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2015

Study Guide Community-Based Practice

Department of Community and Preventive Medicine Study Program of Medicine Faculty of Medicine Udayana University

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Study Guide Community-based Practice

Udayana University Faculty of Medicine, DME 2


L I S T 0 F C O N T E N T

List of Module 2

Curriculum of Community Based Practice 3

Planners Team 7

Facilitators 8

Skill Lab Facilitators 9

Reserve Facilitators 9

Time Table 10

Skill Lab Time Table 22

Assessment Method 22

Introduction 23

Module 1 – 18 24

References 98

Annex 1 Film Summary “And the band played on” 99

Annex 2 Artikel Koran Bali Post 104

Annex 3 Artikel jurnal 109

Skill Lab (1-3) 110

Student Project: Searching, analysis, and interpreting study result 129



L I S T O F M O D U L E MODULE ~ 1 (Mausner & Bahn, p. 1-42) p. 26

Determinants of Health/Diseases, Natural; History of Diseases and Diseases Prevention

MODULE ~ 2 (Kirkwood and Sterne, chapter 2) p.32Population, Sample, Data, and Variables

MODULE ~ 3 (Greenberg p. 15-28) p.35Measurements of Morbidity and Mortality in a Population; Source of Error in Measurements

MODULE ~ 4 (Greenbergp. 51-53) p.40Crude, Specific and Adjusted Rate

MODULE ~ 5 (Skill Lab Manual, Kirkwood & Sterne, Chapter 2) p. 43Skill Lab: Data entry, Data Cleaning, and Data Transformation

MODULE ~ 6 (Greenberg, p. 29-43) p.45Analysis and Interpretation of Descriptive Data

MODULE ~ 7 (Kirkwood & Sterne, Chap 3 &4) p.49Data Presentation and Data Description

MODULE ~ 8 (Skill Lab Manual, Kirkwood & Sterne) p.54Skill Lab Data Presentation and Data Interpretation

MODULE ~ 9 (Kirkwood & Sterne, Chap. 5-8, 10, 14, 15) p.55Inferential Analysis and Interpretation of Analysis Results (Hypothesis Test)

MODULE ~ 10 (Kirkwood & Sterne, Chap. 7, 9, 11, 16, 18, 19) p.59Significance Test for Categorical and Interval Data

MODULE ~ 11 (Skill Lab Manual, Kirkwood & Sterne) p. 64Correlation-Regression, Significance Test For Categorical and Interval Data

MODULE ~ 12 (Greenberg, p. 45 – 73) p. 65Definition, Requirements, Types, and Applications of Surveillance and Outbreaks

MODULE ~ 13 (Greenberg) p. 72Epidemiology Study to Determine Risk Factor of Disease

MODULE ~ 14 (Greenberg, p. 113-123) p.79Epidemiology Study Design: Cohort Study

MODULE ~ 15 (Greenberg, p. 127-136) p.83Epidemiology Study Design: Case – Control Study

MODULE ~ 16 (Greenberg, p.91-113) p. 87Epidemiology Study Design: Clinical Trial

MODULE ~ 17 (Greenberg, p. 127-136) p. 92Epidemiology Study Design: Diagnostic Test



MODULE ~ 18 (Mausner & Bahn, p. 91-110, Greenberg 141-153) p. 95Variability and Biases

CURRICULUMCommunity Based Practice

Competency Statement

Instructional Goals

Learning Objectives Topics

Use community based-practices to conduct studies that improve diseases prevention in the community

Demonstrate ability to apply health prevention principles based on risks and determinant factors of health problem

a) Describe several determinants (models) of diseases and death occurring in the population

b) Explain the applications of understanding diseases and death determinants (models).

c) Identify the strengths and weaknesses of diseases models.

d) Draw figures of the natural history of a certain disease.

e) Explain the applications of the natural history of a disease for prevention.

f) Explain the severity of diseases in a population and its implication to prevention.

g) Explain the Ice Berg phenomena and its implication in diseases prevention.

h) Describe the level of disease prevention based on determinants and natural history.

Determinants of Morbidity and Mortality in a Population, Natural History and Diseases Prevention




Instructional Goals Learning Objectives Topics

Demonstrate ability to search, organize and interpret information/data from different sources in order to assist in diagnostic, therapeutic and health

a) Explain measurements of morbidity and mortality in a population.

b) Describe the definitions of population and sample.

c) Explain the conditions required for a representative sample.

d) Explain several sampling methods.

e) Describe types of data and variables.

f) Be able to prepare software for data entry.

g) Differentiate proportion, ratio, rate, prevalence and incidence.

h) Explain four types of incidence based on their denominators.

i) Describe the source of numerators and denominators for prevalence and incidence.

j) Explain types of errors in rate calculation.

Measurements of Morbidity and Mortality in a Population

k) Explain the differences, application, interpretation, and weaknesses of (slide) crude, specific, and adjusted rate.

Pattern of Morbidity and Mortality Based on Person, Place, and Time



l) Be able to analyze, and interpret crude, specific and adjusted rate.



m) Be able to analyze, present, and interpret descriptive categorical and interval data.

n) Be able to analyze and present data using computers. Be able to interpret the measurements of morbidity and mortality on samples descriptively.

o) Explain the method to determine disease prognosis from the population (survival analysis).

p) Be able to manage, analyze and interpret data inferentially.

q) Describe the definition, requirements, types, and applications of surveillance.

r) Be able to conduct an epidemiologic investigation of an outbreak.

Surveillance and Disease Outbreak

Demonstrate ability to apply methods to determine risk factors of a disease and effectiveness of diseases intervention/ treatment/ prevention

a. Describe epidemiological design, e.g.: cross-section, case-control, and cohort, to determine risk factors of diseases.

b. Explain the advantages and disadvantages of cross-sectional, case-control and cohort design.

c. Describe the

Methods to determine risk factors, effectiveness of treatment, prevention, and intervention of diseases in the community



application of clinical trials to determine the effectiveness of intervention, prevention, and treatment of diseases.



d. Be able to describe the differences between descriptive, cross-sectional, case-control, and clinical study design.

e. Describe the concept of patient variability, variability in medical research, variability of measurement, both in individual and population level.

f. Explain internal validity, external validity, selection bias, information bias, and confounding factors.

g. Describe sources of bias in descriptive research design, cross-sectional, case-control, cohort, and clinical trials and describe how to minimize those biases.

Analyze and interpret data of diseases screening in the community

a. Be able to apply the concept of sensitivity, specificity, and predictive value

b. Describe the concept of cut off points.

Screening of diseases in the community



~ PLANNERS TEAM ~

No Name Department Phone

1 Prof. dr. D.N. Wirawan, MPH (Coordinator)

Community/Preventive 0811394306

2 dr. A.A.Sg. Sawitri (Secretary) Community/Preventive 0817340145

3 dr. I.B. Wirakusuma, MOH Community/Preventive 08124696647

4 dr. Ayu Swandewi, MPH Public Health 088113677930

5 dr. Komang Ayu Kartika Sari, MPH Community/Preventive 082147092348

6 dr. Nyoman Sutarsa, MPH Community/Preventive 087860380028

7 dr. Ni Wayan Septarini, MPH Public Health 081353342409

8 dr. Putu Aryani, S. Ked Community/Preventive 081805664963

9 dr. Ni Luh Putu Ariastuti, MPH Community/Preventive 0818560008

10 dr. Gede Artawan Eka Putra, M. Epid Public Health 087860118004

11 Dr. Putu Cintya Denny Yuliatni Community/Preventive 081353380666



~ FACILITATORS ~

Regular Class (Class A)

No Name Department Phone Group Room Number

1 Dr.rer.Nat. dr. Ni N. Ayu Dewi, M.Si Biochemistry 081337141506 A-1 A.3.09

2 dr. Ni Luh Putu Eka Diarthini, S.Ked Parasitology 081353077733 A-2 A.3.10

3 Dr. I Gusti Nyoman Sri Wiryawan, M.Repro Histology 082341768888 A-3 A.3.11

4 I.B. Putra Dwija, S.Si, M.Biotech Microbiology 08179747502 A-4 A.3.12

5 Drs. I Gede Made Adioka, Apt, M.Kes Pharmacy 081999418471

A-5 A.3.13

6 dr. I Wayan Eka Sutyawan, Sp.M Opthalmology 081338538499 A-6 A.3.14

7 dr. I B Rangga Wibhuti, M.Biomed, Sp.JP Cardiovascular 081237287888 A-7 A.3.15

8 dr. I Putu Kurniyanta, Sp.An Anaesthesiology 081805755222 A-8 A.3.16

9 DR. Luh Seri Ani, SKM, M.KesCommunity/Preventive

08123924326A-9 A.3.17

10 dr. Sri Laksminingsih, Sp.Rad Radiology 08164745561 A-10 A.3.19

English Class (Class B)

No Name Department Phone Group

Room Number

1 Dr. I Putu Adiartha Griadi, M.Fis Phisiology 081999636899 B-1 A.3.09

2 Dr. Komang Ayu Kartika Sari, MPH Community/ Preventive 082147092348 B-2 A.3.10

3 dr. I Nyoman Sutarsa, MPH Community/ Preventive 087860380028 B-3 A.3.11

4 dr. I Nyoman Arcana, Sp.Biok Biochemistry 0811397960 B-4 A.3.12

5 dr. I Made Susila Utama, Sp.PD-KPTI Internal Med 08123815025 B-5 A.3.13

6 dr. I Made Suka Adnyana, Sp.BP Plastic Surgery 081236288975 B-6 A.3.14

7 dr. I Made Sudipta, Sp. THT-KL ENT 08123937063 B-7 A.3.15

8 dr. I Wayan Aryabiantara, Sp.An Anaesthesiology 08123822009 B-8 A.3.16

9dr. I G Kamasan N. Arijana, M.Si.Med Histology 085339644145 B-9 A.3.17



10 dr. I Made Oka Negara, S.Ked Andrology 08123979397 B-10 A.3.19

Skill Lab Facilitators

No Name Department Phone1 dr. G.N. Indraguna Pinatih, MSc. Com/Prev 08123816424

2 dr. Wayan Weta, MSc. Com/Prev 081337005360

3 dr. A.A.Sg. Sawitri, MPH Com/Prev 0817340145

4 dr.Putu Ayu Swandewi, MPH Public Health 081338996006

5 dr. Ni Luh Putu Ariastuti, MPH Com/Prev 0818560008

6 dr.I Wayan Gede Artawan EP, M.Epid Public Health 03617848123

7 dr. Komang Ayu Kartika Sari, MPH Com/Prev 082147092348

8 dr. Nyoman Sutarsa, MPH Com/Prev 087860380028


10 dr. Wulan Citra Sucipta, S.Ked Com/Prev 081805570772

Reserve Facilitators

No Name Department Phone

1 dr. A.A.Sg. Sawitri, MPH Com/Prev 0817340145

2 dr. I.B Wirakusuma, MOH Com/Prev 08124696647

3 dr. Putu Cintya Denny Yuliatni Com/Prev 081353380666


5 dr. Wulan Citra Sucipta, S.Ked Com/Prev 081805570772

6 dr. Ni Luh Putu Ariastuti, MPH Com/Prev 0818560008



~ TIME TABLE ~English Class (B)

Days/date Time Activity Venue Conveyer

1Thursday

12th of March

08.00 – 09.00 Introductory

Theatre 4th Floor

dr. A.A.Sg. Sawitri, MPH

09.00 – 10.30 Movie

10.30 – 12.00 Discussion

12.00 – 13.00 Break/lunch

13.00 – 15.00Independent learning/Student Project (SP)

2Friday

13th of March

08.00 – 09.00Introductory lecture 1

Determinants of morbidity and mortality in a population

Class Room (CR)

3.01

Prof. dr. D.N. Wirawan, MPH

dr. Ni Wayan Septarini, MPH


Natural history of diseases

10.00 – 10.30 Break


Diseases prevention


12.30 – 15.30

SGD : Determinants of morbidity and mortality in a population

Discussion Room (DR)SGD : Natural history of

diseases

SGD : Diseases prevention

3Monday

16th of March

08.00 – 09.00Student presentation and feedback : Determinants of morbidity and mortality in a population

CR 3.01



09.00 – 10.00Student presentation and feedback : Natural history of diseases

10.00 – 10.30 Break

10.30 – 11.30Student presentation and feedback : Diseases prevention

11.30 – 12.30 Break/Lunch



12.30 – 15.00 Independent Learning/SP

4Tuesday

17th of March

08.00 – 09.00

Introductory Lecture

Population, Sample, Data, and Variables

CR 3.01

dr. Putu Ayu Swandewi, MPH

dr. Gd Artawan, M.Epid

09.00 – 10.00


Measurements of Morbidity and Mortality in A Population; Source of Error dr. Ayu Kartika

Sari, MPH

dr. Sawitri, MPH10.00 – 10.30 Break

10.30 – 11.30Introductory Lecture

Crude, Specific and Adjusted Rate

11.30 – 12.30 Break/Lunch DR

12.30 – 15.30

SGD : Population, Sample, Data, and Variables

SGD : Measurements of Morbidity and Mortality in A Population; Source of Error

SGD : Crude, Specific and Adjusted Rate

5Wednesday18th

of March

08.00 – 09.00Student presentation and feedback : Population, Sample, Data, and Variables

CR 3.01 dr. P. Swandewi, MPH

dr. Gede Artawan Eka Putra, M. Epid

09.00 – 10.00

Student presentation and feedback : Measurements of Morbidity and Mortality in A Population; Source of Error

dr. Ayu Kartika Sari, MPH

dr. Sawitri, MPH

10.00 – 10.30 Break

10.30 – 11.30Student presentation and feedback : Crude, Specific and Adjusted Rate



6Thursday

19th of March

Skill lab Schedule

Skill Lab

Data Entry, Data Cleaning, and Data Transformation

CR 3.01 Skill Lab Team

7Monday

23th of March09.00 – 10.00


Analysis and Interpretation of Descriptive Data

CR 3.01 dr. Wirakusuma, MOH

dr. Ariastuti, MPH

10.00 – 11.00 Introductory Lecture

Data Presentation and Data Description


dr. Gede Artawan



Eka Putra, M. Epid



13.00 – 15.00

SGD : Analysis and Interpretation of Descriptive Data

DR

SGD : Data Presentation and Data Description

8Tuesday

24th of March

09.00 – 10.00Student presentation and feedback


CR 3.01

dr. Wirakusuma, MOH

dr. Ariastuti, MPH

10.00 – 11.00Student presentation and feedback : Data Presentation and Data Description





13.00 – 15.00 Independent Learning

9Wednesday

25th of March

Skill lab Schedule

Skill Lab

Data Presentation and Data Interpretation

CR 3.01 Skill lab Schedule

10Thursday

26th of March

09.00 – 10.00


Inferential Analysis and Interpretation of Analysis Results (Hypothesis Test)

CR 3.01 dr. Putu Ayu Swandewi, MPH


10.00 – 11.00Introductory LectureSignificance Test for Categorical and Interval Data



13.00 – 15.00

SGD : Inferential Analysis and Interpretation of Analysis Results (Hypothesis Test)

DR

SGD : Significance Test for Categorical and Interval Data

11Friday

27th of March 09.00 – 10.00

Student presentation and feedback : Inferential Analysis and Interpretation of Analysis Results (Hypothesis Test)



10.00 – 11.00Student presentation and feedback : Significance Test for Categorical and Interval Data





13.00 – 15.00 Independent Learning/ SP

Days/date Time Activity Venue Lecturers

12Monday

30th of March

Skill lab Schedule

Skill LabSignificance Test for Categorical & Interval Data

CR 3.01 Team Skill Lab

13Tuesday

31st of March

09.00 – 10.00


Definition, Requirements, Types, and Applications of Surveillance and Outbreaks

CR 3.01 dr. Ayu Kartika Sari, MPHdr. Ariastuti, MPH

10.00 – 11.00


Epidemiologic studies to determine risk factors of diseases

dr. A.A.Sg. Sawitri, MPHDr. dr. Gede Indraguna, SpGK



13.00 – 15.00

SGD :


DR

SGD:


14Wednesday1st of April

09.00 – 10.00

Student presentation and feedback


CR 3.01 dr. Ayu Kartika Sari, MPH

dr. Ariastuti, MPH

10.00 – 11.00Student presentation and feedback Epidemiologic studies to determine risk factors of diseases

CR 3.01 dr. A.A.Sg. Sawitri, MPHDr. dr. Gd. Indraguna, Sp.GK




15Thursday2nd of April


Epidemiology Study Design: Cohort Study

CR 3.01 dr. Nyoman Sutarsa, MPHdr. Ariastuti, MPH


Epidemiology Study Design: Case Control Study




13.00 – 15.00 SGD : Epidemiology Study Design: Cohort Study

DR



SGD : Epidemiology Study Design: Case Control Study

16Monday

6th of April

09.00 – 10.00Student presentation and feedback Epidemiology Study Design: Cohort Study

CR 3.01 dr. Nyoman Sutarsa, MPH,dr. Ariastuti, MPH

10.00 – 11.00Student presentation and feedback : Epidemiology Study Design: Case Control Study

CR 3.01




17Tuesday7th of April

09.00 – 10.00


Applications of Clinical Trials to Determine the Effectiveness of Intervention, Prevention, and Treatment of Diseases

CR 3.01 dr. A.A.Sg. Sawitri, MPHDr. dr. Gd Indraguna, SpGK


Screening and Diagnostic Testing

CR 3.01 dr. Gede Artawan Eka Putra, M. Epiddr. Sawitri, MPH



13.00 – 15.00

SGD:Applications of Clinical Trials to Determine the Effectiveness of Intervention, Prevention, and Treatment of Diseases Epidemiology Study Design: Case Control Study

DR

SGDScreening and Diagnostic Testing

DR

18Wednesday8th of April

09.00 – 10.00

Student presentation and feedback: Applications of Clinical Trials to Determine the Effectiveness of Intervention, Prevention, and Treatment of Diseases

CR 3.01 dr. A.A.Sg. Sawitri, MPHDr. dr. Indraguna, SpGK

10.00 – 11.00Student presentation and feedback : Screening and Diagnostic Testing

CR 3.01 dr. Gede Artawan Eka Putra, M. Epiddr. A.A.Sg. Sawitri, MPH






19Thursday9th of April

09.00 – 10.00Introductory LectureVariability and bias


10.00 – 11.00 SGD: Variability and bias DR




20Friday

10th of April

09.00 – 10.00 Student presentation and feedback Variability and bias

CR 3.01 dr. A.A.Sg. Sawitri, MPHDr. dr. Indraguna, SpGKTeam

10.00 – 13.00 Presentation of student project and feed back



21Saturday11th of April

Preparation for Final Test

22Monday

13th of April09.00 – 11.00 Assessment Will be arranged

later

Team Resource PersonAnd Facilitators



~ TIME TABLE ~Regular Class (A)

Days/date Time Activity Venue Conveyer

1Thursday

12th of March

08.00 – 09.00 Introductory

Theatre Room 4th

Floor


09.00 – 10.30 Movie

10.30 – 12.00Independent learning / Student Project (SP)


13.00 – 15.00 Discussion

2Friday

13th of March

08.00 – 11.30 Independent learning /SP

Class Room (CR)

3.01





Determinants of morbidity and mortality in a population


Natural history of diseases


Diseases prevention

3Monday

16th of March

08.00 – 11.00

SGD : Determinants of morbidity and mortality in a population

Discussion Room (DR)

SGD : Natural history of diseases

SGD : Diseases prevention


12.00 – 15.00

Student presentation and feedback : Determinants of morbidity and mortality in a population

CR 3.01



Student presentation and feedback : Natural history of diseases

Student presentation and feedback : Diseases prevention

4Tuesday

17th of March



12.30 – 13.30


Population, Sample, Data, and Variables


dr. G.Artawan, M.Epid


Measurements of Morbidity and Mortality in A Population; Source of Error


dr. Sawitri, MPH




Crude, Specific and Adjusted Rate

5Wednesday18t

h of March

08.00 – 11.00

SGD : Population, Sample, Data, and Variables

DR

SGD : Measurements of Morbidity and Mortality in A Population; Source of Error

SGD : Crude, Specific and Adjusted Rate


12.00 – 13.00Student presentation and feedback : Population, Sample, Data, and Variables


dr. Artawan, MEpid

13.00 – 14.00

Student presentation and feedback : Measurements of Morbidity and Mortality in A Population; Source of Error


dr. Sawitri, MPH14.00 – 15.00

Student presentation and feedback : Crude, Specific and Adjusted Rate

6Thursday

19th of March

Skill lab Schedule

Skill Lab

Data Entry, Data Cleaning, and Data Transformation

CR 3.01 Skill Lab Team

7Monday

23th of March






dr. Ariastuti, MPH


Data Presentation and Data Description



8Tuesday

24th of March 09.00 – 11.00

SGD : Analysis and Interpretation of Descriptive Data

DR

SGD : Data Presentation and Data Description



13.00 – 14.00 Student presentation & feedback: Analysis and Interpretation of Descriptive Data


dr. Ariastuti, MPH



14.00 – 15.00Student presentation & feedback: Data Presentation and Data Description



9Wednesday

25th of March

Skill lab Schedule

Skill Lab

Data Presentation and Data Interpretation

CR 3.01 Skill lab Schedule

10Thursday

26th of March

09.00 – 12.00 Independent learning /SP CR 3.01 dr. Putu Ayu Swandewi, MPH



13.00 – 14.00



14.00 – 15.00Introductory LectureSignificance Test for Categorical and Interval Data

11Friday

27th of March

09.00 – 11.00

SGD : Inferential Analysis and Interpretation of Analysis Results (Hypothesis Test)

DR

SGD : Significance Test for Categorical and Interval Data



13.00 – 14.00

Student presentation and feedback : : Inferential Analysis and Interpretation of Analysis Results (Hypothesis Test)



14.00 – 15.00Student presentation and feedback : Significance Test for Categorical and Interval Data

12Monday

30th of March

Skill lab Schedule

Skill LabSignificance Test for Categorical & Interval Data

CR 3.01 Team Skill Lab

13Tuesday

31st of March



13.00 – 14.00



CR 3.01 dr. Ayu Kartika Sari, MPH dr. Sawitri, MPH

14.00 – 15.00



dr. A.A.Sg. Sawitri, MPHDr. dr. Indraguna, SpGK



14Wednesday1st of April

09.00 – 11.00

SGD :


DR

SGD:


11.00 – 12.00 Independent Learning /SP


13.00 – 14.00

Student presentation and feedback : Definition, Requirements, Types, and Applications of Surveillance and Outbreaks

CR 3.01 dr. Ayu Kartika Sari, MPH


14.00 – 15.00Student presentation and feedback : Epidemiologic studies to determine risk factors of diseases


15Thursday2nd of April




Epidemiology Study Design: Cohort Study



Epidemiology Study Design: Case Control Study

16Monday

6th of April

09.00 – 11.00

SGD : Epidemiology Study Design: Cohort Study

DR

SGD : Epidemiology Study Design: Case Control Study



13.00 – 14.00Student presentation and feedback : Epidemiology Study Design: Cohort Study


14.00 – 15.00Student presentation and feedback : Epidemiology Study Design: Case Control Study

CR 3.01

17Tuesday7th of April




Applications of Clinical Trials to Determine the Effectiveness of Intervention, Prevention, and Treatment of Diseases

CR 3.01 dr. Sawitri, MPHDr. dr. Indraguna, SpGK




Screening and Diagnostic Testing

CR 3.01 dr. Artawan Eka Putra, M. Epiddr. Sawitri, MPH

18Wednesday8th of April

09.00 – 11.00

SGD:Applications of Clinical Trials to Determine the Effectiveness of Intervention, Prevention, and Treatment of Diseases

DR

SGDScreening and Diagnostic Testing

DR



13.00 – 14.00

Student presentation and feedback: Applications of Clinical Trials to Determine the Effectiveness of Intervention, Prevention, and Treatment of Diseases

CR 3.01 dr. A.A.Sg. Sawitri, MPHDr. dr. Indraguna P

14.00 – 15.00Student presentation and feedback : Screening and Diagnostic Testing

CR 3.01 dr. Artawan Eka Putra, M. Epiddr. A.A.Sg. Sawitri, MPH

19Thursday9th of April

09.00 – 12.00 Independent Learning/SP CR 3.01 dr. A.A.Sg. Sawitri, MPHDr. dr. Indraguna P12.00 – 13.00 Break/Lunch

13.00 – 14.00 Introductory LectureVariability and bias

14.00 – 15.00 SGD: Variability and bias DR

20Friday

10th of April

09.00 – 12.00 Independent learning /SP CR 3.01 dr. A.A.Sg. Sawitri, MPHDr. dr. Indraguna P12.00 – 13.00 Break/Lunch

13.00 – 14.00 Student presentation (SP) and feedback : Variability and bias

14.00 – 16.00 Presentation of student project and feed back

21Saturday11th of April

Preparation for Final Test

22Monday

13th of April09.00 – 11.00 Assessment Will be arranged

later

Team Resource PersonAnd Facilitators



SKILL LAB TIME TABLE (Regular and English Class)Date: 1. Thursday 19th March 2015

2. Wednesday 25th March 2015

3. Monday 30th March 2015

No Class Group SGD Activity Time/Place

1 English I, II, III, IV, V Skill Lab/ pleno 08.00 – 09.30 / Class room

2 English VI, VII, VIII, IX, X Skill Lab/ pleno 09.30 – 11.00 / Class room

Break/Lunch 11.00 – 12.00 WITA

3 Regular I, II, III, IV, V Skill Lab/ pleno 12.00 – 13.30 / Class room

4 Regular VI, VII, VIII, IX, X Skill Lab/ pleno 13.30 – 15.00 / Class room

Guidance:1. Each student is required to bring their own lap-top and has installed the SPSS

program in to their laptop.

2. Data for practising will be provided before the first skill lab. Each head of the SGD have to contact dr. Putuariastuti or dr. Citra to ask for those files.

3. Skill Lab Guide is provided on the Anexes. Remember to bring your study guide every day.

ASSESSMENT METHOD

Student assessment of this block consists of:

1. a paper test with multiple choice questions at the end of block with proportion 80% of total score

2. a student project with proportion 15% of total score

3. evaluation of activity during the small group discussion with proportion 5% of total score



INTRODUCTION

A movie “And the Band Played On” will be played after introduction session in theatre room 4th floor. Every student should pay attention and carefully watch the movie because proper understanding of the movie is needed to answer learning task question below. The summary of this movie can be seen in the Annex 1 of the Study Guide.

Learning task

Based on the movie “And The Band Played On”, you are required to discuss in your group the following questions:

1. Please explain about steps to discover the cause of AIDS based on this movie. Which one was started first, the lab investigation or epidemiology investigation to find out the cause of AIDS?

2. How were the roles of the following fields shown in the moviea. Statistic b. Clinical c. Sociald. Epidemiologye. Virology

3. Please explain which part of the movie explained the types of social and political threats that influenced the investigation to find out the cause of that mysterious disease

4. Please explain which part of the movie showed ethics and professional aspects

5. Please explain which part of the movie that presented the threats/difficulties in controlling and preventing that disease



M O D U L E ~ 1(Reference Maussner & Bahn, p. 1-42)

Determinants of Health/Diseases, Natural History of Diseases and Diseases Prevention

Prof. dr. Dewa Nyoman Wirawan, MPH & dr. Ni Wayan Septarini, MPH

AIMS:To be able to describe determinants, the natural history of diseases and death occurring in the population and diseases prevention

LEARNING OUTCOMES:1. Describe several determinants (models) of diseases and death occurring in the

population.

2. Explain the applications of understanding diseases and death determinants (models).

3. Identify the strengths and weaknesses of diseases models.

4. Draw figures of the natural history of a certain disease.

5. Explain the applications of the natural history of a disease for prevention.

6. Explain the severity of diseases in a population and its implication to prevention.

7. Explain the Ice Berg phenomena and its implication in diseases prevention.

8. Describe the level of disease prevention based on determinants and natural history

CURRICULUM CONTENTS:1. Determinants (models) of diseases and death occurring in the population.

2. Natural history of certain disease and applications for prevention.

3. Level of disease prevention based on determinants and natural history.



ABSTRACTS In this lecture the difference approaches of community medicine and clinical

medicine are discussed. In clinical medicine, concern is to individual patients who are visit health providers. In community or population medicine concern is to whole population, either sick or healthy in certain geographical area. Community medicine or public health focuses on prevention of diseasesin the population, whereas clinical medicine focuses on treating sick individual patients who come to health providers.

In order to provide appropriate treatments to individual patients, the diagnosis of her/his disease must be established. Similar approach must be established in community medicine. In order to provide appropriate preventions, determinants of health problems in the community must be understood.

To understand determinants or factors which influence the occurrence of diseases in the population, some epidemiological models or theories are discussed in this lecture. Each model has its strengths and weaknesses. There is no single model which is appropriate to explain determinants of all diseases occurrence. Triangle model which was first introduced, explained that the occurrence of diseases in the population is determine by agent, host and environments. Wheel model focused on intrinsic (host factors) and extrinsic (environments factors). Model which was introduced by Blum explained that the occurrence of diseases in the population determine by genetic, behaviour, health programs and environments. Web model explained that a determinant of diseases occurrence in the population is not simple but interrelated of many factors. There are many other models which explain determinants of diseases but not discussed in this lecture.

Beside determinants of diseases occurrence, appropriate prevention also depends on the natural history of the disease. For example, disease where the cause is well established such as HIV/AIDS, has long incubation period and fatal, the focus of prevention are primary prevention such as behavior change educations, secondary prevention such as voluntary HIV testing (VCT) and tertiary prevention such as care support and treatments of secondary (opportunistic) infections. On the other hand, if the cause of disease is not known such as cancers, focus of preventions is secondary and tertiary preventions. Other disease such as dengue fever, which is acute, no vaccine available and no treatments to kill the virus, the focus of prevention is on primary preventions.

SELF DIRECTING LEARNING:

Basic knowledge and its application that students must know, include:1. Disease’s model2. Natural history of disease3. Level of prevention



SCENARIO& LEARNING TASKS

Guidelines: a) During small group discussion (SGD), each group has to select a spokesperson, who

will be presenting the results of discussions at the plenary b) Prior to the plenary, the spokesperson of each group sits in front of the class c) Read carefully the learning tasks (1)and(2) below.d) Each group have to discuss at least 4 (four) learning tasks below which are learning

task (1) and three learning task(2) from (a) to (e)e) Each group must present three learning tasks in the plenary. f) Before the plenary start, every SGD has already put the file of the answer in the

class’ computer/CPUg) Learning task (3) is additional task, however, all of the students MUST watch the

movie carefully in order to be able to answer learning task in day 2.

SGD : DETERMINANTS OF HEALTH / DISEASES

Case 1. Please watch carefully a short video clip on: (you can watch it at your home before the lecture)

http://www.youtube.com/watch?v=5Lul6KNIw_8

Learning Tasks 1:After you carefully watch the video, please discuss the following questions:

1. What factors that influence health mentioned in this video clip?

2. Which model/s of disease morbidity is/are being use?

3. What interventions are being conducted in the two examples given on the clip? What are the focuses of the interventions for each case?

Case 2Please watch carefully as well:

http://www.youtube.com/watch?v=mMDUv2R6tHQ

Learning Tasks 21. What is the main topic of the conversation?2. Please draw in a diagram about what make the guy become a beggar!


http://www.youtube.com/watch?v=mMDUv2R6tHQ

http://www.youtube.com/watch?v=5Lul6KNIw_8


Learning Tasks 3Discuss the following in your group:

a) The high morbidity and mortality due to dengue hemorrhagic fever (DHF) is influenced by many factors (determinants). Discuss these factorsusing Wheels Model. Explain also the most dominant factor.

b) 1. The high incidence of morbidity (morbidity rate) due to traffic accidents are influenced by many factors (determinants). Discuss in groups these factors using Web Model. Explain the most dominant factor.

2.The high mortality rate due to traffic accidents are also influenced by many factors (determinants). Discuss in groups these factors using Blum Model. Explain also the most dominant factor.

c) The high morbidity and mortality due to tuberculosis (TB) are influenced by many factors (determinants). Discuss in groups these factors using Triangle Model. Explain also the most dominant factor.

d) The high morbidity and mortality caused by HIV-AIDS are influenced by many factors (determinants). Discuss in groups these factors usingBlum and Triangle Model. Explain also the most dominant factor.

e) The high morbidity and mortality of children under 5 year olds in the community is influenced by many factors (determinants). Discuss these factors by using Mosley Model. Explain also the mechanisms between these factors by considering the direction of the lines in the Mosley Model.

Learning Tasks 4:

1. At the end of the lecture, each SGD group should give the lecturer a USB with capacity minimum 8 gb.

2. A movie “The Contagion” will be transfered to the USB and the USB will be returned during plenary. This movie will be discussed on the surveillance and disease’s outbreak topic

SGD : NATURAL HISTORY OF DISEASES Guidelines:

1. During small group discussion (SGD), each group has to select a spokesperson, who will be presenting the results of discussions at the plenary

2. Prior to the plenary, the spokesperson of each group sits in front of the class

3. Read carefully the learning tasks (a) to (c) below.



4. Each group has to discuss all of the learning tasks below.

5. Before the plenary start, every SGD has already put the file of the answer in the class’ computer/CPU

LEARNING TASK

a) Discuss the natural history, primary, secondary and tertiary prevention of HIV-AIDS using a diagram.

b) Discuss the natural history, primary, secondary and tertiary prevention of denguehemorrhagic fever (DHF) using a chart.

Please refer to: http://www.medscape.com/viewarticle/725639_2

SGD : DISEASES PREVENTION Guidelines:

a) During small group discussion (SGD), each group has to select a spokesperson, who will be presenting the results of discussions at the plenary

b) Prior to the plenary, the spokesperson of each group sits in front of the class

c) Read carefully the learning tasks (a) to (c) below.

d) Each group has to discuss all of the learning tasks below.e) Before the plenary start, every SGD has already put the file of the answer in the

class’ computer/CPU

LEARNING TASK

Please take a look this PDF file below before answering the following task:

http://www.euro.who.int/__data/assets/pdf_file/0004/129532/Ottawa_Charter.pdf

All the answers for the following task must also refer/consider to “Ottawa Charter” as explained on the above file.

a) Discuss the natural history, primary, secondary and tertiary prevention of tuberculosis.

Please refer to the following website:

http://www.uphs.upenn.edu/bugdrug/antibiotic_manual/iuatld_tb_manual_for_medical_students.pdf

b) Discuss the natural history and primary, secondary and tertiary prevention of malnutrition in children age under 5 years.

Please refer to the following website:

http://www.jped.com.br/conteudo/00-76-s285/ing.pdf


http://www.jped.com.br/conteudo/00-76-s285/ing.pdf



http://www.euro.who.int/__data/assets/pdf_file/0004/129532/Ottawa_Charter.pdf

http://www.medscape.com/viewarticle/725639_2


c) Please refer to the following website:

http://depts.washington.edu/epidem/Epi583/MockLancet.pdf

Please discuss the natural history, primary, secondary and tertiary prevention of traffic accident

Self assessment

1. Explain the determinant factor of diseases occurrence by using the Epidemiological Triangle, the Wheels and the Blum models!

2. A part of the Blum model is the health care factor. If you use the Wheel model, into which factor that factor should be included?

3. The other part of the Blum model is behaviour factor. If you use the Wheel model, that factor should be included into which factor? And if you use the Epidemiological Triangle model, how should you place the behaviour factor?

4. What are the differences between the natural history of HIV/AIDS, DHF, and coronary disease or stroke?

5. Why is it very important to understand the determinant and the natural history of certain diseases in a population?

6. The level of prevention consists of primary preventions, including health promotion (behaviour change & policy/regulation) and specific protection, secondary prevention (early detection and prompt treatment), and tertiary prevention (disability limitation and rehabilitation). Explain which prevention will be effective for the following diseases/incidents: DHF, HIV/AIDS, diarrheal, traffic accident, coronary heart disease, stroke, tuberculosis (TBC), and avian influenza.

7. The health promotion is actually “health education plus” which consists of health education (or behaviour intervention) and structural intervention (or policy/regulation). Give examples of behaviour intervention and policy/regulation to reduce death due to traffic accident, to decrease deficiency of energy and protein (under nutrition) and certain diseases caused by smoking behaviour.

8. Explain the definition of the iceberg phenomena and its consequences regarding the disease prevention in the community and regarding the accuracy of data available at service statistic (primary health services such as private midwives/ doctor, PHC, private clinics, secondary health care service such as district hospital, and tertiary health care service such as referral hospital).


http://depts.washington.edu/epidem/Epi583/MockLancet.pdf


M O D U L E ~ 2(Reference Kirkwood and Sterne, chapter 2)

Population, Sample, Data, and Variablesdr. Putu Ayu Swandewi, MPH & dr. Gede Artawan Eka Putra, M.Epid

AIM:Demonstrate ability to search, collect, organize and interpret information/data from different sources in order to assist in diagnostic, therapeutic and health.

LEARNING OUTCOMES:a) Describe the definitions of population and sample.

b) Explain the conditions required for a representative sample.

c) Explain several sampling methods.

d) Describe types of data and variables.

e) Describe several method of data collection

CURRICULUM CONTENTS:Population, Sample, Data, and Variables

ABSTRACTPopulation is a group in whom the result of certain study can be applied;while sample is part of the population that should represent its population. Two requirements for sample to be representative are concerning on samples size and sample selection.

Sample size are determined by indicators of measurements (mean, proportion) study design, alpha(α) and power(1-β), and tolerated deviation. Besides those above, sample selection also take important role. The recommended sample selection is non-random sampling (simple, systematic, stratified, multistage, etc). In clinic setting, we frequently used consecutive sampling which is a non random sampling and assumed to be represented its population.

Sample allocation can be used if we need more than one group of sample. The easiest way is block allocation. We can also use allocation of simple random sampling, stratified allocation, and systematic allocation.



SCENARIO:Case 1. Study on Maternal and Child HealthThe study was conducted in two phase; 1) household survey to determine the under five years health status and 2) Quasi Experimental study to explore the effect of food supplementation program toward the improvement of nutritional status

First PhaseA household survey was conducted to explore under five years child’s health status at Desa Merdeka in 2011. The objective of the study was to determine several factors that associated with anemia and chronic malnutrition among children in the area. The area has two different characteristics which are easy to reach area (easy) and hard to reach area (hard). The condition of both areaswas suggested to be considered since there might be different characteristics of the family; hence, the samples were randomly selected from both areas.

The subjects of the study were all five under five years old children. Children from the family who are no longer residing in the area were excluded from the study and those with incomplete data were excluded during data analysis. List of the children were withdrawn from the register at the village leader office.

Data collection was performed by interview using structured questionnaire with the mothers and measurement of the children. The characteristics that were explored and measured include: mothers’ and children’s demographic characteristic, haemoglobin level and body weight. More specifically, the variables in the study were ID, name, area, mothers’ age, education, occupation; history of exclusive breastfeeding, parity; and children’s weight, age, hemoglobin level, body weight and height. The hemoglobin level measured with HemoCue, and body weight measured with digital scale. Anemia status was determined when Hb level less than 11 mg/dl and undernourished determined when BMI less than 11kg/m2

Second PhaseAfter the above data collection was completed, the second phase of the study was started. This phase aims to evaluate the impact of food supplementation program to improve nutritional status among undernourished children. All undernourished children were involved in the study, expect those with severe illness. The children were allocated into two groups; first group received food supplementation and second groups continue with the prior daily consumption. The supplementation was provided up to 2 months and at the end of two month the nutritional status (the body weight)were measured again.

Learning Task 1:You are required to discuss the following questions, based on the study:



1. Explain the definition of population (target population and sampled population) and sample!

From each study phase of the case above describe:

1. Target population of the study

2. Sampled population of the study

3. Discuss what are the different between target and sampled population

2. Discuss the reason of taking sample rather than observed all population and the requirement of a good sample?

3. Discuss several conditions for samples to represent the population (to be representative)!

4. Discuss the meanings and objective of inclusion criteria, exclusion criteria, and drop out criteria?

Base on the case study above, for each phase, describe:

1. What are the inclusion criteria for the study

2. What are the exclusion criteria for the study

3. What are the drop out criteria for the study

5. Explain the indication and the technique for sampling methods below

1. Simple random, stratified random, systematic random, multi-stage random, cluster,

2. Quota, convenience, purposive, and “snow-balling technique”3. Based on the case study above, what is the sampling method of the study?

6. Based on the case above (phase 1), describe about sampling frame for the study. What is the importance of constructing a sampling frame and when it is not possible to be constructed?

7. Explain several important parts in sample size calculation including variability, design, power (1-), level of significance (), effect size or precision or margin of error.

a. Draw the relationship between variables above

b. Classify the variables based on their function

8. a. Explain the classification of variables based on the level of measurementb. Among the variables on the study above, classify them based on the level of

measurement9. Describe method of data collection that have been applied on the case study above.

Self Assessments:

1. Explain the definition of population and sample!

2. What are the conditions required for a representative sample?



3. Explain several sampling methods: simple random, proportional, multistage, stratified, systematic, cluster, quota, consecutive, incidental, purposive, and “snow-balling technique”!

4. What is the meaning of exclusion criteria? What is the purpose of excluding some population characteristics?

M O D U L E ~ 3(Reference Greenberg p. 15-28 & Gordis, p. 37-83)

Measurements of Morbidity and Mortality in a Population; Source of Error in Measurements

dr. Ayu Kartika Sari, MPH & dr. A.A.Sg. Sawitri, MPH

AIMSTo demonstrate ability to search, collect, organize and interpret information/data from different sources in order to assist in diagnostic, therapeutic and health.

LEARNING OUTCOMES:At the end of the module, students should be able to:

1. Explain measurements of morbidity and mortality in a population.

2. Differentiate proportion, ratio, rate, prevalence and incidence.

3. Explain four types of incidence based on their denominators.

4. Describe the source of numerators and denominators for prevalence and incidence.

5. Explain types of errors in rate calculation.

6. Explain the differences, application, interpretation, and weaknesses of (slide) crude, specific, and adjusted rate.

7. Analyze, and interpret crude, specific and adjusted rate.

CURRICULUM CONTENTS:1. Measurements of Morbidity and Mortality in a Population

2. Crude, Specific and Adjusted Rate




1. Read previous lectures regarding the natural history of diseases.2. Read Handout 1, p. 62–68, Greenberg, p. 47-53, Gordis, p. 37-83, and power point

slides.

ABSTRACTSAs a medical doctor, either in a clinic or in a public health setting, we will face many problems in a field work which relate to rate, ratio, and proportion. A clinician will certainly consider rate in diagnosing and predicting the fatality or prognosis of a certain disease, a clinician will certainly use rate. Meanwhile, a public health doctor will apply rate, ratio, and proportion to either diagnose a community problem or to evaluate a health program.

Rate, ratio, and proportion, are measurements used to describe the situation, condition, or even a problem among population. Each measurement has specific characteristics and applications. Ratio which is a comparison between 2 independent numbers is usually used for management purpose. Proportion is a comparison between numerator and denominator in which the numerator is included in the denominator. Lastly, rate is a proportion which has population at risk as the denominator. The understanding on measurement characteristics is needed due to giving specific interpretation based on the situation and purpose of measurement.

SCENARIO & LEARNING TASKCase 1. The Jakarta Post, Jakarta | National | Tuesday, December 31 2013, 7:33 PM

http://www.thejakartapost.com/news/2013/12/31/36000-people-with-hivaids-receive-arv-therapy-throughout-2013.html

36,000 people with HIV/AIDS receive ARV therapy throughout 2013

As many as 36,483 people living with HIV/AIDS (ODHA) have received antiretroviral (ARV) treatment in 2013, up from 2,381 in 2005, a senior health official has said.“The development of HIV/AIDS control in Indonesia has shown relatively good results. This can be seen from the increase in the number of people receiving antiretroviral therapy in 2013,” said the Health Ministry’s director general of disease control and environmental health, T, in Jakarta on Tuesday, as quoted by Antara news agency.



However, he said, the ministry also found that the number of early detected HIV cases had continuously increased. In 2013, 20,397 people were recorded as having been infected with HIV, up from 895 in 2005.

Meanwhile, the number of AIDS patients reached 2,763 as of September, down from 4,987 in 2005.

“The Case Fatality Rate (CFR) has also dropped to 0.85 percent as of September from 13.65 percent in 2004,” said T.

The Health Ministry has been intensively carrying out HIV/AIDS control and prevention programs by, among other measures, putting together a national guideline and training modules for health workers such as Training of Trainers (ToT) on Voluntary Counseling and Testing (VCT), which is followed up with VCT as well as Care and Support and Treatment (CST) training sessions.

Learning Task 1

You are required to discuss the following questions, based on the study above.

1. What is the type of measurement underlined in the above case?

2. What is the weakness of that measurement when it is applied to diagnose community health problem?

3. Do you think that measurement is still important to be used in the above case? Please give a reason!

4. Please interpret the data of case fatality rate mentioned in the above case! What is the importance of this rate to clinician?

5. What are the differences between Case Fatality Rate and Cause Specific Death Rate?

Case 2 . Mapping Injecting Drug Users Activity in BaliA study was conducted by team of NGOs (Hatihati, Matahati, dan Yakeba) and Udayana University staff on June-December 2009. The objective of study was to determine the types of HIV risk behaviors among injecting drug users (IDUs) in Bali. Structured interviewed was done to 125 IDUs who randomly selected from a total of 550 reported active IDUs in Denpasar and Badung district. The findings showed that the types of drugs were varied, including heroin (97; 77.6%), buprenorphine (60; 48%) and ATS (1; 0.8%). Some were using both heroin and buprenorphine (35; 28%). Number of injection per day were vary from 1-5 times (mean 1.5). Instead of injecting, they were also using non injected drugs, such as marihuana (22; 17.6%), ATS (23; 18.4%), tranquilizer (39; 31.2%) and ecstasy (15; 12.0%). (Study report of Mapping Injecting Drug Users Activity in Bali, 2010)



Learning Task 2

You are required to discuss the following questions, based on the study above1. What is the type of measurement presented in the case above??2. What is the weakness of that measurement when it is applied to diagnose

community health problem? Explain your answer! 3. If you want to present the data in the form of relative number, which type will be

most appropriate: proportion, prevalence, or incidence? 4. What is the interpretation of 77.6% at above? 5. When there was a total of 550 reported active IDUs, how many of them were

possibly using tranquilizer?6. What are the possibilities of biases of your estimation at number (5)? Provide

your reasons!

Case 3 .

Survey of Tuberculosis in Bali, 2010

Bellow is the result of TB survey in Bali in the end of 2009 (Table 1) that was conducted by the team of Udayana University. The survey was conducted to total available public health centers (120 PHCs) in Bali and 3 main hospitals (RS Sanglah, RS Wangaya, and RS Buleleng). To complete the analysis of the study, researcher took data of population from the Bali Provincial Statistic Office (Table 2).

Table 1. Number of TB cases in Bali

No Sub-province Frequency Proportion Rate1. Buleleng 165 2. Jembrana 73 3. Tabanan 57 4. Badung 119 5. Denpasar 312 6. Gianyar 73 7. Bangli 28 8. Klungkung 57 9. Karangasem 116

Total 1000 Source: HIV prevalence among TB patients in Bali, 2009

Table 2 Number of population in Bali, Based on District and Nationality at 2010

Regencies Indonesian Foreigners TotalChina Others

Male Female Male Female Male Female(1) (2) (3) (4) (5) (6) (7) (8)



1. Jembrana 136.063 136.757 4 1 2 1 272.8282. Tabanan 214.260 216.884 0 0 19 9 431.1723. Badung 197.167 195.619 0 1 161 72 393.0204. Gianyar 199.973 199. 607 15 11 22 32 399.6605. Klungkung 91.067 94.201 0 0 4 0 185.2726. Bangli 107594 108.135 0 0 0 0 215.7297. Karangasem 219.591 218.883 0 1 0 0 438.4758. Buleleng 331.931 330.907 16 17 28 21 662.9209. Denpasar 262.362 260.476 0 0 277 184 523.299

2010 1.760.008 1.761.469 35 31 513 319 3.522.375Source: Bali Provincial Statistic Office, 2010

Learning Task 3

You are required to discuss the following questions, based on the study above

1. Fill in the proportion and the rate based on the above data. What is the interpretation?

2. What is the difference between the proportion and rate at the above? Which one is the appropriate to determine community health problem?

3. What is the rate you calculate: prevalence or incidence? Explain your answers!

4. What are the differences between incidence and prevalence?

5. If you want to calculate incidence of TB, draw the figure of incidence measurement based on the above data (take one sub-district as an example)

6. With regard to the source of data, what could be biases regarding the above result?

Case 4 & Learning Task 4

Morbidity Cases on Newspaper

1. Read carefully the newspaper clipping provided at the annex with the title “Dipertanyakan, Pasien Kurang Mampu Masuk RSUP Denpasar”dan “Masalah Pasien Kurang Mampu, DPRD Pertanyakan Keluhan RSUP”. (Questioned: “Poor patients in Provincial Central Hospital” and “Problem with poor patients, Parliament asks the hospital complaints”).

Discuss in your group and give opinion or comments on that news. Write your comments on a paper and submit it to your lecturer by the next day.



2. Read thoroughly the clipping with title “Korban Kanker Terbanyak Penduduk Pedesaan, Tingkat Sosek Rendah”. (“Mostly cancer patients are from rural areas with low socio-economic status”)

Discuss in your group and give opinion or comments on that news. Write your comments on a paper and submit it to your lecturer by the next day.

M O D U L E ~ 4(Greenbergp. 51-53)

Crude, Specific and Adjusted Ratedr. Ayu Kartika Sari, MPH & dr. A.A.Sg.Sawitri, MPH

___________________________________________________________________ABSTRACTSThe other principal topic of morbidity and mortality measurements is the terms of crude, specific, and adjustment. These measurements are often being applied to those rate, ratio, and proportion. Crude, specific and adjustment are relative measurements. Crude means, generally, if the numerator and the denominator use total incident in a population (ex. CDR, CBR). While specific measurement is when the numerator and the denominator are comes from certain sub-populations. For instances the specific mortality on delivered women and the specific morbidity on tuberculosis. The adjustment is adjusting a certain incident among a group of population to a standard population for comparison purpose.

Case 1

Sero prevalence of HIV among TB patients in BaliStudy of HIV-TB was conducted in 2009 by research team of Udayana University to find out the HIV prevalence among TB patients who visit health services (puskesmas, Sub-province and hospitals) in Bali. Each newly TB diagnosed patients were having short counselling for examination for their HIV status in anonymously unlinked manner. One thousand TB patients (580 male and 420 female) were visiting clinics on June to December 2008. HIV was found to be positive among 39 TB patients (30 male and 9



female), while the distribution based on sub-provinces is presented in the following table. (Report of Sero-Survey of HIV Co Infection among TB patients in Bali, 2009)

Distribution of TB and HIV Patients Based on Sex and Sub-province

Number Disctric Freq. of TB patients (m; f) Freq of HIV patients (m; f)

1. Buleleng 165 (101; 64) 19 (16; 3)2. Jembrana 73 (45; 28) 1 (1; 0)3. Tabanan 57 (31;26) 1 (1; 0)4. Badung 119 (68; 51) 05. Denpasar 312 (171; 141) 16 (11; 5)6. Gianyar 73 (43; 30) 07. Bangli 28 (18; 10) 08. Klungkung 57 (31; 26) 09. Karangasem 116 (75; 41) 2 (1; 1)

Total 1000 (580; 420) 39 (30; 9)

Learning Task 1

1. What is the crude HIV infection among TB patients in Bali? What is the interpretation of that number?

2. What is the specific HIV infection among TB patients in Bali, based on sex? What is the interpretation of that number? Is the number confounded by area or sub-province?

3. What is the specific HIV infection among TB patients in Bali, based on sub-province? What is the interpretation of that number? Is the number confounded by sex?

4. What is the specific HIV infection among TB patients in Bali, based on sex and sub-province? What is the interpretation of that number? Is the number confounded by sex and sub-province?

5. Regarding the above results, explain the weaknesses of crude and specific rate. 6. If you are the Head of Bali Province, what will you do with regard to the above

result (number 1, 2, and 3)?7. If you are the Head of Puskesmas in Buleleng area, what will you do if you

diagnose patient as a TB in Puskesmas?

Case 2

The following graphic shows data of crude birth rate and crude death rate on two countries. Look at carefully, and discuss the following questions. Please be aware that now we are discussing crude, specific and adjusted rate; therefore your answers should be prompted to the concept of those measurements.



Graphic Comparison of CBR and CDR at Canada and Alberta

Learning Task 2

1. First summary: the health status at Canada is better than Alberta. Is that correct? Explain your answer!

2. In order to make fair comparison between those states, what should you do? What data do you need?

3. Second summary: Number of deaths at Canada is greater than Alberta. Is that correct? Explain your answer!

4. Third summary: If adjusted death rate for Canada is 7 per 1000 persons, when the total population of Canada is 33,476,688. The total number of death was 234,339. Is that correct? Explain your answer!

Self Assessment

1. How do you differentiate absolute, ratio, proportion and rate?

2. Incidence and prevalence are not similar in many ways. Explain that!

3. In how many ways can you calculate incidence rate?

4. What are denominators that can be applied for calculating incidence? What is the most ideal enumerator?

5. Which measurement can be used to predict prognosis or trend of mortality for suffering from disease?



M O D U L E ~ 5 (SKILL LAB I)(Reference Skill Lab Manual, Kirkwood & Sterne, Chapter 2)

DATA ENTRY, DATA CLEANING AND DATA TRANSFORMATION

dr. Putu Ayu Swandewi, MPH & dr. Gede Artawan Eka Putra, M.Epid

AIMS:To demonstrate ability to search,organize and interpret information/data from different sources in order to assist in diagnostic, therapeutic and health.

LEARNING OUTCOMES:

1. To make structure of data with SPSS application2. To do data entry3. To clean data before analysis and explain the use4. To do data transformation and explain the use

CURRICULUM CONTENTS:1. Structure of data



2. Data entry3. Data cleaning4. Data transformation

SCENARIO & LEARNING TASKSCase. A population study was done in two sub villages in Desa Merdeka. This study’s purpose was to determine several factors that associated with anaemia and chronic malnutrition among children in the area. The area has two different characteristics which are easy to reach area (easy) and hard to reach area (hard). The condition of both areas was suggested to be considered since there might be different characteristics of the family; hence, the samples were randomly selected from both areas.

First PhaseData collection was performed by interview using structured questionnaire with the mothers and measurement of the children (see questionnaire in the skill lab guide). The characteristics that were explored and measured include: mothers’ and children’s demographic characteristic, haemoglobin level and body weight. More specifically, the variables in the study were ID, name, area, mothers’ age, education, occupation; parity; and children’s age, haemoglobin level, body weight and height. The haemoglobin level measured with HemoCue, and body weight measured with digital scale. Anaemia status was determined when Hb level less than 11 mg/dl and undernourished determined when BMI less than 11kg/m2

Second PhaseAfter the above data collection was completed, the second phase of the study was started. This phase aims to evaluate the impact of food supplementation program to improve nutritional status among undernourished children. All undernourished children were involved in the study, expect those with severe illness. The children were allocated into two groups; first group received food supplementation and second groups continue with the prior daily consumption. The supplementation was provided up to 2 months and at the end of two month the nutritional status (the body weight)were measured again.

Learning Tasks:

See the sheet of data collection and raw data. The data were analysed by computer with software of SPSS. Discuss and analyse the tasks bellow:

1. Explain the types of variables in relation to construction of data entry: variable name (name), type, width, decimal, labels, value labels, missing values!

2. Number (No) consists of 3 numbers (hundreds), name of its field is: number (5 characters, not more than 8 characters for SPSS V.12 or bellow). Fill in the following field structure: type of field: . . . . . . . . . . . . ; Width: . . . . . ; Decimal: . . . . . . . . , Labels: . . . . . . . . . . . . . . . . . . . . . . . . . . , Value labels: . . . . . . . . . . . . . . . . . . . . . ; Missing values: . . .

3. Generally, how can you determine errors in data entry?

4. Explain how you can search and fix the errors!



5. Explain the methods of classifying interval and categorical data!

6. Explain how to calculate composite index of Mass Body Index based on body height and body weight.

Self Evaluation

1. A haemoglobin value consists of 2 numbers and 1 decimal (example: 13.6 gr%). What is its field width?

2. Explain the limits of values that can be used as indicators of error in data entry.

3. Explain the types of variables based on their functions. Give an example of each variable.

4. Explain the types of variables based on measurements scale. Give an example of each.

5. Explain with an example, what is the meaning of ratio variable?6. Explain some methods to control a variable!7. Explain the differences between formal education and IQ variables!

M O D U L E ~ 6(Reference Greenberg, p. 29-43)

Analysis and Interpretation of Descriptive Datadr. Ida Bagus Wirakusuma, MOH & dr. Putu Ariastuti, MPH

AIMS:To demonstrate ability to search, collect, organize and interpret information/data from different sources in order to assist in diagnostic, therapeutic and health.

LEARNING OUTCOMES:1. To analyse, present, and interpret descriptive data.2. To interpret the measurements of morbidity and mortality on samples

descriptively

CURRICULUM CONTENTS:1. Variable of person, place, and time



2. Rate comparison3. Data interpretation

ABSTRACTDescriptive in epidemiology begins with the assumption that disease do not occur in random. Typically three standard questions are posed to characterize the non random distribution of disease: Who get the disease? Where does the disease occur? and When does the disease occur? These questions concern the element of person, place and time, respectively.

At the minimum, the personal attributes examined in relation to disease occurrence are the distribution by age, race and sex. The place of occurrence of the disease may be studied at international, regional and local level. Temporal pattern can be examined across year, month, or days, depending on the time course of the disease in question.


Basic knowledge and its application that students must know include:1. Variables of person, place and time2. Rate comparison and interpretation

SCENARIO & LEARNING TASKS

Case 1. Look at the following figure carefully:

Fig 1 Total deaths by broad cause group, by WHO Region, World Bank income group and by sex, 2008



(Source: Global status report on non communicable diseases 2010)

Learning Tasks 1:Carefully look at the figure above, and discuss the following questions:

1. What are the interpretations of the figure? How many conclusions could you drawn from the figure?

2. When you are living at Indonesia on 2008, what is your risk of death by injuries?

Case 2.

Carefully look at the figure below, and discuss the following questions:

Fig. 2 Most frequently diagnosed cancers world wide



(Source: Global status report on non communicable diseases 2010)

Learning Tasks 2:1. The figures are data of cancer worldwide based on 1) ………; 2) ……….; 3) …………2. Could we conclude that risk of lung cancer among male is higher than liver cancer

among female?3. Could we conclude that risk of breast ca among female is higher than cervix ca?4. Could we conclude that risk of lung cancer in Indonesia is higher than in India?

Case 3:Figure 3. Age-standardized incidence of all cancers (excluding non-melanoma skin cancer), by type, per 100 000 population for both sexes, by WHO Region, 2008

Learning Tasks 3:



Based on the above figure, please answer the following questions:1. The figures are data of cancer worldwide based on 1) …………….; 2) …………..

2. Could we conclude that highest risk of lung cancer was in AMR and EUR?

3. Could we conclude that risk of breast ca among female is higher than cervix ca?

4. Could we conclude that risk of cervix uteri cancer in SEAR is higher than in AFRICA?

5. What are the difference between data provided at figure 2 and 3?

Self Assessments:

1. What is the definition of specific rate?

2. What are the differences between specific and crude rate?

3. Which variables are usually used as a base for specific rate calculation?

4. What is the use/benefit of calculating specific rate?

5. Describe the method to diagnose an undernourish problem among under-five- year old children in one area?

6. A clinician needs to understand the variation (pattern) of diseases based on Who, Where, and When. Why?



M O D U L E ~ 7(Reference Kirkwood & Sterne, Chap 3 &4)

Data Presentation and Data Descriptiondr. Putu Ayu Swandewi, MPH & dr. Gede Artawan Eka Putra, M.Epid


LEARNING OUTCOME:Be able to perform data presentation and data description.

CURRICULUM CONTENTS:Data presentation and data description

ABSTRACTPresenting and describing data are closely related with data classification based on their function, which are: interval (numeric or quantitative), discrete, continue, categorical (qualitative), and ordinal data.

Qualitative data is divided into two categories (dichotomies) and more than two categories (multi-chotomies). Important aspect of qualitative data is the number for each category. The analysis can be made is, either frequency distribution or cross-tab frequency distribution. In the table of frequency distribution, we can calculate incidence, prevalence, and ratio, both for crude and specific measurements based on place, time and person. To summarize the data we can use highest and lowest frequencies and or mode. While for cross-tab frequency distribution, we can make percentage based on column, row, dan total percentages. For specific cross-tab 2x2 (four-fold table), we can calculate some important indicators such as Odds Ratio (OR), Risk Ratio (RR), Specificity (Sp), and Sensitivity (Se). Besides table, we can present this data by graphic, which is most appropriate, is bar chart

Presenting numeric/quantitative data looks more simple than presenting categorical/nominal data. The important aspect to be understood is how to determine appropriate data presentation, either as a single variable or in relation with other variable. Measurements which are used is central tendency (Average) and dispersion. Including in average are mean, mode, median, strength and weakness of average. While



including in dispersion are range, variance, SD; strength and weakness of dispersion. This data can be displayed in graphic such as line chart, hystogram, and box plot.

SCENARIO:Case 1. Data Presentation of Categorical Data

Look at the skill lab Data (appendix) Data which have been collected, include several variables: area, age, education level, occupation, exclusive breastfeeding, parity, children age, Hb level , body weight and height. In the previous session, we’ve discuss variables based on their scale of measurement. Understanding the measurement scale of data is important for selecting the way to present the data. .

For categorical data, the presentation mainly using three different mode which are table, graph/chart and statistics. Table for categorical data are single frequency table, and cross table. Presentation of categorical data in graph can used either bar or pie chart; and statistics that is used are percentage or ratio.

Learning Task 1

Discuss the following:

1. Discuss data presentation in a table and/or chart; and explain the components of table and chart.

2. Discuss the appropriate chart for categorical data based on the variable available in skill lab data

3. Discuss about absolute, relative ad cumulative frequency4. Below is draft of single frequency distribution table

Table-1. The frequency distribution of parity among mothers at Ds. Merdeka, 2011

Parity Frequency Cumulative frequency Relative frequency

Cumulative relative frequency

0 15

1 25

2 30

3 15

4 10

5 or more 5

Total 100- Please fill the blank cells and Interpret the information on the table above!

5. Discuss the presentation of data in a cross tabs frequency distribution table and when we use this type of table. Give example based on the skill lab data



6. Below is cross tabulation between area and type of school and nutritional status among teenager in Province Sentosa

Table 2. Distribution of teenage nutritional status based on type of school in Province Sentosa in 2012

Type of school Undernourished Normal Obese Total

Public 20 (a) 50 (b) 15 (c) 85

Private 10 (d) 50 (e) 5 (f) 65

Total 30 100 20 150

Based on table 2 above,

- calculate, the row and column percentages for each cells

- Interpret the meaning of row percentage in cell a

- Interpret the meaning of column percentage in cell d

- Discuss about chart for presenting data in table 2, please provide an illustration.

7. Specific for 2 by 2 cross tabulation (four-fold table), we can calculate several numbers that useful for describing another method for presenting categorical data, which is ratio. The ratio such as: OR (Odds Ratio), PR (Prevalence Ratio), RR (Relative Risk/risk ratio). Discuss and give example (based on the skill lab data if possible, or your own example)

- The Indication of each ratio

- How to calculate each ratio

- How to interpret the meaning of each ratio

8. There was a cohort study that observed the impact of physical inactivity toward obesity among people age above 40 years old in Province Asri. Underneath is the result after 2 years follow up

Table 3. Cross table between physical activity level and the obesity among people age above 40 years old

Physical activity Obese Normal Total

Less active 30 25 55

Active 10 35 45

Total 40 60 100

From table 3,

- Determine the type of ratio that appropriate for comparing the risk of obesity based on degree of physical activity?



- Calculate and interpret the meaning of the ratio

Case 2. Data Description/Interpretation of Continuous dataRe-open the Skill lab data (appendix). Previously, you have discussed about data presentation of categorical data.

Similar to categorical data, the mode of presentation for continuous data divides into three ways namely; 1) Table, 2) Graphs and 3) Statistics. The tables that can be used are single frequency tables when the range of the data is narrow; on the other hand, using grouped frequency table when the range of data is wide. Several types of graph can be used for presenting continuous data include histogram, box plot, steam and Leaf plot, polygon and scatter plot. Meanwhile, statistics for continuous data comprise measure of central tendency and Measure of dispersion. The measure of central tendency is the value where the data are concentrated. It includes mean, median and modus. Meanwhile, measure of dispersion show the spread of the data includes range, variance, standard deviation, and inter quartile range. It is also important to understand the distribution of data which can be seen by plotting them on a histogram. The distribution will guide us to select the appropriate statistics to represent the data.

Learning Task 2Discuss the following1. Discuss and explain the definition, of central tendency (mean, modus, median). 2. Discuss the distribution of data which can be determine by the value of mean,

median and modus of the data 3. Case 1; If from 100 samples, we found mean Hb level is 12.8 mg/dl, the median is

11.0 mg/dl and the mode is 11.0 mg/dl.- Is the data tend to distribute normally or skewed?

- Which measure of central tendency is appropriate for describing the data? 4. Discuss and explain the definition of measure of dispersion/spread (range,

variance, SD, percentile, quartile and inter quartile range)- Based on case 1 above, what measure of spread is appropriate for describe the

data?5. Based on continuous variables on skill lab data, discuss the appropriate chart that

can be used for data presentation and provide illustration6. In the skill lab data, we have data on children’s age and body weight. Suppose we

wish to show both data in a graphto show the relationship between both variables, what type of graph can be used and make an illustration



Self Assessments:

1. Explain the components of a one-way frequency distribution table! 2. How can you conclude the content of a frequency distribution table?

3. Explain the application of cumulative percentage!4. Cumulative percentage is applied to which kind of data? What is it for?

5. Explain how to conclude the content of a cross tabulation table! 6. Explain briefly the definition of: column, row, and total percentages!

7. Explain some indicators to show the relationship regarding to various categories of variables!

8. Explain the understanding and application of central tendency (average): mean, median, and mode.

9. Explain the understanding and application of dispersion (spread): range, variance, and SD.

10. Explain the advantages and disadvantages of central tendency (average): mean, median, and mode.

11. Explain the advantages and disadvantages of dispersion: range, variance, SD.12. Explain the differences of data presentation and data description of age and formal

education, before and after the categorization. 13. Explain the differences of data presentation and data description of body height,

body weight, and Body Mass Index (BMI).



M O D U L E ~ 8 (SKILL LAB II)

(Skill Lab Manual, Kirkwood & Sterne)

Data Presentation and Data Interpretationdr. Putu Ayu Swandewi, MPH & dr. Gede Artawan Eka Putra, M.Epid

AIMS:To demonstrate ability to search, organize and interpret information/data from different sources in order to assist in diagnostic, therapeutic and health.

LEARNING OUTCOMES:1. To present data into some forms for categorical and continues data2. To interpret data accurately

CURRICULUM CONTENTS:1. Data presentation for categorical data2. Data presentation for continues data


Refer to Cases of Data presentation and Data Description (DAY 9).



M O D U L E ~ 9(Reference Kirkwood & Sterne, Chap. 5-8, 10, 14, 15)


Putu Ayu Swandewi, MD, MPH & Gede Artawan Eka Putra, MD, M.Epid


LEARNING OUTCOME:Be able to interpret inferential and interpretation of result analysis

CURRICULUM CONTENTS:Inferential Analysis and Interpretation of Analysis Results (Hypothesis Test)

ABSTRACT

THEORETICAL DISTRIBUTIONS, CONFIDENCE INTERVAL, AREA UNDER NORMAL DISTRIBUTION, AND HYPOTESIS TEST

For statistical test purpose, we must have basic understanding on theoritical distribution, confidence interval (CI), one-tail and two-tail test.

In a theoritical distribution there are several topics need to be understood, are the probability distribution, characteristics of normal distribution, area under normal distribution curve, definition of Standard Normal Deviate, and also definition and the application of CI. The important point is the understanding on whether a certain data is normally distributed or not. Parametric test require normal data distribution.



Related with normality of data, extreme value outliers may influence data normality. By deleting those values (if the sample size is sufficient), we may produce data with normal distribution. We also can replace those values with the median to gain normal distribution data. If those efforts are not succeed, we may use transformation methods based on power of transformation in the Explore. If the data still not normally distributed, we may choose non parametric test.

In a hypothetic test, we must know the asumptions used, definition of nul and alternative hyphotesis, relation between hyphotesis test with probability (p), confidence interval (CI), the errors in hyphotetis test, and the application of one and two tail tests.

SCENARIO:Case 1.

Theoretical Distributions, Confidence Interval, Area under Normal Distribution

For statistical tests, it is important to understand probability theory, theoretical distributions, confidence intervals (CI), one-tail and two-tail statistical tests, and error in interpreting hypothesis tests.

Learning Task 1Discuss the following

1. Explain the definition of probability, marginal probability, conditional probability and joint probability

2. Explain the definition of “the addition rule and multiplication rule”3. Table 1. Cross tabulation between economic status and nutritional status of

children under 5 years in Desa Amerta 2012

Economic status Under nourish Normal Obese Total

poor 30 35 5 70

affluent 10 55 15 80

Total 30 80 20 150

Based on table above, calculate the probability:a. What is the probability if we selected one child randomly from Desa Amerta,

will be an under nourish kids in? What type of probability it is?b. What is the probability of under-nourish, if the kids from poor family? What

type of probability it is?c. Probability of a child who is randomly selected from this group will be from

poor family or an obese kid?

d. Probability of a child who is randomly selected from this group will be from affluent family and is obese



4. Describe the type of probability distribution of a random variable. Give examples!

5. Describe the characteristic of normal distribution and the use of normal distribution?6. Distinguish the difference between “normal distribution/Gaussian distribution” and

“standard normal distribution” 7. Describe about z score and its use?

8. If from a study we found the mean of Hb level from 200 samples of children is 11.8 mg/dl, and the standard deviation is 0.5mg/dl. It is assumed that data was normally distributed. Based on the used of normal distribution and the area under normal curve (z score)

a. What is the probability of children in the population has Hb level > 12mg/dlb. What is the probability of children in the population has Hb level <11 mg/dl

9. Describe about sampling distribution, standar error of the mean and confidence interval (CI) of the mean

10. Based on case 2 above: - Calculate the standard error of the mean

- Calculate the 95% CI of the mean- Interpret the 95% CI of the mean

Case 2Hypothesis Testing and Linear Correlation Re-open skill lab data (appendix).

Recall that the type of data presentation is based on scale measurement and also the type of hypothesis testing will be based on the scale of measurement and purpose of the analysis. Basically, there are two types of hypothesis testing; namely: testing the difference and testing the association. The basic concept of hypothesis testing should be understood before performing the test. In hypothesis testing we are testing the possibility of the difference or the association is likely to be true in the population. There two type of hypothesis, null hypothesis (Ho) and alternative hypothesis (Ha)

For testing the association between two continuous variables we can use correlation test with correlation coefficient from Pearson (r). In the correlation test, we can determine the strength and direction of the association.

Correlation may be a linear and non-linear, symmetric and non-symmetric, straight and reverse correlation, and relational and causal. In this case, we will only discuss linear correlation.

Learning Task 21. Discuss the indication of linear correlation analysis.

2. From the variables in skill lab data, discuss example of possible correlation analysis



3. Discuss the two ways that we can use for determining correlation between two variables?

4. Discuss the use of coefficient correlation?

5. Discuss the meaning of negative and positive correlation, provide example!6. Discuss the strength of the association, when it is considered low, moderate, high and

perfect, describe with example!

Self Assessments:1. Draw figure of t-distribution, chi-square (2) distribution, F-distribution, Poisson

distribution, Log normal distribution, Binomial distribution. What are the differences of those six distributions?

2. Explain the probability distribution of a random variable!

3. Explain the definition of Normal distribution (Gaussian distribution).4. Explain the characteristics of Normal distribution.

5. What is the total area under the normal curve between: (-) and (+), (-1.96) and (+1.96), (-2.58) and (+2.58).

6. Describe the definition of SND (Standard Normal Deviate).7. Explain one-tail and two-tail statistical tests and when they can be applied.

8. Describe the strength and direction of quantitative variables correlation.9. What does it mean if two quantitative variables have correlation coefficients (r)

which are negative or positive?10. What does it mean if two quantitative variables have correlation coefficients (r) of (0)

or (1)?



M O D U L E ~ 10(Reference Kirkwood & Sterne, Chap. 7, 9, 11, 16, 18, 19)

Significance Test for Categorical and Interval Data dr. Putu Ayu Swandewi, MPH & dr. Gede Artawan Eka Putra, M.Epid


LEARNING OUTCOME:Understand the significance test for categorical and interval data

CURRICULUM CONTENTS:Significance Test for Categorical and Interval Data

ABSTRACT

1. CATEGORICAL DATAFor analytical test, we must remind classification of variables based on their function to determine which variable act as dependent or independent variable.

In a sample taken from certain population, we can calculate proportion, rate, or ratio. Those numbers are used to estimate the parameters in a population. To know whether those numbers are accurate to estimate population parameters, we need to conduct hypothesis test. When conducting the hypothesis test, we need to state statistic hypothesis. There are two types of statistic hypothesis, are null hypothesis



(H0) which shows no difference (similar) in a population, and alternative hypothesis (Ha) which shows there is/are difference(s) (not similar) in a population. The way to write those hypothesis are depending on the study direction and indicator to be measured.

Statistical tests for categorical data include test for 1 proportion (binomial test), two proportions (Chi-square, Mc Nemar, Fisher exact), and three proportions or more (Chi-square). Each of those has requirements to be fulfilled before the test being conducted. In some tests, if the requirement is failed to be fulfilled, there are available the alternative test.

2. INTERVAL DATAFor interval data, the indicators to be calculated in statistical test are mean or median. Statistical hypothesis also stated in H0 andHa in a ways depend study direction and the indicator measured. Requirements for statistical test on interval data are data must be normally distributed and the variance between the groups tested are similar.

Statistical tests for interval data include test for mean of 1 sample (One-sample T-test), Independent-Sample T Test for unmatched 2 samples and Paired-Sample T Test for matched 2 samples if the analysis is conducted for one dependent interval variable with one dichotomous categorical variable. If the data is not normally distributed, we may choose parametric test such as Mann Whitney U Test, Wilcoxon (Wilcoxon Sign Rank)

While for analysis of one dependent interval variable with one multicotomous (more than 2 categories) variable, the choice are One-Way ANOVA (if normally distributed), comparative group test Bonferroni, LSD, Scheffe etc (if variance is similar), Tamhane’s T2/T3 etc (if variance is not similar), and non parametric test Kruskal-Wallis if the data is not normally distributed.

SCENARIO:Case 1. Significancy Test for Categorical Data

Re-open skill lab data (annex).

Data available in the data set include: area, age, education level, occupation, exclusive breastfeeding, parity, children age, Hb level, and body weight. Let’s recall that understanding the measurement scale of data is important for selecting the way to present the data. Beside for purpose of data presentation, measurement scale of data is important on determining the appropriate type of statistical testing (hypothesis testing). Moreover, the classification of data based on the function in the relationship is also important issue to be considered.



We have discussed that there are mainly two type of hypothesis testing which are testing the association and testing the difference (comparison). In the previous section, we learned about correlation test which is one type of test for testing association between variables. In this section, we will discuss hypothesis test for the difference (comparison). The testing will be differentiating based on the type of data (categorical or continuous data).

From a sample taken from a population, we can calculate proportion, rate, and ratio. These numbers are parameter estimations of a population. Whether a number gained from sample may or may not represent its population, it needs to be verified by statistical methods towards the hypothetical test. There are two kinds ofhypothetical statistic statements: Null Hypothesis (H0) and Alternative Hypothesis (Ha). The Null Hypothesis (H0) assumes that there is no difference (similarity) in a population, while the Alternative Hypothesis (Ha) assumes the opposite.

Learning Task 11. Discuss about null hypothesis and alternate hypothesis, and provide example of

hypothetical testing based on the skill lab data2. Discuss two type of error in making decision on statistical analysis

3. Discuss about the p value and its use in statistical testing?4. Discuss

a. If the p value of a test is 0.01, what does it means?b. if the p value of a test is 0.1, what does it means?

5. If from the study we found that the prevalence of malnutrition in Desa Merdeka is 12%. Can we conclude that the proportion is different from national data which is at 10. %? (α=0.05)Discuss:

a. The statistical hypothesis of case 1b. The statistical test should we apply for the analysis of case 1

c. How we determine the statistical decisiond. If the p value for the study is 0.1, what is your decision and interpretation

6. We want to compare the proportion of undernourished children from hard area (1) and easy area (2). (α=0.05). The proportion of undernourished children in hard area and easy area is 18% and 15%, respectively.Discuss:

a. The statistical hypothesis for testing there is a different between both area!b. The statistical hypothesis for testing that the proportion of undernourished

children is higher in the hard area compare to the easy area



c. The test should we apply for the analysis!

d. Create the 2x2 table for this casee. If p value from the study is 0.005, what is the statistical decision and

conclusion of the analysis7. We want to determine wether obesitas is a predictor of diabetes millitus (DM)

among men age above 40 years olds. A cross-sectional study was conducted; among 100 men above 40 years old; 40 of them are obese. Among those 40 with obese 20 of them are diagnosed with DM; meanwhile among the remaining 60 men, 15 of them are diagnosed with DM.

Discuss: a. The statistical hypothesis for testing for determining that obesitas is predictor

of DMb. The test should we apply for the analysis!

c. Create the 2x2 table for this cased. Calculate the ratio of risk/odd that appropriate based on the study design

e. If the 95% CI of the above ratio is between 0.8- 3.5, what is the statistical decision and conclusion of the analysis

Case 2

Significancy Test for Interval Data

Open again “skill lab data” file (annex). Classification of variables has been discussed in previous lesson. Remember about interval variables! For statistical analysis, we also need to remember variable classification based on its function. Variables may act as dependent or independent variables.

The statistical analysis is classify into two group which a parametric test and non parametric test. Parametric test based on the assumption of the distribution of the data. For continuous data that are normally distributed, the statistical analysis includes t test and ANOVA. The tests principally based on comparison of mean between groups.

Learning task 2Discuss the following:

1. Discuss the statistical method to be applied for testing of a sample mean.

We want to compare the mean Hb level of pregnant women in the study, with the normal Hb level for pregnant women which is 11mg/dl. Can we conclude that the mean of Hb level of samples is higher than 11 mg/dl?a. Write down the statistical hypothesis!

b. Discuss, the test that should be applied for the analysis!c. How we determine the statistical decision

2. Discuss about when two groups of samples/ data considered as two independent or two dependent samples?



3. Discuss about homogeneity of variance and when we should determine it?

4. What type of test is used for testing homogeneity of variance, provide an illustration!

5. We want to compare the mean body mass index (BMI) among children from hard area and children from easy area. We wish to conclude that the mean BMI of children in hard are is higher than the children in easy area. Let α=0.05a. Write down the statistical hypothesis for case 2!

b. Explain what test should be applied for the analysis of case 2!c. If the p value from the study is 0.09, what is the statistical decision and

conclusion of the analysis?6. We want to evaluate the impact of nutrition program on the body weight among

the children in the study. Can we conclude that the intervention is effective to increase the children’s body weight? Let α=0.05

a. Write down the statistical hypothesis for case 3!b. Discuss the test should be applied for the analysis for case 3!

d. If the 95% confidence interval of body weight mean difference is 1.2 to 2.8 kg, what is the statistical decision and conclusion of the analysis?

7. We want to compare the mean Hb level pre intervention (Hbpre) of children based on the nutritional status pre intervention (stgizi_pre). Can we conclude that the mean body weight differ based on nutritional status?Discuss:

a. What test should we apply for the analysis?b. Is the analysis one tail or two tails?

c. Write down the statistical hypothesis!d. If the p value from the test is 0.2, discuss about the statistical decision and

the conclusion.8. Explain the conditions for applying those statistical tests above!

If the conditions cannot be met, what statistical method should be applied for testing the hypothesis?

Self Assessments:Significance Test for Categorical Data

1. Explain the statistical method for 2x2 tables and its requirements.2. Explain the statistical methods if those requirements are not fulfilled!

3. Explain the statistical method for pair 2x2 tables.4. Describe the statistics that can be calculated in 2x2 tables for cross-sectional, case-

control, and cohort study.5. Write down the statistical hypothesis statement!



6. Explain the statistics for correlation of two variables: interval-interval, ordinal-ordinal, nominal-interval, nominal-nominal, and similar number of categories.

7. Explain the condition for 2 test of the 2x3 tables or more. Explain how to deal with that if those conditions are not provided.

8. Write down the statistical hypothesis statement for proportion, prevalence, RR, PR, and OR for one-way and two-way tests.

9. What is the meaning of type 1 and type 2 sampling error?

Significance Test for Interval Data

1. Explain the application and requirements for one sample T-test!

2. Which T-test should be applied for independent and pair samples? 3. Write down the statistical hypothesis statement used above!

4. Regarding the tests above, which tests should be used if the condition required for T-test is not met?

5. Explain the non-parametric test to be applied if the condition required for the independent One-Way ANOVA is not met.

6. Write down the statistical hypothesis statement for the above question!

M O D U L E ~ 11 (SKILL LAB III)(Skill Lab Manual, Kirkwood & Sterne)

Correlation-Regressi, Significance Test for Categorical and Interval Data

dr. Putu Ayu Swandewi, MPH & dr. Gede Artawan Eka Putra, M.Epid

AIMS:To demonstrate ability to search, collect, organize and interpret information/data from different sources in order to assist in diagnostic, therapeutic and health.

LEARNING OUTCOMES:1. To conduct data analysis correctly for categorical and interval data2. To interpret data accurately

CURRICULUM CONTENTS:1. Data analysis for categorical and interval data



2. Data interpretation for categorical and interval data

SCENARIO & LEARNING TASKSRefer to Cases of Data analysis and data interpretation for categorical & continues data (DAY 12).

M O D U L E ~ 12(Reference Greenberg, p. 45 – 73)


dr. Ayu Kartika Sari, MPH & dr. Putu Ariastuti, MPH


LEARNING OUTCOMES:At the end of the module, students should be able to:

a) Manage, analyze and interpret data inferentially.

b) Describe the definition, requirements, types, and applications of surveillance. c) Describe how to conduct an epidemiologic investigation of an outbreak.



CURRICULUM CONTENTS:Surveillance and Disease Outbreak

ABSTRACTSURVEILLANCESurveillance was defined as the detection of the occurrence of health-related events or exposures in a target population. Successful surveillance activities require continuity over time; standardize methodology, and timelines of data collection and dissemination.

Surveillance data can be used in different ways, depending on the type of information collected. Persons newly diagnosed with a disease can yield information on incidence rate, death from a disease can be used to describe mortality rate, indices of premature death can be used to assess the impact of a disease of longevity, and prevalence of risk factors can be used to predict future disease occurrence or to assess the status of prevention initiatives.

OUTBREAKSThe investigation of disease outbreaks (sudden and geographically limited epidemics) is an essential role epidemiology. The primary goals of an outbreak investigation are the identification of the causal agent (the pathogen) and prevention for further cases. The propagation of a disease outbreak requires a pathogen, a viable mode of transmission, and an adequate poll of susceptible persons. Elimination of one or more of these three components will terminate the outbreak. Two basic modes of transmission are person-to-person spread and common-source exposure. Infection illnesses can be transmitted by either mode, whereas non-infectious environmental pathogens usually produce disease outbreaks through a common-source transmission.

Not all disease outbreaks warrant investigation. The decision to investigate an outbreak typically is based on the severity of illness, the number of affected persons, uncertainty about the pathogen, and the perceived need to control further spread of the disease. Investigations usually are conducted by local, state or federal public health officials.

A measure of the risk of developing an illness over specified period of time is the attack rate (AR). The traditional epidemiology of food-borne outbreaks-high attack rates, short incubation periods, and clustering of affected individuals in time and place-is changing for a number of reasons. The composition of the topical diet has shifted, with decreased consumption of red meat and increased consumption of poultry, fresh fruits, and vegetables.

Emerging infections can be defined as infections that are appearing in a population for the first time or that have existed in a population but rapidly increasing in incidence or geographic range. A number of factors contribute to the emergence of certain infections:

1) Man-made or natural changes in the environment

2) Demographic shift in population



3) Increased international travel and commerce

4) Technologic and industrial changes.

5) Adaptation of microbes

6) Lapses of the public health system.

Self Directing Learning

1. Read previous lectures 2. Read Greenberg pp. 45-73and power point slides3. Watch the “Contagion” movie


Case 1 & Learning Task 1

Interpretation of Surveillance Results

The following chart contains results of surveillance of dengue hemorrhagic fever in Bali Province, provided by the Bali Provincial Health Department.

Discuss the following questions:

1. The surveillance is conducted to assess morbidity, mortality, or risk factor of dengue? Explain your answer.

2. What is the interpretation of the figure?

3. What is the benefit of conducting surveillance of dengue cases?

4. Think of the natural history and the determinant of DHF and also consider the types of surveillance. What other surveillance might needed for the success of the dengue prevention program?



KASUS DBD PERBULAN SELAMA 5 TAHUN DI BALI

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2003 2004 2005 2006 2007

Case 2. Watch the movie “Contagion” and read the synopsis below

Synopsis of the movie: “Contagion”

1. This movie is a fiction which inspired by the incidence of SARS in several countries around the world.

2. The story is about an outbreak of an unknown disease occurred initially in China and spread to other places including the USA, Japan and England.

3. The movie shows how a disease can be transmitted very fast to cause an outbreak all over the world, how the mode of transmission and the causing agent can be identified, and how the transmission can be finally prevented.

4. The story is started with a woman named Beth Emhoff (Gwyneth Paltrow) departing to Minnesota from her Chicago layover. She had a short term visit to Macao (near Hong Kong) beforehand. She seemed to cough several times at the airport when she talked to her old boyfriend on the phone.

5. The story was continued with the occurrence of a severe flu-like disease in a young man in Hong Kong who then died because of a traffic accident, in a young model in London who then found dead in her bathroom and in a man in Tokyo who had a seizure and died on a bus.

6. The same symptoms that happened to Beth got worsened once she arrived in Minnesota and met her husband and son. She died in a hospital 2 dayslater with severe seizures and the doctor decided to make an autopsy to her body. Soon after her death, her son died after having the same symptoms.



7. Beth’s husband, Mitch (Matt Damon), was then put in isolation but turned out to be immune to the disease.

8. In Switzerland, a briefing led by Dr. Leonora Orantes (Marion Cotillard) was conducted at the WHO office. She emphasized the need to investigate the cases of infection thoroughly and thought that Beth is the potential start of the infection. She then went to Hong Kong to investigate the movements Beth made while there.

9. Dr. Ellis Cheever (Laurence Fishburne), who works at the Atlanta-based Centers for Disease Control and Prevention (CDC), asked Dr. Erin Mears (Kate Winslet) to do an investigation in Minnesota. She gave a briefing at the nearby CDC office about the potential mode of transmission of the disease. She said that the virus is a contagion of touch and the problem is the number of people will be potentially infected (R-naught number), which is very high for this novel disease. She was then unfortunately infected and died during the investigation.

10. There was an issue released by a blogger journalist, Allen (Jude Law), that the virus is being manufactured by drug companies to get profit. Another issue said by Homeland Security agents was that the virus might be a terrorist action to kill people.

11. Two doctors, Ally Hextal (Jennifer Ehle) and David Eisenberg (Demetri Martin) looked at the samples taken from Beth’s body in a secure CDC bio laboratory. They found a virus that taking over the host cell completely, and noticed there were traces of bat and pig in the virus DNA code.

12. Dr. Sussman (Eliott Gould) ran some further tests on the virus samples and he was able to make a stable cultured version of the virus using a particular bat cell line. This finding made them able to develop and test vaccines. The news reported on the virus, now called MEV-1 (Meningoencephalitis virus type 1), and credited Sussman for the discovery.

13. The story continues with the situation of the quarantine and vaccines delivery process to prevent the transmission of the disease. Finally the movie shows the process of how Beth was initially infected by the virus.

Learning Task 2

Based on the story of the movie, please answer the following questions:

1. What is the type of the disease outbreak in the movie? Is it a “person to person outbreak” or a “common source outbreak”? Please explain your answer.

2. Why is it important to find the first person being infected? Describe how the process of identifying the mode of transmission was conducted in the movie.

3. What efforts need to be taken to prevent the disease transmission before the vaccines are available?

4. Describe how the process of finding the virus was conducted in the movie.

5. What is the importance of many stakeholders involvement (WHO, CDC, government, military staff and journalists) in the outbreak investigation and transmission prevention process?



Self Assessment

Interpretation of Surveillance Results

1. The government continuously collects data for smoking behavior among men and patterns of meat consumption among both men and women. In this case, what the government does is:A. New infections surveillanceB. Outbreaks surveillance C. Death surveillanceD. Risk factors surveillanceE. Epidemiologic surveillance

2. A group of Lung Cancer patients who died were identified for their age of death. The difference between the age of death and their average life expectancy was then accumulated. That will produce:A. PYL (Person-years of Life lost).B. MR (Mortality rate).C. YPLL (years of potential life lost).D. YPM (Years of mortality)

3. 60% of lung Cancer patients in stage 2 have the possibility of survival for five years following diagnosis of the cancer. The measurement of 60% follows the criteria as below:A. It is a case fatality rate of Lung Cancer patientsB. Can be used to predict the prognosis of Lung cancer patients in a similar stage. C. It was measured from the lung cancer patients in one periode of time into Lung

cancer patients’ stage 1 – 3.D. One person with Lung cancer stage 2 having possibility to live in the first five

years as of 0.06.E. All Lung cancer patients’ stage 2 may live five years after diagnosed.

4. When compared to the corresponding rate for non-migrants in a low risk country, the incidence rate for a genetically determined disease among offspring in a high-risk country is:A. GreaterB. SmallerC. About the sameD. Cannot be determined from the information providedE. All possible



Outbreak Investigation

1. Which steps of those above are parts of descriptive methods? Which are parts of analytic methods?

2. Not all outbreaks need epidemiologic investigation. Several factors to be considered for an investigation are: 1. Number of apparent cases prevalent2. Public concern3. Aetiology of the outbreak is obviously unclear4. Need for intervention to be stopped

3. The following are parts of the outbreak investigation steps:1. Distribute cases based on person, place and time2. Calculate prevalence rate based on person, place and time 3. Assume temporary hypothesis on transmission’s source4. Analyse aetiology by cross sectional study

4. Due to the outbreak investigation, the analysis of cases based on person, place, and time is done. Its main purpose is:1. Find out the most common group affected by the disease2. Find out the area which was mostly affected by the disease3. Determine the time of outbreak4. Predict the possibility of exposure

5. A result of outbreak investigation is as follows:

Risk Estimate Value95% Confidence Interval

Lower Upper

Odds Ratio for Stall B (Yes / No) 0.048 0.016 0.145

For cohort Sick/healthy = Yes 0.231 0.130 0.410

For cohort Sick/healthy = No 4.846 2.307 10.181

Its interpretation is:a. Risk of diarrhea if having meals at B stall is a half lower than not

having meals at the other stall. b. Risk of diarrhea if having meals at B stall is higher than not having

meals at B stall.c. The OR only affects sample, but not a population. d. The B stall is proven not to be source of transmission.

6. If an OR of eating “es campur” at the B stall is 1.3 with the CI from 0.6–1.9, what is the significance of the data? What conclusions can you draw about the “es campur”



eating within the whole population? Give an example of a more significant OR and CI.

7. A doctor at puskesmas was function as program coordinator for transmitted disease at district with the total population was 500.000 persons. The result of surveillance for several infectious diseases for 5 years was as follows: (source UKDI)

Diseases Year I (%) Year II (%) Year III (%) Year IV (%)

Year V (%)

Malaria 30 35 32 34 31

DHF 0,6 0,5 0,4 0,9 2,1

Typhoid 5,1 5,4 4,8 6,2 5,8

Hepatitis C 2,1 2,2 1,8 1,7 1,6

Influenza 50 45 52 53 53

Which one of the above has become an outbreak? a. Malariab. DHFc. Typhoidd. Hepatitis C

e. Influenza

M O D U L E ~ 13(Greenberg)



Epidemiology Study to Determine Risk Factor of Disease

dr. Anak Agung Sagung Sawitri, MPH & Dr. dr. Gd Indraguna Pinatih, MSc., Sp.GK

AIMS:1. To describe the types and application of epidemiology study designs to determine risk

factors of the disease of interest2. To describe cross sectional & longitudinal study design and to explain the advantages

and disadvantages of cross sectional & longitudinal study to determine risk factors of diseases.

LEARNING OUTCOMES:1. To describe and to draw types of epidemiology study designs and their applications2. To describe advantages and weaknesses of epidemiology study designs3. To describe and to draw cross sectional and longitudinal study design 4. To explain the advantages and disadvantages of cross sectional and longitudinal

study to determine risk factors of diseases5. To explain what is prevalence ratio and how to calculate prevalence ratio in cross

sectional study6. To explain the interpretation of prevalence ratio in cross sectional studies7. To explain the incidence rate and how to calculate incidence in longitudinal study8. To explain the interpretation of incidence rate in longitudinal studies

CURRICULUM CONTENTS:1. Type, application and advantages/weaknesses of epidemiology study designs 2. The cross sectional and longitudinal study design and theirs attributes3. Timing of measurements: cross sectional and longitudinal design4. Data collection in cross sectional and longitudinal studies5. Data analysis in cross sectional studies: prevalence ratio (PR)6. Data analysis in longitudinal studies: incidence rate

ABSTRACTS



To prevent the disease, determinants or risk factors of the disease must be understood. To understand determinants or risk factors, epidemiological studies are used. There are several epidemiological study designs those can be grouped into several ways. Based on the availability of intervention or not, the study designs can be divided into intervention or experimental study and observational study. Intervention study is used to test the efficacy of treatment or prevention of certain disease in a population. Observational studies can be grouped into two groups based on whether there are comparison groups or not. Descriptive study is an observational study without comparison group to study the distribution of the diseases occurrences in a population. Analytic studies have comparison groups to study risk factors of diseases. Analytic studies can be defined into three based on the direction of the observation or the measurement of the outcome. Cross-sectional is when the measurement or the observation of the disease under study (outcome) is at the same time with the measurement of risk factors. Cohort design is prospective or effect to cause while case control design when the direction of the observation is retrospective or cause to effect.

Descriptive studies describe diseases occurrences (“how much or how many”) in the community based on “who”, “when” and “where” attributes. There are two major purposes of this study, are for program planning and evaluation and for foundation of hypothesis testing of analytical studies (experimental, cross-sectional, case-control and cohort).

Each type of analytical study has advantages and disadvantages. Experimental study is not possible to be implemented in human when the aim is to study risk factors (cause) of certain disease but It can be used to study treatment and prevention of certain disease. It is also the strongest design to test the hypotheses. Major disadvantage of cross-sectional design is lack of temporal association since outcome (disease) and risk factors are measured at the same time. Major disadvantages of cohort design are expensive and take long time especially when the disease incidence is low and the time between exposure and disease occurrence is long. Case-control design is less expensive and faster than Cohort, however it has major weakness of information bias and difficult to match between “case group” and “control group”.

SELF DIRECTING LEARNING:Basic knowledge and its application that students must know include:1. Study design of cross sectional and longitudinal studies and their attributes2. The procedures of cross sectional and longitudinal studies3. Findings and its interpretation (including crude PR, adjusted PR) as well as

confounding factors in a cross sectional study4. Findings and its interpretation (including Crude and Specific Incidence Rate)




Case 1. Please carefully study the article entitles: “Dengue and Other Common Causes of Acute Febrile Illness in Asia: An Active Surveillance Study in Children”

Learning Tasks 1:After you carefully read through the article above, please discuss the following questions:1. Draw a figure and explain the design of the study. Provide reasons for your

answer.

2. What is the purpose of the study?

3. What are the uses of this study?

4. Does the study use primary or secondary data?

a. What is the source of the numerator?

b. What is the source of the denominator?

5. Explain how researcher determines and selects the sample!

6. Explain how researcher measures the incidence of acute fever!

7. What are the three most common cause of fever in Asia? And what are the three most common cause of fever in Indonesia? What is the rank of Indonesia among Asean countries in term of dengue?

8. Explain the interpretation of incidence of dengue, chikungunya, and typhoid fever obtained from the study in Indonesia? The denominator is PY, what is the definition and the strength of PY?

9. Carefully observed Table 3. What measurements explained in the Table? Why researcher presenting that table?

10. How you interpret the 95% CI in the below data?



Case 2. Please carefully study the article entitles:“Bacterial vaginosis in female facility workers in north-western Tanzania: prevalence and risk factors”

Learning Tasks 2:After you carefully read through the article above, please discuss the following questions:

1. What is the design of the study which investigated 1305 HSV-2 seropositive women aged 16-35 years and acyclovir 400mg? What is the dependent variable and independent variable? Note: HSV: herpes simplex virus

2. What is the design of the study which investigated prevalence and risk factors for bacterial vaginosis (BV) among HSV-2 seropositive women? What is/are the dependent variable and independents variable?

3. Draw figures (bagan) of study designs. Firstly a figure of study to understand the outcome of acyclovir treatment toward HSV-2. Secondly a figure of study to find out the association between BV and sex in the last week and other risk factors. Note: See Widagdo page 99 and page 148.

4. Calculate the 95% Confidence Interval of BV prevalence using the below formula and explain the interpretation.

Penghitungan CI untuk proporsi (angka prevalens, angka insiden dan proporsi)

CONTOH:

P (proporsi) kurang gizi = 23.2%

N (jumlah sampel) = 400

RUMUS CI:



CATATAN:

(Untuk nilai Z- nya yang dibagi 2 adalah alpha-nya.

Jadi untuk 95% CI, alpha nya berarti 5% di “Table Z” dicari nilai Z dari (1-α/2) yaitu (1-0.025) atau nilai Z ( 0.975) yaitu 1.96

Kalau yang dicari adalah 99% CI dilihat di “Tabel Z” adalah nilai Z (0.995) yaitu 2.57

5. a) List significant risk factors and their adjusted OR of BV which were found in this study b) List not significant risk factors of BV which were found in this study.

6. a) Why the researcher calculate that adjusted OR?

b) Which one is correct term, OR (odds ratio) or prevalence ratio? Note: See Widagdo page 102.

7. a) What is the main weakness of this study?

b) List other weakness of this design. c) List advantageous of this design. Note: See Widagdo page 107.

8. NOTE: Below are the advantageous and weakness of epidemiological study design

KEUNGGULAN & KELEMAHAN RANCANGAN/DISAIN PENELITIAN

RANCANGAN KEUNGGULAN KELEMAHANEKSPERIMENTAL MURNI

1. Merupakan rancangan yang paling kuat/meyakinkan untuk membuktikan hipotesis, karena peneliti sepenuhnya bisa memanipulasi/melakukan kontrol terhadap variabel-variabel yang diteliti, baik variabel pengacau/confounding maupun terhadap variabel independent

1. Pemakaiannya terbatas hanya pada binatang dan tumbuh-tumbuhan saja

EKSPERIMENTAL KUASI(Clinical dan Community Trials)

1. Lebih kuat/meyakinkan dibandingkan penelitian observasional.

2. Bisa dilakukan pada manusia

1. Tidak semua variabel pengacau bisa dihilangkan efeknya

2. Pemakaian pada manusia terbatas hanya untuk meneliti percobaan penyembuhan dan pencegahan penyakit saja, dan tidak bisa dipakai untuk meneliti penyebab penyakit.

COHORT PROSPEKTIF

1. Angka insiden penyakit bisa diperoleh

1. Pada penyakit-penyakit yang angka insidennya rendah, maka jumlah subyek (penduduk sehat)



2. Seringkali secara kebetulan dijumpai hubungan antara exposure/pemaparan yang diteliti dengan penyakit yang sebelumnya tidak terpikirkan oleh peneliti

3. Karena pemaparannya terjadi pada saat sekarang maka bias dalam menetapkan exposure akan lebih kecil dibandingkan rancangan case-control

yang harus diamati/diperiksa secara berkala akan sangat banyak, ini memerlukan biaya, tenaga dan ketekunan yang amat tinggi.

2. Pada penyakit-penyakit yang waktu antara mulainya pemaparan dan timbulnya penyakit panjang, maka akan memerlukan waktu pengamatan yang amat lama. Akibatnya: juga memerlukan biaya, tenaga dan ketekunan yang tinggi.

3. Pada pengamatan yang lama: attrition/lost to follow upakan menjadi tinggi. Ini juga amat dipengaruhi oleh tingkat mobilitas penduduk

4. Ada kemungkinan bias pada saat menentukan pembilang angka insiden, karena kemungkinan adanya kesalahan pada saat menentukan sakit/tidaknya subyek yang diamati.

5. Pada pengamatan yang panjang, bisa jadi KRITERIA atau CARA DIAGNOSIS penyakit telah berubah terutama pada dekade ini dimana teknologi kedokteran berkembang dengan sangat pesat.

COHORT RETROSPEKTIF

1. Relatif lebih murah bila dibandingkan cohort prospektif

2. Waktu penelitian lebih pendek sehingga hasilnya cepat diperoleh

3. Sama halnya dengan cohort prospektif: angka insiden bisa dihitung sehingga RR (relative risk) bisa diperoleh.

1. Rancangan ini 100% tergantung dari kelengkapan pencatatan di masa lalu, baik pencatatan tentang exposure (independent variable), kejadian penyakit (dependent variable), dan variabel-variabel pengacau lainnya. BILA PENCATATAN TIDAK LENGKAP, MAKA BIAS NYA AKAN TINGGI. Karena rancangan ini 100% tergantung dari data sekunder, maka tidak semua variabel confounding mesti tersedia pada catatan pasien.



CASE CONTROL 1. Relatif lebih rendah biayanya dibandingkan cohort prospektif.

2. Jumlah subyek yang dilibatkan dalam penelitian lebih kecil dibandingkan cohort.

3. Waktu pengamatan/penelitian lebih pendek dibandingkan cohort prospektif, sehingga hasilnya lebih cepat diperoleh.

4. Lebih tepat untuk penyakit-penyakit yang angka insidennya rendah atau yang “masa inkubasi-nya” panjang.

1. Karena menelusuri exposure di masa lalu, maka lebih besar kemungkinannya, data tentang exposure yang tersedia DARI CATATAN-CATATAN YANG ADA tidak komplit.

2. Bila exposure ditelusuri melalui wawancara/interview, karena responden harus mengingat kejadian-kejadian di masa lalu, maka lebih besar kemungkinannya mereka lupa (BIAS RECALL)

3. Sering kali dijumpai adanya kesulitan ketika memilih CONTROL yang betul-betul serupa dengan kelompok CASES, terutama pada rancangan mathcing.

4. Tidak bisa diperoleh RELATIVE RISK (tetapi hanya estimasi/ perkiraan relative risk saja). Perkiraan RR ini disebut ODDS RATIO.

ANALITIK CROSS-SECTIONAL

1. Karena baik faktor penyebab maupun akibat (dependent dan independent variable) dan demikian pula confounding variable diamati pada satu titik waktu saja, maka pelaksanaan penelitian relatif lebih cepat dibanding case-control dan cohort.

1. Rancangan ini PALING LEMAH dalam hal pembuktian HUBUNGAN KAUSAL. Kriteria hungnan temporal tidak bisa dipenuhi, karena tidak bisa diketahui, mana yang terjadi terlebih dahulu apakah independent atau independent variabelnya. Angka insiden dan RR tidak bisa diperoleh. Yang dipakai hanya OR.

Self assessment

1. What are the roles of descriptive epidemiologic study designs?

2. What are the types of descriptive epidemiologic study designs?

3. Draw the figure of cross-sectional analytic study design.

4. What is the basic definition of cross-sectional and what is the procedure of the study?

5. Make one example of results analysis for analytic cross sectional design.

6. What are the advantages and disadvantages of analytic cross sectional design?



7. Can analytic cross-sectional design be applied as a basic study to find out aetiology of a disease?

M O D U L E ~ 14(Greenberg, p. 113-123)

Epidemiology Study Design: Cohort Studydr. I Nyoman Sutarsa, MPH & dr. Putu Ariastuti, MPH

AIMS:To describe cohort study design and to explain the advantages and disadvantages of cohort study to determine risk factors of diseases.

LEARNING OUTCOMES:1. To describe and to draw prospective and retrospective cohort study design 2. To explain the advantages and disadvantages of cohort study to determine risk

factors of diseases3. To explain what is relative risk and how to calculate relative risk4. To explain the interpretation of relative risk in cohort studies

CURRICULUM CONTENTS:1. The cohort study design and its attributes2. Timing of measurements: prospective and retrospective design3. Subject selection in cohort studies: exposed and un-exposed groups4. Data collection incohort studies5. Data analysis in cohort studies: risk ratio (RR) and attributable risk (AR)

ABSTRACTS

A cohort study is a type of observational investigation in which subjects are classified on the basis of level of exposure to a risk factor and followed to determine subsequent disease outcome. Prospective cohort studies are conducted by making all observations on exposure and disease status after the onset of the investigation. Retrospective cohort studies involved observations on exposure and disease status prior to the onset of the study. The retrospective approach offers several pragmatic advantages, but may result in less accurate and complete information on exposure and disease status.

Cohort studies are statistically efficient for the study of rare exposures because the exposed individuals can be selectively included in the study. On the other hand, cohort studies are inefficient for the investigation of slowly developing or rare diseases. The evaluation of chronic diseases through the cohort approach requires a long follow-up period and increases the chances that subjects will be lost from the study. The



evaluation of rare diseases with the cohort study approach requires a large sample size and therefore is expensive and labour intensive.

There are several basic strategies to analyse cohort studies. If data are collected on the risk of developing an outcome during a specified period, the summary measure of effect typically is the risk ratio. An alternative approach to contrasting risks is risk difference, which is the risk among exposed persons minus the risks among unexposed persons. If the risk difference is divided by the risk among exposed persons, a measure term the attributable risk percent is derived. The attributable risk percent is an indicator of the proportion of risk that may be attributable to the exposure. When data in a cohort study are based on the rate of disease outcome, the standard measure of effect is the rate ratio.

SELF DIRECTING LEARNING:Basic knowledge and its application that students must know include:1. Study design of cohort studies and its attributes2. The procedures of cohort studies3. Findings and its interpretation (including crude RR, adjusted RR and attributable

risk) as well as confounding factors in a cohort study


Case 1. Please carefully study the article entitles: “Tobacco smoking as a risk factor for depression: A 26-year population-based follow up study”


1. Draw a figure of the study design with its attributes.2. What are the dependent and independent variables of that study?3. What are the confounding variables of that study?4. Describe two major conditions of study group and control group in a cohort study!5. How did the researcher choose the control group?6. How did the researcher control the effect of confounding variables? Is that the way

by design or by analysis?7. Explain how the researcher did that (no 6)!8. Why do the researchers choose this study design? Describe two reasons. 9. What is its CRUDE RR of the study? Provide the interpretation! 10. What is its SPECIFIC RR after being controlled for its tobacco consumption level?

Provide the interpretation!



Case 2. Carefully examine the following table.

Social Class

Non-Smokers Smokers All

Alcohol Intake Alcohol Intake

Heavy Moderate Light Total Heavy Moderate Light Total

I and II 11/84 5/79 11/169 27/332 6/28 3/13 1/26 10/67 37/399

III 4/22 3/25 12/162 19/209 4/17 2/7 6/38 12/62 31/271

IV and V

0/14 1/18 12/91 13/123 7/19 2/18 8/70 17/107 30/230

15/120 9/122 35/422 59/664 17/64 7/38 15/134 39/236 98/900

Learning Tasks 2:After you examined the above table, please calculate the following:

1. What is the CRUDE RR?2. What is the SPECIFIC RR after being controlled for its social class variable?3. What is the SPECIFIC RR after its smoking variable is controlled for?4. What is the SPECIFIC RR after both smoking and social class variables are

controlled for?5. Provide the interpretation of thoseRR!

Case 3:A study at Stockholm had observed 216 children who received BCG immunization when they were aged below 6 years and 358 children who did not have BCG immunization since 1989-1992. The study was set up in 1994. In 1995-1996, all children were examined to find out atopic disease incidence (atopic: a type of allergic disease). The study result shows that 36% who had BCG immunization and 41% who did not have BCG immunization are suffering from atopic disease (2 = 2.6; p > 0,05).

Learning Tasks 3:Based on the above case, please answer the following questions:

1. What is the study design of the case above?2. Draw figure of the study with its attributes?3. What is the main requirement if we want to choose that design?4. What is the RR?5. Does the RR in this study also occur in a population? Describe your reasons!

Case 4.



Carefully read the article entitles: “Risk of herpes zoster among patients with chronic obstructive pulmonary disease: a population-based study”


1. Please draw the study profile/design with its attributes2. Where the exposure and the outcome data were obtained from?3. What is the dependent and independent variables?4. Identify the confounding variables in this study!5. What is the crude RR and what does it mean?

Self Assessments:1. What are the advantages and disadvantages of prospective and retrospective

cohort studies?2. Explain the limitation of cohort studies to determine risk factors of diseases?



M O D U L E ~ 15(Greenberg, p. 127-136)

Epidemiology Study Design: Case – Control Studydr. I Nyoman Sutarsa, MPH & dr. Putu Ariastuti, MPH

AIMS:

At the end of this module, students are expected to be able to describe case-control study and to explain the advantages and disadvantages of case-control study to determine risk factors of diseases.

LEARNING OUTCOMES:1. To describe and to draw case-control study design 2. To explain the advantages and disadvantages of case-control study to determine

risk factors of diseases3. To explain what is odd ratio (OR) and how to calculate OR4. To explain the interpretation of OR in a case control study

CURRICULUM CONTENTS:1. The case-control study design and its attributes2. Case and control definition3. Determination of exposure4. Selection bias in case-control study5. Matching strategy in case-control study6. Data analysis in case control: OR and its interpretation

ABSTRACTS A case-control study is a type of observational investigation in which subjects are enrolled on the basis of the presence or absence of a particular disease and are then evaluated to determine their history of prior exposure to risk factors of interest.

The advantages of this design are primarily logistical. In particular, rare disease and those with long latency periods can be studied efficiently. The sample size required for a case control study tends to be smaller than would be needed for an alternative



design, such as a cohort study. As a result, the expense of conducting a case-control study may be substantially less than the cost of conducting a cohort study. Furthermore, reliance on historical information allows rapid completion of case-control study. The ability to reach a prompt conclusion is particularly important if the disease of interest is potentially life-threatening.

The disadvantages of case-control studies relate primarily to their susceptibility to systematic errors. Because cases and controls are sampled separately, it is possible that these groups may not arise from the same source population. Bias can be introduced into the study results if exposure status is associated with the likelihood of including cases or controls into the study. Reliance on subject recall of earlier exposures or the use of historical records can lead to imprecise or inaccurate classification of exposure.

The decision to conduct a case control study typically is motivated by a desire to exposure the relationship between prior exposure to a specific risk factor and the likelihood of developing a particular disease. Ideally, the cases and the controls should derive from a single well-defined source population, such as a state or metropolitan area (population-based sampling scheme). An attempt may be made to identify all newly diagnosed cases (incident cases) within the source population, particularly when the disease is rare or the source population is modest in size. Cases may be identified from hospital record, surveillance system, death certificates, or other sources. Careful criteria for the presence of disease must be established to minimize false inclusions or exclusions.

Controls typically are sampled from the population that gave rise to the cases. Occasionally, for purposes of convenience, hospital based samples of cases and control are selected. The hospital based approach tends to have the advantages of accessibility to the subjects and cooperative study participants. On the other hand, cases and controls may derive from dissimilar source populations in a hospital-based study and prior exposure status might influence the likelihood of inclusion in this type of investigation.

Matching of controls of cases on the basis of known risk factors for the disease of interest is a common practice in case-control studies. The intent of matching is usually to decrease the possibility of confounding, or mixing of the effectof exposure to the risk factors. Matching can increase the statistical precision of estimates and thereby allow a smaller sample size. On the other hand, matching can be time consuming, and subjects who are not successfully matched must be discarded from the analysis.

The process of selection of subjects in a case-control study precludes the estimation of risk (or rates), and the risk ratio therefore cannot be calculated directly from case-control data. An indirect estimate of the risk ratio, however, can be calculated in a case-control study. This measure is referred to as the odds ratio and is defined as the odds of exposure among cases divided by the odds of exposure among cases divided the odds of exposure among controls. The approach to calculating the odds ratio depends on whether cases and controls were sampled in an unmatched or matched fashion. In instance, a point estimate and 95% confidence interval for the odds ratio can be calculated as a measure of association between prior exposure to the risk factor and occurrence of disease.

SELF DIRECTING LEARNING:Basic knowledge and its application that students must know include:1. Case-control study design and its attributes



2. The procedures of a case control study3. Findings and its interpretation (including crude OR, adjusted OR) as well as

confounding factors in case-control study

SCENARIO & LEARNING TASKSCase 1. Please carefully study the article entitles“Prolonged breastfeeding reduces risk of breast cancer in Sri Lankan women: a case-control study”

Learning Tasks 1:After you carefully read through the article above, please discuss the following questions:1. Draw a figure of the study with its attributes.2. Was this study hospital based or population based or combination?3. Explain its dependent and independent variables!4. Explain its case definition!5. Explain the source and methods of selecting control from the study! 6. Is the above study part of a match-paired design? Why or why not? Describe three

reasons.7. Do the cases represent their population? Do the controls represent their population?8. Are cases and controls comparable? If not, which part is not comparable?9. How does the researcher control the effect of confounding variables?10. What is its CRUDE OR? Provide an interpretation!

Case 2.

Carefully examine the following table.Table 2.1 Use of IUD in sometime pregnant and never pregnant patients and controls in different age groups

Age group (year)

Patients ControlSometime Pregnant

Never Pregnant Total Sometime Pregnant

Never Pregnant Total

IUD No IUD

IUD No IUD

IUD No IUD

IUD No IUD

≤ 15 0 1 0 7 8 0 0 0 3 316-20 13 44 27 93 177 5 19 7 196 22721-25 26 74 28 87 215 25 131 7 163 32626-30 20 50 3 19 92 21 86 3 49 15931-35 7 12 1 3 23 4 21 0 1 26



Total 66 181 59 209 515 55 257 17 412 741

Learning Tasks 2:After you examined the above table, please calculate the following:

1. What is the CRUDE OR? Please provide an interpretation!2. Calculate 95% CI (confidence interval) of the CRUDE OR using below formula:

3. After it is controlled for age variable, what is its SPECIFIC OR? What is the interpretation?

4. What is the SPECIFIC OR after being controlled for obstetric history variable? What is the interpretation?

Case 3:Carefully read the article entitles: “Risk factors for psoriasis: a case control study”

Learning Tasks 3:Based on the above case, please answer the following questions:

1. What is the dependent (or response) variable of this study?2. What are the objectives of this study?3. What are risk factors investigated or what are independent variables of this study?4. Was this study hospital based or population based? 5. How long the cases were collected? 6. Who was the control group?7. Regarding which variables cases and control was comparable?

(a) Regarding which variables cases and control was not comparable? (b)What must be done to control these confounding variables?

8. See Table-3. How do you determine the most dominant risk factors?9. See Table-3. Which risk factor had the highest OR? What was the 95% CI for this

risk factor? 10. What were the OR and 95% CI of variable place of recidence? Explain, what does

it mean?

Self Assessments:

1. What are the advantages and disadvantages of case-control study in determining risk factors of diseases?

2. Explain what is mean by control by design and control by analysis in a case-control study!

Udayana University Faculty of Medicine, DME

95% CI = (OR) exp [+ 1.96Ö1/a+1/b+1/c+1/d]

87


M O D U L E ~ 16(Reference Greenberg, p.91-113 & Gordis, 131-163)

Epidemiology Study Design: Clinical Trialdr. Anak Agung Sagung Sawitri, MPH & Dr. dr. Gd Indraguna Pinatih, MSc., Sp.GK

AIMS:

To be able to describe the application of clinical trials to determine the effectiveness of intervention, prevention, and treatment of diseases.

LEARNING OUTCOMES:

1. To describe and to draw clinical & communitytrial design 2. To explain the advantages and disadvantages of clinical & community trial to

determine the effectiveness of intervention, prevention, and treatment of diseases

3. To explain the definition, aims, subject determination (enrolment, inclusion/exclusion criteria, and randomization of intervention and control group)

4. To explain statistical calculation and consideration in clinical trial 5. To explain outcome evaluation strategy (blinding) and how to calculate outcome6. To explain the interpretation of study outcome7. To explain ethical consideration in certain clinical trial8. To explain the differentiation between clinical and community trial

CURRICULUM CONTENTS:

1. The clinical trial design and its attributes2. Definition of intervention and control group3. Enrolment, Inclusion and exclusion criteria and randomization strategy 4. Blinding strategy in clinical trial



5. Statistical calculation and its parameters6. Data analysis in clinical trial and its interpretation7. Ethical consideration 8. Differentiation of clinical and community trial

ABSTRACTS Evidence-based medicine can be defined as the integration of current best evidence with clinical expertise, pathophysiological knowledge and patient preferences to make health care decisions. Although there are barriers to the practice of evidence-based medicine, such as the skills and time required in appraising the literature, this approach encourages effective management of diseases. It can serve to optimize health outcomes and promote cost-effective management.

Fundamental practice of evidence-based medicine is the ability to critically assess the design, conduct and analysis of clinical studies. For the purpose of assessing the comparative benefits of alternative treatment, the randomized controlled clinical trial is the “gold standard” approach. The evidence-based practitioner, therefore, must be thoroughly familiar with this research method.

The principal strength of this approach derives from assigning treatment to patients by randomization, thereby tending to balance the study groups with respect to both known and unknown prognostic factors.

Before enrolling patient in a clinical trial, the investigation can determine the baseline and follow-up information that will be required to all subjects. Procedures can then be put in place to enable the researches to collect data in a fairly complete and accurate manner. The investigator can also allocate subjects to desire dose level rather than relying on physicians or patients preference. When blinding of the evaluation or patients is feasible, the assessment of clinical outcomes is less likely to be influenced by knowing which treatment was used.

Randomized controlled clinical trial is subject to certain constraints, however. Restrictive criteria for inclusion of subjects may produce a very homogenous study population, which may restrict the ability to extrapolate results to patients with other characteristics. Clinical trial—particularly those involving chronic processes—may require years of follow-up to determine the outcome of the treatment. A prolonged observation period leads to higher costs, increases the likelihood that patient will be lost to follow-up, and delays the time at which a treatment recommendation can be made. The use of intermediate end points, such as measurement blood glucose levels glycosylated hemoglobin in the diabetes therapy trial, can help limit the length of required follow-up. Nevertheless, a definitive conclusion about treatment benefit often requires years of observation.



Large sample sizes typically are required in clinical trial when the magnitude of differences in responses between study groups is small. Furthermore, large numbers of subjects are likely to be required to demonstrate differences between study groups when there is wide variability in responses to treatment. Increasing the size of the study population not only raises the cost of a trial but may also lead to pragmatic difficulties in locating a sufficiently large pool of eligible patients.

Ethical concern may arise in clinical trial if one or more of the treatment options has serious potential effects or if early suggest-but do not establish- a therapeutic advantage for one of the treatments. In this situation, a decision must be made about whether the trial should be continued until a definitive conclusion is reached or should be terminated early so that all patients have the opportunity to receive the apparently superior treatment. To minimize the possible influence of real or perceive conflicts of interests, and external advisory group should review these ethical questions.

An investigator cannot control the behaviour of subjects enrolled in clinical trial. Even after initial informed consent has been given to participate in clinical trial, a subject has the right withdraw at any time. Some subject may elect to remain in the trial but not comply with the assigned regimen. Noncompliance can reduce the statistical power of clinical trial and thereby lead to a false-negative conclusion. Accordingly, every effort must be made to achieve maximal compliance with assigned treatment without infringing on the patients’ right to refuse therapy. Ultimately, treatment decisions should be best on the best evidence available concerning therapeutic benefit. The standard approach to gathering this evidence is the randomized controlled clinical trial. Although this type of investigation labour intensive, time consuming, and expensive it can provide the most convincing evidence of the superiority of one treatment over another. Through the use of randomized control clinical trial such as the diabetes therapy study, decisions concerning patient treatment and care can be based on rigorous scientific information.


Basic knowledge and its application that students must know include:1. Clinical trial design and its attributes2. The procedures of a clinical trial3. The concept of subject selection, restriction and randomization4. Statistical calculation and its parameters5. Findings and its interpretation


Case 1.

Please carefully study the article entitles “Clinical efficacy and safety of a novel tetravalent dengue vaccine in healthy children in Asia: a phase 3, randomised, observer-masked, placebo-controlled trial”

Learning Tasks 1:



After you carefully read through the article above, please discuss the following questions:

1. What is the study intervention?

2. What is the expected outcome (its dependent variable)?

3. Why did the researcher consider to do this study, or in other words, what is the problem in the community?

4. Based on clinical trials definition, describe the purpose of this trial.

5. Describe how researchers design the study, the period, and selected the sites for study. What considerations were put in place when selected the sites?

6. Describe how the researcher selected the subjects of this study. What are the inclusion and exclusion criteria? What are the purposes of defining these criteria?

7. Which type of randomization has been done? What is the purpose in doing this?

8. See the Figure 1. Trial Profile. What is the purpose to describe this figure?

9. Describe how the method of intervention was performed?

10. What are the outcomes (dependent variables)? Describe the operational definitions.

11. Based on measurement scale, that dependent variable is part of which type?

12. Is this study using a blind design? What is the purpose to use blind design?

13. What is one limitation for this longitudinal study with regard to study subjects?

14. Look at Table 1. Baseline Characteristics of the Participants. What is the purpose of researchers for showing the table?

15. What is the main result of this study? Which intervention was more effective?

16. What other outcomes were also resulting from this study?

17. How you interpret the vaccine efficacy of primary analysis (per protocol?)

18. How you compare the vaccine efficacy for the dengue serotype?



19. What are ethical aspects you see from the study?

Case 2. Family planning services throughout all villages are needed to decrease birth rate in Indonesia. In Ayodya Province, almost all reproductive couples chose the vasectomy method. Since the users were high in demand while the doctors in that province were limited, dr. Nyoman Abimanyu, a surgeon who was also the head of the Provincial Health Department, trained a group of nurses to be able to conduct vasectomies. With his authorization for the nurses to do vasectomies, dr. Abimanyu got a lot of criticisms from other surgeons. Therefore, he thought that it was important to have empiric data from a research.

In his study, a number of candidate acceptors (candidate users) were vasectomised by trained nurses, while the other users underwent vasectomies by doctors. Previously, all candidates were informed whether they would be operated by doctors or trained nurses. The number of samples was already sufficient and was already sampled randomly to ensure representatives for their population. The results are as follows: vasectomy failure on the group operated by nurses was 2% while by doctors was 1.25%. Hematoma (subcutaneous bleeding) in the nurses’ group was 8% and in the doctors’ group was 5%.

Learning Tasks 2:

1. What are dependent and independent variables in that study?2. Is the study above an observational or experimental study?3. What is the intervention?4. Is that possible to randomize?5. What randomization can be done?6. What are the advantages and disadvantages of each randomization method?7. Is it possible to do cross-over design? If not, why?8. Is it possible to do blind design?9. Which blind design can be done?10. From the result above, what is the conclusion? Which is more effective?



11. If the result is as follows, what is the conclusion? Which one is more effective? The vasectomy failure in the group operated by nurses is 2% while on the doctor is 1.25% (p > 0.01). Hematoma (subcutaneous bleeding) in the nurses’ group is 8% and in the doctors’ group is 5% (p > 0.01).

Self Assessments:1. Describe the application of clinical trials in diagnosis and patient treatment?

2. Describe the design of parallel clinical trials and cross-over trials

4. Explain the purpose and application of parallel clinical trials and cross-over trials

5. Describe the definition and purpose of single blind and double blind study.

6. Describe the definition of informed consent.

7. On the British Medical Journal 2002, the study was conducted to know the effect of ice consumption to headache with the design study was prospective randomized trial (Source: UKDI)

Headache (+) Headache (-) Total

Accelerated eating group 20 53 73

Cautious eating group 9 63 72

Total 29 116 145

What is the role of the risk factor?

A. 2,9B. 2,2C. 4,4D. 4,6E. 6,8

13. On the study of medication that will be use to suppress cholesterol level; the study procedure was dividing the study group in to two: control and intervention group. Then, the outcome will be compared for both groups. What is the design of the study? (Source: UKDI)

A. True experimentalB. Quasi experimentalC. Cross sectionalD. CohortE. Descriptive



M O D U L E ~ 17(Reference Greenberg, p. 127-136)

Epidemiology Study Design: Diagnostic Testdr. Gede Artawan Eka Putra, M. Epid & dr. Anak Agung Sagung Sawitri, MPH

AIMS:To be able to describe validity and reliability of certain test to apply in the individual and or community context

LEARNING OUTCOMES:1. To describe definition of accuracy, validity and reliability 2. To explain parameters used to indicate validity and reliability3. To describe concept of test validity of the continues or multilevel outcome result4. To choose ideal diagnostic test in certain situations related to individual and

community context



CURRICULUM CONTENTS:1. Definition of diagnostic test and screening program2. Accuracy, validity (sensitivity, specificity, predictive value, and likely hood ratio) and

reliability3. Screening program

ABSTRACTS All clinical information is subject to error. Accounting for the various errors that can arise in diagnostic testing allows the physician to select tests and interpret the result of those tests appropriately. The errors are false- negative, false-positive.

Sensitivity and specificity are characteristics of a diagnostic test. It is useful to consider two other measures, positive predictive value (PV+) and negative predictive value (PV-), which are use to interpret the results of a diagnostic test.

For multilevel or continuous outcome test results, a dividing line or cut off point can be chosen to separate findings considered to be positive or negative. The performance of diagnostic tests also can be assessed by use of likelihood ratios.

SELF DIRECTING LEARNINGBasic knowledge and its application that students must know include:1. Accuracy2. Sensitivity, specificity, predictive value, likelihood ratio3. Cut off point4. Screening programSCENARIO & LEARNING TASKCase 1

The result of screening test was as bellow;

Diabetic retinopathy

Sakit Tidak Sakit Total

Positif 3.200 1.400 4.600

Negatif 150 29.000 29.150

Total 3.350 30.400 33.750

Learning Tasks 1

Udayana University Faculty of Medicine, DME

S

K

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95


1. Calculate the sensitivity, specificity, and predictive value of diabetic retinopathy and Interpret each of your calculation

2. What is the prevalence of diabetic retinopathy among the population?

3. What is the relation between prevalence and predictive value? What is the relation with the target group for screening program?

4. When you conduct screening test among elderly population, which prevention you have done: primary, secondary, or tertiary?

Case 2A detailer has come to dr. Arjuna for offering cheap rapid test for anemia. Dr. Arjuna asked 3 tests for trial. Fortunately, Mrs. Drupadi came for consultation of malaise and continuing dizziness. Dr. Arjuna asked permission from mrs. Drupadi to take the blood sample for checking anemia. At the same time, he also told her that he wanted to check the rapid test for anemia. Using cyan-met HB, the result of hemoglobin was 12.5mg%, while the result of rapid test respectively was 10.5 mg%; 10.2mg%; and 12.0mg%.

Learning Task 2If the criteria of anemia was <11 mg%, how you conclude the result of the rapid test for anemia that just being tried by dr. Arjuna?

Self Assessments:1. What is the definition of sensitivity?

2. What is the definition of specificity?

3. If the prevalence increases, what other value will also increase?

4. If the prevalence of a certain disease is high, while the sensitivity and specificity are stable, than ................... will be low.

M O D U L E ~ 18(Reference Mausner & Bahn, p. 91-110, Greenberg 141-153)

Variability and Biasesdr. Anak Agung Sagung Sawitri, MPH & Dr. dr. Gd Indraguna Pinatih, MSc., Sp.GK

AIMS:Be able to describe variability and biases those might occurred in certain studies



LEARNING OUTCOMES:1. To describe the concept of patient variability, variability in medical research,

variability of measurement, both in individual and population level.2. To explain internal validity, external validity, selection bias, information bias, and

confounding factors.3. To describe sources of bias in descriptive research design, cross-sectional, case-

control, cohort, and clinical trials and describe how to minimize those biases

CURRICULUM CONTENTS:

1. Variability (individual, measurement, and population)2. Validity (internal and external)3. Biases (selection bias, information bias, and confounding bias)

ABSTRACTS In this lecture, the topics of variability and bias (systematic errors) in

epidemiologic measurements are discussed. A distinction is drawn between random variation which is inversely related to precision in measurement, and non random or systematic error, which is related to distortion in measurement.

Variability can arise from (1) the subject under study, (2) differences between individuals, (3) the approach used to sample subjects, or (4) the measurement process itself. Variability related to sampling is likely to diminish as the sample size increases. With extremely large sample size, a very small difference in outcome between study groups can be statistically significant. Whether the magnitude of this difference is sufficient to warrant a change in clinical practice is separate, but equally important question.

Validity concerns the extent to which the findings of a study reflect truth. Internal validity relates to the accuracy of study findings for the persons who are investigated. External validity concerns the extent to which study findings accurately apply to persons who are not studied.

Bias is defined as lack of validity. Conventionally, bias is classified into three major types: selection bias, information (misclassification) bias, and confounding. Selection bias refers to the introduction of systematic errors into study results through the manner in which study subjects are selected. Information bias results in systematic errors in study findings that originate in the approach to collecting information. Two kinds of information bias can exist. Non-differential misclassification occurs when errors in the information about one variable unrelated to the status of another variable. Differential misclassification, on the other hand, occurs when errors in the information about one variable are affected by the status of another variable.

Confounding is concerned with the mixing of the primary effect of interest with the effects of one or more extraneous factors. In experimental studies, the problem of confounding is reduced by randomization, which tends to balance the study groups with respect to both known and unknown determinants of the outcome. In observational



research, however, study groups may differ appreciably in factors that are (1) related to the risk of disease among unexposed persons and (2) are also associated with the exposure of interest, but not as a result of being exposed. The influence of these potential confounders can be addressed in the study design (e.g., through matching or restrictive inclusion criteria) or in the analysis (e.g., through stratification or regression techniques). Only known confounders can be addressed in observational research.

No study is immune from the possibility of bias. The investigator must therefore consider potential sources of bias when sampling subjects, collecting information, analyzing results, and interpreting findings. With planning and forethought, it is possible to anticipate and avoid certain types of error and thus conduct a study that leads to a convincing and valid conclusion

SELF DIRECTING LEARNINGBasic knowledge and its application that students must know include:1. Variability2. Validity3. Bias4. Confounding

SCENARIO & LEARNING TASK

1. Carefully look at Patient Profile in Greenberg page 141. What possibilities may cause the differences in the patient’s cholesterol level?

2. Explain the differences between random and systematic variation (bias) by drawing a figure as shown in Greenberg page 142 (Figure 10.1)

3. Draw a copy of Figure 10-2 in Greenberg page 143 into your work-paper, and then explain with your own words about the definition of sampling variability in research studies. From that example it can be concluded that a smaller sample size in a research study, will cause the variability to be ……………...

4. Draw a copy of Figure 10-3 in Greenberg page 143 into your work-paper.

a. What is demonstrated in A and the B of the study figure?

b. From that example, it can be concluded that the consequence of a bigger sample size in a research study is …………….

c. In a research study like the one above, despite sample size or differences which statistically give significant results, what other things are very important in biological or clinical meaning?

5. Explain with examples, the differences between internal and external validity.

6. Give examples of selection bias.

7. Information bias may be presented in two forms, which are nondifferential misclassification and differential misclassification. Explain those differences using examples.



8. Draw a copy of figure 10-7 in Greenberg page 148 into your work-paper. Ex-plain, with the above examples, nondifferential misclassification which causes underestimated OR and differential misclassification which causes overestimated OR.

9. a. Explain the definition of confounding variable

b. Two methods to control the effect of confounding variables are …… & ………………

c. Carefully see Figure 10-9 in Greenberg page 150. Calculate the OR for all subjects, OR for the obese group, and OR for the non-obese group. What is your conclusion after calculating those three ORs?

d. The method used above (no. c) controls the effect of confounding variables by …

Self Assessments:1. Explain the concept of variability in one patient and in medical research.

2. Explain individual & population variability, &variability related to measurement,

3. Explain the definition of validity and bias.

4. Explain the difference between internal validity and external validity.

5. What is the difference between selection bias, information bias, and confounding bias?

6. A teenager wanted to be a study subject related with lung cancer. His father was death due to lung cancer. Even though he was not selected as sample, what could be most possible bias if he was join the study? (source: UKDI)

Recall bias? or Berkson bias? or Misclasification bias? or Ney man bias? or Volunteer bias?

REFERENCES

Mausner and Bahn, Epidemiology an Introductory text

Kirkwood B.R & Sterne, A.C. (2008), Medical Statistics, Blackwell Publishing Company

Greenberg, R.S. (2004), Medical Epidemiology, 3rded, McGraw-Hill, New York, USA

SPSS V.11.5 Manual



Sastroasmoro, S. dan Ismael, S. (2014), Dasar-Dasar Metodologi Penelitian Klinis, edisi ke-2, CV Sagung Seto, Jakarta.

Bahan SGD (jurnal, hasil penelitian)

ANNEX-1



FILM SUMMARY

And The Band Played On

SINOPSIS DAN PENJELASAN TAMBAHAN FILM “AND THE BAND PLAYED ON”



Film ini merupakan kisah nyata yang menceritakan tentang terjadinya wabah suatu penyakit yang tidak diketahui penyebabnya pada awal tahun 1980.

Kisah yang mirip dengan film juga diuraikan di Buku Greenberg halaman 1 – 7 (lihat buku reference yang telah dibagikan).

Dikisahkan dalam film ini bahwa untuk meneliti atau mengungkapkan penyebab suatu penyakit banyak bidang ilmu yang terlibat, yaitu: ilmu klinik, statistik, epidemilogi, ilmu sosial, imunologi, virologi, etika dan profesionalime. Juga banyak aspek yang berperan, yaitu aspek sosial, politik, dan ekonomi.

Demikian pula dalam pencegahan dan penanggulangannya dikisahkan bahwa suatu penyakit baru bisa dicegah setelah diketahui determinannya, cara penularannya, teknologi untuk diagnosanya (alat test), dan banyak aspek yang berperan antara lain: aspek sosial (stigma pada kelompok gay), politik (rezim yang berkuasa di pemerintahan), ekonomi (pembiayaan).

Film dimulai dengan Dr. Don Francis (seorang dokter yang menekuni Bidang Epidemiologi dan pencegahan)yang diminta oleh Badan Kesehatan Dunia (WHO)untuk meneliti wabah Ebola di Sungai Ebola (Afrika). Ebola adalah suatu penyakit yang tingkat kematiannya hampir 100% dan dijumpai pertama kali di Sungai Ebola.

Setelah itu, ditunjukkan bahwa dokter dan rumah sakit di Kopenhagen, Denmark menemukan seorang pasien yang sakit lalu meninggal dimana penyebab penyakit dan kematiannya tidak diketahui. Yang dijumpai oleh dokter rumah sakit tersebut hanya T-sel (suatu sel yang membentuk kekebalan tubuh manusia) hampir nol dalam pemeriksaan lab-nya.

Selanjutnya bermunculan kasus-kasus serupa yang dijumpai oleh dokter di sejumlah rumah sakit di Amerika, pasien dengan T-sel amat rendah, dan secara klinis dijumpai ada semacam tumor kulit (Kaposi Sarcoma), infeksi oleh jamur di mulut pasien, radang pada otaknya oleh kuman toksoplasmosis. Bakteri ini biasanya dijumpai pada kucing atau anjing.

Hampir semua kasus-kasus tersebut diumpai pada kalangan gay di Amerika terutama Negara Bagian California dan New York. Karena itu, pada saat itu penyakit misterius tsb disebut saja dengan penyakit gay.

Kemudian pihak Communicable Diseases Control (CDC) atau lembaga Kementrian Kesehatan Amerika yang bertanggung jawab terhadap penyakit menular (yang kantor pusatnya di Atlanta, Negara Bagian Georgia) minta bantuan Dr. Don Francis ikut dalam Tim untuk meneliti penyakit ini lebih lanjut. Dia dipilih oleh CDC karena Dr. Don Francis pernah meneliti penularan Hepatitis B dan Wabah Ebola. CDC sama dengan Dirjen P2M (Penanggulangan Penyakit Menular) di Kementrian Kesehatan RI.

Dr. Don Francis bergabung dalam Tim CDC bersama-sama dengan pakar ilmu sosial, statistik, parasitologi, dokter ahli penyakit menular seksual, dll.



Saat itu kegiatan CDC banyak menemui hambatan karena tidak didukung pendanannya oleh Pemerintah Pusat Amerika karena penyakit tersebut banyak dijumpai pada kalangan gay. Partai yang berkuasa saat itu adalah Partai Republik (dengan Reagan sebagai presidennya) yang ideologi politiknya tidak menyetujui atau tidak menyukai homosek-sualitas. Sedangkan Partai Demokrat (partai oposisi) tidak menentang keberadaan komunitas gay. Permintaan CDC untuk meningkatkan laboratoriumnya termasuk untuk membeli mikroskop elektron juga menemui hambatan.

Penelitian yang pertama kali dilakukan oleh CDC adalah menentukan apakah ini penyakit menular dan bagaimana cara menularnya.

Dari data deskriptif dimana hampir semua kasus dijumpai pada kalangan gay dan dengan penelitian contact tracing, kemudian CDC mendapat petunjuk bahwa penyakit ini menular melalui hubungan seksual. Contact tracingatau penelusuran kontak adalah salah satu cara untuk mencari sumber penularan suatu penyakit yang ditularkan melalui kontak langsung termasuk kontak seksual. Catatan: cara penularan penyakit SARS diketahui dengan cara penelitian contact tracing. Catatan: Wabah SARS terjadi tahun 2003 yang bermula di Hongkong kemudian menyebar ke berbagai negara di dunia.

Namun selanjutnya CDC menerima laporan bahwa kasus-kasus yang sama juga dijumpai pada perempuan (migran dari Haiti). Dengan demikian maka penyakit ini bukan lagi penyakit pada gay.

Juga ada laporan bahwa dijumpai pada anak-anak penderita hemofilia (penyakit genetik dimana terjadi kelainan pada sistem pembekuan darah), pasien operasi yang mendapat transfusi darah. Catatan: penderita hemofilia harus rutin mendapat transfusi darah.

Tim CDC semakin bingung karena penyakit ini ternyata bukan saja menular melalui seksual tetapi juga melalui darah atau produk darah.

Karena bukan lagi pada gay saja, kemudian tercetus istilah bahwa penyakit ini adalah suatu Acquired Immunodeficiency Syndrome(AIDS), yaitu suatu syndrome (kumpulan gejala) yang didapat karena terganggunya kekebalan tubuh..

Secara kebetulan Dr. Don Francis melihat temannya yang main game di suatu kantin, dan dia terinspirasi dari game tersebut bahwa ada virus yang menghancurkan T-sel pasien. Tim CDC kemudian mencurigai bahwa penyebab penyakit ini kemungkinan suatu virus yang termasuk dalam famili rotavirus. Pada tahap ini, kemudian penelitian difokuskan untuk bisa menemukan virus tersebut pada pasien. Pada saat itu pakar yang sedang meneliti rotavirus adalah Robert Gallo (seorang ahli virology ternama di Amerika Serikat). Saat itu Robert Gallo meneliti rotavirus pada pasien-pasien leukemia.

Robert Gallo mengklaim bahwa rotavirus yang dia jumpai adalah penyebab pasien-pasien Acquired Immunodeficiency Syndrome pada saat itu.



Karena fasilitas penelitian virus yang saat itu masih terbatas di Amerika, kemudian Dr. Don Francis (CDC) juga minta bantuan kepada Dr. Luc Montagnier (ilmuwan dan peneliti di Lembaga Pasteur di Perancis) untuk menemukan virus yang menyebabkan pasien-pasien AIDS. Saat itu fasilitas di Lembaga Pasteur di Perancis lebih lengkap dibanding di Amerika.

Dalam hal inilah terjadi moral hazard (pelanggaran etika dan kelakuan yang tidak professional) dimana Robert Gallo mengklaim bahwa dirinyalah penemu HIV, padahal yang menemukan pertama kali adalah Tim dari Lembaga Pasteur di Perancis. Menemukan suatu virus baru dalam suatu sampel yang diambil dari pasien tidaklah mudah karena virus harus bisa dibiakkan (dibuat kultur sehingga tidak mati) agar kemudian bisa dilihat dengan elektron mikroskop.

Perselisihan antara Robert Gallo dan Lembaga Pasteur hampir dibawa ke pengadilan (tetapi batal) dan terus berkepanjangan. Karena itu, Panitia Hadiah Nobel dalam waktu lama tidak memutuskan pemberian hadiah Nobel kepada pihak manapun, tetapi akhirnya baru diberikan pada tahun 2008 kepada Tim dariLembaga Pasteur di Perancis setelah HIV dijumpai pada tahun 1984 (setelah 24 tahun).

Test antibodi pertama untuk mengetahui seseorang tertular HIV dijumpai pertama kali pada tahun 1984.

Hal-hal lain yang dikemukakan dalam film ini:

Pencegahan penyakit bisa dilaksanakan ketika cara penularannya telah bisa dipastikan walaupun kuman penyebabnya (HIV) belum diketemukan secara pasti. Hal yang sama juga terjadi pada penyakit-penyakit lainnya. Kejadian kolera dengan penelitian epidemiologi diumpai pada tahun 1854 dan sudah mulai bisa dilakukan pencegahannya saat itu (dengan konsumsi air bersih), sedangkan kuman kolera baru dijumpai 30 tahun kemudian. Demikian pula dengan penyakit-penyakit lain (TBC, polio, dll).

Upaya pencegahan AIDS yang diusulkan oleh Dr. Don Francis saat itu banyak menemukan hambatan, baik hambatan politik (partai republik tidak mau memberikan biaya), hambatan ekonomi (PMI-nya Amerika menolak test pada donor darah karena biayanya akan amat mahal), hambatan sosial (stigma pada komunitas gay dan penolakan penutupan tempat sauna/bathhouse yang biasa dipergunakan oleh komunitas gay untuk berkumpul).

Dalam film juga ditunjukkan bahwa hambatan yang dijumpai bukan saja dalam hal pencegahan tetapi juga dalam hal perawatan pasien AIDS. Dokter di salah satu negara di Eropa dipanggil dan ditegur oleh direkturnya karena banyak merawat pasien AIDS. Direktur RS mengatakan citra RS-nya tercoreng. Si dokter menjawab: “Saya tetap akan merawat mereka, dimanapun saya mendapat tempat”.

Sampai saat ini Dr. Don Francis berdomisili di California dan banyak melaksanakan program-program pencegahan.



Catatan: Dr. MervynSilverman yang dalam film ditunjukan sebagai Kepala Dinas Kesehatan San Franscisco (Public Health Director) saat itu yang terlambat datang satu jam dalam pertemuan di bathhouse dan bersusah payah menengahi upaya penutupan bathhouse komunitas gay, sampai saat ini sering berkunjung ke Bali karena putrinya menikah dengan seorang pemuda dari suatu desa di Kabupaten Gianyar.

=====================



ANNEX -2

ARTIKEL KORANBALI POST



Bali Post, Senin, 19-6-1995

Dipertanyakan, Pasien Kurang Mampu Masuk RSUP DenpasarDenpasar, (Bali Post)

Pihak RSUP Denpasar mempertanyakan pasien kurang mampu masuk RSUP Denpasar yang jumlahnya cenderung terus meningkat.Munculnya pertanyaan itu, karena dari data yang ada jumlah pasien yang kurang mampu masuk RSUP justru paling banyak dari Kabupaten Badung (179 orang), Gianyar (171 orang), dan Tabanan (150 orang).Padahal ketiga daerah ini dinilai sebagai daerah yang memiliki pendapatan perkapita lebih tinggi dari daerah lainnya di Bali.

Hal tersebut diungkapkan Humas RSUP Denpasar, Y.H.L., ketika ditemui Jumat (16/6).Dia menilai tidak masuk akal ketiga daerah yang pendapatan perkapitanya cukup tinggi justru warganya paling banyak menyatakan kurang mampu. Padahal Daerah Bali yang perekonomiannya dinilai semakin maju, sebagai dampak pariwisata, jumlah masyarakatnya yang kurang mampu makin sedikit

“Apakah mungkin mereka sengaja mengaku kurang mampu dan gampang mendapatkan surat keterangan kurang mampu dari aparat desanya,” kata Y.

Ketika ditanya apakah hal itu merugikan RSUP yang kini menjadi swadana, Y tidak berani menjawab.Namun direktur RSUP, dr. IGLMR, ketika dikonfirmasi megatakan hal itu sebenarnya tidak terlalu berpengaruh terhadap keberadaan RSUP.Meskipun kini menjadi swadana, RSUP tetap mempertahankan fungsi sosialnya.

“Jangan hanya mengejar keuntungan belaka, fungsi sosial harus tetap diperhatikan.” Ujarnya.

Menurut R, asalkan tempat tidur berkelas penuh, sebenarnya pasien kurang mampu tidak masalah bagi RSUP. Sebab kalau tempat tidur berkelas itu penuh, keuntungan dari sana yang akan dipakai membantu para pasien kurang mampu yang masuk membludak, tentu hal ini akan menjadi masalah, paparnya.

Seleksi Surat Keterangan

Baik R dan Y mengharapkan pihak aparat terkait lainnya, benar-benar menyeleksi dalam memberikan surat keterangan kurang mampu. Sebab, tidak mungkin pihak RSUP akan mengecek apakah orang itu benar benar kurang mampu sampai ke desanya. Demikian pula pihak terkait, seperti camat, Depsos, dan lain-lain, tidak akan mungkin tahu dengan persis dan detai keadaan pemohon surat keteragan kurang mampu. “Untuk

itu seleksi aparat desa diharapkan lebih ketat lagi dalam mengeluarkan surat keterangan kurang mampu.” ujar Y.

Sejak Januari sampai Juni 1995, tercatat 271 pasien kurang mampu telah dibantu biaya perawatannya oleh pihak RSUP.

Sedangkan sebelumnya dari Januari-Desember 1994 tercatat 606 pasien kurang mampu yang telah ditolong. Itu berarti 877 pasien kurang mampu ditolong keringanan biaya perawatannya oleh RSUP. Dari jumlah tersebut, Kabupaten Badung menempati posisi paling tinggi sebanyak 179 orang, menyusul Gianyar 171 orang , Tabanan 150 orang, Karangasem 97 orang, Kodya Denpasar 75 orang, Klungkung 52 orang, Singaraja 50 oarang, Jembrana 43 orang, Bangli 42 orang, Banyuwangi 9 orang, Timtim 4 orang, NTB 3 orang, dan NTT 1 orang.

Sementara jumlah tempat tidur yang tersedia di RSUP sebanyak 752 buah, masing-masing VIP A



17 buah, VIP B 18 buah, VIP C 15 Buah, dan sisanya tempat tidur kelas I, II, III, dan biasa. Sedangkan tarif yang diberikan untuk tempat tidur berkelas VIP A

Rp 100.000, VIP B Rp 70.000, VIP C Rp 55.000, kelas I Rp 30.000, kelas II Rp 15.000, dan kelas III Rp 4.000 per hari. Dari sinilah keuntungan yang

diharapkan dapat membantu para penderita kurang mampu yang masuk RSUP.

. Bali Post, Selasa, 20-6-1995

Masalah Pasien Kurang MampuDPRD Pertanyakan Keluhan RSUPDenpasar (Bali Post)

Keluhan RSUP Denpasar tentang terus meningkatnya jumlah pasien kurang mampu yang datang berobat (Bali Post, 19/6)) durasakan sebagai hal yang mengherankan oleh Komisi E DPRD Bali. Karena dalam kunjungan terakhir Komisi E ke RSUP sekitar dua bulan lalu, didapat masukan jumlah pasien yang menggunakan kartu miskin menurun, bahkan banyak orang yang kurang mampu tidak menunjukkan surat keterangan miskin karena malu.

Demikian keterangan yang dihimpun Bali Post dari beberapa anggota Komisi E DPRD Bali, Senin (19/6) kemarin. Ketua Komisi E APN mempertanyakan, apakah hanya dalam waktu singkat sudah terjadi perubahan. ”Kami akan mengadakan pertemuan dengan pihak RSUP untuk mendapatkan masukan lebih rinci. Mungkin awal Juli nanti sebagai bagian dari acara rapat koordinasi dengan berbagai instansi yang merupakan mitra kerja Komisi E.” tegasnya.

Berkenaan dengan jumlah pasien kurang mampu yang lebih banyak datang dari daerah yang memiliki pendapatan per kapita tinggi seperti Badung, Tabanan, dan Gianyar, anggota Komisi E lainnya, S berpendapat, masalah ini harus dikaji lebih mendalam.

Bisa saja suatu daerah pendapatan per kapitanya tinggi tetapi banyak masyarakat yang

tergolong kurang mampu. Mungkin ada kesenjangan pendapatan di mana sebagian kecil masyarakat pendapatannya sangat tinggi dan sebagian lainnya rendah. Kemudian setelah dirata-ratakan pendapatan per kapitanya tinggi,” ujarnya.

APN kemudian menambahkan sama saja dengan desa tertinggal. ”Belum tentu penduduk di desa tertinggal tergolong kurang mampu. Misalnya Kintamani, di sana banyak penduduknya kaya-kaya, ” tandasnya.

Baik APN maupun S juga melihat ada hal lain yang perlu dipertimbangkan. Menurut mereka, kesadaran penduduk di Badung, Tabanan, dan Gianyar mungkin lebih tinggi daripada kabupaten lain. ”Meski tergolong tidak mampu, tetapi karena daerahnya relatif lebih maju, penduduk di tiga kabupaten itu memiliki kesadaran untuk berobat ke rumah sakit dibanding tempat lain seperti ke dukun. Sebaliknya, penduduk tak mampu di kabupaten lain lebih memilih ke dukun. Jadi kelihatannya jumlah penduduk miskinnya lebih sedikit,” tutur S.

Kriteria harus jelas

Sebelumnya pihak RSUP juga meminta agar aparat desa melakukan seleksi yang lebih ketat dalam memberikan surat keterangan miskin. Karena, menurut Y, tidak masuk akal daerah



yang pendapatan per kapitanya tinggi memiliki banyak masyarakat miskin.

Terhadap hal ini APN, yang juga Ketua FKP menegaskan, pemberian surat keterangan miskin harus mengikuti kriteria yang ada. Berdasarkan kriteria itulah kemudian ditetapkan apakah seseorang tergolong miskin atau tidak. ”Saya kira kriteria ini perlu diikuti dalam mengeluarkan surat keterangan miskin. Jangan sampai masyarakat merasa aparat desa pilih kasih. Kalau sudah begitu masyarakat bisa ribut.” ujarnya.

Dia menambahkan, ”Kami sebagai wakil rakyat juga menghimbau agar pihak RSUP tetap mengutamakan fungsi sosialnya dibandingkan masalah administrasi. ”Bagaimanapun masyarakat miskin tetap harus dibantu. Kalau ada masyarakat miskin terlanjur datang ke rumah sakit, apakah harus ditolak? Saya kira tidak. RSUP perlu mementingkan aspek sosial daripada urusan administrasi.” ujarnya.

Direktur RSUP dr. IGLMR menegaskan, peningkatan jumlah pasien tidak mampu sebenarnya tidak berpengaruh terhadap keberadaan RSUP asalkan tempat tidur berkelas penuh. RSUP yang kini memiliki status swadana, lanjut dia, tetap mengutamakan aspek sosialnya. (jas)


Study Guide Community-Based Practice 2015

Bali Post, Jumat, 23 September 1983

KORBAN KANKER TERBANYAK PENDUDUK PEDESAAN, TINGKAT SOSEK RENDAH – Perlu Penyebarluasan Pencegahan Lewat PKK dan Media Massa

Denpasar (Bali Post). Korban kanker terbanyak penduduk pedesaan, kaum Ibu dan anak-anak. Data di RSUP Sanglah menunjukkan dari 296 kasus yang diteliti, ternyata 87,50 persen penderita dari golongan petani, 5,07 persen pegawai, 4,73 persen pedagang. Sisanya 2,7 persen tidak diketahui profesinya. Data tersebut memberi petunjuk golongan masyarakat yang tingkat sosial ekonominya rendah paling banyak diancam jiwanya oleh penyakit ganas itu.

Data tersebut terungkap dalam seminar di auditorium unud, Kamis, 22/9 yang diselenggarakan dalam rangka dies natalis Unud ke-21.

Tindakan pencegahan lebih baik daripada pengobatan yang sampai saat ini belum berhasil diselesaikan secara tuntas. Pengobatan hanya untuk memperpanjang penundaan kematian.

Dalam hubungan tersebut, rektor Unud, dr. IBO dalam sambutannya pada acara pembukaan seminar menyarankan pengetahuan tentang kanker dan cara pencegahannya disebarluaskan kepada masyarakat, misalnya melalui kaum Ibu sebagai juru

penerangnya di masing-masing rumah tangga. Penyebarluasan ini juga penting disalurkan lewat PKK dan media massa.

”Masalah pemberantasan penyakit kanker yang semakin tumbuh, menjadi semakin penting. Lebih-lebih dikaitkan dengan upaya meningkatkan kesejahteraan umum.” ujar rektor.

Rektor IBO juga menghimbau dilakukan pengawasan yang baik lewat pelayanan wisatawan, seperti pengawasan kesehatan, kebersihan lingkungan dan kebersihan makanan dan minuman di bar dan restauran.

Tempe Bosok

Dr. DNS mengungkapkan salah satu penyebab timbulnya kanker adalah virus cendawan kuning beracun yang terdapat dalam oncom, tempe bosok, kecap, kacang, kool, kelapa, ketela, dll. Juga disebabkan oleh faktor keturunan, ujar dr. AH, disamping zat-zat kimia, zat warna merah, perekat, iradiasi.

Pengobatan dilakukan dalam berbagai tingkat, yakni antara lain dengan pembedahan, radiasi, kombinasi bedah, diikuti radiasi dan kemoterapi

yang lazimnya dilakukan sebagai tindakan paliatif.

Prof. Dr. IGPA menyinggung terdapatnya pengobatan kanker secara tradisional seperti menggunakan ”ketela gendruwo” di salah satu rumah sakit di Jakarta Barat. Di beberapa daerah lainnya seperti Sulsel, cara pengobatan dilakukan dengan memakai ramuan dedaunan pada kanker payudara, seperti daun ”siput kuning” dicampur ”kumis kucing” dan ”kapur sirih” ditambah air digunakan untuk mengompres payudara yang diserang oleh tumor. Di Sulsel juga dilakukan pengobatan dengan menggunakan besi pijar disertai mantra-mantra, pengobatan dengan pijat, dengan kekuatan batin, mediasi dll.

Seminar yang menarik perhatian besar itu menampilkan delapan pembicara yakni Prof. Dr. IGPA mengenai ’Kanker Masalah Kita:, dr. IBCM ”Beberapa informasi kanker buah dada”, dr. IGPS ”Kanker rahim”, dr. WS ”Beberapa aspek Kanker Nasofaring di RSUP Sanglah”, dr. DSN ”Kanker Hati”, dr. AH ”Kanker Darah pada Anak-anak”, dr. GRT ”Kanker Mata yang Ganas”. Seminar dikoordinasikan oleh panitia penyelenggara diketuai dr. IBT

ANNEX -3

ARTIKEL JURNAL

1. Dengue and other common causes of acute febrile illness in Asia: an active surveillance study in children (Day 14)

2. Bacterial Vaginosis in Female Fascility Workers in North-Western Tanzania: Prevalence and Risk Factors (Day 14)

3. Tobacco Smoking as a Risk Factor for Depression. A 26-year population-based follow up study (Day 16)

4. Risk of Herpes Zoster among Patients with Chronic Obstructive Pulmonary Disease: a population-based study (Day 16)

5. Prolonged breastfeeding reduces risk of breast cancer in Sri Lankan women: A case-control study (Day 17)

6. Risk factors for psoriasis: a case control study (Day 17)

7. Clinical efficacy and safety of a novel tetravalent dengue vaccine in healthy children in Asia: a phase 3, randomized, observer-masked, placebo-controlled trial (Day 18)

DATA ANALYSIS

USING COMPUTERSkill Lab

T O P I C S :- Skill Lab 1: Data Entry Structure, Data Entry,

Data Cleaning, DataTransformation- Skill Lab 2: Presenting and Describing Data

(Table and Graph)- Skill Lab 3: Correlation and Regression,

Hypothesis Testing of Categorical andInterval Data, Survival Analysis

SKILL LAB MANUALBLOCK COMMUNITY-BASED PRACTICE

Skill Lab 1Preparing Data Entry Structure Before preparing structure of data entry, please recall types and classification

of data and variables, and structure of variables regarding to data entry.

1. To make data entry structure consisting of number, variable name, variable type, width, decimal, variable labels, value labels, and missing values

a. Use the variables in Annex 1 (Data collection form) to make data entry structure and Annex 2 (Data skill lab) for data entry practice.

b. Open SPSS program, click Variable View. Make the variables structure (name, type, width, decimal, variable labels, value labels, and missing values), beginning with variable: nomor, nama, area . . . . . . . . etc. (Refer to variable structure for data entry).

c. Save the file you just made, make your own folder and give the name of this file.

d. Notice the variables BBpre (BB before intervention) and BBpost (Bb after intervention)! These variables are a pair (two) of variables of one individual (examination of body weight level before and after intervention). It is important to remember in-paired or dependent tests such as paired test e.g.: paired-sample test, matched-pair test.

2. Data entry. To practice, use the data Annex 2 …. Type the data of variables for ten recods only.

3. Data cleaning. A very important step that must be done before data analysis.

Think of the errors which might occur during data entry. It can be shown by looking for the minimum, maximum, or extreme values, and the average values of the variables.

What should the minimum, maximum, and the mean values of numeric variables be, and what the normal values of the variables are. Just think about it!

a. Open skill lab raw.sav file, … look at the value labels of every variable. Numeric variable doesn’t have value labels, … Write the minimum and maximum values of all categorical variables and the anticipated mean for numerical variables.

b. In this case, to some extent data analysis will be done.

Method: Open skill lab raw.sav file (if it hasn’t so). For numeric variable, use the menu Analyze, Descriptive Statistics, … choose and click Descriptives..., click and move variables to be analyzed to the [Variable(s)] box. Click [Options...] box, leave [Descriptives: Options] window (mean, minimum, and maximun had been marked), click [Continue] box and [OK] box to finish the process.

Watch the results ………. Note if there is any extreme values that might be incorrect (below the minimum or above the maximum values), or whether the mean is in between or out of the range of normal value (eg. the mean of BB above 150 cm).

c. For categorical variable ……… look for frequencies of the categories

Method: Open skill lab raw.sav file (if it hasn’t so).

Use and click the menu: Analyze, Descriptive Statistics, choose and click Frequencies…, click and move variables to be analyzed to the [Variable(s)] box. Click [Statistics] box Click minimum and maximum in the [Dispersion] box, click [Continue] box and [OK] box to finish the process See the results ………. Take note if there are any values beyond the value labels.

Of point b and c, take note of the variables that have incorrect values and the values supposed to be incorrect.

d. To display the incorrect data

Method: Open Skill Lab Raw.sav file (if it has not be opened yet).

Use the menu: Data, choose Select Cases…, click if condition is satisfied in the [Select] box, click if and type the condition of the incorrect data to be looked for in the blank box in the [Select cases] window.

To display the incorrect data use and click the menu: Analyze, Reports, choose and click Case Summaries…, type the specification of data to be displayed in the [Variables] box of the [Summarized cases] window, e.g. variable’s name, record number, etc. Click [OK] box to finish the process. Specifications of the data will make easier to find the incorrect data to be corrected.

e. Data correction

Method: Open skill lab raw.sav file (if it hasn’t so).

Use and click the menu: Data View ……. place cursor on the variable column of the incorrect data. Use the menu: Edit, Finds…, type the incorrect value of the data in the [Find what] box, click Find next, ….. cursor will move to the incorrect data, …. replace the incorrect data with the correct value.

f. Save this corrected file for further analysis!

4. Data Transformation (Grouping)

In some cases, it’s very important to transform numeric variable to catego-rical variable, even categorical variable to other categorical variable of difference categories.

For instance: age can be grouped into several categories of five-year inter-vals, formal educational level to three categories, e.g. high, middle, and low level of education.

Another example, to make an indicator (composite index) based on two or more variables, e.g. BMI (body mass index) based on BW (body weight) and H (height).

A new variable must be made based on some existing variables. This new variable can be numeric or categorical. BMI is a numeric variable and can be trasformed to categorical variable.

a. To make a new categorical variable from quantitative/interval data or nume-ric/interval variable. Make a note of categorical value. …....... Use the menu: Transform, Recode, Into Different Variables…. (Use Skill Lab Raw.sav file).

Method: Open skill lab.sav file.

Use and click the menu: Transform, choose Recode, choose and click Into Different Variables…, choose and move the variable to be recoded to [Input Variabel:] box, .... type the new variable name in the [Output Variabel:] box, .... click Change, .... click Old and New Values, …. give a new code/value (must be integer) for every new category.

To practice: transform variables pendidikan to tk_pendidikan, umur to Klp_umur, hb to anemia, based on the following classi-fication!

- pendidikan to tk_pendidikan (two categories): 1. Low education (tdk pernah sekolah-SMP); 2. High Education (SMA –PT)

- umurAnak to Klp_umur (three categories): 1. Bayi (0-11 months); 2. Batita (12-35 months); 3. Balita (36-59 months).

- hb to anemia (two categories): 1. Anemia (≤ 11 g/dL); 2. Normal (> 11 g/dL).

Edit these new variable structures (type, width, decimal, label, and values).

b. To make composite index

To make an indicator of obesity using BMI (Body Mass Index) based on tb (tinggi badan, height) and bb (berat badan, body weight). From data we can compute BMIpre dan BMIpost.

Furthermore, BMI can be transformed to categorical variable as described before (1. < 17,0: kurang BB tingkat berat, severe underweight, 2. 17,0-18,5: kurang BB tingkat ringan, mild underweight, 3. 18,5-25,0: normal, 4. >25-27,0: kelebihan BB tingkat ringan, mild obesity, 5. >27,0: kelebihan BB tingkat berat, severe obesity). Formula: BMI = BB (kg)2/TB (m)2.


Remember that BMI formula for TB is in metre; therefore you need to compute the TB in cm into m.

Use and click the menu: Transform, click Compute..., Compute Variable window will appear, .... type TBmetre (new variable name) in the [Target Variable:] box, .... type the equation (TB/100) in the [Numeric Expression] box click [OK] box

Then continue to compute BMI variable:

Use and click the menu: Transform, click Compute..., Compute Variable window will appear, .... type bmipre (new variable name) in the [Target Variable:] box, .... type the equation to compute bmi in [Numeric Espression] box (BBpre/TBmetre2), .... click and move variable BBpre to the [Numeric Expression:] box, .... click (“/”) (devide symbol), .... click (“(”) (left bracket), .... click and move variable tbmetre to the [Numeric Expression:] box, .... click (*) (star symbol), .... click and move variable tbmetre to the [Numeric Expression:] box, .... click (“)”) (right bracket), then .... click [OK] box to finish the process. .... A new variable (imt) has been created containing the results of the calculation of the equation just made. ..... Edit the new variable structure, imt (type, width, decimal, label, and values).

Skill Lab 2

a. Presenting and Describing DataFrequency Distribution Tables and Graph

Tables and graphs can be used to present data/variables. Use narration to describe the conclusion of the table and graph. One-way tables can be used to present categorical variables. While association of two or more categorical variables can be presented using cross-tables.

Graph/chart presentation can be used for either categorical or numeric variables, single or associations of two or more variables.

Narration is used to describe the conclusion as depicted by the table or graph, e.g. frequencies, mean, minimum and maximum values, modus, etc.

For those purposes, tables or cross-tables and graphs are needed to be made. Conclusions of the tables and graphs are presented narratively such as some values depicted by the tables and graph (average and spread).

1. Presentation of discrete quantitative data is the same as categorical data (qualitative variable)

a. To make frequency distribution tables, to calculate average and spread, e.g.: mean, minimum and maximum values, SD, modus, percentiles, etc., use the menu: Analyze, Descriptive Statistics, Frequencies….

Method: Open skill lab.sav file (if it hasn’t so).

Use and click the menu: Analyze, choose Descriptive Statistics, choose and click Frequencies..., chose and move the variable(s) to be analysed (paritas) to the [Variable(s):] box in the Frequencies window.

- Leave Display frequency tables (had been marked)

- For statistics option, .... click [Statistics...] box, ….. choose the appopriate statistics in the [Percentile Values, Central Tendency, Dispersion] box in the [Frequencies: Statistics window].

- For graph options, .... click [Charts...] box, ….. choose the appopriate chart options in the [Chart Type] box, e.g.: bar, pie, or histogram (with normal distribution) in the [Frequencies: Charts] window.

click [Continue] and [OK] box to finish the process.

b. For quantitative data (continuous), calculate the mean, minimum, and maxi-mum values, SD; use the menu: Analyze, Descriptive Statistics, …... choose and click Descriptives…


Use and click the menu: Analyze, choose Descriptive Statis-tics, choose and click Descriptives…, choose and move the variable(s) (hbpre) to be analysed to the [Variable(s):] box.

- For descriptive statistics, click [Options] box, ….. choose and marked (Ö) the appropriate statistics in the [Dispersion] box, e.g.: mean, minimum, maximum, and SD) in the [Descriptive: Option] window. ..... Click [Continue] and [OK] box to finish the process.

c. For more statistics options, including trimmed mean, outliers, CI (Confidence Interval), percentiles, normality test, plot, etc.: ….. use the menu: Analyze, Descriptive Statistics, Explore…

Method: Open Skill Lab.sav file (if it hasn’t so).

Use and click the menu: Analyze, choose Descriptive Statis-tics, choose and click Explore..., [Explore] window will be displayed. Choose, click and move the variable(s) will be analysed (hbpre) to the [Dependent List:] box, ….. Leave [Display] box (Both had been marked), ..... click [Statistics...] box for statistic options (e.g. descriptives, outliers), ….. click [Conti-nue] box. .... Click [Plots...] box (for graph options: Boxplots, Descriptive, Normality Plots with Test) ..... click [Continue] box. ..... Click [OK] box to finish the process.

Normality test of data is required in hypothesis test, e.g.: T-test, Analysis of Variance, Correlation, and Multiple Regression.

2. Cross tabulation is used to analyze the association of two categorical variables (dicotomous or multicotomous). It can be descriptive or analytic analysis. If the variables can be classified as dependent or independent variables, the depen-dent variable should be placed as column variable and the independent variable as row variable. Row, column, and total percentages should be considered according to the methodological design. ….. use the menu: Analyze, Descriptive Statistics, Crosstabs…


Use and click the menu: Analyze, choose Descriptive Statis-tics, choose and click Crosstabs..., [Crosstabs] window will be displayed, ..... choose and move the variables to be analysed (choose kab as row variable and BBpre as column variable), ..... choose and move the variable kab to Row(s): box, and the variable BBpre to Column(s): box, ….. click the [Statis-tics...] box (for statistic options: statistic test, association), click [Continue] box (Return to [Crosstabs] window), ….. click the [Cells...] box (for persentage options: row, column, and total percentages). To display the chart, ..... click Display clustered bar charts. ..... click [OK] box to finish the process.

B. Presenting and Describing DataContinued: Frequency Distribution Tables and Charts

1. To Make Several Types of Charts

Other software such as Microsoft Office (Microsoft Word) can be used to make charts. It is more variative and practical, easier, and yields better results.

The type of charts to be used to present the data depends on the type of data/variables and the number of variables. Based on these, the appropriate type of charts should be thought about.

a. One categorical or numeric (discrete) variable: use bar, pie, line chart, histogram. (Bar, pie chart, and histogram, can be made in the chart options of frequency distribution analysis).


Use and click the menu: Graphs, choose and click the type of graphs (Bar..., Line..., Pie..., Area..., Histogram..., etc.), .... Bar... for example. [Bar Charts] window will be displayed. ..... Click the type of chart ( click Simple), .... click Summaries for groups of cases in [Data in Chart Are] box, .... click the [Define] box, .... [Define Simple Bar: Summaries for group of cases] window will be displayed click N of cases or % of cases options in the [Bar Represent] box to display value labels, .... choose and move the variable (choose paritas) for the chart to the [Category Axis:] box, …… click OK to finish the process.

b. Two or more variables:

- Two categorical variables ….. choose bar chart (clustered or stacked), type: Summaries for groups of cases for displaying data in Persentage;


You need make two categorical variabels (if it hasn’t so). For example transform the paritas variable and categorized it into <3 and >3. Transform hemoglobin variable into anaemia (<11 g/dl) and not anaemia (11 g/dl and above)

Use and click the menu: Graphs, choose and click Bar… [Bar Charts] window will be displayed. ..... Click the type of chart ( click Clustered or Stacked,), .... click Summaries for groups of cases in [Data in Chart Are] box, .... click the [Define] box, .... [Define Simple Bar: Summaries for group of cases] window will be displayed click N of cases or % of cases options in the [Bar Represent] box to display value labels, .... choose and move grouping variable (parity= <3 & >3) for the chart to the [Category Axis:] box, .... choose and move anemipre to [Define Clusters by:] box), .... click OK to finish the process.

. . . . . . Follow this procedure to practice making the other charts. Other example is graph of age (year) with Hb (anemia/not anemia). First, you need to transform the continuous variabel in to categorical

The process is almost similar . . . . . . . . .

- More than two variables (one categorical variable for X axis, and two numeric variables for Y axis) ….. choose bar chart (Clustered or Stacked), type: Summaries of separate variables to display Mean);


Use the menu: Graphs, choose Bar..., and click Clustered or Stacked click Define, choose variables for the graph, ….. (choose and move age (categorical variable) into Category Axis: box, and BBpre and BBpos into Bar Represent Box. .... click [OK] box to finish the process.

- Two continuous or ordinal variables for X and Y axis respectively ….. choose scatter diagram (type: simple).


Use the menu: Graphs, choose Scatter/Dot..., [Scatter/ Dot] window will be displayed, and click Simple, ..... click [Define] box, [Simple Sacatterplot] window will be displayed, ..... choose the variable for the graph, ….. (choose BBpre for X axis and BBpos for Y axis). ..... click [OK] box to finish the process.

- Two variables, one categorical variable for X axis and one quantita-tive (continuous/discrete) variable for Y axis……. choose boxplot (type: simple, choose and click Summaries for groups of cases for Data in Chart Are options).

*) If two numeric variable will be displayed separately, choose Clustered, and click Summaries of separate variables in the [Data in Chart Area] box.


Use the menu: Graphs, choose and click boxplot..., [Boxplot] window will be displayed, ..... click Simple for chart option, and click Summaries for groups of cases in the [Data in Chart Are] box, ..... klik Define, choose variable for the chart, …….. (choose and move BBpre or BBpos into [Variable:] box, and age (first: recode age into year) into Category Axis: box). .... Click [OK] box to finish the process.

Method*): Open Skill Lab.sav file (if it hasn’t so).

Use the menu: Graphs, choose and click boxplot..., ..... [Boxplot] window will be displayed, ..... click Clustered for chart option, and click Summaries of separate variables in the [Data in Chart Are] box, ..... klik [Define] box, choose variable for the chart, …….. (choose and move BBpre and BBpos into [Variable:] box, and age (year) into [Category Axis:] box). .... Click [OK] box to finish the process.

- One numeric variable for Y Axis, one categorical variable as grouping variable for Y Axis, and one categorical variable for X Axis ……. choose boxplot (type: clustered, and click Summaries for groups of cases for Data in Chart Area options).


Use the menu: Graphs, choose boxplot..., [Boxplot] window will be displayed, ..... click Clustered for chart type, click Summaries for groups of cases options in [Data in Chart Are] box, ..... click Define, choose the variable for the chart to be made, … (choose

and move age (year) into [Category Axsis:] box, ..... choose and move kab into [Define Clusters by:] box, and BBpre or BBpos into Variable box). .... Click [OK] box to finish the process.

- Two numeric variables for Y Axis, and one categorical variable for X Axis, choose boxplot (type: clustered, and Summaries of separate variables for Data in Chart Area options). (Choose parity for X Axis, and BBpre and BBpos for Y Axis).


Use the menu: Graphs, choose boxplot..., [Boxplot] window will be displayed, ..... click Clustered for type of chart, ..... click Summaries of separate variables options in [Data in Chart Are] box, ..... click Define, choose the variables for the chart to be made, ….. choose and move paritas into [Category Axis:] box, and BBpre and BBpos into [Boxes Represent:] box. ..... Click [OK] box to finish the process.

2. Graph Editing for Better Presentation

Method: Double-click the chart will be edited, click the part/component of chart to be edited, chose the appropriate menu to edit the com-ponent of the chart. Just do the same process for the other component ….. (Trial and Error, patient ....... are absolutely required).

For black and white printing, using pattern will be better than using color for fill color, and dash style will be better than line color.

Skill Lab 3

A. Correlation and RegressionLinear Correlation

Coefficient correlation (r) is an indicator of association of two numeric or interval variables. The values ranged between -1 and +1. The number depics the strength of the association, while the – or + values depict the direction of the association. The data should be normally distributed. If it isn’t so, non-parametric analysis should be choosen (Remember Pearson coefficient correlation and Spearman rho).

1. Coefficient correlation. It doesn’t show the causal-effect association or classify the variables to neither dependent nor independent variables. To com-pute coefficient correlation, choose the menu: Analyze, Correlate, Bivariate…


Choose the menu: Analyze, Correlate, choose and click Bivariate..., choose and move the variables will be analysed into [Variables:] box, (paritas, Hb, BBpre, and BBpos), ..... in the [Correlation Coefficients] box, choose and click Pearson (if the data are normally distributed), or Kendall’s tau-b or Spearman (if the data are not normally distributed), ..…

click Options for statistic options, ..... [Bivariate Correlation Options] window will be displayed, ..... click the appropriate statistics in the [Statistics] box. .... Click [OK] box to finish the process.

2. Regression, is used to predict the value of one dependent variable (Y) based on one or more independent variables (X or X1, X2, X3.......). The variables must be numeric or ordinal. The term Multiple Regression is used if the independent variables are more than one. To predict the value of Y variable based on the change in value of one or more X variables, choose the menu: Analyze, Regression, Linear…

. . . . Not to be discussed in this lecture . . . . Only for who is intrested in.

Method: Open skill lab.sav.sav file (if it hasn’t so).

Choose the menu: Analyze, choose Regression, choose and click Linear..., [Linear Regression] window will appear. ..... Choose and move the dependent variable into [Dependent:] box, and the independent variable(s) into [Independent(s):] box (more than one variables for multiple regression) ….. click [Statistics:] box for statistic options, ..... choose and click the appropriate statistic options in the [Linear Regression: Statistics] window ….. click [Plots:] box, ..... click the appropriate options, ….. Then click [OK] box to finish the process.

Equation: Y = a + b1X1 + b2X2 + b3X3.........

a = intercept

b = slope (regression coefficient)

B. Significancy Test for Categorical DataThere are three types of Significancy Tests for Categorical Data: single proportion for one dicotomous variable, two proportions of two dicotomous variables, and more than two proportions. Two proportions of two dicotomous variables in the form of 2 by 2 table (four-fold table), the larger contingency tables if the categories are more than two.

1. Significancy Test for Single Proportion (Nonparametrik: Binomial test). This significancy test is used to test a single proportion of one dicotomous vari-able (can be multicotomous and specify the cut point; cut point must be one of the categories). Use the menu: Analyze, Nonparametric Test, Binomial…


Use menu Analyze, choose Nonparametric Tests, choose and click Binomial..., choose and move the variabel to be analyzed (choose anemia) to the [Test Variable Lists:] box, ….. type the specified test proportion in the [Test Proportion:] box, ..... in the [Define Dichotomy:] box, ..... choose and click Cut Point (misal=12) type the specified category as cut point in the box. ..... Then click [OK] box to finish the process.

2. Significancy Test for Two Proportion (independent and paired test): four-fold table and larger contingency table

Remember the condition required for Chi-square test:

- Significancy test for 2 by 2 table, if the condition does not meet the require-ment. If it is so, what should be done for a larger contingency table.

- The significancy test for 2 by 2 table for dependent sample or paired sam-ple.

- Remember the expected frequency less than 5, 2-test, McNemar test, Fisher’s Exact Test, regroup of column or row.

- Recall the placement of dependent and independent variables, row and column percentages related to the methodology design.

a. Independent significancy test for two proportions (two dicotomous vari-ables). Use menu: Analyze, Descriptive Statistics, Crosstabs…


Use menu: Analyze, choose Descriptive Statistics, ..... choose and click Crosstabs..., [Crosstabs] window will be displayed, ..... choose and move area (independent variable) to [Row(s):] box, and anemia (dependent variable) to [Column(s):] box, ..... click [Statistics] box, ..... [Crosstabs: Statistics] window will be displayed, …… choose and click Chi-square (for statistical test) and Risk (for: RR and OR), ..... click [Continue] box (Return to [Crosstabs] window), ..... click [Cells] box, ….. [Crosstabs: Cell Display] window will be displayed, ….. click Row and Column to display Row and Column Percentages. ..... Then click [Continue] and [OK] box to finish the process.

- Output: Chi-Square Test, ….. See p (Asymp Sig.) of Chi-Square or Yate’s Continuity Correction (for less than 20 sam-ples).

- See Expected Frequency that less than 5. If more than 20% of the total cells, ……. use p (Exact Sig.) of Fisher’s Exact Test.

- Risk Estimate, ……. use Odds Ratio for case-control design (OR), or for Cohort (RR) of risk factor and not risk factor for Cohort design.

b. Dependent significancy test for two proportions (two dicotomous vari-ables, paired sample test). Use menu: Analyze, Descriptive Statistics, Crosstabs


Use menu: Analyze, use Descriptive Statistics, ..... choose and click Crosstabs..., [Crosstabs] window will be displayed, ..… choose and move the paired variables, one variable to [Row(s):] box, and the other variable to [Column(s):] box, ….. click [Statistics:] box, ..... [Crosstabs: Statistics] window will be displayed, ..… click McNemar (for statistical test), …. click [Continue] box (Return to [Crosstabs] window), ..... click [Cells:] box, ….. click Row or Column (to display Row or Column Percentages). ..... Then click [Continue] and [OK] box to finish the process.

SPSS Program doesn’t provide facility to compute OR for depen-dent sample or paired-sample test. OR can be easily computed manually using the equation: OR = b/c.

C. Significancy Test for Interval Data

Statistical test for two samples mean, independent and dependent or paired- sample test

1. Remember the condition required for the test (normality distribution of the data).

2. Use non-parametric test if the condition does not meet the requirement.

3. For independent-samples T test, read the Levene test to make conclu-sion of the results.

Variable consideration: dependent or test variable should be numeric or interval, and the independent variable should be dicotomous variable.

Recall normality test of data, 1-Sample K-S and Explore

a. Test the normality of BBpre using non-paramtric Kolmogorov-Smirnov test, Use menu: Analyze, Nonparametric Tests, 1-Sample K-S….


Use and click menu: Analyze, choose Nonparametric Tests, choose and click 1-Sample K-S..., [One-Sample Kolmogorov-Smirnov Test] window will be displayed, ..... choose and move the test variable (choose BBpre) to [Test Variable List:] box, ….. click Normal in the [Test Distribution] box. ..... Then click [OK] box to finish the process.

b. Normality test based on grouping variable, ..... Use and click menu: Analyse, Descriptive Statistics, Explore...

Method: Open skill lab.sav file (if it hasn’t so)

Use and click menu: Analyze, choose Descriptive Statistics, ..... choose and click Explore..., [Explore] window will be displayed, ..... choose and move the test variable (BBpre) to the [Dependent List:] box, and grouping variable (area) to the [Factor List:] box, ..… leave the Display box, .... click the [Statistics] box, ..... [Explore: Statistics] window will be displayed, ..... click the appropriate statistics options, .... click [Continue] box, ..... click the Plots box, ..... [Explore: Plots] window will be displayed, ..… click the appropriate options (the most important is Normality plots with test, and Power estimation), .... click [Continue] box, and [OK] box to finish the process.

c. Use Levene test for equality of variances testing. It doesn’t need to do this separately, because it has been included in T test.

d. Whatever the result was, normal or not, assume that the data is normally distri-buted and it is not normally distributed), ….. go further to T-test. Assume that the distribution of the data is normal 1) Use and click menu: Analyze, Compare

Means, One-Sample T Test… (for a single mean test), 2) Use and click menu: Analyze, Compare Means, Independent-Samples T Test ….. (for independent-samples), and 3) Paired-Samples T Test… (for dependent/pair-ed-samples).

1. Statistical Test for One-Sample T Test

This significancy test is used to test the mean of a sample compared with a specified value (standard or determined by the investigator).

Method: - One-Sample T Test

Open skill lab.sav file (if it hasn’t so).

Use and click menu: Analyze, choose Compare Means, ..... choose and click One-Samples T Test..., [One-Sample T Test] window will be displayed. ..... click and move the test variable (BBpre) to the [Test Variable(s):] box, ..... type the standard value (or determined by the investigator; example=15kg ) in the [Test Value] box, …… leave the Options box, and ..... click [OK] box to finish the process.

2. Statistical test for two-samples mean, independent and paired-sample test

- If the data is normally distributed

Method: - Independent-Sample T-Test.

Open skill lab.sav file (if it is not already open).

Use and click menu: Analyze, choose Compare Means, ..... choose and click Independent-Samples T Test..., [Independent-Samples T Test] window will be displayed, ..... choose and move the test variable (BBpre) to the [Test Varible(s):] box, and the grouping variable (area) to the [Grouping Variabel:] box, ..… click [Define Group...] box, ..... [Define Groups] window will be displayed, ..... type code of categories (must be integer) of the grouping variable in Group 1 and Group 2 box respectively, ...... click [Continue] box, ..... and then, ..... click [OK] box to finish the process.

Method: - Paired-Samples T Test.

Use and click menu: Analyze, choose Compare Means, ..... click Paired-Samples T Test..., [Paired-Samples T Test] window will be displayed,..… choose and move the paired-variables (BBpre and BBpost) to [Paired Variables:] box, ..... and click OK to finish the process.

- If the data is not normally distributed, use nonparametric test

Use and click menu: Analyze, Nonparametric Tests, choose [2 Inde-pendent Samples…] (for 2 independent samples) or [2 Related Sam-ples…] (for 2 dependent samples or paired-samples).

Method: - Nonparametric Test for 2 Independent Samples.

Open skill lab.sav file (if it is not already open).Use and click menu: Analyze, choose Nonparametric Tests, .... choose and click 2 Independent Samples..., choose and move the test variable (BBpre) to the [Test Varible(s):] box, and grouping variable (area) to the [Grouping Variable:] box, ... click Define Group, and type the code of categories of grouping variable in Group 1 and Group 2 box respectively, click Continue, … choose and click Mann-Whitney U in Test type: box, … click OK to finish the process.

Method: - Nonparametric Test for 2 Related Samples.

Open skill lab.sav file (if it is not already open).Use and click menu: Analyze, choose Nonparametric Tests, .... choose and click 2 Related Samples..., choose and move the paired-variables to be tested (BBpre and BBpost) to [Test Pair(s) List:] box, ..… choose and click Wilcoxon in [Test type:] box, … click OK to finish the process.

3. Statistical test for three or more samples mean, independent and paired sample test (NOT TO BE DISCUSSED), ....... just for who are interested in.

- If the data is normally distributed

Method: - One-Way ANOVA.

Open skill lab.sav file (if it is not already open).

Use and click menu: Analyze, choose Compare Means, ..... choose and click One-Way ANOVA..., [One-Way ANOVA] window will be displayed, ..... choose and move the test variable (Hb) to the [Dependent List:] box, and the factor variable (klp_umur) to the [Factor:] box, ..… click [Post Hoc...] box, ..... [One-Way ANOVA: Post Hoc Multiple Comparisons] window will be displayed, ..... click Bonferoni or other options in the [Equal Variances Assumed] box, and Tamhane’s T2 or other options in the [Equal Variances Not Assumed] box, ...... click [Continue] box (Return to [One-Way ANOVA] window. ..... Click [Options] box, ..... [One-Way ANOVA: Options] window will be displayed, ..... click Descriptive and Homogeneity of Variance Test in the [Statistics] box, Then, ..... click [Continue] and [OK] box to finish the process.

- If the data is not normally distributed, use nonparametric testUse and click menu: Analyze, Nonparametric Tests, choose [K Independent Samples…] (for 2 independent samples) or [2 Related Samples…] (for 2 dependent samples or paired-samples).

Method: - Nonparametric Test for 3 or more Independent Samples.

Open skill lab.sav file (if it is not already open).Use and click menu: Analyze, choose Nonparametric Tests, ....

choose and click K Independent Samples..., [Test for Several Independent Samples] window will be displayed, ..... choose and move the test variable (Hbpre) to the [Test Varible(s):] box, and grouping variable (klp_umur) to the [Grouping Variable:] box, ... click [Define Range...] box, ..... [Several Independent Samples: Define Range] will be displayed, ..... and type the minimum and maximun range of grouping variable in the [Minimum] and [Maximum] box respectively, ..... click Continue (Return to the [Test for Several Independent Samples] window, ... click [Options...] box, ..... [Several Independent Samples: Options] will be displayed, ..... click Descriptive and Quartile in the [Statistics] box, ..... click Continue (Return to the [Test for Several Independent Samples] window, … choose and click Kruskal-Wallis H in Test type: box, … click OK to finish the process.

Annex 1.

Data Collection Questionnaire and Data Entry Structure

DATA COLLECTIONS QUESTIONNAIRE

KUESIONER PENELITIAN KESEHATAN ANAK PROPINSI MERDEKA

1. Nomor Identitas:

I. IDENTITAS RESPONDEN

1. Nama : . . . . . . . . . . . . . . . . . . . . . . . . .

2. Area : . . . . . . . . . . . . . . . . . . . . . . . . .

3. Pendidikan Ibu : ……………………………….

4. Pekerjaan Ibu : ……………………………….

5. Berapa jumlah anak yang pernah Ibu lahirkan? . . . . . . . orang.

II. HASIL PEMERIKSAAN LABORATORIUM ANAK

6. Hasil pemeriksaan Hb : . . . . . . . . mg%

7. Hasil pemeriksaan TB : …………. cm

8. Hasil pemeriksaan BB sebelum intervensi : . . . . . . .kg

8. Hasil pemeriksaan BB setelah intervensi : . . . . . . . kg

st . . . . . . . . . . . . . . .

DATA ENTRY STRUCTURE

Format variabel

Name Type Width Deci-mals

Label Values Missing

ID No. urut

Area Area tempat tinggal

1 = Mudah dijangkau,2 = sulit dijangkau.

nama Nama

umur Umur ibu

Pendidikan Tingkat pendidikan formal terakhir

1=Tidak Pernah sekolah, 2=Tidak tamat SD

3= SD

4= SMP

5=SMA

6=PT

Pekerjaan Pekerjaan ibu

1= Tidak Bekerja

2=Bertani/berkebun/berternak

3= lainnya

Hb Kadar Hb anak

BBpre BB sebelum intervensi

TB Tinggi badan

BBpost BB setelah intervensi

Pengisian:

- Name (nama variabel), satu kata maksimal 8 huruf jika SPSS versi 12 atau di bawahnya, tidak boleh ada tanda baca, kecuali tanda (_).

- Type (ingat jenis variabel: numeric/string atau teks, date).- Width (jumlah digit atau angka yang diperlukan termasuk tanda baca, koma/titik).

- Decimals (jumlah desimal atau jumlah digit di belakang koma, untuk variabel numerik).

- Label (jelas).

- Values (jelas).

- Missing (biasa dipergunakan angka 9, sesuaikan dengan jumlah digit).

Annex 2. Skill lab data

Student ProjectSEARCHING, ANALYSIS, AND

INTERPRETING STUDY RESULT

T O P I C S :Descriptive longitudinal, cross sectional

Analitic cross sectionalCase Control

CohortClinical & Community Trial

Diagnostic Test

STUDENT PROJECTAbstract

Epidemiologic approaches are utilised to understand the distribution and risk factor of disease, also to know the effectiveness of an intervention. The understanding of these concepts assist doctors in diagnosing, developing evidence based therapy and determining the prognosis of the disease. In community, a doctor is expected to utilise these concepts to tackle health problems.

This student project aims to help student to gain better understanding about epidemiologic approaches and the specific attributes in each approach.

Learning task:

1. Each group need to conduct search on scientific publication that using one of the epidemiology approach (see assignment table for each group).

2. Pay attention on the timeline of this student project3. Each group need to consult and get approval from the planners on the topic4. Pay attention on the specific tasks for each approach. Discuss it with your group.5. Develop short report, maximum 10 A4 pages6. Make 8 copies of your group reports and submit them to dr. Citra timely7. Presentation of the student project will be conducted randomly on day 20 th of CBP

block, at the PLENARY SESSION (See the presentation guide)8. Assessment will be conducted based on several criteria (See evaluation guide)

a. Appropriateness of the answer to the taskb. Participation in group work will be proved by clear job description for each

group member. (dominated work will receive negative mark)c. Student project will contribute 15% of total CBP block mark.

Table 1. Group assign and epidemiologic approach

No SGD Class Approach Planners

1 I, II RegularEnglish

Study Descriptive Cross Sectional

Dr. AyuKartika/dr. Citra

2 III RegularEnglish

Study Descriptive Longitudinal Dr.AyuKartika/dr. Citra

3 IV RegularEnglish

Study Analytic Cross Sectional Dr.Septarini

4 V RegularEnglish

Study Case Control Dr.Sutarsa

5 VI RegularEnglish

Study Case Control (Matched Pair)

Dr.Sutarsa

6 VII RegularEnglish

Study Cohort Prospective Dr.Ariastuti

7 VIII RegularEnglish

Study Cohort Retrospective Dr.Ariastuti

8 IX RegularEnglish

Study Clinical Trial Dr.Sawitri

9 X RegularEnglish

Study Community Trial Dr.Sawitri

10 XI RegularEnglish

Uji Diagnostik Dr.Artawan

Table 2. Student Project Timeline

No Date/week Activities Penanggungjawab

1 Week II Searching and consultation with Planners Planners

2 Week II Discussion with group The chief of SGD

3 Week III Report writing The chief of SGD

4 Week IV Report submission to IKK-IKP The chief of SGD & dr. Ariastuti

5 Week IV Develop presentation slide The chief of SGD

6 Week IV Presentation The chief of CBP block

Table 3. Task for each approach

No Design Task1 Observational study: cross sectional, case control, cohort (source: STROBE Statement)

Introduction Explains the study background Explains the specific objective of the study Explains the study hypothesis (if available)

Method Explains the study design and describe important element of the study Develops study profile explains the setting, location, relevant date include recruitment period,

exposure, follow up and data collection method Participant:

(a) Cohort study—describes eligibility criteria, sources, and selection method. Explain the follow up method(b) Case-control study—describes eligibility criteria, sources and method to select case and control. Explain the rational in selecting case and control. © Cross-sectional study— describes eligibility criteria, sources, and selection method(d)Cohort study— For matched studies, explains the matching criteria,

the number of exposed and unexposed group. (e) Case-control study—For matched studies, explains the matching criteriaand number of control per case

Explains the definition of outcome, exposure, predictor, potential confounder and effect modifier. Explain the diagnostic criteria (if available)

Explains how to determine sample size Explains all statistical methods including methods to control

confounding variablesCohort study—if available, explains how to resolve loss to follow-up Case-control study—if available, explains how to match between case and control that was doneCross-sectional study—if available, explains analytical method that have done related to sample selection

Result Explains main result of the study2 Clinical Trial : (source: CONSORT Statement)

Introduction Explains the background of the study Explains the specific purpose of the study Explains the hypothesis

Methods Describe the trial design and the attributes including ration allocation Developa study profile completely including the attributes Explains the criteria of eligibility for the participant Explains the settings and location where data collected Explains the intervention given to each group, including the possibility

of repetition and real of the intervention Explains specific measurement from primary and secondary outcome Explains how to determine sample size If available, explains about analytical interim that was done Explains the randomization methods Explains the randomization type and details of the restrictions if

available Described the procedure that was done until intervention given Explains who did the randomization, who did the subject inclusion and

and who determine subject allocation for intervention/placebo? Was it done blinded?

Explains the statistical method used to compare the primary and secondary outcome

Result & Discussion

For every primary and secondary outcome, explains the result and significance

Explains the study limitation, how to resolve bias source, whether any precision problems

Explains about generalisability (external validity, applicability) of the result; if available

3 Diagnostic Test Introduction Explains the background of the study

Explains about the special purpose of the study Explains the hypothesis (if available)

Method Explains the study design and important part related to the study design

Develop the study profile Explains about the relevant setting, location, and date including

recruitment period, exposure, follow up and data collection Participant: explains the eligibility criteria, source and selection method Explains the definition of gold standard and diagnostic test. Please

explains the diagnostic criteria if available. Explains how to determine the sample size

Presentation guideline:1. Presentation slides:

Maximum 10 slides

Maximum 12 sentences/point per slide, minimum font size 20, font type ARIAL/TIMES NEW ROMAN/CALIBRI

Minimum animation, simple background

2. Presentation time maximum 12 minutes, followed by 10 minutes discussion

3. Each group will have chance to present their SP as planners will chose presenters randomly.

4. Please ensure to input all group slides to the computer before the presentation session begin, and name them as SGD 1, SGD 2 and so on, and put them in to one folder namely: Student Project Community Based Practice – English/Regular Class

Assesment guideline

No Assesment items Maximum mark

1 Appropriate answer 60

2 Group work 20

3 The ability to answer questions 20

Total mark 100

The mark for study project is an average of marks from planners who assess every SGD groups. The study project mark contribute 15% of total CBP evaluation.

study guide community-based practice - … · web viewcommunity based practice competency statement...

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