Download - Undergraduate Research work
1 x 8 + 1 = 9
12 x 8 + 2 = 98
123 x 8 + 3 = 987
1234 x 8 + 4 = 9876
12345 x 8 + 5 = 98765
123456 x 8 + 6 = 987654
1234567 x 8 + 7 = 9876543
12345678 x 8 + 8 = 98765432
123456789 x 8 + 9 = 987654321
1 x 9 + 2 = 11
12 x 9 + 3 = 111
123 x 9 + 4 = 1111
1 234 x 9 + 5 = 11111
12345 x 9 + 6 = 111111
123456 x 9 + 7 = 1111111
1234567 x 9 + 8 = 11111111
12345678 x 9 + 9 = 111111111
123456789 x 9 +10= 1111111111
9 x 9 + 7 = 88
98 x 9 + 6 = 888
987 x 9 + 5 = 8888
9876 x 9 + 4 = 88888
98765 x 9 + 3 = 888888
987654 x 9 + 2 = 8888888
9876543 x 9 + 1 = 88888888
98765432 x 9 + 0 = 888888888
The Beauty of Mathematics
1 x 1 = 1
11 x 11 = 121
111 x 111 = 12321
1111 x 1111 = 1234321
11111 x 11111 = 123454321
111111 x 111111 = 12345654321
11111 11 x 1111111 = 1234567654321
11111111 x 11111111 = 123456787654321
111111111 x 111111111=12345678987654321
And look at this symmetry:
PROJECT TITLE
Estimation Of The Age Distribution Of Patients Operated And Effect Of Salmonella Typhi
On The Incidence Of Typhoid Complications At The Main Surgical Theatre
Supervisor
Mr. S. K. Appiah
3/25/2010 4
• Komfo Anokye Teaching Hospital (Kath) is not
performing
• Many lives lost both medically and surgically
• The ministry of health ensure well being of the populace
INTRODUCTION
3/25/2010 5
PROBLEM RECOGNITION
• Reconnaissance Visits
• An Interview With A Medical (Surgical) Doctor
3/25/2010 6
THE NEED
• Conduct a survey so as to seek information to questions unanswered
• Find certain conditions that exist in this hospital
3/25/2010 7
QUESTIONS RAISED
• What age group has more surgical complications?
• What category of surgery is high?
• What proportion of the patients were females?
• What are the major complications?
• Probable factors that influence one of the complications?
3/25/2010 8
ASSUMPTIONS MADE
• Most of the patients operated are below 18years
• Most typhoid patients undergo surgery
• The mean age of the patients is in the thirty’s
3/25/2010 9
ASSERTION BY “EMEDICINE GROUP” ON TYPHOID
• Children aged 1 - 5 years have the highest risk of infection, morbidity and mortality
• Typhoid fever in patients is highest in adolescents and young adults
• Disease is generally highest in children aged 3 - 9 years
3/25/2010 10
RESEARCH OBJECTIVES
IDENTIFY THE CONDITIONS IN THE MAIN SURGICAL THEATRE
• To estimate the average age of patients and age range who visit the department
• To determine the ratio of males to females
• To determine the ratio of general surgery to pediatric surgery
• To know the complications frequently observed
3/25/2010 11
RESEARCH OBJECTIVES contd
TO STUDY ONE OF SUCH COMPLICATIONS
• To seek sex-relation
• Whether the environment or location of patients influence the number of cases.
• Does the age of persons have anything to do with the complication?
• Has acquired immunity play a role?
3/25/2010 12
DATA COLLECTION AND DATA ANALYSIS
• DATA was sourced from administrative records of KATH
• January 2006 to October 2007
3/25/2010 13
DATA ANALYSIS
• Microsoft office excel 2007
• SPSS
• Minitab
3/25/2010 14
ORGANIZATION OF THE STUDY
• CHAPTER ONE Overview of the study
• CHAPTER TWO Literature review
• CHAPTER THREE Profile of the coverage
• CHAPTER FOUR Analysis of data
• CHAPTER FIVE Findings and Recommendations
TYPHOID FEVER
is also known as
ENTERIC FEVER
ENDEMIC
Developing Countries
AFRICA & South America
CAUSE
bacterium
Salmonella Typhi
TRANSMISSION
WATERBORNE
OR
FOODBORNE
SYMPTOMS• Fever
• Headache
• Sore throat
• Constipation
• Joint pain
• Abdominal pain
• Loss of appetite
• Fatigue
• Rose spots
PROCESS
BACTERIA food or water stomach
bloodstream tissues
SERIOUS COMPLICATIONS
COMPLICATIONS
• Intestinal perforation
• Peritonitis
• Encephalopathy
• Intestinal hemorrhage
• Hepatosplenomegaly
• Diarrhea
COMPLEX CASES
end up in
SURGERY
KOMFO ANOKYE TEACHING
HOSPITAL (KATH)
• a 1000-bed hospital
• LARGEST in the northern sector
• also known as GEE
LOCATION OF KATH
KATH
A
REFERRAL HOSPITAL
having
POLYCLINIC
as well
THEIR VISION
To become a medical centre of excellence offering Clinical and
Non-Clinical services of the highest quality standards comparable to
any international standards within 5 years (2003-2008)
THEIR MISSION
“to provide quality services to meet the needs and expectations of all clients. This will be achieved
through well-motivated and committed staff applying best
practices and innovation”.
THEATRES
• Main Theatre
• A1 Theatre
• Poly Theatre
MAIN THEATRE
• General Surgery
• Urology
• Neurosurgery
• ENT, Eye
• Paediatric Surgery
• Plastic Surgery
• Gynaecology
LITERATURE
REVIEW
3/25/2010 31
OVERVIEW OF CONCEPTS
3/25/2010 32
Sampling theory is a study of relationships
existing between a population and samples
drawn from the population.
Why sampling over complete
enumeration:-saves time, reduce cost
,saves labour
3/25/2010 33
Sampling Distribution:- It is when samples of size N is been drawn from a given population
Why Use Stratification:-Different classes of surgeryDifferent age groupsDifferent sexes
The Principle Objective Of Stratification:-stratification divides the population into a relative more homogenous age distribution groups with regard to average age sent to the surgical ward for treatment.
STATISTICAL HYPOTHESIS
3/25/2010 34
It is a statement about the parameters of the model
Used to test the claim about the average age
obtained in stratification and the average age
obtained by the random sample generated by
minitab
(Null hypothesis)
(Alternate hypothesis)
3/25/2010 35
The use of P-values in hypothesis testing :-
P-value as the smallest level at which the data
is significant.
State if the null hypothesis was or was not
rejected at a specified α -value or level of
significance
CONFIDENCE INTERVAL
3/25/2010 36
Although hypothesis testing is a useful procedure, it sometimes does not tell the entire story. It is often preferable to provide an interval within which the value of the parameter would be expected to lie.
In many engineering and industrial experiments, the experimenter already knows that the means µ1differ µ2 , consequently, the hypothesis testing on is of little interest.
The experimenter would usually be more interested in a confidence interval on the difference in means . The interval
is called a percent confidence interval for the parameter.
CORRELATION ANALYSIS
3/25/2010 37
CONCERNED WITH THE STRENGTH OF ASSOCIATION BETWEEN THE
VARIABLE OF INTEREST AND THE OTHERS
An error term which caters for the errors due to chance and neglected factors
which we assume are not important
CORRELATION COEFFICIENT
3/25/2010 38
This is a quantitative measure of the strength of
linear relationship between two variables, say x and
y. There are two types of measure:
Pearson Product – Moment
This is used for quantitative data measured on
interval or ratio scale.
Spearman’s Rank Correlation Coefficient
This is used when the data is ranked
Scatter diagram
3/25/2010 39
The scatter diagram is a useful tool in examining
relationships; especially between two
variables.
A plot of the sample data on a graph gives a
visual indication of the degree of association
between two variables say x and y.
TYPES OF REGRESSION MODEL
3/25/2010 40
Regression models are classified
according to the number of predicted
variables and also the form of the
regression function.
Simple Regression model
Multiple regression model
Simple Linear Regression Model
3/25/2010 41
Definition and features of model
The simple linear regression model is given by Y = β0 + β1 x + ε
x - is the value of the response variable in the observation
is the known value of the predictor variables in the ith observation
ε - is the random error term which caters for the errors due to chance are neglected factors which we assumed not important.
are the parameters of the model
β0 - gives the intercept on y axis
β1 - measures the slope of the linear model
ESTIMATION OF LINEAR
REGRESSION MODEL
3/25/2010 42
The linear regression model is estimated by fitting a
best prediction line through the scatter diagram. This
can be done by estimating the parameters of the
model.
3/25/2010 43
METHOD OF LEAST SQUARES
This method finds the estimates
respectively by minimizing the total sum of squares
error( SSE ).
ANALYSIS OF VARIANCE IN
REGRESSION MODEL
3/25/2010 44
The application of analysis of variance (ANOVA) in regression analysis is based on the partitioning of the total variation and its degree of freedom into components.
DEFINITION OF SOME
TERMS(ANOVA):-
3/25/2010 45
The three quantities SSyy, SSE and SSR are measures of dispersion.
The total sum of squares of deviation (SSyy, ) is a measure of dispersion of the total variation in the observed values, y.
The explained sum of squares, (SSR ), measures the amount of the total deviation in the observed values of y that is accounted for by the linear relationship between the observed values of x and y. This is also referred to as sum of squares due to the linear regression model.
The unexplained sum of squares is a measure of dispersion of the observed y values about the regression which is sometimes called the error residual sum of squares (SSE ).
COEFFICIENT OF
DETERMINATION
3/25/2010 46
r2 is called the coefficient of determination which is explainedvariation expressed as fraction of total variation. It is also defined asa square of the correlation coefficient.
3/25/2010 47
MULTIPLE REGRESSION
ANALYSIS Multiple regression analysis will include fitting an
appropriate model to a collected set of data, testing
for the adequacy of the model
The analysis involves a large array of data system of
equations which are conveniently and effectively
performed in matrix
When you have q linear combinations of the k
random variables X 1 , X2…., X k .
3/25/2010 48
That is, for n independent observations on Yi
and the associated independent variables X1, X 2, …, Xk
We have
3/25/2010 49
3/25/2010 50
MULTIPLE LINEAR REGRESSION
MODEL From the general linear regression model for a
multiple regression analysis takes the form
3/25/2010 51
Forms of Multiple Linear Regression
Models1. Polynomials regression models:-
They contain one or more predictor variables in
various powers.
2. Transformed regression models:-
Some non-linear functions may be transformed to
linear regression models.
3.Interaction effects regression model:-
It is the joint effect of two or more predictor
variables(you can use Log etc)
3/25/2010 52
THE BEAUTY OF
MATHEMATICS
ANALYSIS OF DATA AND
DISCUSSION
3/25/2010 53
ANALYSIS OF DATA AND
DISCUSSION
3/25/2010 54
“An unexamined life is not worth living”, similarly an
unexamined organization will not be able to move forward
in the right direction
At the end of this analysis, we will be able to make well
informed decisions as to;
How to raise public awareness on the age group, gender
(sex) that should be extremely vigilant, cared, and etc.
Which class or nature of surgical equipments or devises
that should not be limited in number.
Which complications will need to be attended by the
ministry of health.
3/25/2010 55
CLASSIFICATION OF THE VARIOUS COMPLICATIONS REPAIRED
COMPLICATION NUMBER OF CASES PERCENTAGE
HERNIA 496 27.1
GOITER 80 4.4
TYPHOID 406 22.17
BREAST 127 6.9
APPENDICITIS 72 3.93
OTHERS 650 35.5
TOTAL 1831 100
3/25/2010 56
3/25/2010 57
age range Frequency relative frequency
0-9 32 0.21
10-19 23 0.15
20-29 34 0.23
30-39 27 0.18
40-49 13 0.09
50-59 9 0.06
60-69 5 0.03
70-79 7 0.05
80-89 0 0.00
90-99 0 0.00
3/25/2010 58
3/25/2010 59
Estimating frequency Distribution of age of Patients
9080706050403020100
0.25
0.20
0.15
0.10
0.05
0.00
age point
rela
tiv
e f
req
ue
nc
y
S catterplot of re lative frequency vs age point
3/25/2010 60
STRATIFICATION OF PATIENTS USING THE CLASS OF SURGERY
stratum Nh nh
2
Yh S2h NhYh p=Sh
2/nh V=Nh(Nh-nh)p
General surgery 1310 10
7
37.748 366.4927 49449.88 3.425165421 5397820.941
Pediatric surgery 521 43 5.4419 18.01408 2835.2299 0.418932093 104330.0106
Total 1831 15
0
43.1899 384.5068 52285.1099 3.844097514 5502150.952
The estimate for the mean age was 28.55549 with standard error
1.281085
3/25/2010 61
STRATIFICATION OF PATIENTS BY SEX
stratum Nh nh
2
Yh S2h NhYh p=Sh
2/nh V=Nh(Nh-nh)p
female 684 56 29.518 447.7456 20190.312 7.995457143 3434464.607
male 1147 94 31.511 566.1068 36143.117 6.022412766 7273815.937
Total 1831 150 61.029 1013.852 56333.429 14.01786991 10708280.54
The estimate for the mean age is 30.76648 with standard
error 1.787193
3/25/2010 62
The estimate for the mean age is 29.352 years with standard error
1.133
STRATIFICATION OF PATIENTS BY COMPLICATIONS
3/25/2010 63
STRATIFICATION OF THE PATIENTS IN TERMS OF AGE RANGE
stratum Nh nh: Yh Sh2 NhYh p=Sh
2/nh Nh(Nh-nh)p
0-10 479 10 5.6 6.267 2682.4 0.6267 140788.7817
11-20 240 6 13.5 6.7 3240 1.116666667 62712
21-30 332 8 24.25 7.929 8051 0.991125 106613.334
31-40 245 6 34.5 5.5 8452.5 0.916666667 53675.41667
41-50 202 5 44.8 5.7 9049.6 1.14 45365.16
51-60 142 4 55 9 7810 2.25 44091
61-70 91 3 66 3 6006 1 8008
71-80 71 3 74.33 9.3 5277.43 3.1 14966.8
81-90 20 2 85 2 1700 1 360
91-100 9 1 95 0 855 0 0
Total 1831 48 497.98 55.396 53123.93 12.14115833 476580.4924
The estimate for the mean age is 29.01362 years with standard
error 0.3770333
The Claim!
The Mean Age
is 29 years
3/25/2010 64
STATISTICAL HYPOTHESIS
TESTING
Null Hypothesis:
The Mean Age is 29 years
3/25/2010 65
3/25/2010 66
Descriptive Statistics: factor, formulation1Variable N Mean Median TrMean StDev
SE Mean
factor 750 3.0000 3.0000 3.0000 1.4152
0.0517
formulation 750 28.973 25.000 27.872 21.557
0.787
Variable Minimum Maximum Q1 Q3
factor 1.0000 5.0000 2.0000 4.0000
formulation 1.000 96.000 10.000 43.000
3/25/2010 67
1 2 3 4 5
0
10
20
30
40
50
60
70
80
90
100
factor
form
ula
tio
n1
Boxplots of formulation by factor
(means are indicated by solid circles)
3/25/2010 68
-30 -20 -10 0 10 20 30 40 50 60 70
-3
-2
-1
0
1
2
3
No
rma
l S
co
re
Residual
Normal Probability Plot of the Residuals
(response is f ormulat)
`
3/25/2010 69
282318
95% Conf idence Interv als f or Sigmas
P-Value : 0.191
Test Statistic: 1.532
Levene's Test
P-Value : 0.350
Test Statistic: 4.440
Bartlett's Test
Factor Lev els
5
4
3
2
1
Test for Equal Variances for formulation1
3/25/2010 70
3/25/2010 71
One-way ANOVA: formulation1 versus factorAnalysis of Variance for formulation
Source DF SS MS F P
factor 4 842 210 0.45 0.771
Error 745 347210 466
Total 749 348051
Individual 95% CIs For Mean
Based on Pooled StDev
Level N Mean StDev ----------+---------+---------+------
1 150 27.53 19.68 (-----------*----------)
2 150 28.48 21.37 (-----------*----------)
3 150 28.70 21.90 (-----------*----------)
4 150 29.47 21.47 (----------*-----------)
5 150 30.69 23.36 (----------*-----------)
----------+---------+---------+------
Pooled StDev = 21.59 27.0 30.0 33.0
3/25/2010 72
Multiple Comparisons
Dependent Variable: age formulation of patients
-.9533 2.49280 .995 -7.7698 5.8631-1.1733 2.49280 .990 -7.9898 5.6431-1.9400 2.49280 .937 -8.7565 4.8765-3.1667 2.49280 .710 -9.9831 3.6498
.9533 2.49280 .995 -5.8631 7.7698-.2200 2.49280 1.000 -7.0365 6.5965-.9867 2.49280 .995 -7.8031 5.8298
-2.2133 2.49280 .901 -9.0298 4.60311.1733 2.49280 .990 -5.6431 7.9898
.2200 2.49280 1.000 -6.5965 7.0365-.7667 2.49280 .998 -7.5831 6.0498
-1.9933 2.49280 .931 -8.8098 4.82311.9400 2.49280 .937 -4.8765 8.7565
.9867 2.49280 .995 -5.8298 7.8031
.7667 2.49280 .998 -6.0498 7.5831-1.2267 2.49280 .988 -8.0431 5.58983.1667 2.49280 .710 -3.6498 9.98312.2133 2.49280 .901 -4.6031 9.02981.9933 2.49280 .931 -4.8231 8.80981.2267 2.49280 .988 -5.5898 8.0431-.9533 2.49280 .702 -5.8471 3.9404
-1.1733 2.49280 .638 -6.0671 3.7204-1.9400 2.49280 .437 -6.8337 2.9537-3.1667 2.49280 .204 -8.0604 1.7271
.9533 2.49280 .702 -3.9404 5.8471-.2200 2.49280 .930 -5.1137 4.6737-.9867 2.49280 .692 -5.8804 3.9071
-2.2133 2.49280 .375 -7.1071 2.68041.1733 2.49280 .638 -3.7204 6.0671
.2200 2.49280 .930 -4.6737 5.1137-.7667 2.49280 .759 -5.6604 4.1271
-1.9933 2.49280 .424 -6.8871 2.90041.9400 2.49280 .437 -2.9537 6.8337
.9867 2.49280 .692 -3.9071 5.8804
.7667 2.49280 .759 -4.1271 5.6604-1.2267 2.49280 .623 -6.1204 3.66713.1667 2.49280 .204 -1.7271 8.06042.2133 2.49280 .375 -2.6804 7.10711.9933 2.49280 .424 -2.9004 6.88711.2267 2.49280 .623 -3.6671 6.1204
(J) factor2.003.004.005.001.003.004.005.001.002.004.005.001.002.003.005.001.002.003.004.002.003.004.005.001.003.004.005.001.002.004.005.001.002.003.005.001.002.003.004.00
(I) factor1.00
2.00
3.00
4.00
5.00
1.00
2.00
3.00
4.00
5.00
Tukey HSD
LSD
MeanDifference
(I-J) Std. Error Sig. Lower Bound Upper Bound95% Confidence Interval
3/25/2010 73
Each sample was used for the hypothesis testing of the claim that the mean age was 29 years.
One-Sample Z: sample1Test of mu = 29 vs mu not = 29
The assumed sigma = 21.6
Variable N Mean StDev SE Mean
Sample 1 150 27.53 19.68 1.76
Variable 95.0% CI Z P
Sample 1 ( 24.07, 30.98) -0.84 0.403
One-Sample Z: sample 2Test of mu = 29 vs mu not = 29
The assumed sigma = 21.6
Variable N Mean StDev SE Mean
Sample 2 150 28.48 21.37 1.76
Variable 95.0% CI Z P
Sample 2 ( 25.02, 31.94) -0.29 0.768
One-Sample Z: sample 3Test of mu = 29 vs mu not = 29
The assumed sigma = 21.6
Variable N Mean StDev SE Mean
Sample 3 150 28.70 21.90 1.76
Variable 95.0% CI Z P
Sample 3 ( 25.24, 32.16) -0.17 0.865
One-Sample Z: sample 4Test of mu = 29 vs mu not = 29
The assumed sigma = 21.6
Variable N Mean StDev SE Mean
Sample 4 150 29.47 21.47 1.76
Variable 95.0% CI Z P
Sample 4 ( 26.01, 32.92) 0.26 0.791
One-Sample Z: sample 5Test of mu = 29 vs mu not = 29
The assumed sigma = 21.6
Variable N Mean StDev SE Mean
Sample 5 150 30.69 23.36 1.76
Variable 95.0% CI Z P
Sample 5 ( 27.24, 34.15) 0.96 0.337
3/25/2010 74
3/25/2010 75
AGE NUMBER
OF CASES
PERCENTAG
E
FEMALE
S
TYPHIO
D CASES
PERCENT
AGE
WITH
TYPHIOD
% OF
TYPHIOD
IN RANGE
NUMB
ER
0-17 623 34.00655 221 181 9.8799 44.5813 0-15 162(8.8
4%)
18-
54
944 51.53 360 191 10.4258 47.04 18-30 117
55+ 264 14.47 103 34 1.8559 8.37438
TOT
AL
1831 100 684 406 22.1615
AGE AND TYPHOID STATISTICS
3/25/2010 76
Month PATIENTS ZONGO AGE <6years FEMALE
June 10 1 8 7
July 35 5 25 22
August 41 15 35 24
September 34 7 20 17
October 27 7 16 13
November 46 10 27 20
December 39 7 26 15
January 37 9 23 22
February 31 5 16 16
March 45 11 25 18
April 10 2 6 6
Total 355 79 227 180
DATA FROM THE PEDIATRIC UNIT
3/25/2010 77
Regression Analysis: patients versus zongo, age, femaleThe regression equation is
patients = 4.07 + 0.42 zongo + 0.824 age + 0.500 female
Predictor Coef SE Coef T P
Constant 4.068 5.642 0.72 0.494
zongo 0.420 1.003 0.42 0.688
age 0.8240 0.6766 1.22 0.263
female 0.5002 0.7478 0.67 0.525
S = 5.899 R-Sq = 84.0% R-Sq(adj) = 77.2%
Analysis of Variance
Source DF SS MS F P
Regression 3 1282.58 427.53 12.29 0.004
Residual Error 7 243.60 34.80
Total 10 1526.18
3/25/2010 78
1086420-2-4-6
2
1
0
Residual
Fre
qu
en
cy
Histogram of Residuals
1050
10
0
-10
Observation Number
Re
sid
ua
l
I Chart of Residuals
Mean=-1.8E-04
UCL=13.75
LCL=-13.75
5040302010
10
5
0
-5
Fit
Re
sid
ua
l
Residuals vs. Fits
210-1-2
10
5
0
-5
Normal Plot of Residuals
Normal Score
Re
sid
ual
residual plot for Normal linear
3/25/2010 79
Correlations: patients, zongo, age, femalepatients zongo age
zongo 0.832
0.001
age 0.909 0.886
0.000 0.000
female 0.865 0.791 0.905
0.001 0.004 0.000
Cell Contents: Pearson correlation
P-Value
A NEED FOR MODEL
MODIFICATION
3/25/2010 80
3/25/2010 81
Regression Analysis: patients versus zonagefem
This modification considers the product of the predictor factors as a single variable.
The regression equation is
patients = 24.3 + 0.00230 zonagefem
Predictor Coef SE Coef T P
Constant 24.293 4.256 5.71 0.000
zonagefe 0.0022960 0.0008795 2.61 0.028
S = 9.824 R-Sq = 43.1% R-Sq(adj) = 36.8%
Analysis of Variance
Source DF SS MS F P
Regression 1 657.66 657.66 6.82 0.028
Residual Error 9 868.52 96.50
Total 10 1526.18
THE PRODUCT TRANSFORMATION
3/25/2010 82
Regression Analysis: patients versus sqrt (zonagefem)
This modification considers the square root of the product of the predictor factors as a single
variable.
The regression equation is
patients = 14.1 + 0.353 sqrt(zonagefem)
Predictor Coef SE Coef T P
Constant 14.079 4.162 3.38 0.008
sqrt(zon 0.35299 0.07059 5.00 0.001
S = 6.700 R-Sq = 73.5% R-Sq(adj) = 70.6%
Analysis of Variance
Source DF SS MS F P
Regression 1 1122.2 1122.2 25.00 0.001
Residual Error 9 404.0 44.9
Total 10 1526.2
THE SQUARE ROOT TRANSFORMATION
3/25/2010 83
The regression equation is
patients = - 18.5 + 6.87 Ln(zonagefem)
Predictor Coef SE Coef T P
Constant -18.480 5.142 -3.59 0.006
Ln(zonag 6.8658 0.6790 10.11 0.000
S = 3.704 R-Sq = 91.9% R-Sq(adj) = 91.0%
Analysis of Variance
Source DF SS MS F P
Regression 1 1402.7 1402.7 102.24 0.000
Residual Error 9 123.5 13.7
Total 10 1526.2
THE NATURAL LOG TRANSFORMATION
3/25/2010 84
10 9 8 7 6 5 4
50
40
30
20
10
Ln(zonagefem
patients
S = 3.70396 R-Sq = 91.9 %
R-Sq(adj) = 91.0 %
patients = -18.4801 +
6.86584 Ln(zonagef em
Regression Plot
3/25/2010 85
6420-2-4-6
5
4
3
2
1
0
Residual
Fre
quency
Histogram of Residuals
1050
10
0
-10
Observation Number
Resid
ual
I Chart of Residuals
Mean=0.01085
UCL=12.10
LCL=-12.08
5040302010
5
0
-5
Fit
Resid
ual
Residuals v s. Fits
210-1-2
5
0
-5
Normal Plot of Residuals
Normal Score
Resid
ual
residual plot for Natural log transform
3/25/2010 86
The regression equation is
patients = - 18.5 + 6.87 Ln(zonagefem)
where patients represents the number of
patient admitted with typhoid at the
Pediatric Unit;
zonagefem represents the product of the
environment, age below six years and
number of females. The Ln is the natural
logarithm function.
MAJOR FINDINGS AND
IMPLICATIONS
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The mean age of patients operated was 29 years
The age range which had more surgical complicationswas 0-9 years.
The percentage of cases were relatively high for males.It was realized that about that 62.64 of the casesworked on were males. The ratio of males to femaleswas 1.7:1
The complete data indicates that out of a total of 1831patients 27.1% and 22.17% suffered from hernia andtyphoid complications
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The investigations proved that out of the 22.17% of the typhoid related complications, 44.58% were children. That implied 9.88% of the total cases were children with typhoidcomplications.
It was also observed that 39.9% of the children with typhoidcomplication were aged below 16years. In other words, approximately 8.84% of the cases handled by the theatre were children below 16 years with typhoid fever.
The ratio of the male to female was nearly 1:1 respectively
The known dirty environs (“Zongo”) did not contribute a high percentage in the case of typhoid.
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This could mean that even though most of the patients lived in well sanitary locations, they probably do not take absolute good care of themselves since typhoid is water and food bone. That is to say;
• Nature of the water they drink or use in cooking
• Poor keeping of the kitchen and toilet facilities
• Poor personal hygiene
• Parent Inadequate education of nursing children
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RECOMMENDATIONS The findings and Implications explained above gives
an idea to make good recommendation based on the sample survey.
• The hospital administrators should provide more equipments and surgical devices to accommodated patients especially those with age less 16 years.
• The public should be informed as to the risk of complications of people aged in interval 0-10 years so as to minimize these cases.
• Counseling on ways to minimize some of these related complications should be carried out.
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• The general public should be educated on the incidence and severity of typhoid fever; ways they can minimize its infection.
• The Ministry of Health can help create animations (Cartoons) on our visual media stations so as to educate the children faster.
• Rural Water Projects should be encouraged in way to enhance proper distribution of water to various locations.
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CONCLUSION
Our way of Life, is based on the decisions we make. As such, there is a need for us as citizens to be cautious on the food and water we take into our body.
This survey has revealed to as certain conditions at the main theatre of the KATH. The recommendations outlined, based on the survey, above should be considered so as to ensure that the health of all are stabilize
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Thank
you