dr helen looker mph october 7 th 2014. estimate frequency ◦ disease, behaviour - attribute ◦ at...
Post on 28-Dec-2015
220 Views
Preview:
TRANSCRIPT
Dr Helen LookerMPH
October 7th 2014
Estimate frequency◦ disease, behaviour - attribute ◦ at a point in time
Assess relationship◦ between attribute and other factors
Age in years
16-44 45-64 65-74 75+
Cancer & benign growths
4 10 26 67
Mental disorders
41 52 14 5
Heart & circulatory
16 155 334 306
Skin 14 22 13 6
Musculoskeletal
96 221 262 211* per 1000 men
Scottish Health Survey, 2003
Japan Honolulu San Francisco
“Study that examines the relationship
between disease and factors of interest
in a defined population at a particular time”
Survey a population◦ measure cholesterol level (risk factor)◦ ECG for evidence of CHD (disease)
Calculate prevalence of disease in◦ people with risk factor◦ people free of risk factor
OR calculate prevalence of exposure in
◦ people with disease◦ people free of disease
Total Cholesterol <5.0mmol/l
Total Cholesterol ≥5.0mmol/l
Total
Normal ECG 400 55 455
Abnormal ECG 150 75 225
Total 550 130
Total Cholesterol <5.0mmol/l
Total Cholesterol ≥5.0mmol/l
Total
Normal ECG 400 (72.7%) 55 (42.3%) 455
Abnormal ECG 150 (37.3%) 75 (57.7%) 225
Total 550 130
Total Cholesterol <5.0mmol/l
Total Cholesterol ≥5.0mmol/l
Total
Normal ECG 400 (87.9%) 55 (12.1%) 455
Abnormal ECG 150 (66.7%) 75 (33.3%) 225
Total 550 130
Smoking habit
% with cough in winter
Men Women
Non-smoker 27 9
1-14g per day 46 30
> 15g per day 64 73
Fletcher et al 1959
35-64 years 65+ yearsWith
diabetes (%)
No diabete
s(%)
With diabete
s(%)
No diabet
es(%)
Smoker 20 30 14 17
Obese 60 27 50 22
Alcohol>21 units in last week
13 29 21 19
Low SIMD 22 18 23 18
Scottish Health Survey, 2003
Monitor prevalence of disease◦ service need/ planning◦ groups with most disease
Prevalence of risk factors◦ inter-relationships between risk factors
Age (years)16-24 25-34 35-44 45-
5455-64 65-74 75+
Men 31 30 28 29 29 22 15
Women
23 17 15 18 11 8 3
Scotland, 2003 *Recommended levelMen: 21 unitsWomen: 14 units
monitor prevalence of disease◦ service need/ planning◦ groups with most disease
prevalence of risk factors◦ inter-relationships between risk factors
repeated surveys can monitor change◦ are policy level interventions working
generate hypotheses about causes◦ association with current risk factors◦ association with past exposure or early clinical
signs
Relationships at one point in time◦ prevalent cases◦ prevalent risk factors
Extrapolating data beyond surveyed population ◦ study may miss hospitalised patients
Interactions of risk factors and disease prevalent cases are survivors◦ if risk factor associated with survival from disease or not recovering from disease
◦ exposure may not have preceded disease maybe disease changed level of risk factor
Depression and adverse life events: ◦ depression can occur after adverse life events◦ early onset depression can lead to adverse life
events
Smoking and low respiratory function◦ smoking increases risk of chest diseases which
lower respiratory function◦ chest disease increases susceptibility to smoking
effects Need to follow people over time to see which
comes first
Limitations of cross-sectional studies
relationships at one point in time prevalent cases prevalent risk factors
show association, not causation need very large numbers if
prevalence low especially with acute (rather than
chronic) disease depend on
high response rates accurate answers
could it matter?◦ seasonal effects eg alcohol consumption ◦ time trends eg Hepatitis B infection rates
Study method Time frame
questionnaire to a class of students
instantaneous
postal questionnaire days to weeks
telephone interviews weeks to months
personal interviews months to years
Surveys – at the heart of cross-sectional studies (1)
A simple method Get list of all dentists Visit Take blood sample Calculate prevalence of mercury
poisoning
A question: do dentists get mercury poisoning
Surveys – at the heart of cross-sectional studies (2)
A simple method Get list of all dentists Visit: More than 3000 dentists in
Scotland Take blood sample: Costs for 3000
measures Calculate prevalence of mercury
poisoning
A question: do dentists get mercury poisoning
Surveys – at the heart of cross-sectional studies (3)
A simple method Get list of all dentists take a random sample visit take blood sample
A question: do dentists get mercury poisoning
can make statements about the population by asking a (small) sample
a well taken sample is (almost) as informative as a complete census
sampling is a feature of all research designs
Who was studied
Are they representative of the general population
Could they be different or unusual
Examples◦ patients attending a clinic◦ people interviewed at a community centre◦ health professionals at conference◦ complainants
Problem◦ is the group representative?
commonly used in opinion surveys◦ political◦ consumer preference
interviewer assigned a set number of subjects◦ age, sex, social class, race, geographical area
sometimes good, sometimes not
Biases◦ visibility – people that are around◦ accessibility - easy to recruit◦ affinity – people you like
Sampling for a survey
Group of interest
Sampling frame
Sample
Data
Inference
Selection
list the group
generate random numbers
contact selected individuals
collect data
GP register electoral register professional organizations clinic lists pharmacy records
Bottom line - comprehensive cover
NOT haphazard
each subject has equal chance
number the subjects 1 to n
computer generated random numbers
Dundee post-grad Dundee post-grad studentsstudentsWhat is the frequency of binge
drinking in students?
matriculation list8170 students
sample 300300 r.n.s 1 to 8170
write/e-mail/ interview
what is the final group of interest (ie population will we make inferences about)◦ is this justified
what kind of response rate will we get?◦ what effect might this have?
When population falls into similar groups◦ take a sample of groups◦ then sample within chosen groups
Example - sampling from GP lists◦ randomly select GPs◦ take a sample of their patients
Benefit◦ easier than sampling whole population
Cost◦ lower precision
Prevalence of urinary symptoms
8 GP practice registers
selected 2/3 of men 65+
20% had symptoms
substantial problem exists
Who might be missed out from this study?◦ what effect might this have?
Will answers always be accurate?◦ what effect might this have?
Would answer differ substantially if study was carried out with different GP practices or in another part of the country?
population divided into groups◦ eg income (very rich ..... very poor)◦ eg population density (city ... town ... village)
people in each group tend to be similar◦ major differences between groups
take a random sample from each group
get a better estimate for the same (total) sample size
Inaccurate data
Non-coverage◦ who was missed◦ what might they be like
Non-response◦ who didn’t reply◦ why not
Selection bias: always consider it in cross-sectional studies
How did the people who are participating in your study get to be where they are? Was it related to exposure? Was it related to disease?
Are those who ended up in your study representative of the source population? Could their participation relate to exposure? Could it relate to disease?
You should not do a cross-sectional study without pondering about the answers to these questions
Nature and uses of cross-sectional studies◦ design◦ association not causation
Surveys◦ design◦ conduct
Sampling◦ simple random sampling◦ cluster sampling
Flaws in survey
Definition: observational study with populations or groups (instead of individuals) being unit of observation
compares group averages◦ health◦ risk factors
• In Papua New Guinea, low rate of oral cancer in highlands and high rate in lowlands
• Betel nut grows in lowlands, not highlands
Relationship between per capita meat consumption and colon cancer
Per capita daily consumption - grams
Col
on c
ance
r in
cid
ence
per
100
,000
Armstrong BK, Doll R, 1975
National mortality vs. health care expenditure
Cause and effect: are shops located where there are lots of smokers?or do shops increase the prevalence of smoking?
• Describe associations• Generate hypotheses• Some risk factors may not easily be measurable at an individual level: eg environmental pollutants, GDP
Advantages: Desk top studies
governmental statistics, routine health and social statistics, census data
relatively cheap
relatively quick
0
10
20
30
0 0.2 0.4 0.6 0.8 1
Suicide Rateper 100,000
Proportion Protestant
Problem: protestant countries might differ from catholic ones in other ways
The association seen at the population level is different to that seen at the individual level
Associations at national level◦ internet use
incidence of HIV prevalence of obesity
◦ per capita income incidence of coronary heart disease
Countries with high (or low) exposure differ systematically in many other ways some of which are related to disease risk
OR Complex associations between disease and
exposure◦ Disease incidence among non-exposed differs
between populations◦ Disease incidence among non-exposed varies
systematically with % NOT exposed
% wearing
hats
Lung cancer incidence
(per 100,00)
20 50
40 60
60 70
% wearing hats
Lung cancer incidence(per 100,00)
Hat wearing No Hat Overall incidenc
e
Incidence%
population
Incidence
% populatio
n
A 50 20 50 80 50
B 50 40 66.7 60 60
C 50 60 100 40 70
0 5 10 15 20
% Calories from Saturated Fat
0
1
2
3
4
5C
HD
Dea
ths
and
MI/1
00 R = 0.84
V
MC
DG
SW
B
Z
UN
E
K
Finland, Greece, Italy, Japan, Holland, US, Yugoslavia
Ancel Keys1904 - 2004
Background risk will practically always differ between groups (groups are mostly not made up randomly)
Limit inferences from ecological studies Explore differences between exposed and non-
exposed
But Take advantage of natural experiments Data that are already available
Nature of ecological studies◦ uses◦ limitations
Definition ecological fallacy◦ reason it can occur
top related