Dr Helen Looker MPH October 7 th 2014. Estimate frequency ◦ disease, behaviour - attribute ◦ at...

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