Understanding Health Inequalities

Question 1

Why is it difficult to identify causes of illness in public health data?

Answer 1

Public health data is observational data

We need experimental data​

Question 2

What is meant by experimental data?

Answer 2

A group of people with similar characteristics is randomly divided into 2 groups

The only difference between these 2 groups is exposure status

If you see a difference in outcome, you know it is due to exposure as all the other characteristics are the same

Question 3

What is meant by observational data?

Answer 3

The selection of people in the exposed and unexposed groups is NOT random

The groups will be different in exposure, but also an array of other characteristics

You don’t know which characteristic results in the difference in health

Question 4

Under what circumstances is an association most likely to be causal?

Answer 4

An association is most likely to be causal when the exposure is both necessary and sufficient for the outcome to happen

Question 5

What is meant by necessity?

What is meant by sufficiency?

Answer 5

Necessity:

The exposure must be in place in order for the outcome to happen

Sufficiency:

The exposure always leads to an outcome

Question 6

Why are risk factors in public health data often neither necessary or sufficient?

Answer 6

Illness can happen without the risk factor of interest

e.g. Different exposures may lead to the same illness

presence of the risk factor does not always lead to illness

Question 7

Why are associations often seen in observational data?

Answer 7

1. Exposure could cause the outcome
2. Reverse causation - the outcome may actually be causing the exposure
3. Bi-directional causation - there is a feedback loop between exposure and outcome
4. Confounding - exposure and outcome are both consequences of something else

Question 8

What are the three conclusions that must be considered when it comes to public health data?

Answer 8

1. We cannot observe causal effects in public health data - only associations between risk factors and health outcomes
2. Reverse causation and confounding bias are the main challenges to drawing causal relationships from public health data
3. Statistical association does not necessarily imply a cause and effect relationship

Question 9

Under what 3 circumstances is a causal relationship from public health data more plausible?

Answer 9

1. Potential sources of confounding have been thought about and dealt with in some way
2. There is less potential for reverse causation
3. The association meets a wider set of criteria that are consistent with causality

Question 10

How may potential sources of confounding have been thought about and dealt with in some way?

Answer 10

1. Stratifying into groups can identify whether there is potential for confounding bias
2. Statistical adjustment in a regression model allows us to take into account multiple confounders at the same time

Question 11

How can you identify if there is less potential for reverse causation?

Answer 11

Look at longitudinal data

e.g. Levels of depression amongst unemployed and employed throughout a period of time

Use the same data and swap around

e.g. what is the level of unemployment amongst those who were and weren’t diagnosed with depression within the same time frame

Question 12

What guidelines are used to think about whether something is causal or not?

Answer 12

Bradford Hill Guidelines

This is a set of 9 guidelines

Question 13

What are the 9 components of the Bradford Hill guidelines?

Answer 13

1. Strength
2. Consistency
3. Specificity
4. Temporality
5. Biological gradient
6. Plausibility
7. Coherence
8. Experiment
9. Analogy

Question 14

What is meant by ‘strength’?

Answer 14

Strong associations are more likely to be causal than weak ones

Question 15

What is meant by consistency?

Answer 15

Has the same association been observed in different populations?

Has the same association been observed using different methods?

Question 16

What is meant by specificity?

Answer 16

Is the association with an exposure limited to, or greatly increased in, specific groups?

Question 17

What is meant by temporality?

Answer 17

Did the exposure precede the outcome?

It cannot be a cause if it preceded the outcome

Question 18

What is meant by biological gradient?

Answer 18

Do greater levels of exposure co-occur with a greater risk of occurrence?

higher level of exposure = more likely to get the outcome

Question 19

What is meant by plausibility?

Answer 19

Is there a plausible biological (or social) mechanism linking the exposure with the outcome?

Question 20

What is meant by coherence?

Answer 20

Do the temporal patterns of exposure fit with the observed disease patterns?

e.g. do rising levels of unemployment co-occur with rising levels of depression?

Question 21

What is meant by experiment?

Answer 21

Do preventative actions with regard to the exposure alter the risk of the outcome?

if there is an intervention to take away the risk factor, is there a subsequent improvement in health?

Question 22

What is meant by analogy?

Answer 22

Is the observed association supported by similar associations?

e.g. smoking is a risk factor for lung cancer

is there also an increased risk of lung cancer in those exposed to passive smoking?

Question 23

Why are the Bradford Hill guidelines used?

Answer 23

They are not rules that must be fulfilled before an association can be judged as causal

they are ways of examining if cause and effect is a reasonable inference

Question 24

What is the difference between equality and equity?

Answer 24

In equality, everyone is given the same level of healthcare

You are not providing greater care to the people who need more care

In equity, you are giving someone as much care as they need to become healthy

Question 25

What is the definition of health inequality?

Answer 25

Differences in health status or in the distribution of health determinants between different population groups

Some health inequalities are unavoidable

e.g. Difference in motility in the young and elderly

Question 26

What is the definition of health inequity?

Answer 26

Health inequalities that are unnecessary and avoidable, as well as unfair and unjust

they may be attributable to factors beyond the control of those concerned

Question 27

WHat is the difference between equal and equitable healthcare?

Answer 27

Equal healthcare:

• giving everyone the same level of care
• e.g. Same budget for every patient

Equitable healthcare:

• giving everyone the level of care they need to be healthy
• e.g. Larger/smaller budgets for those with lesser/greater needs

Question 28

How does care need to be changed in order to reduce health inequalities?

Answer 28

Care needs to be equitable in order to reduce health inequalities

Question 29

What is meant by inequalities becoming structural?

Answer 29

There is a structural way that social and health disadvantages develop to affect a group in society

e. g. Poverty, single parenthood
e. g. Social disadvantage can cause mental illness, and the other way around

inter-generational transmission means that the disadvantages that affected the parents may be passed on to the children

Question 30

What is meant by overt racism?

Answer 30

Speech or behaviour that demonstrates a conscious acknowledgement of racist attitudes or beliefs

Question 31

What is meant by structural racism?

Answer 31

A system in which public policies, institutional practices, cultural representation, and other norms work in various, often reinforcing ways to perpetuate racial group inequity

social and political movements in society to eliminate racism mainly target overt racism

Question 32

What are examples related to health of racism in the UK?

Answer 32

There is a relative risk of common mental disorders and psychosis in individuals exposed to racial harassment

black women in the UK are 4 times more likely to die in pregnancy compared with white women