Variation in disease by time, place and person: A framework for

analysis

Raj Bhopal, Bruce and John Usher Professor of Public Health,

Public Health Sciences Section, Division of Community Health Sciences,

University of Edinburgh, Edinburgh EH89AGRaj.Bhopal@ed.ac.uk

Educational objectives

On completion of your studies you should understand: That virtually all diseases vary in their incidence and

prevalence over time, across geographical areas and between population subgroups.

That apparent disease variations can be artefacts of errors or changes in data collection systems.

That variations must be analysed systematically to check that they are real, and not illusory.

Real variations are driven by environmental and social change over the short term, with a genetic contribution in the long term.

Educational objectivesOn completion of your studies you should understand: Real variations help in understanding the causal

pathways of disease. Study of clusters and outbreaks, which reflect

abrupt changes in disease frequency, may yield both causal knowledge, and information to control the public health problem.

Real variations help to develop and target health policy and health care.

Variations generate observations of associations, which in turn spark causal hypotheses.

Educational objectives

On completion of your studies you should understand:

Diseases wax and wane in their population frequency- an axiom of epidemiology

Diseases patterns have undergone massive change in incidence within the last 50 to 100 years

A systematic mode of analysis of disease variations is vital

Ensure that observations of variation are real, and not illusory products of data errors and artefacts

Exercise

Reflect and comment on the following graphs before reading on

UK trends in cardiovascular disease mortality

Standardised mortality ratios for CHD by sex for selected countries of birth, 1989/92, England/ Wales

Source: Wild, S and McKeigue, P BMJ 1997: 314; 705-10.

Exercise: Benefits of studying variations

What potential benefits are there from

investigation of changes in disease frequency?

Is a decline in disease as worthy of

investigation as a rise?

Exercise: Reasons for variation

Why, in general terms, do diseases vary over

time, between places and between subgroups of the populations?

What is the relative importance of genetic and environmental influences in bringing about population differences in disease patterns?

Genetic and environmental influences

All humans belong to one species Genetic variation between populations is small Genetic change arises from a number of processes

including genetic drift and genetic mutation Changes in disease frequency in large populations

occurring over short periods of time are almost wholly environmental

Public health paradox: for populations the environment is the dominant influence on the pattern of disease, for individuals genetic inheritance may be equally or more important

Transitions and disease variations

A decline in birth rates and death rates leads to a shift in the age distribution of the population, with the average age increasing (the demographic transition)

Industrialisation, wealth creation, ageing of the population and the other profound changes alters the pattern of diseases (the epidemiological transition)

These transitions are reversible International differences in disease patterns and

disease variations of migration populations can be conceptualised as a result of populations being at different stages of the demographic and epidemiological transition

Variations and associations: real or artefect?

When changes in disease frequency are natural, or real

This is an experiment of nature, posing a challenge to science

Underlying reasons as discussed above are often exceedingly difficult to pinpoint

Variations and associations: real or artefect?

First step is to exclude artefact The second is to develop a hypothesis

stated as an association The third is to design a test of the

hypothesis The fourth is to assess the results in

relation to frameworks for causal thinking

Variations and associations: real or artefect: CHD (see slide)

In the U.K. coronary heart disease mortality rates rose steadily in the 20th century until the 1970's when they declined

first, demonstrate the association of disease rates and time periods

attempt to explain the time trend by developing our understanding of social, environmental and lifestyle changes over these time periods

test specific hypotheses, e.g. one might be that the rise and fall reflect the changing levels of factors that are known to cause CHD, e.g. exercise patterns

Variations and associations: real or artefect: CHD

be quantitative, e.g. how much of the decline in CHD can be explained by the changing pattern in these factors?

the decline in CHD is too rapid to be a result, solely, of change in the factors mentioned, but risk factors and treatments account for much of the change

think even harder!

Group exercise: Why variations may be illusory

Consider the possible reasons why a variation in disease pattern might be an artefact rather than real. (You may find 7-10 reasons).

Can you group them into 3/4 categories of explanation?

Variations as artefact Chance Errors of observation Changes in the size and structure of the population The likelihood of people seeking health care and

hence being diagnosed The likelihood of the correct diagnosis being reached Changes in the clinical approach to diagnosis Changes in data collection methods Changes in the way diseases are diagnostically coded Changes in the way data are analysed and presented

Variations as artefacts: diagnostic activity

Diagnostic activity measured by the number of tests can be related to the number of cases diagnosed

Test a hypothesis that a high number of cases in a locality or time period reflects excessive diagnostic activity

Predict that a large number of tests would be done for each case diagnosed

By contrast, if a high incidence of disease, and no excessive testing, then the test to case ratio would be low

Figure 3.1

low

high

Testsdonepercase

detected

(test/case

ratio)

Scenario 1:Testing common

disease uncommon

Scenario 2:Testing commondisease common

Scenario 3:Testing uncommondisease uncommon

Scenario 4:Testing uncommondisease common

Tes

ts d

on

e p

er c

ase

det

ecte

d

(tes

t/ca

se r

atio

)/

Variations over time, pretending to be variations in space!

One real, yet potentially misleading cause of geographical variation, is short term fluctuation in disease incidence

This can seem like geographical variation, when in the long-term there is none

Figure 3.2

No.cases

1 2 3year

No.cases

1 2 3year

Place A Place B

Exercise: Explanations for real changes in disease frequency

What explanations can you think of for a real change in disease frequency?

Can you group these into three or four categories of explanation?

Summary of real explanations of disease variations: the causal triad

Host e.g. genetics, behaviour Agent e.g. virulence, introduction of a

new agent Environment e.g. housing, weather

Applying the real-artefact framework: Legionnaires’ disease

You are the epidemiologist responsible for surveillance of infectious diseases in a city of about 1 million people

You are examining the statistics on the numbers of cases of Legionnaires' disease

This pneumonia, acquired by inhaling contaminated aerosol, is rare

About 8 cases per million in your country Examine the surveillance data in table 3.3 Now, make a judgement on whether the

findings represent an outbreak of Legionnaires' disease

Applying the real-artefact framework: Legionnaires’ disease

At 72 cases per million population the incidence rate in this city is exceedingly high

Chance (random fluctuation) seems to be a remote possibility

Is there a problem in the techniques used to handle laboratory specimens leading to false positive results?

Could information on other diseases, say pneumococcal pneumonia or influenza, have been miscoded as Legionnaires' disease?

Has there been a batch of reports in June? Has the number of people at risk altered?

Applying the real-artefact framework: Legionnaires’ disease

is it possible that the cases could be returning from a package tour to a particular destination?

Has the likelihood of diagnosis increased, either because of greater vigilance by doctors or of people using the health care system?

Have there been changes in diagnostic fashion or disease definition?

Have there been changes in the completeness of the data collection methods?

Has there been a deliberate change in the way diseases are coded, analysed or data presented?

Applying the real-artefact framework: Legionnaires’ disease

Once error is excluded, the date of onset of illness in the cases has been checked, and the symptoms and signs found to be of a pneumonic illness..

… the likelihood is that the rise in case numbers is real and there is an outbreak

The challenge now is to develop a testable explanation, a hypothesis, to unveil the underlying reason for the rise in the disease

Applying the real-artefact framework: LD- hypotheses

Is their increased susceptibility to disease? Is there increased virulence of micro-

organisms? Is there an increase in the level of exposure to

the micro-organisms in aerosol? If so, why- Has the weather changed? Have winds and humidity changed? Have protective mechanisms (such as the drift

elimination mechanisms) broken down?

Applying the real-artefact framework: LD- hypotheses

In practice, teasing out the different explanations is a complex task

In studies of the geographical epidemiology of Legionnaires' disease in Scotland, 1978-1986, I prepared a case-list of all 372 potential cases diagnosed over the period

The chart showing the plan of the studies is in figure 3.2

Such an analysis and overview is necessary in all investigations of disease variations

Figure 3.3Prepare a case list

Does the incidence vary?

Incidence by health board, and city of residence

Incidence by post-code sector of residence

Dot maps of place of residence

Map by place of work

Incidence over time

Yes, incidence varies

Why?

Artefact? Real?

Error in case-list and data

Cross check case-lists, compare consultants’ and GP’s opinions on diagnosis, and survey of patients

Differential use of diagnostic facilities

Count serology tests

Examine approach to diagnosis of consultants and laboratories

Host-susceptibility differs by place

Seek variation for other respira-

tory disease

Examine data on socio-economic status by place

Agent virulence differs

Not studied

Environment differs

Study water supply

Cooling tower maintenance and location study

Figure 3.7

Figure 3.8

Disease clustering and clusters in epidemiology

A cluster is a collection of things of the same kind A disease cluster is an aggregation of relatively

rare events or diseases in time or place, or both A cluster is a mini-epidemic or outbreak of a rare

event The concept of cluster is not used for common

diseases because clustering is inevitable due to chance alone, or,for infectious diseases that spread from person-to-person for clustering is the norm

Disease clustering and clusters in epidemiology

A cluster presents a public health problem, and a difficult epidemiological puzzle

Clustering is merely a specialised variant of disease variation so the analysis of clustering follows the principles discussed

Alistair Gregg observed in 1941 that the number of cases of congenital cataract, an exceptionally rare problem, far exceeded the normal

He saw 13 cases of his own, and 7 of his colleagues You know the story!

Do the 5 grapes comprise a cluster?

Reflect on whether the 5 grapes in figure 3.4 comprise a cluster.

What characteristics of the grape makes you think they may be?

Imagine 5 case of acute leukaemia are reported from a single street in a small town i.e. these are the grapes

Perhaps. The challenge is statistical and causal

Is this a cluster?

Figure 3.4

Assessing whether the cluster of grapes and of leukaemia is an artefact or whether there is a common cause

Reflecting on both the cluster of grapes and 5 cases of childhood leukaemia

What evidence would you seek to help you exclude artefact and to ascertain a common cause?

Start with the grapes-what would convince you that they are part of a single cluster

Yes, but, significance unclear i.e. how or why the grapes are together.

The challenge is causal.

Is this a cluster?

Figure 3.5

Assessing whether the cluster of grapes and of leukaemia is an artefact or whether there is a common cause

Evidence that the grapes are bound together by a common stalk would be compelling

Close occurrence of leukaemia cases could be an artefact

If our investigation of leukaemia cases had shown these cases were all bound by common factors such as type of leukaemia, age group, residence, time of disease onset and exposures to causal factors we could be convinced the cluster is real

The next step is to explain mechanisms

Is this a cluster?

Yes. Why?

We know that grapes are held

together by stalks and by a vine.

Figure 3.6

Value of studying variations

Variations in disease patterns are of practical value in helping guide the clinician in both diagnosis and management of disease

Outbreaks and clusters alert clinicians to otherwise rare diseases

Long term trends are important to clinical practice, for example, the changing nature and decline of tuberculosis

Value of studying variations Variations over decades (known as secular trends)

are of special importance in setting priorities and for evaluating whether health objectives have been achieved

Variation in disease by place and by socio-economic status are a guide to the level of inequity in health status

Disease variations help to match resources to need

Health promotors can tailor both the timing and the content of interventions

Epidemiological theory underpinning this subject

Disease variation arises because of either (a) changes in the host, the agent of disease or the environment or (b) changes in interaction between the host, agent and environment

Changes occur at a different pace in different places and sub-populations

Disease variations are, therefore, inevitable In epidemiology we are seeking to uncover the

natural forces that caused them First, the epidemiologist must ensure that

variations are not merely artefacts

Summary Diseases wax and wane in their population frequency The causes of such variations are often difficult to

detect and may remain a mystery Three principal reasons for investigating variations:

1. Bring under control an apparent abrupt rise in disease incidence 2. Gain insight into the causes of disease 3. Make predictions about the future, both in terms of health policy and health care, and the frequency of disease

Analysis of variation in disease begins by differentiating artefactual change from real change

For real change the epidemiological challenge is to pinpoint the causal factors