Disease Variations: Time, Place, and Person

Variation in disease by time, place and person: A framework for analysisRaj 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 objectives On 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 Tests done per case detected (test/case ratio)/

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

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 3.3 Prepare 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 Differential use of diagnostic facilities Host-susceptibility differs by place Environment differs Agent virulence differs Cooling tower maintenance and location study Study water supply Seek variation for other respira-tory disease Examine data on socio-economic status by place Cross check case-lists, compare consultants’ and GP’s opinions on diagnosis, and survey of patients Not studied Count serology tests Examine approach to diagnosis of consultants and laboratories

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

Figure 3.4 Is this a cluster? Perhaps. The challenge is statistical and causal

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

Figure 3.5 Is this a cluster? Yes, but, significance unclear i.e. how or why the grapes are together. The challenge is causal.

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

Figure 3.6 Is this a cluster? Yes. Why? We know that grapes are held together by stalks and by a vine.

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

Disease Variations: Time, Place, and Person