Evaluating environmental/occupational “clusters” of disease Robert J. McCunney, MD

Investigating Clusters of Disease Definition: "cluster" is an unusual aggregation, real or perceived, of health events that are grouped together in time and space A cluster may be useful for generating hypotheses but is not likely to be useful for testing hypotheses

Disease Clusters Focus: usually on increased rate or risk of disease Breast cancer and pesticides Leukemia and contaminated water Increased risk of lung cancer from hexavalent chromium

Cancer Cluster Activities at the CDCP Cancer cluster investigations occasionally have led to the discovery of important pathways in the etiology of specific cancers, such as with angiosarcoma lung cancer Kaposi sarcoma vaginal clear-cell carcinoma bladder cancer scrotal cancer NOTE: The majority of studies that yielded etiologic information were of occupational, drug-induced, or infectious pathogenic exposure rather than studies of environmental exposure

Media survey of cancer cluster reports For the period 1977 through 2001, media reports of 1,440 records of approximately 175 suspected cancer cluster reports These data reflect the breadth of popular concern and awareness regarding issues of exposure types, pollution sites, and specific environmental chemicals.

Clusters: case study 7 cases of kidney cancer at a manufacturing plant Is there a cluster?

Criteria to determine whether to proceed to investigate a cluster identification of a single cancer type; biological plausibility adequate latency political pressure; identification of a common cancer in an unusual age group; identification of a rare cancer; identification of exposure to a carcinogenic substance; elevated ratio of observed/expected confirmed cancer cases

Guidelines for Investigating Clusters of Health Events CDC

GUIDELINES FOR A SYSTEMATIC APPROACH The issue of increased frequency of occurrence should be separated from the issue of potential etiologies Stage 1. Collect information. A variety of diagnoses speaks against a common origin

Stage 2. Assessment Separate two concurrent issues: 1. whether an excess has occurred and 2. whether the excess can be linked etiologically to some exposure

Stage 3. Feasibility Study Conduct detailed literature search with particular attention to known and putative causes of the health effect of concern. Consider an appropriate study design Determine what data should be collected on cases and controls, including laboratory measurements. Determine the nature, extent, and frequency of methods used for environmental measurements. Determine hypotheses to be tested and power to detect differences

Stage 4. Etiologic Investigation Purpose: Perform an etiologic investigation of a potential disease- exposure relationship. Types of studies: Retrospective cohort mortality-most common Case/control-good for rare diseases Cross sectional-good for morbidity assessments

Cluster Investigations A number of problems are encountered in the study of clusters. The health events being investigated (often morbidity or mortality) are usually rare, and increases of these events tend to be small and may occur over a long period. A major complication is that most clusters are chance events

CDC a) provides a centralized, coordinated response system for cancer cluster inquiries, b) supports an electronic cancer cluster list server, c) maintains an informative web page, and d) provides support to states, ranging from laboratory analysis to epidemiologic assistance and expertise

Occupational/Environmental epidemiology Goal: Evaluate exposure- disease relationships Strengths: risks in humans –no need to extrapolate from animal studies Evaluate consequences of exposure in which it actually occurs

Occupational epidemiology Limitations: Low level risks difficult to identify Small increases in risk may be affected by bias, confounding and chance Long latency with cancer Inadequate exposure categorization

Measures of effect RATES are the central metric used to assess disease occurrence in occupational cohort studies Comparison of a study group to a reference group- usually done by assessing the ratio of their respective standardized rates one frequently used outcome measure is the Standardized Mortality Ratio (SMR) SMR: ratio of the sum of the observed events in the study group to the sum of the expected numbers in the study group; expected numbers are based on standardized rates in the reference group

Example of use of Standardized Mortality Ratio (SMR) Null hypothesis (H 0 ): no effect of exposure SMR for lung cancer of % excess compared to reference population P<.05; 95 % Confidence Intervals; ( ); thus, statistically significant ? Role of confounding and bias

Interpreting SMR results Chance: P values; p<.05 and 95% confidence intervals (reflect uncertainty to random error-not confounding Bias: selection, recall Confounding (understates the uncertainty about a true “effect”: smoking and lung cancer (biases SMR upward); alcohol and liver disease, diabetes and neuropathy

Occupational epidemiology Reference population – of paramount importance- ideally should match study group in all important demographics characteristics aside from “exposure” of interest Type of reference populations: General (national, state, county) population disease rates categorized by age, gender, race, geographical area, calendar time Key point: reference and study populations should be identical as much as possible aside from the exposure of interest

Reference population Lung cancer and carbon black: German study SMR: 2.2 (national rates) SMR: 1.8 (local rates)

PRINCIPLES IN EVALUATING WHETHER A DISEASE IS ENVIRONMENTALLY RELATED* Strength of association Consistency of results; does the association hold in different settings and among different study groups? Specificity: How closely are the exposure factors and health outcome associated? Temporality: Does exposure precede disease outcome. Is latency involved? * From Hill AB. The environment and disease: association or causation? Proc R Soc Med 1965: 58:

PRINCIPLES IN EVALUATING WHETHER A DISEASE IS WORK RELATED Biological gradient: does a dose- response relationship exist? Plausibility: Does the association make sense biologically? Coherence: Is the association consistent with the natural history and biology of the disease? Experimental Evidence: Does experimental evidence support the hypothesis of an association? Analogy: Are there other examples with similar risk factors and outcome?

Interpreting occupational epidemiological literature Study design- is the hypothesis clearly defined? Methods: are they adequate to evaluate the hypothesis? How was exposure assessed? How was the cohort defined? Is there adequate ascertainment of vital status? What is the reference population? Is it an appropriate comparison group for the cohort being studied? Results What statistical methods were used? How was chance assessed? How was confounding controlled? How were potential biases addressed in the analysis, such as selection bias and confounding?

Interpreting occupational epidemiological literature Discussion Have the authors contrasted their results with previous scientific literature? Have the authors discussed the limitations of their study? What further work can be done to more fully define the results? Conclusion Have the authors properly assessed the results based on their own analysis ands limitations in light of previous literature?

Renal cell carcinoma

Incidence: increased diagnostic accuracy :CT, ultrasound, MRI Obesity, hypertension, smoking: established risk factors Occupational: ? TCE, solvents

Plant 1200 employees Produces plastics, TDI, benzene, phosgene Built in 1950s Product manufacturing and research

Is there an excess ? Expected rate of renal cancer: 12/100,000 Preliminary analysis: From : 1200 employees for 16 years :~20,000 person years: 7 observed 35 observed over 100,000 person years 12 expected Thus: ~ 3 fold excess???

Disease clusters and students Be critical of media reports Systematic approach to evaluations Recognize role of health departments Most clusters are statistical artifacts Interpret epidemiology studies cautiously Be attentive to possible new links between exposure and disease ) eg. nanoparticles, new materials etc

Discussion Next step?