Cluster Investigations of Non-Infectious Health Events

1. Cluster Investigations of Non-Infectious Health Events

2. Goals • Describe cluster investigations of non-infectious health events • Discuss key factors which should be considered before carrying out a cluster investigation • Outline the basic steps of a cluster investigation

3. Cluster investigations of non-infectious diseases • Critical public health function • May link specific exposures to diseases • Example: Limb deformities in infants related to maternal use of thalidomide in Europe in 1960s (1) • Led to U.S. legislation requiring rigorous testing process for approval of new pharmaceutical products

4. Notable cluster investigations

5. Non-infectious disease cluster investigations may be difficult • Hard to confirm apparent geographic or temporal excess in case numbers • Supposed clusters may represent normal disease patterns • Confounding factors such as age • Different pathogenic processes may result in diseases that look alike but are not linked • Example—primary brain cancer vs. brain metastases spread from cancer in another organ

6. Non-infectious disease cluster investigations may be difficult • Often impossible to establish a definitive cause-and-effect relationship • Small case numbers • Problems isolating single potential exposure • Difficulty in reconstructing exposure histories (2) • Large-scale epidemiologic studies may be required • Difficult to carry out

7. Ensuring a successful investigation • Standardized step-wise process for receiving/evaluating cluster reports • Centralized tracking system, data collection tools, clear lines of communication • Well-trained staff and adequate resources • Experienced investigators, access to laboratories • Perceived problems must be addressed responsibly and sympathetically • Effective, credible communication with public and other agencies

8. To investigate or not? • Investigating a link between exposure and disease may be impossible but it is important to respond to threats perceived by the public • Keep in mind: • Value of using a step-wise process with clear decision points • Share policy of using step-wise process with medical community, general public, media • Deliberate and transparent approach when carrying out any investigation • Recognize local concern but stay within stated investigation process • Develop effective methods of communication

9. Basic steps in investigating non-infectious disease clusters Figure 1. Flowchart of cluster investigation • Each step in a cluster investigation requires: • Collecting and analyzing data • Decision to take immediate action (if needed) • Decision to proceed to next step or not (3)

10. Step 1: Initial ascertainment of cluster • Begin by collecting data: • Identifying information from person reporting the cluster • Demographic information for cluster cases • Clinical information on cluster cases • Identifying information for cluster cases

11. Step 1—continued • Enter information collected into a tracking system • Example: EpiInfo, Microsoft Access or Excel • Notify health department staff, local health officers and appropriate agencies • Begin seeking information on disease causes and compare this information with the reported cluster

12. First decision point • Based on initial information decide whether to continue the investigation • Criteria for continuing include: • Clinically similar health events without a plausible alternative etiology • Apparent excess occurrence of such health events • Plausible temporal association with the possible exposure(s) • A disease present in an demographic group where it is not usually found • One or more cases of a very rare disease

13. If investigation ends • Create a brief summary report and share with person reporting cluster and health department supervisors • If investigation is halted, explain why to person reporting. • Example: variety in diagnoses (e.g., different types of cancers) argues against a common origin

14. Step 2: Assessment of excess occurrence • Estimating excess occurrence • Confirm whether the number of cluster cases is greater than expected • Estimate an occurrence rate Number of people with the health event Total population at risk • Population at risk = all people in the geographic area where the exposure occurred over a designated time period

15. To estimate an occurrence rate • Select an appropriate geographic area and time period • Geographic area should include all persons at risk for the health event but not large enough to include those not at risk • Designated time period should be consistent with time period during which supposed exposure took place • Defining the geographic area and time period too narrowly or too broadly may over- or under-estimate problem

16. How size of geographic area affects occurrence rate Figure 2. Finding the occurrence rate in the population at risk • Occurrence rate of 20% (left) vs. 8% (right)

17. Determining cases and finding a reference population • Determine which cases from the reported cluster to include in a preliminary analysis • Find a reference population comparable to the population in which the cluster appeared • Example—residents from a similar geographic area • Estimate an expected occurrence rate for the reference population from existing surveillance data

18. Compare occurrence rates • Compare observed occurrence rate based on the cluster with the expected rate from the reference population • Use appropriate statistical tests to compare rates • 5 or more cases and appropriate denominator—Chi-square tests or Poisson regression • Small case numbers—group cases across geographic areas or time periods

19. Case Verification • Case definition should include clinical criteria and restrictions on time, place, and person • Sensitive case definition • Broad criteria, may include several related diseases or health events, captures more true cases but includes false positives • Specific case definition • Narrow criteria, focuses on one health event, uses confirmatory testing, excludes true cases (false negatives) • Example—cluster of cancer cases linked to benzene exposure • Sensitive case definition = diagnosis of any form of blood cancer • Specific case definition = diagnosis of leukemia

20. Using multiple case definitions • Example – investigation of childhood cancer cases in Dover Township and Toms River, NJ, 1995 (4) • Industrial pollutants released into Toms River contaminated Dover’s municipal well • Investigation of all childhood cancers and subgroups of selected cancers

21. Childhood cancer cases in New Jersey, 1995 (4) • Observed and expected occurrence rates compared by calculating standardized incidence ratios and 95% confidence intervals • SIR = observed cases (or rate) expected cases (or rate) where = 1 no excess occurrence > 1 possible excess occurrence < 1 observed is less than expected

22. Childhood cancer cases in New Jersey, 1995 • Table 2. Childhood cancer incidence in Toms River census tracts, 1979-1995, children 0-4 years

23. Case-Verification • Examine case-patients’ medical records • Refer to relevant health registries • Obtain copies of relevant laboratory, pathology, or other reports • Obtain clinical/laboratory re-evaluations (e.g. retest biopsy or other specimens) • May need to do additional case-finding

24. Case-Finding • In an expanded assessment: • Reconsider initial case definition • Reassess geographic/time boundaries • Ascertain all potential cases within geographic and time boundaries • Identify appropriate database sources • Perform literature review • Assess likelihood that clustered events are related to supposed exposure(s)

25. Case-Finding • Review additional data sources or medical records • Formal surveys of the community reserved for later stages in the investigation • If excess occurrence of disease confirmed with evidence of association with supposed exposure, consider etiologic study • If excess occurrence not confirmed or confirmed with no plausible relationship to supposed exposure, conclude investigation

26. Step 3: Determining the feasibility of an etiologic study • First, determine epidemiologic and logistical feasibility of an etiologic study • Construct a testable hypothesis • Clearly state hypothesis • Include the target population, health event(s) and exposure(s) of interest

27. Determining feasibility • Pros and cons of different study designs • Potential challenges and ways to address them • Potential for finding additional cases, expanding the case definition and changing the time/geographic periods • Collecting additional data and associated costs

28. Etiologic study—measuring exposure • Do clinical or environmental tests for the exposure exist? • How sensitive are they? • Given the lapse of time since exposure will the test be useful? • Is the reported exposure history a good predictor of true exposure?

29. Determining study benefits • May be difficult to determine whether an etiologic study will justify the effort • Etiologic studies may not be successful unless disease is rare or frequency has suddenly increased • Etiologic agent must be measurable and leave a physiologic response • Appropriate unexposed control group is needed—levels of exposure must vary within population to carry out study (6)

30. Assess study implications • Consider epidemiologic and policy implications • Consider community reactions • If etiologic study is feasible and likely benefits justify the effort, carry out study • If etiologic study is logistically impossible, too expensive or will not affect policies or programs, end investigation

31. Step 4: Conducting an etiologic investigation • Etiologic study should generate knowledge about broader epidemiologic and public health issues raised • Begin by writing a formal study protocol • Lay out steps in data collection, processing, quality assurance and data analysis • Further study design decisions will be unique to the particular study

32. Conclusion • Cluster investigations allow public health officials to interact with the community and be responsive to public needs • May provide information about previously unsuspected exposure-disease relationships • Can be an unproductive drain on public health resources

33. References 1. Lenz W. Kindliche mißbildungen nach medikament-einnahme während der gravidat [Malformations in children after a drug taken during pregnancy]. Dtsch Med Wochenschr. 1961;86:2555–2556. 2. Cartwright RA. Cluster investigations: Are they worth it? Med J Aust. 1999;171(4):172. http://www.mja.com.au/public/issues/ 171_4_160899/cartwright/cartwright.html. Accessed August 13, 2008. 3. CDC. Guidelines for investigating clusters of health events. MMWR Morb Mortal Wkly Rep. 1990;39(RR-11):1-16. http://www.cdc.gov/mmwr/preview/mmwrhtml/00001797.htm. Accessed August 13, 2008. 4. New Jersey Department of Health and Senior Services and ATSDR. Childhood Cancer Incidence Health Consultation: A Review and Analysis of Cancer Registry Data, 1979-1995, for Dover Township (Ocean County), New Jersey. 1997. http://www.state.nj.us/health/eoh/hhazweb/cansumm.pdf. Accessed August 13, 2008.

34. References 5. Bender AP, Williams AN, Johnson RA, Jagger HG. Appropriate public health responses to clusters: The art of being responsibly responsive. Am J Epidemiol. 1990;132:S48-S52. 6. Rothman KJ. A sobering start for the cluster busters’ conference. Am J Epidemiol 1990;132:S6-S13. 7. Fischoff B, Lichtenstein S, Slovic P, et al. Acceptable Risk. Cambridge, UK: Cambridge Univ Press; 1981. 8. Greenberg MR, Wartenberg D. Understanding mass media coverage of disease clusters. Am J Epidemiol. 1990;132:S192-5. 9. Covello VT, Allen F. Seven Cardinal Rules of Risk Communication. Washington, DC: US Environmental Protection Agency, Office of Policy Analysis; 1988. OPA publication 87-020.