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Cluster Investigations of Non-Infectious Health Events PowerPoint PPT Presentation


Cluster Investigations of Non-Infectious Health Events. 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.

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Cluster Investigations of Non-Infectious Health Events

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Cluster investigations of non infectious health events l.jpg

Cluster Investigations of Non-Infectious Health Events


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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


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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


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Notable cluster investigations


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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


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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


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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


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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


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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)


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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


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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


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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


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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


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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


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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


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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)


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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


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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


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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


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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


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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


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Childhood cancer cases in New Jersey, 1995

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


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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


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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)


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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


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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


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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


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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?


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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)


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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


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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


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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


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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.


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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.


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