Cluster investigations of non infectious health events
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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 l.jpg

Cluster Investigations of Non-Infectious Health Events

Goals l.jpg

  • 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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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 difficult

  • 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? difficult

  • 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 difficult

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)

Step 1 initial ascertainment of cluster l.jpg
Step 1: Initial ascertainment of cluster difficult

  • 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 difficult

  • 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 difficult

  • 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 difficult

  • 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 difficult

  • 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 difficult

  • 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 difficult

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 difficult

  • 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 difficult

  • 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 difficult

  • 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 difficult

  • 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 difficult(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 difficult

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

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Case-Verification difficult

  • 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 difficult

  • 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 difficult

  • 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 difficult

  • 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

Determining feasibility l.jpg
Determining feasibility difficult

  • 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 difficult

  • 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 difficult

  • 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 difficult

  • 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 difficult

  • 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 difficult

  • 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

References l.jpg
References difficult

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. 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. 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. Accessed August 13, 2008.

References34 l.jpg
References difficult

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.