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Cluster And Dioxin Exposure / Prof. Jean Francois Viel
 

Cluster And Dioxin Exposure / Prof. Jean Francois Viel

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Cluster And Dioxin Exposure / Prof. Jean Francois Viel Cluster And Dioxin Exposure / Prof. Jean Francois Viel Presentation Transcript

  • How can a cluster investigation generate new knowledge ? The dioxin issue .
    • Professor Jean-François Viel
    • Faculty of Medicine
    • Besançon, France
  • Introduction : environmental justice
    • Issues of environmental justice are attracting increasing public attention, and inherently involve locations of environmental hazards and groups of people.
    • Media reports concerning the perceived excess of rare diseases in populations close to industrials plants increase the demand on public health authorities.
    • Thus, by testing hypotheses relating true clusters to plausible causes, cluster investigations may play a central role in establishing a credible scientific foundation for evaluating environmental justice.
  • Definition of cluster
    • A geographically bounded group of occurrences of sufficient size and concentration to be unlikely to have occurred by chance.
    • Knox 1989
    • An unusual aggregation, real or perceived, of health events that are grouped together in time and space and that are reported to a health agency.
    • CDC 1990
  • In vestigation of disease clusters
    • Since many putative clusters have not stood detailed scrutiny in the past, some researchers believe that their investigation has little potential for developing new etiologic insights.
    • Nevertheless, it is true that interpretating such resulting data is not straightforward.
    • Several basic epidemiological and statistical issues may present obstacles to the handling of such data, but can be addressed satisfactorily, provided some guidelines are followed and some new statistical techniques are used.
  • In vestigation of disease clusters
    • What data are available ?
    • What domain do these data come from ?
    • What is the null hypothesis ?
    • What is the alternative hypothesis ?
  • Pitfalls to avoid
    • Post-hoc analysis
    • Registration bias
    • Boundary shrinkage
    • Inhomogeneous population density
    • Multiple testing
    • Migration bias
    • Confounding
    • Cluster pattern
    • etc.
  • Post-hoc analysis
    • A post hoc investigation invalidates any formal statistical inference because of preselection bias.
    • Validity of the statistical analysis demands that the particular facility is chosen for reasons other than a known or suspected excess of disease in its vicinity.
    • Dioxin (2,3,7,8-TCDD) is carcinogenic to humans (Group 1) in occupationally exposed cohorts (IARC).
    • Whether low doses of dioxin affect the general population remains to be determined.
    • The major source of dioxin release into the environment is represented by municipal solid waste (MSW) incinerators.
  • Dioxin and cancer
    • The strongest evidence for the carcinogenicity of dioxin is for all cancers combined (average relative risk = 1.4) although an increased risk for lung cancer (with about the same relative risk), was found in the most informative studies (IARC 1997).
    • Overall weight of evidence also suggests that soft-tissue sarcomas (STS) and non-Hodgkin’s lymphomas (NHL) are increased in populations occupationally or accidentally exposed to dioxin.
    • In April 1998, the French Ministry of Environment revealed that of 71 MSW incinerators processing more than 6 tons of material per hour, dioxin emissions from 15 of them were above 10 ng I-TEQ/m 3 .
  • The municipal solid waste incinerator of Besançon, France
    • Began operation in 1971.
    • Located in an urbanized area.
    • Capacity: 7.2 metric tons/hour.
    • Processing: 67,000 metric tons (1998).
    • Emissions (1997): dioxin: 16.3 ng I-TEQ/m 3 , dust: 315.6 mg/Nm 3 , hydrogen chlorine: 803.5 mg/Nm 3 .
  • Boundary shrinkage
    • It relates to the selection of geographical boundaries, period, age groups and diagnostic categories of suspected clusters.
    • The more narrowly the population is defined, the less the number of expected cases, the greater the excess rate, and often the more pronounced the statistical significance.
    • Time period (1980-1995), age groups (whole range), and diagnostic categories (non-Hodgkin’s lymphoma, soft-tissue sarcoma) were defined a priori .
    • Geographical boundaries were identified by the statistical algorithm.
  • Registration bias
    • Higher rates can reflect only more complete registration of cases in areas around installations than elsewhere, when an ad hoc survey is conducted.
    • Incidence data were provided by a permanent institution (Doubs cancer registry) in which cases are identified homogeneously and prospectively.
    • To avoid uncertainties in the morphologic classification, all records were reassessed by a medical specialist blind to the location of cases.
  • Spatial scan test
  • Spatial scan test
    • For each location and size of the scanning window, the null hypothesis is that the risk of cancer is the same in all windows (complete spatial randomness), whereas the alternative hypothesis is that there is an elevated rate within compared with outside the window.
    • The window which attains the maximum likelihood is identified as the most likely cluster.
    • To find the distribution of the test statistic under the null hypothesis, Monte Carlo simulations (29 999) are carried out.
  • Inhomogeneous population density
    • Areas with large populations, which are concentrated in and around cities, tend to exhibit more stable rates.
    • False positive auto-correlations may therefore be observed due to uneven population distribution.
    • The spatial scan test adjusts for the inhomogeneous population density.
  • Multiple testing
    • In post hoc analyses, one may well have followed up several leads before deciding to focus on a given cluster.
    • When moving a circular window over the area, and making its radius vary, different sets of neigh-bouring areas are included, and as many likelihood functions are calculated.
    • The analyses were hypothesis and not data-driven.
    • The spatial scan statistic takes multiple testing into account and delivers a single p value for the test of the null hypothesis.
  • Migration bias
    • Daily migration might make the place of residence different from the place of exposure.
    • A substantial proportion of persons might migrate to another geographic area during the time lag between residence near the point source and occurrence of the disease.
    • In- and out-migrations result in a dilution effect, reducing and not over-estimating ecological estimates.
  • Confounding
    • Distance to the University hospital could act as a confounder.
    • People living close to an industrial site are not a random sample of the population, but tend to be socially-disadvantaged, while deprivation itself is strongly associated with ill health.
    • Hodgkin’s disease was also considered, since it follows the same referral pattern as NHL and is not consistently associated with dioxin exposure.
    • The deprivation index from Carstairs and Morris was calculated.
  • Cluster pattern
    • Disease incidences or exposures may not follow simple circular patterns but rather may be anisotropic.
    • The spatial scan statistic implicitly relies on an exposure-distance model. While the most likely cluster is highlighted, it probably does not coincide exactly with the real cluster.
    • The scan test provides only an estimate for the position and the radius of the latter.
  • A priori reasoning
    • If, for STS and NHL:
      • a significant cluster that includes the Besançon area is highlighted by the focused test,
      • a significant space-time interaction involving the recent years is found around the facility,
      • no other cluster is noticeable in the remaining area,
      • sub-analyses across gender are consistent,
    • but not for Hodgkin’s disease.
    • Then:
    • our study supports a relation between plant location and cancer incidence possibly mediated by dioxin emission.
  • Descriptive results
    • 16-year study period (1980-1995)
    • Département of Doubs, France (26 electoral wards)
  • STS results These findings were consistent across gender.
  • Most likely cluster for STS. : Besançon, Audeux : MSWI
  • NHL results These findings were consistent across gender.
  • Most likely cluster for NHL. : Besançon, Audeux : MSWI
  • Hodgkin’s disease results
  • Spatial scan results for Hodgkin’s disease. : Amancey, Ornans, Quingey, Boussières : MSW incinerator
  • Conclusion
    • On the whole, the consistency of our findings for STS and NHL is remarkable. It is reinforced by the fact that no specific cluster was found for the control cancer category.
    • These findings, together with the consistent results across gender, make us suspect an environmental pathway involving dioxin.
    • However, caution should be exercised before these clusters are ascribed to dioxin released by the MSW incinerator.
    • Viel et al. Am J Epidemiol 2000;152:13-19.
  • Further works
    • More sophisticated exposure modelling, with peaked and directional effects as well as radial decline, based on point location data.
    • Case-control study in which dioxins are measured in biologic tissues.
  • Conclusion
    • “ Perhaps the most satisfactory approach is to test a priori hypotheses within geographical database”.
    • “ Thoughtful, careful, and imaginative descriptive analyses of spatial occurrences of diseases do carry the potential to generate new knowledge and to inform us about disease causation and prevention”.
    • P. Elliott
  • Thank you for your kind attention