Decision-Making in Environmental Health: From Evidence to Action

By C. Corvalán; D. Briggs et al. | Go to book overview

Chapter 6*


LINKING ENVIRONMENT AND HEALTH DATA: STATISTICAL AND EPIDEMIOLOGICAL ISSUES

6.1Introduction

Exploration of associations between environment and health is an integral part of environmental epidemiology, either in the search for previously unknown dose-response relations, or to test hypotheses about such relations. The HEADLAMP methodology is an extension of this approach (WHO, 1995). Lying at the interface between epidemiology and public policy, it involves applying known dose-response relations, established in previous investigations and documented in the literature, to new empirical data as a basis for improved decision-making and policy support.

In general, the data used for environment and health linkage as part of HEADLAMP studies are derived from routine monitoring sources, although where necessary additional data may be collected from purpose-designed rapid surveys. In either case, the data often comprise series of data accrued over a long period of time, and gathered in an aggregated form (e.g. at the small-area or regional level). The need to conduct aggregate data studies arises from the difficulty of acquiring individual-level data, especially on environmental exposures and other covariates (Rothman, 1993). As such, the linkage of a health effect variable (e.g. excess mortality) to exposure and other characteristics of populations does not involve the direct use of individual records. Instead, the HEADLAMP methodology relies on analysing grouped data (Nurminen and Nurminen, 1999).

In the HEADLAMP approach, the aim of the environment and health linkage is not to discover new associations, or to confirm suspected ones. Rather it involves using established scientific knowledge to assess the risks that exist, to identify the need for action, to compare the choices available, and to monitor and evaluate the effects of such actions. As part of this process, the associations previously recognised in environment and health data are extrapolated to new data.

*This chapter was prepared by M. Nurminen

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