Symptom patterns as an early warning signal of community health problems

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Abstract

A theoretical argument is presented to justify the use of symptom patterns as early warning indicators of health deterioration in a community. The basic assumption is that people (individuals) and the physical environment (settings) can be treated as probability density functions along a hypothetical health continuum. In practice, the interaction of people and settings results in a bivariate distribution within which a cutoff is placed by decision makers in order to separate the community at large into diagnostic categories of “healthy” and “sick”. It is assumed further that changes in the mix of settings and individuals over time can be tracked by health officials as a change in the nature of symptom patterns. Such indicators can give valuable information about the relative health of a community, complementing the conventional use of the incidence rate of specific diseases. Several suggestions are made about how such a theory can be realized through the systematic collection of symptom data that are subsequently treated by multivariate analysis.

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