In the last decade, time series analysis has become one of the most important tools of surveillance systems. Understanding the nature of temporal fluctuations is essential for successful development of outbreak detection algorithms, aberration assessment, and to control for seasonal variations. Typically, in applying the time series methods to health outcomes collected over an extended period of time it is assumed that population profiles remain constant. In practice, such assumptions have been rarely tested. At best, the temporal analysis is performed using stratification by age or other discriminating factors if heterogeneity is suspected. Any community can experience population changes in various forms. Long-term trends of inflow/outflow migration and rapid transient fluctuations associated with specific events are typical examples of changes in population profile. Seasonality, as an intrinsic property of infectious diseases manifestation in a community, is typically attributed to periodic changes in transmissibility of pathogens. To some extent, seasonal fluctuations in the incidence of infectious diseases could also be associated with the changes in population profiles. The ability to detect and describe such changes would provide valuable clues into seasonally changing factors associated with an infection.
Objective
The objective of this communication is two-fold: 1) to introduce an analytical approach for assessing temporal changes in the surveillance reporting with respect to population profile; and 2) to demonstrate the utility of this method using laboratory-confirmed cases for four reportable enteric infections (cryptosporidiosis, giardiasis, shigellosis, and salmonellosis) recorded by the Massachusetts Department of Public Health over the last 12 years. This new approach for assessing seasonal changes is based on comparison of gender-specific single-year age distributions, which constitute population profiles.