4188.0: Tuesday, November 14, 2000 - 2:50 PM

Abstract #10084

Statistical tools for characterizing the emerging waterborne diseases: A time series approach

Elena N. Naumova, PhD and Robert D. Morris, MD, PhD. School of Medicine, Department of Family Medicine and Community Health, Tufts University, 136 Harrison Avenue, Boston, MA 02111, 617-636-2462, elena.naumova@tufts.edu

Time series analysis is a powerful statistical tool with the potential to provide valuable insight into the epidemiology of emerging waterborne diseases and the temporal properties of water quality parameters associated with an infection. Using a time series analysis, it was shown that an increase in drinking water turbidity, even without exceeding the standard, could result in measurable health effects in the population. At a community level, waterborne infectious diseases occur as a combination of low endemic levels of disease and sudden bursts of outbreaks. Our ability to detect and correctly characterize the temporal relationship between water quality and disease in the community will depend on the measures of exposure and outcome. Temporal properties of exposure measures depend on many characteristics of the water source, treatment procedures, meteorological conditions, and water consumption patterns. Temporal properties of outcome measures would reflect not only the incidence of disease, but also a variety of social factors. This study provides a rationale for statistical assumptions for the analysis of a time series representing a water quality parameter and a time series representing a disease outcome. We will demonstrate a set of analytical methods and graphical tools that were tested by a simulation and the actual data from documented outbreaks of cryptosporidiosis and the surveillance data for giardiasis and cryptosporidiosis. We will provide a set of recommendations for preparing data for analysis, selecting a model, and visualizing the temporal relationship between a health outcome and a water quality parameter using widely used statistical software.

Learning Objectives: During this session, faculty will demonstrate the application of a new statistical approach to analyzing health outcomes associated with water quality

Presenting author's disclosure statement:
Organization/institution whose products or services will be discussed: None
I do not have any significant financial interest/arrangement or affiliation with any organization/institution whose products or services are being discussed in this session.

The 128th Annual Meeting of APHA