Director of Public Health, Dr. Thomas Prendergast, however, was aware of the potential of GIS and welcomed the study. "We had some interest in knowing whether or not we could see any patterns of occurrence, if we could gain any insights that we did not otherwise have by looking at the geographic distribution and sequence of cases."
"It was an opportunity to see what GIS could do," reflected Sarah Mack, department immunization program director, "to actually see the cases from a perspective other than that provided by the bar charts we had created using standard, descriptive statistics. It was certainly the first time we ever had any of our data put into a GIS system."
ACCESS TO RESOURCES
Interning simultaneously with the department and ESRI not only gave Hoffman access to county data but opportunities to work with health care professionals and engineers designing the latest software technology. According to Public Health Program Manager Colleen Tracy, the county provided selected, provisional 1992 resident birth data; communicable disease and demographic data on measles cases reported in the county between 1989-1991; immunization clinic locations; Child Health and Disability Program provider locations; and the Planning Department's street network file.
Hoffman pointed out that demographic data from the 1990 Census was provided by Equifax National Decision Systems, a data provider that allows ESRI to use data in prototype applications such as this.
At ESRI, Hoffman was given almost free rein to explore a wide range of software, including the latest versions of ArcInfo and ArcView, often before they were marketed. "I had access to top-of-the-line output devices and was surrounded by experts who were just a few questions away from any problem I had. It would have cost hundreds of thousands of dollars to equal those resources out in the field."
Outlining the six-month project, Hoffman said, "we took all 2,044 cases from the measles data and address-matched those to the street network file. That enabled us to show, point-by-point, where the persons lived. We overlaid the 1990 census data by census tract and block group onto the addresses of the cases, then merged the two files together. From that we were able to create a shape file, or a map of measles cases by point. That gave us a count of the number of cases per census tract. We did the same thing for births, which told us where the new susceptibles lived; we wanted to take them into account in our predictions.
"We identified the high-risk areas and the centroid of each block group, then determined the allocation of resources [mobile immunization clinics], based on where they would have been most effective; that is, on the distance a woman might walk with small children to get to a clinic. From earlier observations, we estimated this to be about 10 minutes each way."
ASSISTED BY GIS EXPERTS
ESRI software engineer Witold Fraczek assisted Hoffman by conducting a multivariate analysis to identify the populations most at risk. Fraczek used GRID, an ArcView spatial analysis tool, and data from the 1990 Census to determine which independent variables (e.g., age, ethnicity, income, birth rate, etc.) most accurately predicted the dependent variable (measles). The results were incorporated into the new 3.0 version of ArcView.
By combining ArcView with Avenue (an object-oriented programming language), Hoffman created a customized application that enabled public health personnel to query data developed by the project. "The general descriptive epidemiology -- who, where, when, etc., -- was conducted using Epi Info, a statistical analysis program designed for epidemiology by the CDC [Centers for Disease Control]. Epi Info is available as freeware on the Internet."
In the final steps, Hoffman and Fraczek created a model using linear regression analysis to determine the characteristics most likely responsible for the epidemic, and to predict where future cases would occur. Measles