Development of Community Profiles in Missouri

BACKGROUND:  While there is a vast amount of data on a community’s health, environment, and social/demographic; data is scattered in a multitude of places and few datasets are comparable across jurisdictions.  To address this problem the Missouri Environmental Public Health Tracking (EPHT) team created profiles for specific geographic areas across the state.  The team generated profiles at two geographic levels: county and city.

METHODS:  The Missouri EPHT team gathered data from diverse locations, from state and federal resources to private not-for-profit organizations.  Utilizing the consistency of EPHT datasets for health and environment data, and standardized data sets for community level information, EPHT created profiles that are consistent from a local to a national level.  While the county and city profiles follow the same template, they provide slightly different data due to challenges encountered during creation.

RESULTS:  The county level profiles were the easiest to compile since most datasets (locally and nationally) are at a county level.  The Missouri EPHT county profiles include health data from the EPHT nationally consistent data and measures (NCDMs), environmental data includes air and agriculture, and social determinants of health data ranging from family structure to crime and transportation.  City level profiles provide more of a challenge.  First, city boundaries change over time.  Second, it isn’t always clear where city boundaries are located.  To combat this problem the EPHT team talked with city level Local Public Health Agencies (LPHAs) and determined that using zip code boundaries would be best.  Gathered data was aggregated to the city level.  Most health and social determinants data can still be gathered at the zip code level; however, the environmental section of the profile proved to be problematic.  Air and agriculture data was not available at the zip code level; surprisingly, water data turned out to be much more useful.  One issue that was encountered in both profile levels was suppression.  To deal with this, age-adjusted rates were calculated for 10 years.  This had the benefit of creating very stable rates.

CONCLUSIONS:  Community profiles are extremely useful.  They allow end users such as LPHAs to gather a diverse amount of information on their community from one place.  Thanks to data handling consistency, profile information can be compared with other jurisdictions.  The social determinants section can be used to find correlations with health and/or environmental outcomes.