Adult obesity prevalence at the county level in the United States, 2000–2010: Downscaling public health survey data using a spatial microsimulation approach. (August 2018) - Koh, Keumseok; Grady, Sue C.; Darden, Joe T.; Vojnovic, Igor
Highlights: Spatial microsimulation downscales national public health survey data to the county level. U.S. obesity prevalence rates are estimated at the county level from 2000 to 2010. High–High spatial autocorrelation was observed along the Mississippi River, the Appalachian Mountains and Native American reservation sites. Many Western and Northeastern counties were identified as Low–Low clusters. Abstract: Obesity is a growing public health concern in the United States. There is a need to monitor obesity prevalence at the local level to intervene in place-specific ways. However, national public health surveys suppress the local geographic information of respondents due to small sample sizes and the protection of confidentiality. This study therefore, uses a spatial microsimulation approach to estimate obesity prevalence rates at the county level across the United States to visualize temporal, spatial and spatio-temporal changes from 2000 to 2010 for use in the monitoring of obesity prevalence. This method iteratively replicates the demographic characteristics of public health survey respondents with census data for those areas. Following, Local Moran's I was used to identify clusters of high and low obesity prevalence. The findings showed that obesity prevalence rose dramatically over the last decade with substantial variation across counties and states. Counties in Southern states, especially along the Mississippi River and Appalachian Mountains and counties containing or in proximity to Native American reservation sites showed elevated obesity prevalence rates across the decade. Counties in Midwestern states had higher obesity prevalence rates compared to counties in Western and Northeastern states. This study demonstrated the use of spatial microsimulation modeling as an alternative method to obtain reliable obesity prevalence rates at the local-level using existing health survey and census data.