Dr. Glen D. Johnson, Professor at the CUNY Graduate School of Public Health and Health Policy, and colleagues demonstrate how the application of generalized linear mixed models can provide a solution for community health needs assessment when targeted toward a particular health outcome that needs to be “risk-adjusted” for other non-target community-level variables while also incorporating effects of geographic clustering. The findings were published in the Maternal and Child Health Journal.
[Photo: Dr. Glen Johnson]
The objective of these analyses was to estimate community needs with respect to risky adolescent sexual behavior in a way that is risk-adjusted for multiple community factors. Generalized linear mixed modeling was applied for estimating teen pregnancy and sexually transmitted disease incidence by postal ZIP code in New York State, in a way that adjusts for other community co-variables and residual spatial autocorrelation. A community needs index was then obtained by summing the risk-adjusted estimates of pregnancy and STD cases.
Poisson regression with a spatial random effect was chosen among competing modeling approaches. Both the risk-adjusted caseloads and rates were computed for ZIP codes, which allowed risk-based prioritization to help guide funding decisions for a comprehensive adolescent pregnancy prevention program.
The authors’ approach provides quantitative evidence of community needs with respect to risky adolescent sexual behavior, while adjusting for other community-level variables and stabilizing estimates in areas with small populations. The approach was well accepted by the affected groups and proved valuable for program planning.
The authors conclude that the concepts and methodology they used can readily be extended to other public health outcomes. Their approach to synthesizing data from disparate sources can also serve to unite different public health programs to help find solutions for common goals.