Identifying areas at risk of low birth weight using spatial epidemiology: A small area surveillance study

- Spatial epidemiologic analysis can be used to determine spatial patterns in health outcomes.
- Low birth weight was related with area-level social determinants of health.
- A Poisson mixed model with random effect terms for spatial error and overdispersion was used.
- Smoothed maps for predicted prevalence identify areas at high risk of LBW.
- Spatial patterns of residual variation identify unique risk factors.

ObjectivesTo assess the geographic distribution of Low Birth Weight (LBW) in New York State among singleton births using a spatial regression approach in order to identify priority areas for public health actions.MethodsLBW was defined as birth weight less than 2500 g. Geocoded data from 562,586 birth certificates in New York State (years 2008-2012) were merged with 2010 census data at the tract level. To provide stable estimates and maintain confidentiality, data were aggregated to yield 1268 areas of analysis. LBW prevalence among singleton births was related with area-level behavioral, socioeconomic and demographic characteristics using a Poisson mixed effects spatial error regression model.ResultsObserved low birth weight showed statistically significant auto-correlation in our study area (Moran's I 0.16 p value 0.0005). After over-dispersion correction and accounting for fixed effects for selected social determinants, spatial autocorrelation was fully accounted for (Moran's I − 0.007 p value 0.241). The proportion of LBW was higher in areas with larger Hispanic or Black populations and high smoking prevalence. Smoothed maps with predicted prevalence were developed to identify areas at high risk of LBW. Spatial patterns of residual variation were analyzed to identify unique risk factors.ConclusionNeighborhood racial composition contributes to disparities in LBW prevalence beyond differences in behavioral and socioeconomic factors. Small-area analyses of LBW can identify areas for targeted interventions and display unique local patterns that should be accounted for in prevention strategies.