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Background: Direct estimates of rare disease prevalence from public health surveillance may only be available in a few catchment areas. Understanding variation among observed prevalence can inform estimates of prevalence in other locations. The Muscular Dystrophy Surveillance, Tracking, and Research Network (MD STARnet) conducts population-based surveillance of major muscular dystrophies in selected areas of the United States. We identified sources of variation in prevalence estimates of Duchenne and Becker muscular dystrophy (DBMD) within MD STARnet from published literature and a survey of MD STARnet investigators, then developed a logic model of the relationships between the sources of variation and estimated prevalence., Results: The 17 identified sources of variability fell into four categories: (1) inherent in surveillance systems, (2) particular to rare diseases, (3) particular to medical-records-based surveillance, and (4) resulting from extrapolation. For the sources of uncertainty measured by MD STARnet, we estimated each source's contribution to the total variance in DBMD prevalence. Based on the logic model we fit a multivariable Poisson regression model to 96 age-site-race/ethnicity strata. Age accounted for 74% of the variation between strata, surveillance site for 6%, race/ethnicity for 3%, and 17% remained unexplained., Conclusion: Variation in estimates derived from a non-random sample of states or counties may not be explained by demographic differences alone. Applying these estimates to other populations requires caution., (© 2023. The Author(s).)

Conotruncal heart defects (CTDs) are among the most severe birth defects worldwide. Studies of CTDs indicate both lifestyle behaviors and genetic variation contribute to the risk of CTDs. Based on a hybrid design using data from 616 case-parental and 1645 control-parental triads recruited for the National Birth Defects Prevention Study between 1997 and 2008, we investigated whether the occurrence of CTDs is associated with interactions between 921 maternal and/or fetal single nucleotide polymorphisms (SNPs) and maternal obesity and tobacco use. The maternal genotypes of the variants in the glutamate-cysteine ligase, catalytic subunit (GCLC) gene and the fetal genotypes of the variants in the glutathione S-transferase alpha 3 (GSTA3) gene were associated with an elevated risk of CTDs among obese mothers. The risk of delivering infants with CTDs among obese mothers carrying AC genotype for a variant in the GCLC gene (rs6458939) was 2.00 times the risk among those carrying CC genotype (95% confidence interval: 1.41, 2.38). The maternal genotypes of several variants in the glutathione-S-transferase (GST) family of genes and the fetal genotypes of the variants in the GCLC gene interacted with tobacco exposures to increase the risk of CTDs. Our study suggests that the genetic basis underlying susceptibility of the developing heart to the adverse effects of maternal obesity and tobacco use involve both maternal and embryonic genetic variants. These results may provide insights into the underlying pathophysiology of CTDs, and ultimately lead to novel prevention strategies.

The widespread development of comprehensive community initiatives that aim to improve community health is driven by the need to change the systems charged with delivering the services and creating the policies related to a variety of health outcomes. Georgia's Family Connection initiative is the nation's largest statewide network of community collaboratives for health, with collaboratives operating in 159 counties. Data on community context, collaborative processes, engagement in systems change, and changes in programs and activities implemented, gathered consistently at the collaborative level over 3 years, will be used to answer the following questions. How do community contexts and the structure and processes of collaboratives affect implementation of systems change? How do systems changes affect intermediate outcomes such as the type of programs offered in a community? Longitudinal change in systems change and program implementation is described and significant predictors of between-collaborative variation in longitudinal change for each outcome are identified.