Preterm birth persists as a leading cause of infant mortality and morbidity despite decades of intervention effort. Intervention null effects may reflect failure to account for social determinants of health (SDH) or jointly acting risk factors. In some communities, persistent preterm birth trends and disparities have been consistently associated with SDH such as race/ethnicity, zip code, and housing conditions. Health authorities recommend conceptual frameworks for targeted action on SDH and precision public health approaches for preterm birth prevention. We document San Francisco, California's experience identifying the need, rationale, methods, and pilot work for developing a conceptual framework for preterm birth review (PTBR) in San Francisco. The PTBR conceptual framework is intended to enable essential public health services in San Francisco that prevent a range of preterm birth phenotypes by guiding plans for data collection, hypothesis testing, analytical methods, reports, and intervention strategy. Key elements of the PTBR conceptual framework are described including, 10 domains of SDH, 9 domains at the whole person level, such as lived experience and health behaviors, 8 domains at the within-person level, such as biomarkers and clinical measures, 18 preterm birth phenotypes, and the interconnections between domains. Assumptions for the PTBR conceptual framework were supported by a scoping review of literature on SDH effects on preterm birth, health authority consensus reports, and PTBR pilot data. Researcher and health authority interest in each of the domains warrants the framework to prompt systematic consideration of variables in each proposed domain. PTBR pilot data, illustrated in heatmaps, confirm the feasibility of data collection based on the framework, prevalence of co-occurring risk factors, potential for joint effects on specific preterm birth phenotypes, and opportunity for intervention to block SDH effects on preterm birth. The proposed PTBR conceptual framework has practical implications for specifying (1) population groups at risk, (2) grids or heatmap visualization of risk factors, (3) multi-level analyses, and (4) multi-component intervention design in terms of patterns of co-occurring risk factors. Lessons learned about PTBR data collection logistics, variable choice, and data management will be incorporated into future work to build PTBR infrastructure based on the PTBR conceptual framework.
