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Social determinants of health (SDOH) are the conditions in which people are born, grow, live, work, and age that influence health outcomes, and structural and systemic drivers of health (SSD) are the social, cultural, political, and economic contexts that create and shape SDOH. With the integration of constructs from previous examples, we propose an SSD model that broadens the contextual effect of these driving forces or factors rooted in the Centers for Disease Control and Prevention's SDOH framework. Our SSD model (1) presents systems and structures as multidimensional, (2) considers 10 dimensions as discrete and intersectional, and (3) acknowledges health-related effects over time at different life stages and across generations. We also present an application of this SSD model to the housing domain and describe how SSD affect SDOH through multiple mechanisms that may lead to unequal resources, opportunities, and consequences contributing to a disproportionate burden of disease, illness, and death in the US population. Our enhanced SDOH framework offers an innovative and promising model for multidimensional, collaborative public health approaches toward achieving health equity and eliminating health disparities., Competing Interests: Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Introduction: We investigated 2018 gestational diabetes mellitus (GDM) prevalence estimates in three surveillance systems (National Vital Statistics System, State Inpatient Database, and Pregnancy Risk Assessment Monitoring Survey)., Methods: We calculated GDM prevalence for jurisdictions represented in each system; a subset of data was analyzed for people 18-39 years old in 22 jurisdictions present in all three systems to observe dataset-specific demographics and GDM prevalence using comparable categories., Results: GDM prevalence estimates varied widely by data system and within the data subset despite comparable demographics., Discussion: Understanding the differences between GDM surveillance data systems can help researchers better identify people and places at higher risk of GDM., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Background: In 2021, a large petroleum leak contaminated a water source that supplied drinking water to military and civilians in Oahu, Hawaii., Methods: We conducted an Assessment of Chemical Exposures (ACE) survey and supplemented that information with complementary data sources: (1) poison center caller records; (2) emergency department visit data; and (3) a key informant questionnaire., Results: Among 2,289 survey participants, 86% reported ≥1 new or worsening symptom, 75% of which lasted ≥30 days, and 37% sought medical care. Most (n = 1,653, 72%) reported new mental health symptoms. Among equally observable symptoms across age groups, proportions of children ≤2 years experiencing vomiting, runny nose, skin rashes, and coughing (33, 46, 56, and 35%, respectively) were higher than other age groups. Poison center calls increased the first 2 weeks after the contamination, while emergency department visits increased in early December 2021. Key informant interviews revealed themes of lack of support, mental health symptoms, and long-term health impact concerns., Discussion: This event led to widespread exposure to petroleum products and negatively affected thousands of people. Follow-up health surveys or interventions should give special consideration to longer-term physical and mental health, especially children due to their unique sensitivity to environmental exposures.