JAMA Health Forum最新文献

Importance: Twenty-seven states have enacted targeted regulation of abortion providers (TRAP) laws that may disproportionately affect higher-risk pregnancies such as those conceived through fertility treatment.

Objective: To assess the association of TRAP laws with the relative rates of adverse outcomes of pregnancies conceived through fertility treatment.

Design, setting, and participants: This cohort study of singleton births conceived through fertility treatment used National Vital Statistics System data on births between 2012 and 2021. Data were analyzed from August 15, 2024, to September 8, 2025.

Exposure: Included participants were categorized as either living under the legal jurisdiction of states with or without TRAP laws enacted during the study period. As laws were not passed in every state uniformly, the first year of enforcement was excluded.

Main outcomes and measures: Demographic characteristics of individuals who conceived with fertility treatments living in states with and without TRAP laws were compared using χ2 and analysis of variance tests. A maternal composite of adverse outcomes was constructed. Secondary outcomes included a neonatal composite of adverse outcomes and rate of preterm birth. Controlling for potential confounders, generalized estimating equation models with binomial distribution, identity link, and robust sandwich SE estimators were used to assess adjusted absolute percentage point differences comparing states with and without TRAP laws across the enactment of TRAP laws.

Results: This study included 416 019 singleton births (mean [SD] maternal age, 34.5 [5.3] years; mean [SD] gestational age, 38.3 [2.4] weeks; 213 294 males [51.3%]) conceived with fertility treatment. Of these births, 174 671 (42.0%) occurred in states with TRAP laws and 241 348 (58.0%) in states without these laws. Generalized estimating equation models demonstrated a greater increase in the composite of adverse maternal outcomes (absolute adjusted difference-in-differences, 0.25; 95% CI, 0.003-0.50) in states with TRAP laws relative to states without.

Conclusions and relevance: These findings suggest an increase in maternal morbidity among patients using fertility care in states that passed TRAP laws relative to states that did not.

Importance: Public health decisions increasingly rely on large-scale data and emerging technologies such as artificial intelligence and mobile health. However, many populations-including those in rural areas, with disabilities, experiencing homelessness, or living in low- and middle-income regions of the world-remain underrepresented in health datasets, leading to biased findings and suboptimal health outcomes for certain subgroups. Addressing data inequities is critical to ensuring that technological and digital advances improve health outcomes for all.

Observations: This article proposes 10 core concepts to improve data equity throughout the operational arc of data science research and practice in public health. The framework integrates computer science principles such as fairness, transparency, and privacy protection, with best practices in public health data science that focus on mitigating information and selection biases, learning causality, and ensuring generalizability. These concepts are applied together throughout the data life cycle, from study design to data collection, analysis, and interpretation to policy translation, offering a structured approach for evaluating whether data practices adequately represent and serve all populations.

Conclusions and relevance: Data equity is a foundational requirement for producing trustworthy inference and actionable evidence. When data equity is built into public health research from the start, technological and digital advances are more likely to improve health outcomes for everyone rather than widening existing health gaps. These 10 core concepts can be used to operationalize data equity in public health. Although data equity is an essential first step, it does not automatically guarantee information, learning, or decision equity. Advancing data equity must be accompanied by parallel efforts in information theory and structural changes that promote informed decision-making.

Importance: The monthly opioid overdose death rate in the US has declined by 50% from its peak in the summer of 2023 through fall of 2024, and the factors associated with this decline are not fully understood.

Objective: To examine the association between the proportion of fentanyl reports in illicit drug seizures and opioid overdose deaths during periods of rising and falling mortality.

Design and setting: This secondary analysis of state-month level panel data from the National Forensic Laboratory Information System and US Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (CDC WONDER) was conducted from January 2018 to September 2024 and included all 50 US states and Washington, DC. CDC WONDER data were collected from recorded death certificates; National Forensic Laboratory Information System data were obtained from drug reports submitted by forensic laboratories. The data were analyzed from July to August 2025.

Exposure: Percentage of illicit drug seizures that contained fentanyl or fentanyl-related compounds. Illicit drug seizures were defined as seizures that contained any of the following: fentanyl or fentanyl-related compounds, heroin, methamphetamine, cocaine, and xylazine.

Main outcomes and measures: The monthly count of opioid overdose deaths, given by uniform claim descriptor codes X40 to 44, X60 to 64, X85, and Y10 to Y14, with additional multiple cause of death codes of T40.0 to 4 and T40.6. Death rates were calculated using yearly population estimates from the American Community Survey.

Results: From a peak in the summer of 2023 through the fall of 2024, the monthly opioid overdose death rate declined by 50%, from 2.2 to 1.1 per 100 000. This decline was was accompanied by a decline in the fentanyl reports as a proportion of total illicit drug seizures from 28.8% to 23.2%. In a 2-way, fixed-effects model, a 1-percentage point reduction in fentanyl prevalence was associated with 0.018 fewer overdose deaths per 100 000 population per month (95% CI, 0.016-0.019; P < .001). There was evidence that the strength of this association has decreased over time.

Conclusions and relevance: The study results suggest that current decline in the proportion of fentanyl reports in illicit drug seizures is associated with 9.2% of the total observed decline in mortality. Additional contributing factors may include other shifts in the drug supply not captured by fentanyl prevalence in illicit drug seizures, shifts in drug use behavior, and the effect of public health programs, interventions, and policies.

Importance: Federal survey data collection identifies people with disabilities predominantly by using a 6-question sequence asking about different functional impairments known as the American Community Survey-6 (ACS-6). However, little is known about the specific diagnoses identified by the ACS-6 or whether they vary across demographic subgroups.

Objective: To characterize the disability diagnoses identified by the ACS-6 and assess to what extent they identify a consistent population across demographic subgroups.

Design, setting, and participants: This cross-sectional study among people with disabilities responding to the 2023 or 2024 Survey of Income and Program Participation (SIPP) assessed the prevalence of 36 different diagnosis groupings in the ACS-6 as a whole and within each of the individual questions, as well as how the identified diagnoses varied by age group, race and ethnicity, sex, and educational attainment, with further disaggregation by cognitive disability status. Data were analyzed between August 1 and September 15, 2025.

Exposure: Identification as people with disabilities using the ACS-6.

Main outcomes and measures: Diagnoses reported by survey respondents as causing the functional impairments they listed in the SIPP.

Results: A total of 13 341 people with disabilities (52.2% female; mean [SD] age, 53.0 [23.0] years) responding to the SIPP were included. Among people with disabilities aged 22 to 64 years, the most common diagnoses were anxiety or obsessive-compulsive disorders (prevalence, 15.6%; 95% CI, 14.5%-16.9%), depression (15.3%; 95% CI, 14.1%-16.5%), unspecified musculoskeletal issues (13.5%; 95% CI, 12.5%-14.6%), back or spinal problems (11.6%; 95% CI, 10.6%-12.6%), and unspecified neurologic disorders (10.8%; 95% CI, 9.8%-11.8%). The most common disability diagnoses reported by respondents identified by the ACS-6 were different across age groups but similar across demographic groups defined by sex, race and ethnicity, and educational attainment.

Conclusions and relevance: The results of this cross-sectional study suggest that the ACS-6 identifies a similar population across demographic subgroups not characterized by age but highlight substantial heterogeneity in the population of people with disabilities within these subgroups and across age groups. Contemporary debates regarding future revisions to disability data collection in federal population surveys should address the ability to account for this heterogeneity in survey design.