Elizabeth A. Stuart’s research while affiliated with Johns Hopkins Bloomberg School of Public Health and other places

Importance: The impact of recent abortion bans on infant mortality is not fully understood. There is also limited evidence on how these bans may interact with long-standing racial and ethnic disparities in infant health. Objective: To examine the association of abortion bans with changes in infant mortality and to compare this association in racial and ethnic groups based on analyses within and across states. Design, setting, and participants: This population-based, serial, cross-sectional study used a bayesian panel model to examine infant mortality rates in 14 states that implemented complete or 6-week abortion bans and compared them with predictions of infant mortality rates based on pre-ban mortality rates and states without bans. Data included all live births and infant deaths from all 50 US states and the District of Columbia for 2012 through 2023. Models accounted for temporal trends and state-specific factors, with analyses stratified by race and ethnicity, timing of death, and cause of death. Exposure: Complete or 6-week abortion bans. Main outcome and measures: Infant mortality rate, analyzed overall and by subgroups. Results: The analysis found higher than expected infant mortality in states after adoption of abortion bans (observed vs expected, 6.26 vs 5.93 per 1000 live births; absolute increase, 0.33 [95% credible interval (CrI), 0.14-0.51]; relative increase, 5.60% [95% CrI, 2.43%-8.73%]). This resulted in an estimated 478 excess infant deaths in the 14 states with bans during the months affected by bans. The estimated increases were higher among non-Hispanic Black infants compared with other racial and ethnic groups, with 11.81 observed vs 10.66 expected infant deaths per 1000 live births, an absolute increase of 1.15 (95% CrI, 0.53-1.81) and relative increase of 10.98% (95% CrI, 4.87%-17.89%). The observed infant mortality rate due to congenital anomalies was 1.37 vs 1.24 expected (absolute increase, 0.13 [95% CrI, 0.04-0.21]; relative increase, 10.87% [95% CrI, 3.39%-18.08%]), while the rate not due to congenital anomalies was 4.89 observed vs 4.69 expected (absolute increase, 0.20 [95% CrI, 0.02-0.38]; relative increase, 4.23% [95% CrI, 0.49%-8.23%]). Texas had a dominant influence on the overall results and there were larger increases in southern vs nonsouthern states. Conclusions: US states that adopted abortion bans had higher than expected infant mortality after the bans took effect. The estimated relative increases in infant mortality were larger for deaths with congenital causes and among groups that had higher than average infant mortality rates at baseline, including Black infants and those in southern states.

Importance: Abortion bans may lead to births among those who are unable to overcome barriers to abortion. The population-level effects of these policies, particularly their unequal impacts across subpopulations in the US, remain unclear. Objective: To assess heterogeneity in the association of abortion bans with changes in fertility in the US, within and across states. Design, setting, and participants: Drawing from birth certificate and US Census Bureau data from 2012 through 2023 for all 50 states and the District of Columbia, this study used a bayesian panel data model to evaluate state-by-subgroup-specific changes in fertility associated with complete or 6-week abortion bans in 14 US states. The average percent and absolute change in the fertility rate among females aged 15 through 44 years was estimated overall and by state, and within and across states by age, race and ethnicity, marital status, education, and insurance payer. Exposure: Complete or 6-week abortion ban. Main outcome and measures: Fertility rate (births per 1000 reproductive-aged females) overall and by subgroups. Results: There were an estimated 1.01 (95% credible interval [CrI], 0.45-1.64) additional births above expectation per 1000 females aged 15 through 44 years (reproductive age) in states following adoption of abortion bans (60.55 observed vs 59.54 expected; 1.70% increase; 95% CrI, 0.75%-2.78%), equivalent to 22 180 excess births, with evidence of variation by state and subgroup. Estimated differences above expectation were largest for racially minoritized individuals (≈2.0%), unmarried individuals (1.79%), individuals younger than 35 years (≈2.0%), Medicaid beneficiaries (2.41%), and those without college degrees (high school diploma, 2.36%; some college, 1.58%), particularly in southern states. Differences in race and ethnicity and education across states explain most of the variability in the state-level association between abortion bans and fertility rates. Conclusion and relevance: These findings provide evidence that fertility rates in states with abortion bans were higher than would have been expected in the absence of these policies, with the largest estimated differences among subpopulations experiencing the greatest structural disadvantages and in states with among the worst maternal and child health and well-being outcomes.

While telehealth may improve access to healthcare for some, it may also widen gaps in access across different economic groups. Using electronic health records for outpatient mental health care of patients with depression in a large US academic health system, we assessed changes in mental health care utilization from 2016 to 2024 (primary care: n = 42,640 patients, 270,754 visits; psychiatry: n = 12,846 patients, 336,918 visits) and odds of using telehealth relative to in-person care from 2020 to 2024, across national area deprivation index (ADI) percentiles. We found that over 3 years prepandemic (July 2016–June 2019), the volume of mental health care delivered to patients from low-deprivation areas (1st–25th national ADI percentile) was increasing at a steeper rate than for high-deprivation areas (76th–100th national ADI percentile). Visit volume changed rapidly at the onset of the COVID-19 pandemic, and by July 2021 it was increased relative to prepandemic levels. From July 2021 to June 2024, volume of care declined for all deprivation groups, but at a more rapid rate for the high-deprivation group than the low-deprivation group. Further, on average from July 2020 to June 2024, the odds of receiving telehealth relative to in-person care were significantly higher for patients living in low deprivation rather than high-deprivation areas in both primary care and psychiatry. We did not find evidence of telehealth improving access to care for patients in high-deprivation areas. Differences in telehealth use may contribute to sustained disparities in access to mental health care across economic groups.

(Abstracted from JAMA Pediatr 2024;178(8):784–791) Since the 2022 US Supreme Court decision in Dobbs v. Jackson Women’s Health Organization , it has been suggested that abortion restrictions could increase infant mortality. For example, an increase in infant mortality could result from more people being forced to carry a pregnancy, including those with congenital malformations, which accounts for 1 in 5 infants deaths in the United States.

Meta-analysis of randomized controlled trials is commonly used to evaluate treatments and inform policy decisions because it provides comprehensive summaries of all available evidence. However, meta-analyses are limited to draw population inference of treatment effects because they usually do not define target populations of interest specifically, and results of the individual randomized controlled trials in those meta-analyses may not generalize to the target populations. To leverage evidence from multiple randomized controlled trials in the generalizability context, we bridge the ideas from meta-analysis and causal inference. We integrate meta-analysis with causal inference approaches estimating target population average treatment effect. We evaluate the performance of the methods via simulation studies and apply the methods to generalize meta-analysis results from randomized controlled trials of treatments on schizophrenia to adults with schizophrenia who present to usual care settings in the United States. Our simulation results show that all methods perform comparably and well across different settings. The data analysis results show that the treatment effect in the target population is meaningful, although the effect size is smaller than the sample average treatment effect. We recommend applying multiple methods and comparing the results to ensure robustness, rather than relying on a single method.

Background Estimates of the prevalence of preclinical amyloid positivity in the US general population are of great interest to the field, but difficult to measure and thus unavailable in representative studies. A statistical approach from causal inference, ‘transport’, may allow for improved generalizability of findings from a sample of persons from one population to another. We aimed to explore the feasibility and validity of extending results from a deeply‐phenotyped convenience sample, the Alzheimer’s Disease Neuroimaging Initiative (ADNI), to a representative target sample, the Atherosclerosis Risk in Communities Study PET Amyloid Imaging Study (ARIC‐PET) — with “proof of concept” defined by the performance of the transport estimator in recovering the observed prevalence of amyloid positivity in ARIC‐PET. Method Eligible ARIC‐PET and ADNI participants were either white or Black and had normal cognition or mild cognitive impairment. Amyloid positivity was defined using study‐specific cutoffs for standardized uptake value ratio (SUVR). Probability of selection into ADNI (vs. into ARIC‐PET), given harmonized sociodemographic and clinical covariates, was estimated using gradient boosted trees (GBM). The resulting study participation probability scores were used to transport the prevalence of amyloid positivity from ADNI to ARIC using inverse odds of sampling weights. Estimates of amyloid positivity prevalence derived from ADNI and from transporting from ADNI to ARIC‐PET were compared to the observed prevalence in ARIC‐PET, overall and by age, sex, race, education, and APOE e4 status. Result Transported prevalences substantially underestimated observed prevalences of amyloid positivity in most groups, with the exception of white, non‐Hispanic individuals and persons with at least one APOE e4 allele, where prevalence was slightly overestimated. Prevalence of preclinical amyloid positivity based on transporting ADNI was further from the prevalence in ARIC‐PET than the raw prevalence of amyloid positivity in ADNI except for white, non‐Hispanic individuals and those with mild cognitive impairment. Conclusion Transport estimators rely on adequately modeling the selection process. Available data may be insufficient to capture selection biases into deeply‐phenotyped convenience samples, particularly for groups who are under‐represented in research. Transport estimators will have greatest utility when applied to samples derived from known sampling strategies, and with increased diversity in dementia research.

Objectives Heterogeneity of treatment effect (HTE) is a concern in substance use disorder (SUD) treatments but has not been rigorously examined. This exploratory study applied a causal forest approach to examine HTE in psychosocial SUD treatments, considering multiple covariates simultaneously. Methods Data from 12 randomized controlled trials of nine psychosocial treatments were obtained from the National Institute on Drug Abuse Clinical Trials Network. Using causal forests, we estimated the conditional average treatment effect (CATE) on drug abstinence. To assess HTE, we compared CATE variance against total outcome variability, conducted an omnibus test, and applied the Rank‐Weighted Average Treatment Effect (RATE). Results Across nine interventions, CATE variance was lower than total outcome variability, indicating lack of strong evidence of HTE with respect to the baseline covariates considered. The omnibus test and RATE analysis generally support this finding. However, the RATE analysis identified potential HTE in a motivational interviewing trial; this could be a false positive given the multiple analyses; replication is needed to confirm this. Conclusions While causal forests show utility in exploring HTE in SUD interventions, limited baseline assessments in most trials suggest a cautious interpretation. The RATE findings for motivational interviewing highlight potential subgroup‐specific treatment benefits, warranting further research.

Objective Intimate partner violence (IPV) affects an estimated 47% of women living in the USA in their lifetime and is associated with increased risk of physical and mental health concerns. Current prevention efforts focus on individual and family-level interventions rather than macrosystem-level policies. Thus, we sought to test the effects of Medicaid expansion on the rates of IPV and violence more broadly. Methods Present analyses use retrospective longitudinal data from the National Crime Victimization Survey (NCVS). State level rates of total violence and IPV were measured per 1000 population from the NCVS for years 2008–2018 as 3-year averages for each state. A two-way fixed-effects difference-in-differences model was fit to evaluate differences in the change in violence outcomes pre-2014/post-2014 in Medicaid expansion states versus non-expansion states. Results Comparison states had a significantly higher proportion of residents who were black, living below the federal poverty level and with lower educational attainment. Before Medicaid expansion, comparison states had a significantly lower mean rate of total violence and IPV per 1000 population. In two-way fixed effects difference-in-differences models, there was no statistically significant association between Medicaid expansion and IPV or total violence. Discussion Despite null findings, our study adds to the evidence base evaluating the impacts of macro-level policies on different forms of violence. The pathways by which Medicaid expansion could contribute to violence reduction are multifaceted with numerous mediators and those pathways may not be sufficiently strong to generate impacts. Additional work is warranted to further probe Medicaid expansion’s impact on violence prevention.