Katherine Baicker’s research while affiliated with University of Illinois at Chicago and other places

OBJECTIVE To evaluate the effect of an intensive nurse home visiting program on postpartum contraceptive use and birth spacing among individuals with a first pregnancy who were eligible for Medicaid insurance in South Carolina. METHODS We conducted a nonblinded, randomized controlled trial of the Nurse-Family Partnership (NFP), an established intensive home visiting program that provides prenatal and postpartum home visits through 2 years after childbirth. The trial included patients who were eligible for Medicaid insurance with a first pregnancy at less than 28 weeks of gestation between April 1, 2016, and March 17, 2020, who were followed up through 2 years after childbirth. Participants were randomized 2:1 to NFP compared with standard of care treatment. The primary outcome was a birth interval of less than 21 months between the index pregnancy and a subsequent birth. The secondary outcomes were birth intervals of less than 15 and 24 months, receipt of a contraceptive implant or intrauterine device (IUD) immediately postpartum, any contraceptive use and receipt of a family planning visit (at both 6 weeks and 1 year postpartum), and IUD receipt at 1 year postpartum. We assessed outcomes using linked birth certificate records and Medicaid claims data. RESULTS A total of 4,932 trial participants (3,295 in the intervention group and 1,637 in the control group) were included in the study analysis. Within 21 months of the study index birth, 11.0% of individuals in the NFP group and 12.2% of the usual care group had a subsequent birth. The NFP did not have a statistically significant effect on birth intervals of less than 21 months (adjusted coefficient −1.1, 95% CI, −2.9 to 0.8). There were no statistically significant differences between the NFP and control groups for any of the study's eight secondary outcomes related to birth spacing and postpartum contraceptive use. CONCLUSION Home visits with a registered nurse did not affect postpartum contraceptive use or birth spacing. CLINICAL TRIAL REGISTRATION ClinicalTrials.gov, NCT03360539.

Objectives To investigate whether health insurance generated improvements in cardiovascular risk factors (blood pressure and hemoglobin A 1c (HbA 1c ) levels) for identifiable subpopulations, and using machine learning to identify characteristics of people predicted to benefit highly. Design Secondary analysis of randomized controlled trial. Setting Medicaid insurance coverage in 2008 for adults on low incomes (defined as lower than the federal-defined poverty line) in Oregon who were uninsured. Participants 12 134 participants from the Oregon Health Insurance Experiment with in-person data for health outcomes for both treatment and control groups. Interventions Health insurance (Medicaid) coverage. Main outcomes measures The conditional local average treatment effects of Medicaid coverage on systolic blood pressure and HbA 1c using a machine learning causal forest algorithm (with instrumental variables). Characteristics of individuals with positive predicted benefits of Medicaid coverage based on the algorithm were compared with the characteristics of others. The effect of Medicaid coverage was calculated on blood pressure and HbA 1c among individuals with high predicted benefits. Results In the in-person interview survey, mean systolic blood pressure was 119 (standard deviation 17) mm Hg and mean HbA 1c concentrations was 5.3% (standard deviation 0.6%). Our causal forest model showed heterogeneity in the effect of Medicaid coverage on systolic blood pressure and HbA 1c . Individuals with lower baseline healthcare charges, for example, had higher predicted benefits from gaining Medicaid coverage. Medicaid coverage significantly lowered systolic blood pressure (−4.96 mm Hg (95% confidence interval −7.80 to −2.48)) for people predicted to benefit highly. HbA 1c was also significantly reduced by Medicaid coverage for people with high predicted benefits, but the size was not clinically meaningful (−0.12% (−0.25% to −0.01%)). Conclusions Although Medicaid coverage did not improve cardiovascular risk factors on average, substantial heterogeneity was noted in the effects within that population. Individuals with high predicted benefits were more likely to have no or low prior healthcare charges, for example. Our findings suggest that Medicaid coverage leads to improved cardiovascular risk factors for some, particularly for blood pressure, although those benefits may be diluted by individuals who did not experience benefits.

This JAMA Forum discusses the current data collection mechanisms used for Medicare and proposes ways to improve nationwide data collection efforts for Medicaid.

In 2008, Oregon expanded its Medicaid program using a lottery, creating a rare opportunity to study the effects of Medicaid coverage using a randomized controlled design (Oregon Health Insurance Experiment). Analysis showed that Medicaid coverage lowered the risk of depression. However, this effect may vary between individuals, and the identification of individuals likely to benefit the most has the potential to improve the effectiveness and efficiency of the Medicaid program. By applying the machine learning causal forest to data from this experiment, we found substantial heterogeneity in the effect of Medicaid coverage on depression; individuals with high predicted benefit were older and had more physical or mental health conditions at baseline. Expanding coverage to individuals with high predicted benefit generated greater reduction in depression prevalence than expanding to all eligible individuals (21.5 vs 8.8 percentage-point reduction; adjusted difference = +12.7 [95% CI, +4.6 to +20.8]; P = 0.003), at substantially lower cost per case prevented ($16 627 vs$36 048; adjusted difference = −$18 598 [95% CI, −156 953 to −3120]; P = 0.04). Medicaid coverage reduces depression substantially more in a subset of the population than others, in ways that are predictable in advance. Targeting coverage on those most likely to benefit could improve the effectiveness and efficiency of insurance expansion. This article is part of a Special Collection on Mental Health.

This JAMA Forum discusses treatment and health care delivery, innovative use of risk analytics, and spending and coverage priorities in the US health insurance system.

This JAMA Forum discusses the successes, failures, and lessons learned regarding the performance of US social safety-net programs during the COVID-19 pandemic.

There is an urgent need to improve maternal and neonatal health outcomes and decrease their racial disparities in the US. Prenatal nurse home visiting programs could help achieve this by increasing the use and quality of prenatal care and facilitating healthy behaviors during pregnancy. We conducted a randomized controlled trial of 5,670 Medicaid-eligible pregnant people in South Carolina to evaluate how a nurse home visiting program affected prenatal health care and health outcomes. We compared outcomes between the treatment and control groups and found little evidence of statistically significant differences in the intensity of prenatal care use, receipt of guideline-based prenatal care services, other health care use, or gestational weight gain. Nor did we find treatment effects in subgroup analyses of socially vulnerable participants (46.9 percent of the sample) or non-Hispanic Black participants (52.0 percent of the sample). Compared with the broader Medicaid population, our trial participants had more health and social risk factors, more engagement with prenatal care, and similar pregnancy outcomes. Delivering intensive nurse home visiting programs to the general Medicaid population might not be an efficient method to improve prenatal care for those who need the most support during pregnancy.

The United States spends substantially more on health care than most developed countries, yet leaves a greater share of the population uninsured. We argue that incremental insurance expansions focused on addressing market failures will propagate inefficiencies and will fail to facilitate the active policy decisions needed to achieve socially optimal coverage. By instead defining a basic bundle of services that is publicly financed for all, while allowing individuals to purchase additional coverage, policymakers could both expand coverage and maintain incentives for innovation, ensuring universal access to innovative care in an affordable system.

This JAMA Forum discusses alternative ways to achieve universal coverage in the US such as administrative simplification in the Affordable Care Act plans to increase enrollment, having a basic policy that would be available to everyone, and options for supplemental coverage.