The Dependent Coverage Provision Is Good for Mothers, Good for Children, and Good for Taxpayers

Importance The effect of the Affordable Care Act (ACA) dependent coverage provision on pregnancy-related health care and health outcomes is unknown. Objective To determine whether the dependent coverage provision was associated with changes in payment for birth, prenatal care, and birth outcomes. Design, Setting, and Participants Retrospective cohort study, using a differences-in-differences analysis of individual-level birth certificate data comparing live births among US women aged 24 to 25 years (exposure group) and women aged 27 to 28 years (control group) before (2009) and after (2011-2013) enactment of the dependent coverage provision. Results were stratified by marital status. Main Exposures The dependent coverage provision of the ACA, which allowed young adults to stay on their parent’s health insurance until age 26 years. Main Outcomes and Measures Primary outcomes were payment source for birth, early prenatal care (first visit in first trimester), and adequate prenatal care (a first trimester visit and 80% of expected visits). Secondary outcomes were cesarean delivery, premature birth, low birth weight, and infant neonatal intensive care unit (NICU) admission. Results The study population included 1 379 005 births among women aged 24 to 25 years (exposure group; 299 024 in 2009; 1 079 981 in 2011-2013), and 1 551 192 births among women aged 27 to 28 years (control group; 325 564 in 2009; 1 225 628 in 2011-2013). From 2011-2013, compared with 2009, private insurance payment for births increased in the exposure group (36.9% to 35.9% [difference, −1.0%]) compared with the control group (52.4% to 51.1% [difference, −1.3%]), adjusted difference-in-differences, 1.9 percentage points (95% CI, 1.6 to 2.1). Medicaid payment decreased in the exposure group (51.6% to 53.6% [difference, 2.0%]) compared with the control group (37.4% to 39.4% [difference, 1.9%]), adjusted difference-in-differences, −1.4 percentage points (95% CI, −1.7 to −1.2). Self-payment for births decreased in the exposure group (5.2% to 4.3% [difference, −0.9%]) compared with the control group (4.9% to 4.3% [difference, −0.5%]), adjusted difference-in-differences, −0.3 percentage points (95% CI, −0.4 to −0.1). Early prenatal care increased from 70% to 71.6% (difference, 1.6%) in the exposure group and from 75.7% to 76.8% (difference, 0.6%) in the control group (adjusted difference-in-differences, 0.6 percentage points [95% CI, 0.3 to 0.8]). Adequate prenatal care increased from 73.5% to 74.8% (difference, 1.3%) in the exposure group and from 77.5% to 78.8% (difference, 1.3%) in the control group (adjusted difference-in-differences, 0.4 percentage points [95% CI, 0.2 to 0.6]). Preterm birth decreased from 9.4% to 9.1% in the exposure group (difference, −0.3%) and from 9.1% to 8.9% in the control group (difference, −0.2%) (adjusted difference-in-differences, −0.2 percentage points (95% CI, −0.3 to −0.03). Overall, there were no significant changes in low birth weight, NICU admission, or cesarean delivery. In stratified analyses, changes in payment for birth, prenatal care, and preterm birth were concentrated among unmarried women. Conclusions and Relevance In this study of nearly 3 million births among women aged 24 to 25 years vs those aged 27 to 28 years, the Affordable Care Act dependent coverage provision was associated with increased private insurance payment for birth, increased use of prenatal care, and modest reduction in preterm births, but was not associated with changes in cesarean delivery rates, low birth weight, or NICU admission

Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits

Optimizing lower-body exoskeleton walking gaits for user comfort requires understanding users’ preferences over a high-dimensional gait parameter space. However, existing preference-based learning methods have only explored low-dimensional domains due to computational limitations. To learn user preferences in high dimensions, this work presents LINECOSPAR, a human-in-the-loop preference-based framework that enables optimization over many parameters by iteratively exploring one-dimensional subspaces. Additionally, this work identifies gait attributes that characterize broader preferences across users. In simulations and human trials, we empirically verify that LINECOSPAR is a sample-efficient approach for high-dimensional preference optimization. Our analysis of the experimental data reveals a correspondence between human preferences and objective measures of dynamicity, while also highlighting differences in the utility functions underlying individual users’ gait preferences. This result has implications for exoskeleton gait synthesis, an active field with applications to clinical use and patient rehabilitation

Safe Multi-Agent Interaction through Robust Control Barrier Functions with Learned Uncertainties

Robots operating in real world settings must navigate and maintain safety while interacting with many heterogeneous agents and obstacles. Multi-Agent Control Barrier Functions (CBF) have emerged as a computationally efficient tool to guarantee safety in multi-agent environments, but they assume perfect knowledge of both the robot dynamics and other agents' dynamics. While knowledge of the robot's dynamics might be reasonably well known, the heterogeneity of agents in real-world environments means there will always be considerable uncertainty in our prediction of other agents' dynamics. This work aims to learn high-confidence bounds for these dynamic uncertainties using Matrix-Variate Gaussian Process models, and incorporates them into a robust multi-agent CBF framework. We transform the resulting min-max robust CBF into a quadratic program, which can be efficiently solved in real time. We verify via simulation results that the nominal multi-agent CBF is often violated during agent interactions, whereas our robust formulation maintains safety with a much higher probability and adapts to learned uncertainties

A cross-sectional study of predatory publishing emails received by career development grant awardees

OBJECTIVE: To investigate the scope of academic spam emails (ASEs) among career development grant awardees and the factors associated with the amount of time spent addressing them. DESIGN: A cross-sectional survey of career development grant investigators via an anonymous online survey was conducted. In addition to demographic and professional information, we asked investigators to report the number of ASEs received each day, how they determined whether these emails were spam and time they spent per day addressing them. We used bivariate analysis to assess factors associated with the amount of time spent on ASEs. SETTING: An online survey sent via email on three separate occasions between November and December 2016. PARTICIPANTS: All National Institutes of Health career development awardees funded in the 2015 fiscal year. MAIN OUTCOME MEASURES: Factors associated with the amount of time spent addressing ASEs. RESULTS: A total of 3492 surveys were emailed, of which 206 (5.9%) were returned as undeliverable and 96 (2.7%) reported an out-of-office message; our overall response rate was 22.3% (n=733). All respondents reported receiving ASEs, with the majority (54.4%) receiving between 1 and 10 per day and spending between 1 and 10 min each day evaluating them. The amount of time respondents reported spending on ASEs was associated with the number of peer-reviewed journal articles authored (p<0.001), a history of publishing in open access format (p<0.01), the total number of ASEs received (p<0.001) and a feeling of having missed opportunities due to ignoring these emails (p=0.04). CONCLUSIONS: ASEs are a common distraction for career development grantees that may impact faculty productivity. There is an urgent need to mitigate this growing problem

Still Targeting Younger Customers? A Field Experiment on Digital Communication Channel Migration

When encouraging customers to migrate to a digital communication channel, companies often factor age into their targeting strategy. Both the popular press and scholarly work generally believe that younger customers are more likely to opt into communication digitally. However, our empirical evidence from a large-scale field experiment shows that younger customers are not more likely to migrate to a digital communication channel. Besides, we propose two IT-embodied factors to better target customers in the context of digital communication, namely individual digital activeness and information seeking intensity. We find that customers with higher individual digital activeness, or those with lower information seeking intensity, are more likely to migrate to a digital communication channel. Our study thus offers implications for companies to focus more on customer IT-embodied characteristics instead of age

Offering Breakfast in the Classroom and Children’s Weight Outcomes

Obesity is a serious health problem for many children in the United States. Approximately 32% of US children aged 2 to 19 years have overweight or obesity (body mass index [BMI] ≥85th percentile), and nearly 8% of infants and toddlers younger than 2 years have a weight-for-length at the 95th percentile or greater, predisposing them to obesity. Obesity leads to serious, lifelong medical and psychosocial problems and premature death. These consequences disproportionately affect racial/ethnic minority groups and low-income communities, where obesity is most pronounced. Despite previous reports that childhood obesity has remained stable or decreased, more recent evidence shows that the prevalence of obesity and severe obesity is unfortunately increasing, especially among preschool-aged children

On Intelligent Transportation Systems and Road Congestion

Despite substantial investments in transportation infrastructures, road congestion in urban areas has not abated. While there is a growing interest among policymakers in intelligent transportation systems (ITS), the role of ITS in road congestion has not been established. To investigate the effect of ITS on road congestion, we utilized a unique dataset on traffic and ITS adoption from 99 U.S. urban areas in 2001-2008. The results from fixed-effects estimations show that ITS adoption reduces road congestion, saving an average driver 98 minutes of driving time and $38 per year. We also obtained preliminary evidence that ITS reduces carbon emissions by alleviating road congestion. Our findings extend the emerging IS literature on IT value in the public sector and the societal impacts of IT. Our study also contributes to the transportation economics literature and informs transportation policymakers by showing that ITS could be a cost-effective alternative to tackle road congestion