Access to rideshare and satisfaction, prenatal healthcare utilisation, and preterm delivery among pregnant Medicaid recipients: A randomised controlled trial.

BACKGROUND: Lack of access to reliable transportation is a barrier to timely receipt of prenatal care. OBJECTIVES: We aimed to assess the impact of modernisation of non-emergency medical transportation services on patient satisfaction, prenatal care utilisation, and preterm delivery. METHODS: We conducted a randomised controlled pilot trial among pregnant Medicaid recipients in Franklin County, Ohio, a county with high rates of infant mortality. Individuals were randomly assigned to usual non-emergency medical transportation services or enhanced smart transportation (EST) services (i.e. on-demand transportation with access to a mobile application and trips to the grocery store, food bank or pharmacy). The primary outcome was satisfaction with transportation services. Secondary outcomes included adequacy of prenatal care utilisation (APNCU) and preterm delivery <37 weeks. RESULTS: Women were screened between 31 May 2019 and 30 June 2020, with 143 being eligible and enrolling. Evidence of increased satisfaction with transportation was observed in the intervention group compared to usual transportation, with 83.8% and 68.8% reporting being somewhat satisfied or very satisfied respectively (risk difference [RD] 14.8%, 95% confidence interval [CI] 0.5, 29.1). There were no meaningful differences in APNCU or preterm delivery between groups (APNCU: RD 2.1%, 95% CI -14.0, 18.2 and preterm delivery: RD -3.9%, 95% CI -17.0, 9.3). CONCLUSIONS: We found evidence of increased transportation satisfaction among pregnant women randomly assigned to EST versus usual transportation. It remains unclear whether the provision of EST increases prenatal care utilisation or decreases preterm delivery.

A Multi-level Model to Understand Cervical Cancer Disparities in Appalachia.

The Appalachian region experiences higher incidence and mortality due to cervical cancer compared with other regions of the United States. The goal of the Ohio State University Center for Population Health and Health Disparities (CPHHD), called the Community Awareness Resources and Education (CARE) project, was to understand reasons for this disparity. The first wave (2003-2008) of funding included three projects focusing on the known risk factors for cervical cancer, lack of screening, smoking, and infection with human papillomavirus (HPV). On the basis of the results of these projects, the second wave (2011-2017) included four projects, designed to address a multi-level model of factors contributing to cervical disparities in Appalachia. The results of these projects were then used to refine a multi-level model that explains cervical cancer disparities in Appalachia. Future funded projects will take these multi-level explanations for cervical disparities and focus on implementation science strategies to reduce the burden of cervical cancer morbidity and mortality in Appalachia.See all articles in this Special Collection Honoring Paul F. Engstrom, MD, Champion of Cancer Prevention.

Level of Effort and 3 Hour Rule Compliance.

OBJECTIVE: To determine if patients' level of effort (LOE) in therapy sessions during traumatic brain injury (TBI) rehabilitation modifies the effect of compliance with the 3-Hour Rule of the Centers for Medicare & Medicaid Services. DESIGN: Propensity score methodology applied to the TBI Practice-Based Evidence database, consisting of multisite, prospective, longitudinal observational data. SETTING: Acute inpatient rehabilitation facilities (IRF). PARTICIPANTS: Patients (N=1820) who received their first IRF admission for TBI in the United States and were enrolled for 3- and 9-month follow-up. MAIN OUTCOME MEASURES: Participation Assessment with Recombined Tools-Objective-17, FIM Motor and Cognitive scores, Satisfaction with Life Scale, and Patient Health Questionnaire-9. RESULTS: When the full cohort was examined, no strong main effect of compliance with the 3-Hour Rule was identified and LOE did not modify the effect of compliance with the 3-Hour Rule. In contrast, LOE had a strong positive main effect on all outcomes, except depression. When the sample was stratified by level of disability, LOE modified the effect of compliance, particularly on the outcomes of participants with less severe disability. For these patients, providing 3 hours of therapy for 50% or more of therapy days in the context of low effort resulted in poorer performance on select outcome measures at discharge and up to 9 months postdischarge compared to patients with <50% of 3-hour therapy days. CONCLUSIONS: LOE is an active ingredient in inpatient TBI rehabilitation, while compliance with the 3-Hour Rule was not found to have a substantive effect on the outcomes. The results support matching time in therapy during acute TBI rehabilitation to patients' LOE in order to optimize long-term benefits on outcomes.

Mental Health after Unintentional Injury in a Pediatric Managed-Medicaid Population.

OBJECTIVES: The purpose of this study was to compare the rates of mental health diagnoses and psychotropic prescriptions pre- and posthospitalization for injury in a managed-Medicaid population. We hypothesized that children have increased rates of mental health diagnoses and psychotropic prescriptions after injury. STUDY DESIGN: We investigated children (n = 2208) ≤18 years of age treated at a pediatric trauma center from 2005 to 2015 who were enrolled in a managed-Medicaid program at hospital admission and for at least 1 healthcare visit in the preceding year. We used Poisson regression models to estimate rates of mental health diagnoses and psychotropic prescriptions that occurred in the 12 months pre- and postinjury. RESULTS: The rate of mental health diagnoses preinjury was 95.9 per 1000 person-years, which increased to 156.7 per 1000 postinjury (rate ratio [RR] 1.63, 95% CI 1.39-1.92). Children ages 0-4 years with burns were more likely to have mental health diagnoses postinjury (race and ethnicity adjusted RR [aRR] 8.56, 95% CI 3.30-22.2). Children with head injuries were also more likely to have mental health diagnoses postinjury: ages 0-4 years (aRR 3.87, 95% CI 1.31-11.5); ages 5-9 (aRR 3.11, 95% CI 1.27-7.59); ages 10-14 (aRR 2.17, 95% CI 1.27-3.73); and ages 15-18 (aRR 5.37, 95% CI 2.12-13.6). The rate of psychotropic prescriptions preinjury was 121.7 per 1000 person-years and increased to 310.9 per 1000 postinjury (RR 2.55, 95% CI 2.26-2.89). CONCLUSIONS: We identified increased mental health diagnoses and psychotropic prescriptions in children following hospitalization for injury.

Bias associated with using the estimated propensity score as a regression covariate.

The use of propensity score methods to adjust for selection bias in observational studies has become increasingly popular in public health and medical research. A substantial portion of studies using propensity score adjustment treat the propensity score as a conventional regression predictor. Through a Monte Carlo simulation study, Austin and colleagues. investigated the bias associated with treatment effect estimation when the propensity score is used as a covariate in nonlinear regression models, such as logistic regression and Cox proportional hazards models. We show that the bias exists even in a linear regression model when the estimated propensity score is used and derive the explicit form of the bias. We also conduct an extensive simulation study to compare the performance of such covariate adjustment with propensity score stratification, propensity score matching, inverse probability of treatment weighted method, and nonparametric functional estimation using splines. The simulation scenarios are designed to reflect real data analysis practice. Instead of specifying a known parametric propensity score model, we generate the data by considering various degrees of overlap of the covariate distributions between treated and control groups. Propensity score matching excels when the treated group is contained within a larger control pool, while the model-based adjustment may have an edge when treated and control groups do not have too much overlap. Overall, adjusting for the propensity score through stratification or matching followed by regression or using splines, appears to be a good practical strategy.