Application of the Policy Regime Framework to understand COVID-19 policy response in the Southeast U.S.: How RAPID research can provide lessons learned after a public health crisis.

Quick-response research during a time of crisis is important because time-sensitive findings can inform urgent decision-making, even with limited research budgets. This research, a National Science Foundation-funded Rapid Response Research (RAPID), explores the United States (U.S.) government's messaging on science in response to the COVID-19 pandemic, and how this messaging informed policy. Using rapidly emerging secondary data (e.g., policy documents taken from government websites and others), much of which has since been removed or changed, we examined the interactions between governing bodies, non-governmental organizations, and civilian populations in the Southeastern U.S. during the first 2 years of the pandemic. This research helps to better understand how decision-makers at the federal, state, and local levels responded to the pandemic in three states with the lowest vaccine rates and highest levels of poverty, income inequality, and disproportionate impacts borne by people of color in the nation: Alabama, Louisiana, and Mississippi. This study incorporates the Policy Regime Framework to discuss how two foundational concepts (ideas and institutions) helped govern policy implementation during the COVID-19 pandemic. This research fills a significant information gap by providing a better understanding of how policy regimes emerge across multiple levels of government and impact vulnerable populations during times of a public health crisis. We use automated text analysis to make sense of a large quantity of textual data from policy-making agencies. Our case study is the first to use the Policy Regime Framework in conjunction with empirical data, as it emerged, from federal, state, and local governments to analyze the U.S. policy response to COVID-19. We found the U.S. policy response included two distinct messaging periods in the U.S. during the COVID-19 pandemic: pre and post-vaccine. Many messaging data sources (agency websites, public service announcements, etc). have since been changed since we collected them, thus our real-time RAPID research enabled an accurate snapshot of a policy response in a crisis. We also found that there were significant differences in the ways that federal, state, and local governments approached communicating complex ideas to the public in each period. Thus, our RAPID research demonstrates how significant policy regimes are enacted and how messaging from these regimes can impact vulnerable populations.

The Rise in Pediatric Obesity-Related Conditions and Costs in Public Insurance Programs: Evidence from Alabama.

Background: The increase in pediatric obesity rates is well documented. The extent of corresponding increases in diagnoses of obesity-related conditions (Ob-Cs) and associated medical costs for children in public insurance programs is unknown. Methods: Retrospective claims data linked to enrollees' demographic data for Alabama's Children's Health Insurance Program (ALL Kids) 1999-2015 were used. Multivariate linear probability models were used to estimate the likelihood of having any Ob-C diagnoses. Two-part models for inpatient, outpatient, emergency department (ED), and overall costs were estimated. Results: The proportion of enrollees with Ob-C diagnoses almost doubled from 1.3% to 2.5%. The likelihood of diagnoses increased over time (0.0994 percentage points per year, p < 0.001). Statistically higher rates of increase were seen for minority and lowest-income enrollees and for those getting preventive well visits. Costs for those with Ob-Cs increased relative to those without over time, particularly inpatient and outpatient costs. Conclusions: Prevalence of Ob-C diagnoses and costs have increased substantially. This may partly be because of underdiagnoses/underreporting in the past. However, evidence suggests that underdiagnoses are still a major issue.

Assessing the Quality Measure for Follow-up Care After Children's Psychiatric Hospitalizations.

OBJECTIVES: Medicaid and Children's Health Insurance Program plans publicly report quality measures, including follow-up care after psychiatric hospitalization. We aimed to understand failure to meet this measure, including measurement definitions and enrollee characteristics, while investigating how follow-up affects subsequent psychiatric hospitalizations and emergency department (ED) visits. METHODS: Administrative data representing Alabama's Children's Health Insurance Program from 2013 to 2016 were used to identify qualifying psychiatric hospitalizations and follow-up care with a mental health provider within 7 to 30 days of discharge. Using relaxed measure definitions, follow-up care was extended to include visits at 45 to 60 days and visits to a primary care provider. Logit regressions estimated enrollee characteristics associated with follow-up care and, separately, the likelihood of subsequent psychiatric hospitalizations and/or ED visits within 30, 60, and 120 days. RESULTS: We observed 1072 psychiatric hospitalizations during the study period. Of these, 356 (33.2%) received follow-up within 7 days and 566 (52.8%) received it within 30 days. Relaxed measure definitions captured minimal additional follow-up visits. The likelihood of follow-up was lower for both 7 days (-18 percentage points; 95% confidence interval [CI] -26 to -10 percentage points) and 30 days (-26 percentage points; 95% CI -35 to -17 percentage points) regarding hospitalization stays of ≥8 days. Meeting the measure reduced the likelihood of subsequent psychiatric hospitalizations within 60 days by 3 percentage points (95% CI -6 to -1 percentage point). CONCLUSIONS: Among children, receipt of timely follow-up care after a psychiatric hospitalization is low and not sensitive to measurement definitions. Follow-up care may reduce the need for future psychiatric hospitalizations and/or ED visits.

Promoting Continuous Quality Improvement in the Alabama Child Health Improvement Alliance Through Q-Sort Methodology and Learning Collaboratives.

OBJECTIVE: Q-sort methodology is an underutilized tool for differentiating among multiple priority measures. The authors describe steps to identify, delimit, and sort potential health measures and use selected priority measures to establish an overall agenda for continuous quality improvement (CQI) activities within learning collaboratives. METHODS: Through an iterative process, the authors vetted a list of potential child and adolescent health measures. Multiple stakeholders, including payers, direct care providers, and organizational representatives sorted and prioritized measures, using Q-methodology. RESULTS: Q-methodology provided the Alabama Child Health Improvement Alliance (ACHIA) an objective and rigorous approach to system improvement. Selected priority measures were used to design learning collaboratives. An open dialogue among stakeholders about state health priorities spurred greater organizational buy-in for ACHIA and increased its credibility as a statewide provider of learning collaboratives. CONCLUSIONS: The integrated processes of Q-sort methodology, learning collaboratives, and CQI offer a practical yet innovative way to identify and prioritize state measures for child and adolescent health and establish a learning agenda for targeted quality improvement activities.

Planned cesarean delivery compared to induction of labor in women with class III obesity: a cost-minimization analysis (.).

OBJECTIVE: To compare the costs between planned cesarean delivery (CD) and induction of labor (IOL) in women with class III obesity. METHODS: We conducted a cost-minimization analysis using data from a previous study of women with a BMI ≥40 kg/m(2) delivering term singletons via planned CD or after IOL. Decision trees were built with branches for two "treatments": planned CD and IOL (probabilities derived from parent study). Direct and total costs were obtained for each mother-infant pair. Sensitivity analyses for probability of successful IOL were performed. RESULTS: A total of 661 mother-infant pairs were included - 399 IOLs and 262 planned CDs. Of 399 IOLs, 236 (59%) delivered vaginally and 163 (41%) had a CD. IOL was slightly less costly. For IOL and planned CD, respectively, direct costs were $7416 versus $7474, and total costs were $11 545 versus $11 665. Sensitivity analyses indicated that IOL was the least costly strategy if the probability of vaginal delivery after IOL was >57% (0.575 for direct costs; 0.570 for total costs). There was, however, a slight cost advantage to planned CD in women with a prior cesarean. CONCLUSIONS: In women with class III obesity, IOL is less costly than planned CD if the probability of vaginal delivery after IOL in an individual patient is greater than 57%.

Obesity, Physical Activity and Sedentary Behaviors in Children with an Autism Spectrum Disorder.

BACKGROUND AND OBJECTIVES: Previous literature using small sample sizes and limited geographic areas report that overweight/obesity and physical inactivity occur at higher rates among children with autism spectrum disorder (ASD) compared to typically developing peers. The purpose of this study was to examine obesity, overweight, physical activity, and sedentary behavior among children and youth with and without ASD using nationally representative data and controlling for secondary conditions, including intellectual and learning disabilities, ADHD, developmental delay, and other mental, physical, and medical conditions, as well as medication use. METHODS: Data were collected from the 2011-2012 National Survey of Children's Health, a cross-sectional survey of 65,680 (weighted N = 49,586,134) children aged 6-17 (1385 with ASD, weighted N = 986,352). Logistic regression was used to estimate odds ratios, adjusting for demographics and possible secondary conditions. RESULTS: Having a diagnosis of ASD was associated with higher odds of obesity (OR 1.76, CI 1.27-2.43; p = <0.001). However, after additional adjustment for possible secondary conditions, ASD diagnosis was no longer associated with obesity. Those with moderate ASD (OR 0.58, CI 0.36-0.93; p = <0.05) reported lower odds of sedentary behavior, but this association failed to achieve significance after adjustment for secondary conditions and medication use. No significant associations between ASD and overweight or physical activity were found. CONCLUSIONS: These findings suggest that ASD diagnosis is not significantly associated with obesity status after adjustment for possible secondary conditions and medication use. Decision makers, clinicians, and researchers developing interventions for children with ASDs should consider how secondary conditions may impact obesity and related activities.