A comprehensive assessment of statin discontinuation among patients who concurrently initiate statins and CYP3A4-inhibitor drugs; a multistate transition model.

AIMS: The aim of this study was to describe the 1-year direct and indirect transition probabilities to premature discontinuation of statin therapy after concurrently initiating statins and CYP3A4-inhibitor drugs. METHODS: A retrospective new-user cohort study design was used to identify (N = 160 828) patients who concurrently initiated CYP3A4 inhibitors (diltiazem, ketoconazole, clarithromycin, others) and CYP3A4-metabolized statins (statin DDI exposed, n = 104 774) vs. other statins (unexposed to statin DDI, n = 56 054) from the MarketScan commercial claims database (2012-2017). The statin DDI exposed and unexposed groups were matched (2:1) through propensity score matching techniques. We applied a multistate transition model to compare the 1-year transition probabilities involving four distinct states (start, adverse drug events [ADEs], discontinuation of CYP3A4-inhibitor drugs, and discontinuation of statin therapy) between those exposed to statin DDIs vs. those unexposed. Statistically significant differences were assessed by comparing the 95% confidence intervals (CIs) of probabilities. RESULTS: After concurrently starting stains and CYP3A, patients exposed to statin DDIs, vs. unexposed, were significantly less likely to discontinue statin therapy (71.4% [95% CI: 71.1, 71.6] vs. 73.3% [95% CI: 72.9, 73.6]) but more likely to experience an ADE (3.4% [95% CI: 3.3, 3.5] vs. 3.2% [95% CI: 3.1, 3.3]) and discontinue with CYP3A4-inhibitor therapy (21.0% [95% CI: 20.8, 21.3] vs. 19.5% [95% CI: 19.2, 19.8]). ADEs did not change these associations because those exposed to statin DDIs, vs. unexposed, were still less likely to discontinue statin therapy but more likely to discontinue CYP3A4-inhibitor therapy after experiencing an ADE. CONCLUSION: We did not observe any meaningful clinical differences in the probability of premature statin discontinuation between statin users exposed to statin DDIs and those unexposed.

Optimized environmental justice calculations for air pollution disparities in Southern California.

An Environmental Justice (EJ) analysis was carried out using full Chemical Transport Models (CTMs) over Los Angeles, California, to determine how the combination of domain size and spatial resolution affects predicted air pollution disparities in present day and future simulations when data support from measurements is not available. One set of simulations used the Weather Research and Forecasting (WRF) model coupled with Chemistry (WRF/Chem) with spatial resolution ranging from 250 m to 36 km, comparable to census tract sizes, over domains ranging in size from 320 km2 to 10,000 km2. A second set of simulations used the UCD/CIT CTM with spatial resolution ranging from 4 km to 24 km over domains ranging in size from 98,000 km2 to 1,000,000 km2. Overall WRF/Chem model accuracy improved approximately 9% as spatial resolution increased from 4 km to 250 m in present-day simulations, with similar results expected for future simulations. Exposure disparity results are consistent with previous findings: the average Non-Hispanic White person in the study domain experiences PM2.5 mass concentrations 6-14% lower than the average resident, while the average Black and African American person experiences PM2.5 mass concentrations that are 3-22% higher than the average resident. Predicted exposure disparities were a function of the model configuration. Increasing the spatial resolution finer than approximately 1 km produced diminishing returns because the increased spatial resolution came at the expense of reduced domain size in order to maintain reasonable computational burden. Increasing domain size to capture regional trends, such as wealthier populations living in coastal areas, identified larger exposure disparities but the benefits were limited. CTM configurations that use spatial resolution/domain size of 1 km/103 km2 and 4 km/104 km2 over Los Angeles can detect a 0.5 µg m-3 exposure difference with statistical power greater than 90%. These configurations represent a balanced approach between statistical power, sensitivity across socio-economic groups, and computational burden when predicting current and future air pollution exposure disparities in Los Angeles.

Patient Perspectives on Care Transitions From Hospital to Home.

Importance: Understanding the patient's perspective of their care transition process from hospital or skilled nursing facility (SNF) to home may highlight gaps in care and inform system improvements. Objective: To gather data about patients' care transition experiences and factors associated with follow-up appointment completion. Design, Setting, and Participants: A survey tool was developed with input from patient advisors and organizations participating in a collaborative quality initiative. Seventeen hospitals, 12 practitioner organizations, and 6 SNFs in Michigan collaborated to identify shared patients who were aged 18 years and older, had a working telephone number, recently returned home or to an assisted living facility with a diagnosis of congestive heart failure or chronic obstructive pulmonary disease, or after an SNF stay. Using consecutive sampling, interviewers collected 5 telephone surveys per month. From October 2018 to December 2019, patients or caregivers were surveyed via telephone 8 to 12 days after discharge from a hospital or SNF. Data were analyzed from March 2020 to January 2022. Exposure: Care transition experiences. Main Outcomes and Measures: The primary outcome was to identify patient-perceived gaps during care transition experiences, including postdischarge follow-up. Results: On the basis of pilot data, the response rate was estimated at 34%, yielding 1257 surveys. Of 1257 survey respondents (mean [SD] age, 70 [12.94] years for 968 patients for whom age data was available), 654 (52%) were female; 829 (74%) were White, 250 (22%) were Black or African American, and 40 (4%) were another race. Eleven percent of patients reported not receiving a telephone number to call for postdischarge questions. Nearly 80% of patients (977 patients) received a follow-up telephone call, and most found it valuable. Twenty percent of patients (255 patients) reported at least 1 social determinant of health issue. Lack of transportation was associated with reduced likelihood of completing a follow-up visit, decreasing the odds of completing a follow-up by nearly 70% (odds ratio [OR], 0.31; 95% CI, 0.18-0.53; P < .001). Compared with other patient groups, Black patients were less likely to report completing a postdischarge follow-up visit (OR, 0.49; 95% CI, 0.36-0.67; P < .001) or to receive prescribed medical equipment (OR, 4.23; 95% CI, 1.30-13.83; P = .02). Conclusions and Relevance: An examination of patient discharge experiences from a hospital or SNF identified inconsistencies in care transition processes, social determinants of health issues needing to be addressed after discharge, and racial disparities between patients who attend follow-up appointments. Physicians should be aware of these findings and their consequences for patient experiences.

Adoption of low-carbon fuels reduces race/ethnicity disparities in air pollution exposure in California.

An environmental justice (EJ) analysis shows that adoption of low-carbon energy sources in the year 2050 reduces the race/ethnicity disparity in air pollution exposure in California by as much as 20% for PM2.5 mass and by as much as 40% for PM0.1 mass. An ensemble of six different energy scenarios constructed using the energy-economic optimization model CA-TIMES were evaluated in future years. Criteria pollutant emissions were developed for each energy scenario using the CA-REMARQUE model using 4 km spatial resolution over four major geographic areas in California: the greater San Francisco Bay Area including Sacramento (SFBA&SAC), the San Joaquin Valley (SJV), Los Angeles (LA), and San Diego (SD). The Weather Research & Forecasting (WRF) model was used to predict future meteorology fields by downscaling two different climate scenario (RCP4.5 and RCP8.5) generated by two different GCMs (the Community Climate System Model and the Canadian Earth Systems Model). Simulations were performed over 32 weeks randomly selected during the 10 year window from the year 2046 to 2055 to build up a long-term average in the presence of ENSO variability. The trends associated with low-carbon energy adoption were relatively stable across the ensemble of locations and scenarios. Deeper reductions in the carbon intensity of energy sources progressively reduced exposure to PM2.5 mass and PM0.1 mass for all California residents. The greater adoption of low-carbon fuels also reduced the racial disparity in the PM exposure. The three energy scenarios that achieved an ~80% reduction in GHG emissions relative to 1990 levels simultaneously produced the greatest reduction in PM exposure for all California residents and the greatest reduction in the racial disparity of that exposure. These findings suggest that the adoption of low-carbon energy can improve public health and reduce racial disparities through an improvement in air quality.

Out-of-Network Care in Commercially Insured Pediatric Patients According to Medical Complexity.

BACKGROUND: Commercial health plans establish networks and require much higher cost sharing for out-of-network (OON) care. Yet, the adequacy of health plan networks for access to pediatric specialists, especially for children with medical complexity, is largely unknown. OBJECTIVE: To examine differences in OON care and associated cost-sharing payments for commercially insured children with different levels of medical complexity. DESIGN: Cross-sectional study using a nationwide commercial claims database. SUBJECTS: Enrollees 0-18 years old in employer-sponsored insurance plans. The Pediatric Medical Complexity Algorithm was used to classify individuals into 3 levels of medical complexity: children with no chronic disease, children with non-complex chronic diseases, and children with complex chronic diseases. MAIN OUTCOMES: OON care rates, cost-sharing payments for OON care and in-network care, OON cost sharing as a proportion of total health care spending, and OON cost sharing as a proportion of total cost sharing. RESULTS: The study sample included 6,399,006 individuals with no chronic disease, 1,674,450 with noncomplex chronic diseases, and 603,237 with complex chronic diseases. Children with noncomplex chronic diseases were more likely to encounter OON care by 6.77 percentage points with higher cost-sharing by $288 for OON care, relative to those with no chronic disease. For those with complex chronic diseases, these differences rose to 16.08 percentage points and $599, respectively. Among children who saw behavioral health providers, rates of OON care were especially high. CONCLUSIONS: Commercially insured children with medical complexity experience higher rates of OON care with higher OON cost-sharing payments compared with those with no chronic disease.

Cost-sharing payments for out-of-network care in commercially insured adults.

OBJECTIVES: Providers who do not contract with insurance plans are considered out-of-network (OON) providers. There were 2 objectives in this study: (1) to examine the variations of OON cost sharing, both at the state level and by care settings, and (2) to investigate the pattern of OON care use and cost sharing associated with OON care over time. STUDY DESIGN: Secondary data analysis using claims data of employer-sponsored insurance enrollees. METHODS: The study sample included adults aged 18 to 64 years who were continuously enrolled for at least a full calendar year with medical and prescription drug coverage and for whom OON care payment data were available. We examined levels and distributions of cost sharing for OON care from 2012 to 2017, in both emergency department (ED) and non-ED care settings. Outcome measures included annual use of health plan-covered OON care and total out-of-pocket (OOP) cost sharing for OON care. We also measured the use of and cost-sharing spending for OON care based on urgency and site of service. Logistic regression models were constructed to estimate the probability of OON care. Among those with each type of OON care, a generalized linear regression model was used to estimate the OOP spending on OON care. RESULTS: Slowly decreasing rates of OON care over time occurred in different care settings and at different urgency levels. The cost-sharing amounts for OON care rose rapidly from 2012 through 2016, before slowing slightly in 2017. The growth of cost sharing for OON care during nonemergent hospitalizations especially increased from $671 to $1286 during the study period. The amount enrollees spent on OON care grew in most states, but there were substantial variations. CONCLUSIONS: Cost-sharing payments for OON care represent a growing financial burden for some enrollees. Consumers should be held harmless from higher cost sharing for OON care when it occurs without their knowledge or consent. Further, health plan network adequacy may also merit closer scrutiny. Leveraging provider participation in narrow networks must be balanced with broader consumer protections.

Cost-Sharing Disparities for Out-of-Network Care for Adults With Behavioral Health Conditions.

Importance: Individuals in the United States with mental illnesses and substance use disorders can face major access barriers from limited provider (eg, clinicians and facilities) networks in health insurance plans. Objective: To evaluate the cost-sharing payments for out-of-network (OON) care for private insurance plan enrollees with mental health conditions, alcohol use disorders, or drug use disorders compared with those with congestive heart failure (CHF) or diabetes. Design, Setting, and Participants: This cross-sectional study used data from a large commercial claims database from 2012 to 2017. The study included adults with mental health conditions, with alcohol use disorders, with drug use disorders, with CHF, and with diabetes who were aged 18 to 64 years and enrolled in employer-sponsored insurance plans. Main Outcomes and Measures: Main outcomes included OON care during hospitalization, OON care during outpatient care, cost-sharing payments with OON care, OON cost sharing as a proportion of total health care spending, and OON cost sharing as a proportion of total cost sharing. Results: The study sample included 3 209 929 enrollees with mental health conditions (mean [SD] age, 45.9 [12.6] years; 64.8% women), 294 550 with alcohol use disorders (mean [SD] age, 42.8 [13.4] years; 60.9% men), 321 535 with drug use disorders (mean [SD] age, 41.1 [13.9] years; 59.1% men), 178 701 with CHF (mean [SD] age, 53.8 [8.9] years; 62.6% men), and 1 383 398 with diabetes (mean [SD] age, 52.5 [9.0] years; 58.9% men). Enrollees with behavioral conditions were more likely to encounter OON clinicians in inpatient and outpatient settings. For instance, those with drug use disorders were 12.9 percentage points (95% CI, 12.5-13.2 percentage points; P < .001) more likely to have inpatient OON care than those with CHF and 15.3 percentage points (95% CI, 15.1-15.6 percentage points; P < .001) more likely to receive outpatient OON care. Behavioral conditions also had higher cost sharing for OON care. For example, individuals with mental health conditions had cost-sharing payments for OON care $341 (95% CI, $331-$351) higher than those with diabetes (P < .001), individuals with drug use disorders had cost-sharing payments for OON care $1242 (95% CI, $1209-$1276) higher than those with diabetes (P < .001), and individuals with alcohol use disorders had cost-sharing payments for OON care $1138 (95% CI, $1101-$1174) higher than those with diabetes (P < .001). The OON care rates and cost-sharing payments were much higher when enrollees sought care from behavioral clinicians and facilities. Conclusions and Relevance: In this cross-sectional study of enrollees in commercial insurance plans, cost sharing for OON care among those with behavioral health conditions was significantly higher than those with chronic physical conditions. These disparities may be indicative of limited in-network availability for behavioral health care.

Income-Based Disparities in Financial Burdens of Medical Spending Under the Affordable Care Act in Families With Individuals Having Chronic Conditions.

This study examined income-based disparities in financial burdens from out-of-pocket (OOP) medical spending among individuals with multiple chronic physical and behavioral conditions, before and after the Affordable Care Act's (ACA) implementation in 2014. Using the 2012-2015 Medical Expenditure Panel Survey data, we studied changes in financial burdens experienced by nonelderly U.S. populations. Financial burdens were measured by (1) high financial burden, defined as total OOP medical spending exceeding 10% of annual household income; (2) health care cost-sharing ratio, defined as self-paid payments as a percent of total health care payments, excluding individual contributions to premiums; and (3) the total OOP costs spent on health care utilization. The findings indicated reductions in the proportion of those who experienced a high financial burden, as well as reductions in the OOP costs for some individuals. However, individuals with incomes below 138% federal poverty level (FPL) and those with incomes between 251% and 400% FPL who had multiple physical and/or behavioral chronic conditions experienced large increases in high financial burden after the ACA, relative to those with incomes greater than 400% FPL. While the ACA was associated with relieved medical financial burdens for some individuals, the worsening high financial burden for moderate-income individuals with chronic physical and behavioral conditions is a concern. Policymakers should revisit the cost subsidies for these individuals, with a particular focus on those with chronic conditions.