Health Outcome Changes in Individuals With Type 1 Diabetes After a State-Level Insulin Copayment Cap.

Importance: Many insulin users ration doses due to high out-of-pocket costs. Starting January 2020 with Colorado, 25 states and the District of Columbia enacted laws that cap insulin copayments. Objective: To estimate the association of Colorado's $100 copayment cap with out-of-pocket spending, medication adherence, and health care services utilization for diabetes-related complications. Design, Setting, and Participants: In this cohort study using Colorado's All-Payer Claims Database, nonelderly insulin users with type 1 diabetes were analyzed from January 2019 to December 2020. Outcome changes were compared in the prepolicy and postpolicy period among individuals continuously enrolled in state-regulated and non-state-regulated plans using difference-in-differences regressions. Subgroup analyses were conducted based on individuals' prepolicy spending (low: never ≥$100 out-of-pocket vs high: ≥$100 out-of-pocket cost at least once). Data were analyzed from June 2023 to May 2024. Exposure: Enrollment in state-regulated health insurance plans subject to the copayment cap legislation. Main Outcomes and Measures: Adherence to basal and bolus insulin treatment was evaluated using the proportion of days covered measure, out-of-pocket spending reflected prescription cost for a 30-day supply, and health care utilization for diabetes-related complications was identified using primary diagnosis codes from medical claims data. Results: The panel included 1629 individuals with type 1 diabetes (39 096 person-months), of which 924 were male (56.7%), 540 (33.1%) had 1 or more comorbidities, and the mean (SD) age was 40.6 (15.9) years. Overall, the copayment cap was associated with out-of-pocket spending declines of $17.3 (95% CI, -$27.3 to -$7.3) for basal and $11.5 (95% CI, -$24.7 to $1.7) for bolus insulins and increases in adherence of 3.2 (95% CI, 0.0 to 6.5) percentage points for basal and 3.3 (95% CI, 0.3 to 6.4) percentage points for bolus insulins. Changes in adherence were associated with increases within the prepolicy high-spending group (basal, 9.9; 95% CI, 2.4 to 17.4 percentage points; bolus, 13.0; 95% CI, 5.1 to 20.9 percentage points). The policy was also associated with a mean reduction of -0.09 (95% CI, -0.16 to -0.02) medical claims for diabetes-related complications per person per month among high spenders, a 30% decrease. Conclusions and Relevance: In this cohort study of Colorado's insulin copayment cap among individuals with type 1 diabetes, the policy was associated with an overall decline in out-of-pocket spending, an increase in medication adherence, and a decline in claims for diabetes-related complications only among insulin users who spent more than $100 in the prepolicy period at least once.

Colorado Insulin Copay Cap: Lower Out-Of-Pocket Payments, Increased Prescription Volume And Days' Supply.

In 2020, Colorado became the first state to cap out-of-pocket spending for insulin prescriptions, requiring fully insured health plans to cap out-of-pocket spending at $100 for a thirty-day supply. We provide the first evidence on the association of Colorado's Insulin Affordability Program with patient out-of-pocket spending, the amounts paid by plans per insulin prescription, and prescription filling. Using statewide claims data from the period 2018-21, we focused on the first two years that the copay cap law was in effect. We found that Colorado's Insulin Affordability Program was associated with significant reductions in out-of-pocket spending for insulin prescriptions, with the mean out-of-pocket payment per thirty-day supply falling nearly in half (from $62.59 to $35.64). Average plan payments increased slightly more ($31.39) than the decrease in out-of-pocket spending, as the total amount paid per prescription increased by about 1 percent. The average insulin user realized annual savings of $184, while the mean number of fills and the mean days' supply per year increased by 4.2 percent and 11.4 percent, respectively.

Effects of Affordable Care Act on uninsured hospitalization: Evidence from Texas.

OBJECTIVE: To examine the impact of the Affordable Care Act (ACA) health insurance exchanges (Marketplace) on the rate of uninsured discharges in Texas. DATA SOURCE AND STUDY SETTING: Secondary discharge data from 2011 to 2019 from Texas. STUDY DESIGN: We conducted a retrospective study estimating the effects of the ACA Marketplace using difference-in-difference regressions, with the main outcome being the uninsured discharge rate. We stratified our sample by patient's race, age, gender, urbanicity, major diagnostic categories (MDC), and emergent type of admissions. DATA COLLECTION/EXTRACTION METHODS: We used Texas hospital discharge records for non-elderly adults collected by the state of Texas and included acute care hospitals who reported data from 2011 to 2019. PRINCIPAL FINDINGS: The expansion of insurance through ACA Marketplaces led to reductions in the uninsured discharge rate by 9.9% (95% CI, -17.5%, -2.3%) relative to the baseline mean. The effects of the ACA were felt strongest in counties with any share of Hispanic, in counties with a larger population of Black, and other racial groups, in counties with a significant share of female and older age individuals, in counties considered to be urban, in high-volume diagnoses, and emergent type of admissions. CONCLUSIONS: These findings indicate that the ACA facilitated a shift in hospital payor mix from uninsured to insured.

Geographic disparities in telemedicine mental health use by applying three way ANOVA on Medicaid claims population data.

BACKGROUND: Utilization of telemedicine care for vulnerable and low income populations, especially individuals with mental health conditions, is not well understood. The goal is to describe the utilization and regional disparities of telehealth care by mental health status in Texas. Texas Medicaid claims data were analyzed from September 1, 2012, to August 31, 2018 for Medicaid patients enrolled due to a disability. METHODS: We analyzed the growth in telemedicine care based on urban, suburban, and rural, and mental health status. We used t-tests to test for differences in sociodemographic characteristics across patients and performed a three-way Analyses of Variance (ANOVA) to evaluate whether the growth rates from 2013 to 2018 were different based on geography and patient type. We then estimated patient level multivariable ordinary least square regression models to estimate the relationship between the use of telemedicine and patient characteristics in 2013 and separately in 2018. Outcome was a binary variable of telemedicine use or not. Independent variables of interest include geography, age, gender, race, ethnicity, plan type, Medicare eligibility, diagnosed mental health condition, and ECI score. RESULTS: Overall, Medicaid patients with a telemedicine visit grew at 81%, with rural patients growing the fastest (181%). Patients with a telemedicine visit for a mental health condition grew by 77%. Telemedicine patients with mental health diagnoses tended to have 2 to 3 more visits per year compared to non-telemedicine patients with mental health diagnoses. In 2013, multivariable regressions display that urban and suburban patients, those that had a mental health diagnosis were more likely to use telemedicine, while patients that were younger, women, Hispanics, and those dual eligible were less likely to use telemedicine. By 2018, urban and suburban patients were less likely to use telemedicine. CONCLUSIONS: Growth in telemedicine care was strong in urban and rural areas between 2013 and 2018 even before the COVID-19 pandemic. Those with a mental health condition who received telemedicine care had a higher number of total mental health visits compared to those without telemedicine care. These findings hold across all geographic groups and suggest that mental health telemedicine visits did not substitute for face-to-face mental health visits.

Who do freestanding emergency departments treat? Comparing Texas hospitals to satellite and independent freestanding departments in 2021 and 2022.

OBJECTIVE: The objective was to describe characteristics of emergency department visits to Texas satellite and independent freestanding emergency departments (FrEDs) relative to hospital emergency departments (EDs). DATA SOURCES AND STUDY SETTING: The study used all 2021-2022 hospital and FrED discharges from the publicly available Texas Emergency Department Public Use Data Files (PUDF). STUDY DESIGN: We conducted a descriptive analysis, comparing patient and visit characteristics at satellite and independent FrEDs and hospital EDs using chi-square tests. We characterized the top 20 diagnoses and procedures ranked by volume, treatment intensity, and potentially avoidable ED use. DATA COLLECTION/EXTRACTION METHODS: Discharge data from 2021 to 2022 were combined for the analysis, and ED data at critical access hospitals were excluded. PRINCIPAL FINDINGS: Our sample consisted of 21,605,421 ED visits, 76% occurring at hospitals, 12% at satellite FrEDs, and 12% at independent FrEDs. Compared with hospitals and satellite FrEDs, patients to independent FrEDs were younger, healthier, more likely covered by private insurance, and less likely to be identified as non-Hispanic Black or Hispanic. Visits at satellite and independent FrEDs were more likely to be of moderate and low intensity and potentially avoidable. CONCLUSIONS: Our results underscore the need to address potentially avoidable utilization of emergency services.

Concordance in Medical Urgency Classification of Discharge Diagnoses and Reasons for Visit.

Importance: Current policies to divert emergency department (ED) visits for less medically urgent conditions to more cost-effective settings rely on retrospective adjudication of discharge diagnoses. However, patients present to the ED with concerns, making it challenging for clinicians. Objective: To characterize ED visits based on the medical urgency of the presenting reasons for visit and to explore the concordance between discharge diagnoses and reasons for visit. Design, Setting, and Participants: In this retrospective, cross-sectional study, a nationwide sample of ED visits by adults (aged ≥18 years) in the US from the 2018 and 2019 calendar years' ED data of the National Hospital Ambulatory Medical Care Survey was used. An algorithm to probabilistically assign ED visits into medical urgency categories based on the presenting reason for visit was developed. A 3-step, look-back method was applied using an updated version of the New York University ED algorithm, and a map of all possible discharge diagnoses to the same reasons for visit was developed. Analyses were conducted in July and August 2023. Main Outcomes and Measures: The main outcome was probabilistic medical urgency classification of reasons for visits and discharge diagnoses and their concordance. Results: We analyzed 27 068 ED visits (mean age, 48.2% years [95% CI, 47.5%-48.9% years]) representing 190.7 million visits nationwide. Women (mean, 57.0% [95% CI, 55.9%-58.1%]) and patients with public health insurance coverage, including Medicare (mean, 24.9% [95% CI, 21.9%-28.0%]) and Medicaid (mean, 25.1% [95% CI, 21.0%-29.2%]), accounted for the largest share of ED visits, and a mean of 13.2% (95% CI, 11.4%-15.0%) of all visits resulted in a hospital admission. Overall, about 38.5% and 53.9% of all ED visits were classified with 100% and 75% probabilities, respectively, as injury related, emergency care needed, emergent but primary care treatable, nonemergent, or mental health or substance use disorders related based on discharge diagnosis compared with 0.4% and 12.4%, respectively, of all encounters based on patients' reason for visit. Among discharge diagnoses assigned with high certainty to only 1 urgency category using the New York University ED algorithm, between 38.0% (95% CI, 36.3%-39.6%) and 57.4% (95% CI, 56.0%-58.8%) aligned with the probabilistic categorical assignments of their corresponding reasons for visit. Conclusions and Relevance: In this cross-sectional study of 190.7 million ED visits among adults aged 18 years or older, a smaller percentage of reasons for visit could be prospectively categorized with high accuracy to a specific medical urgency category compared with all visits based on discharge diagnoses, and a limited concordance between reasons for visit and discharge diagnoses was found. Alternative methods are needed to identify the medical necessity of ED encounters more accurately.

Determinants of Persistent, Multi-Year, Frequent Emergency Department Use Among Children and Young Adults in Three US States.

OBJECTIVE: This study examines the factors associated with persistent, multi-year, and frequent emergency department (ED) use among children and young adults. METHODS: We conducted a retrospective secondary analysis using the 2012-2017 Healthcare Cost and Utilization Project State Emergency Department Databases for children and young adults aged 0-19 who visited any ED in Florida, Massachusetts, and New York. We estimated the association between persistent frequent ED use and individuals' characteristics using multivariable logistic regression models. RESULTS: Among 1.3 million patients with 1.8 million ED visits in 2012, 2.9% (37,558) exhibited frequent ED use (≥4 visits in 2012) and accounted for 10.2% (181,138) of all ED visits. Longitudinal follow-up of frequent ED users indicated that 15.4% (5770) remained frequent users periodically over the next 1 or 2 years, while 2.2% (831) exhibited persistent frequent use over the next 3-5 years. Over the 6-year study period, persistent frequent users had 31,551 ED visits at an average of 38.0 (standard deviation = 16.2) visits. Persistent frequent ED use was associated with higher intensity of ED use in 2012, public health insurance coverage, inconsistent health insurance coverage over time, residence in non-metropolitan and lower-income areas, multimorbidity, and more ED visits for less medically urgent conditions. CONCLUSIONS: Clinicians and policymakers should consider the diverse characteristics and needs of pediatric persistent frequent ED users compared to broader definitions of frequent users when designing and implementing interventions to improve health outcomes and contain ED visit costs.

Expiration of State Licensure Waivers and Out-of-State Telemedicine Relationships.

This cross-sectional study compares the use of telemedicine in states where COVID-19 pandemic­related licensure waivers expired vs states where waivers continued.

Merging Rural And Urban ACA Rating Areas Improved Choice, Premiums In Rural Texas.

Rural consumers often face a limited choice of carriers and plans and high premiums. To mitigate this issue, Texas recently adjusted its Affordable Care Act Marketplace rating areas to integrate rural areas into nearby urban markets for rating purposes. We found that rural consumers subsequently saw increases in carrier and plan choices, as well as decreases in overall plan premiums.

Association of the affordable care act with racial and ethnic disparities in uninsured emergency department utilization.

BACKGROUND: Disparities in uninsured emergency department (ED) use are well documented. However, a comprehensive analysis evaluating how the Affordable Care Act (ACA) may have reduced racial and ethnic disparities is lacking. The goal was to assess the association of the ACA with racial and ethnic disparities in uninsured ED use. METHODS: This study used data from the Healthcare Cost and Utilization Project (HCUP) State Emergency Department Databases (SEDD) for Georgia, Florida, Massachusetts, and New York from 2011 to 2017. Participants include non-elderly adults between 18 and 64 years old. Outcomes include uninsured rates of ED visits by racial and ethnic groups and stratified by medical urgency using the New York University ED algorithm. Visits were aggregated to year-quarter ED visits per 100,000 population and stratified for non-Hispanic White, non-Hispanic Black, and Hispanic non-elderly adults. Quasi-experimental difference-in-differences and triple differences regression analyses to identify the effect of the ACA and the separate effect of the Medicaid expansion were used comparing uninsured ED visits by race and ethnicity groups pre-post ACA. RESULTS: The ACA was associated with a 14% reduction in the rate of uninsured ED visits per 100,000 population (from 10,258 pre-ACA to 8,877 ED visits per 100,000 population post-ACA) overall. The non-Hispanic Black compared to non-Hispanic White disparity decreased by 12.4% (-275.1 ED visits per 100,000) post-ACA. About 60% of the decline in the Black-White disparity was attributed to disproportionate declines in ED visit rates for conditions classified as not-emergent (-93.2 ED visits per 100,000), and primary care treatable/preventable (-64.1 ED visits per 100,000), while the disparity in ED visit rates for injuries and not preventable conditions also declined (-106.57 ED visits per 100,000). All reductions in disparities were driven by the Medicaid expansion. No significant decrease in Hispanic-White disparity was observed. CONCLUSIONS: The ACA was associated with fewer uninsured ED visits and reduced the Black-White ED disparity, driven mostly by a reduction in less emergent ED visits after the ACA in Medicaid expansion states. Disparities between Hispanic and non-Hispanic White adults did not decline after the ACA. Despite the positive momentum of declining disparities in uninsured ED visits, disparities, especially among Black people, remain.

Factors associated with persistent multiyear frequent emergency department use.

BACKGROUND: Although frequent emergency department (ED) users have been widely studied in cross-sectional settings, there is some evidence suggesting that most frequent ED users do not remain frequent users over multiple consecutive years. The objective of this study was to explore the association between persistent multiyear frequent ED use and individuals' characteristics. METHODS: A retrospective analysis using the Healthcare Cost and Utilization Project State Emergency Department Databases (2012-2017) for individuals aged 18-59 who visited any ED in Florida, Massachusetts and New York was conducted. Multivariable regression models were used to estimate the association between persistent frequent ED use over time (≥4 ED visits in each data year) and individuals' characteristics and clinical factors compared with non-persistent frequent users (≥4 ED visits only in the baseline year). RESULTS: The databases for the three states included 3.3 million patients, who accounted for 4.5 million ED visits in the baseline year (2012). Of those, 3.2% of patients were frequent ED users (≥4 visits) accounting for 13.2% of all ED visits in the baseline year. Longitudinal follow-up revealed that 14.9% (15 617) of frequent users in 2012 remained persistently frequent ED users for 2-3 consecutive years and 3.6% (3774) for 4-6 consecutive years. Persistent frequent ED users differed significantly from non-persistent frequent ED users; they had more ED visits in the index year, were more likely to have no health insurance or public health insurance coverage, and had a higher prevalence of chronic conditions and comorbidities, and more ED visits for less medically urgent conditions. CONCLUSION: Differences exist between persistent and non-persistent frequent ED users that should be considered when implementing interventions designed to improve health outcomes and curtail healthcare expenditures generated by the broad population of frequent ED users.

Adult Cancer-Related Emergency Department Utilization: An Analysis of Trends and Outcomes From Emergency Departments in Maryland and New York.

PURPOSE: To explore emergency department (ED) visits by adults with cancer and to estimate associations between inpatient admissions through the ED and mortality with sociodemographic and clinical factors within this cohort. METHODS: We conducted a retrospective, pooled, cross-sectional analysis of the Healthcare Cost and Utilization State Emergency Department Databases and State Inpatient Databases for Maryland and New York from January 2013 to December 2017. We examined inpatient admissions through the ED and mortality using frequencies. Among patients with cancer, multivariable regressions were used to estimate sociodemographic and clinical factors associated with inpatient admissions and outpatient ED and inpatient mortality overall. RESULTS: Among 22.7 million adult ED users, 1.3 million (5.7%) had at least one cancer-related diagnosis. ED visit rates per 100,000 population increased annually throughout the study period for patients with cancer and were 9.9% higher in 2017 compared with 2013 (2013: 303.5; 2017: 333.6). Having at least one inpatient admission (68.7% v 20.5%; P < .001) and inpatient or ED mortality (6.5% v 1.0%; P < .001) were higher among ED users with cancer compared with those without. Among patients with cancer, being uninsured (adjusted odds ratio, 0.52; 95% CI, 0.44 to 0.62) compared with having Medicare coverage and non-Hispanic Black (adjusted odds ratio, 0.86; 95% CI, 0.80 to 0.92) compared with non-Hispanic White were associated with decreased odds of inpatient admissions. In contrast, patients with cancer without health insurance, non-Hispanic Black patients, and residents of nonlarge metropolitan areas and of areas with lower household incomes had increased odds of mortality. CONCLUSION: High inpatient admissions through the ED and mortality among adult patients with cancer, coupled with an increase in cancer-related ED visit rates and observed disparities in outcomes, highlight the need to improve access to oncologic services to contain ED use and improve care for patients with cancer.

On the impact of predictive analytics-driven disease management interventions.

OBJECTIVES: To evaluate the effect of a predictive algorithm-driven disease management (DM) outreach program compared with non-predictive algorithm-driven DM program participation on health care spending and utilization. STUDY DESIGN: We used propensity score matching forMedicare Advantage members with chronic heart failure (CHF) to evaluate the impact of predictive algorithm-driven DM outreach using claims data from 2013 to 2018 from a large commercial health insurer. METHODS: The insurer ran a predictive algorithm to identify members with CHF with a high likelihood of hospitalization (LOH), and a DM outreach was initiated to those identified as being at high risk of hospitalization (high-LOH intervention group). The intervention group was matched to members with similar concurrent medical risk profiles, based on the DxCG/Verisk score, who received the same DM outreach through the insurer's standard process (low-LOH intervention group). This approach allowed an evaluation of the predictive algorithm in targeting individuals suitable for DM outreach. RESULTS: Regression models showed that high-LOH intervention members had a lower probability of hospitalization (0.032; P = .075) and emergency department (ED) visit (0.039; P = .043) in the year after the outreach compared with low-LOH intervention members, leading to lower total outpatient spending ($1517; P < .001). Analyses for no-intervention members showed that predictive outreach members would have been expected to have higher inpatient and ED utilization and higher medical spending compared with the traditional care members. CONCLUSIONS: A prediction-driven DM outreach program among patients with CHF was effective in reducing medical spending in the year after the outreach compared with traditional DM outreach programs.

Association of Medicaid Expansion With Emergency Department Visits by Medical Urgency.

Importance: Relatively little is known about the association of the Medicaid eligibility expansion under the Patient Protection and Affordable Care Act with emergency department (ED) visits categorized by medical urgency. Objective: To estimate the association between state Medicaid expansions and ED visits by the urgency of presenting conditions. Design, Setting, and Participants: This cross-sectional study used the Healthcare Cost and Utilization Project State Emergency Department Databases from January 2011 to December 2017 for 2 states that expanded Medicaid in 2014 (New York and Massachusetts) and 2 states that did not (Florida and Georgia). Difference-in-differences regression models were used to estimate the changes in ED visits overall and further stratified by the urgency of the conditions using an updated version of the New York University ED algorithm between the states that expanded Medicaid and those that did not, before and after the expansion. Data were analyzed between June 7 and December 12, 2021. Exposure: State-level Medicaid eligibility expansion. Main Outcomes and Measures: Emergency department visits per 1000 population overall and stratified by medical urgency of the conditions. Results: In total, 80.6 million ED visits by 26.0 million individuals were analyzed. Emergency department visits were concentrated among women (59.3%), non-Hispanic Black individuals (28.3%), non-Hispanic White individuals (47.8%), and those aged 18 to 34 years (47.5%) and 35 to 44 years (20.4%). The rates of ED visits increased by a mean of 2.4 visits in nonexpansion states and decreased by a mean of 2.2 visits in expansion states after 2014, resulting in a significant regression-adjusted decrease of 4.7 visits per 1000 population (95% CI, -7.7 to -1.5; P = .003) in expansion states. Most of this decrease was associated with decreases in ED visits by conditions classified as not emergent (-1.5 visits; 95% CI, -2.4 to -0.7; P < .001), primary care treatable (-1.1 visits; 95% CI, -1.6 to -0.5; P < .001), and potentially preventable (-0.3 visits; 95% CI, -0.5 to -0.1; P = .02). No significant changes were observed for ED visits related to injuries and conditions classified as not preventable (-1.4; 95% CI, -3.1 to 0.3; P = .10), as well as for substance use and mental health disorders (0.0; 95% CI, -0.2 to 0.2; P = .94). Conclusions and Relevance: The findings of this study suggest that Medicaid expansion was associated with decreases in ED visits, for which decreases in ED visits for less medically emergent ED conditions may have been a factor.

Disparities in health care use among low-salary and high-salary employees.

OBJECTIVES: To examine how health care utilization and spending vary for low-income employees compared with high-income employees enrolled in an employer-sponsored high-deductible health plan (HDHP). STUDY DESIGN: We use commercial medical claims data and administrative human resource data from a large employer between 2014 and 2018. We link the administrative data, which include details on salary and other benefit choices, to each employee in each year with medical claims. Our variables of interest include medical spending and utilization outcomes grouped into different care settings. METHODS: Using multivariate regressions, we estimate the association between salary buckets and health care utilization and spending, controlling for demographic characteristics, comorbidities of employees, human resource health plan benefits, and geography. RESULTS: Employees earning less than $75,000 show lower rates of utilization and spending on preventive measures, such as outpatient visits and prescription drugs, while having higher rates of utilization of preventable and avoidable emergency department visits and inpatient stays, resulting in lower overall health care spending among lower-salary employees. CONCLUSIONS: Low-salary employees enrolled in HDHPs have higher rates of acute care utilization and spending but lower rates of primary care spending compared with high-salary employees. Results suggest that HDHPs discourage routine physician-patient care among low-salary employees.

Trends in Disenrollment and Reenrollment Within US Commercial Health Insurance Plans, 2006-2018.

Importance: The commercial health insurance market is characterized by consistently high enrollee turnover. Turnover can disrupt care continuity for patients and create challenges for insurers in managing the health of their enrollee populations. Yet the extent to which enrollees reenroll is not widely known. Objective: To characterize rates of disenrollment (hereafter, external turnover) and reenrollment in commercial health plans. Design, Setting, and Participants: In this retrospective longitudinal cohort study, trends in turnover and reenrollment in commercial health plans between January 1, 2006, and August 31, 2018, were analyzed. Data analysis was conducted from January 21, 2020, through December 23, 2021. Participants included 3 018 633 primary members and their dependents with employer-sponsored coverage. Main Outcomes and Measures: Primary outcomes included external turnover from commercial coverage and subsequent reenrollment into any line of business with the insurer (commercial, Medicaid Managed Care, and Medicare Advantage). Within commercial coverage, external turnover was analyzed separately for group (ie, employer-sponsored) and individual markets. Results: In the sample of 3 018 633 members, 50.2% were men; mean (SD) age, including dependents, was 30.68 (19.05) years. A total of 2.2% of members experienced external turnover each month and 21.5% experienced external turnover each year. The individual market had the highest average monthly turnover rate of 3.4% compared with 2.1% in the group market. December had the highest rate of external turnover, with 13.8% experiencing external turnover in the individual market and 6.9% of members experiencing external turnover in the group market. Fourteen percent of the members who left the insurer from an individual plan reenrolled with the insurer after 1 year, and 34% had reenrolled after 5 years. Among members who left the insurer from a group plan, 12% reenrolled after 1 year and 32% reenrolled after 5 years. After 10 years, reenrollment reached 47% in the 2 markets. More than 80% of enrollees returned to the same line of business and within the same state, suggesting findings may generalize to smaller insurers. Conclusions and Relevance: The findings of this cohort study suggest that insurers may benefit from investing in members' long-term health outcomes despite substantial short-term turnover rates.

Frequency and factors associated with foregone and delayed medical care due to COVID-19 among nonelderly US adults from August to December 2020.

OBJECTIVES: To estimate the frequency and factors associated with foregone and delayed medical care attributed to the COVID-19 pandemic among nonelderly adults from August to December 2020 in the United States. METHODS: We used three survey waves from the Urban Institute's Household Pulse Survey (HPS) collected between August 19-31, October 14-26 and December 9-21. The final sample included 155,825 nonelderly (18-64) respondents representing 135,835,598 million individuals in the United States. We used two multivariable logistic regressions to estimate the association between respondents' characteristics and foregone and delayed care. RESULTS: The frequency of foregone and delayed medical care was 26.9% and 35.9%, respectively. Around 60% of respondents reported difficulties in paying for usual household expenses in the last 7 days. More than half reported several days of mental health issues. The regression results indicated that foregone or delayed care were significantly associated with difficulties in paying usual household expenses (p < 0.001), worse self-reported health status (p < 0.001), increased mental health problems (p < 0.001), Veterans Affairs (p <0.001) or Medicaid (p = 0.003) coverage compared to private healthcare coverage, and older age groups. Individuals who participated in the latter two waves of the survey (October, December) were less likely to report foregone and delayed care compared to those who participated in Wave 1 (August). CONCLUSION: Overall, the frequency of foregone and delayed medical care remained high from August to December 2020 among nonelderly US adults. Our findings highlight that pandemic-induced access barriers are major drivers of reduced healthcare provision during the second half of the pandemic and highlight the need for policies to support patients in seeking timely care.

The Affordable Care Act's Coverage Impacts in the Trump Era.

The 2016 US presidential election created uncertainty about the future of the Affordable Care Act (ACA) and led to postponed implementation of certain provisions, reduced funding for outreach, and the removal of the individual mandate tax penalty. In this article, we estimate how the causal impact of the ACA on insurance coverage changed during 2017 through 2019, the first 3 years of the Trump administration, compared to 2016. Data come from the 2011-2019 waves of the American Community Survey (ACS), with the sample restricted to non-elderly adults. Our model leverages variation in treatment intensity from state Medicaid expansion decisions and pre-ACA uninsured rates. We find that the coverage gains from the components of the law that took effect nationally-such as the individual mandate and regulations and subsidies in the private non-group market-fell from 5 percentage points in 2016 to 3.6 percentage points in 2019. In contrast, the coverage gains from the Medicaid expansion increased in 2017 (7.0 percentage points) before returning to the 2016 level of coverage gains in 2019 (5.9 percentage points). The net effect of the ACA in expansion states is a combination of these trends, with coverage gains falling from 10.8 percentage points in 2016 to 9.6 percentage points in 2019.

Informal out-of-pocket payments experience and individuals' willingness-to-pay for healthcare services in Greece.

BACKGROUND: Informal out-of-pocket payments to healthcare providers are not uncommon in the Greek health system. We explore individuals' willingness-to-pay (WTP) to secure zero out-of-pocket full coverage for healthcare services and medications and we estimate the impact of past informal payments and individuals' opinion about the legalization of informal payments on WTP. METHODS: We conducted a survey of 2841 participants from November 2016 to February 2017. We obtained information on WTP using the contingent valuation method. A two-part regression model was used to estimate the association between WTP, informal payments, and respondents' opinion about legalizing such payments. RESULTS: About 80% of the respondents were willing to pay an average of €95 per month to obtain free access to full healthcare coverage and medications. About 65% of the respondents were involved in an informal payment at least once during the past four months with an average payment of €247. Higher informal payments and supportive opinions towards the legalization of informal payments increased the likelihood of WTP and were also positively associated with increased WTP amounts overall (p < 0.001). CONCLUSIONS: This survey reveals that individuals' WTP is critically affected by previous experiences and attitudes towards informal payments. Our results imply that the potential introduction of official fees might not suffice to limit informal payments and suggest the need for stricter regulatory policies.

Model Homes: Evaluating Approaches to Patient-centered Medical Home Implementation.

BACKGROUND: The patient-centered medical home (PCMH) model has been widely adopted, but the evidence on its effectiveness remains mixed. One potential explanation for these mixed findings is variation in how the model is implemented by practices. OBJECTIVE: To identify the impact of different approaches to PCMH adoption on health care utilization in a long-term, geographically diverse sample of patients. DESIGN: Difference-in-differences evaluation of PCMH impact on cost and utilization. SUBJECTS: A total of 5,314,284 patient-year observations from the HealthCore Integrated Research Database, and 5943 practices which adopted the PCMH model in 14 states between 2011 and 2015. INTERVENTION: PCMH adoption, as defined by the National Committee for Quality Assurance. MEASUREMENTS: Six claims-based utilization measures, plus total health care expenditures. We employ hierarchical clustering to organize practices into groups based on their PCMH capabilities, then use generalized difference-in-differences models with practice or patient fixed effects to estimate the effect of PCMH recognition (overall and separately by the groups identified by the clustering algorithm) on utilization. RESULTS: PCMH adoption was associated with a >8% reduction in total expenditures. We find significant reductions in emergency department utilization and outpatient care, and both lab and imaging services. In our by-group results we find that while the reduction in outpatient care is significant across all 3 groups, the reduction in emergency department utilization is driven entirely by 1 group with enhanced electronic communications. CONCLUSION: The PCMH model has significant impact on patterns of health care utilization, especially when heterogeneity in implementation is accounted for in program evaluation.