Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To measure hospital quality based on routine data available in many health care systems including the United States, Germany, the United Kingdom, Scandinavia, and Switzerland.</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>We use the Swiss Medical Statistics of Hospitals, an administrative hospital dataset of all inpatient stays in acute care hospitals in Switzerland for the years 2017–2019.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>We study hospital quality based on quality indicators used by leading agencies in five countries (the United States, the United Kingdom, Germany, Austria, and Switzerland) for two high‐volume elective procedures: inguinal hernia repair and hip replacement surgery. We assess how least absolute shrinkage and selection operator (LASSO), a supervised machine learning technique for variable selection, and Mundlak corrections that account for unobserved heterogeneity between hospitals can be used to improve risk adjustment and correct for imbalances in patient risks across hospitals.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>The Swiss Federal Statistical Office collects annual data on all acute care inpatient stays including basic socio‐demographic patient attributes and case‐level diagnosis and procedure codes.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>We find that LASSO‐selected and Mundlak‐corrected hospital random effects logit models outperform common practice logistic regression models used for risk adjustment. Besides the more favorable statistical properties, they have superior in‐ and out‐of‐sample explanatory power. Moreover, we find that Mundlak‐corrected logits and the more complex LASSO‐selected models identify the same hospitals as high or low‐quality offering public health authorities a valuable alternative to standard logistic regression models. Our analysis shows that hospitals vary considerably in the quality they provide to patients.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>We find that routine hospital data can be used to measure clinically relevant quality indicators that help patients make informed hospital choices.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To evaluate changes in dual enrollment after Affordable Care Act Medicaid expansion by VA priority group, (e.g., service connection), sex, and type of state expansion.</jats:p></jats:sec><jats:sec><jats:title>Study Setting</jats:title><jats:p>Our cohort was all Veterans ages 18–64 enrolled in VA and eligible for benefits due to military service‐connection or low income from 2011 to 2016; the unit of analysis was person‐year.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>Difference‐in‐difference and event‐study analysis. The outcome was dual VA‐Medicaid enrollment for at least 1 month annually. Medicaid expansion, VA priority status, whether a state expanded by a Section 1115 waiver, and sex were independent variables. We controlled for race, ethnicity, age, disease burden, distance to VA facilities, state, and year.</jats:p></jats:sec><jats:sec><jats:title>Data Extraction Methods</jats:title><jats:p>We used data from the VA Corporate Data Warehouse (CDW) regarding age and VA Priority Group to select our cohort of VA‐enrolled individuals. We then took the cohort and crossed checked it with Medicaid Analytic Extract (MAX) and T‐MSIS Analytic Files (TAF) to determine Medicaid enrollment status.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>Service‐connected Veterans experienced lower dual‐enrollment increases across all sex and state‐waiver groups (3.44 percentage points (95% CI: 1.83, 5.05 pp) for women, 3.93 pp (2.98, 4.98) for men, 4.06 pp (2.85, 5.27) for non‐waiver states, and 3.00 pp (1.58 to 4.41) for waiver states) than Veterans who enrolled in the VA due to low income (8.19 pp (5.43, 10.95) for women, 9.80 pp (7.06, 12.54) for men, 10.21 pp (7.17, 13.25) for non‐waiver states, and 7.39 pp (5.28, 9.50) for waiver states).</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Medicaid expansion is associated with dual enrollment. Dual‐enrollment changes are greatest in those enrolled in the VA due to low income, but do not differ by sex or expansion type. Results can help VA identify groups disproportionately likely to have potential care‐coordination issues due to usage of multiple health care systems.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To evaluate the veracity of self‐reports of month‐level health insurance coverage in the Current Population Survey Annual Social and Economic Supplement (CPS).</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>The CHIME (Comparing Health Insurance Measurement Error) study used health insurance enrollment records from a large regional Midwest insurer as sample for primary data collection in spring 2015.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>A sample of individuals enrolled in a range of public and private coverage types (including Medicaid and marketplace) was administered the CPS health insurance module, which included questions about month‐level coverage, by type, over a 17–18‐month time span. Survey data was then matched to enrollment records covering that same time frame, and concordance between the records and self‐reports was assessed.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>Sample was drawn by the insurer's informatics specialists and Census Bureau interviewers conducted the survey. Following data collection, updated enrollment records were matched to the survey data to produce a person‐level file of coverage by type at the month‐level.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>For 91% of the overall sample, coverage status and type were reported accurately for at least 75% of observed months. Results varied somewhat by stability of coverage. Among those who were continuously covered throughout the 17–18 month observation period (which comprised 64% of the overall sample), that level of reporting accuracy was observed for 94% of the sample; for those who had censored spells (34% of the overall sample), the figure was 87%; and among those with gaps and/or changes according to the records (2% of the overall sample), for 82% of the group at least 75% of months were reported accurately.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Findings suggest that reporting accuracy of month‐level coverage in the CPS is high and that the survey could become a valuable new data source for studying the dynamics of coverage, including the Medicaid unwinding.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>Given high rates of un‐ and underemployment among disabled people, adults with intellectual and developmental disabilities rely on Medicaid, Medicare, or both to pay for healthcare. Many disabled adults are Medicare eligible before the age of 65 but little is known as to why some receive Medicare services while others do not. We described the duration of Medicare enrollment for adults with intellectual and developmental disabilities in 2019 and then compared demographics by enrollment type (Medicare‐only, Medicaid‐only, dual‐enrolled). Additionally, we examined the percent in each enrollment type by state, and differences in enrollment type for those with Down syndrome.</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>2019 Medicare and Medicaid claims data for all adults (≥18 years) in the US with claim codes for intellectual disability, Down syndrome, or autism at any time between 2011 and 2019.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>Administrative claims cohort.</jats:p></jats:sec><jats:sec><jats:title>Data Collection and Abstraction Methods</jats:title><jats:p>Data were from the Transformed Medicaid Statistical Information System Analytic Files and Medicare Beneficiary Summary files.</jats:p></jats:sec><jats:sec><jats:title>Principle Findings</jats:title><jats:p>In 2019, Medicare insured 582,868 adults with identified intellectual disability, autism, or Down syndrome. Of 582,868 Medicare beneficiaries, 149,172 were Medicare only and 433,396 were dual‐enrolled. Most Medicare enrollees were enrolled as child dependents (61.5%) Medicaid‐only enrollees (<jats:italic>N</jats:italic> = 819,256) were less likely to be white non‐Hispanic (58.5% white non‐Hispanic vs. 72.9% white non‐Hispanic in dual‐enrolled), more likely to be Hispanic (19.6% Hispanic vs. 9.2% Hispanic in dual‐enrolled) and were younger (mean 34.2 years vs. 50.5 years dual‐enrolled).</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>There is heterogeneity in public insurance enrollment which is associated with state and disability type. Action is needed to ensure all are insured in the program that works for their healthcare needs.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To test the reliability of Medicare claims in measuring vertical integration. We assess the accuracy of a commonly used measure of integration, primary care physician (PCP) practices billing Medicare as a hospital outpatient department (HOPD) in claims.</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>Medicare fee‐for‐service claims, IQVIA, and CPC+ practice surveys for this study.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>We compare measures of integration from Medicare claims to self‐reported indicators of integration from IQVIA and a survey of CPC+ participating practice sites.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>We measure integration by using site‐of‐service billing in the 100% sample of Medicare Carrier claims from 2017–2020. In the IQVIA SK&amp;A (2017–2018), OneKey (2019–2020), and practice survey data (2017–2019), we use self‐reported responses to measure integration.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>We find that currently most PCP practices sites that report themselves as being integrated with a health system do not bill as an HOPD. In 2017, 11% of CPC+ practices were identified as being vertically integrated in claims, while the equivalent numbers in SK&amp;A and surveys were 52% and 54% integration, respectively. A t‐test found that both datasets significantly differed from claims (Survey: 41.3%–45.1%; SK&amp;A: 45.3%–51.1%); this gap persists in 2018–2019.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>Measuring physician‐hospital vertical integration accurately is integral to determining consolidation. The overwhelming majority of PCP practice sites not billing as an HOPD may reflect Medicare regulatory changes that have reduced the financial incentives for doing so. These findings have implications for researchers that study the growth in PCP‐hospital integration in health care markets.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To examine the relationship between the level of state funding for Home‐ and Community‐Based Services (HCBS) and state overall and dimension‐specific performances in Long‐Term Services and Supports (LTSS).</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>We employed state‐level secondary data from the Medicaid LTSS Annual Expenditures Reports, the American Association of Retired Persons (AARP) State Scorecards, the U.S. Census, and Federal Reserve Economic data, spanning the timeframe of 2010–2020.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>Overall state LTSS rankings, along with dimension‐specific rankings, were modeled separately against state Medicaid spending on HCBS relative to total Medicaid spending on LTSS. All models were adjusted for state covariates, secular trend, and state fixed effects.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>The study sample included all 50 states and the District of Columbia. However, California, Delaware, Illinois, and Virginia were excluded from FY2019 due to missing data on Medicaid HCBS expenditures.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>Every 10 percentage‐point increase in the proportion of Medicaid LTSS spending to HCBS demonstrated 2.05 points improvement (95% confidence interval [CI]: −3.88 to 0.22, <jats:italic>p</jats:italic> = 0.03) in rankings for state overall LTSS system performance, 2.92 points improvement (95% CI: −4.87 to 0.98, <jats:italic>p</jats:italic> &lt; 0.01) in rankings for the Choice of Setting and Provider dimension, as well as 1.73 points (95% CI: −3.14 to 0.32, <jats:italic>p</jats:italic> = 0.02) ranking improvement in the dimension of Effective Transitions.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Our study suggested promising effects of increased state funding for HCBS on LTSS performance.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To investigate whether the Veterans Health Administration's (VA) 2019 Referral Coordination Initiative (RCI) was associated with changes in the proportion of VA specialty referrals completed by community‐based care (CC) providers and mean appointment waiting times for VA and CC providers.</jats:p></jats:sec><jats:sec><jats:title>Data Sources/Study Settings</jats:title><jats:p>Monthly facility level VA data for 3,097,366 specialty care referrals for eight high‐volume specialties (cardiology, dermatology, gastroenterology, neurology, ophthalmology, orthopedics, physical therapy, and podiatry) from October 1, 2019 to May 30, 2022.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>We employed a staggered difference‐in‐differences approach to evaluate RCI's effects on referral patterns and wait times. Our unit of analysis was facility‐month. We dichotomized facilities into high and low RCI use based on the proportion of total referrals for a specialty. We stratified our analysis by specialty and the staffing model that high RCI users adopted: centralized, decentralized, and hybrid.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>Administrative data on referrals and waiting times were extracted from the VA's corporate data warehouse. Data on staffing models were provided by the VA's Office of Integrated Veteran Care.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>We did not reject the null hypotheses that high RCI use do not change CC referral rates or waiting times in any of the care settings for most specialties. For example, high RCI use for physical therapy—the highest volume specialty studied—was associated with −0.054 (95% confidence interval [CI]: −0.114 to 0.006) and 2.0 days (95% CI: −4.8 to 8.8) change in CC referral rate and waiting time at CC providers, respectively, among centralized staffing model adopters.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>In the initial years of the RCI program, RCI does not have a measurable effect on waiting times or CC referral rates. Our findings do not support concerns that RCI might be impeding Veterans' access to CC providers. Future evaluations should examine whether RCI facilitates Veterans' ability to receive care in their preferred setting.</jats:p></jats:sec>