Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To model the potential impact of mobile methadone unit implementation in Louisiana on net medication for opioid use disorder (MOUD) treatment rates.</jats:p></jats:sec><jats:sec><jats:title>Data Sources/Study Setting</jats:title><jats:p>We use secondary Louisiana Medicaid claims data between 2020 and 2021.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>We simulate the impact of mobile methadone units in Louisiana using two approaches: (1) a “Poisson regression approach,” which predicts the number of opioid use disorder (OUD) patients that might use methadone at mobile locations based on the underlying association between methadone use and proximity to a brick‐and‐mortar methadone clinic; (2) a “policy approach,” which leverages local treatment uptake rates following the expansion of methadone coverage to Louisiana Medicaid beneficiaries in 2020 to estimate methadone use following mobile unit implementation. Models were run in cases where mobile methadone operators could choose their operation locations freely and in a separate instance where they were restricted to serving rural locations.</jats:p></jats:sec><jats:sec><jats:title>Data Collection</jats:title><jats:p>Our analytic sample includes 43,341 Louisiana Medicaid beneficiaries with one or more primary or secondary diagnoses for opioid dependence.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>We predict that 10 new mobile methadone units in Louisiana would increase the net MOUD treatment rate in the state by 0.54–2.39 percentage points. If these mobile units delivered Methadone exclusively to rural areas, they could increase rural MOUD treatment by 8.54–13.67 percentage points. Further, roughly 20% of all beneficiaries residing in rural areas being treated with methadone would be an average of 24 miles closer to a methadone treatment provider following mobile unit implementation.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Mobile methadone units represent a promising innovation in the delivery of methadone that is likely to increase methadone use, especially in underserved rural locations. However, we find significant variation in their impact conditional on where they choose to operate, and so careful location planning will be required to maximize their benefit.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To study diagnosis coding intensity across Medicare programs, and to examine the impacts of changes in the risk model adopted by the Centers for Medicare and Medicaid Services (CMS) for 2024.</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>Claims and encounter data from the CMS data warehouse for Traditional Medicare (TM) beneficiaries and Medicare Advantage (MA) enrollees.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>We created cohorts of MA enrollees, TM beneficiaries attributed to Accountable Care Organizations (ACOs), and TM non‐ACO beneficiaries. Using the 2019 Hierarchical Condition Category (HCC) software from CMS, we computed HCC prevalence and scores from base records, then computed incremental prevalence and scores from health risk assessments (HRA) and chart review (CR) records.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>We used CMS's 2019 random 20% sample of individuals and their 2018 diagnosis history, retaining those with 12 months of Parts A/B/D coverage in 2018.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>Measured health risks for MA and TM ACO individuals were comparable in base records for propensity‐score matched cohorts, while TM non‐ACO beneficiaries had lower risk. Incremental health risk due to diagnoses in HRA records increased across coverage cohorts in line with incentives to maximize risk scores: +0.9% for TM non‐ACO, +1.2% for TM ACO, and + 3.6% for MA. Including HRA and CR records, the MA risk scores increased by 9.8% in the matched cohort. We identify the HCC groups with the greatest sensitivity to these sources of coding intensity among MA enrollees, comparing those groups to the new model's areas of targeted change.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Consistent with previous literature, we find increased health risk in MA associated with HRA and CR records. We also demonstrate the meaningful impacts of HRAs on health risk measurement for TM coverage cohorts. CMS's model changes have the potential to reduce coding intensity, but they do not target the full scope of hierarchies sensitive to coding intensity.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To examine the effect of Medicaid immediate postpartum long‐acting reversible contraception (IPP LARC) reforms on self‐reported mental health among low‐income mothers aged 18–44 years.</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>We used national secondary data on self‐reported mental health status in the past 30 days from the core component (2014–2019) of the Behavioral Risk Factor Surveillance System (BRFSS).</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>We estimated linear probability models for reporting any days of not good mental health in the past 30 days. We adjusted for individual‐level factors, state‐level factors, and state and year fixed effects. Our primary independent variable was an indicator for IPP LARC payment reform. We examined the effect of the Medicaid payment reforms on self‐reported mental health status in the past 30 days using difference‐in‐differences and event‐study designs.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>Not applicable.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>State adoption of Medicaid IPP LARC reforms was associated with significant reductions (between 5.7% and 11.5%) in the predicted probability of reporting any days of not good mental health among low‐income mothers. Treatment effects appeared to be driven by respondents reporting two or more children (less than 18 years of age) in the household (ATT = ‐0.028, <jats:italic>p</jats:italic> = 0.04). Results are robust to a series of sensitivity tests and alternative estimation strategies.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Our findings suggest that contemporary efforts to improve access to contraceptive methods may have important benefits beyond reproductive autonomy. These findings have implications for policymakers as the landscape related to family planning services continues to shift.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To examine racial/ethnic differences in emergency department (ED) transfers to public hospitals and factors explaining these differences.</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>ED and inpatient data from the Healthcare Cost and Utilization Project for Florida (2010–2019); American Hospital Association Annual Survey (2009–2018).</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>Logistic regression examined race/ethnicity and payer on the likelihood of transfer to a public hospital among transferred ED patients. The base model was controlled for patient and hospital characteristics and year fixed effects. Models II and III added urbanicity and hospital referral region (HRR), respectively. Model IV used hospital fixed effects, which compares patients within the same hospital. Models V and VI stratified Model IV by payer and condition, respectively. Conditions were classified as emergency care sensitive conditions (ECSCs), where transfer is protocolized, and non‐ECSCs. We reported marginal effects at the means.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>We examined 1,265,588 adult ED patients transferred from 187 hospitals.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>Black patients were more likely to be transferred to public hospitals compared with White patients in all models except ECSC patients within the same initial hospital (except trauma). Black patients were 0.5–1.3 percentage points (pp) more likely to be transferred to public hospitals than White patients in the same hospital with the same payer. In the base model, Hispanic patients were more likely to be transferred to public hospitals compared with White patients, but this difference reversed after controlling for HRR. Hispanic patients were − 0.6 pp to −1.2 pp less likely to be transferred to public hospitals than White patients in the same hospital with the same payer.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Large population‐level differences in whether ED patients of different races/ethnicities were transferred to public hospitals were largely explained by hospital market and the initial hospital, suggesting that they may play a larger role in explaining differences in transfer to public hospitals, compared with other external factors.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To describe the prevalence of maternal chronic hypertension (MCH), assess how frequently blood pressure is controlled before pregnancy among those with MCH, and explore management practices for antihypertensive medications (AHM) during the pre‐pregnancy and pregnancy periods.</jats:p></jats:sec><jats:sec><jats:title>Data Sources, Study Setting, and Study Design</jats:title><jats:p>We conducted a descriptive observational study using data abstracted from the Veterans Health Administration (VA) inclusive of approximately 11 million Veterans utilizing the VA in fiscal years 2010–2019.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>Veterans aged 18–50 were included if they had a diagnosis of chronic hypertension before a documented pregnancy in the VA EMR. We identified chronic hypertension and pregnancy with diagnosis codes and defined uncontrolled blood pressure as ≥140/90 mm Hg on at least one measurement in the year before pregnancy. Sensitivity models were conducted for individuals with at least two blood pressure measurements in the year prior to pregnancy. Multivariable logistic regression explored the association of covariates with recommended and non‐recommended AHMs received 0–6 months before pregnancy and during pregnancy.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>In total, 8% (3767/46,178) of Veterans with a documented pregnancy in VA data had MCH. Among 2750 with MCH meeting inclusion criteria, 60% (<jats:italic>n</jats:italic> = 1626) had uncontrolled blood pressure on at least one BP reading and 31% (<jats:italic>n</jats:italic> = 846) had uncontrolled blood pressure on at least two BP readings in the year before pregnancy. For medications, 16% (<jats:italic>n</jats:italic> = 437) received a non‐recommended AHM during pregnancy. Chronic kidney disease (OR = 3.2; 1.6–6.4) and diabetes (OR = 2.3; 1.7–3.0) were most strongly associated with use of a non‐recommended AHM during pregnancy.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Interventions are needed to decrease the prevalence of MCH, improve preconception blood pressure control, and ensure optimal pharmacologic antihypertensive management among Veterans of childbearing potential.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To validate imputation methods used to infer plan‐level deductibles and determine which enrollees are in high‐deductible health plans (HDHPs) in administrative claims datasets.</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>2017 medical and pharmaceutical claims from OptumLabs Data Warehouse for US individuals &lt;65 continuously enrolled in an employer‐sponsored plan. Data include enrollee and plan characteristics, deductible spending, plan spending, and actual plan‐level deductibles.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>We impute plan deductibles using four methods: (1) parametric prediction using individual‐level spending; (2) parametric prediction with imputation and plan characteristics; (3) highest plan‐specific mode of individual annual deductible spending; and (4) deductible spending at the 80th percentile among individuals meeting their deductible. We compare deductibles’ levels and categories for imputed versus actual deductibles.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>Not applicable.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>All methods had a positive predictive value (PPV) for determining high‐ versus low‐deductible plans of ≥87%; negative predictive values (NPV) were lower. The method imputing plan‐specific deductible spending modes was most accurate and least computationally intensive (PPV: 95%; NPV: 91%). This method also best correlated with actual deductible levels; 69% of imputed deductibles were within $250 of the true deductible.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>In the absence of plan structure data, imputing plan‐specific modes of individual annual deductible spending best correlates with true deductibles and best predicts enrollees in HDHPs.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objectives</jats:title><jats:p>To examine whether community health centers (CHCs) are effective in offsetting mental health emergency department (ED) visits.</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>The HRSA Uniform Data System and the HCUP State ED Databases for Florida patients during 2012–2019.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>We identified CHC‐year‐specific service areas using patient origin zip codes. We then estimated panel data models for number of ED mental health visits per capita in a CHC's service area. Models measured CHC mental health utilization as number of visits, unique patients, and intensity (visits per patient).</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>CHC mental health utilization increased approximately 100% during 2012–2019. Increased CHC mental health provision was associated with small reductions in ED mental health utilization. An annual increase of 1000 CHC mental health care visits (5%) was associated with 0.44% fewer ED mental health care visits (<jats:italic>p</jats:italic> = 0.153), and an increase of 1000 CHC mental health care patients (15%) with 1.9% fewer ED mental health care visits (<jats:italic>p</jats:italic> = 0.123). An increase of 1 annual mental health visit per patient was associated with 16% fewer ED mental health care visits (<jats:italic>p</jats:italic> = 0.011).</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Results suggest that mental health provision in CHCs may reduce reliance on hospital EDs, albeit minimally. Policies that promote alignment of services between CHCs and local hospitals may accelerate this effect.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To determine whether annual changes in prices for clinician‐administered drugs are associated with changes in patient out‐of‐pocket costs.</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>National commercial claims database, 2009 to 2018.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>In a serial, cross‐sectional study, we calculated the annual percent change in manufacturer list prices and net prices after rebates. We used two‐part generalized linear models to assess the relationship between annual changes in price with (1) the percentage of individuals incurring any out‐of‐pocket costs and (2) the percent change in median non‐zero out‐of‐pocket costs.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>We created annual cohorts of privately insured individuals who used one of 52 brand‐name clinician‐administered drugs.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>List prices increased 4.4%/yr (interquartile range [IQR], 1.1% to 6.0%) and net prices 3.3%/yr (IQR, 0.3% to 5.5%). The median percentage of patients with any out‐of‐pocket costs increased from 38% in 2009 to 48% in 2018, and median non‐zero annual out‐of‐pocket costs increased by 9.6%/yr (IQR, 4.1% to 15.4%). There was no association between changes in prices and out‐of‐pocket costs for individual drugs.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>From 2009 to 2018, prices and out‐of‐pocket costs for brand‐name clinician‐administered drugs increased, but these were not directly related for individual drugs. This may be due to changes to insurance benefit design and private insurer drug reimbursement rates.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To understand the determinants and benefits of cross‐sector partnerships between Veterans Affairs Medical Centers (VAMCs) and geographically affiliated AmericaServes Network coordination centers that address Veteran health‐related social needs.</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Setting</jats:title><jats:p>Semi‐structured interviews were conducted with AmericaServes and VAMC staff across seven regional networks. We matched administrative data to calculate the percentage of AmericaServes referrals that were successfully resolved (i.e., requested support was provided) in each network overall and stratified by whether clients were also VAMC patients.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>Convergent parallel mixed‐methods study guided by Himmelman's Developmental Continuum of Change Strategies (DCCS) for interorganizational collaboration.</jats:p></jats:sec><jats:sec><jats:title>Data Collection</jats:title><jats:p>Fourteen AmericaServes staff and 17 VAMC staff across seven networks were recruited using snowball sampling and interviewed between October 2021 and April 2022. Rapid qualitative analysis methods were used to characterize the extent and determinants of VAMC participation in networks.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>On the DCCS continuum of participation, three networks were classified as networking, two as coordinating, one as cooperating, and one as collaborating. Barriers to moving from networking to collaborating included bureaucratic resistance to change, VAMC leadership buy‐in, and not having VAMCs staff use the shared technology platform. Facilitators included ongoing communication, a shared mission of serving Veterans, and having designated points‐of‐contact between organizations. The percentage of referrals that were successfully resolved was lowest in networks engaged in networking (65.3%) and highest in cooperating (85.6%) and collaborating (83.1%) networks. For coordinating, cooperating, and collaborating networks, successfully resolved referrals were more likely among Veterans who were also VAMC patients than among Veterans served only by AmericaServes.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>VAMCs participate in AmericaServes Networks at varying levels. When partnerships are more advanced, successful resolution of referrals is more likely, especially among Veterans who are dually served by both organizations. Although challenges to establishing partnerships exist, this study highlights effective strategies to overcome them.</jats:p></jats:sec>