To examine whether different caregiving arrangements influence hospitalization risk and frequency among older adults with functional limitations. This longitudinal study used linear probability and Poisson regression models with individual and wave fixed effects, lagged predictors, and household-clustered standard errors to assess associations between different caregiving arrangements and hospitalization outcomes among community-dwelling older adults in the United States. Caregiving was categorized as no help, family help, formal help, or combined help. Outcomes included any hospitalization and the number of hospital stays over a two-year period. Data came from eight waves of the Health and Retirement Study (2004-2018). The samples included adults aged 65 and older who reported difficulty with at least one activity of daily living and participated in at least two survey waves. The final analytic sample comprised 2926 individuals contributing 5595 person-wave observations. While the overall hospitalization risk did not differ significantly by caregiving type, receiving combined support was associated with a reduced number of hospital stays among those hospitalized (IRR = 0.712, p < 0.01), compared to receiving no support. Cognitive functioning modified these relationships, with formal help linked to fewer hospitalizations among those with impairment but more among those without. Differences also emerged across racial and ethnic groups, where formal help was linked to lower hospitalization rates for Black individuals, and combined help was associated with increased hospitalizations among Hispanic older adults. Policies that expand access to both formal and family caregiving support may help reduce hospitalizations among older adults with functional limitations, particularly when tailored to care recipient characteristics.

To estimate the effect of Medicaid continuous coverage provision (CCP) during the COVID-19 public health emergency on postpartum insurance for low-income noncitizen immigrants compared to citizens, and to assess whether state use of the from-conception-to-the-end-of-pregnancy (FCEP) policy, which excluded many immigrants from postpartum coverage, modified this effect. This study used a quasi-experimental design (difference-in-differences, or DID) to compare the insurance coverage gains between low-income postpartum noncitizens and citizens, using 2019 as the reference year. We further applied a triple difference (DDD) approach to assess the effect of the FCEP-only coverage policy, hypothesizing that noncitizens in states using FCEP as the sole pathway would experience smaller gains than in other states using various pathways to cover pregnant noncitizens otherwise ineligible. Using the 2016-2022 American Community Survey individual-level data, we identified 61,572 low-income citizens (weighted N = 7,553,823) and 10,444 low-income noncitizens (weighted N = 1,487,373), with 8 FCEP-only states and 23 other states with various pathways. During the study period, the average postpartum uninsurance rate was 44.6% for noncitizens and 14.0% for citizens, while Medicaid coverage was 34.5% for noncitizens and 57.5% for citizens. Medicaid gains among noncitizens were statistically similar to gains among citizens in 2021 compared to 2019 in both FCEP-only states (DID = 2.3 percentage points [pp], 95% CI: -13.3, 8.7) and states with various pathways (DID = 4.6 pp, 95% CI: -1.2, 10.4), but these trends did not persist in 2022. We found no differences in insurance coverage gains among noncitizens between FCEP-only and states with various pathways. Findings suggest that the CCP did not substantially reduce postpartum coverage inequities for noncitizen immigrants in either FCEP-only states or states with various pathways. Postpartum coverage inequities persisted under the CCPs, leaving many noncitizens uninsured.

To assess the impact of increased Medicaid income eligibility during pregnancy on payment source for prenatal care and birth and on gestational age at birth (GAb). We performed a quasi-experimental, difference-in-differences study comparing two increases in Medicaid income eligibility during pregnancy to two control states with data from 2007 to 2010: (Dyad 1) Ohio (expanded from 150% to 200% of the Federal Poverty level [FPL]) versus Pennsylvania and (Dyad 2) Wisconsin (185% to 250% FPL) versus Michigan. We performed multinomial logistic regression to assess the impact of increased Medicaid eligibility on the following key outcome variables: payment source for prenatal care and birth and GAb. We utilized CDC Pregnancy Risk Assessment Monitoring System (PRAMS) data (2007-2010) and limited analysis to singleton, in-state live births. After re-weighting for PRAMS survey design, our analytical sample represented about 540,000 births. In the higher-income Wisconsin-Michigan dyad, increased Medicaid eligibility during pregnancy significantly increased exclusive Medicaid coverage for prenatal care (7.0%, 95% CI 2.9% to 11.1%) and birth (8.3%, 4.3% to 12.4%). Simultaneously, private insurance coverage dropped for prenatal care (-4.0%, -7.7% to -0.3%) and birth (-3.7%, -7.2% to -0.2%) while self-payment decreased only for birth (-1.8%, -3.5% to -0.2%). In the lower-income Ohio-Pennsylvania dyad, the only statistically significant effects on payment source were decreases in the likelihood of a payment source of other for prenatal care (-3.3%, -6.2% to -0.3%) and birth (-4.7%, -7.9% to -1.6%). There were no statistically significant effects on GAb across both dyads. Increased Medicaid eligibility during pregnancy for individuals of higher income seems to improve utilization of exclusive Medicaid with diminished uninsurance but also less private insurance after accounting for indicators of socioeconomic advantage but has no clear impact on GAb. Medicaid policy should balance reducing uninsurance with directing scarce resources to high-risk individuals.

To generate evidence regarding the specific critical incidents that prompt patients to switch care providers. Building on existing work on customer switching behavior, we applied the critical incident technique (CIT) to the health services research context and analyzed primary data obtained from 555 US-based patients who reported switching providers between 2018 and 2022 to develop a typology of the critical incidents that prompt patients to switch healthcare providers. Data were obtained from an online survey of adult US-based patients who reported switching primary care providers (PCPs) for non-insurance-related reasons. The survey was conducted from August to September 2022 using a quota sampling approach. We found eight critical incident categories associated with patient switching: service encounter failures, pricing, competitor attraction, inconvenience, core service failures, involuntary switching, breakdown in shared decision-making, and service environment perception. We offer explanations and suggest potentially useful evidence-based strategies for further investigation.

To examine the association between state Home- and Community-Based Services (HCBS) caregiver payment policies and timely follow-up visits (in-person and telehealth) within 14 days of hospital discharge among Medicare-Medicaid dual-eligible older adults with dementia. We categorized state HCBS caregiver payment policies into three groups: no caregiver payment, payment eligible for other friends/family, and payment eligible for two caregiver types (legally responsible relatives or other friends/family). The primary outcome was the mode of follow-up visit within 14 days post-hospital discharge (in-person, telehealth, or no visit). We used multinomial logistic regression with hospital random effects, adjusting for individual- and area-level and HCBS factors. Marginal effects were estimated. We analyzed 2021 Medicare claims data linked with publicly available datasets. The analytic cohort comprised 51,633 dual-eligible Medicare beneficiaries with dementia who were hospitalized and discharged to the community in 2021. State HCBS caregiver payment policies were significantly associated with the mode of timely follow-up visits. Compared to states without providing caregiver payments, states providing payments to two caregiver types had a 6.8 percentage point higher probability (p < 0.01) of timely in-person visits but a 3.2 percentage point lower probability (p < 0.01) of timely telehealth visits. Similar, though smaller, significant differences were observed between states that provided payments to only other family or friends and those with no caregiver payments. Other HCBS generosity measures, as well as racial, ethnic, and geographic locations, were also associated with the mode of post-discharge visits. Providing financial support to family caregivers through state HCBS policies may increase the rate of timely post-discharge visits, primarily driven by an increase in in-person visits. The effects were particularly prominent among states that allow payments to both types of caregivers.

To examine changes in children's Medicaid and CHIP enrollment during the Families First Coronavirus Response Act unwinding and assess whether CHIP enrollment offsets Medicaid declines. We used linear probability models with monthly indicators to estimate changes in enrollment from April 2023 to September 2024 overall and by CHIP structure type. We used monthly children's enrollment data from the U.S. Centers for Medicare & Medicaid Services for 32 states and the District of Columbia. During the unwinding, Medicaid enrollment declined from 48.1% to 41.2% of children, while CHIP enrollment remained stable (8.7% to 8.6%). We found average declines of 62,032 (95% confidence interval [CI]: -108,018 to -16,045) Medicaid-enrolled children per state (6.5 percentage points [pp], 95% CI: -8.1 to -5.0). Medicaid declines were larger in states with combination CHIP (-8.7 pp, 95% CI: -10.3 to -7.2) than Medicaid expansion CHIP (-4.5 pp, 95% CI: -6.0 to -3.1). We found no evidence of significant changes in CHIP enrollment overall or by CHIP structure. Children's Medicaid enrollment fell sharply without offsetting CHIP gains during the unwinding, underscoring the need for policies that prevent administrative disenrollment and ensure seamless coverage transitions.

To evaluate how adding kidney transplantation waitlisting measures-the Standardized First Kidney Transplant Waitlist Ratio for Incident Dialysis Patients (SWR) and Percentage of Prevalent Patients Waitlisted (PPPW)-affects Dialysis Facility Care Compare Star Ratings. In this observational, cross-sectional study, we calculated the difference between facilities' published (with waitlisting measures) and counterfactual (without waitlisting measures) Star Ratings. We used multinomial regression to examine associations between Star Rating changes after waitlisting measure inclusion and facility characteristics and calculated corresponding average risk differences. We used comprehensive clinical and administrative data from the Centers for Medicare/Medicaid Services from 2021 to investigate the impact of waitlisting measure addition on Star Ratings. Facility characteristics included demographic and patient mix, area deprivation index (ADI), dialysis organization affiliation, and urbanicity. 36.5% of facilities' ratings changed after waitlisting measures were added. Facility characteristics associated with a higher average risk of Star increase included location in low-ADI (0.091; 95% CI: 0.072, 0.109) or urban areas (0.061; 95% CI: 0.034, 0.087), independent/small dialysis organization affiliation (0.062; 95% CI: 0.041, 0.083), and having more PD patients (0.115; 95% CI: 0.093, 0.138). Characteristics associated with a higher average risk of Star decrease included high-ADI (0.075; 95% CI: 0.054, 0.095) or rural (0.056; 95% CI: 0.028, 0.083) location, large dialysis organization affiliation (0.058; 95% CI: 0.039, 0.078), having more patients with dual Medicare/Medicaid eligibility (0.052; 95% CI: 0.032, 0.071), and having fewer peritoneal dialysis patients (0.100; 95% CI: 0.081, 0.120). Including waitlisting measures significantly impacts the Star Ratings and captures a new dimension of care quality. Worse socioeconomic status-related facility characteristics were strongly associated with worse Star Rating outcomes. These findings can inform future discussions about risk adjustment among the developers of the SWR and PPPW measures.

To understand perceived successes and challenges of the HEART payment, and opportunities for similar value-based payment mechanisms aiming to address health-related social needs. This study analyzes perceptions of primary care practices participating in the Maryland Primary Care Program (MDPCP) on the HEART payment, a value-based payment designed to support patients' social and medical needs. After a year of payment implementation, we gathered feedback through participant surveys and focus groups. From February to March 2023, we administered a survey with 112 responses and held seven focus groups to collect primary data. For quantitative survey data, we summarized descriptive statistics and performed regression analyses to determine predictors of perceived value of the HEART payment. For qualitative focus group data, we coded and analyzed data to understand key themes on success factors and barriers to HEART payment implementation. The HEART payment was rated as high value for 61.3% of survey respondents. In bivariate regression analysis, the level of funds received and affiliation with a Care Transformation Organization (CTO) were associated with perceived value of the HEART payment; however, these associations were not significant in multivariate models. In focus groups, we found that the biggest perceived success of HEART was its unique ability to enable direct support for patients' health-related social needs, with practices using the payment to provide patients with resources such as transportation, medically necessary home remediations, and support for loneliness. Perceived challenges included the need for more precise patient eligibility targeting and administrative burdens. The HEART payment is a promising new payment model that enables primary care practices to directly address patients' social needs. Future value-based payment models that incorporate social risk adjustments in provider payments may consider alternate methods to identify patients with a high burden of health-related social needs. This may include adjusting data points used to identify beneficiaries or allowing providers to directly identify patients.

To estimate the reliability of two outcome quality measures in Veterans Health Administration (VHA) data using three different methods. We created two cohorts of VHA patients meeting criteria for two measures: (1) risk-standardized complication rates following elective primary total hip arthroplasty and/or total knee arthroplasty (THA/TKA), and (2) risk-standardized mortality rates following acute myocardial infarction hospitalization (AMI). We fit hierarchical logistic regression models and calculated facility-level risk-standardized rates. We estimated entity-level reliability using three commonly applied methods: (1) delta method approximation; (2) latent scale model; (3) split-sample method. For each measure, we extracted risk adjustment and outcome data from the VHA Corporate Data Warehouse for patients meeting eligibility criteria in fiscal years 2021 and 2022. Most facilities had complication rates following total hip and/or knee arthroplasty and mortality rates following hospitalization for acute myocardial infarction that, statistically, were no different from the national average. Reliability estimates based on delta method approximation (0.14 for THA/TKA; 0.12 for AMI) and the split-sample method (0.12 for THA/TKA; 0.19 for AMI) were very low for both measures. As we varied the sample sizes, we found that much higher sample sizes would be needed to reliably differentiate quality of care across facilities. On the other hand, reliability estimates based on the latent scale model were substantially higher than the other two methods (0.64 for THA/TKA; 0.41 for AMI), suggesting that there is substantially more between-facility variation in latent quality than manifests in observed outcomes. Reliability estimates based on the latent scale approach are not numerically or conceptually interchangeable with estimates based on the other two approaches. Given that health outcomes are generally reported using observed outcomes, reliability estimation based on the latent scale approach should not be used without a strong rationale.