Health Services Research最新文献

Objective: To identify state strategies to increase access to medications for opioid use disorder (MOUD) through Section 1115 Substance Use Disorder waivers.

Study setting and design: We conducted a qualitative analysis of 27 waiver applications that were implemented between 2015 and 2020. We identified barriers and proposed strategies for expanding MOUD access and utilization.

Data sources and analytic sample: After excluding five states due to insufficient information, we analyzed 22 applications.

Principal findings: We identified six barriers and eight corresponding strategies. Barriers included care delays, limited MOUD facilities, lack of care transition support, limited MOUD access in residential treatment, insufficient care coordination, and prescriber shortages. Commonly proposed strategies were requiring access to MOUD in residential treatment, which was stipulated by the Centers for Medicare & Medicaid Services, addressing prescriber shortages through education and technical assistance, campaigns to address stigma, and increased reimbursement. Other strategies included changes to prior authorization requirements, efforts to increase the number of facilities that offer MOUD, and changes to improve care transitions.

Conclusions: States proposed a variety of strategies to expand access to and use of MOUD. Future research could investigate how these approaches, implemented individually or in combination, are associated with outcome change and impact.

Objective: To develop and validate a hierarchical algorithm for assigning prenatal care (PNC) encounters using claims data while ensuring continuity of care.

Study setting and design: We conducted a retrospective cohort study among South Carolina Medicaid beneficiaries. Using a six-step hierarchical algorithm-incorporating specialty designations, diagnostic/procedure codes, and adjustments for inpatient stays and supplemental visits-we assigned PNC encounters and identified predominant PNC providers. To assess predictive validity, we examined associations between predominant provider status and adverse birth outcomes (obtained from linked birth certificates and claims data) using logit-binomial generalized estimating equations with robust standard errors, and we compared models' performance using both model fit statistics and 10-fold cross-validation.

Data sources and analytic sample: We used South Carolina Medicaid data on live-birth pregnancies from 2016 to 2021. We followed participants from conception until delivery.

Principal findings: Initial screening identified 302 package/bundle payment claims, leading to the exclusion of 299 pregnancies (0.3%) from further analysis. The final analytic dataset contained 1,072,615 confirmed PNC encounters for 90,581 (97%) pregnancies. This study identified predominant providers for 87,573 pregnancies (98% of cases with at least two PNC encounters). The analysis of predictive validity revealed significant protective associations for two outcomes when comparing pregnancies with versus without predominant providers: preterm birth (adjusted RR: 0.68, 95% CI: 0.59-0.77) and low-birth-weight (adjusted RR: 0.68, 95% CI: 0.57-0.80).

Conclusions: This study developed and validated a claims-based algorithm to identify PNC utilization in South Carolina Medicaid data. Predictive validity tests revealed that predominant provider status was associated with reduced adverse birth outcomes, suggesting care continuity may improve perinatal health. Future research could apply this algorithm to examine causal relationships between predominant provider status and specific outcomes (e.g., preterm birth, low birth weight), while accounting for institutional and socioeconomic confounders. These findings offer a foundation for optimizing PNC delivery through continuity-focused interventions.

Objective: To enhance National Provider Identifier (NPI) and specialty information available in Medicare Advantage (MA) encounter data and use the enhanced data to evaluate methods for retrospective attribution of the patient's usual clinician, comparing results across MA and Traditional Medicare (TM) populations.

Study setting and design: We fill in missing clinician identifiers and specialty codes in MA encounter data using Centers for Medicare and Medicaid Services (CMS) and publicly available provider datasets. We attributed patients to the usual clinician using 16 methodological options, comparing the performance of these attribution methods in MA and TM.

Data sources and analytic sample: We used a 20% sample of MA encounter data and TM claims data for 2016-2022, incorporating information from CMS's Medicare Data on Provider Practice and Specialty, archived data from the National Plan and Provider Enumeration System, and specialty-taxonomy crosswalks derived from CMS publications.

Principal findings: For MA, we identified individual NPIs for 83% of medical claims in 2016, improving to 89% in 2022. Among MA medical claims billed by physicians and advanced practice providers, 95% of NPIs were for individual clinicians by 2022. In total, we identified individual or organization NPIs and specialty codes for over 99% of medical encounters in both TM and MA in all years. Rates of patient attribution were stable over time, and most methods had similar performance in MA and TM. We recommend a hierarchical attribution method that resulted in the highest fraction attributed with good consistency of attributed clinician year over year. Published reference files and SAS code make these NPI identification and patient attribution methods accessible.

Conclusions: Our methods allow researchers to identify provider NPIs that can be matched to external clinician data, used to attribute patients to a usual source of care, or to fit clinician fixed effects in studies of MA and TM.

Objective: To provide a comprehensive overview of the different types of hospital discharge planning (DP) interventions and outcomes examined in systematic reviews, and to assess the strength of evidence (SoE) for the associations between DP and these outcomes.

Study setting and design: Umbrella review ("review of systematic reviews").

Data sources: We searched five databases (PubMed, CINAHL, Web of Science, Cochrane, and Business Source Complete) from inception through February 2024 for systematic reviews examining associations between hospital DP and various outcomes. We conducted backward and forward citation searches to identify additional systematic reviews. Altogether, these searches yielded 1817 records, of which 34 met the inclusion criteria. We assessed the methodological quality of the included reviews using the AMSTAR 2 tool, summarized DP intervention types and the reviews' subgroup analyses narratively, and evaluated the SoE for 19 outcomes using a recently developed method.

Principal findings: We identified 20 distinct DP intervention types which we grouped into six intervention categories. Patient education was the most frequently investigated type. We rated SoE as high for five outcomes, moderate for eight, and low for six. We found the strongest evidence for associations between hospital DP and reduced readmissions, fewer medication discrepancies, and greater patient satisfaction. Evidence for associations with quality of life, emergency department visits, mortality, and overall patient health, however, was weak or lacking. Our synthesis of the reviews' subgroup analyses indicated that the effects of hospital DP varied across patient populations and intervention types. Overall, the most effective interventions appeared to be high-intensity, bundled programs, incorporating medication-related interventions and follow-ups, particularly for reducing readmissions.

Conclusion: This umbrella review synthesizes evidence on associations between hospital DP and various outcomes. The findings support the development of tailored DP strategies and point to research gaps. Future studies should prioritize standardizing intervention definitions, outcome measures, and subgroup classifications, and investigate unexamined causal mechanisms.

Objective: 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.

Study setting and design: 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.

Data sources and analytic sample: 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.

Principal findings: 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.

Conclusions: 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.

Objective: To examine which hospital-payer contracts include Diagnosis Related Group (DRG) codes and whether they set prices as a consistent multiple of hospital list prices or Medicare's DRG fee schedule.

Study setting and design: We study the cash rates and negotiated contracts (including commercial group, Medicare Advantage, Medicaid Managed Care, and individual market health plans) of US general and surgical acute care hospitals. We develop bunching and regression-based methods to classify the pricing bases of DRGs within contracts. We show the unadjusted and regression-adjusted variation in DRG inclusion and pricing across hospital and insurer characteristics.

Data sources and analytic sample: Hospital price transparency data from Turquoise Health (May 2024) is joined with hospital characteristics from the American Hospital Association, insurer market concentration from Clarivate, and Medicare DRG rates. We observe 4033 hospitals with 157,313 hospital-health plan contracts and 3902 sets of cash rates.

Principal findings: About 17% of hospitals do not include DRGs in any of their negotiated contracts or cash rates, while 54% include them in some, but not all contracts. Nearly half (48%) of hospitals exclude DRGs from their cash rates. Among commercial group contracts with DRGs, 25%-27% benchmark their DRG prices to hospital list prices, while 32%-36% are based on Medicare's fee schedule. Medicare Advantage contracts are more likely to be benchmarked to Medicare (64%), while most hospitals base their cash rates on list prices (85%). Hospitals facing less competition had lower rates of DRG contracting but were observed to be more likely to negotiate prices based on list prices conditional on including DRGs.

Conclusions: Our findings suggest that hospital market power may influence hospital-health plan negotiations beyond the average price levels. Policies aimed at standardizing these contracts must account for the wide variation in payment and pricing bases currently used in the private market.

Objective: To investigate the relationship between community care (CC) treatment, surgical complexity, and postoperative surgical outcomes in spine surgeries among Veterans.

Data sources and study setting: Veterans Health Administration (VHA) sample with data sourced from the Corporate Data Warehouse and CC claims.

Study design: To evaluate differences in VHA and CC spine surgical complexity and outcomes, we first characterized VHA patients with lumbar spinal stenosis (LSS) who received spine surgery in the VHA or CC. Then, we estimated adjusted naïve logistic regression models to calculate the effect of CC on the probability of having a complex spine surgery, 30-day readmission, 30-day complication, and 1-year reoperation. Finally, we estimated adjusted 2-stage models using an instrument for primary care provider's historical CC referral rates and imaging rates as a semi-parametric Newey correction for sample selection.

Analytic sample: LSS-diagnosed patients living ≤ 80 miles from a VHA facility that performed at least one spine surgery between January 1, 2019 and December 31, 2022.

Principal findings: Of the 41,726 LSS-diagnosed patients, 7496 (18.0%) had spine surgery within 1 year of diagnosis. 2920 (39.0%) were VHA surgeries and 4576 (61.0%) were in CC. In the naïve model, CC surgery was associated with a 26.61 percentage point (pp) increase in the probability of having a complex surgery (95% CI 24.17, 29.05), a 4.31 pp increase in readmission (95% CI 2.76, 5.85), and a 6.80 pp increase in reoperation (95% CI 5.21, 8.40). After accounting for characteristics associated with the likelihood of surgery, CC, and outcomes, only the effect of CC use on the probability of a complex surgery was significant (36.48; 95% CI 22.69, 50.27).

Conclusions: We found no difference in surgical outcomes between VHA and CC patients. Since CC patients were more likely to receive complex spine surgeries, the VHA paid for more costly, resource-intensive procedures with no improvements in quality.

Objective: To assess the impact of pandemic-related changes in state scope of practice law (SOPL) for certified registered nurse anesthetists (CRNAs) on the utilization of anesthesia services during the COVID-19 pandemic, which served as a natural experiment.

Study setting and design: We used a difference-in-differences approach to compare changes in the utilization of anesthesia services during the COVID-19 pandemic in areas that expanded SOPL (removed requirements for supervision or direction) to those that did not. Additionally, we examined if the impact of changes in SOPL on patient access differed by underserved status.

Data sources and analytic sample: To understand patient access to anesthesia care, we used a large, national administrative claims database of privately insured and Medicare Advantage enrollees to measure utilization rates per 1000 members from 2018 through 2022. We used the county-level density of anesthesia providers to identify underserved areas. We used data on the changes in SOPL at the state level and assessed changes in utilization prior to and during COVID-19.

Principal findings: In the areas that changed SOPL, removing requirements for supervision or direction, utilization of anesthesia procedures increased by 18 procedures per 1000 members over the study period (17% increase; p-value 0.066) compared with an increase of 9 procedures per 1000 members (7% increase; p-value 0.031) in areas that maintained SOPL requiring supervision. However, increases in utilization in underserved and not underserved areas were similar across SOPL statuses.

Conclusions: This study provides evidence that the SOPL that allows CRNAs to practice without the requirement of supervision or direction results in greater access to anesthesia services compared with a more restrictive SOPL requiring supervision.

Objective: To evaluate the effects of the Family First Coronavirus Response Act (FFCRA) on work outcomes of women for whom the FFCRA effectively expanded income eligibility for Medicaid beyond 60 days postpartum by prohibiting states from redetermining Medicaid eligibility between March 2020 and March 2023.

Study setting and design: We use a difference-in-differences design that leverages the differences in income eligibility between pregnancy and non-pregnancy across states, and compares outcome changes pre-post FFCRA over these differences.

Data sources and analytic sample: Data come from the 2016-2022 American Community Survey. The sample includes 205,104 women aged 19-49 years who reported giving birth within the past 12 months in 41 states and Washington D.C.

Principal findings: On average, the FFCRA increased postpartum Medicaid coverage by 2.8 percentage points (95% CI: 0.7-4.8) or by 9.3% relative to the 2019 Medicaid coverage rate. In contrast, the FFCRA effects on work outcomes were small and not significant: the average effect was 0.10 percentage points for labor force participation (95% CI: -1.0 to 1.2), 0.7 percentage points for employment (95% CI: -0.02 to 1.4), 0.04 h for weekly work hours (95% CI: -0.4 to 0.5), and 0.2 percentage points for full-time employment (95% CI: -1.1 to 1.5). These confidence intervals rule out an employment decline above 0.02 percentage points and full-time employment decline above 1.1 percentage points. The increase in Medicaid coverage is concentrated among states with a larger difference between pregnancy and non-pregnancy eligibility (+5.9 percentage points; 95% CI: 0.9 to 10.9) and estimates in this group also rule out relatively small declines in work outcomes.

Conclusion: There is no evidence of declines in work outcomes following the increase in Medicaid coverage beyond 60 days postpartum that resulted from the FFCRA. The findings suggest that subsequent postpartum Medicaid coverage extensions for 12 months under the American Rescue Plan are unlikely to disincentivize work among beneficiaries.