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Introduction: Methamphetamine detoxification before entering formal and longer term treatment may have a positive impact on treatment retention and success. Understanding geographic distribution of methamphetamine specialty detox services and differential access by race/ethnicity is critical for establishing policies that ensure equitable access across populations.

Methods: We used the Mental health and Addiction Treatment Tracking Repository to identify treatment facilities that offered any substance use detoxification in 2021 (N=2346) as well as the census block group in which they were located. We sourced data from the US Census Bureau to identify the percentage of a census block group that was White, Black, and Hispanic. We used logistic regression to model the availability of methamphetamine-specific detox, predicted by the percentage of a block group that was Black and Hispanic. We adjusted for relevant covariates and defined state as a random effect. We calculated model-based predicted probabilities.

Results: Over half (60%) of detox facilities offered additional detox services specifically for methamphetamine. Sixteen states had <10 methamphetamine-specific detox facilities. The predicted probability of methamphetamine-specific detox availability was 60% in census block groups with 0%-9% Black residents versus only 46% in census block groups with 90%-100% Black residents, and was 61% in census block groups with 0%-9% Hispanic residents versus 30% in census block groups with 90%-100% Hispanic residents.

Conclusions: During an unprecedented national methamphetamine crisis, access to a critical health care service was disproportionately lower in communities that were predominately Black and Hispanic. We orient our findings around a discussion of health disparities, residential segregation, and the upstream causes of the systematic exclusion of minoritized communities from health care.

Background: HCAHPS' 2008 initial public reporting, 2012 inclusion in the Hospital Value-Based Purchasing Program (HVBP), and 2015 inclusion in Hospital Star Ratings were intended to improve patient experiences.

Objectives: Characterize pre-COVID-19 (2008-2019) trends in hospital consumer assessment of healthcare providers and systems (HCAHPS) scores.

Research design: Describe HCAHPS score trends overall, by phase: (1) initial public reporting period (2008-2013), (2) first 2 years of HVBP (2013-2015), and (3) initial HCAHPS Star Ratings reporting (2015-2019); and by hospital characteristics (HCAHPS decile, ownership, size, teaching affiliation, and urban/rural).

Subjects: A total of 3909 HCAHPS-participating US hospitals.

Measures: HCAHPS summary score (HCAHPS-SS) and 9 measures.

Results: The mean 2007-2019 HCAHPS-SS improvement in most-positive-category ("top-box") responses was +5.2 percentage points/pp across all hospitals (where differences of 5pp, 3pp, and 1pp are "large," "medium," and "small"). Improvement rate was largest in phase 1 (+0.8/pp/year vs. +0.2pp/year and +0.1pp/year for phases 2 and 3, respectively). Improvement was largest for Overall Rating of Hospital (+8.5pp), Discharge Information (+7.3pp), and Nurse Communication (+6.5pp), smallest for Doctor Communication (+0.8pp). Some measures improved notably through phases 2 and 3 (Nurse Communication, Staff Responsiveness, Overall Rating of Hospital), but others slowed or reversed in Phase 3 (Communication about Medicines, Quietness). Bottom-decile hospitals improved more than other hospitals for all measures.

Conclusions: All HCAHPS measures improved rapidly 2008-2013, especially among low-performing (bottom-decile) hospitals, narrowing the range of performance and improving scores overall. This initial improvement may reflect widespread, general quality improvement (QI) efforts in lower-performing hospitals. Subsequent slower improvement following the introduction of HVBP and Star Ratings may have reflected targeted, resource-intensive QI in higher-performing hospitals.

Research design: Community-engaged qualitative study using inductive thematic analysis of semistructured interviews.

Objective: To understand Latine immigrants' recent prenatal care experiences and develop community-informed strategies to mitigate policy-related chilling effects on prenatal care utilization.

Background: Decreased health care utilization among immigrants due to punitive immigration policies (ie, the "chilling effect") has been well-documented among Latine birthing people both pre and postnatally.

Patients and methods: Currently or recently pregnant immigrant Latine people in greater Philadelphia were recruited from an obstetric clinic, 2 pediatric primary care clinics, and 2 community-based organization client pools. Thematic saturation was achieved with 24 people. Participants' pregnancy narratives and their perspectives on how health care providers and systems could make prenatal care feel safer and more comfortable for immigrants.

Results: Participants' recommendations for mitigating the chilling effect during the prenatal period included training prenatal health care providers to sensitively initiate discussions about immigrants' rights and reaffirm confidentiality around immigration status. Participants suggested that health care systems should expand sources of information for pregnant immigrants, either by partnering with community organizations to disseminate information or by increasing access to trusted individuals knowledgeable about immigrants' rights to health care. Participants also suggested training non-medical office staff in the use of interpreters.

Conclusion: Immigrant Latine pregnant and birthing people in greater Philadelphia described ongoing fear and confusion regarding the utilization of prenatal care, as well as experiences of discrimination. Participants' suggestions for mitigating immigration-related chilling effects can be translated into potential policy and programmatic interventions which could be implemented locally and evaluated for broader applicability.

Objective: Fragmented readmissions, when admission and readmission occur at different hospitals, are associated with increased charges compared with nonfragmented readmissions. We assessed if hospital participation in health information exchange (HIE) was associated with differences in total charges in fragmented readmissions.

Data source: Medicare Fee-for-Service Data, 2018.

Study design: We used generalized linear models with hospital referral region and readmission month fixed effects to assess relationships between information sharing (same HIE, different HIEs, and no HIE available) and total charges of 30-day readmissions among fragmented readmissions; analyses were adjusted for patient-level clinical/demographic characteristics and hospital-level characteristics.

Data extraction methods: We included beneficiaries with a hospitalization for acute myocardial infarction, congestive heart failure, chronic obstructive pulmonary disease, syncope, urinary tract infection, dehydration, or behavioral issues with a 30-day readmission for any reason.

Principal findings: In all, 279,729 admission-readmission pairs were included, 27% of which were fragmented (n=75,438); average charges of fragmented readmissions were $64,897-$71,606. Compared with fragmented readmissions where no HIE was available, the average marginal effects of same-HIE and different-HIE admission-readmission pairs were -$2329.55 (95% CI: -7333.73, 2674.62) and -$3905.20 (95% CI: -7592.85, -307.54), respectively. While the average marginal effects of different-HIE pairs were lower than those for no-HIE fragmented readmissions, the average marginal effects of same-HIE and different-HIE pairs were not significantly different from each other.

Conclusions: There were no statistical differences in charges between fragmented readmissions to hospitals that share an HIE or that do not share an HIE compared with hospitals with no HIE available.

Background: Housing is a critical social determinant of health that can be addressed through hospital-supported community benefit programming.

Objectives: To explore the prevalence of hospital-based programs that address housing-related needs, categorize the specific actions taken to address housing, and determine organizational and community-level factors associated with investing in housing.

Research design: This retrospective, cross-sectional study examined a nationally representative dataset of administrative documents from nonprofit hospitals that addressed social determinants of health in their federally mandated community benefit implementation plans. We conducted descriptive statistics and bivariate analyses to examine hospital and community characteristics associated with whether a hospital invested in housing programs. Using an inductive approach, we categorized housing investments into distinct categories.

Measures: The main outcome measure was a dichotomous variable representing whether a hospital invested in one or more housing programs in their community.

Results: Twenty percent of hospitals invested in one or more housing programs. Hospitals that addressed housing in their implementation strategies were larger on average, less likely to be in rural communities, and more likely to be serving populations with greater housing needs. Housing programs fell into 1 of 7 categories: community partner collaboration (34%), social determinants of health screening (9%), medical respite centers (4%), community social determinants of health liaison (11%), addressing specific needs of homeless populations (16%), financial assistance (21%), and targeting high-risk populations (5%).

Conclusions: Currently, a small subset of hospitals nationally are addressing housing. Hospitals may need additional policy support, external partnerships, and technical assistance to address housing in their communities.

Background: Individuals with limited English proficiency (LEP) have long faced barriers in navigating the health care system. More information is needed to understand whether their care was limited further during the early period of the COVID-19 pandemic.

Objective: To assess the impact of English proficiency on delayed and forgone health care during the early COVID-19 pandemic.

Research design: Multivariate logistic regression analysis of National Health Interview Survey data (July-December 2020; n=16,941). Outcomes were self-reported delayed and forgone health care because of cost or the COVID-19 pandemic. Delayed health care included medical, dental, mental health, and pharmacy care. Forgone health care also included care at home from a health professional.

Results: A greater percentage of LEP adults reported delayed (49%) and forgone (41%) health care than English-proficient adults (40% and 30%, respectively). However, English proficiency was not significantly associated with delayed or forgone health care, after adjusting for demographic, socioeconomic, and health factors. Among LEP adults, multivariate models showed that being uninsured, having a disability, and having chronic conditions increased the risk of delaying and forgoing health care. LEP adults of Asian race and Hispanic ethnicity were also more likely to forgo health care while those with 65+ years were less likely to forgo health care.

Conclusions: Adults with LEP were more likely to experience challenges accessing health care early in the pandemic. Delayed and forgone health care were explained by low socioeconomic status and poor health. These findings highlight how during a period of limited health resources, deficiencies in the health care system resulted in an already disadvantaged group being at greater risk of inequitable access to care.

Background: Although federal legislation made COVID-19 vaccines free, inequities in access to medical care may affect vaccine uptake.

Objective: To assess whether health care access was associated with uptake and timeliness of COVID-19 vaccination in the United States.

Design: A cross-sectional study.

Setting: 2021 National Health Interview Survey (Q2-Q4).

Subjects: In all, 21,532 adults aged≥18 were included in the study.

Measures: Exposures included 4 metrics of health care access: health insurance, having an established place for medical care, having a physician visit within the past year, and medical care affordability. Outcomes included receipt of 1 or more COVID-19 vaccines and receipt of a first vaccine within 6 months of vaccine availability. We examined the association between each health care access metric and outcome using logistic regression, unadjusted and adjusted for demographic, geographic, and socioeconomic covariates.

Results: In unadjusted analyses, each metric of health care access was associated with the uptake of COVID-19 vaccination and (among those vaccinated) early vaccination. In adjusted analyses, having health coverage (adjusted odds ratio [AOR] 1.60; 95% CI: 1.39, 1.84), a usual place of care (AOR 1.58; 95% CI: 1.42, 1.75), and a doctor visit within the past year (AOR 1.45, 95% CI: 1.31, 1.62) remained associated with higher rates of COVID-19 vaccination. Only having a usual place of care was associated with early vaccine uptake in adjusted analyses.

Limitations: Receipt of COVID-19 vaccination was self-reported.

Conclusions: Several metrics of health care access are associated with the uptake of COVID-19 vaccines. Policies that achieve universal coverage, and facilitate long-term relationships with trusted providers, may be an important component of pandemic responses.

Background: Direct and indirect standardization are well-established approaches to performing risk adjustment when comparing outcomes between healthcare providers. However, it is an open question whether they work well when there is an association between the center effects and the distributions of the patient characteristics in these centers.

Objectives and methods: We try to shed further light on the impact of such an association. We construct an artificial case study with a single covariate, in which centers can be classified as performing above, on, or below average, and the center effects correlate with center-specific mean values of a patient characteristic, as a consequence of differential quality improvement. Based on this case study, direct standardization and indirect standardization-based on marginal as well as conditional models-are compared with respect to systematic differences between their results.

Results: Systematic differences between the methods were observed. All methods produced results that partially reflect differences in mean age across the centers. This may mask the classification as above, on, or below average. The differences could be explained by an inspection of the parameter estimates in the models fitted.

Conclusions: In case of correlations of center effects with center-specific mean values of a covariate, different risk adjustment methods can produce systematically differing results. This suggests the routine use of sensitivity analyses. Center effects in a conditional model need not reflect the position of a center above or below average, questioning its use in defining the truth. Further empirical investigations are necessary to judge the practical relevance of these findings.