Health Services Research最新文献

Objectives: To examine how long COVID is associated with financial hardship (food insecurity, inability to pay bills, or threat of losing service) across income and education levels, and to assess the role of employment loss or reduced work hours in this hardship.

Data source and study setting: We used nationally representative data on 271,076 adults from the 2022 Behavioral Risk Factor Surveillance System (BRFSS).

Study design: We used multivariable binomial logistic regression models to estimate the average marginal effect of long COVID on financial hardships across multiple income and education groups.

Principal findings: In general, we found a significant positive association between long COVID and the three measures of financial hardships across income and education groups (1-11 percentage points increase, 95% CI 0.00-0.02 and 0.07-0.14, respectively). Mediation analysis showed that lost or reduced hours of employment accounted for a significant portion (6%-20%) of the changes in financial distress.

Conclusions: Long COVID has affected the economic wellbeing of people from all socioeconomic statuses, although at a higher rate for lower income groups. Policy attention is needed to address its economic impacts across income and education levels.

Objective: To analyze the variability in new infliximab biosimilar starts as well as switching from bio-originator to biosimilar infliximab, across insurance payers and rheumatology practices nationally.

Study setting and design: Data came from Rheumatology Informatics System for Effectiveness, a national registry with electronic health records from over 1100 US rheumatologists. Key outcomes include ever use of a biosimilar, date of initiation, and date of switching. Key variables of interest include insurance payer and practice.

Data sources and analytic sample: Secondary analysis of 37,560 patients aged ≥18 years administered infliximab (bio-originator or biosimilar) between April 2016 and September 2022 in Rheumatology Informatics System for Effectiveness. We tested for differences in use of biosimilar infliximab by demographic characteristics, socioeconomic status, and diagnosis using standard mean differences and multivariable modified Poisson regression. We used generalized estimating equations to assess the adjusted effect of insurance and year of initiation on new biosimilar starts. We analyzed variation in biosimilar switching by insurance, date of switch, and practice.

Principal findings: A total of 8196 (21.8%) infliximab users ever used a biosimilar and use did not differ significantly by demographic or clinical characteristics. In 2022, uptake among new users was higher among those with Medicaid (55%; 95%CI 43%-68%) and private insurance (51%; 95%CI 46%-57%) compared to Medicare (36%; 95%CI 29%-43%). Few prevalent bio-originator infliximab users switched to a biosimilar, and switching was lowest among Medicare beneficiaries (7% vs. 14.2% in Medicaid and 16.9% among privately insured). In adjusted analyses, practice level differences explained 37% of variation among new biosimilar starts and 34% of variation among those switching to a biosimilar.

Conclusions: Our findings underscore two critical areas for enhancing biosimilar infliximab usage: increasing switching among prevalent users and increasing uptake among Medicare beneficiaries initiating treatment. Significant variation in uptake across practices also suggests that local switching policies are likely key drivers of uptake.

Objective: To ascertain how an instrumental variables (IV) model can improve upon the estimates obtained from traditional cost-of-illness (COI) models that treat health conditions as predetermined.

Study setting and design: A simulation study based on observational data compares the coefficients and average marginal effects from an IV model to a traditional COI model when an unobservable confounder is introduced. The two approaches are then applied to real data, using a kinship-weighted family history as an instrument, and differences are interpreted within the context of the findings from the simulation study.

Data sources and analytic sample: The case study utilizes secondary data on type 2 diabetes mellitus (T2DM) status to examine healthcare costs attributable to the disease. The data come from Utah residents born between 1950 and 1970 with medical insurance coverage whose demographic information is contained in the Utah Population Database. Those data are linked to insurance claims from Utah's All-Payer Claims Database for the analyses.

Principal findings: The simulation confirms that estimated T2DM healthcare cost coefficients are biased when traditional COI models do not account for unobserved characteristics that influence both the risk of illness and healthcare costs. This bias can be corrected to a certain extent with instrumental variables. An IV model with a validated instrument estimates that 2014 costs for an individual age 45-64 with T2DM are 27% (95% CI: 2.9% to 51.9%) higher than those for an otherwise comparable individual who does not have T2DM.

Conclusions: Researchers studying the COI for chronic diseases should assess the possibility that traditional estimates may be subject to bias because of unobserved characteristics. Doing so may be especially important for prevention and intervention studies that turn to COI studies to assess the cost savings associated with such initiatives.

Objective: To evaluate whether race- and sex-based biases are present in a predictive model of avoidable hospital (AH) events.

Study setting and design: We examined whether Medicare fee-for-service (FFS) beneficiaries in Maryland with similar risk scores differed in true AH event risk on the basis of race or sex (n = 324,834). This was operationalized as a logistic regression of true AH events on race or sex with fixed effects for risk score percentile.

Data sources and analytic sample: Beneficiary-level risk scores were derived from 36 months of Medicare FFS claims (April 2019-March 2022) and generated in May 2022. True AH events were observed in claims from June 2022.

Principal findings: Black patients had higher average risk scores than White patients; however, the likelihood of experiencing an AH event did not differ by race when controlling for predicted risk (Marginal Effect [ME] = 0.0003, 95%CI -0.0003 to 0.0009). AH event likelihood was lower in males when controlling for risk level; however, the effect was small (ME = -0.0008, 95% CI -0.0013 to -0.0003) and it did not differ by sex for the target group for intervention (ME = 0.0002, 95% CI -0.0031 to 0.0036).

Conclusions: We implemented a simple bias assessment methodology and found no evidence of meaningful race- or sex-based bias in this model. We encourage the incorporation of bias checks into predictive model development and monitoring processes.

Objective: To test quantitative process measures characterizing the work of social needs case managers as they assisted patients with diverse health-related needs-spanning both medical and social domains.

Study setting and design: The study analyzed secondary data on 7076 patients working with 147 case managers from the CommunityConnect social needs case management program in Contra Costa County, California from 2018 to 2021. The service-designed to be holistic with a focus on social determinants as root causes of health issues-helped patients navigate social services, health care, and mental health care.

Data sources and analytic sample: We used cross-sectional analyses to quantitatively characterize electronic health records (EHRs) derived measures of case management intensity (goal updates), duration (days goal was open), and outcomes for 19 different categories of health and social goals. Mixed-effects regression models were used to examine how work process measures varied according to goal categories. Models nested goals within patients within case managers and adjusted for patient-level covariates.

Principal findings: The most common goals were dental care (53%), food (40%), and housing (39%). In adjusted analyses, housing goals had significantly more case manager updates than any other type of goal with a marginal mean of 14.0 updates (95% CI: 13.4-14.7), were worked on for significantly longer (marginal mean of 417 days, 95% CI: 360-474) than any goal except dental care, and were least likely to be resolved. Utilities, insurance, and medication coordination goals were most likely to be resolved.

Conclusions: Case managers and patients repeatedly worked on goals over many months. Meeting housing needs and accessing dental care were issues that were not easily resolved and required extensive follow-up. One-time referral interventions may need follow-up systems to meaningfully support social and health needs.

Objective: To evaluate healthcare cost and utilization changes among Medicaid and dually eligible participants of a supportive housing program implemented by a managed care organization and community-based organization.

Study setting and design: Healthcare claims were reviewed retrospectively for 80 program participants in one urban Pennsylvania county between 1/1/2018 and 9/28/2023 who had ≥6 months of claims data in both pre- and post-housing periods. Eligibility included age >18 years, Medicaid/Special Needs Plan enrollment, and housing need. Due to limited housing units, potential participants were prioritized by medical need and history of unplanned care.

Data sources and analytic sample: Healthcare cost and utilization were compared during pre- (i.e., 12 months before housing initiation) and post-periods (i.e., 12 months after housing initiation).

Principal findings: Compared to the pre-period, significantly lower medical (-40.4%, p = 0.004), emergency department (-62.7%, p = 0.02), and total (-33.3%, p = 0.02) costs of care were observed in the post-period. Significantly lower primary care (-50.0%, p = 0.0003), specialist (-31.3%, p = 0.02), and emergency department (-50.0%, p = 0.03) utilization were also observed.

Conclusions: Healthcare cost and utilization among medically complex individuals were lower with supportive housing. Future evaluations with randomized designs can address the potential causal impact of supportive housing as a healthcare intervention on specific outcomes.

Objective: To investigate provider and administrators' perspectives about the impact of the Department of Veterans Affairs' (VA) Community Care program on acute and residential mental health treatment of rural Veterans.

Data sources and study setting: Primary data were collected from participants via interviews. Participants were employees of VA Healthcare Systems located in Northern New England, or employees of non-VA mental health treatment settings affiliated with VA in Northern New England.

Study design: This study was informed by the Consolidated Framework for Implementation Research (CFIR), with Community Care as the implemented program. Individual, semi-structured interviews were conducted.

Data collection/extraction methods: Individual interviews were transcribed, coded deductively using the CFIR, and inductively coded by locating themes.

Principal findings: Twenty-one people completed interviews. Commonly reported challenges included community programs not focused on Veterans' needs, poor coordination of care, communication challenges, and problems tracking Veteran care. Facilitators included increased access to care and strengthening coordination of care.

Conclusions: The VA's Community Care program can address the acute or residential mental health needs of Veterans in rural settings in some circumstances, however there are challenges to successful implementation.