Health Services Research最新文献

Objective: To use a practical approach to examining the use of Expert Recommendations for Implementing Change (ERIC) strategies by Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) dimensions for rural health innovations using annual reports on a diverse array of initiatives.

Data sources and study setting: The Veterans Affairs (VA) Office of Rural Health (ORH) funds initiatives designed to support the implementation and spread of innovations and evidence-based programs and practices to improve the health of rural Veterans. This study draws on the annual evaluation reports submitted for fiscal years 2020-2022 from 30 of these enterprise-wide initiatives (EWIs).

Study design: Content analysis was guided by the RE-AIM framework conducted by the Center for the Evaluation of Enterprise-Wide Initiatives (CEEWI), a Quality Enhancement Research Initiative (QUERI)-ORH partnered evaluation initiative.

Data collection and extraction methods: CEEWI analysts conducted a content analysis of EWI annual evaluation reports submitted to ORH. Analysis included cataloguing reported implementation strategies by Reach, Adoption, Implementation, and Maintenance (RE-AIM) dimensions (i.e., identifying strategies that were used to support each dimension) and labeling strategies using ERIC taxonomy. Descriptive statistics were conducted to summarize data.

Principal findings: A total of 875 implementation strategies were catalogued in 73 reports. Across these strategies, 66 unique ERIC strategies were reported. EWIs applied an average of 12 implementation strategies (range 3-22). The top three ERIC clusters across all 3 years were Develop stakeholder relationships (21%), Use evaluative/iterative strategies (20%), and Train/educate stakeholders (19%). Most strategies were reported within the Implementation dimension. Strategy use among EWIs meeting the rurality benchmark were also compared.

Conclusions: Combining the dimensions from the RE-AIM framework and the ERIC strategies allows for understanding the use of implementation strategies across each RE-AIM dimension. This analysis will support ORH efforts to spread and sustain rural health innovations and evidence-based programs and practices through targeted implementation strategies.

Objective: To compare theoretical strengths and limitations of common immortal time adjustment methods, propose a new approach using multiple imputation (MI), and provide practical guidance for using MI in precision medicine evaluations centered on a real-world case study.

Study setting and design: Methods comparison, guidance, and real-world case study based on previous literature. We compared landmark analysis, time-distribution matching, time-dependent analysis, and our proposed MI application. Guidance for MI spanned (1) selecting the imputation method; (2) specifying and applying the imputation model; and (3) conducting comparative analysis and pooling estimates. Our case study used a matched cohort design to evaluate overall survival benefits of whole-genome and transcriptome analysis, a precision medicine technology, compared to usual care for advanced cancers, and applied both time-distribution matching and MI. Bootstrap simulation characterized imputation sensitivity to varying data missingness and sample sizes.

Data sources and analytic sample: Case study used population-based administrative data and single-arm precision medicine program data from British Columbia, Canada for the study period 2012 to 2015.

Principal findings: While each method described can reduce immortal time bias, MI offers theoretical advantages. Compared to alternative approaches, MI minimizes information loss and better characterizes statistical uncertainty about the true length of the immortal time period, avoiding false precision. Additionally, MI explicitly considers the impacts of patient characteristics on immortal time distributions, with inclusion criteria and follow-up period definitions that do not inadvertently risk biasing evaluations. In the real-world case study, survival analysis results did not substantively differ across MI and time distribution matching, but standard errors based on MI were higher for all point estimates. Mean imputed immortal time was stable across simulations.

Conclusions: Precision medicine evaluations must employ immortal time adjustment methods for unbiased, decision-grade real-world evidence generation. MI is a promising solution to the challenge of immortal time bias.

Objective: To generate evidence regarding the offensive (customer acquisition) versus defensive (customer retention) motivation for pharmaceutical manufacturer coupons.

Data sources and study setting: Retail prescriptions from IQVIA's Formulary Impact Analyzer data between 2017 and 2019.

Study design: Ordinary least squares regression models with person, therapeutic class, drug, and time-fixed effects to measure the association between switching medications and coupon usage as well as the association between patient out-of-pocket spending and switching to a drug and using a coupon. To study switching type heterogeneity, reanalysis of associations for any type of switch, generic-brand switches, and brand-brand switches. Reestimation of baseline analyses for sodium-glucose cotransporter-2 inhibitors, anticoagulants, and inhaled corticosteroids/long-acting beta2-agonists to assess heterogeneity by drug class and market maturity.

Data collection: 1,167,132 privately insured patients that utilized at least one coupon between 2017 and 2019 for one or more prescriptions.

Principal findings: Coupon usage was associated with a 1.0 percentage point reduction in any kind of drug switch in the full sample and by 0.65-2.9 percentage points for the drug class subgroups. However, these estimates are governed by market dynamics; the probability of switching increased by 40% on the first coupon usage before declining by more than 50% on subsequent coupons. Switching after the first coupon use may be explained by systematic savings implied by coupon use; we find coupons reduced patient out-of-pocket spending by $45.00 (i.e., the majority of patient out-of-pocket costs). In subgroup analyses, coupon savings were $6.43 larger than average for anticoagulants, characterized by the highest levels of brand and generic competition among the considered therapeutic classes.

Conclusions: Pharmaceutical manufacturers may be using coupons to acquire customers and then build brand loyalty, especially in markets with more generic competition. Antitrust authorities and other regulators should scrutinize the impact of coupons on market competitiveness and drug spending.

Objective: To compare morbidity burden captured from multimorbidity indices and aggregated measures of clinically meaningful categories captured in primary care community-based health center (CBHC) patients.

Data sources and study setting: Electronic health records of patients seen in 2019 in OCHIN's national network of CBHCs serving patients in rural and underserved communities.

Study design: Age-stratified analyses comparing the most common conditions captured by the Charlson, Elixhauser, and Multimorbidity Weighted (MWI) indices, and Classification Software Refined (CCSR) and Chronic Condition Indicator (CCI) algorithms.

Data collection/extraction methods: Active ICD-10 conditions on patients' problem list in 2019.

Principal findings: Approximately 35%-56% of patients with at least one condition are not captured by the Charlson, Elixhauser, and MWI indices. When stratified by age, this range broadens to 9%-90% with higher percentages in younger patients. The CCSR and CCI reflect a broader range of acute and chronic conditions prevalent among CBHC patients.

Conclusion: Three commonly used indices to capture morbidity burden reflect conditions most prevalent among older adults, but do not capture those on problem lists for younger CBHC patients. An index with an expanded range of care conditions is needed to understand the complex care provided to primary care populations across the lifespan.

Objective: To understand whether and how primary care providers and staff elicit patients' past experiences of healthcare discrimination when providing care.

Data sources/study setting: Twenty qualitative semi-structured interviews were conducted with healthcare staff in primary care roles to inform future interventions to integrate data about past experiences of healthcare discrimination into clinical care.

Study design: Qualitative study.

Data collection/extraction methods: Data were collected via semi-structured qualitative interviews between December 2018 and January 2019, with health care staff in primary care roles at a hospital-based clinic within an urban safety-net health system that serves a patient population with significant racial, ethnic, and linguistic diversity.

Principal findings: Providers did not routinely, or in a structured way, elicit information about past experiences of healthcare discrimination. Some providers believed that information about healthcare discrimination experiences could allow them to be more aware of and responsive to their patients' needs and to establish more trusting relationships. Others did not deem it appropriate or useful to elicit such information and were concerned about challenges in collecting and effectively using such data.

Conclusions: While providers see value in eliciting past experiences of discrimination, directly and systematically discussing such experiences with patients during a primary care encounter is challenging for them. Collecting this information in primary care settings will likely require implementation of multilevel systematic data collection strategies. Findings presented here can help identify clinic-level opportunities to do so.

Objective: To examine the relationship between the penetration (or reach) of a national program aiming to integrate mental health clinicians into all primary care clinics (PC-MHI) and rates of guideline-concordant follow-up and treatment among clinic patients newly identified with depression in the Veterans Health Administration (VA).

Data sources/study setting: 15,155 screen-positive patients 607,730 patients with 2-item Patient Health Questionnaire scores in 82 primary care clinics, 2015-2019.

Study design: In this retrospective cohort study, we used established depression care quality measures to assess primary care patients who (a) newly screened positive (score ≥3) and (b) were identified with depression by clinicians via diagnosis and/or medication (n = 15,155; 15,650 patient-years). Timely follow-up included ≥3 mental health, ≥3 psychotherapy, or ≥3 primary care visits for depression. Minimally appropriate treatment included ≥4 mental health visits, ≥3 psychotherapy, or ≥60 days of medication. In multivariate regressions, we examined whether higher rates of PC-MHI penetration in clinic (proportion of total primary care patients in a clinic who saw any PC-MHI clinician) were associated with greater depression care quality among cohort patients, adjusting for year, healthcare system, and patient and clinic characteristics.

Data collection/extraction methods: Electronic health record data from 82 VA clinics across three states.

Principal findings: A median of 9% of all primary care patients were seen by any PC-MHI clinician annually. In fully adjusted models, greater PC-MHI penetration was associated with timely depression follow-up within 84 days (∆P = 0.5; SE = 0.1; p < 0.001) and 180 days (∆P = 0.3; SE = 0.1; p = 0.01) of a positive depression screen. Completion of at least minimal treatment within 12 months was high (77%), on average, and not associated with PC-MHI penetration.

Conclusions: Greater PC-MHI program penetration was associated with early depression treatment engagement at 84-/180-days among clinic patients newly identified with depression, with no effect on already high rates of completion of minimally sufficient treatment within the year.

Objective: To assess the effects of an evidence-based family caregiver training program (implementation of Helping Invested Families Improve Veteran Experiences Study [iHI-FIVES]) in the Veterans Affairs healthcare system on Veteran days not at home and family caregiver well-being.

Data sources and study setting: Participants included Veterans referred to home- and community-based services with an identified caregiver across 8 medical centers and confirmed family caregivers of eligible Veterans.

Study design: In a stepped wedge cluster randomized trial, sites were randomized to a 6-month time interval for starting iHI-FIVES and received standardized implementation support. The primary outcome, number of Veteran "days not at home," and secondary outcomes, changes over 3 months in measures of caregiver well-being, were compared between pre- and post-iHI-FIVES intervals using generalized linear models including covariates.

Data collection/extraction methods: Patient data were extracted from the electronic health record. Caregiver data were collected from 2 telephone-based surveys.

Principal findings: Overall, n = 898 eligible Veterans were identified across pre-iHI-FIVES (n = 327) and post-iHI-FIVES intervals (n = 571). Just under one fifth (17%) of Veterans in post-iHI-FIVES intervals had a caregiver enroll in iHI-FIVES. Veteran and caregiver demographics in pre-iHI-FIVES intervals were similar to those in post-iHI-FIVES intervals. In adjusted models, the estimated rate of days not at home over 6-months was 42% lower (rate ratio = 0.58 [95% confidence interval: 0.31-1.09; p = 0.09]) post-iHI-FIVES compared with pre-iHI-FIVES. The estimated mean days not at home over a 6-month period was 13.0 days pre-iHI-FIVES and 7.5 post-iHI-FIVES. There were no differences between pre- and post-iHI-FIVES in change over 3 months in caregiver well-being measures.

Conclusions: Reducing days not at home is consistent with effectiveness because more time at home increases quality of life. In this study, after adjusting for Veteran characteristics, we did not find evidence that implementation of a caregiver training program yielded a reduction in Veteran's days not at home.

Objective: Evaluate whether cost-sharing decreases led high-deductible health plans (HDHP) enrollees to increase their use of healthcare.

Data sources, study setting: National sample of chronically-ill patients age 18-64 from 2018 to 2020 (n = 1,318,178).

Study design: Difference-in-differences analyses using entropy-balancing weights were used to evaluate the effect of a policy shift to $0 cost-sharing for telehealth on utilization for HDHP compared with non-HDHP enrollees. Due to this shock, HDHP enrollees experienced substantial declines in cost-sharing for telehealth, while non-HDHP enrollees experienced small declines. Event study models were also used to evaluate changes over time.

Data collection/extraction methods: Outcomes included use of any outpatient care; use of $0 telehealth; use of $0 telehealth as a proportion of all outpatient care; and use of any telehealth. To test whether any differences were due to preferences for care modality versus cost-sharing, we further evaluated use of non-$0 telehealth as a placebo test.

Principal findings: There was no difference in change in overall outpatient visits (p = 0.84), with chronicall-ill HDHP enrollees using less care both before and after the policy shift. However, compared with non-HDHP enrollees, HDHP enrollees increased their use of $0 telehealth by 0.08 visits over a 9-month period, a 27% increase (95% CI 0.07-0.09, p < 0.001) and shifted 1.2 percentage points more of their care to $0 telehealth, a 15% increase (ß = 0.01, 95% CI 0.01, 0.01, p < 0.001). However, HDHP enrollees had lower uptake of non-$0 telehealth than non-HDHP enrollees (ß = -0.01, 95%CI -0.02, 0.00, p = 0.04).

Conclusions: Recent-but-expiring federal legislation exempts telehealth from HDHP deductibles for care provided in 2023 and 2024. Our results indicate that extending the protections provided by this legislation could help reduce the gap in access to care for chronically-ill persons enrolled in HDHPs.