Health Services Research最新文献

Objective: To examine how a novel payer-provider joint venture (JV) between one payer and multiple competitive delivery systems in New Hampshire (NH), which included value-based payment, care management, and non-financial supports, impacted healthcare value and payer and provider group experiences.

Study setting and design: We conducted a mixed-methods study. We used a quasi-experimental longitudinal difference-in-differences design to examine the impact of the JV (which started in January 2016 and ended in December 2020) on healthcare utilization, quality, and spending, using members in Maine (ME) as a control group. We also analyzed patient uptake of the JV's care management program using routinely collected administrative data and assessed payer and provider group leaders' perspectives about the JV via semi-structured interviews.

Data sources and analytic sample: We used administrative and claims data from 2013 to 2019 in a commercially insured population under 65 years in NH and ME. We also used administrative data on care management eligibility and uptake and conducted semi-structured interviews with payer and provider group leaders affiliated with the JV.

Principal findings: The JV was associated with no sustained change in medical utilization, quality, and spending throughout the study period. In the first year of the JV, there was a $142 (95% confidence interval: $41, $243) increase in pharmaceutical spending per member and a 13% (4.4%, 25%) relative increase in days covered for diabetes medications. Only 15% of eligible members engaged in care management, which was a key component of the JV's multi-pronged approach. In a disconnect from the empirical findings, payer and provider group leaders believed that the JV reduced healthcare costs and improved quality.

Conclusions: Our findings provide evidence for future payer-provider JVs and demonstrate the importance of having a valid control group when evaluating JVs and value-based payment arrangements.

Objective: To examine the response of community health center (CHC) quality to quality levels at neighboring CHCs in the presence of non-price competition.

Data setting and design: A quasi-experimental study of US community health centers. Outcome variables were indices that measured overall quality of CHC care. Using patient flow data, we constructed CHC-specific Hirschman-Herfindahl index (HHI) and competitors' composite quality measure. The plausibly exogenous change in characteristics of "competitors' competitors" was exploited to identify the relationship between competition and quality of care, using a generalized two-stage least square model with instrumental variables.

Data sources and analytic sample: Using the Health Center Program Uniform Data System (2014-2018), linked with American Community Survey and Medical Expenditure Panel Survey, we analyzed 1098 unique federally funded CHCs in 50 states and District of Columbia which had at least one neighboring CHC and had non-missing data for 2015-2018 (4226 CHC-years).

Principal findings: Most of CHCs served populations in overlapping geographic markets, with median market concentration decreasing during the study period. A one-percent increase in competitors' quality was associated with a 0.71-percent increase in an index CHC's composite quality (p < 0.01), consisting of a 0.59-percent increase in chronic condition control rates (p < 0.01); a 0.68-percent increase in the screening and assessment rates (p < 0.01); and a 0.78-percent increase in medication management rates (p < 0.01). The association was stronger at CHCs serving a smaller proportion of uninsured patients. No significant quality reaction was observed at CHCs with a percentage of uninsured patients larger than the 75th percentile. We observed no significant associations between HHI and quality.

Conclusions: Increasing competition does not harm quality of care at CHCs. A CHC appears to improve its quality if its competitors improved quality. The beneficial quality effect was less pronounced in CHCs providing a significant proportion of care to uninsured patients, suggesting lack of incentives faced by these CHCs.

Objective: To investigate women's perceptions of telemedicine for reproductive health care services, focusing on how perceived benefits and privacy risks influence their intentions to adopt telemedicine and their willingness to disclose personal health information.

Study setting and design: A cross-sectional survey was conducted. The study applied the privacy calculus theory to the context of telemedicine for reproductive health, using adapted, validated variables to develop the survey. Outcome variables included intentions to adopt telemedicine and willingness to disclose accurate personal health information.

Data sources and analytic sample: Data were collected in May and June 2023 using Qualtrics online panel services, targeting women across the United States who had not used telemedicine for reproductive health. The sample comprised 847 women aged 18 and older. Structural equation modeling was employed using AMOS v28.0 to test the hypothesized relationships between perceived benefits, perceived risks, and adoption intentions. The analysis controlled for age, household income, political affiliation, religious views, and prior births.

Principal findings: Perceived benefits were positively related to intention to adopt telemedicine for reproductive care (β: 0.600, p < 0.001), and willingness to disclose accurate personal health information (β: 0.453, p < 0.001). Unexpectedly, perceived privacy risks were positively related to adoption intentions (β: 0.128, p < 0.001), but negatively related to willingness to disclose (β: -0.282, p < 0.001). Intentions to adopt were positively associated with willingness to disclose (β: 0.089, p < 0.05). Lastly, older women and women located in states with abortion restrictions expressed lower intentions to adopt. The model explained 40.2% of variance in intention to adopt and 38.3% of variance in willingness to disclose.

Conclusions: The study demonstrates the importance of perceived benefits and privacy risks in driving telemedicine adoption and disclosure intentions among women in the reproductive health context. These findings suggest the need for targeted strategies to address privacy concerns and support telemedicine adoption, particularly in restrictive regulatory environments.

Objective: To characterize the landscape of policies that determine eligibility for family planning services through Medicaid programs and describe trends in eligibility and its determinants over time.

Data sources and study setting: Secondary data were collected for all states in the United States for the years 2008 through 2023. Data on economic and demographic characteristics came from the American Community Survey (ACS).

Study design: Our descriptive study characterized state adoptions of Medicaid family planning section 1115 waivers and state plan amendments (SPA) and their eligibility criteria. We then estimated the proportion of women aged 19-44 years who were eligible for family planning services through Medicaid and identified the key determinants of changes in eligibility, by state and year.

Data collection/extraction methods: Information on state Medicaid policies was extracted from documentation on the Centers for Medicare & Medicaid Services website. When estimating the eligible population sizes, the denominator was women aged 19-44 years, the group most likely to be eligible for Medicaid family planning programs. Supplemental data on program enrollment or utilization were collected from states' websites and reports.

Principal findings: Though eligibility limits for family planning through Medicaid generally increased over time, the proportion of women aged 19-44 years eligible for at least limited benefits decreased from 45.0% in 2012 to 39.4% in 2022, largely because of increases in household income. Trends varied considerably across states and by eligibility pathway. Among women with incomes below the poverty level, the proportion who were not eligible for Medicaid family planning services decreased from 6.3% in 2013 to 1.5% in 2022.

Conclusions: Our data demonstrated substantial geographic and temporal variation in eligibility for family planning services through Medicaid. We identified key drivers of eligibility changes that may have important implications for health services analyses of means-tested public programs.

Objective: To examine how private Medicare Advantage (MA) plans responded to slower growth in federal payments after the Affordable Care Act (ACA).

Data sources and study setting: We used publicly available data from the Centers for Medicare and Medicaid Services on MA plan subsidies ("benchmarks"), asking prices ("bids"), plan premiums, cost-sharing, and covered benefits. Data covered all counties with MA plans between 2006 through 2019.

Study design: We examined plan responses to changes in benchmark subsidies by comparing changes in bids, rebates, and other outcomes between counties experiencing larger changes in benchmarks and counties with smaller changes, pre- and post-ACA. We used longitudinal fixed effects regression models to assess heterogeneity in how plans adjusted premiums and benefits across more and less competitive markets.

Data collection: Analyses included all counties with at least one MA plan available to individual beneficiaries. Plans targeting special populations were excluded.

Principal findings: Average plan benchmarks fell by $89 per month post-ACA, adjusted for inflation. Plans responded similarly to benchmark subsidy decreases and increases, increasing bids by 62 cents for every dollar increase in subsidies pre-ACA (95% confidence interval [CI]: 0.56 to 0.67) and decreasing them by 57 cents for every dollar reduction in subsidies post-ACA (95% CI: 0.49-0.65). However, post-ACA, plans altered less salient benefits, such as cost-sharing, by about twice as much as they had pre-ACA. Premiums changed by similar amounts before and after the ACA (-$0.07, 95% CI: from -$0.09 to -$0.06). Plans in more competitive markets responded less to payment changes than plans did in less competitive markets, suggesting the former are operating closer to marginal costs. Finally, payments to plans declined far less than projected due in part to other changes in MA policy.

Conclusions: Plans used partial pass-through of benchmark subsidy decreases to shield beneficiaries from cuts and allocated benchmark decreases to benefits that were less salient to the average enrollee. These findings, combined with higher-than-projected payments post-ACA, may explain the continued growth in MA enrollment.

Objective: To examine the association of Medicaid home- and community-based services (HCBS) expenditures on the home care workforce.

Data sources/study setting: We use two national, secondary data sources from 2008 to 2019: state-level Medicaid HCBS expenditures and the American Community Survey, in which we identify direct care workers in the home (i.e., home care workers), defined as nursing, psychiatric, and home health aides or personal care aides working in home health care services, individual and family services, and private households.

Study design: Our key explanatory variable is HCBS expenditures per state per year. To estimate the association between changes in Medicaid HCBS expenditures and the workforce size, hourly wages and hours worked, we use negative binomial, linear, and generalized ordered logit regression, respectively. All models include demographic and socioeconomic characteristics, the number of potential HCBS beneficiaries (individuals with a disability and income under the federal maximum income eligibility limits), indicators for minimum wage and/or overtime protections for direct care workers, wage pass-through policies, and state and year fixed effects.

Data collection/extraction methods: We exclude states with incomplete reporting of expenditures.

Principal findings: States' HCBS expenditures increased between 2008 and 2019 after adjusting for inflation and the number of potential HCBS beneficiaries. Yet, home care workers' wages remained stagnant at $11-12/h. We find no association between changes in Medicaid HCBS expenditures and wages. For every additional $1 million in Medicaid HCBS expenditures, the expected number of workers increases by 1.2 and the probability of working overtime increased (0.0015% points; p < 0.05). Results are largely robust under multiple sensitivity analyses.

Conclusions: We find no evidence of a statistically significant relationship between changes in state-level changes in Medicaid HCBS expenditures and worker wages but do find a significant, but small, association with hours worked and workforce size.

Objective: To evaluate whether having previously disenrolled from Medicare Advantage (MA) is associated with lower hazards of future MA enrollment.

Data sources and study setting: Secondary data from Medicare.

Study design: We examined beneficiaries with baseline FFS enrollment from 2017-2019 using a 20% sample of Medicare claims. Cox proportional hazard models were used to examine the association of prior MA enrollment (in the three years prior to baseline FFS enrollment) with MA re-enrollment, and whether this association is modified by Alzheimer's Disease and Related Dementias (ADRD), prior nursing home use, chronic illness, dual eligible status, and availability of MA plans and quality.

Data collection: Not applicable.

Principal findings: Overall, 3.3% of beneficiaries switched to MA annually. Of those with prior MA enrollment, MA switching percentages were 9.0%, 4.6%, and 6.8% for those whose most recent MA enrollments were 1, 2, and 3 years prior to their baseline FFS year. Comparatively, the switching percentages was 3.2% for those with no prior MA enrollment. The hazards of switching to MA were 2.73 (p < 0.001), 1.29 (p < 0.001), and 1.97 (p < 0.001) times greater than remaining in FFS for beneficiaries whose most recent MA enrollments were one, two, and three years prior to their baseline FFS year. Hazards of switching were generally similar between those with and without ADRD, stratified by recency in prior MA experience, except those with dual eligibility. Among those with ADRD, switching hazards were greatest for 3 years prior MA enrollees in counties with the fewest available (HR: 3.84, p < 0.001) and lowest-rated plans (HR: 4.02, p < 0.001).

Conclusions: Recency of switching from MA to FFS was the strongest predictor of a FFS-to-MA switch, identifying a population of beneficiaries who multiply switch regardless of health status or MA access. Future health policy considerations should more closely examine the vulnerabilities and long-term outcomes of this population.

Objective: To decompose the mental health disparities between breast cancer patients and survivors (hereafter survivors) of racial and ethnic minority groups and non-Hispanic White survivors into the contributions of individual-, interpersonal-, community-, and societal-level determinants.

Data sources and study setting: We used data from the 2010-2020 Medical Expenditure Panel Survey Household Component (MEPS-HC). Our primary outcome was whether the person had mental health conditions or not.

Study design: We employed the Kitagawa-Oaxaca-Blinder (KOB) method to understand to what extent the differences in outcomes were explained by the differences in the determinants between non-Hispanic Black or Hispanic breast cancer survivors and non-Hispanic White survivors. We also bifurcated the Hispanic sample analysis by the US-born status (and county of origin).

Data collection/extraction methods: Confidential geographic identifiers are utilized to supplement the MEPS-HC data with information on community characteristics and local healthcare resources.

Principal findings: The prevalence of mental health conditions among non-Hispanic Black and Hispanic breast cancer survivors was 26.1% (95% CI: 20.4, 31.7) and 28.3% (95% CI: 21.9, 34.6), respectively. These rates were higher than those for their non-Hispanic White counterparts, 19.7% (95% CI: 17.4, 21.9). In our KOB model, the disparity between non-Hispanic Black and White survivors was fully explained by differences in education, health, and family structure, with community- and societal-level determinants playing no significant role. Conversely, our KOB model did not explain any of the overall differences between Hispanic and non-Hispanic White survivors. However, for foreign-born Hispanic survivors, the disparity was fully explained by a combination of individual- and societal-level determinants.

Conclusions: Our findings, which identify specific individual-, interpersonal-, and societal- determinants that were associated with racial and ethnic differences in mental health, can be used by clinicians and policymakers to proactively address racial and ethnic disparities in health.