External Determinants of Veterans’ Utilization of<scp>VA</scp>Health Care

Hanchate, Amresh; Frakt, Austin B.; Kressin, Nancy R.; Trivedi, Amal N.; Linsky, Amy; Abdulkerim, Hassen; Stolzmann, Kelly; Mohr, David C.; Pizer, Steven D.

doi:10.1111/1475-6773.13011

Cited by 10 publications

(9 citation statements)

References 23 publications

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“…Quantitative survivorship was overestimated compared with national registries with captured patient populations. As with all health systems in the United States, VHA patients with other insurance options 20 may receive revision surgery elsewhere. Quantitative comparisons with other registries are difficult because our calculated survivorship is based on isolated implant component revision rates, whereas the AOANJRR and other national registries commonly report on implant pairs and define failure as revision for any reason, which may or may not include removal of the implant of interest.…”

Section: Discussionmentioning

confidence: 99%

Assessment of Extractability and Accuracy of Electronic Health Record Data for Joint Implant Registries

et al. 2021

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IMPORTANCE Implant registries provide valuable information on the performance of implants in a real-world setting, yet they have traditionally been expensive to establish and maintain. Electronic health records (EHRs) are widely used and may include the information needed to generate clinically meaningful reports similar to a formal implant registry. OBJECTIVES To quantify the extractability and accuracy of registry-relevant data from the EHR and to assess the ability of these data to track trends in implant use and the durability of implants (hereafter referred to as implant survivorship), using data stored since 2000 in the EHR of the largest integrated health care system in the United States.

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Section: Discussionmentioning

confidence: 99%

Assessment of Extractability and Accuracy of Electronic Health Record Data for Joint Implant Registries

et al. 2021

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“…16,17 Among working-age males, Hanchate and colleagues found that greater Medicaid generosity and higher proportion of employer-sponsored insurance are associated with lower utilization of VHA care. 16 We extend those findings to show that higher private health insurance coverage in the region is associated with lower risk of preventable hospitalization or death for VHA patients. A higher proportion of health insurance coverage among enrollees, including Medicare, Medicaid or private insurance, reduces demand for VHA care.…”

Section: Discussionmentioning

confidence: 99%

“…Studies have found that veterans with higher household median income tend to have private insurance and are less likely to use VHA care 7,15. The HPI measures changes in housing prices since 1991, representing financial gains for homeowners but increased financial pressure for renters 16. Hanchate et al16 found an effect of the HPI consistent with a higher proportion of renters among veterans, namely that a higher HPI increased utilization of VHA care.…”

Section: Methodsmentioning

confidence: 99%

“…The HPI measures changes in housing prices since 1991, representing financial gains for homeowners but increased financial pressure for renters 16. Hanchate et al16 found an effect of the HPI consistent with a higher proportion of renters among veterans, namely that a higher HPI increased utilization of VHA care. The veteran unemployment rate was associated with higher demand for VHA care, as unemployed veterans could lose both income and employer-sponsored insurance 16,17…”

Section: Methodsmentioning

confidence: 99%

“…The veteran unemployment rate was associated with higher demand for VHA care, as unemployed veterans could lose both income and employer-sponsored insurance. 16,17 These annual market variables were reported at the county-level or state-level, and aggregated to the VHA facility-level by calculating weighted average values. Enrollees' residential information was used to create county-level weights (ie, the proportion of enrollees from each county) and to calculate weighted average values at the VHA facilitylevel.…”

Section: Covariatesmentioning

confidence: 99%

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Regional Economic Conditions and Preventable Hospitalization Among Older Patients With Diabetes

et al. 2021

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Objective: The aim was to explore the relationship between changes in regional economic conditions and quality of care-preventable hospitalization or death among older patients with diabetes at Veterans Health Administration (VHA), safety-net system for veterans.Subjects: VHA patients aged 65 years and older with a diabetes diagnosis between July 2012 and June 2014, who had at least 1 primary care visit in the past year.Measures: County-level and state-level public data were used to characterize regional health insurance coverage and affluence surrounding the VHA facilities. Each patient was associated with a VHA facility and its corresponding regional market variables, and followed up to 48 months or until they experienced diabetes-related Prevention Quality Indicators or death.Results: Discrete-time Cox proportional hazards models estimated that changes in regional market variables characterizing regional health insurance coverage and affluence were significant factors associated with preventable hospitalization or death. All regional market variables were combined into a demand index, where 1 SD decrease in the demand index was associated with a 2.0-point increase in predicted survival for an average patient at an average VHA facility. For comparison, a 1 SD increase in primary care capacity was associated with 4.7-point increase.Conclusions: Downturns in regional economic conditions could increase demand for VHA care and raise the risk of diabetes-related preventable hospitalization or death among older VHA patients diagnosed with diabetes. Safety-net hospitals may be unfairly penalized for lower quality of care when experiencing higher demand for care because of an economic downturn.

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Early Impact of VA MISSION Act Implementation on Primary Care Appointment Wait Time

et al. 2022

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Background Through Community Care Networks (CCNs) implemented with the VA MISSION Act, VA expanded provider contracting and instituted network adequacy standards for Veterans’ community care. Objective To determine whether early CCN implementation impacted community primary care (PC) appointment wait times overall, and by rural/urban and PC shortage area (HPSA) status. Design Using VA administrative data from February 2019 through February 2020 and a difference-in-differences approach, we compared wait times before and after CCN implementation for appointments scheduled by VA facilities that did (CCN appointments) and did not (comparison appointments) implement CCNs. We ran regression models with all appointments, and stratified by rural/urban and PC HPSA status. All models adjusted for Veteran characteristics and VA facility–level clustering. Appointments 13,720 CCN and 40,638 comparison appointments. Main Measures Wait time, measured as number of days from authorization to use community PC to a Veteran’s first corresponding appointment. Key Results Overall, unadjusted wait times increased by 35.7 days ([34.4, 37.1] 95% CI) after CCN implementation. In adjusted analysis, comparison wait times increased on average 33.7 days ([26.3, 41.2] 95% CI, p < 0.001) after CCN implementation; there was no significant difference for CCN wait times (across-group mean difference: 5.4 days, [−3.8, 14.6] 95% CI, p = 0.25). In stratified analyses, comparison wait time increases ranged from 29.6 days ([20.8, 38.4] 95% CI, p < 0.001) to 42.1 days ([32.9, 51.3] 95% CI, p > 0.001) after CCN implementation, while additional differences for CCN appointments ranged from 13.4 days ([3.5, 23.4] 95% CI, p = 0.008) to −15.1 days ([−30.1, −0.1] 95% CI, p = 0.05) for urban and PC HPSA appointments, respectively. Conclusions After early CCN implementation, community PC wait times increased sharply at VA facilities that did and did not implement CCNs, regardless of rural/urban or PC HPSA status, suggesting community care demand likely overwhelmed VA resources such that CCNs had limited impact.

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External Determinants of Veterans’ Utilization ofVAHealth Care

Abstract: Changes in alternative insurance coverage and other external determinants may affect VA health care spending. Policy makers should consider these factors in allocating VA resources to meet local demand.

Cited by 10 publications

References 23 publications

Assessment of Extractability and Accuracy of Electronic Health Record Data for Joint Implant Registries

Assessment of Extractability and Accuracy of Electronic Health Record Data for Joint Implant Registries

Regional Economic Conditions and Preventable Hospitalization Among Older Patients With Diabetes

Early Impact of VA MISSION Act Implementation on Primary Care Appointment Wait Time

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