Contextual factors influencing the association between the Affordable Care Act's Medicaid expansion and Veteran VA-Medicaid dual enrollment.

OBJECTIVE: To evaluate changes in dual enrollment after Affordable Care Act Medicaid expansion by VA priority group, (e.g., service connection), sex, and type of state expansion. STUDY SETTING: Our cohort was all Veterans ages 18-64 enrolled in VA and eligible for benefits due to military service-connection or low income from 2011 to 2016; the unit of analysis was person-year. STUDY DESIGN: Difference-in-difference and event-study analysis. The outcome was dual VA-Medicaid enrollment for at least 1 month annually. Medicaid expansion, VA priority status, whether a state expanded by a Section 1115 waiver, and sex were independent variables. We controlled for race, ethnicity, age, disease burden, distance to VA facilities, state, and year. DATA EXTRACTION METHODS: We used data from the VA Corporate Data Warehouse (CDW) regarding age and VA Priority Group to select our cohort of VA-enrolled individuals. We then took the cohort and crossed checked it with Medicaid Analytic Extract (MAX) and T-MSIS Analytic Files (TAF) to determine Medicaid enrollment status. PRINCIPAL FINDINGS: Service-connected Veterans experienced lower dual-enrollment increases across all sex and state-waiver groups (3.44 percentage points (95% CI: 1.83, 5.05 pp) for women, 3.93 pp (2.98, 4.98) for men, 4.06 pp (2.85, 5.27) for non-waiver states, and 3.00 pp (1.58 to 4.41) for waiver states) than Veterans who enrolled in the VA due to low income (8.19 pp (5.43, 10.95) for women, 9.80 pp (7.06, 12.54) for men, 10.21 pp (7.17, 13.25) for non-waiver states, and 7.39 pp (5.28, 9.50) for waiver states). CONCLUSIONS: Medicaid expansion is associated with dual enrollment. Dual-enrollment changes are greatest in those enrolled in the VA due to low income, but do not differ by sex or expansion type. Results can help VA identify groups disproportionately likely to have potential care-coordination issues due to usage of multiple health care systems.

Beyond Hospital-Level Aggregated Data: A Methodology to Adapt Clinical Data From the Electronic Health Record for Nursing Unit-Level Research.

BACKGROUND: Studies of nurse staffing frequently use data aggregated at the hospital level that do not provide the appropriate context to inform unit-level decisions, such as nurse staffing. OBJECTIVES: Describe a method to link patient data collected during the provision of routine care and recorded in the electronic health record (EHR) to the nursing units where care occurred in a national dataset. RESEARCH DESIGN: We identified all Veterans Health Administration acute care hospitalizations in the calendar year 2019 nationwide. We linked patient-level EHR and bar code medication administration data to nursing units using a crosswalk. We divided hospitalizations into segments based on the patient's time-stamped location (ward stays). We calculated the number of ward stays and medication administrations linked to a nursing unit and the unit-level and facility-level mean patient risk scores. RESULTS: We extracted data on 1117 nursing units, 3782 EHR patient locations associated with 1,137,391 ward stays, and 67,772 bar code medication administration locations associated with 147,686,996 medication administrations across 125 Veterans Health Administration facilities. We linked 89.46% of ward stays and 93.10% of medication administrations to a nursing unit. The average (standard deviation) unit-level patient severity across all facilities is 4.71 (1.52), versus 4.53 (0.88) at the facility level. CONCLUSIONS: Identification of units is indispensable for using EHR data to understand unit-level phenomena in nursing research and can provide the context-specific information needed by managers making frontline decisions about staffing.

Determinants of referral network size for screening colonoscopies in the Veterans Health Administration after the implementation of the MISSION Act.

OBJECTIVE: To measure key characteristics of the Veterans Health Administration's (VHA) Community Care (CC) referral network for screening colonoscopy and identify market and institutional factors associated with network size. DATA SOURCES: VHA electronic health records, CC claim data, and National Plan and Provider Enumeration System. STUDY DESIGN: In this retrospective cross-sectional study, we measure the size of the VHA's CC referral networks over time and by VHA parent facility (n = 137). We used a multivariable linear regression to identify factors associated with network size at the market-year level. Network size was measured as the number of physicians who performed at least one VHA-purchased screening colonoscopy per 1000 enrollees at baseline. DATA EXTRACTION: Data were extracted for all Veterans (n = 102,119) who underwent a screening colonoscopy purchased by the VHA from a non-VHA physician from 2018 to 2021. PRINCIPAL FINDINGS: From 2018 to 2021, median network volume of screening colonoscopies per 1000 enrollees grew from 1.6 (IQR: 0.6, 4.6) to 3.6 (IQR: 1.6, 6.6). The median network size grew from 0.63 (IQR: 0.30, 1.26) to 0.92 (IQR: 0.57, 1.63). Finally, the median procedures per physician increased from 2.5 (IQR: 1.6, 4.2) to 3.2 (IQR: 2.4, 4.7). After adjusting for baseline market characteristics, volume of screening colonoscopies was positively related to network size (ß = 0.15, 95% CI: [0.10, 0.20]), negatively related to procedures per physician (ß = -0.12, 95% CI: [-0.18, -0.05]), and positively associated with the percent of rural enrollees (ß = 0.01, 95% CI: [0.00, 0.01]). CONCLUSIONS: VHA facilities with a higher volume of VHA-purchased screening colonoscopies and more rural enrollees had more non-VHA physicians providing care. Geographic variation in referral networks may also explain differences in the effects of the MISSION Act on access to care and patient outcomes.

Veterans Health Administration enrollees' choice of care setting relates to the expansion of care options: Evidence from screening colonoscopies before and after the MISSION Act.

OBJECTIVE: To estimate whether those enrolled in the Veterans Health Administration (VHA) were less likely to use VHA-delivered colorectal cancer screening colonoscopies after the MISSION Act. DATA SOURCES AND STUDY SETTING: Secondary data were collected on VHA-enrolled Veterans from FY2017-FY2021. STUDY DESIGN: This retrospective cross-sectional study measured the volume and share of screening colonoscopies that were VHA-delivered over time and by drive time eligibility-defined as living more than 60 min away from the nearest VHA specialty-care clinic. We used a multivariable logistic regression to adjust for patient and facility factors. DATA EXTRACTION: Data were extracted for VHA enrollees (n = 773,766) who underwent a screening colonoscopy either performed or purchased by the VHA from FY2017-FY2021. PRINCIPAL FINDINGS: In the 9 months after the implementation of the MISSION Act, and before the onset of the Covid-19 pandemic, the average monthly VHA-share of screening colonoscopies decreased by 3 percentage points (pp; 95% confidence interval [CI] = [-4 to -2 pp]) for the non-drive time eligible group and it decreased by 16 pp (95% CI = [-22 to -9 pp]) for the drive time eligible group. The total number of screening colonoscopies did not significantly change in either group during this time period. After adjusting for patient characteristics, a linear time trend, and parent facility fixed effects, implementation of the MISSION Act was associated with a reduction in the probability of a VHA-delivered screening colonoscopy (average marginal effect [AME]: -2.5 pp; 95% CI = [-5.1 to 0.0 pp]) for the non-drive time eligible group. The drive time eligible group (AME: -9.4 pp; 95% CI = [-13.2 to -5.5 pp]) experienced a larger change. CONCLUSIONS: The VHA-share of screening colonoscopies among VHA enrollees fell in the 9 months immediately after the passage of the MISSION Act. This decline was larger for VHA enrollees who were targeted for eligibility due to a longer drive time. These results suggest that the MISSION Act led to more VHA-purchased care among targeted VHA enrollees, though it is unclear whether total utilization increased.

Case Sampling for Evaluating Hospital Postoperative Morbidity in US Surgical Quality Improvement Programs.

Importance: US surgical quality improvement (QI) programs use data from a systematic sample of surgical cases, rather than universal review of all cases, to assess and compare risk-adjusted hospital postoperative complication rates. Given decreasing postoperative complication rates over time and the types of cases eligible for abstraction, it is unclear whether case sampling is robust for identifying hospitals with higher than expected complications. Objective: To compare the assessment of hospital 30-day complication rates derived from sampling strategy used by some US surgical QI programs relative to universal review of all cases. Design, Setting, and Participants: This US hospital-level analysis took place from January 1, 2016, through September 30, 2020. Data analysis was performed from July 1, 2022, through December 21, 2022. Quarterly, risk-adjusted, 30-day complication observed to expected (O-E) ratios were calculated for each hospital using the sample (n = 502 730) and universal review (n = 1 725 364). Outlier hospitals (ie, those with higher than expected mortality) were identified using an O-E ratio significantly greater than 1.0. Patients 18 years and older who underwent a noncardiac operation at US Department of Veterans Affairs (VA) hospitals with a record in the VA Surgical Quality Improvement Program (systematic sample) and the VA Corporate Data Warehouse surgical domain (100% of surgical cases) were included. Main Outcome Measure: Thirty-day complications. Results: Most patients in both the representative sample and the universal sample were men (90.2% vs 91.2%) and White (74.7% vs 74.5%). Overall, 30-day complication rates were 7.6% and 5.3% for the sample and universal review cohorts, respectively (P < .001). Over 2145 hospital quarters of data, hospitals were identified as an outlier in 15.0% of quarters using the sample and 18.2% with universal review. Average hospital quarterly complication rates were 4.7%, 7.2%, and 7.4% for outliers identified using the sample only, universal review only, and concurrent identification in both data sources, respectively. For nonsampled cases, average hospital quarterly complication rates were 7.0% at outliers and 4.4% at nonoutliers. Among outlier hospital quarters in the sample, 54.2% were concurrently identified with universal review. For those identified with universal review, 44.6% were concurrently identified using the sample. Conclusion: In this observational study, case sampling identified less than half of hospitals with excess risk-adjusted postoperative complication rates. Future work is needed to ascertain how to best use currently collected data and whether alternative data collection strategies may be needed to better inform local QI efforts.

Medicaid Expansion's Final Frontier: Breaking State-Level Partisan Opposition.

This Viewpoint discusses the reasons why Medicaid has not been expanded in all US states and explains how expansion might finally be achieved in the 10 holdout states.

Case Sampling vs Universal Review for Evaluating Hospital Postoperative Mortality in US Surgical Quality Improvement Programs.

Importance: Representative surgical case sampling, rather than universal review, is used by US Department of Veterans Affairs (VA) and private-sector national surgical quality improvement (QI) programs to assess program performance and to inform local QI and performance improvement efforts. However, it is unclear whether case sampling is robust for identifying hospitals with safety or quality concerns. Objective: To evaluate whether the sampling strategy used by several national surgical QI programs provides hospitals with data that are representative of their overall quality and safety, as measured by 30-day mortality. Design, Setting, and Participants: This comparative effectiveness study was a national, hospital-level analysis of data from adult patients (aged ≥18 years) who underwent noncardiac surgery at a VA hospital between January 1, 2016, and September 30, 2020. Data were obtained from the VA Surgical Quality Improvement Program (representative sample) and the VA Corporate Data Warehouse surgical domain (100% of surgical cases). Data analysis was performed from July 1 to December 21, 2022. Main Outcomes and Measures: The primary outcome was postoperative 30-day mortality. Quarterly, risk-adjusted, 30-day mortality observed-to-expected (O-E) ratios were calculated separately for each hospital using the sample and universal review cohorts. Outlier hospitals (ie, those with higher-than-expected mortality) were identified using an O-E ratio significantly greater than 1.0. Results: In this study of data from 113 US Department of Veterans Affairs hospitals, the sample cohort comprised 502 953 surgical cases and the universal review cohort comprised 1 703 140. The majority of patients in both the representative sample and the universal sample were men (90.2% vs 91.1%) and were White (74.7% vs 74.5%). Overall, 30-day mortality was 0.8% and 0.6% for the sample and universal review cohorts, respectively (P < .001). Over 2145 quarters of data, hospitals were identified as an outlier in 11.7% of quarters with sampling and in 13.2% with universal review. Average hospital quarterly 30-day mortality rates were 0.4%, 0.8%, and 0.9% for outlier hospitals identified using the sample only, universal review only, and concurrent identification in both data sources, respectively. For nonsampled cases, average hospital quarterly 30-day mortality rates were 1.0% at outlier hospitals and 0.5% at nonoutliers. Among outlier hospital quarters in the sample, 47.4% were concurrently identified with universal review. For those identified with universal review, 42.1% were concurrently identified using the sample. Conclusions and Relevance: In this national, hospital-level study, sampling strategies employed by national surgical QI programs identified less than half of hospitals with higher-than-expected perioperative mortality. These findings suggest that sampling may not adequately represent overall surgical program performance or provide stakeholders with the data necessary to inform QI efforts.

Risk-Adjusted Cumulative Sum for Early Detection of Hospitals With Excess Perioperative Mortality.

Importance: National surgical quality improvement programs lack tools for early detection of quality or safety concerns, which risks patient safety because of delayed recognition of poor performance. Objective: To compare the risk-adjusted cumulative sum (CUSUM) with episodic evaluation for early detection of hospitals with excess perioperative mortality. Design, Setting, and Participants: National, observational, hospital-level, comparative effectiveness study of 697 566 patients. Identification of hospitals with excess, risk-adjusted, quarterly 30-day mortality using observed to expected ratios (ie, current criterion standard in the Veterans Affairs Surgical Quality Improvement Program) was compared with the risk-adjusted CUSUM. Patients included in the study underwent a noncardiac operation at a Veterans Affairs hospital, had a record in the Veterans Affairs Surgical Quality Improvement Program (January 1, 2011, through December 31, 2016), and were aged 18 years or older. Main Outcome and Measure: Number of hospitals identified as having excess risk-adjusted 30-day mortality. Results: The cohort included 697 566 patients treated at 104 hospitals across 24 quarters. The mean (SD) age was 60.9 (13.2) years, 91.4% were male, and 8.6% were female. For each hospital, the median number of quarters detected with observed to expected ratios, at least 1 CUSUM signal, and more than 1 CUSUM signal was 2 quarters (IQR, 1-4 quarters), 8 quarters (IQR, 4-11 quarters), and 3 quarters (IQR, 1-4 quarters), respectively. During 2496 total quarters of data, outlier hospitals were identified 33.3% of the time (830 quarters) with at least 1 CUSUM signal within a quarter, 12.5% (311 quarters) with more than 1 CUSUM signal, and 11.0% (274 quarters) with observed to expected ratios at the end of the quarter. The CUSUM detection occurred a median of 49 days (IQR, 25-63 days) before observed to expected ratio reporting (1 signal, 35 days [IQR, 17-54 days]; 2 signals, 49 days [IQR, 26-61 days]; 3 signals, 58 days [IQR, 44-69 days]; ≥4 signals, 49 days [IQR, 42-69 days]; trend test, P < .001). Of 274 hospital quarters detected with observed to expected ratios, 72.6% (199) were concurrently detected by at least 1 CUSUM signal vs 42.7% (117) by more than 1 CUSUM signal. There was a dose-response relationship between the number of CUSUM signals in a quarter and the median observed to expected ratio (0 signals, 0.63; 1 signal, 1.28; 2 signals, 1.58; 3 signals, 2.08; ≥4 signals, 2.49; trend test, P < .001). Conclusions: This study found that with CUSUM, hospitals with excess perioperative mortality can be identified well in advance of standard end-of-quarter reporting, which suggests episodic evaluation strategies fail to detect out-of-control processes and place patients at risk. Continuous performance evaluation tools should be adopted in national quality improvement programs to prevent avoidable patient harm.

Utilization Rates of SGLT2 Inhibitors Among Patients With Type 2 Diabetes, Heart Failure, and Atherosclerotic Cardiovascular Disease: Insights From the Department of Veterans Affairs.

BACKGROUND: Multiple clinical trials have demonstrated significant cardiovascular benefit with use of sodium-glucose cotransporter-2 (SGLT2) inhibitors in patients with type 2 diabetes (T2DM) and heart failure (HF) irrespective of ejection fraction. There are limited data evaluating real-world prescription and practice patterns of SGLT2 inhibitors. OBJECTIVES: The authors sought to assess utilization rates and facility-level variation in the use among patients with established atherosclerotic cardiovascular disease (ASCVD), HF, and T2DM using data from the nationwide Veterans Affairs health care system. METHODS: The authors included patients with established ASCVD, HF, and T2DM seen by a primary care provider between January 1, 2020, and December 31, 2020. They assessed the use of SGLT2 inhibitors and the facility-level variation in their use. Facility-level variation was computed using median rate ratios, a measure of likelihood that 2 random facilities differ in use of SGLT2 inhibitors. RESULTS: Among 105,799 patients with ASCVD, HF, and T2DM across 130 Veterans Affairs facilities, 14.6% received SGLT2 inhibitors. Patients receiving SGLT2 inhibitors were younger men with higher hemoglobin A1c and estimated glomerular filtration rate and were more likely to have HF with reduced ejection fraction and ischemic heart disease. There was significant facility-level variation of SGLT2 inhibitor use, with an adjusted median rate ratio of 1.55 (95% CI: 1.46-1.64), indicating a 55% residual difference in SGLT2 inhibitor use among similar patients with ASCVD, HF, and T2DM receiving care at 2 random facilities. CONCLUSIONS: Utilization rates of SGLT2 inhibitors are low in patients with ASCVD, HF, and T2DM, with high residual facility-level variation. These findings suggest opportunities to optimize SGLT2 inhibitor use to prevent future adverse cardiovascular events.

Disparities in Antifibrotic Medication Utilization Among Veterans With Idiopathic Pulmonary Fibrosis.

BACKGROUND: Two antifibrotic medications, pirfenidone and nintedanib, are approved for the treatment of idiopathic pulmonary fibrosis (IPF). Little is known about their real-world adoption. RESEARCH QUESTION: What are the real-world antifibrotic utilization rates and factors associated with uptake among a national cohort of veterans with IPF? STUDY DESIGN AND METHODS: This study identified veterans with IPF who received care either provided by the Veterans Affairs (VA) Healthcare System or non-VA care paid for by the VA. Patients who had filled at least one antifibrotic prescription through the VA pharmacy or Medicare Part D between October 15, 2014, and December 31, 2019, were identified. Hierarchical logistic regression models were used to examine factors associated with antifibrotic uptake, accounting for comorbidities, facility clustering, and follow-up time. Fine-Gray models were used to evaluate antifibrotic use by demographic factors, accounting for the competing risk of death. RESULTS: Among 14,792 veterans with IPF, 17% received antifibrotics. There were significant disparities in adoption, with lower uptake associated with female sex (adjusted OR, 0.41; 95% CI, 0.27-0.63; P < .001), Black race (adjusted OR, 0.60; 95% CI, 0.49-0.73; P < .001), and rural residence (adjusted OR, 0.88; 95% CI, 0.80-0.97; P = .012). Veterans who received their index diagnosis of IPF outside the VA were less likely to receive antifibrotic therapy (adjusted OR, 0.15; 95% CI, 0.10-0.22; P < .001). INTERPRETATION: This study is the first to evaluate the real-world adoption of antifibrotic medications among veterans with IPF. Overall uptake was low, and there were significant disparities in use. Interventions to address these issues deserve further investigation.

Nurse Leader Perceptions of Data in the Veterans Health Administration: A Qualitative Evaluation.

Healthcare systems and nursing leaders aim to make evidence-based nurse staffing decisions. Understanding how nurses use and perceive available data to support safe staffing can strengthen learning healthcare systems and support evidence-based practice, particularly given emerging data availability and specific nursing challenges in data usability. However, current literature offers sparse insight into the nature of data use and challenges in the inpatient nurse staffing management context. We aimed to investigate how nurse leaders experience using data to guide their inpatient staffing management decisions in the Veterans Health Administration, the largest integrated healthcare system in the United States. We conducted semistructured interviews with 27 Veterans Health Administration nurse leaders across five management levels, using a constant comparative approach for analysis. Participants primarily reported using data for quality improvement, organizational learning, and organizational monitoring and support. Challenges included data fragmentation, unavailability and unsuitability to user need, lack of knowledge about available data, and untimely reporting. Our findings suggest that prioritizing end-user experience and needs is necessary to better govern evidence-based data tools for improving nursing care. Continuous nurse leader involvement in data governance is integral to ensuring high-quality data for end-user nurses to guide their decisions impacting patient care.

A Novel Use of Bar Code Medication Administration Data to Assess Nurse Staffing and Workload.

OBJECTIVE: The aim of the study is to introduce an innovative use of bar code medication administration (BCMA) data, medication pass analysis, that allows for the examination of nurse staffing and workload using data generated during regular nursing workflow. METHODS: Using 1 year (October 1, 2014-September 30, 2015) of BCMA data for 11 acute care units in one Veterans Affairs Medical Center, we determined the peak time for scheduled medications and included medications scheduled for and administered within 2 hours of that time in analyses. We established for each staff member their daily peak-time medication pass characteristics (number of patients, number of peak-time scheduled medications, duration, start time), generated unit-level descriptive statistics, examined staffing trends, and estimated linear mixed-effects models of duration and start time. RESULTS: As the most frequent (39.7%) scheduled medication time, 9:00 was the peak-time medication pass; 98.3% of patients (87.3% of patient-days) had a 9:00 medication. Use of nursing roles and number of patients per staff varied across units and over time. Number of patients, number of medications, and unit-level factors explained significant variability in registered nurse (RN) medication pass duration (conditional R2 = 0.237; marginal R2 = 0.199; intraclass correlation = 0.05). On average, an RN and a licensed practical nurse (LPN) with four patients, each with six medications, would be expected to take 70 and 74 minutes, respectively, to complete the medication pass. On a unit with median 10 patients per LPN, the median duration (127 minutes) represents untimely medication administration on more than half of staff days. With each additional patient assigned to a nurse, average start time was earlier by 4.2 minutes for RNs and 1.4 minutes for LPNs. CONCLUSION: Medication pass analysis of BCMA data can provide health systems a means for assessing variations in staffing, workload, and nursing practice using data generated during routine patient care activities.

Medicaid Expansion and Veterans' Reliance on the VA for Depression Care.

Background: In 2001, before the Affordable Care Act (ACA), some states expanded Medicaid coverage to include an array of mental health services, changing veterans' reliance on US Department of Veterans Affairs (VA) services. Methods: Using Medicaid and VA administrative data from 1999 to 2006, we used a difference-in-difference design to calculate shifts in veterans' reliance on the VA for depression care in New York and Arizona after the 2 states expanded Medicaid coverage to adults in 2001. Demographically matched, neighbor states Pennsylvania and New Mexico/Nevada were used as paired comparisons, respectively. Fractional logit was used to capture the distribution of inpatient and outpatient depression care utilization between the VA and Medicaid, while ordered logit and negative binomial regressions were applied to model Medicaid-VA dual users and per capita utilization of total depression care services, respectively. Results: Medicaid expansion was associated with a 9.50 percentage point (pp) decrease (95% CI, -14.61 to -4.38) in reliance on the VA for inpatient depression care among service-connected veterans and a 13.37 pp decrease (95% CI, -21.12 to -5.61) among income-eligible veterans. For outpatient depression care, VA reliance decreased by 2.19 pp (95% CI, -3.46 to -0.93) among income-eligible veterans. Changes among service-connected veterans were nonsignificant (-0.60 pp; 95% CI, -1.40 to 0.21). Conclusions: After Medicaid expansion, veterans shifted depression care away from the VA, with effects varying by health care setting, income- vs service-related eligibility, and state of residence. Issues of overall cost, care coordination, and clinical outcomes deserve further study in the ACA era of Medicaid expansions.

State-Level Social Vulnerability Index and Healthcare Access in Patients With Atherosclerotic Cardiovascular Disease (from the BRFSS Survey).

We analyzed the association between social vulnerability index (SVI) and healthcare access among patients with atherosclerotic cardiovascular disease (ASCVD). Using cross-sectional data from the Behavioral Risk Factor Surveillance System 2016 to 2019, we identified measures related to healthcare access in individuals with ASCVD, which included healthcare coverage, presence of primary care clinician, duration since last routine checkup, delay in access to healthcare, inability to see doctor because of cost, and cost-related medication nonadherence. We analyzed the association of state-level SVI (higher SVI denotes higher social vulnerability) and healthcare access using multivariable-adjusted logistic regression models. The study population comprised 203,347 individuals aged 18 years or older who reported a history of ASCVD. In a multivariable-adjusted analysis, prevalence odds ratios (95% confidence interval) for participants residing in states in the third tertile of SVI compared with those in the first tertile (used as reference) were as follows: absence of healthcare coverage = 1.03 (0.85 to 1.24), absence of primary care clinician = 1.33 (1.12 to 1.58), >1 year since last routine checkup = 1.09 (0.96 to 1.23), delay in access to healthcare = 1.39 (1.18, 1.63), inability to see a doctor because of cost = 1.21 (1.06 to 1.40), and cost-related medication nonadherence = 1.10 (0.83 to 1.47). In conclusion, SVI is associated with healthcare access in those with pre-existing ASCVD. Due to the ability of SVI to simultaneously and holistically capture many of the factors of social determinants of health, SVI can be a useful measure for identifying high-risk populations.

State-Level Social Vulnerability Index and Healthcare Access: The Behavioral Risk Factor Surveillance System Survey.

INTRODUCTION: Access to health care is affected by social determinants of health. The social vulnerability index encompasses multiple social determinants of health simultaneously and may therefore be associated with healthcare access. METHODS: Cross-sectional data were used from the 2016‒2019 Behavioral Risk Factor Surveillance System, a nationally representative U.S. telephone-based survey of adults aged ≥18 years. State-level social vulnerability index was derived using county-level social vulnerability index estimates from the Centers for Disease Control and Prevention Agency for Toxic Substances and Disease Registry. Analyses were performed in October 2021. Social vulnerability index was ranked according to percentiles, which were divided into tertiles: Tertile 1 (0.10-0.32), Tertile 2 (0.33-0.53), and Tertile 3 (0.54-0.90). RESULTS: In multivariable-adjusted models comparing U.S. states in Tertile 3 with those in Tertile 1 of social vulnerability index, there was a higher prevalence of absence of healthcare coverage (OR=1.39 [95% CI=1.22, 1.58]), absence of primary care provider (OR=1.34 [95% CI=1.22, 1.48]), >1-year duration since last routine checkup (OR=1.18 [95% CI=1.10, 1.27]), inability to see a doctor because of cost (OR=1.38 [95% CI=1.23, 1.54]), and the composite variable of any difficulty in accessing healthcare (OR=1.15 [95% CI=1.08, 1.22]). CONCLUSIONS: State-level social vulnerability is associated with several measures related to healthcare access. These results can help to identify targeted interventions to improve access to health care in U.S. states with high social vulnerability index burden.

A Mistake Not to Be Repeated: What Can We Learn from the Underutilization of Statin Therapy for Efficient Dissemination of Cardioprotective Glucose Lowering Agents?

PURPOSE OF REVIEW: To review the factors contributing to underutilization of guideline-directed therapies, identify strategies to alleviate these factors, and apply these strategies for effective and timely dissemination of novel cardioprotective glucose-lowering agents. RECENT FINDINGS: Recent analyses demonstrate underutilization of cardioprotective glucose lowering agents despite guideline recommendations for their use. Major contributors to underutilization of guideline-directed therapies include therapeutic inertia, perceptions about side effects, and factors found at the level of the clinicians, patients, and the healthcare system. The recent emergence of several novel therapies, such as sodium-glucose cotransporter-2 inhibitors and glucagon-like peptide-1 receptor agonists, for use in cardiovascular disease provides a unique avenue to improve patient outcomes. To effectively utilize novel cardioprotective glucose lowering agents to improve cardiovascular outcomes, clinicians must recognize and learn from prior barriers to application of guideline-directed therapies. Further endeavors are prudent to ensure uptake of novel agents.