BACKGROUND: Previous studies found that documentation of comorbidities differed when Veterans received care within versus outside Veterans Health Administration (VHA). Changes to medical center funding, increased attention to performance reporting, and expansion of Clinical Documentation Improvement programs, however, may have caused coding in VHA to change. METHODS: Using repeated cross-sectional data, we compared Elixhauser-van Walraven scores and Medicare Severity Diagnosis Related Group (DRG) severity levels for Veterans admissions across settings and payers over time, utilizing a linkage of VHA and all-payer discharge data for 2012-2017 in seven US states. To minimize selection bias, we analyzed records for Veterans admitted to both VHA and non-VHA hospitals in the same year. Using generalized linear models, we adjusted for patient and hospital characteristics. RESULTS: Following adjustment, VHA admissions consistently had the lowest predicted mean comorbidity scores (4.44 (95% CI 4.34-4.55)) and lowest probability of using the most severe DRG (22.1% (95% CI 21.4%-22.8%)). In contrast, Medicare-covered admissions had the highest predicted mean comorbidity score (5.71 (95% CI 5.56-5.85)) and highest probability of using the top DRG (35.3% (95% CI 34.2%-36.4%)). CONCLUSIONS: More effective strategies may be needed to improve VHA documentation, and current risk-adjusted comparisons should account for differences in coding intensity.

Veterans enrolled in the Veterans Affairs (VA) health care system gained greater access to non-VA care beginning in 2014. We examined hospital and Veteran characteristics associated with hospital choice. We conducted a longitudinal study of elective hospitalizations 2011 to 2017 in 11 states and modeled patients choice of VA hospital, large non-VA hospital, or small non-VA hospital in conditional logit models. Patients had higher odds of choosing a hospital with an academic affiliation, better patient experience rating, location closer to them, and a more common hospital type. Patients who were male, racial/ethnic minorities, had higher VA enrollment priority, and had a mental health comorbidity were more likely than other patients to choose a VA hospital than a non-VA hospital. Our findings suggest that patients respond to certain hospital attributes. VA hospitals may need to maintain or achieve high levels of quality and patient experience to attract or retain patients in the future.

PURPOSE: To examine changes in rural and urban Veterans utilization of acute inpatient care in Veterans Health Administration (VHA) and non-VHA hospitals following access expansion from the Veterans Choice Act, which expanded eligibility for VHA-paid community hospitalization. METHODS: Using repeated cross-sectional data of VHA enrollees hospitalizations in 9 states (AZ, CA, CT, FL, LA, MA, NY, PA, and SC) between 2012 and 2017, we compared rural and urban Veterans probability of admission in VHA and non-VHA hospitals by payer over time for elective and nonelective hospitalizations using multinomial logistic regression to adjust for patient-level sociodemographic features. We also used generalized linear models to compare rural and urban Veterans travel distances to hospitals. FINDINGS: Over time, the probability of VHA-paid community hospitalization increased more for rural Veterans than urban Veterans. For elective inpatient care, rural Veterans probability of VHA-paid admission increased from 2.9% (95% CI 2.6%-3.2%) in 2012 to 6.5% (95% CI 5.8%-7.1%) in 2017. These changes were associated with a temporal trend that preceded and continued after the implementation of the Veterans Choice Act. Overall travel distances to hospitalizations were similar over time; however, the mean distance traveled decreased from 39.2 miles (95% CI 35.1-43.3) in 2012 to 32.3 miles (95% CI 30.2-34.4) in 2017 for rural Veterans receiving elective inpatient care in VHA-paid hospitals. CONCLUSIONS: Despite limited access to rural hospitals, these data demonstrate an increase in rural Veterans use of non-VHA hospitals for acute inpatient care and a small reduction in distance traveled to elective inpatient services.

Adipocyte and β-cell dysfunction and macrophage-related chronic inflammation are critical for the development of obesity-related insulin resistance and type 2 diabetes mellitus (T2DM), which can be negatively regulated by Tregs. Our previous studies and those of others have shown that activation of γ-aminobutyric acid (GABA) receptors inhibits inflammation in mice. However, whether GABA could modulate high fat diet (HFD)-induced obesity, glucose intolerance and insulin resistance has not been explored. Here, we show that although oral treatment with GABA does not affect water and food consumption it inhibits the HFD-induced gain in body weights in C57BL/6 mice. Furthermore, oral treatment with GABA significantly reduced the concentrations of fasting blood glucose, and improved glucose tolerance and insulin sensitivity in the HFD-fed mice. More importantly, after the onset of obesity and T2DM, oral treatment with GABA inhibited the continual HFD-induced gain in body weights, reduced the concentrations of fasting blood glucose and improved glucose tolerance and insulin sensitivity in mice. In addition, oral treatment with GABA reduced the epididymal fat mass, adipocyte size, and the frequency of macrophage infiltrates in the adipose tissues of HFD-fed mice. Notably, oral treatment with GABA significantly increased the frequency of CD4(+)Foxp3(+) Tregs in mice. Collectively, our data indicated that activation of peripheral GABA receptors inhibited the HFD-induced glucose intolerance, insulin resistance, and obesity by inhibiting obesity-related inflammation and up-regulating Treg responses in vivo. Given that GABA is safe for human consumption, activators of GABA receptors may be valuable for the prevention of obesity and intervention of T2DM in the clinic.