Healthcare service utilization for formerly homeless veterans in permanent supportive housing: Do neighborhoods matter?

Neighborhood characteristics are associated with residents' healthcare use. However, we understand less about these relationships among formerly homeless persons, who often have complex healthcare needs, including mental health and substance use disorders. Among formerly homeless Veterans, we examined: (a) how neighborhood characteristics are associated with Veteran Health Administration (VHA) healthcare use and, (b) if these relationships varied by Veterans' level of healthcare need. We obtained data on our cohort of 711 Veterans housed through VHA's permanent supportive housing program (HUD-VASH) in 2016-2017 from VHA's Homeless Registry, VHA's electronic health records, and the U.S. Census. We studied the relationships between neighborhood characteristics (% Veteran, % in poverty, % unemployed, % using public transportation, and % vacant properties) and VA healthcare use (primary care visits, outpatient mental health visits, and "high use" of emergency departments [> 4 visits]) using mixed-effects logistic and negative binomial regression models, controlling for patient demographics. We further examined moderation by patient healthcare need (calculated from cost and clinical data). We found that veterans in neighborhoods with higher percentages of residents who (a) were Veterans or (b) used public transportation were more likely to have high emergency department use. Those in neighborhoods with higher public transportation use had more primary care visits while those in neighborhoods with more property vacancies had more outpatient mental health visits. Among those with high healthcare needs, residents of areas with more Veterans had higher emergency department use. Promoting public transportation use and social engagement with other Veterans in residential neighborhoods may influence HUD-VASH Veterans' VA healthcare use. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Military Sexual Trauma and Adverse Mental and Physical Health and Clinical Comorbidity in Women Veterans.

INTRODUCTION: Military sexual trauma (MST)-exposure to sexual harassment or assault during military service-is a major health priority for the Veterans Health Administration (VHA). We examined the health correlates of MST in the largest sample of U.S. women veterans studied to date. METHODS: Using national VHA electronic medical record data, we identified 502,199 women veterans who enrolled in the VHA between January 1, 2000, and December 31, 2017, had at least one VHA visit, and were screened for MST (exclusive of those who declined to answer the screening). We conducted logistic regression analyses to examine associations of a positive MST screen with various mental and physical health conditions-defined by administrative diagnostic codes-and comorbidity of mental and/or physical health conditions. Models were adjusted for demographic and military service characteristics, along with duration in the VHA. RESULTS: Approximately 26% (n = 130,738) of women veterans screened positive for MST. In fully adjusted models, a positive MST screen was associated with greater risk of having all mental and physical health conditions examined, except cancer-related conditions, ranging from 9% greater odds of rheumatic disease to 5.4 times greater odds of post-traumatic stress disorder. MST was also associated with greater comorbidity, including greater odds of having ≥2 mental health conditions (odds ratio [OR], 3.28; 99% confidence interval [CI], 3.20-3.37), having ≥2 physical health conditions (OR, 1.26; 99% CI, 1.22-1.29), and having ≥1 mental health condition and ≥1 physical health condition (OR, 2.05; 99% CI, 2.00-2.11). CONCLUSIONS: Findings suggest that MST is common in women veterans and may play a role in the clinical complexity arising from comorbid conditions.

The Utility of Clinical Notes for Sexual Minority Health Research.

INTRODUCTION: Despite improvements in electronic medical record capability to collect data on sexual orientation, not all healthcare systems have adopted this practice. This can limit the usability of systemwide electronic medical record data for sexual minority research. One viable resource might be the documentation of sexual orientation within clinical notes. The authors developed an approach to identify sexual orientation documentation and subsequently derived a cohort of sexual minority patients using clinical notes from the Veterans Health Administration electronic medical record. METHODS: A hybrid natural language processing approach was developed and used to identify and categorize instances of terms and phrases related to sexual orientation in Veterans Health Administration clinical notes from 2000 to 2019. System performance was assessed with positive predictive value and sensitivity. Data were analyzed in 2019. RESULTS: A total of 2,413,584 sexual minority terms/phrases were found within clinical notes, of which 439,039 (18%) were found to be related to patient sexual orientation with a positive predictive value of 85.9%. Documentation of sexual orientation was found for 115,312 patients. When compared with 2,262 patients with a record of administrative coding for homosexuality, the system found mentions of sexual orientation for 1,808 patients (79.9% sensitivity). CONCLUSIONS: When systemwide structured data are unavailable or inconsistent, deriving a cohort of sexual minority patients in electronic medical records for research is possible and permits longitudinal analysis across multiple clinical domains. Although limitations and challenges to the approach were identified, this study makes an important step forward for the Veterans Health Administration sexual minority research, and the methodology can be applied in other healthcare organizations.

Characterizing VA Users with the OMOP Common Data Model.

In 2015, the VA Informatics and Computing Infrastructure, a resource center of the Department of Veterans Affairs, began to transform parts of its Corporate Data Warehouse (CDW) into the Observational Medical Outcomes Partnership) Common Data Model for use by its research and operations communities. Using the hierarchical relationships within the clinical vocabularies in OMOP we found differences in visits, disease prevalence, and medications prescribed between male and female veterans seen between VA fiscal years 2000-17.

Comparison of automated and retrospectively calculated estimated glomerular filtration rate in electronic health record data.

BACKGROUND: Estimated glomerular filtration rate (eGFR) is the clinical standard for assessing kidney function and staging chronic kidney disease. Automated reporting of eGFR using the Modification of Diet in Renal Disease (MDRD) study equation was first implemented within the Department of Veterans Affairs (VA) in 2007 with staggered adoption across laboratories. When automated eGFR are not used or unavailable, values are retrospectively calculated using clinical and demographic data that are currently available in the electronic health record (EHR). Due to the dynamic nature of EHRs, current data may not always match past data. Whether and to what extent the practice of re-calculating eGFR on retrospective data differs from using the automated values is unknown. METHODS: We assessed clinical data for patients enrolled in VA who had their first automated eGFR lab in 2013.We extracted the eGFR value, the corresponding serum creatinine value, and patient race, gender, and date of birth from the EHR. The MDRD equation was applied to retrospectively calculate eGFR. Stage of chronic kidney disease (CKD) was defined using both eGFR values. We used Bland-Altman plots and percent agreement to assess the difference between the automated and calculated values. We developed an algorithm to select the most parsimonious parameter set to explain the difference in values and used chart review on a small subsample of patients to determine if one approach more accurately describes the patient at the time of eGFR measurement. RESULTS: We evaluated eGFR data pairs from 266,084 patients. Approximately 33.0% (n = 86,747) of eGFR values differed between automated and retrospectively calculated methods. The majority of discordant pairs were classified as the same CKD stage (n = 74,542, 85.93%). The Bland-Altman plot showed differences in the data pairs were centered near zero (mean difference: 0.8 mL/min/1.73m2) with 95% limits of agreement between - 6.4 and 8.0. A change in recorded age explained 95.6% (n = 78,903) of discordant values and 85.02% (n = 9371) of the discordant stages. CONCLUSIONS: Values of retrospectively calculated eGFR can differ from automated values, but do not always result in a significant classification change. In very large datasets these small differences could become significant.