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OBJECTIVE: To identify organizational service features associated with positive patient ratings of primary care within primary care clinics tailored to accommodate persons with ongoing and recent experiences of homelessness (PEH). DATA SOURCES AND STUDY SETTING: PEH receiving primary care in 29 United States Veterans Health Administration homeless-tailored clinics were surveyed about their primary care experience using the validated Primary Care Quality-Homeless (PCQ-H) survey. Characteristics of the clinics were assessed through surveys of clinic staff using a new organizational survey developed through literature review, site visits, statistical analysis, and consensus deliberation. STUDY DESIGN: Cross-sectional examination of patients ratings of care based on surveys of patients, and of clinic characteristics, analyzed with Classification and Regression Tree (CART) analysis, a form of machine learning. DATA COLLECTION METHODS: Patient surveys (n = 3394) were obtained from a random sample of enrolled patients by both mail and telephone by an external survey contractor. Staff (n = 52 from 29 clinics) were interviewed by telephone. PRINCIPAL FINDINGS: This analysis identified service features that impact patient experience favorably, including aspects of patient-centeredness, team identity, strong external leadership support, and service that reach beyond traditional primary care clinic confines. Results varied according to the patient experience scale analyzed. Individual characteristics of PEH, such as degree of social support, general health, and unsheltered status, were also correlated with how they rate care. CONCLUSIONS: Organizational characteristics correlate with ratings of primary care from patients with recent and ongoing homelessness. Primary care programs serving homeless individuals can assure better care based on who they hire, how they foster team identity, what services they provide, and the strength of leadership support to protect a homeless-focused mission.

BACKGROUND: Chronic pain and problematic substance use are prevalent among Veterans with homeless experience (VHE) and may contribute to a challenging primary care experience. OBJECTIVE: To examine the association of chronic pain and problematic substance use with unfavorable primary care experiences among VHE and to explore the association of pain treatment utilization and unfavorable care experiences in VHE with chronic pain. METHODS: We surveyed VHE (n = 3039) engaged in homeless-tailored primary care at 29 Veterans Affairs Medical Centers (VAMCs). We assessed unfavorable primary care experiences with four validated Primary Care Quality-Homeless (PCQ-H) scales: multivariable logistic regressions explored associations between unfavorable care experiences for VHE with chronic pain and problematic substance use, chronic pain alone, problematic substance use alone, or neither. We then examined the association between receipt of pain treatments and unfavorable experiences among VHE with chronic pain. Last, we identified PCQ-H items that had the greatest difference in unfavorable response rates between VHE with and without chronic pain. RESULTS: The prevalence of unfavorable primary care experience was higher on all four scales for patients reporting chronic pain (with or without problematic substance use) (all p

INTRODUCTION: Initiation of medications for opioid use disorder (MOUD) within the hospital setting may improve outcomes for people who inject drugs (PWID) hospitalized because of an infection. Many studies used International Classification of Diseases (ICD) codes to identify PWID, although these may be misclassified and thus, inaccurate. We hypothesized that bias from misclassification of PWID using ICD codes may impact analyses of MOUD outcomes. METHODS: We analyzed a cohort of 36 868 cases of patients diagnosed with Staphylococcus aureus bacteremia at 124 US Veterans Health Administration hospitals between 2003 and 2014. To identify PWID, we implemented an ICD code-based algorithm and a natural language processing (NLP) algorithm for classification of admission notes. We analyzed outcomes of prescribing MOUD as an inpatient using both approaches. Our primary outcome was 365-day all-cause mortality. We fit mixed-effects Cox regression models with receipt or not of MOUD during the index hospitalization as the primary predictor and 365-day mortality as the outcome. RESULTS: NLP identified 2389 cases as PWID, whereas ICD codes identified 6804 cases as PWID. In the cohort identified by NLP, receipt of inpatient MOUD was associated with a protective effect on 365-day survival (adjusted hazard ratio, 0.48; 95% confidence interval, .29-.81; P < .01) compared with those not receiving MOUD. There was no significant effect of MOUD receipt in the cohort identified by ICD codes (adjusted hazard ratio, 1.00; 95% confidence interval, .77-1.30; P = .99). CONCLUSIONS: MOUD was protective of all-cause mortality when NLP was used to identify PWID, but not significant when ICD codes were used to identify the analytic subjects.

BACKGROUND: The United States has been grappling with the opioid epidemic, which has resulted in over 75,000 opioid-related deaths between April 2020 and 2021. Evidence-based pharmaceutical interventions (buprenorphine, methadone, and naltrexone) are available to reduce opioid-related overdoses and deaths. However, adoption of these medications for opioid use disorder has been stifled due to individual- and system-level barriers. External facilitation is an evidence-based implementation intervention that has been used to increase access to medication for opioid use disorder (MOUD), but the implementation costs of external facilitation have not been assessed. We sought to measure the facility-level direct costs of implementing an external facilitation intervention for MOUD to provide decision makers with estimates of the resources needed to implement this evidence-based program. METHODS: We performed a cost analysis of the pre-implementation and implementation phases, including an itemization of external facilitation team and local site labor costs. We used labor estimates from the Bureau of Labor and Statistics, and sensitivity analyses were performed using labor estimates from the Veterans Health Administration (VHA) Financial Management System general ledger data. RESULTS: The average total costs for implementing an external facilitation intervention for MOUD per site was $18,847 (SD 6717) and ranged between $11,320 and $31,592. This translates to approximately $48 per patient with OUD. Sites with more encounters and participants with higher salaries in attendance had higher costs. This was driven mostly by the labor involved in planning and implementation activities. The average total cost of the pre-implementation and implementation activities were $1031 and $17,816 per site, respectively. In the sensitivity analysis, costs for VHA were higher than BLS estimates likely due to higher wages. CONCLUSIONS: Implementing external facilitation to increase MOUD prescribing may be affordable depending on the payers budget constraints. Our study reported that there were variations in the time invested at each phase of implementation and the number and type of participants involved with implementing an external facilitation intervention. Participant composition played an important role in total implementation costs, and decision makers will need to identify the most efficient and optimal number of stakeholders to involve in their implementation plans.

BACKGROUND: Persons who experience homelessness (PEH) have high rates of depression and incur challenges accessing high-quality health care. Some Veterans Affairs (VA) facilities offer homeless-tailored primary care clinics, although such tailoring is not required, within or outside VA. Whether services tailoring enhances care for depression is unstudied. OBJECTIVE: To determine whether PEH in homeless-tailored primary care settings receive higher quality of depression care, compared to PEH in usual VA primary care. DESIGN: Retrospective cohort study of depression treatment among a regional cohort of VA primary care patients (2016-2019). PARTICIPANTS: PEH diagnosed or treated for a depressive disorder. MAIN MEASURES: The quality measures were timely follow-up care (3 + completed visits with a primary care or mental health specialist provider, or 3 + psychotherapy sessions) within 84 days of a positive PHQ-2 screen result, timely follow-up care within 180 days, and minimally appropriate treatment (4 + mental health visits, 3 + psychotherapy visits, 60 + days antidepressant) within 365 days. We applied multivariable mixed-effect logistic regressions to model differences in care quality for PEH in homeless-tailored versus usual primary care settings. KEY RESULTS: Thirteen percent of PEH with depressive disorders received homeless-tailored primary care (n = 374), compared to usual VA primary care (n = 2469). Tailored clinics served more PEH who were Black, who were non-married, and who had low income, serious mental illness, and substance use disorders. Among all PEH, 48% received timely follow-up care within 84 days of depression screening, 67% within 180 days, and 83% received minimally appropriate treatment. Quality metric attainment was higher for PEH in homeless-tailored clinics, compared to PEH in usual VA primary care: follow-up within 84 days (63% versus 46%; adjusted odds ratio [AOR] = 1.61, p = .001), follow-up within 180 days (78% versus 66%; AOR = 1.51, p = .003), and minimally appropriate treatment (89% versus 82%; AOR = 1.58, p = .004). CONCLUSIONS: Homeless-tailored primary care approaches may improve depression care for PEH.

Surveys of underserved patient populations are needed to guide quality improvement efforts but are challenging to implement. The goal of this study was to describe recruitment and response to a national survey of Veterans with homeless experience (VHE). We randomly selected 14,340 potential participants from 26 U.S. Department of Veterans Affairs (VA) facilities. A survey contract organization verified/updated addresses from VA administrative data with a commercial address database, then attempted to recruit VHE through 4 mailings, telephone follow-up, and a $10 incentive. We used mixed-effects logistic regressions to test for differences in survey response by patient characteristics. The response rate was 40.2% (n=5,766). Addresses from VA data elicited a higher response rate than addresses from commercial sources (46.9% vs 31.2%, p

OBJECTIVE: High BMI is associated with many comorbidities and mortality. This study aimed to elucidate the overall clinical risk of obesity using a genome- and phenome-wide approach. METHODS: This study performed a phenome-wide association study of BMI using a clinical cohort of 736,726 adults. This was followed by genetic association studies using two separate cohorts: one consisting of 65,174 adults in the Electronic Medical Records and Genomics (eMERGE) Network and another with 405,432 participants in the UK Biobank. RESULTS: Class 3 obesity was associated with 433 phenotypes, representing 59.3% of all billing codes in individuals with severe obesity. A genome-wide polygenic risk score for BMI, accounting for 7.5% of variance in BMI, was associated with 296 clinical diseases, including strong associations with type 2 diabetes, sleep apnea, hypertension, and chronic liver disease. In all three cohorts, 199 phenotypes were associated with class 3 obesity and polygenic risk for obesity, including novel associations such as increased risk of renal failure, venous insufficiency, and gastroesophageal reflux. CONCLUSIONS: This combined genomic and phenomic systematic approach demonstrated that obesity has a strong genetic predisposition and is associated with a considerable burden of disease across all disease classes.