Socioeconomic Disparities and Metabolic Risk in Veterans with Serious Mental Illness

Association of homelessness and diet on the gut microbiome: a United States-Veteran Microbiome Project (US-VMP) study

Military veterans account for 8% of homeless individuals living in the United States. To highlight associations between history of homelessness and the gut microbiome, we compared the gut microbiome of veterans who reported having a previous experience of homelessness to those from individuals who reported never having experienced a period of homelessness. Moreover, we examined the impact of the cumulative exposure of prior and current homelessness to understand possible associations between these experiences and the gut microbiome. Microbiome samples underwent genomic sequencing and were analyzed based on alpha diversity, beta diversity, and taxonomic differences. Additionally, demographic information, dietary data, and mental health history were collected. A lifetime history of homelessness was found to be associated with alcohol use disorder, substance use disorder, and healthy eating index compared to those without such a history. In terms of differences in gut microbiota, beta diversity was significantly different between veterans who had experienced homelessness and veterans who had never been homeless (P = 0.047, weighted UniFrac), while alpha diversity was similar. The microbial community differences were, in part, driven by a lower relative abundance of Akkermansia in veterans who had experienced homelessness (mean; range [in percentages], 1.07; 0-33.9) compared to veterans who had never been homeless (2.02; 0-36.8) (P = 0.014, ancom-bc2). Additional research is required to facilitate understanding regarding the complex associations between homelessness, the gut microbiome, and mental and physical health conditions, with a focus on increasing understanding regarding the longitudinal impact of housing instability throughout the lifespan.IMPORTANCEAlthough there are known stressors related to homelessness as well as chronic health conditions experienced by those without stable housing, there has been limited work evaluating the associations between microbial community composition and homelessness. We analyzed, for the first time, bacterial gut microbiome associations among those with experiences of homelessness on alpha diversity, beta diversity, and taxonomic differences. Additionally, we characterized the influences of diet, demographic characteristics, military service history, and mental health conditions on the microbiome of veterans with and without any lifetime history of homelessness. Future longitudinal research to evaluate the complex relationships between homelessness, the gut microbiome, and mental health outcomes is recommended. Ultimately, differences in the gut microbiome of individuals experiencing and not experiencing homelessness could assist in identification of treatment targets to improve health outcomes.

Care Retention Among Veterans with Serious Mental Illness who were once lost-to-Veterans Health Administration care

OBJECTIVE

To evaluate care retention among Veterans with serious mental illness (SMI) who were lost to Veterans Health Administration (VHA) care for at least one year and subsequently returned to VHA care via the SMI Re-Engagement program, an outreach program for Veterans with SMI who are lost-to-care.

METHODS

For the 410 Veterans with SMI who returned to care via SMI Re-Engagement between April 4th, 2016 and January 31, 2018, we assessed VHA in-person and telehealth utilization (overall, primary care, mental health care) for two years following the date of return to care.

RESULTS

Care retention was common: 70.2% of Veterans had at least one encounter in each year of the two-year follow-up period and an additional 22.7% had at least one encounter during one of the two years. During the two-year follow-up period, 72.4% of Veterans had at least one primary care encounter and 70.7% of Veterans had at least one mental health care encounter. Adjusted binomial logistic regression analyses found a return-to-care encounter in primary care (OR = 2.70; 95% CI: 1.34, 5.42) predicted primary care retention, and a return-to-care encounter in mental health care (OR = 4.01; 95% CI: 2.38, 6.75) predicted mental health care retention.

CONCLUSION

Most Veterans who return to care via the SMI Re-Engagement program remain in VHA care for the subsequent two years.

Development and Validation of a Clinical Risk Score to Predict Hospitalization Within 30 Days of Coronavirus Disease 2019 Diagnosis

INTRODUCTION

Early identification of patients with coronavirus disease 2019 (COVID-19) who are at risk for hospitalization may help to mitigate disease burden by allowing healthcare systems to conduct sufficient resource and logistical planning in the event of case surges. We sought to develop and validate a clinical risk score that uses readily accessible information at testing to predict individualized 30-day hospitalization risk following COVID-19 diagnosis.

METHODS

We assembled a retrospective cohort of U.S. Veterans Health Administration patients (age ≥ 18 years) diagnosed with COVID-19 between March 1, 2020, and December 31, 2020. We screened patient characteristics using Least Absolute Shrinkage and Selection Operator logistic regression and constructed the risk score using characteristics identified as most predictive for hospitalization. Patients diagnosed before November 1, 2020, comprised the development cohort, while those diagnosed on or after November 1, 2020, comprised the validation cohort. We assessed risk score discrimination by calculating the area under the receiver operating characteristic (AUROC) curve and calibration using the Hosmer-Lemeshow (HL) goodness-of-fit test. This study was approved by the Veteran's Institutional Review Board of Northern New England at the White River Junction Veterans Affairs Medical Center (Reference no.:1473972-1).

RESULTS

The development and validation cohorts comprised 11,473 and 12,970 patients, of whom 4,465 (38.9%) and 3,669 (28.3%) were hospitalized, respectively. The independent predictors for hospitalization included in the risk score were increasing age, male sex, non-white race, Hispanic ethnicity, homelessness, nursing home/long-term care residence, unemployed or retired status, fever, fatigue, diarrhea, nausea, cough, diabetes, chronic kidney disease, hypertension, and chronic obstructive pulmonary disease. Model discrimination and calibration was good for the development (AUROC = 0.80; HL P-value = .05) and validation (AUROC = 0.80; HL P-value = .31) cohorts.

CONCLUSIONS

The prediction tool developed in this study demonstrated that it could identify patients with COVID-19 who are at risk for hospitalization. This could potentially inform clinicians and policymakers of patients who may benefit most from early treatment interventions and help healthcare systems anticipate capacity surges.

A follow-up study of metabolic syndrome in schizophrenia

The prevalence of metabolic syndrome (MetS) in schizophrenia patients is increasing worldwide. The aim of the current study was to examine the progress of MetS in a schizophrenia cohort we had previously investigated and determine the role of various related factors, including sociodemographic and clinical variables, nutritional status and physical activity. Of the 319 patients investigated in the first study, 149 patients agreed to be included in the follow-up. Physical measurements and laboratory tests were performed in addition to evaluations with the Positive and Negative Syndrome Scale, Udvalg for Kliniske Undersogelser Side Effects Scale, International Physical Activity Questionnaire, 24 h dietary recall method and Nutrition Information Systems Package Program. According to the ATPIII, ATPIIIA and IDF criteria, the MetS prevalences had increased from 35.6 to 44.3%, 38.9 to 53% and 43.6 to 55.7%, respectively. Patients with MetS had a shorter period of hospitalization and a higher UKU total side effects score, and most of them were married or divorced/widowed. Patients with MetS also had a higher daily consumption of added sugar, cholesterol, polyunsaturated fatty acids and omega 3 fatty acid, and the daily added sugar intake was found to be related to the increase in MetS. Unexpectedly, the physical activity level was not found to significantly differ in the patients with and without MetS. In conclusion, the MetS prevalence was found to be increased among schizophrenia patients over time, and the increase in the young age group was particularly striking. Among all of the factors investigated, nutritional status was found to play a major role in this increased prevalence.

Cumulative Burden of Illness in Veterans With Tardive Dyskinesia and Serious Mental Disorders

PURPOSE/BACKGROUND

To inform cost-benefit decisions for veterans, the risk of tardive dyskinesia (TD) and its impact on comorbidities and outcomes were assessed.

METHODS/PROCEDURES

In a retrospective study, veterans with schizophrenia/schizoaffective, and bipolar and major depressive disorders receiving antipsychotics during the period October 1, 2014, to September 30, 2015, were identified. Tardive dyskinesia was determined by International Classification of Diseases, Ninth Revision, Clinical Modification codes. Correlates of TD were examined using χ or t tests. Odds ratios (ORs) and β parameters with 95% confidence intervals (CIs) for categorical and continuous variables associated with TD were derived from a multivariate logistic and linear regression, respectively.

FINDINGS/RESULTS

Among 7985 veterans, 332 (4.2%) were diagnosed as having possible TD. The odds of having TD were higher for older veterans (OR, 1.04; 95% CI, 1.03-1.05; P < 0.0001) and veterans with schizophrenia/schizoaffective disorder (OR, 1.54; 95% CI, 1.23-1.91; P < 0.0001) or diabetes (OR, 1.64; 95% CI, 1.30-2.06; P < 0.0001). Veterans with TD received more antipsychotic prescriptions (mean ± SD, 18.4 ± 30.3 vs 13.3 ± 26.4; P = 0.003) and days of supply (233.9 ± 95.4 vs 211.4 ± 102.0; P < 0.0001). They were more likely to have received 2 or more antipsychotics (27.1% vs 19.7%, P = 0.0009) and benztropine (OR, 2.25: 95% CI 1.73-2.91; P < 0.0001). Veterans with TD had a higher Charlson Comorbidity Index score (β = 0.32; SE, 0.09; 95% CI, 0.14-0.49; P = 0.0003) and higher odds of any medical hospitalization (OR, 1.45; 95% CI, 1.07-1.95; P = 0.001).

IMPLICATIONS/CONCLUSIONS

The diagnosis of possible TD was associated with older age, schizophrenia/schizoaffective disorder, medical comorbidity, and hospitalization. Tardive dyskinesia may be a marker for patients at risk of adverse health care outcomes and diminished quality of life.