Relationship between mental health diagnoses and COVID-19 test positivity, hospitalization, and mortality in Southeast Wisconsin.

BACKGROUND: There is a paucity of literature on the relationship between pre-existing mental health conditions and coronavirus disease-2019 (COVID-19) outcomes. The aim was to examine the association between pre-existing mental health diagnosis and COVID-19 outcomes (positive screen, hospitalization, mortality). METHODS: Electronic medical record data for 30 976 adults tested for COVID-19 between March 2020 and 10th July 2020 was analyzed. COVID-19 outcomes included positive screen, hospitalization among screened positive, and mortality among screened positive and hospitalized. Primary independent variable, mental health disorders, was based on ICD-10 codes categorized as bipolar, internalizing, externalizing, and psychoses. Descriptive statistics were calculated, unadjusted and adjusted logistic regression and Cox proportional hazard models were used to investigate the relationship between each mental health disorder and COVID-19 outcomes. RESULTS: Adults with externalizing (odds ratio (OR) 0.67, 95%CI 0.57-0.79) and internalizing disorders (OR 0.78, 95% CI 0.70-0.88) had lower odds of having a positive COVID-19 test in fully adjusted models. Adults with bipolar disorder had significantly higher odds of hospitalization in fully adjusted models (OR 4.27, 95% CI 2.06-8.86), and odds of hospitalization were significantly higher among those with externalizing disorders after adjusting for demographics (OR 1.71, 95% CI 1.23-2.38). Mortality was significantly higher in the fully adjusted model for patients with bipolar disorder (hazard ratio 2.67, 95% CI 1.07-6.67). CONCLUSIONS: Adults with mental health disorders, while less likely to test positive for COVID-19, were more likely to be hospitalized and to die in the hospital. Study results suggest the importance of developing interventions that incorporate elements designed to address smoking cessation, nutrition and physical activity counseling and other needs specific to this population to improve COVID-19 outcomes.

Effectiveness of Social Needs Screening and Interventions in Clinical Settings on Utilization, Cost, and Clinical Outcomes: A Systematic Review.

Objective: This systematic review examined and synthesized peer-reviewed research studies that reported the process of integrating social determinants of health (SDOH) or social needs screening into electronic health records (EHRs) and the intervention effects in the United States. Methods: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines, a systematic search of Scopus, Web of Science Core Collection, MEDLINE, and Cochrane Central Register of Clinical Trials was performed. English language peer-reviewed studies that reported the process of integrating SDOH or social needs screening into EHRs within the U.S. health systems and published between January 2015 and December 2021 were included. The review focused on process measures, social needs changes, health outcomes, and health care cost and utilization. Results: In total, 28 studies were included, and half were randomized controlled trials. The majority of the studies targeted multiple SDOH domains. The interventions vary by the levels of intensity of their approaches and heterogeneities in outcome measures. Most studies (82%, n=23) reported the findings related to the process measures, and nearly half (43%, n=12) reported outcomes related to social needs. By contrast, only 39% (n=11) and 32% (n=9) of the studies reported health outcomes and impact on health care cost and utilization, respectively. Findings on patients' social needs change demonstrated improved access to resources. However, findings were mixed on intervention effects on health and health care cost and utilization. We also identified gaps in implementation challenges to be overcome. Conclusion: Our review supports the current policy efforts to increase U.S. health systems' investment toward directly addressing SDOH. While effective interventions can be more complex or resource intensive than an online referral, health care organizations hoping to achieve health equity and improve population health must commit the effort and investment required to achieve this goal.

Retrospective observational study of the robustness of provider network structures to the systemic shock of COVID-19: a county level analysis of COVID-19 outcomes.

OBJECTIVE: To evaluate whether certain healthcare provider network structures are more robust to systemic shocks such as those presented by the current COVID-19 pandemic. DESIGN: Using multivariable regression analysis, we measure the effect that provider network structure, derived from Medicare patient sharing data, has on county level COVID-19 outcomes (across mortality and case rates). Our adjusted analysis includes county level socioeconomic and demographic controls, state fixed effects, and uses lagged network measures in order to address concerns of reverse causality. SETTING: US county level COVID-19 population outcomes by 3 September 2020. PARTICIPANTS: Healthcare provider patient sharing network statistics were measured at the county level (with n=2541-2573 counties, depending on the network measure used). PRIMARY AND SECONDARY OUTCOME MEASURES: COVID-19 mortality rate at the population level, COVID-19 mortality rate at the case level and the COVID-19 positive case rate. RESULTS: We find that provider network structures where primary care physicians (PCPs) are relatively central, or that have greater betweenness or eigenvector centralisation, are associated with lower county level COVID-19 death rates. For the adjusted analysis, our results show that increasing either the relative centrality of PCPs (p value<0.05), or the network centralisation (p value<0.05 or p value<0.01), by 1 SD is associated with a COVID-19 death reduction of 1.0-1.8 per 100 000 individuals (or a death rate reduction of 2.7%-5.0%). We also find some suggestive evidence of an association between provider network structure and COVID-19 case rates. CONCLUSIONS: Provider network structures with greater relative centrality for PCPs when compared with other providers appear more robust to the systemic shock of COVID-19, as do network structures with greater betweenness and eigenvector centralisation. These findings suggest that how we organise our health systems may affect our ability to respond to systemic shocks such as the COVID-19 pandemic.

Cost and Length of Stay for Department of Defense Hospitalizations Attributed to COVID-for Adults with Diabetes

Healthcare costs in 2020 increased 9.7% from the prior year reaching $4.1 trillion dollars. This increase is considered largely due to the COVID-pandemic. Since adults with diabetes were at increased risk of poor outcomes from COVID-19, the objective of this study was to investigate cost and length of stay for Department of Defense (DoD) hospitalizations attributed to COVID-for adults with diabetes. Data on hospitalizations within military facilities between 2020-2021 for patients with diabetes were investigated. 6,265 hospitalizations occurred at DoD facilities, of which 7.2% (n=458) were attributed to COVID-19. Generalized linear models using a gamma distribution for total cost and Poisson distribution for length of stay were run to investigate outcomes adjusting for age, sex, race/ethnicity, active duty status, service category (Army, Coast Guard, Marine, Air Force, or Navy) , and comorbidity count. In adults with diabetes hospitalized at military facilities, those with COVID-cost over $8,500 more than those without COVID- (8792.98, 95%CI 5850.38,11735.57) after adjustment. There were no significant differences by sex or race/ethnicity, however, active duty hospitalizations cost on average $2,200 more than not active duty (2239.26, 95%CI 738.72,3739.81) . Length of stay was over 2 days longer for COVID-hospitalizations (2.20, 95%CI 1.98,2.42) after adjustment. There were no differences by sex, however African Americans and Asian/Pacific Islanders had slightly longer lengths of stay (AA: 0.37, 95%CI 0.26,0.48;A/PI: 0.26, 95%CI 0.05,0.46) , as did those on active duty (0.24, 95%CI 0.08,0.40) . Total costs for hospitalizations attributed to COVID-were higher and length of stay longer for adults with diabetes at military facilities. Further work is needed to understand long term consequences of COVID-on cost and utilization for adults with diabetes.

Short-term impact of COVID-19 on quality of life, perceived stress, and serious psychological distress in an adult population in the midwest United States.

PURPOSE: This study aimed to investigate changes over time in quality of life, perceived stress, and serious psychological distress for individuals diagnosed with COVID-19 in an urban academic health system. METHODS: Phone-based surveys were completed with adult patients tested for COVID-19 during emergency department visits, hospitalizations, or outpatient visits at the Froedtert and Medical College of Wisconsin Health Network. Data were then matched to medical record data. Unadjusted and adjusted mixed effects linear models using random intercept were run for each outcome (physical health-related quality of life, mental health-related quality of life, perceived stress, and serious psychological distress) with time (baseline vs 3-month follow-up) as the primary independent variable. Individuals were treated as a random effect, with all covariates (age, sex, race/ethnicity, payor, comorbidity count, hospitalization, and intensive care unit (ICU) stay) treated as fixed effects. RESULTS: 264 adults tested positive for COVID-19 and completed baseline and 3-month follow-up assessments. Of that number, 31.8% were hospitalized due to COVID-19, and 10.2% were admitted for any reason to the ICU. After adjustment, patients reported higher physical health-related quality of life at 3 months compared to baseline (0.63, 95% CI 0.15, 1.11) and decreased stress at 3 months compared to baseline (- 0.85, 95% CI - 1.33, - 0.37). There were no associations between survey time and mental health-related quality of life or serious psychological distress. CONCLUSIONS: Results suggest the influence of COVID-19 on physical health-related quality of life and stress may resolve over time, however, the influence of mental health on daily activities, work, and social activities may not.

Relationship between physical and mental health comorbidities and COVID-19 positivity, hospitalization, and mortality.

BACKGROUND: Understanding the association between separate and combined mental and physical health diagnoses and COVID-19 outcomes is greatly needed to address the severity of illness. METHODS: Data on 24,034 patients screened for COVID-19 as of July 2020 were extracted from the Froedtert/Medical College of Wisconsin Epic medical record. COVID-19 outcomes were defined as positive screens, proportion hospitalized among positive screens, and proportion that died among positive and hospitalized population. The primary independent variable was a 3-category variable: physical health diagnosis alone, mental health diagnosis alone, and combined mental and physical health diagnoses. Logistic regression and Cox proportional hazard models were used to examine the independent relationship between separate and combined diagnoses and COVID-19 outcomes. RESULTS: Compared to physical health diagnosis alone, mental health diagnosis alone had lower odds of screening positive (OR=0.68, CI=0.51;0.92) and was not associated with hospitalization or mortality among positive screens. Combined had lower odds of screening positive (OR=0.78, CI=0.69;0.88) and higher odds of hospitalization among positive screens after adjusting for demographics (OR=1.58, CI=1.20;2.08) but lost significance in the fully adjusted model. No category of diagnoses was associated with mortality. LIMITATIONS: Analysis is cross-sectional and cannot speak to any causal relationships. CONCLUSIONS: Overall, compared to physical health diagnosis alone, mental health diagnosis and combined had lower odds of positive screens. However, individuals with combined were more likely to be hospitalized, after adjusting for demographics only. These findings add new evidence for risk of COVID-19 and related hospitalization in individuals who have a physical and mental health diagnosis.

Team Science, Population Health, and COVID-19: Lessons Learned Adapting a Population Health Research Team to COVID-19.

Our multidisciplinary research team is composed of 6 faculty with expertise in internal medicine, nephrology, maternal/fetal medicine, health services research, statistics, and community-based research, and 36 program staff including biostatisticians, nurses, program coordinators, program assistants, and medical assistants/phlebotomists. With the emergence of the COVID-19 pandemic and the impact it was having on our community, especially the ethnic minority population in inner-city Milwaukee, we felt it was critical to stay engaged and figure out how to ask meaningful research questions that are important to the community, are relevant to the times, and will lead to lasting change. While navigating this unprecedented challenge, our research team made difficult decisions but were able to engage our staff and respond to community needs. We organized our lessons learned to serve as a perspective on how to effectively remain committed to vision and serve our communities, while collecting evidence that can inform policy in difficult times.

Racial/Ethnic Differences In COVID-19 Screening, Hospitalization, And Mortality In Southeast Wisconsin.

This study aimed to understand racial/ethnic differences in coronavirus disease 2019 (COVID-19) screening, symptom presentation, hospitalization, and mortality, using data from 31,549 adults tested for COVID-19 between March 1 and July 10, 2020, in Milwaukee and Southeast Wisconsin. Racial/ethnic differences existed in adults who screened positive for COVID-19 (4.5 percent of non-Hispanic Whites, 14.9 percent of non-Hispanic Blacks, and 14.8 percent of Hispanics). After adjustment for demographics and comorbidities, Blacks and Hispanics were more than three times more likely to screen positive and two times more likely to be hospitalized relative to Whites, and Hispanics were two times more likely to die than Whites. Given the long-standing history of structural racism, residential segregation, and social risk in the US and their role as contributors to poor health, we propose and discuss the part these issues play as explanatory factors for our findings.