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Purpose: Few have studied the relationship between employment and health in the Medicaid population. The purpose of this study is to estimate the impact of job loss on the mental health of working Medicaid beneficiaries. Design/methodology/approach: This study conducted a post hoc analysis of 1,538 adult Medicaid beneficiaries enrolled in a prospective cohort study. The authors matched participants who lost their job to participants who remained employed based on demographics, illness severity and social determinants of health. The authors estimated the effect of job loss on the odds of a diagnosis of depression and/or anxiety and self-reported mental health during a one-year follow-up period, stratified by prior history of depression and/or anxiety as documented in the Medicaid claims. Findings: Among participants with no preexisting depression or anxiety, the incidence of depression or anxiety was 17% versus 7% (aOR = 2.85; 95% confidence interval (CI): 1.88 to 4.34) between those who lost versus kept their job, respectively, and the mean difference in self-reported mental health was −4.3 (95% CI: −6.02 to −2.58). Self-reported mental health was also poorer between those who lost versus kept their job among participants with preexisting depression and/or anxiety (x = −4.78 (95% CI: −8.90 to −0.66). Research limitations/implications: Limitations of this study are as follows: we may not have matched on all factors that influence retaining a job; we do not distinguish between involuntary and voluntary job loss; generalizability is limited; and employment information is based on self-report. Practical implications: Our society should invest more resources into supporting low-wage workers such as Medicaid beneficiaries. Social implications: Active labor policies that connect people to jobs, help them retain their job and support skills training to secure a better quality job, could reduce health disparities in the Medicaid population. Originality/value: Use of both claims and self-reported mental health information to evaluate the impact of job loss on working Medicaid beneficiaries. [ABSTRACT FROM AUTHOR]

Policy PointsSocial determinants of health are an important predictor of future health care costs.Medicaid must partner with other sectors to address the underlying causes of its beneficiaries' poor health and high health care spending. Context: Social determinants of health are an important predictor of future health care costs but little is known about their impact on Medicaid spending. This study analyzes the role of social determinants of health (SDH) in predicting future health care costs for adult Medicaid beneficiaries with similar past morbidity burdens and past costs. Methods: We enrolled into a prospective cohort study 8,892 adult Medicaid beneficiaries who presented for treatment at an emergency department or clinic affiliated with two hospitals in Washington, DC, between September 2017 and December 31, 2018. We used SDH information measured at enrollment to categorize our participants into four social risk classes of increasing severity. We used Medicaid claims for a 2‐year period; 12 months pre‐ and post‐study enrollment to measure past and future morbidity burden according to the Adjusted Clinical Groups system. We also used the Medicaid claims data to characterize total annual Medicaid costs one year prior to and one year after study enrollment. Results: The 8,892 participants were primarily female (66%) and Black (91%). For persons with similar past morbidity burdens and past costs (p < 0.01), the future morbidity burden was significantly higher in the upper two social risk classes (1.15 and 2.04, respectively) compared with the lowest one. Mean future health care spending was significantly higher in the upper social risk classes compared with the lowest one ($2,713, $11,010, and $17,710, respectively) and remained significantly higher for the two highest social risk classes ($1,426 and $3,581, respectively), given past morbidity burden and past costs (p < 0.01). When we controlled for future morbidity burden (measured concurrently with future costs), social risk class was no longer a significant predictor of future health care costs. Conclusions: SDH are statistically significant predictors of future morbidity burden and future costs controlling for past morbidity burden and past costs. Further research is needed to determine whether current payment systems adequately account for differences in the care needs of highly medically and socially complex patients. [ABSTRACT FROM AUTHOR]

Background Males experience increased severity of illness and mortality from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) compared with females, but the mechanisms of male susceptibility are unclear. Methods We performed a retrospective cohort analysis of SARS-CoV-2 testing and admission data at 5 hospitals in the Maryland/Washington DC area. Using age-stratified logistic regression models, we quantified the impact of male sex on the risk of the composite outcome of severe disease or death (World Health Organization score 5–8) and tested the impact of demographics, comorbidities, health behaviors, and laboratory inflammatory markers on the sex effect. Results Among 213 175 SARS-CoV-2 tests, despite similar positivity rates, males in age strata between 18 and 74 years were more frequently hospitalized. For the 2626 hospitalized individuals, clinical inflammatory markers (interleukin-6, C-reactive protein, ferritin, absolute lymphocyte count, and neutrophil:lymphocyte ratio) were more favorable for females than males (P <.001). Among 18–49-year-olds, male sex carried a higher risk of severe outcomes, both early (odds ratio [OR], 3.01; 95% CI, 1.75 to 5.18) and at peak illness during hospitalization (OR, 2.58; 95% CI, 1.78 to 3.74). Despite multiple differences in demographics, presentation features, comorbidities, and health behaviors, these variables did not change the association of male sex with severe disease. Only clinical inflammatory marker values modified the sex effect, reducing the OR for severe outcomes in males aged 18–49 years to 1.81 (95% CI, 1.00 to 3.26) early and 1.39 (95% CI, 0.93 to 2.08) at peak illness. Conclusions Higher inflammatory laboratory test values were associated with increased risk of severe coronavirus disease 2019 for males. A sex-specific inflammatory response to SARS-CoV-2 infection may underlie the sex differences in outcomes. [ABSTRACT FROM AUTHOR]

Background: Predicting the clinical trajectory of individual patients hospitalized with coronavirus disease 2019 (COVID-19) is challenging but necessary to inform clinical care. The majority of COVID-19 prognostic tools use only data present upon admission and do not incorporate changes occurring after admission.Objective: To develop the Severe COVID-19 Adaptive Risk Predictor (SCARP) (https://rsconnect.biostat.jhsph.edu/covid_trajectory/), a novel tool that can provide dynamic risk predictions for progression from moderate disease to severe illness or death in patients with COVID-19 at any time within the first 14 days of their hospitalization.Design: Retrospective observational cohort study.Settings: Five hospitals in Maryland and Washington, D.C.Patients: Patients who were hospitalized between 5 March and 4 December 2020 with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) confirmed by nucleic acid test and symptomatic disease.Measurements: A clinical registry for patients hospitalized with COVID-19 was the primary data source; data included demographic characteristics, admission source, comorbid conditions, time-varying vital signs, laboratory measurements, and clinical severity. Random forest for survival, longitudinal, and multivariate (RF-SLAM) data analysis was applied to predict the 1-day and 7-day risks for progression to severe disease or death for any given day during the first 14 days of hospitalization.Results: Among 3163 patients admitted with moderate COVID-19, 228 (7%) became severely ill or died in the next 24 hours; an additional 355 (11%) became severely ill or died in the next 7 days. The area under the receiver-operating characteristic curve (AUC) for 1-day risk predictions for progression to severe disease or death was 0.89 (95% CI, 0.88 to 0.90) and 0.89 (CI, 0.87 to 0.91) during the first and second weeks of hospitalization, respectively. The AUC for 7-day risk predictions for progression to severe disease or death was 0.83 (CI, 0.83 to 0.84) and 0.87 (CI, 0.86 to 0.89) during the first and second weeks of hospitalization, respectively.Limitation: The SCARP tool was developed by using data from a single health system.Conclusion: Using the predictive power of RF-SLAM and longitudinal data from more than 3000 patients hospitalized with COVID-19, an interactive tool was developed that rapidly and accurately provides the probability of an individual patient's progression to severe illness or death on the basis of readily available clinical information.Primary Funding Source: Hopkins inHealth and COVID-19 Administrative Supplement for the HHS Region 3 Treatment Center from the Office of the Assistant Secretary for Preparedness and Response. [ABSTRACT FROM AUTHOR]