Poverty and Suicidal Ideation Among Hispanic Mental Health Care Patients Leading up to the COVID-19 Pandemic.

Introduction: Suicide rates have risen in Hispanic communities since 2015, and poverty rates among Hispanics often exceed the national average. Suicidality is a complex phenomenon. Mental illness may not alone explain whether suicidal thoughts or behaviors will occur; it remains uncertain how poverty affects suicidality among Hispanic persons with known mental health conditions. Our objective was to examine whether poverty was associated with suicidal ideation among Hispanic mental healthcare patients from 2016 to 2019. Methods: We used de-identified electronic health record (EHR) data from Holmusk, captured using the MindLinc EHR system. Our analytic sample included 4,718 Hispanic patient-year observations from 13 states. Holmusk uses deep-learning natural language processing (NLP) algorithms to quantify free-text patient assessment data and poverty for mental health patients. We conducted a pooled cross-sectional analysis and estimated logistic regression models. Results: Hispanic mental health patients who experienced poverty had 1.55 greater odds of having suicidal thoughts in a given year than patients who did not experience poverty. Conclusion: Poverty may put Hispanic patients at greater risk for suicidal thoughts even when they are already receiving treatment for psychiatric conditions. NLP appears to be a promising approach for categorizing free-text information on social circumstances affecting suicidality in clinical settings.

A Machine Learning Approach to Predicting Higher COVID-19 Care Burden in the Primary Care Safety Net: Hispanic Patient Population Size a Key Factor

Introduction The federal government legislated supplemental funding to support community health centers (CHCs) in response to the COVID-19 pandemic. Supplemental funding included standard base payments and adjustments for the number of total and uninsured patients served before the pandemic. However, not all CHCs share similar patient population characteristics and health risks. Objective To use machine learning to identify the most important factors for predicting whether CHCs had a high burden of patients diagnosed with COVID-19 during the first year of the pandemic. Methods Our analytic sample included data from 1342 CHCs across the 50 states and D.C. in 2020. We trained a random forest (RF) classifier model, incorporating 5-fold cross-validation to validate the RF model while optimizing the model's hyperparameters. Final performance metrics were calculated following the application of the model that had the best fit to the held-out test set. Results CHCs with a high burden of COVID-19 had an average of 65.3 patients diagnosed with COVID-19 per 1000 patients in 2020. Our RF model had 80.9% accuracy, 80.1% precision, 25.0% sensitivity, and 98.1% specificity. The percentage of Hispanic patients served in 2020 was the most important feature for predicting whether CHCs had high COVID-19 burden. Conclusions Findings from our RF model suggest patient population race and ethnicity characteristics were most important for predicting whether CHCs had a high burden of patients diagnosed with COVID-19 in 2020, though sensitivity was low. Enhanced support for CHCs serving large Hispanic patient populations may have an impact on addressing future COVID-19 waves.

Estimating Excess Deaths by Race/Ethnicity in the State of California During the COVID-19 Pandemic.

INTRODUCTION: To examine excess mortality among minorities in California during the COVID-19 pandemic. METHODS: Using seasonal autoregressive integrated moving average time series, we estimated counterfactual total deaths using historical data (2014-2019) of all-cause mortality by race/ethnicity. Estimates were compared to pandemic mortality trends (January 2020 to January 2021) to predict excess deaths during the pandemic for each race/ethnic group. RESULTS: Our findings show a significant disparity among minority excess deaths, including 7892 (24.6% increase), 4903 (20.4%), 30,186 (47.7%), and 22,027 (12.6%) excess deaths, including deaths identified as COVID-19-related, for Asian, Black, Hispanic, and White non-Hispanic individuals, respectively. Estimated increases in all-cause deaths excluding COVID-19 deaths were 1331, 1436, 3009, and 5194 for Asian, Black, Hispanic, and White non-Hispanic individuals, respectively. However, the rate of excess deaths excluding COVID-19 recorded deaths per 100 k was disproportionately high for Black (66 per 100 k) compared to White non-Hispanic (36 per 100 k). The rates for Asians and Hispanics were 23 and 19 per 100 k. CONCLUSIONS: Our findings emphasize the importance of targeted policies for minority populations to lessen the disproportionate impact of COVID-19 on their communities.

The role of good governance in the race for global vaccination during the COVID-19 pandemic.

Governments have developed and implemented various policies and interventions to fight the COVID-19 pandemic. COVID-19 vaccines are now being produced and distributed globally. This study investigated the role of good governance and government effectiveness indicators in the acquisition and administration of COVID-19 vaccines at the population level. Data on six World Bank good governance indicators for 172 countries for 2019 and machine-learning methods (K-Means Method and Principal Component Analysis) were used to cluster countries based on these indicators and COVID-19 vaccination rates. XGBoost was used to classify countries based on their vaccination status and identify the relative contribution of each governance indicator to the vaccination rollout in each country. Countries with the highest COVID-19 vaccination rates (e.g., Israel, United Arab Emirates, United States) also have higher effective governance indicators. Regulatory Quality is the most important indicator in predicting COVID-19 vaccination status in a country, followed by Voice and Accountability, and Government Effectiveness. Our findings suggest that coordinated global efforts led by the World Health Organization and wealthier nations may be necessary to assist in the supply and distribution of vaccines to those countries that have less effective governance.

The impact of school opening model on SARS-CoV-2 community incidence and mortality.

The role that traditional and hybrid in-person schooling modes contribute to the community incidence of SARS-CoV-2 infections relative to fully remote schooling is unknown. We conducted an event study using a retrospective nationwide cohort evaluating the effect of school mode on SARS-CoV-2 cases during the 12 weeks after school opening (July-September 2020, before the Delta variant was predominant), stratified by US Census region. After controlling for case rate trends before school start, state-level mitigation measures and community activity level, SARS-CoV-2 incidence rates were not statistically different in counties with in-person learning versus remote school modes in most regions of the United States. In the South, there was a significant and sustained increase in cases per week among counties that opened in a hybrid or traditional mode versus remote, with weekly effects ranging from 9.8 (95% confidence interval (CI) = 2.7-16.1) to 21.3 (95% CI = 9.9-32.7) additional cases per 100,000 persons, driven by increasing cases among 0-9 year olds and adults. Schools can reopen for in-person learning without substantially increasing community case rates of SARS-CoV-2; however, the impacts are variable. Additional studies are needed to elucidate the underlying reasons for the observed regional differences more fully.

Analysis of Excess Deaths During the COVID-19 Pandemic in the State of Florida.

Objectives. To determine the number of excess deaths (i.e., those exceeding historical trends after accounting for COVID-19 deaths) occurring in Florida during the COVID-19 pandemic.Methods. Using seasonal autoregressive integrated moving average time-series modeling and historical mortality trends in Florida, we forecasted monthly deaths from January to September of 2020 in the absence of the pandemic. We compared estimated deaths with monthly recorded total deaths (i.e., all deaths regardless of cause) during the COVID-19 pandemic and deaths only from COVID-19 to measure excess deaths in Florida.Results. Our results suggest that Florida experienced 19 241 (15.5%) excess deaths above historical trends from March to September 2020, including 14 317 COVID-19 deaths and an additional 4924 all-cause, excluding COVID-19, deaths in that period.Conclusions. Total deaths are significantly higher than historical trends in Florida even when accounting for COVID-19-related deaths. The impact of COVID-19 on mortality is significantly greater than the official COVID-19 data suggest.

The Impact of Halting Elective Admissions in Anticipation of a Demand Surge Due to the Coronavirus Pandemic (COVID-19).

BACKGROUND: In anticipation of a demand surge for hospital beds attributed to the coronavirus pandemic (COVID-19) many US states have mandated that hospitals postpone elective admissions. OBJECTIVES: To estimate excess demand for hospital beds due to COVID-19, the net financial impact of eliminating elective admissions in order to meet demand, and to explore the scenario when demand remains below capacity. RESEARCH DESIGN: An economic simulation to estimate the net financial impact of halting elective admissions, combining epidemiological reports, the US Census, American Hospital Association Annual Survey, and the National Inpatient Sample. Deterministic sensitivity analyses explored the results while varying assumptions for demand and capacity. SUBJECTS: Inputs regarding disease prevalence and inpatient utilization were representative of the US population. Our base case relied on a hospital admission rate reported by the Center for Disease Control and Prevention of 137.6 per 100,000, with the highest rates in people aged 65 years and older (378.8 per 100,000) and 50-64 years (207.4 per 100,000). On average, elective admissions accounted for 20% of total hospital admissions, and the average rate of unoccupied beds across hospitals was 30%. MEASURES: Net financial impact of halting elective admissions. RESULTS: On average, hospitals COVID-19 demand for hospital bed-days fell well short of hospital capacity, resulting in a substantial financial loss. The net financial impact of a 90-day COVID surge on a hospital was only favorable under a narrow circumstance when capacity was filled by a high proportion of COVID-19 cases among hospitals with low rates of elective admissions. CONCLUSIONS: Hospitals that restricted elective care took on a substantial financial risk, potentially threatening viability. A sustainable public policy should therefore consider support to hospitals that responsibly served their communities through the crisis.

US Policies Increase Vulnerability of Immigrant Communities to the COVID-19 Pandemic.

The adverse policy environment in the United States (US) has made immigrant communities particularly vulnerable to uncontrolled community spread of COVID-19. Past and recent federal and state policy actions may exacerbate undetected community spread in immigrant communities and commensurate economic impact. Given the importance of immigrants to the US economy and society, and the human toll this pandemic is having on migrants worldwide, federal and state policies should pivot to find ways to improve access to healthcare for immigrants.

The importance of widespread testing for COVID-19 pandemic: systems thinking for drive-through testing sites.

On 11 March 2020, the World Health Organisation (WHO) declared COVID-19 a pandemic. Early epidemiological estimates show that COVID-19 is highly transmissible, infecting populations across the globe in a short amount of time. WHO has recommended widespread clinical testing in order to contain COVID-19. However, mass testing in emergency department (ED) settings may result in crowded EDs and increase transmission risk for healthcare staff and other ED patients. Drive-through COVID-19 testing sites are an effective solution to quickly collect samples from suspected cases with minimal risk to healthcare personnel and other patients. Nevertheless, there are many logistical and operational challenges, such as shortages of testing kits, limited numbers of healthcare staff and long delays for collecting samples. Solving these problems requires an understanding of disease dynamics and epidemiology, as well as the logistics of mass distribution. In this position paper, we provide a conceptual framework for addressing these challenges, as well as some insights from prior literature and experience on developing decision support tools for public health departments.