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.

Early Data on Predictors of COVID-19 Treatment Frequency at Community Health Centers.

INTRODUCTION: Federally-funded community health centers (CHCs) serve on the front lines of the COVID-19 pandemic, providing essential COVID-19 testing and care for vulnerable patient populations. Overlooked in the scholarly literature is a description of how different characteristics and vulnerabilities shaped COVID-19 care delivery at CHCs in the first year of the pandemic. Our research objective was to identify organization- and state-level factors associated with more or fewer COVID-19 care and testing visits at CHCs in 2020. METHODS: Multilevel random intercept regression models examined associations among organization and state-level predictor variables and the frequency of COVID-19 care and testing visits at CHCs in 2020. The study sample included 1267 CHCs across the 50 states and the District of Columbia. RESULTS: The average CHC provided 932 patient visits for COVID-19-related care in 2020. Yet, the CHC's role in delivering COVID-19 services proved as diverse as the populations and localities CHCs serve. For example, after adjusting for other factors, each percentage-point increase in a CHC's Hispanic patient population size was associated with a 1.3% increase in the frequency of patient visits for COVID-19 care in 2020 (P < .001). Serving a predominantly rural patient population was associated with providing significantly fewer COVID-19-related care visits (P = .002). Operating in a state that enacted a mask-wearing policy in 2020 was associated with a 26.2% lower frequency of COVID-19 testing visits at CHCs in 2020, compared to CHCs operating in states without mask-wearing policies (P = .055). CONCLUSIONS: In response to the pandemic, the federal government legislated funding to help CHCs address challenges associated with COVID-19 and provide services to medically-underserved patient populations. Policymakers will likely need to provide additional support to help CHCs address population-specific vulnerabilities affecting COVID-19 care and testing delivery, especially as highly contagious COVID-19 variants proliferate (eg, Delta and Omicron).

Examining the Relationship Between COVID-19 Prevalence and Depression Care at Community Health Centers: Assessing 2020 Data.

OBJECTIVE: The author examined associations between COVID-19 prevalence at community health centers (CHCs) and the percentage of eligible CHC patients who had ongoing depression care documented in 2020. METHODS: Using 2020 Uniform Data System data, the author analyzed 1,113 CHCs in the 50 U.S. states (representing 26,865,015 total patients). Multivariable linear regression models were used to examine associations between COVID-19 prevalence and the percentage of patients who screened positive for depression and had follow-up depression care documented at CHCs in 2020. RESULTS: On average, each increase of 1 percentage point in COVID-19 prevalence within a CHC patient population was independently associated with a 0.47-percentage point decrease of eligible patients with a follow-up depression care plan documented in 2020. CONCLUSIONS: Findings appear to be consistent with recent evidence indicating disruptions in health care delivery coinciding with the COVID-19 pandemic. This observation is concerning given the history of mental health disparities experienced by patients with lower incomes.

Community Health Centers Maintained Initial Increases in Medicaid Covered Adult Patients at 5-Years Post-Medicaid-Expansion.

The Affordable Care Act (ACA) Medicaid expansion created new financial opportunities for community health centers (CHCs) providing primary care in medically-underserved communities. However, beyond evidence of initial policy effects, little is understood in the scholarly literature about whether the ACA Medicaid expansion affected longer-lasting changes in CHC patient insurance mix. This study's objective was to examine whether the ACA Medicaid expansion was associated with lasting increases in the annual percentage of adult CHC patients covered by Medicaid and decreases in the annual percentage of uninsured adult CHC patients at expansion-state CHCs, compared to non-expansion-state CHCs. This observational study examined 5353 CHC-year observations from 2012 to 2018 using Uniform Data System data and other national data sources. Using a 2-way fixed-effects multivariable regression approach and marginal analysis, intermediate-term policy effects of the Medicaid expansion on annual CHC patient coverage outcomes were estimated. By 5-years post-expansion, the Medicaid expansion was associated with an overall average increase of 11.7 percentage points in the percentage of adult patients with Medicaid coverage at expansion-state CHCs, compared to non-expansion-state CHCs. Among expansion-state CHCs, 39.8% of adult patients were predicted to have Medicaid coverage 5-years post-expansion, compared to 19.0% of non-expansion-state adult CHC patients. A state's decision to expand Medicaid was similarly associated with decreases in the annual percentage of uninsured adult CHC patients. Primary care operations at CHCs critically depend on patient Medicaid revenue. These findings suggest the ACA Medicaid expansion may provide longer-term financial security for expansion-state CHCs, which maintain increases in Medicaid-covered adult patients even 5-years post-expansion. However, these financial securities may be jeopardized should the ACA be ruled unconstitutional in 2021, a year after CHCs experienced new uncertainties caused by COVID-19.