Utah Latina/o/x suicide decedents less likely to die by firearm, even in rural areas: examining population-wide data from the Utah Office of the Medical Examiner.

Introduction: Suicide death remains a significantly rarer event among Latina/o/x populations compared to non-Latina/o/x populations. However, the reasons why Latina/o/x communities experience relatively lower suicide rates are not fully understood. Critical gaps exist in the examination of Latina/o/x suicide death, especially in rural settings, where suicide death by firearm is historically more common within non-Latina/o/x populations. Method: We tested whether the prevalence of Latina/o/x firearm suicide was meaningfully different in urban and rural environments and from non-Latino/a/x decedents when controlling for age, sex, and a social deprivation metric, the Area Deprivation Index. Suicide death data used in this analysis encompasses 2,989 suicide decedents ascertained in Utah from 2016 to 2019. This included death certificate data from the Utah Office of the Medical Examiner on all Utah suicide deaths linked to information by staff at the Utah Population Database. Results: Compared to non-Latina/o/x suicide decedents, Latina/o/x suicide decedents had 34.7% lower adjusted odds of dying by firearm. Additionally, among the firearm suicide decedents living only in rural counties, Latina/o/x decedents had 40.5% lower adjusted odds of dying by firearm compared to non-Latina/o/x suicide decedents. Discussion: The likelihood of firearm suicide death in Utah differed by ethnicity, even in rural populations. Our findings may suggest underlying factors contributing to lower firearm suicide rates within Latina/o/x populations, e.g., aversion to firearms or less access to firearms, especially in rural areas, though additional research on these phenomena is needed.

Botánicas Function as Health Care Safety Nets Among Hispanic American Immigrants: A Brief Report.

As the U.S. population becomes more racially/ethnically diverse, the Hispanic American immigrant population has slowly grown in recent years. In the face of anti-immigrant policies, limited access to care, fear of deportation, discrimination, stigmatization, poverty, and other stressors, Hispanic American immigrants seek services from botánicas for religious, spiritual, medical, and psychosocial health reasons, including the accessibility and affordability of services from folk practitioners in these herbal dispensaries. Hispanics are the primary consumers of herbal remedies and complementary and alternative medicines in the United States. The purpose of this article is to emphasize the critical role of botánicas in the health and wellness of Hispanic American immigrants. Recommendations for health professionals are provided in the care of Hispanic Americans who utilize folk, traditional, and herbal medicines for health and healing.

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.

Emergency Department and Inpatient Utilization Reductions and Cost Savings Associated With Trauma Center Mental Health Intervention: Results From a 5-year Longitudinal Randomized Clinical Trial Analysis.

OBJECTIVE: To identify and refer patients at high risk for the psychological sequelae of traumatic injury, the American College of Surgeons Committee on Trauma now requires that trauma centers have in-place protocols. No investigations have documented reductions in utilization and associated potential cost savings associated with trauma center mental health interventions. BACKGROUND: The investigation was a randomized clinical trial analysis that incorporated novel 5-year emergency department (ED)/inpatient health service utilization follow-up data. METHODS: Patients were randomized to a mental health intervention, targeting the psychological sequelae of traumatic injury (n = 85) versus enhanced usual care control (n = 86) conditions. The intervention included case management that coordinated trauma center-to-community care linkages, psychotropic medication consultation, and psychotherapy elements. Mixed model regression was used to assess intervention and control group utilization differences over time. An economic analysis was also conducted. RESULTS: Over the course of the 5-year intervention, patients demonstrated significant reductions in ED/inpatient utilization when compared with control patients [ F (19,3210) = 2.23, P = 0.009]. Intervention utilization reductions were greatest at 3 to 6 months (intervention 15.5% vs control 26.7%, relative risk = 0.58, 95% CI: 0.34, 1.00) and 12 to 15 months (intervention 16.5% vs control 30.6%, relative risk = 0.54, 95% CI: 0.32, 0.91) postinjury time points. The economic analysis suggested potential intervention cost savings. CONCLUSIONS: Mental health intervention is associated with significant reductions in ED and inpatient utilization, as well as potential cost savings. These findings could be productively integrated into future American College of Surgeons Committee on Trauma policy discussions.

Employee attitudes toward suicide prevention and Counseling on Access to Lethal Means: initial findings from an academic medical center implementing the Zero Suicide framework.

Introduction: Zero Suicide is a strategic framework designed to transform a healthcare system's suicide prevention activities. In 2020, University of Utah Health launched a Zero Suicide program and Counseling on Access to Lethal Means (CALM) training for its employees. In 2022, the healthcare system surveyed its workforce's attitudes toward suicide prevention and CALM. We sought to evaluate employees' attitudes and behaviors toward suicide prevention and CALM training following the launch of the Zero Suicide program. Methods: A Zero Suicide Workforce Survey was administered online through REDCap to all University of Utah Health employees. The analytic sample included 3,345 respondents. We used two-portion z-tests to compare the proportions of respondents who (1) completed CALM training and (2) did not yet complete the CALM training because they felt it was irrelevant to their position by different employee characteristics. Results: More than half of the respondents in the analytic sample were directly interacting with patients who may be at risk for suicide (57.6%). About 8.4% of the respondents had completed CALM training. Among those who had not yet completed CALM training, 9.5% indicated they did not think CALM was relevant to their job. Respondents knowledgeable about warning signs for suicide and respondents who were confident in their ability to respond when suspecting elevated suicide risk were significantly more likely to complete CALM training. Discussion: This evaluation provides important insights from the workforce of a large academic medical center implementing a Zero Suicide program, including insights on opportunities for improving program implementation and evaluation.

Identifying Rare Circumstances Preceding Female Firearm Suicides: Validating A Large Language Model Approach.

BACKGROUND: Firearm suicide has been more prevalent among males, but age-adjusted female firearm suicide rates increased by 20% from 2010 to 2020, outpacing the rate increase among males by about 8 percentage points, and female firearm suicide may have different contributing circumstances. In the United States, the National Violent Death Reporting System (NVDRS) is a comprehensive source of data on violent deaths and includes unstructured incident narrative reports from coroners or medical examiners and law enforcement. Conventional natural language processing approaches have been used to identify common circumstances preceding female firearm suicide deaths but failed to identify rarer circumstances due to insufficient training data. OBJECTIVE: This study aimed to leverage a large language model approach to identify infrequent circumstances preceding female firearm suicide in the unstructured coroners or medical examiners and law enforcement narrative reports available in the NVDRS. METHODS: We used the narrative reports of 1462 female firearm suicide decedents in the NVDRS from 2014 to 2018. The reports were written in English. We coded 9 infrequent circumstances preceding female firearm suicides. We experimented with predicting those circumstances by leveraging a large language model approach in a yes/no question-answer format. We measured the prediction accuracy with F1-score (ranging from 0 to 1). F1-score is the harmonic mean of precision (positive predictive value) and recall (true positive rate or sensitivity). RESULTS: Our large language model outperformed a conventional support vector machine-supervised machine learning approach by a wide margin. Compared to the support vector machine model, which had F1-scores less than 0.2 for most infrequent circumstances, our large language model approach achieved an F1-score of over 0.6 for 4 circumstances and 0.8 for 2 circumstances. CONCLUSIONS: The use of a large language model approach shows promise. Researchers interested in using natural language processing to identify infrequent circumstances in narrative report data may benefit from large language models.

A Comparison of the Adverse Effects and Utility of Different Monoclonal Antibodies for SARS-CoV-2: A Retrospective Cohort Study.

Introduction Multiple monoclonal antibody (mAb) treatments have been developed to combat the growing number of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strains. These treatments have been shown to be effective in reducing the risk of hospitalization and death from SARS-CoV-2 infection with a low risk of adverse effects; however, more data is required to evaluate the comparative efficacy of mAbs. The primary objective of this study is to describe the hospitalization rate, length of stay (LOS), and mortality rate in SARS-CoV-2 patients treated with four different mAb treatments, including bamlanivimab plus etesevimab, casirivimab plus imdevimab, sotrovimab, and bebtelovimab. Methods A retrospective chart review and prospective phone surveys of SARS-CoV-2 patients treated with mAbs in a 400-bed tertiary, suburban medical center were conducted between June 2020 and April 2022. Eligibility criteria for mAbs included non-hospitalized patients over the age of 18 with less than 10 days of SARS-CoV-2 symptoms and no oxygen requirement on emergency department (ED) admission. Data were collected from the retrospective chart review and subjective patient surveys. A chi-squared test was used. Significance was assessed at p < 0.05. Results The study population included 3249 patients, with 1537 males and 1712 females and an average age of 62.48 ± 17.54 years. Five hundred forty-two patients received bamlanivimab plus etesevimab; 849 received bebtelovimab; 1577 received casirivimab plus imdevimab; and 281 received sotrovimab. The overall hospitalization rate was 1.0%, and the mortality rate was 0.2% following mAb treatment. The hospitalization rate was greatest among patients administered Sotrovimab (2.1%) and least among patients administered Bebtelovimab (0.1%) (p = 0.010). 2.4% of patients who were discharged from the ED after receiving one of the four mAbs returned within 30 days with SARS-CoV-2 symptoms. The average length of stay was 4.75 ± 4.56 days, with no significant differences between the mAbs. The provider-reported adverse event rate was 2.2%, with significant differences in adverse event rates between mAbs. Bamlanivimab-etesevimab was associated with the highest adverse event rate (4.6%), and sotrovimab was associated with the lowest adverse event rate (1.4%) (p < 0.001). Conclusion This study shows a low hospitalization and mortality rate following mAb infusion in patients with mild and moderate COVID-19. However, there were significant differences in hospitalization and mortality among patients receiving each of the four mAb treatments. There was a high degree of patient-reported symptom improvement, and adverse reactions were reported in only 2.2% of patients with no severe reactions. Multiple monoclonal antibody treatments are not effective as monotherapy; however, this study shows the potential benefits of including a mAb infusion as part of a SARS-CoV-2 treatment plan.

Disparities in food insecurity between sexual minority and heterosexual adults - a higher burden on bisexual individuals.

Background: Sexual minorities-individuals who identify as gay/lesbian, bisexual, or other non-heterosexual individuals-experience higher rates of food insecurity (FI) compared to heterosexual individuals. During the COVID-19 pandemic, discrimination and structural racism, which are known risk factors for food insecurity, were perpetuated against sexual and racial/ethnic minorities. However, to our knowledge, a nationally representative analysis of the impact of the pandemic on food insecurity by sexual minority status and based on race/ethnicity is missing. We aimed to determine the degree of association between FI and sexual minority adults overall, before (2019) and during (2020-2021) the pandemic, and stratified by race/ethnicity. Methods: We used nationally representative data from the 2019-2021 National Health Interview Survey (NHIS). We specified multivariable logistic regression models to determine the association between FI and identifying as a sexual minority adult (≥18 years old), including gay/lesbian, bisexual, and other non-heterosexual individuals. Results: Overall, we only observed FI disparities between bisexuals and heterosexuals (aOR 1.61 [95% CI 1.31-1.99]). Stratified by year, this association was significant only during the pandemic. Stratified by race/ethnicity, non-Hispanic white and non-Hispanic black individuals identifying as bisexual also experienced a significantly higher FI rate than their heterosexual counterparts. Conclusion: Our results may be a manifestation of the disproportionate impact of discrimination on bisexual individuals' FI experiences. With the growing number of legislative bills targeting the rights of sexual minorities, we expect to see a higher burden of FI among bisexuals, particularly, bisexual people of color. Future intersectional research regarding FI among bisexual and racial/ethnic minority individuals would further elucidate how membership in multiple minority groups may contribute to a higher risk of FI.

Characterizing Female Firearm Suicide Circumstances: A Natural Language Processing and Machine Learning Approach.

INTRODUCTION: Since 2005, female firearm suicide rates increased by 34%, outpacing the rise in male firearm suicide rates over the same period. The objective of this study was to develop and evaluate a natural language processing pipeline to identify a select set of common and important circumstances preceding female firearm suicide from coroner/medical examiner and law enforcement narratives. METHODS: Unstructured information from coroner/medical examiner and law enforcement narratives were manually coded for 1,462 randomly selected cases from the National Violent Death Reporting System. Decedents were included from 40 states and Puerto Rico from 2014 to 2018. Naive Bayes, Random Forest, Support Vector Machine, and Gradient Boosting classifier models were tuned using 5-fold cross-validation. Model performance was assessed using sensitivity, specificity, positive predictive value, F1, and other metrics. Analyses were conducted from February to November 2022. RESULTS: The natural language processing pipeline performed well in identifying recent interpersonal disputes, problems with intimate partners, acute/chronic pain, and intimate partners and immediate family at the scene. For example, the Support Vector Machine model had a mean of 98.1% specificity and 90.5% positive predictive value in classifying a recent interpersonal dispute before suicide. The Gradient Boosting model had a mean of 98.7% specificity and 93.2% positive predictive value in classifying a recent interpersonal dispute before suicide. CONCLUSIONS: This study developed a natural language processing pipeline to classify 5 female firearm suicide antecedents using narrative reports from the National Violent Death Reporting System, which may improve the examination of these circumstances. Practitioners and researchers should weigh the efficiency of natural language processing pipeline development against conventional text mining and manual review.

A 1.2 Billion Pixel Human-Labeled Dataset for Data-Driven Classification of Coastal Environments.

The world's coastlines are spatially highly variable, coupled-human-natural systems that comprise a nested hierarchy of component landforms, ecosystems, and human interventions, each interacting over a range of space and time scales. Understanding and predicting coastline dynamics necessitates frequent observation from imaging sensors on remote sensing platforms. Machine Learning models that carry out supervised (i.e., human-guided) pixel-based classification, or image segmentation, have transformative applications in spatio-temporal mapping of dynamic environments, including transient coastal landforms, sediments, habitats, waterbodies, and water flows. However, these models require large and well-documented training and testing datasets consisting of labeled imagery. We describe "Coast Train," a multi-labeler dataset of orthomosaic and satellite images of coastal environments and corresponding labels. These data include imagery that are diverse in space and time, and contain 1.2 billion labeled pixels, representing over 3.6 million hectares. We use a human-in-the-loop tool especially designed for rapid and reproducible Earth surface image segmentation. Our approach permits image labeling by multiple labelers, in turn enabling quantification of pixel-level agreement over individual and collections of images.

Critical gaps in understanding firearm suicide in Hispanic communities: demographics, mental health, and access to care.

Suicide rates increased by 26.7% among Hispanics from 2015 to 2020, driven at least in part by highly lethal firearm suicide deaths. However, there are critical gaps in characterizing firearm suicide risks and prevention opportunities in Hispanic communities. We examined Hispanic adult firearm suicide decedents reported through the National Violent Death Reporting System from 2013-2019, focusing on demographic characteristics, firearm choices, suicidal thoughts/behaviors, mental health, and mental health treatment, compared with non-Hispanic adult firearm suicide decedents. Only 13.8% of Hispanic firearm suicide decedents were known to be undergoing treatment for a mental health or substance use problem prior to death, compared to 18.8% of non-Hispanic firearm suicide decedents. On average, Hispanic firearm suicide decedents were significantly less likely than non-Hispanic firearm suicide decedents known to have been treated for a mental health or substance use problem. These results may underscore the critical need for public health agencies and policymakers to promote initiatives integrating mental health screening into medical care, reducing mental health stigma among Hispanics, and expanding mental health treatment capacity in Hispanic communities.

We examined Hispanic adult firearm suicide decedents reported through the National Violent Death Reporting System from 2013­2019, focusing on demographic characteristics, firearm choices, suicidal thoughts and behaviors, mental health, and mental health treatment, compared with non-Hispanic adult firearm suicide decedents. We found key differences in demographic characteristics, firearm choices, and known history of suicidal thoughts and behaviors, mental ill health, and mental health treatment between Hispanic and non-Hispanic adult firearm suicide decedents. One key finding from this study was that Hispanic adult firearm suicide decedents were significantly less likely than non-Hispanic adult firearm suicide decedents known to be undergoing treatment for mental health or substance use problems prior to death. Additionally, compared with non-Hispanic decedents, Hispanic decedents were more likely to have a known history of suicidal thoughts/plans and suicide attempts. Handguns were used in three-fourths (74.9%) of non-Hispanic firearm suicide deaths but more than 8 in 10 (81.9%) Hispanic firearm suicide deaths. These results may indicate opportunities for public health agencies and policymakers to promote initiatives integrating mental health screening into primary medical care, reducing mental health stigma among Hispanics, and expanding mental health treatment capacity in Hispanic communities.

Endogenous Circadian Clock Machinery in Cortical NG2-Glia Regulates Cellular Proliferation.

The molecular circadian clock can be found throughout the body and is essential for the synchronizing cellular physiology with the 24 h day. However, the role of the clock in regulating the regenerative potential of the brain has not been explored. We report here that murine NG2-glia, the largest population of proliferative cells in the mature central nervous system, rhythmically express circadian clock genes in a 24 h period, including the critical clock component Bmal1 RNA and BMAL1 protein. Interestingly, daily NG2-glia proliferation preferentially occurs during the time of day in which Bmal1 expression is high, while conditional knockout of Bmal1 decreases both cortical NG2-glia density and cellular proliferation. Furthermore, in a neurotrauma model, we show that pathology-induced NG2-glia proliferation is also dependent on Bmal1 expression. Because circadian rhythm disturbances are common in neurologic disorders across the life span, including in traumatic brain injury, these findings bear significant implications for cellular regeneration in brain injuries and disease.

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.

Trends in Department of Defense Disability Evaluation System Ratings and Awards for Posttraumatic Stress Disorder and Traumatic Brain Injury, 2002-2017.

Since 2001, more than 3 million service members have deployed in support of multiple combat operations in Afghanistan, Iraq, and other theaters. Many have been diagnosed with the ""signature wounds"" of these conflicts: posttraumatic stress disorder (PTSD) and/or traumatic brain injury (TBI). During the intervening years, the process by which service members are evaluated for disability has evolved significantly, including a complete overhaul of the Disability Evaluation System (DES) beginning in 2007. Meanwhile, the Department of Defense (DoD) and the services made policy changes and initiated other efforts to improve screening for PTSD and TBI, encourage service members to seek treatment, improve quality of care, and reduce the stigma associated with treatment for these conditions. To explore these changes, as well as their potential effects on the numbers and characteristics of service members who are evaluated through DES, the authors identify and assess trends in DES outcomes for PTSD and TBI between 2002 and 2017.

Examining the policy effects of Arizona's 2016 pre-emption law on firearm suicide rates in the greater Tucson area: an observational study.

OBJECTIVE: In 2016, Arizona enacted SB 1487 to nullify Tucson's ordinance permitting the municipality to destroy confiscated and forfeited firearms and instead require the firearms to be resold to the public through an auctioneer. Our objective was to examine whether firearm suicide rates increased in Pima County (greater Tucson area) relative to other Arizona counties following the enactment of Arizona's 2016 pre-emption law. DESIGN: An observational study of a natural policy experiment. We used a difference-in-differences approach to estimate the effects of Arizona enacting SB 1487 on firearm suicide rates in Pima County. Our statistical analyses adjusted for county-level differences in population demographics (age, gender and race) and unemployment rates, as well as a proxy for firearm availability and mental health professional shortage area status. SETTING: 9 Arizona counties from 2014 to 2019. PARTICIPANTS: A policy group was constructed using Pima County (Tucson area) observations. A comparison group was created using data from eight other Arizona counties. 54 county-year observations were analysed. INTERVENTION: SB 1487, which pre-empted Tucson law and allowed firearms that were seized/surrendered to law enforcement to be recirculated instead of destroyed. OUTCOMES AND MEASURES: Annual rates of firearm and non-firearm suicides per 100 000 persons extracted from the Centers for Disease Control and Prevention WONDER system. RESULTS: Over the study period, comparison group counties had an average of 14.87 firearm suicides per 100 000 persons per year, compared with 11.56 firearm suicides per 100 000 persons per year in Pima County. A 1.13 increase in Pima County's firearm suicides per 100 000 persons coincided with the enactment of Arizona's 2016 pre-emption law, relative to comparison group counties over the same period. CONCLUSIONS: SB 1487 was associated with higher firearm suicide rates in Pima County relative to other areas not targeted by the law, assuming fewer firearms were destroyed and more firearms re-entered the greater Tucson area through 2019.

Oxidative Stress-Induced Damage to the Developing Hippocampus Is Mediated by GSK3ß.

Neonatal brain injury renders the developing brain vulnerable to oxidative stress, leading to cognitive deficit. However, oxidative stress-induced damage to hippocampal circuits and the mechanisms underlying long-term changes in memory and learning are poorly understood. We used high oxygen tension or hyperoxia (HO) in neonatal mice of both sexes to investigate the role of oxidative stress in hippocampal damage. Perinatal HO induces reactive oxygen species and cell death, together with reduced interneuron maturation, inhibitory postsynaptic currents, and dentate progenitor proliferation. Postinjury interneuron stimulation surprisingly improved inhibitory activity and memory tasks, indicating reversibility. With decreased hippocampal levels of Wnt signaling components and somatostatin, HO aberrantly activated glycogen synthase kinase 3 ß activity. Pharmacological inhibition or ablation of interneuron glycogen synthase kinase 3 ß during HO challenge restored progenitor cell proliferation, interneuron development, inhibitory/excitatory balance, as well as hippocampal-dependent behavior. Biochemical targeting of interneuron function may benefit learning deficits caused by oxidative damage.SIGNIFICANCE STATEMENT Premature infants are especially vulnerable to oxidative stress, as their antioxidant defenses are underdeveloped. Indeed, high oxygen tension is associated with poor neurologic outcomes. Because of its sustained postnatal development and role in learning and memory, the hippocampus is especially vulnerable to oxidative damage in premature infants. However, the role of oxidative stress in the developing hippocampus has yet to be explored. With ever-rising rates of neonatal brain injury and no universally viable approach to maximize functional recovery, a better understanding of the mechanisms underlying neonatal brain injury is needed. Addressing this need, this study uses perinatal hyperoxia to study cognitive deficits, pathophysiology, and molecular mechanisms of oxidative damage in the developing hippocampus.

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.

Preventing Adolescent and Young Adult Suicide: Do States With Greater Mental Health Treatment Capacity Have Lower Suicide Rates?

PURPOSE: Youth suicide is increasing at a significant rate and is the second leading cause of death for adolescents. There is an urgent public health need to address the youth suicide. The objective of this study is to determine whether adolescents and young adults residing in states with greater mental health treatment capacity exhibited lower suicide rates than states with less treatment capacity. METHODS: We conducted a state-level analysis of mental health treatment capacity and suicide outcomes for adolescents and young adults aged 10-24 spanning 2002-2017 using data from Centers for Disease Control and Prevention, U.S. Bureau of Labor Statistics, Federal Bureau of Investigation, and other sources. Multivariable linear fixed-effects regression models tested the relationships among mental health treatment capacity and the total suicide, firearm suicide, and nonfirearm suicide rates per 100,000 persons aged 10-24. RESULTS: We found a statistically significant inverse relationship between nonfirearm suicide and mental health treatment capacity (p = .015). On average, a 10% increase in a state's mental health workforce capacity was associated with a 1.35% relative reduction in the nonfirearm suicide rate for persons aged 10-24. There was no significant relationship between mental health treatment capacity and firearm suicide. CONCLUSIONS: Greater mental health treatment appears to have a protective effect of modest magnitude against nonfirearm suicide among adolescents and young adults. Our findings underscore the importance of state-level efforts to improve mental health interventions and promote mental health awareness. However, firearm regulations may provide greater protective effects against this most lethal method of firearm suicide.