Predicting COVID-19 new cases in California with Google Trends data and a machine learning approach.

BACKGROUND: Google Trends data can be a valuable source of information for health-related issues such as predicting infectious disease trends. OBJECTIVES: To evaluate the accuracy of predicting COVID-19 new cases in California using Google Trends data, we develop and use a GMDH-type neural network model and compare its performance with a LTSM model. METHODS: We predicted COVID-19 new cases using Google query data over three periods. Our first period covered March 1, 2020, to July 31, 2020, including the first peak of infection. We also estimated a model from October 1, 2020, to January 7, 2021, including the second wave of COVID-19 and avoiding possible biases from public interest in searching about the new pandemic. In addition, we extended our forecasting period from May 20, 2020, to January 31, 2021, to cover an extended period of time. RESULTS: Our findings show that Google relative search volume (RSV) can be used to accurately predict new COVID-19 cases.  We find that among our Google relative search volume terms, "Fever," "COVID Testing," "Signs of COVID," "COVID Treatment," and "Shortness of Breath" increase model predictive accuracy. CONCLUSIONS: Our findings highlight the value of using data sources providing near real-time data, e.g., Google Trends, to detect trends in COVID-19 cases, in order to supplement and extend existing epidemiological models.

An economic evaluation of the expansion of electronic case reporting in an academic healthcare setting.

Objectives: Determine the economic cost or benefit of expanding electronic case reporting (eCR) for 29 reportable conditions beyond the initial eCR implementation for COVID-19 at an academic health center. Materials and methods: The return on investment (ROI) framework was used to quantify the economic impact of the expansion of eCR from the perspective of an academic health system over a 5-year time horizon. Sensitivity analyses were performed to assess key factors such as personnel cost, inflation, and number of expanded conditions. Results: The total implementation costs for the implementation year were estimated to be $5031.46. The 5-year ROI for the expansion of eCR for the 29 conditions is expected to be 142% (net present value of savings: $7166). Based on the annual ROI, estimates suggest that the savings from the expansion of eCR will cover implementation costs in approximately 4.8 years. All sensitivity analyses yielded a strong ROI for the expansion of eCR. Discussion and conclusion: Our findings suggest a strong ROI for the expansion of eCR at UHealth, with the most significant cost savings observed implementing eCR for all reportable conditions. An early effort to ensure data quality is recommended to expedite the transition from parallel reporting to production to improve the ROI for healthcare organizations. This study demonstrates a positive ROI for the expansion of eCR to additional reportable conditions beyond COVID-19 in an academic health setting, such as UHealth. While this evaluation focuses on the 5-year time horizon, the potential benefit could extend further.

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.

Associations between food insecurity and other social risk factors among U.S. adults.

BACKGROUND: Food insecurity (FI) often co-exists with other social risk factors, which makes addressing it particularly challenging. The degree of association between FI and other social risk factors across different levels of income and before and during the COVID-19 pandemic is currently unknown, impeding the ability to design effective interventions for addressing these co-existing social risk factors. OBJECTIVE: To determine the association between FI and other social risk factors overall and across different levels of income-poverty ratios and before (2019) and during (2020-2021) the pandemic. DESIGN: We used nationally representative data from the 2019-2021 National Health Interview Survey for our cross-sectional analysis. Social risk factors available in NHIS included difficulties paying for medical bills, difficulties paying for medications, receiving income assistance, receiving rental assistance, and "not working last week". SUBJECTS: 93,047 adults (≥18 years old). KEY RESULTS: Individuals with other social risk factors (except receiving income assistance) were more likely to report FI, even after adjusting for income and education inequalities. While poverty leads to a higher prevalence of FI, associations between FI and other social risk factors were stronger among people with higher incomes, which may be related to their ineligibility for social safety net programs. Associations were similar before and during the pandemic, perhaps due to the extensive provision of social safety net programs during the pandemic. CONCLUSIONS: Future research should explore how access to a variety of social safety net programs may impact the association between social risk factors. With the expiration of most pandemic-related social supports, further research and monitoring are also needed to examine FI in the context of increasing food and housing costs. Our findings may also have implications for the expansion of income-based program eligibility criteria and screening for social risk factors across all patients and not only low-income people.

Navigating the Landscape: Barriers and Facilitators in Electronic Case Reporting Implementation Across Public Health Agencies.

OBJECTIVE: The objectives were to identify barriers and facilitators for electronic case reporting (eCR) implementation associated with "organizational" and "people"-based knowledge/processes and to identify patterns across implementation stages to guide best practices for eCR implementation at public health agencies. DESIGN: This qualitative study uses semistructured interviews with key stakeholders across 6 public health agencies. This study leveraged 2 conceptual frameworks for the development of the interview guide and initial codebook and the organization of the findings of thematic analysis. SETTING: Interviews were conducted virtually with informants from public health agencies at varying stages of eCR implementation. PARTICIPANTS: Investigators aimed to enroll 3 participants from each participating public health agency, including an eCR lead, a technical lead, and a leadership informant. MAIN OUTCOME MEASURES: Patterns associated with barriers and facilitators across the eCR implementation stage. RESULTS: Twenty-eight themes were identified throughout interviews with 16 informants representing 6 public health agencies at varying stages of implementation. While there was variation across these levels, 3 distinct patterns were identified, including themes that were described (1) solely as a barrier or facilitator for eCR implementation regardless of implementation stages, (2) as a barrier for those in the early stages but evolved into a facilitator for those in later stages, and (3) as facilitators that were unique to the late-stage implementation. CONCLUSION: This study elucidated critical national, organizational, and person-centric best practices for public health agencies. These included the importance of engagement with the national eCR team, integrated development teams, cross-pollination, and developing solutions with the broader public health mission in mind. While the implementation of eCR was the focus of this study, the findings are generalizable to the broader data modernization efforts within public health agencies.

Association of social vulnerability and influenza vaccination rates for Annual Medicare Enrollees at the county-level in the United States.

INTRODUCTION: Influenza is a preventable acute respiratory illness with a high potential to cause serious complications and is associated with high mortality and morbidity in the US. We aimed to determine the specific community-level vulnerabilities for different race/ethnic communities that are most predictive of influenza vaccination rates. METHODS: We conducted a machine learning analysis (XGBoost) to identify community-level social vulnerability features that are predictive of influenza vaccination rates among Medicare enrollees across counties in the US and by race/ethnicity. RESULTS: Population density per square mile in a county is the most important feature in predicting influenza vaccination in a county, followed by unemployment rates and the percentage of mobile homes. The gain relative importance of these features are 11.6%, 9.2%, and 9%, respectively. Among whites, population density (17% gain relative importance) was followed by the percentage of mobile homes (9%) and per capita income (8.7%). For Black/African Americans, the most important features were population density (12.8%), percentage of minorities in the county (8.0%), per capita income (6.9%), and percent of over-occupied housing units (6.8%). Finally, for Hispanics, the top features were per capita income (8.4%), percentage of mobile homes (8.0%), percentage of non-institutionalized persons with a disability (7.9%), and population density (7.6%). CONCLUSIONS: Our study may have implications for the success of large vaccination programs in counties with high social vulnerabilities. Further, our findings suggest that policies and interventions seeking to increase rates of vaccination in race/ethnic minority communities may need to be tailored to address their specific socioeconomic vulnerabilities.

Food Insecurity Disparities Among Immigrants in the U.S.

Introduction: Food insecurity negatively impacts public health and costs the U.S. healthcare system $53 billion annually. Immigrants are at higher risk of food insecurity. We sought to (1) characterize the prevalence of food insecurity among immigrants (i.e., noncitizens and naturalized citizens) and U.S.-born citizens and (2) determine whether Supplemental Nutrition Assistance Program utilization and income-poverty ratio levels impact the relationship between immigration status and food insecurity. Methods: Multivariable logistic regression models were used to determine the odds of food insecurity (dependent variables) using nationally representative data from the 2019-2020 National Health Interview Survey. Independent variables included immigration status, Supplemental Nutrition Assistance Program utilization, income-poverty ratio, and other important demographics. AORs with their 95% CIs are reported. Analysis was conducted in 2022. Results: After controlling for independent variables, noncitizens had 1.28 (95% CI=1.02, 1.61) times higher odds of food insecurity than U.S.-born citizens. There was no food insecurity disparity between naturalized citizens and U.S.-born citizens. However, the association between immigration status and food insecurity varied significantly at different levels of Supplemental Nutrition Assistance Program utilization and income-poverty ratio. There were no food insecurity disparities between immigrants and U.S.-born citizens when they utilized the Supplemental Nutrition Assistance Program and when they had an income below 200% federal poverty level. Noncitizens who did not utilize the Supplemental Nutrition Assistance Program or those with an income above 200% federal poverty level were more likely to report food insecurity than their U.S.-born counterparts (AOR=1.32, 95% CI=1.01, 1.73 and AOR=1.88, 95% CI=1.24, 2.86, respectively). Moreover, naturalized citizens with an income above 200% federal poverty level were also more likely to report food insecurity than their U.S.-born counterparts (AOR=1.61, 95% CI=1.21, 2.14). Conclusions: Supplemental Nutrition Assistance Program utilization may likely eliminate food insecurity disparities among immigrants and U.S.-born citizens. However, among non-Supplemental Nutrition Assistance Program utilizers, significant food insecurity disparities remained between noncitizens and U.S.-born citizens after adjusting for independent variables. In addition, among individuals with incomes above 200% federal poverty level, significant food insecurity disparities were observed between immigrants and U.S.-born citizens. More research is needed to further understand the role that fear of deportation, ineligibility or lack of awareness about eligibility for the Supplemental Nutrition Assistance Program, and other factors such as structural racism play in food insecurity disparities between immigrants and U.S.-born citizens.

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.

A systematic review for the impacts of global approaches to regulating electronic nicotine products.

Background: The rapid increase in electronic nicotine product (ENP) use among young people has been a global public health challenge, given the potential harm of ENPs and nicotine dependence. Many countries have recently introduced legislations to regulate ENPs, but the impacts of these policies are poorly understood. This systematic review aims to critically synthesise empirical studies on the effects of global regulations regarding ENPs on the prevalence of use, health outcomes and their determinants, using the 4A marketing mix framework (acceptability, affordability, accessibility and awareness). Methods: Following the PRISMA guideline, we searched PubMed, Embase, Scopus, Web of Science, Academic Search Complete, Business Source Complete, and APA PsycINFO databases from inception until June 14, 2022 and performed citation searches on the included studies. Reviewed literature was restricted to peer-reviewed, English-language articles. We included all pre-post and quasi-experimental studies that evaluated the impacts of e-cigarette policies on the prevalence of ENP use and other health outcomes. A modified Joanna Briggs Institute (JBI) Critical Appraisal checklist for quasi-experimental studies was used for quality assessment. Due to heterogeneity of the included studies, we conducted a narrative synthesis of evidence. Results: Of 3991 unduplicated records screened, 48 (1.2%) met the inclusion criteria, most were from high-income countries in North America and Europe and 26 studies measured self-reported ENPs use. Flavour restrictions significantly decreased youth ENP use and taxation reduced adult use; mixed results were found for the impacts of age restrictions. Indoor vaping restrictions and the European Tobacco Products Directive (TPD) did not seem to reduce ENP use based on existing studies. Changes in determinants such as sales and perceptions corroborated our conclusions. Few studies assessed the impacts of other regulations such as advertising restrictions and retail licensing requirements. Conclusions: Flavour restrictions and taxes have the strongest evidence to support effective control of ENPs, while others need powerful enforcement and meaningful penalties to ensure their effectiveness. Future research should focus on under-examined policies and differential impacts across sociodemographic characteristics and countries. Registration: PROSPERO CRD42022337361.

Is use of a smuggler to cross the US-Mexico border associated with mental health problems among undocumented immigrants from Mexico?

Although numerous studies have found that Latine immigrants to the United States (US) have better health outcomes on average than persons born in the US, studies of persons living in Mexico have found that undocumented immigrants have worse health, especially those that were deported, compared to Mexican citizens that never migrated or migrated with authorization. However, the health outcomes of Mexican migrants using a smuggler to cross the US-Mexico border is a gap in the literature. We hypothesized that undocumented immigrant adults who used a smuggler to cross the US-Mexico border would be more likely to report mental health problems upon return to Mexico compared with undocumented immigrant adults that did not use a smuggler. We analyzed nationally representative, cross-sectional survey data of 1,563 undocumented immigrants currently living in Mexico. Most undocumented immigrants in the sample (87%) used a smuggler. Use of a smuggler by undocumented immigrant adults was associated with a 4.7% higher prevalence of emotional or psychiatric problems compared to undocumented immigrant adults that did not use a smuggler. We conclude that modality of ingress into the US is a risk factor for poorer mental health among undocumented immigrant adults.

Immigrant-focused Medical Legal Partnerships: A Practical Innovation to Improve Immigrant Health and Social Well-being.

THE PROBLEM: Immigration status is an important social determinant of health that can limit access to health care and related services. Few medical legal partnerships (MLPs) address immigration-related legal needs of patients. PURPOSE OF ARTICLE: An immigrant-focused MLP (IMLP) addresses immigration legal needs and provides advice to vulnerable patient-clients and their families regarding potential legal consequences of accessing health care and other public benefits. In this article, we outline the core elements of an IMLP and provide case examples from our ongoing IMLPs. KEY POINTS: Although many of the core elements of an IMLP are similar to those of an MLP, an IMLP focuses specifically on immigration-related legal needs of patient-clients. IMLPs can complement and extend MLP services by providing legal assistance to non-citizen immigrant individuals and mixed status families. CONCLUSIONS: IMLPs may improve immigrant patient-clients' health and social well-being and create eligibility for public benefits. An IMLP can be a scalable innovation to improve access to health and legal services. Challenges to institutionalizing an IMLP may include limited sustainable funding, few legal service providers with immigration expertise and capacity, patient-client identification, and the politicized nature of immigration.

Social vulnerability and initial COVID-19 community spread in the US South: a machine learning approach.

BACKGROUND AND OBJECTIVES: More than 93 million COVID-19 cases and more than 1 million COVID-19 deaths have been reported in the USA by August 2022. The disproportionate effect of the pandemic and its severe impact on vulnerable communities raised concerns. This research aimed to identify and rank Social Vulnerability Index (SVI) factors highly predictive of the spread of COVID-19 in the US South at the beginning of the pandemic. METHODS: We used Extreme Gradient Boosting (XGBoost) machine learning methodology and SVI data, and the number of COVID-19 cases across all counties in the US South to predict the number of positive cases within 30 days of a county's first case. RESULTS: Our results showed that the percentage of mobile homes is the most important feature in predicting the increase in COVID-19. Also, population density per square mile, per capita income, percentage of housing in structures with 10+ units, percentage of people below poverty and percentage of people with no high school diploma are important predictors of COVID-19 community spread, respectively. CONCLUSIONS: SVI can help assess the vulnerability or resilience of communities to the spread of COVID-19 and can help identify communities at high risk of COVID-19 spread.

A budget impact analysis of cost to implement a whole child health focused, family-based intervention in primary care for children with elevated BMI.

BACKGROUND: Although the cost of implementing evidence-based interventions (EBIs) is a key determinant of adoption, lack of cost information is widespread. We previously evaluated the cost of preparing to implement Family Check-Up 4 Health (FCU4Health), an individually tailored, evidence-based parenting program that takes a whole child approach, with effects on both behavioral health and health behavior outcomes, in primary care settings. This study estimates the cost of implementation, including preparation. METHODS: We assessed the cost of FCU4Health across the preparation and implementation phases spanning 32 months and 1 week (October 1, 2016-June 13, 2019) in a type 2 hybrid effectiveness-implementation study. This family-level randomized controlled trial took place in Arizona with n = 113 predominantly low-income, Latino families with children ages > 5.5 to < 13 years. Using electronic cost capture and time-based activity-driven methods, budget impact analysis from the perspective of a future FCU4Health adopting entity-namely, ambulatory pediatric care clinicians-was used to estimate the cost of implementation. Labor costs were based on 2021 Bureau of Labor Statistics Occupational Employment Statistics, NIH-directed salary cap levels or known salaries, plus fringe benefits at a standard rate of 30%. Non-labor costs were based on actual amounts spent from receipts and invoices. RESULTS: The cost of FCU4Health implementation to 113 families was $268,886 ($2380 per family). Actual per family cost varied widely, as individual tailoring resulted in families receiving a range of 1-15 sessions. The estimated cost of replicating implementation for future sites ranged from $37,636-$72,372 ($333-$641 per family). Using our previously reported preparation costs (i.e., $174,489; $1544 per family), with estimated replication costs of $18,524-$21,836 ($164-$193 per family), the total cost of delivering FCU4Health was $443,375 ($3924 per family), with total estimated replication costs of $56,160-$94,208 ($497-$834 per family). CONCLUSIONS: This study provides a baseline for costs associated with implementation of an individually tailored parenting program. Results provide critical information for decision makers and a model for future economic analysis and can be used to inform optimization thresholds for implementation and, when necessary, benchmarks for program adaptation to promote scale-up. TRIAL REGISTRATION: This trial was prospectively registered on January 6, 2017, at ClinicalTrials.gov (NCT03013309).

Social vulnerability predictors of drug poisoning mortality: A machine learning analysis in the United States.

BACKGROUND AND OBJECTIVES: Drug poisoning is a leading cause of unintentional deaths in the United States. Despite the growing literature, there are a few recent analyses of a wide range of community-level social vulnerability features contributing to drug poisoning mortality. Current studies on this topic face three limitations: often studying a limited subset of vulnerability features, focusing on small sample sizes, or solely including local data. To address this gap, we conducted a national-level analysis to study the impacts of several social vulnerability features in predicting drug mortality rates in the United States. METHODS: We used machine learning to investigate the role of 16 social vulnerability features in predicting drug mortality rates for US counties in 2014, 2016, and 2018-the most recent available data. We estimated each vulnerability feature's gain relative contribution in predicting drug poisoning mortality. RESULTS: Among all social vulnerability features, the percentage of noninstitutionalized persons with a disability is the most influential predictor, with a gain relative contribution of 18.6%, followed by population density and the percentage of minority residents (13.3% and 13%, respectively). Percentages of households with no available vehicles, mobile homes, and persons without a high school diploma are the following features with gain relative contributions of 6.3%, 5.8%, and 5.1%, respectively. CONCLUSION AND SCIENTIFIC SIGNIFICANCE: We identified social vulnerability features that are most predictive of drug poisoning mortality. Public health interventions and policies targeting vulnerable communities may increase the resilience of these communities and mitigate the overdose death and drug misuse crisis.

Can allostatic load in pregnancy explain the association between race and subsequent cardiovascular disease risk: A cohort study.

OBJECTIVE: To assess the relationship between allostatic load, a measure of cumulative chronic stress in early pregnancy and cardiovascular disease risk, 2-7 years postpartum, and pathways contributing to racial disparities in cardiovascular disease risk. DESIGN: Secondary analysis of a prospective cohort study. SETTING MULTICENTER POPULATION: Pregnant women. METHODS: Our primary exposure was high allostatic load in the first trimester, defined as at least 4 of 12 biomarkers (systolic blood pressure, diastolic blood pressure, body mass index, cholesterol, low-density lipoprotein, high-density lipoprotein, high-sensitivity C-reactive protein, triglycerides, insulin, glucose, creatinine and albumin) in the unfavourable quartile. Logistic regression was used to test the association between high allostatic load and main outcome adjusted for confounders: time from index pregnancy and follow up, age, education, smoking, gravidity, bleeding in the first trimester, index adverse pregnancy outcomes, and health insurance. Each main outcome component and allostatic load were analysed secondarily. Mediation and moderation analyses assessed the role of high allostatic load in racial disparities of cardiovascular disease risk. MAIN OUTCOME MEASURE: Incident cardiovascular disease risk: hypertension, or metabolic disorders. RESULTS: Cardiovascular disease risk was identified in 1462/4022 individuals (hypertension: 36.6%, metabolic disorder: 15.4%). After adjustment, allostatic load was associated with cardiovascular disease risk (adjusted odds ratio [aOR] 2.0, 95% CI 1.8-2.3), hypertension (aOR 2.1, 95% CI 1.8-2.4) and metabolic disorder (aOR 1.7, 95% CI 1.5-2.1). Allostatic load was a partial mediator between race and cardiovascular disease risk. Race did not significantly moderate this relationship. CONCLUSIONS: High allostatic load during pregnancy is associated with cardiovascular disease risk. The relationships between stress, subsequent cardiovascular risk and race warrant further study.

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.

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.

Allostatic Load and Adverse Pregnancy Outcomes.

OBJECTIVE: To assess the association between allostatic load, as an estimate of chronic stress, and adverse pregnancy outcomes. METHODS: This was a secondary analysis of nuMoM2b (Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be), a prospective observational cohort study. Our primary exposure was dichotomous high allostatic load in the first trimester, defined as 4 or more of 12 biomarkers in the "worst" quartile. The primary outcome was a composite adverse pregnancy outcome: hypertensive disorders of pregnancy (HDP), preterm birth, small for gestational age (SGA), and stillbirth. Secondary outcomes included components of the composite. Multivariable logistic regression was used to test the association between high allostatic load and adverse pregnancy outcomes, adjusted for potential confounders. Mediation and moderation analyses were conducted to assess the role of allostatic load along the causal pathway between racial disparities and adverse pregnancy outcomes. RESULTS: Among 4,266 individuals, 34.7% had a high allostatic load. Composite adverse pregnancy outcome occurred in 1,171 (27.5%): 14.0% HDP, 8.6% preterm birth (48.0% spontaneous and 52.2% indicated), 11.0% SGA, and 0.3% stillbirth. After adjustment for maternal age, gravidity, smoking, bleeding in the first trimester, and health insurance, high allostatic load was significantly associated with a composite adverse pregnancy outcome (adjusted odds ratio [aOR] 1.5, 95% CI 1.3, 1.7) and HDP (aOR 2.5, 95% CI 2.0-2.9), but not preterm birth or SGA. High allostatic load partially mediated the association between self-reported race and adverse pregnancy outcomes. The association between allostatic load and HDP differed by self-reported race, but not for a composite adverse pregnancy outcome, preterm birth, or SGA. CONCLUSION: High allostatic load in the first trimester is associated with adverse pregnancy outcomes, particularly HDP. Allostatic load was a partial mediator between race and adverse pregnancy outcomes. The association between allostatic load and HDP differed by self-reported race.