The relationship between Social Determinants of Health (SDoH) and death from cardiovascular disease or opioid use in counties across the United States (2009-2018)

Impact of Social Determinants of Health on Cardiovascular Disease

An increasing number of studies have shown the impact of social determinants of health (SDoHs) on different cardiovascular outcomes. SDoHs influence the regional incidence of heart failure, heart failure outcomes, and heart failure readmission rates; can prevent use of advanced heart failure therapies in minorities with an indication for their use; can influence the incidence of coronary artery disease and peripheral artery disease outcomes; and can also prevent providing equal quality of care to all patients with myocardial infarction. In the setting of arrhythmias, specific SDoHs can increase the incidence of atrial fibrillation and adversely affect major outcomes in these patients. In congenital heart diseases, SDoHs can affect major outcomes, as well. In conclusion, SDoHs significantly impact cardiovascular morbidity and death and specific outcomes of patients with cardiovascular disease. Policy measures that aim to improve those SDoHs that negatively affect health outcomes hold promise for improving cardiovascular outcomes at individual and population levels.

Elucidating The Impact of Community-Level Social Determinants of Health on Pre-operative Frailty: A Data-Driven Study in Florida

Frailty, an age-related syndrome, is associated with poor post-operative outcomes. The impact of community-level social determinants of health (SDoH) on pre-operative frailty has not been investigated yet. We developed a machine learning model to predict pre-operative frailty using an institutional dataset and applied it to a more geographically diverse population from the OneFlorida+ Clinical Research Consortium. Computable phenotyping for SDoH stratification using unsupervised learning was employed to identify distinct patient profiles based on zip code-level SDoH characteristics. We applied multivariate logistic regression to examine the association between SDoH profiles and pre-operative frailty risk. Adverse community-level SDoH profiles are independently associated with higher pre-operative frailty risk; patients from the disadvantaged SDoH profile had 1.21 times higher odds (95% CI 1.16-1.26) of being frail compared to the advantaged SDoH cluster after adjusting for potential confounders. Considering patients' social context could improve pre-operative care and surgical outcomes, informing clinical practice and policies.

Socioeconomic inequalities in high-risk fertility behaviour among Nigerian women: A non-experimental population-based study

BACKGROUND

High-risk fertility behaviour (HRFB) has adverse health, social and economic effects on women. An understanding of socioeconomic inequalities is needed to design effective interventions targeted to lower maternal morbidity and mortality due to HRFB.

OBJECTIVES

The objective was to quantify socioeconomic inequalities in HRFB among Nigerian women.

DESIGN

This was a cross-section study that used population-based data.

METHODS

A sample of 21,792 Nigerian women aged 15-49 years from the 2018 Nigeria Demographic Health Survey was analyzed. Percentage was employed in univariate analysis. In addition, concentration index was used to assess the extent of inequalities in HRFB. This was further decomposed to ascertain the explanatory components' relative contributions to the socioeconomic inequalities in HRFB.

RESULTS

The weighted prevalence of HRFB (63.5%; 95% CI: 62.6-64.4%), included <18 years at childbirth (4.9%; 95% CI:4.5-5.3%), >34 years at childbirth (18.3%; 95% CI: 17.6-19.0%), >3 children birth order (49.6%; 95% CI: 48.7-50.5%) and <24 months at preceding birth interval (17.0% 95% CI: 16.3-17.6%) were estimated. Education (Contri: 50.3997%, Ec: 0.2771), wealth (Contri: 27.2811%, Ec: 0.2665), socioeconomic disadvantaged (Contri: 14.9163%, Ec: -0.0996), religion (Contri: 13.8636%, Ec: -0.0496), region (Contri: 11.1724%, Ec: 0.0711), partner education (Contri: 7.1351%, Ec: 0.2138), media use (Contri: 4.5064%, Ec: 0.1449) and family motility (Contri: 3.7890%, Ec: -0.0281) were positive contributors to HRFBs among Nigerian women. However, age (Contri: -14.6237, Ec: 0.0089) and history of contraceptive use (Contri: -8.7723, Ec: -0.2094) were negative contributors to HRFBs among women of reproductive age in Nigeria.

CONCLUSION

Women who have low socioeconomic level reported higher prevalence of HRFB. Targeted interventions are required to lower HRFB among Nigerian women from poor households and with no formal education. Women should get counselling and assistance from healthcare and educational institutions to help them adopt healthy sexual and reproductive practices.

Social Determinants of Health and US Health Care Expenditures by Insurer

Importance

US health expenditures have been growing at an unsustainable rate, while health inequities and poor outcomes persist. Targeting social determinants of health (SDOH) may contribute to identifying and controlling health care expenditures.

Objective

To determine whether SDOH are associated with US health care expenditures by Medicare, Medicaid, and private insurers.

Design, Setting, and Participants

Cross-sectional study of adults, representing the US civilian, noninstitutionalized population with Medicare, Medicaid, or private coverage, from the 2021 Medical Expenditure Panel SDOH Survey. Data analysis was conducted from October 2023 to April 2024.

Exposure

SDOH as individual-level, health-related social needs categorized by Healthy People 2030 domains: (1) educational access and quality, (2) health care access and quality, (3) neighborhood and built environment,(4) economic stability, and (5) social and community context.

Main Outcomes and Measures

The primary outcome was health care expenditures (US dollars) by Medicare, Medicaid, and private insurers. A 2-part econometric model (probit regression model and generalized linear model with gamma distribution) was used.

Results

Among the 14 918 insured adults in the analytic sample (mean [SD] age, 52.5 [17.9] years; 8471 female [56.8%]), the majority had middle to high family income (10 524 participants [70.5%]) and were privately insured (10 227 participants [68.5%]). Annual median (IQR) expenditure was $1648 ($389-$7126) for Medicaid, $3643 ($1321-$10 519) for Medicare, and $1369 ($456-$4078) for private insurers. Educational attainment and social isolation were associated with Medicaid expenditures. Medicaid beneficiaries with a high school diploma or general educational development certificate had on average (mean difference) $2245.39 lower annual Medicaid expenditures (95% CI, -$3700.97 to -$789.80) compared with beneficiaries with less than high school attainment. Compared with those who never felt isolated, Medicaid beneficiaries who often felt isolated had on average $2706.94 (95% CI, $1339.06-$4074.82) higher annual Medicaid expenditures. Health care access, built environment, and economic stability were associated with Medicare expenditures. Medicare beneficiaries living in neighborhoods with lower availability of parks had on average $5959.27 (95% CI, $1679.99 to $10 238.55) higher annual Medicare expenditures. Medicare beneficiaries who were very confident in covering unexpected expenses had on average $3743.98 lower annual Medicare expenditures (95% CI, -$6500.68 to -$987.28) compared with those who were not confident. Medical discrimination and economic stability were associated with private expenditures. Private insurance beneficiaries who experienced medical discrimination had on average $2599.93 (95% CI, $863.71-$4336.15) higher annual private expenditures compared with those who did not. Private beneficiaries who were contacted by debt collections in the past year had on average $2033.34 (95% CI, $896.82 to $3169.86) higher annual private expenditures compared with those who were not contacted.

Conclusions and Relevance

In this cross-sectional study of 14 918 insured adults, individual-level SDOH were significantly associated with US health care expenditures by Medicare, Medicaid, and private insurers. These findings may inform health insurers and policymakers to incorporate SDOH in their decision-making practices to identify and control health care expenditures, advancing health equity.

Drug Use Patterns in Wastewater and Socioeconomic and Demographic Indicators

Importance

Measuring drug use behaviors in individuals and across large communities presents substantial challenges, often complicated by socioeconomic and demographic variables.

Objectives

To detect spatial and temporal changes in community drug use by analyzing concentrations of analytes in influent wastewater and exploring their associations with area-based socioeconomic and sociodemographic metrics like the area deprivation index (ADI) and rural-urban commuting area (RUCA) codes.

Design, Setting, and Participants

This longitudinal, cross-sectional wastewater study was performed from May 2022 to April 2023 and included biweekly influent wastewater samples of 39 analytes from 8 sampling locations across 6 wastewater treatment plants in southern Nevada. Statistical analyses were conducted in December 2023.

Main Outcomes and Measures

It was hypothesized that wastewater monitoring of pharmaceuticals and personal care products (PPCPs) and high-risk substances (HRSs) could reveal true spatial and temporal drug use patterns in near-real time. Data collection of samples for PPCPs and HRSs was performed using mass spectrometry. Both ADI and RUCA scores were utilized to characterize neighborhood contexts in the analysis. The false discovery rate (FDR) method was utilized to correct for multiple comparisons (PFDR).

Results

Over the 12-month wastewater monitoring period, 208 samples for PPCPs and HRSs were collected, and analysis revealed an increase in the consumption of HRSs and the seasonal variation in PPCP use in southern Nevada. There was a significant increase in levels of stimulant-associated analytes, such as cocaine (β = 9.17 × 10-4; SE = 1.29 × 10-4; PFDR = 1.40 × 10-10), and opioids or their metabolites, notably norfentanyl (β = 1.48 × 10-4; SE = 1.88 × 10-4; PFDR = 1.66 × 10-12). In contrast, DEET, an active ingredient in mosquito and tick repellents, demonstrated a seasonal use pattern (β = -4.85 × 10-4; SE = 2.09 × 10-4; PFDR = 4.87 × 10-2). Wastewater from more disadvantaged or rural neighborhoods, as assessed through ADI and RUCA scores, was more likely to show a significant positive correlation with HRSs, such as cocaine (β = 0.075; SE = 0.038; P = .05) and norfentanyl (β = 0.004; SE = 0.001; P = 1.64 × 10-5).

Conclusions and Relevance

These findings suggest that wastewater monitoring of PPCPs and HRSs offers a complementary method to existing public health tools, providing timely data for tracking substance use behaviors and use of PPCPs at a population level.

Leverage pre-requisite courses to teach bachelor of science in nursing students about social determinants of health

BACKGROUND

Nurses have frequent opportunities to address social determinants of health (SDOH) in practice. However, many nurses graduate without completing coursework in SDOH, and there remain barriers to incorporating SDOH content into nursing curricula.

PURPOSE

We propose the revision of nursing pre-requisites to include substantive, introductory coursework on SDOH.

METHOD

We explored the history and professional context surrounding nursing's current pre-requisite course expectations. We also performed an assessment of the credit hour requirements and pre-requisite course titles for the nation's 100 top-ranked nursing programs.

FINDINGS

Our assessment revealed that the allocation of credit hour requirements for most programs leaves little room for SDOH-focused credits once nursing coursework starts; also, based on title analyses, foundational coursework on SDOH may be missing from most programs' pre-requisite listings.

DISCUSSION

Nursing pre-requisites should include SDOH content for reasons including the limited availability of credit hours for SDOH-focused electives and the importance of students developing a strong foundation in SDOH before their nursing coursework begins.

Injection Drug Use Alters Plasma Regulation of the B Cell Response

The opioid epidemic continues to be a major public health issue that includes millions of people who inject drugs (PWID). PWID have increased incidence of serious infections, including HIV as well as metabolic and inflammatory sequelae. We sought to discern the extent of systemic alterations in humoral immunity associated with injection drug use, including alterations in the plasma proteome and its regulation of B cell responsiveness. Comprehensive plasma proteomics analysis of HIV negative/hepatitis C negative individuals with a history of recent injection heroin use was performed using mass spectrometry and ELISA. The effects of plasma from PWID and healthy controls on the in vitro proliferation and transcriptional profile of B cell responses to stimulation were determined by flow cytometry and RNA-Seq. The plasma proteome of PWID was distinct from healthy control individuals, with numerous immune-related analytes significantly altered in PWID, including complement (C3, C5, C9), immunoglobulin (IgD, IgM, kappa light chain), and other inflammatory mediators (CXCL4, LPS binding protein, C-reactive protein). The plasma of PWID suppressed the in vitro proliferation of B cells. Transcriptome analysis indicated that PWID plasma treatment increased B cell receptor and CD40 signaling and shifted B cell differentiation from plasma cell-like toward germinal center B cell-like transcriptional profiles. These results indicate that the systemic inflammatory milieu is substantially altered in PWID and may impact their B cell responses.

Examining the relationship between social determinants of health, measures of structural racism and county-level overdose deaths from 2017-2020

INTRODUCTION

Despite being an important determinant of health outcomes, measures of structural racism are lacking in studies examining the relationship between the social determinants of health (SDOH) and overdose deaths. The aim of this study is to examine the association between per capita revenue generated from fines and forfeitures, a novel measure of structural racism, and other SDOH with county-level overdose deaths from 2017-2020.

METHODS

This longitudinal analysis of 2,846 counties from 2017-2020 used bivariate and multivariate Generalized Estimating Equations models to estimate associations between county overdose mortality rates and SDOH characteristics, including the fines and forfeitures measure.

RESULTS

In our multivariate model, higher per capita fine and forfeiture revenue (5.76; CI: 4.76, 6.78), households receiving food stamps (1.15; CI: 0.77, 1.53), residents that are veterans (1.07; CI: 0.52, 1.63), substance use treatment availability (4.69; CI: 3.03, 6.33) and lower population density (-0.002; CI: -0.004, -0.001) and percent of Black residents (-0.7`; CI: -1.01, -0.42) were significantly associated with higher overdose death rates. There was a significant additive interaction between the fines and forfeitures measure (0.10; CI: 0.03, 0.17) and the percent of Black residents.

CONCLUSIONS

Our findings suggest that structural racism, along with other SDOH, is associated with overdose deaths. Future research should focus on connecting individual-level data on fines and forfeitures to overdose deaths and other health outcomes, include measures of justice-related fines, such as court fees, and assess whether interventions aimed at increasing economic vitality in disadvantaged communities impact overdose deaths in a meaningful way.

Identifying social determinants of health from clinical narratives: A study of performance, documentation ratio, and potential bias

OBJECTIVE

To develop a natural language processing (NLP) package to extract social determinants of health (SDoH) from clinical narratives, examine the bias among race and gender groups, test the generalizability of extracting SDoH for different disease groups, and examine population-level extraction ratio.

METHODS

We developed SDoH corpora using clinical notes identified at the University of Florida (UF) Health. We systematically compared 7 transformer-based large language models (LLMs) and developed an open-source package - SODA (i.e., SOcial DeterminAnts) to facilitate SDoH extraction from clinical narratives. We examined the performance and potential bias of SODA for different race and gender groups, tested the generalizability of SODA using two disease domains including cancer and opioid use, and explored strategies for improvement. We applied SODA to extract 19 categories of SDoH from the breast (n = 7,971), lung (n = 11,804), and colorectal cancer (n = 6,240) cohorts to assess patient-level extraction ratio and examine the differences among race and gender groups.

RESULTS

We developed an SDoH corpus using 629 clinical notes of cancer patients with annotations of 13,193 SDoH concepts/attributes from 19 categories of SDoH, and another cross-disease validation corpus using 200 notes from opioid use patients with 4,342 SDoH concepts/attributes. We compared 7 transformer models and the GatorTron model achieved the best mean average strict/lenient F1 scores of 0.9122 and 0.9367 for SDoH concept extraction and 0.9584 and 0.9593 for linking attributes to SDoH concepts. There is a small performance gap (∼4%) between Males and Females, but a large performance gap (>16 %) among race groups. The performance dropped when we applied the cancer SDoH model to the opioid cohort; fine-tuning using a smaller opioid SDoH corpus improved the performance. The extraction ratio varied in the three cancer cohorts, in which 10 SDoH could be extracted from over 70 % of cancer patients, but 9 SDoH could be extracted from less than 70 % of cancer patients. Individuals from the White and Black groups have a higher extraction ratio than other minority race groups.

CONCLUSIONS

Our SODA package achieved good performance in extracting 19 categories of SDoH from clinical narratives. The SODA package with pre-trained transformer models is available at https://github.com/uf-hobi-informatics-lab/SODA_Docker.

Social associations and cardiovascular mortality in the United States counties, 2016 to 2020

BACKGROUND

The positive aspects of social interaction on health have been described often, with considerably less attention to their negative aspect. This study aimed to assess the impact of social associations on cardiovascular mortality in the United States.

METHODS

The Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (CDC WONDER) data sets from 2016 to 2020 were used to identify death records due to cardiovascular disease in the United States population aged 15 years and older. The social association rate defined as membership associations per 10,000 population, accessed from the 2020 County Health Rankings data was used as a surrogate for social participation. All United States counties were grouped into quartiles based on their social association rate; Q1 being the lowest quartile of social association, and Q4 the highest quartile. Age-adjusted mortality rate (AAMR) was calculated for each quartile. County health factor rankings for the state of Texas were used to adjust the AAMR for baseline comorbidities of county population, using Gaussian distribution linear regression.

RESULTS

Overall, the AAMR was highest in the 4th social association rate quartile (306.73 [95% CI, 305.72-307.74]) and lowest in the 1st social association rate quartile (266.80 [95% CI, 266.41-267.20]). The mortality rates increased in a linear pattern from lowest to highest social association rate quartiles. After adjustment for the county health factor ranks of Texas, higher social association rate remained associated with a significantly higher AAMR (coefficient 15.84 [95% CI, 12.78-18.89]).

CONCLUSIONS

Our study reported higher cardiovascular AAMR with higher social associations in the United States, with similar results after adjustment for County Health Rankings in the state of Texas.

Lack of leisure time physical activity and variations in cardiovascular mortality across US communities: a comprehensive county-level analysis (2011-2019)

OBJECTIVES

To investigate the associations between county-level proportions of adults not engaging in leisure-time physical activity (no LTPA) and age-adjusted cardiovascular mortality (AACVM) rates in the overall US population and across demographics.

METHODS

Analysing 2900 US counties from 2011 to 2019, we used the Centers for Disease Control and Prevention (CDC) databases to obtain annual AACVM rates. No LTPA data were sourced from the CDC's Behavioural Risk Factor Surveillance System survey and county-specific rates were calculated using a validated multilevel regression and poststratification modelling approach. Multiple regression models assessed associations with county characteristics such as socioeconomic, environmental, clinical and healthcare access factors. Poisson generalised linear mixed models were employed to calculate incidence rate ratios (IRR) and additional yearly deaths (AYD) per 100 000 persons.

RESULTS

Of 309.9 million residents in 2900 counties in 2011, 7.38 million (2.4%) cardiovascular deaths occurred by 2019. County attributes such as socioeconomic, environmental and clinical factors accounted for up to 65% (adjusted R2=0.65) of variance in no LTPA rates. No LTPA rates associated with higher AACVM across demographics, notably among middle-aged adults (standardised IRR: 1.06; 95% CI (1.04 to 1.07)), particularly women (1.09; 95% CI (1.07 to 1.12)). The highest AYDs were among elderly non-Hispanic black individuals (AYD=68/100 000).

CONCLUSIONS

Our study reveals a robust association between the high prevalence of no LTPA and elevated AACVM rates beyond other social determinants. The most at-risk groups were middle-aged women and elderly non-Hispanic black individuals. Further, county-level characteristics accounted for substantial variance in community LTPA rates. These results emphasise the need for targeted public health measures to boost physical activity, especially in high-risk communities, to reduce AACVM.

Spatial and Temporal Drug Usage Patterns in Wastewater Correlate with Socioeconomic and Demographic Indicators in Southern Nevada

Evaluating drug use within populations in the United States poses significant challenges due to various social, ethical, and legal constraints, often impeding the collection of accurate and timely data. Here, we aimed to overcome these barriers by conducting a comprehensive analysis of drug consumption trends and measuring their association with socioeconomic and demographic factors. From May 2022 to April 2023, we analyzed 208 wastewater samples from eight sampling locations across six wastewater treatment plants in Southern Nevada, covering a population of 2.4 million residents with 50 million annual tourists. Using bi-weekly influent wastewater samples, we employed mass spectrometry to detect 39 analytes, including pharmaceuticals and personal care products (PPCPs) and high risk substances (HRS). Our results revealed a significant increase over time in the level of stimulants such as cocaine (pFDR=1.40×10-10) and opioids, particularly norfentanyl (pFDR =1.66×10-12), while PPCPs exhibited seasonal variation such as peak usage of DEET, an active ingredient in insect repellents, during the summer (pFDR =0.05). Wastewater from socioeconomically disadvantaged or rural areas, as determined by Area Deprivation Index (ADI) and Rural-Urban Commuting Area Codes (RUCA) scores, demonstrated distinct overall usage patterns, such as higher usage/concentration of HRS, including cocaine (p=0.05) and norfentanyl (p=1.64×10-5). Our approach offers a near real-time, comprehensive tool to assess drug consumption and personal care product usage at a community level, linking wastewater patterns to socioeconomic and demographic factors. This approach has the potential to significantly enhance public health monitoring strategies in the United States.

The Imperative of Regulation: The Co-creation of a Medical and Non-medical US Opioid Crisis

The ravaging COVID-19 pandemic has almost pushed into oblivion the fact that the United States is still struggling with an immense addiction crisis. Drug overdose deaths rose from 16,849 in 1999 to nearly 110000-of which an estimated 75,000 involved opioids-in 2022. On a yearly basis, the opioid casualty rate is higher than the combined number of victims of firearm violence and car accidents. The Covid-19 epidemic might have helped to worsen the addiction crisis by stimulating drug use among adolescents and diverting national attention to yet another public health crisis. In the past decade the sharpest increase in deaths occurred among those related to fentanyl and fentanyl analogs (illicitly manufactured, synthetic opioids of greater potency). In the first opioid crisis wave (1998-2010), opioid-related deaths were mainly associated with prescription opioids such as Oxycontin (oxycodone hydrochloride). The mass prescription of these narcotic drugs did anything but control the pervasive phenomenon of 'addiction on prescription' that played such an important role in the emergence and robustness of the US opioid crisis. Using a long-term drug lifecycle analytic approach in this article I will show how opioid producing pharmaceutical companies created a medical market for opioid painkillers. They thus fueled a consumer demand for potent opioid drugs that was eagerly capitalized on by criminal entrepreneurs and their international logistic networks. I will also point out the failure of US authorities to effectively respond to this crisis due to the gap between narcotic product regulation, regulation of marketing practices and the rise of a corporate dominated health care system. Ironically, this turned the most powerful geopolitical force in the war against drugs into its greatest victim. Due to formulary availability and regulatory barriers to accessibility European countries have been relatively protected against following suit the US opioid crisis.

The Future of the United States Overdose Crisis: Challenges and Opportunities

Policy Points People are dying at record numbers from overdose in the United States. Concerted action has led to a number of successes, including reduced inappropriate opioid prescribing and increased availability of opioid use disorder treatment and harm-reduction efforts, yet ongoing challenges include criminalization of drug use and regulatory and stigma barriers to expansion of treatment and harm-reduction services. Priorities for action include investing in evidence-based and compassionate policies and programs that address sources of opioid demand, decriminalizing drug use and drug paraphernalia, enacting policies to make medication for opioid use disorder more accessible, and promoting drug checking and safe drug supply.

County-level predictors of US drug overdose mortality: A systematic review

BACKGROUND

This systematic review summarized published literature on county-level predictors of drug overdose mortality in the United States (US).

METHODS

Peer-reviewed studies and doctoral dissertations published in English between 1990 and July 19, 2022 were identified from PubMed, Web of Science, ProQuest Dissertations & Theses, PsycINFO, CINAHL, and EconLit. Eligible studies examined at least one county-level predictor of drug overdose mortality in US counties. Two reviewers independently completed screening, quality assessment (with an adapted National Institutes of Health Quality Assessment Tool), and data extraction. Results were qualitatively summarized and grouped by predictor categories.

RESULTS

Of 56 studies included, 42.9% were subnational, and 53.6% were limited to opioid overdose. In multiple studies, measures related to opioid prescribing, illness/disability, economic distress, mining employment, incarceration, family distress, and single-parent families were positively associated with drug overdose mortality outcomes, while measures related to cannabis dispensaries, substance use treatment, social capital, and family households were negatively associated with drug overdose mortality outcomes. Both positive and negative associations were documented for smoking, uninsurance, healthcare professional shortage status, physicians per capita, unemployment, income, poverty, educational attainment, racial composition, and rurality. Findings within studies also differed by subpopulation (by race/ethnicity, gender, age, or rurality) and the type of drugs involved in overdose.

CONCLUSIONS

The findings of this review provide relatively mixed evidence regarding many county-level predictors of overdose mortality, several of which also vary between subpopulations, supporting the importance of additional research to elucidate pathways through which the county context may shape risk of fatal overdose.