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In Michigan, the COVID-19 pandemic severely impacted Black and Latinx communities. These communities experienced higher rates of exposure, hospitalizations, and deaths compared to Whites. We examine the impact of the pandemic and reasons for the higher burden on communities of color from the perspectives of Black and Latinx community members across four Michigan counties and discuss recommendations to better prepare for future public health emergencies.
Methods:
Using a community-based participatory research approach, we conducted semi-structured interviews (n = 40) with Black and Latinx individuals across the four counties. Interviews focused on knowledge related to the pandemic, the impact of the pandemic on their lives, sources of information, attitudes toward vaccination and participation in vaccine trials, and perspectives on the pandemic’s higher impact on communities of color.
Results:
Participants reported overwhelming effects of the pandemic in terms of worsened physical and mental health, financial difficulties, and lifestyle changes. They also reported some unexpected positive effects. They expressed awareness of the disproportionate burden among Black and Latinx populations and attributed this to a wide range of disparities in Social Determinants of Health. These included racism and systemic inequities, lack of access to information and language support, cultural practices, medical mistrust, and varied individual responses to the pandemic.
Conclusion:
Examining perspectives and experiences of those most impacted by the pandemic is essential for preparing for and effectively responding to public health emergencies in the future. Public health messaging and crisis response strategies must acknowledge the concerns and cultural needs of underrepresented populations.
Objectives: Leveraging the non-monolithic structure of Latin America, which represents a large variability in social determinants of health (SDoH) and high levels of genetic admixture, we aim to evaluate the relative contributions of SDoH and genetic ancestry in predicting dementia risk in Latin American populations
Methods: Community-dwelling participants aged 65 and older (N = 3808) from Cuba, Dominican Republic, Mexico, and Peru completed the 10/66 protocol assessments. Dementia was diagnosed using the cross-culturally validated 10/66 algorithm. The primary outcome measured was the risk of developing dementia. Multivariate linear regression models adjusted for SDoH were used in the main analysis.
Results: We observed extensive three-way (African/European/Native American) genetic ancestry variation between countries. Individuals with higher proportions of Native American (>70%) and African American (>70%) ancestry were more likely to exhibit factors contributing to worse SDoH, such as lower educational levels (p <0.001), lower SES (p < 0.001), and higher frequency of vascular risk factors (p < 0.001). In unadjusted analysis, American individuals with predominant African ancestry exhibited a higher dementia frequency (p = 0.03) and both Native and African ancestry predominant groups showed lower cognitive performance relative to those with higher European ancestry (p < 0.001). However, after adjusting for measures of SDoH, there was no association between ancestry proportion and dementia probability, and ancestry proportions no longer significantly accounted for the variance in cognitive performance (African predominant p = 0.31 [–0.19, 0.59] and Native predominant p = 0.74 [–0.24, 0.33]).
Conclusions: The findings suggest that social and environmental factors play a more crucial role than genetic ancestry in predicting dementia risk in Latin American populations. This underscores the need for public health strategies and policies that address these social determinants to reduce dementia risk in these communities effectively.
Black and Latino individuals are underrepresented in COVID-19 treatment and vaccine clinical trials, calling for an examination of factors that may predict willingness to participate in trials.
Methods:
We administered the Common Survey 2.0 developed by the Community Engagement Alliance (CEAL) Against COVID-19 Disparities to 600 Black and Latino adults in Baltimore City, Prince George’s County, Maryland, Montgomery County, Maryland, and Washington, DC, between October and December 2021. We examined the relationship between awareness of clinical trials, social determinants of health challenges, trust in COVID-19 clinical trial information sources, and willingness to participate in COVID-19 treatment and vaccine trials using multinomial regression analysis.
Results:
Approximately half of Black and Latino respondents were unwilling to participate in COVID-19 treatment or vaccine clinical trials. Results showed that increased trust in COVID-19 clinical trial information sources and trial awareness were associated with greater willingness to participate in COVID-19 treatment and vaccine trials among Black and Latino individuals. For Latino respondents, having recently experienced more challenges related to social determinants of health was associated with a decreased likelihood of willingness to participate in COVID-19 vaccine trials.
Conclusions:
The willingness of Black and Latino adults to participate in COVID-19 treatment and vaccine clinical trials is influenced by trial awareness and trust in trial information sources. Ensuring the inclusion of these communities in clinical trials will require approaches that build greater awareness and trust.
Social determinants of health (SDOH) are an important contributor to health status and health outcomes. In this analysis, we compare SDOH measured both at the individual and population levels in patients with high comorbidity who receive primary care at Federally Qualified Health Centers in New York and Chicago and enrolled in the Tipping Points trial.
Methods:
We analyzed individual- and population-level measures of SDOH in 1,488 patients with high comorbidity (Charlson Comorbidity Index ≥ 4) enrolled in Tipping Points. At the individual level, we used a standardized patient-reported questionnaire. At the population level, we employed patient addresses to calculate the Social Deprivation Index (SDI) and Area Deprivation Index. Multivariable regressions were conducted in addition to qualitative feedback from stakeholders.
Results:
Individual-level SDOH are distinct from population-level measures. Significant component predictors of population SDI are being unhoused, unable to pay for utilities, and difficulty accessing medical transportation. Qualitative findings mirrored these results. High comorbidity patients report significant SDOH challenges at the individual level. Fitting a binomial generalized linear model, the comorbidity score is significantly predicted by the composite individual SDOH index (p < 0.0001) controlling for age and race/ethnicity.
Conclusions:
Individual- and population-level SDOH measures provide different risk assessments. The use of community-level SDI data is informative in the aggregate but should not be used to identify patients with individual unmet social needs. Health systems should implement a standardized individualized assessment of unmet SDOH needs and build strong, enduring partnerships with community-based organizations that can provide those services.
The COVID-19 pandemic is a disaster event. Exposure to stressors during and after disaster events is associated with negative mental health symptoms. To inform targeted COVID-19 recovery efforts, data are needed to understand which stressors play a key role in this relationship.
Methods
Cross-sectional survey data (demographics, impacts of COVID-19, social determinants of health, depression, and anxiety) were collected online from adults living in New York state between May and June 2020. Differences in the proportion of stressors (COVID-19 and social determinants) experienced by race/ethnicity were assessed using chi-square analyses. Logistic regression was used to assess which factors were associated with increased odds of depression and anxiety.
Results
A majority (n = 258, 62.2%) of the 415 respondents reported being directly impacted by the pandemic. Non-white respondents reported a significantly larger proportion of stressors compared to white respondents. Under half of respondents reported depression (n = 171, 41.2%) and anxiety (n = 164, 39.5%). Healthcare and food concerns were associated with increased odds of depression and anxiety, and economic concerns were associated with increased odds of anxiety.
Conclusions
Findings underscore the need to respond to the COVID-19 mental health crisis by addressing social determinants of health.
Social determinants of health (SDoH), such as socioeconomics and neighborhoods, strongly influence health outcomes. However, the current state of standardized SDoH data in electronic health records (EHRs) is lacking, a significant barrier to research and care quality.
Methods:
We conducted a PubMed search using “SDOH” and “EHR” Medical Subject Headings terms, analyzing included articles across five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions.
Results:
Of 685 articles identified, 324 underwent full review. Key findings include implementation of tailored screening instruments, census and claims data linkage for contextual SDoH profiles, NLP systems extracting SDoH from notes, associations between SDoH and healthcare utilization and chronic disease control, and integrated care management programs. However, variability across data sources, tools, and outcomes underscores the need for standardization.
Discussion:
Despite progress in identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical for SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately, widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.
Given the dramatic growth in the financial burden of cancer care over the past decades, individuals with cancer are increasingly susceptible to developing social needs (e.g., housing instability and food insecurity) and experiencing an adverse impact of these needs on care management and health outcomes. However, resources required to connect individuals with needed social and community services typically exceed the available staffing within clinical teams. Using input from focus groups, key informant interviews, user experience/user interface testing, and a multidisciplinary community advisory board, we developed a new technology solution, ConnectedNest, which connects individuals in need to community based organizations (CBOs) that provide services through direct and/or oncology team referrals, with interfaces to support all three groups (patients, CBOs, and oncology care teams). After prototype development, we conducted usability testing, with participants noting the importance of the technology for filling a current gap in screening and connecting individuals with cancer with needed social and community services. We employ a patient-empowered approach that engages the support of an individual’s healthcare team and community organizations. Future work will examine the integration and implementation of ConnectedNest for oncology patients, oncology care teams, and cancer-focused CBOs to build capacity for effectively addressing distress in this population.
This study was conducted to provide empirical evidence of geographical variations of neonatal mortality and its associated social determinants with a view to improving neonatal survival at the subnational level in Nigeria. With a combination of spatial analysis and artificial intelligence techniques, this study analysed data from the 2016/2017 Nigeria Multiple Indicator Cluster Survey. The analysis focused on the neonatal period of a weighted national representative population of 30,924 live births delivered five years before the survey commencement. Global Moran’s I index and local indicator of spatial autocorrelation cluster maps were used to determine hot and cold spots. A multilayer perceptron neural network was used to identify the key determinants of neonatal mortality across the states and geopolitical zones in Nigeria. The overall neonatal mortality rate was 38 deaths per 1000 live births. There is evidence of geographic clustering of neonatal mortality across Nigeria (worse in the North-Central and North-West zones), majorly driven by poor maternal access to mass media (which plays a critical role in promoting positive health behaviours), short birth interval, a higher position in a family birth order, and young maternal age at child’s birth. This study highlights the need for a policy shift towards implementing state and region-specific strategies in Nigeria. Gender-responsive, culturally, and regionally appropriate reproductive, maternal, and child health-targeted interventions may address geographical inequity in neonatal survival.
Multisector stakeholders, including, community-based organizations, health systems, researchers, policymakers, and commerce, increasingly seek to address health inequities that persist due to structural racism. They require accessible tools to visualize and quantify the prevalence of social drivers of health (SDOH) and correlate them with health to facilitate dialog and action. We developed and deployed a web-based data visualization platform to make health and SDOH data available to the community. We conducted interviews and focus groups among end users of the platform to establish needs and desired platform functionality. The platform displays curated SDOH and de-identified and aggregated local electronic health record data. The resulting Social, Environmental, and Equity Drivers (SEED) Health Atlas integrates SDOH data across multiple constructs, including socioeconomic status, environmental pollution, and built environment. Aggregated health prevalence data on multiple conditions can be visualized in interactive maps. Data can be visualized and downloaded without coding knowledge. Visualizations facilitate an understanding of community health priorities and local health inequities. SEED could facilitate future discussions on improving community health and health equity. SEED provides a promising tool that members of the community and researchers may use in their efforts to improve health equity.
Social determinants of health (SDoH), such as food and financial insecurity and food assistance, are potentially modifiable factors that may influence breastfeeding initiation and duration. Knowledge gaps exist regarding the relationship between these SDoH and infant feeding practices. We explored the relationships of food and financial insecurity and food assistance with the continuation of breastfeeding at four months postpartum among mothers and whether race and ethnicity modified these associations.
Design:
Mothers retrospectively reported food and financial insecurity and receipt of food assistance (e.g. Women, Infants and Children and Supplemental Nutrition Assistance Program) during pregnancy with their first child and infant feeding practices (exclusive/mostly breastfeeding v. exclusive/mostly formula feeding) following the birth of their first child. Sociodemographic-adjusted modified Poisson regressions estimated prevalence ratios and 95 % CI.
Setting:
Minneapolis-St. Paul, Minnesota.
Participants:
Mothers who participated in the Life-course Experiences And Pregnancy study (LEAP) (n 486).
Results:
Ten percent of mothers reported food insecurity, 43 % financial insecurity and 22 % food assistance during their pregnancies. At four months postpartum, 63 % exclusively/mostly breastfed and 37 % exclusively/mostly formula-fed. We found a lower adjusted prevalence of breastfeeding at four months postpartum for mothers who reported experiencing food insecurity (0·65; 0·43–0·98) and receiving food assistance (0·66; 0·94–0·88) relative to those who did not. For financial insecurity (aPR 0·92; 0·78, 1·08), adjusted estimates showed little evidence of an association.
Conclusions:
We found a lower level of breastfeeding among mothers experiencing food insecurity and using food assistance. Resources to support longer breastfeeding duration for mothers are needed. Moreover, facilitators, barriers and mechanisms of breastfeeding initiation and duration must be identified.
This study explores the transformative effects of the Community Plunge, an educational program at the Wake Forest University School of Medicine (WFUSOM), on healthcare delivery, community engagement, and trainee perspectives. It addresses the broader context of health outcomes, where clinical care only accounts for 20%, emphasizing the critical role of social determinants of health (SDOH) and individual behaviors in the remaining 80%.
Methods:
WFUSOM’s Community Plunge, established in 2002, involves a guided tour of the community, discussions with residents, and debriefing sessions. Qualitative interviews with 20 clinicians were conducted to extract key themes and insights.
Results:
The study identified several key outcomes. First, participants gained crucial insights into the community’s history, structural challenges, and prevalent SDOH, enhancing their understanding of the diverse patient populations they serve. Second, the program positively influenced clinician attitudes, fostering empathy, reducing paternalism, and promoting holistic patient care. Third, participants expressed a desire for increased community involvement and reported career trajectory changes toward advocacy and volunteerism. However, challenges such as time constraints were acknowledged.
Conclusions:
The study advocates for collaborative efforts to enhance the program’s impact, including proactive measures to ensure respectful engagement during community tours. It positions the Community Plunge as an innovative, scalable, and transformative strategy for experiential SDOH exposure, crucial for the evolving social consciousness of healthcare learners.
The minority stress model, based on the theory of minority stress, serves as a primary framework to understand mental health disparities among LGBTQ+ individuals. In this chapter, we describe the model and its relationship with the social determinants of health and multiple minority stress. Interventions and evidence-based practices incorporating this model and barriers to care are discussed.
DOHaD research in economics finds inequitable health and labour market outcomes but lacks insight into structural factors that contribute to disparities. In practice, social relations like racism, sexism, and ableism can translate into inequitable ‘returns to investment’ in ‘human capital’. DOHaD literature in economics could contribute more to understanding the determinants of health. It is limited by a narrow focus on molecular factors and the decontextualised use of demographic variables, which should be interpreted as proxies for hierarchical power relations. Excluding systems of oppression from analyses renders inequity-generating social structures less visible instead of clarifying their unjust consequences. Egalitarian economic approaches can address the failure to adequately integrate social structures with historically grounded, socially informed analyses. This chapter demonstrates how by tracing the devaluation of reproductive labour in economic thought to the reduction of women and girls to their reproductive roles in the DOHaD literature. The marginalisation of women’s labour and of women’s economic research contributes to the dehumanising instrumentalisation of women in orthodox economic research in DOHaD. The analysis reveals risks for women and girls, linking DOHaD literature to debates about ’foetal personhood’, women’s autonomy, and gender inequity.
Social determinants of health (SDOH) can contribute to disparities that negatively impact health outcomes and healthcare utilization. Comprehensive screening is frequently overlooked during inpatient clinical care. This pilot aimed to evaluate the capturability of a multi-domain SDOH screening tool during hospitalization, as well as correlation of SDOH needs to readmissions.
Methods:
The Protocol for Responding to and Assessing Patients’ Assets, Risks and Experiences (PRAPARE) screening tool was implemented on admission with adult inpatients at an academic tertiary medical center in central Pennsylvania. A total of 80 patients were screened over an 8-week period using the PRAPARE tool.
Results:
43.7% of participants were identified as having at least one SDOH need and 21.2% were identified as having two or more needs. Of the participants identified as having at least one SDOH need through PRAPARE screening, 42.5% experienced a readmission within 30 days, compared to 15% readmissions among participants with no identified SDOH needs. For each additional SDOH need a patient had, the odds they experienced a readmission increased by 2.2 times.
Conclusions:
The study findings suggest that utilization of the PRAPARE screening tool has the ability to capture significant SDOH needs among hospitalized patients. This study also suggests that higher SDOH needs correlate to increased odds of experiencing a hospital readmission.
Few people in my memory have a name that more appropriately defines the life they have lived. “Charitable purpose” as defined in O.C.G.A. § 43-17-2 includes any charitable or benevolent purpose including health, education, or social welfare. Anyone who knew Charity Scott knows that she lived a life devoted to providing and improving the health of her community, the education of law students about health law and its use to improve the health of her community, and social welfare by addressing the socio-economic determinants of health. If she had not been assigned that name at birth, those of us who knew her could have easily assigned Charity as a nickname.
It was a great privilege to know Professor Charity Scott. I first met her when I was finishing Emory University’s joint law and public health program in the early 2000s, through the Office of General Counsel at the U.S. Centers for Disease Control (CDC), in the early days of CDC’s Public Health Law Program, now the Office of Public Health Law Services. In those days, introductions were generous and frequent for excited students beginning their careers, but meeting Professor Scott made an impression on me. She was the first and only female health law professor in the field that I had the opportunity to know in the early years of my career.
Leveraging the National COVID-19 Cohort Collaborative (N3C), a nationally sampled electronic health records repository, we explored associations between individual-level social determinants of health (SDoH) and COVID-19-related hospitalizations among racialized minority people with human immunodeficiency virus (HIV) (PWH), who have been historically adversely affected by SDoH.
Methods:
We retrospectively studied PWH and people without HIV (PWoH) using N3C data from January 2020 to November 2023. We evaluated SDoH variables across three domains in the Healthy People 2030 framework: (1) healthcare access, (2) economic stability, and (3) social cohesion with our primary outcome, COVID-19-related hospitalization. We conducted hierarchically nested additive and adjusted mixed-effects logistic regression models, stratifying by HIV status and race/ethnicity groups, accounting for age, sex, comorbidities, and data partners.
Results:
Our analytic sample included 280,441 individuals from 24 data partner sites, where 3,291 (1.17%) were PWH, with racialized minority PWH having higher proportions of adverse SDoH exposures than racialized minority PWoH. COVID-19-related hospitalizations occurred in 11.23% of all individuals (9.17% among PWH, 11.26% among PWoH). In our initial additive modeling, we observed that all three SDoH domains were significantly associated with hospitalizations, even with progressive adjustments (adjusted odds ratios [aOR] range 1.36–1.97). Subsequently, our HIV-stratified analyses indicated economic instability was associated with hospitalization in both PWH and PWoH (aOR range 1.35–1.48). Lastly, our fully adjusted, race/ethnicity-stratified analysis, indicated access to healthcare issues was associated with hospitalization across various racialized groups (aOR range 1.36–2.00).
Conclusion:
Our study underscores the importance of assessing individual-level SDoH variables to unravel the complex interplay of these factors for racialized minority groups.
Type 2 diabetes (T2DM) poses a significant public health challenge, with pronounced disparities in control and outcomes. Social determinants of health (SDoH) significantly contribute to these disparities, affecting healthcare access, neighborhood environments, and social context. We discuss the design, development, and use of an innovative web-based application integrating real-world data (electronic health record and geospatial files), to enhance comprehension of the impact of SDoH on T2 DM health disparities.
Methods:
We identified a patient cohort with diabetes from the institutional Diabetes Registry (N = 67,699) within the Duke University Health System. Patient-level information (demographics, comorbidities, service utilization, laboratory results, and medications) was extracted to Tableau. Neighborhood-level socioeconomic status was assessed via the Area Deprivation Index (ADI), and geospatial files incorporated additional data related to points of interest (i.e., parks/green space). Interactive Tableau dashboards were developed to understand risk and contextual factors affecting diabetes management at the individual, group, neighborhood, and population levels.
Results:
The Tableau-powered digital health tool offers dynamic visualizations, identifying T2DM-related disparities. The dashboard allows for the exploration of contextual factors affecting diabetes management (e.g., food insecurity, built environment) and possesses capabilities to generate targeted patient lists for personalized diabetes care planning.
Conclusion:
As part of a broader health equity initiative, this application meets the needs of a diverse range of users. The interactive dashboard, incorporating clinical, sociodemographic, and environmental factors, enhances understanding at various levels and facilitates targeted interventions to address disparities in diabetes care and outcomes. Ultimately, this transformative approach aims to manage SDoH and improve patient care.
Housing is an environmental social determinant of health that is linked to mortality and clinical outcomes. We developed a lexicon of housing-related concepts and rule-based natural language processing methods for identifying these housing-related concepts within clinical text. We piloted our methods on several test cohorts: a synthetic cohort generated by ChatGPT for initial infrastructure testing, a cohort with substance use disorders (SUD), and a cohort diagnosed with problems related to housing and economic circumstances (HEC). Our methods successfully identified housing concepts in our ChatGPT notes (recall = 1.0, precision = 1.0), our SUD population (recall = 0.9798, precision = 0.9898), and our HEC population (recall = N/A, precision = 0.9160).
To evaluate differences in the percentage of expenditure on food groups in Mexican households according to the gender of the household head and the size of the locality.
Design:
Analysis of secondary data from the National Household Income and Expenditure Survey (ENIGH) 2018. We estimated the percentage of expenditure on fifteen food groups according to the gender of the head of household and locality size and evaluated the differences using a two-part model approach.
Setting:
Mexico, 2018.
Participants:
A nationally representative sample of 74 647 Mexican households.
Results:
Female-headed households allocated a lower share of expenditure to the purchase of sweetened beverages and alcoholic beverages and higher percentages to milk and dairy, fruits and water. In comparison with metropolitan households, households in rural and urban localities spent more on cereals and tubers, sugar and honey, oil and fat and less on food away from home.
Conclusions:
Households allocate different percentages of expenditure to diverse food groups according to the gender of the head of the household and the size of the locality where they are located. Future research should focus on understanding the economic and social disparities related to differences in food expenditure, including the gender perspective.