Adding socioeconomic status to Framingham scoring to reduce disparities in coronary risk assessment

Assessing cardiovascular disease risk and social determinants of health: A comparative analysis of five risk estimation instruments using data from the Eastern Caribbean Health Outcomes Research Network

BACKGROUND

Accurate assessment of cardiovascular disease (CVD) risk is crucial for effective prevention and resource allocation. However, few CVD risk estimation tools consider social determinants of health (SDoH), despite their known impact on CVD risk. We aimed to estimate 10-year CVD risk in the Eastern Caribbean Health Outcomes Research Network Cohort Study (ECS) across multiple risk estimation instruments and assess the association between SDoH and CVD risk.

METHODS

Five widely used CVD risk estimation tools (Framingham and WHO laboratory, both laboratory and non-laboratory-based, and ASCVD) were applied using data from ECS participants aged 40-74 without a history of CVD. SDoH variables included educational attainment, occupational status, household food security, and perceived social status. Multivariable logistic regression models were used to compare differences in the association between selected SDoH and high CVD risk according to the five instruments.

FINDINGS

Among 1,777 adult participants, estimated 10-year CVD risk varied substantially across tools. Framingham non-lab and ASCVD demonstrated strong agreement in categorizing participants as high risk. Framingham non-lab categorized the greatest percentage as high risk, followed by Framingham lab, ASCVD, WHO lab, and WHO non-lab. Fifteen times more people were classified as high risk by Framingham non-lab compared with WHO non-lab (31% vs 2%). Mean estimated 10-year risk in the sample was over 2.5 times higher using Framingham non-lab vs WHO non-lab (17.3% vs 6.6%). We found associations between food insecurity, those with the lowest level compared to the highest level of education, and non-professional occupation and increased estimated CVD risk.

INTERPRETATION

Our findings highlight significant discrepancies in CVD risk estimation across tools and underscore the potential impact of incorporating SDoH into risk assessment. Further research is needed to validate and refine existing risk tools, particularly in ethnically diverse populations and resource-constrained settings, and to develop race- and ethnicity-free risk estimation models that consider SDoH.

Global trends in heart failure from 1990 to 2019: An age-period-cohort analysis from the Global Burden of Disease study

AIMS

This study aimed to analyse the global prevalence and disability trends of heart failure (HF) from 1990 to 2019, considering both sexes and country-specific economic strata.

METHODS

This study conducted a secondary analysis employing data from the Global Burden of Disease (GBD) study. The analysis is stratified by sex and Socio-demographic Index (SDI) levels. Through age-period-cohort and Joinpoint regression analyses, we investigated the temporal trends in HF prevalence and years lived with disability (YLDs) during this period.

RESULTS

Between 1990 and 2019, the global prevalence of HF surged by 106.3% (95% uncertainty interval: 99.3% to 114.3%), reaching 56.2 million cases in 2019. While all-age prevalence and YLDs increased over the 30 year span, age-standardized rates decreased by 2019. Countries with higher SDI experienced a more pronounced percentage decrease compared with those with lower SDI. Longitudinal analysis revealed an overall improvement in both prevalence and YLDs for HF, albeit with notable disparities between SDI quintiles and sexes. Ischaemic heart disease and hypertensive heart disease emerged as the most rapidly increasing and primarily contributing causes of HF, albeit with variations observed across different countries. The average annual percentage change for prevalence and YLDs over the period was -0.26% and -0.25%, respectively.

CONCLUSIONS

This study offers valuable insights into the global burden of HF, considering factors such as population aging, regional disparities, sex differences and aetiological variations. The findings hold significant implications for healthcare planning and resource allocation. Continued assessment of these trends and innovative strategies for HF prevention and management are crucial for addressing this pressing global health concern.

Socio-economic inequalities and heart failure morbidity and mortality: A systematic review and data synthesis

Socio-economic status (SES) has been associated with incident and prevalent heart failure (HF), as well as its morbidity and mortality. However, the precise nature of the relationship between SES and HF remains unclear due to inconsistent data. This study aims to provide a comprehensive assessment and data synthesis of the relationship between SES and HF morbidity and mortality. We performed a systematic search and data synthesis using six databases following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Guidelines. The included studies comprised observational studies that reported on HF incidence and prevalence, HF hospitalizations, worsening HF (WHF) and all-cause mortality, as well as treatment options (medical, device and advanced HF therapies). SES was measured on both individual and area levels, encompassing single (e.g., income, education, employment, social risk score, living conditions and housing characteristics) and composite indicators. Among the 4124 studies screened, 79 were included, with an additional 5 identified through cross-referencing. In the majority of studies, a low SES was associated with an increased HF incidence (72%) and prevalence (75%). For mortality, we demonstrated that low SES was associated with increased mortality in 45% of the studies, with 18% of the studies showing mixed results (depending on the indicator, gender or follow-up) and 38% showing non-significant results. Similar patterns were observed for the association between SES, WHF, medical therapy prescriptions and the utilization of devices and advanced HF therapies. There was no clear pattern in the used SES indicators and HF outcomes. This systematic review, using contemporary data, shows that while socio-economic disparity may influence HF incidence, management and subsequent adverse events, these associations are not uniformly predictive. Our review highlights that the impact of SES varies depending on the specific indicators used, reflecting the complexity of its influence on health disparities. Assessment and recognition of SES as an important risk factor can assist clinicians in early detection and customizing HF treatment, while also aiding policymakers in optimizing resource allocation.

Race/ethnicity and socioeconomic status affect the assessment of lipoprotein(a) levels in clinical practice

BACKGROUND AND OBJECTIVE

High lipoprotein(a) [Lp(a)] levels are a risk factor for atherosclerotic cardiovascular disease (ASCVD), however Lp(a) ordering in clinical practice is low. This study examines how race/ethnicity and socioeconomic status influence Lp(a) ordering.

METHODS

This is a single center, retrospective study (2/1/2020-6/30/2023) using electronic medical records of adults with at least one personal ICD-10 diagnosis of ASCVD, aortic valve stenosis, resistant hypercholesterolemia (low-density lipoprotein cholesterol >160 mg/dL on statin therapy), and family history of ASCVD or high Lp(a). We evaluated Lp(a) level differences among racial/ethnic groups and sexes. We also assessed associations between diagnosis type, diagnosis number, age at diagnosis, race/ethnicity, socioeconomic score (based on zip codes), public health coverage and the presence of Lp(a) orders.

RESULTS

4% of our cohort (N=2,249 in 56,833) had an Lp(a) order (17.3% of whom identified as Hispanic, 8.7% non-Hispanic Black, 47.5% non-Hispanic White, and 27% Asian/other). Non-Hispanic Black and Hispanic patients had lower rates of Lp(a) orders (0.17% and 0.28%, respectively) when compared to non-Hispanic White patients (2.35%), p < 0.001, however, their median Lp(a) levels were higher, p < 0.001. Individuals on Medicaid or belonging to deprived socioeconomic groups were less likely to have an Lp(a) order (incidence rate ratio [IRR] = 0.40, p < 0.001 and IRR = 0.39, p < 0.001 respectively). Certain diagnosis (carotid stenosis, family history of ASCVD and familial hypercholesterolemia) and multiple diagnoses (>2) resulted in more Lp(a) orders compared to only one diagnosis (p < 0.001).

CONCLUSIONS

Lp(a) ordering is low in patients with or at risk for ASCVD. Non-Hispanic Black and Hispanic patients are less likely to have an Lp(a) order. Individuals on Medicaid and residing in socioeconomically deprived neighborhoods are less likely to have an Lp(a) order. Lp(a) orders depend on the type and number of patients' diagnoses.

Addition of Social Determinants of Health to Coronary Heart Disease Risk Prediction: The Multi-Ethnic Study of Atherosclerosis

BACKGROUND

Social determinants of health (SDoH) are associated with cardiovascular risk factors and outcomes; however, they are absent from risk prediction models. We aimed to assess if the addition of SDoH improves the predictive ability of the MESA (Multi-Ethnic Study of Atherosclerosis) Risk Score.

METHODS AND RESULTS

This was a community-based prospective population cohort study that enrolled 6286 men and women, ages 45-84 years, who were free of clinical coronary heart disease (CHD) at baseline. Data from 10-year follow-up were examined for CHD events, defined as myocardial infarction, fatal CHD, resuscitated cardiac arrest, and revascularization in cases of anginal symptoms. Participants included 53% women with average age of 62 years. When adjusting for traditional cardiovascular risk factors, SDoH, and coronary artery calcium, economic strain, specifically low family income, was associated with a greater risk of CHD events (hazard ratio [HR], 1.42 [95% CI, 1.17-1.71], P value<0.001). Area under the curve of risk prediction with SDoH was 0.822, compared with 0.816 without SDoH. The calibration slope was 0.860 with SDoH and 0.878 in the original model.

CONCLUSIONS

Significant associations were found between economic/financial SDoH and CHD risk factors and outcomes. Incorporation of SDoH into the MESA Risk Score did not improve predictive ability of the model. Our findings do not support the incorporation of SDoH into current risk prediction algorithms.

The burden of premature coronary heart disease among adults with low socioeconomic status in Argentina: A modeling study

BACKGROUND

The well-established inverse relationship between socioeconomic status (SES) and risk of developing coronary heart disease (CHD) cannot be explained solely by differences in traditional risk factors.

OBJECTIVE

To model the role SES plays in the burden of premature CHD in Argentina.

MATERIALS AND METHODS

We used the Cardiovascular Disease Policy Model-Argentina to project incident CHD events and mortality in low and high-SES Argentinean adults 35 to 64 years of age from 2015 to 2024. Using data from the 2018 National Risk Factor Survey, we defined low SES as not finishing high-school and/or reporting a household income in quintiles 1 or 2. We designed simulations to apportion CHD outcomes in low SES adults to: (1) differences in the prevalence of traditional risk factors between low and high SES adults; (2) nontraditional risk associated with low SES status; (3) preventable events if risk factors were improved to ideal levels; and (4) underlying age- and sex-based risk.

RESULTS

56% of Argentina´s 35- to 64-year-old population has low SES. Both high and low SES groups have poor control of traditional risk factors. Compared with high SES population, low SES population had nearly 2-fold higher rates of incident CHD and CHD deaths per 10 000 person-years (incident CHD: men 80.8 [95%CI 76.6-84.9] vs 42.9 [95%CI 37.4-48.1], women 39.0 [95%CI 36.-41.2] vs 18.6 [95%CI 16.3-20.9]; CHD deaths: men 10.0 [95%CI 9.5-10.5] vs 6.0 [95%CI 5.6-6.4], women 3.2 [95%CI 3.0-3.4] vs 1.8 [95%CI 1.7-1.9]). Nontraditional low SES risk accounts for 73.5% and 70.4% of the event rate gap between SES levels for incident CHD and CHD mortality rates, respectively.

DISCUSSION

CHD prevention policies in Argentina should address contextual aspects linked to SES, such as access to education or healthcare, and should also aim to implement known clinical strategies to achieve better control of CHD risk factors in all socioeconomic levels.

Race/ethnicity and socioeconomic status affect the assessment of lipoprotein(a) levels in clinical practice

Background and Objective

High Lp(a) levels are a risk factor for ASCVD, however Lp(a) ordering in clinical practice is low. This study examines how race/ethnicity and socioeconomic status influence Lp(a) ordering.

Methods

This is a single center, retrospective study (2/1/2020-6/30/2023) using electronic medical records of adults with at least one ICD-10 diagnosis of ASCVD or resistant hyperlipidemia (LDL-C >160 mg/dL on statin therapy). We evaluated Lp(a) level differences among racial/ethnic groups and sexes. We also assessed associations between diagnosis type, diagnosis number, age at diagnosis, race, socioeconomic score (based on zip codes), public health coverage and presence of Lp(a) orders.

Results

4% of our cohort (N=56,833) had an Lp(a) order (17.3% Hispanic, 8.7% non-Hispanic Black, 47.5% non-Hispanic White and, 27% Asian/others). Non-Hispanic Black and Hispanic patients had lower rates of Lp(a) orders (0.17%, 0.28%, respectively) when compared to non-Hispanic White patients (2.35%), p<0.001, however, their median Lp(a) levels were higher. Individuals belonging to deprived socioeconomic groups or on Medicaid, were less likely to have an Lp(a) order (RR=0.39, p<0.001 and RR=0.40, p<0.001 respectively). Certain diagnoses (carotid stenosis, family history of ASCVD and FH) and multiple diagnoses (>2) resulted in more Lp(a) orders compared to those with only one diagnosis (p<0.001).

Conclusions

Lp(a) ordering is low in patients with ASCVD. Non-Hispanic Black and Hispanic patients at risk are less likely to have an Lp(a) order. Individuals residing in socioeconomically deprived neighborhoods and on Medicaid are also less like have Lp(a) order. Lp(a) orders depend on the type and number of patients' diagnoses.

The Canadian Women's Heart Health Alliance ATLAS on the Epidemiology, Diagnosis, and Management of Cardiovascular Disease in Women - Chapter 7: Sex, Gender, and the Social Determinants of Health

Women vs men have major differences in terms of risk-factor profiles, social and environmental factors, clinical presentation, diagnosis, and treatment of cardiovascular disease. Women are more likely than men to experience health issues that are complex and multifactorial, often relating to disparities in access to care, risk-factor prevalence, sex-based biological differences, gender-related factors, and sociocultural factors. Furthermore, awareness of the intersectional nature and relationship of sociocultural determinants of health, including sex and gender factors, that influence access to care and health outcomes for women with cardiovascular disease remains elusive. This review summarizes literature that reports on under-recognized sex- and gender-related risk factors that intersect with psychosocial, economic, and cultural factors in the diagnosis, treatment, and outcomes of women's cardiovascular health.

Polysocial Risk Scores: Implications for Cardiovascular Disease Risk Assessment and Management

PURPOSE OF REVIEW

To review current evidence, discuss key knowledge gaps and identify opportunities for development, validation and application of polysocial risk scores (pSRS) for cardiovascular disease (CVD) risk prediction and population cardiovascular health management.

RECENT FINDINGS

Limited existing evidence suggests that pSRS are promising tools to capture cumulative social determinants of health (SDOH) burden and improve CVD risk prediction beyond traditional risk factors. However, available tools lack generalizability, are cross-sectional in nature or do not assess social risk holistically across SDOH domains. Available SDOH and clinical risk factor data in large population-based databases are under-utilized for pSRS development. Recent advances in machine learning and artificial intelligence present unprecedented opportunities for SDOH integration and assessment in real-world data, with implications for pSRS development and validation for both clinical and healthcare utilization outcomes. pSRS presents unique opportunities to potentially improve traditional "clinical" models of CVD risk prediction. Future efforts should focus on fully utilizing available SDOH data in large epidemiological databases, testing pSRS efficacy in diverse population subgroups, and integrating pSRS into real-world clinical decision support systems to inform clinical care and advance cardiovascular health equity.

Effects of socioeconomic status on physical activity and cardiovascular diseases prior to and during the COVID-19 pandemic in the older adults

Purpose

This research seeks to evaluate the repercussions of socioeconomic status (SES) on physical activity (PA) among the older population, both pre and intra-COVID-19 pandemic. The study aims to scrutinize whether alteration in PA behaviors based on SES impacts cardiovascular diseases (CVDs). It is well established that PA has a significant association with CVDs and the pandemic has restricted PA in the older population. We endeavor to discern whether SES modulates PA levels and whether these levels of PA behavior subsequently influence the incidence of CVDs among older adults.

Methods

The analytical framework of this study relies on the data procured from the Fact-Finding on the Status of Senior Citizens (FSSSC) survey conducted in 2017 and 2020, involving 10,299 (75 ± 6 years) and 10,097 (74 ± 6 years) participants, respectively. We employ Structural Equation Modeling (SEM) to elucidate the ramification of the COVID-19 pandemic on CVDs while accommodating potential mediating and confounding variables, including socioeconomic status, PA levels, body mass index (BMI), and gender, in the context of the pandemic and CVDs.

Results

Our empirical models indicated a tendency for older adults of lower socioeconomic status (SES) to exhibit diminished levels of physical activity (PA) compared to their counterparts of higher SES, particularly considering the influence of the COVID-19 pandemic. Furthermore, prolonged engagement in PA is associated with a reduced risk of hypertension (p = 0.010), and congestive heart failure & arrhythmia (p < 0.001), when accounting for confounding factors.

Conclusion

The COVID-19 pandemic has generated an SES-based disparity in PA among older adults, despite PA time being greater in older individuals with higher SES. Interestingly, this did not result in a reduction in CVDs. Therefore, the study emphasizes the need for targeted exercise programs may be necessary to mitigate health inequality among the older population.

Risk Factors for Premature Coronary Heart Disease in Women Compared to Men: Systematic Review and Meta-Analysis

Background: We aimed to systematically examine literature on the prevalence of known modifiable and nonmodifiable risk factors for premature coronary heart disease (PCHD) in women compared with men. Materials and Methods: PubMed, CINAHL, Embase, and Web of Science databases were searched. Review protocol is registered in PROSPERO (CRD42020173216). Quality was assessed using the National Heart, Lung, and Blood Institute tool. Review Manager 5.3 was used for meta-analysis. Effect sizes were expressed as odds ratio (OR) and mean differences/standardized mean differences (SMD) with 95% confidence intervals (CIs) for categorical and continuous variables. Results: In this PCHD cohort (age <65 years), the mean age of presentation in women was 3 years older than men. Women had higher total cholesterol (SMD 0.11; 95% CI 0.00 to 0.23) and higher high-density lipoprotein cholesterol (SMD 0.49; 95% CI 0.29 to 0.69). Women were more likely to have hypertension (OR 1.51, 95% CI 1.42 to 1.60), diabetes mellitus (OR 1.78, 95% CI 1.55 to 2.04), obesity (OR 1.33, 95% CI 1.24 to 1.42), metabolic syndrome (OR 3.73, 95% CI 1.60 to 8.69), stroke (OR 1.63, 95% CI 1.51 to 1.77), peripheral vascular disorder (OR 1.67, 95% CI 1.43 to 1.96), and depression (OR 2.29, 95% CI 1.96 to 2.67). Women were less likely to be smokers (OR 0.60, 95% CI 0.55 to 0.66), have reported alcohol intake (OR 0.36, 95% CI 0.33 to 0.40), and reported use of illicit drug (OR 0.32, 95% CI 0.16 to 0.62). Conclusions: Risk factor profile in PCHD has a clear sex difference that supports early, aggressive, holistic, but sex-specific, approach to prevention.

Education degree predicts cardiovascular outcomes in men suffering from erectile dysfunction

BACKGROUND

The level of education has been recognized as a cardiovascular risk factor; nevertheless, it is often neglected in cardiovascular risk prediction.

OBJECTIVES

To evaluate the psychobiological correlates of the level of education and if it could predict incident major adverse cardiovascular events in men consulting for erectile dysfunction.

METHODS

Total 3733 men (49.8 ± 13.7 years old) attending an andrology outpatient clinic for erectile dysfunction were studied. Sexual and psychological symptoms, hormonal and metabolic, as well as instrumental (penile color Doppler ultrasound) parameters were evaluated according to the education level (university, upper secondary, lower secondary, and primary degree). For a subset of 956 patients, data on incident major adverse cardiovascular events were retrospectively collected for 3.9 ± 2.4 years.

RESULTS

As compared with men with university degree, those with a lower education had an increased frequency of moderate-severe erectile dysfunction (odds ratio = 1.21 [0.99;1.48], 1.41 [1.14;1.73], 1.70 [1.26;2.30] for upper secondary, lower secondary, and primary school, respectively) and reduced flaccid peak systolic velocity at penile color Doppler ultrasound. Men with a lower level of education tend to suffer from metabolic syndrome (odds ratio = 1.38 [1.06;1.79], 1.73 [1.34;2.24], 1.72 [1.24;2.37] for upper secondary, lower secondary, and primary school, respectively) and were more likely to have history of previous cardiovascular events. In the longitudinal study, men with a higher level of education had a significantly lower incidence of major adverse cardiovascular events. The role of higher education as an independent predictor of major adverse cardiovascular events was established by multivariable Cox regressions (hazard ratio = 2.14 [1.24-3.69]).

DISCUSSION

In erectile dysfunction subjects, lower level of education is associated with a more severely impaired erectile function with atherogenic pathogenesis and with a worse cardio-metabolic profile. In addition, a lower level of education predicts forthcoming major adverse cardiovascular events. Therefore, education level should be considered as a costless but valuable information in the assessment of cardiovascular risk in patients with erectile dysfunction.

2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guideline for the Management of Patients With Chronic Coronary Disease: A Report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines

AIM

The "2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guideline for the Management of Patients With Chronic Coronary Disease" provides an update to and consolidates new evidence since the "2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS Guideline for the Diagnosis and Management of Patients With Stable Ischemic Heart Disease" and the corresponding "2014 ACC/AHA/AATS/PCNA/SCAI/STS Focused Update of the Guideline for the Diagnosis and Management of Patients With Stable Ischemic Heart Disease."

METHODS

A comprehensive literature search was conducted from September 2021 to May 2022. Clinical studies, systematic reviews and meta-analyses, and other evidence conducted on human participants were identified that were published in English from MEDLINE (through PubMed), EMBASE, the Cochrane Library, Agency for Healthcare Research and Quality, and other selected databases relevant to this guideline.

STRUCTURE

This guideline provides an evidenced-based and patient-centered approach to management of patients with chronic coronary disease, considering social determinants of health and incorporating the principles of shared decision-making and team-based care. Relevant topics include general approaches to treatment decisions, guideline-directed management and therapy to reduce symptoms and future cardiovascular events, decision-making pertaining to revascularization in patients with chronic coronary disease, recommendations for management in special populations, patient follow-up and monitoring, evidence gaps, and areas in need of future research. Where applicable, and based on availability of cost-effectiveness data, cost-value recommendations are also provided for clinicians. Many recommendations from previously published guidelines have been updated with new evidence, and new recommendations have been created when supported by published data.

2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guideline for the Management of Patients With Chronic Coronary Disease: A Report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines

AIM

The "2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guideline for the Management of Patients With Chronic Coronary Disease" provides an update to and consolidates new evidence since the "2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS Guideline for the Diagnosis and Management of Patients With Stable Ischemic Heart Disease" and the corresponding "2014 ACC/AHA/AATS/PCNA/SCAI/STS Focused Update of the Guideline for the Diagnosis and Management of Patients With Stable Ischemic Heart Disease."

METHODS

A comprehensive literature search was conducted from September 2021 to May 2022. Clinical studies, systematic reviews and meta-analyses, and other evidence conducted on human participants were identified that were published in English from MEDLINE (through PubMed), EMBASE, the Cochrane Library, Agency for Healthcare Research and Quality, and other selected databases relevant to this guideline.

STRUCTURE

This guideline provides an evidenced-based and patient-centered approach to management of patients with chronic coronary disease, considering social determinants of health and incorporating the principles of shared decision-making and team-based care. Relevant topics include general approaches to treatment decisions, guideline-directed management and therapy to reduce symptoms and future cardiovascular events, decision-making pertaining to revascularization in patients with chronic coronary disease, recommendations for management in special populations, patient follow-up and monitoring, evidence gaps, and areas in need of future research. Where applicable, and based on availability of cost-effectiveness data, cost-value recommendations are also provided for clinicians. Many recommendations from previously published guidelines have been updated with new evidence, and new recommendations have been created when supported by published data.

Association between socio-economic status and estimated atherosclerotic cardiovascular disease risk: results from a middle-aged population-based study

OBJECTIVES

The association between cardiovascular disease (CVD) risk and socio-economic status (SES) remains poorly studied. The purpose of this study was to investigate the relationship between SES and estimated 10-year atherosclerotic cardiovascular disease (ASCVD) risk among the general UK Biobank population.

STUDY DESIGN

This was a population-based study.

METHODS

Among 311,928 volunteers (47.7% men) of the UK Biobank population, SES was assessed by a questionnaire, and ASCVD risk was calculated using pooled cohort equation models. Associations between SES and ASCVD risk were estimated using multiple gender-specific regressions.

RESULTS

The findings from this study showed that men had higher estimated 10-year ASCVD risk than women (8.6% vs 2.7%; P < 0.001), higher education level (38.3% vs 36.2%; P < 0.001), higher income level (31.0% vs 25.1%; P < 0.001), higher levels of employment (65.4% vs 60.5%; P < 0.001) and higher scores of Townsend deprivation (P < 0.001). Using the multiple logistic regression model, a decreased 10-year ASCVD risk in men was associated with high income level (odds ratio [OR] = 0.64 [95% confidence interval {CI} 0.61-0.68]; P < 0.001), high educational level (OR = 0.71 [95% CI 0.68-0.74]; P < 0.001), higher Townsend deprivation quintile (OR = 0.81 [95% CI 0.78-0.85]; P < 0.001) and employed status (OR = 0.74 [95% CI 0.69-0.80]; P < 0.001). The same results were observed in women, with high income level (OR = 0.68 [95% CI 0.55-0.68]; P < 0.001), high educational level (OR = 0.87 [95% CI 0.82-0.93]; P < 0.001), higher Townsend deprivation quintile (OR = 0.74 [95% CI 0.69-0.80]; P < 0.001) and employed status (OR = 0.53 [95% CI 0.45-0.63]; P < 0.001) being associated with a lower 10-year ASCVD risk. When considering the false discovery rate logworth analysis, SES factors presented a similar contribution to CVD risk as lifestyle factors.

CONCLUSIONS

Health policies should consider the SES factors identified in this study, in addition to traditional risk factors, when designing prevention campaigns for CVD. Further research is required to improve the ASCVD risk prediction models among different SES variables.

Socioeconomic status and modification of atherosclerotic cardiovascular disease risk prediction: epidemiological analysis using data from the atherosclerosis risk in communities study

OBJECTIVE

Examine whether the relationship between the pooled cohort equations (PCE) predicted 10-year risk for atherosclerotic cardiovascular disease (ASCVD) and absolute risk for ASCVD is modified by socioeconomic status (SES).

DESIGN

Population-based longitudinal cohort study-Atherosclerosis Risk in Communities (ARIC)-investigating the development of cardiovascular disease across demographic subgroups.

SETTING

Four communities in the USA-Forsyth County, North Carolina, Jackson, Mississippi, suburbs of Minneapolis, Minnesota and Washington County, Maryland.

PARTICIPANTS

We identified 9782 ARIC men and women aged 54-73 without ASCVD at study visit 4 (1996-1998).

PRIMARY OUTCOME MEASURES

Risk ratio (RR) differences in 10-year incident hospitalisations or death for ASCVD by SES and PCE predicted 10-year ASCVD risk categories to assess for risk modification. SES measures included educational attainment and census-tract neighbourhood deprivation using the Area Deprivation Index. PCE risk categories were 0%-5%, >5%-10%, >10%-15% and >15%. SES as a prognostic factor to estimate ASCVD absolute risk categories was further investigated as an interaction term with the PCE.

RESULTS

ASCVD RRs for participants without a high school education (referent college educated) increased at higher PCE estimated risk categories and was consistently >1. Results indicate education is both a risk modifier and delineates populations at higher ASCVD risk independent of PCE. Neighbourhood deprivation did modify association but was less consistent in direction of effect. However, for participants residing in the most deprived neighbourhoods (referent least deprived neighbourhoods) with a PCE estimated risk >10%-15%, risk was significantly elevated (RR 1.65, 95% CI 1.05 to 2.59). Education and neighbourhood deprivation inclusion as an interaction term on the PCE risk score was statistically significant (likelihood ratio p≤0.0001).

CONCLUSIONS

SES modifies the association between PCE estimated risk and absolute risk of ASCVD. SES added into ASCVD risk prediction models as an interaction term may improve our ability to predict absolute ASCVD risk among socially disadvantaged populations.

Using Geomarkers and Sociodemographics to Inform Assessment of Caregiver Adversity and Resilience

BACKGROUND AND OBJECTIVES

A high level of caregiver adverse childhood experiences (ACEs) and/or low resilience is associated with poor outcomes for both caregivers and their children after hospital discharge. It is unknown if sociodemographic or area-based measures (ie, "geomarkers") can inform the assessment of caregiver ACEs or resilience. Our objective was to determine if caregiver ACEs or resilience can be identified by using any combinations of sociodemographic measures, geomarkers, and/or caregiver-reported household characteristics.

METHODS

Eligible participants for this cohort study were English-speaking caregivers of children hospitalized on a hospital medicine team. Caregivers completed the ACE questionnaire, Brief Resilience Scale, and strain surveys. Exposures included sociodemographic characteristics available in the electronic health record (EHR), geomarkers tied to a patient's geocoded home address, and household characteristics that are not present in the EHR (eg, income). Primary outcomes were a high caregiver ACE score (≥4) and/or a low BRS Score (<3).

RESULTS

Of the 1272 included caregivers, 543 reported high ACE or low resilience, and 63 reported both. We developed the following regression models: sociodemographic variables in EHR (Model 1), EHR sociodemographics and geomarkers (Model 2), and EHR sociodemographics, geomarkers, and additional survey-reported household characteristics (Model 3). The ability of models to identify the presence of caregiver adversity was poor (all areas under receiver operating characteristics curves were <0.65).

CONCLUSIONS

Models using EHR data, geomarkers, and household-level characteristics to identify caregiver adversity had limited utility. Directly asking questions to caregivers or integrating risk and strength assessments during pediatric hospitalization may be a better approach to identifying caregiver adversity.

Socioeconomic Deprivation and Premature Cardiovascular Mortality in the United States

OBJECTIVE

To determine the variability in county cardiovascular (CV) premature mortality explained by integrated metrics of socioeconomic deprivation and to explore temporal trends in CV mortality by county socioeconomic deprivation.

METHODS

This is a cross-sectional analysis of US county-level death certificate data from 1999 to 2018 of age-adjusted premature (25 to 64 years) CV mortality. Integrated metrics of socioeconomic deprivation (Social Deprivation Index [SDI] and county Area Deprivation Index [ADI]) were associated with mortality using linear regression analysis. Relative change in county CV mortality from 1999 to 2018 was associated with indices using linear regression analysis.

RESULTS

Counties with higher quartile SDI and ADI had significantly higher total, non-Hispanic Black/African American, and female premature CV mortality (P<.001). Both SDI and ADI were significantly associated with CV mortality by linear regression (P<.001) explaining 40% and 44% of county variability in CV mortality, respectively. Counties with lower deprivation indices experienced a larger decreased in premature CV mortality (P<.001).

CONCLUSION

This study demonstrates an association between multiple integrated metrics of socioeconomic deprivation and premature cardiovascular mortality and shows potentially worsening disparities.

Patient-centric characterization of multimorbidity trajectories in patients with severe mental illnesses: A temporal bipartite network modeling approach

Multimorbidity is a major factor contributing to increased mortality among people with severe mental illnesses (SMI). Previous studies either focus on estimating prevalence of a disease in a population without considering relationships between diseases or ignore heterogeneity of individual patients in examining disease progression by looking merely at aggregates across a whole cohort. Here, we present a temporal bipartite network model to jointly represent detailed information on both individual patients and diseases, which allows us to systematically characterize disease trajectories from both patient and disease centric perspectives. We apply this approach to a large set of longitudinal diagnostic records for patients with SMI collected through a data linkage between electronic health records from a large UK mental health hospital and English national hospital administrative database. We find that the resulting diagnosis networks show disassortative mixing by degree, suggesting that patients affected by a small number of diseases tend to suffer from prevalent diseases. Factors that determine the network structures include an individual's age, gender and ethnicity. Our analysis on network evolution further shows that patients and diseases become more interconnected over the illness duration of SMI, which is largely driven by the process that patients with similar attributes tend to suffer from the same conditions. Our analytic approach provides a guide for future patient-centric research on multimorbidity trajectories and contributes to achieving precision medicine.

Role of confirmed and potential predictors of an unfavorable outcome in heart failure in everyday clinical practice

Heart failure (HF) is the only cardiovascular disease with an ever increasing incidence. HF, through reduced functional capacity, frequent exacerbations of disease, and repeated hospitalizations, results in poorer quality of life, decreased work productivity, and significantly increased costs of the public health system. The main challenge in the treatment of HF is the availability of reliable prognostic models that would allow patients and doctors to develop realistic expectations about the prognosis and to choose the appropriate therapy and monitoring method. At this moment, there is a lack of universal parameters or scales on the basis of which we could easily capture the moment of deterioration of HF patients' condition. Hence, it is crucial to identify such factors which at the same time will be widely available, cheap, and easy to use. We can find many studies showing different predictors of unfavorable outcome in HF patients: thorough assessment with echocardiography imaging, exercise testing (e.g., 6-min walk test, cardiopulmonary exercise testing), and biomarkers (e.g., N-terminal pro-brain type natriuretic peptide, high-sensitivity troponin T, galectin-3, high-sensitivity C-reactive protein). Some of them are very promising, but more research is needed to create a specific panel on the basis of which we will be able to assess HF patients. At this moment despite identification of many markers of adverse outcomes, clinical decision-making in HF is still predominantly based on a few basic parameters, such as the presence of HF symptoms (NYHA class), left ventricular ejection fraction, and QRS complex duration and morphology.

Explaining the Variance in Cardiovascular Disease Risk Factors: A Comparison of Demographic, Socioeconomic, and Genetic Predictors

BACKGROUND

Efforts to explain the burden of cardiovascular disease (CVD) often focus on genetic factors or social determinants of health. There is little evidence on the comparative predictive value of each, which could guide clinical and public health investments in measuring genetic versus social information. We compared the variance in CVD-related outcomes explained by genetic versus socioeconomic predictors.

METHODS

Data were drawn from the Health and Retirement Study (N = 8,720). We examined self-reported diabetes, heart disease, depression, smoking, and body mass index, and objectively measured total and high-density lipoprotein cholesterol. For each outcome, we compared the variance explained by demographic characteristics, socioeconomic position (SEP), and genetic characteristics including a polygenic score for each outcome and principal components (PCs) for genetic ancestry. We used R-squared values derived from race-stratified multivariable linear regressions to evaluate the variance explained.

RESULTS

The variance explained by models including all predictors ranged from 3.7% to 14.3%. Demographic characteristics explained more than half this variance for most outcomes. SEP explained comparable or greater variance relative to the combination of the polygenic score and PCs for most conditions among both white and Black participants. The combination of SEP, polygenic score, and PCs performed substantially better, suggesting that each set of characteristics may independently contribute to the prediction of CVD-related outcomes. Philip R. Lee Institute for Health Policy Studies, Department of Family & Community Medicine, UCSF.

CONCLUSIONS

Focusing on genetic inputs into personalized medicine predictive models, without considering measures of social context that have clear predictive value, needlessly ignores relevant information that is more feasible and affordable to collect on patients in clinical settings. See video abstract at, http://links.lww.com/EDE/B879.

Non-medical patient-related factor influence in proximal humeral fracture outcomes: a multicentric study

PURPOSE

Age, sex, and type of fracture have traditionally been described as prognostic factors for proximal humeral fractures (PHFs). Some non-medical patient-related factors may play a role in the outcome. This paper evaluates the association of comorbidities and socioeconomic factors with clinical outcomes for PHF.

METHODS

A total of 217 patients with PHF were evaluated according to Neer's classification with X-ray. Comorbidities were assessed through the Charlson comorbidity index and, non-medical patient-related factors were determined with a 52-item questionnaire concerning personal behaviors such as social activities, family support, economic solvency, and leisure-time activities. The clinical outcome was assessed with the Constant-Murley Score (CMS), with a minimum 1-year follow-up. The minimal clinically relevant difference for the CMS was set at 10 points. A multivariable analysis was performed to adjust for comorbidities and non-medical patient-related factors, such as age, sex, fracture classification, and treatment.

RESULTS

One hundred and eighty-three patients completed the initial research protocol, while 126 of them completed the 1-year follow-up. The mean age was 71.6 years (SD ± 13.3), and 79.3% of the patients were women. In the bivariable analysis, age and comorbidities were correlated with the CMS (correlation coefficient: - 0.34 [- 0.49, 0.17] and 0.35 [0.18, 0.50], respectively), as well as non-medical patient-related factors and the fracture pattern (p value ANOVA < 0.001). In the multivariable regression model, the effects of considering oneself socially active, without economic problems, and self-sufficient were associated with a higher CMS than the effect of the fracture pattern (beta coefficient: 11.69 [6.09-17.30], 15.54 [8.32-22.75], and 10.61 [3.34-17.88], respectively).

CONCLUSION

Socioeconomic status had a higher impact on functional outcomes than fracture pattern in patients with PHF.

A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects

BACKGROUND

Machine learning (ML) has spread rapidly from computer science to several disciplines. Given the predictive capacity of ML, it offers new opportunities for health, behavioral, and social scientists. However, it remains unclear how and to what extent ML is being used in studies of social determinants of health (SDH).

METHODS

Using four search engines, we conducted a scoping review of studies that used ML to study SDH (published before May 1, 2020). Two independent reviewers analyzed the relevant studies. For each study, we identified the research questions, Results, data, and algorithms. We synthesized our findings in a narrative report.

RESULTS

Of the initial 8097 hits, we identified 82 relevant studies. The number of publications has risen during the past decade. More than half of the studies (n = 46) used US data. About 80% (n = 66) utilized surveys, and 70% (n = 57) employed ML for common prediction tasks. Although the number of studies in ML and SDH is growing rapidly, only a few studies used ML to improve causal inference, curate data, or identify social bias in predictions (i.e., algorithmic fairness).

CONCLUSIONS

While ML equips researchers with new ways to measure health outcomes and their determinants from non-conventional sources such as text, audio, and image data, most studies still rely on traditional surveys. Although there are no guarantees that ML will lead to better social epidemiological research, the potential for innovation in SDH research is evident as a result of harnessing the predictive power of ML for causality, data curation, or algorithmic fairness.

Predictors of tooth loss: A machine learning approach

Introduction

Little is understood about the socioeconomic predictors of tooth loss, a condition that can negatively impact individual’s quality of life. The goal of this study is to develop a machine-learning algorithm to predict complete and incremental tooth loss among adults and to compare the predictive performance of these models.

Methods

We used data from the National Health and Nutrition Examination Survey from 2011 to 2014. We developed multiple machine-learning algorithms and assessed their predictive performances by examining the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values.

Results

The extreme gradient boosting trees presented the highest performance in the prediction of edentulism (AUC = 88.7%; 95%CI: 87.1, 90.2), the absence of a functional dentition (AUC = 88.3% 95%CI: 87.3,89.3) and for predicting missing any tooth (AUC = 83.2%; 95%CI, 82.0, 84.4). Although, as expected, age and routine dental care emerged as strong predictors of tooth loss, the machine learning approach identified additional predictors, including socioeconomic conditions. Indeed, the performance of models incorporating socioeconomic characteristics was better at predicting tooth loss than those relying on clinical dental indicators alone.

Conclusions

Future application of machine-learning algorithm, with longitudinal cohorts, for identification of individuals at risk for tooth loss could assist clinicians to prioritize interventions directed toward the prevention of tooth loss.

The Potential for Health Information Technology Tools to Reduce Racial Disparities in Maternal Morbidity and Mortality

Health information technology (health IT) potentially is a promising vital lever to address racial and ethnic, socioeconomic, and geographic disparities in maternal morbidity and mortality (MMM). This is especially relevant given that approximately 60% of maternal deaths are considered preventable.1-36 Interventions that leverage health IT tools to target the underlying drivers of disparities at the patient, clinician, and health care system levels potentially could reduce disparities in quality of care throughout the continuum (antepartum, intrapartum, and postpartum) of maternity care. This article presents an overview of the research (and gaps) on the potential of health IT tools to document SDoH and community-level geocoded data in EHR-based CDS systems, minimize implicit bias, and improve adherence to clinical guidelines and coordinated care to inform multilevel (patient, clinician, system) interventions throughout the continuum of maternity care for health disparity populations impacted by MMM. Telemedicine models for improving access in rural areas and new technologies for risk assessment and disease management (e.g., regarding preeclampsia) also are discussed.

Development and validation of two SCORE-based cardiovascular risk prediction models for Eastern Europe: a multicohort study

AIMS

Cardiovascular disease (CVD) risk prediction models are used in Western European countries, but less so in Eastern European countries where rates of CVD can be two to four times higher. We recalibrated the SCORE prediction model for three Eastern European countries and evaluated the impact of adding seven behavioural and psychosocial risk factors to the model.

METHODS AND RESULTS

We developed and validated models using data from the prospective HAPIEE cohort study with 14 598 participants from Russia, Poland, and the Czech Republic (derivation cohort, median follow-up 7.2 years, 338 fatal CVD cases) and Estonian Biobank data with 4632 participants (validation cohort, median follow-up 8.3 years, 91 fatal CVD cases). The first model (recalibrated SCORE) used the same risk factors as in the SCORE model. The second model (HAPIEE SCORE) added education, employment, marital status, depression, body mass index, physical inactivity, and antihypertensive use. Discrimination of the original SCORE model (C-statistic 0.78 in the derivation and 0.83 in the validation cohorts) was improved in recalibrated SCORE (0.82 and 0.85) and HAPIEE SCORE (0.84 and 0.87) models. After dichotomizing risk at the clinically meaningful threshold of 5%, and when comparing the final HAPIEE SCORE model against the original SCORE model, the net reclassification improvement was 0.07 [95% confidence interval (CI) 0.02-0.11] in the derivation cohort and 0.14 (95% CI 0.04-0.25) in the validation cohort.

CONCLUSION

Our recalibrated SCORE may be more appropriate than the conventional SCORE for some Eastern European populations. The addition of seven quick, non-invasive, and cheap predictors further improved prediction accuracy.

Association of Low Socioeconomic Status With Premature Coronary Heart Disease in US Adults

Importance

Individuals with low socioeconomic status (SES) bear a disproportionate share of the coronary heart disease (CHD) burden, and CHD remains the leading cause of mortality in low-income US counties.

Objective

To estimate the excess CHD burden among individuals in the United States with low SES and the proportions attributable to traditional risk factors and to other factors associated with low SES.

Design, Setting, and Participants

This computer simulation study used the Cardiovascular Disease Policy Model, a model of CHD and stroke incidence, prevalence, and mortality among adults in the United States, to project the excess burden of early CHD. The proportion of this excess burden attributable to traditional CHD risk factors (smoking, high blood pressure, high low-density lipoprotein cholesterol, low high-density lipoprotein cholesterol, type 2 diabetes, and high body mass index) compared with the proportion attributable to other risk factors associated with low SES was estimated. Model inputs were derived from nationally representative US data and cohort studies of incident CHD. All US adults aged 35 to 64 years, stratified by SES, were included in the simulations.

Exposures

Low SES was defined as income below 150% of the federal poverty level or educational level less than a high school diploma.

Main Outcomes and Measures

Premature (before age 65 years) myocardial infarction (MI) rates and CHD deaths.

Results

Approximately 31.2 million US adults aged 35 to 64 years had low SES, of whom approximately 16 million (51.3%) were women. Compared with individuals with higher SES, both men and women in the low-SES group had double the rate of MIs (men: 34.8 [95% uncertainty interval (UI), 31.0-38.8] vs 17.6 [95% UI, 16.0-18.6]; women: 15.1 [95% UI, 13.4-16.9] vs 6.8 [95% UI, 6.3-7.4]) and CHD deaths (men: 14.3 [95% UI, 13.0-15.7] vs 7.6 [95% UI, 7.3-7.9]; women: 5.6 [95% UI, 5.0-6.2] vs 2.5 [95% UI, 2.3-2.6]) per 10 000 person-years. A higher burden of traditional CHD risk factors in adults with low SES explained 40% of these excess events; the remaining 60% of these events were attributable to other factors associated with low SES. Among a simulated cohort of 1.3 million adults with low SES who were 35 years old in 2015, the model projected that 250 000 individuals (19%) will develop CHD by age 65 years, with 119 000 (48%) of these CHD cases occurring in excess of those expected for individuals with higher SES.

Conclusions and Relevance

This study suggested that, for approximately one-quarter of US adults aged 35 to 64 years, low SES was substantially associated with early CHD burden. Although biomedical interventions to modify traditional risk factors may decrease the disease burden, disparities by SES may remain without addressing SES itself.

Cumulative Social Risk and Cardiovascular Disease Among Adults in South Korea: A Cross-Sectional Analysis of a Nationally Representative Sample

INTRODUCTION

The Framingham risk score (FRS) is widely used to predict cardiovascular disease (CVD), but it neglects to account for social risk factors. Our study examined whether use of a cumulative social risk score in addition to the FRS improves prediction of CVD among South Korean adults.

METHODS

We used nationally representative data on 19,147 adults aged 19 or older from the Korea National Health and Nutrition Examination Survey 2013-2016. We computed a cumulative social risk score (range, 0-3) based on 3 social risk factors: low household income, low level of education, and single-living status. CVD outcomes were stroke, myocardial infarction, and angina. Weighted logistic regression examined the associations between cumulative social risk, FRS, and CVD. McFadden pseudo-R² and area under receiver operating characteristic curve (AUC) assessed model performance. We conducted mediation analyses to quantify the association between cumulative social risk score and CVD outcomes that is not mediated by the FRS.

RESULTS

A unit increase in social risk was associated with 89.4% higher risk of stroke diagnosis, controlling for the FRS (P < .001). The FRS explained 8.0% of stroke diagnosis (R²) with fair discrimination (AUC = 0.728), and adding the cumulative social risk score enhanced R² and AUC by 2.4% and 0.039. In the association between cumulative social risk and stroke, the proportion not mediated by the FRS was 65% (P < .001). We observed similar trends in myocardial infarction and angina, such that an increase in social risk was associated with increased relative risk of disease and improved disease diagnosis, and a large proportion of the association was not mediated by the FRS.

CONCLUSION

Controlling for the FRS, cumulative social risks predicted stroke, myocardial infarction, and angina among adults in South Korea. Future research is needed to examine non-FRS mediators between cumulative social risk and CVD.

Social Determinants of Health Improve Predictive Accuracy of Clinical Risk Models for Cardiovascular Hospitalization, Annual Cost, and Death

BACKGROUND

Risk models in the private insurance setting may systematically underpredict in the socially disadvantaged. In this study, we sought to determine whether US minority Medicare beneficiaries had disproportionately low costs compared with their clinical outcomes and whether adding social determinants of health (SDOH) into risk prediction models improves prediction accuracy.

METHODS AND RESULTS

Retrospective observational cohort study of 2016 to 2017 Medicare Current Beneficiary Survey data (n=3614) linked to Medicare fee-for-service claims. Logistic and linear regressions were used to determine the relationship between race/ethnicity and annual costs of care, all-cause hospitalization, cardiovascular hospitalization, and death. We calculated the observed-to-expected (O:E) ratios for all outcomes under 4 risk models: (1) age+sex, (2) model 1+clinical comorbidity adjustment, (3) model 2+SDOH, and (4) SDOH alone. Our sample was 44% male and 11% black or Hispanic. Among minorities, adverse clinical outcomes were inversely related to cost. After multivariable adjustment, blacks/Hispanics had higher rates of cardiovascular hospitalization (incidence rate ratio, 1.78; P=0.012) but similar annual costs ($-336, P=0.77) compared with whites. Among whites, models 1 to 4 all showed similar O:E ratios, suggesting high accuracy in risk prediction using current models. Among minorities, adjustment for age, sex, and comorbidities underpredicted all-cause hospitalization by 20% (O:E, 1.20) and cardiovascular hospitalization by 70% (O:E, 1.70) and overpredicted death by 21% (O:E, 0.79); adding SDOH brought O:E near 1 for all outcomes. Among both groups, the SDOH risk model alone performed with equal or superior accuracy to the model based on clinical comorbidities.

CONCLUSIONS

A paradoxical relationship was observed between clinical outcomes and costs among racial and ethnic minorities. Because of systematic differences in access to care, cost may not be an appropriate surrogate for predicting clinical risk among vulnerable populations. Adjustment for SDOH improves the accuracy of risk models among racial and ethnic minorities and could guide use of prevention strategies.

Screening Children for Social Determinants of Health: A Systematic Review

CONTEXT

Screening children for social determinants of health (SDOHs) has gained attention in recent years, but there is a deficit in understanding the present state of the science.

OBJECTIVE

To systematically review SDOH screening tools used with children, examine their psychometric properties, and evaluate how they detect early indicators of risk and inform care.

DATA SOURCES

Comprehensive electronic search of PubMed, Cumulative Index to Nursing and Allied Health Literature, Embase, Cochrane Central Register of Controlled Trials, and Web of Science Core Collection.

STUDY SELECTION

Studies in which a tool that screened children for multiple SDOHs (defined according to Healthy People 2020) was developed, tested, and/or employed.

DATA EXTRACTION

Extraction domains included study characteristics, screening tool characteristics, SDOHs screened, and follow-up procedures.

RESULTS

The search returned 6274 studies. We retained 17 studies encompassing 11 screeners. Study samples were diverse with respect to biological sex and race and/or ethnicity. Screening was primarily conducted in clinical settings with a parent or caregiver being the primary informant for all screeners. Psychometric properties were assessed for only 3 screeners. The most common SDOH domains screened included the family context and economic stability. Authors of the majority of studies described referrals and/or interventions that followed screening to address identified SDOHs.

LIMITATIONS

Following the Healthy People 2020 SDOH definition may have excluded articles that other definitions would have captured.

CONCLUSIONS

The extent to which SDOH screening accurately assessed a child's SDOHs was largely unevaluated. Authors of future research should also evaluate if referrals and interventions after the screening effectively address SDOHs and improve child well-being.

Evaluation of a Novel Financial Navigator Pilot to Address Patient Concerns about Medical Care Costs

CONTEXT

Interventions are required that address patients' medically related financial needs.

OBJECTIVE

To evaluate a Financial Navigator pilot addressing patients' concerns/needs regarding medical care costs in an integrated health care system.

METHODS

Adults (aged ≥ 18 years) enrolled at Kaiser Permanente Northwest, who had a concern/need about medical care costs and received care in 1 of 3 clinical departments at the intervention or comparison clinic were recruited between August 1, 2016, and October 31, 2016. Baseline and 30-day follow-up participant surveys were administered to assess medical and nonmedical socioeconomic needs, satisfaction with medical care, and satisfaction with assistance with cost concerns. Physicians at both clinics were invited to complete a survey on medical care costs. We assessed participant characteristics and survey responses using descriptive statistics and 30-day change in satisfaction measures using multivariable linear regression models.

RESULTS

Eighty-five intervention and 51 comparison participants completed the baseline survey. At baseline, intervention participants reported transportation (52.9%), housing (38.2%), and social isolation (32.4%) needs; comparison participants identified employment (33.3%), food (33.3%), and housing (33.3%) needs. Intervention participants reported higher satisfaction with care (p = 0.01) and higher satisfaction with cost concerns assistance (p = 0.01) vs comparison participants at 30-day follow-up, controlling for baseline responses. Although most physicians (80%) reported discussing medical care costs with their patients, only 18% reported knowing about their patients' financial well-being.

CONCLUSION

We demonstrated the promise of a novel Financial Navigator pilot intervention to address medical care cost concerns and needs, and underscored the prevalence of nonmedical social needs in an economically vulnerable population.

Sugar-sweetened beverage prices: Variations by beverage, food store, and neighborhood characteristics, 2017

Sugar-sweetened beverage (SSB) consumption is associated with obesity, type 2 diabetes, and cardiovascular disease. A number of U.S. jurisdictions have levied volume-based specific SSB taxes. This study estimated baseline mean SSB prices across categories and sizes as this will help to determine the percentage increase in price resulting from the imposition of specific taxes. Data on food store SSB prices were collected in 2017 in Cook County, IL, St. Louis City/County, MO, Oakland, CA, and Sacramento, CA (N = 11,767 product-level observations from 581 stores). Data were weighted to represent volume sold by category and size. Mean prices per ounce were computed across categories and sizes. Linear regression models, clustered on store, were run to estimate associations between price per ounce and product characteristics, neighborhood (linked by census tract) characteristics, store type, and site. Weighted summary statistics show that the mean price of SSBs was 4.8 cents/oz. Soda was least expensive (3.4 cents/oz), followed by sports drinks (4.8 cents/oz), juice drinks (5.2 cents/oz), ready-to-drink tea/coffee (7.8 cents/oz), and energy drinks (19.9 cents/oz). Prices were higher for individual-sized (9.6 cents/oz) compared to family-sized drinks (>1 L/multi-pack; 3.5 cents/oz). Regression results revealed that prices were lower in stores in majority non-Hispanic black tracts and varied by beverage characteristics and store type but not tract-level socioeconomic status. Given substantial variation in prices by SSB category, a penny-per-ounce SSB tax, if fully passed through, would increase soda prices by 29% versus 5% for energy drinks, highlighting the potential importance of different specific tax rates across beverage categories.

A Validation Study of the Mini-IPIP Five-Factor Personality Scale in Adults With Cancer

The Mini International Personality Item Pool (Mini-IPIP) is a brief measure of the Five-Factor Model of personality with documented validity in healthy samples of adults and could be useful for assessing personality in patient populations such as individuals with cancer. The purpose of this study was to examine the psychometric properties of the Mini-IPIP in 2 samples of adults with cancer. A sample of 369 (Sample 1) and a sample of 459 (Sample 2) adults with cancer completed an online survey including the Mini-IPIP. To assess criterion validity, Sample 2 completed measures of emotional distress. Analyses included internal consistency (Samples 1 and 2), confirmatory factor analyses (CFAs; Samples 1 and 2), and correlations and a structural regression model to examine the associations between the 5 personality factors and emotional distress (Sample 2 only). Results showed that the Mini-IPIP demonstrated levels of internal consistency and CFA model fit that were similar to previous validation studies conducted in the general population. Consistent with prior research and theory, this study also found that personality factors measured by the Mini-IPIP were associated with measures of emotional distress in Sample 2. These findings suggest the potential utility of the Mini-IPIP in both research and clinical settings involving individuals with cancer.

Resilience in the midst of chaos: Socioecological model applied to women with depressive symptoms and socioeconomic disadvantage

Socioeconomic disadvantage is extremely common among women with depressive symptoms presenting for women's health care. While social stressors related to socioeconomic disadvantage can contribute to depression, health care tends to focus on patients' symptoms in isolation of context. Health care providers may be more effective by addressing issues related to socioeconomic disadvantage. It is imperative to identify common challenges related to socioeconomic disadvantage, as well as sources of resilience. In this qualitative study, we interviewed 20 women's health patients experiencing depressive symptoms and socioeconomic disadvantage about their views of their mental health, the impact of social stressors, and their resources and skills. A Consensual Qualitative Research approach was used to identify domains consisting of challenges and resiliencies. We applied the socioecological model when coding the data and identified cross-cutting themes of chaos and distress, as well as resilience. These findings suggest the importance of incorporating context in the health care of women with depression and socioeconomic disadvantage.

Documenting social determinants of health-related clinical activities using standardized medical vocabularies

OBJECTIVES

Growing recognition that health is shaped by social and economic circumstances has resulted in a rapidly expanding set of clinical activities related to identifying, diagnosing, and intervening around patients' social risks in the context of health care delivery. The objective of this exploratory analysis was to identify existing documentation tools in common US medical coding systems reflecting these emerging clinical practices to improve patients' social health.

MATERIALS AND METHODS

We identified 20 social determinants of health (SDH)-related domains used in 6 published social health assessment tools. We then used medical vocabulary search engines to conduct three independent searches for codes related to these 20 domains included in common medical coding systems (LOINC, SNOMED CT, ICD-10-CM, and CPT). Each of the 3 searches focused on one of three clinical activities: Screening, Assessment/Diagnosis, and Treatment/Intervention.

RESULTS

We found at least 1 social Screening code for 18 of the 20 SDH domains, 686 social risk Assessment/Diagnosis codes, and 243 Treatment/Intervention codes. Fourteen SDH domains (70%) had codes across all 3 clinical activity areas.

DISCUSSION

Our exploratory analysis revealed 1095 existing codes in common medical coding vocabularies that can facilitate documentation of social health-related clinical activities. Despite a large absolute number of codes, there are addressable gaps in the capacity of current medical vocabularies to document specific social risk factor screening, diagnosis, and interventions activities.

CONCLUSIONS

Findings from this analysis should help inform efforts both to develop a comprehensive set of SDH codes and ultimately to improve documentation of SDH-related activities in clinical settings.

Leveraging the Electronic Health Record to Create an Automated Real-Time Prognostic Tool for Peripheral Arterial Disease

Background Automated individualized risk prediction tools linked to electronic health records ( EHR s) are not available for management of patients with peripheral arterial disease. The goal of this study was to create a prognostic tool for patients with peripheral arterial disease using data elements automatically extracted from an EHR to enable real-time and individualized risk prediction at the point of care. Methods and Results A previously validated phenotyping algorithm was deployed to an EHR linked to the Rochester Epidemiology Project to identify peripheral arterial disease cases from Olmsted County, MN, for the years 1998 to 2011. The study cohort was composed of 1676 patients: 593 patients died over 5-year follow-up. The c-statistic for survival in the overall data set was 0.76 (95% confidence interval [CI], 0.74-0.78), and the c-statistic across 10 cross-validation data sets was 0.75 (95% CI, 0.73-0.77). Stratification of cases demonstrated increasing mortality risk by subgroup (low: hazard ratio, 0.35 [95% CI, 0.21-0.58]; intermediate-high: hazard ratio, 2.98 [95% CI, 2.37-3.74]; high: hazard ratio, 8.44 [95% CI, 6.66-10.70], all P<0.0001 versus the reference subgroup). An equation for risk calculation was derived from Cox model parameters and β estimates. Big data infrastructure enabled deployment of the real-time risk calculator to the point of care via the EHR . Conclusions This study demonstrates that electronic tools can be deployed to EHR s to create automated real-time risk calculators to predict survival of patients with peripheral arterial disease. Moreover, the prognostic model developed may be translated to patient care as an automated and individualized real-time risk calculator deployed at the point of care.

Value of Neighborhood Socioeconomic Status in Predicting Risk of Outcomes in Studies That Use Electronic Health Record Data

IMPORTANCE

Data from electronic health records (EHRs) are increasingly used for risk prediction. However, EHRs do not reliably collect sociodemographic and neighborhood information, which has been shown to be associated with health. The added contribution of neighborhood socioeconomic status (nSES) in predicting health events is unknown and may help inform population-level risk reduction strategies.

OBJECTIVE

To quantify the association of nSES with adverse outcomes and the value of nSES in predicting the risk of adverse outcomes in EHR-based risk models.

DESIGN, SETTING, AND PARTICIPANTS

Cohort study in which data from 90 097 patients 18 years or older in the Duke University Health System and Lincoln Community Health Center EHR from January 1, 2009, to December 31, 2015, with at least 1 health care encounter and residence in Durham County, North Carolina, in the year prior to the index date were linked with census tract data to quantify the association between nSES and the risk of adverse outcomes. Machine learning methods were used to develop risk models and determine how adding nSES to EHR data affects risk prediction. Neighborhood socioeconomic status was defined using the Agency for Healthcare Research and Quality SES index, a weighted measure of multiple indicators of neighborhood deprivation.

MAIN OUTCOMES AND MEASURES

Outcomes included use of health care services (emergency department and inpatient and outpatient encounters) and hospitalizations due to accidents, asthma, influenza, myocardial infarction, and stroke.

RESULTS

Among the 90 097 patients in the training set of the study (57 507 women and 32 590 men; mean [SD] age, 47.2 [17.7] years) and the 122 812 patients in the testing set of the study (75 517 women and 47 295 men; mean [SD] age, 46.2 [17.9] years), those living in neighborhoods with lower nSES had a shorter time to use of emergency department services and inpatient encounters, as well as a shorter time to hospitalizations due to accidents, asthma, influenza, myocardial infarction, and stroke. The predictive value of nSES varied by outcome of interest (C statistic ranged from 0.50 to 0.63). When added to EHR variables, nSES did not improve predictive performance for any health outcome.

CONCLUSIONS AND RELEVANCE

Social determinants of health, including nSES, are associated with the health of a patient. However, the results of this study suggest that information on nSES may not contribute much more to risk prediction above and beyond what is already provided by EHR data. Although this result does not mean that integrating social determinants of health into the EHR has no benefit, researchers may be able to use EHR data alone for population risk assessment.

Performance of a Framingham cardiovascular risk model among Indians and Europeans in New Zealand and the role of body mass index and social deprivation

Objectives

To evaluate a Framingham 5-year cardiovascular disease (CVD) risk score in Indians and Europeans in New Zealand, and determine whether body mass index (BMI) and socioeconomic deprivation were independent predictors of CVD risk.

Methods

We included Indians and Europeans, aged 30–74 years without prior CVD undergoing risk assessment in New Zealand primary care during 2002–2015 (n=256 446). Risk profiles included standard Framingham predictors (age, sex, systolic blood pressure, total cholesterol/high-density lipoprotein ratio, smoking and diabetes) and were linked with national CVD hospitalisations and mortality datasets. Discrimination was measured by the area under the receiver operating characteristics curve (AUC) and calibration examined graphically. We used Cox regression to study the impact of BMI and deprivation on the risk of CVD with and without adjustment for the Framingham score.

Results

During follow-up, 8105 and 1156 CVD events occurred in Europeans and Indians, respectively. Higher AUCs of 0.76 were found in Indian men (95% CI 0.74 to 0.78) and women (95% CI 0.73 to 0.78) compared with 0.74 (95% CI 0.73 to 0.74) in European men and 0.72 (95% CI 0.71 to 0.73) in European women. Framingham was best calibrated in Indian men, and overestimated risk in Indian women and in Europeans. BMI and deprivation were positively associated with CVD, also after adjustment for the Framingham risk score, although the BMI association was attenuated.

Conclusions

The Framingham risk model performed reasonably well in Indian men, but overestimated risk in Indian women and in Europeans. BMI and socioeconomic deprivation could be useful predictors in addition to a Framingham score.

New technological devices for the assessment of systemic inflammation in the primary prevention of cardiovascular disease

PURPOSE OF REVIEW

The prediction of cardiovascular disease (CVD) events is of strategic importance for the primary prevention of one of the big killers in the world. Predictive models have a history of decades, but still the desired accuracy is not reached by any of the existing models. The inclusion of inflammatory factors in the models did not increase their accuracy. In this review, we discuss the possible reasons for that failure and we propose a paradigm shift.

RECENT FINDINGS

Systemic inflammation is a very volatile phenomenon. The blood concentration of inflammatory biomarkers may change considerably in one individual with a timescale of seconds. Sudden changes in environmental conditions can trigger rapid modifications in the inflammatory profile of an individual. In routine clinical practice, the blood tests for inflammation are carried out at one point in time, not in standard environmental conditions, and are therefore inadequate.

SUMMARY

We have to direct CVD research toward the understanding of the synchronic relationship between external environmental conditions and internal physiological reactions. CVD risk assessment must be carried out by using continuous real-time monitoring of external and internal parameters together, something that may become possible with the advent of new technological devices.

Integrating Social And Medical Data To Improve Population Health: Opportunities And Barriers

Recent efforts in medical settings to identify social determinants of health have focused primarily on screening for the purpose of improving care for individual patients and getting standardized data into electronic health records (EHRs). Relatively little attention has been given to processes needed to extract data on social determinants of health out of medical records with adequate validity and efficiency to facilitate analysis across individual encounters to inform population health efforts relevant to the health care sector. In this article we describe the rationale for extracting data on social determinants of health from EHRs, including the potential influence of aggregated data on quality improvement activities and health care payment reform. We then discuss opportunities and challenges to pulling these data from EHRs to enable population-level applications, focusing on the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, as one potential data aggregation resource. Standardizing methods for extracting data on social determinants of health from EHRs will require understanding current challenges and refining existing translation tools.

Individual- and Area-Level SES in Diabetes Risk Prediction: The Multi-Ethnic Study of Atherosclerosis

INTRODUCTION

The purpose of this study was to evaluate if adding SES to risk prediction models based upon traditional risk factors improves the prediction of diabetes.

METHODS

Risk prediction models without and with individual- and area-level SES predictors were compared using the prospective Multi-Ethnic Study of Atherosclerosis. Cox proportional hazards models were utilized to estimate hazard ratios for SES predictors and to generate 10-year predicted risks for 5,021 individuals without diabetes at baseline followed from 2000 to 2012. C-statistics were used to compare model discrimination, and the proportion of individuals reclassified into higher or lower risk categories with the addition of SES predictors was calculated. The accuracy of risk prediction by SES was assessed by comparing observed and predicted risks across tertiles of the SES variables. Statistical analyses were performed in 2015-2016.

RESULTS

Over a median of 9.2 years of follow-up, 615 individuals developed diabetes. Individual- and area-level SES variables did not significantly improve model discrimination or reclassify substantial numbers of individuals across risk categories. Models without SES predictors generally underestimated risk for low-SES individuals or individuals residing in low-SES areas (underestimates ranging from 0.31% to 1.07%) and overestimated risk for high-SES individuals or individuals residing in high-SES areas (overestimates ranging from 0.70% to 1.30%), and the addition of SES variables largely mitigated these differences.

CONCLUSIONS

Standard diabetes risk models may underestimate risk for low-SES individuals and overestimate risk for those of high SES. Adding SES predictors helps correct this systematic misestimation, but may not improve model discrimination.

Association of cumulative social risk with mortality and adverse cardiovascular disease outcomes

BACKGROUND

Quantifying the cumulative effect of social risk factors on cardiovascular disease (CVD) risk can help to better understand the sources of disparities in health outcomes.

METHOD AND RESULTS

Data from the Heart Strategies Concentrating on Risk Evaluation (HeartSCORE) study were used to create an index of cumulative social risk (CSR) and quantify its association with incident CVD and all-cause mortality. CSR was defined by assigning a score of 1 for the presence of each of 4 social factors: i) racial minority status (Black race), ii) single living status, iii) low income, and iv) low educational level. Hazard ratios (HRs) were computed using Cox-regression models, adjusted for CVD risk factors. Over a median follow-up period of 8.3 years, 127 incident events were observed. The incidence of the primary outcome for subgroups of participants with 0, 1, and ≥2 CSR scores was 5.31 (95% CI, 3.40-7.22), 10.32 (7.16-13.49) and 17.80 (12.94-22.67) per 1000 person-years, respectively. Individuals with CSR score of 1 had an adjusted HR of 1.85 (1.15-2.97) for incident primary outcomes, compared to those with score of 0. The corresponding HR for individuals with CSR score of 2 or more was 2.58 (1.60-4.17).

CONCLUSION

An accumulation of social risk factors independently increased the likelihood of CVD events and deaths in a cohort of White and Black individuals.

Analysis of cerebrovascular disease mortality trends in Andalusia (1980-2014)

INTRODUCTION

In recent decades, mortality rates for cerebrovascular diseases (CVD) have decreased significantly in many countries. This study analyses recent tendencies in CVD mortality rates in Andalusia (1980-2014) to identify any changes in previously observed sex and age trends.

PATIENTS AND METHODS

CVD mortality and population data were obtained from Spain's National Statistics Institute database. We calculated age-specific and age-standardised mortality rates using the direct method (European standard population). Joinpoint regression analysis was used to estimate the annual percentage change in rates and identify significant changes in mortality trends. We also estimated rate ratios between Andalusia and Spain.

RESULTS

Standardised rates for both males and females showed 3 periods in joinpoint regression analysis: an initial period of significant decline (1980-1997), a period of rate stabilisation (1997-2003), and another period of significant decline (2003-2014).

CONCLUSIONS

Between 1997 and 2003, age-standardised rates stabilised in Andalusia but continued to decrease in Spain as a whole. This increased in the gap between CVD mortality rates in Andalusia and Spain for both sexes and most age groups.

Performance of the Atherosclerotic Cardiovascular Disease Pooled Cohort Risk Equations by Social Deprivation Status

BACKGROUND

The atherosclerosis cardiovascular disease (ASCVD) Pooled Cohort risk equations have shown different calibration across US populations with varied levels of social deprivation.

METHODS AND RESULTS

We analyzed the calibration and discrimination of the Pooled Cohort risk equations by social deprivation status among 9066 REGARDS (REasons for Geographic And Racial Differences in Stroke) study participants not taking statins for whom ASCVD risk may lead to statin initiation. Patients were aged 45 to 79 years, had no ASCVD or diabetes mellitus, and had a low-density lipoprotein cholesterol level 70 to 189 mg/dL. Social deprivation was defined using 3 indicators: annual household income <$25 000, less than a high school education, and living without a partner. At baseline in 2003-2007, 54.6%, 27.4%, and 18.0% of participants had 0, 1, and 2 or 3 indicators showing deprivation, respectively. From baseline through December 2012, 457 participants developed ASCVD (nonfatal/fatal stroke, myocardial infarction, or coronary heart disease death). Predicted and observed ASCVD incidence per 1000 person-years were 8.02 and 6.23 (95% CI, 5.31-7.31), respectively, among participants with 0 indicators of deprivation (Hosmer-Lemeshow P=0.01); 8.05 and 6.61 (95% CI, 5.29-8.24), respectively, with 1 indicator (P=0.09); and 9.83 and 11.40 (95% CI, 9.23-14.05), respectively, with 2 or 3 indicators (P=0.12). The C-index (95% CI) was 0.72 (0.69-0.75), 0.73 (0.69-0.78), and 0.70 (0.65-0.75) among participants with 0, 1, and 2 or 3 indicators of deprivation, respectively. The net reclassification improvement after adding deprivation data to the Pooled Cohort risk equations was modest (0.12; 95% CI, 0.03-0.21).

CONCLUSIONS

The Pooled Cohort risk equations have good calibration among individuals with social deprivation but overestimate ASCVD risk among those with less social deprivation.

The interaction between systemic inflammation and psychosocial stress in the association with cardiac troponin elevation: A new approach to risk assessment and disease prevention

We have previously shown that there is a complex and dynamic biological interaction between acute mental stress and acute release of inflammatory factors into the blood stream in relation to heart disease. We now hypothesize that the presence of chronic psychosocial stress may modify the weight of single test results for inflammation as a predictor of heart disease. Using a cross-sectional design, 500 participants free from heart disease drawn from the Whitehall II study in UK in 2006-2008 were tested for plasma fibrinogen as an inflammatory factor, financial strain as a marker of chronic psychosocial stress, coronary calcification measured using computed tomography, and for plasma high-sensitivity cardiac troponin T (HS-CTnT) as a marker of cardiac risk. Fibrinogen concentration levels above the average were associated with a 5-fold increase in the odds of HS-CTnT positivity only among individuals with financial strain (N=208, OR=4.73, 95%CI=1.67 to 13.40, P=0.003). Fibrinogen was in fact not associated with HS-CTnT positivity in people without financial strain despite the larger size of that subsample (n=292, OR=0.84, 95%CI=0.42 to 1.67, P=0.622). A test for interaction on the full sample (N=500) showed a P value of 0.010 after adjusting for a range of demographics, health behaviours, traditional cardiovascular risk factors, psychosocial stressors, inflammatory cytokines, and coronary calcification. In conclusion, elevated fibrinogen seems to be cardio-toxic only when is combined with financial strain. Chronic psychosocial stress may modify the meaning that we should give to single test results for inflammation. Further research is needed to confirm our results.

Impact of Individual and Neighborhood Factors on Cardiovascular Risk in White Hispanic and Non-Hispanic Women and Men

Cardiovascular disease (CVD) is the leading cause of mortality for adults in the US, regardless of ethnicity. A cross-sectional correlational design was used to describe and compare CVD risk and cardiac mortality in White Hispanic and non-Hispanic women and men. Data from 3,317 individuals (1,523 women and 1,794 men) hospitalized for non-cardiac causes during 2012-2013, and data from the 2010 United States Census were included. The sex-specific 10-year Framingham General Cardiovascular Risk Score (FRS-10) was used to estimate long-term risk for major cardiac events. Approximately three-quarters of the sample was White Hispanic. FRS-10 scores were generally low, but a high prevalence of risk factors not included in the standard FRS-10 scoring formula was seen. White Hispanic women had significantly lower estimated CVD risk scores compared to White Hispanic and non-Hispanic men despite higher non-FRS-10 risks. Neighborhood median household income had a significant negative relationship and Hispanic neighborhood concentration had a significant positive relationship with cardiac mortality. Hispanic concentration was the only predictor of estimated CVD risk in a multilevel model. CVD risk assessment tools that are calibrated for ethnic groups and socioeconomic status may be more appropriate for Hispanic individuals than the FRS-10. Neighborhood-level factors should be included in clinical cardiac assessment in addition to individual characteristics and behavioral risks. Researchers should continue to seek additional risk factors that may contribute to or protect against CVD in order to close the gap between estimated CVD risk and actual cardiac mortality for Hispanics in the US. © 2016 Wiley Periodicals, Inc.

Sex Differences in Cardiovascular Disease Risk of Ghanaian- and Nigerian-Born West African Immigrants in the United States: The Afro-Cardiac Study

BACKGROUND

The number of African immigrants in the United States grew 40-fold between 1960 and 2007, from 35 355 to 1.4 million, with a large majority from West Africa. This study sought to examine the prevalence of cardiovascular disease (CVD) risk factors and global CVD risk and to identify independent predictors of increased CVD risk among West African immigrants in the United States.

METHODS AND RESULTS

This cross-sectional study assessed West African (Ghanaian and Nigerian) immigrants aged 35-74 years in the Baltimore-Washington metropolitan area. The mean age of participants was 49.5±9.2 years, and 58% were female. The majority (95%) had ≥1 of the 6 CVD risk factors. Smoking was least prevalent, and overweight or obesity was most prevalent, with 88% having a body mass index (in kg/m(2)) ≥25; 16% had a prior diagnosis of diabetes or had fasting blood glucose levels ≥126 mg/dL. In addition, 44% were physically inactive. Among women, employment and health insurance were associated with odds of 0.09 (95% CI 0.033-0.29) and 0.25 (95% CI 0.09-0.67), respectively, of having a Pooled Cohort Equations estimate ≥7.5% in the multivariable logistic regression analysis. Among men, higher social support was associated with 0.90 (95% CI 0.83-0.98) lower odds of having ≥3 CVD risk factors but not with having a Pooled Cohort Equations estimate ≥7.5%.

CONCLUSIONS

The prevalence of CVD risk factors among West African immigrants was particularly high. Being employed and having health insurance were associated with lower CVD risk in women, but only higher social support was associated with lower CVD risk in men.