The Impact of Social Determinants of Health and Lifestyle on the Association of Lipoprotein(a) with Myocardial Infarction and Stroke.

Background: In European cohorts, a higher Mediterranean diet or Life's Simple 7 (LS7) score abolished or attenuated the risk associated with increasing Lipoprotein(a) [Lp(a)] on cardiovascular outcomes. This is unstudied in US cohorts. The impact of social determinants of health (SDOH) on the association of Lp(a) with cardiovascular outcomes remains unstudied. We sought to test if a SDOH score and LS7 score impacts the association of Lp(a) with myocardial infarction (MI) or stroke. Methods: Observational Cohort of US Adults from the Atherosclerosis Risk in Communities (ARIC) and Multi-Ethnic Study of Atherosclerosis (MESA) cohorts. We performed sequential multivariable Cox proportional hazard analysis, first adjusting for age, gender, non-HDL-C, race and ethnicity, then added SDOH and LS7 scores sequentially. The primary outcomes were time until first fatal or nonfatal MI or stroke. Results: ARIC (n=15,072; median Lp(a)=17.3 mg/dL) had 16.2 years average follow up. MESA (n=6,822; median Lp(a)=18.3 mg/dL had 12.3 years average follow-up. In multivariable analyses adjusted for age, gender, race and ethnicity, and non-HDL-C, Lp(a) was associated (HR, p-value) with MI in ARIC (1.10, <0.001) and MESA (1.09, <0.001), and stroke in ARIC (1.08, <0.001) but not MESA (0.97, 0.50). With SDOH and LS7 added to the model associations remained similar (association of Lp(a) with MI in ARIC 1.09, <0.001 and in MESA 1.10, 0.001, with stroke in ARIC 1.06, <0.003 and in MESA 0.96, 0.39). In models with all covariates, each additional SDOH correlated positively with MI (ARIC 1.13, <0.001; MESA 1.11, <0.001) and stroke (ARIC 1.17, <0.001; HR 1.07, p=0.11) and each additional LS7 score point correlated negatively with MI (ARIC 0.81, <0.001; MESA 0.84, <0.001) and stroke (ARIC 0.82, <0.001; MESA 0.84, <0.001). Conclusions and Relevance: SDOH and lifestyle factors were predictors for MI and stroke that did not impact the association between Lp(a) and cardiovascular events. Our findings support that Lp(a) is an independent risk factor for MI and possibly stroke.

Disparities in Cardiovascular Health by Food Security Status and Supplemental Nutrition Assistance Program Participation Using Life's Essential 8 Metrics.

This cross-sectional study examines the associations among household food security, Supplemental Nutrition Assistance Program participation, and cardiovascular health among 2013-2018 National Health and Nutrition Examination Survey participants.

Assessing and Addressing Social Determinants of Cardiovascular Health: JACC State-of-the-Art Review.

Social determinants of health (SDOH) are the social conditions in which people are born, live, and work. SDOH offers a more inclusive view of how environment, geographic location, neighborhoods, access to health care, nutrition, socioeconomics, and so on are critical in cardiovascular morbidity and mortality. SDOH will continue to increase in relevance and integration of patient management, thus, applying the information herein to clinical and health systems will become increasingly commonplace. This state-of-the-art review covers the 5 domains of SDOH, including economic stability, education, health care access and quality, social and community context, and neighborhood and built environment. Recognizing and addressing SDOH is an important step toward achieving equity in cardiovascular care. We discuss each SDOH within the context of cardiovascular disease, how they can be assessed by clinicians and within health care systems, and key strategies for clinicians and health care systems to address these SDOH. Summaries of these tools and key strategies are provided.

An Analysis of SNAP Online Purchasing Behavior in California: A Review of the First 7 Months of Program Implementation and Lessons Learned.

PURPOSE: The Supplemental Nutrition Assistance Program (SNAP) Online Purchasing Pilot (OPP) allows for the online purchase of groceries using SNAP benefits. First implemented in California in late April 2020, little is known about program usage. This study assessed initial implementation of SNAP Online in California using SNAP OPP transaction data from April - October 2020. Insights can identify usage differences by demographics, store availability, and rurality to help inform future pilot programs and nutrition initiatives. DESIGN: Using generalized estimating equations, we modeled county-level associations between transactions and county-level demographics, rurality, and retailer availability. SETTING: Transaction data from California's Department of Social Services (CDSS) was linked with publicly-available, county-level demographics. SUBJECTS: Anonymized county-level data on SNAP Online transactions and CalFresh households. MEASURES: The primary outcome was successful SNAP Online food transactions per county. ANALYSIS: Generalized estimating equation models with clustering by county was used. RESULTS: During the first 7 months, median SNAP Online transactions per county per month was 665; 2.7% of total SNAP redemptions were from SNAP Online. Counties with more female-led, disabled, Latino, or Asian CalFresh households had fewer Amazon transactions. Each additional Walmart per county corresponded to 260.7 more Walmart transactions (P < .001). Each percent increase in county zip codes covered by Amazon Fresh corresponded to 45.4 fewer Walmart transactions (P < .05) and 37.3 more Amazon transactions (P < .001). CONCLUSION: Number of stores per county was associated with greater online grocery transactions, whereas rurality was not. County-level SNAP demographics correlated with transactions at particular retailers.

The Utility of rRT-PCR in Diagnosis and Assessment of Case-fatality rates of COVID-19 In the Iranian Population. Positive Test Results are a Marker for Illness Severity.

The utility of PCR-based testing in characterizing patients with COVID-19 and the severity of their disease remains unknown. We performed an observational study among patients presenting to hospitals in Iran who were tested for 2019-nCoV viral RNA by rRT-PCR between the fourth week of February 2020 to the fourth week of March 2020. Frequency of symptoms, comorbidities, intubation, and mortality rates were compared between COVID-19 positive vs. negative patients. 96103 patients were tested from 879 hospitals. 18754 (19.5%) tested positive for COVID-19. Positive testing was more frequent in those 50 years or older. The prevalence of cough (54.5% vs. 49.7%), fever (49.5% vs. 44.7%), and respiratory distress (43.0% vs. 39.0%) but not hypoxia (46.9% vs. 56.7%) was higher in COVID-19 positive vs. negative patients (p<0.001 for all). More patients had cardiovascular diseases (10.6% vs. 9.5%, p<0.001) and type 2 diabetes mellitus (10.8% vs. 8.7%, p<0.001) among COVID-19 positive vs. negative patients. There were fewer patients with cancer (1.1%, vs. 1.4%, p<0.001), asthma (1.9% vs. 2.5%, p<0.001), or pregnant (0.4% vs. 0.6%, =0.001) in COVID-19 positive vs. negative groups. COVID-19 positive vs. negative patients required more intubation (7.7% vs. 5.2%, p<0.001) and had higher mortality (14.6% vs. 6.3%, p<0.001). Odds ratios for death of positive vs negative patients range from 2.01 to 3.10 across all age groups. In conclusion, COVID-19 test-positive vs. test-negative patients had more severe symptoms and comorbidities, required higher intubation, and had higher mortality. rRT-PCR positive result provided diagnosis and a marker of disease severity in Iranians.

Association of cardiovascular risk factor profile and financial hardship from medical bills among non-elderly adults in the United States.

BACKGROUND: While optimal cardiovascular risk factor (CRF) profile is associated with lower mortality, morbidity, and healthcare expenditures among individuals with atherosclerotic cardiovascular disease (ASCVD), less is known regarding its impact on financial hardship from medical bills. Therefore, we assessed whether an optimal CRF profile is associated with a lower burden of financial hardship from medical bills and a reduction in cost-related barriers to health. METHODS: We used a nationally representative sample of adults between 18 and 64 years from the National Health Interview Survey between 2013 and 2017. We assessed ASCVD status and the number of risk factors to categorize the study population into 4 mutually exclusive categories: ASCVD (irrespective of CRF profile) and non-ASCVD with poor, average, and optimal CRF profile. Adjusted logistic regression model was used to determine the association of ASCVD/CRF profile with financial hardship from medical bills and cost-related barriers to health (cost-related medication non-adherence (CRN), foregone/delayed care, and high financial distress). RESULTS: We included 119,388 non-elderly adults, representing 189 million individuals annually across the United States. Non-ASCVD/optimal CRF profile individuals had a lower prevalence of financial hardship and an inability paying medical bills when compared with individuals with ASCVD (24% vs 45% and 6% vs 19%, respectively). Among individuals without ASCVD and an optimal CRF profile, the prevalence of each cost-related barrier to health was <50% compared with individuals with ASCVD. Poor/low income and uninsured individuals within non-ASCVD/average CRF profile strata had a lower prevalence of financial hardship and an inability paying medical bills when compared with middle/high income and insured individuals with ASCVD. Non-ASCVD individuals with optimal CRF profile had the lowest odds of all barriers to health. CONCLUSION: Optimal CRF profile is associated with a lower prevalence of financial hardship from medical bills and cost-related barriers to health despite lower income and lack of insurance.

How Genomics Is Personalizing the Management of Dyslipidemia and Cardiovascular Disease Prevention.

PURPOSE OF THE REVIEW: To summarize advances in genomic medicine and anticipated future directions to improve cardiovascular risk reduction. RECENT FINDINGS: Mendelian randomization and genome-wide association studies have given significant insights into the role of genetics in dyslipidemia and cardiovascular disease (CVD), with over 160 gene loci found to be associated with coronary artery disease to date. This has enabled the creation of genetic risk scores that have demonstrated improved risk prediction when added to clinical markers of CVD risk. Incorporation of genomic data into clinical patient care is on the horizon. Genomic medicine is expected to offer improved risk assessment, determination of targeted treatment strategies, and improved detection of lipid disorders causal to CVD development.