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Background Preventive screenings in children encourage maintenance of optimal cardiovascular health, but gaps may exist between recommendations and clinical practice. We evaluated adherence to pediatric guidelines for universal age‐based and risk‐based screening for body mass index, blood pressure, lipids, and blood glucose. Methods and Results We used 2010 to 2018 ambulatory visit data from children aged 2 to 12 years within CAPRICORN (Chicago Area Patient‐Centered Outcomes Research Network), an electronic health record network in Chicago. This study included 87 549 children who attended 197 559 well‐child encounters. Across all encounters, children were 51.5% male and mean (SD) age 6.4 (3.3) years. For each child who attended a well‐child visit and met age and/or risk‐based criteria, receipt of body mass index, blood pressure, lipids, and/or hemoglobin A1c or fasting blood glucose measurements were assessed. We used generalized estimating equations to calculate proportion adherence for each metric overall and stratified by age, sex, race and ethnicity, and insurance status. Universal age‐based screening prevalence (95% CI) per 100 eligible visits was 77.1 (76.8–77.3) for body mass index, 33.4 (33.1–33.7) for blood pressure, and 9.6 (9.3–9.9) for lipids. Risk‐based screening prevalence (95% CI) per 100 eligible visits was 13.9 (12.2–15.9) for blood pressure, 6.9 (6.4–7.5) for lipids, and 13.3 (12.6–14.1) for blood glucose. Conclusions Early screening of cardiovascular health risk factors could lead to earlier interventions, which could alter cardiovascular health trajectories across the lifetime. Low‐to‐moderate levels of adherence to universal age‐based and risk‐based cardiovascular health screening highlight the gap between recommendations and clinical practice, emphasizing the need to understand and address barriers to screening in pediatric populations.

Abstract It is increasingly clear that longitudinal risk factor levels and trajectories are related to risk for atherosclerotic cardiovascular disease (ASCVD) above and beyond single measures. Currently used in clinical care, the Pooled Cohort Equations (PCE) are based on regression methods that predict ASCVD risk based on cross-sectional risk factor levels. Deep learning (DL) models have been developed to incorporate longitudinal data for risk prediction but its benefit for ASCVD risk prediction relative to the traditional Pooled Cohort Equations (PCE) remain unknown. Our study included 15,565 participants from four cardiovascular disease cohorts free of baseline ASCVD who were followed for adjudicated ASCVD. Ten-year ASCVD risk was calculated in the training set using our benchmark, the PCE, and a longitudinal DL model, Dynamic-DeepHit. Predictors included those incorporated in the PCE: sex, race, age, total cholesterol, high density lipid cholesterol, systolic and diastolic blood pressure, diabetes, hypertension treatment and smoking. The discrimination and calibration performance of the two models were evaluated in an overall hold-out testing dataset. Of the 15,565 participants in our dataset, 2170 (13.9%) developed ASCVD. The performance of the longitudinal DL model that incorporated 8 years of longitudinal risk factor data improved upon that of the PCE [AUROC: 0.815 (CI 0.782–0.844) vs 0.792 (CI 0.760–0.825)] and the net reclassification index was 0.385. The brier score for the DL model was 0.0514 compared with 0.0542 in the PCE. Incorporating longitudinal risk factors in ASCVD risk prediction using DL can improve model discrimination and calibration.

Background: Social and psychosocial determinants are associated with cardiovascular health (CVH). Objectives: To quantify the contributions of social and psychosocial factors to racial/ethnic differences in CVH. Methods: In the Multi-Ethnic Study of Atherosclerosis and Mediators of Atherosclerosis in South Asians Living in America cohorts, Kitagawa-Blinder-Oaxaca decomposition quantified the contributions of social and psychosocial factors to differences in mean CVH score (range 0–14) in Black, Chinese, Hispanic, or South Asian compared with White participants. Results: Among 7,978 adults (mean age 61 [SD 10] years, 52 % female), there were 1,892 Black (mean CVH score for decomposition analysis 7.96 [SD 2.1]), 804 Chinese (CVH 9.69 [1.8]), 1,496 Hispanic (CVH 8.00 [2.1]), 1,164 South Asian (CVH 9.16 [2.0]), and 2,622 White (CVH 8.91 [2.1]) participants. The factors that were associated with the largest magnitude of explained differences in mean CVH score were income for Black participants (if mean income in Black participants were equal to White participants, Black participants’ mean CVH score would be 0.14 [SE 0.05] points higher); place of birth for Chinese participants (if proportion of US-born and foreign-born individuals among Chinese adults were equivalent to White participants, Chinese participants’ mean CVH score would be 0.22 [0.10] points lower); and education for Hispanic and South Asian participants (if educational attainment were equivalent to White participants, Hispanic and South Asian participants’ mean CVH score would be 0.55 [0.11] points higher and 0.37 [0.11] points lower, respectively). Conclusions: In these multiethnic US cohorts, social and psychosocial factors were associated with racial/ethnic differences in CVH.

Key points Question Is statin use prior to hospital admission for COVID-19 associated with reducing severe inpatient outcomes? Findings In this observational study using electronic health records from a multi-hospital health system in Chicago, we used robust statistical methods to account for confounding and found that adults 40 years or older who were prescribed statins prior to admission for COVID-19 had minimally lower rates of intubation and admission to the intensive care unit. However, inpatient mortality was comparable between statins users and non-users. Meaning Consistent with current COVID-19 treatment guidelines, we did not find evidence supporting the utilization of statins for clinically significant reduction in severe inpatient COVID-19 outcomes.

Abstract Background Mindfulness-based interventions have been shown to improve psychological outcomes including stress, anxiety, and depression in general population studies. However, effectiveness has not been sufficiently examined in racially and ethnically diverse community-based settings. We will evaluate the effectiveness and implementation of a mindfulness-based intervention on depressive symptoms among predominantly Black women at a Federally Qualified Health Center in a metropolitan city. Methods In this 2-armed, stratified, individually randomized group-treated controlled trial, 274 English-speaking participants with depressive symptoms ages 18–65 years old will be randomly assigned to (1) eight weekly, 90-min group sessions of a mindfulness-based intervention (M-Body), or (2) enhanced usual care. Exclusion criteria include suicidal ideation in 30 days prior to enrollment and regular (>4x/week) meditation practice. Study metrics will be assessed at baseline and 2, 4, and 6 months after baseline, through clinical interviews, self-report surveys, and stress biomarker data including blood pressure, heart rate, and stress related biomarkers. The primary study outcome is depressive symptom score after 6 months. Discussion If M-Body is found to be an effective intervention for adults with depressive symptoms, this accessible, scalable treatment will widely increase access to mental health treatment in underserved, racial/ethnic minority communities. Trial registration ClinicalTrials.gov NCT03620721. Registered on 8 August 2018.

Abstract Background Geographic heterogeneity in COVID-19 outcomes in the United States is well-documented and has been linked with factors at the county level, including sociodemographic and health factors. Whether an integrated measure of place-based risk can classify counties at high risk for COVID-19 outcomes is not known. Methods We conducted an ecological nationwide analysis of 2,701 US counties from 1/21/20 to 2/17/21. County-level characteristics across multiple domains, including demographic, socioeconomic, healthcare access, physical environment, and health factor prevalence were harmonized and linked from a variety of sources. We performed latent class analysis to identify distinct groups of counties based on multiple sociodemographic, health, and environmental domains and examined the association with COVID-19 cases and deaths per 100,000 population. Results Analysis of 25.9 million COVID-19 cases and 481,238 COVID-19 deaths revealed large between-county differences with widespread geographic dispersion, with the gap in cumulative cases and death rates between counties in the 90th and 10th percentile of 6,581 and 291 per 100,000, respectively. Counties from rural areas tended to cluster together compared with urban areas and were further stratified by social determinants of health factors that reflected high and low social vulnerability. Highest rates of cumulative COVID-19 cases (9,557 [2,520]) and deaths (210 [97]) per 100,000 occurred in the cluster comprised of rural disadvantaged counties. Conclusions County-level COVID-19 cases and deaths had substantial disparities with heterogeneous geographic spread across the US. The approach to county-level risk characterization used in this study has the potential to provide novel insights into communicable disease patterns and disparities at the local level.

Background Cardiovascular disease mortality related to heart failure (HF) is rising in the United States. It is unknown whether trends in HF mortality are consistent across geographic areas and are associated with state‐level variation in cardiovascular health (CVH). The goal of the present study was to assess regional and state‐level trends in cardiovascular disease mortality related to HF and their association with variation in state‐level CVH. Methods and Results Age‐adjusted mortality rates (AAMR) per 100 000 attributable to HF were ascertained using the Centers for Disease Control and Prevention's Wide‐Ranging Online Data for Epidemiologic Research from 1999 to 2017. CVH at the state‐level was quantified using the Behavioral Risk Factor Surveillance System. Linear regression was used to assess temporal trends in HF AAMR were examined by census region and state and to examine the association between state‐level CVH and HF AAMR. AAMR attributable to HF declined from 1999 to 2011 and increased between 2011 and 2017 across all census regions. Annual increases after 2011 were greatest in the Midwest (β=1.14 [95% CI, 0.75, 1.53]) and South (β=0.96 [0.66, 1.26]). States in the South and Midwest consistently had the highest HF AAMR in all time periods, with Mississippi having the highest AAMR (109.6 [104.5, 114.6] in 2017). Within race‒sex groups, consistent geographic patterns were observed. The variability in HF AAMR was associated with state‐level CVH (P

Background Given the increasing prevalence of diabetes mellitus (DM) in the United States, estimating the effects of population‐level increases in obesity on incident DM has substantial implications for public health policy. Therefore, we determined the population attributable fraction, which accounts for the prevalence and excess risk of DM associated with obesity. Methods and Results We included non‐Hispanic White, non‐Hispanic Black, and Mexican American participants without DM at baseline from MESA (Multi‐Ethnic Study of Atherosclerosis) with available data on body mass index and key covariates from 2000 to 2017 to calculate unadjusted and adjusted (age, study site, physical activity, diet, income, and education level) hazard ratios (HR) for obesity‐attributable DM. We calculated national age‐adjusted prevalence estimates for obesity using data from NHANES (National Health and Nutrition Examination Survey) in 4 pooled cycles (2001–2016) among adults with similar characteristics to MESA participants. Last, we calculated unadjusted and adjusted population attributable fractions from the race/ethnic and sex‐specific HR and prevalence estimates. Of 4200 MESA participants, the median age was 61 years, 46.8% were men, 53.9% were non‐Hispanic White, 32.9% were non‐Hispanic Black, and 13.3% were Mexican. Among MESA participants, incident DM occurred in 11.6% over a median follow‐up of 9.2 years. The adjusted HR for obesity‐related DM was 2.7 (95% CI, 2.2–3.3). Adjusted population attributable fractions were 0.35 (95% CI, 0.29–0.40) in 2001 to 2004 and 0.41 (95% CI, 0.36–0.46) in 2013 to 2016, and greatest among non‐Hispanic White women. Conclusions The contribution of obesity towards DM in the population remains substantial and varies significantly by race/ethnicity and sex, highlighting the need for tailored public health interventions to reduce obesity. Registration URL: https://www.clinicaltrials.gov; Unique identifiers: NC00005487, NCT00005154.

Summary: Researchers are often interested in estimating the causal effects of sustained treatment strategies, i.e., of (hypothetical) interventions involving time-varying treatments. When using observational data, estimating those effects requires adjustment for confounding. However, conventional regression methods cannot appropriately adjust for confounding in the presence of treatment-confounder feedback. In contrast, estimators derived from Robins's g-formula may correctly adjust for confounding even if treatment-confounder feedback exists. The package gfoRmula implements in R one such estimator: the parametric g-formula. This estimator can be used to estimate the effects of binary or continuous time-varying treatments as well as contrasts defined by static or dynamic, deterministic, or random interventions, as well as interventions that depend on the natural value of treatment. The package accommodates survival outcomes as well as binary or continuous outcomes measured at the end of follow-up. This paper describes the gfoRmula package, along with motivating background, features, and examples. The Bigger Picture: Causal inference is a core task of data science. When data from randomized experiments are not available, data analysts often rely on nonexperimental (observational) data to estimate causal effects. The parametric g-formula is a statistical method to estimate the causal effects of sustained treatment strategies from observational data with time-varying treatments, confounders, and outcomes. Although this methodology was introduced in the 1980s, it has not been widely used due to the lack of open-source software. This article presents the gfoRmula package, an implementation of the parametric g-formula in R. The aim of this software is to facilitate the application of the parametric g-formula to complex, observational data to answer causal questions. Furthermore, this package helps provide a way to compare the performance of the parametric g-formula to other methods in the causal inference literature.

BackgroundEmulation of the "target trial" (TT), a hypothetical pragmatic randomized controlled trial (RCT), using observational data can be used to mitigate issues commonly encountered in comparative effectiveness research (CER) when randomized trials are not logistically, ethically, or financially feasible. However, cardiovascular (CV) health research has been slow to adopt TT emulation. Here, we demonstrate the design and analysis of a TT emulation using electronic health records to study the comparative effectiveness of the addition of a disease-modifying anti-rheumatic drug (DMARD) to a regimen of methotrexate on CV events among rheumatoid arthritis (RA) patients.MethodsWe used data from an electronic medical records-based cohort of RA patients from Northwestern Medicine to emulate the TT. Follow-up began 3 months after initial prescription of MTX (2000-2020) and included all available follow-up through June 30, 2020. Weighted pooled logistic regression was used to estimate differences in CVD risk and survival. Cloning was used to handle immortal time bias and weights to improve baseline and time-varying covariate imbalance.ResultsWe identified 659 eligible people with RA with average follow-up of 46 months and 31 MACE events. The month 24 adjusted risk difference for MACE comparing initiation vs non-initiation of a DMARD was -1.47% (95% confidence interval [CI]: -4.74, 1.95%), and the marginal hazard ratio (HR) was 0.72 (95% CI: 0.71, 1.23). In analyses subject to immortal time bias, the HR was 0.62 (95% CI: 0.29-1.44).ConclusionIn this sample, we did not observe evidence of differences in risk of MACE, a finding that is compatible with previously published meta-analyses of RCTs. Thoughtful application of the TT framework provides opportunities to conduct CER in observational data. Benchmarking results of observational analyses to previously published RCTs can lend credibility to interpretation.

BackgroundAdults in rural counties in the United States (US) experience higher rates broadly of cardiovascular disease (CVD) compared with adults in urban counties. Mortality rates specifically due to heart failure (HF) have increased since 2011, but estimates of heterogeneity at the county-level in HF-related mortality have not been produced. The objectives of this study were 1) to quantify nationwide trends by rural-urban designation and 2) examine county-level factors associated with rural-urban differences in HF-related mortality rates.Methods and findingsWe queried CDC WONDER to identify HF deaths between 2011-2018 defined as CVD (I00-78) as the underlying cause of death and HF (I50) as a contributing cause of death. First, we calculated national age-adjusted mortality rates (AAMR) and examined trends stratified by rural-urban status (defined using 2013 NCHS Urban-Rural Classification Scheme), age (35-64 and 65-84 years), and race-sex subgroups per year. Second, we combined all deaths from 2011-2018 and estimated incidence rate ratios (IRR) in HF-related mortality for rural versus urban counties using multivariable negative binomial regression models with adjustment for demographic and socioeconomic characteristics, risk factor prevalence, and physician density. Between 2011-2018, 162,314 and 580,305 HF-related deaths occurred in rural and urban counties, respectively. AAMRs were consistently higher for residents in rural compared with urban counties (73.2 [95% CI: 72.2-74.2] vs. 57.2 [56.8-57.6] in 2018, respectively). The highest AAMR was observed in rural Black men (131.1 [123.3-138.9] in 2018) with greatest increases in HF-related mortality in those 35-64 years (+6.1%/year). The rural-urban IRR persisted among both younger (1.10 [1.04-1.16]) and older adults (1.04 [1.02-1.07]) after adjustment for county-level factors. Main limitations included lack of individual-level data and county dropout due to low event rates (ConclusionsDifferences in county-level factors may account for a significant amount of the observed variation in HF-related mortality between rural and urban counties. Efforts to reduce the rural-urban disparity in HF-related mortality rates will likely require diverse public health and clinical interventions targeting the underlying causes of this disparity.