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To examine the association between potentially modifiable risk factors with cardiovascular disease (CVD) and all-cause mortality and to quantify their population attributable fractions (PAFs) among a sample of Tehran residents. Overall, 8108 participants (3686 men) aged≥30 years, were investigated. To examine the association between risk factors and outcomes, multivariate sex-adjusted Cox proportional hazard regression analysis were conducted, using age as time-scale in two models including general/central adiposity: 1)adjusted for different independent variables including smoking, education, family history of CVD and sex for both outcomes and additionally adjusted for prevalent CVD for all-cause mortality 2)further adjusted for obesity mediators (hypertension, diabetes, lipid profile and chronic kidney disease). Separate models were used including either general or central adiposity. During median follow-up of >10 years, 827 first CVD events and 551 deaths occurred. Both being overweight (hazard ratio (HR), 95%CI: 1.41, 1.18-1.66, PAF 13.66) and obese (1.51, 1.24-1.84, PAF 9.79) played significant roles for incident CVD in the absence of obesity mediators. Predicting CVD, in the presence of general adiposity and its mediators, significant positive associations were found for hypercholesterolemia (1.59, 1.36-1.85, PAF 16.69), low HDL-C (1.21, 1.03-1.41, PAF 12.32), diabetes (1.86, 1.57-2.27, PAF 13.87), hypertension (1.79, 1.46-2.19, PAF 21.62) and current smoking (1.61, 1.34-1.94, PAF 7.57). Central adiposity remained a significant positive predictor, even after controlling for mediators (1.17, 1.01-1.35, PAF 7.55). For all-cause mortality, general/central obesity did not have any risk even in the absence of obesity mediators. Predictors including diabetes (2.56, 2.08-3.16, PAF 24.37), hypertension (1.43, 1.11-1.84, PAF 17.13), current smoking (1.75, 1.38-2.22, PAF 7.71), and low education level (1.59, 1.01-2.51, PAF 27.08) were associated with higher risk, however, hypertriglyceridemia (0.83, 0.68-1.01) and being overweight (0.71, 0.58-0.87) were associated with lower risk. Modifiable risk factors account for more than 70% risk for both CVD and mortality events.

We identified risk patterns associated with incident coronary heart disease (CHD) using survival tree, and compared performance of survival tree versus Cox proportional hazards (Cox PH) in a cohort of Iranian adults. Data on 8,279 participants (3,741 men) aged ≥30 yr were used to analysis. Survival trees identified seven subgroups with different risk patterns using four [(age, non-HDL-C, fasting plasma glucose (FPG) and family history of diabetes] and five [(age, systolic blood pressure (SBP), non-HDL-C, FPG and family history of CVD] predictors in women and men, respectively. Additional risk factors were identified by Cox models which included: family history of CVD and waist circumference (in both genders); hip circumference, former smoking and using aspirin among men; diastolic blood pressure and lipid lowering drug among women. Survival trees and multivariate Cox models yielded comparable performance, as measured by integrated Brier score (IBS) and Harrell’s C-index on validation datasets; however, survival trees produced more parsimonious models with a minimum number of well recognized risk factors of CHD incidence, and identified important interactions between these factors which have important implications for intervention programs and improve clinical decision making.

Objectives: To quantify the Black/Hispanic disparity in COVID-19 mortality in the United States (US). Methods: COVID-19 deaths in all US counties nationwide were analyzed to estimate COVID-19 mortality rate ratios by county-level proportions of Black/Hispanic residents, using mixed-effects Poisson regression. Excess COVID-19 mortality counts, relative to predicted under a counterfactual scenario of no racial/ethnic disparity gradient, were estimated. Results: County-level COVID-19 mortality rates increased monotonically with county-level proportions of Black and Hispanic residents, up to 5.4-fold (≥43% Black) and 11.6-fold (≥55% Hispanic) higher compared to counties with <5% Black and <15% Hispanic residents, respectively, controlling for county-level poverty, age, and urbanization level. Had this disparity gradient not existed, the US COVID-19 death count would have been 92.1% lower (177,672 fewer deaths), making the rate comparable to other high-income countries with substantially lower COVID-19 death counts. Conclusion: During the first 8 months of the SARS-CoV-2 pandemic, the US experienced the highest number of COVID-19 deaths. This COVID-19 mortality burden is strongly associated with county-level racial/ethnic diversity, explaining most US COVID-19 deaths.

[This corrects the article DOI: 10.3389/ijph.2021.1604004.].

Identifying an appropriate set of predictors for the outcome of interest is a major challenge in clinical prediction research. The aim of this study was to show the application of some variable selection methods, usually used in data mining, for an epidemiological study. We introduce here a systematic approach. The P-value-based method, usually used in epidemiological studies, and several filter and wrapper methods were implemented to select the predictors of diabetes among 55 variables in 803 prediabetic females, aged ≥20 years, followed for 10–12 years. To develop a logistic model, variables were selected from a train data set and evaluated on the test data set. The measures of Akaike information criterion (AIC) and area under the curve (AUC) were used as performance criteria. We also implemented a full model with all 55 variables. We found that the worst and the best models were the full model and models based on the wrappers, respectively. Among filter methods, symmetrical uncertainty gave both the best AUC and AIC. Our experiment showed that the variable selection methods used in data mining could improve the performance of clinical prediction models. An R program was developed to make these methods more feasible and visualize the results.

Regarding the second and the third comment; we selected variables using 10-fold cross-validation approach. [...]our decision to include and/or exclude a variable is not based on a single split but the average over 10 test data sets.