Relative Risk ( RR), Hazard Ratio ( HR), and Odds Ratio ( OR) are commonly used statistical measures in the field of public health to assess the magnitude of the effect of exposure factors on outcomes. These indicators have different calculation principles and implications in public health. However, a few researchers misused or misinterpreted RR, HR, and OR values when interpreting study results. Therefore, this article explores the relationships and differences among these measures, as well as the correct selection and application of RR, HR, and OR in both cohort study and case-control study.

Relative Risk ( RR), Hazard Ratio ( HR), and Odds Ratio ( OR) are commonly used statistical measures in the field of public health to assess the magnitude of the effect of exposure factors on outcomes. These indicators have different calculation principles and implications in public health. However, a few researchers misused or misinterpreted RR, HR, and OR values when interpreting study results. Therefore, this article explores the relationships and differences among these measures, as well as the correct selection and application of RR, HR, and OR in both cohort study and case-control study.

Objective:To analyze the association between air pollution, genetic susceptibility, and the risk of all-cause mortality and cardiovascular outcomes in patients with atrial fibrillation (AF).Methods:AF patients aged between 40-69 years old registered in the United Kingdom Biobank from 2006 to 2010 were included. After excluding those lost to follow-up or with incomplete data during follow-up, 5 814 subjects were analyzed. Long-term exposure to air pollution was estimated at the geocoded residential address of each participant. Genetic risk scores for all-cause mortality, cardiovascular disease, heart failure, myocardial infarction, and stroke were constructed separately for each object to assess the corresponding genetic susceptibility. The Cox proportional hazards model was used to analyze the association between air pollution, genetic susceptibility, and the risk of all-cause mortality and cardiovascular outcomes in AF patients.Results:During a median follow-up of 12.4 years, there were 929 of all-cause mortality (15.98%) and 1 772 of cardiovascular events (30.48%). Multivariable-adjusted analyses revealed that higher exposure to PM 2.5, PM 10, NO x, and NO 2 was associated with an increased risk of cardiovascular disease mortality, heart failure, myocardial infarction, and stroke, with hazard ratios ( HRs) ranging from 1.26 to 1.48. Specifically, for each interquartile range ( IQR) increase in PM 2.5 exposure, the HRs for the outcomes mentioned above were 1.33 (95% CI: 1.14-1.54), 1.42 (95% CI: 1.31-1.54), 1.46 (95% CI: 1.30-1.64), and 1.43 (95% CI: 1.27-1.61), respectively. Both NO x and NO 2 exposures were associated with a 9% increased risk of all-cause mortality per IQR increment, with corresponding HRs of 1.09 (95% CI: 1.02-1.17) and 1.09 (95% CI: 1.01-1.17), respectively. Individuals with high genetic susceptibility to AF had a higher risk of myocardial infarction and stroke compared to those with low genetic susceptibility, with corresponding HRs of 1.39 (95% CI: 1.04-1.87) and 1.46 (95% CI: 1.09-1.95), respectively. Compared to AF patients with low air pollution exposure, those with high air pollution exposure have adjusted population attributable fractions of up to 33.57% (95% CI: 17.87%-46.26%) for cardiovascular mortality, 28.61% (95% CI: 20.67%-35.75%) for heart failure, 33.35% (95% CI: 20.97%-43.79%) for myocardial infarction, and 42.29% (95% CI: 30.05%-52.71%) for stroke. Furthermore, there was an additive interaction between PM 2.5, NO x, and NO 2 exposure and high genetic susceptibility on the incidence of myocardial infarction. An additive interaction was also observed between NO x, NO 2 exposure, and high genetic susceptibility on the incidence of heart failure (all P<0.05). Conclusions:Both air pollution and genetic susceptibility increase the risk of all-cause mortality and cardiovascular outcomes in AF patients.

Objective To establish a simple model for arteriosclerosis(AS)screening to provide a viable tool for the timely identification of AS risk among residents aged 40～65 years.Methods Data were obtained from the Sleep and Chronic Diseases Program in Fuquan City.The original dataset was divided into a training subset and a validation subset(80%:20%).LASSO and logistic regression models were used to screen variables,perform multivariate regression analyses.Internal validation was performed using the Bootstrap method.Nomogram Plot was constructed,and risk score thresholds were determined based on ROC curves to classify high-risk populations.Results RS Model was established to include age,gender,napping,sleep efficiency,sleep disorders,hyperten-sion and diabetes,with AUC=74.80%and a model risk score threshold=84.20.PHC Model was established to include age,gender,napping,sleep efficiency,systolic blood pressure,fasting blood glucose,and pulse variables,with AUC=82.80%and a risk score threshold of 78.00.Decision curves showed that both models performed well in terms of calibration and actual benefits for health management.Conclusion The two AS screening models exhibit acceptable accuracy and differentiation.Therefore,it can be applied in residents'self-health management and in primary care organizations'screening work in a large scale.

Objective:To analyze the association between body health score and the risk of hypertension among health examination population aged 40-65 years.Methods:This study was a cross-sectional study, and 1 104 people aged 40-65 years who underwent physical examination at the Physical Examination Centre of the First People′s Hospital of Fuquan City from March to November 2022 were selected. Clinical data, such as general information, physical examination, body composition and history of hypertension diseases, were collected. The body health score was reported by the Xiaomi Body Fat Scale′s accompanying exercise health software, and was calculated by combining body fat, water and other body composition data. The association between body health score and the risk of hypertension was analyzed using restricted cubic spline regression models, while a sensitivity analysis and sex-stratified analyses were performed. Multivariate logistic regression combined with stratified analysis was used to explore the association between dimensions of body composition and the risk of hypertension.Results:The body health score was significantly lower in hypertensive patients than in non-hypertensive patients among the 1 104 health examination population [52.0(30.0) vs 69.0(35.8) points] ( Z=-8.547, P<0.001). The lower the body health score, the higher the risk of hypertension ( χ2=18.48, PNonlinear<0.001). In the total population, high body mass index was associated with an increased risk of hypertension ( OR=1.744, 95% CI: 1.104-2.765), high protein content was associated with a reduced risk of hypertension ( OR=0.587, 95% CI: 0.344-0.982) (both P<0.05). Gender-stratified analyses showed that high protein content was associated with a reduced risk of hypertension only in men ( OR=0.233, 95% CI: 0.080-0.592) ( P=0.004). High body mass index was positively associated with the risk of hypertension when the body health score was ≥60 points ( OR=2.378, 95% CI: 1.255-4.542) ( P=0.008). High visceral adiposity index (VAI) was positively associated with the risk of hypertension when the body health score was <60 points ( OR=4.395, 95% CI: 1.466-13.620), and high protein content was negatively associated with the risk of hypertension ( OR=0.255, 95% CI: 0.091-0.638) (all P<0.05). Conclusions:Health examination population aged 40-65 years with lower scores of physical health are more likely to have a risk of hypertension. Men should pay attention to the impact of body protein in hypertension risk prevention and control. The effect of body mass index should be noted when body health scores are ≥60 points, and the effect of VAI and body protein should be considered when body health scores are <60 points.