A study of factors impacting disease based on the Charlson Comorbidity Index in UK Biobank - Wang, Changcong; Zhang, Xinyue; Li, Bai; Mu, Dongmei (Frontiers in Public Health, 2023)
Objective: With advances in medical diagnosis, more people were found to have more than one disease. The damage caused by different diseases varies, so relying solely on the number of diseases to represent comorbidities was not reasonable. Charlson comorbidity index (CCI) was a widely used comorbidity measure and was considered the gold standard indicator for assessing comorbidity in clinical studies. However, CCI’s demographic and behavioral risk factors have not been explored much. Methods: We conducted multivariate logistic regression analysis and restricted cubic splines to examine the influence factors of CCI and the relationship between covariates and risk of CCI, respectively. Our research employed the Multivariate Imputation by Chained Equations method to interpolate missing values. In addition, the CCI score for each participant was calculated based on the inpatient's condition using the International Classification of Diseases, edition 10 (ICD10). Considering the differences in the burden of disease between males and females, a subgroup analysis was eventually conducted by gender. Results: A total of 502,411 participants (229,086 men) with CCI scores ranging from 0 to 98 were included in this study. All covariates differed between CCI groups. High waist-hip ratio (WHR) increases the risk of CCI in both males [OR=19.439, 95% CI= (16.261,23.241)] and females [OR=12.575, 95% CI= (11.005,14.370)], and the effect of WHR on CCI may be greater in males. Associations between age, Body Mass Index (BMI) and WHR, and CCI risk were J-shaped for all participants, males, and females. Concerning the association between TDI and CCI risk, the U-shape was found in all participants, and males and varied to a greater extent in males, but was a J-shape in females. Conclusions: Increased WHR, BMI, and Townsend deprivation index (TDI) were significant predictors of poor health and WHR showed a greater role. The impact of deprivation indices on health showed differences by gender. Although inequalities were declining, the impact of socio-economic factors on health still needed to be considered. Factors might interact with each other; therefore, a comprehensive, rational and robust intervention will be necessary for health.