Complex patterns and determinants of regional multiple chronic conditions across the United States

Abstract

Noncommunicable chronic diseases (NCDs) are a rapidly growing global public health concern, posing substantial challenges to healthcare systems. The presence of multiple (≥2) chronic conditions (MCC) exacerbates these challenges. In this study, we constructed an integrated MCC network to comprehensively evaluate the impact of NCD prevalence and associated factors on MCC patterns. We identified four distinct MCC patterns, each with its unique set of associated risk factors. Firstly, we found that race, sedentary lifestyles, and smoking habits were significant contributors to the co-occurrence of diabetes, chronic kidney disease, and cancer. Secondly, smoking habits and mental health were identified as risk factors associated with the clusters of high cholesterol, hypertension, coronary heart disease, and arthritis. Furthermore, the comorbidity of chronic obstructive pulmonary disease and asthma was affected by socioeconomic status, smoking habits, and educational attainment, and a noteworthy reciprocal relationship existed between these two MCC combinations. Thirdly, the combination of asthma and obesity is associated with risk factors such as mental health, smoking habits, sedentary lifestyles, and binge drinking behaviors. Finally, the pattern of depression-stroke comorbidity was influenced by risk factors including mental health, age, and sleep duration. Our findings hold valuable implications for healthcare system optimization, offering a pathway to mitigate the escalating burden of NCDs. Additionally, they provide a foundation for scientific strategies aimed at the joint prevention and management of these complex conditions, ultimately enhancing public health and safety on a global scale.

Keywords: multiple chronic conditions; network analysis; noncommunicable chronic diseases; public health; risk factors.

Fig. 1.

The age-adjusted distribution of the prevalence of NCDs among adults aged 18 years and older in 2020.

Fig. 2.

Bivariate interaction analysis of NCD prevalence; a) Pearson correlation heatmap. b) Monomodal network diagram.

Fig. 3.

The influence of covariates on the prevalence of NCDs; a) OLS coefficient heatmap. b) Multimodal network diagram.

Fig. 4.

Schematic representation of multimodal network construction.

Fig. 5.

Schematic representation of MCC network construction.

Fig. 6.

Conceptual mapping of disease roles in MCC networks.