Status and annual change of vulnerability among middle-aged and elderly people with cardiometabolic multimorbidity in a division of Xinjiang Production and Construction Corps, 2017 – 2021: baseline and follow-up data analysis - Ziqi WANG; Jieting CHEN; Mei ZHANG; Mingxia JING (Zhongguo gonggong weisheng)
ObjectiveTo analyze the status, annual change, and major factors of vulnerability among middle-aged and elderly people with cardiometabolic multimorbidity (CMM) in a division of the Xinjiang Production and Construction Corps from 2017 to 2021 to provide a basis for population health interventions. MethodsStudy data were collected from 839 patients aged ≥ 45 years with cardiovascular metabolic disease (CMD) who participated in a baseline survey in July 2017 and 3 follow-up surveys in July 2018, July 2019, and July 2021 in 3 urban and 2 regimental communities in a division of the Xinjiang Production and Construction Corps. Based on the vulnerability conceptual framework of "risk factors-resilience-social service system", a vulnerability assessment index system for the CMM population was constructed. The entropy weighting method was used to dynamically evaluate the vulnerability level of the CMM population from 2017 to 2021. Kernel density estimation was used to analyze the spatial and temporal evolution characteristics of the vulnerability level of the CMM population in different community types. The contribution rate of each vulnerability dimension was calculated to explore the vulnerability level and its hierarchical changes in different comorbidity combinations. The obstacle degree model was introduced to identify the influencing factors of vulnerability. ResultsThe mean scores of overall and risk factor vulnerability of the CMM population increased from – 0.190 ± 0.106 and 0.021 ± 0.030 in 2017 to – 0.101 ± 0.019 and 0.045 ± 0.039 in 2021. The mean scores of health resilience and health service system vulnerability decreased from 0.087 ± 0.069 and 0.125 ± 0.070 in 2017 to 0.084 ± 0.073 and 0.063 ± 0.077 in 2021, respectively. The results of the spatial and temporal analysis showed a decreasing order of absolute difference in annual peak vulnerability scores and an increasing order of variation in annual peak nuclear density of vulnerability scores for the CMM population from urban, suburban, and rural communities. The average percentages of the combined contribution of the three-dimensional indicators to the overall vulnerability of the CMM population over the four-year period were 52.29% for health care, 41.84% for health resilience, and 5.87% for health risk, respectively. Vulnerability scores increased annually from 2017 to 2021 for CMM populations with any of the CMD combinations, with the vulnerability level changing for CMM populations with 63.64% of all CMD combinations and unchanged for CMM populations with 36.36% of all CMD combinations. In terms of high and low vulnerability, there was no significant change over time for CMM populations with any of the CMD combinations, except for CMM populations with combinations of ischemic heart disease and stroke, hypertension and diabetes, ischemic heart disease and hypertension and diabetes, and ischemic heart disease and hypertension and stroke. The results of the influence factor analysis showed that at the dimensional level, the indicators of health resilience were of the highest contribution (60.99%) to the vulnerability of the CMM population, followed by the contribution of health care indicators (34.54%) and health risk indicators (13.42%); while the main contributing secondary indicators included the level of education, frequency of health care follow-ups, type of residential community, family size, family income, and with application approval of chronic disease severity assessment. ConclusionsThe vulnerability level of the CMM population in a division of the Xinjiang Production and Construction Corps increased annually from 2017 to 2021, among which the CMM population in urban communities is a major target of concern. The vulnerability of the CMM population in this area is mainly influenced by the health resilience and health care system.