上海市徐汇区常住老年居民慢性病共病模式与日常活动能力的关联

Association between chronic disease comorbidity patterns and activities of daily living among permanent elderly residents in Xuhui District of Shanghai

  • 摘要:
    目的 了解上海市徐汇区社区常住老年居民的慢性病共病模式,分析不同共病模式对老年人日常活动能力的影响。
    方法 基于2015年和2021年上海市徐汇区常住居民健康状况与卫生服务利用调查数据,纳入完整参与2次调查,2015年参与调查期间已满60周岁,自报患慢性病且2次调查中慢性病状况未发生显著变化的常住居民。采用前瞻性队列研究设计,以2015年数据为基线数据,2021年调查数据作为随访数据。由上海市徐汇区疾病预防控制中心分别于2015年和2021年的4—7月于辖区街镇内实施体格检查和问卷调查。采用潜在类别分析(LCA)对11种慢性病的共病模式进行分类。赤池信息准则(AIC)、贝叶斯信息准则(BIC)、样本校正的贝叶斯信息准则(aBIC)值越小,模型拟合越好。通过Barthel指数(BI)与Lawton和Brody制定的工具性日常活动能力量表(LB⁃IADL)评估老年人的日常活动能力(ADL)和工具性日常活动能力(IADL)。用多元线性回归和多元logistic回归分析不同共病模式与日常活动能力的关联。
    结果 共纳入1 608名研究对象。其中671人(41.73%)自报无任何慢性病,11种慢性病患病情况为高血压743人(46.21%)、心脏病148人(9.20%)、脑血管疾病37人(2.30%)、糖尿病或血糖异常310人(19.28%)、其他内分泌系统疾病(非糖尿病)39人(2.43%)、恶性肿瘤37人(2.30%)、慢性肺部疾病16人(1.00%)、痛风8人(0.50%)、肌肉骨骼疾患34人(2.11%)、胃部或消化系统疾病27人(1.68%)、血脂异常15人(0.93%)。运用LCA将老年人慢性病共病模式依次分为1~9种潜在类别组合。模型5(即5种潜在类别)的BIC (7 112.69)和aBIC(6 909.38)最低,AIC(6 768.20)较低,拟合效果最佳,即5种潜在类别是本研究中最佳的类别组合。5种潜在类别以及涉及的研究对象占比分别为无病健康组(41.73%)、高血压组(31.28%)、糖尿病⁃高血压组(19.28%)、重疾组(5.29%)及其他代谢性疾病组(2.43%)。经多元线性回归分析,与无病健康组相比,糖尿病⁃高血压组BI⁃20得分(b=-0.952,95%CI:-1.317~-0.587,P<0.001)、IADL得分(b=-0.744,95%CI:-0.999~-0.489,P<0.001)更低,重疾组(b=-0.644,95%CI:-1.072~-0.217,P=0.003)和高血压组(b=-0.344,95%CI:-0.562~-0.125,P=0.002)的IADL得分更低。经多因素logistic回归分析,与无病健康组相比,糖尿病⁃高血压组老年人的残疾(OR=2.835,95%CI:1.392~5.774)、ADL功能受损(OR=2.470,95%CI:1.637~3.726)及IADL功能受损(OR=1.739,95%CI:1.264~2.392)风险更高;重疾组老年人ADL功能受损风险更高(OR=2.206,95%CI:1.171~4.155)。
    结论 在参与上海市徐汇区常住居民健康状况与卫生服务利用调查的老年人中,糖尿病⁃高血压是关键的慢性病共病模式之一,且对老年人的ADL和IADL均有不良影响。社区健康管理应优先关注糖尿病⁃高血压共病的老年人,加强早期筛查及综合干预,以延缓其ADL和IADL的下降。

     

    Abstract:
    Objective To investigate the patterns of chronic disease comorbidity among permanent elderly residents in Xuhui District of Shanghai, and to analyze the impact of different comorbidity patterns on the elderly’s ability to perform activities of daily living.
    Methods Based on data from the 2015 and 2021 Surveys on Health Status and Health Service Utilization among Permanent Residents in Xuhui District of Shanghai, the study included permanent residents who fully participated in both surveys, were aged 60 years or older at the 2015 survey, self-reported having a chronic disease, and reported no significant changes in their chronic disease status between the two surveys. A prospective cohort study design was adopted, using the 2015 data as the baseline and the 2021 survey data as follow-up data. Physical examinations and questionnaire surveys were conducted by the Shanghai Xuhui District Center for Disease Control and Prevention in the subdistricts and towns within the jurisdiction from April to July in both 2015 and 2021. Latent class analysis (LCA) was used to classify comorbidity patterns across 11 types of chronic diseases. The smaller the values of the Akaike information criterion (AIC), the Bayesian information criterion (BIC), and the sample-corrected Bayesian information criterion (aBIC), the better the model fits. The Barthel index (BI) and the Lawton and Brody Instrumental Activities of Daily Living Scale (LB-IADL) were used to assess the participants’ activities of daily living (ADL) and instrumental activities of daily living (IADL). Multiple linear regression and multivariate logistic regression models were used to analyze the association between different comorbidity patterns and ADL and IADL.
    Results A total of 1 608 study participants were enrolled. Among them, 671 (41.73%) self-reported having no chronic diseases. The prevalence of 11 types of chronic diseases was as follows: hypertension in 743 participants (46.21%), heart disease in 148 participants (9.20%), cerebrovascular disease in 37 participants (2.30%), diabetes or abnormal blood glucose in 310 participants (19.28%), other endocrine system diseases (non-diabetic) in 39 participants (2.43%), malignant tumors in 37 participants (2.30%), chronic lung disease in 16 participants (1.00%), gout in 8 participants (0.50%), musculoskeletal disorders in 34 participants (2.11%), gastric or digestive system diseases in 27 participants (1.68%), and dyslipidemia in 15 participants (0.93%). LCA was used to classify comorbidity patterns of chronic diseases among older adults into 1 to 9 latent category combinations. Model 5 (i.e., 5 latent categories) had the lowest BIC (7 112.69) and aBIC (6 909.38), with a relatively low AIC (6 768.20), indicating the best model fit; thus, five latent categories represented the optimal combination in this study. The five latent categories (and their respective proportions of study participants) were the healthy control group (41.73%), the hypertension group (31.28%), the diabetes-hypertension group (19.28%), the severe illness group (5.29%), and the other metabolic diseases group (2.43%). According to multiple linear regression analyses, compared with the healthy control group, the diabetes-hypertension group had significantly lower BI-20 scores (b=-0.952, 95%CI: -1.317‒ -0.587, P<0.001) and lower IADL scores (b=-0.744, 95%CI: -0.999‒ -0.489, P<0.001); the severe illness group (b=-0.644, 95%CI: -1.072‒ -0.217, P=0.003) and the hypertension group (b=-0.344, 95%CI: -0.562‒ -0.125, P=0.002) had lower IADL scores. According to multivariate logistic regression analyses, compared with the healthy control group, older adults in the diabetes-hypertension group had a higher risk of disability (OR=2.835, 95%CI: 1.392‒5.774), impaired ADL function (OR=2.470, 95%CI: 1.637‒3.726), and impaired IADL function (OR=1.739, 95%CI: 1.264‒2.392); older adults in the severe illness group had a higher risk of impaired ADL function (OR=2.206, 95%CI: 1.171‒4.155).
    Conclusion Among the elderly participating in the Survey on Health Status and Health Service Utilization of Permanent Residents in Xuhui District of Shanghai, diabetes-hypertension is one of the key chronic disease comorbidity combinations and has adverse effects on both ADL and IADL. Community health management should prioritize the elderly with diabetes-hypertension comorbidity, and strengthen early screening and comprehensive interventions to slow the decline in ADL and IADL.

     

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