Functional status and annual hospitalization in multimorbid and non-multimorbid older adults: a cross-sectional study in Southern China

Wang, Xiaoxiao; Chen, Zhaobin; Chen, Xu-Jia; Huang, Lingling; Song, Xiaoyue; Wu, Xiao Y.; Fu, Li-Ying; Wang, Pei‐Xi

doi:10.1186/s12955-018-0864-4

Cited by 16 publications

(18 citation statements)

References 42 publications

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“… 27 Most of the studies (n = 46, 60.5%) had a primary purpose of reporting the prevalence and patterns of multimorbidity. Of the total 76 studies, 52 (68.4%) were conducted in only six middle-income countries: Brazil, 27 – 42 China, 43 – 54 South Africa, 25 , 26 , 55 – 64 India, 65 – 72 Mexico 73 , 74 and Iran. 75 , 76 Studies based on multicountry data (n = 11) were based on data obtained from the World Health Organization’s (WHO’s) Study on Global Ageing and Adult Health (SAGE) and World Health Surveys.…”

Section: Resultsmentioning

confidence: 99%

Multimorbidity of chronic non-communicable diseases in low- and middle-income countries: A scoping review

et al. 2020

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Background: Multimorbidity is rising in low- and middle-income countries (LMICs). However, the evidence on its epidemiology from LMICs settings is limited and the available literature has not been synthesized as yet. Objectives: To review the available evidence on the epidemiology of multimorbidity in LMICs. Methods: PubMed, Scopus, PsycINFO and Grey literature databases were searched. We followed the PRISMA-ScR reporting guideline. Results: Of 33, 110 articles retrieved, 76 studies were eligible for the epidemiology of multimorbidity. Of these 76 studies, 66 (86.8%) were individual country studies. Fifty-two (78.8%) of which were confined to only six middle-income countries: Brazil, China, South Africa, India, Mexico and Iran. The majority (n = 68, 89.5%) of the studies were crosssectional in nature. The sample size varied from 103 to 242, 952. The largest proportion (n = 33, 43.4%) of the studies enrolled adults. Marked variations existed in defining and measuring multimorbidity. The prevalence of multimorbidity in LMICs ranged from 3.2% to 90.5%. Conclusion and Recommendations: Studies on the epidemiology of multimorbidity in LMICs are limited and the available ones are concentrated in few countries. Despite variations in measurement and definition, studies consistently reported high prevalence of multimorbidity. Further research is urgently required to better understand the epidemiology of multimorbidity and define the best possible interventions to improve outcomes of patients with multimorbidity in LMICs.

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Section: Resultsmentioning

confidence: 99%

Multimorbidity of chronic non-communicable diseases in low- and middle-income countries: A scoping review

et al. 2020

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“…Specifically, the independent contributions of sociodemographic characteristics, health-related variables, and sleep quality to central obesity were assessed using clustered multiple logistic regression analyses [24,25]. There was the possibility of multi-directional links between the three clusters and the dependent variable as shown in Consequently, variables in the prior cluster might have an impact on variables in the subsequent cluster [24]. We determined the final regression model in three phases, the details were shown in previous studies [24].…”

Section: Discussionmentioning

confidence: 99%

“…There was the possibility of multi-directional links between the three clusters and the dependent variable as shown in Consequently, variables in the prior cluster might have an impact on variables in the subsequent cluster [24]. We determined the final regression model in three phases, the details were shown in previous studies [24].…”

Section: Discussionmentioning

confidence: 99%

Association between sleep quality and central obesity among southern Chinese reproductive-aged women

Liu

Guo

et al. 2020

Preprint

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Background Females are more prone to complain about sleep problems. Poor sleep quality in this group may make them more vulnerable to suffering obesity and other diseases, which can seriously affect their fitness and quality of life. Nevertheless, the connections between sleep quality and central obesity among reproductive-aged women are not clear. The aims of this study were to explore the association between sleep quality as measured by the Pittsburgh Sleep Quality Index (PSQI) and central obesity among Chinese reproductive-aged women and to identify the independent contributions of sociodemographic characteristics, health-related factors, and sleep quality to central obesity. Methods In this cross-sectional survey, 2449 Chinese reproductive-aged women aged 18-49 years were studied. Sleep quality was assessed using the Chinese version of the PSQI. Central obesity as the outcome of interest was a binary variable; women were categorized as with versus without central obesity. Clustered logistic regression was used to analyze the independent contribution of sleep quality to central obesity.Results The risk of central obesity increased significantly with poor sleep quality (assessed by global PSQI score) (adjusted OR(Odds Ratio)=2.20 per SD increase; P=0.004), increased sleep disturbance score (adjusted OR=1.11 per SD increase; P=0.042) and decreased subjective sleep quality score (adjusted OR=0.81 per SD increase; P<0.001). The independent contribution of sleep quality was 9.9%, less than those of sociodemographic (73.3%) and health-related (16.8%) variables. Among complaints related to sleep disturbance, the inability to breathe comfortably and having bad dreams showed significant associations with central obesity. Conclusions There exists some degree of correlation between sleep quality and central obesity amongChinese reproductive-aged women. Understanding this link has public health relevance because of the important role sleep plays in health. It also has some implications for the discovery of novel strategies to prevent central obesity and the implementation of weight maintenance programs in this population group.

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“…Speci cally, clustered multiple linear regression analyses [18][19][20]were used to explore the impacts of socio-demographic characteristics, health-related factors, and Family APGAR (3 clusters based on the nature of the study variables and study purpose) on each domain of HRQOL. There was the possibility of multidirectional links among the 3 clusters of independent variables and the dependent variable.…”

Section: Discussionmentioning

confidence: 99%

Family Function and Health-Related Quality of Life Among Low-Income Residents with Hypertension: A Cross-Sectional Study in Central China

Wang¹,

Zhang²,

Zhang³

et al. 2020

Preprint

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Backgroud Family is the most important social support available to hypertensive patients, which may affect their health-related quality of life (HRQOL) and health outcomes. However, data on the relationship between family function and HRQOL among hypertensive residents are sparse, particularly for those low-income residents with hypertension. In this study, we aimed to examine the effects of family function on physical and mental health among low-income residents with hypertension in Central China, and to explore the independent contributions of socio-demographic variables, health-related factors and family function to each domain of HRQOL. Methods This cross-sectional, community-based survey, studied 295 low-income residents with hypertension. Family function was measured using the Family APGAR Index (Adaptation, Partnership, Growth, Affection, and Resolve). HRQOL was assessed using the SF-12 Questionnaire. Clustered multiple linear regressions were used to analyze the independent contributions of family function to each domain of HRQOL.Results 35.90% of low-income hypertensive residents had highly functional family. Multiple regression analyses showed that those with higher Family APGAR scores obtained higher general health (β=0.168, P=0.008), bodily pain(β=0.167, P=0.008), mental component summary (β=0.330, P<0.001), role limitations due to emotional problems (β=0.138, P=0.022), mental health (β=0.302, P<0.001), vitality (β=0.264, P<0.001), and social function (β=0.312, P<0.001) scores, whereas no independent contribution of family function to physical component summary was observed. On these subscales, the independent contributions of family function accounted for 15.75%, 14.29%, 39.63%, 5.47%, 94.67%, 51.92% and 57.58%, respectively (more than all socio-demographic and health-related variables in the MH, VT and SF domains). Conclusion Family function was significantly associated with HRQOL among low-income hypertensive residents. This relation holds for both mental component summary and each of its individual domains, as well as partial physical domains.

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Functional status and annual hospitalization in multimorbid and non-multimorbid older adults: a cross-sectional study in Southern China

Cited by 16 publications

References 42 publications

Multimorbidity of chronic non-communicable diseases in low- and middle-income countries: A scoping review

Multimorbidity of chronic non-communicable diseases in low- and middle-income countries: A scoping review

Association between sleep quality and central obesity among southern Chinese reproductive-aged women

Family Function and Health-Related Quality of Life Among Low-Income Residents with Hypertension: A Cross-Sectional Study in Central China

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