Xinye Zou, Siyu Zou, Ruolin Zhang, Kefan Xue, Yi Guo, Hewei Min, Yibo Wu, Xinying Sun
{"title":"中国生活方式因素与多病风险的关系:一项全国代表性研究","authors":"Xinye Zou, Siyu Zou, Ruolin Zhang, Kefan Xue, Yi Guo, Hewei Min, Yibo Wu, Xinying Sun","doi":"10.1007/s11482-024-10291-3","DOIUrl":null,"url":null,"abstract":"<div><p>Multimorbidity significantly impacts health, well-being, and the economy; therefore, exploring notable factors associated with multimorbidity across all age groups is critical. For this investigation, we focused on the relationship between four lifestyle factors and multimorbidity risk. We recruited 11,031 Chinese citizens aged ≥ 12 years from 31 provinces between July 2021 and September 2021 using a quota sampling strategy to ensure that the socioeconomic characteristics (sex, age, rural–urban distribution) of those participating in this research were representative of national demographics. In the first stage, multivariable logistic regression models were utilized as a means of investigating the relationship between lifestyle factors and multimorbidity. Then, a multinomial logistic regression model was used with the aim of examining the Healthy Lifestyle Profile (HLP) related to the number of chronic diseases. Multivariable logistic regression models assessed the interaction effects and joint association among the four lifestyle factors. Overall, 18% of the participants had at least one disease, and 5.9% had multimorbidity. Approximately two-thirds of the participants were physically inactive, 40% had consumed alcohol, 39% were underweight or overweight, and 20% were or had been smokers. Participants who maintained one HLP showed a 34% lower multimorbidity risk (adjusted OR, 0.66; 95% CI, 0.48 to 0.92), while participants who maintained 4 HLP showed a 73% lower multimorbidity risk (adjusted OR, 0.27; 95% CI, 0.17 to 0.43), as compared to those who had 0 HLP. The joint association analysis revealed that participants with all four healthy lifestyle factors had 0.92 times lower odds of multimorbidity (95% CI: 0.90, 0.94) in comparison with the all-unhealthy reference cluster. Notably, individuals with a combination of healthy smoking status and healthy body weight had the highest minimized odds of multimorbidity (OR: [0.92], 95% CI: 0.91, 0.94). Common lifestyle habits, alone or in combination, are associated with multimorbidity risk. This study provides insights for public health programs to promote a healthy lifestyle at a younger age and to alleviate multimorbidity risk in older people.</p></div>","PeriodicalId":51483,"journal":{"name":"Applied Research in Quality of Life","volume":"19 3","pages":"1411 - 1435"},"PeriodicalIF":2.8000,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11482-024-10291-3.pdf","citationCount":"0","resultStr":"{\"title\":\"Association of Lifestyle Factors with Multimorbidity Risk in China: A National Representative Study\",\"authors\":\"Xinye Zou, Siyu Zou, Ruolin Zhang, Kefan Xue, Yi Guo, Hewei Min, Yibo Wu, Xinying Sun\",\"doi\":\"10.1007/s11482-024-10291-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Multimorbidity significantly impacts health, well-being, and the economy; therefore, exploring notable factors associated with multimorbidity across all age groups is critical. For this investigation, we focused on the relationship between four lifestyle factors and multimorbidity risk. We recruited 11,031 Chinese citizens aged ≥ 12 years from 31 provinces between July 2021 and September 2021 using a quota sampling strategy to ensure that the socioeconomic characteristics (sex, age, rural–urban distribution) of those participating in this research were representative of national demographics. In the first stage, multivariable logistic regression models were utilized as a means of investigating the relationship between lifestyle factors and multimorbidity. Then, a multinomial logistic regression model was used with the aim of examining the Healthy Lifestyle Profile (HLP) related to the number of chronic diseases. Multivariable logistic regression models assessed the interaction effects and joint association among the four lifestyle factors. Overall, 18% of the participants had at least one disease, and 5.9% had multimorbidity. Approximately two-thirds of the participants were physically inactive, 40% had consumed alcohol, 39% were underweight or overweight, and 20% were or had been smokers. Participants who maintained one HLP showed a 34% lower multimorbidity risk (adjusted OR, 0.66; 95% CI, 0.48 to 0.92), while participants who maintained 4 HLP showed a 73% lower multimorbidity risk (adjusted OR, 0.27; 95% CI, 0.17 to 0.43), as compared to those who had 0 HLP. The joint association analysis revealed that participants with all four healthy lifestyle factors had 0.92 times lower odds of multimorbidity (95% CI: 0.90, 0.94) in comparison with the all-unhealthy reference cluster. Notably, individuals with a combination of healthy smoking status and healthy body weight had the highest minimized odds of multimorbidity (OR: [0.92], 95% CI: 0.91, 0.94). Common lifestyle habits, alone or in combination, are associated with multimorbidity risk. 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Association of Lifestyle Factors with Multimorbidity Risk in China: A National Representative Study
Multimorbidity significantly impacts health, well-being, and the economy; therefore, exploring notable factors associated with multimorbidity across all age groups is critical. For this investigation, we focused on the relationship between four lifestyle factors and multimorbidity risk. We recruited 11,031 Chinese citizens aged ≥ 12 years from 31 provinces between July 2021 and September 2021 using a quota sampling strategy to ensure that the socioeconomic characteristics (sex, age, rural–urban distribution) of those participating in this research were representative of national demographics. In the first stage, multivariable logistic regression models were utilized as a means of investigating the relationship between lifestyle factors and multimorbidity. Then, a multinomial logistic regression model was used with the aim of examining the Healthy Lifestyle Profile (HLP) related to the number of chronic diseases. Multivariable logistic regression models assessed the interaction effects and joint association among the four lifestyle factors. Overall, 18% of the participants had at least one disease, and 5.9% had multimorbidity. Approximately two-thirds of the participants were physically inactive, 40% had consumed alcohol, 39% were underweight or overweight, and 20% were or had been smokers. Participants who maintained one HLP showed a 34% lower multimorbidity risk (adjusted OR, 0.66; 95% CI, 0.48 to 0.92), while participants who maintained 4 HLP showed a 73% lower multimorbidity risk (adjusted OR, 0.27; 95% CI, 0.17 to 0.43), as compared to those who had 0 HLP. The joint association analysis revealed that participants with all four healthy lifestyle factors had 0.92 times lower odds of multimorbidity (95% CI: 0.90, 0.94) in comparison with the all-unhealthy reference cluster. Notably, individuals with a combination of healthy smoking status and healthy body weight had the highest minimized odds of multimorbidity (OR: [0.92], 95% CI: 0.91, 0.94). Common lifestyle habits, alone or in combination, are associated with multimorbidity risk. This study provides insights for public health programs to promote a healthy lifestyle at a younger age and to alleviate multimorbidity risk in older people.
期刊介绍:
The aim of this journal is to publish conceptual, methodological and empirical papers dealing with quality-of-life studies in the applied areas of the natural and social sciences. As the official journal of the ISQOLS, it is designed to attract papers that have direct implications for, or impact on practical applications of research on the quality-of-life. We welcome papers crafted from interdisciplinary, inter-professional and international perspectives. This research should guide decision making in a variety of professions, industries, nonprofit, and government sectors, including healthcare, travel and tourism, marketing, corporate management, community planning, social work, public administration, and human resource management. The goal is to help decision makers apply performance measures and outcome assessment techniques based on concepts such as well-being, human satisfaction, human development, happiness, wellness and quality-of-life. The Editorial Review Board is divided into specific sections indicating the broad scope of practice covered by the journal. The section editors are distinguished scholars from many countries across the globe.