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Shen J, Shi H, Zhang J, Meng X, Zhang C, Kang Y. Household polluting cooking fuels and intrinsic capacity among older population: A harmonized nationwide analysis in India and China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:169031. [PMID: 38042204 DOI: 10.1016/j.scitotenv.2023.169031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Revised: 11/29/2023] [Accepted: 11/29/2023] [Indexed: 12/04/2023]
Abstract
BACKGROUNDS Household polluting cooking fuels, as an important changeable behavior, are related to various detrimental health effects among the elderly. There is limited research on the association between polluting cooking fuel use and intrinsic capacity (IC) as an indicator of healthy aging. This study aimed to evaluate the above-mentioned association in India and China, where polluting cooking fuel use is common. METHODS We enrolled 33,803 participants aged ≥60 years from two nationally representative studies: the Longitudinal Aging Study in India and the China Health and Retirement Longitudinal Study. Polluting cooking fuel use was defined as a self-report of using wood, coal, kerosene, crop residue, or dung. IC was measured by five aspects, including locomotion, cognition, vitality, sensory, and psychological capacity. The random-effects mixed linear regression and logistic regression with population weighting were performed. Multivariable-adjusted model and propensity score were used to adjust for potential confounders. RESULTS A total of 47.54 % and 59.32 % of elderly adults reported primary cooking using polluting fuels in India and China, respectively. Using polluting cooking fuels was consistently associated with IC decline; particularly, cognitive capacity was the most susceptible domain. In India, participants using polluting fuels had a 1.062 (95 % confidence interval [CI]: 1.047-1.078) times risk for IC deficits, whereas more prominent results were observed in China (odds ratio [OR]: 2.040, 95 % CI: 1.642-2.533). Such harmful effects might be alleviated by transferring from polluting to clean fuels. Additionally, the duration of polluting fuel use was also positively associated with IC deficits. CONCLUSION This study provided substantial public implications on healthy aging for the elderly population at a global scale, strengthening the importance of health education and policy efforts to accelerate the transition from polluting to clean fuels.
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Affiliation(s)
- Ji Shen
- Department of Geriatric Medicine, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, No. 1, Dahua Road, Dongdan, Dongcheng District, Beijing 100730, PR China
| | - Hong Shi
- Department of Geriatric Medicine, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, No. 1, Dahua Road, Dongdan, Dongcheng District, Beijing 100730, PR China
| | - Jie Zhang
- Department of Geriatric Medicine, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, No. 1, Dahua Road, Dongdan, Dongcheng District, Beijing 100730, PR China
| | - Xue Meng
- Office of National Clinical Research for Geriatrics, Department of Scientific Research, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, No. 1, Dahua Road, Dongdan, Dongcheng District, Beijing 100730, PR China
| | - Chi Zhang
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, No. 1, Dahua Road, Dongdan, Dongcheng District, Beijing 100730, PR China
| | - Yuting Kang
- Office of National Clinical Research for Geriatrics, Department of Scientific Research, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, No. 1, Dahua Road, Dongdan, Dongcheng District, Beijing 100730, PR China.
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Zhang Z, Lin H. Air Pollution: A Pressing Threat to Functioning in the Older Adults. J Gerontol A Biol Sci Med Sci 2023; 78:2307-2308. [PMID: 37536283 DOI: 10.1093/gerona/glad145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/05/2023] Open
Affiliation(s)
- Zilong Zhang
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Hualiang Lin
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
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Li W, Huang G, Tang N, Lu P, Jiang L, Lv J, Qin Y, Lin Y, Xu F, Lei D. Association between co-exposure to phenols, phthalates, and polycyclic aromatic hydrocarbons with the risk of frailty. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:105181-105193. [PMID: 37713077 DOI: 10.1007/s11356-023-29887-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 09/11/2023] [Indexed: 09/16/2023]
Abstract
The phenomenon of population aging has brought forth the challenge of frailty. Nevertheless, the contribution of environmental exposure to frailty remains ambiguous. Our objective was to investigate the association between phenols, phthalates (PAEs), and polycyclic aromatic hydrocarbons (PAHs) with frailty. We constructed a 48-item frailty index using data from the National Health and Nutrition Examination Survey (NHANES). The exposure levels of 20 organic contaminants were obtained from the survey circle between 2005 and 2016. The association between individual organic contaminants and the frailty index was assessed using negative binomial regression models. The combined effect of organic contaminants was examined using weighted quantile sum (WQS) regression. Dose-response patterns were modeled using generalized additive models (GAMs). Additionally, an interpretable machine learning approach was employed to develop a predictive model for the frailty index. A total of 1566 participants were included in the analysis. Positive associations were observed between exposure to MIB, P02, ECP, MBP, MHH, MOH, MZP, MC1, and P01 with the frailty index. WQS regression analysis revealed a significant increase in the frailty index with higher levels of the mixture of organic contaminants (aOR, 1.12; 95% CI, 1.05-1.20; p < 0.001), with MIB, ECP, COP, MBP, P02, and P01 identified as the major contributors. Dose-response relationships were observed between MIB, ECP, MBP, P02, and P01 exposure with an increased risk of frailty (both with p < 0.05). The developed predictive model based on organic contaminants exposure demonstrated high performance, with an R2 of 0.9634 and 0.9611 in the training and testing sets, respectively. Furthermore, the predictive model suggested potential synergistic effects in the MIB-MBP and P01-P02 pairs. Taken together, these findings suggest a significant association between exposure to phthalates and PAHs with an increased susceptibility to frailty.
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Affiliation(s)
- Wenxiang Li
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences, Nanning, 530021, People's Republic of China
| | - Guangyi Huang
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences, Nanning, 530021, People's Republic of China
| | - Ningning Tang
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences, Nanning, 530021, People's Republic of China
| | - Peng Lu
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences, Nanning, 530021, People's Republic of China
| | - Li Jiang
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences, Nanning, 530021, People's Republic of China
| | - Jian Lv
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences, Nanning, 530021, People's Republic of China
| | - Yuanjun Qin
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences, Nanning, 530021, People's Republic of China
| | - Yunru Lin
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences, Nanning, 530021, People's Republic of China
| | - Fan Xu
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences, Nanning, 530021, People's Republic of China
| | - Daizai Lei
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences, Nanning, 530021, People's Republic of China.
- Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, 6 Taoyuan Road, Qingxiu District, Nanning, 530000, China.
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