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Preplanned Studies: Population Attributable Fractions for Modifiable Factors of Longevity and Healthy Longevity Among the Late-Elderly Aged 75 Years or Older — China, 1998–2018

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  • Summary

    What is already known about this topic?

    Limited evidence on healthy longevity was provided in the world, and no studies investigated the fractions of healthy longevity attributed to modifiable factors.

    What is added by this report?

    Incidences of longevity and healthy longevity in China are provided. It reveals that the total weighted population attributable fractions for lifestyles and all modifiable factors were 32.8% and 83.7% for longevity, respectively, and 30.4% and 73.4% for healthy longevity, respectively.

    What are the implications for public health practice?

    China has a high potential for longevity and healthy longevity. Strategies may be targeted at education and residence in early life as well as healthy lifestyles, disease prevention, and functional optimization in late life.

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  • Funding: Supported by the National Natural Science Foundation of China (grant numbers 82025030 and 81941023), Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (grant number 2021-JKCS-028), and Claude D. Pepper Older Americans Independence Centers grant (grant number 5P30 AG028716 from NIA)
  • [1] United Nations, Department of Economic and Social Affairs Population Division. World population prospects 2022. 2022. https://population.un.org/wpp/Download/Standard/CSV/. [2022-10-18].https://population.un.org/wpp/Download/Standard/CSV/
    [2] World Health Organization. Decade of healthy ageing: baseline report. Geneva: World Health Organization. 2020. https://apps.who.int/iris/handle/10665/338677. [2022-10-12].https://apps.who.int/iris/handle/10665/338677
    [3] Zeng Y, Feng QS, Hesketh T, Christensen K, Vaupel JW. Survival, disabilities in activities of daily living, and physical and cognitive functioning among the oldest-old in China: a cohort study. Lancet 2017;389(10079):1619-29. http://dx.doi.org/10.1016/S0140-6736(17)30548-2CrossRef
    [4] Sabia S, Singh-Manoux A, Hagger-Johnson G, Cambois E, Brunner EJ, Kivimaki M. Influence of individual and combined healthy behaviours on successful aging. CMAJ 2012;184(18):1985-92. http://dx.doi.org/10.1503/cmaj.121080CrossRef
    [5] Mukadam N, Sommerlad A, Huntley J, Livingston G. Population attributable fractions for risk factors for dementia in low-income and middle-income countries: an analysis using cross-sectional survey data. Lancet Glob Health 2019;7(5):e596 − 603. http://dx.doi.org/10.1016/S2214-109X(19)30074-9CrossRef
    [6] Willcox BJ, He QM, Chen RD, Yano K, Masaki KH, Grove JS, et al. Midlife risk factors and healthy survival in men. JAMA 2006;296(19):2343-50. http://dx.doi.org/10.1001/jama.296.19.2343CrossRef
    [7] Shadyab AH, Manson JE, Li WJ, Gass M, Brunner RL, Naughton MJ, et al. Parental longevity predicts healthy ageing among women. Age Ageing 2018;47(6):853-60. http://dx.doi.org/10.1093/ageing/afy125CrossRef
    [8] Li ZH, Zhang XR, Lv YB, Shen D, Li FR, Zhong WF, et al. Leisure activities and all-cause mortality among the Chinese oldest-old population: a prospective community-based cohort study. J Am Med Dir Assoc 2020;21(6):713-9.e2. http://dx.doi.org/10.1016/j.jamda.2019.08.003CrossRef
    [9] Arumai Selvan D, Mahendiran D, Senthil Kumar R, Kalilur Rahiman A. Garlic, green tea and turmeric extracts-mediated green synthesis of silver nanoparticles: phytochemical, antioxidant and in vitro cytotoxicity studies. J Photochem Photobiol B Biol 2018;180:243-52. http://dx.doi.org/10.1016/j.jphotobiol.2018.02.014CrossRef
    [10] Rillamas-Sun E, LaCroix AZ, Waring ME, Kroenke CH, LaMonte MJ, Vitolins MZ, et al. Obesity and late-age survival without major disease or disability in older women. JAMA Intern Med 2014;174(1):98-106. http://dx.doi.org/10.1001/jamainternmed.2013.12051CrossRef
    [11] Newson RS, Witteman JCM, Franco OH, Stricker BHC, Breteler MMB, Hofman A, et al. Predicting survival and morbidity-free survival to very old age. Age (Dordr) 2010;32(4):521-34. http://dx.doi.org/10.1007/s11357-010-9154-8CrossRef
  • TABLE 1.  Person-years of follow-up, number of outcome events, and incidence per 1,000 person years of observation by residence, sex, and age groups among Chinese late-elderly, 1998–2018.

    GroupsAll participantsLongevityHealthy longevityUsual longevity
    No. of participantsPerson-yearsNo. of eventsIncidenceNo. of eventsIncidenceNo. of eventsIncidence
    Total11,00543,1543,45480.073517.02,71963.0
    Residence
     Urban4,68317,9801,37176.331117.31,06059.0
     Rural6,32225,1742,08382.742416.81,65965.9
    Sex
     Men5,59521,1031,57274.541719.81,15554.7
     Women5,41022,0511,88285.331814.41,56470.9
    Age group (years)
     75–791,5169,33917919.2404.313914.9
     80–844,79120,95293844.81858.875336.0
     85–894,69812,8622,337181.751039.71,827142.0
    Download: CSV

    TABLE 2.  Odds ratios (95% CIs) for factors associated with longevity and healthy longevity among Chinese late-elderly, 1998–2018.

    CharacteristicsLongevityHealthy longevity
    OR (95% CI)P-valueOR (95% CI)P-value
    Women1.45 (1.30–1.61)<0.0010.71 (0.580.87)0.001
    Rural1.16 (1.05–1.28)0.003Not included
    Education years (per 1 year)Not included1.05 (1.03–1.08)<0.001
    Smoking
    Never smokers1 (ref)1 (ref)
    Current smokers0.86 (0.76–0.97)0.0160.79 (0.62–0.99)0.044
    Former smokers0.83 (0.72–0.95)0.0071.14 (0.89–1.46)0.310
    Performing houseworkNot included
    Rarely or not1 (ref)
    Occasionally1.25 (1.09–1.44)0.002
    Often1.42 (1.29–1.57)<0.001
    Raising domestic animals
    Rarely or not1 (ref)1 (ref)
    Occasionally1.04 (0.88–1.22)0.6701.51 (1.13–2.01)0.006
    Often1.23 (1.09–1.38)0.0011.35 (1.09–1.65)0.005
    Reading newspapers or bookNot included
    Rarely or not1 (ref)
    Occasionally0.94 (0.78–1.13)0.490
    Often1.19 (1.03–1.38)0.019
    Watching TV or listening to the radioNot included
    Rarely or not1 (ref)
    Occasionally1.11 (0.98–1.27)0.110
    Often1.27 (1.14–1.43)<0.001
    Garlic consumptionNot included
    Rarely or not1 (ref)
    Occasionally1.14 (1.04–1.26)0.008
    Often1.05 (0.92–1.19)0.510
    Tea drinkingNot included
    Rarely or not1 (ref)
    Occasionally1.37 (1.10–1.71)0.005
    Often1.21 (0.99–1.47)0.062
    Good chewing ability1.14 (1.03–1.25)0.0081.25 (1.05–1.48)0.014
    ADL score (per 1 point)0.84 (0.80–0.88)<0.0010.85 (0.75–0.97)0.018
    MMSE score (per 1 point)1.02 (1.01–1.03)<0.001
    Psychological resources (per 1 point)1.02 (1.01–1.03)0.0051.05 (1.03–1.08)<0.001
    Hearing lossNot included0.49 (0.33–0.74)0.001
    Heart disease0.73 (0.61–0.87)0.001
    Cerebrovascular disease0.77 (0.60–0.98)0.036Not included
    Respiratory disease0.79 (0.69–0.91)0.001Not included
    BMINot included
    Underweight0.82 (0.68–0.98)0.032
    Normal1 (ref)
    Overweight1.08 (0.81–1.43)0.620
    Obese0.45 (0.22–0.89)0.022
    Self-rated health statusNot included
    Good1.07 (0.97–1.19)0.19
    So so1 (ref)
    Bad0.76 (0.65–0.89)0.001
    Note: Data are adjusted OR (95% CI). A value higher than 1 indicates participants are more likely to be longevity or healthy longevity. Not included means factors were not selected or non-significant (P>0.05) in the stepwise logistic models and not retained in the final models.
    Abbreviation: CI=confidence interval; OR=odds ratio; ADL=activities of daily living; MMSE=mini-mental state examination; BMI=body mass index.
    Download: CSV

    TABLE 3.  Population attributable fractions for modifiable factors of longevity and healthy longevity among the late-elderly in China, 1998–2018.

    FactorsPAFs for longevity (%)PAFs for healthy longevity (%)
    RawWeightedRawWeighted
    Lifestyle factors
    Smoking*9.83.321.48.8
    Tea drinking (often or occasionally)Not includedNot included12.04.9
    Garlic consumption (often or occasionally)7.52.5Not includedNot included
    Performing housework (often or occasionally)19.96.7Not includedNot included
    Reading newspapers or books (often)3.41.1Not includedNot included
    Raising domestic animals (often or occasionally)4.81.69.84.0
    Watching TV or listening to the radio (often or occasionally)14.54.9Not includedNot included
    BMI (normal weight or overweight; 18.5–27.9 kg/m2)Not includedNot included14.76.0
    Other potentially modifiable factors
    Residence (rural)7.92.7Not includedNot included
    Education years (≥6)Not includedNot included5.52.3
    Chewing ability (good)6.42.210.74.4
    ADL limitations (no)38.813.137.515.4
    Cognitive impairment (no)27.69.3Not includedNot included
    Mental health (good)9.73.317.37.1
    Hearing function (good)Not includedNot included49.820.5
    Heart disease (no)24.88.4Not includedNot included
    Cerebrovascular disease (no)27.19.1Not includedNot included
    Respiratory disease (no)19.06.4Not includedNot included
    Self-rated health status (good or so so)26.79.0Not includedNot included
    Combined factors
    All modifiable lifestyle factors47.532.846.830.4
    All potentially modifiable factors94.183.788.373.4
    Note: All models were adjusted for age, sex, and all other potentially modifiable factors. Not included means factors were not selected or non-significant (P>0.05) in the stepwise logistic models and not retained in the final models.
    Abbreviation: CI=confidence interval; PAF=population attributable fraction; BMI=body mass index; ADL=activities of daily living.
    * Based on the results of separate logistic models, never smoking was considered in the calculation of PAFs for longevity, and never or quit smoking was considered in the calculation of PAFs for healthy longevity.
    Weighted PAFs were the relative contributions of each factor to the overall PAF of all potentially modifiable factors when adjusted for communality.
    Download: CSV

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Population Attributable Fractions for Modifiable Factors of Longevity and Healthy Longevity Among the Late-Elderly Aged 75 Years or Older — China, 1998–2018

View author affiliations

Summary

What is already known about this topic?

Limited evidence on healthy longevity was provided in the world, and no studies investigated the fractions of healthy longevity attributed to modifiable factors.

What is added by this report?

Incidences of longevity and healthy longevity in China are provided. It reveals that the total weighted population attributable fractions for lifestyles and all modifiable factors were 32.8% and 83.7% for longevity, respectively, and 30.4% and 73.4% for healthy longevity, respectively.

What are the implications for public health practice?

China has a high potential for longevity and healthy longevity. Strategies may be targeted at education and residence in early life as well as healthy lifestyles, disease prevention, and functional optimization in late life.

  • 1. China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
  • 2. Department of Nutrition and Food Hygiene, School of Public Health, Institute of Nutrition, Fudan University, Shanghai, China
  • 3. Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, Jilin, China
  • 4. School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
  • 5. Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, Guangdong, China
  • 6. Division of Non-communicable Disease and Aging Health Management, Chinese Center for Disease Control and Prevention, Beijing, China
  • 7. Duke Molecular Physiology Institute and Division of Rheumatology, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA
  • 8. Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
  • Corresponding authors:

    Xiaoming Shi, shixm@chinacdc.cn

    Chen Mao, maochen9@smu.edu.cn

    Yuebin Lyu, lvyuebin@nieh.chinacdc.cn

  • Funding: Supported by the National Natural Science Foundation of China (grant numbers 82025030 and 81941023), Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (grant number 2021-JKCS-028), and Claude D. Pepper Older Americans Independence Centers grant (grant number 5P30 AG028716 from NIA)
  • Online Date: January 13 2023
    Issue Date: January 13 2023
    doi: 10.46234/ccdcw2023.005
  • Globally, the population of the late-elderly (aged ≥75 years) was expected to rise from 275 million in 2020 to 768 million by 2050 (1), which might result in large disease burdens accompanied by increased life years of ill-health (23). However, the lack of data on multiple health measurements of older adults in large long-term cohorts increases the difficulty in studying healthy longevity. To fill the gap, analyses were conducted using data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) from 1998 to 2018. Separate logistic regression models were performed to identify factors associated with longevity and healthy longevity among 11,005 late-elderly participants, who were more likely to survive to 90 years old than younger adults. The corresponding population attributable fractions (PAFs) were also calculated. The incidence densities were 17.0/1,000 person-years for healthy longevity and 63.0/1,000 person-years for usual longevity. Considering early life factors, lifestyles, functional health, and diseases, the total weighted PAFs were 83.7% for longevity and 73.4% for healthy longevity, suggesting great potential for longevity and healthy longevity in China. Effective strategies may include targeting people through education and residence in early life, healthy lifestyles, disease prevention, and functional optimization in late life.

    Study participants were recruited from the CLHLS study, which is the first study to investigate factors of healthy longevity in China covering 23 provincial-level administrative divisions (PLADs) (3). The study included 11,005 late-elderly participants who had the potential to survive to age 90 years by 2018, with complete measurement of outcomes and candidate factors for analysis (Supplementary Figure S1). Healthy longevity was defined with reference to the WHO definition of healthy ageing and participants were classified into three categories (2): 1) healthy longevity: age at death ≥90 years, with good physical performance, cognitive function, mental health, visual function, and hearing function; 2) usual longevity: age at death ≥90 years, with at least one kind of functional impairment or disability mentioned above; 3) non-survival: age at death <90 years, irrespective of functional status (Supplementary Methods). Candidate influencing factors, including demographic characteristics, lifestyles, functional status, self-rated health, and diseases, were collected through face-to-face interviews with all participants (Supplementary Methods).

    Factors of longevity and healthy longevity were identified with separate logistic regression models via a bidirectional stepwise procedure and inclusion of candidate factors with P<0.1 in the raw estimates only adjusted for age: 1) non-longevity versus longevity; 2) usual longevity versus healthy longevity. As participants with different baseline ages will have different periods to age 90, age was included as a confounder as opposed to a risk factor in the analyses. The PAFs of modifiable factors were calculated by transforming them into binary or multifactorial variables according to previous studies and were adjusted for communality, which explains the overlap between factors (4-5). We performed a principal component analysis to calculate the communality for each factor and took into account how much each unobserved component explained each measured factor (5). Both age and sex were considered fixed variables. In this study, the PAF is interpreted as the probability gains of longevity or healthy longevity if all participants kept to healthy lifestyles or were in the absence of adverse health status factors (4). All analyses were finished with R 4.1.3 for Windows (R Foundation for Statistical Computing, Vienna, Austria). Statistical significance was set at two-tailed with a P-value <0.05.

    Overall, 3,454 (31.4%) participants survived to age 90, of which 735 (6.7%) were classified as having healthy longevity and 2,719 (24.7%) were classified as having usual longevity. The corresponding incidence densities were 17.0/1,000 and 63.0/1,000 person-years, respectively. Rural residents and women were more likely to be classified as having longevity, but most of them were classified as having usual longevity, indicative of a lower likelihood of being classified as having healthy longevity. The incidences of longevity, healthy longevity, and usual longevity were higher in older adults with advanced ages (Table 1).

    GroupsAll participantsLongevityHealthy longevityUsual longevity
    No. of participantsPerson-yearsNo. of eventsIncidenceNo. of eventsIncidenceNo. of eventsIncidence
    Total11,00543,1543,45480.073517.02,71963.0
    Residence
     Urban4,68317,9801,37176.331117.31,06059.0
     Rural6,32225,1742,08382.742416.81,65965.9
    Sex
     Men5,59521,1031,57274.541719.81,15554.7
     Women5,41022,0511,88285.331814.41,56470.9
    Age group (years)
     75–791,5169,33917919.2404.313914.9
     80–844,79120,95293844.81858.875336.0
     85–894,69812,8622,337181.751039.71,827142.0

    Table 1.  Person-years of follow-up, number of outcome events, and incidence per 1,000 person years of observation by residence, sex, and age groups among Chinese late-elderly, 1998–2018.

    Odds ratios [95% confidence intervals (CIs)] for factors retained in the final logistic regression models are presented in Table 2. The PAFs for modifiable factors associated with longevity and healthy longevity are displayed in Table 3. Of all modifiable lifestyles, performing housework, watching TV or listening to the radio, and never smoking are the top three factors contributing to longevity (weighted PAFs were 6.7%, 4.9%, and 3.3%, respectively); never smoking or having quit, normal weight or overweight, and tea-drinking are the top three factors contributing to healthy longevity (weighted PAFs were 8.8%, 6.0%, and 4.9%, respectively). All modifiable lifestyles combined may explain 32.8% probability gains of longevity and 30.4% probability gains of healthy longevity. The probability gains reached 83.7% and 73.4% when all potentially modifiable factors, excluding age and sex, were considered. Among factors other than lifestyles, interventions at early ages also contribute to considerable probability gains of longevity and healthy longevity (weighted PAF of residence areas was 2.7% for longevity, weighted PAF of education years was 2.3% for healthy longevity); early prevention and usage of supporting tools or equipment to optimize functional abilities are also important in promoting longevity [weighted PAFs of chewing ability, activities of daily living (ADL) limitations, and cognitive impairment were 2.2%, 13.1%, and 9.3%, respectively] and healthy longevity (weighted PAFs of chewing ability, ADL limitations, and hearing function were 4.4%, 15.4%, and 20.5%, respectively).

    CharacteristicsLongevityHealthy longevity
    OR (95% CI)P-valueOR (95% CI)P-value
    Women1.45 (1.30–1.61)<0.0010.71 (0.580.87)0.001
    Rural1.16 (1.05–1.28)0.003Not included
    Education years (per 1 year)Not included1.05 (1.03–1.08)<0.001
    Smoking
    Never smokers1 (ref)1 (ref)
    Current smokers0.86 (0.76–0.97)0.0160.79 (0.62–0.99)0.044
    Former smokers0.83 (0.72–0.95)0.0071.14 (0.89–1.46)0.310
    Performing houseworkNot included
    Rarely or not1 (ref)
    Occasionally1.25 (1.09–1.44)0.002
    Often1.42 (1.29–1.57)<0.001
    Raising domestic animals
    Rarely or not1 (ref)1 (ref)
    Occasionally1.04 (0.88–1.22)0.6701.51 (1.13–2.01)0.006
    Often1.23 (1.09–1.38)0.0011.35 (1.09–1.65)0.005
    Reading newspapers or bookNot included
    Rarely or not1 (ref)
    Occasionally0.94 (0.78–1.13)0.490
    Often1.19 (1.03–1.38)0.019
    Watching TV or listening to the radioNot included
    Rarely or not1 (ref)
    Occasionally1.11 (0.98–1.27)0.110
    Often1.27 (1.14–1.43)<0.001
    Garlic consumptionNot included
    Rarely or not1 (ref)
    Occasionally1.14 (1.04–1.26)0.008
    Often1.05 (0.92–1.19)0.510
    Tea drinkingNot included
    Rarely or not1 (ref)
    Occasionally1.37 (1.10–1.71)0.005
    Often1.21 (0.99–1.47)0.062
    Good chewing ability1.14 (1.03–1.25)0.0081.25 (1.05–1.48)0.014
    ADL score (per 1 point)0.84 (0.80–0.88)<0.0010.85 (0.75–0.97)0.018
    MMSE score (per 1 point)1.02 (1.01–1.03)<0.001
    Psychological resources (per 1 point)1.02 (1.01–1.03)0.0051.05 (1.03–1.08)<0.001
    Hearing lossNot included0.49 (0.33–0.74)0.001
    Heart disease0.73 (0.61–0.87)0.001
    Cerebrovascular disease0.77 (0.60–0.98)0.036Not included
    Respiratory disease0.79 (0.69–0.91)0.001Not included
    BMINot included
    Underweight0.82 (0.68–0.98)0.032
    Normal1 (ref)
    Overweight1.08 (0.81–1.43)0.620
    Obese0.45 (0.22–0.89)0.022
    Self-rated health statusNot included
    Good1.07 (0.97–1.19)0.19
    So so1 (ref)
    Bad0.76 (0.65–0.89)0.001
    Note: Data are adjusted OR (95% CI). A value higher than 1 indicates participants are more likely to be longevity or healthy longevity. Not included means factors were not selected or non-significant (P>0.05) in the stepwise logistic models and not retained in the final models.
    Abbreviation: CI=confidence interval; OR=odds ratio; ADL=activities of daily living; MMSE=mini-mental state examination; BMI=body mass index.

    Table 2.  Odds ratios (95% CIs) for factors associated with longevity and healthy longevity among Chinese late-elderly, 1998–2018.

    FactorsPAFs for longevity (%)PAFs for healthy longevity (%)
    RawWeightedRawWeighted
    Lifestyle factors
    Smoking*9.83.321.48.8
    Tea drinking (often or occasionally)Not includedNot included12.04.9
    Garlic consumption (often or occasionally)7.52.5Not includedNot included
    Performing housework (often or occasionally)19.96.7Not includedNot included
    Reading newspapers or books (often)3.41.1Not includedNot included
    Raising domestic animals (often or occasionally)4.81.69.84.0
    Watching TV or listening to the radio (often or occasionally)14.54.9Not includedNot included
    BMI (normal weight or overweight; 18.5–27.9 kg/m2)Not includedNot included14.76.0
    Other potentially modifiable factors
    Residence (rural)7.92.7Not includedNot included
    Education years (≥6)Not includedNot included5.52.3
    Chewing ability (good)6.42.210.74.4
    ADL limitations (no)38.813.137.515.4
    Cognitive impairment (no)27.69.3Not includedNot included
    Mental health (good)9.73.317.37.1
    Hearing function (good)Not includedNot included49.820.5
    Heart disease (no)24.88.4Not includedNot included
    Cerebrovascular disease (no)27.19.1Not includedNot included
    Respiratory disease (no)19.06.4Not includedNot included
    Self-rated health status (good or so so)26.79.0Not includedNot included
    Combined factors
    All modifiable lifestyle factors47.532.846.830.4
    All potentially modifiable factors94.183.788.373.4
    Note: All models were adjusted for age, sex, and all other potentially modifiable factors. Not included means factors were not selected or non-significant (P>0.05) in the stepwise logistic models and not retained in the final models.
    Abbreviation: CI=confidence interval; PAF=population attributable fraction; BMI=body mass index; ADL=activities of daily living.
    * Based on the results of separate logistic models, never smoking was considered in the calculation of PAFs for longevity, and never or quit smoking was considered in the calculation of PAFs for healthy longevity.
    Weighted PAFs were the relative contributions of each factor to the overall PAF of all potentially modifiable factors when adjusted for communality.

    Table 3.  Population attributable fractions for modifiable factors of longevity and healthy longevity among the late-elderly in China, 1998–2018.

    • In this large cohort study of 11,005 late-elderly participants, incidences of longevity and healthy longevity in China were provided and 19 modifiable factors for longevity and healthy longevity were identified. The weighted PAFs of longevity and healthy longevity are 32.8% and 30.4%, respectively, for all modifiable lifestyle factors and would increase to 83.7% for longevity and 73.4% for healthy longevity when all potentially modifiable factors were considered. Longevity and healthy longevity should be considered from a life course perspective, in which education and residence in early life and lifestyles in late life would continuously influence both the health status (including functional status and diseases) of a person and the probability of longevity and healthy longevity. Although functional status and diseases are probably irreversible in late life, the benefits are still substantial if supporting environments are strengthened to optimize the intrinsic capacity and functional abilities of late-elderly populations.

      In this study, the probability of healthy longevity in China was suggested to be similar of that in younger Americans (67). Sex and 19 modifiable factors associated with longevity and healthy longevity in China were identified. These results are consistent with previous findings. The PAF for modifiable lifestyles of healthy longevity is comparable to a previous study that investigated successful aging in the United Kingdom when not adjusted for communality (4). Sociodemographically, women, rural residents, and participants with higher education levels were more likely to be classified as having longevity and/or healthy longevity (6). Regarding lifestyles, never smoking and engaging in leisure activities were beneficial to longevity and/or healthy longevity in late-elderly participants, which may be explained by their effects on reducing the risk of cognitive impairment, ADL disability, and mortality (4,6,8); garlic consumption and tea drinking may protect the late-elderly participants by the antidotal effect, inhibiting the growth of cancer cells, and reducing inflammatory levels (9); late-elderly participants who were underweight or obese may suffer from higher risk of chronic diseases and disabilities, reducing the likelihood of healthy longevity (10). Regarding other health measurements, late-elderly participants with good chewing ability, ADL independence, cognitive function, mental health, and hearing function were more likely to reach longevity and/or healthy longevity. These late-elderly participants may suffer from slower functional declines, and good chewing ability also enabled the adequate intake of essential nutrients. Conversely, late-elderly participants with heart disease, cerebrovascular disease, respiratory disease, and bad self-rated health status were more likely to suffer from functional declines and less likely to reach longevity (11).

      Several limitations need to be considered when interpreting these findings. First, this study did not include data on genetic information and biomarkers, which need to be further investigated in future studies. Second, the inclusion of Chinese late-elderly participants might limit the generalizability of these findings to other racial groups.

      In summary, incidences of longevity and healthy longevity in China in a large cohort of late-elderly participants were provided and revealed that education and residence in early life, healthy lifestyles, disease prevention, and functional optimization in late life together contribute to longevity and healthy longevity. The findings of this study reinforced the importance of a life course perspective in health management and may be applied to develop intervention strategies against the burdens attributed to the ageing population in China.

    • No conflicts of interest.

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