Leading Determinants for Disease-Free Status in Community-Dwelling Middle-Aged Men and Women: A 9-Year Follow-Up Cohort Study

Conclusions : Body mass index, diets, smoking, alcohol pulmonary tuberculosis, and physical activity are key factors for disease-free status promotion. Individuals with low socioeconomic condition are more in necessitate of concern. Results : Disease-free status was found in about half of both men and women during a average 9-year follow-up. The five most common leading predictors were body mass index ( 6.4–9.5 % of total discrepancy ), self-rated health ( 5.2–8.2 % ), self-rated quality of life ( 4.1–6.8 % ), red kernel consumption ( 4.5–6.5 % ), and chicken intake ( 4.5–5.9 % ) in both genders. modifiable behavioral factors including body mass index, diets, smoking, alcohol pulmonary tuberculosis, and physical action, contributed to 37.2–40.3 % of sum division. Participants having six or more modifiable health factors were 1.63–8.76 times more likely to remain disease-free status and had 0.60–2.49 more disease-free years ( out of 9-year follow-up ) than those having two or fewer. Non-behavioral factors including low levels of education and income and eminent proportional socioeconomic disadvantage, were leading hazard factors for disease-free status. Methods : We included 52,036 participants aged 45–64 years from the 45 and Up Study who were dislodge of 13 predefined chronic conditions at service line ( 2006–2009 ). Disease-free status was defined as participants aging from 45–64 years at service line to 55–75 years at the end of the follow-up ( December 31, 2016 ) without developing any of the 13 chronic conditions. We used machine learning methods to evaluate the importance of 40 electric potential predictors and analyzed the affiliation between the total of leading modifiable healthy factors and disease-free condition. background : Identifying leading determinants for disease-free status may provide evidence for action priorities, which is imperative for populace health with an expanding aged population global. This survey aimed to identify lead determinants, particularly modifiable factors for disease-free condition using machine learning methods.

Introduction

The ball-shaped population is aging, and it is estimated that 16 % of the sum population will be 65 years or older by 2050 ( 1 ). In Australia, 15 % of the population were aged ≥65 years in 2014, and the percentage is expected to increase to 23 % by 2050 ( 2, 3 ). physiologic degeneracy with aging is associated with numerous complications, including cardiometabolic disorders, cancer, mental disorders, dementia, Parkinson ‘s disease, musculoskeletal disorders, and asthma ( 4, 5 ). These conditions account for a overriding proportion of global deathrate with cardiovascular disease and cancer as the first two leadership contributors ( 6 ). The promotion of disease-free condition is an authoritative public health priority, as the prevention of these chronic conditions would notably improve individuals ‘ choice of animation and importantly reduce health care costs ( 7 – 9 ) .
In 2015, the first earth report card on healthy aging was released by the World Health Organization ( 10 ), and an increasing phone number of studies have investigated the hazard factors for healthy aging ( 11 ). previous studies have linked socioeconomic status and life style, behavioral, psychological, and biological factors to goodly aging ( 12 – 15 ), however ; these studies are limited by their cross-sectional design and/or humble sample sizes. Although goodly aging is not only the absence of disease ( 10, 16, 17 ), disease-free condition is the fundamental of healthy ripening and is defined by diagnosis of diseases preferably than self-rated health with more measurement bias. The importance of determinants in rank on disease-free status is less known ( 12, 14 ), thus determining the leading modifiable and non-modifiable predictors based on large data using prediction models particularly machine learning considering its advantage in prediction operation is imperative for prioritizing populace health actions ( 18 ). Middle age represents an important period for chronic disease prevention, consequently identifying the leading determinants for disease-free condition during this time period is essential ( 16 ) .
We aimed to prospectively examine the association of life style behaviors, family history of chronic disease, socioeconomic condition, psychological and geographic factors with disease-free status and evaluated the importance of 40 electric potential predictors using machine learning methods based on a large cohort study and claims databases. We besides aimed to analyze whether clustering selected leading modifiable factors were associated with disease-free condition in men and women .

Materials and Methods

Participants

The 45 and Up Study is a prospective cogitation of 266,896 participants aged 45 years and complete from New South Wales ( 19 ). Participants were randomly sampled from the general population through the Department of Human Services ( once Medicare Australia ) registration database and an 18 % reaction rate was achieved, corresponding to 11 % of the stallion New South Wales population in the prey age group ( 20 ). Baseline datum including life style behaviors, medical history, kin history of chronic disease, socioeconomic status, and geographic factors were collected between 2006 and 2009. These data were linked to the Medicare Benefits Schedule and Pharmaceutical Benefits Scheme datum ( July 1, 2004–December 31, 2016 ) by the Sax Institute using a unique identifier provided by the Department of Human Services. The 45 and Up sketch has ethical blessing from the UNSW Human Research Ethics Committee. The learn protocol was approved by the Royal Victorian Eye and Ear Hospital Human Research Ethics Committee. Participants provided consent to follow-up and link their data to routine health datasets .
This psychoanalysis excluded participants with any of the 13 chronic conditions at service line, including cancer ( excluded non-melanoma hide cancer ), heart disease, stroke, high blood pressure, dyslipidemia, diabetes, asthma, depression, anxiety, dementia, Parkinson ‘s disease, hep substitute, and osteoarthritis based on self-reported history of previous diagnosis, Medicare Benefits Schedule, or pharmaceutical Benefits Scheme claims ; those with Department of Veterans ‘ Affairs cards ; or those aged 65 years or over ; those who needed avail with daily tasks because of long-run illness/disability at service line ( Figure 1 ) .

FIGURE 1

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Figure 1. Flowchart of player choice for the analysis in this cogitation. The main prospective analyses included 52,036 participants aged 45–64 years who were rid of major chronic conditions at baseline. We besides conducted cross-section analysis of the association of individual predictive variable with “ disease-free ” status and evaluation the importance of variables in 152,813 participants .

Independent Variables

Baseline data were collected using a self-administrative questionnaire, which is available at hypertext transfer protocol : //www.saxinstitute.org.au/our-work/45-up-study/questionnaires/ .
demographic information including age, sex, ethnicity, income, education, marital status, working status, number of children, and health policy was assessed. life style behaviors including dietary intake, smoke, alcohol pulmonary tuberculosis, physical natural process, rest and sitting time were self-reported based on a questionnaire. Body aggregate index ( BMI ) was calculated as weight in kilograms divided by the squarely of height in meters based on self-reported assessments .
psychological distress, social interaction, quality of life, and overall health were individually measured using specific indices. Socioeconomic status was assessed using the Index of Relative Socio-economic Disadvantage ( 21 ), while geographic aloofness was measured using the Accessibility Remoteness Index of Australia ( 22 ). Family history of heart disease, stroke, high blood pressure, cancer, diabetes, Alzheimer ‘s disease, Parkinson ‘s disease, low, arthritis, and hip fracture was self-reported. The classification of each independent varying is detailed in Supplementary Methods .

Outcome Variables

Disease-free status was defined as participants aging from 45–64 years at baseline to 55–75 years at the end of the follow-up without developing any of the 13 chronic conditions. The incidence of the 13 chronic conditions during follow-up was determined by medications and medical services claimed by study participants via the Pharmaceutical Benefits Scheme or Medicare Benefits Schedule ( Table S1 ) .

Statistical Analysis

descriptive data were summarized as frequency and percentage according to senesce and gender. We used the Chi-square examination to examine whether the incidence of chronic conditions differed by sex and age and Benjamin-Hochberg operation was used to control the fake discovery rate at level 5 % for multiple comparisons ( 23 ) .
The association of likely predictors with disease-free condition was assessed using Poisson arrested development models with full-bodied variation. The multivariable analysis adjusted for old age, follow-up period, country of birth, income, education, BMI, psychological distress, smoke, passive fume, alcohol consumption, physical activity, sleep fourth dimension, breakfast cereal inhalation, chicken inhalation, loss kernel consumption, vegetable consumption, fruits intake, health insurance, and sociable interaction .
We used four established machine learning models including logistic regression, random forest, gradient boost machine, and deep learn to analyze the importance of potential predictors for disease-free condition and compared the accuracy of these models ( details in Supplementary Methods and Table S2 ). Twenty go predictors and 10 leading modifiable factors were obtained according to their contribution derived from car memorize. Poisson regression models with robust variance were then used to analyze the affiliation of clustering 10 leading modifiable healthy factors with disease-free condition. A general linear mix model was used to evaluate the multivariable-adjusted think of dispute of disease-free years between participants with a different numeral of healthy factors. Missing data on each variable examined are listed in table 1, and those missing values are assigned as a individual class .

table 1

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Table 1. Baseline characteristics by gender and age .

sensitivity analysis was conducted to examine the cross-section associations of electric potential predictors with “ disease-free ” and leading predictors using the baseline data with 152,813 participants aged 45–64 years, where disease-free was defined as being free of the 13 chronic conditions at service line .
We realized these machine learning modeling exercises using the statistical software R 3.4.1. other analyses were performed using SAS version 9.4 ( SAS Institute Inc. ), and all P -values were bilateral .

Results

Participant Characteristics

As shown in table 1, 52,036 participants aged 45–64 years ( 56.9 % female ) with a mean follow-up of 8.9 ± 0.9 ( range : 7.0, 11.5 ) years were included in the analysis. Individuals aged 45–54 years had higher income, education, the preponderance of overweight/obesity, smoking preponderance and consumed less vegetable, fruit, and fish and more chicken compared with those aged 55–64 years in both men and women ( all P < 0.0001 ). Younger individuals were less likely to report an excellent self-rated quality of life or overall health compared to their older counterparts ( all P < 0.0001 ) .

Disease-Free Status by Age and Gender

During follow-up, 50.0 % of the participants were determined as disease-free status with a like proportion in women ( 49.8 % ) and men ( 50.1 % ). In the multivariable-adjusted model, men aged 55–64 years had a 45 % ( 95 % confidence interval [ CI ] : 41, 48 % ) lower likelihood of disease-free status compared to those aged 45–54 years ( P < 0.0001 ). While women aged 55–64 years had a 38 % ( 95 % CI : 35, 41 % ) lower likelihood of disease-free condition than those aged 45–54 years ( P < 0.0001, Figure 2 ) . FIGURE 2 www.frontiersin.org
Figure 2. proportion of disease-free status by old age and sex during follow-up. Disease-free condition was defined as participants aging from 45–64 years at service line to 55–75 years at the end of the follow-up without developing any of the 13 chronic conditions .

Incidence of Individual Chronic Conditions by Age and Gender

Individuals aged 55–64 years had a higher incidence of all chronic conditions except low than those aged 45–54 years ( all P < 0.0001 ). man had a higher incidence of affection disease, stroke, high blood pressure, dyslipidemia, diabetes, Parkinson 's disease, osteoarthritis, and hip successor, while women had a higher incidence of natural depression, anxiety, cancer, and asthma ( all P < 0.0001, Figure 3 ) . FIGURE 3 www.frontiersin.org
Figure 3. incidence of 13 chronic conditions by age and sex. (A,B) Show the incidence of 13 chronic conditions by age and by sex, respectively .

Relative Risk for Disease-Free Status Associated With Potential Predictors

In the multivariable analysis, a smaller proportion of disease-free status was observed in participants with corpulence [ relative risk ( RR ) 0.72 ( 95 % CI : 0.69, 0.75 ) ] or fleshiness [ 0.48 ( 95 % CI : 0.46, 0.51 ) ], older historic period [ 0.88 ( 95 % CI : 0.86, 0.89 ) ] for each year addition, smoking [ 0.67 ( 95 % CI : 0.63, 0.72 ) ], passive smoke [ 0.94 ( 95 % CI : 0.90, 0.98 ) ], excessive alcohol consumption [ 0.93 ( 95 % CI : 0.87, 0.99 ) ], diets high in chicken [ 0.77 ( 95 % CI : 0.70, 0.85 ) ], moderate [ 0.81 ( 95 % CI : 0.77, 0.86 ) ] or high psychological straiten [ 0.66 ( 95 % CI : 0.59, 0.73 ) ], poor/fair self-rated health [ 0.56 ( 95 % CI : 0.50, 0.63 ) ], or quality of life [ 0.56 ( 95 % CI : 0.50, 0.63 ) ], and family history of affection disease [ 0.80 ( 95 % CI : 0.77, 0.83 ) ], stroke [ 0.86 ( 95 % CI : 0.82, 0.89 ) ], or high blood pressure [ 0.86 ( 95 % CI : 0.82, 0.89 ) ] ( all P < 0.05 ). high level of physical action [ RR : 1.13 ( 95 % CI : 1.07, 1.20 ) ], sleep time between 7 and 9 heat content [ 1.07 ( 95 % CI : 1.02, 1.13 ) ], senior high school education [ 1.57 ( 95 % CI : 1.44, 1.71 ) ] were associated with a higher likelihood of disease-free condition ( all P < 0.05, Table 2 ) . table 2 www.frontiersin.org

Table 2. relative risks for disease-free condition associated with likely predictors .

Importance of Contributors to Disease-Free Status

Random Forest exhibited a higher prediction performance ( as assessed by area under the curvature ) compared with the other three car learning models ( Table S3 ). Figure 4 depicts the precede predictors for disease-free status in women and men, stratified by age group, as derived from Random Forest. For both men and women, although in different orders, the six contribute predictors for disease-free condition were BMI ( roll : 6.4, 9.5 % of entire variation ), self-rated life quality ( 4.1, 6.8 % ), self-rated health ( 4.1, 6.0 % ), bolshevik meat consumption ( 4.5, 6.5 % ), chicken consumption ( 4.5, 5.9 % ), and age ( 3.9, 9.5 % ). Age was ranked as the sixth precede predictor ( 4.0 % total division ) for disease-free status in men aged 45–54 years but was the most crucial forecaster ( 9.5 % ) at senesce 55–64 years. Results from other car learning methods shown in Table S4 .

FIGURE 4

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Figure 4. Age- and gender-specific 20 conduct predictors for disease-free status derived from random forest. (A–D) Show the 20 contribute predictors for disease-free status in men aged 45–54 and 55–64 years and women aged 45–54 and 55–64 years. Disease-free condition was defined as participants aging from 45–64 years at service line to 55–75 years at the conclusion of the follow-up without developing any of the 13 chronic conditions. Machine learning methods including random forest, gradient boost machine, deep learn, and logistic regression were applied to evaluate the importance of predictors and results from random forest with the best prediction operation are shown in this figure. eVariables were inversely associated with disease-free status proportion. fVariables were positively associated with disease-free status symmetry. gVariables were non-linearly associated with disease-free status proportion .

Clustering Modifiable Healthy Factors and Disease-Free Status Years and Proportion According to Age and Gender

The 10 leading modifiable factors for disease-free condition contributed to 37.2–40.3 % of the sum discrepancy across all subgroups. We defined these 10 modifiable healthy factors as normal weight, high physical bodily process, control alcohol consumption, never smoke, none passive smoking, and diets high in fruit, vegetables, and whole milk and low in red and chicken according to their association with disease-free condition. A higher proportion of women ( 59.5 % ) displayed six or more goodly factors than men ( 43.1 % ), and older participants had more healthy factors than their younger counterparts ( both P < 0.0001 ). In the multivariable analysis, the likelihood of disease-free status increased well with the phone number of healthy factors introduce across different subgroups ( P < 0.0001 ). That is, men displaying six to ten healthy factors were 2.05–8.76 times more likely to be classified as disease-free condition compared to those with two or fewer, while the comparable count for women was 1.63–3.54. Each extra healthy factor was associated with a 15–17 % higher likelihood of disease-free status. man with six or more healthy factors had 1.0–2.5 longer disease-free years compared with those with two or less. The corresponding number for women was 0.6–2.0 years ( Figure 5 ) . FIGURE 5 www.frontiersin.org
Figure 5. Number of modifiable healthy factors and disease-free years and proportion. Disease-free condition was defined as participants aging from 45–64 years at service line to 55–75 years at the end of the follow-up without developing any of the 13 chronic conditions. The 10 leading modifiable healthy factors included BMI between 18.5 and 24.9 kg/m2, yield inhalation ≥ 2 servings/day, vegetables intake ≥ 3 servings/day, forcible action ≥ 5 sessions/week, red kernel intake ≤ 1 serving/week, chicken intake ≤ 1 serving/week, alcohol consumption between 1 and 4 drinks/week, never smoke, none passive smoke, regular whole milk drinking. aGeneralized linear arrested development model was used to evaluate the bastardly remainder of disease-free years between unlike participants with number of healthy factors with the same covariates adjusted for in the Poisson regression analysis. bMultivariate analysis was conducted using Poisson regression model with full-bodied discrepancy adjusted for age, follow-up period, country of parentage, income, education, psychological straiten, aloofness, marital condition, healthy policy, self-rated health, self-rated quality of life smoke, and family history of cardiovascular disease, cancer, diabetes, high blood pressure, hip fracture, Parkinson ‘s disease, and dementia .

Sensitivity Analysis

cross-section analysis of 152,813 participants showed that the lead predictors for disease-free were alike to those obtained in the longitudinal analysis, although in different orders. Overweight/obesity, physical inaction, smoking, passive fume, and diets first gear in vegetables and fruits and high in crimson kernel and chicken were associated with a lower likelihood of disease-free ( Table S5 ). modifiable factors accounted for 30.0–40.0 % of total variation as derived from Random Forest. Self-rated health and timbre of life were the two lead predictors of disease-free across subgroups ( Figure S1 ). The results for leading predictors from early methods can be seen in Table S6 .

Discussion

In the present analyze, we report that approximately half of all participants remained disease-free status over a mean 9-year follow-up. The six leadership predictors for disease-free condition in both men and women were BMI, self-rated health, self-rated quality of life, red kernel consumption, chicken inhalation, and long time. Participants with healthy diet habits, high physical activity, non-smokers, moderate alcohol consumption, tone down sleep clock time, a high socioeconomic status, and low psychological distress had a higher likelihood of disease-free status. A greater issue of modifiable goodly factors was associated with a higher likelihood of disease-free status and longer disease-free years, highlighting the importance of intervention on these factors .
Our learn agrees with former studies ( 24 – 27 ), showing that men had a higher incidence of cardiometabolic disorders, Parkinson ‘s disease, osteoarthritis, and hip substitution, while women were more probable to develop depressive disorder and asthma. however, unlike some studies ( 28 ), we found women had a higher incidence of cancer than men. This may be partially explained by the age range of our study participants as women aged 45–54 years had a higher incidence of cancer than their male counterparts in Australia ( 29 ). In the rankings of leading predictors, age moved from the sixth position in men aged 45–54 years to first at 55–64 years and from one-sixth to third in women. We argue that men are more affect by chronological age than women, which is coherent with previous studies that women had an advantage in life anticipation than men ( 30 ). This sex remainder might be partially attributed to the more healthy factors clustered in women than men .
We observed BMI was the leading risk agent for disease-free condition. This is consistent with a late multi-cohort study showing that fleshiness was associated with a passing of 1.0–2.5 in 10 potential disease-free years during middle and later adulthood ( 31 ). Whilst, having a high gear BMI, was ranked as the fourth leading contributor to the global effect of disease in 2015, accounting for 4.9 % of disability-adjusted life years ( 32 ). The increasing prevalence of overweight/obesity in both children and adults during 1980–2015 indicates that overweight/obesity represents a health challenge in the long-run ( 33 ). The increasing vogue in BMI might be curved by healthy diets or eminent physical activity, which may need to be intervened by governments ( 34 ). We found that diets high in fruits and vegetables and low in red meat may help promote disease-free condition, which is reproducible with previous studies investigating chronic disease and mortality ( 32, 35 ). We besides report that chicken intake was inversely associated with disease-free status likelihood, being among the leading five predictors in both women and men. This witness might be explained by the big proportion of wimp being fried, which contains higher levels of trans-fats and energy concentration resulting in an increased risk of chronic disease ( 35, 36 ). reduction of bolshevik kernel and fried chicken consumption deserves examination for populace health strategies to promote disease-free status .
Although there has been a decreasing swerve in smoking prevalence in Australia ( 35 ), levels of passive smoke ( 29.1 % ), peculiarly in public places ( 25.5 % ), were eminent in our study. We observed, on average, one current smoker affects three passive smokers and our multivariable analysis demonstrated stream smoking accounted for 3.9 % of the incidence of chronic conditions, compared with 1.8 % caused by passive smoke. frankincense, policy-responsive passive fume restraint besides deserves examination, given both direct and passive fume are major threats to disease-free condition .
The likelihood of disease-free status increased well with an increase number of modifiable healthy factors in our study. We besides observed a low proportion of participants with more than six healthy factors, suggesting public health interventions promoting modifiable healthy factors would probable help curb the increasing incidence of chronic conditions in the aging population. Our study besides demonstrated that participants having six or more modifiable healthy factors had 0.60–2.49 more disease-free years out of a 9-year follow-up. This underlines that modifications on these healthy factors may help maximize disease-free status in middle-aged individuals. Participants with lower socioeconomic status are inescapably more likely to display higher rates of unhealthy behaviors, be of elevated psychological distress and less low-cost and accessible to healthy foods or built environments in physical natural process ( 34, 37 ). Improving modifiable healthy factors among these vulnerable people should be a priority .
Self-reported overall and health quality of liveliness are stronger predictors for disease-free condition than psychological distress or individual socioeconomic factors, including income, education, health indemnity, and relative socioeconomic disadvantage in our analyze. This may be attributable to the fact that self-rated health, a quantify of socioeconomic inequality, besides reflects the sensing of the biological and psychological status of individuals in given cultural and social circumstances ( 38 ). Individuals at different stages of animation may differ in the evaluation of their health status ( 38 ). This is consistent with our findings that self-rated health and quality of life ranked lower as a predictor for disease-free status in individuals aged 55–64 years compared with those aged 45–54 years. consistent with former studies ( 39 ), the hazardous effects of psychological distress on disease-free condition was observed in our study, peculiarly among the younger population .
To our cognition, this is the first discipline to comprehensively probe associations of multiple predictors including biological, socioeconomic, psychological, and geographic factors with disease-free status in a community-dwelling population with big sample distribution size and long-run follow-up. We exploited multiple machine learning methods to analyze the leave predictors given the first gear prediction performance of traditional arrested development exemplar ( 18, 40 ) and examined the affiliation of clustering modifiable healthy factors with disease-free condition .
Some limitations should besides be considered. first, we did not utilize the traditional definition of disease-free status that involves physical conditions, and self-rated genial and cognitive routine. Despite this, we included the majority of chronic conditions that contribute to mortality and caused by deterioration of physical, mental, or cognitive function. second, although some chronic conditions that may be related to worse healthy aging were not included in our analysis because of the inaccessibility, the necessitate conditions contributed to a prevailing proportion of total mortality in Australia ( 6 ). third, all data regarding exposures ( apart from geographic information ) were self-reported ; therefore, we can not deny the likely influences of self-reporting bias. however, the measurement errors would be more probable to bias true associations to the null as the data were collected before any of the chronic conditions of pastime occurred. Fourthly, participants in our study were, on average, healthier than the general population in New South Wales ; however, exchangeable associations between exposures and health outcomes in this cohort study have been reported previously compared with a population spokesperson study ( 20 ). Fifthly, the definition of incident chronic conditions based on MBS and PBS data in our study may be biased because the awareness of diagnosis of a disease is dependent of health care seeking behavior and handiness, although we controlled the refer confounders including department of education, family income, health insurance, psychological distress, overall health, geographic aloofness, family history of chronic diseases, senesce, and sex in the multivariable-analysis. Sixthly, the definition of chronic conditions was based on both self-reported and MBS/PBS data at service line but MBS/PBS data alone during follow-up, which might have introduced some bias. seventhly, it seems that disease events for individuals with general beneficiaries might be less likely to be captured using PBS data compared to those with concessional beneficiaries before July 2012 ( 41 ). however, the combination of PBS and MBS data to detect chronic conditions in our analyze might have largely reduced this bias, which is reflected in the gradual decrease course of disease-free condition without astute decrease over 10 years in trope 2. Eightly, although the participation rate of our sketch was similar to former studies of this kind ( 42, 43 ), the relatively low engagement rate ( 18 % ) might limit the generalization of our findings.

In termination, despite chronological senesce plays an authoritative function in disease-free condition, modifiable factors including BMI, diets, physical natural process, direct, and collateral smoke, and alcohol pulmonary tuberculosis accounted for a overriding proportion of total division suggesting that improvement in healthy behaviors may substantially promote disease-free status in the middle-age population. Participants with low socioeconomic condition, high psychological distress, or poor/fair self-rated health are more in necessitate of health services and social support. The findings provide evidence on priorities of health strategy to promote disease-free status in middle-aged men and women, resulting in increased population longevity in the long-run .

Data Availability Statement

The datasets for this manuscript are not publicly available because The data that support the findings of this discipline are available from The Sax Institute but restrictions apply to the handiness of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with license of The Sax Institute. Requests to access the datasets should be directed to MH, mingguang.he @ unimelb.edu.au .

Ethics Statement

The 45 and Up study has ethical blessing from the UNSW Human Research Ethics Committee. The study protocol was approved by the Royal Victorian Eye and Ear Hospital Human Research Ethics Committee. Participants provided consent to follow-up and link their data to routine health datasets .

Author Contributions

ten, LZ, and MH conceived and designed the research. ten and LZ conducted data analysis and interpretation. XS wrote the initial blueprint of the manuscript. x, LZ, WW, SK, JW, and MH revised the manuscript. All authors read and approved the final manuscript .

Funding

MH received support from the University of Melbourne at Research Accelerator Program and the Centre for Eye Research Australia Foundation. The Centre for Eye Research Australia received operational Infrastructure Support from the victorian State Government. The specific project was funded by the Australia China Research Accelerator Program at Centre for Eye Research Australia. MH was besides supported by the Fundamental Research Funds of the State Key Laboratory in Ophthalmology, National Natural Science Foundation of China ( 81420108008 ). The sponsor or fund organization had no role in the purpose or behave of this research .

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or fiscal relationships that could be construed as a electric potential battle of interest .

Acknowledgments

This research was completed using data collected through the 45 and Up Study ( www.saxinstitute.org.au ). The 45 and Up Study is managed by the Sax Institute in collaboration with major partner Cancer Council NSW ; and partners : the National Heart Foundation of Australia ( NSW Division ) ; NSW Ministry of Health ; NSW Government Family and Community Services–Aging, Carers and the Disability Council NSW ; and the Australian Red Cross Blood Service. We thank the other investigators, staff, and participants of the 45 and Up Study cohort for their significant contributions .

Supplementary Material

The Supplementary Material for this article can be found on-line at : hypertext transfer protocol : //www.bestofcalgary.city/articles/10.3389/fpubh.2019.00320/full # supplementary-material

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