Association Between Regional Difference in Heart Rate Variability and Inter-prefecture Ranking of Healthy Life Expectancy: ALLSTAR Big Data Project in Japan

Author(s):  
Emi Yuda ◽  
◽  
Yuki Furukawa ◽  
Yutaka Yoshida ◽  
Junichiro Hayano
2019 ◽  
Vol 7 ◽  
pp. 205031211985225 ◽  
Author(s):  
Junichiro Hayano ◽  
Masaya Kisohara ◽  
Yutaka Yoshida ◽  
Hiroyuki Sakano ◽  
Emi Yuda

Objectives: Senility death is defined as natural death in the elderly who do not have a cause of death to be described otherwise and, if human life is finite, it may be one of the ultimate goals of medicine and healthcare. A recent survey in Japan reports that municipalities with a high senility death ratio have lower healthcare costs per late-elderly person. However, the causes of regional differences in senility death ratio and their biomedical determinants were unknown. In this study, we examined the relationships of the regional difference in senility death ratio with the regional differences in heart rate variability and physical activity. Methods: We compared the age-adjusted senility death ratio of all Japanese prefectures with the regional averages of heart rate variability and actigraphic physical activity obtained from a physiological big data of Allostatic State Mapping by Ambulatory ECG Repository (ALLSTAR). Results: The age-adjusted senility death ratio of 47 Japanese prefectures in 2015 ranged from 1.2% to 3.6% in men and from 3.5% to 7.8% in women. We compared these ratios with the age-adjusted indices of heart rate variability in 108,865 men and 136,536 women and of physical activity level in 16,661 men and 21,961 women. Heart rate variability indices and physical activity levels that are known to be associated with low mortality risk were higher in prefectures with higher senility death ratio. Conclusion: The regional senility death ratio in Japan may be associated with regional health status as reflected in heart rate variability and physical activity levels.


2020 ◽  
Vol 15 (5) ◽  
pp. 35-55
Author(s):  
N.P. STARYKH ◽  
◽  
A.V. EGOROVA ◽  

The purpose of the article is to analyze the current state of healthcare in Russia. Scientific novelty of the study: the authors suggest that the efficiency of the health care system depends on the state of such indicators of public health as life expectancy and healthy life expectancy. Life expectancy is an integrated demographic indicator that characterizes the number of years that a person would live on average, provided that the age-specific mortality rate of a generation would be at the level for which the indicator was calculated throughout life. The indicator ‘healthy life expectancy’ is formed by subtracting the number of years of unhealthy life (due to chronic diseases, disabilities, mental and behavioral disorders, etc.) from the life expectancy indicator. Results: the article presents an analysis of the current state of Russian healthcare based on statistical data provided by the Federal State Statistics Service, the World Health Organization, and world rankings. Attention is focused on the perceptions of Russians about the quality of medical services and Russian healthcare. Conclusions about the current state of health care in Russia are formulated by the authors, based on a secondary analysis of statistical data, as well as data from sociological research presented by leading Russian sociological centers.


2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 505-505
Author(s):  
Matthew Farina ◽  
Phillip Cantu ◽  
Mark Hayward

Abstract Recent research has documented increasing education inequality in life expectancy among U.S. adults; however, much is unknown about other health status changes. The objective of study is to assess how healthy and unhealthy life expectancies, as classified by common chronic diseases, has changed for older adults across education groups. Data come from the Health and Retirement Study and National Vital Statistics. We created prevalence-based life tables using the Sullivan method to assess sex-specific life expectancies for stroke, heart disease, cancer, and arthritis by education group. In general, unhealthy life expectancy increased with each condition across education groups. However, the increases in unhealthy life expectancy varied greatly. While stroke increased by half a year across education groups, life expectancy with diabetes increased by 3 to 4 years. In contrast, the evidence for healthy life expectancy provides mixed results. Across chronic diseases, healthy life expectancy decreased by 1 to 3 years for respondents without a 4-year degree. Conversely, healthy life expectancy increased for the college educated by .5 to 3 years. While previous research shows increases in life expectancy for the most educated, trends in life expectancy with chronic conditions is less positive: not all additional years are in lived in good health. In addition to documenting life expectancy changes across education groups, research assessing health of older adults should consider the changing inequality across a variety of health conditions, which will have broad implications for population aging and policy intervention.


2021 ◽  
Vol 46 (3) ◽  
pp. 395-408
Author(s):  
Jennifer Carter ◽  
John Mathers ◽  
Susan Fairweather‐Tait ◽  
Susan Jebb ◽  
Naveed Sattar ◽  
...  

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