Longevity Industry Dashboards

Longevity Governance

Dashboard

Introduction & Big Data Comparative Analysis Framework

Life expectancy is increasing all around the world. While there have been obvious fluctuations in the dynamics of this statistically measured demographic indicator, life expectancy at birth overall has been steadily increasing for many years. It has more than doubled in the last two centuries.
 
This increase was previously driven by reductions in infant mortality. But since around the 1950s, the main factor of steady increase has been reductions in mortality at older ages. This has contributed to the ageing of the population and critical changes in age distribution, which can be described with old-age dependency ratio.
 
The major problem with merely increasing life expectancy is that it also increases morbidity because people live long enough to get more age-related disease, disability, dementia, and dysfunction. Many serious diseases have increased prevalence with age, including cancer, heart disease, stroke, respiratory disease, kidney disease, dementia, arthritis, and osteoporosis.
 
Consequently, it is unclear why countries are investing so much money in research focused on reducing death rates in the elderly, if the consequence is advancing ageing, that can be described as the increase in disability years, plus pension, and social and medical costs, in an unsustainable way.
 
Ageing is caused by many different processes, that is why healthy longevity goes far beyond demographic characteristics and medical research problems on how to increase the quantity of life.
 
This paper seeks to identify which health system characteristics, socio-economic factors, and environmental conditions are likely to increase health-adjusted life expectancy and improve the quality of life.
 
The analysis is based on the 200 parameters that define healthy longevity across the chosen 50 countries and their impact on the gap between health-adjusted life expectancy and life expectancy at birth.

200 Analysed Parameters per country

The Framework of Healthy Longevity

HALE, a specific measure of healthy Longevity, is an indispensable metric for Aging Analytics Agency.
 
Today’s increased global Longevity is a “problem of success”, an inevitable consequence of sharp increases in sanitation, diet, health care, elderly care, and geriatric medicine, a set of changes which have occurred suddenly within the lifetimes of today’s elderly. But this increased Longevity is not a consequence of decreased aging; this life extension is not accompanied by a commensurate extension in health. As a result, increased global Longevity is producing a global aging demographic, an impending crisis frequently referred to as the “silver tsunami”.
 
In order to float rather than sink, Longevity must become an asset. And this means altering the nature of aging entirely, reducing the period of financially and socially inactive decrepitude at the end of life. Specifically, it means utilizing technology to ensure that these longer lives are also healthy, productive, financially active lives, and creating a system of government frameworks and financial incentives to create and sustain this case of affairs.
 
The most important technical metric for this task is HALE (health-adjusted life-expectancy). It belongs to a set of metrics known as HALYs (health-adjusted life-year). It includes HALE, a measure of population health that takes into account mortality and morbidity, Quality-adjusted life years (QALYs) and disability-adjusted life years (DALYs), the latter being types of HALY whose original purposes were at variance.
HALE can be estimated at international, national or local levels to:
  • Compare population health across communities and over time;
  • Provide a full picture of which diseases, injuries, and risk factors contribute the most to poor health in a specific population (this is probably the most common use of summary measures of health);
  • Assess which information or sources of information are missing, uncertain, or of low quality;
  • Measures of HALE are normally presented by age, sex and geographical region.

The Uses of HALE

 
Research on healthy ageing encompasses: the biological processes contributing to ageing per se; the socio-economic and environmental exposures across life which modulate ageing and the risk of age-related frailty, disability and disease; and the development of interventions which may modulate the ageing trajectory.
 
 
Such research needs measures of health span which, in addition to chronological age, can characterise and quantify important functions which are subject to decline at faster, or slower, rates during individual human ageing. Furthermore, it is impossible to determine whether biotechnologies for aging have been successful if we cannot tell how advanced the aging process is in any given individual
 
The role of government strategy is of immediate importance in advancing the Longevity industry from its present point, and governments must be able to monitor and describe biomedical progress. Metrics for tangible progress are absolutely essential component of any government strategic agenda. It will be impossible to make concrete claims regarding global progress in biotechnology - and in preventive medicine in particular - without an agreed set of metrics.
 
HALE serves as a crucial metric type in many Aging Analytics Agency reports and publications, most notably National Longevity Development Plans: Global Overview 2019 (First Edition).

Goals of the Research

 
This report aims to answer the following questions:
  • What specific features of healthcare systems, socio-economic conditions, environmental factors affect public health?
  • How does the impact of factors differ across countries?
  • What constellation of factors contributes the most to healthy longevity?
  • Which factors are the main drivers of disability adjusted years?
  • What countries are leaders in longevity governance?
  • Why disproportionate healthcare expenditures in the United States contributes to bad health care system performance and reduction in life expectancy in recent years?
  • Why the health care system in Singapore is considered to be one of the most efficient in the world?
  • What can be done to improve HALE in each country globally?
Global Longevity Governance Landscape is an analytical report that focuses on 50 countries Big Data comparative Analysis of longevity progressiveness. The goal is to find and determine metrics and methods that could better assess the health status and capture effectiveness of healthcare system in terms of rising trend of longevity.
 
Nowadays such complex indicators as life expectancy and health-adjusted life expectancy goes beyond the traditional measures of demographic potential of a particular countries, major causes of death, and probabilities of premature death (based on life tables).

Report Methodology

The focus of this study was primarily on 6 levels of analysis and aimed at perceptions of actual changes of interconnections between the nearly 200 parameters.
 
Quantitative data analysis in this report include approximately 10 000 numerical values to indicate trends in Longevity. Therefore, the six step approach was used to conduct this systematic search:
 
1. Providing of statistical analysis for absolute values.
2. Indexes research.
3. Ratios estimating.
4. Counting of growth rates.
5. Growth rates of ratios valuation.
6. Deep analysis of effectiveness ratios.
 
Report Methodology The Report Methodology is needed to develop health systems performance indicators, data collection strategies and tools for monitoring at national and global levels. Using of key metrics is aimed at evaluation what factors have the greatest influence on HALE and life expectancy in a particular country from the ranking. In some extension, this metrics system is well-defined performance measurement that is used to analyze and optimize all relevant healthcare processes to increase the level of industry effectiveness.
 
Therefore, metrics which are covered by this report could be applied for the assessment of healthcare system and strategies for health improvement. As well as all above indicators are complex and tangible, this metrics system can be used for deep analysis of current state of a country, its prospects and overall industry optimization. Secondary data sources are reliable and accurate: local health authorities, government, WHO, OECD, The World Bank.

Report Structure

The Introduction begins by defining each of the HALYs, explaining why HALE may be the most useful of the HALY metrics, and the normal difference between HALE and life expectancy, and then lists data sources to be used in the analysis that follows.
 
Main Patterns identifies the patterns that emerge across the countries, providing healthcare system overviews, healthcare expenditures, eHealth efficiency and air pollution across countries with high medium and low HALE and life expectancies.
 
Ranking of Countries provides a detailed methodology for ranking countries according to their healthy longevity-determining factors, divided into five groups: Economy, Health and Healthcare, Society, Demography, Environment and Infrastructure and then provides longevity ranking and scores for each of sub-rankings.
 
Current trends in Life Expectancy and Healthy Longevity examines gap between life expectancy by gender and potential explanations for the slowdown in improvements in recent years across chosen countries.
 
Public Spendings and Healthcare Efficiency shows that changes in demography and health conditions are putting pressure on public finance. Yet, a considerable part of this health expenditure makes little or no contribution to improving people's health.
Climate and Healthy Longevity briefly analyses the effects of climate on HALE in every world region.
 
Healthy Longevity and Metabesity discusses the relationship between healthy longevity and non-communicable diseases that have common metabolic roots.
Singapore and USA Healthy Longevity Comparison summarises specific features of healthcare systems and derives factors that affect public health both in Singapore and the United States. The main focus is made on healthcare system efficiency of Singapore and disproportionate healthcare expenditures in the U.S.
 
Singapore and Hong Kong Healthy Longevity Comparison compares determining factors of healthy longevity between Hong Kong, SAR and Singapore. The emphasis is made on healthcare accessibility and affordability of healthcare provision.
 
HALE and Supercentenarian Distributions discusses the distribution of centenarians across nations.
 
Analytical Methodology explains in detail the analytical process behind the report, beginning by illustrating the multiple layers of metrics and then methodologies for ratios, growth rates, growth rates of ratios, meteorological analysis impact and indexes of health status, society, retirement, immunization, economy and mental health.

HALE |  QALY | DALY: Definitions

Health Adjusted Life Expectancy (HALE) is a measure of population health that takes into account mortality and morbidity. It adjusts overall life expectancy by the amount of time lived in less than perfect health. Global HALE at birth for females was only 3 years greater than that for males. In comparison, female life expectancy at birth was almost 5 years higher than that for males.
 
Health-adjusted life years (HALYs) are population health measures permitting morbidity and mortality to be simultaneously described within a single number. They are useful for overall estimates of burden of disease, comparisons of the relative impact of specific illnesses and conditions on communities, and in economic analyses. Quality-adjusted life years (QALYs) and disability-adjusted life years (DALYs) are types of HALYs whose original purposes were at variance.
 
QALY is a generic measure of disease burden, including both the quality and the quantity of life lived. It is used in economic evaluation to assess the value for money of medical interventions. One QALY equates to one year in perfect health. If an individual's health is below this maximum, QALYs are accrued at a rate of less than 1 per year. To be dead is associated with 0 QALYs.
 
DALYs measure the amount of life lost in a population as a result of premature death or disability. They can be used to estimate the burden of disease on populations. DALYs were used in the Global Burden of Disease study to enable mortality and morbidity comparisons to be made across countries. Weightings were applied to conditions by using the time trade off approach, in which people were asked to consider living more years in imperfect health compared with fewer years in perfect health. One DALY can be thought of as one lost year of "healthy" life. The sum of these DALYs across the population, or the burden of disease, can be thought of as a measurement of the gap between current health status and an ideal health situation where the entire population lives to an advanced age, free of disease and disability.

Why HALE?

Disability-adjusted life expectancy (DALE) integrates data on mortality, long-term institutionalization and activity limitations in the population and represents a comprehensive index of population health status. Thus, the emphasis is not exclusively on the length of life, but also on the quality of life. Quality-Adjusted Life Year (QALY) specifically refers to the balance between the length of time someone lives and the quality of life in terms of the absence of disease.
 
Director of the National Institutes of Health (NIH) Francis Collins, have called DALYs and similar metrics like the QALY (DALY = Lifetime - QALY) “only partially successful in providing the kind of information that policy-makers need,” and urged the NIH to fund the “development and application of more rigorous models.”
 
HALE provides a summary of overall health conditions for a population, which are in turn an integral part of development. While communicable diseases such as HIV/AIDS, tuberculosis and malaria continue to cause substantial loss of health and mortality in developing countries, particularly African countries, non-communicable diseases and injuries are responsible for more than half of all lost years of healthy life in developing as well as developed countries. HALE thus provides a more complete picture of the impact of morbidity and mortality on populations, than DALY, QALY or simple Life Expectancy alone.

Health-Adjusted Life Expectancy and Life Expectancy

This table represents the distribution of countries by their Health Adjusted Life Expectancy (HALE) and estimated average life expectancy (LE) and the gap. The gap is measured as absolute difference between life expectancy and HALE in a particular country.
 
Countries are distributed unevenly, because the major countries are developed countries with approximately the same level of development and welfare.
 
As can be seen, there are 10 countries in the group that combines a high level of HALE and LE and a big gap between the two indicators, which makes it the biggest group in the sample.

Big Data Comparative Analysis Framework

Data collection is an essential stage of the research. Accurate data collection is essential to maintaining the integrity of research. To answer relevant questions of the working paper and evaluate outcomes, data used for this analysis was collected from credible sources. These include the following:
Introduction & Big Data Comparative Analysis Framework

Global Longevity Governance Landscape
Conceptual Framework

Global Longevity Governance Landscape is an analytical report that focuses on 50 countries Big Data comparative Analysis of longevity progressiveness. The goal was to find and determine metrics and methods that could better assess the health status and capture effectiveness of healthcare system in terms of rising trend of longevity.

Nowadays such complex indicators as life expectancy and health-adjusted life expectancy goes beyond the traditional measures of demographic potential of a particular countries, major causes of death, and probabilities of premature death (based on life tables).

First, longevity progressiveness is important for driving economic progress and competitiveness—both for developed and developing economies. Many governments are putting policies on longevity at the center of their growth strategies and budget planning. Second, the definition of longevity has broadened—it is no longer quantitative increase in life expectancy at birth. Longevity could be and is more general and horizontal in nature. Today longevity is about social inclusiveness, high quality of life, technical innovations in care delivery and medical treatment, and modified business and governmental models. Last, but foremost, longevity progressiveness focuses not on increase life span but to reduce number of years in poor health.

This paper seeks to identify which health system characteristics, socio-economic factors, and environmental conditions are likely to increase health-adjusted life expectancy and improve the quality of life.

The analysis is based on the 200 parameters that define healthy longevity across the chosen 50 countries and their impact
on the gap between health-adjusted life expectancy and life expectancy at birth.

The rich data base, including absolute values, ratios, indexes can be used to monitor performance of longevity progressiveness across countries over time and to benchmark developments against economies within the same region, income group classification or a particular initiated cluster.

Big Data Comparative Analysis Framework

Big Data comparative analysis is based on the specific nature of parameters and their relationships that determine the development of healthy longevity progressiveness across countries of different levels of economic development and income group.

Global Longevity Governance Landscape
Conceptual Framework

Longevity Ranking
The rankings show how countries compare in terms of health and wellbeing. The values, on which the rankings are based,
show how countries are performing. In particular, they show how different countries compare with the best-performing countries and their potential for improvement. The difference in Index values between countries is sometimes minimal, as there several countries with high level of life expectancy and of the same level of development. A difference of 0.1 or more, points can be considered statistically significant.

The Ranking has been calculated using the most relevant, reliable data for 2016 from international sources that is comparable across countries. Data from national sources is often more up to date than international data sets because of the time it takes to process, standardise and introduce data into international data sets. This means that the Ranking does not necessarily reflect the current situation, such as the outcomes of policies that have recently been introduced.
Sub-indexes:

● Economy
Measured by unemployment rate, poverty rate in old age, living standards using GDP per capita, income Gini coefficient.

● Health and Healthcare
Measured by life expectancy at birth, healthy life expectancy at birth, chronicle disease burden, healthcare expenditures and psychological well-being. Good physical and mental health is critical to social and economic engagement of people.

● Environment and Infrastructure
Measured by access to safe water sources, physical safety, natural factors. These indicators capture the enabling attributes of the communities in which older people live.

● Society
Measured by social connection and development of human capital.

● Demography
Measured by major demographic indicators.

Global Longevity Governance Landscape
Conceptual Framework

Determining Healthy Longevity Factors
The major problem with merely increasing life expectancy is that it also increases morbidity because people live long
enough to get more age-related disease, disability, dementia, and dysfunction. Many serious diseases have increased
prevalence with age, including cancer, heart disease, stroke, respiratory disease, kidney disease, dementia, arthritis, and
osteoporosis.
Ageing is caused by many different processes, that is why healthy longevity goes far beyond demographic characteristics
and medical research problems on how to increase the quantity of life.
To define major risks and favorable factors and their compound impact on healthy longevity we use multiple linear
regression analysis, which is a quantitative method used to test the nature of relationships between a dependent variable
and two or more independent variables. Gap between life expectancy and health-adjusted life expectancy was chosen as
dependent variable. All independent parameters were divided into six pillars: general economic conditions, mortality rates,
lifestyle factors, environment, demography and healthcare. Variables are evaluated by what they add to the prediction of the
dependent variable which is different from the predictability afforded by the other predictors in the model.
Consequently, it is unclear why countries are investing so much money in research focused on reducing death rates in the
elderly, if the consequence is advancing ageing, that can be described as the increase in disability years, plus pension, and
social and medical costs, in an unsustainable way.
To help formulate and prioritize among social and health government expenditures, estimations of relationship between
HALE and public spendings for countries that differ solely in their national plans, target programmes can provide valuable
information. The estimator of the relationship between HALE and public spending is intraclass correlation coefficient (ICC).

Big Data Analysis

Absolute Values, Indices, Ratios

Overall, there are 6 levels of proprietary metrics, which differ based on the nature of the parameters they consist of. Together, they comprise 200 separate metrics. 

Indicators, their growth rates and their ratios are calculated separately and then integrated in the final metrics system.

The whole of the metrics can also be subdivided into 2 categories based on the logic of the parameters, namely:

● Stimulators (variables that favorably affect average life expectancy and health-adjusted life expectancy); 
● Destimulators (variables that negatively affect average life expectancy and health-adjusted life expectancy).
Thus, the ranking system reflects both strengths and opportunities of different countries regarding the development of
healthcare system and strategies for health improvement. It can be applied for the evaluation of the current state of a country, as well as of its prospects.

Growth Rates, Growth Rates of Ratios, Effectiveness Ratios

Absolute values are enhanced by relative ones, and the use of both in combination enables a clearer understanding of interconnections between the parameters and provides the opportunity to investigate what factors have the greatest influence on HALE and life expectancy in a particular country.

There is multicollinearity between some metrics. It is caused by use of dummy variables and by the inclusion of a variable which is computed from other variables in the data set.

Each level of metrics is based upon the extension, further subdivision or comparative combination of the metrics in the preceding level, or is derived from insights provided by them.

The research is based on open source data and information given by WHO, OECD, The World Bank, and different institutions of each specific country.

50 Countries and 200 Parameters

Patterns recognition is based on a comparison of 200 parameters across 50 countries according to their distribution and variation. It aims to derive interconnection between metrics and classify countries into groups.

Big Data Comparative Analysis of Longevity

Patterns recognition is based on a comparison of 200 parameters across 50 countries according to their distribution and variation. It aims to derive interconnection between metrics and classify countries into groups.