Longevity Industry Dashboards
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.
200+ Analysed Parameters per country
The Framework of Healthy Longevity
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.
In the methodology, we have identified several metrics that most correlate with healthy longevity. Among those metrics are HALE, Life Expectancy, DALY rates, Healthcare Efficiency Index, Current Health Expenditure per Capita, Gross domestic expenditure on R&D and a few others. Those metrics received higher weight and categories in which they are allocated represent higher importance to final evaluation.
Chosen metrics can be estimated at international, national or local levels to:
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Compare population health across communities and over time;
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Provide a full picture of which diseases, injuries, and risk factors contribute the most to poor health in a specific population;
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Highlight the state's contribution to development and maintenance of healthy longevity;
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Measures of HALE are normally presented by age, sex and geographical region;
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Compare countries in order to share the knowledge and experience regarding healthy longevity.
Application of Metrics
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.
Goals of the Methodology
This methodology answers the following questions:
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What specific features of healthcare systems, socio-economic conditions, environmental factors affect public health?
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How does the impact of factors differ across countries?
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What constellation of factors contributes the most to healthy longevity?
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Which factors are the main drivers of disability adjusted years?
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What countries are leaders in longevity governance?
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What can be done to improve HALE, Life Expectancy and other correlated metrics in each country globally?
Longevity Progressiveness Methodology is an analytical tool that focuses on 50 countries. The goal is to evaluate the current state of longevity and related initiatives among analyzed countries, find and determine strengths and weaknesses of the healthcare system in terms of the 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).
Methodology Description
The tool was created for comparing the level of development of the country's current longevity state, longevity policies and initiatives among other analyzed countries. Quantitative data analysis in this methodology includes more than 7 000 numerical values to indicate Longevity. Therefore, the five step approach was used to conduct this systematic search:
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Providing research for absolute values.
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Values comparison evaluation.
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Weight ratios estimating.
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Summarizing scores.
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Results analysis.
The methodology includes consistent and logical assessments of quantitative and qualitative indicators of the countries being evaluated and delivers a dynamic score for each country in the analysis set. The score for each country may vary depending on the number of countries in the analysis set.
Metrics which are covered by this report could be applied for the assessment of the 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 the 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.
Methodology Structure
The Methodology is divided into three main parts
1st Part:
Data input. Filling in all the necessary data for all countries.
The Data Input Part contains a table which is structured into 7 main categories:
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Health Status
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Government care
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Gov policy
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Demography
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Society
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Ecolog
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Economy
A total of +200 evaluation metrics is given in the table. The data for all metrics may be found in open-source databases of international organizations related to Healthcare and Longevity respectfully, such as World Health Organization, World Bank, Organization for Economic Co-operation and Development.
2nd Part:
Data Evaluation. All the data is being processed, evaluated and weighted due to the importance and correlation with longevity.
The Data Evaluation Part contains an assessment table which assigns a score for each metric for each country. The score is formed by analyzing data provided in the Data input Part.
Each set of data for each metric from the Data input Part is divided by normal distribution method into three groups in order to find out the allocation of a particular country’s metric within the analyzed set. Afterwards, depending on the allocation of a particular country’s metric within the analyzed set, a metric receives a score from 1 to 3 (where 1 is a low score and 3 is a high score). A country’s metric receives a score of 1 when its value is below average within the set. A country’s metric receives a score of 2 when its value is within the range of average plus normal distribution deviation value within the set. Finally, a country's metric receives a score of 3 when its value is above the range of average plus normal distribution deviation value within the set. A set of metrics within Gov policy category receive a score from 0 to 1 (where 0 is “exists” and 1 is “does not exist”).
All the assessment occurs automatically using the MS Excel formulas. Afterwards, all metrics’ points are evaluated one more time in the Weighted Evaluation Table and assigned with a particular weight.
3rd Part:
Results. After evaluation the methodology presents final scores for Longevity Progressiveness for all countries.
The Result Part provides the user of the tool with a final view on total scores for each category for each country and an overall total score for each country. A higher score for category and an overall total score mean a higher level of development within the Longevity Industry of a particular country.
Despite providing in the ResultsTable the most comprehensive default view that can be achieved by a user using this Methodology, a user can adjust and filter the table depending on his needs. Slicer filters let users distinguish different levels of detalization. Those levels include filtering an amount of desirable for a view of countries, categories and also metrics.
Metrics 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.
Health-Efficiency Index is an index conducted by Bloomberg since 2013, which tracks life expectancy and medical spending to determine which health-care systems have the best outcomes.
Gross domestic spending on R&D is defined as the total expenditure (current and capital) on R&D carried out by all resident companies, research institutes, university and government laboratories, etc., in a country. It includes R&D funded from abroad, but excludes domestic funds for R&D performed outside the domestic economy. This indicator is measured in USD constant prices using 2010 base year and Purchasing Power Parities (PPPs) and as percentage of GDP.
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 the 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 2019 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.
● Government Care
Measured by government activities and spendings regarding healthcare..
● Government Policy
Measured by amount of laws, policies and plans for longevity initiatives .
● Health Status
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.
● Ecology
Measured by access to safe water sources, environment pollution, natural factors.
● 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.
