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Big Data Comparative Analysis: Healthy Longevity in 50 Regions 

The Network Graph is used to display relations between various factors that determine Longevity. All parameters are divided into six pillars: general economic conditions, mortality rates, lifestyle factors, environment, demography and healthcare.

The graph itself visualizes how metrics are interconnected with each other. The relationship between them are displayed with lines. Bold arrows indicate direct impact on health longevity, which is determined as difference between life expectancy at birth and health-adjusted life expectancy. Dashed lines reveal multicollinearity, a state of very high intercorrelations or inter-associations among the independent variables, factors across different groups.
62 Healthy Longevity Determining Factors

Comparative Longevity Analysis

The Network Graph is used to display relations between various factors that determine Longevity. All parameters are divided into six pillars: general economic conditions, mortality rates, lifestyle factors, environment, demography and healthcare.
The graph itself visualizes how metrics are interconnected with each other. The relationship between them are displayed with lines. Bold arrows indicate direct impact on health longevity, which is determined as difference between life expectancy at birth and health-adjusted life expectancy. Dashed lines reveal multicollinearity, a state of very high intercorrelations or inter-associations among the independent variables, factors across different groups.
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Conceptual Model of the Gap Determinants

The determinants of the gap between life expectancy and HALE are complex and comprise multiple policy domains. One basic but important conceptual model that can be used to illustrate the breadth of these determinants is shown above. The determinants are presented in a set of boxes, the size of which represents the strength of relationship with gap and the color represents its significance.
The biggest box belongs to general economic conditions that have important long-term health effects. The next box contains society's basic health institution, which can both sustain and impair a healthy existence. The next сell emphasizes the critical role of living conditions. The box below to the living conditions highlights the importance of individual behavioral choices (cigarette smoking, risk-taking behaviors) in the determination of the gap between life expectancy and HALE. The last box but not the least implicates the assessment of causes of death contribution to the gap.

Because of the issue of multicollinearity as all the mentioned factors are interconnected we will build five different models
to identify unmixed impact of each individual group of factors on the gap.
64 Conceptual Model of the Gap Determina

Methodology of Multiple Linear Regression Analysis

Multiple linear regression analysis is a quantitative method used to test the nature of relationships between a dependent variable and two or more independent variables.

Dependent and Independent Variables
The variable whose value is to be predicted is known as the dependent variable and the ones whose known values are used for prediction are known independent (exploratory) variables. Sometimes the dependent variable is also called endogenous variable, criterion variable, prognostic variable or regressand. The independent variables are also called exogenous variables, predictor variables or regressors.

Significance and Goodness of Fit
At the center of the multiple linear regression analysis lies the task of fitting a single line through a scatter plot. More specifically, the multiple linear regression fits a line through a multi-dimensional cloud of data points. 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. The F-test is used to assess whether the set of independent variables collectively predicts the dependent variable. R-squared—the multiple correlation coefficient of determination—is reported and used to determine how much variance in the dependent variable can be accounted for by the set of independent variables. Beta coefficients are used to determine the magnitude of prediction for each independent variable. For significant predictors, every one unit increase in the predictor, the dependent variable will increase or decrease by the number of unstandardized beta coefficients. A standardized beta coefficient compares the strength of the effect of each individual independent variable to the dependent variable. The higher the absolute value of the beta coefficient, the stronger the effect.

Basic Modeling Assumptions
● Assumption of linearity. There is a linear relationship between dependent and independent variables.
● Assumption of homoscedasticity. Data values for dependent and independent variables have equal variances.
● Assumption of absence of collinearity or multicollinearity. There is no correlation between two or more independent
variables.
● Assumption of normal distribution. The data for the independent variables and dependent variable are normally
distributed.

Economic Instability and Gap between HALE and Life Expectancy

The Consumer Price Index (CPI) is a measure of the average change over time in the prices paid by consumers for a market basket of consumer goods and services. CPI characterises prices instability and economic instability in general as rapid inflation indicates recession or systemic crises.

The lowest level of CPI in 2019 was observed in Switzerland and the highest was in Iran.

Unemployment and Healthy Longevity

High unemployment leads to reduction of health-adjusted life expectancy. Countries with low unemployment (close to natural level of unemployment) have higher HALE. Hgh unemployment rate leads to social disproportions and unaffordability of basic basket of goods and services.
 
But calculations also show that increase in unemployment leads to decrease in gap. Such inverse relations can be explained parameters. Both life expectancy and HALE are modeled indicators, but HALE is inertial by nature and has lower elasticity comparing to life expectancy.

Public Healthcare Expenditure and Out-of-pocket Expenditure

As a result of the study, we found out, that mentioned above dimensions of national healthcare systems have a significant impact on the gap and its change. Every single unit rise of domestic private health expenditures leads to 0.044 years or 16 days decrease in the gap between life expectancy and HALE. It is necessary to pay attention to the sign of the coefficient for “Public health care expenditure” as it shows that increase in the level of public expenditure can cause the gap to increase. This indicates health care system inefficiency. In general, the variance of healthcare peculiarities explains 24.1 % of the gap variance. According to F-test and p-value, this model is significant. Therefore, there is enough evidence in the data to suggest that the linear relation between the gap and healthcare systems exists. The standard error of estimate measures that on average prediction values of the model and actual values of the gap differ by 0.718 years.
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Public Healthcare Expenditure and Out-of-pocket Expenditure

Unregulated direct charges often constitute a major access barrier to needed health care and contribute to high out-of-pocket payments generating problems of financial protection. Out-of-pocket payments absorb household’s financial resources and make healthcare unaffordable for low socioeconomic groups as a result large discrepancies appears in healthcare status. In contrast, public spending on health is central to universal health coverage and social protection, but there is no clear trend of. In the United States high healthcare expenditure is a result of high administrative cost and corruption in healthcare.

Healthcare Accessibility

The balance of medical facilities and healthcare professionals is vital to provide high-quality healthcare services. Countries that are below the circle and extreme outliers, such as Cuba and China face problems of staff shortage, long waiting time or both of them.
 
A key issue is that the supply of doctors has not kept pace with demographic trends and the increasing demands of an ageing population. Timeliness of healthcare services closely relates to staff shortage. These problems hinders governments to focus efforts on care-delivery improvements.

Ageing Population and Current Healthcare Expenditure

The total-age-dependency ratio is the ratio of the sum of the number of young and the number of elderly people at an age when both groups are generally economically inactive, (i.e. under 15 years of age and aged 65 and over), compared to the number of people of working age (i.e. 15-64 years old). Steady increase in share of old age group in the population leads to increase in financial burden. The youngest population across chosen countries live in South Africa and Qatar. The oldest nations are in Israel and Japan, where the value of age dependency ratio is bigger than 600 elderly people per 1000 of people of working age. High values of current healthcare expenditures in the United States and Switzerland show that healthcare is enough expensive in both countries and private insurance providers set high fees.

Gap between HALE and Life Expectancy and Living Conditions

An important factor that determines life expectancy and HALE is a general environmental condition. In particular, we focused on living conditions including the level of using at least basic sanitation services (%), level of using at least basic drinking water services (%), ambient and household air pollution.
 
According to our research and computed standardised beta coefficients, the highest strength of the effect belongs to the level of using at least basic drinking water. Every single unit increase in the percentage of people using at least basic drinking water services provided the other factors remain constant, leads to 0.110 years or 40 days gap increase. It can be explained by the fact that life expectancy at birth will change at a faster pace that HALE. We can draw the opposite conclusion regarding ambient and household air pollution: here HALE will increase or decrease in faster pace than life expectancy.
 
Adjusted R-squared shows that 18.0 % variance in the dependent variable can be accounted for by the set of environment condition variables. According to F-test and p-value, this model is significant. Therefore, there is enough evidence in the data to suggest that the linear relation between the gap and general living conditions exists.
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Improved Water Sources and Healthy Longevity

Improved drinking water source is a source that, by nature of its construction, adequately protects the water from outside contamination, in particular from faecal matter. Bad water supply causes the burden of communicable diseases and increases the risk of premature death, such a situation is observed in big cities and remote areas in South Africa, Indonesia, India, Brazil. Waterborne diseases are caused by drinking contaminated or dirty water. Contaminated water can cause many types of diarrheal diseases, including Cholera, and other serious illnesses such as Guinea worm disease, Typhoid, and Dysentery. Water related diseases cause 3.4 million deaths each year.

Ambient Air Pollution

Increase in ambient air pollution, concentration of fine particulate matter (PM2.5) contributes to exponential growth of ambient and household air pollution attributable death rate (per 100 000 population). The highest level of death ration is in India and China, the biggest industrial producers in the world. Diseases as a result of the pollution include acute lower respiratory infections, chronic obstructive pulmonary disease, stroke, ischemic heart disease, and lung cancer. The highest level of ambient air pollution across chosen countries is in Qatar. However Qatar’s pollution readings are some of the worst in the world, the number of deaths attributed to poor air quality is not as high. So, air pollution has health impacts even at very low concentrations.

Gap between HALE and Life Expectancy and Lifestyle Factors

The above factors indicating general lifestyle such as the prevalence of undernourishment, smoking, overweight among adults and alcohol consumption have a significant impact on the gap prediction. According to the standardised beta coefficient values, we can not highlight any dimension as the most powerful.
 
For instance, every single unit rise of overweight prevalence leads to 0.014 years or 5 days decrease in the gap between life expectancy and HALE. The effect of total alcohol consumption is the same: every single unit increase provided the other factors remain constant, leads to 0.038 year or 14 days decrease in the gap.
 
In general, the variance in lifestyle factors determines 15.5 % variance in the gap between life expectancy and HALE. The overall F-test defines that an assumed linear relationship is statistically significant whereas p-value for the model is less than the accepted significance level.
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Obesity and Health-adjusted Life Expectancy

There is negative correlation between both HALE and life expectancy and prevalence of obesity. Singapore have the highest level of HALE and one of the lowest obesity rate, which is a result of compound impact of high quality of life, healthier behaviour and effective healthcare policies that tackle the rising burden of non-communicable diseases. The most recently available data from both the OECD and the WHO indicate that the U.S. has the greatest prevalence of obesity among high-income countries. Over a third of the U.S. is obese, compared to just over a fifth on average in comparable countries. The higher-than average rates of obesity across observed countries may contribute in some ways to the higher disease burden from cardiovascular conditions. Though rates of disease burden caused by these conditions have improved across countries, they still cause fairly large negative impact on HALE.

Alcohol Consumption and Gap between Life Expectancy and HALE

The graph shows that higher alcohol consumption leads to increase in gap between life expectancy at birth and health-adjusted life expectancy. Alcohol abuse cause increase in risk of premature deaths and it prevails among younger population (South Africa is an evidence). Higher alcohol consumption is associated with a greater risk of stroke, heart failure, and fatalities due to high blood pressure or a bulging or ruptured aorta.
 
Countries that consume less alcohol or more than global average are exposed to disaster of non-communicable diseases. So, any amount of drinking appeared to increase these risks.

Smoking and Life Expectancy

Smoking is one of the biggest causes of preventable deaths. The poisons from the tar in cigarettes enter your blood. These poisons then make blood thicker, and increase chances of clot formation. Smoking damages heart and blood circulation, increasing the risk of conditions such as coronary heart disease, heart attack, stroke, peripheral vascular disease (damaged blood vessels) and cerebrovascular disease (damaged arteries that supply blood to your brain).
 
The level of smoking varies significantly across countries. Cigarettes cause harmful impact on health even at very low consumption.

Gap between HALE and Life Expectancy and Causes of Deaths

As a result of our research, the mentioned above reasons of death have a significant impact on the gap and its change. The highest prediction power belongs to communicable diseases and maternal, prenatal and nutrition conditions. The sign of its coefficient emphasizes that increase in a number of deaths caused by illnesses that result from the infection (HIV, hepatitis A, B and C, measles, salmonella) leads to 0.104 year decline in the gap - the fact that life expectancy at birth will decrease at a faster pace than HALE (health-adjusted life expectancy). The same conclusion we can make regarding non-communicable diseases.
 
In general, the variance in causes of death determines 32.3 % variance in the gap between life expectancy and HALE. The overall F-test defines that an assumed linear relationship is statistically significant whereas p-value for the model is less than the accepted significance level.
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Communicable diseases and Life Expectancy

Worldwide, developed and developing countries are facing the double burden of communicable and noncommunicable diseases. However, developing countries are more exposed and more vulnerable due to a multitude of factors, including geographic, demographic and socio-economic factors.
 
Burden of communicable diseases prevails in developing and low-income countries. South Africa, India and Indonesia face the challenge to reduce deaths from communicable diseases and maternal, prenatal and nutrition conditions in younger age group (15-34 years).
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Methodology of Analysis of Variance (ANOVA)

Analysis of variance (ANOVA) is a quantitative method used in statistics that splits an observed aggregate variability found inside a data set into two parts: systematic factors and random factors. The systematic factors have a statistical influence on the given data set. while the random factors do not. The ANOVA test used to determine the influence that independent variables have on the dependent variable in a regression study.
 
Dependent and Independent Variables
The dependent variable must be a continuous (interval or ratio) level of measurement. The independent variables must be categorical (nominal or ordinal) variables. A one-way ANOVA has just one independent and one dependent variable. MANOVA is used when there are two or more dependent variables.
 
Statistical Significance
The purpose of analysis of variance is to test for significant differences between means in different groups or variables, usually arranged by an experimenter in order to evaluate the effects of different treatments or experimental conditions on one or more outcome measures. The null hypothesis for an ANOVA is that there is no significant difference among the groups (the mean is the same). The alternative hypothesis assumes that there is at least one significant difference among the groups. To determine whether a set of means are all equal F-test is calculated. The test statistic is a measure that allows us to assess whether the differences among the sample means (numerator) are more than would be expected by chance if the null hypothesis is true. In general, if the p-value associated with the F is sm