Health Research, Vol. 3, Issue 3, Sep  2019, Pages 8-18; DOI: 10.31058/ 10.31058/

Global Health Quality Assessment Using Statistical Control Monitoring Tools Based on WHO Database Record: A Descriptive Analysis

, Vol. 3, Issue 3, Sep  2019, Pages 8-18.

DOI: 10.31058/

Mostafa Essam Ahmed Mostafa Eissa 1*

1 Microbiology and Immunology Department, Faculty of Pharmacy, Cairo University, Cairo, Egypt

Received: 26 August 2019; Accepted: 29 August 2019; Published: 8 October 2019


Human life expectancies and mortality rates are of crucial concern for national and international health organizations because they provide good estimation for monitoring of health quality. World Health Organization (WHO) provides comprehensive database record for its regular observations for the nations globally. Analysis of web-based data of WHO using statistical software was conducted using statistical programs after stratification and processing of database. Results showed that life expectancy and Health Adjusted Life Expectancy (HALE) for developed and wealthy countries are much higher than that for developing and poor countries. Box plot diagrams demonstrated the pattern of the global distribution of these parameters with aberrant low values of survivability and high incidence of mortalities pertained for poor nations. The quality of life is also reflected by death rates at different age groups and the maternal probability of mortality. These markers are highly correlated and each one could be used as predictor or indicator for the other parameters which are evident in Contour plot. Modeling of worldwide survivability distribution estimated that the best pattern describing data is Generalized Extreme Value distribution (GEV). On the other hand, the best distribution fitting for mortality rates could be described by both Log-normal and Weibull (3) distributions. The study showed that despite the great advancement in health sciences and technologies in the recent decades and the massive efforts done by national and international organizations to improve human life around the world, a huge gap still exists at different geographical regions globally between rich and needy nations which is a reflection of inherent and may be persistent challenges that still affect the quality of the life environment in these suffering countries.  


GEV, Log-Normal, Weibull (3), Pareto Chart, HALE, WHO, Box Plot, Contour Plot, Outliers, Correlation Coefficient

1. Introduction

Despite great advancement that has been achieved by the human being in the new millennium, a huge gap in healthy life quality still exists between many nations and countries worldwide. Generally, western countries are leading in life quality based on the latest surveillance study, especially European nations [1]. For instance, Sweden, Denmark, Norway, Switzerland and Finland are among the best-ranked countries in the world in 2019 [1]. Developed countries have systems to ensure that their citizens are in best states of health, life quality and well-being in contrast to the developing nations where most of its populations are subjected to an unhealthy environment that is stemmed from many sources which are poorly controlled or regulated (if at all) [2].

Several measures have been designed to measure the level of life quality quantitatively. One method includes life expectancy which is the sum of healthy year’s lives and years lived with illness. On the other hand, Healthy Life Expectancy (HALE) is a modification of the ordinary life expectancy in which the years that are lived with disability are multiplied by a fraction weight of the degree of sickness [3].

The present study aimed to investigate the magnitude of the gap quantitatively using statstical analysis and techniques by using analytical software and statistical process control programmed methodologies. Global modeling of the distribution of survivability years and death ratios has been established with defined parameters that help in the simulation of the worldwide events of population dynamicity.

2. Materials and Methods

The principle of the present study is dependent on the statistical analysis of database of international organization that provides detailed global record for survivability on mortalities at its official internet site. Another complementary study would be required to provide statistical analysis based on various geographical regions for comparison. Detailed finding could be elucidated in a different separate work.

2.1. Abbreviations and Acronyms

The following abbreviations are going to be used in the current study: African Region (AFR), Regions of Americas (AMR), South East Asia Region (SEAR), European Region (EUR), Eastern Mediterranean Region (EMR),Western Pacific Region (WPR), Health-Adjusted Life Expectancy (HALE), Male (M), Female (F), Live Births (L.B.), Cumulative Percent (Cum %), Days (D), Years (Y), Probability (Prob.), World Health Organization (WHO) and Box Plot (Boxplot = box-and-whisker plot or diagram).

2.2. Data Source Subject of Study

WHO dataset for mortality status 2005 from official website: http: // [4]. Data were subjected to stratification, filtration and processing for categorization and statistical analysis.

2.3. Statistical Analysis Software

Several statistical processing platforms were used in conjunction to analyze data complementarily from different perspectives. Programs, tools and analysis scheme were done similar to previous analytical study [5]. Pareto charts were constructed for geographical regions using weighted contribution of each as the product of number of survivability years or mortality rates combined for each country i.e. number of countries of each geographical region affect the final Pareto weight for each one (Regional weighted life quality).

2.4. Assessment of Defective Fraction of Life Lived Poorly

Calculation of defective proportion of life lived suboptimal (P) for both males (Pm) and females (Pf) were done according to previous study [6]. This parameter was used to determine the magnitude of the average of poorly-lived portion of life for each WHO geographical region.

2.5. Basic Equations [3, 7]

Life Expectancy (LE) = Years lived healthy (A) + Years lived with disabilities (B) (1)

HALE = Years lived healthy (A) + Years lived with disabilities (B) x f (2)


f = Fraction weight of the disease or illness.

3. Results and Discussion

Human life expectancies and mortality rates from different worldwide geographical areas based on WHO classification could provide useful information regarding the quality of life in relation to the economic and social status of the regions' countries [8].

3.1. Pareto Diagram in Prioritization of the Health Risk Geographical Group

Pareto charts in Figures 1 to 3 shows the descending contribution in life/mortality balance from different areas globally taken into account the number of countries involved in each group. For M and F both EUR followed by AMR are showing greatest life expectancies and HALE values while EMR and SEAR have the lowest expectancies. It should be noted that F showed slightly greater survivability in comparison to M. Despite that the survivability of M and F of AFR region was in the middle-of-road mortality rates were very high for all groups, sex and age categories if compared with the other geographical groups i.e. contribution from about 43% to 67%. The longer life expectancies for women generally could be contributed to not only the biological factors but also from social and economic intervention [9]. EMR healthcare is affected strongly by economic status and growth combined with social status pollution and other negative obstacles in the region' countries which impact heath quality of the affected populations [10]. However, it should be noted that as weighted Pareto takes into consideration the number of nations, EMR and SEAR are composed of much fewer number of countries compared to AFR i.e. 21 and 11 versus 46 nations. Nevertheless, considering life expectancy and HALE regardless of the number of nations will put AFR at the end of the list after EUR, AMR, WPR, EMR and SEAR. While AFR, SEAR and EMR are leading groups in the probability of mortality lists.

3.2. Box Plot Analysis and the Aberrant Countries

Box-and-whisker diagrams in Figure 4 demonstrate the distribution patterns of life expectancies and death rates with outlier values shown as asterisks for exceptionally high or low values. For example, Swaziland showed the lowest life expectancy for males at 33 years and females at 36 years which is common for the latest with Botswana and Zimbabwe. Another country in the African region, Sierra Leone had very low HALE value at 27 years for males which demonstrated also the aberrant value of maternal mortality 20 deaths per 1000 individuals. On the other hand, Mali showed the outlier values for children death rates below age five years (0.22) in addition to other developing nations that showed aberrantly high rates of death. Moreover, other African countries demonstrated exceptionally high rates of mortality for other age groups.

Figure 1. Pareto chart showing global life assessment by WHO for males.

Figure 2. Pareto chart showing global life assessment by WHO for females.

Figure 3. Pareto chart showing global child and maternal mortality.

Figure 4. Box-and-whisker plot for life expectancies and mortality rates.

3.3. Correlation between Life Expectancies and Mortality Rates

Strong correlation exists between survivability and death rates for all groups. Contour plots in Figure 5 illustrate this multidimensional relation in 2D-graph. Global record correlation matrix could indicate that any single category of WHO record would provide effective marker for the other groups.

Figure 5. Death probability as a function of life expectancy and HALE in males and females.

3.4. Distribution Fitting and Data Pattern Modeling

Distribution fitting test for survivability and death groups with parameter data for each one could be found in Table 1. Generalized extreme value (GEV) distribution was found to be the best one that fits life expectancy and HALE for both females and males world populations. Concerning mortality rates, log-normal showed appropriate approximation foradult death for both males and females. On the other hand, maternal, childhood (lower than five years) and neonatal (below 28 days) distributions were best described by Weibull (3).

Table 1. Distribution fitting based on WHO global record for each country.

WHO Health Category

Assumed Distribution

Estimated Parameters

Life expectancy M





Life expectancy F





Healthy life expectancy (HALE)M





Healthy life expectancy (HALE)F





Prob. dying per 1000 (15-60Y)M




Prob. dying per 1000 (15-60Y)F




Prob. dying per 1000 (< 5Y)

Weibull (3)




Prob. dying per 1000 (< 28D)

Weibull (3)




Maternal Mort./100,000 L.B.

Weibull (3)




1Generalized extreme value distribution

3.5. Proportion of Non-Optimal Life

Determination of sub-optimal fraction of life lived in poor health conditions for each geographical region is shown in Figure 6. Generally, female populations are living suffering from ill health conditions for greater time than male individuals despite their life expectancies are higher than them. This observation is in agreement with that of other researchers who have estimated a longer but not healthier life for women citizens [11].

Figure 6. Non-optimal proportion of life (P) lived by males (Pm) and females (Pf) for WHO geographical regions.

3.6. Discussion of the Results Analysis

Normally, HALE could be found correlated with ordinary life expectancy years (as was found in this study). However, since HALE is a measure of the quality of life and deaths, it was found to be highly correlated with mortality rates data as well [12]. HALE is a more accurate estimator for full functioning citizens in a society being productive gears for their countries which in turn a reflection for governments' policies and control over healthcare industry and services [11]. Accordingly, it is not surprising to find that the developed nations (such as in Europe) have a high level of healthcare regulations and controls to maintain a high rank of survivability and a low probability of mortality at all ages where the surveillance and care start from newborns to elderly. Thus, people in EUR are living longer time in healthier conditions than populations in the other countries in other regions [11, 13]. This would be evident from equation:

P = B.(1-f)/(A + B)         (3)

Thus, the fractions of life lived in less than optimal conditions are affected by both time and the illness impact weight.

4. Conclusions

The study demonstrated that although there is great advancement in health sciences and technologies that have been achieved in the recent decades and massive efforts are being implemented by national and international organizations to improve human life around the globe, but a wide rift is still existing between different geographical regions globally between prosperous and destitute nations which is a reflection of inherent and may be persistent challenges that still affect the quality of the living environment in these suffering countries. Cosmopolitan modeling of survivability and mortality indicators could be approximated mathematically for reasonable prediction of human life quality. However, it is recommended that extensive data mining and collection for each country over statistically-valid period of times derive useful trends and accurate assessments of the existing situation, future prediction and the degree of improvement required for each case.

Conflicts of Interest

The author declares that there is no conflict of interest regarding the publication of this article.


© 2017 by the authors. Licensee International Technology and Science Press Limited. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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