Evaluating the health of nations: a Libyan perspective

LETTER TO THE EDITOR

Evaluating the health of nations: a Libyan perspective

Published: 25 February 2011

Citation: Libyan J Med 2011, 6: 6021 - DOI: 10.3402/ljm.v6i0.6021

Libyan J Med 2011. © 2011 Sliman Abdalah M. Al-Lagilli. This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.


Researchers leave no doubt regarding the importance of a health system, since health is considered to be a fundamental contributor to the welfare of every country (1). As the evaluation and ranking of countries are based on their health status, it is therefore a crucial issue. Despite numerous attempts, health systems are a difficult issue to measure. The vast majority of researchers use mortality rates as an indicator of the country's health status (2). However, this approach assumes that health is a one-dimensional concept, which is not precisely true (3, 4).

To create a synthesised health status indicator, more variables are incorporated into the analysis by using the statistical I2-distance method (5, 6). The I2-distance method was proposed by Ivanovic (5) and Jeremic and Radojicic (6). They devised this method in order to rank countries according to their level of socio-economic development.

For a selected set of variables XT=(X1, X2, …, Xk) chosen to characterise the entities, the I2-distance between two entities er=(x1r, x2r, …, xkr) and es=(x1s, x2s, …, xks) is defined as



where is the square distance between the values of variable Xi for er and es (e.g. discriminate effect),



σi the standard deviation of Xi, and rji. 12…j–1 is a partial coefficient of the correlation between Xi and Xj (j<i).

Each of the Eastern Mediterranean Region (EMR) countries’ health status is quantified by use of the I2-distance ranking method. The selection of the indicators was chosen in order to reflect the health of individuals and state of health services (4). Data from the Statistical Information System of the World Health Organisation and the WHO Eastern Mediterranean Region Office was used (3, 7).

Indicators of the health of individuals

  Total life expectancy at birth (years)
  Neonatal mortality rate (per 1,000 live births)
  Infant mortality rate (per 1,000 live births)
  Under five mortality rate (per 1,000 live births)
  Maternal mortality rate (per 1,000 live births)

Indicators of health services
  Population with access to local health services, total (%)
  The number of dentists per 10,000 people
  The number of nurses per 10,000 people
  The number of physicians per 10,000 people
  The number of pharmacists per 10,000 people
  One year olds immunised with measles vaccine (%)
  One year olds immunised with DTP3 (%)
  One year olds immunised with HBV3 (%)
  One year olds immunised with BCG (%)
  One year olds immunised with OPV3 (%)
  Total expenditure on health (per capita) average US$
  Government expenditure on health (per capita) average US$
  Total expenditure on health of percentage of GDP

Qatar tops the list of EMR ‘healthiest countries’, and Libya is in 5th position (Table 1). On the other hand, Afghanistan and Yemen are at the bottom of the list. To fully understand the rankings, it was essential to find which of the input variables is the most important for measuring the health status of countries (7). We used Pearson correlation test and correlation coefficient of each variable, with the I2-distance value presented in Table 2.


Table 1. The results of the I2-distance method, I-distance values and rank
Country I2-distance Rank
Qatar 50.420 1
The United Arab Emirates 30.923 2
Jordan 28.337 3
Kuwait 27.993 4
Libya 27.253 5
Egypt 26.993 6
Oman 26.168 7
Bahrain 24.775 8
Palestine 24.529 9
Saudi Arabia 23.952 10
Lebanon 23.064 11
Tunisia 22.571 12
Syria 21.377 13
Iran 19.108 14
Morocco 16.922 15
Sudan 14.674 16
Djibouti 10.382 17
Pakistan 8.733 18
Iraq 7.068 19
Afghanistan 4.260 20
Yemen 3.291 21


Table 2. The correlation between the I2-distance and input variables
  r
The number of nurses 0.891**
Under five mortality rate 0.819**
Infant mortality rate 0.811**
Total life expectancy at birth 0.797**
Neonatal mortality rate 0.794**
Total expenditure on health 0.779**
Government expenditure on health 0.762**
One year olds immunised with OPV3 0.705**
One year olds immunised with measles vaccine 0.663**
One year olds immunised with DTP3 0.654**
The number of physicians 0.615**
One year olds immunised with BCG 0.601**
The number of pharmacists 0.578**
The number of dentists 0.534*
Population with access to local health services 0.441*
Maternal mortality rate 0.335
One year old immunised with HBV3 0.130
Total expenditure on health of percentage of GDP 0.05

**p<.01.

*p<.05.

The most significant variable for determining the health status of a country is its number of nurses. Various papers have already elaborated upon the number of nurses as being a key factor for a country's health (8). This is precisely one of the key reasons why Qatar was able to take the first rank as it has the largest number of nurses (73.8 per 10,000 people). Following Qatar is Libya with the second largest number of nurses (54 per 10,000 people). Thus, it is crucial for Libya to maintain such a high number of medical staff.

The mortality rate for children under five is the second most significant variable. Libya has a much higher mortality rate than the two ‘healthiest’ countries, Qatar and the United Arab Emirates. We must point out that mortality rates for children are three of the top five most significant health indicators. Thus, child health service is essential and it must be improved (9).

Conclusion

The health system performance of EMR countries by applying the statistical I2-distance method has clearly shown a great disparity. In addition, the I2-distance method has provided information as to which input variables are crucial for determining a country's health system performance. Libya is in a good position to improve the key health indicators elaborated in this paper.

Sliman Abdalah M. Al-Lagilli
Faculty of Economics
Seventh April University
Libyan Arab Jamahiriya

Veljko Jeremic
Faculty of Organizational Sciences
University of Belgrade
Belgrade, Serbia
E-mail: jeremicv@fon.rs

Kristina Seke
Institute of Public Health of Serbia ‘Dr Milan Jovanovic Batut’
Dr Subotica 5
Belgrade, Serbia

Danka Jeremic
Institute of Endocrinology
Diabetes and Diseases of Metabolism
University Clinical Centre
Belgrade, Serbia

Zoran Radojicic
Faculty of Organizational Sciences
University of Belgrade
Belgrade, Serbia

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Libyan Journal of Medicine eISSN 1819-6357, ISSN 1993-2820

This journal is published under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License. Responsible editor: Omran Bakoush