Literature DB >> 21526042

Evaluating the health of nations: a Libyan perspective.

Sliman Abdalah M Al-Lagilli1, Veljko Jeremic, Kristina Seke, Danka Jeremic, Zoran Radojicic.   

Abstract

Entities:  

Year:  2011        PMID: 21526042      PMCID: PMC3081873          DOI: 10.3402/ljm.v6i0.6021

Source DB:  PubMed          Journal:  Libyan J Med        ISSN: 1819-6357            Impact factor:   1.657


× No keyword cloud information.
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 X=(X1, X2, …, Xk) chosen to characterise the entities, the I2-distance between two entities e=(x, x, …, x) and e=(x, x, …, x) is defined aswhere is the square distance between the values of variable X for e and e (e.g. discriminate effect), σ the standard deviation of X, and r is a partial coefficient of the correlation between X and X(j 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 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

CountryI2-distanceRank
Qatar50.4201
The United Arab Emirates30.9232
Jordan28.3373
Kuwait27.9934
Libya27.2535
Egypt26.9936
Oman26.1687
Bahrain24.7758
Palestine24.5299
Saudi Arabia23.95210
Lebanon23.06411
Tunisia22.57112
Syria21.37713
Iran19.10814
Morocco16.92215
Sudan14.67416
Djibouti10.38217
Pakistan8.73318
Iraq7.06819
Afghanistan4.26020
Yemen3.29121
Table 2

The correlation between the I2-distance and input variables

r
The number of nurses0.891**
Under five mortality rate0.819**
Infant mortality rate0.811**
Total life expectancy at birth0.797**
Neonatal mortality rate0.794**
Total expenditure on health0.779**
Government expenditure on health0.762**
One year olds immunised with OPV30.705**
One year olds immunised with measles vaccine0.663**
One year olds immunised with DTP30.654**
The number of physicians0.615**
One year olds immunised with BCG0.601**
The number of pharmacists0.578**
The number of dentists0.534*
Population with access to local health services0.441*
Maternal mortality rate0.335
One year old immunised with HBV30.130
Total expenditure on health of percentage of GDP0.05

p <.01.

p <.05.

The results of the I2-distance method, I-distance values and rank The correlation between the I2-distance and input variables 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.
  5 in total

1.  Factors in health initiative success: learning from Nepal's newborn survival initiative.

Authors:  Stephanie L Smith; Shailes Neupane
Journal:  Soc Sci Med       Date:  2010-12-09       Impact factor: 4.634

2.  Measuring the health of nations: updating an earlier analysis.

Authors:  Ellen Nolte; C Martin McKee
Journal:  Health Aff (Millwood)       Date:  2008 Jan-Feb       Impact factor: 6.301

3.  Measuring Health: A Multivariate Approach.

Authors:  Jeroen Klomp; Jakob de Haan
Journal:  Soc Indic Res       Date:  2009-05-26

4.  Inequalities that hurt: demographic, socio-economic and health status inequalities in the utilization of health services in Serbia.

Authors:  Janko Janković; Snezana Simić; Jelena Marinković
Journal:  Eur J Public Health       Date:  2009-11-23       Impact factor: 3.367

5.  Impact of health reforms on child health services in Europe: the case of Bulgaria.

Authors:  Boika Rechel; Nick Spencer; Clare Blackburn; Richard Holland; Bernd Rechel
Journal:  Eur J Public Health       Date:  2009-03-19       Impact factor: 3.367

  5 in total
  4 in total

1.  An Evaluation of European Countries' Health Systems through Distance Based Analysis.

Authors:  V Jeremic; M Bulajic; M Martic; A Markovic; G Savic; D Jeremic; Z Radojicic
Journal:  Hippokratia       Date:  2012-04       Impact factor: 0.471

2.  Healthcare workers satisfaction and patient satisfaction - where is the linkage?

Authors:  I Janicijevic; K Seke; A Djokovic; T Filipovic
Journal:  Hippokratia       Date:  2013-04       Impact factor: 0.471

3.  Mental health services in new Libya: the way forward.

Authors:  Selim M El-Badri
Journal:  Libyan J Med       Date:  2013-07-22       Impact factor: 1.743

4.  Evaluating the health system financing of the Eastern Mediterranean Region (EMR) countries using Grey Relation Analysis and Shannon Entropy.

Authors:  Kimia Pourmohammadi; Payam Shojaei; Hamed Rahimi; Peivand Bastani
Journal:  Cost Eff Resour Alloc       Date:  2018-09-17
  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.