AfDB 2014 www.afdb.org Economic Brief
What policies should be implemented CONTENTS to address inequalities in health care in Tunisia? Summary p.2
1 – General Introduction p.4 Key Messages
2 – Indicators of Health Status • Despite the progress achieved, health inequalities remain considerable and relatively little known in Tunisia. In light of the analysis conducted, there is significant elbow room for reducing these inequalities. and Use of Health Care In Tunisia, there are significant inequalities in care consumption between governorates for similar needs Services p.5 (those related to reproductive health, for example). There are also significant differences in the health status of the population of these governorates. The life expectancy of 74.5 years in 2009 does not exceed 70 years in Kasserine and Tataouine, but reaches 77 years in the governorates of Tunis and Sfax. The analysis indicates that: 3- T erritorial Inequalities in - The overall inequality in health spending declined from 2000 to 2010. The breakdown of the Gini index Health Care Facilities p.9 shows that this movement is almost entirely explained by the decrease in inequality in pharmaceuticals spending, which accounted for 42.2% of health spending in 2010. This trend can be attributed to a greater availability of pharmacies throughout the national territory. 4- Trend in inequalities in - The items where inequality has worsened and that had an inertia effect were long-term illnesses (17% of expenditure), hospital stay and medical surgery (8.6%) and radio and scans (8% of health spending). health spending in Tunisia Such spending is related to the demographic and epidemiological transition. between 2000 and 201 0 p.27 - Dental care is characterized by unusually high levels of inequality and lack of access for the disadvantaged classes.
• The main recommandations in this context are as follow: 5- General Conclusion p.4 1 - From the supply side: (i) In the public sector, it is necessary to revitalize primary health care by improving the operation (ii) It is also important to strengthen Level II which seems to be the weak link in the system. Better coverage of the territory in terms of Level II beds should necessarily go hand-in-hand with the Bibliography p.42 provision of more specialized physicians for the poorest regions in light of the demographic and epidemiological transition. (iii) Efforts should be made to ensure that at each level the system performs its assigned tasks under the best possible conditions. These tasks should be clearly defined. Each Annexes p.44 hospital institution should have a scheme of work that allows for coherent strategic management. (iv) The specific incentives that were introduced to encourage physicians to settle in deserted areas should be evaluated. Public-public and possibly private-public partnerships should be instituted. Also, it is important to negotiate with corporations an institutional framework to better regulate the opening of private practices. (v) It is necessary to determine measures that should be implemented to enhance health care delivery at local or regional level, as part of an overall regional development policy. - On the demand side: (i) It is important to reduce financial barriers to health care access by better targeting the poor who benefit from free medical assistance. (ii) Pharmaceuticals are a significant drain on the budgets of the poorest households and it is necessary to reduce this weight by ensuring good governance of public pharmacies. (iii) There is a need to ensure a better collective coverage of Zondo Sakala longterm illness, hospital stay and medical surgery, x-rays and scans. Knowing the profile of households that incur these expenses will make it possible to better target them, if need be. (iv) Dental care continues Vice President to be characterized by extremely high inequalities in expenses. Improved coverage of the territory in [email protected] terms of availability of dental practices and greater public awareness of the importance of dental health should curb one of the causes of the inequality. Similarly, a special processing of reimbursement for dental expenses by health insurance, apart from the recurrent expenses, should contribute to reducing inequalities in dental care access. - On the institutional side: (i) It is necessary to aim at reducing social and regional inequalities in health care. Jacob Kolster (ii) There is a need to produce and monitor indicators for assessing the progress of specific categories not Director ORNA only at the national level but also at the local level. It is important to conduct periodic surveys on the [email protected] status of health, health care use, or the failure to seek health care for financial reasons. +216 7110 2065
This paper was prepared by Salma Zouari, Ines Ayadi and Yassine Jmal, under the supervision of Vincent Castel (ORNA) and Sahar Rad (ORNA) et Laurence Lannes (OSHD). Overall guidance was received from Jacob Kolster (Director, ORNA). Ahmed Rekik and Chokri Arfa suggested improvements to the preliminary version of this research. Asma Baklouti, Mariem Ellouze, Rahim Kallel and Abdessalem Gouider each made an input.
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Summary Therefore, there is clearly a need to develop a strategy for strengthening and revitalizing primary health in the country as well as enhancing Level II. n Tunisia, there are significant inequalities in care consumption between Igovernorates for similar needs (those related to reproductive health, 1-2- Regarding human resource allocations, the inequality between for example). There are also significant differences in the health status governorates has decreased, except for physicians whether in the of the population of these governorates. The life expectancy of 74.5 public or private sector. Although there has been a significant drop years in 2009 does not exceed 70 years in Kasserine and Tataouine, in the number of inhabitants per physician from 2002 to 2010, but reaches 77 years in the governorates of Tunis and Sfax. the gaps have widened between the better endowed governorates and the less endowed ones, while the variation coefficients have Three hypotheses were then made: increased.
l Households, whatever the level of their resources and even when they The availability of free medical practitioners is characterized by high benefit from social security coverage, have unequal access to care levels of inequality; the relationship between the most endowed and because of inequities in the provision of health care services in their the least endowed governorates is 14.3. This is followed by dental immediate environment. practices (ratio of 11.3) and hospital beds (10.7). The most evenly l Despite the importance of social coverage, households assume an distributed resources are pharmacies and paramedical staff. average of 41% of health spending in the form of out-of-pocket expenditures. Therefore, households have unequal access to care It would be advisable to review the criteria for opening positions of arising from inequalities in income distribution and illustrated by public health physician at regional level and the institutional framework unequal health spending. governing private practices. Like the practice of pharmacy, the practice l Due to the importance of out-of-pocket health care spending, the of dentistry and medicine on a free basis should be better regulated. regressive (or progressive) nature of care spending and its inelasticity Similarly, public-private and especially public-public partnerships compared to income, can give them a potentially catastrophic and (such as agreements between academic physicians and regional impoverishing character that makes unequal access to care even hospitals) that may make disadvantaged areas more attractive as is more acute. being considered for specialists could be a solution. However, the implementation of such partnerships should be accompanied by These hypotheses were tested on the basis of available statistical measures to ensure their effectiveness for all stakeholders. data. Health policy recommendations have been made. 1-3- Lastly, since the status of health care facilities in a governorate 1- On the assumption that the availability of care provision, whether cannot be analysed by reference to a single determinant, all the public or private, and good coverage of the national territory in health components of the sector and the complementarity between different infrastructure contribute to the decline in inequality in access to care, providers should be taken into account simultaneously. For this purpose, we analysed the trend of provision indicators by governorate and the we have integrated the various determinants of facilities (by category dispersion of these indicators through the use of cross-sectional data and overall) in order to arrive at relatively homogeneous groups (called of the 2010 health map and various longitudinal indicators published clusters) and calculated for each governorate, a composite indicator in the Statistical Yearbook of the National Institute of Statistics for the of care provision that measures its position compared to other period 1997-2010. Three aspects were analysed: infrastructure, the governorates as well as the progress that may be achieved over time. availability of beds and the provision of human resources. Among the three components of health care facilities, the geographic 1-1- With regard to infrastructure and bed availability, it turned out distribution of medical human resources stands out as the most unequal, that only the availability of PHCs declined over the last decade. Level with a significant concentration on the coast. Despite an increase in the II, which is the reference for Level I, would not be very effective because density of physicians, regional disparities have widened. Qualitatively, it lacks adequate technical equipment and specialized physicians. We the inequalities are even more blatant and more than 2/3 of specialists suspect that patients are referred to Level III which takes the place are found on the coast as regards not only rare specialties but also the of Level II, thus causing inefficiencies. most common such as gynaecology and paediatrics.
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Three governorates constantly fall within the most favoured cluster l the contribution of inequality in each SPY category or sub-category whatever the aspect considered. They are Tunis, Sousse and Monastir. to total health spending inequality;
Conversely, four governorates always fall within the most disadvantaged l the marginal effect - equalizer or non-equalizer – of the variation of a cluster: Jendouba, Kairouan, Kasserine and Sidi Bouzid. Between these particular SPY on total health spending inequality. two groups, the various governorates show more or less substantial deficits depending on the nature of the resources analysed. The scope 2-1- The overall inequality in health spending declined from 2000 to of intervention required by each governorate may then be defined. 2010. The breakdown of the Gini index shows that this movement is almost entirely explained by the decrease in inequality in pharmaceuticals Government intervention is necessary when there is a build-up of spending, which accounted for 42.2% of health spending in 2010. This inequalities. However, the choices related to the health sector and efforts trend can be attributed to a greater availability of pharmacies throughout to better allocate resources to priority areas can only be effective if they the national territory. form part of a comprehensive local development strategy in these areas. The reduction of economic, cultural and social differences between the 2-2- The items where inequality has worsened and that had an inertia governorates can only facilitate and strengthen health reforms. effect were long-term illnesses (17% of expenditure), hospital stay and medical surgery (8.6%) and radio and scans (8% of health spending). 2- Working from the assumption that inequalities in access to health Such spending is related to the demographic and epidemiological are linked to income inequalities, we assessed the inequality of out-of- transition. pocket household health expenditure and analysed the trends thereof and training through inequality indicators and their breakdown by The reduction of the corresponding inequality requires specific expenditure category. For this purpose, we referred to the individual government policies that target the most vulnerable groups. The data of the national surveys on household budget and consumption in collective management of these expenditures still seems insufficient. 2000, 2005 and 2010. This data provides information on total health Knowing the profile of households that incur these expenditures will spending per person per year (SPY) and the various expenditure help to better target them. categories: routine medical care, special medical care, pharmaceuticals and medical devices or expenditure sub-categories (medical 2-3- Dental care is characterized by unusually high levels of inequality consultations; dental care; radio, scanner and medical analysis; medical and lack of access for the disadvantaged classes. stay and surgery; special dental care; special radiology expenditure; childbirth; long-term diseases; drugs; other pharmaceuticals, etc.). Improved coverage of the territory by dental practices and greater awareness of people about the importance of oral and dental health This approach gave information about: should curb one of the causes of this inequality. Similarly, a specific treatment for reimbursement made by health insurance that is non- l Overall inequality in health spending and its trends; concurrent with current spending should contribute to the reduction of l inequality of SPY in each care spending item and sub-item; inequalities in access to dental care.
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1. General Introduction
ince 1956, the foundations for a universal system of health care is heavily subsidized or free for the beneficiaries of free health coverage Sdelivery have been established in Tunisia. For three decades, who are estimated at 27% of the population. the resulting benefits have been improved over time and a social security system has been put in place for employees. However, public Despite the important size of community coverage of medical care by expenditure on the health sector has slowed since the 1990s, and insurance or by the State budget, private spending still remains very private practices have gradually substituted public health services that high and is increasing. In 2010, health care was financed by public have experienced some decline in their quality and availability. Household budgets to the tune of 23.8%, by health insurance resources (CNAM health spending has risen sharply, sometimes leaving a heavy dent on with 27.7% and private insurance with 7%) and out-of-pocket the household budget. The most vulnerable segments are not spared household spending that covered 41.2%. (Arfa, ElGazzar, 2013). Thus, the health system faces many challenges, and it is important to: Since January 2011, there is heightened awareness of inequalities in the health status of the population and health care use. More attention l Reduce regional disparities in the provision of health care services; is paid to issues of equity in access to care and there is more concern l reduce inequalities in household health spending; about a more egalitarian distribution of health services throughout the l limit the amount of out-of-pocket household spending. country. Therefore, it is necessary to better define the situation of health Indeed, although health infrastructure covers almost the entire inequalities and its recent trend, and to identify policies that can help country 1, there are inequalities in availability between the different address the above challenges. regions. The health system is predominantly public, with 87% of bed capacity in public hospitals and 13% in private clinics. On average, We will begin by recalling some household health status indicators Tunisia has 123 physicians per 100 000 inhabitants. However, and the use of health services (Section I). We will then analyse the physician density is much lower in the poorest regions where most inequities in health care provision (Section II). Lastly, we will also of the beneficiaries of free health care are found. 2 address the spending inequalities and their sources (Section III). In this respect, we will mainly use data from the 2010 Tunisia Health With regard to the financing of the demand for care, the National Health Map published by the Ministry of Health as well as data relating to Insurance Fund (CNAM) covers about 68% of the total population. It the health sector published by the National Institute of Statistics (INS) covers both public and private health care services in the country. The in the Statistical Yearbook of Tunisia between 1997 and 2010. We majority of physicians, laboratories, dentists and pharmacists are will further use the individual databases of the National Surveys on contracted with CNAM. There are three branches: the public branch, Household Budget and Consumption conducted by the INS in 2000, the private branch and the reimbursement branch. The public branch 2005 and 2010.
1 The health system includes: (a) primary health centres or primary health centres and local or district hospitals; (b) regional hospitals; and (c) university teaching hospitals. 2 Medical density in Tunisia is lower than the European average, which is more than 300 physicians per 100 000 people. It is the highest in the Maghreb (Algeria and Libya 120, Morocco 60, Mauritania 10) and occupies the ninth place in the EMRO region behind Lebanon (330), Bahrain (300), Qatar (280), Jordan (260), Egypt (240), Kuwait and Oman (180), Saudi Arabia (160) and ahead of Iran (90), Pakistan (80) , Syrian Arab Republic and Iraq (50 ), Sudan and Yemen (30) (MH, Tunisia Health Map 2010).
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2. Indicators of Health Status and Use of Health Care Services 3
here are few studies and statistics on this issue. However, we because reproductive health concerns the entire population in the T will refer to data known for their relevance and published regularly same manner. by the INS. The health status will be assessed through life expectancy and the infant mortality rate (IMR). These two indicators are particularly 1- Health status indicators suited to the health inequalities study (Jusot, 2003). However, they are published in a systematic way only at the national level. The Differences in life span may be seen as a synthetic indicator of social mortality rate, which is a poor indicator of health status because it is differences affecting health throughout the life cycle (Aïach, 2000). sensitive to the structure of the population by age, is however available There has been a remarkable increase in life expectancy at birth in at the governorate level. It will only be used to assess the evolution Tunisia (Figure 1). From only 58 years in 1956, life expectancy has of its variation coefficient. 4 Health service use will be analysed through risen to 74.9 years in 2011. The continuous improvement of this the data on reproductive health because of their availability and indicator is applicable to both men and women.
Figure 1: Life expectancy by gender (1990-2011)
78 76 74 72 70 68 66 64
1 7 1 7 1 0 4 5 6 8 9 0 4 5 6 8 9 0 2 3 2 3 9 9 0 0 1 9 9 9 9 9 9 0 0 0 0 0 0 1 9 9 0 0 9 9 0 0 0 9 9 9 9 9 9 0 0 0 0 0 0 0 9 9 0 0 1 1 2 2 2 1 1 1 1 1 1 2 2 2 2 2 2 2 1 1 2 2
Male Female
The increase in life expectancy is due to a decrease in mortality The mortality rate showed a decreasing trend. From 19.1 per thousand rates in general, and the sharp decline in infant mortality in in 1960, it dropped to 5.5 per thousand in 2011 (Figure 2). The infant particular. mortality rate fell from 120 per thousand births in 1966 to 14 per thousand in 2011 (Figure 2).
3 All the statistics in this section are derived from Tunisian Statistical Yearbooks published by the National Institute of Statistics. 4 For a Y distribution of mortality rates with average Y, the coefficient of variation, noted CV, is derived from the variance. It is defined as the ratio of the standard deviation σ to the mean mortality rates: CV = σ /Y where σ2 = 1/ N ∑(Yi - Y)2 CV is used to compare the dispersions of distributions with different averages. The higher the CV the more dispersed the distribution will be.
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Figure 2: Mortality rate F i g u r e 3 : I n f ant mortality rate
25.0 200 20.0 150 15.0 100 10.0 5.0 50 0.0 0 8 8 8 0 4 2 6 0 4 2 6 0 1 0 4 8 2 6 0 4 8 2 6 0 4 8 6 8 0 9 6 7 7 8 8 9 9 0 0 6 6 6 7 7 8 8 8 9 9 0 0 0 9 9 0 9 9 9 9 9 9 9 9 0 0 9 9 9 9 9 9 9 9 9 9 0 0 0 1 1 2 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 2 2 2
Infant mortality is an indicator widely used in international comparisons. The life expectancy of 74.5 years in 2009 did not exceed 70 years in It is an indicator of robust health, revealing a country’s development Kasserine and Tataouine, but reached 77 years in the Tunis or Sfax level and the quality of its health care system. It depends on several governorates (Map 1). Similarly, the decline in infant mortality has not factors, including income, maternal educational level and the been equally beneficial to all children regardless of their place of birth effectiveness of preventive care provided to mother and child (Map 1). In 2009, the infant mortality rate was 17.8 ‰ at the national (Bouchoucha and Vallin, 2007). The decline in infant mortality rate may level. In the South, it was 21 ‰, while in the Midwest it rose to 23.6 ‰ be attributed to both factors inherent in the health system (modernization (Ministry of Regional Development, 2011). Map 1 shows two groups of and better coverage of the country) and the evolution of Tunisian society governorates at odds with each other: the first (Tunis, Sousse, Monastir (improvement in the quality of life and an increase in the living standards and Sfax) recorded the lowest infant mortality rates (IMR), while the and the educational level of the population). second (Kasserine, Sidi Bouzid and Kairouan) had the highest rates.
However, this overall positive trend hides significant disparities between Lastly, the mortality rate by governorate (indicator sensitive to the age structure rural and urban areas as well as between socioeconomic groups and of the population) shows contrasting trends and especially an increase in between the various governorates. We will focus on the regional aspect the variation coefficient, indicating a rise in inter-governorate inequality and of inequality and health status indicators. greater heterogeneity of living conditions prevailing there (Figure 4).
Figure 4: Coefficient of variation in mortality rates by governorate (1978-2010)
0.25 0.20 0.15 0.10 0.05 0.00 1997 1998 1999 2000 2000 2001 2002 2003 2004 2005 2007 2008 2009 2010
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2- Health care use indicators women’s perception of health and medicine, and it depends on the alternative care available to them and their medical environment. In general, health care use is related to health condition and inequality However, it is often subject to social and family control (Gastineau, in health care use primarily reflects unequal needs. Hence it is not 2003). Belonging to a family or community group, social habits and the necessarily unfair. It is therefore necessary to analyse the inequalities environment are likely to influence women's choices in this matter. observed only when facing the same need. As such, the indicators related to reproductive health are well suited for such an analysis. They help to With reference to the number of deliveries by governorate, we calculated identify women who give birth and analyse inequalities between them. three indicators: the home delivery rate, the hospital delivery rate and the clinic delivery rate. 5 Tunisian Statistical Yearbooks each year publish statistics on the use of reproductive health care. We will try to see to what extent the use of In 2010, the home delivery rate (or rate of medically unassisted childbirth) such care is egalitarian. was 7.6%. It varied greatly between governorates. Governorates with high home delivery rates include Monastir, Nabeul and Mahdia, all of However, it is important to note that reproductive health care use reflects which have relatively good health infrastructure (Figure 5).
Figure 5: Home delivery rate in 2010 by governorate
50% 40% 30% 20% 10% 0% l i f r r s s s x l a a a a a a a e e e e e n n d A i i i j i u e t I s u s e a u a a b n n b n n n n z f r f b e d i i e i s s S K e b o a a u u u u
I r a S e u u e i i z h B a r u b n u a l e z o o o o r T e a i N o K a a o i e n o o A r L s G
h n d i S A T G B a S d B o U s N
g a M a n t n i e T a a a K e e M d K M J i T Z B M S
Clinic delivery rate was 12.3%. With the exception of Monastir, it was private health infrastructure (Figure 6). generally higher in the major urban centres of the coast which have
Figure 6: Clinic delivery rate in 2010 by governorate
50% 40% 30% 20% 10% 0% i l f r r s s s x l a a a a a a a e e e e e n d n A i i i j i u e t I s u e s a u a b n b a n n n n n z f r f b e d i i i e s s S K e b o a a u u u u
I r a S e u u e i i z h B a r n u b u a l e z o o o o r T e i N o K a a G u i e n o o A r L s G
d n h i S A B a S o d B o U s N
g a M a n t n T i é T a a a K e e M d K M J i T Z B M S
5 The sum of these three rates is not a unit because some women do not report the place where they gave birth.
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Lastly, hospital delivery is the dominant standard in Tunisia. On are those where the private sector is important (Sfax, Tunisia...) average, 67.5% of women give birth in hospital. Governorates or where the use of medicine is relatively limited (Sidi Bouzid, where the propensity to give birth in hospitals is lower (Figure 7) Kasserine…).
Figure 7: Hospital delivery rate in 2010 by governorate
100% 80% 60% 40% 20% 0% i l f r r s s s x l a a a a a a a e e e e e n d n A i i i j u i e t I s u e s a u b n n a b a n n n n z f r f b e d e i i i s s S K e b o a a u u u u
I r a S e u u e i i z h B a r b u n u a l e z o o o o r T e i N o K a a o G i e n o o A r L s G
d n h i S A T B a S d B o U s N
g a M a n t n i e T a a a K e e M d K M J i T Z B M S
By comparing the number of antenatal or postnatal visits in the public show a lower frequency rate for these procedures. These governorates sector to the total number of births registered in a year (assisted or are better off in private infrastructure, and many women consult their unassisted, in hospital or private clinic), the frequency of these procedures gynaecologist. The number of public postnatal visits is much lower (0.57 may be determined. There is an average of three antenatal visits per in 2010), but it is just as unevenly distributed among the governorates delivery. Only Tunis, Sousse, Sfax, Monastir and Mahdia governorates as the number of antenatal visits (Figures below).
Figure 8: Antenatal visits per delivery F i g u r e 9 : P o s t n atal visits per delivery
1.5 8 6 1.0 4 0.5 2 0 0.0 i l i l f r r f s s s r r x s s s l a a a a a A e e e e e x l d n n a a a a a a e e e e e n d n i A a i i i j I i i i j u e i u t e s u t e s I a u s u e s a u n n a a n n n a n b a n n n n n z f r f S b n b z e f d r i i i f b s e s e d K i i e i s s S K e b e o I b a a o u u
a a u u u
r I a S r e u u a e i i S u e u u z h B a r e u n i i u z b h B a r b n u u a l a l e z o o r e T N e z o o o r T e i o K i a N o o G o i e K n a a o a o G o i e A n o r o A L r s L G s
h G i S A
h d T i S A T U B a d S d B B o a S d B s o U N s N
g M a n t g M a n t n i T i e n a e T a a a a K e a K e e M d M d e K K i T J Z i T Z J B M B M S S
More generally, the various governorates do not fall in the same • availability of funding or means of support to make the demand category for the two indicators, reflecting the importance of community effective; and social determinants in health care use. It would be interesting to • existence of an offer or several offers to meet the need. consider the factors that explain these differences in order to assess the fairness of the system (Fleurbaey and Schokkaert, 2011). The use When one of the last two elements is absent, access to care becomes of health care which determines the health status of individuals is impossible and care will not be provided at the risk of leading to serious mainly due to the interaction of three determinants: vital and economic consequences. Disease causes a loss of income and can propel the individual into poverty. As such, these two dimensions • Existence of demand related to the expression of a need for health deserve special attention because they have a determinant impact on (disease prevention, disease treatment, reproduction, etc.); access to care. We will devote the following sections to them.
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3. Territorial Inequalities in Health Care Facilities
he availability of a health service whether public or private and hospitals represent approximately 50% of all public sector beds. The T good coverage of the national territory in health infrastructure health system further includes the polyclinics of the National Social contribute significantly to the reduction of inequality in access to Security Fund, hospitals under the Ministry of National Defence and health care (Gold Zeynep et al., 2009). After an overview of Tunisia’s the facilities of the Ministry of the Interior and Local Development health care system, we will analyse the availability of public and private (Arfa and Elgazzar, 2013). health resources in the 24 governorates and try to build a composite indicator of health care facilities that can help to assess inter-regional In Tunisia, access to the various levels is open and not by referral. Hence, inequalities and to monitor their trend. the first line may start at the primary, secondary or tertiary level. The intense activity of emergency services in hospitals is an example. 1- Overview of the health care system in 2010 6 The private health care sector has grown significantly: it accounts for In Tunisia, the health care delivery system is primarily public although about 14% of total bed capacity and 70% of advanced technology there is a growing private sector (Arfa, 2007). Nationally, more than services. In terms of human resources, it employs 48.3% of physicians 86% of hospital beds are in the public sector. 7 The leading care (55.6% of specialists and 42% of general practitioners), 77.5% of dentists provider is the Ministry of Health. The public provision of health services and 81.5% of pharmacists. Private clinics are mostly concentrated in is structured in three levels of care. Primary health care 8 is provided major coastal urban areas (Arfa and Elgazzar, 2013). by 2 085 primary health centres (PHC), with 2 923 district hospital beds (consisting of small facilities with an average of 27 beds per Despite an equalizing trend and geographical accessibility deemed facility) and maternity centres, which together account for about 15% acceptable for front-line facilities, the distribution of health services of public sector bed capacity. This care level implements preventive in the country is characterized by a certain inequality that should be health policy. It handles 60% of public sector medical outpatients and evaluated and corrected. more than 1.3 million reproductive health visits (perinatal consultations, contraception, STI, screening for female cancers, etc.). It manages 2- Trend in the distribution of health facilities the health activities of all pupils and students at all levels (pre-school, primary, secondary, university, vocational training and others). To study the trend in the distribution of health facilities in the country, we will analyse the allocations of the 24 governorates in primary health Level II health care is provided by 33 regional hospitals (RH), which centres and hospital beds over the past decade. These two indicators account for 35% of total bed capacity and medical specialists in the characterize public health provision. 9 We will complete them with public sector. human resource indicators (public sector physicians and paramedical staff). We will also analyse the availability of private practices, Level III health care consists of a network of 24 hospitals and pharmacies and dental offices in governorates. These three indicators academic institutions with an average size of 405 beds. These characterize private health provision.
6 The main source is the 2010 Health Map of Tunisia (Ministry of Health). 7 In 2010, the theoretical public bed capacity is 19 565 beds, while private clinics account for only 3 029 beds. 8 When people need health care, they turn most often to primary health care services, which are the first point of contact with the system. In general, primary health care has a double function. First, it provides preventive and curative support for common diseases. Then it acts as an interface and when necessary, refers patients to higher levels; it facilitates their movement within the health system when more specialized care is needed. 9 We will not discuss any issues related to the efficient use of the infrastructure, (World Bank, 2008). Overall, the potential of district hospitals has been under-utilized because of the weakness of their technical facilities, which limits the scope of diagnostic and therapeutic care management. Regarding regional hospitals, despite generally satisfac - tory technical facilities, productivity is affected by the lack of specialists, who are more attracted to university hospital careers or private practice. Lastly, UTHs control most of the heavy equipment in the public sector. Skill levels are high, but the sector suffers from consultation congestion, due to the weakness of the second level, as well as an increasingly strong tendency for brain drain to private practice that offers significantly higher income levels (WHO, 2010).
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For each indicator, we will refer to the per capita endowment or its 2003 (Figure 10). It increased from 4 795 in 2003 to 5 051 in 2010. This inverse (the number of inhabitants per unit). We will see if, on average, trend could mean a more efficient use of PHCs for the benefit of a denser the indicator improves. In addition, the use of the coefficient of population. It may also indicate less easy access to these centres. In variation 10 will reveal whether, overall, the distribution of health the latter case, the PHC facility, as the access point of the population resources has become more or less unequal. Comparing the values to the health system, is fulfilling its role of health prevention and curative of the indicator at the beginning of the last decade and at its end, for treatment of common diseases less than before. each governorate, will allow description of the trend of the governorate. The data used are drawn from the Tunisian Statistical Yearbook for However, the coefficient of variation of the number of inhabitants per 2006-2010 (Serial No. 53). PHC according to the governorate shows a downward trend indicating a reduction in inter-governorate inequality (Figure 10). The decrease 2-1 Trend in the distribution of public health care facilities in inequalities is due primarily to the deterioration of the situation in governorates such as Monastir, Bizerte, Sfax, Sousse, Nabeul, Ben 2-1-1. Primary Health Centres (PHCs) Arous, Ariana and Tunis, as shown in Figure 12. These governorates are relatively well served by Level III and PHCs providing consultations The number of inhabitants by PHC globally reflects a reversal trend from almost daily.
Figure 10: Inhabitants per PHC (1998-2010) F i g u r e 1 1 : C V i n h a b itants per PHC (1998-2010)
5100 5100 5000 5000 4900 4900 4800 4800 4700 4700 4600 4600 8 9 1 5 7 8 9 8 9 1 5 7 8 9 0 2 3 4 6 0 0 2 3 4 6 0 9 9 0 0 0 0 0 9 9 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 9 9 0 0 0 0 0 9 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
Figure 12: Inhabitants per PHC by governorate
25 000 20 000 15 000 10 000 5 000 2000 0 2010 l f i s s s r r x a a a a a a a e e e e e d l n e i n i j l i i u e t s s e a u u b b n n n n a a n n z f r f e d b i i b e s K s i b e o a a
u u u u a S e u u r i i e h B r u n b z u a a l e z m o o r o T o e i o K a a G i e o o o n A r L G s e
d n h S A i T B a S d B o N s s
g a M n t n a i e a n a a K e e M d K M J E i T Z B M S
10 For a Y expenditure distribution with average Y, the coefficient of variation (CV) is derived from the variance. It is defined as the ratio of the standard deviation σ to the average of expenses: CV = σ /Y where σ2 = 1/ N ∑(Yi - Y)2 CV is used to compare the dispersions of distributions with different averages. The higher the CV the more dispersed the distribution will be.
10 African Development Bank Economic Brief AfDB
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In this respect, it should be noted that this indicator does not allow improving the frequency of primary health care consultations rather proper assessment of the availability of health services in regions than the multiplication of small health centres. 12 because it does not take into account the pace of consultations in the PHC. Indeed, primary health centres differ in their type and the 2-1-2. Hospital beds pace of medical consultation observed there. In 2010, 1040 of the 2085 PHCs provided at most one day of consultation per week. The Unlike the first indicator, the bed equipment rate or number of public 2 085 primary health centres are in fact equivalent to 870 full-time beds per 1000 inhabitants shows a significant increasing trend in the centres. 11 In addition, the pace of consultations is not equally distributed public provision of care (Figure 13). The number of public beds per 1000 among the PHCs. The proportion of PHCs providing medical inhabitants increased from 1.74 in 2001 to 1.85 in 2010. Similarly, the consultation six days per week is only 4.4% in Medenine, 4.8% in coefficient of variation of the bed rate in governorates decreased (Figure Tataouine, 9.3% in Tozeur, 8% in Mahdia, 8.6% in Kébili, 8.9% in Sidi 13). This therefore means reduced inter-governorate inequality. Bouzid and 9.6% in Beja. Thus, in these governorates, most of the population does not have daily access to mobile, community primary Despite these positive developments, the public bed equipment rate health care services. Accordingly, efforts should focus more towards varied in 2010 from 0.4 in Ben Arous to 4 in Tunis (Figure 15).
Figure 13: Bed availability rate (1998-2010) F ig u r e 14: CV bed availability rate
1.9 0.60 1.8 0.50 0.40 1.7 0.30 1.6 0.20 1.5 0.10 1.4 0.00 9 8 7 5 1 9 8 9 1 4 6 7 8 8 0 2 3 5 9 0 6 4 2 0 0 0 0 0 0 9 9 9 0 0 0 0 0 9 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 9 9 9 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 2 2 2 2 2 1 1 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2
Figure 15: Public hospital beds per 1 000 inhabitants by governorate 4.50 4.00 3.50 3.00 2.50 2.00 1.50 1.00 0.50 2000 0.00 l i f r r s s 2010 e x a a a a a a a s e e e e d l n e n r i j i i i u t e s s e a u u b n b n n n a a n è z f r f e d n b i i e s s K i e t b o a a u u u
u a S e u r i i e h B u r n u z b a u n a l z o o r e o o e i T o K a G a i e o e o n o A r
L G s
n d h S A i T B a S d B o e s N
g a i M n t n a i e a s a i a K e e M d K M J i T Z n B M S u T
11 The 2010 Health Map can be used to calculate for each governorate the number of PHC full-time equivalent and the number of inhabitants per PHC full-time equivalent, but this statistic is not available for earlier years. 12 Three evaluations of the primary health care system (1997, 2000 and 2004) were carried out by the Ministry of Health as part of the Health Districts National Development Programme. It is a programme developed since 1994 by the Directorate of Primary Health Care. The overall objective of PNDCS is to make all health units in the country able to manage the health status of the population through a set of preventive, curative, promotional and rehabilitation activities, and ensure coordination within and between sectors involving all health stakeholders. The PNDCS has two specific objectives: firstly, improving the (technical and relational) quality and efficiency of care at the primary health centres (PHC) and the district hospital and secondly, strengthening and involving the population in health management. The main recommendations were: (i) optimizing health delivery by moving from a logic of coverage with infrastructure (number of hospital beds, number of DHCs) to an approach of coverage with effective services (number of medical consultation days offered, range of hospital services with appropriate technical facilities); (ii) improving the dimensions of care quality, for example by adapting opening hours to the rhythm of the patient population.
11 African Development Bank AfDB Economic Brief
2014 • www.afdb.org
Reducing inequality reflects both: to ensure that at each of its levels, the tasks assigned are carried out in the best conditions. These missions should be clearly specified. Each l An improvement in bed equipment in many governorates among the hospital institution should have a roadmap that allows strategic least well off, notably: Sidi Bouzid, Kasserine, Siliana, Tataouine, Beja, management consistent with the whole system. Kébili, Le Kef, Gafsa, Tozeur... l a worsening situation in other governorates. The number of beds per 2-1-3. Public sector physicians 1 000 population decreased in Ariana, Sfax, Monastir, Sousse, Manouba and Zaghouan. These governorates are experiencing strong There has been a significant decrease in the number of inhabitants per population growth induced in particular by migration. Almost ten years physician in the public sector in all governorates with the exception of of stagnation of the bed equipment rate in Tunis or the reduction Ariana. This decrease reflects an increase in public health care provision. thereof in large cities such as Sfax, Monastir, Sousse, Ariana and Overall, the number of inhabitants per physician dropped from 2176 in Manouba are all the more disturbing trends since these cities are 2002 to 1569 in 2010 (Figure 16). Until 2008, this trend increased university teaching hospital centres. The quality of training at the inequality between governorates. However, since 2008, the inequality patient’s bedside may be affected by the increasingly less favourable gap is closing but is still significant (Figure 16). The number of inhabitants conditions in which it takes place. As such, there is the risk of a vicious per physician in the public sector varied, in 2010, between 493 in Tunis circle that reproduces mediocrity. To decongest level III and allow it and 3377 in Kasserine, a ratio of 1 to 6.8. to devote more time to training and research missions, level II should be developed. There is a clear improvement in physician availability in many governorates (Figure 18). However, at the same time, the situation Indeed, the bed equipment rate analysed below takes into account beds has changed very little in governorates like Kasserine, Medenine, at the three levels. It therefore hides disparities within levels. In the absence Nabeul and Kébili. Yet these governorates were initially less endowed of detailed statistics for the study period, it was not possible for us to with physicians. appraise the paces of matching developments. However, interviews with stakeholders led us to conclude that the weak link in the system is level II. Five governorates are better provided with public health physicians Often this level is ineffective or non-existent and therefore needs to be than the country as a whole. The number of inhabitants per physician strengthened. there is less than 1569. They are Tunis, Sousse, Monastir, Sfax (which enjoy level III services) and Tozeur. Conversely, five governorates have Finally, the quest for greater equity in the health system should not result twice less the number of physicians; the number of physicians per in levelling from the bottom, or in a substantial carrying forward of inhabitant is higher than 3 000 in Kairouan, Jendouba, Sidi Bouzid, activities from a certain level to a higher one. The system should be able Medenine and Kasserine.
Figure 17: CV Inhabitants per public health Figure 16: Inhabitants per public health physician physician 3000 0.37 2500 0.36 2000 0.35 1500 0.34 0.33 1000 0.32 500 0.31 0 0.30 9 8 9 1 5 7 8 0 0 2 3 4 6 0 9 8 7 6 5 4 3 2 0 9 9 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 9 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
12 African Development Bank Economic Brief AfDB
2014 • www.afdb.org
Figure 18: Inhabitants per public health physician by governorate
5000 4500 4000 3500 3000 2500 2000 1500 2002 1000 500 2010
0
l f i s s r r x s a a a a a a a e e e e e e d l n n i i j l i u i e t s s e a u u a n n b b n n n a n z f r f e d i b i e i b s K s b e o a a u
u u u u r a S e u i i e h B r u b n u z a a T l o e z m o o e r o i o K a a G i e o o o r n A s L G e i
d n h S A T B a S d s B o N s
a g a M n t n i a e n K a a e e d M K M J E i T Z B M S
The overall improvement in the availability of physicians veils significant in 2002 to 308 in 2010. The indicator has improved in all governorates deficits in medical specialists (surgery, obstetrics, ophthalmology, except Sousse, Monastir and Ariana. The trend of the coefficient of orthopaedics, anaesthesiology ...). variation across governorates indicates a gradual reduction of inter- governorate inequalities (Figure 19). Better coverage of the national territory in level II beds is necessarily concomitant with better provision of these regions with physicians in However, there are still significant inequalities. The number of general and specialist physicians in particular. inhabitants per public sector paramedical staff varied in 2010 between 150 in Tunis and 720 in Ariana, a ratio of 1 to 4.8. The six governorates 2-1-4. Public sector paramedical staff best equipped with paramedical staff are Tunis, Tozeur, Sousse, Monastir, Gafsa and Kef. The six governorates least equipped are The number of inhabitants per senior technician is a clear indication of Zaghouane, Nabeul, Kasserine, Sidi Bouzid, Ben Arous and Ariana, the increase in public health care provision (Figure 19). It went from 341 as shown in Figure 21.
Figure 19: Inhabitants per paramedical staff F i g u r e 2 0 : CV Inhabitants per paramedical staff
0.60 360 0.50 340 0.40 320 0.30 0.20 300 0.10 280 0.00 9 8 9 1 5 7 8 0 0 2 3 4 6 8 9 1 5 7 8 9 0 2 3 4 6 0 0 9 9 0 0 0 0 1 0 0 0 0 0 9 9 0 0 0 0 0 0 0 0 0 0 1 0 9 9 0 0 0 0 0 0 0 0 0 0 9 9 0 0 0 0 0 0 0 0 0 0 0 1 1 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2
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Figure 21: Inhabitants per paramedical staff by governorate 1200 1000 800 600 400 2002 200 2010 0 i l f s s s r r l x a a a a a a a e e e e e e d e n i i i j l u i e t s e s a u u n a n b b n n n n n b z f r f e d i e i i b s K s b e o a a u
a u u r e a S e u u i i h B r b u n u z a a l o e z u m o o e r T K I o a a G i e o o o r n A s L G e i o
d n S A T B a S d s B o N s
a a h M n t n i a e n K g a e e d M K M J E i T a B M S Z
Once more, better coverage of the territory with level II beds will involve better. In these areas, even when the number of inhabitants per physician better provision of these regions with senior health technicians and is already low, this number has continued to decrease. By contrast, in nurses. the hinterland and in the least developed governorates, the propensity of physicians to settle there was low and the number of inhabitants per 2.2 Trend in the distribution of private health care facilities physician remained high and/or was on the increase. To counteract this spontaneous location of physicians, it is important to negotiate with the 2-2-1. Private practice offices medical corps to revise the institutional framework governing the opening of private practice offices and/or to grant special incentives to physicians The number of private practice offices has increased considerably who settle in priority areas. The Order of 24 December 2009 in part throughout the past two decades such that the number of inhabitants enshrined the idea by providing for compensation for medical per office has on average been divided by 1.66 in 10 years (Figure 22). specialists 13 practising in the private sector and contracted with health Inequalities between governorates declined until 2004, but have since facilities in priority areas (defined by the Order of 1 March 1995 issued increased (Figure 22). Between 2004 and 2010, the situation improved by the Prime Minister laying down priority health areas for the granting in all governorates except Siliana, Sidi Bouzid and Tataouine. of certain benefits). The effects could be assessed. 14 In France, a solution to the “medical desert” phenomenon was the establishment of “medical In 2004, the ratio between the governorate best provided with private home”, that is to say, a structure whose main advantage is that of practice offices (Tunis) and the least provided governorate (Siliana) bringing together in one place many practitioners and several specialties was 1 to 11.6. In 2010, this ratio rose to 14.3 (Figure 24). This problem with the purpose of saving by pooling and sharing certain costs. However, is not specific to Tunisia; the same situation prevails in several these structures instead resulted in the creation of a wider geographical developed countries, including France where it is called “medical network of rural and “desert” areas. Proposals have been made to desert” (Potvin Moquet, Jones, 2010; High Council of Public Health, hamper the freedom of installation of physicians rather than encourage 2009 and Senate 2013). them to open offices in areas where medical facilities are scarce. In particular, this means excluding from health insurance physicians who Thus, in a context where physicians are free to choose their location, choose to settle in already saturated areas. As a result, since their patients there was a craze for major urban centres and areas where the are not reimbursed by social security, it would be impossible for a young purchasing power of the patient base is higher and the quality of life physician to build a patient base (Senate, 2013).
13 TND 500 for specialists in surgery and obstetrics and gynaecology, TND 400 for all other specialties. 14 There is concern that physicians may abuse this situation by diverting patients from the hospital to their private practice or by using hospital equipment for private purposes. Ethical standards and rules of governance should be enacted. Very strict controls must be implemented.
14 African Development Bank Economic Brief AfDB
2014 • www.afdb.org
Figure 22: Inhabitants per private practice Figure 23: CV Inhabitants/Private practice office office
0.70 4 000 0.60 3 000 0.50 0.40 2 000 0.30 1 000 0.20 0.10 0 0.00 8 9 0 1 2 3 4 5 6 7 8 9 0 1 5 7 8 9 0 2 3 4 6 0 9 9 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 9 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
Figure 24: Number of inhabitants per private practice office by governorate 16000 14000 12000 10000 8000 6000 4000 2001 2000 0 2010 i l f r r s s s l x a a a a a a a e e e e e e d n n i i i j l i u e t s u e s a u a n b b n n n n a n b z f r f e d i i b i e s s K e b o a a u u u
u r e a S e u u i i z h B a r n u b u a l o e z m o o e r o T K i o a a o G i e n o o r A s L G e i
d n h S A T B a S d s B o N s
a g a M n t n i a e n K a a e e M d K M J E i T Z B M S
2-2-2. Dental offices per office was divided on average by 1.76 between 2001 and 2009 (Figure 25). Inequalities between governorates have significantly The number of dental offices has increased significantly over the decreased, as shown by the trend of the variation coefficients last two decades to the extent that the number of inhabitants (Figure 25).
Figure 25: Inhabitants per dental office F igure 26: CV Inhabitants per dental office
14 000 0.80 12 000 10 000 0.60 8 000 6 000 0.40 4 000 0.20 2 000 0 0.00 9 8 9 1 5 7 8 0 2 3 4 6 9 1 5 7 8 9 0 2 3 4 6 0 9 9 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 9 9 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 1 1 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2
15 African Development Bank AfDB Economic Brief
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Figure 27: Number of inhabitants per dental office by governorate 60000 50000 40000 30000 20000 2004 10000 2009 0 i l r r s s s f l x a a a a a a a e e e e e e e d n n i i i j l l i u t s u s e e a u a n b b n n n n a n b z f r f e d i i b b e i s s e K b o a a u u u u r e a
S e u u i i z h B a r u n b u a l o z m m o o r o T e K e i o a a o G i e n o o r A s G e e i L
d n h S A T B a S d B o s N s s
a g a M n t n i e a n n K a a e e M d K M J E E i T Z B M S
Between 2001 and 2009, the situation improved in all governorates 2-2-3. Pharmacies and is particularly striking in Zaghouan, Siliana, Sidi Bouzid and Kasserine (Figure 27). Overall, the number of pharmacies has increased faster than the country’s population, such that the number of inhabitants per pharmacy In 2004, the ratio between the governorate most provided with dental has been divided by 1.2 in 10 years (Figure 28). Accordingly, the offices (Tunis) and the least provided governorate (Zaghouan) was 1 to inequalities between governorates decreased significantly as shown by 30. In 2009, this ratio dropped to 11 (between Tunis and Kebili). So the variation coefficient (Figure 28). The situation improved in all there are still margins to reduce inequalities in dental office availability. governorates between 2001 and 2010 (Figure 30).
Figure 28: Inhabitants per pharmacy Figure 29: CV Inhabitants per pharmacy
8 000 0.50 6 000 0.40 0.30 4 000 0.20 2 000 0.10 0 0.00 9 8 7 5 1 8 9 1 5 7 9 8 0 6 4 3 2 0 0 2 3 4 6 0 0 0 0 0 0 1 0 0 0 0 0 9 9 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 9 9 0 0 0 0 0 0 0 0 0 0 0 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2
Figure 30: Inhabitants per pharmacy by governorate 60000 50000 40000 30000 20000 2004 10000 2009 0 i l f r r s s s l x a a a a a a a e e e e e e e d n n i i i j l l i u t e s e s u a u a n b n b n n n a n b z f r f e d b i b i i e s s K e b o a a u u u u
r e a S e u u i i h B z r u n b a u a l o z m m e o o r o T e K i o a a G i e o o n o r A L s G e e i
n d h S A T B a S d B o s N s s
a g a M n t n i e a n n K a a e e M d K M J E E i T Z B M S
16 African Development Bank Economic Brief AfDB
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The improvement has been very significant in governorates that were increased. Hence, it is necessary to develop an appropriate strategy less provided with pharmacies. 15 Despite this, in 2010, the governorate for addressing this challenge. It would be advisable to review the most provided with pharmacies (Tunis) had per capita 2.63 times criteria for opening positions of public health physician at regional more pharmacies than that least provided (Kasserine). It should be level and the institutional framework governing practices at the private noted that the inequalities in the number of pharmacies nationwide level. Similarly, public-public partnerships (such as agreements are much lower than inequalities in private practice offices due to very between academic physicians and regional hospitals) or, failing that, strict regulations. public-private partnerships likely to make disadvantaged areas more attractive, could be considered for medical specialists. 2.3 Summary The availability of private medical practitioners is characterized by high The analysis shows a generally favourable trend that however hides levels of inequality; the ratio between the most endowed and the least internal distortions (Table 1 below). The availability of PHCs is declining. endowed governorates is 14.3. They are followed by dental offices (ratio Level II, which is the reference for Level I, would not be very effective of 11.3) and hospital beds (10.7). The most fairly distributed resources because it is poorly resourced. Therefore, there is an important carry are pharmacies and paramedical staff. Like in pharmacy, the private over to Level III, which thus replaces Level II, thereby causing practice of dentistry and medicine should be better regulated. inefficiencies. Obviously, there is a need to develop a strategy that strengthens and revitalizes primary health care in the country and Lastly, the status of health care facilities in one governorate cannot be enhances Level II. analysed by reference to a single determinant. As such, all components of the sector and complementarity between the various providers should Similarly, it has been shown that the trend of all dispersion indicators be considered simultaneously. Consequently, it seems worthwhile to is favourable, with the exception of the indicator for physicians, conduct an analysis of health care provision that integrates all the whether in the public or private sector. Although the number of determinants of the provision so as to end up with relatively homogeneous physicians per inhabitant witnessed a significant drop between 2002 groups. It would further be interesting to develop for each governorate and 2010 (28.5% for the public health sector and 21% for private a composite indicator of health care facilities so that a governorate could practice), the gaps have widened between the better endowed and gauge its position in relation to other governorates as well as the progress less endowed governorates, and the variation coefficients have it may achieve over time.
15 The operation of pharmacies is strictly subject to a numerus clausus, which is established on the basis of five areas according to delegations. It is regularly reviewed to adapt to the new realities of the profession and demographics.
17 African Development Bank AfDB Economic Brief
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Table 1: Level and distribution of major health care provision indicators
Disadvantaged Indicator Statistics 2002 2010 2010/2002 Appraisal governorates
Average 4807 5051 +5% Unfavourable Tunis Inhabitants /PHC CV 0.94 0.89 Favourable Ariana
Max/min 8.63 8.66 Moderate Ben Arous
Average 1.7 1.85 +8.8% Favourable Ben Arous Hospital beds/ CV 0.49 0.41 Favourable Ariana 1 000 inhabitants Max/min 47.4 10.7 High Sidi Bouzid
Average 2196 1569 -28.5% Favourable Inhabitants/ Kasserine Physician (public CV 0.32 0.35 Unfavourable Medenine sector) Max/min 5.27 6.85 Moderate Sidi Bouzid
Average 341 308 -8.7% Favourable Inhabitants/ Ariana Paramedical CV 0.44 0.3 Favourable Ben Arous staff Max/min 6.68 4.8 Low Sidi Bouzid
Average 2128 1681 -21% Favourable Inhabitants/ Siliana Private practice CV 0.5 0.6 Unfavourable Kasserine (2004-2010) Max/min 11.6 14.3 High Sidi Bouzid
Average 8847 5774 -34.7% Favourable Inhabitants/ Tozeur Dental office CV 0.66 0.58 Favourable Kasserine (2002-2009) Max/min 24 11.3 High Tataouine
Average 6756 5604 -17% Favourable Tozeur Inhabitants/ CV 0.39 0.27 Favourable Kasserine Pharmacy Max/min 4.4 2.6 Low Tataouine
3- Health care facilities in 2010: cluster analysis 1. Average distance to get to a regional hospital 2. Average distance to get to a general hospital 3.1 Indicators 3. Inhabitants by PHC 4. Proportion of PHCs providing medical consultation 6 days of 6 To analyse the distribution of health care facilities between the 24 governorates 5. Inhabitants per PHC full-time equivalent (FTE) in Tunisia, we will refer to a broad set of indicators that characterize such 6. Inhabitants per primary care physician facilities. These are indicators relating to health infrastructure, human resources 7. Frontline bio-medical laboratory unit per 100 000 inhabitants in public and private health facilities, and equipment. These indicators are 8. Frontline radiology unit per 100 000 inhabitants drawn from the Tunisia 2010 Health Map published by the Ministry of 9. Frontline dental chairs per 100 000 inhabitants Health and/or from the Tunisia Statistical Yearbook published by the INS. 10. Inhabitants per day pharmacy 11. Inhabitants per night pharmacy 3-1-1. Health infrastructure indicators 12. Private bio-medical laboratories per 100 000 inhabitants 13. Haemodialysis machines per 100 000 inhabitants (public and These are indicators on: private)
18 African Development Bank Economic Brief AfDB
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3-1-2. Common equipment indicators 16 This cluster covers 626 PHCs, 21 district hospitals (341 beds), 13 regional hospitals (1 315 beds) 22 university hospitals (92% of the overall with 9 032 1. Hospital bed equipment rate (public and private) beds) and 4 590 private medical practices out of 6 273 (general practitioners 2. Public hospital bed equipment rate and specialists). It is characterized by good positioning in terms of access 3. Private bed equipment rate (in clinics) to hospitals and backed by sustained availability of alternative types of 4. General surgery bed equipment rate infrastructure and also by poor positioning in terms of the number of 5. Gynaecology and obstetrics bed equipment rate inhabitants per Primary Health Centre (PHC). Hence, we have: 6. Paediatric bed equipment rate
7. Ophthalmology bed equipment rate l The best location in terms of access to regional hospitals and general 8. ENT bed equipment rate hospitals (except Médenine due to its low density);
9. Orthopaedic bed equipment rate l The largest proportion of PHC providing medical consultation 6 days 10. Cardiology bed equipment rate a week;
11. Anaesthesiology bed equipment rate l The highest number of inhabitants per PHC on average;
12. Psychiatric bed equipment rate l The highest number of inhabitants per PHC in full time equivalent;
l A moderate number of inhabitants per primary care physician;
3-1-3. Human resource indicators l The lowest number of primary biomedical laboratory units for 100 000 inhabitants;
1. Density of physicians (per 100 000 inhabitants) l The lowest number of primary radiology units for 100 000 inhabitants;
2. Density of general practitioners (per 100 000 inhabitants) l The lowest number of primary health care dental chairs for 100 000 3. Density of general practitioners in the public sector (per 100 000 inhabitants; inhabitants) l The highest number of inhabitants per day-time pharmacy;
4. Density of general practitioners in the private sector (per 100 000 l Moderate number of inhabitants per overnight pharmacy; inhabitants) l The highest number of medical analysis laboratories for 100 000 5. Density of specialists (per 100 000 inhabitants) inhabitants;
6. Density of public sector specialists (per 100 000 inhabitants) l The highest number of haemodialysis machines for 100 000 7. Density of private sector specialists (per 100 000 inhabitants) inhabitants. 8. Medical density per specialty (all physicians) 9. Density of pharmacists (per 100 000 inhabitants) Besides Médenine, this cluster is composed of university hospital 10. Density of dentists (per 100 000 inhabitants) cities. 18 It can be observed that there is a predominance of tertiary 11. Density of nurses, nursing aides and senior technicians (per 100 000 care, including emergency services that are particularly in demand inhabitants). and are overriding the PHC. 19 The question then is not so much whether or not to increase the density of PHCs but also to understand 3-2 Heath Infrastructure Distribution the motives underlying the people's preference for emergency room services to PHCs. Should it be blamed on the overly broad primary Based on health infrastructure indicators in the 24 governorates observed health network or the discrepancy between its temporal accessibility in 2010 (Tables 15, 16, 17 and 18 in Annex 1), a dynamic clusters analysis 17 and the quality of care it provides? 20 These two aspects certainly was conducted in four clusters (Map 3). deserve special consideration and it is appropriate to both standardize the availability of infrastructure and upgrade the operation of all 3-2-1- The first cluster (Table 13 in Annex 1) includes the Tunis, Ariana, Ben structures at all levels. The certification of hospitals would be entirely Arous, Manouba, Sousse, Monastir, Sfax and Médenine governorates. appropriate. In this context, an agency for the accreditation and
16 We did not consider indicators for equipment that has a regional scope and serves several governorates, such as the equipment rate for public beds with university status by major region (north, centre and south); the MRI equipment rate by major region; the scanner equipment rate and the equipment rate for other heavy equipment. 17 The method for classifying dynamic clusters is essentially based on the distribution of a population into homogeneous groups (classes or clusters) using the core concept associated with each class. It may involve, as in our study, for example, discovering the main governorates with the closest health facilities.
19 African Development Bank AfDB Economic Brief
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certification of health services was established by Decree No. 2012- an intensive care unit, with the technical equipment needed to support 1709 of 06/09/2012. different types of emergencies, district hospitals currently perform only a single real hospital function: carrying out eutocic deliveries. Regional 3-2-2- The second cluster (Table 14 in Annex 1) includes the Bizerte, hospitals that are supposed to provide Level II care, lack specialists (the Nabeul and Kebili governorates. This cluster is characterized by a cluster is very poorly staffed with specialist physicians) and equipment. very average positioning with respect to all criteria relating to public Furthermore, quality certification will help bring these structures up to infrastructure (hospitals, PHC ...) or private facilities (pharmacies, standard. Lastly, it would be appropriate to develop specific incentives dental offices, laboratories, etc.). It includes 273 PHC, 9 CH and 6 to induce private stakeholders to settle in these governorates. RH (2 135 beds i.e. 11% of the overall nominal capacity), 709 private practices, 286 pharmacies, 221 dental offices... 3-2-4- The fourth cluster (Table 16 in Annex 1) includes the Jendouba, Kairouan, Kasserine and Sidi Bouzid governorates. This cluster is 3-2-3- The third cluster (Table 15 in Annex 1) includes the Béja , Gabès , characterized by even low rate of access to hospitals and available Gafsa, Le Kef, Mahdia, Siliana, Tataouine, Tozeur and Zaghouan basic infrastructure (with 27 CH, 470 PHC and only 4 RH). Furthermore, governorates. the most reduced availability of private facilities can be observed (350 private practices). The shortfall of private health services is probably These nine governorates have a rather low hospital access rate (due due to the low standard of living in these governorates and the lack of to low population density) and limited availability of various types effective demand for health services. of infrastructure, especially those that are private-owned. This shortcoming is partly offset by proper positioning in terms of the number Hence, this fourth cluster comprises all priority governorates in terms of inhabitants per PHC and per primary care physician. of infrastructure wherein an intervention to enhance public health coverage would allow coverage similar to the rest of the country, and Yet these governorates have 46 district hospitals out of a total of 109, seems to be a necessary step to boost private coverage through the 10 regional hospitals (about 33 throughout Tunisia) and two university ripple effect. In this regard, it would be wise to develop specific incentives hospitals (in Mahdia and Zaghouan), with a nominal capacity of 4 133 to induce private stakeholders to settle in these governorates. beds (21 % of national capacity). The table below summarizes the specificities of each cluster with respect However, while a hospital should have at least one surgical ward and to health infrastructure.
18 Tunis, Sousse, Monastir and Sfax. 19 In a study on the reasons for recourse to the emergency services of major hospitals in Greater Tunis (Ben Gobrane et al., 2012), the major reasons given by patients are quick and easy access to emergency services, the availability of equipment as compared to PHCs and, for the populations, inappropriate working hours of primary care facilities that work only in the morning. Hence, recourse to emergency services is partly due to the shortcomings of primary care medicine. 20 In most of these structures, consultations are carried out only in the morning. In rural areas, the length of consultations is notoriously reduced given the number of consultations conducted. In urban areas, opening hours do not match the time users are available for consultation. The result is threefold: either unwarranted recourse to hospital emergency services at different levels, or delay in recourse or forced and costly recourse to private primary care facilities.
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Table 2: Distribution of health infrastructure by clusters
Characteristics of Allocations Governorates Other front-line Private medical Hospitals PHC structures structures
Tunis, Ariana, Ben Arous, Manouba, Sousse, + - - + Cluster 1 Monastir, Sfax and Medenine.
Cluster 2 Bizerte, Kebili et Nabeul +/- +/- +/- +/-
Beja, Gabes, Gafsa, Le Kef, Mahdia, Siliana, - + + +/- Cluster 3 Tataouine, Tozeur et Zaghouan
Jendouba, Kairouan, Kasserine and +/- +/- +/- - Cluster 4 Sidi Bouzid
The analysis below focused on hospital availability in terms of average located. Moreover, the quality of private technical equipment in Tunis distance access to a hospital, but did not provide information on the and the concentration of specialists observed there have fostered the availability of the said hospitals. Furthermore, to better clarify this aspect, export of health services. Standardization and certification procedures we will consider some indicators of bed availability rates. would allow a greater development of this sector.
3-3 Bed Availability Rate 3-3-2- The second cluster (Table 18 in Annex 1) includes the Sfax, Mahdia, Le Kef and Médenine governorates. This cluster is characterized Based on the bed availability rate in the 24 governorates observed in 2010 by its leadership in private infrastructure. It ranks first in terms of bed (Table 19, 20, 21 and 22 in the annex), a dynamic cluster analysis was availability rates in clinics. However, the number of its public beds is conducted in four clusters (Listing 2: Map 4). relatively small (3 567 out of 19 565 beds, i.e. 18%). Overall, it ranks third in terms of hospital beds as well as specialties in general surgery, 3-3-1- The first cluster (Table 17 in Annex 1) includes the Tunis, orthopaedics, ORL, ophthalmology, paediatrics and anaesthesiology. Sousse, Monastir, Tozeur and Manouba governorates. This cluster is However, this cluster has the highest number of beds in psychiatric characterized by: wards and ranks immediately after the first cluster with respect to specialized beds in gynaecology and cardiology wards. • The best positioning in terms of bed availability rates and hospital bed availability rates (public beds). This positioning is observed for In this cluster, Sfax is a university hospital city. Its two university hospital all specialties except ORL and psychiatry; and centres 21 , which are adjacent to each other, suffer various shortcomings. • The second positioning in terms of clinic beds (private rooms) as They are congested and overwhelmed by a workload exceeding their well as public beds in ORL and psychiatry. nominal capacity and thereby leading to long waiting periods. They service not only the Sfax governorate but also the southern population In this cluster, Tunis, Monastir and Sousse are three university hospitals of more than 4 million inhabitants, which implies overuse of equipment. with a total of 6 664 beds (34% of the national capacity), including 2 Because of such anomalies, these UH increasingly face difficulty in 961 by specialty. These UH are regional in scope and assigned to the meeting academic training, specialization, high-level care and medical North and Centre, and even national for certain specialties. They service research needs in good condition. For over a decade, a new UH has a much larger population than that of the governorate in which they are been programmed for Sfax but has not yet been implemented.
21 The Habib Bourguiba Hospital (506 beds) is home to the surgical specialty services; the Hedi Chaker Hospital (889 beds), which is older, provides medical pathology ser - vices.
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However, Sfax is characterized by a significant private health sector, previous investment code provided for some tax incentives 23 for with clinics (mono-disciplinary or polyclinic), radiology centres, medical equipment. laboratories and pharmacies. It serves as a medical platform for the South and for the export of health services primarily for Libyans. 3-3-4- Cluster 4 (Table 20 in Annex 1) comprises the less affluent governorates in terms of beds: Ariana, Ben Arous, Zaghouan, Bizerte, 3-3-3- The third cluster (Table 19 in Annex 1) comprises the Béja, Gafsa, Nabeul, Jendouba, Kairouan, Kasserine, Sidi Bouzid and Gabes. There Kébili and Tataouine governorates. This cluster is relatively very well have, on average, the lowest public bed availability rates and, to a lesser equipped in terms of public beds but is poorly equipped with respect to extent, low private bed availability rates. This is true for almost all specialties. private beds. It ranks second with respect to public infrastructure (1 831 However, the Northern governorates (Ariana, Ben Arous, Zaghouan, hospital beds in Levels 1 and 2). The significance of the public sector is Bizerte and Nabeul) seem to be of lower priority due to their proximity to highlighted by the specialized beds availability rate in ORL, surgery, Tunis and the importance of the private sector. In the other governorates orthopaedics, ophthalmology, paediatrics and anaesthesiology. However, (Jendouba, Kairouan, Kasserine, Sidi Bouzid and Gabes), not only is the there is a shortfall of equipment for gynaecology, cardiology and psychiatry. per capita bed ratio low, but a significant proportion of the beds are district hospital beds, and district hospitals are often poorly equipped and poorly In addition, this cluster is characterized by a relatively underdeveloped staffed with specialists. The lack of equipment and resources in DHs has private sector. It is the most underprivileged group in terms of transformed these centres into intermediate facilities incapable of resolving clinic beds. The establishment of clinics is governed by clinical the problems they encounter, thereby causing users to bypass this level specifications 22 , considered stringent as compared to that of public and resort to the private sector or the secondary and tertiary levels, usually structures (e.g. it is required to have a nurse for two intensive care in other governorates. Table 3 below summarizes the distribution of beds beds, whereas there are no set standards for the public sector). The between the public and private facilities for each cluster.
Table 3: Bed distribution by cluster
Governorates Public Beds Private Beds
Cluster 1 Tunis, Sousse, Monastir, Manouba and Tozeur + +/-
Cluster 2 Sfax, Mahdia, Le Kef and Medenine +/- +
Cluster 3 Beja, Gafsa, Kebili et Tataouine +/- -
Ariana, Ben Arous, Zaghouan, Bizerte, Nabeul, Cluster 4 Jendouba, Siliana, Kairouan, Kasserine, Sidi Bouzid - +/- and Gabes
22 Decree No. 93-1915 of 31 August 1993 to determine structures and specialties, and standards in terms of capacity, equipment and staffing of private health institutions, as supplemented and amended by Decree No. 99-2833 of 21 December 1999 and Decree No. 2001-1082 of 14 May 2001. 23 Decree No. 94-1056 of 9 May 1994, establishing a list of equipment needed for health and hospital institutions that may qualify for the tax incentives under Section 49 of the Investment Incentives Code and the conditions for granting these benefits, as amended and supplemented by Decree No. 98-967 of 27 April 1998 and Decree No. 2006-382 of 6 February 2006.
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3-4 Human Resources Tataouine and Tozeur (Table 24 in Annex 1). In the public hospitals o f the 10 governorates, there are only 1 134 physicians (general Based on human resource indicators from the 24 governorates, reported practitioners and specialists), i.e. 17% of public health physicians. The in 2010 (Tables 23, 24, 25 and 26 in Annex 1), a dynamic cluster analysis private sector has only 601 physicians (308 general practitioners and was conducted in four clusters (Listing 3: Map 5). 221 specialists) and relatively few dentists.
3-4-1- The first cluster (Table 21 in Annex 1) comprises the Tunis, Ariana, 3-4-3- Ben Arous, Bizerte, Gabes, Mahdia, Manouba, Médenine, Sousse, Monastir, and Sfax Governorates. Overall, these five Nabeul and Zaghouan constitute an intermediate group with moderate governorates have the highest rates of human resource allocation both human resource allocation for almost all categories. 25% of doctors in the public sector (3957 to 6723 physicians, i.e. 59% of all physicians and 27% of paramedical personnel in the public and private sector in the public sector) and the private sector (47% of physicians in the have settled in these 8 governorates (Table 22 in Annex 1). private sector practice in these five governorates), regardless of the area of specialization. 56% of private dentists and 43% of pharmacies 3-4-4- Béja (Table 23 in Annex 1) is a governorate hinged between the are located in the governorates of this cluster. second cluster and the cluster of human resource-deficient governorates.
3-4-2- The cluster of human resource-deficient governorates comprises The table below compares the distribution of human resources in both Gafsa, Jendouba Kairouan, Kasserine, Kebili, Le Kef, Sidi Bouzid, Siliana, sectors for the 4 clusters.
Table 4: Distribution of health workers by cluster and by sector
Governorates Public Beds Private Beds
Cluster 1 Tunis, Ariana, Sousse, Monastir, Sfax + +
Ben Arous, Bizerte, Gabes, Mahdia, Manouba, +/- +/- Cluster 2 Medenine, Nabeul and Zaghouan
Cluster 3 Beja +/- +/-
Gafsa, Jendouba, Kairouan, Kasserine, Kebili, - - Cluster 4 Le Kef, Sidi Bouzid, Siliana, Tataouine and Tozeur
Overall, among the three types of indicators of health care provision, of health care provision. Hence, it is important to summarize the geographic distribution of health human resources turns out to be infrastructure, equipment and human resource indicators into a single the most unequal and reveals a significant concentration on the coast. indicator. Despite an increase in physician density, regional disparities have widened. Qualitatively, the inequalities are even more egregious and 4- Profile of governorates and composite indicator more than 2/3 of the specialists are concentrated in the coast, not only of health care facilities by governorate. for rare specialties but also for the most common ones such as gynaecology and paediatrics. 4-1 Profile of governorates
Such regional breakdown reflects the geographic dichotomy that shows The ranking of the various governorates into homogenous groups with a clear regional imbalance in favour of the coast to the detriment of the respect to their medical infrastructure, their availability in terms of beds North West and Central West of Tunisia. It is important to consider the and human resources, help to develop a profile for each governorate impact of this inequality in human resource allocation on the inequality according to the groups to which it belongs (see Table below).
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Table 5: Assignment of governorates to clusters (R ji )
Governorates Human Resources Infrastructure Equipements
MONASTIR 1 1 1
SOUSSE 1 1 1
TUNIS 1 1 1
SFAX 1 1 2
MANOUBA 2 1 1
ARIANA 1 1 4
MEDENINE 2 1 2
BEN AROUS 2 1 4
TOZEUR 4 3 1
MAHDIA 2 3 2
BIZERTE 2 2 4
NABEUL 2 2 4
GABES 2 3 4
KEBILI 4 2 3
LE KEF 4 3 2
ZAGHOUAN 2 3 4
BEJA 3 3 3
GAFSA 4 3 3
TATAOUINE 4 3 3
SILIANA 4 3 4
JENDOUBA 4 4 4
KAIROUAN 4 4 4
KASSERINE 4 4 4
SIDI BOUZID 4 4 4
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The table shows that three governorates still belong to the most This indicator is the average of the three scores awarded to each advantaged cluster irrespective of the criterion applied. They are Tunis, governorate. Sousse and Monastir. By contrast, four governorates still belong to the most disadvantaged cluster. They are Jendouba, Kairouan, Kasserine If r ji is the ranking of the cluster to which governorate j belongs with and Sidi Bouzid (Map 6). respect to resource i, score s ji may be defined as the inverse of r ji
Between these two groups, the other governorates have more or less sji = 1/rji given that rji = 1, 2, 3, 4 significant shortfalls depending on the type of resources being analysed. The table defines the scope of intervention required by each governorate. Thus, the score of governorate j for resource i (sji) is considered a unit Hence, Sfax, for example, requires greater bed availability rates in order score where the governorate belongs to the higher class. The score to measure up to Tunis, Sousse and Monastir. decreases as the governorate moves away from this class.
4-2 Composite indicator of health care provision The composite indicator of a governorate’s health care provision is equal to the arithmetic average of its scores. I j = ∑ (s ji )/3 Each governorate, depending on its profile, may be assigned a composite indicator of health care provision. The outcomes are presented in the table below.
Table 4: Score by resource and composite indicator of health care facilities by governorate.
Governorates Human Resources Infrastructure Equipements Total MONASTIR 1.00 1.00 1.00 1.00 SOUSSE 1.00 1.00 1.00 1.00 TUNIS 1.00 1.00 1.00 1.00 SFAX 1.00 1.00 0.50 0.83 MANOUBA 0.50 1.00 1.00 0.83 ARIANA 1.00 1.00 0.25 0.75 MEDENINE 0.50 1.00 0.50 0.67 BEN AROUS 0.50 1.00 0.25 0.58 TOZEUR 0.25 0.33 1.00 0.53 MAHDIA 0.50 0.33 0.50 0.44 BIZERTE 0.50 0.50 0.25 0.42 NABEUL 0.50 0.50 0.25 0.42 GABES 0.50 0.33 0.25 0.36 KEBILI 0.25 0.50 0.33 0.36 LE KEF 0.25 0.33 0.50 0.36 ZAGHOUAN 0.50 0.33 0.25 0.36 BEJA 0.33 0.33 0.33 0.33 GAFSA 0.25 0.33 0.33 0.31 TATAOUINE 0.25 0.33 0.33 0.31 SILIANA 0.25 0.33 0.25 0.28 JENDOUBA 0.25 0.25 0.25 0.25 KAIROUAN 0.25 0.25 0.25 0.25 KASSERINE 0.25 0.25 0.25 0.25 SIDI BOUZID 0.25 0.25 0.25 0.25
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The analysis is based primarily on quantitative indicators. However, while it is true that the choice of the location of pharmacies, given qualitative aspects are still essential. The same is true for those relating that it is highly regulated, resulted in a fairly equal space coverage by to the efficiency of structures. It is obvious that it is equally important pharmacies, it is worth noting also that such was not the case for to improve the functioning of the existing infrastructure to make the dental practices and especially for private medical practices which, most out of them, as it is to further increase the density of infrastructure instead of correcting the deficiencies resulting from the lack of public (World Bank, 2008). health services, exacerbate existing inequalities. It is then clear that in the absence of a regulatory framework for the opening of private To this end, a certification of existing structures should be considered medical practices, it is important to introduce special incentives for (at least the most important). The missions and responsibilities of each young doctors to settle in priority governorates, despite the absence level of care should be laid down, and resources should be allocated of an adequately solvent demand. It may include conventional accordingly. Establishment projects should be stopped for each cooperation between regional hospitals and specialists in private structure. practice for the missing specialties.
The analysis revealed that, despite the reduction in public budgets However, the choices relating to the health sector and the efforts to allocated to health, there is reduced inequality in the allocation of better allocate resources to priority areas can be effective only if they infrastructure to the various regions. This decrease in inequality is partly form part of a comprehensive local development strategy for these due to the concentration of government efforts on the most resource- areas. The reduction of economic, cultural and social gaps between deprived areas. However, it stems mostly from a reduction of resources the governorates can only facilitate and strengthen the health reforms. allocated to the major urban centres with high population growth dynamics. This movement is synonymous with a decline in access to To better support our recommendations, we would have liked to test a health care for vulnerable populations in these regions. It is also panel model which explains the population’s health status by governorate symptomatic of reduced resources allocated to structures responsible (life expectancy and infant mortality rate) and by the prevailing health for the training of future doctors and paramedical personnel. status in each governorate. We were not able to do so because health indicators are not published at the governorate level. Such work would The dwindling resources allocated to the public sector and the ensuing be very informative and could be conducted later. reduced access to care would be offset by an increase in private sector resources and a greater availability of its services. In fact, there Reduction of health inequalities may be achieved only by understanding is an interesting private sector dynamics in its three components: the determinants, setting the corresponding objective, informing health private medical practices, dental offices and pharmacies. However, care professionals and monitoring the achievements.
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4. Trend in inequalities in health spending in Tunisia between 2000 and 2010
n recent years, the State’s effort in favour of health has been on the The analysis of the contribution of the various out-of-pocket health Idecline. Public health expenditures accounted for 2.7% of GDP in expenditure items to the total health expenditure inequality and the 1995 and for only 2.3% in 2011. By contrast, private spending by trends of this contribution between 2000 and 2010 should help to households rose sharply. In 2010, household spending amounted to better understand some of the causes of the inequality in out-of- 51% of total health expenditures, with approximately 80% being out- pocket health spending and to possibly identify policies that should of-pocket payments and 20% corresponding to health insurance be implemented to reduce this inequality. In this regard, we will use premiums. In fact, out-of-pocket payments made by households for the breakdown of the inequality index by source 25 (or component). health purposes account for 41.2% of national health expenditures This technique indicates which expenditure categories contributed (Arfa et al., 2013). most to the formation of inequality. Any policy aimed at reducing health care inequalities should primarily target these categories The importance of out-of-pocket health expenditure in national health (Wagstaff and Van Doorslaer, 2000). accounts (Arfa et al., 2007 and 2008) implies that the search for greater equity in health should entail efforts to achieve more equitable Traditionally, inequalities in expenditures are captured through the distribution of the out-of-pocket spending of households. Therefore, Lorenz curve and the Gini index that analyse the distribution of health it is important to evaluate the inequality of out-of-pocket health expenditures in themselves. However, despite its merits, this approach expenditures of households and to analyse their trends using inequality presents only a partial picture of the inequalities in health expenditures. indicators. A more comprehensive picture may be obtained through the concentration curve and index, which analyse the distribution of health To that end, we will refer to data from the national surveys on budget expenditures in relation to the distribution of the population’s living and household consumption of 2000, 2005 and 2010. These surveys standards ranked from the poorest to the richest. were conducted on a representative sample of households across the country. 24 They provide information on individual consumption of We will start off by focusing on the Gini index, considered as an indicator goods and services, and therefore make it possible to study the trends of inequality in health spending, to see how its breakdown by source in the living standards of households through their expenditures. can provide information on the formation and trend of inequality. Next, These data provide information on expenditures per person per year we will follow the same approach for the concentration index. 26 Lastly, (SPY) and their structure by various expenditure items, especially we will analyse the inequality in health spending and its trend in light of those related to health. The data also facilitates the assessment of the available data. the degree of inequality of these expenditures and the analysis of their trends. Various health spending items were covered in the For estimates of the Gini and concentration indices, we use the STATA consumer surveys (see nomenclature in Annex 1: Health indicators 12 and DASP 27 version 2.2 software, which enable us to calculate and by cluster 2). They correspond to disbursements made by households. break down the various indices.
24 For the three surveys, the initial sample is drawn from a stratified random sampling conducted in two stages in each governorate. The sample frame consisted of the data files of the general population census (1994 and 2004 respectively). Regarding the 2000 survey, 12 960 households were sampled and 12 249 responded (representing a response rate of 95%). Concerning the 2005 survey, 13 392 households were sampled and 12 317 responded (that is, a response rate of 92%). As for the 2010 survey, of the 13 392 households initially sampled, 11 291 responded (representing a response rate of 85%). 25 The breakdown of inequality indices was introduced in health economics by Wagstaff et al. (2003). 26 The literature on inequalities shows that there is a range of relevant inequality indicators, including the Theil indicator and the log deviation. In addition, other break - down techniques, such as groups, are interesting and very informative. In subsequent research, we plan to break down the inequalities in health expenditures using various geographic (environment, region, governorate) and socioeconomic (occupational status of the household head, household size, vulne - rability. etc.) criteria, and to identify the relative importance of intra-group and inter-group inequalities and their trend. 27 Distributive Analysis Stata Package, developed by Araar and Duclos (2007). PEP, World Bank, UNDP and Laval University. http://dasp.ecn.ulaval.ca/
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1- Lorenz curve and the Gini index 1.3. Breakdown of the Gini index by expenditure categories
To describe the distribution of health expenditures as well as the Analytically, the Gini index has several expressions. Lerman and Yitshaki degree and origins of their inequality, we start by adopting the Lorenz (1985) showed that: classification method and then the method that consists in calculating acceptable inequality indicators. G = 2 cov (Y ; F(Y)) / Y (Equation 1)
1.1. Lorenz Curve Y being the expenditure vector Y : (Y 1, Y 2, .... Y n) and F(Y) representing the cumulative distribution considered as random variable uniformly distributed Inequality in the distribution of a variable (health expenditure, for between [0, 1]. example) may be highlighted using the Lorenz curve (Figure 31) which matches the proportion of the population classified by increasing order The breakdown adopted makes it possible to analyse the contributions of health expenditures with the share of total health expenditures to total inequality of the various expenditure categories (consultations, incurred by that population. radiological procedures, drug purchases, etc.). It also facilitates the measurement of their specific inequality contribution to the total Figure 31: Lorenz Curve for health spending inequality (Lerman and Yitshaki 1985).
Let Y= (Y 1, Y 2, .... Y n) be the distribution of total health expenditures and k Yi the expenditure of the person i in item or category k where k=1....K.
k Y1,Y 2,...,Y k are therefore the expenditure components and Y = ∑ Y (Equation 2)
Given the properties of the covariance, we have:
G = 2 ∑cov (Y k ; F(Y)) / Y (Equation 3)
where cov (Y k ; F(Y)) represents the covariance of the expenditure in In the case of two distributions X and Y, X dominates Y if the Lorenz item k with the cumulative distribution of total expenditure. curve relating to distribution “X” constantly lies above the curve relating to distribution “Y”. Distribution “X” is then more egalitarian than “Y”. When cov (Y k , F(Y k)) Yk, is multiplied and divided by cov(Yk ;F(Y k )) Nevertheless, when two curves intersect, comparison of the inequality and by Y k, we obtain the rule of breakdown according to the source trend becomes impossible. Therefore, the digital indicators for evaluating (or component), that is: inequality must be calculated as the Gini coefficient. G = ∑ [ [cov (Y k ; F(Y)) / cov (Y k ; F(Y k))] * [2cov (Y k ; F(Y k)/ Y k] * ( Y k /Y ) ] 1.2. GINI coefficient (Equation 4)
The Gini coefficient seems to be the most popular of the various Let’s note: inequality indices. It is derived from the Lorenz curve (concentration curve) in that it is the ratio of the area between this curve and the first - the Gini correlation between the component k and the total diagonal line and the half-square in which the curve lies. The Gini expenditure: index lies within the range [0, 1]. The more it tends towards 1, the k k k more unequal is the distribution of expenditures. On the other hand, Rk = cov (Y ; F(Y)) / cov (Y ; F(Y )) (Equation 5) when the indicator declines, the distribution of expenditure becomes more egalitarian. - the Gini coefficient related to the component k: G k = 2cov
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(Yk ; F(Y k) / Yk (Equation 6) living, ranked from the poorest to the richest (horizontal axis). In other words, it links the share of health expenditures to the quintiles of total - the proportion of the component k in the total expenditure : expenditures (or other variable indicating the standard of living). 28
Sk = Y k /Y (Equation 7) Figure 32: Concentration curve for health spending in 2010 Then G = ∑ R k Gk Sk (Equation 8) e
The relative contribution of an expenditure source k to the total r u t i d n inequality is: e p x e
h t a e H
f o
P = R Gk S / G. (Equation 9)
k k k n o i t u b i r t s i d
The sum of the relative contributions of various sources (components) e v i t a l is equal to the unit. The breakdown of the Gini index also helps to u m u determine the marginal effect of variation in each expenditure category C k on total expenditure inequality. Let ek be a scalar slightly superior Population in order of increasing Heath expenditure to the unit, an increase in expenditures derived from the source k results in the passage to vector ekYk and will involve a variation of G. The variation in the value of G brought about by this change at 2.2. Concentration index the margin of the expenditure category is obtained from the partial derivative of G in relation to ek. We show that: The concentration index (Kakwani, 1980), which derives directly from the concentration curve, quantifies the degree of socioeconomic ∂G/ ∂ek = S k (R k Gk - G) (Equation 10) inequality related to the variable being examined (health expenditure). The concentration index is equal to twice the area between the
∂G/ ∂ek ek is the marginal contribution of source k to total inequality. concentration curve and the first bisector. If there is no socioeconomic inequality, the concentration curve is confounded with the bisector The relative marginal effect is obtained by dividing the above expression by and the concentration index is equal to zero. By convention, the G, that is to say: concentration index is negative when the concentration curve lies above the first bisector. In this case, health expenditures are highly (∂G/ ∂ek)/ G = ( R k Gk Sk / G ) - S k (Equation 11) concentrated among the poor. Conversely, the concentration index is positive when the concentration curve lies below the first bisector. It is clear that the sum of the relative marginal effects is nil, multiplying Consequently, it is important not to focus on the inequality of health all the sources of income by e leaves Gini’s global index unchanged. expenditures as reflected by the Gini index for these expenditures, but instead on the inequality of health expenditures as revealed by 2. Concentration curve and index the concentration index.
2.1. Concentration Curve 2.3. Breakdown of the concentration index by expenditure categories The concentration curve (Figure 32) represents the cumulative percentage of health expenditures (vertical axis) associated with the According to Kakwani (1980), the expenditure concentration index percentage of the population classified by increasing standards of X is:
28 The Gini curve appears as a special case of the concentration curve. For the Lorenz curve, the vertical axis and the horizontal axis refer to the same variable. For the concentration curve, the variables are different.
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C = 2 cov (X ; F(Y)) / Y (Equation 12) 3. Kakwani index
X being the health spending vector and Y the vector of total The comparison of the health expenditures concentration curve with expenditure: (Y 1, Y 2, .... Y n) and F (Y) representing the cumulated the Gini curve of total expenditures gives an indication of the progressivity distribution considered as a random variable evenly distributed of the variables being studied, which, in our case, are health expenditures. between [0 and 1]. If health expenditures are proportional to the standard of living, then the inequality in health expenditures is similar to that of living standards, The Gini index appears as a special case of the concentration index and the concentration curve of health expenditures is confounded with where X=Y. that of living standards (or Lorenz curve). When the poor spend proportionately less on their health, their share of health spending is That is, X 1,X 2,...,X k the expenditure components with X = ∑ X k (Equation 13) lower than their share in the total expenditures. In this case, the Lorenz curve dominates (or is above) the concentration curve. The opposite The concentration index C may be written as follows (O’Donnell et al., phenomenon is observed when the system is regressive. 2008): The Kakwani index is equal to twice the area between the Gini curve C = ∑ S k Ck (Equation 14) and the concentration curve. It is equal to the difference between the health spending concentration index and the Gini index of total k Where S k = X /X represents the specific budget coefficient relating expenditures. to expenditure item k or the expenditure elasticity Xk in relation to total expenditure X. I = C – G
Ck is the concentration index specific to the category k The value of I lies between -2 and 1. A negative value means that health spending is regressive; the Gini curve is below the concentration The contribution of category k to total inequality is: curve. A positive value implies the progressivity of health spending; the Gini curve is above the concentration curve. There is uncertainty Pk = S kCk/C (Equation 15) when the curves intersect. In which case, it becomes necessary, in addition to the graphical analysis, to use the Kakwani index to This leads to: ∑ S k Ck /C = 1 distinguish between these cases.
The sum of the relative contributions of various sources (components) Figure 33: Concentration curves for the health is equal to the unit. The breakdown of the concentration index also SPY and total SPY in 2010 helps to determine the contribution of variation in each expenditure category k to total expenditure inequality.
Thus, the breakdown of the Gini index as proposed by Lerman and Yitshaki (1985) and that of the concentration index as presented by Kakwani (2000) makes it possible to measure the contribution of an expenditure category to the total inequality. The breakdown also helps to gauge the impact of a marginal increase in a particular expenditure category on total inequality. We will use these breakdown techniques to analyse how the various items of health expenditure have contributed to the inequality in private expenditures on health in Tunisia.
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4. Trend in and sources of inequality of health SPY: Table 8: Trend in the concentration index and 2000-2005-2010 Kakwani index
4.1. Overall inequality trends Years 2000 2005 2010
Gini indices show that inequalities in health expenditures are much C 0.444437 0.448276 0.410342 greater than inequalities in total household expenditures (Table 7). In 2010, the Gini curve of health spending revealed a high concentration G 0.408558 0.414008 0.384674 of health expenditures (Figure 31). Twenty per cent (20%) of the population accounts for over 75% of health spending. The high levels I 0.035879 0.034268 0.025668 of inequality stem from unequal health status of the population, given that health expenditures are closely linked to the need for care, which is expressed only by those in poor health. The concentration index shows that inequality in health spending increased between 2000 and 2005, but dropped sharply between Table 7: Trend in Gini index for total SPY and the 2005 and 2010 (Table 5). Despite this decline, inequality in health health SPY 29 spending continues to be higher than inequality in total expenditures Years 2000 2005 2010 (Figure 33).
Total SPY 0.408558 0.414008 0.384674 The Kakwani index is positive. Health expenditures are progressive (Figure 34). The Kakwani index declined between 2000 and 2010, Health SPY 0.744939 0.715508 0.711574 the progressivity of health spending was constrained. santé
Therefore, it is important to consider not the inequality in health The scope of the inequalities in health care spending explains why spending as reflected by the Gini index for these expenses, but the they should be paid close attention and the need to identify ways and inequality in health spending as shown by the concentration index. means of reducing these inequalities.
Figure 34: Budget coefficient of health spending by decile of total expenditures in 2010
7 % 6 % 5 % 4 % 3 % 2 % 1 % 0 % d1 d2 d3 d4 d5 d6 d7 d8 d9 d10
29 The Gini index for total SPY 2000 is equal to that published in the report of the 2000 consumption survey (p.27). The 2000 and 2005 indices differ from those published in 2012 in the poverty report (p. 23). However, the 2010 index does not differ from the latter.
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4.2. Contribution of expenditure items to total inequality special radiology services (scans and MRI), child delivery, medical in health spending treatment abroad, long-term care (long-term consultations and medication) and cosmetic surgery (since 2010); In the national budget and household consumption surveys of 2000, 2005 and 2010, health expenditures are classified into four categories: • Pharmaceuticals (drugs) and such other pharmaceuticals as baby products (talc, soap, etc.), adhesive bandages, etc.; • Routine medical care which includes medical consultations, dental care, paramedical services (x-rays, analyses and nursing • Medical devices, including optical glasses, blood pressure services) in both public and private institutions. This item also measuring devices, hearing aids, etc. includes the use of traditional medicine (healers and medicinal plants); Therefore, one may wonder to what extent each of these items contributed to changes in health spending inequalities. To answer this • Special medical care which includes stays in the hospital question, we will proceed to break down the health inequalities by or clinic, surgical procedures, special dental surgery procedures, category (Table 9).
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Table 9: Breakdown of health spending inequality by expenditure category
Kakwani Year Item Y in D S Gini Index Concentration Index k k Index
Marginal G R S G /G G S C /C C -G k k k k effect k k k k
Routine medical 15.471 21.8% 0.877% 22.6% 0.008 0.471 23.14% 0.062442 care
Special medical 27.974 39.4% 0.872 40.6% 0.012 0.456 40.78% 0.050442 care
2000 Pharmaceuticals 26.351 37.2% 0.799 35.3% -0.019 0.408 34.10% -0.000558
Medical 1.130 1.6% 0.984 1v5% -0.001 0.550 1.97% 0.141442 equipment
Total 70.927 100% 0.745 100.0% 0.0000 0.444 100.0% 0.035442
Routine medical 30.287 27.1% 0.846 28.6% 0.015 0.486 29.45% 0.071992 care
Special medical 32.054 28.7% 0.888 30.4% 0.017 0.453 29.01% 0.038992 care
2005 Pharmaceuticals 47.510 42.6% 0.740 39.5% -0.031 0.417 39.57% 0.002992
Medical 1.742 1.6% 0.977 1.5% -0.001 0.565 1.97% 0.150992 equipment
Total 111.672 100% 0.716 100.0% 0.0000 0.448 100.0% 0.033992
Routine medical 39.062 27.1% 0.843 28.6% 0.015 0.433 26.04% 0.048326 care
Special medical 42.067 29.2% 0.903 32.1% 0.029 0.493 39.99% 0.108326 care
2010 Pharmaceuticals 60.790 42.2% 0.724 37.8% -0.044 0.331 31.91% -0.053674
Medical 2.210 1.5% 0.986 1.5% -0.000 0.625 2.06% 0.240326 equipment
Total 144.251 100% 0.730 100.0% 0.0000 0.410 100.0% 0.025326
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Inequality of the SPY for routine medical care (G = 0.843 and C = were progressive as evidenced by the Kakwani index and budgetary 0.433 in 2010) was higher than the overall inequality in health coefficients by decile of total SPY (Figure 35). expenditures (G = 0.730 and C = 0.410 in 2010). However, on account of their average share in these expenditures (the Sk stood at Of all health expenditures, pharmaceutical expenditures were the most 27.1% in 2010), their relative contribution to inequality was average substantial and the least unequal component (Table 6). In 2010, they (28.6% in 2010). From 2000 to 2010, the relative marginal effect of accounted for 42.2% of total health expenditures, while their contribution spending on routine medical care was positive throughout the period, to the formation of inequality stood at 37.8%, according to the Gini index, meaning that this source of inequality instead had an inertia effect on and at 42.2%, according to the concentration index. From 2000 to 2010, inequalities in health spending. The Kakwani index was positive and the Gini index specific to pharmaceutical expenditures declined from routine health care expenditures were progressive. Indeed, in 2010, 0.799 to 0.724. 30 The marginal effect of the growth of these expenditures the budget coefficient of these expenses decreased for the 9th and on inequality was negative throughout the period analysed, reflecting an 10 deciles (Figure 35). equalizing impact. In 2010, pharmaceutical expenditures were regressive from the fifth decile (Figure 35) and the Kakwani index was negative.
The inequality of the SPY for special medical care was on the uptrend Expenditures on medical equipment were the smallest component (GK2000 = 0.872; Gk2010 = 0.903 and = 0.459 CK2000; Ck2010 = of health expenditures (Table 6), yet they were the most unequal 0.493). Throughout the period, their relative marginal effect remained (Gk201=0.986 and Ck2010=0.625). In 2010, they accounted for positive. This development shows that spending on special medical 1.5%, while their contribution to the formation of inequality stood at care is a source of inequality and its increase helped to worsen the 1.5%, according to the Gini index. These expenditures were highly inequality in overall care expenditures, given that these expenditures progressive and had the highest Kakwani index.
Figure 35: Budget coefficients of health spending by decile of total SPY, by category, in 2010 31
0.03 Roune medical 0.025 care 0.02 Special medical 0.015 care 0.01 Pharmaceucals 0.005 0 Medical equipement d2 d3 d4 d5 d6 d7 d8 d9 d10
30 The concentration index went from 0.408 to 0.331 31 Our calculations from the pooled budget data on the 2010 survey published on site of the National Institute of Statistics (INS) http://www.ins.nat.tn/indexfr.php
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4.3. Contribution of routine medical care to the total (G 2010 = 0.993 and C 2010 = 0.600). This situation reflects the difficulty that inequality poor people have in accessing health care other than the one provided free of charge. The often exorbitant cost of these procedures and care Routine care expenses include medical consultations, dental care, would lead to high elasticity and discourage low-income patients from radiology and biological procedures in both public and private sector using the services. 32 In actual fact, the expenses entailed amount to a facilities, and the use of traditional medicine (Table 7). high proportion of their income. The budget coefficient for these expenses increases exponentially with income (Figure 36). The Kakwani Between 2000 and 2010, the decline in inequalities in routine medical index is very high, compared to other items of expenditure. There is care expenses was driven by the decline in inequalities in medical therefore considerable elbow room for improving the access of poor consultations (G k2000 = 0.877; G k2010 = 0.849 and G k2010 = 0.443; C k2010 people to such care. = 0.433). In 2010, the specific budget coefficient of these expenditures decreased for the 9th and 10th deciles (Figure 36). The Kakwani index The trends are similar to those recounted in the literature. was positive but very small. Socioeconomic status is identified as a key determinant of health expenditure. Similarly, people of higher socio-economic status generally The decline in inequalities in spending on medical consultations may be use more health care services, especially the specialized services, than attributed to the greater availability of physicians (reflected in the other people (Allin et al. 2006). In contrast, when people of lower socio- reduction in the ratio of the number of inhabitants per physicians). The economic status are not entitled to free care, they are more likely, than decline is observable despite an increase in inequality in the availability other people, to forgo health expenditures. of physicians nationwide. This suggests that when patients do not have easy access to medical services and physicians, they tend to move For De Looper and Lafortune (2009), despite having higher rates of towards the latter. illness, disease and death, poorer or less educated persons often have difficulties in accessing appropriate specialists and preventive health Spending on radiological and biological procedures continued to be services. They make less use of these goods and services, some of highly unequal (G 2010 = 0.947 and C 2010 = 0.464). The same is true for which are very expensive compared to their income. We would expect dental care, which is generally not considered a priority by the poor the same to be true for special medical expenses.
Figure 36: Budget coefficients of routine health spending by deciles of total SPY, and by sub-item, in 2010 33
0.015 Medical consultaons 0.01 Dental 0.005
X-Rays, Scans and 0 Medical analyses d1 d2 d3 d4 d5 d6 d7 d8 d9 d10
32 For these types of care, there is balance and the demand nil. 33 Our calculations from the pooled budget data on the 2010 survey published on site of the National Institute of Statistics (INS) http://www.ins.nat.tn/indexfr.php
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Table 10: Breakdown of inequality in routine health spending by expenditure sub-category
Kakwani Year Item Y in D S Gini Index Concentration Index k k Index
Marginal G R S G /G G S C /C C -G k k k k Effect k k k k
Medical 11.571 16.3% 0.877 16.3% 0.0000 0.443 16.26% 0.03444 consultations
Dental care 0.806 1.14% 0.996 1.3% 0.0016 0.607 1.55% 0.19844
X-rays, scans 2000 and medical 3.037 4.28% 0.98 4.9% -0.0062 0.544 5.24% 0.13544 analyses
Traditional 0.056 0.08% 0.99 0.08% -0.0000 0.525 0.09% 0.11644 medicine
ROUNTINE MEDICAL 15.471 21.8% 0.877 22.6% 0.008 0.471 23.14% 0.06244 CARE
Medical 21.177 19.0% 0.849 19.5% 0.005 0.600 19.66% 0.18599 consultations
Dental care 1.506 1.3% 0.995 1.6% 0.003 0.464 2.02% 0.04999
X-rays, scans 2005 and medical 7.515 6.7% 0.954 7.4% 0.007 0.822 7.66% 0.40799 analyses
Traditional 0.089 0.08% 0.999 0.09% 0.000 0.578 0.10% 0.16399 medicine
ROUNTINE MEDICAL 30.287 27.1% 0.846 28.6% 0.015 0.486 29.45% 0.07199 CARE
Medical 25.749 17.9% 0.849 18.2% 0.003 0.406 16.08% 0.02133 consultations
Dental care 1.640 1.1% 0.993 1.2% 0.001 0.600 1.65% 0.21533
X-rays, scans 2010 and medical 11.515 8.0% 0.947 8.9% 0.009 0.464 8.23% 0.07933 analyses
Traditional 0.157 0.1% 0.999 0.1% 0.000 0.822 0.07% 0.43733 medicine
ROUNTINE MEDICAL 39.062 27.1% 0.843 28.6% 0.015 0.433 26.04% 0.04833 CARE
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4.4. Contribution of special medical expenditures Increasing inequalities in special health care services are observed in all expenditure items with the exception of special expenditures for Les dépenses de soins exceptionnels regroupent les maladies de radiological services (MRI and scans). Whenever radiological services longue durée, les dépenses de séjour et chirurgie médicale, les soins are called for, the special expenditures for the services are most often dentaires exceptionnels, les dépenses exceptionnelles de radiologie et waived in hospitals or covered by health insurance, and the patients les accouchements (Tableau 11). themselves seldom have to make any payments.
Table 11: Breakdown of inequality in special care spending by expenditure sub-category
Concentration Kakwani Gini index Year Item Yk in D Sk index index Marginal G R S G /G G S C /C C -G k k k k effects k k k k
Hospital stay and medical 8.703 12.3% 0.94 13.5% 0.0123 0.509 14.05% 0.1004 surgery Special dental care 0.710 1.0% 0.99 11.7% 0.0017 0.768 1.73% 0.3594
Special radiology expenses 0.671 0.9% 0.99 1% 0.0008 0.503 1.07% 0.0944
2000 Child Delivery 1.110 1.6% 0.98 1.4% -0.0017 0.341 1.20% -0.0676
Treatment abroad 0.215 0.3% 0.99 0.4% 0.0008 0.769 0.53% 0.3604
Long-term illnesses 16.565 23.4% 0.90 23.1% -0.0024 0.423 22.20% 0.0144
SPECIAL MEDICAL CARE 27.974 39.4% 0.872 40.6% 0.012 0.459 40.78% 0.0504
Hospital stay and medical 11.244 10.1% 0.96 11.3% 0.0125 0.470 10.56% 0.0560 surgery Special dental care 0.837 0.7% 0.99 0.7% 0.00015 0.575 0.96% 0.1610
Special radiology expenses 1.474 1.3% 0.98 1.4% 0.0005 0.430 1.27% 0.0160
Child Delivery 1.793 1.6% 0.98 1.7% 0.00045 0.448 1.61% 0.0340 2005
Treatment abroad 0.034 0.03% 0.99 0.04% 0.0001 0.750 0.05% 0.3360
Long-term illnesses 16.673 14.9% 0.93 15.2% 0.0034 0.437 14.57% 0.0230
SPECIAL MEDICAL CARE 32.054 28.7% 0.888 30.4% 0.017 0.453 29.01% 0.0390
Hospital stay and medical surgery 12.472 8.6% 0.977 10.3% 0.0174 0.515 12.42% 0.1303
Special dental care 1.156 0.8% 0.996 0.8% 0.0006 0.665 1.42% 0.2803
Special radiology expenses 2.101 1.5% 0.981 1.5% 0.0004 0.396 1.55% 0.0113
Child Delivery 1.870 1.3% 0.987 1.3% 0.0004 0.444 0.98% 0.0593 2010 Treatment abroad 0.000 0.0% 0.999 0.0% -0.000 0.392 0.00% 0.0073
Long-term illnesses 24.464 17.0% 0.933 18.0% -0.0101 0.485 23.61% 0.1003
SPECIAL MEDICAL CARE 0.009 0.0% 0.999 0.0% 0.000 0.411 12.42% 0.0263
SOINS MEDICAUX 42.067 29.2% 0.903 32.1% 0.029 0.493 39.99% 0.1083 EXCEPTIONNELS
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Long-term illnesses had the most substantial marginal effect, accounting coefficients were strongly on the uptrend, reflecting the magnitude of the for 17% of total health expenditures in 2010, including expenses inequalities (Figure 37). The highest Kakwani index observed was for for hospital stay and medical surgery. The corresponding budget special dental care and treatment abroad in 2000 and 2005.
Figure 37: Budget coefficients of special health spending by deciles of total SPY and by sub-item, in 2010 34
0.014 0.012 Hospital Stay and Surgery 0.01 0.008 Special Dental Care 0.006 Special Radiology 0.004 Expenditures 0.002 Child Delivery 0 d1 d2 d3 d4 d5 d6 d7 d8 d9 d10 Long-term Illnesses
This trend reflects the demographic and epidemiological transitions 35 inequality in pharmaceutical expenditures and the weight of these experienced by the country. The health indicators in Tunisia show expenditures on the poorest populations, although they are entitled to an overall decline in the incidence of infectious diseases and a free or virtually free care and medicines. 37 simultaneous increase in the incidence of non-communicable diseases and chronic diseases among aging populations. Analysis of mortality The Kakwani indices were negative or extremely low. data 36 shows that the main causes of death are diseases of the circulatory system, metabolic diseases and cancer. These diseases may The decline in inequality in pharmaceutical expenditures was due to the be avoided through primary (healthier lifestyles) or secondary prevention decrease in inequality relating to these two types of expenditures. Their (screening, early diagnosis). Their prevention and, above all, treatment contribution to inequality diminished and their marginal effect was call for increased spending by households and the community. negative, pointing to an equalizing effect.
4.5. Contribution of pharmaceutical expenditures The decline in inequality of pharmaceutical expenditures may be attributed to the increasingly equal availability of pharmacies nationwide. The pharmaceutical expenditures include expenses relating to purchase Faced with the difficulty of accessing physicians, patients - especially of drugs and those relating to the purchase of pharmaceutical products the poorer ones and those of the middle class - often resign themselves (Table 9). In 2010, the corresponding budget coefficients were on the to self-medication. This is also true for patients who live in areas that are downtrend depending on the deciles (Figure 38), reflecting the low under-served in terms of health infrastructure and doctors.
34 Our calculations from the pooled budget data on the 2010 survey published on site of the National Institute of Statistics (INS) http://www.ins.nat.tn/indexfr.php 35 The infant mortality rate dropped from 51.4 ‰ in 1984 to 16 ‰ in 2011. Life expectancy at birth was 74.9 years in 2011 (source: National Institute of Statistics - INS). 36 National Statistics on Medical Causes of Death. Tunis 2009). Research Unit on Aging and Medical Causes of Death – National Public Health Institute. 37 It would appear patients entitled to free drugs are sometimes forced to buy their drugs from private pharmacies due to shortages in hospital pharmacies or Primary Health Centres (PHCs). These shortages are caused by governance problems and would be resolved.
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Figure 38: Budgetary coefficients for pharmaceuticals by deciles of total SPY, and by sub-items, in 2010 38
0.025 0.02 0.015 Drugs 0.01 Other Pharmaceucals 0.005 0 d1 d2 d3 d4 d5 d6 d7 d8 d9 d10
Table 12: Breakdown of inequality in spending on pharmaceuticals by expenditure sub-category
Concentration Kakwani Gini Index Index Index Year Item Y in D S k k Marginal G R S G /G G S C /C C -G k k k k Effect k k k k
Medicines 24.448 34.4% 0.813 33.1% -0.013304 0.404 31.31% -0.004558
Other 2000 1.904 2.7% 0.963 2.1% -0.005545 0.462 2.79% 0.053442 pharmaceuticals
PHARMACEUTICALS 26.351 37.2% 0.799 35.3% -0.019 0.408 34.10% -0.000558
Medicines 39.783 35.6% 0.782 34.7% -0.008982 0.435 34.59% 0.020992
Other 2005 7.728 6.9% 0.857 4.7% -0.02157 0.322 4.98% -0.092008 pharmaceuticals
PHARMACEUTICALS 47.510 42.6% 0.740 39.5% -0.031 0.417 39.57% 0.002992
Medicines 47.795 33.1% 0.792 32.3% -0.008436 0.366 28.69% -0.018674
Other 2010 12.995 9.1% 0.788 5.5% -0.035153 0.202 3.23% 0.182674 pharmaceuticals
PHARMACEUTICALS 60.790 42.2% 0.724 37.8% -0.044 0.331 31.91% -0.053674
38 Our calculations from the pooled budget data on the 2010 survey published on site of the National Institute of Statistics (INS) http://www.ins.nat.tn/indexfr.php
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The analysis focused on the trends in the inequality in health spending spending in 2010. Medical consultations (16.3% of health spending) in Tunisia and the contribution of the various items of expenditure to the and medical devices (1.5% of health spending) also had equalizer formation of this inequality. The analysis is based on data on total health effects. These changes may be attributed to the increased availability of spending per person per year (SPY), as established by the national physicians and the improved national coverage in terms of pharmacies. budget and household consumption surveys for 2000, 2005 and 2010. The items where inequality has worsened and produced an inertia effect The trends and sources of inequality are captured mainly through the were long-term illness (17% of spending), the spending on hospital stay calculation and breakdown of the Gini index and the concentration index and medical surgery (8.6%) and paramedical or x-ray, scanner and by expenditure category. This approach is likely to furnish us with biology services (8% of health spending). These expenses are related to information on: the demographic and epidemiological transition. To reduce the corresponding inequality, government authorities need to adopt specific • The overall inequality in health spending and its trends; policies targeting the most vulnerable groups among the young and the • the inequality of the SPY in each health care expenditure item elderly. and sub-item; • the contribution of the inequality of each SPY to the total inequality; Dental care continues to be characterized by unusually high levels of • the marginal effect (equalizer or non-equalizer) of the variation of inequality and lack of access suffered by disadvantaged classes. a particular SPY on the total inequality in health expenditure. Improved coverage of the territory by dental practices and increased public awareness of the importance of oral health should curb one of the The outcomes showed that overall inequality declined from 2000 causes of this inequality. Similarly, special processing of their dental to 2010. The breakdown of inequality indicators reveals that this expenditure refund by health insurance, with no competition with trend was almost exclusively due to a decrease in inequality in recurrent expenditures, should contribute to reducing inequalities in pharmaceutical expenditures, which accounted for 42.2% of health dental care access.
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5. General Conclusion
espite the progress achieved, health inequalities remain 2- On the demand side: Dconsiderable and relatively little known in Tunisia. In light of the analysis conducted, there is significant elbow room for reducing these 2-1- It is important to reduce financial barriers to health care access by inequalities. better targeting the poor who benefit from free medical assistance.
1- From the supply side: 2-2- Pharmaceuticals are a significant drain on the budgets of the poorest households and it is necessary to reduce this weight by 1-1- In the public sector, it is necessary to revitalize primary health ensuring good governance of public pharmacies. care by improving the operation 2-2- There is a need to ensure a better collective coverage of long- 1-2- It is also important to strengthen Level II which seems term illness, hospital stay and medical surgery, x-rays and scans. to be the weak link in the system. Better coverage of the territory in Knowing the profile of households that incur these expenses will make terms of Level II beds should necessarily go hand-in-hand with the it possible to better target them, if need be. provision of more specialized physicians for the poorest regions in light of the demographic and epidemiological transition. 2-3- Dental care continues to be characterized by extremely high inequalities in expenses. Improved coverage of the territory in terms of 1-3- Efforts should be made to ensure that at each level the system availability of dental practices and greater public awareness of the performs its assigned tasks under the best possible conditions. importance of dental health should curb one of the causes of the These tasks should be clearly defined. Each hospital institution should inequality. Similarly, a special processing of reimbursement for dental have a scheme of work that allows for coherent strategic expenses by health insurance, apart from the recurrent expenses, management. should contribute to reducing inequalities in dental care access.
1-4- The specific incentives that were introduced to encourage 3- On the institutional side: physicians to settle in deserted areas should be evaluated. Public- public and possibly private-public partnerships should be instituted. 3-1- It is necessary to aim at reducing social and regional inequalities Also, it is important to negotiate with corporations an institutional in health care . framework to better regulate the opening of private practices. 3-2- There is a need to produce and monitor indicators for assessing the 1-5- It is necessary to determine measures that should be progress of specific categories not only at the national level but also at the implemented to enhance health care delivery at local or regional level, local level. It is important to conduct periodic surveys on the status of health, as part of an overall regional development policy. health care use, or the failure to seek health care for financial reasons.
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• Maurey H. (2013), Rapport d’information Sénat n°335, Session ordinaire (Senate information report No. 335, Regular Session), 2012-2013 «Présence médicale sur l’ensemble du territoire» (Nationwide Medical Presence). http://www.senat.fr/rap/r12-335/r12-3351.pdf
• Ministry of Regional Development (2011), Livre Blanc (White Paper) http://eeas.europa.eu/delegations/tunisia/documents/more_info/livreblanc_devreg_nov11_fr.pdf
• Ministry of Health (2010), Health Map 2010 http://www.santetunisie.tn/msp/images/CSfinale2010.pdf.
• National Office for the Family and the Population/UNICEF (2007), Multiple Indicator Cluster Survey. MICS Tunisia.
• Haut Conseil de la santé publique (Public Health Council) (2009), Les inégalités sociales de santé : sortir de la fatalité. (Social Inequalities in Health: Overcoming the Fatality). www.hcsp.fr/explore.cgi/hcspr20091112_inegalites.pdf
• O’Donnell O. et al. (2008), Analyzing Health Equity Using Household Survey Data. A Guide to Techniques and Their Implementation. WBI Learning Resources Series The Wold Bank
• WHO (2010), Country Cooperation Strategy for WHO and Tunisia (2010-2014) www.emro.who.int/docs/CCS_Tunisie_2010_FR_14489.pdf, Consulted on 1-3-2101
• Or Zeynep et al. (2009), «Inégalités de recours aux soins en Europe. Quel rôle attribuable aux systèmes de santé ? » (Inequalities in health care use in Europe. What role for health systems?), Revue économique, 2009/2 Vol. 60, p. 521-543. DOI: 10.3917/reco.602.0521.
• Potvin L., Moquet M.-J., Jones C. (2010), Réduire les inégalités sociales en santé (Reducing Inequalities in Health). Saint-Denis : INPES, coll. Santé en action, 2010.
•Van Doorslaer E, Koolman X. (2004), “Explaining the differences in income-related health inequalities across European countries”, Health Economics, 13, 7.
• Van Doorslaer E., Koolman X, Jones A. (2004), “Explaining income-related inequalities in doctor utilisation in Europe”, Health economics 13,7.
• Van Doorslaer, E., Koolman X, Jones, Puffer F. (2002). “Equity in the use of physician visits in OECD countries: has equal treatment for equal need been achieved?” In Measuring Up: Improving Health Systems Performance in OECD Countries
• Van Ourti T. (2004), “Measuring horizontal inequity in Belgian health care using a Gaussian random effects two part count data model”. Health economics, 13, 7.
• Wagstaff A., van Doorslaer E., Watanabe N. (2003), “On decomposing the causes of health sector inequalities with an application to malnutrition inequalities in Vietnam”. Journal of Econometrics 112.
• Wagstaff A., van Doorslaer E. (2000), “Income inequality and health: what does the literature tell us?” Annual Review of Public Health, 21.
43 African Development Bank
Economic Brief AfDB
2014 • www.afdb.org
Annex 1: Indicators of Health care Facilities by Cluster
Table 13: Health infrastructure indicators: Cluster 1.1
, l - s 0 a y e l t 0 d n a i i o 0 n
7 6 6 4 9 3 1 h T d ...... ) ( a 9 0
c
1 o 3 4 7 9 3 8 0 e . 1 0 a c t 2 2 2 1 2 1 3 i b l m 1 a
m a e b r
v i h a s e u r i n H s p i p p 0 y 0 r
0 l o t 0 a 8 9 8 3 6 6 9 a ...... 4 1 0 c r i 3 1 0 2 5 3 6 b 1 o
d a r b e h e a n M L p i 3 0 0 6 9 0 9 8 - . t 7 5 7 5 6 5 8 6 - y b h 2 2 8 6 7 5 9 4 3 r c e a g 5 1 6 0 7 6 3 5 a i a h 4 4 3 5 1 4 3 4 h m N i n I / t p m e 5 3 9 5 3 5 3 7 . m i 8 0 2 4 2 7 8 7 - y t b 1 r 3 2 2 8 9 5 8 8 c y a a 6 5 7 5 5 5 5 3 a a h h d n m / p I e 0 n l . 1 9 4 6 4 i 0 l s 9 5 7 a t b r 6 3 . 5 6 . 9 . t 1 0 i . . . . . n a 1 1 0 r n 0 a 1 1 1 4 2 o h e e 0 r h n 1 i c p d F y e r . g 0 n t 2 4 8 2 6 4 i e i l o 2 2 0 l t . 5 5 8 5 8 1 . p b 1 ...... 0
o n 0 0 i t a 0 0 0 2 0 1 i 0 o d h r n 0 a n u 1 F r i y r e l r . o 0 a n 2 4 4 3 6 4 t t i e i 6 2 l 0 c . 5 5 5 3 8 1 . a t 1 i p ...... b r 0
0 0 n d 0 0 1 2 0 1 t a o 0 i o e h b r 0 n n a 1 u i l F m . y t r 2 4 0 8 6 7 8 8 i - i a 4 4 9 9 1 7 1 8 b 2 s
2 1 0 4 8 6 1 5 e a n y r m r i 5 6 7 9 4 5 6 5 h a h e r a i n p c p p I c ) t E e n 9 9 7 0 4 0 4 T e 5 . l m 9 2 4 3 4 4 1 F i t i 2
a 2 5 8 0 1 6 4 4 T 4 v b - i C 2 8 4 5 3 0 0 l l a 8 u 2 1 1 1 1 1 2 H u h q P n F I / E ( s n y - k o i a l
a 1 4 6 3 t e 6 5 t a g l d r 5 3 4 2 6 C e
7 . 2 1 1 c n u o . 5 0 . 2 2 i i 6 . 0 . . . H w s r
0 0 p
d 0 0 0 0 r e P n n o e
f r e f f o o i p t c o o m P . y 0 6 4 t 8 5 8 6 0 r i e ) 2 8 1 r 1 3 6 0 8 a b t 9 7 4 4 C 2 0 3 0 1
a n 9 1 0 m r i 9 4 5 6 6 H h e 1 1 2 r e P n ( C P p I l l e e a a c t g i r 9 a n
2 7 0 9 7 2 a e p ) 1 2 1 r a 2 1 2 1 3 2 n s t m e 2 H s e o v i o r G d f G H ( A l l e a e a c t n g i a 4 0 4 8 0 n
o a 8 1 p i r ) 1 2 1 2 2 a s t g m e H s o e v i o r R R d f H ( A
e E R A t g I
N a B n
T I r i
E f
U S A o
d N S S r o e a n
i A
n S N O E e r U S h I r t X k e t e N A N D e
U O I s N N n v A
p t i A E O u o E a R R F O e U n l r T B M A A M M S d S R c o c G
45 African Development Bank AfDB Economic Brief
2014 • www.afdb.org
Table 14: Health infrastructure indicators, Cluster 1.2 . s i b r s a e c y i l h p l
a n b i i s 2 2 5
. . . u d e 3 0 ) 4 9 8 p n o
i 0 e 2 1 1 , t l h 0 m
a a c e t 0 v d i a a o r 0 n T H ( a p m 1 0 y r 0
o 0 l t 8 3 5 0 . . . a 2 a . r 0 c 1 1 2 i b 1 o
d a r b e h e a n M L i p - r a 0 0 7 h - 4 4 6 . t p 4 1 3 1 y
b h 6 0 1 c e a g i a 3 3 3 h m N i n I / t m y c e 7 3 1 a 4 . m 3 9 i 0 2 m t b 8 7 r y 0 a 6 5 a a 1 h h d n I / p e 0 4 9 9 n l . i 0 l s 7 9 7 a t 3 b r . . . t 0 i n a 2 1 2 n r 0 a o h e e 0 r h n d c 1 F p i y e r . g 0 1 9 6 n t i e i l o 0 0 9 4 l 2 t . . . p b 0
o n i 2 1 1 t a i 0 o d h r n 0 a n F r u 1 i - a l
e l y r . 1 9 6 0 r a n t i e i 0 9 4 l 0 2 c o . . . t i t p b 0
2 1 1 n a d t a r i 0 o e h r o n 0 n u 1 b i F m n a . i y 9 2 2 t r i c 9 3 8 i a 3
b 6 4 6 s
e a y r 7 4 7 m r i h e r h a n I p p c p ) t E e n 7 8 T e 7 . l m 9 5 i F t 8 i a
1 8 2 T 6 v b - i 4 4 C l 4 l a u 1 1 H u h q P n F I / ( E - n e r o n i e t m o i
a p t
t % % g l r % C 3 s 4 4 n u o 9 i y l k H 1 1 s r p a a e e P n o
f d c e r i f f
o w 6 c d o o P . y t r i e 3 4 2 ) r a 7 8 2 b t 3 C 0 7 0
a n m r i 6 1 6 H h e r e n P p P I C ( l l e e a a c t g 9 i r a 6 4 n
a 9 3 e p ) r 8 8 a n s 2 t m e H s e o v i o r G d f G H ( A l l e a e a c t n g i 3 0 4 a n 2
o a p i 2 4 2 r ) a s t g m e H s o e v i o r R R A d f H (
e
t E a L r g
T I o U n i n L R E r I k E e B B n v Z A I a E o B N K R G
46 African Development Bank Economic Brief AfDB
2014 • www.afdb.org
Table 15: Health infrastructure indicators, Cluster 1.3 r s i , e l s a p y
t l d s a o i 4 3 8 4 7 3 9 2 n ...... e
T 1 0 d ) ( a 2
4 6 4 9 6 5 0 8 n
2 o i 0 e . 1 1 2 1 1 2 3 2 c t i h 0 b l m
a c a e b v 0 i a h a u r 0 n H m 1 i p p r - e a p L
l y 7 6 8 4 7 2 r 0 a ...... 1 1 0 3 . o c 0 1 0 0 0 0 1 i t b 0 a d a r 0 e h o 0 n M b i 1 y c 5 0 3 7 5 0 0 0 7 a - 7 5 6 5 8 5 4 0 1 . t m 2 1 2 6 4 2 2 5 4 2 b h r 8 6 2 6 0 9 9 4 8 e a g a i 3 3 4 3 3 2 2 3 2 h h m N i n I / t p y e c 0 9 4 8 8 7 0 2 4 a . m 0 i 7 9 3 4 0 1 6 7 t b 7 3 m 2 6 1 8 0 3 9 9 r y a 1 9 6 9 8 9 7 7 8 a a h 1 h d n I / p e 0 n l 1 4 6 1 8 2 3 3 . i 1 0 l s a 6 0 6 5 7 4 . 8 9 t b r 4 ...... t 0 i 4 n a r n 2 3 2 3 2 3 4 2 0 a o h e e 0 r h n 1 i c p d F y e g r 0 n 6 1 5 3 2 5 2 6 5 i e . o l l 0 9 2 2 7 5 8 4 8 3 t 4 p b ...... 0 o n i t a 1 2 3 2 2 3 3 3 2 i 0 o d h r n 0 a n u 1 r i F y r e l o r t 6 9 6 1 8 5 3 0 a n 1 8 i e . l 9 4 6 5 7 8 . . 9 a 0 c 4 ...... t i r p b 4 5 0
1 2 2 3 2 3 2 n d o t a i 0 o e h b r n 0 n a u 1 i l m F n a y i 7 1 9 2 3 0 7 6 6 r . c i 7 2 7 3 3 0 7 9 3 a b 1 s 7 8 3 8 8 5 1 9 7
e a y r m r 5 6 6 5 4 4 4 3 4 i h r e h a n p p c p I ) t E e n 7 T e 6 6 8 9 6 1 5 9 m l 6 i F 2 1 9 7 1 4 9 0 .
a 0 1 T 7 3 9 9 1 1 2 8 v b - i C 1 l 9 9 7 7 8 8 8 7 l a u 1 H u h q P F n ( E I / n r n o e i o t i
l
p t a
% % % % % % g a t r l C % % % s 3 0 5 0 9 8 0 c n o u i i y 8 5 6 k H 1 2 2 1 1 2 r s p a d e e P n o e
f d e r f f
o o o c P m 6 w y 7 9 5 9 8 9 8 9 0 r . e ) 5 2 3 5 3 2 5 0 8 r a 1 b t 2 9 6 7 5 6 3 7 4 C a n m r 3 3 3 2 3 2 2 4 3 i H h e r e P n ( C P p I l l e e a a c t g i r 7 8 6 6 3 7 3 a n
4 9 a e p ) 2 6 2 0 4 0 3 4 r a 4 6 s n t 1 1 2 2 1 3 3 m e H e s o v o i r G f H ( A d G l l e a e a c t n g i a 2 2 6 4 4 0 9 2 9 n
o a 3 p i r ) 2 3 3 4 4 4 5 4 2 a s t g m e H s o e v i o r R ( d f R A H
N E e t A
N a
I r U
R
A g A o U I F O S A U n n N i D O r E E H S E A k A e A H I K F B Z J G n v
T L A A A o E a I A O E A M S T B G L T R G Z G
47 African Development Bank AfDB Economic Brief
2014 • www.afdb.org
Table 16: Health infrastructure indicators, Cluster 1.4 r s i , e l s a p y
t l d s a o i 8 6 6 n 8 . . . e
T 0 d . ) ( a 4 5 1 3
n
o i 0 8 e . 1 1 1 c t i h 0 b l m
a c a e b v 0 i a h a u r 0 n H m 1 i p p - 0 a 0 L
0 l y r 5 5 2 2 0 a . . . . 4 . o 0 c 0 0 0 0 t i b 1 a
d a r r e h o e n M b p i y c 3 4 8 0 a - 3 5 3 5 . t m 5 0 0 2 4 b h r 0 3 4 1 e a g a i 7 4 5 4 h h m N i n I / t p y e c 6 6 8 5 a . m 7 7 5 i 9 t 0 3 4 4 b m 9 r y a 0 1 1 9 a a h 1 1 1 h d n I / p e 0 n 9 7 1 1 l . i 0 l s a 8 9 3 9 t b 4 r . . . . t 0 i n a 1 1 2 2 n r 0 a o h e e 0 r h n c 1 i d p F y e g r 1 0 n 5 7 8 i e . o l 9 l 0 6 9 0 t 2 p . . . b 0 0 o
n i t a 1 1 2 2 i 0 o d h r n 0 a n u 1 F r i y r e l o r t 2 7 8 7 0 a n i e . l 4 9 7 6 a 0 c 3 . . . . t i r p b 0
1 1 2 2 n d o t a i 0 o e h b r n 0 a n u 1 l i F m n a y i 7 4 0 8 r . c i 3 5 2 3 a b 4 s 6 3 7 4
e a y r m r 8 8 5 3 i h e r h a n c p I p p ) t E e n 2 6 0 0 T e m l 3 9 3 3 i F .
a 4 5 5 1 3 T v b - i C 2 3 2 3 l l a u 1 1 1 1 H u h q P n F E I / ( n r n o e i o t i
l
p t a
% % % g a t r l C % s 2 1 7 6 c n o u i i y 9 k H 1 1 1 r s p a d e e P n o e
f d e r f f
o c w m o 6 o P y 2 5 8 7 r . e ) 1 0 3 2 r a 2 b t 7 3 2 3 C
a n m r 3 4 4 3 i H h e r e P n ( C P p I l l e e a a c t g i r 7 7 7 a n 8
a e p 5 5 5 2 ) r a 9 s n t 1 2 1 m e H e s o v o i r G H ( f G A d l l e a e a c t n g i a 6 6 0 4 n
o a 4 p i r ) 3 4 5 4 a s t g m e H s o e v i o r R ( A d f R H
E A e N t N B I a
A r U R g D U o I E O n n i O r Z S
D k e I R U I S n v N D o a O A A I E K K J S R B G
48 African Development Bank Economic Brief AfDB
2014 • www.afdb.org
Table 17: Bed availability indicators, Cluster 2.1 0 c i 0 r 0 t 1 0 2 9 a 4 i . 6 4 1 . 0 0 1 . . / h 6 b 0 0 s c 1 a y d h s e n i b P - - s e n i d h 9 6 5 7 9 t e e d 0 . 7 6 7 9 9 s 1 b v . . . . . n 0
i b e 0 0 0 0 0 a 0 s e a a r 0 n h a n a i 1 e n A s t c / i d - e 7 8 7 2 . o i b 0 4 1 9 2 0 1 b
. . . . 0 d y a 1 1 0 2 r r 0 h g a e 0 n o p 1 I l C r e s 5 4 8 3 p c - . i
0 2 5 9 7 o 0 1 b . . . . s d 0 h a 3 0 0 1 d e t 0 h r e a 0 n 1 i b p O s d e 4 7 7 6 0 . b 7 6 9 6 0 2
0 . . . . b 0 0 0 0 0 a R 0 h N 1 n E / i - l 6 8 3 6 y a 0 . 6 9 9 9 0 1 g h . . . . 0 b t s o 0 0 1 1 l 0 a h d 0 o h p e 1 n m b / i O s c 5 i r 1 n 1 t 1 8 6
4 . e a 8 2 . / 0 1 i 0 r , , 1 w s 1 s 0 d r 8 7 2 d o l 1 l d e i 0 a a e e 0 h e P b y b 1 c 3 9 1 3 o 0 6 3 1 . . c A 0 1 / . . 0 0 5 e s 8 8 B 0 1 1 n d 0 C y e 0 G b 1 W l 3 3 3 3 y a 9 6 8 0 r / . 0 1 0 r . . . .
e b 0 e 2 1 4 5 s g a 0 n d
r h e e 0 u n s b G 1 i 3 / 0 4 8 .
. . 0 0 1 c b 0 2 i s 1 2 1 a 0 n d i h l e 0 n i C 1 b l a t / . 5 9 9 4 0 i
. . . . 6 b 0 1 p s 2 3 0 0 2 a 0 s 2 2 3 4 d
h o e 0 n i 1 H b r e ) 9 6 9 7 p e 0 . . . .
+ 6 t
1 b 0 3 6 0 2 s 2 a b 2 2 3 5 a 0 d v
i u h e r 0 p n i 1 ( B p
e R A t I
a B T r E R g o S U
S U n n A i S O r S I E k e N N U Z N n v O A o a O O U M R M S T T G
49 African Development Bank AfDB Economic Brief
2014 • www.afdb.org
Table 18: Bed availability indicators, Cluster 2.2 0 c i 0 r 0 t 8 3 3 0 a i 5 6 1 1 2 . . . . / h 0 0 0 b s c a y d h s e n i b P e v - i 1 5 3 0 . s s a 3 2 5 0 3 i b . . . e s
n 0 s e a 0 0 0 a d e d r 0 e h t n e a n 1 h n n c b / i i a t A d - e 7 6 6 . o i b 0 1 8 6 2 b
. . . 0 d y a 0 0 0 r r 0 h g a e 0 n o p 1 I C l r e s 8 1 7 p c - . i
0 5 8 7 o 3 b . . . s d 0 h a 0 0 0 d e t 0 h r e a 0 n 1 i b p O s d e 8 1 0 . 5 b 5 . 6 3
0 . . b 0 0 0 0 a R 0 h N 1 n E / i - l 8 5 2 y a 0 . 5 2 7 3 g h . . . 0 b t s o 0 0 0 l 0 a h d 0 o h p e 1 n m b / i O s 5 c i n r 1 t
4 4 9 e a / 0 r 0 9 8 i 3 w s . . . s 0 r d d 7 4 8 o l l d i 0 e a e e a 0 h e b y b 1 c P 2 7 9 o 3 9 9 c A 2 / . . . 0 e s 8 6 7 B 0 n d 0 C y e 0 G b 1 W
l 3 4 8 y a 7 9 6 r / . 3 0 r . . .
e b 0 e 2 1 2 s g a 0 n d
r h e e 0 u n s b G 1 i / . 0 6 6 3
. . . 2 c b 0 i s 0 1 4 a 0 n d
i h l e 0 n i C 1 b l a t / . 2 9 6 0 i
. . . b 0 3 p s 1 4 6 a 0 s 2 1 1 d
h o e 0 n 1 i H b r e ) 7 5 9 p e 0 . . .
+ t
2 b 0 1 6 0 s a b 2 1 2 a 0 d v
i u h e r 0 p n i 1 ( B p
e E t a
N r I
A g o I N F n n i r D E E k e H D K v n
A E o a E M M G L R
50 African Development Bank Economic Brief AfDB
2014 • www.afdb.org
Table 19: Bed availability indicators, Cluster 2.3 0 c i 0 r 0 t 0 a i 1 0 0 0 0 4 . / h b s c a y d h s e n i b P e - v 3 8 3 8 0 . i s a 3 1 5 6 s 0 i 2 i b . . . . e s
0 n s e a 0 0 0 0 a d d r 0 e e h n e t a n 1 h n c b / i n a t A d - e . o 8 i b 0 . 0 0 0 4 b
0 0 d y a r r 0 h g a e 0 n o p 1 I C l r e s 8 6 2 p c - . i
0 9 8 8 o 0 2 b . . . s d 0 h a 0 0 0 d e t 0 h r e a 0 n 1 i b p O s d e 5 3 8 0 . 8 b 6 3 . 6 1
0 . . . b 0 0 0 0 0 a R 0 h N 1 n E / i - l 5 3 3 8 y a 0 . 6 3 5 6 2 g h . . . . 0 b t s o 0 1 0 0 l 0 a h d 0 o h p e 1 n m b / i O 5 - n 1
3 6 3 a e 4 i / 0 r 0 6 . 3 2 w s . . . d s 8 0 r d s 6 6 8 o l e l d i 0 a c i a e e 0 h e r b y b 1 c t P 5 1 2 o 1 9 1 5 . c A 3 / . . . 0 9 e s 5 7 6 B 0 n d 0 C y e 0 G b 1 W l 5 6 8 5 y a 4 9 9 0 r / . 2 0 r . . . .
e b 0 e 2 2 3 2 s g a 0 n d
r h e e 0 u n s b G 1 i / . 0 2 3
. . 0 0 4 c b 0 i s 1 1 a 0 n d i h l e 0 n i C 1 b l a t / . 9 5 7 0 i
. . . 2 b 0 2 p s 7 8 7 2 a 0 s 1 1 1 d
h o e 0 n 1 i H b r e ) 9 2 8 7 p e 0 . . . .
+ t
3 b 0 7 3 9 7 s a b 1 2 1 1 a 0 d v
i u h e r 0 p n i 1 ( B p
E e t N a I r
g o U I A n n
i L O r S I A k e A F J B n v T A o E a E A B G K G T R
51 African Development Bank AfDB Economic Brief
2014 • www.afdb.org
Table 20: Bed availability indicators, Cluster 2.4 0 c i 0 r 0 t 7 1 0 a i 5 7 1 0 0 0 0 0 0 0 0 0 3 . . . / h b 0 0 s c a y d h s e n i b P e v - i 1 1 3 7 3 7 5 0 . 7 s s a . 2 1 8 2 2 2 1 0 i 0 0 0 4 b ...... e s
n 0 0 s e a 0 0 0 0 0 0 0 a d e d r 0 e h t n e a n 1 h n n c b / i i a t A d - e 3 1 1 4 8 7 . o 6 4 i b 0 . 7 4 7 5 . 4 7 b 0 0 0 3
...... 0 d 0 0 y a 0 0 0 0 0 0 r r 0 h g a e 0 n o p 1 I l C r e s p c 5 4 5 - . i 8
0 5 5 6 . o b 0 0 0 0 0 0 0 4 . . . s d 0 0 h a 0 0 0 d e t 0 h r e a 0 n 1 i b p O s d e 7 5 5 6 6 7 9 3 0 . b 3 5 3 3 4 2 2 4 0 0 0 4
0 ...... b 0 0 0 0 0 0 0 0 0 a R 0 h N 1 n E / i - l 7 7 5 6 6 7 9 3 y a 0 . 3 9 3 3 4 2 3 4 g 0 0 0 4 h ...... 0 b t s o 0 0 0 0 0 0 0 0 l 0 a h d 0 o h p e 1 n m b / i O 5 - n 1
7 7 8 7 8 4 9 5 9 a e i / 0 r 4 1 5 9 0 9 6 9 4 0 0 4 w s ...... d s 0 r d s o 8 5 2 3 4 3 3 3 4 l e l d i 0 a c i a e e 0 h e r b y b 1 c t P 9 6 3 1 1 7 9 4 7 4 2 o 8 0 8 9 3 7 8 7 5 8 2 c A 4 / 0 ...... e s 3 2 6 7 3 3 4 6 4 4 5 B 0 n d 0 C y e 0 G b 1 W
l 5 7 6 7 4 6 6 5 9 7 y a 4 4 6 7 1 1 4 4 7 4 r / . 0 0 4 r ......
e b 0 e 0 2 1 1 1 1 1 1 1 1 s g a 0 n d
r h e e 0 u n s b 1 i G / . 0 3 7 5 1 5 6 6 2
...... c 0 0 0 3 b 0 i s 4 1 0 2 1 0 0 2 a 0 n d
i h l e 0 n i C 1 b l a t / . 0 5 3 1 3 7 7 7 9 i
3 8 ...... 7 . . b 0 4 p s 6 4 2 2 2 0 6 7 1 8 3 a 0 s 1 1 1 1 1 1 1 2 d
h o e 0 n 1 i H b r e ) 6 1 8 8 8 9 7 7 9 p e 0 5 ......
+ 7 t .
4 b 0 2 9 5 2 2 4 0 6 7 s 1 a 5 b 1 1 1 1 1 1 1 1 2 a 0 d v
i u h e r 0 p n ( p i 1 B
e E S N A t N N U A B a I
A r U
O L U E R
A o D U g I A R O U T E O n S N n O r Z i A E N H R S
E D
A e I R k I U A B E I S G B N I v N n D L Z A O A A I I A E o A I E a R K Z K N S B S J G B G A B R
52 African Development Bank Economic Brief AfDB
2014 • www.afdb.org
Table 21: Human resource indicators: Cluster 3.1 . ) l / l 0 a s a l 0 t t c a 0 i o 0 3 6 7 6 1 4 s
t 5 3 6 8 ...... i t d l . . . . ( 0 o 9 3 0 7 6 9 3
1 0 1 t e 5 4 3 a 7 . 0
0 6 1 3 4 0 1 i . . 0 5 9 9 / 6 1 b 2 m 1 4 3 4 7 4 c / b b 0 s f a a e t f a a 0 r h a s h p h a 0 i t n n r n 1 i S P s i 2 1 7 t i 5
6 6 0 . a 0 1 . . . i . 0 0 t 1 1 1 h 0 b c c 0 e a l a 0 t y 0 r h a 0 a s 0 9 c p n 0 v i 9 3 6 1 . 1 3 3 6 8 i
i
. . . . 1 P . 7 . . . . l
r d 8 . 0 1 1 9 0 8 4 2 0 2 1 4 a 0 p e 5 9 0 r 6 8 9 2 2 1 4 2 ) 6 . 0 1 l 1 / e m . / b 0 a f s a n b t f a 0 t r e a a h o s a 0 t i t n h t ( 1 G P s i 3 n e 7 1 9 . i . . .
2 1 h 0 2 4 3 t . 0 1 s 0 b / e 0 a
l 0 c a i 0 s h a l 0 3 6 2 9 9 2 9 4 3 5 6 n 0 . . . . . c n n ...... 0 b i i
4 5 1 7 1 A 2 9 0 5 6 6 1 a 1 u 1 d i 0 2 7 9 6 3 0 1 3 6 9 3 0 p e c 0
1 1 1 3 1 4 3 4 6 3 ) i 1 0 . l 1 s m s / 0 b t a f a y t f a 0 r s i a h h o 0 a t t g n ( 1 P P s i 3 5 7 9 . o 0 . . . l 6 1 1 0 2 3 3 . o . 1 i 0 b b d / 0 a a r - s i 0 h h a r n 2 2 n 1 n 3 7 . 2 . i 1 9 8 3 2 5 p i / C . . . a
...... ( i 7 5 1 1
9 2 s 0 0 4 6 4 7 5 3 3 0 6 . t c 4 8 0 i 2 3 2 3 4 3 0 8 s 1 1 ) b i 0 s . 0 t l e a 0 y 0 b n t a h t 0 h e 0 a a n 1 o v 1 i P h / / D t s n 4 1 4 6 6 i . . . . . 1
n 0 2 2 4 3 7 0 o 0 0 b e 0 0 / a g 0 c r s 0 i h 0 l 9 t u 5 5 6 4 . 0 9 n 1 b . . . . . s 8 . 4 3 4 0 i / 6 ) i . . . . . S 1 1
1 1 0 4 u l 9 6 0 e 4 2 8 7 8 s 0 2 6 4 p 1 a 4 t t 1 i 0 . s a i c 0 b t v . e i a 0 - n r b p h e 0 o p l a n ( 1 S D i o h 6 8 3 7 4 . n . . . . m i 1 l 3
3 3 5 5 0 1 . a . 0 t 0 h 0 / b c t 0 s 0 a e a 0 h t t 0 r h 0 s 9 1 7 6 3 p a 3 0 5 0 8 5 i 0 . . . . . p n ...... 1 v i
1 i / ) 1 8 6 7 8 g O 1 4 l
1 8 5 7 5 5
r 2 4 3 5 e 3 a 2 1 2 2 3 2 0 s p t t r
0 . s a e i 0 b t v n i 0 a n r s e h 0 e t p n ( 1 G s 4 D i i 6 6 6 2 .
n . . . . r 1 7 t 0 e 7 6 6 8 1 c r a . i 0 i l d / / b 0 l d b i s s a 0 t o u h h n 7 7 s 1 i 0 9 0 . . 7 0 3 2 9 0 p n c P / a . . . i ...... ( c i 9 2
1 )
4 8 1 0 8 9 1 3 0 0 9 a r . 0 0 c 0 i 7 6 0 4 1 3 2 3 4 2 - 1 2 o b 0 m s 0 t a r l 0 a y 0 c e a a 0 - h t h i 0 e h b 0 n r o s 1 i P t P 1 t d l e i 0 t ) ) h 0 r / s s / 3 4 8 5 c 3 r s 0 c o s 8 , 8 7 8
b i t 1 l , , , . t t f a 0 2 s o c 2 2 3 6 b i s
e 2 o 0 - i 1 8 2 2 .
e 5 8 l u c e . . . . y 1 2 7 2 . . 5 o
2 s
0 n 1 . . . . a / a p
3 4 9 . 1 8 g 0 0 2 i
0 c 9 0 2 8 3 7 e . c 3 3 4 1 b i a 0 1 1 m s c 1 l e 4 0
b r a 0 - m e n b n 0 a g a 0 h u 5 a o y p 0 h n h i 0 i n p 1 n i ( 1 S w c r ( G P 1 i
. t b . c /
a b a r s a h t p h n 6 s s i
9 1 8 5 i .
3 5 3 1 8 1 5 l n
. . . . l 4 3 5 3 . i ...... n c 0 0 1 . . . .
) 1 a 1 3 0 0 1 9 a 0 7 2 0 9 8 1 a 0 o 0 . r 2 3 4 3 0 c r 4 3 6 0 2 1 i 1 2 2 1 2 2 e 1 l e 0 e 0 b t e m 0 r b 0 0 g a n a n 0 r a v 0 u 0 e h i e 0 h u r 0 p 1 n ( 1 G i s / G P 1 p
e e e t t t
R R R a I I I a a E E E r r r
T T T S S S o o o g g g S S S A A A A
n S S S n n n n n
r A A A i i i r r N S N N S S N S S S I I I e X X X k k k e e N N N A A A A U U U I I I I v v v N N N n n n A A A O O O o o o a a a R R R R F F O O F O U U U A G S T R G A M S S R G A S T S T M R M S A
53 African Development Bank AfDB Economic Brief
2014 • www.afdb.org
Table 22: Human resource indicators. Cluster 3.2 . ) . l / / b a s s t t a t s o s h i 5 9 1 0 0 9 7 6 i t 9 ...... l r ( n 7 8 5 1 3 7
t i 2 2 0 9 7 2 7 9 2 8 4 a .
1 1 5 2 . 5 1 0 2 i a ...... 0 3 3 3 2 2 3 3 2 i 3 0 b c 0 0 0 1 0 1 0 1 h 0 a e 0 c 0 h p 0 y 0 n 1 i S s 0 1 P . t b c a a / . r h s b p n t 1 4 6 3 7 8 5 9 i
...... a l
s 2 i 4 5 5 9 9 7 0 3 h a 0 t r 6 5 4 5 5 4 5 6 ) n 0 7 9 8 3 7 2 e l i ...... e 1 0 2
0 h a 0 0 0 0 0 1 n t t 0 0 e s o 0 0 t e 0 ( 1 G a 0 n 0 1 A ) l / a s t 5 n o s . 3 7 3 7 7 1 6 t ...... t a ( 3 i 2 3 7 6 8 0 9 8 s
0 i 0 . c 9 7 8 8 8 8 8 i 0 1 g b s 0 6 4 4 5 5 1 2 o a ...... 0 y 2 3 l 0 1 2 1 0 0 2 1 h 0 . h o 0 i n 0 b 1 i P d 0 a r 0 h a 1 n i / C / s n 2 3 8 6 9 7 0 8 . ) ...... a i 2 e b 8 8 6 4 6 0 5 5
0 . t c a 6 4 3 3 3 5 5 2 i 0 a b s h / 0 v a y i s n 0 6 2 4 9 8 i r h . . . . . 2 1 2 2 h
n 0 2 2 1 1 1 p n 0 o 1 i ( P 0 e 0 g r 0 u 0 b S 1 / a s h t 0 5 2 4 6 1 n . . . . 0 . . 0 . s i . . ) i 2
0 2 0 1 4 7 l 9 7 e 0 3 2 2 1 2 2 a t . i - 0 a b c 0 o v l a e i 0 r o h p 0 2 3 5 1 1 7 3 ...... p 2 2 n ( 1 S m i 4 3 2 1 3 3 2 l
a 0 h 0 / t . s 0 . t h t 0 b c s p i 0 a a g 1 O r h 3 8 6 2 9 1 9 8 ...... p n i
2 ) 8 5 6 3 7 6 7 8 l
3 2 1 2 2 2 2 1 e a 0 t r 0 a e 0 s v t n i 0 r s e 7 7 8 3 6 1 4 9 i 0 ......
n 2 p r ( 1 G 5 5 2 3 4 3 4 2 t 0 e r a 0 i d 0 c l d i i l 0 / o h 1 b s c P / u n 3 0 9 7 8 1 1 8 p a ...... ( i 3 ) g
5 5 0 1 1 0 4 2 0 r . c n 3 4 4 5 5 3 3 6 i 0 i o b r s 0 t ) a a - y 0 c i s e h r h r 0 e t b n a s P 1 i e d 0 t e l i 0 6 2 2 4 5 7 5 s y ) 5 h
r 3 8 1 . 0 5 1 2 0 / 2 b ...... c 9 o 1
0 s t 2 1 1 1 1 2 1 o f t 4 0 - c o - s
1 e i o 4 9 6 1 6 6 5 l / 5 . . . . 3 . . n s
. . c 0
2 1 a 5 1 5 0 1 7 . e i ( s 9 8 e c 0
1 1 1 2 1 1 i b l c n m 0 n e a b e a y o 0 i g h u p 0 w c a G n p i ( 1 S . t b c a a r h p s n
i
8 6 0 1 7 7 6 2 6 5 8 5 7 3 l l
...... n 0 2 0 2 4 a ) a 5 9 9 6 1 1 2 5 0 1 2 1 3 2 2 o 0 . r r c 2 2 2 3 3 2 2 4 0 i e 0 b e l e 0 0 g a n b n r 0 0 h e u e u 0 1 n p i s / G 1 G (
e e S S N N
t t E E A A A A U U
a a
N N r r
I I U U B B O O L L E E
o o A A g g N N U U O O I I R R U U T T n n S S n n E E r r i i A A E E H H D D O O R R E E
e e k k D D B B E E H H N N G G B B v v N N n n E E Z Z A A A A A A A A A A o o I I a E E a M N N Z M Z B G B G R M G B M M G B M R
54 African Development Bank Economic Brief AfDB
2014 • www.afdb.org
Table 22: Human resource indicators. Cluster 3.2 e l 0 t a 0 a c 0 v i i
5 4 2 1 8 2 . . . . . 7 . 2 9 r d . . . 0 2 3 2 0 1 0 4 p 5 1 9 e 0
1 1 1 1 1 1 . 1 m / b f a f a r a h a t n i s P l 0 c i a l 0 2 4 0 0 3 1 7 6 5 c 0 b ...... i
5 4 3 2 2 6 1 1 9 u 4 d 0 7 9 8 0 7 6 2 6 5 p e 0
1 2 2 3 2 2 2 2 2 . 1 m / b f a f a r a h a t n i s P . b a h n i 9 9 5 1 7 6 4 5 1 ......
2 s 3 0 5 5 2 5 9 3 7 0 t 2 2 1 1 1 1 1 1 1 0 s i 0 t l 0 n a t 0 e 1 o t / D 0 0 0 0 c i 0 l 1 b 8 3 6 3 8 9 5 3 7 / ......
3 u 2 3 3 3 3 2 4 5 3 s p t
. s i b t a n h e n i D 0 0 0 e 0 t 0 a 1 6 9 9 7 9 4 1 . . . . 0 . . 2 . v . . i / 2 1 7 1 1 2 4 3
r 9 8 2 1 1 1 1 1 1 s p t
. s i b t a n h e n i D . / b s a t h s i 1 3 5 9 3 9 0 6 2 n ...... i c
2 6 2 0 5 0 3 3 7 1 a 0 2 2 2 1 2 2 2 1 2 0 m l r 0 a a 0 t h 0 o t P 1 . / b s a t h s i n i c 4 6 5 3 8 4 5 8 4
...... a 2 0 1 1 2 3 3 2 2 1 2 c 0 m i r l 0 a b 0 h u 0 p 1 P . /
b s a t h s i 8 7 0 6 5 5 5 8 8 n ...... i c
2 4 0 8 2 6 1 0 5 8 a 0 2 2 1 1 1 2 2 1 1 e t 0 m r a 0 a v 0 i h r 0 p 1 P
S e S N t E U A A U a
N r
O I U B O L E o A R g N U O I R U T n S n A E r i A E H O D
R E e k D B E N H N G B v N n E Z A E A A A A o E I a N Z B M G G B B M R M
55 African Development Bank AfDB Economic Brief
2014 • www.afdb.org
Table 23: Human resource indicators. Cluster 3.3 l a ) l / c i a l s t t 1 d a . 0 o t s é 4 i t 4 0 2 l . o ( 3
3 0 t 0 9 m a .
3 i . . 0 2 / a 0 b c b r 0 b s 0 a e t a a 0 a 1 h s p h 0 i h / P n r n 1 i S 3 t i
3 l a 4 0 e . i 0 . t t a 0 0 h 0 b a c c 0 i c 0 v
a a i y d 0 r r 0 h 6 s e 0 p 0 . p n 3
7 i
. 1 P . 4 l 1
m 3 / 4 b a 0 f a r 4 f ) a 0 r l / e . a h 0 a a t s n b t n t 0 i s P e a s o 0 i t h t ( 1 G n e i l 0 c
0 4 i h a l 0 t 0 . c 0 b s i 0 b
5 / u e . 0 d a 0
a 9 p 0 2 e s h 0
2 n . 0 1 n 1 n . m i 3 / b 1 A
a 3 f 4 i a f a 0 r 7 c a ) h i / 0 a l t s s n 0 a i s P t y t 0 s i h o 0 t g ( 1 P l 6 o . l 2 a 0 t . 1 o 0 i o b 7 s 0 t 0 . d / t
a 3 r 3 0 . 0 s s h i a 1 0 0 b n t n 4
0 C 1 i a a . n i 3 ) e 0
9 0 h e . r t c 2 1 i 0 n a o i D / b s . 0 t v a y i 0 b c r h h 0 a e p n c 1 i ( s P h / i l s n i b 2 3
n u 0 . o 9 s . 0 0 p e 4 b t -
3 0 0 . / a g s c i r 0 0 s b h t e t u 3 0 0 a n n . s s i
i S 1 0
3 h 6 e l e 1 0 n 1 a t i D / i 0 a c 0 . v . . ) e i 0 - r r b b p 0 o o p l a a ( t 1 S o h h 6 n n . m i i 3 l
8 2 . s 3 . a 0 0 . t t 9 h e 0 0 / b s c t t i s 0 0 a t a h a t 0 0 r h n v s 1 p i i 0 0 . p n e r i
3 ) g O 1 3 1 l
p D / 1 e a 0 t r 0 a e 0 v . n i 0 / r s b e 0 t s p a t ( 1 G s i 9 h s
n . r i 3 7 n t 0 e . 2 i c c r
3 a 4 i 0 i a l 0 d / 1 0 l d b i 0 s m 0 o l r u h 0 n 1 7 a a c P / p 0 a . t ( i 2 h )
0 4 0 o r . c 4 i t P 1 0
o b s s 0 f t a n y 0 c o a h
i h . e 0 e n n c / s 1 i P i b g r s e a a t t
) e h s m t s i g . 2 / n ) r o s 0 i - c n 4 s r . 3
i . a t b a u w r 2 o 1 0
e s o t i p a 1 c - 0 y l m 0 ( . i c
e r l 0 o 3 0 a 0 3 . e i 9 a 0 b c b 0 1 0 b s c 4 e 0 h
d u 0 0 a - l e n i 0 c p P 1 0 i 5 h y p l 0 h 1 1 n i b 1 S G / c ( . / )
b c i s l a t b h s i 7 u n . s i
c 7 3 l l
. 3 2 p n 0 . a ( 3 4 a a 1 0 0 1
/ e o 0 1 . r r . 3 t 0 0 m t e 0 b e e b r a 0 0 c 0 g a a n n a v 0 0 r a i 0 h h e e h r r 0 0 u 1 n n i s / G G p i 1 p P 1 e e e t t t a a a r r r o o o g g g n n n n n n r r r i i i A e A e A e k k k v v v J J J n n n o o o E E E a a a B G B G B R R R G
56 African Development Bank Economic Brief AfDB
2014 • www.afdb.org
Table 24: Human resource indicators. Cluster 3.4 ) l / a s t / b n e o a 4 5 2 3 0 3 3 3 3 2 r t a ...... 1 2 9 9 7 ( t i h 3 5
4
0 0 1 7 5 3 4 6 7 4 1 . 7 8 1 3 . 9 . 0 0 0 3 a c . . . . . i i 0 0 7 5 5 4 6 6 4 5 6 8 0 0 b 0 0 0 0 0 s h 0 0 a y c 0 0 h h y 0 0 n s 0 1 i P 1 P ) l a s / t t s o 8 6 2 3 3 3 4 s . . . 7 . . 7 . 2 . i t e . . . l 0 ( 4 t 9 5 8 3 8 0 6
b 9 9 8 a 0 . s 1 1 1 1 1 1 1 i i a 3 2 9 2 7 0 b . . . . . c s 0 0 0 0 0 3 h 0 a 0 0 0 0 0 e é 0 h 0 p h 1 t n 0 / i S s 0 e 0 ) n 0 l 1 A a R t / P s o
6 9 9 6 8 0 6 0 1 7 r t l ...... ( e
4 a 0 0 5 8 5 9 6 6 7 6 4 . r n 0 5 3 3 3 4 4 3 4 5 6 / b e o 0 s i a n 0 t e h b e 0 c u n a 2 9 1 2 7 4 5 9 7 1 i a G ...... g 1 4 h 1 0 1 0 0 0 0 0 0
o l 0 a i 0 / 0 d - s r i 0 r n a 0 3 4 9 7 6 2 0 5 4 4 p ...... a 1 C ( i 4 6 9 3 5 0 4 6 1 6 7
0 . c 2 1 2 1 2 2 1 1 1 1 i 0 ) b s 0 e a y 0 t h h 0 a
/ n v 1 i P s b n a 9 9 7 7 6 2 9 7 9 e h ...... 2 4 i
. 0 0 0 0 1 0 0 0 1 g 0 b r 0 / a u 0 r s h i 0 t 2 5 n h . 3 3 0 0 . 1 7 2 8 s 0 i ...... ) i 4
1 0 l 1 C 7 9 6 8 4 1 6 4 e 0 1 1 a t i 0 a c 0 v e i 0 r p
0 . p ( 1 S b / a . 8 9 4 7 3 2 3 4 9 h ...... 1 4
m 1 0 1 0 1 1 1 1 1 b l 0 a s a 0 r t h 0 e h n 0 1 1 7 6 6 9 3 6 i n 7 8 ...... p
0 . . ) 4 l 5 2 4 2 3 1 0 2 o 0 9 9 1 O i e a 1 1 1 1 1 1 1 1 t 0 t r i t 0 a e c v 0 n i a 0 r e r 1 p / ( / p G s
5 1 1 6 7 3 7 7 1 9 e ...... 4 r s
t 1 2 2 1 0 2 1 1 2 3 t 0 a / n i 0 - s a d f 0 u n é 1 1 2 6 5 1 3 7 9 8 0 n p a ...... ( i 4 1 e P
4 2 3 9 2 0 0 5 7 3 0 . c 4 3 3 2 4 4 3 4 4 6 i 0 b s 0 ) a y 0 c e i h h l 0 - d n i P 1 i b
r t e 0 é g
t 0 ) â s
9 1 2 9 7 7 3 7 0 s 5 3
0 9 8 4 6 9 . . 4 6 b - n s 4 n ...... 0 t 0 0 u e o
a 1 0 0 0 0 0 0 0
0 s 6 r - i p 6 3 9 7 3 8 6 5 1 . s l ...... ( 1 e 9 o 4 1
0 / e a 8 8 8 3 5 7 5 8 2 . é
4 c i 1 0
r s b é c m – 0 ) c
a n n e 0 c o m 5 i e h y p r l i 0 e 1 n G ( p f c i b 1 S ) 0
c 0 i
s l b 0 s e b a 0 t n u h 0 s 5 9 3 9 2 3 7 2 8 2
e i ...... 8 7 8 7 7 3 7 3 7 9 p i l l 1 ...... ( 2 3 0
5 3 4 5 7 2 4 7 5 2 a g a 1 1 1 0 0 2 0 1 0 1 / . 0 3 2 2 2 3 3 2 3 4 5 r r r . t 0 b e u é c r 0 a n n i a 0 h e é h r 1 n i G p G / C
t t
E E E E a a A A r r N N N N N N B B I I I I o o A A R R A A U U n n D D R R U U g g
U U I I F F r r I I U U A A N N E E O O n n O O e e Z Z E E i i O O L L E E
S S I I S S A A v v D D I I k k A A I I K K U U R R Z Z
F F u u I S S I B B T T n n N N D D L L O O I I I I O O E E A o o A A A a a A A E A E A E E L S B S B S T S T T L T K G J G K K G K K G K R R J
57 African Development Bank AfDB Economic Brief
2014 • www.afdb.org
Table 24: Human resource indicators. Cluster 3.4 l 0 c i 4 3 3 8 9 4 4 3 4 6 a l 0 ...... c 0 3 5 2 5 6 4 2 9 9 7 b 3 i
0 7 7 1 8 9 0 8 1 9 u d 0 4 2 2 2 3 3 2 2 3 4 p e 0
. 1 m / b f a f a r a h a t n i s P 0 0 0 0 l 0 a 9 7 0 7 6 0 7 7 4 8 1 t ...... 4 / o 5 1 2 0 4 9 2 1 1 4 s t t
. s i b t a n h e n i D 0 0 0 c 0 5 6 3 1 2 4 8 6 1 8 i ...... l 0 7 3 0 5 2 5 9 7 8 2 b 3 1 9 7 7 1 8 8 9 8 1 9 / u 3 2 2 2 3 3 1 2 3 4 s p t
. s i b t a n h e n D i 0 0 0 e t 0 9 6 3 6 5 4 5 . . . 9 . . 9 . 2 . a 0 . . . 4 0 0 2 2 0 2 3 v 1 7 9 8 i 1 1 1 1 1 1 1 / r s p t
. s i b t a n h e n i D
. / b s t a s h 8 6 9 8 0 3 2 1 4 8 i ...... 3 c n 3 2 2 2 4 4 3 5 3 5 i
a 0 m 0 l r 0 a a
t h 0 o t P 1 . /
b s a t h s i n 1 0 5 1 6 2 8 3 8 7 i ...... c 4
7 8 9 5 8 6 6 7 4 7 a 0 c 0 m i r l 0 a b 0 h u 0 1 p P . / b s a t h 1 3 3 5 9 0 9 8 2 4 s i ...... n 4 5 1 2 2 5 4 0 5 9 7 i c
1 1 1 1 1 1 1 1 1 1 a 0 e t 0 m r a 0 a v 0 i h r 0 p 1 P
D
I
t E E A Z a N N N r B I I U A o R A U R U g
O n U F I U r A N E O n O B E i O L e E
S I S A D I k A v I K R Z
F I S B T u n N D L I I O E A A o a E A A E L S S T T G J K K K R G
58 African Development Bank Economic Brief AfDB
2014 • www.afdb.org
Annex 2: Health Expenditure Nomenclature 2000 in 2005 (source : www.ins.nat.tn )
41 ROUTINE MEDICAL CARE 423 MRI SCAN 4231 Mri scan in a public institution 411 MEDICAL CONSULTATIONS 4232 Mri scan in a private institution 4111 Consultations by public institutions 4239 Mri scan in a with no indication 4112 Consultations by private institutions 424 CHILD DELIVERY 4119 Consultation with no indication 4241 Child delivery in a public institution 4242 Child delivery in a private institution 412 DENTAL CARE 4249 Child delivery with no indication 4121 Dental care in public institutions 4122 Dental care in private institutions 425 MEDICAL TREATMENT ABROAD 4129 Dental care with no indication 426 LONG-TERM CARE 413 X-RAY AND ANALYSES 4261 Consultations for long-term illnesses 4131 X-ray and medical analyses in public institutions 4262 Drugs for long-term illness 4263 Non-classified expenditure on long-term illnesses 4132 X-ray and medical analyses in private institutions 4139 X-ray and medical analyses with no indication 43 PHARMACEUTICALS
416 TRADITIONAL HEALER 431 DRUGS, NURSING 4161 Traditional healer 4321 Baby products 4322 Other pharmaceuticals 42 SPECIAL MEDICAL CARE 432 AUTRES PRODUITS PHARMACEUTIQUES 421 STAY AND SURGERY 4321 Produits pour bebe 4211 Stay and surgery in a public institution 4322 Autre produit pharmaceutique 4212 Stay and surgery in a private institution 4219 Stay and surgery with no indication 44 MEDICAL EQUIPMENT
441 MEDICAL EQUIPMENT 422 SPECIAL DENTAL SURGERY 4411 Medical eyeglasses 4221 Special dental care in a public institution 4222 Special dental care in a private institution 4412 Hearing aids 4229 Special dental care with no indications 4419 Other medical equipment
59 African Development Bank L A Y / t i n U n o i t a c i n u m m o C d n a s n o i t a l e R l a n r e t x E
, n g i s e D
-
B D f A
-
0 1 0 2
©
African Development Bank