Appraising financial protection in health: the case of Tunisia

Int J Health Care Finance Econ (2013) 13:73–93DOI 10.1007/s10754-013-9123-8

Appraising financial protection in health: the caseof Tunisia

Mohammad Abu-Zaineh · Habiba Ben Romdhane ·Bruno Ventelou · Jean-Paul Moatti · Arfa Chokri

Received: 22 November 2011 / Accepted: 12 January 2013 / Published online: 5 February 2013© Springer Science+Business Media New York 2013

Abstract Despite the remarkable progress in expanding the coverage of social protectionmechanisms in health, the Tunisian healthcare system is still largely funded through directout-of-pocket payments. This paper seeks to assess financial protection in health in the partic-ular policy and epidemiological transition of Tunisia using nationally representative surveydata on healthcare expenditure, utilization and morbidity. The extent to which the healthcaresystem protects people against the financial repercussions of ill-health is assessed using thecatastrophic and impoverishing payment approaches. The characteristics associated with thelikelihood of vulnerability to catastrophic health expenditure (CHE) are examined using mul-tivariate logistic regression technique. Results revealed that non-negligible proportions of theTunisian population (ranging from 4.5 % at the conservative 40 % threshold of discretionarynonfood expenditure to 12 % at the 10 % threshold of total expenditure) incurred CHE. Interms of impoverishment, results showed that health expenditure can be held responsible forabout 18 % of the rise in the poverty gap. These results appeared to be relatively higher whencompared with those obtained for other countries with similar level of development. Nonethe-less, although households belonging to richer quintiles reported more illness episodes and

M. Abu-Zaineh · J.-P. Moatti · B. VentelouINSERM-IRD-UMR 912 (SESSIM), 13006 Marseille, France

M. Abu-Zaineh (B) · J.-P. MoattiAix-Marseille University, Aix-Marseille School of Economics (AMSE), 13006 Marseille, Francee-mail: [email protected]

H. B. RomdhaneCardiovascular Diseases Research Laboratory, Faculty of Medicine, 15, Rue Djebel Akhdhar-1007Bab Saâdoun, Tunis, Tunisia

B. VentelouFrench National Center for Scientific Research,Research Group in Quantitative Economics of Aix-Marseille (CNRS-GREQAM-IDEP),Aix-Marseille School of Economics (AMSE), 13006 Marseille, France

A. ChokriNational Institute of Labour and Social Studies (INTES)-University of Carthage-Tunis,44, Rue de l’artisanat Charguia 2, Tunis, Tunisia

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received more treatment than the poor households, the latter households were more likely toincur CHE at any threshold. Amongst the correlates of CHE, health insurance coverage wassignificantly related to CHE regardless of the threshold used. Some implications and policyrecommendations, which might also be useful for other similar countries, are advanced toenhance the financial protection capacity of the Tunisian healthcare system.

Introduction

“Financial protection in health” has increasingly been recognized as a major policy goal ofany healthcare system (WHO 2007). Yet, latest world health statistics (WHO 2011) indicatethat healthcare expenditures in many low- and middle-income countries remain largely fundedthrough direct households’ out-of-pocket payments at the point of services delivery. Such out-of-pocket payments represent from one-third to three-quarters of total health expendituresin developing countries, and have a well-recognized negative impact on households’ welfare(Xu, Evans et al. 2007). Indeed, the unforeseen nature of “health shocks” may sometimesculminate into “catastrophic” expenditures, mobilizing significant shares of households’financial resources, disturbing their living standards, or even pushing them below povertylines (Wagstaff 2007). Financial protection mechanisms, such as pre-payment insuranceschemes, have therefore been recommended as a key policy for reducing financial catastropheand promoting equity in both healthcare financing and utilization (WHO 2008). A growingnumber of developing countries currently try to expand the breadth of financial protectioncoverage for health with respect to both population groups and services (Gottret, Schieber etal. 2008). On the one hand, it is obvious that the economic burden of diseases may wipe outprior development achievements (Chia 2007) and that reliance on direct payments for healthcare declines with the level of country’s development (O’Donnell, Doorslaer et al. 2008).On the other hand, extending health insurance coverage may sometimes compromise theefficiency performance of the healthcare system, increase the cost of the needed healthcare,and consequently jeopardize the risk protection function of insurance mechanisms themselves(Goudge, Russell et al. 2009).

The case of Tunisia, one of the upper-middle income countries in political and epidemi-ological transition, is especially interesting on these matters. In recent decades, the countryhas witnessed a sustained economic and social development, rising from 0.516 on the UNDPHuman Development Index in 1975 to 0.766 in 2005 (UNDP 2007). If extreme poverty(referring to the officially reported rates) has been noticeably reduced in Tunisia, it is dueto both economic growth and the implementation of various social programs (UNDP 2004).Indeed, since 1969, several national polices and measures including income transfers andaid to indigent families have been put in place. In addition, a national solidarity fund tomaintain the development of basic infrastructures and services, in particular, in low-incomesettings was set up in 1993. The government has committed more than half of its budgetto the social sector (about 19 % of the country’s GDP), whereas public total investments insocial programs has doubled between 1996 and 2005 (Ben Romdhane and Grenier 2009).

In terms of healthcare, official figures from government sources(Ministry of Public Health 2009) indicated that Tunisia has achieved close to universal cover-age with circa 99 % of the population being reportedly covered whether through the NationalHealth Insurance Fund “Caisse Nationale d’Assurance Maladie—CNAM”, (66 %) or theMedical Assistance Schemes (MAS) (33 % of the population). However, some discrepancybetween the official and the de facto coverage emerges as soon as data from nationally repre-sentative surveys are released following the collapse of the former political regime in 2011.

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Appraising financial protection in health 75

Such discrepancy, which is mainly due to differences in the proportions of population reportedto benefit from MAS (a difference of 11 %), can be explained by the fact that, by law, allthose who are not covered by the CNAM can, in principle, benefit from MAS. However, inpractice, some ad hoc criteria restricting entitlement to these schemes are followed (NationalInstitute of Public Health 2008). In addition to these limitations in actual coverage, signifi-cant increases in out-of-pocket expenditures on healthcare have been concomitantly observed(Arfa and Achouri 2008). This has been partly due to the shift away from public providers toprivate healthcare providers, and to cost-sharing tariffs that exist even under state-subsidizedschemes.

The 2006 Tunisian Health Care Utilization and Morbidity Survey (THCUMS) gave us theopportunity to assess the extent to which the Tunisian healthcare system has succeeded in pro-tecting people against the financial consequences of ill-health. We apply standard assessmentmeasures of financial protection in health, using both “catastrophic” and “impoverishing”payment approaches (Wagstaff, Bilger et al. 2011). Although informative, the ex-post mea-sures of “catastrophic” and “impoverishing” payments do not tell us who are more likely toovershoot a specified threshold (Wagstaff and van Doorslaer 2003), nor do they tell whichcharacteristics are associated with the vulnerability to financial catastrophe (O’Donnell,van Doorslaer et al. 2005). Therefore, we complement our analysis to answer these questionsthrough (i) examining the distributional incidence of catastrophic healthcare payments acrosssocio-economic groups, using the concentration index approach, and then (ii) examining thecharacteristics associated with the likelihood of vulnerability to catastrophic payments usingthe multivariate logistic regression technique. The remainder of the paper is organized asfollows. The following Section summarizes the main features of “the Tunisian healthcare sys-tem”. Section “Materials and Mothods” presents the methodology and the dataset used in theeconometric analysis. Sections “Major Findings” and “Discussion” present and discuss thestudy results. The paper concludes with some recommendations in Section “Conclusions”.

The Tunisian healthcare system

The structure, function and capacity of the Tunisian healthcare system have largely beeninfluenced by the country’s historical dynamics of institutional development. Until the1980s, Tunisia’s healthcare system, founded in the colonial tradition of a hospital-centeredhealth infrastructure, was concentrated in large urban areas. In the past three decades,several institutional and organizational reforms were undertaken to expand both accessto healthcare services and to health insurance coverage. For instance, the “large-scale”reform initiated in 1998 has expanded coverage of the social insurance system—initiallydesigned for public sector employees—through extending eligibility of enrolment to othergroups of the population, particularly, the self-employed and workers in the agricul-tural and informal sectors. Together with other measures such as the introduction ofMAS, the “free-care cards” (where eligible population are defined according to nationalpoverty line and regional quotas) and the “reduced-fee plans” (where eligible popula-tion are defined based on the minimum wage rate), such reform has raised the cover-age from 54.6 % of the total population in 1995 to 88 % of the population in 2008 (ofwhom 22 % are covered by MAS). This expanded coverage was, however, not associ-ated with a parallel improvement in the supply capacity of the public healthcare sectorresulting in some deterioration in the quality and availability of some basic services—including essential supplies and medications (Achouri 2005). Indeed, despite the grow-ing demand for healthcare, the share of government expenditure on health has stagnated

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at about 8 % of the total government expenditure during the last decade. Consequently,private sector providers have been increasingly incorporated in the healthcare deliverysystem despite the higher financial burden that this entails for households (Arfa and Achouri2008).

A further reform was introduced in 2005 with the aim of merging several insurance planscovering different professional groups under the National Health Insurance Fund (CNAM).Prior to the advent of CNAM, socially insured patients were only entitled to all healthcareservices provided at the public facilities with copayments paid for medical consultations andspecified medications. Following the 2005 reform, coverage was extended to include inpatientand outpatient services provided by the private sector, although reimbursement mechanismslimit coverage for a predetermined list of chronic illnesses and surgical interventions andremain subject to an annual expenditure cap (Arfa and Achouri 2008). In addition to CNAM,several private insurance schemes have developed in response to insufficiencies in coverage ofthe public insurance mechanism. However, due to high premiums, these schemes still covera tiny proportion of the population (estimates from the THCUS-2006 suggest a coveragelevel of 9 % of total surveyed population). In general, current private schemes exclude manypre-existing conditions whereas some medical services, such as pre- and post-natal care,are available upon payment of additional premiums. In many cases, maximum ceiling forclaims is imposed (Achouri 2005). As elsewhere (Abu-Zaineh, Mataria et al. 2009), the roleof private schemes in Tunisia have been somewhat controversial, not only because schemesoften reach the better-off segments of the population, but also due to the lack of adequateregulations that mandate certain practices such as premium calculations and acceptance ofapplicants.

Three main stockholders are involved in the provision of healthcare services: the publicsector, whose facilities are managed by the Ministry of Public Health (MoPH), operates 85 %of total hospital beds and employs more than 55 % of medical personnel; the private sector,which has flourished rapidly since the beginning of the 1990s, operates 81 clinics, representing12.5 % of the total national hospital beds capacity in 2004; and thirdly, the parastatal sector,which is managed by other public departments such as the social security fund (SSF), operates4 hospitals, representing 2.5 % of hospital beds, 6 polyclinics, and nine healthcare facilitiesoffering in-house medical services (Arfa and Achouri 2008). Despite the rise in the totalnumber of hospital beds (an increase of 9 % during the period 1998–2005) resulting in anaverage number of two beds per thousand population, the distribution of healthcare facilitiesacross the county was described (Arfa and Achouri 2008) as inappropriate and inadequate interms of the number, level and type of services. The spatial-inequalities in the distribution ofhealthcare facilities are, especially, pronounced in the case of secondary and tertiary services,with about 69 % of the total hospital capacity being concentrated in the eastern coastal regionof the country. In terms of healthcare delivery, about 67.7 % of total healthcare visits tookplace at public and parastatal facilities as per 2006 (of which 37.5 % made by householdsbelonging to the lower half of income deciles), while the remaining took place at the privatesector clinics (of which 21.6 % made by households belonging to the lower half of incomedeciles).

Until the end of the 1980s, health financing was largely supported by the state budgetand the SSF with about 65 % of total health expenditures being funded from both fiscal andsocial contributions. In spite of recent increases in SSF contributions, total public financinghas, however, decreased to 53 % of total health expenditures following the financial crisisduring the second half of the 1980s and the subsequent adjustment programs that impactedgovernment budgets. This reduction was compensated by a significant rise in household out-of-pocket expenditures. Total health expenditures were estimated at about 2,502.6 million

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Table 1 Funding and provision of healthcare by public and private sectors in Tunisia

Funding ProvisionShare of total healthcare expenditure (%)

Share of total hospital beds (%) Share of total visitsb (%)

Public sectora 54,5 87.5 67.7

Private sector 45.5 12.5 32.3

Total 100 100 100

Source: Arfa et al. (2007), “National Health Accounts Report for the years 2004 and 2005”. Tunis: NationalInstitute of Public Healtha Including parastatal facilities managed by other public departmentsb Authors’ calculations of market share based on number of visits as per THCUMS-2006

USD in 2008, indicating a per capita health expenditure of 174.9 USD. Currently, healthcareexpenditures are respectively financed though general government revenues (26.2 %); socialinsurance contributions (27.0 %); private insurance premiums (6.5 %), and direct out-of-pocket payments (43.3 %). Private sector receives the lion’s share of total direct households’expenditures (81 % in 2006 of which 48 % was dedicated to medications). External sourcesof financing were insignificant representing less than 1 % of total health expenditures (Arfaet al. 2007; Arfa and Achouri 2008). Table 1 provides a summary crossing the provision andfunding of healthcare by public-private sectors.

Demographic and health indicators reveal that the 10.6 million Tunisians are undergoingrapid demographic and epidemiological changes. This is mainly due to the falling infantmortality (from 41 per thousand in 1990 to 20 per thousand in 2005) and the improvement inlife expectancy at birth (a gain of 22.25 years over the last four decades), resulting in one of thehighest life expectancy in the MENA Region (WHO 2006). A review of the epidemiologicalprofile shows that non-communicable diseases, which are associated with high treatmentcosts, such as cardiovascular diseases, cancer, and diabetes, have dominated communicableand traditional infectious diseases as the main causes of morbidity (70.8 % of cases) andmortality (79.7 % of deaths) (Ben Romdhane, Belhani et al. 2005).

Materials and methods

Our analysis is based on national representative cross-sectional data from the Tunisian HealthCare Utilization and Morbidity Survey (THCUMS), which was conducted by the TunisianNational Institute of Public Health in 2006. The sampling frame was derived by the TunisianNational Institute of Statistics using the database of the Population Census of 2004. Thesampling procedure based on two-stage stratified cluster-random sample gave a target of6,538 households, of which 5,799 households were effectively reached for the interview(a response rate of 88.7 %). However, when it came to success in effectively completing theinterviews, the response rate fell but only slightly to 84.3 % (5,508 households included inthe present analysis); i.e., there was only a 1 in 8 chance that an interview was not completed.Although, these unit non-response cases accounted for only 291 cases (or 5 %) of the requestsfor interview, efforts were made to assure the representativeness of the sample. This is doneby comparing response rates across geographic areas and comparing the data with externaldata source: the population profile of the 2004 census. The data have been weighted using theweighted estimators provided by the survey to compensate for unit non-response cases, andto recover the population profile as per the Population Census of 2004. Estimated weights

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are defined as the inverse of the probability of selecting a subject from the study populationto be included in the study sample. The weights have been estimated taking into account thedistribution of population as per three key characteristics—for which differences betweenthe target sample and the final effective sample were observed—age, gender and region.

The survey provides information on households’ health experiences—morbidity, expen-diture and utilization, a set of socio-demographic characteristics as well as alternative typesof data upon which a measure of household’s standard of living can be constructed. Theseinclude: total monthly income, gross consumption expenditures and household amenities.Among these, gross consumption expenditures are generally advocated on the grounds that itcan give a more reliable measure of household standard of living in the context of developingcountries (Deaton and Grosh 2000), and can better reflect the disruptive impact of healthpayments on household current consumption expenditure (O’Donnell, van Doorslaer et al.2008). The latter is apprehended in our survey using a list of questions tracking the amountspent on two sets of items, which are consumed by a typical Tunisian household over anentire month: alimentary items (e.g., vegetables and fruits, pastry products, etc) and nonfooditems (e.g., housing, electricity, education services etc). Payments for healthcare are derivedfrom a question on the total amount of healthcare expenditures (covering ambulatory, out-patient and in-patient services, medical examinations and medications, treatment tools andequipments, dental services and the expenditure related to transportation) that were incurredby household members over the last 12 months, and were not reimbursed by any third.

Household standard of living or ability-to-pay (ATP) is thus measured in the present analy-sis using two alternative definitions: total household consumption expenditures, on the onehand, and household consumption net of nondiscretionary food expenditures, on the otherhand. Measures of expenditures are scaled to the same period used to compute total amountof healthcare payments and both are equivalised to generate an average expenditure (pay-ment) per equivalent-adult, using the WHO/FAO equivalence scale proposed for developingcountries (Deaton and Grosh 2000). Lastly, data on income and amenities are used to checkfor the robustness of our ATP measure, using the method explored in Wagstaff and Watanabe(2003). Results, which are available upon request, offered us a high degree of confidenceabout the robustness of our proxy for households’ standards of living.

The extent to which the healthcare system protects people against the financial con-sequences of ill-health is assessed using both catastrophic and impoverishing paymentapproaches (Wagstaff and van Doorslaer 2003). Details of technical derivations and compu-tations are fully documented in O’Donnell, van Doorslaer et al. (2008). However, to illustratethe methods used, we briefly point to the relevant definitions and measures adopted.

Health expenditures are deemed “catastrophic” if they exceed a certain fraction of house-hold total (or net of nondiscretionary food) expenditures, and “impoverishing” if they aresufficiently large to make household fall below the poverty line (Wagstaff 2008). Prevalenceand intensity of catastrophic and impoverishing payments are respectively assessed by thehead count (H) and the gap measures (G). Both H and G are, then, linked by the meanpositive gap measure (MPG). Hence, in the case of the catastrophic payment approach, thehead count measure (Hcat) is given by the proportion of households whose healthcare budgetshares exceed a predefined threshold (zcat). The catastrophic payment gap measure (Gcat)

is the average amount by which households overshot zcat, estimated over all householdsin the quintile of equivalent expenditure, irrespective of their healthcare payments. Lastly,the MPGcat measures the intensity of catastrophic payments computed for the subsample ofhouseholds with catastrophic payments in their quintile of equivalent expenditure.

The analogous impoverishing health payment head count (Hpov) and gap (Gpov) measuresare obtained by comparing poverty estimates derived from household resources gross and net

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of healthcare expenditures. Thus, the impoverishing payment head count (Hpov) representsthe difference in the proportions of households below the poverty (or deep poverty) linebefore and after accounting for healthcare payments (Hpre and Hpost, respectively). Theimpoverishing gap (Gpov) is the combined amounts by which poor households fail to reachthe poverty line in the population, obtained by comparing intensity in poverty before and afterpaying for healthcare (Gprevs.Gpost). Lastly, the two measures, Hpov and Gpov are relatedthrough the mean positive gap (MPGpov), which represents the intensity of impoverishment.

The catastrophe threshold zcat is typically set at different values ranging between 5 and25 % of households’ total expenditures, or alternatively at 40 % of their discretionary (non-food) expenditures—referred to as capacity-to-pay (Xu, Evans et al. 2003). Therefore, wepresent a set of results using several values for zcat ranging from 5 to 40 %. In addition,because of the obvious sensitivity of impoverishing measures to the chosen poverty line, weuse two alternative definitions. The first is the local poverty line of 0.86 USD per personper day, which was proposed by the Tunisian National Institute of Statistics (2005), and thesecond is the international poverty line of 1.08 USD per person per day, which was pro-posed by the World Bank for international comparisons (World_Bank 2004). For practicalreasons, we also normalize the MPGpov on the poverty lines used. Lastly, considering theinterest in revealing whether it is the poor or the better-off who overshoot zcat, we examinethe prevalence and intensity of CHE across equivalent household expenditure quintiles andwe compute a concentration index (C) for each measure—labelled CH and CG , respectively.The two measures of prevalence and intensity of CHE are then adjusted to take into accountthe distribution of healthcare payments across socio-economic groups. This is done by con-structing rank-weighted measures of the catastrophic payment, Hw and Gw—obtained bymultiplying Hcat and Gcat by the complement of their respective C—where the weight isdecreasing according to the person’s rank in the income distribution.

Finally, the probability of CHE occurrence is predicted using a Logit regression tech-nique. As others, (e.g., Gotsadze, Zoidze et al. 2009; Yardim, Cilingiroglu et al. 2010), weassume that the probability of households having CHE is affected by the health status ofits members and a set of household characteristics, including insurance status, place of resi-dence, education of the head of household and household’s economic status (measured by theequivalent expenditure quintile). All explanatory variables were entered in the Logit modelusing forward stepwise entry function and selected if the probability of its score statistic isless than 0.05 and removed if the probability was greater than 0.1. The model goodness-of-fit was assessed using Hosmer–Lemeshow test (Hosmer and Lemeshov 2000). Lastly, thestatistical analyses were performed using STATA/SE 12.1 and ADePT statistical packages.Detailed codes of computations were adapted from (O’Donnell, van Doorslaer et al. 2008;Wagstaff, Bilger et al. 2011). All estimates were corrected for clustering (at 2 levels) andheteroskedasticity-robust standard errors were produced.

Major findings

Table 2 presents the results on the prevalence (head counts) and intensity (overshoot) ofcatastrophic payments for healthcare in Tunisia. Results are presented for health expendi-tures as share of both total and discretionary nonfood expenditures—using the cut-off pointsof 5, 10, 15, 25 and 40 % for both cases. As expected, for both shares of household expenditure(total and nonfood), the proportions of the households (Hcat) exceeding the threshold (zcat),and the mean excess (Gcat) unsurprisingly fall shall the threshold used to define catastrophicexpenditures (zcat) be raised. For instance, considering total household expenditure as the

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Table 2 Incidence and intensity of catastrophic health expenditures among Tunisian households (n = 5,508)a

Threshold budget share of household total expenditures5 % 10 % 15 % 25 % 40 %

Head count (Hcat) 33.43 12.07 4.85 1.00 0.10

(Standard error) (0.96) (0.58) (0.36) (0.16) (0.06)

Gap (Gcat) 1.74 0.69 0.30 0.07 0.00

Standard error (0.08) (0.05) (0.03) (0.01) (0.00)

Mean positive gap (MPGcat) 5.20 5.68 6.16 6.71 1.89

Threshold budget share of discretionary nonfood expenditures

Head count (Hcat) 65.80 48.99 34.62 17.20 4.54

(Standard error) (1.14) (1.07) (1.03) (0.73) (0.39)

Overshoot (Gcat) 9.15 6.31 4.25 1.75 0.29

(Standard error) (0.26) (0.22) (0.18) (0.10) (0.03)

Mean positive gap (MPGcat) 13.91 12.88 12.27 10.17 6.30

aAll estimates are corrected for clustering (2 levels) and robust standard errors are reported

denominator, the magnitude of the prevalence of CHE falls from a substantial 33 % of house-holds spending in excess of 5 % on healthcare to only 1 % of households spending in excess ofa quarter of their total expenditure on healthcare. More interestingly, however, when health-care payments are compared to household discretionary expenditures, the proportions of thehouseholds exceeding the thresholds appear to be much more pronounced compared withtotal expenditure and for all the thresholds in the range explored (lower panel of Table 2).For example, using 10 % as threshold, one finds that around 49 % of the Tunisian householdswere exposed to CHE, whereas the mean catastrophic payment gap ( MPGcat), reflectingboth the incidence and intensity of CHE, has, on average, almost doubled using discretionaryexpenditures as denominator. Given that the latter is obtained through subtracting expenditureon basic necessities—food—from total household budget, findings on the higher thresholdsreveal that large fractions (ranging from 15 to 40 %) of the remaining household budget arespent on healthcare by substantial proportions of the Tunisian households (ranging from circa35 to 5 %, respectively).

Turning to the intensity of catastrophic payments, results indicate that those overshootingthe threshold of 5 % of total expenditure spent, on average, 10.20 % (5 + 5.20 %), whereasthose overshooting the 25 % threshold spent, on average, 31.71 % (25 + 6.71) on healthcare.Considering the discretionary nonfood expenditure, such intensity appears to be even morepronounced with those spending more than 5 % of their nonfood expenditure on healthcarespent, on average, 18.91 % (5 + 13.91), whereas those spending more than 40 % on healthcarespent, on average, 46.30 % of their remaining household budget.

Detailed results examining whether catastrophic health expenditures was borne by thepoor or by the better-off are displayed in Table 3 as per the two common thresholds: the 10 %of total expenditure and the 40 % of discretionary expenditure. The distribution of CHE isfirst examined across equivalent expenditure quintiles and summarized by the concentrationindices (CH and CG), and the rank weighted head count and gap measures (HW and GW). Itis worth noting, first, that households belonging to richer quintiles reported more illness andtreatment episodes than households in lower-quintiles. However, the prevalence of CHE fallswith expenditure quintiles whatever the threshold (zcat): for both the 10 and 40 % thresholds,the prevalence of CHE is circa four times higher in the poorest quintile compared with that in

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the highest quintile. Moreover, the worse-off appear to be more likely to overshoot the thresh-old (zcat) by larger amounts, and for each zcat there is more concentration of overshootingamongst the worse-off than the better-off. Those in the lowest expenditure quintile overshoot-ing the 10 % threshold of total expenditure spent, on average, 17.0 % (10 + 7.0) on healthcarecompared to 13.2 % (10 + 3.2) for the highest quintile. Similar patterns are observed whencomparisons are held against the 40 % of discretionary expenditure. As a consequence, theconcentration indices (CH and CG) are, with no exception negative, confirming the abovetrends, whereas the rank-weighted head count and gap (HW and GW) are larger than theunweighted indices given in Table 2.

Table 3 also presents the distribution of CHE as per health insurance status of the headof the household. Unsurprisingly, the prevalence and intensity of CHE appear to be highestamongst the uninsured and lowest amongst those covered by private insurance schemes.Significant proportions of those who are covered by the social health insurance (4.87 %) andof those who are supposed to benefit from free care and reduced tariffs (2.54 %) appear toincur CHE using the 10 % threshold while similar patterns are still observed when using the40 % threshold.

Results on the impoverishing effect of health expenditures are summarized in Table 4. Byconsidering the World Bank’s poverty line and the total consumption basis, results indicatethat circa 3.69 % of Tunisian households live in extreme poverty (<$1.08). This percentagerises to 4.35 % when healthcare payments are netted out of household consumption. Expressedas a percentage of the poverty line the poverty gap increases from 0.87 % of the 1.08 USDline to 1.01 % when healthcare payments are netted out of household consumption. Also ofnote, when healthcare payments are netted out of household consumption, the normalizedmean positive poverty gap falls by 0.17 %, indicating that the rise in the poverty gap israther due to an increase in the number of households being brought into poverty ratherthan a deepening of the poverty of the already poor. The pattern of results is similar whenconsidering the specific Tunisian poverty threshold, but the relative difference in povertyis less when poverty is assessed on consumption net of healthcare payments. For instance,while according to the Tunisian poverty line, 0.32 % of households falls under poverty line,and thus, become impoverished due to healthcare payment—the percentage is doubled whenwe consider the World Bank’s poverty line. Lastly, it is worth noting that the estimated robuststandard errors are relatively small when compared to the point estimates, and for all measuresthe differences in the estimates of poverty based on household consumption gross and net ofhealthcare payments is significantly different from zero at 5 % or less.

Results of the multivariate analysis, using logistic regression, of the factors associatedwith the risk of facing CHE are presented in Table 5 as per the two common thresh-olds: the 10 % of total expenditure and the 40 % of discretionary expenditure.1 Variablesselected in the final model include: (a) households with at least one member having disabilityand chronic illness, and (b) households that faced hospital expenditure (all included in themodel as dichotomous variable), (c) expenditure quintiles, (d) insurance status, (e) education,(f) gender of the household head, and (d) place of residence. It is of interest to note first thatbased on the Hosmer–Lemeshow test the model goodness-of-fit is satisfactory. Nevertheless,almost all the variables included in the analysis exhibit broadly similar patterns at the twothresholds, though at different levels of significance and sometimes different magnitudes.

1 It is worth noting that we have also conducted the multivariate analysis for the other thresholds/cut-off levels.However, given that our results exhibited broadly similar patterns in that the coefficients of the key covariateswere found to be significant, though at different levels and slightly different magnitudes. Therefore, we haveopted to present results for the most commonly used thresholds: the 10 and 40 % of total and discretionaryexpenditure, respectively. Results on the other thresholds are available upon request.

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–0.0

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.017

)–

HW

orG

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–13

.43

%0.

757

%–

5.54

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0.31

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–

Hea

lthin

sura

nce

stat

usSo

cial

heal

thin

sura

nce

4.87

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uced

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ffs

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5.12

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0.32

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ate

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nce

1.75

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0.11

(0.0

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nsur

ed6.

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.03)

2.92

(0.0

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55(0

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10.3

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ted

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the

othe

rth

resh

olds

expl

ored

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ble

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tatio

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eth

resh

olds

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ble

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little

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ntag

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that

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ilar

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ribu

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tern

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ere

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rved

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ster

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rent

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astic

ityan

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ring

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).St

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cate

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ifica

ntat

10%

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ss.

123


Tabl

e4

Mea

sure

sof

impo

veri

shin

gef

fect

sof

heal

thca

repa

ymen

tsa

Dif

fere

nce

Gro

ssof

heal

thca

repa

ymen

ts(1

)N

etof

heal

thca

repa

ymen

ts(2

)A

bsol

ute

(3)=

(2)-

(1)

Rel

ativ

e[(

3)/(

1)]

(%)

Wor

ldba

nk’s

pove

rty

Lin

e=

1.08

USD

per

capi

tape

rda

y

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rty

head

coun

t(H

pov)

3.69

%(0

.003

8)4.

35%

(0.0

042)

0.66

%(0

.001

1)17

.77

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rty

gap

(Gpo

v)

21.5

51(2

.933

)24

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(3.1

63)

3.38

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)15

.72

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mal

ized

pove

rty

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0.87

%(0

.001

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013)

0.14

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mal

ized

mea

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PGpo

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23.7

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(0.0

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−0.1

7%

−0.7

0

Tun

isia

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ecifi

cpo

vert

ylin

e=

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USD

per

capi

tape

rda

y

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rty

head

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t(H

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2.14

%(0

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2.46

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.98

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rty

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v)

10.7

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mal

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%(0

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177)

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0

aSt

anda

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rors

are

betw

een

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over

allh

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hold

sbe

low

the

pove

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line

123


Tabl

e5

Fact

ors

asso

ciat

edw

ithth

esi

skof

cata

stro

phic

heal

thex

pend

iture

(sim

ulta

neou

slo

gist

icm

odel

)

CH

E≥

0.10

ofto

talc

onsu

mpt

ion

expe

nditu

reC

HE

≥0.

40of

disc

retio

nary

nonf

ood

expe

nditu

re

βSE

P>

|z|

OR

[95

%C

Ifo

rO

R]

βSE

P>

|z|

OR

[95

%C

Ifo

rO

R]

Hea

lthst

atus

a

Hea

lthdi

sabi

lity

(atl

east

one

hous

ehol

dm

embe

r)−0

.724

0.22

10.

531

0.48

6[0

.127

;1.6

63]

−0.5

820.

144

0.00

00.

611*

[0.4

13;9

.211

]

Chr

onic

heal

thdi

seas

es(a

tlea

ston

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old

mem

ber)

0.97

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143

0.03

22.

531*

*[1

.055

;2.9

63]

0.35

00.

162

0.02

91.

454*

[1.0

12;1

.734

]

Hos

pita

lizat

ion

(atl

east

one

hous

ehol

dm

embe

r1.

871

0.12

00.

017

5.78

5**

[1.4

62;7

.013

]1.

566

0.16

40.

004

3.51

2***

[2.9

11;4

.503

]

Hou

seho

ldch

arac

teri

stic

sa

Gen

der:

Mal

e−0

.654

0.10

50.

008

0.40

3*[0

.213

;1.8

32]

−0.2

050.

144

0.11

10.

800*

*[0

.626

;1.4

42]

Edu

catio

nals

tatu

sof

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ad(s

econ

dary

orhi

gh)

−0.2

160.

124

0.04

30.

716*

[0.4

03;1

.741

]−0

.673

0.33

50.

209

0.67

3**

[0.4

91;0

.810

]

Settl

emen

t(ur

ban)

−3.3

620.

042

0.00

10.

326*

*[0

.102

;1.8

67]

−0.3

010.

123

0.01

30.

691*

[0.5

10;1

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]

Em

ploy

men

tsta

tus

ofho

useh

old

head

a(E

mpl

oyed

)−2

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0.24

30.

042

0.25

6[0

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;0.9

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−0.2

800.

152

0.05

00.

739*

[0.4

02;1

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]

Hea

lthin

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nce

stat

usof

hous

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Priv

ate

insu

ranc

e−2

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0.75

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4**

[0.1

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.805

]−1

.984

0.64

30.

005

0.13

4*[0

.111

;1.7

24]

Soci

alhe

alth

insu

ranc

e−0

.839

0.04

70.

307

0.33

1*[0

.104

;1.0

48]

−1.4

600.

422

0.03

00.

357*

*[0

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;1.0

12]

Stat

e-su

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ized

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rage

(fre

eor

redu

ced

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ff)

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−0.2

160.

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;2.2

05]

−1.2

330.

285

0.05

00.

527*

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;1.6

81]

Eco

nom

icst

atus

Hou

seho

ldex

pend

iture

quin

tilec

Q2

0.21

40.

121

0.20

11.

759*

[0.6

02;4

.508

]−0

.424

0.20

20.

039

0.52

5**

[0.4

81;8

.112

]

Q3

0.53

10.

145

0.47

31.

331*

*[0

.734

;1.4

88]

−0.3

660.

230

0.02

80.

722*

[0,5

21;1

,053

]

Q4

−1.5

460.

274

0.30

70.

803*

*[0

.121

;3.2

05]

−0.8

020.

221

0.00

00.

556*

[0.3

61;9

.002

]

Q5

−4.2

010.

402

0.00

10.

415*

[0.1

97;2

.912

]−1

.134

0.25

50.

000

0.42

5**

[0.2

57;7

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]

Con

stan

t−6

.180

0.34

20.

000

––

−5.0

280.

256

0.00

0–

–

LR

χ2(1

7)=

477.

61Pr

ob>

χ2

=0.

0000

Pseu

doR

2=

0.18

7H

osm

er–L

emes

how

test

χ2

(8)

=8.

32

LR

χ2(1

7)=

512.

44Pr

ob>

χ2

=0.

0000

Pseu

doR

2=

0.15

4H

osm

er–L

emes

how

test

χ2

(8)

=7.

30

123


Tabl

e5

cont

inue

d

CH

E≥

0.10

ofto

talc

onsu

mpt

ion

expe

nditu

reC

HE

≥0.

40of

disc

retio

nary

nonf

ood

expe

nditu

re

βSE

P>

|z|

OR

[95

%C

Ifo

rO

R]

βSE

P>

|z|

OR

[95

%C

Ifo

rO

R]

Con

stan

t−6

.180

0.34

20.

000

––

−5.0

280.

256

0.00

0–

–

LR

χ2(1

7)=

477.

61Pr

ob>

χ2

=0.

0000

Pseu

doR

2=

0.18

7H

osm

er–L

emes

how

test

χ2

(8)

=8.

32

LR

χ2(1

7)=

512.

44Pr

ob>

χ2

=0.

0000

Pseu

doR

2=

0.15

4H

osm

er–L

emes

how

test

χ2

(8)

=7.

30

Sign

ifica

ntle

vels

are:

*1,

**5,

***

10%

,res

pect

ivel

ySE

Rob

usts

tand

ard

erro

rs.I

nad

ditio

nto

hete

rosc

edas

ticity

,SE

sar

eco

rrec

ted

for

clus

teri

ngat

(2le

vels

)a

All

incl

uded

inth

em

odel

asdi

chot

omou

sva

riab

leb

Hea

lthin

sura

nce

stat

usof

hous

ehol

dhe

ad=

1,0

othe

rwis

e.R

efer

ence

grou

p=

Not

cove

red

byan

yhe

alth

insu

ranc

esc

hem

esc

Ref

eren

cegr

oup

=Po

ores

tqui

ntile

All

vari

able

sw

ere

ente

red

inth

eL

ogit

mod

elus

ing

forw

ard

step

wis

een

try

func

tion

and

sele

cted

ifth

epr

obab

ility

ofits

scor

est

atis

ticis

less

than

0.05

and

rem

oved

ifth

epr

obab

ility

was

grea

ter

than

0.1

123


Indeed, in contrast to disability, the presence of a chronic disease and hospital admissioncosts (for at least one household member) are associated with higher probability of incurringCHE (by 2.5 and 6 times at the 10 % threshold and 3.5 and 1.5 times at the 40 % threshold,respectively). This is not surprising given the fact that treatment of chronic diseases andhospitalization care are rather costly compared to disability which may not necessarily beassociated with undue healthcare expenditure. On the other hand, gender of household headis also associated with the likelihood of vulnerability to CHE: female-headed householdsappear to be at higher risk of facing CHE compared with households headed by male (about2.5 times higher at the 10 % threshold and 1.25 times at 40 % thresholds). Among the otherhousehold head characteristics, education level (having secondary or higher) and employ-ment status (being employed) are seen as protective factors against CHE. By contrast, placeof living (urban vs. rural) appeared to be a contributor to face CHE with rural householdsbeing at higher risk of CHE (almost three times higher at the 10 % thresholds and 1.5 timeshigher at the 40 % threshold) than their urban counterparts.

Even after adjustment for these characteristics, health insurance coverage of the householdhead is significantly related to occurrence of CHE with similar patterns being observedregardless of the threshold. Compared to the portion of the population who does not have anyhealth insurance coverage or state-subsidized coverage, the odds of facing CHE are circa 8times lower when the household head is affiliated to a complementary private insurance, 3times lower when subscribed to social insurance system, while it is almost two times lowerfor those who are exempted or beneficiary of reduced tariffs of care. Lastly, results also showa socio-economic gradient in the probability of facing CHE: the odds of facing catastrophichealth expenditure decline as we move from the lower to the upper expenditure quantilewith households belonging to the richest quintile being almost two times less likely to facecatastrophic expenditure when compared with the poorest quintile.

Discussion

As noted at the outset, unlike other countries with similar standards of living, Tunisia haswitnessed during the last two decades a fairly rapid and sustained improvement in severaldimensions of well-being. Unfortunately, however, debates on the issues of fairness andfinancial protection in health in the particular context of Tunisia has been so far parochialin character, lacking coherent evidence, and measures against which to evaluate and judgethe capacity of the current healthcare system to ensure a fair financial protection in health tothe entire population. This study hoped to provide policy-makers with some insights aboutthe disruptive and impoverishing effects associated with health expenditure.

In line with previous literature, catastrophic health expenditure was defined here withrespect to both household total expenditures and to expenditures after netting nondiscre-tionary food expenditures and estimated using a range of internationally-recognized budgetthresholds for both cases (Wagstaff and van Doorslaer 2003). This allowed us to assess thereliability of our estimates and facilitate comparison with other studies adopting differentdefinitions. Indeed, while results from the two approaches confirm that non-negligible pro-portions of the Tunisian population (ranging from 4.54 % at the conservative 40 % thresholdto 12.10 % using the 10 % threshold of total expenditure) incurred catastrophic health expen-ditures, these results appear to be relatively higher when compared with those obtained forother countries with virtually similar level of development (Elgazzar, Raad et al. 2010). Forinstance, using the 40% threshold, less than 1.0 % of households were estimated to face CHEin Turkey (Yardim, Cilingiroglu et al. 2010) and Palestine (Mataria, Raad et al. 2010), while

123


Table 6 A summary comparison of the proportions of households facing catastrophic expenditures in low-tomiddle-upper income countries

Country As share of total expenditure As share of nonfood expenditurezcat = 5 % zcat = 10 % zcat = 25 % zcat = 40 %

Tunisia (present study) 33.43 12.07 17.20 4.54

Turkey – – – 0.60

Palestine – 8.00 – 0.97

Iran – – – 3.00

Indonesia 9.57 4.43 4.40 1.95

Malaysia 6.62 2.01 0.78 0.21

Philippines 9.21 4.60 3.81 1.58

Sri Lanka 10.97 2.95 3.40 1.31

Kyrgyz 15.53 5.84 9.29 2.64

Nepal 14.72 5.90 1.83 4.57

Thailand 8.43 3.52 1.83 0.71

China 28.37 12.61 11.23 4.81

India 25.59 10.84 9.76 3.44

Bangladesh 27.63 15.57 14.73 7.13

Vietnam 33.77 15.11 15.10 5.97

Source: compiled based on authors’ review of studies cited in the text– Not available

in a study amongst fourteen Asian countries (van Doorslaer, O’Donnell et al. 2007) suchproportion varied from less than 1.0 % in Malaysia and Thailand up to 5.97 % in Vietnamand 7.13 % in Bangladesh. Using the 10 % threshold, the incidence of CHE in Tunisia stillappears to be high compared with many low- to middle-income countries where often lessthan 10 % of households were found to incur health expenditure of more than 10 % of theirtotal expenditures (Table 6). Changing the threshold (downward or upward) does not sub-stantially alter the picture. For instance, 33.43 % of Tunisian households were found to spendin excess of 5 % of the total household budget on health and a substantial 17.20 % spend inexcess of a quarter of the nonfood budget on health, both proportions remain comparativelyhigher than many other countries (Table 6).

Moreover, the analysis of the distributional incidence of catastrophic health expendituredemonstrates the high burden on welfare that health expenditure poses on the most eco-nomically worse-off groups of the population. Both incidence and intensity of catastrophicpayments, whether defined as a share of household total expenditure or as a share of discre-tionary expenditure, appeared to be more concentrated amongst the poor. For the impover-ishing impact, results also showed that out-of-pocket expenditure can be held responsible forabout 18 % of the rise in the poverty gap which is also quite high compared to similar studies(Mataria, Raad et al. 2010; Yardim, Cilingiroglu et al. 2010). Quite surprisingly, impover-ishment due to healthcare expenditure remains significant even when considering the officialTunisian poverty threshold, which has often been criticized for being “too low” to accountfor the real cost-of-living in the country (Muller 2008).

Going beyond measurement, analysis of the correlates of CHE was useful in sheddingsome light on the question of who are more likely to be vulnerable to financial catastrophein Tunisia and whether these groups are the same as those found in other countries with

123


similar level of development. Generally, our results are consistent with the factors usuallyassociated with CHEs in previous literature (e.g., Xu, Evans et al. 2007; Amaya Lara and RuizGomez 2011; Boyer, Abu-Zaineh et al. 2011; Hajizadeh and Nghiem 2011). However, unlikeothers (Pradhan and Prescott 2002; O’Donnell, van Doorslaer et al. 2005) where measuresof health status were captured through the age-sex composition of the household as a proxyfor healthcare needs, these were apprehended in our analysis using direct measures such asthe presence of disability, chronic disease and hospitalization. Unsurprisingly, the latter twovariables appear to be significantly associated with exposure to risk of catastrophic payments.But, there are also other differences. For instance, in contrast to the results reported for asimilar upper-middle-income country, Turkey (Yardim, Cilingiroglu et al. 2010), and a low-income country, Burkina Faso (Su, Kouyaté et al. 2006), where household head characteristicssuch as education, employment and gender were shown to be unrelated to CHE, our resultsshowed that households with less educated-heads, unemployed-heads, and female-headedhouseholds are more likely to be vulnerable to CHE. However, in line with the findingsreported for Turkey, households living in rural areas are found to be more vulnerable to CHEthan those living in urban areas.

While these findings can help policy-makers target the most vulnerable groups forimproved financial protection, some caution should be exercised as regards the causal inter-pretation. For instance, findings on education and employment do not necessarily reflectcausal relationships as they may capture unobserved individual abilities: an educated house-hold can be more efficient in maintaining health and preventing disease (Grossman 1972),hence, be less likely to incur large expenditures. Nonetheless, inasmuch as education capturesbetter job seeking ability allowing for better lifetime income it can reflect a negative effectof this on health expenditures through better health (O’Donnell, van Doorslaer et al. 2005).Similarly, the rural effect may probably reflect, inter alia, not only variations in the travelcosts, but also, characteristics of living conditions: individuals living in rural areas are morelikely to differ from those living in urban areas. Unfortunately, due to lack of data on theseconditions, we were not able to control for such determinants directly through, for exam-ple, including indicators of access to sanitary toilets and safe drinking water, nor did we findplausible instruments for abilities, which may determine education but not directly determinehealth. Nevertheless, gender of the household head, which was found to be correlated withthe risk of CHE, may reflect differences in the characteristics of male- and female-headedhouseholds. Indeed, data from our survey was used to compare both de facto female-headedhouseholds (i.e., households where the male head is absent the majority of the time or unableor unwilling to work) and de jure female-headed households (i.e., those headed by widowsor divorced women) with those headed by males. Interestingly, results (not reported) showedsignificant differences between the two types of households as per health insurance coverage:female-headed households, constituting circa 14 % of all households, were found to be circa6 times less likely to be covered by the social health insurance scheme and 4 times less likelyto benefit from medical aid schemes compared with their men-headed counterparts.

Catastrophic health expenditure has also been used to measure the performance of prevail-ing health insurance schemes (Waters, Anderson et al. 2004; Devadasan, Criel et al. 2007;Ekman 2007). Accordingly, a large fraction of households experiencing CHE is likely tobe associated with an insufficient coverage in relation to health insurance schemes. Recentcountry-level studies have concluded that the expansion of insurance schemes tend to reducethe risk of catastrophic and impoverishing expenditure (Knaul, Arreola-Ornelas et al. 2006;Limwattananon, Tangcharoensathien et al. 2007; Hajizadeh and Nghiem 2011). Other stud-ies (Habicht, Xu et al. 2006; Xu et al. 2006; Wagstaff and Lindelow 2008) have, however,showed that this is not always necessarily the case.

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Turning to the Tunisian case, our results indicate that CHE was also concentrated on thosecovered by social health insurance and those who were supposed to benefit from free-care andreduced-tariffs, even if health coverage appeared to almost halve the probability of incurringCHE compared with the excluded. Such result can be attributed in part to the rising andextensive use of co-payments (and hence a decrease in the depth of coverage), and in part,to a greying of the population and the chronically-ill having shallower coverage, especiallyfor some expensive inpatient care and medications. Indeed, the implementation of structuraladjustment programs was accompanied by adoption of policies aiming at cost-sharing andcost-recovery in public facilities and a decline (in real terms) in public health spending. Userfees are collected from patients using public services in order to offset budget reductionsand to rationalize the use of free care. Nonetheless, a rise in the role of private sector wasalso noticeable with a rapid increase in fee-for-services group practices and private insuranceschemes in the past few years. The frequent unavailability of some prescribed examinationsand medications at government facilities following budget reductions forced patients to goto private facilities, and thus has contributed to the increase in the role of the private sector.

The current arrangements appear to be rather inadequate to fully ensure against vulner-ability to CHE and signal the need for more effective interventions that can reconsider thecurrent role and structure of the Tunisian health insurance system in order to strengthen itsfinancial protective capacity. Some economic implications for the design of funding arrange-ments, which emerged from the Tunisian experience, may worth making. One is that neitherexemptions nor reduced-tariffs policies can alone be enough to succeed. Of course, eachpolicy can have its pros and cons, but the answer to the question of which one should beadopted depends rather on the availability of the key factors to success implementation,particularly, the question of how these should be funded and managed? As elsewhere (Abu-Zaineh, Mataria et al. 2008), lessons from Tunisian experience showed that both policies canhave little success unless sufficient funding and monitoring arrangements are put in place tooffset the revenues lost by health facilities and insurance schemes, otherwise they would notbe able to maintain the provision of the needed health services. Indeed, previous report onhealthcare in Tunisia (Arfa and Achouri 2008) has already shown problems with implemen-tation, often, due to shortage of medications, lack of adequate coordination of healthcare andinsufficient and ineffective use of resources, resulting in a weak public service delivery andpublic dissatisfaction with health services.

While increasing overall public funding can help secure the provision of good-qualityservices delivered at public sector facilities, the capacity to significantly increase alloca-tions from this source to the health sector remains, at least in the short-term, highly con-strained given the current economic hardships accompanying the transitional period in thecountry (Arieff 2012). Even so, more money alone will not attain the desired outcomesunless other reform measures are taken into account. Currently, the state plays a “passiverole”—transferring the pooled funds to services providers on behalf of the population. Today,however, a growing body of literature (e.g., Preker and langenbrunner 2005) has brought evi-dence in favour of a more active purchasing role of the state on the grounds that this can helpachieve both equity and efficiency objectives of the healthcare system through purchasing,contracting and commissioning services from the other providers (e.g., the parastatal andprivate sectors) (McIntyre 2007). This is particularly germane in the cases where an activeprivate sector already exists and the use of its services comprises a large share of healthcareexpenditure as is the case of Tunisia where the private sector received about 81 % of totaldirect households’ expenditures on health.

Another key issue for healthcare system reform in Tunisia concerns the design of thebenefit package to which beneficiaries are entitled, including type of services and providers.

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Indeed, it has been noted that the benefit package has not yet been appropriately concep-tualized and designed in the health insurance system in Tunisia (Arfa and Achouri 2008).The current system offers beneficiaries what can be called “a negative list” of services (asopposed to a “positive list” where each services included is itemized)—i.e., a benefit packagethat covers all healthcare except for a number of specified services such as treatment for a listof chronic conditions, organ transplantation and so on, with an annual expenditure cap perhousehold being imposed. However, given the goal of financial protection, it is increasinglyargued (Preker and Carrin 2004; Preker and langenbrunner 2005) that the design of the benefitpackage should take into account services that are mostly associated with catastrophic expen-diture; e.g., inpatient care and other high-cost and low-frequency services. Moving towardsan active purchasing role with the purchaser regularly compiling and analyzing relevant epi-demiological and actuarial data on the beneficiaries was shown (Kutzin 2001) to be usefulin designing a benefit package that can meet the likely population’s needs for healthcare.

Conclusion

Akin to many other developing countries, Tunisia is now facing a new challenge of theemergence of non-communicable diseases as a result of the growth of life-style-related riskfactors. Such serious and chronic diseases, which are associated with high treatment costs, canseverely disrupt households’ standards of living and even push households into poverty traps.The health system as a whole appears to be unable to cope with the current epidemiologicaltransition and to reduce the financial burden associated with ill-health. The limited resourcesavailable to health and the increasing contributions of households in the total health expen-ditures can have a negative effect on the health outcomes especially for the poorest groups ofthe population. Equitable access to health services is a crucial issue in Tunisia and a challengeto policy-makers especially in the context of the epidemiological transition. Acknowledgingthe presence of the problem is a vital step to ensure follow-up action. Thus, as far as pol-icy recommendations are concerned, the findings and the discussion reported in this studycan help shape policy towards reinforcing the protective capacity of the current healthcaresystem. While pro-poor financing schemes shall be pursued to mitigate the negative impactof recurrent shocks prevailing in the country, it is recommended that policies should seekto expand not only the breadth of social protection coverage but also its depth by includinglong-term care and inpatient services to the benefit package, with a special attention to thepoor and chronically-ill. An effective and accurate information system is called for to enablegreater information sharing between the Ministry of Social Affairs and the Ministry of PublicHealth. Such system can then be used to better identifying the number of people who are eli-gible for exemptions or subsidies, estimating their expected use of services, the likely cost ofexemptions/subsidies, and thus, identifying the funding requirements for expanding effectivecoverage. Unfortunately, until now, there has been no attempt to provide accurate and reliableestimations of the cost of expanding effective coverage in Tunisia. There is a crucial needto conduct such analysis which can help identify the funding requirements and the policyinterventions needed to enhance the financial protection capacity of the healthcare system.

Acknowledgements We would like to thank The French National Research Agency (ANR) for its financialsupport to the research project (INEGSANTE-Les Suds-Aujourd’hui II-2010). Thanks are also due to theTunisian National Institute of Public Health (INSP-Tunis) and to TAHINA Team. We are grateful to ProfessorPedro P. Barros and two anonymous referees for helpful comments and suggestions. The authors are alsograteful to Dr. Yves Arrighi for reading and commenting on the paper.

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Appraising financial protection in health: the case of Tunisia