Health equity in Lebanon: a microeconomic analysis
© Salti et al; licensee BioMed Central Ltd. 2010
Received: 14 July 2009
Accepted: 14 April 2010
Published: 14 April 2010
The health sector in Lebanon suffers from high levels of spending and is acknowledged to be a source of fiscal waste. Lebanon initiated a series of health sector reforms which aim at containing the fiscal waste caused by high and inefficient public health expenditures. Yet these reforms do not address the issues of health equity in use and coverage of healthcare services, which appear to be acute. This paper takes a closer look at the micro-level inequities in the use of healthcare, in access, in ability to pay, and in some health outcomes.
We use data from the 2004/2005 Multi Purpose Survey of Households in Lebanon to conduct health equity analysis, including equity in need, access and outcomes. We briefly describe the data and explain some of its limitations. We examine, in turn, and using standardization techniques, the equity in health care utilization, the impact of catastrophic health payments on household wellbeing, the effect of health payment on household impoverishment, the equity implications of existing health financing methods, and health characteristics by geographical region.
We find that the incidence of disability decreases steadily across expenditure quintiles, whereas the incidence of chronic disease shows the opposite pattern, which may be an indication of better diagnostics for higher quintiles. The presence of any health-related expenditure is regressive while the magnitude of out-of-pocket expenditures on health is progressive. Spending on health is found to be "normal" and income-elastic. Catastrophic health payments are likelier among disadvantaged groups (in terms of income, geography and gender). However, the cash amounts of catastrophic payments are progressive. Poverty is associated with lower insurance coverage for both private and public insurance. While the insured seem to spend an average of almost LL93,000 ($62) on health a year in excess of the uninsured, they devote a smaller proportion of their expenditures to health.
The lowest quintiles of expenditures per adult have less of an ability to pay out-of-pocket for healthcare, and yet incur healthcare expenditures more often than the wealthy. They have lower rates of insurance coverage, causing them to spend a larger proportion of their expenditures on health, and further confirming our results on the vulnerability of the bottom quintiles.
Out-of-pocket spending on health is a major concern for policymakers, especially in developing countries where direct household payments for health care can account for the single largest component of household spending after food expenditures. High private health spending is related to the incidence of illnesses and chronic diseases, but it can grow exponentially and affect the living conditions of individuals in situations of crisis, conflict and natural disasters. These catastrophic health payments can push households into poverty or into deeper poverty. Households facing these health expenses may cut back on other essential household spending such as food and clothing. Households may also reduce their consumption of healthcare services, thus causing the health condition of family members to further deteriorate. To date, there has been little work done on the level and distribution of household out-of-pocket payments for health care in the Middle East, and to what extent household expenditures on health care affect living standards. Given that the region is prone to recurrent conflict and political instability, it becomes very important to determine who the most vulnerable groups with regards to healthcare access and financing are. We focus in our analysis on the Lebanese economy, where health equity issues have not yet been thoroughly studied.
One of the structural weaknesses in the Lebanese health care system is related to the fact that the role of the Ministry of Health has focused almost exclusively on the provision of services, while its role in prevention, planning and regulation remained limited. This is particularly true in light of the expanding role of the private sector (Figure 1). Both these factors also explain the prevalence of the more costly and arguably less effective culture of curative care rather than the more efficient strategy of preventive care in the Lebanese healthcare system.
Recognizing the inefficiency of the health care sector in Lebanon, the World Bank initiated with the Lebanese Government under the Paris III reform framework a Social Protection Development Policy Loan (DPL) including several health sector reforms. The loan package comprises key reforms in the health insurance sector, measures to rationalize health expenditures, and critical reforms in public health policy. The public health reforms supported by the DPL consist of:
i. Implementation, by the Ministry of Health of a health expenditure rationalization plan to better contain rising hospitalization expenditures.
ii. Reform initiative to revitalize primary healthcare through the implementation of a five-year action plan based on a recently developed primary healthcare strategy for the country.
iii. Reform initiative to fully develop and operationalize the national Expanded Program on Immunization (EPI).
iv. Reform initiative to significantly upgrade the core public health function of disease surveillance.
Public sector health spending (2006)
Health expenditure item
2006 Budget Proposal (000 LL)
as a % of total budgetary expenditures
as a % of GDP
Hospitalization in the private sector
Purchase of Medication
Hospitalization of public sector employees
Maternity and sickness allowance
Health inequity can be defined as "a particular type of difference in health or in the most important influences on health that could potentially be shaped by policies; it is a difference in which disadvantaged social groups (such as the poor, racial/ethnic minorities, women, or other groups that have persistently experienced social disadvantage or discrimination) systematically experience worse health or greater health risks than more advantaged groups" . Thus, inequity in health reflects the systematic differences across socio-economic groups in one or more aspects of health . While the definition we use more closely matches the definition of health equity as equality in health, it is also consistent with the understanding of health inequality as an indicator of general injustice in society [5, 6].
In recent years, rising global interest in the area of health equity has spawned numerous international research and policy initiatives . These initiatives have largely focused on measuring and explaining inequalities in health status outcomes (infant mortality or maternal mortality), health service use (antenatal care visit), and public subsidies supporting health service utilization. Policy initiatives have sought to alleviate disparities by addressing modifiable factors through organizational, economic and/or regulatory reforms. Another focal area of health equity research has revolved around the progressivity of health care payments and the catastrophic and impoverishing impact of these payments on individual households . A number of global evaluations mainly conducted by the WHO have highlighted the variations in household payments for health care and their implications, shedding light on the international focus afforded to the issue of financial protection in health care .
This paper takes a closer look at the micro-level inequities in the use of healthcare, in access to healthcare, in the ability to pay for it, and in some health outcomes. The findings contribute to the international literature on health inequity by providing more evidence from Middle Eastern countries. The results of our analysis are especially useful for policymakers in Lebanon and other countries that face the issue of promoting access to health services by targeting the most needy, while at the same time maintaining efficiency and financial stability in volatile security environments.
In the methods section of this paper we briefly describe the data and explain our choice of measures. We also explain in several subsections the methods used, including two techniques for standardization of our use measures. We describe, in turn, our measures of health care utilization, of catastrophic health payments, of existing health financing methods. In the results and discussion section, we illustrate our major findings regarding the equity of healthcare use, the incidence of catastrophic health payments, the impoverishing effect of healthcare spending, the distribution of health financing methods and a geographical breakdown of health characteristics in the country. We also qualify our results with a discussion of the limitations of our data and our approach. In the conclusion section, we summarize our results and present policy recommendations based on our findings.
This paper uses micro-data from the 2004/2005 Multi-Purpose Survey of Households conducted by the United Nations Development Programme (UNDP), the Ministry of Social Affairs (MoSA) and the Central Administration for Statistics (CAS). This is the most recent national survey of household living conditions to be conducted in Lebanon. The survey collects data on socio-demographics, household characteristics (including data on expenditures, assets and geographical characteristics), labor market characteristics and some health variables. The data contain information on close to 56,000 individuals from 13,000 households in all 6 Lebanese mohafazas (governorates). The survey focuses exclusively on Lebanese nationals and therefore excludes other residents in Lebanon (Palestinian refugees, foreign migrant workers, etc.).
Access to raw data from the survey was secured through MoSA, however all income data, including variables measuring financial assistance in healthcare from the government or non-profit sectors, remain inaccessible, as the Central Administration for Statistics has yet to release the income measures from the survey. The lack of access to income data constrains our ability to conduct welfare analysis. As has become standard practice in the literature, we use the distribution of household expenditures per adult equivalent in our investigation of equity in access and use. Adult equivalent scaling follows the OECD scale of weighing as 1 count the first adult and as 0.7 every subsequent adult, and as 0.5 count every individual under age 15 in the household. Our poverty line is also expenditure based, as are some of our variables measuring the use of healthcare services. This presents some technical difficulties in the analysis of the poverty implications and impoverishing effects of healthcare expenditures.
Measurement of variables
a. Need variables
the presence of a disability
the presence of a chronic condition
being a mother of a newborn child (within the last 12 months) as we take childbearing to be an indicator of the need for some healthcare
A list of the disabilities and chronic conditions included is provided in additional file 1. We include all three of these variables in our standardizations (described below).
b. Use variables
We define two broad classes of household-level use variables (which are then scaled to household size to get per-capita measures):
1. An indicator variable measuring the presence of any health related expense, including spending on health insurance.
2. A variable measuring the dollar amounts of health care spending.
The reason we are able to use the presence of any health related expense as an indicator of use is that no health care financing plans in Lebanon involves complete coverage of health expenses, with the exception of the government provided insurance plan for military and security personnel. For all other Lebanese citizens, even the most generous coverage involves some out-of-pocket expenditures, so we take the presence of health-related spending as an indicator of the use of healthcare services .
c. Catastrophic health payments
We then look at the incidence of catastrophic health payments (payments over 25 percent of expenditures per adult equivalent) across different population groups and socioeconomic characteristics.
d. Insurance coverage
We identify two broad classes of insurance plans: publicly provided plans (the National Social Security Fund, or NSSF, the Civil Servants' Cooperatives, municipal government plans, and plans of the security and armed forces) and privately provided plans (for the employed, the self-employed and the syndicated).
a. Means and concentration indexes
The methods that are employed to quantify the degree of equity in health care include descriptive and regression techniques using national household survey data described above. Descriptive methods include comparison of means and the 'concentration index' technique. The concentration index is a measure of how equally a health variable is distributed across a population ranked by income level. As a single numeric, the concentration index allows degrees of equity to be easily captured and compared, in order to determine the extent and nature of policy reform that is necessary.
b. Standardization: direct, indirect
A proper assessment of the equity of healthcare must control through regression analysis for the confounding effects of need and demographics, as well as other sources of heterogeneity that might affect healthcare use. We standardize our measures of the use of healthcare on three measures of need, as well as the main demographics we take to be correlated to utilization, using both the direct and indirect standardization techniques .
Direct standardization predicts the distribution of use by expenditure quintile that would be observed if the distribution of the confounding variables (need, age and sex) was the same for each quintile, but confounding variables had quintile-specific effects. Indirect standardization involves predicting the value of use in the same way, using the observed values for the confounding variables, but constraining their effect on use to be the same across all quintiles. In standardizing, we control for health confounding effects such as age, gender, and non-confounding effects such as education, the log of household expenditures and employment.
c. Methodology in poverty analysis
Our analysis of catastrophic health payments involves the calculation of headcounts and overshoots. The un-weighted headcount treats all households equally in calculating the share of households that incur a catastrophic health payment. This statistic assumes constant marginal utility of income. A measure that is more sensitive to equity concerns and that assigns more weight to households at the bottom of the distribution is the rank-weighted headcount. The concentration index measures the degree of equity in the incidence of catastrophic health payments across the income distribution and a negative index is indication that households at the lower end of the distribution of total household expenditures have a greater tendency to exceed the spending threshold on health and incur catastrophic health payments .
The depth of the impact of catastrophic payments is calculated using the overshoot, which calculates the average excess above the threshold. Like the headcount, the un-weighted version of the overshoot treats any dollar in excess of the threshold equally, regardless of which household is spending it. The rank-weighted overshoot instead assigns greater weight to overshoot spending by households at the bottom of the expenditure distribution. The concentration index measures the equity of the overshoot across the distribution of household expenditures: a positive value indicates that the overshoot tends to be greater among the better off.
When it comes to the analysis of poverty, we are unable to capture the impoverishing effects of healthcare spending (particularly when its scale is catastrophic) when our poverty line is defined on the basis of expenditure levels: the larger an individual's spending on healthcare, the likelier the individual's overall expenditures exceed the poverty line, which leads to an artificially lower poverty headcount. One approach that is commonly used to address this difficulty in constructing a valid measure of poverty is to calculate net poverty rates and net poverty gaps, which include only non-health spending . The weakness of this approach is that insofar as the poverty line is constructed to include spending on healthcare, net figures will overstate poverty. Another approach that we propose is to extrapolate from the overall poverty line and the composition of the mean household's spending a poverty line for non-health expenditures. Poverty rates and gaps calculated based on this line are not affected by the extent of healthcare spending, nor should the threshold itself include expenditures on health. This method is similar in essence to the one developed by Wagstaff et al. (chapter 19) . One of the major limitations in this approach is the arbitrariness of choosing the breakdown in the expenditures of the mean household to extract from the poverty line the amount of spending on non-health related goods.
Because health expenditures are not disaggregated into different classes or types of healthcare goods and services, we have no information on the nature of healthcare consumption. Thus, for example we have no means of discerning publicly provided healthcare from health services that are privately provided. Similarly, we cannot distinguish between inpatient, outpatient and specialist care.
Another limitation of the household survey is the paucity of variables measuring health outcomes. Disabilities and chronic conditions are recorded; however data on health status, whether self-assessed or measured by a healthcare professional, were not collected in this survey. Furthermore, unlike many surveys of the living conditions of households, this survey fails to record recent illness or injury, recent visits to healthcare centers or the recent use of the services of a healthcare professional.
Results and Discussion
Distribution of need
Need across expenditure quintiles
%of quintile with disability
%of quintile with disability ind. std.
%of quintile with disability dir. std.
% of quintile with chron. cond.
% of quintile with chron. cond. ind. std.
% of quintile with chron. cond. dir. std.
The incidence of disability decreases steadily across expenditure quintiles moving from close to 3% for the poorest fifth to 1.2% for the richest quintile. When standardized on age and gender whether directly or indirectly, these figures become slightly higher than the non-standardized figures particularly for the poorer quintiles. The incidence of chronic disease across quintiles shows the opposite trend to the one for disabilities: the incidence of chronic disease increases monotonically from 14.3% in the poorest quintile to 18.6% in the top quintile. This difference may be due to a difference in the frequency and accuracy of diagnoses that the various expenditure classes have access to. When standardized on age and gender, the differences across quintiles shrink somewhat as the standardized figures are slightly higher for poorer quintiles and lower for richer ones.
The distribution of health care expenditures
a. The incidence of health-related expenses
Incidence of health related expenditures by expenditure quintile
We note the remarkable difference between the indirectly standardized measures and the observed directly standardized rates of healthcare use. Thus, when overall means of the non-confounding variables are used (in indirect standardization), predicted healthcare spending is much lower for all five quintiles than if we assumed quintile means for household expenditures, employment and education. The absolute difference in predicted usage rates across quintiles is roughly the same for all three measures, which makes the difference proportionately much larger when use is indirectly standardized. This is the result of the substantial differences in the non-confounding variables (expenditures, employment and education) across quintiles.
b. Out-of-pocket expenditures on health
Out-of-pocket health related expenditures by expenditure quintile
c. The disability card
Use of the MOSA disability card
The use of healthcare services, whether measured by the presence of any health-related expense, the use of a disability card to access government provided health services, or the value of out-of-pocket expenditures on health appears to be regressive when measured against the distribution of overall household expenditures per adult equivalent. This result holds for the observed values of healthcare use as well as values standardized on need and other health determinants. Our results for equity in need showed disparity in the patterns of each need variable, but the more prevalent of our two need variables (the presence of a chronic health condition) put upper expenditure quintiles at more of a disadvantage. The results we find for use, even when standardized for need show patterns that consistently favor the rich.
Ministry of Social Affairs disability card by expenditure quintile
The impact of catastrophic health payments
As a background to the discussion of catastrophic health payments and their impact on welfare, we look at the breakdown across expenditure quintiles of the share of healthcare in household expenditures as well as the share of healthcare in non-food expenditures. We also look at the breakdown of the characteristics of individuals making catastrophic payments . The characteristics that we look at are gender, geographical regions (mohafaza) and age.
Share of health in expenditures by quintile
Health share in expenditures
Health share in non-food expenditures
a. Who incurs catastrophic health payments?
Characteristics of individuals facing catastrophic-health payments
Non-catastrophic (health payments < 25% of expenditures)
Catastrophic (health payments >= 25% of expenditures)
The determinants of catastrophic health payments.
Dependent variable: Presence of catastrophic health payments
N = 13,944
Log household expenditures per adult equivalent
Presence of a recent mother
Private insurance (employer)
Private insurance (own)
Private insurance (mutual)
Private insurance (syndicate)
b. Welfare effects of catastrophic health payments
Percentage of households incurring catastrophic payments for healthcare
Share of overall expenditures
Share of non-food expenditures
Weighted Head Count
Mean Positive Overshoot
Table 10 shows that the concentration index of headcounts is consistently negative, indicating that households at the bottom of the distribution are more likely to incur catastrophic health payments, using a variety of different possible thresholds for catastrophic payments. This might be the result of a rigid payment structure that is not a function of income. The concentration index is greatest for the lowest value of the threshold, which thereby results in the biggest discrepancy between the weighted and un-weighted measures of the headcount.
Interestingly, however, the concentration index of overshoots is positive indicating that while poorer households are more at risk of catastrophic payments, the dollar amounts of these payments is progressive: the better off tend to face larger overshoots. This result may be driven by non-linearity in health consumption which requires a minimal level of health spending regardless of total expenditure, but then increases more than proportionately as total expenditures increase.
Using only non-food expenditures (and adjusting the thresholds accordingly) gives qualitatively similar results in terms of the analysis of progressivity and poverty impact: head counts have a negative concentration index which makes the discrepancy between un-weighted and weighted headcounts largest when the threshold is lowest. Overshoots are progressive.
The impoverishing effect of healthcare spending
a. Poverty analysis using national and World Bank poverty lines
Conventional poverty analysis is conducted using 4 different poverty lines: a deep national poverty line equivalent to expenditures of $2.2 per person per day, a national poverty line of $4 per person per day, the deep poverty line of $1 (or 1.08 in PPP) and the poverty line of $2 (or 2.15 in PPP) used by the World Bank, keeping in mind that the rate of exchange of the dollar to the Lebanese Pound is LL1,500/$. Poverty rates are calculated as a headcount of individuals lying below the poverty line, as a fraction of the overall population. Poverty gaps are calculated as the average shortfall from the poverty line (per year). And normalized poverty gaps express poverty gaps as a percentage of the poverty line. For poverty rates, poverty gaps and normalized poverty gaps, we calculate both gross and net measures, where health expenditures are included in the gross measures and netted out in the net measures.
Measures of poverty based on consumption gross and net of spending on health care
Gross of health payments
Net of health payments
(3) = (2)-(1)
[(3)/(1)] × 100
$2.2 per day poverty line
Poverty gap (LL)
$4.4 per day poverty line
Poverty gap (LL)
$1.08 per day poverty line (WB)
$2.15 per day poverty line (WB)
Health spending profile of the poor and non-poor
($4.4 per day)
($4.4 per day)
Share of spending on healthcare
Catastrophic health payments
Out of pocket expenditures per head (LL)
b. Poverty analysis using a "non-health" poverty line
Non-health poverty line
$3.3 per day poverty line
$2.9 per day non-health poverty line
Poverty gap (LL)
Health-induced and health-obscured poverty
Share of spending on health
Out of pocket expenditures on health (LL)
Risk of catastrophic health payments
Share of spending on health
Out of pocket expenditures on health (LL)
Risk of catastrophic health payments
Out of pocket expenditures (LL)
Risk of catastrophic health payments
The spending profile on non-health related goods by the health-induced poor is comparable to that of individuals above the poverty line. It is the shortfall in their spending on health that pushes them into poverty.
In any analysis of the welfare effects of health payments, the health-obscured poor is a group that deserves attention if we are concerned about the validity of our methodology for measuring poverty: if we abstracted from healthcare payments, this group's spending profile on non-health goods would put them in poverty. So not only are the health-obscured trailing behind on non-health consumption, they are also spending more on healthcare than the typical "poor" household, which at once, obscures their poverty status and indicates that they are incurring large health expenses which is cause for concern in its own right. Thus, conventional poverty analysis fails to capture households whose poverty is health-obscured. To the extent that these households' spending on health hinders their ability to spend on other goods, health spending has an impoverishing effect on these households. While an-income based approach to poverty analysis would be able to detect the poverty of health impoverished households, this effect is missed by any expenditure based poverty analysis, as households whose health spending is substantial will show up as non-poor even when their spending on non-health goods is low.
The equity implications of health financing methods
In this section we compare the insurance type by wealth (using wealth quintiles and poverty headcounts), in an attempt to answer the question: who is insured and what type of plan do they have?
The results obtained, are analyzed by contrasting the quality of services provided by either class of plan. As a last task in this section we investigate the effect of the type of plan on the amount spent on health.
a. Who is insured and what type of plan do they have?
Among the insured, Figure 7 shows that the breakdown across insurance schemes also changes across expenditure quintiles: publicly provided insurance is the dominant form of insurance for all but the top quintile and it represents an overwhelmingly large fraction of the insurance plans of the third quintile (close to 92%). Similarly, the rate of private coverage is around 5% of the insured in the bottom 60th percentile, rises to 10% for the next quintile and accounts for a quarter of the insured in the top fifth of the expenditure distribution.
Health care use by insurance plan
Use (ind. stand.)
OOP expenditures on health
OOP expenditures on health (ind. stand.)
b. Hospitalization class and the quality of services
c. Health outcomes and health behavior by insurance plan
Expenditures on health by insurance type
Share of expenditures on health
The geographical distribution of healthcare expenditures
In this section, we map our quintile analysis to geographical regions (mohafazas). We break down each mohafaza by expenditure quintile. We also investigate the difference in the incidence of disabilities and the presence of chronic health conditions across mohafazas. We then examine geographical disparities in use variables (disability cards, health expenditure, insurance coverage, hospitalization class).
Disabilities and chronic disease by Mohafaza (province)
Incidence of disability
Presence of chronic disease
Disability card and insurance coverage for the disabled, by Mohafaza (province)
Incidence of use and OOP expenditures by Mohafaza (province) Source: Authors' estimate from 2004/2005 Household Survey
Use ind. stand.
OOP on health
OOP on health ind. stand.
The results of our analysis highlight the vulnerability of the lowest quintile of expenditures per adult equivalent, particularly when measures of healthcare use are standardized for need and demographic and economic health determinants. Not only is the healthcare use of the lowest quintiles through spending on healthcare substantially lower, they also appear to have less access to non-spending healthcare services as they are far less likely to benefit from health insurance.
Our analysis of the effect of insurance on health spending shows that once use, out of pocket expenditures and the share of spending on health are standardized, the uninsured spend both less money on health and a larger proportion of their total expenditures on health, further confirming our results on the extreme vulnerability of the lowest quintiles.
Furthermore, we take note of the weakness of running expenditure-based poverty analysis when healthcare use is also primarily measured through expenditures on health and we adjust our poverty calculations by introducing a measure of poverty that abstracts from health payments. This new measure allows us to hone in on segments of the expenditure distribution that are incorrectly labeled as poor or non-poor under the standard total expenditure based approach. Any welfare and equity analysis of healthcare reforms should correct such misclassification.
Our results call for a serious reconsideration of the targeting of health financing in Lebanon, as the lack of formal health insurance for the poorest strata of the Lebanese society makes it disproportionally exposed to adverse conditions, especially in times of conflict and instability. As it stands, the uninsured in Lebanon can benefit from medical care and hospitalization at the expenses of the Ministry of Public Health, either by going to public hospitals or by seeking preadmission to private ones, where services' payments are subject to a predetermined ceiling. But the Ministry's mandate in terms of coverage is ad-hoc and there are issues with the control of patient flows across various levels of the health care system . The Ministry has limited ability to direct any of the uninsured to its own hospitals and has no way of knowing (in advance) of its full financial liability for providing these inpatient benefits. Because the Ministry cannot turn away any uninsured patients (except for those going to private hospitals who may not be guaranteed admission if annual budget ceilings have been reached), its total current (and future) expenditures on hospital care are unpredictable. Payment ceilings at private hospitals, indeed, sometimes can be exceeded when hospitals successfully petition the Ministry of Finance for payment after services have been provided.
The Ministry of Public Health in Lebanon is now responsible for the hospital care of more than half the population. It is implicitly liable for the more expensive care required by the rising number of retired and elderly persons who are not covered under any other insurance fund. As our results have shown, there is a need to revise the Ministry's strategy of covering health care for the uninsured by designing an efficient and inclusive health insurance system, which could have at least two pillars: one that covers critical and essential healthcare for the Lebanese at no cost, and an additional fully-funded pillar where insurance contributions would be proportional to income. This health insurance scheme would not only insure equity, but will also reduce in the long run the out-of-pocket expenditures of the Lebanese households.
This paper has benefited from the support of the World Bank, as part of the Bank's regional health sector flagship report. We also acknowledge Wael Moussa, Ali Abboud and Rawan Nassar for excellent assistance in research.
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