Cross-Country Measures for Monitoring Epilepsy Care


Address correspondence and reprint requests to Charles E. Begley, School of Public Health, The University of Texas Health Science Center, 1200 Herman Pressler, Houston, Texas 77030, U.S.A. E-mail:


Summary: Purpose: The International League Against Epilepsy (ILAE) Commission on Healthcare Policy in consultation with the World Health Organization (WHO) examined the applicability and usefulness of various measures for monitoring epilepsy healthcare services and systems across countries. The goal is to provide planners and policymakers with tools to analyze the impact of healthcare services and systems and evaluate efforts to improve performance.

Methods: Commission members conducted a systematic literature review and consulted with experts to assess the nature, strengths, and limitations of the treatment gap and resource availability measures that are currently used to assess the adequacy of epilepsy care. We also conducted a pilot study to determine the feasibility and applicability of using new measures to assess epilepsy care developed by the WHO including Disability-Adjusted Life Years (DALYs), responsiveness, and financial fairness.

Results: The existing measures that are frequently used to assess the adequacy of epilepsy care focus on structural or process factors whose relationship to outcomes are indirect and may vary across regions. The WHO measures are conceptually superior because of their breadth and connection to articulated and agreed upon outcomes for health systems. However, the WHO measures require data that are not readily available in developing countries and most developed countries as well.

Conclusion: The epilepsy field should consider adopting the WHO measures in country assessments of epilepsy burden and healthcare performance whenever data permit. Efforts should be made to develop the data elements to estimate the measures.

A major contributor to the global burden of epilepsy is the lack of adequate healthcare services, particularly in developing countries but also in parts of developed countries as well. One of the greatest challenges to health planners and policymakers is obtaining meaningful data to measure these inadequacies at the community or country level. While it is easy to conceptualize indicators that might provide some insights, obtaining the data can be difficult.

Relatively simple measures that have been developed to assess the adequacy of epilepsy healthcare at the country level are the treatment gap and availability of key resources. These measures have been useful because they provide a basic indication of unmet need and require data that is relatively easy to obtain for most countries. In the 1990s, the World Health Organization (WHO) defined a broader set of healthcare measures to be used across diseases and countries to assess healthcare performance. Disability-Adjusted Life Years (DALYs) is proposed as an indicator of the health effects of healthcare systems. Patients' perceptions of treatment responsiveness are proposed as a measure. Financial fairness defined as healthcare spending relative to capacity to pay (CTP) is proposed as a measure.

Over the last 3 years, the members of the International League Against Epilepsy (ILAE) Commission on Healthcare Policy examined the general strengths and weaknesses of the existing measures used to assess epilepsy care at the country level and the feasibility and applicability to epilepsy of the recently developed WHO indicators. The members of the Commission included two neurologists, two neuropsychologists, a health economist, an epidemiologist, and a health care administrator. In addition, an economist from the WHO was added as a consultant to assist Commission members in understanding and applying the WHO approach to epilepsy. The Commission conducted workshops and organized panel presentations at international congresses, held meetings, and worked with the WHO consultant. This paper summarizes the methods and findings of the Commission on the adequacy of existing measures and the pros and cons of adopting the WHO indicators as a way of extending and improving upon them.


Overview of treatment gap and resource availability measures

The treatment gap was defined by the ILAE Commission on the Developing World (1) as: The difference between the number of people with active epilepsy and the number whose seizures are being appropriately treated in a given population at a given point of time, expressed as a percentage. The Commission defined an operational measure of active epilepsy as: two or more unprovoked epileptic seizures on different days in the prior year that are disabling to the individual. Appropriate treatment was defined as: diagnosis and treatment of underlying cause, and treatment of recurrent seizures according to international standards.

The treatment gap typically measures underutilization in terms of the lack of available drug therapies. If data are available, the gap can be derived for any service such as diagnostic services (the diagnostic gap) or the availability of specialized treatment for severe epilepsy, such as surgery (the surgery gap). The treatment gap has been used to assess need in demonstration projects sponsored by the ILAE, International Bureau for Epilepsy (IBE), and WHO in China, Brazil, Senegal, Zimbabwe, and elsewhere (Wang et al., 2003; Ndoye et al., 2005).

Another approach to determining community need, as defined by the ILAE Commission on European Affairs, involves determining the availability of a specified set of key resources for epilepsy care (Malmgren et al., 2003). This resource availability measure consists of the quantity of certain types of health care resources involved in the direct provision of medical care, such as physicians and hospitals, as well as key nonmedical resources such as the presence of lay associations for people with epilepsy, and the availability of funding for services. Data on each of a series of key resources are related to population data to derive per capita estimates of resource availability that can be compared to other communities in the region and world. The availability of key resources measure has recently been applied in a unique collaborative effort of the WHO, ILAE, and IBE: “Atlas on Epilepsy Care in the World 2005 (World Health Organization/International Bureau of Epilepsy/International League Against Epilepsy, 2005).”

Evaluation methods and results

The members of the Commission conducted a systematic review of the literature on the development and application of the treatment gap and resource availability measures in assessing epilepsy care. In order to identify relevant articles we searched three databases: Ovid Medline, EBSCO MEDLINE, and PubMed. We also searched for online articles using the Scirus scientific information search engine, as well as Google. Search keywords pertaining to epilepsy epidemiology, health care assessment, needs assessment, economic evaluation, and health planning were used alone and in combination. In addition, all issues of Epilepsia and Epilepsy Research since 1980 were reviewed for relevant titles. Relevant articles were identified and discussed by members of the Commission. Kristina Malmgren, Chair, ILAE Commission of European Affairs, and Harry Meinardi, Chair, ILAE Commission on the Developing World, were invited to two of the Commission's meetings for interviews on the pros and cons of existing measures of assessment and their application.

There is widespread agreement that the main strength of the treatment gap measure is its relative simplicity and ease of estimation. Direct methods for measuring the treatment gap involve finding out through population-based prevalence studies how many detected cases there are in an area that are not receiving treatment. Indirect methods, which can be applied when data are scarce, involve comparing prevalence data on cases to the estimated number of people in treatment, estimated by dividing the amount of antiepileptic drugs sold in an area by the defined daily dose. Lacking data on prevalence, an estimate may be made by applying a reasonable rate of active epilepsy in a population, such as a minimum of 0.5% (Kale, 2002).

A major limitation of the treatment gap is that it reveals little about the alternative factors that may cause a gap. Such factors may include, for example, insufficient availability of drugs, poorly organized health care systems, access barriers such as lack of transportation and health insurance, or inhibiting population characteristics about healthcare seeking behavior. Understanding the influence of these factors can be vital to policymakers in developing a definitive diagnosis of community need or pinpointing an intervention to address the need. The likelihood of reducing the treatment gap in an area by expanding healthcare resources may not be effective if people with epilepsy are reluctant to seek treatment either due to a lack of knowledge about their condition, a sense of shame or guilt, or because they feel they will be discriminated against if they disclose their condition.

Like the treatment gap measure, the resource availability measure is relatively easy to estimate but provides only a basic indication of the lack of services for epilepsy in an area. The application of this approach recently accomplished for European countries (Malmgren et al., 2003) divided the task into two parts, the first involved obtaining official population data and data about the medical profession (e.g., total number of physicians and number of physicians in specialties relevant for epilepsy treatment). The second involved obtaining subjective assessments of epilepsy experts of the availability of specialty care resources in a country and the most important problems with epilepsy care.

A major limitation of the resource availability measures is the lack of community standards of what is considered a minimal, satisfactory, or optimal amount of epilepsy healthcare resources that could be applied across countries. Healthcare problems are suggested by data showing wide variation in resource availability in European countries (Malmgren et al., 2003) and around the world (World Health Organization/International Bureau of Epilepsy/International League Against Epilepsy, 2005). However, in a low-income country the most cost-effective way to expand epilepsy care may be to train primary care doctors or nurses to diagnose and treat epilepsy, yet the resource availability measure may point to a severe shortage of epilepsy specialists in the country as the problem. Until there are clear connections drawn between the level of resources needed to achieve a specific outcome in the quality and effectiveness of care, this measure can only be used as a suggestive indicator of a problem, at best. Another limitation of the resource availability approach is that, like the treatment gap approach, it reveals little about other possible factors that cause underutilization and poor outcomes besides the availability of resources, such as access barriers, or the attitudes and beliefs of patients and their families.


Overview of the measure

In developing its framework for health systems performance assessment in the 1990s, the WHO established a common set of measures for evaluating healthcare services in systems (World Health Organization, 2000; Murray and Evans, 2003). The primary motivation of the work was to provide analysts with standardized tools to assess the performance of healthcare systems across diseases and countries. In the WHO framework, maintaining and improving population health was defined by the WHO as the ultimate goal of healthcare services. DALYs is proposed as the operational indicator of the extent to which the health goal is being achieved. It defines population health in negative terms taking into account both premature death caused by disease and the loss of healthy life due to disability (Murray, 1996; Murray et al., 2002). As a broad indicator of the adequacy of healthcare services, DALYs reflect a number of factors including disease incidence, disability and mortality effects, and the availability, accessibility, and effectiveness of treatments.

DALYs are the sum of years of life lost to premature death (YLL) and years lived with a disabling condition (YLD) among those who survive. YLL is calculated as the number of deaths attributable to the condition, multiplied by the years that would have been lived in the absence of a premature death. Similarly, YLD is calculated as the years of life lived with the condition, adjusted for the perceived severity of the condition. For a given calendar year and condition, YLL and YLD are calculated for age- and gender-specific cohorts. YLL and YLD are then summed across cohorts to yield global DALYs per condition.

YLL and YLD that occur in the relatively young and relatively old are discounted using an age-weight function that reflects the broader societal impact of losses of life and health in the more productive middle years. YLL and YLD that occur in the future are discounted to reflect the greater social value of preventing near-term death and disability. As discounting reflects nonuniversal value judgments, results may be presented with and without age-weighting and discounting.

Evaluation methods and results

The Commission's examination of the DALY was guided by Dr. Chisholm who attended the Commission's meetings and conferences and led us in obtaining and translating literature on this WHO measure. With Dr. Chisholm's assistance, we performed an exercise to test the applicability of DALYs as an indicator of epilepsy healthcare in five developed countries (Canada, France, Italy, United Kingdom, and the United States) and one developing country (South Africa) using basic epidemiological and health care data. The goals of the exercise were to: (1) test the feasibility of calculating country-specific DALYs, using published epidemiologic data; (2) identify gaps in the data required for estimating DALYs for specific regions, and (3) identify aspects of the DALY method that require qualification or further refinement to ensure that it validly captures the burden of epilepsy. We did not set out to provide definitive burden estimates for each country, which would have required more systematic and precise data estimates than resources permitted.

Calculations of epilepsy-related DALYs were made using a modified version of the WHO spreadsheet (available from dalycalculationtemplate.xls). We identified region-, condition-, age-, and gender-specific data on population, incidence, prevalence, duration, mortality, and disability via literature review. We chose the most representative data, based on consultation with epidemiological experts and used the standard WHO age-weights and a 3% discount rate to facilitate comparisons. Of necessity, we included data only from studies whose age strata overlapped the strata in the WHO worksheet.

Country-specific parameter estimates are shown in Table 1. Country-specific data were not available in all cases, so estimates based on regional studies or studies of other countries were applied where populations, economic conditions, or healthcare systems were considered sufficiently similar. Epidemiologic estimates for epilepsy are not available for South Africa, for example, so parameters were selected from studies of other sub-Saharan countries. In the absence of country-specific data, standardized mortality ratios (1.1–4.0, depending on age) obtained through literature review were applied to all countries.

Table 1. Epidemiologic parameters for calculating country-specific DALYs
CountryPopulation death rate (per 1,000)Incidence (per 1,000)Prevalence (per 1,000)Treated (%)Controlled (%)
  1. Sources:

  2. aMoran et al., 2000

  3. bMoeller and Sonntag, 2002

  4. cDepartment of Economic and Social Affairs, 2003

  5. dMacDonald et al., 2000

  6. ePreux and Druet-Cabanac, 2005

  7. fHauser and Kurland, 1975

  8. gTellez-Zenteno et al., 2004

  9. hLeonardi and Ustun, 2002

  10. iBegley et al., 2001

  11. jJacoby et al., 1998

  12. kJallon et al., 2001

  13. lKwan and Brodie, 2001

United States0.095a0.33–1.01b2.8–10.2f72i63i
United Kingdom0.125a0.33–1.01b2.8–10.2f90j63l
South Africa0.281a0.83e3.1–15.8h10h63l

In the WHO calculation of DALYs, different disability weights due to epilepsy depend only on treatment status. We modified the WHO worksheet to reflect both the proportion of patients in each age and gender stratum who were treated versus the untreated and the proportion with controlled versus uncontrolled seizures. Remission rates were estimated to be 0.05–0.09 per year, depending on age, based on the aforementioned review. We used disability weights for treated and untreated cases from the original WHO study as well as additional weights derived from our own literature review to illustrate the importance of accurately measuring disability associated with epilepsy.

Epilepsy-related DALYs for the five developed countries are shown in Fig. 1, expressed as DALYs per 1,000 population. For comparison, dashed lines show DALYs per 1,000 for different conditions in the Established Market Economy region, as estimated in the 1996 WHO Global Burden of Disease study (Murray et al., 2002). The analysis indicates the sensitivity of the burden estimates to the epidemiologic parameters chosen. For example, the original WHO estimates that in the Established Market Economy countries the burden of epilepsy (0.51/1,000) was between that of multiple sclerosis (0.26/1,000) and Parkinson's disease (0.62/1,000)(not shown in Fig. 1) whereas the parameters for France used in our exercise equate epilepsy's burden with that of diabetes. The variability between the five countries is mainly due to differences in the estimated incidence (higher in France), treatment gap (higher in the United States), and population death rates (higher in the United States, United Kingdom, and France)(Table 1).

Figure 1.

Effects of uncertainty on DALY estimates.

Key methodological differences account for our higher estimates of burden, compared to the 1996 WHO study. Disability weights are particularly influential, because much of the epilepsy burden is accounted for by YLDs. Disability weights from the literature were elicited directly from persons with epilepsy, using validated preference-based methods. The range for persons with uncontrolled seizures (0.29–0.44) (Messori et al., 1998; Wiebes et al., 2002; Langfitt et al., 2006), and with relatively well-controlled seizures (0.12–0.19) (Stavem et al., 2001), considerably exceeds original WHO disability weights (0.15 for untreated cases, 0.065 for treated cases,) that were derived from a person-trade-off protocol administered to panels of disease experts (Murray, 1996).

Epidemiologic estimates used in the exercise included idiopathic, cryptogenic, and symptomatic epilepsies. The WHO excluded symptomatic cases, resulting in considerably lower estimates of incidence, prevalence, mortality, and percent uncontrolled. The WHO assigns one primary condition per case in order to avoid overestimating global burden of disease by double-counting cases with multiple conditions. Under this approach, the burden of symptomatic epilepsies should theoretically be incorporated into the burden estimates for the primary conditions (e.g., stroke, traumatic brain injury, etc.). While the WHO explicitly accounts for epilepsy as a consequence of meningitis, it does not do so for those conditions that account for a large proportion of symptomatic epilepsies. The WHO's exclusion of symptomatic cases likely leads to an underestimate of total epilepsy burden.

Due to a different scale of burden, results for South Africa are shown separately in Fig. 2, along with WHO-estimated DALYs/1,000 for different conditions in the sub-Saharan African region. Uncertainty about the incidence, prevalence, and the appropriate disability weights produces even wider variation in burden estimates for South Africa. Epilepsy burden estimates using the lower and higher end incidence and prevalence estimates for sub-Saharan Africa (Preux and Druet-Cabanac, 2005) are compared to other conditions. Depending on the estimates used, the burden of epilepsy may be comparable to that of Alzheimer's disease (0.66/1,000) or greater than that of war.

Figure 2.

Effects of uncertainty on DALY estimates.


Overview of measure

To assess responsiveness involves determining individuals' perceptions of how respectfully they were treated and the extent that the services were patient oriented (Valentine et al., 2000, 2003). The measure of responsiveness is estimated both in terms of the overall level and the distribution across populations. A fair health care system will receive the same rating of responsiveness on every element for every group in the population (Murray and Evans, 2003). Respect for persons is evaluated in terms of four domains:

  • 1Respecting the individual as a person and not just a patient, and guaranteeing the person's rights to security and freedom from discrimination.
  • 2Respect for the person's right to be involved in decisionmaking, with an entitlement to relevant information, and a right to voluntarily accept or refuse service, request a second opinion, or explore alternatives.
  • 3Respect for privacy and confidentiality by ensuring personal health information is kept private and confidential.
  • 4Respect of a person's entitlement to obtain prompt service with access to closely located facilities, and to be taken care of in a timely manner.

Patient orientation consists of the following four domains:

  • 1Adequate quality of basic amenities of healthcare facilities.
  • 2Health care workers' ability to positively and effectively interact with patients and their families during the delivery of care, taking into account language or cultural preferences.
  • 3Access to social support networks, whether such support is received from family or community.
  • 4Choice of care provider. Healthcare systems should respect a person's right to choose.

The domains are weighted according to importance as follows: Respect for persons (50% which includes: Respect for dignity—1/3; Confidentiality—1/3; and Autonomy—1/3), and Client orientation (50% which includes Prompt attention—20%; Quality of amenities—15%; Access to support networks—10%; Choice of provider—5%).

In a recent WHO survey, no single preferred instrument for measuring responsiveness was identified (DeSilva, 2000). However, the Consumer Assessment of Health Plans (CAHPS) questionnaire, a measure covering several domains, was identified as an appropriate tool for measuring responsiveness. The question format reduces subjectivity by asking about the frequency of events in the form of a Likert scale (always, usually, sometimes, never). Nine questions from CAHPS were included in the WHO responsiveness module of the key informant's survey, which estimated responsiveness levels in 191 countries (Darby et al., 2000).

Evaluation methods and results

To examine the applicability of the WHO responsiveness measure, we explored the psychometric properties (reliability and validity) of the CAHPS, as a measure of responsiveness in people with epilepsy. We drew evidence from the published data produced by the WHO for assessing responsiveness in other health conditions. There was no data in existence for epilepsy and as a consequence we designed our study to obtain such data. An integral part of the study involved developing a questionnaire that incorporated responsiveness items from the WHO CAHPS. The first section contained questions about the clinical and demographic details of the participants; the second section contained the nine questions from the CAHPS scale pertaining to responsiveness; and the third section included the Liverpool Impact of Epilepsy Scale, Liverpool Stigma of Epilepsy Scale, and Liverpool Adverse Drug Effects Profile. One thousand questionnaires were randomly distributed in the U.K. to members of Epilepsy Action, a large epilepsy support group, of which there were 279 responses. The reliability of the scale was assessed by assessing the internal structure using Chronbach's alpha. Equal weighting was applied to each of the eight elements on the Likert scale. The criterion validity of the scale was assessed by comparing the results of the responsiveness scales with other measures. The a priori hypothesis was that there would be a strong correlation between results of the responsiveness scale and patient perceived impact of epilepsy. Correlation analysis was conducted to determine the strength of the relationships.

There were 279 respondents who completed the questionnaire of which 48% were male. The mean age was 47 years. In terms of marital status, 36% were single, 53% were married, 7% were separated, and 3% were widowed. Of the sample 45% were in paid employment, 7% were unemployed, 23% retired, 13% permanent sick, and 8% were students, the remaining 3% were unclassified. Respondents were asked to provide information about their location: 10% reported living in the inner city, 57% reported living in an urban district, and 33% reported living in a rural area. In terms of their clinical status, 54% reported being seizure free in the last 12 months, 32% reported having less than 1 seizure per month, and 14% more than 1 seizure per month.

The mean score on the responsiveness scale was 14 (SD = 5.68). Overall, people with epilepsy report a high level of satisfaction with healthcare services in the U.K. (see Table 2). Respondents reported particularly high levels of satisfaction with the domains of dignity and choice with more than 50% of participants stating that the service was always responsive to their expectations. Ten per cent of respondents expressed dissatisfaction with their interaction with healthcare providers and a further 10% indicated that they had not sought healthcare services in the last 12 months. Lower satisfaction scores were observed in the domains of communication and prompt attention. The lowest score was provided for the domain of autonomy.

Table 2. Experiences with the U.K. healthcare services results of responsiveness scale % response
DomainQuestionAlwaysUsuallySometimesNeverNot applicable
Prompt attentionIn the last 12 months, when you wanted care, how often did you get care as soon as you wanted? n = 22738.025.810.86.811.1
DignityIn the last 12 months, when you sought care, how often did doctors, nurses, or other healthcare providers treat you with respect? n = 23455.919.45  3.610.8
In the last 12 months, when you sought care, how often did the office staff, such as receptionists or clerks there, treat you with respect? n = 23645.226.9 7.9 4.710.4
CommunicationIn the last 12 months, how often did doctors, nurses, or other healthcare providers listen carefully to you? n = 23143.422.911.8 4.710.0
In the last 12 months, how often did doctors, nurses, or other healthcare providers there, explain things in a way you could understand? n = 23040.126.211.15 10.0
In the last 6 months, how often did doctors, nurses, or other healthcare providers give you time to ask questions about your health problem or treatment? n = 22837.324  12.5 7.910.0
AutonomyIn the last 12 months, how often did doctors, nurses, or other healthcare providers there involve you as much as you wanted to be in deciding about the care, treatment, or tests? n = 23237.621.912.910.8 9.7
No problemMild problemModerate problemSevere problemExtreme problem
ChoiceIn the last 12 months, with the doctors, nurses and other health care providers available to you, how big a problem, if any, was it to get a health care provider you were happy with? n = 23356.610. (N/A 9.0%)

The results of the assessment of reliability revealed a Chronbach's Alpha score of 0.90 for the whole population indicating that the CAHPS scale has high internal consistency in this population. Correlation analysis was conducted between the responsiveness score and the variables: age, sex, seizure type, and duration of epilepsy, location, impact of epilepsy, seizure frequency, stigma and total adverse effects score. No significant correlation was found between responsiveness and age or sex in this sample. Significant correlations with responsiveness are presented (see Table 3). Significant correlations (p > 0.01) were found between responsiveness and total impact of epilepsy score, total stigma score, duration of the epilepsy, and adverse effects profile. Significant correlations (p > 0.05) were found between responsiveness and areas of residents and seizure frequency.

Table 3. Significant correlations with responsiveness results of responsiveness scale
 Responsiveness scoreTotal impactTotal stigmaType of areaSeizure frequencyDurationSide effects
  1. acorrelation is significant at the 0.05 level.

  2. bcorrelation is significant at the 0.01 level.

 Pearson's correlation1−0.305b0.268b−0.137a0.115a0.163b0.283b
 Sig. (1-tailed) 0.0000.0000.0180.040.0080.000
Total impact
 Pearson's correlation−0.305b1−0.585b0.058−0.556b−0.122a−0.533b
 Sig. (1-tailed)0.000 0.0000.1740.0000.0250.000
Total stigma score
 Pearson's correlation0.268b−0.585b1−0.0210.426b232b0.493b
 Sig. (1-tailed)0.000 0.3650.0000.0000.000
Type of area (city/urban/rural)
 Pearson's correlation−0.137a0.058−0.02110.005−0.0350.04
 Sig. (1-tailed)0.180.1740.365 0.4680.2880.28
Seizure frequency
 Pearson's correlation0.115a−0.556b0.426b0.00510.190b0.453b
 Sig. (1-tailed)0.400.0000.0000.468 0.0010.000
Duration of epilepsy
 Pearson's correlation0.163b−0.122a0.232b−0.0350.190b10.178b
 Sig. (1-tailed)0.0080.0250.0000.2880.001 0.005
Total side effects
 Pearson's correlation0.283a−533b0.493b0.0400.453b0.178b1
 Sig. (1-tailed)0.0000.0000.0000.2800.0000.005 


Overview of the measure

The WHO financial fairness measure is concerned with the magnitude of the financial burden of acquiring healthcare and its distribution. To operationalize the measure, the WHO prioritizes two broad goals:

  • 1Minimize catastrophic payments. As healthcare can be catastrophically costly, it is vital for people to be protected from having to choose between financial ruin and loss of health. Therefore, achieving greater fairness in financing is through risk pooling and prepayment.
  • 2Minimize burdensome payments by equivalent households. Prepayment should not be based on health risk, as in a pure insurance system, but should reflect the distribution of income. The rich should contribute relatively more for healthcare services than the poor. (Murry and Evans, 2003)

Reflecting these concerns, the WHO adopted the normative claim that: a health system is fairly financed if the ratio of the financial contribution of each household through all payment mechanisms to that household's capacity to pay (CTP) is identical for all households, independent of the household's health status or use of the health system.

To derive the index of Financial Fairness Contribution (FFC) from the defining statements, one must first consider the household contribution then compare across households. The Health Financing Contribution (HFC) for a household is the ratio of total financial contribution for healthcare services (through taxes, social security contributions, private insurance, and direct out-of-pocket payments), which may be called Household Healthcare Expenditure (HHE), compared to the CTP for this household, defined as effective income (including assets, future earnings potential, as well as current income) minus subsistence expenditure (minimally including expenditure on food, basic shelter, and minimal clothing). In an ideal world the ratio should be equal for all households.

The index of FFC is the sum of the inequality in the distribution of HFC across households. The index is defined by a number that runs from 0 (extreme inequality) to 1 (perfect equality). The FFC index should be equal to 1 if all households with the same income pay the same amount for healthcare services. It will be less than 1 either if households with the same income pay different shares of their income (a problem of horizontal equity) or if households with different income spend a different proportion of their income (a problem of vertical equity).

Evaluation methods and results

If data for estimating the FFC for households with epilepsy compared to households without the condition were available, we could analyze financial fairness among households with epilepsy and compare this to the fairness situation in the general population of the country of interest. But as such data are not generally available, our study of this indicator examined only the first part of the FFC concept that is concerned with minimizing the out-of-pocket proportion of payment for healthcare services. In this way, the fairness of the healthcare services could be defined according to the breakdown of health care expenditure for patients with epilepsy between:

  • 1a prepaid share, including all kinds of prepayment systems (compulsory or voluntary insurance),
  • 2and the remaining share corresponding to out-of-pocket payments made by households.

Fairness would be higher the higher the amount of prepaid share and the lower the out-of-pockets payment.

The Commission tested whether this modified version of the WHO index of financial fairness could be derived for people with epilepsy in several different countries. Implementing such an index for any country of interest involves combining two series of data:

  • 1the mean amount of expenditure per patient per year on epilepsy-related services (impatient care, outpatient care, exams, AEDs, …)
  • 2the mean share of financing (prepaid versus out-of-pocket) for each epilepsy-related service.

We developed a spreadsheet to derive both the amount of expenditure and relative share of prepaid and out-of-pocket payments made by people with epilepsy. We then conducted a literature review to determine if country-level data existed to estimate the required data elements of the spreadsheet. Based on our examination of the literature, comparable data of sufficient detail were available for only three of the six developed countries represented on the Commission (France, Italy, and the United States), and one developing country (Burundi), not represented on the Commission.

For these countries, estimates of the mean annual amount of expenditure per patient on each epilepsy-related service was available from studies of newly diagnosed patients in year 1 and year 2 in France, Italy, and the United States, and from a study of prevalent cases in Burundi. The mean share of financing by source of funds (prepaid vs. out-of-pocket) for total healthcare expenditure (all patients, all diseases) were available from National Health Accounts for each country, either for all services (Beghi et al., 2004; Nsengiyumva et al., 2004) or for each service category (De Zelicourt et al., 2000; Begley et al., 2001).

As may be seen in Table 4, the absolute amount of annual expenditure is higher in the first year of diagnosis than in year 2, but in different proportion according to the country considered (from a 20% decrease in Italy to a four-fold decrease in France and the United States). Among developed countries, differences in annual expenditure are greater for the first year of diagnosis (a four-fold difference between Italy and the United States) than for year 2 (40% variation between France and the United States). There was a dramatic difference between developed countries and Burundi in annual costs and the distribution by payment source.

Table 4. Financial fairness of healthcare services
CountryAnnual epilepsy-related healthcarePrepaid shareOut-of-pocket share
  1. Sources: Italy (Beghi et al. 2004), Burundi (Nsengiyuumva et al. 2004), France (De Zelicourt et al. 2000), United States (Begley et al. 2001).

  2. a Burundi cost data are for prevalent cases and, therefore, are presented for year 2 only.

Year 1
 United States$4,20991.3%$3,8458.7%$365
Year 2
 United States$94682.7%$78217.3%$164

Concerning the financial burden of epilepsy, in France and the United States the percentage prepaid share is higher in year 1 than in subsequent years, indicating that more intensive use of more specialized healthcare services (inpatient care, CT scan or MRI, lab exams) in the year of onset is associated with a higher prepaid share. French healthcare services have the highest prepaid share, so that the out-of-pocket payment is less than $182 (out-of-pocket share 6%) in year 1 and $73 in subsequent years (out-of-pocket share 10%). In the United States, the prepaid share is high in year 1 but significantly decreases for subsequent years; leaving an out-of-pocket share of 17.3% and a corresponding expenditure of nearly $165. In Italy where it was not possible to apply differentiated prepaid shares according to service, the prepaid share is somewhat lower than in the other developed countries (around 80% compared to more than 90% in France and the United States in year 1) but because of the lower total cost the out-of-pocket payment amount is not very different.


Given that an estimated 80% of all people in the world with epilepsy live in developing countries, where healthcare resources are constrained and healthcare data are limited, we also examined from the perspective of a single developing country, the feasibility of deriving all three of the WHO indicators. South Africa was selected to illustrate the developing country perspective simply due to the fact that it was represented by a Commission member who had knowledge of the availability of relevant information. Many other developing countries, even in sub-Saharan Africa, might have been a more representative selection.

South Africa, as a developing country, exhibits marked inequities in the distribution of healthcare resources and these inequities would likely be reflected in low responsiveness and financial fairness indicators. The dual healthcare system serves 45 million people. The private healthcare system, which serves15% of the population, consumes 57% of the financial resources and has at its disposal approximately 65% of the healthcare personnel, 75% of neurologists, 73% of EEG machines, 91% of CT scanners, and 94% of MRI scanners (Benatar, 1997). Government provided healthcare services make up the balance. Neither the treatment gap nor resource availability measures have ever been measured in South Africa (Shorvon and Farmer, 1988) although it appears that the data are available to make such estimates.

Can reasonably accurate epilepsy-related DALYs be derived for South Africa? While there are relatively sound epilepsy mortality data in South Africa to estimate YLL due to epilepsy, the most important determinant of years lost to disability, namely age and sex-specific incidence, has never been measured. Since the societal burden of epilepsy is primarily related to morbidity rather than mortality, quantifying morbidity, and therefore incidence, is of considerable importance. Prevalence of epilepsy, a potential substitute measure, has also not been estimated in South Africa. Duration and distribution by severity have not been determined, but since these variables have far less of a determining effect on DALYs, they may deserve a lower research priority.

There are strong indications that the incidence and prevalence of epilepsy is greater in developing compared to developed nations (Preux and Druet-Cabanac, 2005). Estimates of the incidence of epilepsy in sub-Saharan African countries have varied between 64 and 156 per 100,000, compared with estimates in the range of 40–70 per 100,000 for developed countries (South African Health Review, 2005). Studies of prevalence have similarly been higher in sub-Saharan African countries (median estimates of 1.5%, ranging from 0.5% to 7.4%) compared to developed countries (estimates of approximately 0.5–0.9%). In South Africa the effects of the HIV epidemic, estimated at between 5 and 6.5 million people in the total population of about 45 million, on the burden of epilepsy remains unknown (available from; HIV infection in the brain and common secondary infections, such as tuberculosis, may increase the burden of epilepsy in the population. Homicide, motor vehicle accidents, and infections such as neurocysticercosis too may contribute to an increased burden of epilepsy in the South African population, as it may in other developing nations (Thomson, 1993; Fingerhut et al., 1998; Leary et al., 1999; Preti and Miotto, 2000; Mafojane et al., 2003; Phiri et al., 2003; Sander, 2003 available from

There are no cost studies of epilepsy in South Africa and no studies of the population's experience with healthcare services. Therefore, estimating financial fairness and obtaining representative responsiveness information for a country such as South Africa will require considerable new efforts in primary data collection.

In summary, limited health care resources are typically paralleled by a paucity of research and data availability for making even approximate estimates of the WHO measures of healthcare performance. If the WHO measures are to be estimated for South Africa, high quality incidence studies merit priority, since incidence is the principle determinant of DALYs. Financial information and healthcare service experience data will also have to be gathered.


Our overall findings are that the treatment gap and resource availability measures have limited value for planners and policymakers and the epilepsy field should consider adopting the broader WHO measures in country assessments of epilepsy burden and healthcare performance whenever data permit. The WHO measures are clearly superior at the conceptual level and have several potential advantages. The DALY directly measures the ultimate, distal outcome of interest (health) on a single scale that is applicable to all conditions. DALY estimates also create the opportunity to discuss the flip side of burden, that is, avoidable burden, which in the case of epilepsy is a large percentage of attributable burden that can be achieved at relatively low cost (Chisholm, 2005).

Responsiveness is a desirable measure because it reflects a consumer perspective on healthcare treatment and outcomes. Our survey indicated that the responsiveness scale is a reliable and valid instrument in people with epilepsy in the U.K. No problems were found with its use and the findings of this study are in line with previous responsiveness studies in other health conditions (World Health Organization, 2000).

The modified WHO financial fairness index gives us the mean prepaid share for all patients with epilepsy but no information concerning differences among patients with epilepsy or persons with epilepsy compared to the general population. It was possible to estimate for three of the seven countries represented on our Commission.

Our findings on the WHO measures must be qualified by a number of limitations, however. There is ongoing debate about how certain assumptions (e.g., differential age-weighting or keeping disability weights constant across regions) may skew DALY estimates and result in inequitable resource distribution (Allotey et al., 2003; Arnesen and Kapiriri, 2004). Current DALY methods do not reflect the burden attributable to comorbid epilepsy in stroke, traumatic brain injury, and other conditions that account for the majority of symptomatic epilepsies. In addition, the original WHO disability weights underestimate burden relative to weights obtained in actual persons with epilepsy using standard survey methods.

The reliability and validity study of the responsiveness scale undertaken by the Commission had several limitations including: the sample was drawn from members of a U.K. support group; the response rate was relatively low, both these factors may have implications for the generalizability of the results. The psychometric properties of the scale need to be tested further, across different cultures before results may be meaningfully compared on an international basis.

The primary limitation of all the WHO measures is that they require data that are not available in developing countries and most developed countries. At the present time, their main value is that they define data elements that need to be obtained if more complete and meaningful assessments of epilepsy-related healthcare services are to be made. Until then, planners and policymakers will have to rely on existing measures, perhaps supplemented by WHO measures that are based on hypothetical estimates.


Acknowledgments:  The authors would like to thank Epilepsy Action for supporting the responsiveness study, Margaret Rawnsley (Research Officer, Epilepsy Action) for her assistance in administering the questionnaire and Dr. Jayne Brookes for her help with statistical analysis. We would also like to thank the ILAE Executive Committee for their support of the Commission's work and Dr. Leonid L. Prilipko for his assistance with the WHO framework.