IJE Advance Access published online on April 13, 2008
International Journal of Epidemiology, doi:10.1093/ije/dyn063
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Out-of-pocket health expenditure in a population covered by the Family Health Program in Brazil
1Centro de Pesquisas Epidemiológicas, Universidade Federal de Pelotas. Pelotas, RS, Brasil.
2Programa de Pós-graduação em Saúde Coletiva, Universidade do Vale do Rio dos Sinos, São Leopoldo, RS, Brasil.
Corresponding author. Centro de Pesquisas Epidemiológicas, Universidade Federal de Pelotas, R. Mal. Deodoro, 1160 - 3° Piso, 96020-220 Pelotas, RS, Brazil. E-mail: abarros.epi{at}gmail.com
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Abstract |
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Background The Brazilian public health system, free and universal, should limit out-of-pocket health expenses. However, Brazil was reported as one of the countries with the highest proportion of families experiencing catastrophic expenditure. This study was designed to assess occurrence of high health spending in a low-income population, as well as the pattern of out-of-pocket health payments.
Methods A cross-sectional study was done in Porto Alegre, Brazil, in 2003, with a sample representative of families covered by the Family Health Program. Health expenses were recorded with reference to 30 days prior to the interview and income data were collected with reference to the previous calendar month. Health expenditure was explored in terms of total household health expenditure >5, 10 and 20% of household income and >40% households capacity to pay.
Results The final study sample included 869 households. Medicines were responsible for 47% of household expenditure with health; second came private health plans which accounted for 22%. The richest spent, on average, 70 times more them the poorest with health plans, 26 times more with dental treatment and six times more with medicines. About 16% households committed 20% or more of their income with health, independent of economic position. Similarly, 12% of the households had health expenditure in excess of 40% of their capacity to pay.
Conclusion The proportion of income spent on health was similar across economic groups, but this ‘equality’ is achieved at an unacceptably high level. Specific strategies to reduce such vulnerability are needed.
Keywords out-of-pocket health expenditure, catastrophic expenditure, health inequities, public health services
Accepted 5 March 2008
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Introduction |
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In 1988, within the new Constitution, the Brazilian Unified Health System (SUS) was created to offer free health care to the population, based on three pillars: universal coverage, integral health care and equity. Since then, much has been done to achieve this goal, including two important policies: decentralization and primary health care. With the first, management of health services has been transferred to the local governments. The second implements the provision of wide coverage of primary care to the population, including several programmes such as antenatal care, well-baby clinics and immunization, among others. Health care is delivered through a mix of public, private and non-profit facilities. Financing comes from the three levels of government—federal, state and municipal. Most of the more than 40 000 primary health centres are public, usually run by local governments. About a third of the 6400 hospitals are public, the others run by private or non-profit organizations. The latter are contracted by the health care authorities to offer free care to the population. User fees are not charged from patients receiving care through the SUS, and are strictly forbidden under any circumstances.1 Free medication is distributed by pharmacies or primary care centres, but it is limited to selection of drugs considered essential. Dental care is offered in a much smaller scale than medical care, and presents much greater variation between municipalities. Generally, access to health services through the SUS is high, with <5% lack of access.2 A detailed account of how the SUS is financed is given elsewhere.3
In 1994 the Family Health Program (known in Brazil by the acronym PSF) was created by the Ministry of Health. This new programme was designed to solve some issues with the traditional approach to primary health care through a few main strategies: (i) a team formed by a full-time family doctor, a nurse, an auxiliary nurse and four community health workers (CHWs); (ii) a well defined catchment area; (iii) prioritize health promotion and prevention, along with curative care. CHWs are expected to visit homes at least once a month, especially those with children or elder citizens. They should reinforce the link between the health service and the population, encouraging attendance to antenatal care, immunization, and helping to identify people with special needs. Besides offering free services, PSF units should offer a selection of free medicines covering most of the needs, in a way to minimize health out-of-pocket expenditure (more details available at http://portal.saude.gov.br/saude/area.cfm?id_area=149).
Despite the fact that SUS provides universal coverage, the proportion of the Brazilian population buying private health plans is around 25%, as estimated by two national surveys done in 19984 and 20035 and by the World Health Survey also carried out in 2003.6 Most health plans in Brazil are prepaid, delivering health services through a set of predefined facilities. Reimbursement plans are rare, usually only available in top-class contracts.
A study about health expenditure, using data from the 1995–96 survey on family budget (POF, done by the Brazilian Institute of Geography and Statistics—IBGE) and from the 1998 national household survey (PNAD, done by IBGE), showed that health is responsible, on average, for 9% of the Brazilian families’ expenditure. It also showed that the poorest families spent proportionally more on medicines than the better-off, while the opposite was observed for private health plans. Considering only those who reported some expenditure, the proportion of the family income destined to health was higher in the lowest income deciles of the population.7
Catastrophic health expenditure has been defined as payments that consume a high proportion of the household income. Different operational definitions have been used in the literature: health expenditure exceeding 10 or 20% of the family income8,9 and 40% of the household capacity to pay.10 Poor families are likely to be more vulnerable to catastrophic expenditure. But also, healthier families can be pushed into poverty by having a considerable part of its budget consumed by health expenses.
The Brazilian public health system, free and universal, should protect people, especially poor people, from catastrophic expenditure, if not from health expenditure at all. However, a 59-country study put Brazil and Vietnam as the two countries with the highest proportion of families (around 10%) experiencing catastrophic expenditure.10 This result is surprising, even if we consider that dental service coverage is limited in Brazil,11 and that a significant proportion of the medicines used by the poor population is not obtained for free in the public health service network.12
We set out to study out-of-pocket health expenditure using data from a low-income population covered by the PSF, instead of national data. It was also our objective to estimate the proportion of household budget allocated to health by socioeconomic position, and to estimate the proportion of households incurring in health expenditure at levels considered excessive in the literature.
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Methods |
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A cross-sectional study was carried out in Porto Alegre, capital of Rio Grande do Sul State, Southern Brazil, between July and September 2003. Its main objective was to study patterns of access and utilization of health services, along with health expenditure in a population covered by the Family Health Program (PSF). The city of Porto Alegre had
1.3 million people (IBGE, 2000), being one of the richest cities in Brazil. Like all other large cities in the country it comprises large poor areas, with a deeply deprived population. When the study started, there were 56 PSF units in the city operating for more than 6 months—the criterion used for inclusion in the study. The population covered by these units was estimated to be 143 000, slightly over 10% of the city population.
A sample size of 900 households was chosen to estimate focus and coverage13 of the PSF in the city with an error of four percentage points, allowing for design effect, control of confounding and missing information.
Two-stage cluster sampling was used, with the PSF coverage areas as primary sampling units. From the 56 eligible units, 45 were selected through systematic sampling with probability proportional to size. In each of these areas 20 households were selected. The geographic area covered by each PSF unit is divided into four sub-areas of the same size, each under responsibility of one CHW. In each of such areas a starting point (household) was selected randomly from the agents’ registry books. The next household to the left was selected, and then every sixth household was selected to complete five. This process was done by one of the study supervisors, aided by the CHW responsible for the sub-area.
All residents in the sampled households were recruited for the study. Individuals aged 13 or more were interviewed directly, while information from those younger than 13 were obtained through their parents. Information collected included: household assets, infrastructure, income, health services seeking profile, access and utilization, out-of-pocket health expenditure, private health plan coverage and satisfaction with the health services used. A standardized, pre-coded questionnaire was used for the interviews, which were carried out in the households by people hired and trained for the task.
Data quality control involved repeating 10% of the interviews using a shortened version of the questionnaire and telephone contact with most families who had a phone line (about 50% of the sample). Data were entered twice and cross-checked until all discrepancies were resolved using Epi-Info 6.04.14 Data consistency and all analyses were done with Stata 8.0.15
The economic classification of the sample was done using the IEN, acronym for National Economic Indicator (in Portuguese), an asset index proposed by Barros and Victora16 that has reference distributions described for all states and state capitals in Brazil. The index was created by principal components analysis, using 13 asset indicators available in the Brazilian Census. The IEN distribution for Porto Alegre was used to classify the sample into reference quintiles, so that those under the first quintile cut-off point were allocated to the first reference quintile, and so on. As our study population is poorer than the population of the city, the lowest reference quintiles account for more than 20% of the sample.
The main outcomes in this article are out-of-pocket health household expenditure, global and by items (medicines, private health plans, medical consultations, diagnostic exams, dental treatment and other). Expenses were obtained with reference to the 30 days prior to the interview. Income data were collected with reference to the previous calendar month. In order to avoid spurious correlations with proportions of health expenditure in relation to income or capacity to pay, the IEN was used to classify the sample in terms of socioeconomic position instead of income. Analyses involved the calculation of means and proportions, with all P-values obtained through linear and logistic models taking into account the sample design effect (Stata svy commands).
Situations of excessively high health expenditure were identified using a several criteria: total household health expenditure as a proportion of household income and capacity to pay. Household income was estimated by adding the reported monetary inputs by all residents. Household capacity to pay was estimated as household income less its adjusted food expenditure. When the result was <R$ 1.00, it was set to R$ 1.00 (US$ 1 was rated at R$ 3.00 by the time of fieldwork).
The median household food expenditure in Brazil was R$ 230.00 for an average household size of 3.68. This value was obtained from the 2003 Family Budget Survey carried out by IBGE. Adjusted food expenditure for each household in the sample was calculated as 
, where hhdsize is the household size. An adjusted food expenditure estimate was used instead of a crude estimate because food consumption is not directly proportional to household size due to economies of scale. This approach was adapted from Xu et al.10
Instead of choosing a single level for excessive expenditure, as some authors have done, we calculated the proportion of households spending more than 5, 10 and 20% of their income and more than 40% of their capacity to pay, an approach similar to Wagstaff and van Doorslaer.17
This research project was approved by the Ethics and Research Committee of the Federal University of Pelotas Medical School and by the Ethics Committee of the Porto Alegre Municipal Health Secretariat. Informed written consent was obtained from all adults and from one parent in case of individuals under 18 years of age.
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Results |
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The final study sample included 869 households and 2988 individuals. Among the eligible, 138 were lost or refused to participate (4.4%), 60% of them male. Approximately 40% of the sample was composed of children and adolescents, 30% of adults aged <40 and nearly 30% of adults 40 or more and elderly. There was a slightly higher proportion of women (53%). The sample included 61% of white, 18% of blacks and 21% of mixed colour—see Table 1.
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Details of the sample economic profile are presented in Table 2. The population covered by the PSF in Porto Alegre was concentrated in the poorest economic reference quintiles—38% of the sample was below the cut-off point for the lowest IEN reference quintile, while only 5% fell in the highest reference quintile. A strong association between per capita and total household income was observed with IEN reference quintiles. Average income in the highest reference quintile was more than four times bigger than in the first reference quintile. The average number of residents per household was 3.6.
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The average out-of-pocket household expenditure with health was R$ 89.43. Medicines were responsible for the biggest share (47%) with a mean value of R$ 42.08. Second came private health plans which accounted for 22% of the total average expenditure, with a mean value of R$ 19.76. Next figured expenses with dental treatment, averaging R$ 10.53. Expenditure by economic position and spending item is presented in Table 3, along with inequality measures. Socioeconomic groups were different in respect of all spending items.
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The better-off in our sample spent 70 times more on private health plans and 26 times more in dental treatment than the poorest group. On the other hand, spending on medicines among the top quintile was <6 times than the bottom quintile. This is probably related to patterns of use of the public health system, more often used by the poor, and its failure to provide all the necessary drugs.
The share of each spending item for different economic reference quintiles is shown in Figure 1. There was a clear decreasing participation of medicines, from 82% in the bottom quintile to 47% in the top quintile. Conversely, expenditure with private health plans rose from 8% among the poorest to 36% among the better-off.
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Table 4 shows the proportion of family income used up by health expenditure, by spending item and by socioeconomic position. On average, 10.5% of family income was spent with health, ranging from 6.3% in the bottom quintile to 16.2% in the top quintile (P < 0.001). Medicines ranked first, consuming 5.8% of the family income, with a difference between socioeconomic groups (P < 0.001). The lowest reference quintile had 4.5% of family income consumed by medicines while the highest had 7.3%, but there was not a clear trend. With respect to private health plans, on the other hand, the trend is very clear. Better-off families spent, on average, 5.3% of their income with health plans, more than 10 times compared with the poorest group (0.5%).
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Table 4 also shows the proportion of households that had non-zero expenditure with health, in total and for selected items. Overall, nearly 70% of households had some expenditure in the previous 30 days. Just over 60% households had some expenditure with medicines, 21.8% with private health plans and 7.2% with a dental treatment. Variation across economic quintiles was important and significant for these three items. But medicines were the commonest reason for spending in the poorest families: 47.2%. In this group, only 8.1 and 2.2% of the households spent with private health plans and dental treatment, respectively. The order, among the richest, was preserved, but a larger proportion of households reported spending in all items: 92.3% with medicines, 64.1% with private health plans and 23.1% with dental treatment.
The proportions of households with health expenditure exceeding 5, 10 and 20% of their income is presented in Table 5. Overall, these proportions were 46.5, 28.8 and 16.1%, respectively. Reference quintiles four and five were merged for this analysis in order to give more stability to the estimates, given the small numbers in the top quintile. The proportion of households exceeding 5 and 10% of their income increased with economic position. However, no trend was observed for the proportion of households with health expenditure exceeding 20% of their income—despite increasing absolute figures.
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Table 5 also shows the proportion of households with health expenditure in excess of 40% of their capacity to pay. Overall, 12% of the households fell in this category, with no difference among reference quintiles.
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Discussion |
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This article is based on a survey representative of a population covered by the Family Health Program (PSF) in Porto Alegre, Rio Grande do Sul State capital, which deserves such attention given the priority the programme has been given by the Brazilian Ministry of Health as the new model for primary health care. The PSF is expected to offer high quality health care coupled with a stronger component of community-oriented policies and increased equity.
The sampling process in our study was designed with great care, so that every household in the catchment areas of each sampled PSF unit would have the same probability of selection, thus avoiding biases in the results. The low losses (4.4%) also contribute for data quality and confidence in the interpretation of results. On the other hand, some limitations, inherent to the data collection process, were unavoidable. Information based on recall can be subject to bias, especially underreporting of events that occurred long ago, or, relevant to our case, small expenses. To minimize such problem all expenditure data was collected with reference to the 30 days previous to the interview. Health care utilization was based on a longer recall period (6 months), but this usually involves events important to the individuals that are less likely to be forgotten. In the literature even longer recall periods, such as 12 months, are common.18
Consistent with PSF policy, covered households are poorer than the general population of Porto Alegre with 37.5% of studied households in the first reference quintile and only 4.5% in the highest reference quintile. The coverage of the poor, however, was still limited to around 19%.19 Our data are also consistent with the high-income concentration present in Brazil, with average per capita income in the richest reference quintile nearly four times bigger than the poorest quintile.
Out-of-pocket expenditure was markedly lower among the 20% poorest—the average out-of-pocket spending with health in the top economic quintile was 11 times higher. Mean spending with medicines was higher than other items for all economic groups except the richest, where mean expenditure with health plans was slightly higher than with medicines. Not only average expenditure with health plans increased with wealth, but also the proportion of households relying on private health care—a 3-fold increase in this proportion was observed from the poorest to the richest reference quintiles.
For all items except complementary exams and other expenditure, there was a positive association of average expenditure with socioeconomic position. Expenditure with health plans in the richest reference quintile was 70-fold those in the poorest quintile, while for medicines the ratio was nearly six—that is, expenditure more equally distributed.
The share of each spending item reflected closely what was presented by Kilsztajn et al.,20 with medicines being the main source of expenditure among the poorest, and having its share reduced as wealth increased. Silveira et al.7 analysing the same dataset (a national survey done in 1998) showed a similar pattern for a different stratification of the population. In both cases the share of medicines was lower (around 70%) than what we found for the poorest. A report using national data from a survey done in 2002 presented results very similar to ours, where the share of expenses with medicines varied from 42% in the richest decile to 86% in the poorest.
In relation to the proportion of family income spent with health, our findings were very different from Silveira et al.7 In their study, only households incurring in expenditure were taken into account. This strategy can actually overestimate the proportion of income spent with health, especially with short-term reference periods. In this case, a large number of households would not have incurred in expenses and will be ignored in the analysis. With longer reference periods most households will contribute to the averages. Using all households with and without expenditure, as representative of a longer time period we found much lower proportions of income spent with health. In fact, as much more poor households did not report any spending with health care, we found an inverse trend compared with Silveira et al.7—that is, a smaller proportion of income was taken by health expenses among the poor compared with the richer groups.
The variable used for socioeconomic position classification can also be related to these results. Silveira et al.7 used family income and we used an asset-based indicator that has the advantage of avoiding short-term income fluctuations. Also, using the same variable for classifying the families and for calculating the proportion of income spent with health may bias the results, as families where income was underreported will be classified as poorer than they are and will incur disproportionately high health expenditure. Also, spurious correlation may derive from using family income as both the classifying variable and the denominator for the ratio spending/income.
We found a positive association between percentage of family income spent on health and socioeconomic position—contrary to what was found by Ruger and Kim21 and by Habicht et al.22 The situation in Korea21 is different since the absolute spending on health was similar between income quintiles. In our case, absolute spending was strongly and positively associated with wealth. In Estonia,22 absolute out-of-pocket expenditure increased with wealth, as in our study. But the proportion of income spent on health was negatively associated with wealth status. Clearly, the lack of resources may limit spending with health, but universal coverage provided by the SUS in Brazil probably plays an important role in reducing health expenditure and making the proportion of income spent on health by the poor smaller than the better-off (who much more frequently pay for private health insurance).
Catastrophic expenditure has been defined in previous studies in a variety of ways.10 But one important aspect in differentiating high spending from catastrophe is that the time period covered is long enough. As pointed out by Thuan et al.,23 ‘catastrophic health spending for a household is not usually the result of a single disastrous event, but rather a series of events’. Our study covered spending over a period of 30 days, so it is not possible to assess catastrophe. Anyhow, the proportion of households with high spending in health was similar to that reported by Xu et al.10—around 12%. Despite the possibility that a family is able to cope with a single event of high expenditure, this situation is undesirable. The risk of impoverishment related to high health spending has been demonstrated, as well as the protective impact of universal care.24 In a country like Brazil, where large investments are being made in a universal health system, high health spending, especially among the poor, is unacceptable.
In summary, we found that the poor spend less with health than the better-off, and spend a smaller proportion of their income as well. However, the proportion of families spending 20% or more of their monthly income with health was similar across the wealth spectrum. The same was found for expenditure over 40% of the household capacity to pay. Medicines were responsible for the largest share of out-of-pocket expenditure in all wealth groups, except for the richest. It is clear, thus, that action is needed to reduce out-of-pocket expenditure in Brazil, and a wider range of free or heavily subsidized medicines is the priority if the poorest are to benefit most.
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Acknowledgements |
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This work was funded by the World Bank through the ‘Reaching the Poor’ program. We also thank the Porto Alegre municipal Health Secretariat for their support.
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