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Are people willing and able to pay for health services?
SW. Sci. Med. Vol. 29. No. I, pp. 35-42. 1989 Printed in Great Britam. All rights reserved
Copyright
ARE PEOPLE WILLING FOR HEALTH Business Department,
0277-9536,89 $3.00 + 0.00 C 1989 Pergamon Press plc
AND ABLE TO PAY SERVICES?
RICHARDA. YODER Eastern Mennonite College, Harrisonburg,
VA 22801. U.S.A.
Abstract-Following a nationwide increase in user fees for health services in Swaziland. this paper analyzes the effect of the fee increase on overall patient use of health services, on which types of services, curative vs preventive, were most affected, and on changes in utilization by higher paying and lower paying groups. Patient attendance data from a 71% sample of government and mission health facilities, suggests that the ‘people are willing and able to pay for health services’ assertion is not supported by the Swaziland case. Following the fee increase, average attendance decreased at government facilities by 32.4%, increased at mission facilities by 10.2%, leading to a combined decline of approx. 17%. Patient visits designed to protect against childhood diseases, BCG and DPT immunizations, or against dehydration in children, show average attendance declines of - 16, - 19, and -24%. respectively, while visits for musculoskeletal diseases, a less serious disease, declined 1.2%. The analysis also suggests that up to 34% of the overall decline in attendance was among patients who previously had paid the least for health care with part of this decline likely including fewer multiple visits. Key words-health
financing, cost recovery, user fees, privatization
services does not matter, having a minimal if any effect on the decision to seek health care” [l 11.In the Philippines and Malaysia it was found that price had a minimal impact on the demand for services [1%21]. In Kenya, however, it was found that cash price was a deterrent to health care use [22]. Following a careful review of these studies, Lewis observes that drawing conclusions from these studies is difficult because of differences in dependent variables, methods of analysis, study design, study location and the typically small sample size [l 11. In a more controlled study, Meyer [15] found in Mali that, among those who used modern care, there was an insensitivity to price. This inelastic demand (own price elasticity = -0.017) suggests that there would be little or no change in expenditure pattern in the event that prices for modern care were raised and all else held constant.* Thus, while the ‘willing and able to pay’ rationale has some grounds for support, at least in village and regional level studies, it does leave unanswered a number of additional questions. What happens to the aggregate demand for health services when user fees are raised on a nationwide level? When willing and able to pay data are disaggregated by different groups of users-higher paying and lower paying users, curative and preventive service users-what are the results? How much are the different groups willing and able to pay? When fees are raised how are the costs and benefits distributed across the population. What is the impact of higher user fees on cost recovery and on the quality of services? This study presents the experience of Swaziland which raised user fees for services on a nationwide level. Specifically, it examines the impact of increased fees on overall patient use of health services, on types of services (curative vs preventive) affected, and on health service utilization by higher paying and lower paying groups.
world economic conditions, the expansion of social services in developing countries over the last 15 years, and the Reagan administration’s emphasis on the private sector, there has been a surge of interest in methods of financing health services from sources other than the public purse [l-7]. Alternative methods of financing health services typically include four areas: providing insurance or other risk coverage, charging users of health facilities, decentralizing health services, and using nongovernment resources effectively. Of these alternatives, introducing or raising user fees for health services is receiving increased attention and, for some, legitimacy as a means of strengthening the health system and increasing access to health care for the poor [8-151. User fees typically are justified on the grounds that people are willing and able to pay for health services. Evidence in support of this assumption generally derives from household or aggregate expenditure surveys [ 10, 15, 161 or from attitude surveys in which people are asked how much they would be willing to pay for a particular service (17, 181. Although generating revenue through user fees is receiving increased attention, controlled studies assessing the impact of user fees on utilization are sparse, particularly at a national level. Of the work that has been done, the findings are mixed. Lewis, in an analysis of seven different health demand and utilization studies in developing countries at a subnational level, concludes that, “In general the price of Concurrent
with
tightening
*An elasticity coefficient of -0.017 indicates that for every 1% increase in the price of health care, utilization will decrease by approx. 0.017%. More generally, if demand is inelastic (< 1.0) a price increase will result in an increase in total expenditure; if demand is elastic (z 1.O), a price increase will decrease total expenditure. 35
36
RICHARD A. YODER
The Kingdom of Swaziland, one of the smallest countries in Africa, is bordered by the Republic of South Africa and Mozambique. Its estimated 1984 population of 721,000 is 85% rural. Although a per capita income of U.S. $790 (1984) makes Swaziland a middle income country by World Bank standards, the distribution of income is reported to be highly skewed reflecting the dualistic nature of the economy. Swaziland’s health care system has been characterized by a relatively high per capita expenditure on health, a relatively well developed infrastructure and a disproportionate emphasis on the provision of curative services. Total health expenditures averaged in the early 1980s approx. $45.00 per capita-among the highest in Africa. Yet, by contemporary southern African standards, the country historically has had high mortality rates and a high incidence of communicable and infectious diseases. Estimates of infant mortality, for example, range from 113/1000 [23] to 150/1000, [24] an estimated 60% of the deaths of children under age 15 in 198 1 were caused by diseases related to poor environmental sanitation [25], and life expectancy is about 54 years; the annual rate of population growth is 3.6%. The relatively poor health status indicators reflect in part the historical emphasis on hospital based curative services. Although the government is attempting to reorient the health care system towards primary care in more recent years, progress has been slow and the long standing emphasis on hospital based curative services remains in evidence. The government and church missions provide the majority of modern care with missions operating some 40% of the not-for-profit health facilities in the country. Mission health services are considered to be of higher quality given the more constant availability of drugs and equipment, more highly motivated and committed personnel, and better facilities. However. in spite of the perceived higher quality of services provided by the mission facilities, demand for their services has been less than that of government, partially because of the higher fees charged by the missions. On 1 October, 1984, the Government of Swaziland introduced a new fee structure for health services provided by the government and mission sectors. The primary objective was to equalize the fees charged by the two sectors who together provided the majority of modern health services, while keeping net revenues neutral. This objective was designed to increase access to health services and to increase the potential for a more integrated health service system between the government and mission sectors, particularly in terms of the patient referral and supervisory system. The fee equalization policy generally meant a 300-400% increase in the price of government provided curative services with mission sector fees remaining essentially the same. Fees for preventive services increased to E0.50* from what essentially was a free service for both government and mission services. Specifically, under the old fee structure, fees for government provided services were 20 cents per
*E = Emalangeni the Swazi currency; El.00 = U.S. $0.90.
general (nonprivate) outpatient visit in clinics and 30 cents in hospitals. For preventive services, there was no charge after paying an initial 50 cents for the ‘red card’, a patient-retained card of health record. In the mission sector, fees for general outpatient services were typically E1.OO, although one facility charged E2.00. Preventive services were generally free except for one mission facility where fees ranged from E2.00 (child welfare) to a once only fee of E5.00 for antenatal visits. Under the new fee struture, fees for general services for government and mission (outpatient, inpatient and support services) were equalized at El.00 for outpatient services. Fees for preventive services were increased to 50 cents per visit in addition to the one time 50 cent red card fee. METHODS
Service based data from the Ministry of Health’s information system were used as the primary source. Only those facilities which had complete data for the study period were included in the analysis: 35 government facilities and 20 mission facilities, representing 71% of the nonspecialty government and mission facilities. The 1.8: 1 ratio of government to mission facilities used in the sample is slightly larger than the 1.3 : 1 ratio found in the population. As a means of controlling for nonprice influences on utilization that could occur in any single month, average attendance over a 3-month period is used. Mean attendance during the first 3 months of the new equalized fee structure is compared with the 3-month average attendance reported for the same 3 months in the previous year under the old unequal fee structure. These comparisons are broken down by mission and government sectors as well as the combined totals. In order to corroborate findings from the above sample of facilities, a follow-up study, involving 42 facilities, was conducted 1 year after the new fee structure was introduced [26]. Although this study represents one of the few nationwide analyses of the impact of increased user fees on utilization and various measures were taken to validate the findings, there are several limitations that should be noted. These include (a) a representative, rather than a random, sample of facilities, (b) lack of a control group, and (c) absence of complete congruence between facilities used in the original study and facilities used in the follow-up study to cross check and validate the findings. In addition, the utilization data are service based rather than household or individual based which, along with the absence of household income data, make it more difficult to attribute causality in utilization changes. In light of this, a model is constructed and tested to sift out how people’s choices change and possible reasons for these choices. RESULTS
The impact of fees on patient use of health services
Results reported in Table 1 show that for government facilities there is a 32.4% decline in attendance while the data for the mission facilities show an
Are Table
1.Monthly
sector
average attendance under unequal fee structures by sector
and equalized
Pm-fee change, attendance 10/83-12/83
Post-fee change, attendance 10184-12184
% Change
817 783 805
552 862 665
-32.4* 10.1 - 17.4t
Government Mission Totals ‘P <0.001;
people willing and able to pay for health services?
tP
increase of approx. 10%. Combined, the government and mission facilities experienced a decrease in attendance of 17.4%. A comparison of attendance figures for the latest month of the analysis, December 1983 (old fees) and December 1984 (new fees), shows a decline of 28% which is greater than the 17.4% decline when using the 3-month average of Table 1. The inelastic demand of 0.32 for government facilities indicates that for every 1% increase in the price of health care, utilization is expected to decrease by approx. 0.32%. It further indicates that even with the 32.4% decline in attendance at government facilities, total spending on government provided health services is expected to increase. Because outpatient fees at most mission sector facilities remained essentially the same at $1.00 per visit, the price elasticity of demand cannot be calculated at an aggregate level. For the same reason, calculating elasticities for government and mission facilities combined is more complex. To account for this complexity it was necessary to calculate an average fee per patient that is weighted according to differential patient use by sector. This method, which resulted in an average fee per outpatient of $0.52, is reasonable so long as there is substitution between mission and government facilities. The resulting inelastic demand of 0.30 for use of government and mission facility combined indicates that total spending will increase even though total utilization has declined. If one were to use the initial price and initial quantity demanded in estimating elasticities, instead of the average price and quantity demanded as was done above. estimated elasticity for the government sector would be 0.11 (instead of 0.30) and 0.09 (bmsnsiadof 0.30) for government and mission comThe decrease in use of government facilities and increase in use of mission facilities indicates that some mission facilities are being substituted for some government facilities. In addition, the existence of Table 2. Changes
in patient
over 5000 traditional healers in Swaziland, resulting in a healer-population ratio of approx. 1: 110 [27], along with estimates indicating that 85% of the Swazi population use traditional healers at least once per year, suggests that traditional health services may also be substituted for modem health services. Results reported in Table 1 are consistent with what was anticipated. Since the government fees increased, a decrease in the use of services was expected and since the mission fees remained essentially the same for the majority of facilities, an increase in the use of mission services was expected, particularly given the perceived higher quality of care provided by the mission facilities. What could not be predicted was what the magnitude of change would be. Although data from Mbabane Government Hospital, the main government hospital, have been excluded from the analysis because of the unreliability of the data, observations made by senior staff at the hospital indicate that attendance was down by 40-50%. Validation of these observations would exacerbate the overall decline in attendance. Declines in patient use of the sample of health facilities reported in Table 1 above were for the first 3 months of the new fee structure. Do these declines represent an accurate picture of utilization patterns over time or were they simply an initial response? To answer this question, a similar study was done 1 year after the new fee structure was introduced. These findings are shown in Table 2. Results reported in Table 2 support the findings of the original study although there is some variation. Declines in use of government facilities have increased further from 32.4% (Table 1) to 38.5%. For mission facilities, the increase in use is less pronounced showing a rise of only 1% instead of the 10.1% found in the earlier study. The combined decline in patient use of health facilities, however, is nearly the same at 17.1%. The findings reported in Table 2 are of particular significance in that the analysis was done 1 year after the new fee structure was introduced, thus allowing more than adequate time for changes in utilization patterns to level off, and the sample of facilities analyzed was different from the original study but yielded similar results. The data also show that, among government facility users, the greatest decline occurred at the government hospitals (-45.6%), corroborating observations made by senior staff at Mbabane Government Hospital 3 months into the new fee structure, with the lowest decline at the government health centers (19.1 %)-the second level of health care.
use of health facilities Monthly
Facility
IYKX
GOVI Hospitals Govt Health Centers Govt Clinics Total: Govt Average Mission Hospitals Mission Health Centers Mission Clinics Total: Mission Average Total:
ALL FACILITIES
Source: Ref. [26].
37
I year into new fee structure
average attendance
n
Jan. 1984
Sept. 1985
3 2 12 17 2 2: 25
2456 1334 560 1031 2429 548 647 752
1337 1079 341 634 2815 887 614 758
42
858
712
% Change -45.6 -19.1 -39.1 -38.5 15.9 61.9 -5.1 1.0 -17.1
RNXAIUI
38
Among mission health facilities, the greatest decline occurred at the clinics (-5.1%) with the greatest increase occurring at the one health center that was sampled (61.9%). It should be noted, however, that it was at this health center, a mission facility, where there was a 50% decline in fees following the fee change. In addition, senior health and administrative staff at this health center were primarily foreign missionaries well versed in Swazi culture and language and managed what was perceived to be a high quality service. This combination of factors likely explains a substantial part of the 61.9% increase in utilization. Changes in health system use by type
ofpatient visit
A second question to address, given the 17% overall decline in patient use of health services, is which services or types of patient visits show the greatest changes in use. Is it those with more serious illnesses that need medical attention? Is it children who are no longer being immunized against childhood diseases? Or, is it diseases that tend to be self-limiting such as minor aches and pains and skin irritations. Losses in the latter group would represent positive efficiencies in the system while losses in immunizations and serious illnesses represent negative efficiencies. To answer this set of distributive questions, changes in health service use were examined for seven different types of patient visits, and are shown in Table 3. According to the data in Table 3, musculoskeletal diseases such as arthritis and rheumatism, less serious diseases in terms of morbidity and mortality, show the lowest decline (- 1.2%) in average attendance. On the other hand, patient visits which result in protection against childhood diseases, BCG and DPT immunizations, or against dehydration in children, each show substantial declines in average attendance at - 16, - 19, and -24%, respectively. The consistent declines in utilization of government provided preventive services are not surprising; indeed, what is surprising is the decision to increase fees for preventive services.* What could not be predicted was what the magnitude of change would be. These declines lend support to assertions of a weak health education program in Swaziland in particular as well as of people generally having a propensity to care for present ailments over possible future ailments. Unlike the government sector, declines in utilization of preventive services are not consistent in the mission sector. Patient visits for BCG immunizations and diarrhea both conform to expected declines; however, the increase, though marginal, in DPT 1 immunizations (5.1%), is contrary to expected patterns. Although reasons for the variation in trends for the mission sector are not clear, there are several possible explanations. As stated in the introductory section, mission sector fees for outpatient treatment generally remained the same following introduction of the new fee equalization structure. However, under the old fee *The Ministry of Health has since taken away the fee for preventive services.
A. YODER
Are
people willing and able to pay for health
structure there was some variation in fees for preventive services at different mission clinics which led to a decline in fees for preventive services at these clinics. This could explain the 5.1% increase in DPT 1 immunizations. Musculoskeletal diseases, which one would not expect to change significantly from year to year, had the greatest increase at mission facilities (32.6%) and the greatest decrease at government facilities (-46.6%). These changes, rather than reflecting changes in the prevalence of musculoskeletal disease in the population, are probably instead an indicator of people’s preference for using mission facilities over government facilities. This would be consistent with the popular perception that mission facilities provide higher quality care than the government facilities. Thus when fees are the same between the two sectors, but perceived quality of care is different, people will migrate to the sector providing the higher quality care, i.e. the mission sector. For STDs and respiratory tract infections, which are also likely to change only slightly in the absence of either intervention or an epidemic-neither of which occurred-attendance remained approximately the same for the mission sector. The decline in attendance for STDs at government facilities without an accompanying increase at mission facilities suggests that there is now more untreated STD in the public that is lost to the government and not picked up by the mission sector. Another explanation is that perhaps the public considers STD and respiratory diseases to be minor ailments. If so, use of health services for minor ailments should decline with fee increases, as in fact happened with government facility use, and should not increase noticeably where fees remain essentially the same, as happened with mission facility use. Fees and financial access
The above analysis has shown that, following the change in fee structure, there was an overall 17% decline in use of health services. Who were these people and what was the reason for the decline? In particular, what part of the decline was due to reduced financial access? When health service fees rise, at least three types of patients can be expected to drop out, or reduce their use, of health services: low income patients for whom the fee is not longer affordable, patients who decide their ailment is not serious enough to justify the costs, and patients making multiple visits. In this section, we will attempt to systematize anecdotal information gathered informally from patients by constructing and testing a model for identifying what percentage of the decline may be attributed to a reduction in financial access. One way of examining this question is from the standpoint of what people pay for health services. The two relevant monetary costs involved in attending a health facility are fees and transport. (There are other costs, such as income foregone for wage labor because of absence from the workplace, but which are excluded from the model because of the unavailability of data.) From Table 4, we can see that prior to the change in fee structure, patients paying the
39
services?
Table 4. Typology of health care costs under unequal fee structure Sector
Attended facility nearest home
Bypassed any facility to attend facility of choice
Government
(Cell B) (Cell A) Least cost: Low fee Low fee High transport costs Low transport costs
Mission
(Cell C)
(Cell D) Highest cost: High fee High fee High transport costs Low transport costs
least cost were those who attended a government facility closest to their home (Cell A). Patients paying the most for health care were those who attended a mission facility and who bypassed another facility to get to the facility of their choice (Cell D). Although it is more difficult to ascertain what the relative costs of health care are between the other two groups (Cells B and C), we can say that for government facility users who bypassed another facility (Cell B), the fee was lower but the transport costs higher while for mission facility users that did not bypass another facility, the fees were higher but the transport costs lower. Under the new fee structure the only difference in costs under the four cells would be transportation so that the costs of health care in Cells A and C would be the same and the costs in Cells B and D would be the same. By answering the question of how the new fee structure would affect the choice of facility by the people in each of the cells, and if we accept the economic principle that, through the income effect, consumer spending on health care will decrease as consumer income falls, we may also get some indication of how the burden of a fee increase may be differentially distributed across higher paying and lower paying groups. Table 5 suggests how the people might change their choice of facility. All people attending government facilities will experience an increase in the costs of health care under the new fee structure and thus can be expected to reduce their use of services. The greatest decrease in utilization is expected to be concentrated in those using the nearest government facility (Cell A). Since the majority of the lower income groups will tend to chocse the least costly source of health care, i.e. the nearest government facility, we may hypothesize that, following the fee change, declines in this group will be dominated by the lower income groups. If cost is not a problem for people using the nearest government facility (Cell A) they may choose, under the new fee structure, to bypass a government or mission facility to use the mission facility of their choice (Cell D) to obtain what they perceive to be. better care, although this would probably be minimal. It is more Table 5. Expected utilization patterns under equalized fee structure Sector
Attended facility nearest home
Bypassed any facility to attend facility of choice
Government
(Cell A) Decrease
(Cell B) Decrease
Mission
(Cell C)
(Cell D) Neutral (or slight increase)
1llCltASC
40
RICHARD
likely though, that these people would choose to reduce their transport costs and obtain better care by using the nearest mission facility (Cell C). People who previously bypassed one government facility to use another government facility further away (Cell B) would likely begin using a mission facility (Cell C), thereby not only reducing transport costs but also receiving better care for the same price. People who used the nearest mission facility (Cell C) are not likely to change the facility they use. Rather, they are more likely to appreciate the reduced fee and stay where they are. They may possibly move to Cells B or D, although it is not likely. Mission facility users who bypassed one mission facility to use the mission facility of their choice farther away (Cell D) are not likely to change because under the old fee structure they chose to purchase what they perceived to be the best health care the government or mission facilities could provide, regardless of cost. Since the cost of the best health care has not changed significantly under the new fee structure, there is little motivation to change. Consequently, there should be no significant change in Cell D. In summary, following the fee structure change we are hypothesizing that the greatest declines will be in the group using the nearest government facility, while the greatest increases will be with people using the nearest mission facility. The data in Table 6 show what will actually happen. Table 6 shows that in each case, except Cell B, the data support what was hypothesized. Among those who paid the most for health care under the unequal fee structure (Cell D), there is essentially no change (1.6% decline) in attendance. The reasons for the increase in those who bypassed any facility to attend a government facility (Cell B), which is contrary to the hypothesis, are not clear. It is clear that such behavior is contrary to mainstream economic explanations in which price increases typically result in reduced demand, particularly where there are substitutes available. It is clear also that people are more selective than commonly thought in choosing a facility. Perhaps some undetermined cultural phenomenon or other noneconomic factor could be part of the explanation. Further research would be required to develop a fuller understanding of what these phenomenon may be. The 21.5% increase in mission facility attenders who did not bypass another facility (Cell C) is consistent with our hypothesis. When prices between government and mission facilities are equalized, and mission facilities are perceived to provide higher quality care, people will migrate to the mission facilities where they can receive better care. Table 6. Changes in utilization patterns between the unequal and equalized fee structure
A. YODER Among those who previously paid the least for health care (Cell A), there was a 34.4% decline in attendance. This finding is also consistent with our hypothesis. From an equity standpoint, it is this group which presents the greatest concern. Where did these people go? According to our model, there are four possibilities: the nearest mission facility (Cell C), a farther away mission facility (Cell D), a farther away government facility (Cell B), or drop out of the modern health care system. If the decline in use of government facilities (- 32.4%) was totally offset by the increase in use of mission facilities (10.1 %), then changes in patient use patterns as shown in the data of Table 6 simply would reflect a redistribution of use among the four cells. Given the 17% overall decline in utilization, it is reasonable to conclude that declines in government facility use were not completely offset by increases in mission facility use, and, that a percentage of this net decline consists of people who left the system. What share of the 17% who left the system did so for reasons of unaffordability? The model hypothesized that decreases in use would be denominated by people who, prior to the fee change, chose the least costly source of health care (the nearest government facility) because they have the lowest incomes and consequently are the ones who could least afford a free increase. The data show that declines were greatest among those people who previously paid the least for health care; the data also suggest that some, at most 34.4%, felt that they could no longer afford health care after the increase in fees and consequently left the system. However, because these are service based data and not household or individual based, it cannot be stated that all the 34% from the lower paying group (Cell A) did so for reasons of unaffordability; nor can it be stated with certainty that 17% fewer people were using the modern health systemonly that there were 17% fewer visits. Are there other plausible explanations for the decline in utilization? Assuming that Swazi users sometimes visit more than one facility for the same ailment, as is the case in the Philippines for example [19], then part of the decline may be attributed to fewer multiple visits. The lack of a constant supply of drugs, typical in many Third World health care systems, is an unlikely part of the explanation since there were no significant differences in price or supply during the period of the study. Nor can the decline in utilization be attributed to differences in weather, farm production or consumer income during the study period since- here also there were no significant changes [28]. Consequently, we are left with the conclusion that people did not simply transfer their use from one facility to another within the system. Rather, some people left the modern health care system and reduced financial access is the most plausible explanation for the reduction in utilization.
% Change Attended facility nearest home
Bypassed any facility to attend facility of choice
Government
(Cell A) - 34.4%
(Cell B) 63. I %
Mission
(Cell C) 21.5%
(Cell D) - 1.6%
Sector
IMPLICATIONS
Bearing in mind its limitations, there are several observations that may be drawn fromn this study. First, the assertion that ‘people are willing and able to pay for health services’ does not appear to be
Are people willing and able to pay for health services?
supported by the Swaziland data. With the exception of government facility users who bypassed another facility, the hypotheses presented in the model were supported by the data. Specifically, when disaggregating the overall 17% decline by higher paying and lower paying groups, it was found that up to onethird of those who stopped using government or mission health services were from the lower paying group, although, as has been discussed, an unknown part of this decline may be attributed to fewer multiple visits. The hope that the 17% fewer patient visits would consist primarily of minor ailments or self-limiting diseases is not supported by the data. The decreases in immunizations and treatment for diarrhea1 and sexually transmitted diseases suggest that the ‘wrong people’ have left the system. What was the affect of the price increase on total revenues? Although there was an overall decline of approx. 17% in use of health services, total revenues will have increased because of the inelastic demand. From a strictly financial point of view, it would make sense to raise fees even further since such a measure would result in additional net revenues. However, because user fees now recover about 2.0% of the total MOH recurrent budget (and make a 0.16% gross contribution to total government revenue), the present fee structure amounts to a low yield and inefficient tax [29]. To make a more substantial contribution to government revenue of, say, I%, estimates indicate that fees would have to be increased seven times above their current level [29]. The trade-off, however, would be a further reduction in financial access for the lower paying groups-a trade-off that would be difficult to defend on either equity or political criteria. The methodological question of using service based data is more complex and does present some difficulties in answering the question posed in the title of the article. It is for this reason, however, that the model represented in Tables 4-6 was developed. Is there a way of using the service based data many Ministries of Health routinely collect as part of their information system to give responses to health financing and utilization questions? The answer, of course, is an empirical one: does the data support the hypotheses in the model? So in part, the model may be seen as a satisficing alternative to the household income and related surveys which, because of their cost and complexity, are often left undone-and whose validity and reliability are often questionable. When combined with results of other studies, what these findings suggest is that the empirical evidence supporting the ‘willing and able to pay’ assertion is mixed. In some cases, people have both a willingness and an ability to pay, in others they do not. The Swaziland data do appear to suggest that a nondiscriminatory, flat increase in user fees, as was introduced in Swaziland, is neither an equitable nor an efficient solution to the financing problem. At the same time, there is evidence suggesting that user fees can have beneficial affects when accompanied by complementary measures such as a differential fee structure, return of revenues from user fees to the Ministries of Health instead of the central treasury (as is now not practiced in Swaziland), and provision
41
of insurance coverage or other forms of shared risk [7,91. Finally, these findings, along with the mixed results of other similar studies, are suggestive of additional areas of needed research. Of particular importance are disaggregated analyses that provide indications of how the burdens and benefits of user fees are distributed across the population, their impact on demand and quality and quantity of supply, and their impact on cost recovery. Utilization and income data that are household or individual based could also lead to less tentative conclusions. Acknowledgements-Research conducted during author’s 3-year term as Chief of Party/Health Planning Advisor, Ministry of Health, Swaziland under USAID Grant No. 645-0215. Appreciation is expressed to Dale Herman for his assistance in data collection and analysis and to Jonathan Meyer and Russell Smith for their helpful comments.
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Birdsall N. and Chuhan P. Willingness 10 Payfor Health and Wafer in Rural Mali: Do WTP Questions Work?
World Bank Country Policy Department, Washington, DC., 18 February, 1983. 17. Birdsall N. and Orivel F. The demand for and wilhngness to pay for health and schooling in Mali. The World Bank, mimeo, Washington, D.C., February 1983. 18. Akin J. S., Guilkey D. K., Griffin C. C: et al. The Demand for Primarv Health Services in rhe Third World.
Rowman & Allanheld, Totawa, N.J., 1985. 19. Akin J. S. The demand for child health services in the Philippines. Sot. Sci. Med. 15, 1981. 20. Akin J. S., Guilkey D. K., Griffin C. C. et al. The demand for primary health care services in the Bicol Region of the Philippines. Paper presented at the National Council for Inrernational Healrh (NCIH) ference, June 1982.
Con-
21. Heller P. S. A model of the demand for medical health services in peninsular Malaysia. Sot. Sci. Med. 16, 1982. 22. Mwabu G. M. Conditional logit analysis of household choice of medical treatments in rural villages in Kenya. Kenyatta University College, P.O. Box 43844.
Africa Studies Association Conference,
Boston, Mass., December 1983. 23. Government of Swaziland, Centers for Disease Control, U.S. Agency for International Development. Swaziland National Nutrition Survey, 1983.
Ministry of Health. National Health Policy Statemenr. July 1983. 25. Government of Swaziland. 4th Narional Developmenr Plan, 1983/84-1987/88.
24. Swaziland Government,
26. Grant D. S. Health services fee structure assessment. Consulram’s Report IOMinistry of Health, Government of Swaziland, 1985. 27. Green E. C. and Makhuba L. P. Tradirional Healers in Swaziland: Toward Improved Cooperarion Between the Tradirional and Modern Health Secrors. Swaziland
Government. Ministry of Health, June 1983. 28. Kunene P. Department of Economic Planning and Statistics, Government of Swaziland. Personal communication, 17 April, 1985. 29. Stevens C. M. Alternatives for Financing Health Services in Swaziland. Government of Swaziland, Ministry of Health, November 1984.
Copyright
ARE PEOPLE WILLING FOR HEALTH Business Department,
0277-9536,89 $3.00 + 0.00 C 1989 Pergamon Press plc
AND ABLE TO PAY SERVICES?
RICHARDA. YODER Eastern Mennonite College, Harrisonburg,
VA 22801. U.S.A.
Abstract-Following a nationwide increase in user fees for health services in Swaziland. this paper analyzes the effect of the fee increase on overall patient use of health services, on which types of services, curative vs preventive, were most affected, and on changes in utilization by higher paying and lower paying groups. Patient attendance data from a 71% sample of government and mission health facilities, suggests that the ‘people are willing and able to pay for health services’ assertion is not supported by the Swaziland case. Following the fee increase, average attendance decreased at government facilities by 32.4%, increased at mission facilities by 10.2%, leading to a combined decline of approx. 17%. Patient visits designed to protect against childhood diseases, BCG and DPT immunizations, or against dehydration in children, show average attendance declines of - 16, - 19, and -24%. respectively, while visits for musculoskeletal diseases, a less serious disease, declined 1.2%. The analysis also suggests that up to 34% of the overall decline in attendance was among patients who previously had paid the least for health care with part of this decline likely including fewer multiple visits. Key words-health
financing, cost recovery, user fees, privatization
services does not matter, having a minimal if any effect on the decision to seek health care” [l 11.In the Philippines and Malaysia it was found that price had a minimal impact on the demand for services [1%21]. In Kenya, however, it was found that cash price was a deterrent to health care use [22]. Following a careful review of these studies, Lewis observes that drawing conclusions from these studies is difficult because of differences in dependent variables, methods of analysis, study design, study location and the typically small sample size [l 11. In a more controlled study, Meyer [15] found in Mali that, among those who used modern care, there was an insensitivity to price. This inelastic demand (own price elasticity = -0.017) suggests that there would be little or no change in expenditure pattern in the event that prices for modern care were raised and all else held constant.* Thus, while the ‘willing and able to pay’ rationale has some grounds for support, at least in village and regional level studies, it does leave unanswered a number of additional questions. What happens to the aggregate demand for health services when user fees are raised on a nationwide level? When willing and able to pay data are disaggregated by different groups of users-higher paying and lower paying users, curative and preventive service users-what are the results? How much are the different groups willing and able to pay? When fees are raised how are the costs and benefits distributed across the population. What is the impact of higher user fees on cost recovery and on the quality of services? This study presents the experience of Swaziland which raised user fees for services on a nationwide level. Specifically, it examines the impact of increased fees on overall patient use of health services, on types of services (curative vs preventive) affected, and on health service utilization by higher paying and lower paying groups.
world economic conditions, the expansion of social services in developing countries over the last 15 years, and the Reagan administration’s emphasis on the private sector, there has been a surge of interest in methods of financing health services from sources other than the public purse [l-7]. Alternative methods of financing health services typically include four areas: providing insurance or other risk coverage, charging users of health facilities, decentralizing health services, and using nongovernment resources effectively. Of these alternatives, introducing or raising user fees for health services is receiving increased attention and, for some, legitimacy as a means of strengthening the health system and increasing access to health care for the poor [8-151. User fees typically are justified on the grounds that people are willing and able to pay for health services. Evidence in support of this assumption generally derives from household or aggregate expenditure surveys [ 10, 15, 161 or from attitude surveys in which people are asked how much they would be willing to pay for a particular service (17, 181. Although generating revenue through user fees is receiving increased attention, controlled studies assessing the impact of user fees on utilization are sparse, particularly at a national level. Of the work that has been done, the findings are mixed. Lewis, in an analysis of seven different health demand and utilization studies in developing countries at a subnational level, concludes that, “In general the price of Concurrent
with
tightening
*An elasticity coefficient of -0.017 indicates that for every 1% increase in the price of health care, utilization will decrease by approx. 0.017%. More generally, if demand is inelastic (< 1.0) a price increase will result in an increase in total expenditure; if demand is elastic (z 1.O), a price increase will decrease total expenditure. 35
36
RICHARD A. YODER
The Kingdom of Swaziland, one of the smallest countries in Africa, is bordered by the Republic of South Africa and Mozambique. Its estimated 1984 population of 721,000 is 85% rural. Although a per capita income of U.S. $790 (1984) makes Swaziland a middle income country by World Bank standards, the distribution of income is reported to be highly skewed reflecting the dualistic nature of the economy. Swaziland’s health care system has been characterized by a relatively high per capita expenditure on health, a relatively well developed infrastructure and a disproportionate emphasis on the provision of curative services. Total health expenditures averaged in the early 1980s approx. $45.00 per capita-among the highest in Africa. Yet, by contemporary southern African standards, the country historically has had high mortality rates and a high incidence of communicable and infectious diseases. Estimates of infant mortality, for example, range from 113/1000 [23] to 150/1000, [24] an estimated 60% of the deaths of children under age 15 in 198 1 were caused by diseases related to poor environmental sanitation [25], and life expectancy is about 54 years; the annual rate of population growth is 3.6%. The relatively poor health status indicators reflect in part the historical emphasis on hospital based curative services. Although the government is attempting to reorient the health care system towards primary care in more recent years, progress has been slow and the long standing emphasis on hospital based curative services remains in evidence. The government and church missions provide the majority of modern care with missions operating some 40% of the not-for-profit health facilities in the country. Mission health services are considered to be of higher quality given the more constant availability of drugs and equipment, more highly motivated and committed personnel, and better facilities. However. in spite of the perceived higher quality of services provided by the mission facilities, demand for their services has been less than that of government, partially because of the higher fees charged by the missions. On 1 October, 1984, the Government of Swaziland introduced a new fee structure for health services provided by the government and mission sectors. The primary objective was to equalize the fees charged by the two sectors who together provided the majority of modern health services, while keeping net revenues neutral. This objective was designed to increase access to health services and to increase the potential for a more integrated health service system between the government and mission sectors, particularly in terms of the patient referral and supervisory system. The fee equalization policy generally meant a 300-400% increase in the price of government provided curative services with mission sector fees remaining essentially the same. Fees for preventive services increased to E0.50* from what essentially was a free service for both government and mission services. Specifically, under the old fee structure, fees for government provided services were 20 cents per
*E = Emalangeni the Swazi currency; El.00 = U.S. $0.90.
general (nonprivate) outpatient visit in clinics and 30 cents in hospitals. For preventive services, there was no charge after paying an initial 50 cents for the ‘red card’, a patient-retained card of health record. In the mission sector, fees for general outpatient services were typically E1.OO, although one facility charged E2.00. Preventive services were generally free except for one mission facility where fees ranged from E2.00 (child welfare) to a once only fee of E5.00 for antenatal visits. Under the new fee struture, fees for general services for government and mission (outpatient, inpatient and support services) were equalized at El.00 for outpatient services. Fees for preventive services were increased to 50 cents per visit in addition to the one time 50 cent red card fee. METHODS
Service based data from the Ministry of Health’s information system were used as the primary source. Only those facilities which had complete data for the study period were included in the analysis: 35 government facilities and 20 mission facilities, representing 71% of the nonspecialty government and mission facilities. The 1.8: 1 ratio of government to mission facilities used in the sample is slightly larger than the 1.3 : 1 ratio found in the population. As a means of controlling for nonprice influences on utilization that could occur in any single month, average attendance over a 3-month period is used. Mean attendance during the first 3 months of the new equalized fee structure is compared with the 3-month average attendance reported for the same 3 months in the previous year under the old unequal fee structure. These comparisons are broken down by mission and government sectors as well as the combined totals. In order to corroborate findings from the above sample of facilities, a follow-up study, involving 42 facilities, was conducted 1 year after the new fee structure was introduced [26]. Although this study represents one of the few nationwide analyses of the impact of increased user fees on utilization and various measures were taken to validate the findings, there are several limitations that should be noted. These include (a) a representative, rather than a random, sample of facilities, (b) lack of a control group, and (c) absence of complete congruence between facilities used in the original study and facilities used in the follow-up study to cross check and validate the findings. In addition, the utilization data are service based rather than household or individual based which, along with the absence of household income data, make it more difficult to attribute causality in utilization changes. In light of this, a model is constructed and tested to sift out how people’s choices change and possible reasons for these choices. RESULTS
The impact of fees on patient use of health services
Results reported in Table 1 show that for government facilities there is a 32.4% decline in attendance while the data for the mission facilities show an
Are Table
1.Monthly
sector
average attendance under unequal fee structures by sector
and equalized
Pm-fee change, attendance 10/83-12/83
Post-fee change, attendance 10184-12184
% Change
817 783 805
552 862 665
-32.4* 10.1 - 17.4t
Government Mission Totals ‘P <0.001;
people willing and able to pay for health services?
tP
increase of approx. 10%. Combined, the government and mission facilities experienced a decrease in attendance of 17.4%. A comparison of attendance figures for the latest month of the analysis, December 1983 (old fees) and December 1984 (new fees), shows a decline of 28% which is greater than the 17.4% decline when using the 3-month average of Table 1. The inelastic demand of 0.32 for government facilities indicates that for every 1% increase in the price of health care, utilization is expected to decrease by approx. 0.32%. It further indicates that even with the 32.4% decline in attendance at government facilities, total spending on government provided health services is expected to increase. Because outpatient fees at most mission sector facilities remained essentially the same at $1.00 per visit, the price elasticity of demand cannot be calculated at an aggregate level. For the same reason, calculating elasticities for government and mission facilities combined is more complex. To account for this complexity it was necessary to calculate an average fee per patient that is weighted according to differential patient use by sector. This method, which resulted in an average fee per outpatient of $0.52, is reasonable so long as there is substitution between mission and government facilities. The resulting inelastic demand of 0.30 for use of government and mission facility combined indicates that total spending will increase even though total utilization has declined. If one were to use the initial price and initial quantity demanded in estimating elasticities, instead of the average price and quantity demanded as was done above. estimated elasticity for the government sector would be 0.11 (instead of 0.30) and 0.09 (bmsnsiadof 0.30) for government and mission comThe decrease in use of government facilities and increase in use of mission facilities indicates that some mission facilities are being substituted for some government facilities. In addition, the existence of Table 2. Changes
in patient
over 5000 traditional healers in Swaziland, resulting in a healer-population ratio of approx. 1: 110 [27], along with estimates indicating that 85% of the Swazi population use traditional healers at least once per year, suggests that traditional health services may also be substituted for modem health services. Results reported in Table 1 are consistent with what was anticipated. Since the government fees increased, a decrease in the use of services was expected and since the mission fees remained essentially the same for the majority of facilities, an increase in the use of mission services was expected, particularly given the perceived higher quality of care provided by the mission facilities. What could not be predicted was what the magnitude of change would be. Although data from Mbabane Government Hospital, the main government hospital, have been excluded from the analysis because of the unreliability of the data, observations made by senior staff at the hospital indicate that attendance was down by 40-50%. Validation of these observations would exacerbate the overall decline in attendance. Declines in patient use of the sample of health facilities reported in Table 1 above were for the first 3 months of the new fee structure. Do these declines represent an accurate picture of utilization patterns over time or were they simply an initial response? To answer this question, a similar study was done 1 year after the new fee structure was introduced. These findings are shown in Table 2. Results reported in Table 2 support the findings of the original study although there is some variation. Declines in use of government facilities have increased further from 32.4% (Table 1) to 38.5%. For mission facilities, the increase in use is less pronounced showing a rise of only 1% instead of the 10.1% found in the earlier study. The combined decline in patient use of health facilities, however, is nearly the same at 17.1%. The findings reported in Table 2 are of particular significance in that the analysis was done 1 year after the new fee structure was introduced, thus allowing more than adequate time for changes in utilization patterns to level off, and the sample of facilities analyzed was different from the original study but yielded similar results. The data also show that, among government facility users, the greatest decline occurred at the government hospitals (-45.6%), corroborating observations made by senior staff at Mbabane Government Hospital 3 months into the new fee structure, with the lowest decline at the government health centers (19.1 %)-the second level of health care.
use of health facilities Monthly
Facility
IYKX
GOVI Hospitals Govt Health Centers Govt Clinics Total: Govt Average Mission Hospitals Mission Health Centers Mission Clinics Total: Mission Average Total:
ALL FACILITIES
Source: Ref. [26].
37
I year into new fee structure
average attendance
n
Jan. 1984
Sept. 1985
3 2 12 17 2 2: 25
2456 1334 560 1031 2429 548 647 752
1337 1079 341 634 2815 887 614 758
42
858
712
% Change -45.6 -19.1 -39.1 -38.5 15.9 61.9 -5.1 1.0 -17.1
RNXAIUI
38
Among mission health facilities, the greatest decline occurred at the clinics (-5.1%) with the greatest increase occurring at the one health center that was sampled (61.9%). It should be noted, however, that it was at this health center, a mission facility, where there was a 50% decline in fees following the fee change. In addition, senior health and administrative staff at this health center were primarily foreign missionaries well versed in Swazi culture and language and managed what was perceived to be a high quality service. This combination of factors likely explains a substantial part of the 61.9% increase in utilization. Changes in health system use by type
ofpatient visit
A second question to address, given the 17% overall decline in patient use of health services, is which services or types of patient visits show the greatest changes in use. Is it those with more serious illnesses that need medical attention? Is it children who are no longer being immunized against childhood diseases? Or, is it diseases that tend to be self-limiting such as minor aches and pains and skin irritations. Losses in the latter group would represent positive efficiencies in the system while losses in immunizations and serious illnesses represent negative efficiencies. To answer this set of distributive questions, changes in health service use were examined for seven different types of patient visits, and are shown in Table 3. According to the data in Table 3, musculoskeletal diseases such as arthritis and rheumatism, less serious diseases in terms of morbidity and mortality, show the lowest decline (- 1.2%) in average attendance. On the other hand, patient visits which result in protection against childhood diseases, BCG and DPT immunizations, or against dehydration in children, each show substantial declines in average attendance at - 16, - 19, and -24%, respectively. The consistent declines in utilization of government provided preventive services are not surprising; indeed, what is surprising is the decision to increase fees for preventive services.* What could not be predicted was what the magnitude of change would be. These declines lend support to assertions of a weak health education program in Swaziland in particular as well as of people generally having a propensity to care for present ailments over possible future ailments. Unlike the government sector, declines in utilization of preventive services are not consistent in the mission sector. Patient visits for BCG immunizations and diarrhea both conform to expected declines; however, the increase, though marginal, in DPT 1 immunizations (5.1%), is contrary to expected patterns. Although reasons for the variation in trends for the mission sector are not clear, there are several possible explanations. As stated in the introductory section, mission sector fees for outpatient treatment generally remained the same following introduction of the new fee equalization structure. However, under the old fee *The Ministry of Health has since taken away the fee for preventive services.
A. YODER
Are
people willing and able to pay for health
structure there was some variation in fees for preventive services at different mission clinics which led to a decline in fees for preventive services at these clinics. This could explain the 5.1% increase in DPT 1 immunizations. Musculoskeletal diseases, which one would not expect to change significantly from year to year, had the greatest increase at mission facilities (32.6%) and the greatest decrease at government facilities (-46.6%). These changes, rather than reflecting changes in the prevalence of musculoskeletal disease in the population, are probably instead an indicator of people’s preference for using mission facilities over government facilities. This would be consistent with the popular perception that mission facilities provide higher quality care than the government facilities. Thus when fees are the same between the two sectors, but perceived quality of care is different, people will migrate to the sector providing the higher quality care, i.e. the mission sector. For STDs and respiratory tract infections, which are also likely to change only slightly in the absence of either intervention or an epidemic-neither of which occurred-attendance remained approximately the same for the mission sector. The decline in attendance for STDs at government facilities without an accompanying increase at mission facilities suggests that there is now more untreated STD in the public that is lost to the government and not picked up by the mission sector. Another explanation is that perhaps the public considers STD and respiratory diseases to be minor ailments. If so, use of health services for minor ailments should decline with fee increases, as in fact happened with government facility use, and should not increase noticeably where fees remain essentially the same, as happened with mission facility use. Fees and financial access
The above analysis has shown that, following the change in fee structure, there was an overall 17% decline in use of health services. Who were these people and what was the reason for the decline? In particular, what part of the decline was due to reduced financial access? When health service fees rise, at least three types of patients can be expected to drop out, or reduce their use, of health services: low income patients for whom the fee is not longer affordable, patients who decide their ailment is not serious enough to justify the costs, and patients making multiple visits. In this section, we will attempt to systematize anecdotal information gathered informally from patients by constructing and testing a model for identifying what percentage of the decline may be attributed to a reduction in financial access. One way of examining this question is from the standpoint of what people pay for health services. The two relevant monetary costs involved in attending a health facility are fees and transport. (There are other costs, such as income foregone for wage labor because of absence from the workplace, but which are excluded from the model because of the unavailability of data.) From Table 4, we can see that prior to the change in fee structure, patients paying the
39
services?
Table 4. Typology of health care costs under unequal fee structure Sector
Attended facility nearest home
Bypassed any facility to attend facility of choice
Government
(Cell B) (Cell A) Least cost: Low fee Low fee High transport costs Low transport costs
Mission
(Cell C)
(Cell D) Highest cost: High fee High fee High transport costs Low transport costs
least cost were those who attended a government facility closest to their home (Cell A). Patients paying the most for health care were those who attended a mission facility and who bypassed another facility to get to the facility of their choice (Cell D). Although it is more difficult to ascertain what the relative costs of health care are between the other two groups (Cells B and C), we can say that for government facility users who bypassed another facility (Cell B), the fee was lower but the transport costs higher while for mission facility users that did not bypass another facility, the fees were higher but the transport costs lower. Under the new fee structure the only difference in costs under the four cells would be transportation so that the costs of health care in Cells A and C would be the same and the costs in Cells B and D would be the same. By answering the question of how the new fee structure would affect the choice of facility by the people in each of the cells, and if we accept the economic principle that, through the income effect, consumer spending on health care will decrease as consumer income falls, we may also get some indication of how the burden of a fee increase may be differentially distributed across higher paying and lower paying groups. Table 5 suggests how the people might change their choice of facility. All people attending government facilities will experience an increase in the costs of health care under the new fee structure and thus can be expected to reduce their use of services. The greatest decrease in utilization is expected to be concentrated in those using the nearest government facility (Cell A). Since the majority of the lower income groups will tend to chocse the least costly source of health care, i.e. the nearest government facility, we may hypothesize that, following the fee change, declines in this group will be dominated by the lower income groups. If cost is not a problem for people using the nearest government facility (Cell A) they may choose, under the new fee structure, to bypass a government or mission facility to use the mission facility of their choice (Cell D) to obtain what they perceive to be. better care, although this would probably be minimal. It is more Table 5. Expected utilization patterns under equalized fee structure Sector
Attended facility nearest home
Bypassed any facility to attend facility of choice
Government
(Cell A) Decrease
(Cell B) Decrease
Mission
(Cell C)
(Cell D) Neutral (or slight increase)
1llCltASC
40
RICHARD
likely though, that these people would choose to reduce their transport costs and obtain better care by using the nearest mission facility (Cell C). People who previously bypassed one government facility to use another government facility further away (Cell B) would likely begin using a mission facility (Cell C), thereby not only reducing transport costs but also receiving better care for the same price. People who used the nearest mission facility (Cell C) are not likely to change the facility they use. Rather, they are more likely to appreciate the reduced fee and stay where they are. They may possibly move to Cells B or D, although it is not likely. Mission facility users who bypassed one mission facility to use the mission facility of their choice farther away (Cell D) are not likely to change because under the old fee structure they chose to purchase what they perceived to be the best health care the government or mission facilities could provide, regardless of cost. Since the cost of the best health care has not changed significantly under the new fee structure, there is little motivation to change. Consequently, there should be no significant change in Cell D. In summary, following the fee structure change we are hypothesizing that the greatest declines will be in the group using the nearest government facility, while the greatest increases will be with people using the nearest mission facility. The data in Table 6 show what will actually happen. Table 6 shows that in each case, except Cell B, the data support what was hypothesized. Among those who paid the most for health care under the unequal fee structure (Cell D), there is essentially no change (1.6% decline) in attendance. The reasons for the increase in those who bypassed any facility to attend a government facility (Cell B), which is contrary to the hypothesis, are not clear. It is clear that such behavior is contrary to mainstream economic explanations in which price increases typically result in reduced demand, particularly where there are substitutes available. It is clear also that people are more selective than commonly thought in choosing a facility. Perhaps some undetermined cultural phenomenon or other noneconomic factor could be part of the explanation. Further research would be required to develop a fuller understanding of what these phenomenon may be. The 21.5% increase in mission facility attenders who did not bypass another facility (Cell C) is consistent with our hypothesis. When prices between government and mission facilities are equalized, and mission facilities are perceived to provide higher quality care, people will migrate to the mission facilities where they can receive better care. Table 6. Changes in utilization patterns between the unequal and equalized fee structure
A. YODER Among those who previously paid the least for health care (Cell A), there was a 34.4% decline in attendance. This finding is also consistent with our hypothesis. From an equity standpoint, it is this group which presents the greatest concern. Where did these people go? According to our model, there are four possibilities: the nearest mission facility (Cell C), a farther away mission facility (Cell D), a farther away government facility (Cell B), or drop out of the modern health care system. If the decline in use of government facilities (- 32.4%) was totally offset by the increase in use of mission facilities (10.1 %), then changes in patient use patterns as shown in the data of Table 6 simply would reflect a redistribution of use among the four cells. Given the 17% overall decline in utilization, it is reasonable to conclude that declines in government facility use were not completely offset by increases in mission facility use, and, that a percentage of this net decline consists of people who left the system. What share of the 17% who left the system did so for reasons of unaffordability? The model hypothesized that decreases in use would be denominated by people who, prior to the fee change, chose the least costly source of health care (the nearest government facility) because they have the lowest incomes and consequently are the ones who could least afford a free increase. The data show that declines were greatest among those people who previously paid the least for health care; the data also suggest that some, at most 34.4%, felt that they could no longer afford health care after the increase in fees and consequently left the system. However, because these are service based data and not household or individual based, it cannot be stated that all the 34% from the lower paying group (Cell A) did so for reasons of unaffordability; nor can it be stated with certainty that 17% fewer people were using the modern health systemonly that there were 17% fewer visits. Are there other plausible explanations for the decline in utilization? Assuming that Swazi users sometimes visit more than one facility for the same ailment, as is the case in the Philippines for example [19], then part of the decline may be attributed to fewer multiple visits. The lack of a constant supply of drugs, typical in many Third World health care systems, is an unlikely part of the explanation since there were no significant differences in price or supply during the period of the study. Nor can the decline in utilization be attributed to differences in weather, farm production or consumer income during the study period since- here also there were no significant changes [28]. Consequently, we are left with the conclusion that people did not simply transfer their use from one facility to another within the system. Rather, some people left the modern health care system and reduced financial access is the most plausible explanation for the reduction in utilization.
% Change Attended facility nearest home
Bypassed any facility to attend facility of choice
Government
(Cell A) - 34.4%
(Cell B) 63. I %
Mission
(Cell C) 21.5%
(Cell D) - 1.6%
Sector
IMPLICATIONS
Bearing in mind its limitations, there are several observations that may be drawn fromn this study. First, the assertion that ‘people are willing and able to pay for health services’ does not appear to be
Are people willing and able to pay for health services?
supported by the Swaziland data. With the exception of government facility users who bypassed another facility, the hypotheses presented in the model were supported by the data. Specifically, when disaggregating the overall 17% decline by higher paying and lower paying groups, it was found that up to onethird of those who stopped using government or mission health services were from the lower paying group, although, as has been discussed, an unknown part of this decline may be attributed to fewer multiple visits. The hope that the 17% fewer patient visits would consist primarily of minor ailments or self-limiting diseases is not supported by the data. The decreases in immunizations and treatment for diarrhea1 and sexually transmitted diseases suggest that the ‘wrong people’ have left the system. What was the affect of the price increase on total revenues? Although there was an overall decline of approx. 17% in use of health services, total revenues will have increased because of the inelastic demand. From a strictly financial point of view, it would make sense to raise fees even further since such a measure would result in additional net revenues. However, because user fees now recover about 2.0% of the total MOH recurrent budget (and make a 0.16% gross contribution to total government revenue), the present fee structure amounts to a low yield and inefficient tax [29]. To make a more substantial contribution to government revenue of, say, I%, estimates indicate that fees would have to be increased seven times above their current level [29]. The trade-off, however, would be a further reduction in financial access for the lower paying groups-a trade-off that would be difficult to defend on either equity or political criteria. The methodological question of using service based data is more complex and does present some difficulties in answering the question posed in the title of the article. It is for this reason, however, that the model represented in Tables 4-6 was developed. Is there a way of using the service based data many Ministries of Health routinely collect as part of their information system to give responses to health financing and utilization questions? The answer, of course, is an empirical one: does the data support the hypotheses in the model? So in part, the model may be seen as a satisficing alternative to the household income and related surveys which, because of their cost and complexity, are often left undone-and whose validity and reliability are often questionable. When combined with results of other studies, what these findings suggest is that the empirical evidence supporting the ‘willing and able to pay’ assertion is mixed. In some cases, people have both a willingness and an ability to pay, in others they do not. The Swaziland data do appear to suggest that a nondiscriminatory, flat increase in user fees, as was introduced in Swaziland, is neither an equitable nor an efficient solution to the financing problem. At the same time, there is evidence suggesting that user fees can have beneficial affects when accompanied by complementary measures such as a differential fee structure, return of revenues from user fees to the Ministries of Health instead of the central treasury (as is now not practiced in Swaziland), and provision
41
of insurance coverage or other forms of shared risk [7,91. Finally, these findings, along with the mixed results of other similar studies, are suggestive of additional areas of needed research. Of particular importance are disaggregated analyses that provide indications of how the burdens and benefits of user fees are distributed across the population, their impact on demand and quality and quantity of supply, and their impact on cost recovery. Utilization and income data that are household or individual based could also lead to less tentative conclusions. Acknowledgements-Research conducted during author’s 3-year term as Chief of Party/Health Planning Advisor, Ministry of Health, Swaziland under USAID Grant No. 645-0215. Appreciation is expressed to Dale Herman for his assistance in data collection and analysis and to Jonathan Meyer and Russell Smith for their helpful comments.
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