doi: 10.1093/heapol/czh036
HEALTH POLICY AND PLANNING; 19(5): 310–321
Health Policy and Planning 19(5),
© Oxford University Press, 2004; all rights reserved.
The impact of the introduction of user fees at a district hospital
in Cambodia
BART JACOBS1 AND NEIL PRICE2
Phnom Penh, Cambodia and 2Centre for Development Studies, University of Wales,
Swansea, UK
1Enfants&Développement,
Proponents of user fees in the health sector in poor countries cite a number of often interrelated rationales,
relating inter alia to cost recovery, improved equity and greater efficiency. Opponents argue that dramatic
and sustained decreases in service utilization follow the introduction of user fees, highlighting evidence that
user fees reduce service utilization when they fail to result in improved quality of care and/or when services
are priced higher than those charged by private health care providers. Utilization of public health services in
Cambodia is low. Supply-side factors are significant determinants of such low public sector utilization, including low official salaries of service providers (forcing many to seek additional income in the private sector),
and operations budgets which are erratic and often insufficient to cover running costs of service delivery
outlets. The Cambodia Ministry of Health (MOH) encourages user fee schemes at operational district level.
By allowing revenue to be retained at the health facility level, the MOH aims to improve health care delivery
– and consequently service utilization – through increased salaries to health facility staff and increases in
operations budgets.
This case study of the introduction of user fees at a district referral hospital in Kirivong Operational District
in Cambodia, using the findings from empirical research, examines the impact of user fees on health-careseeking behaviour, ability to pay and consultation prices at private practitioners. The research showed that
consultation fees charged by private providers increased in tandem with price increases introduced at the
referral hospital. It further demonstrates – for the first time that we are aware of from the available literature
– that the introduction and subsequent increase in user fees created a ‘medical poverty trap’, which has
significant health and livelihood impact (including untreated morbidity and long-term impoverishment).
Addressing the medical poverty trap will require two interventions to be implemented immediately: regulation of the private sector, and reimbursing health facilities for services provided to patients who are
exempted from paying user fees because of poverty. A third, longer-term initiative is also suggested: the
establishment of a social health insurance mechanism.
Key words: user fees, poverty, private sector, Cambodia, health financing
Introduction
The international development discourse presents
numerous, often interrelated, rationales for the introduction
of user fees in the health sector, relating inter alia to cost
recovery, improved equity and greater efficiency (Price
2002). A major argument for the generation of revenues
through cost-recovery strategies based on user fees relates to
covering operational costs: ‘Facilities that retain revenues
generally performed substantially better than facilities that
sent all their revenues to the treasury’ (Shaw and Griffin
1995, p. 26). With regards to equity, it is claimed that
universal free health care reinforces inequitable distribution
of resources in that it provides better access to services in
wealthy urban areas at the expense of poor and rural populations (Birdsall 1989; Foreit and Levine 1993). Proponents
of this view claim that user fees avoid the provision of subsidies to those who can afford to pay all or some of the costs,
and in doing so, free-up funds to pay all or part of the costs
of services for those less able to pay. The dominant theme in
the efficiency rationale for user fees is that charging attaches
value to a service, i.e. it increases demand by increasing
perception of quality and deterring unnecessary use of health
care systems. Some advocates of user fees claim that free
services reduce utilization because of inefficiencies leading to
quality and time costs borne by the users, and because of the
low value ascribed to free services (Lewis 1986). Foreit and
Levine (1993) argue that selective user fees encourage clients
to use appropriate service delivery outlets, by charging
higher prices at tertiary level (hospital outpatient clinics)
than at primary level (health posts). ‘Correct pricing’
schemes, according to Shaw and Griffin (1995), thus signal to
consumers to use health resources effectively and efficiently,
and serve as a warning that those who choose to bypass the
referral system will be required to pay the full cost of the
service.
Opponents of user fees refer to studies indicating dramatic
and sustained decreases in service utilization following their
introduction (Waddington and Enyimayew 1989, 1990;
Mbugua et al. 1995; Mwabu et al. 1995). Creese (1991)
provides extensive evidence that user fees divert those who
cannot pay to other sources of health care or away from the
health care system. Other studies highlight how user fees
The impact of user fees
reduce service utilization when they fail to result in substantial and sustained improvement in the quality of care and/or
when services are priced higher than those charged by private
health care providers (Audibert and Mathonnat 2000;
Chawla and Ellis 2000; Ha et al. 2002).
Despite very poor health indicators (maternal and infant
mortality rates of 437 per 100 000 live births and 95 per 1000
live births respectively [Ministry of Health Cambodia 2002]),
public health service utilization is low in Cambodia with 0.3
annual contacts per capita (Ministry of Health Cambodia
1999). Supply-side factors are significant determinants of
such low public sector utilization, including the low official
salaries of service providers (often in the range of US$15–30
per month) that force many public health professionals to
seek additional income in the private sector, and operations
budgets which are erratic and often insufficient to cover the
running expenses of service delivery outlets. The Cambodia
Ministry of Health (MOH) encourages the development of
user fee schemes at operational district level, with all revenue
but 1% being retained at the health facility. The overarching
rationale of the MOH is that user fees will improve health
care delivery – and consequently service utilization – by
providing funds for increased salaries for health facility staff
and increases in operations budgets. A prerequisite to the
introduction of user fees is community participation, which
has to be ensured by establishing Health Centre Co-Management and Feedback Committees comprised of elected
community members (Ministry of Health Cambodia
1997a,b). Prior to initiation of a user fee scheme, approval
has to be obtained from the MOH (Ministry of Health
Cambodia 1997a,b), although the scheme can be piloted
prior to applying for official recognition (Ministry of Health
Cambodia/UNICEF 2000). Health financing schemes must
be transparent to the local community, can only be developed
in consultation with community representatives, and a
system for identification of the poorest for exclusion from
payment has to be established (Ministry of Health Cambodia
1997b).
This case study of the introduction (and subsequent increase)
of user fees at a district referral hospital in Kirivong in
Cambodia draws on empirical research to examine the
impact of user fees on health-care-seeking behaviour, ability
to pay and consultation prices at private practitioners. The
case study demonstrates that the introduction and subsequent increase in user fees created a ‘medical poverty trap’,
which has the potential to lead to untreated morbidity,
reduced access to care, long-term impoverishment and
irrational drug use.
Background
Kirivong Operational District (KOD) is located in Takeo
Province, in the southeast of Cambodia, bordering Vietnam.
It consists of four administrative districts with 31 communes
and 290 villages, with a total population of 201 870 (1998
census), whose main economic activity is subsistence farming
supplemented by fishing and gathering. Two of the administrative districts are located in the Tonle Bassac Delta;
because of widespread irrigation through canals and flooding
311
during the rainy season, these two districts have different
farming patterns to the two administrative districts on higher
ground, which have very limited irrigation. KOD has 20
health centres and an 80-bed referral hospital, and is run
through a ‘contracting-in’ agreement whereby a non-governmental organization (NGO) is responsible for its management and administration.
KOD has a flourishing private health sector. A census
conducted in October 2002 (Jacobs, unpublished data) indicated that there were 511 private practitioners operating in
KOD, of whom 75 were qualified and operating from their
homes where they also ran a small pharmacy (henceforth
referred to as ‘qualified private practitioners’). At the time
of our study these qualified practitioners were not required
to register with the MOH. Most outlets for prescription
drugs, however, are small shops run by unqualified personnel who sell other retail products such as rice and sugar.
There were 336 such vendors identified. An additional 50
persons were found to sell prescription drugs at local
markets, while an equal number reportedly went from house
to house to sell and administer injections. Lay persons selling
prescription drugs are referred to as ‘drug sellers’ in the text.
All curative and preventive health care in the public health
sector was, until November 2001, officially provided free at
the point of delivery. Health staff were paid (by the
contracted-in NGO) a monthly salary supplement of
US$20–45. In November 2001, it was decided to pilot user
fees at the referral hospital. Prices for services were based on
the findings from a cross-sectional survey of women with
children undertaken in July 2001, regarding willingness and
ability to pay for curative services (Jacobs, unpublished
data).1 Prices were initially kept low, as hospital management
recognized that quality of care was poor (defined by the
inability to provide services such as obstetrics and surgery).
The user fee scheme was piloted for 5 months, following
which a final scheme based on higher prices was elaborated
and implemented, at the same time that obstetric and surgery
services were introduced.
To formulate the final scheme, 151 patients or their carers
were interviewed during the piloting phase (see research
methods below): 115 patients aged more than 5 years paid
fees while 36 children of 5 years or less were admitted free
of charge. No differences in health-seeking behaviour or
ability to pay were observed between the two groups, nor was
there a statistical correlation between the total expenditure
on health care and ability to pay. Mutual assistance relations
were found to be common whereby only 10% of borrowers
obtained money from private lenders and 31% paid interest.
These observations – and the commencement of obstetric
and surgery services – were considered appropriate to justify
an increase in the user fee prices when the final scheme was
introduced. An overview of the user fee price schemes during
the pilot and implementation phases is provided in Annex 1.
Methods
Randomly selected patients admitted at KOD Referral
Hospital were interviewed using a piloted pre-coded
312
Bart Jacobs and Neil Price
questionnaire administered before the introduction of the
pilot user fee scheme, during the pilot phase, and after the
implementation of the final user fee scheme. For patients
aged less than 18 years, the accompanying carer was interviewed. Questions concerned personal details, place of residence and distance from the hospital, care-seeking behaviour
and its associated costs, income forgone by patients and/or
carers during hospitalization, and ability to pay for both
hospitalization and any consultation costs in the private
sector. The latter was defined as having sufficient cash to pay
for all direct costs incurred with the illness episode without
having to borrow or sell assets.
Data were analyzed using the statistical package Epi-Info
6.04b and differences in proportions were compared using
the χ2 test. Significance was determined at 5%. For skewed
data a non-parametric test was used. Data were stratified
according to period of interview: before introduction of user
fees (baseline), during the piloting period (pilot) and following the increase of user fees (implementation). To calculate
total costs of hospitalization, direct costs (transport,
consultation charges at private providers, hospitalization
costs) were used.2 Indirect costs were estimated by questioning interviewees regarding daily amount of revenue lost by
patients and carers during the hospitalization period. This
question was only asked during piloting and implementation,
and the respective findings were used to assess the influence
of seasonality on ability to pay all costs related to hospitalization without having to borrow or sell assets.
Data regarding the monthly number of patients admitted at
the hospital were derived from the facility’s Health Information System (HIS) reports. We further assessed whether
the introduction of user fees had an effect on the case mix of
patients being hospitalized. For this purpose we selected the
five most common conditions reported in the HIS during the
three phases. These data were also used to calculate hospital
mortality rates, by dividing the total number of patients
admitted by the respective number of deaths. Tuberculosis
patients and deaths due to tuberculosis were not included.3
Results
Patients’ profile
The total number of patients/carers interviewed was 101 at
baseline, 151 during the pilot phase and 152 during
implementation. The respective median age of interviewed
patients (or their carers) was 24 years (range 3 months – 47
years), 21 years (range 2 months – 80 years) and 21 years
(range 4 months – 78 years). The literacy rate and proportion
of patients/carers who were farmers remained statistically
similar across the three surveys (Table 1). The socioeconomic status of respondents (using landownership4 and
possession of a motorbike as proxies), however, changed
considerably, whereby the proportion of landless interviewees decreased from 16% during baseline to 7% during
piloting and 5% during implementation. The proportion of
interviewees who possessed a motorbike increased by 50%:
from 17% during baseline to 25% in the two study periods
thereafter.
At baseline (i.e. when services were provided free of charge)
interviewees resided on average 6km from the hospital
(range 0–30), statistically similar to the 8km (range 0–45)
observed during the user fee pilot phase. During final scheme
implementation, however, interviewees resided significantly
further from the hospital: 12.5km (range 0–55) (KruskalWallis, difference between piloting and implementation, p <
0.001). During implementation, 76% of patients resided
within the administrative district where the hospital is
located, compared with 93% during piloting and 85% at
baseline.
Care-seeking and prices of private practitioners
When there were no official charges at the hospital, only 20%
of patients consulted private providers before presenting at
the hospital. This proportion increased to 54% during
piloting, and 73% during implementation (Table 1). The
proportion of interviewees who initially consulted drug
Table 1. Patient profile and health seeking behaviour
Variable
Literate
Farmer
Owns land
Owns motorbike
Resides within administrative district of hospital
Initial consultation at private sectora:
Traditional healer
Drug seller
Private qualified practitioner
Consulted provider in Vietnam
a During
Baseline
Pilot
n = 101
n (%)
n = 151
n (%)
Implementation
—————————————————–
n = 152
p-value
n (%)
df = 2
42 (42)
79 (78)
85 (84)
17 (17)
86 (85)
20 (20)
4 (4)
6 (6)
10 (10)
0
69 (46)
103 (68)
140 (93)
38 (25)
140 (93)
82 (54)
2 (1)
48 (32)
29 (19)
3 (2)
61 (40)
119 (78)
144 (95)
38 (25)
116 (76)
111 (73)
0
38 (25)
73 (48)
0
0.4
0.1
0.01
0.04
<0.001
<0.001
<0.001
<0.001
baseline, four patients consulted additional private providers before going to hospital: one a drug seller and three private qualified
practitioners. During piloting, three consulted additionally a traditional healer, one a drug seller and four private qualified practitioners. At
implementation, all went to the hospital following failed consultation at the private sector.
5 (13)
8 (21)
38
23 (32)
1 (1)
21 (19)
41 (37)
111
25 (66)
3 (6)
48
1 (2)
5 (6)
3 (4)
8(10)
82
3 (10)
1 (3)
4 (14)
29
48 (43)
1 (1)
21 (72)
44 (92)
1 (17)
1 (17)
6
4 (67)
1 (10)
10
8 (40)
1 (5)
7 (35)
1 (5)
3 (15)
20
Close to home
Cheap
Trust in provider
Advised to go there
Seriousness of condition
Total
4 (40)
1 (10)
4 (40)
Baseline
n (%)
——————————————————
All
Drug
Private
providers
sellers
qualified
Reason
The means of reported costs for initial consultation with
private sector providers (all categories) were R22 629
(US$5.8) at baseline, R43 962 (US$11.3) during piloting, and
R54 011 (US$13.8) during implementation. These prices,
however, are not adjusted for disease/condition or type of
provider. Table 3 shows that the average consultation cost at
drug sellers doubled from US$2.7 during baseline to US$5.4
during piloting and implementation. The treatments
provided by private qualified practitioners tripled from US$6
during baseline to ≈US$20 thereafter.
Table 2. Main reasons for initially consulting a private provider
Distance from the hospital was inversely correlated with the
initial contact being with this facility. During piloting 50%
(29/58) of interviewees residing within 5km came straight to
the hospital, compared with 30% (28/93) of interviewees
living further away (p < 0.001). The respective figures during
implementation were 32% and 11% (p < 0.001). Also during
this period, interviewees reportedly knowing the hospital
user fee prices beforehand were significantly more likely to
first contact the public sector than those who did not know
the prices: 34% versus 13%, respectively (p = 0.006). The
former proportion, however, was significantly lower than the
60% (39/65) observed during piloting (p = 0.001).
Pilot
n (%)
–——————————————————
All
Drug
Private
providers
sellers
qualified
Table 2 provides an overview of the reasons mentioned for
initially consulting private practitioners by all interviewees,
and for those who consulted drug sellers and private qualified practitioners. Geographical proximity of the private
provider was the reason mentioned most frequently for
initially consulting such a practitioner during the three study
periods, but was most pronounced during piloting compared
with the other periods: 80% versus ≈45%, respectively. Proximity to provider was most mentioned for consulting drug
sellers during piloting (90%) and least mentioned for
consulting private qualified practitioners during implementation (32%). One in three interviewees (35%) initially going
private during baseline did so because of trust in the
consulted provider. This was mentioned by only 6% during
piloting and not at all during implementation. During
baseline and piloting, about 5% of persons who initially
consulted the private sector did so because of advice from
peers. This rose to 19% during implementation. The proportion of interviewees seeking care initially in the private sector
because of seriousness of the condition was 10–15% during
the first two study periods. Following implementation of the
final user fee scheme, 37% reported going first to private
providers because of the seriousness of the condition. This
was mentioned considerably more by interviewees consulting qualified practitioners (45%) than by interviewees going
to drug sellers (21%).
66 (80)
Implementation
n (%)
—————————————————————
All
Drug
Private
providers
sellers
qualified
sellers increased from 6% at baseline to 32% during piloting,
and reduced slightly to 25% during implementation. The
respective figures for consultations at private qualified practitioners were 10%, 19% and 48%. These figures indicate
that the majority of patients who initially consulted private
practitioners opted mainly for drug sellers during piloting but
switched to private qualified providers during implementation.
313
16 (22)
33 (45)
73
The impact of user fees
314
Bart Jacobs and Neil Price
Table 3. Average costs of consultations with drug sellers and private qualified practitioners
Provider consulted
Baseline
US$ (No. of patients)
Pilot
US$ (No. of patients)
Implementation
US$ (No. of patients)
Drug seller
Private qualified practitioner
2.7 (7)
6.0 (13)
5.9 (49)
18.9 (33)
5.4 (38)
20.6 (73)
Case mix of patients and mortality rate
Table 4 provides an overview of the patient case mix for the
five most common diseases/conditions of patients admitted at
the hospital during the 3 months of each of the study periods.
The proportions of admitted patients suffering from malaria
remained similar during the baseline and piloting but
decreased during implementation. There were no changes
observed in proportions of patients admitted for diarrhoea/dysentery and respiratory infection, and for women
who delivered at the facility. The proportion of admitted
dengue patients halved after baseline. The proportion of
‘other’ conditions that are not specified in the HIS increased
from 35% during baseline and piloting to 50% during
implementation. The mortality rate was 6.6/1000 admitted
patients during baseline and increased to 13.6/1000 admissions thereafter.
Total cost for hospitalization
Table 5 and Figure 1 provide an overview of the total direct
costs associated with hospitalization during the three study
periods. The total average direct cost per illness episode per
patient was US$3.2 during baseline, US$10.9 during piloting
and US$19 during implementation. Costs of consultations at
private practitioners formed the major proportion of total
direct costs during the study periods: 72% during baseline,
67% during piloting and 59% during implementation.
Table 4. Patient case mix and mortality
Disease/condition
Baseline
n (% of total)
Pilot
n (% of total)
Implementation
n (% of total)
Malaria
Diarrhoea/dysentery
Respiratory infections
Dengue
Deliveries
Othera
Total patients (non-TB)
Deaths (non-TB)
Death/1000 patients
91 (10)
80 (9)
182 (20)
122 (13)
85 (9)
308 (34)
915
6
6.6
73 (9)
100 (12)
156 (19)
51 (6)
93 (11)
282 (35)
810
11
13.6
42 (4)
111 (11)
228 (22)
45 (4)
85 (8)
518 (50)
1032
14
13.6
conditions not registered in the health information system.
12
10
direct costs in US$
a includes
8
baseline
6
piloting
final
4
2
0
Private practitioners
Figure 1.
Transport
Hospitalisation
Direct costs incurred during baseline, pilot and implementation phases
315
59
7
34
100
Hospitalization fees – at least officially – were nil during
baseline and amounted to 25% and 34% of the total direct
costs during piloting and implementation, respectively.
Transport costs accounted for 28% of total direct costs during
baseline and became 7–8% during the periods thereafter.
Note: The baseline data do not include ‘unofficial’ charges at the hospital, which according to Keller and Schwartz (2001) are likely to have been significant.
233
94
0
327
2.3
1
0
3.2
72
28
0
100
1123
124
408
1655
7.4
0.8
2.7
10.9
67
8
25
100
1697
200
988
2885
11.2
1.3
6.5
19.0
Ability to pay
Private providers
Transport
Hospital
Total
Pilot (n = 151)
——————————————————————–
Total
Per
% of
spent
patient
total
Baseline (n = 101)
——————————————————————–
Total
Per
% of
spent
patient
total
Table 5. Direct costs associated with hospitalization during baseline, piloting and implementation (US$)
Implementation (n = 152)
———————————————————————
Total
Per
% of
spent
patient
total
The impact of user fees
Despite the provision of free hospital care, 40% of respondents in the baseline reported having insufficient cash to
cover all expenses incurred. This was significantly higher for
those who initially sought care at the private sector than for
those who did not: 65% versus 33% (p = 0.01), although such
difference was no longer observed during piloting and
implementation. During the pilot phase, however, 60% of
interviewees were unable to cover all expenses, despite the
fact that 25% were exempted from hospital fees (Table 6).
During implementation, the proportion of interviewees
unable to cover all expenses reduced to 41%, with only one
patient exempted.
Respondents in the pilot phase survey who knew hospital
prices beforehand reported significantly more availability of
sufficient cash than those not knowing the prices: 55%
(36/66) versus 28% (24/85) (p < 0.001). During implementation, the proportions were 74% (73/99) and 30% (16/53)
respectively (p < 0.001). During implementation, 66% of
interviewees knew the hospital prices beforehand, significantly higher than the 43% during piloting (p < 0.001).
The majority of interviewees who reported having insufficient cash resorted to borrowing to cover the direct costs of
hospitalization. The remainder sold an animal to obtain
money. Of those borrowing, the majority secured loans from
relatives: 61%, 60% and 63% during baseline, piloting and
implementation, respectively. Of people borrowing throughout the three periods, only 30% paid interest.5 At baseline,
interviewees borrowed together US$253, a ratio of 0.77:1 for
borrowed money to direct costs. The respective amounts and
ratio were US$1398 and 0.84:1 during piloting; and US$1542
and 0.53:1 during implementation. During baseline and
piloting none of the borrowers reported difficulty repaying
the loan. During implementation, however, 8% of borrowers
or 3% of all interviewees reported having to sell land to repay
their loan.
At the pilot phase, patients and carers reported losing a total
of US$1768 (US$12 per patient) because of being unable to
work as a result of hospitalization. During implementation a
total of US$346 (US$2.3 per patient) was reportedly lost due
to inability to work.
Effects of introducing and increasing user fees on monthly
number of admissions
Figure 2 presents a one-year overview of the number of
paediatric cases and total number of patients admitted at the
hospital during July 2001–July 2002. The average monthly
total number of admissions was 287 at baseline. It fell slightly
during the first 2 months following the start of the user fee
pilot phase, whereupon it returned to its previous level.
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Bart Jacobs and Neil Price
Table 6. Ability to pay costs of hospitalization
Variable
Baseline
(n = 101)
N (%)
Pilot
(n = 151)
N (%)
Implementation
(n = 152)
N (%)
Proportion exempted from payment
Unable to pay all costs
Resort to borrowing
101 (100)
40 (40)
36 (36)
37 (25)
91 (60)
84 (56)
1 (0.7)
62 (41)
62 (41)
fees during the implementation of the final scheme at KOD
Referral Hospital was the pursuit of financial sustainability,
in line with the national policies on health financing (Ministry
of Health Cambodia 1997b).
Following the increase of user fee prices (at implementation),
the number of admissions fell by a quarter for the first 2
months, whereupon it returned to its previous level.
The monthly number of paediatric cases remained unaffected by user fees: an average of 63 per month before user
fees and 66 following their introduction. During piloting,
however, children aged less than 60 months were exempted
from payment. At implementation, when user fees were
introduced for this age group (April 2002), the average
number of monthly admissions decreased for 2 months
before recovering.
Significant planning went into the design and implementation
of the user fee scheme at KOD, including the cross-sectional
survey regarding willingness and ability to pay for public
health services, and the piloting of the user fees in tandem
with an assessment of patients’ ability to pay the newly introduced fees. Prior to increasing user fees (the implementation
of the final user fee scheme), efforts were directed towards
improving the quality of care at the referral hospital through
strengthening the managerial capacity of its staff, and their
diagnosis and treatment competence. Fee collection practices
were limited to a single payment, irrespective of the duration
of hospitalization or additional diagnostic tests. The fee scale
was simple to facilitate the public’s ability to remember
prices. These price schedules were disseminated to the public
by the use of flyers through village authorities and pagoda
volunteers.
Discussion
The main aim of introducing user fees at the hospital was to
stimulate its staff to provide quality care through increasing
their salaries. It has been observed elsewhere in Cambodia
that considerable improvement in quantity and quality of
services resulted from staff incentive payments derived from
user fees (van Damme et al. 2001; Meessen et al. 2002;
Ministry of Health Cambodia/Swiss Red Cross/WHO 2002;
Soeters and Griffith 2003; Akashi et al. 2004). An additional
objective behind the decision to subsequently increase user
Following the increase in user fees during implementation,
significantly more patients came from districts other than
450
409
400
350
307
300
323
300
298
297
271
Total
Paediatric
361
350
250
267
276
239
221
200
150
100
54
50
82
78
71
47
58
54
98
86
52
51
50
65
0
July
Aug
Sep
Oct
Nov
Dec
Jan
Introduction of user fees
Figure 2.
Feb
Mar
Apr
May June July
Increase user fees
Total number of patients and number of paediatric patients admitted at the hospital (July 2001–July 2002)
The impact of user fees
KOD: 24% versus 7–15% before. Consequently, these
patients tended to reside significantly further from the
hospital than those patients hospitalized during the user-fee
pilot phase: 12.5km versus 8km respectively. These changes
most likely result from introducing surgery at the hospital, as
patients and carers would have been less likely to be referred
to the Provincial hospital where prices were much higher.
Furthermore, the introduction of surgery probably resulted
in a public perception of better quality of care, resulting in a
willingness to travel further distances for services.
There were, however, considerable changes in the socioeconomic profile of patients following the pilot phase of user
fees, as defined by the two variables of landownership and
ownership of motorcycles. The proportion of interviewees
who were landless decreased from 16% during baseline to
7% following introduction of user fees. Landlessness is a
major indicator of poverty, and households without land have
much less income than those who have it (Ministry of
Planning 1999; Chan and Sarthi 2003). For their socioeconomic stratification of patients in Sotnikum, Cambodia,
Hardeman et al. (2004) utilize ownership of a motorbike as
an indicator for ‘medium rich’ and ‘rich’ households. Following the introduction of user fees, the proportion of
patient/carers who owned a motorbike increased from 17%
to 25%. Although only based on two socio-economic indicators, these data suggest an alteration of the socio-economic
profile of patients following the introduction and increase of
user fees, suggesting that a proportion of the poor and vulnerable have been deterred from seeking care at the hospital.
The introduction and subsequent increase of user fees appear
to have led more patients initially to seek care from the
private sector. Whereas 20% and 54% of interviewees during
baseline and piloting, respectively, first consulted private
providers, 73% did so during implementation. The mostmentioned reason for initially consulting private practitioners was geographical proximity during all the study
periods. This was especially pronounced during the pilot
phase (80%), when most patients who initially went private
consulted drug sellers.
These findings are in line with Mbugua et al. (1995), who
observed a shift from public fee-charging facilities to the
private sector in Kenya following the introduction of user
fees. As pointed out by Ensor and Cooper (2004), selection
of provider requires a multitude of complex choices to be
made. Factors that play a role are the perceived quality of
care offered, physical and financial accessibility of services
and knowledge of services offered. The limited knowledge of
the population about appropriate health care may force them
to seek advice from nearby private providers as a perceived
cost-saving measure (Asenso-Okyere et al. 1998), as indicated in KOD by the shift of initial consultations from the
hospital to drug sellers during piloting. In line with observations by Paphassarang et al. (2002) in neighbouring Laos,
such drug sellers offer treatments based on ability to pay,
overcoming uncertainties related to costs of treatment – in
our case, hospitalization.
During implementation, however, most interviewees initially
317
consulting private providers went to qualified providers. Only
32% of such interviewees mentioned ‘proximity to home’ as
a reason for consulting such providers. Instead, the most
reported reason for consulting qualified private practitioners
was the seriousness of their condition, 45%, compared with
10–14% during the previous periods. Whereas 35% of interviewees initially going to private practitioners mentioned
‘trust in provider’ as a reason for this consultation at baseline,
none mentioned this during implementation. During
implementation, 19% of interviewees initially consulting
private providers sought advice from their peers compared to
≈5% during free care and piloting. These data suggest that
uncertainties related to the cost of hospitalization were most
pronounced following the increase of fees at that facility and
forced patients to seek care further from home at unfamiliar
private providers, following consultation of their peers. The
fact that seriousness of condition was the second most
mentioned reason during implementation may indicate that
patients delayed seeking care (due to uncertainties of cost of
treatment), as observed by Paphassarang et al. (2002) and
Asenso-Okyere et al. (1998).
However, not all observed changes in health-care-seeking
behaviour can be explained by uncertainties related to price.
For example, during piloting, interviewees who knew the
hospital prices beforehand were significantly more likely to
first contact the public sector (60%) than those who did not
know the prices (35%). During implementation, the respective figures were 34% and 13%, a considerable decrease,
which suggests that patients may have had difficulties
meeting the financial requirements for paying the hospitalization fees.
Private practitioners appeared to increase their fees in
tandem with the price increases introduced at the referral
hospital. The average price per treatment obtained from drug
sellers doubled from US$2.7 during baseline to US$5.4 thereafter, and the price per treatment from private qualified practitioners tripled from US$6 to about US$20 respectively.
These price increases suggest profiteering by private
providers, who operate without supervision or regulation and
abuse the patients’ limited knowledge of appropriate health
care (Mills et al. 2002). It may also indicate that patients
delayed seeking health care, resulting in their presenting with
more serious conditions that required more expensive treatment.
Patient case-mix and mortality data at the hospital suggest
that the price increase at private practitioners was mainly due
to delayed care seeking. The case mix for the five most
common conditions of patients, for example, altered little
over the study periods, but the overall mortality rate for
admitted patients increased from 6.6/1000 during baseline to
13.6/1000 thereafter. This increase in mortality6 may be due
to delayed care seeking because of uncertainties related to
the total costs of treatment (at private and public facilities),
but may also result from unsuccessful initial treatment in the
private sector. The HIS, however, did not provide data to
allow us to assess whether patients presented with more
severe conditions at pilot and implementation phases than at
baseline.
318
Bart Jacobs and Neil Price
The total average direct costs associated with hospitalization
were US$3.2 at baseline, although the real direct costs are
likely to have been higher due to charging of unofficial fees
by hospital staff (Keller and Schwartz 2001), despite none of
the interviewees reporting such practice. In line with observations elsewhere in Cambodia, charging unofficial payments
at Kirivong hospital decreased following the introduction of
user fees (Wilkinson et al. 2001a; Soeters and Griffith 2003;
Akashi et al. 2004; Hardeman et al. 2004). Increased salaries
for staff and contractual agreements forbidding unofficial
charges, along with the establishment of facility-level financial committees (responsible for service fees and salary
supplements), installation of suggestion/complaint boxes in
visible places and staff on duty wearing identification badges,
have all facilitated this reduction in unofficial charges in
Cambodia (van Damme et al. 2001; Ministry of Health
Cambodia/Swiss Red Cross/WHO 2002; Soeters and Griffith
2003; Akashi et al. 2004; Hardeman et al. 2004). A combination of these approaches was introduced at the KOD
hospital, along with the establishment of a staff disciplinary
committee. Nevertheless, the reported total direct costs
increased to US$10.9 during piloting and US$19 during
implementation. The major proportion (59–72%) of direct
costs related to consultations at private practitioners,
whereas hospitalization fees accounted for 0–34% during the
observation periods.
The proportions of interviewees who were unable to pay all
costs related to hospitalization without resorting to borrowing or selling assets were similar during baseline and
implementation (40%). During piloting, significantly more
interviewees reported an inability to pay (60%), while 25%
were exempted from user fees in comparison with the
implementation phase when only one interviewee was
exempted.
Several factors may influence the ability to pay for all costs
associated with hospitalization. For example, the data
suggest that prior knowledge of the user fees influences the
need to borrow money to pay for services. During piloting,
43% of interviewees knew the prices beforehand, significantly lower than the 66% observed during implementation.
Interviewees who knew the fees in advance were significantly
more likely to have sufficient available cash to cover all
expenses (74%) than those who did not know the fees
beforehand (31%). They were also significantly more likely
to initially consult public health providers (34%) than those
who did not know the prices (13%), reducing total expenditure. Being acquainted with prices beforehand tackles uncertainty in knowing how much a hospitalization will cost and
may consequently reduce the need for borrowing (Nyonator
and Kutzin 1999).
The difference in ability to pay between pilot and implementation – when the socio-economic status of interviewees was
similar – is likely to be due to seasonality: the pilot phase was
conducted during the peak period of agricultural activities,
when households have less cash than after harvest when
crops have been marketed. Opportunity costs per interviewee (for patients and carers) were US$12 at pilot and
US$2.3 during implementation. The ratios of borrowed
money to direct costs were 0.84:1 and 0.53:1 respectively. As
pointed out by Chan and Sarthi (2003), Cambodian farmers
are highly dependent upon credit during the planting season.
The proportion of interviewees who could pay hospital fees
without borrowing cannot, however, be taken as representative of the wider population because it understates
the proportion of the population that was deterred by the
fees (Mwabu et al. 1995). As suggested by the decrease in
landless interviewees and an increase in motorbike owners
from baseline to pilot, a major proportion of the most vulnerable may have been deterred from seeking care at the
hospital. The failing exemption system may have contributed
to this.
Only 31% of borrowers had to pay interest, which is similar
to the proportion observed during baseline and piloting.
However, 3% of respondents in the implementation phase
survey reportedly had to sell land in order to repay the loan,
a major cause for impoverishment in Cambodia (Oxfam GB
2000). The introduction and subsequent increase in user fees
at the hospital has created what may be termed a ‘medical
poverty trap’, which occurs when user fees are introduced at
public health facilities concurrently with an increase in outof-pocket expenses for private sector services (Whitehead et
al. 2001). The main effects of a ‘medical poverty trap’ fall into
four categories: untreated morbidity, reduced access to care,
long-term impoverishment, and irrational use of drugs
leading inter alia to drug resistance.
In line with findings from other countries (de Béthune et al.
1989; Wilkinson et al. 2001b), hospitalization rates declined
following increase of user fees. The phased approach, the
simultaneous quality improvement and intensive efforts to
disseminate information on the user fee scheme allowed
hospitalization rates to recover following a 2-month decline.
Addressing the impact of the medical poverty trap described
above will require two interventions to be implemented
immediately at operational district level: regulating the
private sector and exempting the poor from user fees. A
third, longer-term initiative might also justify serious
consideration, namely the establishment of a social health
insurance mechanism.
The research undertaken at KOD shows that the greatest
proportion of costs incurred during an illness episode was
due to consulting private providers. Currently, within the
context of its strategic 5-year plan for 2003–2007, the
Cambodia Ministry of Health (2002) envisages taking steps
to develop a legislative and regulatory framework for the
private health sector. The private sector in Cambodia, as in
many other low-income countries, is unregulated: providers
emphasize curative over preventive services, treatment is
often according to ability to pay, best practice is often
ignored, etc. Integrating the private health sector into an
overall public policy framework remains a considerable and
long-term challenge (Mills et al. 2002). In the short term,
strategies should, therefore, focus on influencing healthseeking behaviour to promote initial consultation with public
providers, and bringing first-line curative services closer to
The impact of user fees
the people by linking these to monthly outreach activities
(and when resources allow, by establishing health posts).
Exempting the poor from user fee payment is an important
component of any viable user fee system (Mwabu et al. 1995).
However, exemption mechanisms strain already limited
administrative capacity, and are expensive to operate, with
start-up costs particularly high: ‘tested and low-cost models
for identifying those who simply cannot afford to pay for
health care are as rare today as they were a decade ago’
(Shaw and Griffin 1995, p. 42). Moreover, schemes aimed at
targeting the poor with exemptions often miss the intended
beneficiaries (Mills 1991; Willis and Leighton 1995). Gilson
et al. (1995) note three main problems with targeting the
poor. First, judging who should be eligible for exemptions
faces a number of constraints. Means testing depends largely
upon household income data, the paucity of such information
leading to inappropriate or subjective eligibility (Mills 1991;
Willis and Leighton 1995). As measures of poverty become
more sophisticated, the costs of their calculation rise: budget
constraints limit accuracy. Secondly, the administration and
monitoring of exemptions is fraught with difficulties and
requires an efficient administrative system, with clear guidelines on the application of exemptions (which are often
missing, resulting in misallocation of benefits). Finally,
insufficient information and stigma may prevent the poor
from taking up exemptions. These three problems combine
to make targeting expensive and ineffective: ‘the expenses
incurred in targeting may undermine the revenue generated
from the implementation of user fees. Hence, either user fees
are imposed with ineffective targeting, harming the poor, or
the user fee initiative is financially unviable’ (Thomas et al.
1998, p. 51).
The exemption system at Kirivong hospital is constrained by
all the above problems, reflected in the fact that only one
respondent in the implementation phase interviews was
exempted from paying hospital fees. Furthermore, an exemption scheme requires operational arrangements which will
ensure that exemptions do not result in loss of income for
hospital staff. Fear of loss of income may result in refusal to
grant exemptions to those most in need, and/or discrimination
by staff against those unable to pay (Whitehead et al. 2001),
thus exacerbating the stigma reported by Gilson et al. (1995).
Reducing the risk of discrimination by staff will require
mandating a ‘third party’ to reimburse fees to the hospital.
This is currently being piloted in Cambodia at several
operational districts where non-governmental organizations
are responsible for the reimbursement through the operation
of an equity fund (Hardeman et al. 2004). Alternative and
more sustainable approaches are being considered which will
require exploration of existing social networks and development of strategies to stimulate the community cash contributions to protect the most vulnerable.
In the longer term, in order to address the medical poverty
trap, consideration needs to be given to changing from direct
payments at the point of service delivery to a social health
insurance system in which healthy, high-income groups subsidize health care for low-income groups (Nyonator and
Kutzin 1999; Whitehead et al. 2001). Jütting (2003) has shown
319
that social health insurance schemes can be viable in
resource-poor settings, but because they continue to exclude
the poorest, well-targeted subsidies may be necessary. In
Cambodia such subsidies may take the form of reimbursement of the membership fee for the poorest by the equity
fund.
Endnotes
1 The cross-sectional survey interviewed a sample of 400
mothers with a child aged under 18 months in 40 villages.
2 Costs for private consultations or hospitalization were not
adjusted for disease or condition.
3 During the study period, the initial treatment for TB patients
was 2 months of hospitalization. By decree, TB patients are
exempted from paying user fees at public health facilities. The
hospital has 16 beds for TB patients.
4 With more than three-quarters of the population engaged in
agriculture in Cambodia, land is the most important asset in rural
areas (Ministry of Planning 1999, p. 16).
5 The remaining 70% were able to borrow from relatives,
friends or neighbours without paying interest.
6 We recognize the problems of interpreting hospital death
rates, which require adjustment for severity of illness, length of
hospital stay, age, diagnosis, type of admission (Seagrott and
Goldacre 1994; Jarman et al. 1999).
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Acknowledgements
The authors would like to thank Jeff Sine for comments on an earlier
draft of this article, and two anonymous reviewers for Health Policy
and Planning. The project in Kirivong was implemented by
Enfants&Développement, funded by the Cambodian Ministry of
Health. The usual disclaimer applies: the opinions expressed herein
are those of the authors and do not necessarily reflect the views of
Enfants&Développement or the Government of Cambodia. We
accept responsibility for any errors.
Biographies
Bart Jacobs currently manages the Kirivong Operational Health
District in Cambodia. He holds an MSc in Health Planning and
Development, and has worked extensively in Southeast Asia, East
Africa, the former Soviet Union, and Australia as a researcher,
consultant and manager in a wide variety of health projects including TB control, blood safety, STD/HIV control, social marketing,
indigenous health, and health sector reform. His main areas of
research and operational interest and expertise are in health-seeking
behaviour and the development of locally appropriate public health
interventions.
Neil Price is Senior Lecturer and Deputy Head at the Centre for
Development Studies, University of Wales Swansea. He holds a
doctorate in social anthropology and has undertaken ethnographic
fieldwork in Cambodia, the Caribbean, China, Kenya and Zambia.
He has extensive experience of advisory and commissioned research
with international development agencies throughout Sub-Saharan
Africa, Asia, Latin America and the Middle East. His main areas of
advisory expertise are in social, policy and institutional analysis in
the health sector, and in the appraisal, monitoring and evaluation of
HIV/AIDS and reproductive health programmes.
Correspondence: Neil Price, Centre for Development Studies,
University of Wales Swansea, Swansea, SA2 8PP, UK. Email:
[email protected]
The impact of user fees
321
Annex 1. Overview of initial and final user fee scheme at KOD Referral Hospital (Riels)
Service
Age group
OPD consultation
≤5 yrs
>5yrs
≤5 yrs
6–15yrs
referred
non-referred
Adults
referred
non-referred
Hospitalization
Delivery (≥2 ANC)
(≤1 ANC)
Major surgery
Minor surgery
≤5 yrs
6–15yrs
Adults
≤5 yrs
6–15yrs
Adults
Initial fee
Final fee
% increase
0
0
0
2 500
3 500
5 000
n.a.
n.a.
n.a.
4 000
6 000
12 000
15 000
200
150
10 000
15 000
10 000
20 000
n.a.
n.a.
n.a.
0
6 000
15 000
22 000
30 000
20 000
30 000
5 000
50 000
100 000
5 000
10 000
20 000
120
100
100
50
n.a.
n.a.
n.a.
n.a.
67
33
ANC = antenatal care consultations; n.a. = not applicable; OPD = outpatient department.
US$1 = 3900 Riels.