doi: 10.1093/heapol/czh027
HEALTH POLICY AND PLANNING; 19(4): 218–233
Health Policy and Planning 19(4),
© Oxford University Press, 2004; all rights reserved.
Household decision-making on child health care in developing
countries: the case of Nepal
SUBHASH POKHREL AND RAINER SAUERBORN
Department of Tropical Hygiene and Public Health, University of Heidelberg, Germany
Quantitative studies on health care utilization often overlook the importance of capturing the ‘pathway’ of
household decision-making processes. This paper offers a four-step construct which maps out a hierarchical scale of household decision-making regarding child health care. The construct begins with the perception of illness, moves on to choice of care and provider, and finally ends with health care expenditure. The
construct is substantiated by means of a descriptive analysis of nationally representative data from the 1996
round of the Nepal Living Standards Survey. About 10% of the total population reported illness, 69% of whom
sought care, and depending upon the provider they chose, spent between 2.5 to 4.3% of their per capita
household total annual expenditure on health care. Bivariate analysis detected age and gender biases in the
perception of illness, but if a child was reported ill, all subsequent steps were found to be free from such
differences. Further analysis, that took into account the changing effects of income and mother’s education,
indicated that there may be conceptually different household dynamics that underlie boys’ and girls’ illness
perception; this ultimately determines whether or not health care is sought. The authors put forward a
hypothesis to be tested by future studies. They argue that gender role significantly affects the perception of
illness, but not necessarily the subsequent care-seeking. The relevance of this hypothesis to explaining the
typical South Asian characteristic of differential child mortality rates is discussed.
Key words: child health care, perception of illness, gender bias, age bias, health expenditure, provider, household decisions,
South Asia, Nepal
Introduction
How a country fares in terms of its population’s health is
largely dependent on how the available health care services
are being utilized. Analysis of health care utilization has
therefore increasingly been a major focus for researchers,
policymakers and programme managers. Several conceptual
models of health care utilization have been proposed, of
which the behavioural model (Anderson 1968), health belief
model (Hochbaum 1958) and economic model (Grossman
1972) are commonly accepted.
price and income covariate with a set of other socio-demographic and need factors, producing the demand for health
care, usually represented by health care utilization. Important in the economic model is the assumption that individuals
produce a commodity called ‘health’ by combining their time
and other inputs purchased from the market (one of the
inputs could be medical/health care), and as such, the
demand for health care is derived from a more basic demand
for health (Grossman 1972). Application of the economic
model in the analysis of health care utilization in developing
countries is growing (see Jack 1999).
The behavioural model consists of three major components
– predisposing factors (e.g. age, sex, family size, education,
employment), enabling factors (e.g. income, insurance, residence) and need factors (e.g. perceived health status,
symptoms of illness, disability days) – which determine the
use or non-use of care. Applications of this model are found
in several studies (Kroeger 1983). The psychosocial model
(e.g. health belief model), on the other hand, postulates that
individual perception, which is influenced by health beliefs
about vulnerability to a particular health threat and the
consequences of the health problem, effects the individual’s
state of readiness to take an action. This state interacts with
modifying factors such as demographic, socio-psychological
and structural variables, and the perceived benefits of the
health services lead to the likelihood of service utilization.
This model has been applied to examine the utilization of
preventive care (Carmel 1991).
From an analytical viewpoint, the three conceptual models
are not mutually exclusive in that rarely can all factors be
collected at the same time. A mapping from a conceptual
framework to an analytical one is therefore needed.
McKinlay (1972) identified six analytically different
approaches to health-seeking behaviour: socio-demographic,
socio-psychological, socio-cultural, geographic, organizational and economic approaches; each having strengths and
weaknesses. Kroeger (1983) categorized the health care
utilization studies broadly into two tracks: (a) the pathway
model, which describes different steps in decision making and
in the process of illness behaviour, and (b) the determinant
model, which focuses on a set of explanatory variables or
determinants that are associated with the health care choice.
He found that empirical studies using the pathway model
were mainly of a qualitative nature, while those employing
the determinant model were mainly of a quantitative nature.
The economic model, however, assumes that factors such as
As the determinant model allows the estimation of the affect
Household decisions on child health care
of price and income, holding all other factors constant, utilization studies based on an economic framework appear to
have focused almost exclusively on the determinant model.
If the purpose of the study is to estimate the net effect of an
explanatory variable on the utilization, determinant models
are far more popular, even for studies that are based on other
conceptual frameworks.
Moreover, the choice of dependent variable representing
health care utilization has been diverse. Some studies use a
binary choice of whether or not an individual sought care or
a multinomial indicating the type of provider chosen from a
list of formal and informal healers (e.g. Levin et al. 2001; Ha
et al. 2002), while others consider health expenditure
incurred to buy health care goods and services given the use
of certain healers (e.g. Rous and Hotchkiss 2003).
A question arises as to what track of analysis and/or which
kind of dependent variable one should choose in analyzing
how health care is being utilized (Jack 1999). Ideally, both
quantitative and qualitative approaches are needed to better
understand health-seeking behaviour (Ward et al. 1997), but
resource constraints and availability of data often tempt
investigators to choose one or the other. Although the choice
of the analytical framework may not alter the findings, the
choice of the dependent variable might do so, because household decision-making on health care is usually complex – it
is a ‘process’ involving several steps, not an end in itself.
Health care decisions can therefore not be viewed as a single
choice.
On the qualitative approach, the ‘pathway model’ mentioned
by Kroeger (1983) seems to capture this process to a large
extent, but the quantitative dimension of such a model has
not been explored sufficiently. More recently, Flessa (2002)
has linked the scarcity of health to the demand for health care
through a series of events in between them, i.e. needs
(defined as the subjective experience of scarcity of health)
and want (defined as willingness to seek health care). He
argues that since the aim of any utilization study is to find
factors which could contribute to the reduction of scarcity of
health, such a study cannot ignore the various steps between
scarcity and demand. It therefore follows that if one fails to
capture the whole process in the quantitative analysis by
taking into account only one variable of interest (which is
usually one of the steps in the process), it is likely that the
results will be of little use or even misleading because the
analysis may not be able to identify the step or steps (in
the process) where a policy could play a substantial role. For
example, Sauerborn et al. (1996a) report age bias, but not
gender bias, in households’ decisions to allocate scarce
resources for health care in West Africa, while gender bias in
intra-household resource allocation in South Asia has long
been reported (Chen et al. 1981; Das Gupta 1987).
Being able to detect age or gender bias in household
decisions (as in the case of the above analyses) may help in
devising policies to improve access to heath care. However,
it is probably not adequate to do so in a more targeted
manner because this requires an understanding of the stage
of the household decision-making process at which the age
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or gender bias enters, what factors really determine this bias,
and whether or not these factors affect all steps of the
decision-making process.
Although a limited number of recent quantitative studies
point towards this direction, their primary focus has been
either on looking at differential patterns of choosing
providers (e.g. Levin et al. 2001; Ha et al. 2002), or on getting
unbiased estimates of their main dependent variable (e.g.
Rous and Hotchkiss 2003). Therefore, they do not address
adequately the problem that exists at one or more steps
within the decision process.
The purpose of this paper is thus two-fold. First, it creates a
conceptual construct that maps out household decisionmaking on child health care in a hierarchy of several steps.
Secondly, it attempts to substantiate this construct by means
of a descriptive analysis using the nationally representative
data from the 1996 round of the Nepal Living Standards
Survey.
Conceptual construct
Drawing heavily on the established qualitative approach of
health seeking behaviour – the pathway model (Kroeger
1983) – we create a conceptual construct that maps out the
decision-making process in order for us to analyze it quantitatively. It is assumed that decisions regarding child health
care are ‘household decisions’ in that these decisions are
largely influenced by household factors such as the relation
to other members of the household (say, parents), their
educational and occupational exposures, and household
income (Levin et al 2001; Pillai et al. 2003).
The conceptual construct has four hierarchical steps (Figure
1). The first step is the ‘perception of illness’. Whether or not
a health care utilization really requires a person to first
perceive illness may be debated in a developed country
context, but in developing countries people are likely to seek
health care only when they perceive themselves to be ill
(Ward et al. 1997; Rous and Hotchkiss 2003). While qualitative research may contribute greatly to understanding
‘perception of illness’, it is equally difficult to measure this
perception quantitatively. Fortunately, many household
surveys ask individuals if they were ill or injured in the past
month prior to the survey date. This information (selfreported morbidity) can be used to proxy ‘perception of
illness’.
The second step in the decision-making process is ‘seeking
care’, which is generally measured by the ‘proportion of individuals who sought care given perceived illness’. A household may choose to seek care from external sources (formal
or informal) or decide to go for self- or home-care.
Having preferred to seek external care, the third step in the
decision process is to ‘choose a provider’ (from various types
available, i.e. public, private, traditional healers, etc.).
The fourth step in the decision process is ‘health expenditure’, the level of which depends on the type of provider
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3rd level: Health systems
characteristics
• Access to health care provider;
• Price of health care goods/services;
• Quality of health care, etc.
2nd level: Household
characteristics
• Household income;
• Mother’s education &
employment;
• Whether mother heads the
household;
• Number of children living;
1st level: Individual
characteristics
• Age;
• Gender, etc.
Figure 1. Household decision-making on child health care as a process involving four hierarchical steps and determined by factors at three levels
Step1: Perception of illness
Crude but available proxy for measurement:
Self-reported morbidity or self-reported health-status from
household surveys
2
3
Step 3: Choice of provider
Proxy for measurement:
% choosing a particular health provider given
external care sought
4
Step 4: Health care expenditure
Proxy for measurement:
$ spent as the result of first
consultation with a particular
provider
Step 2: Care seeking
Proxy for measurement:
Whether or not external health care was sought given
perceived illness
1 episode
of household
decision making
st
2 and higher episodes of decision making process
if an ‘illness episode’ itself requires several follow-ups (influenced
largely by providers’ decisions) or in case of ‘healer shopping’
3
3
2
1
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Subhash Pokhrel and Rainer Sauerborn
Household decisions on child health care
chosen. Since household members and health care providers
jointly determine the level of expenditure after the child is
taken to the provider, households may have unforeseen
expenditure. This is why our construct assumes health expenditure as the result of step 3 that might later feed back into
decision-making at step 3, rather than it (health expenditure)
being an influence over decision-making at step 3.
It is important to acknowledge the limitations of restricting
the construct to only four steps. First, it is likely that a second
or subsequent visit to the health care provider is necessary
(e.g. if the treatment requires a follow-up, or if the illness
does not improve). Secondly, it ignores the so-called ‘healer
shopping’ (Kroeger 1983); for example, if people are not
happy with the service they get from an allopathic doctor, or
the illness does not improve, they may turn to a traditional
provider (and can pay very high prices) without any advice
from the doctor. Thirdly, because poor people are unsure of
the potential costs they will face on seeking care, they do not
seek care and hence previous decisions will affect the current
one being examined. Fourthly, the fear of borrowing or
selling assets as a strategy to cope with the costs of illness may
also affect the earlier steps.
Despite these shortcomings, the construct is still relevant
because the understanding of how households ‘initiate’
health care will arguably have the most significant contribution to reforming national health care systems that suffer
from low utilization, as in the case of Nepal. Moreover, it is
likely that after the first consultation, decisions on health care
no longer remain ‘household’ decisions as they are largely, if
not completely, dependent upon the provider’s decision.
Finally, in most occasions, all required data are not available,
and without limiting the construct to four steps, it is impractical to carry out the analysis.
The second part of the construct is the identification of
factors that determine one or all steps of the decision process.
Health care decisions depend on several factors, some of
which are inter-related. Individuals’ attitudes, beliefs and
perceptions are important determinants of health care utilization, especially with regard to their initial contact with the
health care system. Health status, income and education
often determine the demand for health care (Grossman 1972;
Levin et al. 2001; Ha et al. 2002). While a person’s health
status affects his/her perceived benefit of medical treatment,
which may in turn be affected by education, income can
determine the ability to pay. Other characteristics such as age
and sex are also important with regard to health care
decisions because they determine differences in health status,
partly due to inherent biological disparity between men and
women and between age groups, and partly due to social
roles and responsibilities assigned to men and women and to
different age groups (Kroeger 1983; WHO 1998).
There are many other factors which may be important in
determining the decision process. For example, whether or
not one or both parents live in the household may be instrumental in resource allocation decisions. The husband’s
education may determine not only whether a woman works,
but whether she decides how to spend the funds, which is
221
very important for women’s control over resources. A
mother’s access to credit or social resources and her status
within the household might also be important at the household level (Levin et al. 2001; Matsumura and Gubhaju 2001).
Moreover, the structure, process and outcome related to
quality of care (such as availability of drugs, attitudes of
health care workers, and perceived quality of care) might be
important at the health system level (Acharya and Cleland
2000; Mariko 2003), and so is the price of health care goods
and services, including drugs (Sauerborn et al. 1994; Ching
1995).
In this construct, it is therefore assumed that the decisionmaking process is determined by several factors acting at
three different levels. These may include, but are not limited
to: (a) individual-level characteristics such as age and gender
of the child; (b) household-level characteristics such as family
income, mother’s socio-economic status (as measured by her
education, employment and whether or not she heads the
household), number of children living in the household and
rural/urban status; and (c) health systems-level characteristics
such as access to health facility/provider.
However, we included only these factors in our empirical
analysis with Nepal data for two reasons. First, we intended
to use a descriptive study to explore this construct for the
following reasons, and therefore could not accommodate a
large number of variables in the descriptive analysis. Since
we have four decision steps, we would encounter four simultaneous equations in the multivariate analysis. The estimation of such a system of equations becomes more complex
for several reasons, and therefore, should be the subject of
another paper. For example, different steps in the decision
process have different measurement scales, such as binary
(illness), multinomial (provider) or continuous (expenditure), the joint estimation of which are not usually handled
by common statistical software programs. Moreover, all four
dependent variables are basically choice variables (endogenous). In econometric terms, there could be unobserved
factors that are correlated with any of the dependent variables (say illness reporting) and the health expenditure
decision. Then, parameter estimates in the health expenditure model that include these factors as independent variables will be biased. This calls for a correction in the
estimation procedure (see Akin et al. (1998) for a twoequation system, and Rous and Hotchkiss (2003) for a threeequation system).
Second, the data we intended to use lacked some of the variables mentioned above (for instance, the drug prices and the
quality of care). While the data shortcoming is critical in
formulating policy implications that relate to unavailable
data, it nevertheless has little impact on the exploration of
the construct using available data only.
Context, data and statistical methods
Context
As mentioned in the introduction, we attempted to substantiate the above conceptual construct using data from Nepal.
222
Subhash Pokhrel and Rainer Sauerborn
With a population of 23 million (80% of which is rural) and
per capita income of US$220, Nepal’s health care system
relies heavily on the public provision of service delivery, but
health care financing is organized predominantly by out-ofpocket expenditures. The Ministry of Health is primarily
responsible for providing both curative and preventive health
care through 79 hospitals located at central, regional and
district levels, 178 primary health centres, 705 health posts
and 3132 sub-health posts located at the community level
(MOH 2002). Private provision has expanded rapidly since
1991, the year a new health policy was implemented.
Although the number of service outlets has increased significantly in the recent past, Nepal’s health care infrastructure,
especially in rural areas, is characterized by a low level of
utilization and poor quality of care (NPC 1997; Acharya and
Cleland 2000).
Nepal spends about 5% of its gross national product (GNP)
on health, of which 74% is accounted for by out-of-pocket
payments (Hotchkiss et al. 1998). The infant and under-5
child mortality rates have declined steadily in the past
decade, but they are still one of the highest in the world and
are characterized by substantial gender differentials. The
number of deaths of female children aged 1–4 per 100 male
child deaths in 1970, 1985 and 1996 were, respectively, 108.7,
128.2 and 124.2. These figures illustrate the progressively
worsening situation for girls relative to boys during the 1970s
and lack of progress thereafter (WHO 2000). Child health
services focus on the expanded programme on immunization,
nutrition, and control of diarrhoeal and acute respiratory
infections – programmes that address leading causes of
mortality and morbidity (MOH 2002).
There is scant evidence on health care utilization in Nepal.
The Ministry of Health estimates per capita new visits to
government health facilities as 0.36 for the year 2001–02
(MOH 2002), a typically lower level of utilization in a country
which relies heavily on public provision. The National
Planning Commission (NPC 1997) reveals that 12% of
households reported one or more contacts with government
health services. The use of maternal services is limited; only
43% of expectant mothers made first antenatal visits in
government facilities in 2001–02 (MOH 2002). Most often,
treatment for illness is sought only after home remedies have
failed; women are the primary care givers at home and a large
proportion of the population (42%) do not visit modern
health facilities, instead seeking the help of traditional
healers (Niraula 1994). The most recent study documenting
health seeking behaviour in a hill village reports that of the
69% of households that sought health care when an illness
occurred, 26% visited traditional healers exclusively while
only 19% first visited formal health care institutions (Jimba
et al. 2003). No studies represent the overall health seeking
behaviour at a national level; Ministry of Health estimates
ignore private provision and traditional healers, and other
studies do not represent the whole country.
Data
The data for this study come from the Nepal Living Standards Survey (NLSS), which was administered in 1996 by
Nepal’s Central Bureau of Statistics (CBS) with assistance
from the World Bank. A total of 3338 households (18 855
individuals) in 275 communities were selected through a twostage stratified sampling procedure in order to represent the
whole country. Sampling weights were constructed to correct
for different selection probabilities across households. The
details of the survey design and implementation are
described in Prennushi and Central Bureau of Statistics
(1996). The definition of ‘household’ adopted by the NLSS
was: ‘a group of people who normally live and eat their meals
together (that is, the person concerned has lived in the household for at least 6 of the past 12 months)’. NLSS asked all
sampled individuals questions on various economic, demographic and health related behaviours. For children under 5
years, the information was collected from their respective
mothers. Questions related to health included: (a) whether
each household member was perceived to suffer from a
chronic disease or from an illness or injury in the past month,
(b) whether the individual/s used health care services, and (c)
if so, the type of place and practitioner that was consulted,
and the consultation and travel costs for the first two visits.
If tests were carried out and drugs were given as the result of
the consultation during the visit, they were also included. No
costs for drug items were recorded separately, however.
We analyzed 8112 individual observations from 2847 households in 274 communities. These observations include all
children aged 15 years or less captured by the survey. We
retain individuals of age 10–15 years under ‘children’, despite
the WHO definition of ‘adolescent’, which ranges from 10–19
years of age, assuming that this would make little significant
difference to our study questions.
Statistical methods
Descriptive analysis was carried out, taking into account the
sampling weights to correct for different selection probabilities across households. Pearson’s χ2 test was used in order to
determine whether children with specific individual, household and health systems level characteristics differed from
each other in terms of health care decisions. The difference
in means (such as average health care expenditure) across
groups was examined by using adjusted Wald tests, which
performed similar to Student’s t-test if there were only two
groups. Since expenditure data were skewed and had positive
values together with some zeros, we transformed them into
a logarithmic scale [u = log10(x + 1)] before doing significance
tests. All calculations were done using ‘survey commands’
from STATA version 7 (StataCorp 2001), thus giving population estimates.
Throughout the analysis, we use the term ‘child health care’
to mean any action taken by households in response to their
child’s acute illness; it includes the process of obtaining
curative services at different providers of health care. We
have excluded the decision-making on non-acute diseases
because of the unavailability of data. We have confined our
analysis to the first visits (consultations) only, as discussed in
the section outlying conceptual construct.
We constructed the income quartiles as the quartile of the
Household decisions on child health care
household’s total annual expenditure, assuming that households make expenditure decisions based on the amount of
income they expect to earn on average; therefore, annual
expenditure could serve a better proxy for the household’s
regular income than using reported income, which can vary
from year to year (Rous and Hotchkiss 2003). We measured
mothers’ socio-economic status by three key variables: her
education in terms of whether or not she attended school, her
employment in terms of whether she is in self- or wageemployment or does not work (is a housewife), and her
guardianship in terms of whether or not she heads the household (Matsumura and Gubhaju 2001). We used the availability of a primary health care facility within or over 1 hour
of travel time as a proxy for access, the health system variable
(Magadi et al. 2000).
There are various types of health care provider in Nepal, and
based on the information collected by the survey, we classified them into four categories for the purpose of our analysis:
(a) public providers, who are the government or other public
hospitals/primary health care facilities; (b) pharmacy, which
includes all medical shops (these are mostly run by private
owners, provide informal advice on care, sell drugs on their
own and are spread across the country at both large and small
scales); (c) home visits/traditional healers (about two-thirds of
home visits were made by traditional healers and were
particularly common in rural areas); and (d) private/NGO
providers, which includes clinics and facilities run by nongovernmental organizations or missionaries.
223
Results
Table 1 gives an overview of the four-step decision process
with population estimates. About 10% of the children aged
15 years or less were perceived to have suffered from an
acute illness or injury 1 month prior to the survey date. The
most commonly reported illnesses were diarrhoea and fever,
which together accounted for more than 60% of the total
(not shown in the table). Nearly one-third of the sick children
did not seek care. This translates to an approximate 30% risk
of a sick child going without any external health care. From
the data we have, we do not know what really happens to
those not seeking any external care (it could be self-care,
home-care or no care at all). Once the households decided to
seek health care for their ill or injured children, more than
80% were taken to either public or private providers who
offered formal and mostly allopathic forms of medicine.
However, one-tenth of the sick children received care via
informal advice from a pharmacy or medical shop, while a
little less than this proportion were served by traditional
healers. The pattern of care-seeking indicates that public
providers are most commonly consulted in any illness
episode, followed by private providers, but informal care still
exists in a visible proportion, even after controlling for
income and rural/urban status (Figure 2).
While the average expenditure on child health care was 210
Nepal Rupees (Rs.) (about US$4 in 1996), it varied across
the type of providers, as expected (Table 1). The cost of
Table 1. The four-step hierarchy of household decision-making on child health care in Nepal (2847 households with 8112 children 15 years
or below)
Step Decision
No. of observations
1
8112
Population estimates
% (standard error)
2
3
4
a Two
Perception of illness as measured by self-reported morbidity
(whether or not one was reported ill/injured 1 month prior to
the survey date)
No
Yes
Health care seeking (whether or not external care was sought
given illness)
Yes
No
Choice of provider for the first consultation given external
care was sought
Public
Pharmacy
Home visit/traditional healers
Private/NGO
Health care expenditure (fees for first consultation and travel
expenses) in Rupees 1995/96 value
Public
Pharmacy
Home visit/traditional healers
Private/NGO
Average (weighted)
90.0 (0.6)
10.0 (0.6)
788
68.9 (2.7)
31.1 (2.7)
556
56.7 (2.9)
10.0 (1.8)
7.8 (1.5)
25.5 (2.7)
observations with extreme values were dropped from the analysis.
care spending expressed as percentage of per capita household total annual expenditure.
b Health
Mean (95% CI)
% (95% CI)b
210 (175–244)
166 (80–252)
115 (63–167)
256 (204–308)
210 (184–236)
4.3 (3.2–5.4)
2.5 (1.6–3.4)
2.5 (1.3–3.6)
4.3 (3.3–5.4)
4.0 (3.3–4.7)
554a
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Quartile 1
(lowest)
Public
3
Pharmacy
Quartile 4
(highest)
Home visit/TH
Urban
Rural
Private
Figure 2. Percentage of children seeking first consultation by type of providers and residence or income
2
All
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Subhash Pokhrel and Rainer Sauerborn
Household decisions on child health care
consultation included the costs of the first visit plus the travel
costs. Since there could be more than one visit, this may
underestimate the total expenditure (and expenditure as a
percentage of income). However, for the reasons discussed
in the section outlining the conceptual construct, we did not
consider the costs of the second and subsequent visits in our
analysis. The costs of the first consultation included examination fees, costs of diagnostic tests and drugs, if they were the
result of the consultation. The average expenditure associated with private providers was appreciably higher compared
with public ones; this indicates that private care was, as
expected, more expensive for many. When expressed as a
percentage of total per capita household annual expenditure,
the average spending on child health care (first consultation
plus travel costs) was 4% (range 3.3–4.7). This points to the
fact that there is a huge burden on households for child
health care, because the overall household health care
225
spending in Nepal is 5% of total per capita annual expenditure (Hotchkiss et al. 1998).
Table 2 disaggregates the household decision-making process
into the four-steps of our conceptual framework (Figure 1)
and stratifies it by the three levels of possible determinants.
At the first step, several characteristics appeared significant.
While households were more likely to report illness/injury of
infants than of elder children, girls were less likely to be
reported ill than boys. The mother’s status also affected the
likelihood of reporting an illness/injury. Mothers who
attended schools or were household heads reported
children’s illnesses/injuries more often. However, whether or
not a mother was a housewife (not-working) or had any sort
of self- or wage-employment had an insignificant effect on
reporting illness/injury. Having only one child in the household was associated with an increased chance of reporting
Table 2. Household decision-making on child health care disaggregated into four steps by three levels of possible determinants
Levels of determinants
1. Individual
Age group:
< 1 year
1–5 yrs
6–15 yrs
Gender:
Male
Female
2. Household
Mother’s education:
Never attended school
Attended school
Mother’s employment:
Not working
Wage employment
Self-employment
Mother head of household:
Yes
No
Number of children in family:
Only 1
2–3
>3
Quartile of per capita income:
1 (lowest)
2
3
4 (highest)
Residence:
Rural
Urban
3. Health systems
Time to health-facility:
Within 1 hour
More than 1 hour
Steps of decision-making process
—————————————————————————————————————————————
1. Reporting
2. Seeking health
3. Choice of public
4. Expenditure for first
illness/injury
care
provider
consultation plus travel cost
(n = 8112)a
(n = 788)
(n = 556)
(Rupees) (n = 554)
% (95% CI)
% (95% CI)
% (95% CI)
Mean (95% CI)% of income
20 (16–24)*
15 (13–17)
6 (5–7)
71 (60–80)
71 (65–77)
65 (57–72)
57 (43–69)
52 (46–59)
63 (54–71)
212 (148–275)
215 (179–250)
202 (159–246)
4.7
4.3
3.2
11 (9–12)**
9 (8–11)
71 (64–76)
67 (60–73)
58 (51–65)
55 (47–62)
221 (186–255)
196 (161–231)
4.5
3.3
10 (9–11)**
12 (10–15)
67 (61–72)*
84 (71–92)
58 (52–64)
48 (37–59)
210 (182–238)
195 (145–245)
4.4
2.0
11 (9–14)
11 (9–14)
10 (8–11)
69 (60–77)
71 (60–80)
69 (61–75)
60 (49–70)
46 (32–60)
58 (50–65)
199 (150–249)
193 (124–261)
212 (178–246)
3.3
3.8
4.3
15 (12–19)*
10 (9–11)
58 (45–70)*
71 (65–76)
77 (62–87)*
54 (48–60)
168 (130–205)
212 (184–239)
4.0
4.0
13 (10–17)**
10 (9–12)
10 (8–11)
70 (56–80)
68 (61–75)
69 (62–76)
61 (46–74)
58 (50–66)
55 (47–63)
244 (134–353)
204 (166–251)
205 (169–241)
2.5
3.8
4.5
9 (8–12)
10 (8–11)
11 (10–13)
10 (8–13)
52 (42–62)*
68 (58–77)
75 (66–81)
82 (73–89)
63 (50–74)
58 (46–68)
56 (48–65)
52 (40–63)
190 (123–258)*
133 (108–158)
209 (168–250)
311 (245–378)
7.9
3.2
3.2
2.6
10 (9–11)
10 (8–13)
68 (62–73)
81 (63–92)
58 (52–64)*
39 (31–47)
203 (176–229)* 4.0
301 (210–392) 3.3
10 (9–11)
10 (9–12)
76 (71–81)*
53 (43–63)
56 (49–63)
59 (47–70)
209 (180–238)
214 (162–267)
Sample size (n) is the number of children.
a proxy used for measuring perception of illness.
* significant at 5% level; ** significant at 10% level.
3.6
5.0
226
Subhash Pokhrel and Rainer Sauerborn
illness/injury. Other variables such as the type of residence
(rural/urban) and access to a health facility showed no
statistically significant difference in this very first step of the
decision-making process. Income did not influence the likelihood of a child being reported ill or injured.
The trend observed in the first step changed when households entered the second step in the decision process. While
the mother’s status in terms of her educational background
and whether or not she headed the household remained
significant determinants of seeking health care, economic
characteristics like income and travel time to the nearest
health facility became significant at this step. Interestingly,
once the child was reported ill, his/her individual characteristics such as age and sex made no difference in subsequent
decision-steps. This was true for a disease-specific analysis,
such as for diarrhoea, as well (not shown in the Table). The
effect of a mother being the head of the household, although
positive in reporting illness, was negative in health care
seeking. Once the decision to seek health care was made,
time taken to bring the child to the health facility made no
difference to the choice of provider and subsequent expenditure decisions. The pattern of health expenditure differed
significantly across different income groups and was regressive, the poorest spending a much higher share of their
income (7.9%) on child health care than the richest (2.6%).
Moreover, there was a large gap between rich and poor in
terms of spending on child health care (on average, quartile
4 spent Rs.121 more than quartile 1).
Given the complexity of the household decision-making
process, relying on bivariate analysis to draw conclusions
would be injudicious, because it is unable to disclose how
different elements interact or change with other variables
(e.g. with changing income levels). One way to improve on
this is to pick up one interesting variable from Table 2 and
analyze it further.1 As an illustration, we chose gender
because the vast literature from South Asia speaks a great
deal on gender bias in health care decisions (Chen et al. 1981;
Das Gupta 1987; Pandey et al. 2002; Borooah, forthcoming).
Table 2 shows the existence of gender bias in reporting illness
but not in the subsequent steps of the decision-making
process, such as while seeking care or spending money on
sick children. This necessitated further analysis.
Table 3 summarizes the female to male ratio of household
decision-making by type of illness reported. The table clearly
shows that, having perceived their children as ill, households
did not discriminate between boys and girls in any of the
subsequent steps in the decision-making process, except that
they spent significantly more money on treating diarrhoea in
boys than in girls (p < 0.01). In this group (diarrhoea), boys
were relatively younger than girls (mean age 2.4 vs. 4.1 years;
p = 0.01) but the average number of days missed due to illness
was not statistically significant. Also, spending to treat diarrhoea in boys was higher regardless of what types of provider
were chosen. After controlling for the age of the children, the
bias in reporting illness was still evident in infants (boys
24.5% against girls 14.4%; p < 0.01) but disappeared as the
age of the child increased (not shown in the table). The mean
age was 7.3 years in both gender groups (p = 0.93). The mean
days missed due to illness was similar in both gender groups
(2.2 days in girls vs. 2.3 days in boys; p = 0.73) with no significant difference observed by sex even after controlling for
age.
Figure 3 depicts nicely the non-linear relationship of
perceived illness with income; lower for the poorer who can’t
afford to be ill, rising with wealth, and then falling again as
health and wealth are associated. However, this relationship
was not true for boys. The difference in reporting illness
between boys and girls was significant in the lowest income
quartile only (p = 0.02).
Educated mothers were more likely to perceive illness in
both boys and girls. About 14% of boys and 11% of girls were
reported ill if the mother attended school, compared with
10% and 9%, respectively, if the mother did not attend the
school. The difference was trivial in the case of girls, but
significant for boys (p < 0.05).
Since a large number of the school-attending mothers
belonged to the richest income group (30%), further analysis
examined how perceived illness of boys and girls changed
with income between school-attending and non-schoolattending mothers. Regardless of income level, mothers who
never attended school reported the illness of boys on a
similar level, and mothers who had been to school tended to
report the illness of boys more often. The effect was significant in the highest income quartile (9.6% vs. 15.3% in two
respective education groups; p = 0.01). This could partly be
due to a larger sample size in this income quartile. If the child
was a girl, the likelihood of reporting illness by mothers who
attended school increased as income increased, whereas the
Table 3. Female to male ratio of household decision-making by type of illness reported
Type of illness
reported
Seek care
(n = 788)
————————————
n
F/M ratio
Choose public provider
(n = 556)
————————————
n
F/M ratio
Health expenditure
(n = 554)
————————————
n
F/M ratio
Diarrhoea
Fever
Others
149
336
303
114
230
212
114
229
211
a Significant
at 1% level.
0.87
0.97
0.96
0.81
1.07
0.88
0.44a
0.94
1.13
Percentage
0.0
2.0
4.0
6.0
8.0
10.0
12.0
Quartile 1 (lowest)
7.9
9.4
10.8
Total
Male
Income quartile
Female
3
Figure 3. Percentage reporting illness with changing income level by sex of the child
2
8.7
9.5
10.4
11.1
10.7
10.4
Quartile 4 (highest)
9.8
11.0
10.5
Household decisions on child health care
227
Percent
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
18.0
Quartile 4 (highest)
8.3
9.6
Figure 4. Percentage reporting illness of male and female children by mother’s education and income quartiles
Female child with mothers attending school
Income quartile
3
10.1
Female child with mothers never attending school
2
9.1
10.0
12.2
Male child with mothers attending school
9.6
11.8
10.3
11.8
15.3
Male child with mothers never attending school
Quartile 1 (lowest)
5.3
8.2
9.7
10.7
14.7
228
Subhash Pokhrel and Rainer Sauerborn
Household decisions on child health care
other group showed the non-linear relationship between
perceived illness and income as mentioned above (Figure 4).
The differences were insignificant in both groups, however.
Table 4 presents the gender characteristics of the sampled
population. While there was no difference between boys and
girls by individual, household and health systems level variables, the difference was apparent in mothers who attended
and did not attend school. For example, mothers who had
attended school were relatively younger and had, on average,
younger children compared with mothers who did not attend
school. They had higher income (about double of nonschool-attending mothers), and relatively more of them were
housewives living in urban areas with more access to health
facilities.
Turning to how income is inter-related to higher steps of the
decision process with regard to boys and girls, three indications clearly emerged. First, the child’s age was an important determinant of the differences in the decision to seek care
between boys and girls. For infants, for example, households
were more likely to seek care for boys than for girls (79 vs.
56%; p < 0.05). However, when the data were disaggregated
by income quartiles, no sex difference was observed among
infants, nor was it observed among grown-up children.
Secondly, mothers who did not attend school were more
likely to differentiate between boys and girls while seeking
care (70 vs. 63%; p < 0.10). Again, the difference turns out
to be insignificant when disaggregated by income quartile.
Third, although income in general determined the likelihood
of seeking care in a pooled sample, households treated boys
and girls similarly in care-seeking decisions regardless of
income. The difference by the sex of the child in choice of
provider was insignificant at all income levels. However,
health care expenditure was significantly higher for boys in
the lowest income quartile (Rs.236 as compared to Rs.128 in
females; p = 0.01), but this difference ceased with increase
in income.
229
Discussion
The analysis of Nepalese data based on the four-step
construct has resulted in some interesting research and policy
relevant findings. Before we discuss these findings, we would
like to identify a few shortcomings of the construct as well as
of the data used. First, household surveys have long been
recognized as ‘notoriously weak’ in capturing childhood
morbidity because obviously sick children are often not
reported thus (Caldwell et al. 1983). This argument alone
may be enough to criticize our approach of using ‘illness
reporting’ as a proxy for perceiving it (perceiving a child’s
illness does not necessarily mean that it is reported). Sauerborn et al. (1996a) argue that any difference in health care
utilization by a certain characteristic (say, age) detected in a
sample based on those who have reported ill would not invalidate the finding (but rather make it a lower-bound estimate),
if there was indeed an under-reporting.
Secondly, the NLSS data report that 68.9% sought treatment
from a provider for all acute child illnesses, which for Nepal,
one could argue, may be an incredibly high figure. Niraula
(1994) found that 58.4% of the respondents used modern
health facilities in hill villages in Nepal. Recently, Jimba et
al. (2003) found that 69% of the households in which illness
had occurred sought health care, and the rest used home
care. Of those who sought health care, 26% visited
traditional healers exclusively, whereas 55% first visited
traditional healers and then visited the government primary
care facilities. While our estimate of overall treatment
seeking rate is consistent with these studies (none of which
was nationally representative, however), we suspect that the
NLSS suffered from an under-reporting in the case of
traditional healers.2
Thirdly, one may argue that the intra-household decisionmaking process cannot be captured through a living standards survey; rather, such a survey captures the outcome of
Table 4. The gender characteristics of the sampled population (n = 8112)
Characteristics
Mean age of the child (years)
Mean age of the mother (years)
% of female children
% of mothers who attended school
% of mothers who were not in selfor wage-employment
% of mothers heading the household
Mean income per capita (Rs.)
% living in rural areas
Mean time taken to reach the
nearest health facility (minutes)
Sex of the child
——————————————————
Male
Female
p-value
Mother’s educational background
—————————————————–
Never
Attended
p-value
attended
school
school
7.2
33.3
–
11.5
7.2
33.4
–
12.1
0.91
0.56
–
0.42
7.2
34.1
48.6
–
5.9
28.1
50.3
–
0.00
0.00
0.42
–
22.3
8.0
6187
93.9
22.3
7.4
6065
94.0
0.64
0.39
0.22
0.79
20.5
7.8
5522
96.2
35.9
6.8
10 236
77.7
0.00
0.52
0.00
0.00
80.7
82.9
0.55
84.7
53.4
0.01
230
Subhash Pokhrel and Rainer Sauerborn
those decisions. This is true if one examines only one
outcome variable such as ‘treatment sought’ or ‘choice of
provider’, as has widely been the practice in many utilization
studies (e.g. Levin et al. 2001; Ha et al. 2002). In this paper,
this issue has been addressed to a large extent.
Fourthly, our data reflect a cross-sectional picture and hence
are unable to capture the seasonal variations in decisionmaking by the same household, if any (Sauerborn et al.
1996b).
Nevertheless, creating a four-step construct to map out the
decision-making process in hierarchical steps has been useful
in two respects. First, the variation between different choices
within the same decision episode has been well captured. For
example, all perceived illness does not end up in treatment,
nor is equal treatment predictable between different groups
having the same need. This observation clearly has tremendous implications for policy, in particular that related to
distributional problems such as removing barriers to access.
Secondly, the same background characteristic which explains
variation in one choice may not explain the variation in other
choices in the decision process. This makes it possible to see
where (in which step) a particular factor appears as a policy
lever and, as we shall discuss shortly, also opens up a window
to explaining gender differential in child mortality in South
Asian countries, including Nepal.
There is already a large body of literature on gender bias in
the allocation of health care and food within households
(Chen et al. 1981; Das Gupta 1987; Pandey et al. 2002;
Borooah, forthcoming) or in mortality rates (D’Souza and
Chen 1980; WHO 2000) in South Asia. We also found,
though by bivariate analysis, that households were less likely
to perceive illness (or at least report it) of girls than of boys.
Once the child was reported ill, households’ subsequent
decisions on health care remained free from such biases.
Upon further analysis, however, we found that households
sought care for infant boys more often than for infant girls,
but the difference was insignificant in elder children. Health
care needs of boys are usually higher in the neonatal period
(1 month after the birth) for biological reasons, but after that
any differences in care-related decisions by the sex of the
child are more likely due to social discrimination. Unfortunately, we could not make a distinction between neonatal and
post-neonatal age for infants. Nonetheless, we cannot readily
assume that the difference in seeking care for infants by sex
of the child is due to an overwhelming number of neonates
in this age-group, and therefore boys needed more care than
girls for biological reasons, especially in the light of our
findings that sex difference did not exist by the type of illness
reported. Except for diarrhoea, gender was not associated
with any of the subsequent decision steps (expenditure on
treating diarrhoea of boys was significantly higher). Since
treating diarrhoea of boys cost households more at all types
of provider, it could be a systematic preference in favour of
boys. Unfortunately, we do not have data on the outcome of
the treatment and it is therefore not clear whether spending
less money on girls could have led to increased mortality of
girls from diarrhoea – an implication for future research.
Our results indicate that the underlying household dynamics
of illness perception are different for boys and girls, as illustrated by Figures 3 and 4. Strong son-preference exists in
many South Asian countries, including Nepal, because
parents need old age security and continuity of lineage (Das
Gupta 1987; Niraula and Morgan 1995; Leone et al. 2003). As
investing in girls may be equivalent to investing in a human
resource that will shift outside of the family after marriage,
investment in daughters is not perceived to contribute in the
same manner as investment in sons (Chen et al. 1981). This
suggests that discrimination against females would be worse
in low-income households. However, we did not find that
income played a strong role on the perception of illness in
the full sample, but it held almost a horizontal association
with perception of illness in the sample of boys only, as
opposed to the non-linear relationship in the sample of girls
only.
This apparently contradictory finding is consistent with Das
Gupta’s (1987) observation that discrimination against
female children in India may not be motivated primarily by
economic hardship, but scarcity of resources may heighten
discrimination against females in societies where a culture of
‘son-preference’ exists. Although sons are undoubtedly
preferred in Nepalese society because they are the ones who
can perform death and post-death rituals and continue the
family name, daughters are wanted because of the special
role girls play during religious occasions (Karki 1988). Poor
households are faced with the dilemma of spending money
on a female child at the onset of her illness when there is still
a chance she may get better by herself, or waiting until she is
really sick and then spending more money in fear of losing
her. Increased income is likely to reduce resource constraints
so that household decisions do not become a trade-off
between who (a boy or a girl) should be treated.
Our study indicates that gender counts in illness perception,
but not necessarily in the subsequent choices related to
provider and expenditure. It is important to note that our
study looked at only one health-seeking action, and therefore, the above observation holds good only for that one
action. Although it requires to be tested by future research
that employs a more robust multivariate analysis, this observation is the outcome of the four-step construct which looked
at the different choices households faced in the decisionmaking process, unlike past studies that concentrated on the
analysis of a particular choice variable (Chen et al. 1981; Das
Gupta 1987; Pandey et al. 2002). The use of a similar
construct in a recent study reported from India (which
concentrated on only two choices) also found that gender was
not associated with the decision to seek care, but it influenced
the selection of allopathic compared with alternative medical
systems (Pillai et al. 2003).
The above hypothesis that gender counts in illness perception but not necessarily in the subsequent care-seeking
behaviours could be linked to worsening gender bias in child
mortality during the 1970s and lack of progress thereafter.
Most of the causes of death among children in Nepal (acute
respiratory infection, diarrhoea, etc.) are avoidable by appropriate and timely health interventions. The focus of the
Household decisions on child health care
health care system in Nepal has therefore been on the expansion of health facilities, creating better access for the
predominantly rural population living in a rather difficult
terrain. Despite this, the utilization of such facilities has been
quite low (Niraula 1994; NPC 1997; MOH 2002). As our
results indicate, use of health care is largely dependent on the
perception of illness. While the low level of utilization could
simply be the reflection of a low level of illness perception
(10%), the differential illness perception by the sex of the
child ultimately leads to even lower health care use by female
children.3 Lower perception of illness does not necessarily
mean higher health status (Sen 2002). For common diseases
like respiratory infections and diarrhoea, this could involve
fatal consequences.
The effect of the mother’s status on child health care
decisions is noteworthy. The results indicate that it is not only
the gender of the child, but also the gender of the household
head, that may influence the decision-making process. For
example, mothers with schooling were more likely to
perceive illness in their children and seek care. The mothers
who attended school in our sample had different economic,
geographic and demographic status than mothers who never
attended school. The differential decision-making pattern
could therefore be attributed to this difference in the
mother’s status. The receipt of higher income may facilitate
participation in certain social institutions, which may be so
organized as to bring their members into contact with
medical facilities (McKinlay 1972).
If a woman heads the household, she is more likely to
perceive (or at least report) her child’s illness, but she is less
likely to seek care. Being the head of the household, she will
probably face resource constraints (her time as well as
support from male members in the family, for instance) while
seeking health care for her child (Levin et al. 2001). The
maternal employment argument, as put forward by Basu
(1990) to explain the lower health care use for children in
India, is unlikely to be applicable in Nepal; whether or not a
mother was in self- or wage-employment made no difference
in any of the decision-steps. This may be due to the low level
of formal employment in our study population, as opposed
to Basu’s study population where many mothers had formal
employment far away from home.
Turning to policy implications, since the perception of illness
is probably the single most important factor determining
subsequent care-seeking,4 any policy or intervention needs to
aim at understanding or, if needed, influencing the way in
which the illness of a boy or a girl is being perceived (or at
least reported) in the community. Policies like lowering
financial barriers to health care for women and children or
improving health care provision in rural areas, in anticipation
that more women and children would use the services, may
not, on their own, help to achieve the desired results in the
long run, because they do not address the problem (gender
bias) in that step of the household decision-making process
where such a policy could have played a substantial role. This
adds to the considerable debate as to whether the mere
provision of health services will lead to increased utilization
(Obermeyer 1993; Magadi et al. 2000) and the reality as seen
231
in the Nepalese context – the low level of utilization despite
substantial supply of health care (NPC 1997; Niraula 1994;
MOH 2002).
As our results indicate, in the lowest income group, reducing
resource constraints by means of a subsidy to child health
care services may help reduce this bias because the household decisions do not then become a trade-off between who
(a boy or a girl) should be treated. In the higher income
groups, mother’s education could be a policy lever.
Matsumura and Gubhaju (2001) document that even
primary-level education can significantly increase the
chances of a woman using maternal health care from a
modern health facility in Nepal, and one can expect the same
in the case of their children. In the long run, a mother’s
education, on the one hand, may reduce her likelihood of
resource constraint by increasing income, and on the other
hand, helps her better perceive illness in her children regardless of their sex, as higher education and income may link her
to certain social institutions that foster health care utilization
(McKinlay 1972) and also discourage discrimination by
gender.
Lastly, our study has substantiated several points that have
already been raised in the health care utilization literature
from developing countries. First, the fact that there is high
risk associated with a sick child going without any sort of
external health care raises concerns, not only for the health
of the individual child, but also from a disease control point
of view, particularly pertaining to infectious diseases.
Secondly, a market is clearly visible for child health care in
Nepal, as private providers hold one-quarter of the total
stakes; therefore, private provision may be encouraged in
urban and rich areas, shifting public resources to improve
quality of care in rural areas (Ha et al. 2002). However,
although people show willingness to pay, this may not reflect
their ability to pay, and if private markets are not regulated,
the consequences of this redistribution could be devastating
(Hsiao 1995). The government may therefore utilize the
freed-up resources in strongly regulating private provision.
Thirdly, child health care expenditure in Nepal is regressive;
poor people have to spend more of their earnings on child
health care than the rich, and in order to protect their livelihood, financing reforms like community-based insurance
could be sought (CMH 2001; Ranson 2002). Unless the
national health system addresses distributional problems first
(e.g. stimulating need-based demand through financing
mechanisms such as insurance or subsidy to child care), the
benefits of medical technologies, such as oral rehydration
therapy to deal with diarrhoea, can never be realized fully
(Pokhrel and Sauerborn 2003).
Conclusion
This paper explored the quantitative dimension of the qualitative ‘pathway’ model of health-seeking behaviour by
creating a four-step construct that mapped out household
decision-making on child health care in developing countries.
Several interesting points emerged from the descriptive
analysis based on Nepalese data. First, the same background
characteristic which explains variation in one choice may not
232
Subhash Pokhrel and Rainer Sauerborn
explain the variation in other choices within the same
decision episode. This makes it possible to see where (in
which step) a particular factor appears as a policy lever.
Secondly, there may be conceptually different household
dynamics that underlie illness perception regarding boys and
girls. Policies such as lowering costs of health care use for
women and children or improving health care provisions in
rural areas alone may therefore not work, as they do not
address the step in the decision-making process where the
gender bias enters. Thirdly, the differential child mortality
rate in South Asia, in particular in Nepal, is likely to be the
outcome of differential access to health care arising primarily
from differences (by the gender of the child) in illness
perception. Care-seeking or spending decisions when illness
is perceived may be free from such biases. However, this
hypothesis needs to be tested by future research that employs
a more robust multivariate analysis.
Endnotes
1 We are thankful to the anonymous referee who showed us this
adventurous analytical path.
2 NLSS asked the individuals, “Was anyone consulted (e.g. a
doctor, nurse or other healer) for the illness or injury?” followed by
another question, “Who was consulted first?” Note that modern
healers came first when interviewers read out the examples of
healers. This might have tempted respondents to appear ‘modern’
and conceal the choice of traditional healers (‘unmodern’ action)
from survey enumerators.
3 What is implicit here is the assumption that we would expect,
in general, the likelihood of boys and girls getting sick to be equal.
Differences in perception of illness could lead to larger differences
in subsequent care seeking and spending behaviour. However, as we
have found, once households perceive illness of girls, they do not
discriminate against them in care seeking. Analysis based on the
sample of ‘reported ill’ only, as has been a practice, will therefore
give biased results. Hence, the need to capture all steps in the
decision-making process, as was the case with the four-step
construct.
4 As discussed in the introduction, all conceptual as well as
empirical models of health seeking behaviour acknowledge this
assumption. Our empirical data also justifies it.
References
Acharya LB, Cleland J. 2000. Maternal and child health services in
rural Nepal: does access or quality matter more? Health Policy
and Planning 15: 223–9.
Akin JS, Guilkey DK, Hutchinson PL, McIntosh MT. 1998. Price
elasticities of demand for curative health care with control for
sample selectivity on endogenous illness: an analysis for Sri
Lanka. Health Economics 7: 509–31.
Anderson R. 1968. A behavioral model of families’ use of health
services. Research Series No. 25. Chicago: Center for Health
Administration Studies, University of Chicago.
Basu AM.1990. Cultural influences on health care use: two regional
groups in India. Studies in Family Planning 21: 275–86.
Borooah VK. forthcoming. Gender bias among children in India in
their diet and immunization against disease. Social Science and
Medicine (in press).
Caldwell JC, Reddy PH, Caldwell P. 1983. The social component of
mortality decline: an investigation in South India employing
alternative methodologies. Population Studies 37: 185–206.
Carmel S. 1991. The Health Belief Model in the research of AIDSrelated preventive behavior. Public Health Review 18: 73–85.
Chen L, Huq E, D’Souza S. 1981. Sex bias in the family allocation
of food and health care in rural Bangladesh. Population and
Development Review 7: 55–70.
Ching P. 1995. User fees, demand for children’s health care and
access across income groups: the Philippine case. Social Science
and Medicine 41: 37–46.
CMH. 2001. Macroeconomics and health: investing in health for
economic development. Report of the commission on macroeconomics and health. Geneva: World Health Organization.
Das Gupta M. 1987. Selective discrimination against female children
in rural Punjab, India. Population and Development Review 13:
77–100.
D’souza S, Chen L. 1980. Sex differentials in mortality in rural
Bangladesh. Population and Development Review 6: 257–70.
Flessa S. 2002. Gesundheitsreformen in Entwicklungslandern
[Health sector reform in developing countries]. Frankfurt aM:
Lembeck.
Grossman M. 1972. On the concept of health capital, the demand
for health. Journal of Political Economy 80: 223–55.
Ha NT, Berman P, Larsen U. 2002. Household utilization and expenditure on private and public health services in Vietnam. Health
Policy and Planning 17: 61–70.
Hochbaum GM. 1958. Public participation in medical screening
programs: A sociopsychological study. PHS publication no. 572.
Washington, DC: US Government Printing Office.
Hotchkiss DR, Rous JJ, Karmacharya K, Sangraula P. 1998. Household health expenditures in Nepal: implications for health care
financing reform. Health Policy and Planning 13: 371–83.
Hsiao WC. 1995. Abnormal economics in the health sector. In:
Berman P (ed). Health sector reform in developing countries:
making health sector reform sustainable. Boston, MA: Harvard
School of Public Health.
Jack W. 1999. Principles of health economics for developing countries. Washington, DC: the World Bank.
Jimba M, Poudyal AK, Wakai S. 2003. The need for linking healthcare-seeking behavior and health policy in rural Nepal. Southeast Asian Journal of Tropical Medicine and Public Health 34:
462–3.
Karki YB. 1988. Sex preference and the value of sons and daughters
in Nepal. Studies in Family Planning 19: 169–78.
Kroeger A. 1983. Anthropological and socio-medical health care
research in developing countries. Social Science and Medicine
17: 147–61.
Leone T, Matthews Z, Dalla Zuanna G. 2003. Impact and determinants of sex preference in Nepal. International Family Planning
Perspectives 29: 69–75.
Levin A, Rahman MA, Quayyum Z, Routh S, Barkat-e-Khuda.
2001. The demand for child curative care in two rural thanas of
Bangladesh: effect of income and women’s employment. International Journal of Health Planning and Management 16:
179–94.
Magadi MA, Madise NJ, Rodrigues RN. 2000. Frequency and timing
of antenatal care in Kenya: explaining the variations between
women of different communities. Social Science and Medicine
51: 551–61.
Mariko M. 2003. Quality of care and the demand for health services
in Bamako, Mali: the specific roles of structural, process, and
outcome components. Social Science and Medicine 56: 1183–96.
Matsumura M, Gubhaju B. 2001. Women’s status, household structure and the utilization of maternal health services in Nepal.
Asia Pacific Population Journal 16: 23–44.
McKinlay J. 1972. Some approaches and problems in the study of
the use of services: an overview. Journal of Health and Social
Behavior 13: 115–52.
MOH. 2002. Department of Health Services Annual Report 2058/59
(2001/02). Kathmandu: Ministry of Health.
Niraula BB. 1994. Use of health services in hill villages in central
Nepal. Health Transition Review 4: 151–66.
Niraula BB, Morgan SP. 1995. Son and daughter preferences in
Benighat, Nepal: implications for fertility transition. Social
Biology 42: 256–73
Household decisions on child health care
NPC. 1997. Service delivery survey: health and agriculture services.
Nepal Multiple Indicators Surveillance, Sixth Cycle. November.
Kathmandu: National Planning Commission Secretariat.
Obermeyer CM. 1993. Culture, maternal health care, and women’s
status: a comparison of Morocco and Tunisia Studies in Family
Planning 24: 354–65.
Pandey A, Sengupta PG, Mondal SK et al. 2002. Gender differences
in healthcare-seeking during common illnesses in a rural
community of West Bengal, India. Journal of Health, Population and Nutrition 20: 306–11.
Pillai RK, Williams SV, Glick HA et al. 2003. Factors affecting
decisions to seek treatment for sick children in Kerala, India.
Social Science and Medicine 57: 783–90.
Pokhrel S, Sauerborn R. 2003. Medical spending and health outcome
in Nepal: problems with the technology or its distribution?
Bulletin of the World Health Organization 81: 844–5.
Prennushi G, Central Bureau of Statistics. 1996. Nepal living standards survey I (1995/96): Survey design and implementation.
Washington, DC: World Bank. Available from [http://www.
worldbank.org/lsms/country/nepal/nep96bidr.pdf].
Ranson MK. 2002. Reduction of catastrophic health care expenditures by a community-based health insurance scheme in
Gujarat, India: current experiences and challenges. Bulletin of
the World Health Organization 80: 613–21.
Rous JJ, Hotchkiss DR. 2003. Estimation of the determinants of
household health care expenditures in Nepal with controls for
endogenous illness and provider choice. Health Economics 12:
431–51.
Sauerborn R, Nougtara A, Latimer E. 1994. The elasticity of
demand for health care in Burkina Faso: differences across age
and income groups. Health Policy and Planning 9: 185–92.
Sauerborn R, Berman P, Nougtara A. 1996a. Age bias, but no
gender bias, in the intra-household resource allocation for
health care in rural Burkina Faso. Health Transition Review 6:
131–45.
Sauerborn R, Nougtara A, Hien M, Diesfeld HJ. 1996b. Seasonal
variations of household costs of illness in Burkina Faso. Social
Science and Medicine 43: 281–90.
Sen A. 2002. Health: perception versus observation. British Medical
Journal 324: 860–1.
StataCorp. 2001. Stata Statistical Software: Release 7.0. College
Station. Texas: Stata Corporation.
Ward H, Mertens TE, Thomas C. 1997. Health seeking behaviour
233
and the control of sexually transmitted disease. Health Policy
and Planning 12: 19–28
WHO. 1998. Gender and health. Technical paper. Geneva: World
Heath Organization.
WHO. 2000. Women of South-East Asia: a health profile. New Delhi:
South East Asia Regional Office, World Health Organization.
Acknowledgements
This study is a part of a broader research on demand for child health
care in Nepal carried out under the auspices of the Health Systems
Research Junior Group supported by Tropical Medicine Heidelberg
(TMH). The authors would like to thank Cheryl Cashin, Rachel
Snow, Steffen Flessa, Budi Hidayat, Hengjin Dong, KR Nayar,
Heiko Becher, Manuela De Allegri, Anayo Akunne and Devendra
Gnawali for their useful comments at various stages. Special thanks
go to the two anonymous referees whose input substantially
improved this paper. The NLSS dataset was provided by the Central
Bureau of Statistics, Kathmandu, Nepal. The views expressed in this
paper are those of the authors and not necessarily of the organization they represent.
Biographies
Subhash Pokhrel holds an MSc in Statistics and Health Economics
and is a doctoral student at the Department of Tropical Hygiene and
Public Health, University of Heidelberg, Germany. He has worked
at the Institute of Medicine, Tribhuvan University, Kathmandu, and
has several years of experience in health policy and systems research
in Nepal.
Rainer Sauerborn, MD, MPH, Ph.D., is Professor of Public Health
and Director of the Department of Tropical Hygiene and Public
Health at the University of Heidelberg, Germany. He has published
extensively on the demand for and financing of health care services
in rural Burkina Faso.
Correspondence: Subhash Pokhrel, Department of Tropical Hygiene
and Public Health, Im Neuenheimer Feld 324, D-69120 Heidelberg,
Germany. Tel: +49 6221–564886; Fax: + 49 6221–565948; Email:
[email protected].