Original Article
Page 1 of 11
Analysis of equity in utilization of health services in Afghanistan
using a national household survey
Farhad Farewar 1, Khwaja Mir Ahad Saeed 1, Abo Ismael Foshanji 1, Said Mohammad Karim Alawi 1,
Mohammad Yonus Zawoli2, Omarzaman Sayedi2, Wu Zeng3
1
Health Economics and Financing Directorate, Ministry of Public Health, Kabul, Afghanistan; 2Health Sector Resiliency (HSR) Project, Palladium,
Kabul, Afghanistan; 3Georgetown University, Washington, DC, USA
Contributions: (I) Conception and design: F Farewar, O Sayedi, W Zeng; (II) Administrative support: None; (III) Provision of study materials or
patients: None; (IV) Collection and assembly of data: KMA Saaed, AI Foshanji, SMK Alawi, MY Zawoli; (V) Data analysis and interpretation: F
Farewar, KMA Saeed, AI Foshanji, SMK Alawi, W Zeng; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.
Correspondence to: Farhad Farewar. Health Economics and Financing Directorate, Ministry of Public Health, Kabul, Afghanistan.
Email: hefd.farewar@gmail.com.
Background: Afghanistan has made significant progress in improving the health status of its population
by improving access, coverage, and quality of health services since 2002. As a result, child and maternal
mortality rates have considerably decreased. Despite this progress, however, concerns have been increasing
over inequity in the utilization of health care.
Methods: Data from the Afghanistan Living Conditions Survey (ALCS 2016/17) were analyzed to examine
inequities in using health care. Wealth was measured using consumption of both consumables and durable
goods. Key health services studied were inpatient and outpatient care use in the public and private sectors.
The use of inpatient and outpatient care was compared by wealth status, marriage status, age group, gender,
and education level using F tests. Logistic and negative binomial regression models were used to examine
factors associated with the utilization of outpatient and inpatient care, respectively. Concentration indexes
(CIs), the composite measure of inequalities, were generated for both outpatient and inpatient services, and
CIs were broken down by potential drivers of the inequalities.
Results: The study shows that households in the wealthiest quintile used more outpatient and inpatient
health care compared to those in the poorest quintile. Overall utilization of inpatient and outpatient care
was pro-rich, with a CI of 0.123 and 0.174, respectively. There was greater inequality in utilization of health
services provided by private health facilities, with a CI of 0.288 and 0.234 for outpatient and inpatient care,
respectively. The use of health services in public facilities was more evenly distributed among the population,
with CIs close to zero (0.014 and 0.093 for outpatient and inpatient services, respectively). The breakdown
of CIs shows that location was one of key drivers of inequalities in utilization of care, which prevailed in both
inpatient and outpatient health services.
Conclusions: There is significant inequality in the use of inpatient and outpatient care in Afghanistan.
Although the utilization of health services in public facilities is more equal, the utilization of care in private
facilities is pro-rich. As the private sector provides more than half of outpatient care services, it is critical
to address this inequality. Improving physical access and quality of care in public facilities, and expanding
programs that address potential financial barriers, could help reduce the inequity.
Keywords: Inequity; inequality; utilization of care; Afghanistan
Received: 07 May 2020. Accepted: 28 September 2020; Published: 25 December 2020.
doi: 10.21037/jhmhp-20-63
View this article at: http://dx.doi.org/10.21037/jhmhp-20-63
© Journal of Hospital Management and Health Policy. All rights reserved.
J Hosp Manag Health Policy 2020;4:34 | http://dx.doi.org/10.21037/jhmhp-20-63
Page 2 of 11
Introduction
Since 2002, Afghanistan has made significant progress in
improving the health status of its population by expanding
access to and coverage of health services, in addition to
enhancing the quality of health services. The population’s
access to health services within a two-hour travel distance
has increased substantially, from nine percent in 2002
to nearly 87% in 2014, and to over 90% in 2018 (1,2).
Accompanying the upward trend in health services coverage
is the downward trend in infant, under-five, and maternal
mortality rates, which exemplifies the improvement in
health outcomes. Despite the overall improvement in
the country’s health status and health services, improving
equity in health services utilization is of concern to the
Government of Afghanistan, particularly with regard to
services used by the poor and other vulnerable populations.
According to findings from the Afghanistan Health
Survey 2018 (AHS), three-quarters of people who had a
health complaint sought treatment outside their homes. In
addition, the AHS suggest that access to treatment varies
from 64.8% among those in the lowest wealth quintile to
80.5% in the high-income quintile. The AHS also estimated
that utilization of private health sector services accounted
for nearly two-thirds of the cases that sought care. Clinics
funded or operated by the Ministry of Public Health
(MoPH) served 21.1% of cases, and MoPH hospitals served
9.4% of cases. MoPH clinics were more frequently used
by rural residents (25.9%) compared to urban (9.9%) and
by those in the lowest quintile (35.6%) (2). These statistics
suggest the existence of inequality in the use of health care
in Afghanistan.
As equity is a critical element in evaluating the
performance of a country’s health system, many countries
have conducted equity analyses to provide evidence for
more informed decision making by identifying at-risk and
vulnerable populations that are left behind in using health
care services. For example, an equity study examining
maternal and child health services (MCH) in Thailand
showed that socioeconomic factors, especially education,
had effects on health outcomes (3). Mothers or caregivers
with the highest level of education had a better result across
all health outcome indicators compared to those without
formal education (4).
A study in Malawi (5) found that the rich used selected
health services more often than the poor. In some countries,
although interventions to increase overall utilization
of health care were implemented, the benefits varied
© Journal of Hospital Management and Health Policy. All rights reserved.
Journal of Hospital Management and Health Policy, 2020
significantly among populations, with the rich sometimes
benefiting more from such interventions. A study of poor
and non-poor pregnant women in western rural China
reported that the use of MCH services was unequal (6).
Since early 2000, health insurance coverage in China
has expanded substantially. However, it was found that
insurance benefited the rich more than the poor, and
that resources were disproportionately used by the rich,
exacerbating the inequality (7).
Afghanistan has been facing a triple-disease burden of
communicable diseases, non-communicable diseases, and
injuries. In addressing preventable MCH conditions, the
Government of Afghanistan, with support from donors,
has collaborated with development partners to pilot,
implement, and monitor health programs, including
those targeting the poorest populations. As a result, the
country has systematically recorded core MCH health
indicators through household surveys, such as the Multiple
Indicator Cluster Survey, and the 2018 AHS. Indicators
such as skilled birth attendance, antenatal care coverage,
contraceptive prevalence rate, and child immunization
are regularly tracked. These surveys contain information
critical to understanding the inequality of health care use
among different populations.
In this study, we used the Afghanistan Living Conditions
Survey (ALCS 2016/17), which contains comprehensive
information about the socioeconomic status of the
population to estimate inequity in the use of inpatient
and outpatient care in the public and private sectors. The
survey produced information at the national and provincial
levels, tracked seasonality of indicators, and is the only
national survey that includes Afghanistan’s nomadic Kuchi
population.
We present the following article in accordance with
the SURGE reporting checklist (available at http://dx.doi.
org/10.21037/jhmhp-20-63).
Methods
Conceptually, inequality considers any difference in health
utilization among population groups, while inequity
includes only unjustified inequality. Thus, to understand
inequity, a breakdown of inequality into justifiable and
unjustifiable inequality is needed.
Data source
To conduct an inequality and inequity analysis of
J Hosp Manag Health Policy 2020;4:34 | http://dx.doi.org/10.21037/jhmhp-20-63
Journal of Hospital Management and Health Policy, 2020
utilization of health services requires information on
(I) wealth status, such as household assets, income, and
expenditure; (II) utilization of health services, such as use
of inpatient care, outpatient care, child care, or maternal
care; and (III) factors that explain the inequality (e.g.,
age, gender, education, and location). In this study, we
extracted data from the ALCS 2016/17, which was the most
recent national survey of household living conditions in
Afghanistan (8). The survey collected data not only on the
socioeconomics and demographics of individuals (e.g., age,
gender, education, employment), but also on household
characteristics. It provided data on expenditures and assets,
detailed information on utilization of health care, education
attendance, and food security.
The survey used a two-stage sampling approach,
consisting of 35 strata with 34 provinces and one stratum
for the nomadic Kuchi population. In the first stage, for
each stratum, enumeration areas (EAs) were sampled with
probability proportional EA size. In the second stage,
ten households were randomly selected as the Ultimate
Sampling Unit. In total, about 21,000 households and more
than 150,000 persons were included in the survey. The
detailed sampling process is described in the ALCS (8).
Measurement
As mentioned above, key data for conducting the equity
analysis included wealth status, use of health services, and
potential explanatory variables for inequality. Below, we
provide more detailed information on how these three types
of variables were measured.
Measurement of wealth status
Wealth status was measured using the annual total
consumption within a household. In order to identify a
precise measure of wealth status, the survey asked about
monthly consumption of 21 consumables (such as food,
cigarettes, tobacco, soap, shampoo, charges for mobile
phone, and transportation), and yearly consumption of
19 durable goods (such as shoes, clothing, education fees,
and textbooks). The monthly consumption of consumables
was multiplied by 12 to generate yearly estimates. The
study estimated the total annual consumption by summing
the annual consumption of consumables and durable goods.
Adjusting total annual consumption for household size,
by generating annual consumption per capita, the study
provided an estimate of the wealth status of the household.
The households were then divided into five quintiles based
© Journal of Hospital Management and Health Policy. All rights reserved.
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on the consumption per capita.
Measurement of utilization of health care services
The survey asked if respondents had been admitted to a
hospital for inpatient care in the last 12 months, and if they
sought care for outpatient care in the month prior to when
the survey was administered. For inpatient care, respondents
were asked how many admissions occurred during the last
year. Therefore, the outcome variable took values such as 0,
1, 2, 3, 4 or more. The survey did not ask for the number of
visits for outpatient care, and so we only know if individuals
sought care in the last month. Additionally, the survey
provided information on where services were obtained
for the last admission/visit. This allowed us to conduct
an analysis of service utilization by health facility type at
public facilities (e.g., national hospital, regional hospitals,
provincial hospitals, district hospitals, poly clinics, and
other public clinical units) or private facilities (e.g., private
hospitals, private clinics, and other private clinical units).
Measurement of potential explanatory variables
The study used five variables as potential explanatory variables:
age, gender, marriage status, education status, and location of
households. The survey asked the age of individual members
of the household and age is noted as a continuous variable. For
our analysis, we recoded age as an ordered variable, with age
groups of 0–10 years old, 11–20 years old, 21–30 years old,
31–40 years old, 41–50 years old, 51–60 years old, and
60+ years old. The study categorized gender as male (coded
as 1) and female (coded as 0). And for marriage status, the
study created five categories: married, widowed, divorced, or
separated, engaged, and never married.
For our analysis, we re-categorized marriage status as
married (coded as 1, consisting of the first three categories)
and not married (coded as 0, consisting of the latter two
categories). We also reduced the number of categories for
location of households from three (urban, rural, and Kuchi)
to two (urban and rural, where rural combines rural and
Kuchi). Given that the majority of the Afghan population
does not receive an education, we also recoded the
education level into two levels: Those who did not attend
school were classified as a “no education” group (education
=0) and those who attended school were classified as a “some
education” group (education =1).
Analysis
A descriptive analysis of the population was conducted. Use
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Journal of Hospital Management and Health Policy, 2020
Table 1 Demographic characteristics of surveyed sample
Characteristics
Mean ± standard error or percentage
Household size
7.70±0.03
Age (years)
20.50±0.05
Age groups (years)
0–10
33.81%
11–20
24.77%
21–30
16.11%
31–40
9.55%
41–50
6.94%
51–60
4.45%
60+
4.37%
magnitude of inequality. If the index takes a negative value
when the curve lies above the line of equality, this indicates
a disproportionate concentration of the health variable
among the poor. The CI takes a positive value when it lies
below the line of equality.
We conducted all analyses using Stata. To estimate the
CI and generate the concentration curves, we followed
the approach and used the Stata command proposed by
O’Donnell et al. (9). The study was conducted in accordance
with the Declaration of Helsinki (as revised in 2013). Since
we were using the secondary data for which an IRB approval
and informed consent was already secured, we assume no
further consent and IRB approval was required for this.
Results
Marriage
Not married
60.82%
Married
39.18%
Residency
Rural
76.24%
Urban
23.76%
Education
No education
69.78%
Some education
30.22%
of inpatient and outpatient care was compared by wealth
status, marriage status, age group, gender, and education
using F tests or t-tests. We used logistic and negative
binomial regression models to examine factors associated
with the utilization of outpatient and inpatient care,
respectively.
We conducted the key analysis of inequality by
generating concentration indexes (CIs) and concentration
curves. The CI is defined in terms of the concentration
curve and takes a value of between zero and one. The
concentration curve plots the cumulative percentage of
the health variable on the Y axis against the cumulative
percentage of the population, ranked by socioeconomic
status beginning at the poorest and ending with the richest
on the X axis. If everyone has exactly the same value of
the health variables, the concentration curve will be a
45-degree line (line of equality). The further the curve is
from the line of equality, the more concentrated the degree
of health inequality (9). The CI provides a measure of the
© Journal of Hospital Management and Health Policy. All rights reserved.
The ALCS survey contained 21,000 households and more
than 150,000 persons. The average age in the sampled
population was 20.50 years old (Table 1). Children 0–10 years
old accounted for 33.81% of the sampled population, the
largest share in the sample; those 11–20 years old accounted
for 24.77%, the second largest in the sample. The survey also
shows that the majority of the population lived in rural areas,
accounting for 76.24% of the sampled population; 69.78% of
the sampled population had no education.
Table 2 shows the utilization of outpatient and inpatient
care by individuals based on their affiliation to one of the
wealth quantiles, age group, gender, marital status, location,
and education. The results show that with the increase
of wealth status, the use of outpatient care and private
outpatient care consistently increased. Just 3.9% of the
population in the lowest quintile used outpatient care in
the month prior to the survey, as compared to 13.0% of
those in the highest wealth quintile. However, using public
outpatient care did not show a consistent pattern. The use
of public outpatient care peaked among those in the third
quintile.
Overall, the utilization of care among different age
groups was in a “U” shape, with relatively higher utilization
among those 1–10 years old and lower utilization among
those 11–20 years old, and a continuous increase of
utilization as people aged. The percentage of the population
using overall outpatient care in the month before the survey
ranged from 0.046 to 0.171; the percentage using inpatient
care in the year before the survey ranged from 0.0022 to
0.141. There were differences of use of overall, public,
and private outpatient and inpatient care among different
age groups (P<0.05). Females used more outpatient and
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Journal of Hospital Management and Health Policy, 2020
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Table 2 Utilization of outpatient and inpatient care by household characteristics
Outpatient care (mean ± Std. Err)
Inpatient care (mean ± Std. Err)
Characteristics
All
Public
Private
All
Public
Private
1st quintile
0.039±0.001
0.017±0.001
0.021±0.001
0.030±0.002
0.025±0.002
0.005±0.001
2nd quintile
0.066±0.002
0.029±0.001
0.036±0.001
0.040±0.002
0.028±0.002
0.011±0.001
3rd quintile
0.083±0.002
0.030±0.001
0.054±0.002
0.037±0.002
0.025±0.001
0.012±0.001
4th quintile
0.102±0.002
0.029±0.001
0.073±0.002
0.045±0.002
0.028±0.001
0.017±0.001
5th quintile
0.130±0.002
0.025±0.001
0.105±0.002
0.071±0.003
0.040±0.002
0.031±0.003
388.81*
33.52*
411.94*
31.75*
8.28
43.37*
0–10
0.095±0.002
0.033±0.001
0.062±0.001
0.035±0.002
0.025±0.001
0.010±0.001
11–20
0.046±0.001
0.013±0.001
0.032±0.001
0.022±0.001
0.016±0.001
0.006±0.001
21–30
0.075±0.002
0.020±0.001
0.054±0.002
0.052±0.003
0.034±0.002
0.017±0.002
31–40
0.095±0.003
0.027±0.002
0.067±0.003
0.057±0.004
0.037±0.003
0.020±0.002
41–50
0.127±0.004
0.036±0.002
0.090±0.003
0.072±0.005
0.036±0.003
0.036±0.004
51–60
0.138±0.005
0.033±0.003
0.105±0.005
0.086±0.007
0.047±0.005
0.038± 0.005
60+
0.171±0.006
0.042±0.003
0.128±0.005
0.141±0.007
0.088±0.006
0.052±0.004
221.7***
70.67***
147.98***
77.35***
37.26***
42.96***
Female
0.106±0.001
0.032±0.001
0.074±0.001
0.057±0.002
0.036±0.001
0.020±0.001
Male
0.069±0.001
0.020±0.001
0.048±0.001
0.036±0.001
0.023±0.001
0.012±0.001
457.34***
156.88***
282.97***
96.35***
67.18***
29.95***
Not married
0.073±0.001
0.024±0.001
0.049±0.001
0.029±0.001
0.020±0.001
0.009±0.001
Married
0.109±0.002
0.029±0.001
0.079±0.001
0.073±0.002
0.045±0.002
0.027±0.001
F value
365.69***
30.79***
341.15***
345.18***
200.33***
151.52***
Rural
0.080±0.001
0.027±0.001
0.052±0.001
0.044±0.001
0.029±0.001
0.014±0.001
Urban
0.111± 0.002
0.023±0.001
0.088±0.002
0.052±0.003
0.031±0.002
0.021±0.002
F value
162.39***
9.72***
256.61***
8.09**
0.51
11.78***
No education
0.103±0.002
0.029±0.001
0.074±0.001
0.070±0.002
0.043±0.002
0.026±0.001
Some education
0.065±0.002
0.015±0.001
0.050±0.002
0.035±0.002
0.021±0.001
0.013±0.001
238.46***
132.05***
119.78***
148.24***
102.87***
48.06***
0.213±0.006
0.041±0.010
0.288±0.008
0.174±0.016
0.093±0.018
0.324±0.033
∆
Wealth
F value
Age (years)
F value
Gender
F value
Marital status
Location
Education
F value
Total
Δ
*, P<0.05; **, P<0.01; ***, P<0.001. , 1st quintile are the poorest households, 5th quintile are the richest households; Std. Err., standard
error.
© Journal of Hospital Management and Health Policy. All rights reserved.
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Journal of Hospital Management and Health Policy, 2020
Table 3 Logistic regression model for outpatient care (odds ratio)
Explanatory variables
Overall
Public
Private
Wealth quintile
1.356***
1.073***
1.475***
Age
1.269***
1.208***
1.268***
Male
0.388***
0.350***
0.428***
Marriage
1.435***
1.503***
1.398***
Urban
1.168***
0.863*
1.280***
Education
0.882***
0.947
0.862**
Constant
0.015***
0.011***
0.007***
*, P<0.05; **, P<0.01; ***, P<0.001.
inpatient care compared to males. For example, on average,
10.6% of females sought outpatient care in the month
before the survey was conducted, compared to 6.9% of
males. The difference was statistically significant (P<0.001).
Health care utilization among married people was higher
compared to not-married people, both for outpatient and
inpatient care, no matter whether it was public or private
outpatient or inpatient care. On average, 10.9% of married
people, compared to 7.3% of not-married people, sought
outpatient care during the month prior to the survey. The
difference was statistically significant (P<0.01). Similarly,
7.3% of married people, in comparison to 2.9% of notmarried people, were admitted to the hospital for inpatient
care one year prior to the survey. The difference was
statistically significant (P<0.001). Overall, people who lived
in urban areas used more inpatient and outpatient care than
those who resided in rural areas.
Taking overall outpatient care as an example, about
3.1% more people sought outpatient care in urban areas
than in rural areas (11.1% vs. 8.0%). The difference was
statistically significant (P<0.001). However, in the case of
public outpatient care, use of care was lower in urban areas
compared to rural areas (2.3% vs. 2.7%). In private facilities,
whether for outpatient or inpatient care, people in urban
areas used more care than those in the rural areas. Health
care utilization among people with no education was higher
than among people with some education (Table 2, 10.3%
vs. 6.5% for overall outpatient, and 7.0% vs. 3.5% for
overall inpatient care). These differences were statistically
significant (P<0.001).
The logistic regression model examining factors
associated with outpatient care utilization shows that wealth
quintile, age, education, location, and marital status were
all associated with the utilization of outpatient health care
© Journal of Hospital Management and Health Policy. All rights reserved.
(Table 3). Wealth, age, marital status, and location were
positively associated with the use of outpatient care (overall
private and public outpatient care), while being male and
with some education was negatively associated with the use
of outpatient care.
If the wealth were increased by one quintile, the odds
of using overall outpatient care would increase by 35.6%;
increasing category in one age ladder would increase the
odds by 26.9%; being male reduced odds by 61.2%; being
married was associated with 43.5% higher odds; living in
urban areas was associated with 16.8% higher odds; and
have some education was associated with 11.8% reduced
odds. All these factors were statistically significant (P<0.001).
For the use of outpatient care in public facilities, the
odds of using the care would increase by 7.3% if wealth
increased by one quintile. Raising one category in the
age ladder increased the odds by 20.8%. Being married
increased the odd by 53.6%. Being male and residing in an
urban area was negatively associated with the utilization
of outpatient care in public facilities. The odds of males
using public outpatient care was 35.0% of females using
care. Those residing in urban areas had 13.7% lower odds
of using public outpatient care than those residing in rural
areas. Education was not associated with the use of public
outpatient care.
For outpatient care in private health facilities, we found
that wealth status, age, marital status, and location were
positively associated with the use. If the wealth quintile were
to increase by one unit, the odds of utilization of private
outpatient care would increase by 47.5%. Increasing age by
one category in the age ladder up, the odds of using private
outpatient care would increase by 26.8%. Being married
was associated to increased odds by 39.8%. Residence in
urban areas increased the odds by 28.0%. Being male and
having some education reduced the odds of use of private
outpatient care by 57.2% and 13.8%, respectively.
The negative binomial regression model for examining
factors associated with inpatient care utilization shows that
wealth, age, gender, and marital status were associated with
the use of inpatient care, while location and education were
not (Table 4).
If the wealth quintile and age ladder increased by one
unit, the use of overall inpatient care would increase by
18.3% and 26.3%, respectively (P<0.001). People who
were married used 40.1% more inpatient care than those
who were not married (P<0.001). Males used 75.6% less
inpatient care than females did (P<0.001).
For determinants of inpatient care in public facilities,
J Hosp Manag Health Policy 2020;4:34 | http://dx.doi.org/10.21037/jhmhp-20-63
Table 4 Negative binomial regression model for inpatient care
Explanatory variables
Overall
Public
Private
Wealth quintile
0.183***
0.107***
0.344***
Age
0.262***
0.227***
0.327***
Male
−0.756***
−0.810***
−0.633***
Marriage
0.401***
0.434***
0.346**
Urban
−0.044
−0.084
0.014
Education
−0.041
−0.047
−0.046
Constant
−4.54***
−4.574***
−6.484***
Page 7 of 11
Cumulative outcome
proportion
Journal of Hospital Management and Health Policy, 2020
1.0
0.8
0.6
0.4
0.2
0.0
0
20
40
60
80
Population percentage
(ordered by consump_percapita)
inptimes
inpprivatetimes
100
inppublictimes
Figure 2 Concentration curve for inpatient care.
*, P<0.05; **, P<0.01; ***, P<0.001.
Table 5 Concentration index for outpatient and inpatient care
(mean ± Std. Err)
Outpatient
Inpatient
Overall
0.213±0.006
0.174±0.016
Public
0.041±0.010
0.093±0.018
Private
0.288±0.008
0.324±0.033
Cumulative outcome proportion
Sector
1.0
0.8
0.6
0.4
0.2
0.0
0
20
40
60
80
Population percentage
(ordered by consump_percapita)
outptimes
outpprivatetimes
100
outppublictimes
Figure 1 Concentration curve for outpatient care.
the same pattern occurred as for overall inpatient care.
Increasing the wealth status by one unit was associated
with a 10.7% increase in the utilization of public inpatient
care (P<0.001). Those married used 43.4% more public
inpatient care than those not married (P<0.001). Gender
was also associated with health care utilization. Being male
significantly reduced utilization of inpatient care by 81%
(P<0.001).
Being wealthy, older, and married were associated with
increased use of inpatient care from private facilities. Rising
one step in the wealth ladder increased utilization of private
© Journal of Hospital Management and Health Policy. All rights reserved.
inpatient care by 34.4% (P<0.001); each category up the age
ladder increased the use of private inpatient care by 32.7%
(P<0.001). Those married used inpatient care from private
health care facilities 34.6% more than their not-married
comparators (P<0.001). Gender was also associated with use
of private inpatient care. Being male decreased inpatient
health care utilization from a private health care facility.
Males used 63.3% less private inpatient health care than
females (P<0.001).
The estimated CIs (Table 5) show that the overall
utilization of outpatient care and inpatient care tended to be
pro-rich, with values of 0.213 and 0.174 for outpatient care
and inpatient admissions, respectively. When separating
care by public and private settings, we found that the
CIs were greater for the utilization of private facilities,
with 0.288 and 0.324 for outpatient visits and inpatient
admission, respectively. This shows that there was a greater
inequality in seeking care in private facilities and that it
was pro-rich. The CIs for the use of health care in public
facilities were close to zero (0.041 and 0.093 for outpatient
and inpatient services, respectively), indicating both poor
and rich used public health facilities almost equally.
Figures 1,2 show the concentration curves of outpatient
and inpatient visits, respectively. If the curve falls above
the line of equality, it indicates a tendency of pro-poor,
whereas if curves fall below the line of equality, it favors the
wealthy. As shown in Figures 1,2, concentration curves for
outpatient and inpatient care fell below the line of equality,
indicating that the use of care was in favor of the wealthy.
Concentration curves for health services in public facilities
tended to fall closer to the line of equality as compared to
those for services obtained at private facilities.
Figures 3,4 show the decomposition of inequality for
outpatient and inpatient care, respectively, by potential
determinant (e.g., age, gender, marriage, urban, and
education). It is clear that a significant portion of inequality
J Hosp Manag Health Policy 2020;4:34 | http://dx.doi.org/10.21037/jhmhp-20-63
Page 8 of 11
Journal of Hospital Management and Health Policy, 2020
0.3500
use of inpatient and outpatient health care in Afghanistan.
Overall, the inequality is pro-rich, suggesting that the
rich use more health care services than the poor. Our
findings also show that wealth status, age, marital status,
location, and education are associated with the use of
outpatient and inpatient care. Further decomposition of
inequality suggests that the major inequality lies in the use
of inpatient and outpatient care from private providers; the
use of health services from public health facilities is more
evenly distributed among the population with different
socioeconomic status.
The existence of inequality in use of health services is
consistent with findings from earlier studies conducted in
Afghanistan (10,11). On average, utilization remains low
among those in the lower income quintile, compared to
those in the higher-income quintile. Despite this, public
health facilities are more equally used regardless of wealth
status, which suggests that significant investment in public
health facilities helps reduce the inequality of health service
utilization. In fact, a prior study in Afghanistan showed
that the use of public health services was pro-poor (11) and
facilitated the timely use of care and treatment.
The core of Afghanistan’s health service delivery system
comprises the Basic Package of Health Services (BPHS),
which focuses on primary care, and the Essential Package
of Hospital Services (EPHS), which covers secondary
care. Both packages are provided free of charge through
nongovernmental organizations (NGOs) or government
facilities. Service utilization improved substantially upon
implementation of the two packages. Given the high
poverty rate among Afghans, with the majority living with
limited resources, it is expected that the government and
NGOs will continue to play a critical role in meeting the
population’s health needs, particularly of those in rural
areas. Since majority of out of pocket payment made in
2017 is on medicine and diagnostics, maintaining sufficient
stock of medicine in public health facilities is paramount in
improving equity.
Private health facilities comprise various categories from
doctors’ offices to complex hospitals. Consistent to the prior
studies in Afghanistan (12,13), we found that more than half
of outpatient care is provided by private health facilities.
While this means there is a vibrant private market for
health service delivery (more flexible hours of operation and
relatively better treatment of personnel may be two factors
for this), it may also signal that the public health service
delivery system is not functioning as expected specially
when all of the private health facilities are financed out of
0.3000
0.2500
0.2000
0.1500
0.1000
0.0500
0.0000
All
Public
Private
−0.0500
Age Gender Marriage
Urban Education
Residual
Figure 3 Decomposition of concentration index for outpatient
visits.
0.3500
0.3000
0.2500
0.2000
0.1500
0.1000
0.0500
0.0000
−0.0500
All
Age Gender Marriage
Public
Private
Urban Education
Residual
Figure 4 Decomposition of concentration index for inpatient
admission.
could not be explained by the above factors as the residual
presented the largest portion of the CIs.
Among the known factors, location was the major
factor contributing to the inequality for outpatient care.
It contributed to 14% and 16% of the CI for overall and
private outpatient care, respectively. Age was another factor
contributing to the inequality, accounting for 8.3% and
6.7% of inequality for overall and private outpatient care,
respectively.
In terms of the contributors for inpatient care, age was
the key factor, accounting for 14.5%, 20.4%, and 11.5%
of inequality for overall, public, and private inpatient care,
respectively. Location was also a factor explaining the
inequality for overall inpatient care and private inpatient
care, contributing 4.3% and 7.5% of the inequality,
respectively.
Discussion
This study shows that there is substantial inequality in the
© Journal of Hospital Management and Health Policy. All rights reserved.
J Hosp Manag Health Policy 2020;4:34 | http://dx.doi.org/10.21037/jhmhp-20-63
Journal of Hospital Management and Health Policy, 2020
pocket. Despite the impressive improvement in expanding
access to health care under BPHS and EPHS, there remain
many challenges, of which the poor quality of care is a major
concern (14,15). Developing quality improvement processes
and interventions targeting specific quality concerns would
help address the inequality concern in using the care.
As expected, wealth is strongly associated with the use
of health care, both public and private, which suggests
affordability is a prevalent concern of access to health
services, even for free care provided in public health
facilities. However, ability to pay decides access to services
from the private sector, anecdotes suggest that those not
financially privileged incur catastrophic health spending
when accessing services. Despite free consultations in public
facilities, when seeking care, the poor often incur out-ofpocket expenses to cover such costs as transportation, meals,
medicine, diagnostic tests, and accommodations.
For instance, it is not unusual for patients to have to
purchase drugs from private pharmacies, due to the stock
out of medicine in public facilities, and to pay for laboratory
tests and imaging services conducted in the private sector
due to unavailability of such services in public facilities. In
fact, out-of-pocket health expenditures account for more
than 75.5% of the country’s total health expenditures (14),
which puts a great financial burden on households and
exacerbates inequity. Approximately 14% of the population
is dragged into poverty for having to pay to access health
care at the point of services (14).
The decomposition of inequality and the regression
models show that location is an important factor explaining
the disparity in utilization of care. For instance, living in
urban areas is positively associated with care in private
health facilities. There are several reasons for this finding.
First, there is greater availability of private-sector health
services in urban areas. The number of private health
facilities increased substantially due to the government’s
support, as it considers them a vital part of the national
health system (16). Taking licensed private hospitals as an
example, the number increased from 14 in 2003 to 319 in
2014 (12), most of them in the urban areas. Second, urban
health facilities are more accessible; physical access to care
in remote areas has long been an issue for those seeking
care. The Government of Afghanistan has taken some
initiatives to improve access, such as establishing mobile
clinics and providing referral transportation support.
Contrary to our expectation, those with no education used
more healthcare as compared to those with some education.
This might be due to the fact that majority in the sample
© Journal of Hospital Management and Health Policy. All rights reserved.
Page 9 of 11
has no education. Also, important to note that there might
be little difference in level of education between these two
categories. Other important factor in healthcare utilization
by those with no education can be the fact that those with
no education be relatively poor as well. Therefore, chances
are they become ill more often and use services at a higher
frequency.
Our finding of a negative association of residing in urban
areas with health care utilization of public health facilities is
interesting. It is probably due to the BPHS targeting rural
areas, over urban areas. Another possibility of the negative
association is the growing network of private health facilities
in urban areas. In areas where there is an issue of physical
access to health care, particularly public health services,
using private health facilities to fill the service gap would be
critical in expanding health services among the population.
Establishing a prepayment arrangement that allows for
purchasing services from the private sector could alleviate
financial catastrophe caused by people incurring out-ofpocket expenses when using private sector health services.
Public and private outpatient and inpatient care
utilization follows a “U” shape pattern. That is the
utilization is higher among the age group of 1–10, which
might be due to the still higher prevalence of infectious
disease among children, followed with lower utilization
among the age group of 11–20, and increase of utilization as
people get older. The last is consistent with the pattern of
healthcare utilization elsewhere in the world as we grow old,
we are more likely to develop chronic diseases as a result of
which our healthcare need increases. Based on our analysis
moving one category up in the age categories increases the
use of health care services from both public and private
health facilities. This is understandable: As people age, the
need and demand for health services increases. However,
the current BPHS and EPHS packages supported by donors
offer very limited services for older people, which might
put them at risk of incurring catastrophic health expenses.
Therefore, any revisions to the scope of the packages
should consider including services for adults, especially
interventions for common non-communicable diseases that
occur more often in the elderly.
Conclusions
Finally, inequality of using inpatient and outpatient care
remains a concern in Afghanistan. The BPHS and EPHS
packages that target the poor and remote populations have
reduced the magnitude of the inequality. Addressing quality
J Hosp Manag Health Policy 2020;4:34 | http://dx.doi.org/10.21037/jhmhp-20-63
Page 10 of 11
of care concerns, improving physical access to care, and
expanding the availability of care by progressively increasing
the prepaid share of total health spending, could potentially
address the inequality in the utilization of health services
in Afghanistan. Also important is to make all efforts to
address demand side factors such as socio-cultural barriers
to obtaining services when making expansion decisions.
Acknowledgments
Analyzing equity in health service utilization is critical to
identify risk or vulnerable population who are left behind
in using health services. This study, “An Equity Analysis
of Health Service Utilization in Afghanistan”, was made
possible by the joint effort of dedication from the Ministry
of Public Health (MoPH) and technical partners. Seizing
this opportunity, we would like to appreciate the effort that
was taken by Health Economics and Financing Directorate
(HEFD), led by Dr. Farhad Farewar, to carry out the
assignment. We would also appreciate Dr. Abo Ismael
Foshanji, Dr. Sayed Karim Alawi and Khwaja Mir Ahad for
their participation and effort in collecting, and analyzing
data and producing the report. Finally, we would like to
highly value the support of USAID-funded Health Sector
Resilience (HSR) project, principally Dr. Wu Zeng and Mr.
Mohammad Yonus Zawoli, for the vital role they played
in technically assisting HEFD with the data analysis and
finalizing this report.
The authors acknowledge and appreciate the work of
all officers in HEFD who provided them with backstop
support providing them with data required as and when
needed.
Funding: The study has been supported by the USAID
financed HSR project.
Footnote
Journal of Hospital Management and Health Policy, 2020
jhmhp-20-63
Conflicts of Interest: All authors have completed the ICMJE
uniform disclosure form (available at http://dx.doi.
org/10.21037/jhmhp-20-63). The series “Incentives
and health system efficiency in low- and middle-income
countries” was commissioned by the editorial office without
any funding or sponsorship. Wu Zeng served as the unpaid
Guest Editor of the series and serves as an unpaid editorial
board member of Journal of Hospital Management and Health
Policy from August 2019 to July 2021. The authors have no
other conflicts of interest to declare.
Ethical Statement: The authors are accountable for all
aspects of the work in ensuring that questions related
to the accuracy or integrity of any part of the work are
appropriately investigated and resolved. The study was
conducted in accordance with the Declaration of Helsinki (as
revised in 2013). Since we were using the secondary data for
which an IRB approval and informed consent was already
secured, we assume no further consent and IRB approval
was required for this.
Open Access Statement: This is an Open Access article
distributed in accordance with the Creative Commons
Attribution-NonCommercial-NoDerivs 4.0 International
License (CC BY-NC-ND 4.0), which permits the noncommercial replication and distribution of the article with
the strict proviso that no changes or edits are made and the
original work is properly cited (including links to both the
formal publication through the relevant DOI and the license).
See: https://creativecommons.org/licenses/by-nc-nd/4.0/.
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doi: 10.21037/jhmhp-20-63
Cite this article as: Farewar F, Saeed KMA, Foshanji AI, Alawi
SMK, Zawoli MY, Sayedi O, Zeng W. Analysis of equity in
utilization of health services in Afghanistan using a national
household survey. J Hosp Manag Health Policy 2020;4:34.
© Journal of Hospital Management and Health Policy. All rights reserved.
J Hosp Manag Health Policy 2020;4:34 | http://dx.doi.org/10.21037/jhmhp-20-63