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R E S E A R C H A R T I C L E Open Access
Impact of the free healthcare initiative on
wealth-related inequity in the utilization of
maternal & child health services in Sierra
Leone
Mohamed Boie Jalloh
1,2*
, Abdulai Jawo Bah
3,4
, Peter Bai James
3,5
, Steven Sevalie
2,3,4
, Katrina Hann
4
and
Amir Shmueli
1
Abstract
Background: As a result of financial barriers to the utilization of Maternal and Child Health (MCH) services, the
Government of Sierra Leone launched the Free Health Care Initiative (FHCI) in 2010. This study aimed to examine the
impact of the FHCI on wealth related inequity in the utilization of three MCH services.
Methods: We analysed data from 2008 to 2013 Sierra Leone Demographic Health Surveys (SLDHS) using 2008 SLDHS as
a baseline. Seven thousand three hundred seventy-four and 16,658 women of reproductive age were interviewed in the
2008 and 2013 SLDHS respectively. We employed a binomial logistic regression to evaluate wealth related inequity in the
utilization of institutional delivery. Concentration curves and indices were used to measure the inequity in the utilization
of antenatal care (ANC) visits and postnatal care (PNC) reviews. Test of significance was performed for the difference in
odds and concentration indexes obtained for the 2008 and 2013 SLDHS.
Results: There was an overall improvement in the utilization of MCH services following the FHCI with a 30% increase in
institutional delivery rate, 24% increment in more than four focused ANC visits and 33% increment in complete PNC
reviews. Wealth related inequity in institutional delivery has increased but to the advantage of the rich, highly educated,
and urban residents. Results of the inequity statistics demonstrate that PNC reviews were more equally distributed in 2008
than ANC visits, and, in 2013, the poorest respondents ranked by wealth index utilized more PNC reviews than their
richest counterparts. For ANC visits, the change in concentration index was from 0.008331[95% CI (0.008188, 0.008474)] in
2008 to −0.002263 [95% CI (−0.002322, −0.002204)] in 2013. The change in concentration index for PNC reviews was
from −0.001732 [95% CI (−0.001746, −0.001718)] in 2008 to −0.001771 [95% CI (−0.001779, −0.001763)] in 2013. All
changes were significant (pvalue < 0.001).
Conclusion: The FHCI appears to be improving access to and utilization of MCH services, narrowing the inequity in ANC
visits and PNC reviews, but is insufficient in addressing wealth- related inequity that exists for institutional deliveries. If
Sierra Leone is to realize a significant reduction in maternal and child mortality rates, it needs to strengthen the effective
implementation of FHCI considering incorporating a sector wide approach (SWAp) or a “Health in all Policy”framework to
reach the less educated, rural residents and ensuring culturally sensitive quality services.
Keywords: Antenatal care, Postnatal care, Inequity, Institutional delivery, Concentration index, Maternal health, Sierra
Leone
© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
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the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
* Correspondence: mboie1537@gmail.com
1
Department of Health Management and Economics, School of Public
Health, The Hebrew University of Jerusalem, Jerusalem, Israel
2
34 Military Hospital Wilberforce, Freetown, Sierra Leone
Full list of author information is available at the end of the article
Jalloh et al. BMC Health Services Research (2019) 19:352
https://doi.org/10.1186/s12913-019-4181-3
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Background
Despite the gains in improving the health status of vulner-
able segments of the society over the century, inequity in
health and healthcare continue to persist globally [1]and
obeying the inverse care law –the availability of good
quality healthcare seems to be inversely related to the
need for it [2]. Such gap in health status between the rich
and the poor is prevalent in many developing countries.
In recognising the need to bridge the equity gap, most
governments and international organisations have in-
cluded key provisions in their primary healthcare delivery
policy initiative to address such disparities [1,3–5]. Not-
withstanding such commitments, the health status among
the poor in sub- Saharan Africa is suboptimal [6].
Monitoring trends in equity in health and access to es-
sential health interventions is important in order to
tailor scarce public resources to those who are most in
need, particularly poor and underserved communities.
While low income countries in sub-Saharan Africa face
many challenges in collecting and analysing relevant in-
formation for observing trends in equity, such challenges
which should not be an excuse for inaction [7].
The 2008 Sierra Leone Demographic and Health Sur-
vey (SLDHS) identified cost as the main barrier to
utilization of maternal and child health (MCH) services
and a key contributing factor for the high maternal and
infant mortality rates [8]. In order to address the high
maternal and infant mortality rates, the government
launched the free healthcare initiative (FHCI) for preg-
nant women, lactating mothers and children under the
age of five in April 2010, which eliminates medical fees
and provides drugs and treatments at no cost in every
public health facility in the country [9,10]. However, the
FHCI remains challenged by increasing demand, low
staffing, and stock-outs of essential laboratory equip-
ment (86–97%), other equipment (13–47%) and drugs
(12%), resulting in patients being required to pay out of
pocket for services falling under the FHCI [11–13]. Al-
though an increasing number of women and children
are reportedly utilizing healthcare services, the FHCI
may not have eradicated differential distribution of ser-
vices among the different wealth quintiles [11,14].
Despite the FHCI, Sierra Leone was unable to meet its
target of the millennium development goals 4 and5
(MDG4 and MDG5) –reducing maternal mortality ratio
to 450 per 100,000 births and child mortality to 95 per
1000 live births. The FHCI has since entered into the
sustainable development goals (SDG) era with significant
gaps in the health sector remaining to achieve SDG 3 –
health and wellbeing for all [15,16]. In Sierra Leone, the
current neonatal and under-five mortality rates are at 39
and 156 deaths per 1000 live births respectively and the
maternal mortality ratio is 1165 death per 10,000 live
births [17]. These infant and maternal mortality indices
are far short of the 70 deaths per 100,000 live births tar-
get set out in the 2030 sustainable development goal
agenda [18]. Even though the FHCI has made MCH ser-
vices free, indirect costs, among other factors, may still
contribute to the disparity in the utilization of MCH ser-
vices. However, little is known on the impact of the
FHCI in narrowing the wealth-related inequity in the
utilization of MCH services. For instance, studies have
reported that despite the FHCI, women in rural commu-
nities, many of which are poor, still experience difficulty
in accessing health services [10,11].
Inequity studies are urgently needed to understand the
FHCI’s ability to close the gap between wealth quintiles,
which will provide evidence to guide policies aiming to
reduce inequalities in access to such services in order to
achieve universal health coverage in Sierra Leone. There-
fore, we aimed to evaluate the change in the utilization
of MCH services among wealth quintiles before (2008)
and after FHCI (2013) implementation in Sierra Leone.
Further analysis is aimed at demonstrating the impact of
secondary factors that affect utilization of MCH services
such as education level, residence, ethnicity, age, occupa-
tion, religion and number of children of respondents.
Methods
Settings
Sierra Leone, which is a low-income country, is approxi-
mately 71,740 km
2
land area divided into four administra-
tive regions namely Northern, Southern, Eastern
provinces and the Western area where the capital
Freetown is located. The country has a long historical and
geopolitical context of poverty, high illiteracy rate. Sierra
Leone is also a country that is recovering from disasters
including the prolonged 11-year civil war that ended in
2002, followed by the 2012 Cholera outbreak [19]andof
recent the 2014–2016 Ebola Virus disease epidemic [20].
Sierra Leone is a low-income country with a reported
Gross National Income (GNI) per capita (current dollar,
purchasing power parity (PPP) of $1690 while the gross
domestic product (GDP) growth rate was 6% in 2013
and the Human Development Index rank for Sierra
Leone is 177 out of 187 countries [21]. It has an esti-
mated 2015 population of 7075,64 [22]andthenatureof
its geography poses significant challenges for the delivery of
health services to the population in some of these districts.
Sierra Leone currently faces a triple burden of diseases
(communicable diseases, 70%; NCDs, 22% and injuries, 7%)
[23] common to a growing number of LMICs with life ex-
pectancy for both male and female at 50 years [24].
Data source and sample size
This study was based on the secondary analysis of data
obtained from two nationally representative household
surveys that interviewed a total of 7374 and 16,658
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women of reproductive age (15–49 years) in 2008 [8]
and 2013 [25]. Response rates among eligible individuals
in the target samples were 94% [8] and 97.2% [25]in
2008 and 2013 respectively.
Sampling method of SLDHS
All the two Sierra Leone Demographic and Health Sur-
veys (SLDHS) used a multi-stage cluster sampling tech-
nique [8,25]. Initially, the Enumeration Areas (EA) —a
cluster that conventionally encompasses 85 adjacent
households each were selected as primary sampling units
from the sampling frame developed based on the 2004
Census [26]. In each of the selected EAs, a complete list-
ing of households was carried out from which secondary
sampling units were drawn using systematic random
sampling technique. In the two surveys, 353 EAs were
sampled of which 145 were urban and 208 were rural,
with each EA having 85 households from which 22 were
selected in the second stage of the two-stage sampling
[8,25]. For this study, all data collected from women
who gave birth in the preceding 5 years of the survey
were included. In cases, where women had more than
one birth in the reference period, the most recent one
was considered. An algorithm of the number of women
interviewed in each of the SLDHS and the women in-
cluded in the final analysis of antenatal care (ANC) &
postnatal care (PNC) (Additional file 1).
Data analysis
Data analysis were done using Excel Microsoft Corporation
and SPSS Package version 22 (SPSS, Inc. Chicago). This
study first explored the background characteristics of study
participants and then the analysis of MCH utilization by
wealth quintile and other individual characteristics. An un-
adjusted and adjusted binary logistic regression was run for
institutional delivery and a concentration curve with subse-
quent concentration indices generated for ANC visits and
PNC reviews for 2008 and 2013 SLDHS.
For MCH utilization variables, we defined the number
of antenatal visits (ANC) and post-natal reviews made
(PNC) as discrete variables; we considered the number
of visits to be complete if it reached the recommended
number of visits as per the WHO guidelines [27,28]
(four or more for ANC and four or more for PNC). For
ease of analysis, ANC was transformed into three sub-
categories (none, up to four and more than four visits)
and PNC into two subcategories (incomplete and
complete). Complete includes all four reviews: post-
delivery, prior to discharge, a week after discharge, and
6 weeks post-delivery. If any of these visits were missed,
then that constitutes an incomplete PNC. We defined
Institutional delivery as the use of a healthcare institu-
tion for delivery for the pregnancy under review, regard-
less of the package of care provided as a binary
categorical variable (Yes vs No). We defined wealth
quintiles as poorest (1st quintile); poorer (2nd quintile);
middle (3rd quintile); richer (4th quintile); and richest
(5th quintile). Additional covariates were defined as cat-
egorical i.e. education level, occupation, residence (rural/
urban), ethnicity, religion, and mother’s age as well as
discreet (number of children) variables. All the inde-
pendent variables were categorical variables except for
number of children, which was a quantitative variable.
The undermentioned operational definitions of the
dependent and independent variables (see Additional
files 2and 3) were the same as defined in the DHS data-
set except for PNC (a composite variable) ethnicity and
religion, which were redefined to suit the study design.
The concentration curves were built using two key vari-
ables: the independent wealth index variable on the one
hand and maternal & child health services utilization out-
come variables on the other hand (ANC& PNC). The con-
centration indices estimated the magnitude of wealth
related inequality in the selected MCH services utilization.
During analysis, the cases were grouped according to
wealth quintiles into: Poorest: 1st quintile; Poorer: 2nd
quintile; Middle: 3rd quintile; Richer: 4th quintile; Rich-
est: 5th quintile.The sum of each outcome variable noted
for the five wealth quintiles and then expressed as a per-
centage of the total outcome variable of interest. Each
curve, therefore, represents the cumulative percent of
the outcome variable of interest against the cumulative
percent of the wealth quintile of the sample analyzed. If
ANC visits or PNC reviews utilization were equally dis-
tributed across the different wealth quintiles, a 45-
degree line representing perfect equality would be gener-
ated. This line known as the line of equality (LOE) runs
from the bottom left corner of the graph (0,0) to the
upper right corner of the graph (100, 100) [29]. If these
services were however utilized more by the rich than the
poor, the curve falls below the LOE and the further it is
away from the LOE the more the wealth-related inequal-
ity in the distribution of the MCH services utilization.
Since the aim was to compare the wealth related in-
equality in ANC visits or PNC reviews utilization across
a period using the 2008 and 2013 SLDHS, the concen-
tration curves for each outcome variable were plotted on
the same graph. Thus, if the curve of one of the time pe-
riods (2008 vs 2013) lies above the other (closer to the
LOE), then the former is said to dominate the latter, but
the extent is unknown. In order to get an exact measure
of the degree of inequality, a concentration index is built
from each curve and it is defined as double the area be-
tween the curve and the LOE [29]. The concentration
indexes obtained were then used to rank these two-
time periods by the degree of inequality. If the two
curves cross each other, a case of non-dominance
maybedemonstrated.
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In this study, the concentration index was calculated
first as twice the area between the curve and the line of
equality. However, since the area under-the-curve ap-
proach to calculating the confidence interval (CI) does
not give the standard error of the curve and hence the
CI, the CIs were therefore computed using the conveni-
ent regression method. The CI was computed in the
convenient regression method as twice the weighted
variance of fractional living standard variable squared
(δ
2)
and the health variable (h
i
= ANC or PNC) divided
by the mean of the health variable (μ) based on the left
hand of eq. 1 below:
2δ2hi=μðÞ¼αþβriþƐið1Þ
The computation of the fractional rank of wealth index
(r
i)
was based on equation below for the weighted data.
ri¼ΣWjþWi=2ðÞ ð2Þ
r
i
was then sorted in ascending order and its variance
calculated. βproduced during the convenient regression
of the CI variable against the fractional rank variable
represents the unadjusted estimate of the concentration
index generated on the right hand of eq. 1.
The standardized or adjusted estimate of the concen-
tration index was computed using SPSS statistical soft-
ware using the generated model to predict the health
variable (ANC or PNC) based on eq. 3 below:
Yi¼boþb1x1þb2x2þb3x3ð3Þ
Yi represents the predicted health variable. During the
adjustment or standardization of the wealth variable for the
other covariates, the adjusted values were predicted using
eq. 3 while keeping all covariates at their mean values.
In order to calculate the standard error of the standard-
ized estimate of the concentration index, the sampling
variability was taken into account, and thus the conveni-
ent regressions were run without transforming the
dependent health variable but instead using the trans-
formed living standard variable (i.e. RWealthi).The stand-
ard error of the adjusted concentration index was
estimated as the coefficient of the transformed living
standard variable (RWealthi).The variance of the fractional
rank, which was also used in the transformation,
depended only on the sample size and so has no sampling
variability. It can be treated as a constant. This way the
sampling variability was considered because the estimate
and its standard error were written as a function of regres-
sion coefficients based on eqs. 4, 5, and 6 below.
hi¼α1þβ1riþuið4Þ
Ḃ¼2δr2=μ
_
Bð5Þ
Ḃ¼2δr2=α1þḂ=2
hi
_
Bð6Þ
An unadjusted and adjusted binary logistic regression
were run to identify how wealth in relation to the other
independent variables serves as a predictor of utilization
of healthcare institutions for delivery. The generated
model predicts whether a pregnant woman will deliver
in a health facility or at home based on her wealth index
and other independent variables. Logistic regression
models were used to obtain unadjusted and adjusted
odds ratios with 95% confidence interval for the associa-
tions between the different independent variables and
institutional delivery. The significant standardized con-
tribution of each covariate was assessed using the ad-
justed Wald test to obtain the p-value. All p-values <
0.05 were considered statistically significant.
Ethical considerations
The DHS program-ICF International, (Rockville, USA),
granted access to the data after a submission of a written
request through their online platform. The Sierra Leone
Ethics and Scientific Review Committee granted a waiver
since this is a secondary analysis of de-identified data.
Results
Sociodemographic characteristics
The results in Table 1show that of the women included
in the analysis, 75 and 66% had no formal education in
2008 and 2013 respectively; about 70% were rural resi-
dents in both 2008 and 2013; about 80% were Muslims
in both 2008 and 2013; and 55 and 50% of children had
one to four siblings in 2008 and 2013 respectively.
MCH services utilization rates
Table 2highlights MCH services (ANC, Institutional De-
livery and & PNC reviews) utilization rates in 2008 and
2013. Although more than 50% of women attended the
four ANC visits recommended by WHO focus antenatal
care guideline in 2008, this number increased to 75% in
2013. Institutional delivery among women respondents
increased from 27% in 2008 to 57% in 2013. There was
also a reduction in the number of incomplete postnatal
visits from 92% in 2008 to 59% in 2013.
Inequality analysis of ANC visits
The curves in Fig. 1(a and b) show the unadjusted and
adjusted concentration curves respectively for ANC
visits in both 2008 and 2013 SLDHS.
The ANC concentration curve for 2013 lies slightly
above the line of equality indicating that the poor made
more ANC visits than the rich. On the other hand, the
2008 ANC concentration curve lies below and above the
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Table 1 Weighted Number of Study Participants by Sociodemographic Characteristics 2008 & 2013
Weighted number of study partipcipants by sociodemographic
charactetristics 2008
Weighted number of study partipcipants by sociodemographic
charactetristics 2013
Background
characteristics
ANC
2008 Freq
(n= 3346)
Percent
(%)
PLOD
2008 Freq
(n= 4053)
Percent
(%)
PNC 2008
Freq (n=
3504)
Percent
(%)
ANC
2013 Freq
(n= 7478)
Percent
(%)
PLOD
2013 Freq
(n= 8625)
Percent PNC 2013
Freq (n=
7971)
Percent
(%)
Wealth Index
Poorest 721 22 885 22 665 19 1667 22 1901 22 1663 21
Poorer 707 21 849 21 623 18 1524 20 1809 21 1550 19
Middle 748 22 893 22 690 19 1556 21 1797 20 1527 19
Richer 629 19 793 19 798 23 1491 20 1694 20 1849 23
Richest 541 16 683 16 728 21 1240 17 1447 17 1382 17
Education level
None 2510 75 3051 74 2441 70 4920 66 5768 67 5233 66
Primary 411 12 515 13 497 14 1079 14 1203 14 1085 14
Secondary 386 12 482 12 515 14 1374 18 1559 18 1540 19
Higher 39 01 55 01 51 02 105 01 117 01 114 01
Occupation
Yes 2577 77 3122 77 2585 74 5596 75 6476 75 5791 73
No 769 23 950 23 919 26 1882 25 2148 25 2181 27
Residence
Urban 1036 32 1183 29 1267 36 2075 28 2387 28 2586 32
Rural 3238 68 2920 71 2236 64 5404 72 6260 72 5385 68
Ethnicity
Temne,Loko, &
Limba
1589 48 1898 46 1369 39 3283 44 3724 43 3211 40
Mende, Sherbro
& Kono
1192 36 1512 37 1598 46 3156 42 3714 43 3460 43
Others Sierra
Leonean &
Foreign
565 17 687 17 536 15 1039 14 1183 14 1300 17
Religion
Christianity 636 19 794 19 883 25 1388 19 1590 18 1581 20
Islam 2667 80 3247 79 2593 74 6067 81 7005 81 6372 80
Others 43 01 50 01 27 01 24 0.3 25 0.3 18 0.2
Mother’s age
19–15 270 08 330 08 290 08 751 10 859 10 824 10
24–20 664 20 804 20 722 08 1527 20 1773 21 1683 21
29–25 953 29 1213 30 1018 21 1853 25 2142 25 1945 24
34–30 579 17 704 17 614 29 1421 19 1644 19 1493 19
39–35 559 17 673 16 553 16 1152 15 1354 16 1250 16
40–44 209 06 251 06 208 06 485 07 554 06 491 06
45–49 111 03 127 03 98 03 290 04 322 04 285 04
Siblings
None 707 21 871 21 737 21 1811 24 2112 24 1948 24
1–4 1863 56 2269 56 1935 55 3631 50 4166 49 3835 49
> 4 776 23 96 23 832 24 2036 26 2369 27 2188 27
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line of equality, suggesting that in 2008 there was little
wealth related inequality in the number of ANC visits.
Inequality analysis of PNC reviews
Figure 2(a and b) show the unadjusted and adjusted con-
centration curves respectively for PNC reviews. In com-
parison with Figs. 1(a and b), unadjusted and adjusted
concentration curves in Figs. 2(a and b) demonstrate
that PNC reviews were more equally distributed in 2008
than ANC visits and this is evident in the values of con-
centration indices in 2008 for ANC visits and PNC re-
views. Fig. 2(a and b) shows that in 2013, the poorest
Table 2 Weighted Profile Distribution of MCH Services
Utilization in 2008 & 2013
MCH Services Distribution 2008 2013
ANC None 8.2% 2.2%
Up to four visits 41% 22.6%
More than four visits 51% 75.2%
Institutional delivery Yes 27% 57.2%
No 73% 42.8%
Postnatal reviews Complete 8.3% 41.4%
Incomplete 91.7% 58.6%
a
b
Fig. 1 aWeighted Unadjusted Concentration Curves for ANC visits in 2008 and 2013 SLDHS. bWeighted adjusted concentration curves for ANC
visits in 2008 and 2013 SLDHS
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respondents ranked by wealth index utilized more PNC
reviews than the richest.
Results of the inequality statistics for ANC visits and
PNC reviews in 2008 & 2013 SLDHS are presented in
Table 3. The differences in the adjusted concentration
indices was statistically significant for ANC (t = 76.80,
p< 0.001) and PNC (t = 4.84, p< 0.001) over the two
survey periods.
Determinants of institutional delivery
In the 2013 SLDHS, institutional delivery coverage was
57.2% (Table 2), 50.4% among the poorest wealth quintile
and 72.2% among the richest wealth quintile (Fig. 3b).
Women in the richest wealth quintile were [AOR= 1.75;
95% CI (1.41, 2.17)] more likely to give birth at a health fa-
cility compared to women in the poorest wealth quintile
(Table 4). The level of inequality in institutional delivery
utilization increased, as the overall coverage increased,
from a baseline utilization rate of 27% (Table 2). In 2008
SLDHS, the rate of institutional delivery was 18.2% among
the poorest wealth quintile and 41.9% among the richest
wealth quintile (Fig. 3b).Women in the richer wealth
quantile were [AOR = 1.37;95%CI (1.05, 1.78)] times more
likely to birth in a health facility compared to their poorest
counterparts (Table 4a). .
The proportion of institutional delivery also varied sig-
nificantly across education levels and residence. In 2008
SLDHS, 69.1% of women with higher than secondary
school education had institutional delivery compared to
the 22.2% of women with no education, representing a
46.9% difference in institutional delivery utilization rate
(Fig. 4). Thus, women with higher than secondary school
education were [AOR = 3.76; 95% CI (2.04, 6.94)] times
more likely to give birth at a health facility compared to
women with no formal education (Table 4a). In 2013
SLDHS, the overall coverage for institutional delivery
improved for all education levels and the inequality gap
narrowed. Women with higher than secondary school
a
b
Fig. 2 aWeighted Unadjusted Concentration Curves for PNC reviews in 2008 and 2013. bWeighted Adjusted Concentration Curves for PNC
reviews in 2008 and 2013
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education had 88% institutional delivery rate compared
to the 52.1% of those with no formal education, repre-
senting a 35.9% difference in institutional delivery
utilization rate (Fig. 4). Women with higher than second-
ary school education in 2013 were [AOR = 3.4; 95% CI
(1.89, 6.11)] times more likely to birth at a health facility
compared to women with no formal education (Table 4b).
Institutional delivery was lowest among women in
rural settings (21.4% & 52.3% in 2008 & 2013 respect-
ively) compared to their counterparts in urban settings
(40.9% & 70.3% in 2008 and 2013 respectively) (Fig. 4).
Women in rural areas were 32% less likely in 2008
[AOR = 0.68; 95% CI (0.58, 0.80)] and 53% less likely in
2013 [AOR = 0.47 [95% CI (0.37, 0.58)] to give birth at a
health facility compared to their counterparts in urban
settings (Table 4).
The institutional delivery rate varied significantly
across ethnic and religious subgroups of the respondents
in both 2008 and 2013 SLDHS. In 2013 SLDHS, women
from tribes found predominantly in the South and
South-east of the country (Mende, Sherbro & Kono) had
23.1% more utilization rate of institutional delivery than
women from tribes predominantly located in the North-
ern and Western parts of the country (Temne, Loko &
Limba). A 13.3% difference in institutional delivery rate
in 2008 between tribes in the South and Southeast and
those in Northern and Western of the country (Fig. 4b).
Women from tribes in the South & South-eastern re-
gions were more likely to birth at a health facility com-
pared to their counterparts from tribes in the Northern
& Western parts of the country in 2013 [AOR = 3.08;
95% CI (2.78, 3.42)] and 2008 [AOR = 2.22; 95% CI
(1.88, 2.63)] (Table 4).
The percentage point difference between Christian
and Muslim women in the utilization rate of institu-
tional delivery was 11.4% in 2008, and this difference in-
creased to 12.8% in 2013 (Fig. 4b). Muslim women in
2013 were 14% less likely [AOR = 0.86; 95% CI (0.76,
0.97)] to deliver at a health facility compared to their
Christian counterparts (Table 4a).
We observed a significant difference in the unadjusted
(t = 1.80, p= 0.036) and adjusted (t = 1.73, p= 0.042) odds
ratios of the richest-poorest subgroups of society with
regards to the utilization of institutional delivery in 2008 &
2013 (Table 5). This represents a significant gap in wealth
related inequality in institutional delivery utilization be-
tween the rich and the poor over the study period.
Discussion
.Our results show changes in distribution of utilization
of MCH services across wealth quintiles over time
alongside a significant increase in the proportion of
women eligible for free MCH services utilizing such ser-
vices before and 3 years after the introduction of FHCI
in Sierra Leone. We found that while utilization of ANC
was unequally distributed to the advantage of the richest
women prior to FHCI, it was unequally distributed to
the advantage of the poorest women in 3 years after the
introduction of the FHCI in 2013
. This finding is consistent with a similar study in
Afghanistan [30] but inconsistent with many published
studies elsewhere [31–35]. The observed inconsistency
may have arisen from minor differences in variable def-
inition [31–35] variable types [32,34] included the use
of cross sectional study data with much shorter periods
and not DHS by others [31,34]. It may also be due to
differences in the economic profile and health systems
of the different countries [32–35] or the use of single
DHS dataset as opposed to a time trend review [35].
Our findings have also demonstrated that PNC reviews
which were slightly unequally distributed in favor of the
poor in 2008 were in 2013 significantly unequally dis-
tributed in favor of the poor suggesting that other im-
portant factors besides wealth may be at play. Children
of poorer households are more likely to get sick than
those of richer households [36,37] and that poorer
women are more likely to be fertile [38,39], therefore
increasing health needs among this group. Trends in
health seeking behavior in the country may play a role.
Table 3 Test of Significance for Means & Standard Errors
Obtained from Convenient Regressions
Antenatal Care Visits
Unadjusted
Year 2008 2013 Test
statistic
P
value
Estimate of concentration
index
0.009612 0.002264 10.78 <0.001
Standard error 0.000584 0.000351
Adjusted
Year 2008 2013 Test
statistic
P
value
Estimate of concentration
index
0.008331 -0.002263 76.80 <0.001
Standard error 0.000073 0.000030
Postnatal reviews
Unadjusted
Year 2008 2013 Test
statistic
P
value
Estimate of concentration
index
-0.000386 -0.001769 6.70 <0.001
Standard error 0.000133 0.000158
Adjusted
Year 2008 2013 Test
statistic
P
value
Estimate of concentration
index
-0.001732 -0.001771 4.84 <0.001
Standard error 0.000007 0.000004
Jalloh et al. BMC Health Services Research (2019) 19:352 Page 8 of 15
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
People tend to utilize the informal healthcare before
seeking the formal sector as a form of last resort [40–
42] and recent Sierra Leonean studies suggest that preg-
nant women, lactating mothers and infertile women
practice medical pluralism [43–45].. Therefore that may
be the reason why mild to moderates ailments may not
warrant PNC reviews utilization within richer house-
holds. Our finding is inconsistent with findings in Ghana
and other published literature globally [35,46,47]. The
inconsistency of our finding with the globally literature
may reflect the unique cultural, political and social con-
text of Sierra Leone. The inconsistency may be due to
differences in the data source used and the analytical
approaches. For instance, while Ghana shares a similar
cultural profile to that of Sierra Leone, the study examin-
ing the impact of the free user policy on utilization ana-
lyzed data from the Ghana Maternal Health Survey 2007
[48]. The difference in findings may also be reflective of
differences in health policy to healthcare delivery design.
For example, in Bangladesh [35]userfeeswereabolished
alongside the implementation of a sector wide approach
(SWAp), which resulted in.the narrowing the gap in
wealth-related inequity between the rich and the poor.
We found that wealth-related inequality in the
utilization of health facilities for delivery increased over
the study period to the disadvantage of the poor. Our
a
b
Fig. 3 aWeight adjusted proportion of institutional delivery in 2008 and 2013 by Mother’s age group. bWeight adjusted proportion of
institutional delivery in 2008 and 2013 by Economic status
Jalloh et al. BMC Health Services Research (2019) 19:352 Page 9 of 15
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Table 4 Adjusted and Unadjusted Odds Ratios of the Association between Demographic Characteristics of Women Respondents
and Institutional Delivery in (a) 2008 SLDHS and (b) 2013 SLDHS
2008 SLDHS (a) 2013 SLDHS (b)
Background
Characteristic
Adjusted OR (95%
CI)
P-value Unadjusted OR (95%
CI)
Pvalue Adjusted OR
(95% CI)
P-value Unadjusted OR (95%
CI)
Pvalue
Wealth index
Poorest 1 1 1 1
Poorer 1.49 (1.17, 1.89) 0.001 1.44 (1.14, 1.82) 0.002 1.22 (1.07,
1.40)
0.004 1.07 (0.94, 1.22) 0.290
Middle 1.52 (1.19, 1.93) 0.001 1.54 (1.22, 1.94) <
0.001
1.28 (1.11,
1.47)
<
0.001
1.07 (0.94, 1.21) 0.335
Richer 1.37 (1.05, 1.78) 0.020 1.82 (1.45, 2.30) <
0.001
1.72 (1.47,
2.00)
<
0.001
1.68 (1.47, 1.92) < 0.001
Richest 1.30 (0.94, 1.80) 0.115 3.24 (2.57, 4.08) <
0.001
1.75 (1.41,
2.17)
<
0.001
2.55 (2.20, 2.95) < 0.001
Education Level
None 1 1 1 1
Primary 1.40 (1.13, 1.75) 0.002 1.78 (1.45, 2.18) <
0.001
1.28 (1.12,
1.47)
<
0.001
1.37 (1.21, 1.56) < 0.001
Secondary 1.98 (1.56, 2.52) <
0.001
2.90 (2.38, 3.54) <
0.001
1.76 (1.52,
2.03)
<
0.001
2.36 (2.09, 2.66) < 0.001
Higher 3.76 (2.03, 6.96) <
0.001
7.61 (4.27, 13.55) <
0.001
3.40 (1.89,
6.11)
<
0.001
6.80 (3.88, 11.91) <
0.001>
Occupation
No 1 1 1 1
Yes 1.20 (1.00, 1.43) 0.047 0.86 (0.73, 1.01) 0.066 0.72 (0.64,
0.81)
<
0.001
0.58 (0.52, 0.64) < 0.001
Residence
Urban 1 1 1 1
Rural 0.47 (0.38, 0.58) <
0.001
0.39 (0.34, 0.45) <
0.001
0.68 (0.58,
0.80)
<
0.001
0.46 (0.42, 0.51) < 0.001
Ethnicity
Temne, Loko, & Limba 1 1 1 1
Mende, Sherbro &
Kono
2.22 (1.88, 2.63) <
0.001
1.98 (1.69, 2.31) <
0.001
3.08 (2.78,
3.41)
<
0.001
2.62 (2.39, 2.88) < 0.001
Others 1.47 (1.19, 1.82) <
0.001
1.53 (1.25, 1.87) <
0.001
1.62 (1.41,
1.86)
<
0.001
1.58 (1.38, 1.80) < 0.001
Religion
Christianity 1 1 1 1
Islam 0.87 (0.72, 1.04) 0.130 0.58 (0.49, 0.69) <
0.001
0.86 (0.76,
0.97)
0.016 0.58 (0.52, 0.65) < 0.001
Others 0.41 (0.15, 1.11) 0.079 0.17 (0.07, 0.46) <
0.001
0.89 (0.37,
2.12)
0.791 0.56 (0.25, 1.26) 0.162
Mother’s age
15–19 1 1 1 1
20–24 1.19 (0.88, 1.61) 0.265 1.19 (0.89, 1.58) 0.249 1.09 (0.91,
1.30)
0.367 1.00 (0.84, 1.18) 0.976
25–29 1.04 (0.77, 1.40) 0.791 0.96 (0.72, 1.26) 0.749 1.25 (1.05,
1.50)
0.014 1.02 (0.87, 1.20) 0.841
30–34 1.25 (0.91, 1.71) 0.162 1.22 (0.91, 1.64) 0.179 1.03 (0.86,
1.25)
0.739 0.79 (0.67, 0.94) 0.006
35–39 1.08 (0.78, 1.49) 0.654 0.96 (0.71, 1.29) 0.775 1.06 (0.87,
1.29)
0.562 0.77 (0.64, 0.91) 0.003
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finding is consistent with other studies, which reported
increasing wealth related inequity in institutional deliv-
ery between the poor and the rich. [30–32,46,49,50].
The observed increase in wealth related inequality in
utilization of institutional delivery that favors the rich may
be a significant pointer to the fact that poor and less edu-
cated women view the conventional healthcare setting as
a hostile environment that is not culturally sensitive to the
needs of women delivering at these institutions. For ex-
ample, women in rural areas prefer to squat during deliv-
ery unlike the lithotomy position promoted in health
facilities [51]. Some tribes have specific rituals observed
around the time of delivery, such as burial of the placenta
by specific family members or its consumption as food,
which may not be accommodated in the healthcare setting
[52,53]. Also, women of secret traditional societies do not
prefer to be attended to during delivery by women who
are not members of these secret societies or worse still by
men [54]. The healthcare delivery system may therefore
need to re-think its approach and re-evaluate its policies
to providing institutional delivery to accommodate the le-
gitimate concerns of women and therefore promote insti-
tutional delivery, which is key to reducing the current
high maternal and infant mortality in Sierra Leone.
We found that despite an encouraging decrease in
home delivery rates from 73% in 2008, the 43% delivery
rate in 2013 remains high, which may indicate barriers
beyond a policy of free services. Our results showed
higher levels of education and urban residence have a re-
lationship to utilization of MCH services, consistent
with other evidence [31,35,55,56]. The influence of
residence on inequality in the utilization of MCH ser-
vices may be due to the availability of more health facil-
ities in urban settings than in rural settings [57].In
addition, rural health facilities are usually under staffed
and this may serve as a disincentive to seeking MCH
services in rural residences [58]. Tackling such inequit-
able distribution of health facilities and addressing the
human resource for health gap is a fundamental goal in
the free health care initiative [13]. Residents in rural set-
tings hold strong cultural beliefs that limit their seeking
of institutional delivery such as that labor is a normal
process that can only requires hospitalization and sur-
gery for weak women or those who have invited a curse
upon themselves [59].
Policy and practice implications
FHCI has been successful at increasing utilization of
MCH services over time, but the serious gaps in equity
of utilization of services across different wealth quintiles
remain problematic. In addition, the increase in inequity
of utilization of facility-based delivery services, a factor
with strong correlation to maternal mortality [49],
among the poorest women, warrants immediate action
to ensure that policies are benefitting all levels of society
in order to achieve universal health coverage. In order
for Sierra Leone to meet its commitment to achieving
SDG3, a review of the implementation strategies sup-
porting the FHCI with specific reference to equity is re-
quired. Such a review should include consideration of
implementation approaches to address specific equity
gaps. Bangladesh has shown that a sector-wide approach
(SWAp) that harnesses the significant inputs of other
sectors such as agriculture, infrastructure, education,
and traditional leadership, has promise [35]. Such an
adaption of “Health in all Policy”approach allows for de-
velopments in the agricultural sector to enhance the
rural incomes, thus helping to address indirect costs of
accessing free services [60]. Interventions that address
quality of care in relation to delivery services, with a spe-
cific focus on accommodating social and cultural prefer-
ences of the poorest women, should be considered.
Similarly, investments in, strategic deployment of, and
retention of human resources for health in rural and re-
mote communities is needed to create a more balanced
and fair of services. Finally, strategies to understand and
target services preferences, health promotion needs, and
other barriers to accessing institutional delivery services
for the poorest, uneducated, and/or rural women and
their families should be reviewed.
Limitations
Our study has several limitations. Our methods limit
our ability to attribute causality in the changes in MCH
Table 4 Adjusted and Unadjusted Odds Ratios of the Association between Demographic Characteristics of Women Respondents
and Institutional Delivery in (a) 2008 SLDHS and (b) 2013 SLDHS (Continued)
2008 SLDHS (a) 2013 SLDHS (b)
Background
Characteristic
Adjusted OR (95%
CI)
P-value Unadjusted OR (95%
CI)
Pvalue Adjusted OR
(95% CI)
P-value Unadjusted OR (95%
CI)
Pvalue
40–44 0.99 (0.66, 1.49) 0.950 0.85 (0.58, 1.25) 0.404 1.05 (0.83,
1.33)
0.702 0.71 (0.57, 0.88) 0.002
45–49 0.96 (0.57, 1.64) 0.883 0.67 (0.40, 1.11) 0.116 0.80 (0.60,
1.06)
0.122 0.60 (0.46, 0.78) < 0.001
Number of children 1.02 (0.99, 1.05) 0.119 1.02 (0.99, 1.05) 0.235 1.00 (0.99,
1.02)
0.823 1.00 (0.98, 1.02) 0.948
Jalloh et al. BMC Health Services Research (2019) 19:352 Page 11 of 15
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utilization and distribution across wealth quintiles
over time to FHCI. The lack of a comparison group
means that this study cannot rule out the contribu-
tion of other factors to the recorded incremental
changes in the utilization of MCH services. Recall
bias on events that happened within the 5 years prior
to each survey is another limitation. The inclusion of
only women who gave birth to their last child in the
5 years prior to each survey may have reduced the
number of eligible women from the richest wealth
quintile who are known to be less willing to give
birth to more kids. This effect is however expected to
be minimal and is counteracted by the exclusion of
women whose children died within the first 2 months
a
b
Fig. 4 aWeight adjusted proportion of institutional delivery in 2008 and 2013 by Education level and Residence. bWeight adjusted proportion
of institutional delivery in 2008 and 2013 by Ethnicity and Religion
Table 5 Test of Equality of the Odds Ratios obtained from
Binomial Logistic Regression
Institutional Delivery
a
Unadjusted
Year 2008 2013 Test statistic Pvalue
Rich-poor odds ratio 3.24 2.55 1.80 0.03593
Standard error of the odds ratio 0.340 0.177
Adjusted
Year 2008 2013 Test statistic Pvalue
Rich-poor odds ratio 1.3 1.75 1.73 0.04182
Standard error of the odds ratio 0.186 0.173
a
All values were obtained at a 95% confidence interval
Jalloh et al. BMC Health Services Research (2019) 19:352 Page 12 of 15
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
for PNC reviews since infant mortality is more com-
mon among the poor. However, this study may be
one of the first in Sierra Leone to utilize the DHS in
the evaluation of the impact of FHCI on inequity of
MCH services across wealth quintiles.
Conclusion
Although it is difficult to draw a conclusive causal
link between the increase in the utilization rate of
the selected MCH services and the free healthcare
initiative, it appears that the initiative is at the least
not pro-rich for ANC visits and PNC reviews. Steps
need to be taken to address the growing wealth re-
lated inequality to the disadvantage of the poor that
accompanies the overall increase in institutional de-
livery rate. Pronounced level of inequality in institu-
tional delivery was also linked with women level of
education and residence, revealing that women with
no formal education or residents in rural settings
were the most underserved subpopulations. It is ob-
vious that in addition to wealth differences, other
sociodemographic characteristics like education level,
residence, ethnicity, and religion contribute to the
existing inequities. Promoting the education level of
women and increasing the number of qualified staff
at health facilities in rural settings, and ensuring cul-
turally sensitive, quality care should be prioritized to
improve the odds against socioeconomically disad-
vantaged women.
Additional files
Additional file 1: Number of women interviewed and included in the
final analysis in the 2008 and 2013 SLDHS. An algorithm of the number
of women interviewed and included in the final analysis for antenatal
care (ANC), postnatal care (PNC) and place of delivery (PLOD) in the 2008
and 2013 SLDHS. (DOCX 42 kb)
Additional file 2: Dependent variables. Operational definitions of the
dependent variables. (DOCX 14 kb)
Additional file 3: Independent variables. Operational definitions of the
independent variables. (DOCX 15 kb)
Abbreviations
ANC: Antenatal care; FHCI: Free healthcare initiative; MCH: Maternal child
health; MDG: Millennium development goals; PLOD: Place of delivery;
PNC: Postnatal care; SDG: Sustainable development goals; SLDHS: Sierra
Leone Demography Health survey; SWAp: Sector wide approach
Acknowledgements
We extend our thanks to MEASURE DHS for providing us with Sierra Leone
Demography Health survey data for 2008 and 2013.
Authors’contributions
MBJ and AS contributed to the study conceptualization, MBJ, AS, AJB & PBJ
contributed in developing the study design. MBJ analysed the data and
wrote the first draft of the manuscript. AJB, PBJ, KH, SS and AS contributed
to the intellectual content of the manuscript. All authors read and approved
the final version of the manuscript.
Funding
The authors did not receive any funding for this work.
Availability of data and materials
The dataset for this study can be access from the DHS program-ICF Inter-
national, Rockville, data after the submission of a written request. It is avail-
able at https://dhsprogram.com/data/available-datasets.cfm
Ethics approval and consent to participate
Access to the dataset was granted by the DHS program-ICF International,
Rockville, the USA after the submission of a written request through their on-
line platform and a waiver was granted by the Sierra Leone Ethics and Scien-
tific Review Committee since this is a secondary data analysis study. Written
informed consent was obtained from all participants at the time the two sur-
veys were conducted.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Department of Health Management and Economics, School of Public
Health, The Hebrew University of Jerusalem, Jerusalem, Israel.
2
34 Military
Hospital Wilberforce, Freetown, Sierra Leone.
3
College of Medicine and Allied
Health Sciences, University of Sierra Leone, Connaught Hospital, Freetown,
Sierra Leone.
4
Sustainable Health Systems, Freetown, Sierra Leone.
5
Australian
Research Centre in Complementary and Integrative Medicine, Faculty of
Health, University of Technology Sydney, Level 8, Building 10, 235-253 Jones
Street, Ultimo, Sydney, NSW 2007, Australia.
Received: 24 September 2018 Accepted: 24 May 2019
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