RESEARCH ARTICLE
Effect of corruption on perceived difficulties in
healthcare access in sub-Saharan Africa
Amber HsiaoID*, Verena Vogt, Wilm Quentin
Technische Universita
¨t Berlin, Department of Health Care Management, Berlin, Germany
*amber.hsiao@gmail.com
Abstract
Background
Achieving Universal Health Coverage (UHC) by improving financial protection and effective
service coverage is target 3.8 of the Sustainable Development Goals. Little is known, how-
ever, about the extent to which paying bribes within healthcare acts as a financial barrier to
access and, thus, UHC.
Methods
Using survey data in adults from 32 sub-Saharan African countries in 2014–2015, we con-
structed a multilevel model to evaluate the relationship between paying bribes and reported
difficulties of obtaining medical care. We controlled for individual-, region-, and country-level
variables.
Results
Having paid bribes for medical care significantly increased the odds of reporting difficulties
in obtaining care by 4.11 (CI: 3.70–4.57) compared to those who never paid bribes, and
more than doubled for those who paid bribes often (OR = 9.52; 95% CI: 7.77–11.67).
Respondents with higher levels of education and more lived poverty also had increased
odds. Those who lived in rural areas or within walking distance to a health clinic had reduced
odds of reporting difficulties. Sex, age, living in a capital region, healthcare expenditures per
capita, and country Corruption Perception Index were not significant predictors.
Conclusions
We found that bribery in healthcare is a significant barrier to healthcare access, negatively
affecting the potential of African countries to make progress toward UHC. Future increases
in health expenditures—which are needed in many countries to achieve UHC—should be
accompanied by greater efforts to fight corruption in order to avoid wasting money. Measur-
ing and tracking health sector-specific corruption is critical for progress toward UHC.
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OPEN ACCESS
Citation: Hsiao A, Vogt V, Quentin W (2019) Effect
of corruption on perceived difficulties in healthcare
access in sub-Saharan Africa. PLoS ONE 14(8):
e0220583. https://doi.org/10.1371/journal.
pone.0220583
Editor: David A. Larsen, Syracuse University,
UNITED STATES
Received: April 2, 2019
Accepted: July 18, 2019
Published: August 21, 2019
Copyright: ©2019 Hsiao et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All data files are
available from the Afrobarometer database (http://
www.afrobarometer.org/data/merged-data).
Funding: The author(s) received no specific
funding for this work.
Competing interests: The authors have declared
that no competing interests exist.
Introduction
Universal health coverage (UHC) is recognized by the United Nations (UN) as one of the key
strategies for improving global health and wellbeing [1]. As target 3.8 of the Sustainable Devel-
opment Goals (SDG), global goals set by the UN to improve health and development by 2030,
achieving UHC requires that all people have access to quality essential health services without
encountering financial barriers or hardships [1]. Although the implementation and financing
of UHC varies by country, UN Member States have committed to a broad framework to
achieve UHC that includes healthcare financing reform, removing financial risks and barriers
to access, and promoting health system efficiency by eliminating waste and corruption [2–4].
In the African region, many countries have made gains in personal healthcare access and
quality, which have led to significant improvements in health outcomes. Progress toward
UHC has been particularly slow, however, in central and east sub-Saharan African countries
[2]. A pan-African survey, the Afrobarometer, found that in 2014–2015, nearly half of Africans
forewent needed healthcare, and 4 in 10 of those who accessed care in the prior year found it
difficult to access that care [5]. There are several reasons for lack of access to healthcare in
Africa. Studies from specific African countries, including Malawi, Nigeria, South Africa, Zam-
bia, and Burkina Faso, have shown that some of these reasons include fear of discrimination or
stigmatization [6], lack of education [7], lack of transportation [7–10], and direct financial bar-
riers, such as out-of-pocket payments or user fees [11].
Having to pay illegal fees or bribes to receive medical care is a particular type of financial
barrier that has important implications for access to care. This is especially true for poorer
patients who are more reliant on public services and thus more vulnerable to bribery [12].
Patients who are frequently confronted with having to pay a bribe at the point of care may
decide to delay seeking care until they are much sicker (or may not seek care at all); they may
also rely instead on traditional/spiritual healers or informal drug sellers that could exacerbate
their health [13]. Of course, patients who decide to pay the bribes are left with fewer resources
as well. The repeated action of paying a bribe for medical care may uproot one’s trust in the
healthcare system and consequently lead one to perceive that access is limited. While institu-
tional corruption also has consequences for a patient’s access to care, patients do not experi-
ence it first-hand as they do with bribery. Public officials may misappropriate or pocket funds,
which reduces healthcare system funding overall. Consequently, this leads to fewer resources
to purchase necessary medicines, hire qualified healthcare workers, or make improvements to
healthcare facilities, which all ultimately impact patient care and quality access. Previous stud-
ies have found that perceived national corruption is associated with poorer health across all
socioeconomic groups, particularly among the less educated [14]. More generally, poor gover-
nance is associated with poorer health outcomes, including lower levels of life expectancy [15],
higher mortality rates [15,16], and lower levels of subjective health feelings [15,17].
We know that generalized corruption in healthcare has detrimental effects on health in Africa,
such as low immunization rates, increased mortality for patients [18], and poor management of
chronic conditions [19]. No studies to our knowledge, however, have investigated and quantified
the relationship between paying bribes for healthcare—a distinctly different type of corruption in
healthcare than institutionalized corruption—and perceived difficulty of access to healthcare
across Africa. If the goal is to attain UHC in order to prevent mortality and disability, it is impor-
tant to understand whether experiencing corruption negatively affects one’s ability or desire to
improve health by seeking care [20]. Therefore, our primary study objective is to examine whether
paying bribes makes one more likely to report difficulty in obtaining medical care in sub-Saharan
Africa. As a second objective, we assess what proportion of these reported difficulties can be
explained by individual-level factors, versus region- or country-level factors.
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Methods
We used data from Round 6 of the Afrobarometer survey (2014–2015), a nationally represen-
tative publicly available dataset that surveys public attitudes on democracy, governance, eco-
nomic conditions, and related issues across African countries. Face-to-face interviews are
conducted by Afrobarometer staff in the respondent’s language with a randomly selected sam-
ple of 1,200 or 2,400 respondents in each country [21]. Only countries in sub-Saharan Africa
were included in our analysis, which included 32 countries from Round 6. The Afrobarometer
applies a clustered, stratified, multi-stage, area probability sample such that all citizens of vot-
ing age have an equal and known chance of selection for interview.
Our outcome of interest was perceived difficulty of obtaining medical treatment. Respon-
dents who had contact with a public clinic or hospital in the past 12 months were asked, “How
easy or difficult was it to obtain the medical treatment you needed?” We coded the response as
a binary outcome: “easy” (“very easy” and “easy”) and “difficult” (“very difficult” and “diffi-
cult”). Our main independent variable of interest was reported frequency of having to pay a
bribe for medical treatment. Respondents who had contact with a public clinic or hospital
were asked, “How often, if ever, did you have to pay a bribe, give a gift, or do a favor for a
health worker or clinic or hospital staff in order to get the medical care you needed? (in the
past 12 months)” Possible responses were “never,” “once or twice,” “a few times,” or “often.”
We controlled for the individual-level covariates in our model that have been shown to be
associated with access to healthcare. We hypothesized a priori that these same covariates might
influence an individual’s perceived difficulty in obtaining medical treatment. We predicted
that proximity to a nearby health clinic (as determined by the interviewer to be within easy
walking distance) [22], higher levels of education [14,23], and being male [24,25] would be
correlated with less perceived difficulty in obtaining medical treatment. In contrast, we pre-
dicted that those living in rural areas [23,26] and those with a higher lived poverty index (LPI)
[27,28] would have more perceived difficulty in obtaining care. The LPI is a measure within
the Afrobarometer survey that assesses how frequently the individual surveyed went without
basic necessities during the course of the prior year. The index ranges from 0 (no lived pov-
erty) to 5 (extreme lived poverty), and is an average of 5 components that individuals are asked
about (frequency of foregoing food, water, medicine, cooking fuel, and cash; the score for each
component also ranges from 0 to 5). The LPI is meant to provide a complement to other exist-
ing indices of poverty that provides an assessment of the extent to which the interviewee’s
basic needs are met [29]. Finally, we hypothesized that those in the younger age groups would
have fewer perceived difficulty in obtaining care, though the literature has shown that the type
of care sought matters [24,30]. Missing data was not imputed for our analysis.
We collapsed the number of levels for some of the variables. For education, “some primary
schooling” and “primary school completed” were combined, as were “intermediate school or
some secondary school/high school” and “secondary school/high school completed.” The four
levels of education including and beyond post-secondary qualifications were collapsed into a
single category. For urbanity, the Afrobarometer distinguishes between urban, semi-urban,
peri-urban, and rural, but only 2 countries (Botswana and Malawi) use the semi-urban and
peri-urban values; therefore, we collapsed rural, semi-urban, and peri-urban into a single level
“rural” after verifying that the population density of the district was more similar to rural areas
of the country as compared with urban areas.
We also controlled for selected region- and country-level variables. A respondent living in
the capital may perceive fewer barriers to medical care relative to other regions, since capital
regions are often more developed and may have more healthcare resources. Therefore, we
accounted for variability in regional development by creating a “capital region” variable.
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Within each country, the capital region within the Afrobarometer dataset was identified and
coded.
At the country level, we merged country-level data from the World Health Organization’s
(WHO) Global Health Expenditure database and Transparency International, a global coali-
tion against corruption. From the WHO data, we used healthcare expenditures per capita by
country (in constant 2011 international dollars) [31]. From Transparency International, we
used the 2015 Corruption Perception Index (CPI) for each country, which is a composite indi-
cator from country experts that measures perception of public sector corruption [32]. We did
not find collinearity between CPI and our primary predictor variable (having paid a bribe) (r =
–0.13) and thus included both predictors in our models.
We also considered the inclusion of covariates from the WHO related to healthcare access
and availability of health services: community health workers per 1,000 people, physicians per
1,000 people, and hospital beds per 1,000 people. Due to sparse and/or outdated data, however,
these covariates could not be included in our analysis.
We specified a 3-level multivariable analysis using a random-intercept multilevel logistic
regression model. Using a multilevel regression model allowed us to investigate the extent to
which there was clustering of outcomes across regions within a country, and between coun-
tries. The 3 levels within the model were individuals (n = 29,788), regions within countries
(n = 384), and countries (n = 32). With the addition of covariates from each level (i.e., individ-
ual-, region-, and country-level), we tested whether the inclusion of the covariates improved
our model fit by comparing the Akaike’s Information Criterion for each model to the null mul-
tilevel logistic regression. We conducted all analyses in Stata/IC 13.1 (College Station, TX).
Results
In our survey year, 31,322 respondents had contact with a public clinic or hospital (Table 1).
Of these, 14% reported paying a bribe or giving a gift at least once in the past year in order to
obtain medical care. Our study population was similar in sociodemographic characteristics to
the general weighted Afrobarometer population (Table 1).
Forty-one percent of respondents (n = 12,821) said it was difficult to obtain medical care
(Table 1). By country, the highest proportions of respondents that reported it being difficult to
obtain medical care were in Gabon (65%), Liberia (63%), Sudan (62%), and Senegal (60%). The
highest proportions of respondents that reported ever paying bribes were in Liberia (53%),
Sudan (32%), Cameroon (30%), Guinea (25%), and Sierra Leone (25%) (Fig 1 and S1 Table).
In Model 1, where we only included the main predictor (frequency of paying bribes), we
found that those who paid bribes once or twice had 4.11 (95% confidence interval [CI]: 3.70–
4.57) times the odds of reporting that it was difficult to obtain medical care compared with
those who never paid bribes (Table 2). The odds further increased for those who paid bribes a
few times (OR = 4.90; 95% CI: 4.25–5.65) and more than doubled for those who paid bribes
often (OR = 9.52; 95% CI: 7.77–11.67).
In Model 2, individual-level covariates were added to our multilevel Model 1. We found
that the odds of reporting that it was difficult to obtain medical care did not substantially
change when compared to the null model (Model 1). Respondents with at least some second-
ary education (OR = 1.16–1.20) or who had a higher LPI (OR = 1.38–4.06) had increased odds
of reporting difficulties in obtaining care. We observed that respondents with geographic
access to a health clinic (OR = 0.86; 95% CI: 0.81–0.91) and those living in a rural area
(OR = 0.85; 95% CI: 0.79–0.91) had reduced odds of reporting difficulties in obtaining care.
In Model 3, we added the capital region covariate, which was not found to be significant. A
sensitivity analysis was also conducted by including “secondary” cities (e.g., Johannesburg,
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South Africa) in the coding of the capital region covariate, but there was no effect on the analy-
sis. In Model 4, our full model with the country-level covariates added, both CPI and health
expenditures per capita were not significant. The ORs (and associated p-values) for our pri-
mary exposure of interest (paying a bribe) in all of the models remained stable with each itera-
tion of the model.
We found that the intraclass correlation coefficient in Model 4 was 0.05 for the between-
country variation and 0.13 for the between-region variation, indicating that approximately 5%
and 13% of the total variance in the outcome was due to unexplained between-country and
between-region variation in reporting difficulties in obtaining care, respectively.
Table 1. Sociodemographic characteristics of study population vs. Afrobarometer total weighted population, 2014–2015�.
Study Pop.
N (%)
Total Pop.
N (%)
Total 31,322 (100) 49,137 (100)
Frequency of paying a bribe or gift
Never 26,391 (86) — N/A —
Once or twice 2,122 (7)
A few times 1,218 (4)
Often 805 (3)
Sex (%)
Male 15,020 (48) 19,126 (50)
Female 16,302 (52) 19,275 (50)
Age group (%)
18–25 yrs 7,242 (23) 9,352 (24)
26–35 yrs 9,769 (31) 11,534 (30)
36–45 yrs 6,533 (21) 7,682 (20)
46–55 yrs 3,838 (12) 4,793 (13)
>55 yrs 3,772 (12) 4,847 (13)
Education (%)
No formal schooling 4,092 (13) 5,886 (15)
Informal schooling only 1,473 (5) 2,110 (6)
Some or primary schooling completed 9,531 (30) 10,680 (28)
Intermediate or some secondary/high 6,620 (21) 8,534 (22)
Secondary/high school completed 5,060 (16) 5,503 (14)
Post-secondary or higher 4,480 (14) 5,597 (15)
Urban/rural
Urban 11,693 (37) 14,903 (39)
Rural 19,629 (63) 23,499 (61)
Health clinic access in EA or PSU
No 12,582 (41) 14,942 (39)
Yes 18,461 (59) 23,170 (61)
Lived poverty index
No lived poverty 4,680 (15) 6,292 (17)
>0 to �1 10,726 (35) 12,540 (33)
>1 to �2 10,058 (32) 12,079 (32)
>2 to �3 4,771 (15) 6,057 (16)
>3 to �4 830 (3) 1,090 (3)
�Sum of counts for each characteristic may not add up to column total due to missing data.
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We also plotted the estimated country- and region-level residuals to examine the country-
and region-level effects (S1 and S2 Figs). The figures show that for approximately 13 countries
and a substantial number of regions, the perceived difficulty of obtaining care is significantly
above or below the average values.
Discussion
To our knowledge, our study is the first to explore whether individually perceived corruption
in the form of bribes might impact one’s perception of how difficult it is to obtain medical care
in the African region. We found that patients who had paid bribes had between 4.00–9.11
times the odds of reporting difficulties in obtaining medical care, compared to those who
never paid bribes. Only poverty was associated with a similarly large increase in the odds of
reporting difficulties in obtaining medical care (OR 1.41–4.20, depending on the level of pov-
erty). Country level factors, such as health expenditures per capita and corruption as measured
by the CPI were not important. Our findings have important implications for policymakers
and researchers in the context of the current quest of African countries to reach UHC.
First, we find a high incidence of patients having to pay bribes for medical care in some
African countries, reaching as high as 53% in Liberia, and incidence is above 10% in half of the
countries. This is a reason for concern, as the pervasiveness of paying bribes makes it difficult
for many patients to access care. Therefore, fighting health sector corruption should be an inte-
gral part of current efforts to reach UHC [20]. As corruption is often deeply-engrained in soci-
eties, this will likely require substantial changes to the health system in addition to changes to
policies that reach beyond the health sector [33]. Studies have highlighted that fighting corrup-
tion in the health sector must start from the top: Ministers of Health must demonstrate strong
leadership, and high morality and integrity in order to change the attitudes of providers and
patients who may have accepted bribery as a cultural trait of the health system [19]. In addi-
tion, moving away from direct payments can reduce corruption because it eliminates the
exchange of money at the point of access to care [4]. Furthermore, more transparency between
Fig 1. Proportion of respondents who paid bribes versus reported difficulties in obtaining care in sub-Saharan Africa, 2014–2015.
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Table 2. Odds ratio and 95% confidence intervals for associations between having paid a bribe to obtain medical care and perceived difficulty of obtaining medical
care.
Model 1 Model 2 Model 3 Model 4
OR 95% CI OR 95% CI OR 95% CI OR 95% CI
Paid a bribe to obtain medical care
Never 1 Reference 1 Reference 1 Reference 1 Reference
Once or twice �4.11 (3.70–4.57) �4.00 (3.59–4.46) �4.00 (3.59–4.46) �3.99 (3.58–4.45)
A few times �4.90 (4.25–5.65) �4.73 (4.08–5.47) �4.72 (4.08–5.47) �4.69 (4.05–5.43)
Often �9.52 (7.77–11.67) �9.12 (7.36–11.30) �9.11 (7.35–11.29) �9.07 (7.32–11.24)
Health clinic access within walking distance
No 1 Reference 1 Reference 1 Reference
Yes �0.86 (0.81–0.91) �0.86 (0.81–0.91) �0.87 (0.81–0.92)
Urbanity
Urban 1 Reference 1 Reference 1 Reference
Rural �0.85 (0.79–0.91) �0.85 (0.80–0.91) �0.86 (0.80–0.92)
Lived poverty index
No lived poverty 1 Reference 1 Reference 1 Reference
>0 to �1�1.38 (1.26–1.50) �1.38 (1.26–1.51) �1.41 (1.28–1.54)
>1 to �2�2.18 (1.98–2.39) �2.18 (1.98–2.39) �2.22 (2.02–2.45)
>2 to �3�2.97 (2.66–3.31) �2.97 (2.66–3.31) �3.06 (2.74–3.41)
>3 to �4�4.06 (3.37–4.89) �4.06 (3.37–4.89) �4.20 (3.48–5.07)
Education
No formal schooling 1 Reference 1 Reference 1 Reference
Informal schooling only (including Koranic) 1.01 (0.87–1.17) 1.01 (0.87–1.17) 1.01 (0.87–1.18)
Some or primary schooling completed 1.02 (0.93–1.12) 1.02 (0.93–1.12) 1.03 (0.93–1.13)
Intermediate school or some secondary/high school �1.16 (1.05–1.28) �1.16 (1.04–1.28) �1.17 (1.06–1.30)
Secondary/high school completed �1.20 (1.08–1.34) �1.20 (1.07–1.34) �1.20 (1.07–1.34)
Post-secondary or higher �1.14 (1.02–1.28) �1.14 (1.02–1.28) �1.15 (1.02–1.29)
Sex
Male 1 Reference 1 Reference 1 Reference
Female 0.95 (0.90–1.00) 0.95 (0.90–1.00) 0.96 (0.91–1.01)
Age group
18–25 yrs 1 Reference 1 Reference 1 Reference
26–35 yrs 1.08 (1.00–1.16) 1.08 (1.00–1.16) 1.07 (0.99–1.15)
36–45 yrs 1.05 (0.97–1.14) 1.05 (0.97–1.14) 1.05 (0.97–1.14)
46–55 yrs 1.00 (0.91–1.09) 1.00 (0.91–1.09) 0.99 (0.90–1.09)
>55 yrs 0.93 (0.84–1.03) 0.93 (0.84–1.03) 0.93 (0.84–1.02)
Capital region
Yes 1 Reference 1 Reference
No 1.22 (0.99–1.51) 1.24 (1.00–1.54)
Corruption Perceptions Index (CPI)
1.00 (0.98–1.02)
Healthcare expenditures per capita
1.00 (1.00–1.00)
ICC (SE) for country 0.07 (0.02) 0.05 (0.02) 0.06 (0.02) 0.05 (0.02)
ICC (SE) for region 0.14 (0.02) 0.13 (0.02) 0.13 (0.02) 0.13 (0.02)
Akaike's information criterion (AIC),% change 36,893.04, –6.8% 35,325.45, –10.8% 35,323.99, –10.8% 34,526.47, –12.8%
Notes: The ICC (SE) in the null model for country was 0.09 (0.02) and for region 0.16 (0.02). The AIC for the null model was 39,595.94. The proportional change in AIC
compares each iteration of the model with the null (e.g, Model 1 compared to null, Model 2 compared to Model 1).
�Statistically significant at α= 0.05
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patients and healthcare providers is needed; for example, information on user charges and
exemptions should be clear and easily accessible to patients. Fighting corruption also requires
strong institutions that are willing to proactively collect patient complaints, investigate corrup-
tion allegations, and punish corruption [19,34].
On the patient side, there is evidence that beyond the healthcare system itself, the social net-
works of the patients may play a role in perpetuating corruption. A recent study found that in
Tanzania and Uganda, those who reported strong social networks perceived fewer barriers to
health access and were less prone to extortive bribing. If patients know that health access is
challenging, they may mobilize their existing social networks for financial assistance and/or
direct connections to health workers for favors and prioritized access [34]. In contrast, in
Rwanda, authorities used a “naming and shaming” approach to actively publish the names of
those engaged in crimes of corruption (along with the names of their parents and their com-
munities of origin). This aggressive approach has reduced reliance on social networks for
favors. These findings suggest that fighting corruption has local and country-specific nuances
that must be understood before implementing policies, but also that social networks beyond
the health system itself are potential a point of intervention for battling corruption.
Second, there has been significant international attention on health system financing to
achieve UHC [35,36]. The WHO has emphasized that low-income countries will require addi-
tional financial support in order to achieve UHC and expand access to services [4]. However,
we found that healthcare expenditures per capita had no significant impact on the odds of
reporting difficulties in obtaining medical care, and this finding did not change in our sensitiv-
ity analyses, when we used public expenditures per capita or the log of healthcare expenditures.
This suggests that there may be inefficiencies in how available healthcare resources in sub-
Saharan Africa are currently being used to improve patients’ access to care, though there is
likely significant variation within and between countries that our current study does not
attempt to address. Some of this variation is likely due to the presence of social networks
described above that may pervert equitable access, especially for the poorest [34]. At the same
time, it may also suggest that more resources will not lead to better outcomes unless efforts are
increased to fight corruption in the healthcare sector [19].
Third, when we compare our data on reported difficulties in obtaining medical care with
the UHC service coverage index, which is used by the UN to monitor progress toward SDG
3.8.1 [3], we find that countries that have a low UHC index score (e.g., Liberia has a UHC
index score of 34/100) also tend to have a higher proportion of respondents saying it is difficult
to obtain medical care (63% in Liberia), but the correlation between the two is relatively low.
This may indicate that the UHC coverage index is not a good reflection of the experiences of
patients living in African countries: Niger has a low UHC index score of 33, but also has one of
the lowest proportions (23%) of respondents saying that they had difficulty obtaining medical
care. Conversely, Gabon has a UHC index score of 52, but 65% of respondents (the highest in
our study population) say it was difficult to obtain medical care. The UHC index is based on
information that has generally been available in most countries, including vaccination cover-
age and health worker density; however, various standalone components in the index for a spe-
cific country are often outdated (i.e., more than 10 years old) and recency of primary data
availability impacts a country’s UHC score. Further, since the index does not include a compo-
nent that measures actual patient access or patient satisfaction, this lack of correlation is not
entirely surprising. Our findings suggest that survey data concerning difficulty of obtaining
medical care may provide additional insights on effective service coverage that could be used
to supplement existing metrics as part of the SDG monitoring process. Furthermore, given the
strong influence of bribes on difficulty in obtaining medical care, monitoring country-level
healthcare corruption levels is important for achieving UHC because fighting corruption is
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possible only if change is measured [33]. Many African countries implement Demographic
and Health Surveys that routinely monitor the impact of various indicators on health; a bribery
question similar to the Afrobarometer question could be added to country surveys to routinely
track healthcare corruption over time.
Fourth, our analysis confirms some existing knowledge and raises several questions that
require further analysis. The findings from our study confirm that the poor have more diffi-
culty obtaining care—up to 4.20 times more—which is in line with existing literature that the
impoverished experience more financial hardships in accessing care [1]. Similarly, geographic
proximity to a health clinic makes it easier to obtain care (OR = 0.87; 95% CI: 0.81–0.92).
However, interestingly and against our expectation, living in a rural area also reduces the odds
of reporting difficulties in obtaining care (OR = 0.86; 95% CI: 0.80–0.92). This could be related
to greater social inequalities in urban areas [37], and a greater general awareness of the difficul-
ties in obtaining care in an urban setting. For example, urban areas may attract more experi-
enced health professionals who are more in demand, and poorer individuals may feel
especially disadvantaged when they seek care due to finances or discrimination. Another possi-
ble explanation could be that the (in)existence of geographic access problems is captured by
the covariate “proximity to a nearby clinic,” whereas the “rural” covariate only captures factors
unrelated to physical access. One study with similar findings has speculated that rural popula-
tions have lower expectations of access to health services [38]; therefore, the “rural” covariate
may address these lower expectations because geographic access is already captured in the
“proximity to a nearby clinic” covariate. Furthermore, the more educated reported more diffi-
culties in obtaining care. We theorize that one possible explanation for this is that those who
are more educated may be better informed about their rights and may demand more from the
healthcare system [39]. Finally, national level corruption as measured by the CPI was not
found to be significant. This might indicate that while the CPI may be an appropriate measure
of broader institutionalized corruption, it may not necessarily capture corruption in the form
of bribery as experienced by patients accessing care. Further, because the CPI relies on a range
of data sources that reflect expert opinions, it may differ from the experiences of the general
population [20,33,40].
Our study has limitations. The Afrobarometer survey asked only those who received medi-
cal care in the prior year about difficulties of obtaining medical care. Thus, our results may
underestimate the effect of paying bribes on difficulties in obtaining care because our analysis
does not include those who attempted to obtain care, but never received it. Furthermore, the
pervasiveness of bribes within a healthcare system may normalize the behavior so much that it
may be difficult for patients to distinguish which payments are legal versus illegal (i.e., under-
reporting of bribes paid, confusing it with user fees). Our analysis also excludes respondents
who may have avoided the medical system altogether even when it was necessary. US-based
studies suggest that individuals may avoid medical care when there are barriers to access—
even when they are insured [41], and the same is likely to be true in African setting. Distrust in
the medical profession may also decrease one’s willingness to seek care [42]. Finally, our study
does not allow us to identify the reasons for the considerable variation across countries. More
research is needed to understand corruption within the context of each individual country’s
health policies and systems, and to identify possible actions to prevent bribery.
In summary, our study found that bribery is strongly associated with increased difficulties
in obtaining medical care in sub-Saharan Africa. Therefore, national governments and inter-
national actors should increase their efforts to fight corruption and to measure it in order to
make progress toward achieving UHC. Given our finding that higher health expenditures are
not associated with easier access to medical care, future increases in health expenditures
should be accompanied by even greater efforts to fight corruption to avoid wasting money.
Corruption and healthcare access in Africa
PLOS ONE | https://doi.org/10.1371/journal.pone.0220583 August 21, 2019 9 / 12
Finally, monitoring progress toward UHC would benefit from including survey data on the
incidence of corruption and on perceived difficulty in obtaining medical care.
Supporting information
S1 Table. Difficulty of obtaining medical care by country, 2014–2015 (n = 31,322).
(DOCX)
S1 Fig. Country-level residuals for reported difficulty of obtaining care.
(PNG)
S2 Fig. Region-level residuals for reported difficulty of obtaining care.
(PNG)
Author Contributions
Conceptualization: Amber Hsiao.
Data curation: Amber Hsiao.
Formal analysis: Amber Hsiao, Verena Vogt, Wilm Quentin.
Investigation: Amber Hsiao.
Methodology: Amber Hsiao, Verena Vogt, Wilm Quentin.
Supervision: Wilm Quentin.
Validation: Amber Hsiao, Verena Vogt, Wilm Quentin.
Writing – original draft: Amber Hsiao.
Writing – review & editing: Amber Hsiao, Verena Vogt, Wilm Quentin.
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