Journal Article Critique
For this assignment, you are going to do a thorough critique of a journal article based somewhere in the field of your future career in healthcare, or a personal interest of yours (i.e. a medical condition you or a loved one have, a medical treatment or issue that interests you, etc.).really take the time to pick an article that you think is well writtenand analyze every aspect of it- from the title, to the way they present their research question, to the way they wrote their methodology, to their use of subheadings, to how they wrap up the paper in a thoughtful conclusion. Look at their use of language- consider how they present the process they went through. Does everything make sense to you? How could the author(s) have done better? Read it a few times if you can. Be critical! Even great articles have weaknesses, and even boring/monotonous articles have good points.Remember, you are not critiquingthe study, butthe actual article itself.This is also not a place to just summarize the article or give your opinion about the topic. (P.S I will attach the article below)
Critiques must be one to two (1-2) full pages of writing, excluding cover page. The entire document must be in APA formatting. That means 12 point font, one inch margins, double spacing, page headers, references, and a standard APA cover page (see APA examples in module)-but you don’t need an author’s note or abstract.APA adherence will be part of your grade! Don’t use bullets or lists in your critique- it should be in paragraph form using full sentences. You don’t need any external resources for this aside from your article.
RESEARCH ARTICLE Open Access
Socio-economic inequalities in the multiple
dimensions of access to healthcare: the
case of South Africa
Tanja Gordon1*, Frederik Booysen2 and Josue Mbonigaba3
Abstract
Background: The National Development Plan (NDP) strives that South Africa, by 2030, in pursuit of Universal Health
Coverage (UHC) achieve a significant shift in the equity of health services provision. This paper provides a diagnosis
of the extent of socio-economic inequalities in health and healthcare using an integrated conceptual framework.
Method: The 2012 South African National Health and Nutrition Examination Survey (SANHANES-1), a nationally
representative study, collected data on a variety of questions related to health and healthcare. A range of concentration
indices were calculated for health and healthcare outcomes that fit the various dimensions on the pathway of access. A
decomposition analysis was employed to determine how downstream need and access barriers contribute to upstream
inequality in healthcare utilisation.
Results: In terms of healthcare need, good and ill health are concentrated among the socio-economically advantaged
and disadvantaged, respectively. The relatively wealthy perceived a greater desire for care than the relatively poor.
However, postponement of care seeking and unmet need is concentrated among the socio-economically disadvantaged,
as are difficulties with the affordability of healthcare. The socio-economic divide in the utilisation of public and private
healthcare services remains stark. Those who are economically disadvantaged are less satisfied with healthcare services.
Affordability and ability to pay are the main drivers of inequalities in healthcare utilisation.
Conclusion: In the South African health system, the socio-economically disadvantaged are discriminated against across
the continuum of access. NHI offers a means to enhance ability to pay and to address affordability, while disparities
between actual and perceived need warrants investment in health literacy outreach programmes.
Keywords: Access, Health inequality, Healthcare, Concentration index, Decomposition, South Africa
Background
The United Nations Sustainable Development Goal
(SDG) 3.8 strives towards the achievement of access to
quality, effective, and affordable medical care for all and
the assurance of universal coverage [1]. In addition,
mandated in South Africas National Development Plan
(NDP) is the goal to provide universal equitable, efficient
and quality healthcare [2]. In light of these global and
national policy prerogatives, socio-economic inequalities
in access to healthcare remain high on the policy
agenda.
Research finds that over one billion people in low- and
middle-income countries (LMIC) are unable to afford
healthcare and that the poor within these countries
benefit least from healthcare utilisation [3, 4]. In the case
of South Africa, the socio-economically disadvantaged
are more likely to experience poor health status, disabil-
ity, the simultaneous occurrence of more than one con-
dition/disease (multi-morbidity) and are less likely to use
inpatient care [57]. The South African health system is
two-tiered with the least advantaged heavily dependent
on the under-resourced public sector, while the wealthy
(many of whom have private medical insurance) use the
private sector [815]. Since 1996, user fees were waived
for all seeking primary public healthcare, although eligi-
bility for free care at public sector hospitals is subject to
The Author(s). 2020 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
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: [emailprotected]
1Research Impact Assessment programme (RIA), Human Sciences Research
Council (HSRC), HSRC Building 134 Pretorius Street, Pretoria 0002, South
Africa
Full list of author information is available at the end of the article
Gordon et al. BMC Public Health (2020) 20:289
https://doi.org/10.1186/s12889-020-8368-7
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http://creativecommons.org/publicdomain/zero/1.0/
mailto:[emailprotected]
a means-test [16, 17]. In order to access a private health-
care facility one has to pay out-of-pocket (OOP) or be
covered by health insurance (even then the patient may
incur a co-payment). In 2015/16, private healthcare ex-
penditure was 4.4% and OOP expenditure 0.06% of
GDP, whereas public healthcare expenditure amounted
to 4.1% of GDP and is funded from general tax [8, 17].
Although each health sector makes an almost equal con-
tribution to GDP, the public sector services approxi-
mately 84% of the population while the private sector
services a mere 16% [8, 9].
South African studies on health inequalities, however,
with the exception of Harris et al. [18], are rather unidi-
mensional in nature, generally focusing only on a limited
number of outcomes rather than a wide variety of di-
mensions of access to healthcare. Studies tend to look at
single dimensions on the pathway of access, for example,
healthcare outcomes such as multi-morbidity and dis-
ability [6], life-style diseases [19, 20], child [21, 22] and
maternal health [23, 24], and healthcare utilisation [7].
Current research, therefore, is limited in that it fails to
examine the full spectrum of the dimensions of access.
Another important point to note is that inequality in ac-
cess, where it has been analysed comprehensively [18],
has only been measured descriptively, whereas this study
adopts a more standard method and makes use of the
concentration index and employs a decomposition ana-
lysis to determine the main contributors to inequality in
healthcare utilisation. As the country embarks on the
implementation of National Health Insurance (NHI) [8],
advancing the understanding of inequalities in access to
healthcare and tracking these inequalities remains a priority.
The one purpose, therefore, of this study is to describe
socio-economic inequalities in South Africans access to
healthcare using a standardised indicator of inequality
applied to an integrated conceptual framework. The
other purpose is to determine how upstream need and
access barriers contribute to downstream inequality in
healthcare utilisation in the private and public sectors
with the aid of a decomposition analysis.
Conceptual framework
Elsewhere, access has been defined as availability (the lo-
cation of the healthcare facility and the ability of the in-
dividual to access the facility), affordability (direct/
indirect costs of healthcare utilisation and the ability of
the individual to meet these costs); and acceptability (the
point at which the service from the provider meets the
expectation of the patient) [25]. This study however,
uses the even more detailed framework adopted by Lev-
esque et al. [26] to conceptualise the various dimensions
of access to healthcare (Fig. 1). These authors define ac-
cess as realised utilisation. More intrinsically, access
comprises the perception of an individuals need for
care, healthcare seeking, healthcare reaching and the
utilisation of healthcare and its consequences. The path-
way is influenced by individual and community-level
health system supply-side factors: 1) approachability;
2) acceptability; 3) availability and accommodation; 4) af-
fordability and; 5) appropriateness as well as demand-
side factors: 1) ability to perceive; 2) ability to seek;
3) ability to reach; 4) ability to pay and; 5) ability to engage.
Given the broad dynamics of this definition, this study uses
proxies that best fit the applicable stages or dimensions of
access and selected demand- and supply-side factors.
Methods
Data
Data analysis was conducted using the nationally repre-
sentative 2012 South African National Health and Nutri-
tion Examination Survey (SANHANES-1). The objective
of the survey was to examine the current health and nu-
trition status of South Africans in relation to non-
communicable disease (NCD) prevalence and their asso-
ciated risk factors. For the purpose of the survey, 500
Enumerator Areas (EAs) representative of the demo-
graphic profile of South Africa were identified from the
2007 HSRC Master Sample of 1000 EAs selected from
the 2001 population census. Thereafter, 20 visiting
points were randomly selected from each EA totalling a
sample of 10,000 visiting points (VPs). Of the 10,000
households (VPs) sampled, 8168 were valid households
of which 6307 (77.2%) were successfully interviewed.
From the total number of valid households who con-
sented to participate in the study, 27,580 individuals
aged 15 years and older were eligible for interview. Over-
all, 92.6% of all qualified individuals completed the indi-
vidual interview. The SANHANES-1 survey received
ethical clearance from the Research Ethics Committee
(REC) of the Human Science Research Council (HSRC)
(REC 6/16/11/11) [27].
Health and healthcare outcomes
Table 1 below maps out the variables selected to repre-
sent each dimension of access to healthcare based on
the studys conceptual framework (see Fig. 1).
Wealth index
To investigate the socio-economic gradient in each of
the health and healthcare outcomes in the access path-
way, a wealth index and corresponding wealth quintiles
were constructed by applying Multiple Correspondence
Analysis (MCA) to the household survey data. Use was
made of a total of 16 variables, including housing type,
water and sanitation services, and ownership of 13
household assets. The percentage inertia explained by
the first dimension is approximately 90%. The wealth
index was used as it is considered a more reliable
Gordon et al. BMC Public Health (2020) 20:289 Page 2 of 13
measure of socio-economic status (SES) in developing
countries as compared to income [28].
The concentration index
The concentration curve plots the cumulative propor-
tion of the population by SES, beginning with the least
advantaged and ending with the most advantaged,
against the cumulative proportion of health or ill health.
The line of equality or the diagonal signifies the absence
of inequality. If the curve lies above the line, ill health
falls on the least advantaged in the population, and if it
lies below, the more advantaged. The further the curve
lies from the diagonal the greater the degree of inequal-
ity. The concentration index is defined as twice the area
between the curve and the line of equality. It takes on a
positive value when it lies below the line of equality and
a negative value when it lies above. A positive value can
be interpreted as the concentration of health among the
relatively wealthy and a negative value among the rela-
tively poor. The minimum value the index can take is
1 and the maximum value is + 1. Should the index be
equal to zero (or not statistically significantly different
from zero), no inequality exists [2931].
According to the literature, the standardised concen-
tration index is suitable for variables with a ratio scale,
the equation of which is as follows:
C 2
cov h; r 1
where C is the standardised concentration index, h is the
healthcare variable, is the mean of the healthcare vari-
able, and r is the ith- ranked individual in the socio-
economic distribution from the relatively poorest to the
richest [28, 29, 31, 32].
Bounded variables, on the other hand, complicate the
measurement of inequality. Given that bounded variables
can take the form of attainments or short falls the mir-
ror property that requires absolute values of health I(h)
and ill health I(1 h) to be equal with different signs, is
not satisfied with the standardised concentration index
[32]. In this regard, one common practice concerning
variables with a limit is the use of the Erregyer corrected
concentration index. The index is desirable as it satisfies
properties required for bounded variables [33]. The
equation for the Erregyer index is as follows:
CCI 4
ba
C 2
where CCI is the corrected concentration index, is the
mean of the attained healthcare, b and a the maximum
and minimum values, respectively, and C the standar-
dised concentration index [3234].
Decomposition analysis
A decomposition analysis was conducted to determine
how upstream factors such as health status, need and ac-
cess barriers contribute to downstream socio-economic
inequality in healthcare utilisation. Following Wagstaff
Fig. 1 Dimensions of access to healthcare: a conceptual framework
Gordon et al. BMC Public Health (2020) 20:289 Page 3 of 13
Table 1 Health and healthcare outcomes, by access dimension
Access dimension Outcome Survey question
Healthcare need:
Self-reported health (SRH) Binary: Very good and good 1, 0
otherwise
In general how would you rate your health today? [AQ]
World Health Organisation Disability
Schedule (WHODASscore)
Continuous In the last 30 days, how much difficulty did you have in ?
(12 questions) [AQ]
Kessler Psychological Distress
Scale (K10)
Binary: Psychological distressed 1, 0
otherwise
The following questions concern how you have been feeling
over the past 30 days. (10 questions) [AQ]
Post-Traumatic Stress Disorder
(PTSD)
Binary: PTSD 1, 0 otherwise In the past week, how much trouble have you had with the
following symptoms? (17 questions) [AQ]
Perceived healthcare need:
Needed care Binary: Needed care 1, 0 otherwise When was the last time you needed health care (from a doctor
or hospital)? [AQ]
Healthcare seeking:
Household healthcare postponed Binary: Household healthcare
postponed 1, 0 otherwise
In the last 12 months, have you put off or postponed getting
the healthcare you need? [VPQ]
Availability:
Household distance to a
healthcare facility
Binary: 010 Km away from a
healthcare facility 1, 0 otherwise
How far do you live from the nearest health clinic or hospital?
[VPQ]
Healthcare reaching:
Unmet need Binary: Unmet need 1, 0
otherwise
The last time you needed health care, did you get health
care? [AQ]
Affordability:
Household difficulty affording cost
of care
Binary: Yes 1, 0 otherwise In the past 12 months, have you had difficulty affording the
cost of necessary medical care? [VPQ]
Household difficulty affording
prescription medicine
Binary: Yes 1, 0 otherwise In the past 12 months, have you had difficulty affording the
cost of prescription medication? [VPQ]
Ability to pay:
Household private medical
insurance
Binary: In my own name/
through a family member
1, 0 otherwise
Do you have private medical aid/ health insurance either in
your own name or through another family member? [VPQ]
Healthcare utilisation:
Household private care Binary: Private 1, 0 otherwise Where do you usually get your healthcare from? [VPQ]
Household public care Binary: Public 1, 0 otherwise
Individual private care Binary: Private doctor/hospital/
clinic in the last year 1, 0
otherwise
When was the last time that you received health care from a
private doctor/hospital/clinic? [AQ]
Individual public care Binary: Public doctor/hospital
in the last year 1, 0 otherwise
When was the last time that you received health care from a
public doctor/hospital/clinic? [AQ]
Overall individual care Binary: Individual private or
public care in the last year
1, 0 otherwise
Healthcare consequences:
Healthcare service satisfaction Binary: Very satisfied and
satisfied, 0 otherwise
In general, how satisfied were you with how the health care
services were run in your area? [AQ]
Healthcare service provider
satisfaction
Binary: Very satisfied and satisfied, 0
otherwise
How would you rate the way health was provided in your area?
[AQ]
AQ adult individual questionnaire, VPQ visiting point household questionnaire
Gordon et al. BMC Public Health (2020) 20:289 Page 4 of 13
[35], Eq. 3 depicts the linear relationship between the
health variable (utilisation) and its determinants:
hi 0
XK
k1
kxik i 3
where hi is the healthcare variable of interest, xik the set of
demographic and socio-economic contributing factors,
and i the error term. Like the concentration indices, the
decomposition technique used for the standard concentra-
tion index (C) (not shown here) [3537] is modified to
suit the corrected concentration index (CCI) as follow:
CCI h 4
XK
k1
kxkC xk GC
” #
4
The decomposed CCI is the summed product of the
degree of responsiveness, i.e. the elasticity kxk to
health changes and the degree of socio-economic in-
equality C(xk) in that determinant, plus the generalised
concentration index of the error term (GC), all multi-
plied by 4. All things being equal, a positive contribution
(x % > 0) by a factor would decrease socio-economic in-
equality. Alternatively, a negative contribution (x % < 0),
all things being equal, would increase socio-economic
inequality [20, 38, 39]. The unexplained part of the con-
tribution of factors to inequality, the residual, can take
on negative values, with an explained percentage in ex-
cess of 100%, which, by interpretation, suggests that
measured inequality is completely explained by the
models explanatory variables [40], as has been the case
in other decomposition studies [4044]. To determine
whether actual and perceived need and access barriers
are sector-specific, the decomposition analysis was strati-
fied by private/public healthcare utilisation as charac-
terised by the two-tiered South African health system.
The Generalised Linear Model (GLM) from the binomial
family with a link function was used as it is considered
the least sensitive to the choice of reference group when
the dependent variable is a binary health outcome [45].
The decomposition analysis was bootstrapped at 500 rep-
lications to obtain standard errors and p-values for the
statistical significance of the absolute contributions [46].
Data analysis was conducted in STATA software version
15 and weighted with post stratified sample weights.
Results
Description
Table 2 describes the adult samples socio-demographic
characteristics and each of the access variables. The
adult sample comprised slightly more females than
males (52% versus 48%). The average age of respondents
was 37 years. Respondents mainly comprised Africans
(78%) and lived mainly in urban areas (67%).
Table 2 Summary statistics
Variable Mean (%) SE n
A. Demographics
Sex:
Male 47.96 0.004 15,911
Female 52.04 0.004 15,911
Age:
Age 36.75 0.128 15,886
Race:
African 77.64 0.003 15,839
non-African 22.36 0.003 15,839
Geographical area:
Urban 66.70 0.004 15,405
Rural 33.30 0.004 15,405
B. Access dimension
Healthcare need:
Self-reported health 78.49 0.003 14,351
WHODAS score 5.29 0.096 13,407
Psychological distress 6.46 0.002 14,215
Perceived healthcare need:
Needed care 50.57 0.005 9937
Healthcare seeking:
Household healthcare
postponed
21.19 0.005 5651
Availability:
Household distance to a
healthcare facility
77.46 0.005 5817
Healthcare reaching:
Unmet need 3.16 0.002 6852
Affordability:
Household difficulty affording
cost of care
27.64 0.006 5613
Household difficulty affording
prescription medicine
26.09 0.006 5603
Ability to pay:
Household private medical
insurance
21.09 0.005 5804
Healthcare utilisation:
Household private care 27.38 0.006 5823
Household public care 71.32 0.006 5823
Individual private care 30.52 0.004 11,029
Individual public care 42.37 0.005 10,489
Overall individual care 59.49 0.005 10,293
Healthcare consequences:
Healthcare service satisfaction 71.37 0.004 14,143
Healthcare service dissatisfaction 69.35 0.004 14,059
Note: All estimates are weighted proportions, SE Standard error, WHODAS score
World Health Organisation Disability Assessment Schedule, K10 Kessler
Psychological Distress Scale
Gordon et al. BMC Public Health (2020) 20:289 Page 5 of 13
Overall, 78% of individuals self-reported good or very
good health. On average, 5% of individual respondents
found it difficult to complete basic physical, cognitive and
social activities. In addition, 6% of respondents experi-
enced high or very high levels of psychological distress.
From the results, just over 50% of the population received
the healthcare they required and just about 21% of house-
holds postponed seeking healthcare. Unmet need was low,
at 3%, and just over three quarters of households lived
within 10 km from a healthcare facility. Roughly 21% of
households had private medical insurance. In addition, an
estimated 28% of households had difficulty affording their
medical care and 26% their prescription medication.
Among individual respondents, 31% used private care and
42% public care in the year prior to the survey, with 59%
having used either a private or public healthcare facility.
Approximately seven in ten households used a public
healthcare facility compared to only 27% of households
that used a private facility. In terms of satisfaction, 71 and
69% of respondents were satisfied or very satisfied with
their healthcare services and service provider, respectively.
These averages, however, mask substantial socio-
economic inequalities, as illustrated by the patterns across
the wealth quintiles (Table 3) and the estimates of the
concentration indices (Table 4).
Socio-economic inequalities in access to healthcare
Healthcare need and perceived healthcare need
Table 4 shows the concentration index for good self-
reported health to be positive in value and statistically sig-
nificant in margin. That is, relatively wealthier individuals
perceived their current health state as very good or good
(CCI + 0.074, p < 0.001). Concentration indices for respon-
dents who had difficulty completing physical, cognitive
and social tasks (C 0.101, p < 0.001) or reported psycho-
logical distress (CCI 0.041, p < 0.001) lie below zero. In
other words, the socio-economically disadvantaged are
more likely to have poor health outcomes. In terms of per-
ceived healthcare need, relatively economically better-off
respondents were more likely to perceive a need for
healthcare (CCI + 0.060, p = 0.022).
Healthcare seeking and reaching
Socio-economically disadvantaged households were more
likely to postpone seeking healthcare compared to those
at an advantage (CCI 0.154, p < 0.001). Relatively
wealthy households were more likely to be located within
a 10 km radius of a healthcare facility in comparison to
relatively poorer households (CCI + 0.210, p < 0.001).
From Fig. 2, the most common reason households post-
poned obtaining healthcare was because they could not af-
ford care, followed by high transportation costs. The
socio-economically disadvantaged were also more likely
than those at an advantage to need healthcare but to re-
port not receiving care (CCI 0.029, p < 0.001).
Affordability, healthcare utilisation and healthcare
consequences
In terms of affordability and ability to pay, which pro-
vides a bridge between reaching and using healthcare
[26], results show households at an economic advantage
to be more likely to have private medical insurance
when compared to those at a socio-economic disadvan-
tage (CCI + 0.490, p < 0.001). Economically disadvan-
taged households found it difficult to pay for their
medical care (CI 0.162, p < 0.001) and prescription
medicine (CI 0.169, p < 0.001). Although individual
overall utilisation was unequally distributed across the
five wealth quintiles, the summary measure of inequality
was not significantly different from zero (CCI + 0.033,
p = 0.257) and hence overall utilisation was not decom-
posed. The concentration indices depicted in Table 4
also differentiate the private and public sectors, respect-
ively, in terms of the nature of healthcare utilisation. Pri-
vate care (CCI + 0.247, p < 0.001) was concentrated
among relatively better-off individuals, while those indi-
viduals who were economically worse-off depended on
the public sector (CCI 0.231, p < 0.001). Sector-specific
household-level socio-economic inequalities were even
more pronounced, with concentration indices as high as
CCI + 0.490 (p < 0.001) for private healthcare and CCI
0.462 (p < 0.001) for public healthcare utilisation. In
terms of healthcare consequences, the results show that
relatively wealthy individuals were more likely to report
being satisfied or very satisfied with their healthcare ser-
vice (CI + 0.074, p = 0.008) and service provider (CI +
0.078, p = 0.006), respectively.
Decomposition of socio-economic inequality in healthcare
utilisation
Table 5 shows the results of the decomposition analysis.
The columns report the margins, absolute contributions
(the product of each determinants elasticity and CI) and
their bootstrapped standard errors and p-values, as well
as the percentage contributions of each explanatory fac-
tor. In terms of sector-based healthcare utilisation, two
factors, namely household wealth (45.20%) and access to
private medical insurance (46.40%), together explained
almost all of the observed inequality in private sector
healthcare utilisation. The same two factors (household
wealth 34.76% and private medical insurance
48.58%), together with being African (20.24%), were all
statistically significant and large contributors to inequal-
ity in public sector healthcare utilisation. Subjectively
perceived need (12.81%, p = 0.001), and challenges with
the affordability of care ( 6.62%, p = 0.008) made mod-
est but statistically significant contributions to inequality
Gordon et al. BMC Public Health (2020) 20:289 Page 6 of 13
in private sector healthcare utilisation. Need also made a
modest ( 12.44%) but statistically significant (p = 0.002)
contribution to public sector healthcare utilisation. For
private sector healthcare utilisation, the contribution of
age was statistically significant (p = 0.004), but small
(1.96%). In the case of public sector healthcare utilisa-
tion, the contribution of self-reported health was small
(2.12%) yet statistically significant (p = 0.001). The unex-
plained residuals for both the private ( 11.13) and pub-
lic ( 0.48) decomposition models are negative and, as a
result, the need, access and other variables explain all of
the measured inequality in healthcare utilisation.
Discussion
Levesque et al. [26] provide an in-depth conceptualisa-
tion of the term access to healthcare. In essence, a path-
way is described beginning with healthcare need,
followed by perceived healthcare, healthcare seeking,
healthcare reaching, healthcare utilisation and lastly
healthcare consequences. This paper provides an expos-
ition of socio-economic inequalities across this con-
tinuum of access using a set of 17 indicators.
All three measures of health status used in the analysis
exhibited a socio-economic gradient, with healthcare
need (poorer health status) concentrated in the poor.
Another study also found that those socio-economically
disadvantaged were most likely to report disability in re-
lation to their intellect and emotions [5]. Concerning
psychological distress, other studies also have found a
lower prevalence among individuals with high incomes
groups compared to those who belong to low income
groups [4749].
The ability to identify ones healthcare needs is the next
stage along the pathway of access to healthcare [26]. In
SANHANES-1, respondents reported when last they
needed healthcare. Financially better-off respondents were
Table 3 Health and healthcare outcomes in each access dimension, by wealth quintile
Access dimension Quintile 1 (%) Quintile 2 (%) Quintile 3 (%) Quintile 4 (%) Quintile 5 (%) F-statistic p-value
Healthcare need:
Self-reported health 74.52 75.98 75.94 78.47 83.42 20.1 0.000
WHODAS score 6.10 6.09 5.65 5.00 3.74 20.7 0.000
Psychological distress 8.48 6.87 8.06 6.92 2.99 21.9 0.000
Perceived healthcare need:
Needed care 49.00 45.45 46.78 53.72 54.54 12.0 0.000
Healthcare seeking:
Household healthcare postponed 28.88 26.21 23.22 15.19 10.65 39.4 0.000
Availability:
Household distance to a healthcare facility 61.75 73.47 80.15 86.53 86.64 73.4 0.000
Healthcare reaching:
Unmet need 5.55 3.80 2.96 3.26 1.59 7.9 0.000
Affordability:
Household difficulty affording cost of
care
36.45 31.47 29.38 24.32 15.22 36.1 0.000
Household difficulty affording
prescription medicine
34.01 31.83 26.85 22.99 12.61 41.4 0.000
Ability to pay:
Household private medical insurance 3.01 3.69 10.73 23.50 66.53 683.7 0.000
Healthcare utilisation:
Household private care 8.01 10.09 16.44 32.75 70.92 513.5 0.000
Household public care 88.47 88.70 82.29 65.75 30.05 430.1 0.000
Individual private care 19.85 18.62 25.02 34.30 48.26 153.8 0.000
Individual public care 52.39 50.36 46.97 42.81 24.18 108.2 0.000
Overall individual care 59.13 56.73 57.25 60.65 62.18 4.1 0.003
Healthcare consequences:
Healthcare service satisfaction 70.77 68.34 66.81 68.35 79.91 38.6 0.000
Healthcare service provider satisfaction 69.25 66.25 66.13 64.20 79.41 49.5 0.000
Note: All estimates are weighted proportions; WHODAS score World Health Organisation Disability Assessment Schedule, K10 Kessler Psychological Distress Scale
Gordon et al. BMC Public Health (2020) 20:289 Page 7 of 13
Table 4 Socio-economic inequality in access to healthcare, by dimension
Access dimension C/CCI SE p-value
Healthcare need:
Self-reported health 0.074 0.020 0.000
WHODAS score 0.101 0.025 0.000
Psychological distress 0.041 0.008 0.000
Perceived healthcare need:
Needed care 0.060 0.026 0.022
Healthcare seeking:
Household healthcare postponed 0.154 0.013 0.000
Availability:
Household distance to a healthcare facility 0.210 0.013 0.000
Healthcare reaching:
Unmet need 0.029 0.008 0.000
Affordability:
Household difficulty affording cost of care 0.162 0.014 0.000
Household difficulty affording prescription medicine 0.169 0.014 0.000
Ability to pay:
Household private medical insurance 0.490 0.011 0.000
Healthcare utilisation:
Household private care 0.490 0.012 0.000
Household public care 0.462 0.013 0.000
Individual private care 0.247 0.026 0.000
Individual public care 0.231 0.027 0.000
Overall individual care 0.033 0.029 0.257
Healthcare consequences:
Healthcare service satisfaction 0.074 0.028 0.008
Healthcare service provider satisfaction 0.078 0.028 0.006
Note: C Standard concentration index, CCI Erregyer corrected concentration index, SE Standard error, WHODAS score World Health Organisation Disability
Assessment Schedule, K10 Kessler Psychological Distress Scale
Fig. 2 Most common reasons for households postponing healthcare
Gordon et al. BMC Public Health (2020) 20:289 Page 8 of 13
