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R E S E A R C H A R T I C L E Open Access
Corruption, inequality and population perception
of healthcare quality in Europe
Zlatko Nikoloski
*
and Elias Mossialos
Abstract
Background: Evaluating the quality of healthcare and patient safety using general population questionnaires is
important from research and policy perspective. Using a special wave of the Eurobarometer survey, we analysed
the general population’s perception of health care quality and patient safety in a cross-country setting.
Methods: We used ordered probit, ordinary least squares and probit analysis to estimate the determinants of
health care quality, and ordered logit analysis to analyse the likelihood of being harmed by a specific medical
procedure. The models used population weights as well as country-clustered standard errors.
Results: We found robust evidence for the impact of socio-demographic variables on the perception of quality of
health care. More specifically, we found a non-linear impact of age on the perception of quality of health care and
patient safety, as well as a negative impact of poverty on both perception of quality and patient safety. We also
found robust evidence that countries with higher corruption levels were associated with worse perceptions of
quality of health care. Finally, we found evidence that income inequality affects patients’perception vis-à-vis safety,
thus feeding into the poverty/health care quality nexus.
Conclusions: Socio-demographic factors and two macro variables (corruption and income inequality) explain the
perception of quality of health care and likelihood of being harmed by adverse events. The results carry significant
policy weight and could explain why targeting only the health care sector (without an overall reform of the public
sector) could potentially be challenging.
Keywords: Quality of healthcare, Access to healthcare, Corruption, Inequality, EU
Background
The health care systems of the European Union (EU)
member states have been subject to continuous reform over
the last twenty years, mainly stemming from the pressures
of aging populations and challenges in reforming public
budgets. Over the same period, the EU witnessed its biggest
enlargement (the so called big bang) that brought in
ten new countries –almost all of which belong to the
group of ‘transition’countries. The enlargement in 2004
(and the subsequent one in 2007)
a
also allowed for the
possibility of both patients and medical staff to migrate
across borders, thus adding further layers of complexity to
the extant national health care systems. Given this back-
ground, an evaluation of people’sperceptionofhealthcare
quality (and of patient safety) is a high-priority task, not
least because a careful examination of factors that influence
perceptions of health care quality could provide the basis
for effective policy action aimed at improving access to
services and the quality of national health care. Having
said that, evaluating the quality of health care is not a new
topic. Indeed, to date, there have been many systematic
cross-country reviews (and subsequent ranking) of health
care systems. Some of them have tried to understand
the quality of national health care from a comparative
perspective, while others (relying on surveys of patient
satisfaction/perception of quality) help to provide a
deeper understanding of the determinants of health care
quality at national level.
Using the Eurobarometer 327 (2009) survey, we look
at the general population’s perception of health care quality
and patient safety in a cross-country EU setting. This paper
adds to the extant literature in three crucial ways: (i) we
study the macro-level and socio-demographic determi-
nants of the general population’s perception of the quality
* Correspondence: z.nikoloski@lse.ac.uk
London School of Economics, Houghton Street, WC2A 2AE London, UK
© 2013 Nikoloski and Mossialos; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the
Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is properly cited.
Nikoloski and Mossialos BMC Health Services Research 2013, 13:472
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of healthcare; (ii) building on (i), we disentangle which
aspects of health care are the most important in shaping
the perception of health care quality; and (iii) we analyt-
ically explore the macro-level and socio-demographic
determinants of the likelihood of being harmed by med-
ical procedures. While doing so, we employ additional
robustness checks. It is important to note that there are
two limitations to our findings: the lack of control variables
vis-à-vis personal-level health care utilisation rates and
subjective health evaluations. We have addressed these
shortcomings by using macro-proxy variables for health
care utilisation (health care expenditure as a percentage
of gross domestic product (GDP)) as well as controlling for
age, which is usually strongly correlated with subjective
health status evaluation.
A substantial literature focuses on the issue of quality of
health care (both qualitative and quantitative). The existing
qualitative body of research can be divided into two main
groups, one relying on medical professionals’perceptions of
quality (see, for example, Robinson et al [1]) and the other
solely focusing on patients
b
. Patient surveys could offer
a better overview of perception of health care quality
and safety [2]. According to the authors, evidence
shows that patient perceptions are distinctive, and often
conflicting, compared with those of the health care pro-
viders, which ultimately leads to different assessments of
health care quality. Given that patients are the final users
of health care services, considering their perceptions
would be more important. Furthermore, most of the
studies using patient surveys are small-scale studies
that use patient interviews and focus groups (Anderson
et al [3], Attree [4], Concato and Feinstein [5], Gerteis
et al [6], Irurita [7], Jun et al [8], Larrabee and Bolden
[9], Ngo-Metzger et al [10], Radwin [11], Stichler and
Weiss [12], and Ware and Stewart [13]). Finally, within
the body of research that focuses on patients, a sub-section
concentrates on patient satisfaction with health care services
as the only measure of health care quality (Taylor and Cronin
[14], Babakus and Mangold [15], Sohail [16]). However,
this subsection of literature has been fairly controver-
sial and has received significant criticism from the research
community (for a detailed discussion , see Crow et al [17]).
Another strand of literature has looked at perceptions of
quality of health care from a quantitative and cross-country
perspective. To date, there have been two papers that have
looked at the issue of patient quality from this perspective.
Cleary et al [18] analysed the quality of health care using
patient-reported measures of quality in the United States,
Australia, Canada, New Zealand and the United Kingdom.
Wendt et al [19] used a truncated sample of 14 EU coun-
tries to assess the preferences for state involvement, and
subsequently, quality of health care across the EU.
In addition, a significant body of literature has looked
at the determinants of what constitutes the core attributes
of health service quality. Gronroos [20] argues that these
attributes can be divided intotwogroups:functional
(ambience, i.e. description of the form in which the
service is delivered) and technical (outcome, i.e. the
quality of what is delivered). Similar reasoning is found
in other studies on the topic (Zifko-Baliga and Krampf
[21], DeRuyter and Wetzels [22]).
To the best of our knowledge, there is one study concern-
ing patient safety in a cross-country setting, assessing the
likelihood of medication and medical errors in five OECD
countries (Cleary et al [18]). Finally, in the context of the
US, a burgeoning literature has looked at various aspects of
quality and performance of HMOs (Health Maintenance
Organisation). The overall finding of that sizeable litera-
ture points to a negative link between age and perception
of quality as well as a negative link between chronic illness
and perception of quality (Miller and Luft [23,24],).
Following the strands of literature outlined above, in
this paper we answer the following three questions:
(i) What individual and macro-level variables drive the
perception of quality of healthcare among EU nationals?;
(ii)What attributes of healthcare play the biggest role
when evaluating the quality of healthcare?;
(iii)What individual and macro-level variables drive the
perception of the likelihood of being harmed by
hospital or non-hospital care?
Methods
Data
The empirical analysis in this paper is based on data from
the special Eurobarometer Survey 327, which is part of
a set of surveys conducted on behalf of the European
Commission. This special Eurobarometer survey was con-
ducted during September and October 2009 and features
attitudes towards quality of health care as well as the
likelihood of experiencing adverse events. Most na-
tional samples were drawn with a multi-stage, random
probability sampling design from the population aged
15 years and over. In each country, approximately 1000
standardised face-to-face interviews were conducted
(for the smaller countries, like Luxembourg, Malta and
Cyprus, the number was about 500). The German and
UK samples consist of 1500 respondents. The total size
of the sample was around 26,000 people. In this paper
we analysed data for the 27 countries that currently make
up the European Union. Data were weighted according to
the national weighting procedure with sex, age, region and
size of locality entering the iteration procedure. The data
used in the paper are widely available.
Dependent variables
In order to analyse respondents’perceptions vis-à-vis the
quality of health care and the likelihood of being harmed
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by hospital or non-hospital care, we focused on two ques-
tions. The first was: “How would you evaluate the overall
quality of health care in our country? Answers were: (1)
very good; (2) fairly good; (3) fairly bad; and (4) very bad”
c
.
The question was used in an ordered probit and OLS
analysis. As a robustness check we also constructed an
alternative measure (0–1) that was subsequently used
in a probit analysis. Table 1 provides a snapshot of the
average values for this variable. As expected, the Nordic
countries and the oldest welfare states of Western Europe
tend to show perceptions of the highest levels of health
care quality. Incidentally, these are the countries with the
highest health expenditures both, per capita and as a per-
centage of GDP.
Second, to analyse the possibility of being harmed by
hospital or non-hospital care, we relied on the question:
“How likely do you think it is that patients could be
harmed by hospital (or non-hospital) care in our country?
Answers were: (1) very likely; (2) fairly likely; (3) not very
likely; (4) not at all likely”. Following established practice
in analysing questions of likelihood, an ordered logit
model was used
d
.
Independent variables –individual characteristics
As in previous studies (Carlson et al [25], Cleary et al [26],
Haviland et al [27], Roohan et al [28]) we use age as one of
the main explanatory variables. Rather than using dummies
for age categories (Wendt et al [19]), we use age and an
age-squared term to test the possibility of a non-linear
effect of age on health care quality perception. The
Eurobarometer does not include variables on health status
and thus we cannot control for it
e
. As in other studies, we
Table 1 Patients’perception of quality of healthcare and selected healthcare systems variables
Rank Country Eurobarometer mean
score for perception of
quality of healthcare
Health expenditure per
capita (current USD)
average 2008-2010
Total health expenditure
(as % of GDP) average
2008-2010
Number of physicians
per 1000 people,
average 2008-2010
1 Austria 1.613 5048.8 10.8 4.9
2 Belgium 1.671 4707.2 10.5 3.0
3 Sweden 1.74 4641.3 9.6 3.8
4 Netherlands 1.862 5527.0 11.3 2.9
5 Finland 1.866 4105.6 8.8 2.8
6 Denmark 1.895 6406.2 11.0 3.4
7 UK 1.905 3581.3 9.4 2.7
8 Luxembourg 1.918 8166.1 7.5 2.8
9 Malta 1.922 1653.6 8.5 3.1
10 France 1.955 4823.9 11.7 3.5
11 Germany 1.998 4703.1 11.3 3.6
12 Spain 2.064 3015.2 9.4 4.0
13 Czech Rep. 2.133 1482.8 7.6 3.6
14 Cyprus 2.217 1805.5 6.0 2.6
15 Estonia 2.265 959.5 6.2 3.4
16 Slovenia 2.299 2214.3 9.0 2.5
17 Italy 2.52 3347.6 9.3 3.9
18 Ireland 2.527 4732.1 9.2 3.2
19 Slovak Rep. 2.531 1428.3 8.6 3.0
20 Lithuania 2.653 850.5 7.1 3.6
21 Portugal 2.664 2371.7 10.6 3.9
22 Latvia 2.791 816.1 6.6 3.0
23 Poland 2.837 906.4 7.3 2.2
24 Hungary 2.92 1006.0 7.4 3.1
25 Bulgaria 2.931 475.2 7.0 3.7
26 Greece 2.974 2941.1 10.3 3.6
27 Romania 2.984 458.8 5.6 2.3
Sources: Eurobarometer special survey on health (2010), World Development Indicators. The perception of quality of healthcare variable takes values from 1 - very
good to 4 - very bad.
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alsocontrolforlevelofeducation(Roohanetal[28],
Carlson et al [25]). Previous research attempts to con-
trol for respondents’gender although the results from
the extant research, as well as meta-analysis, tend to be
inconclusive (Cleary et al. [18]).
In addition to the variables above, we also control for
marital status, differences between rural and urban dwellers
and size of households. Differences between income groups
are an important determinant (Schoen et al [29]), especially
in health care in the context of EU countries, which
rely on high levels of redistribution. For this purpose,
we constructed a category called poor (corresponding
to the people who have difficulty paying bills most of
the time over the last 12 months). Finally, to capture
the relative standing of the people in the country, rather
than using subjective income assessment, we used an asset
index, constructed using factor analysis (for a detailed
explanation of asset index creation and usage in survey
analysis please refer to Nikoloski and Ajwad [30]).
Independent variables –macro level variables
Even though the EU comprises countries that are at a
high level of economic development, there is significant
variation among countries in terms of macro (economic)
developments. Hence, it is important to control for them.
The level of development is captured by the log of the
average GDP per capita (PPP) for the period 2008–2010.
We also control for the level of economic inequality
(3-year average Gini coefficient). There is also significant
institutional variation among the countries. Given that all
of them are, more or less, democratic, while there are
significant differences in terms of bureaucratic quality,
we use a three year average (2008–2010) of Transparency
International’s Corruption Perception Index. Finally, we
experiment with different health care variables. To capture
the monetary input, we use data on total health care ex-
penditure (per capita and as a percentage of GDP). The
extant research uses the level of health care expenditure
as an indication of the interventionist power of the state
in the field of health care. As an indicator for real input, we
included the number of physicians in relation to the popu-
lation (per 1,000). Physicians are often the first point of
contact for patients and make decisions affecting a major
part of total health care resources. In many countries, they
act as gatekeepers and are often responsible for transferring
patients to specialist health care and further care givers.
Additional file 1: Table A2 provides a more detailed de-
scription of the macro level independent variables.
Table 2 provides a snapshot of the basic macro-level vari-
ables used in our analysis. As mentioned previously, for the
analytical purpose of our exercise we used ordered probits,
OLS and probits (for perception of quality of health care)
and ordered logits (for likelihood of being harmed by
hospital or non-hospital care).
Independent variables –criteria for selecting a doctor
Building on this, we then analysed the importance of
different criteria when assessing the quality of healthcare.
For this purpose we used the question: “Of the following
criteria, which are the three most important criteria when
you think of high quality healthcare in our country?: (a)
proximity of hospital and doctors; (b) free choice of doctor;
(c) respect of a patient’s dignity; (d) medical staff that is well
trained; (e) a clean environment at the healthcare facility;
(f) treatment that works; (g) free choice of hospital; (h)
healthcare that keeps you safe from harm; (i) no waiting
list to get seen and treated; (j) a welcoming and friendly
environment and (k) modern medical equipment”.
Results
Perception of quality of care
The results of the ordered probit analysis are captured
in Table 3. We find a non-linear relationship between age
and perception of quality of health care (the inflection
point of the curve occurs around the age of 40 years). In-
dividuals living in households with a higher wealth index
(and therefore enjoying a better socio-economic status)
tend to be associated with better perceptions of health
care quality. When it comes to gender, we find evidence
that females are more negative (vis-à-vis perceptions of
quality of health care) than males (although the magnitude
of the coefficient is minuscule). Finally (and closely corre-
sponding to our wealth index findings), the poor tend to
have more negative perceptions of health care quality than
the non-poor. Of the macro-level variables the Corruption
Perception Index is the one which is the most interesting,
lending evidence to the fact that in more transparent
countries, the perception of the quality of health care
tends to be higher
f
.
As a robustness check, we repeated the analysis using
an OLS and probit methodology, respectively. Additional
file 2: Table A1 presents these results, which, confirmed
our main hypotheses distilled from the ordered probit
analysis outlined above
g,h
.
We also conducted a secondary robustness check by
including an interaction term between the age category and
the proxy for corruption in order to test if the perception of
quality of healthcare stems from the older generation (i.e.
the one which is the heaviest user of healthcare services).
While informative, the results did not yield statistically
significant results
i
.
Determinants of quality of healthcare
Building on the analysis and findings above, we pro-
ceeded with determining the statistical significance of
different quality of health care determinants, while alternat-
ing between models that include individual characteristics,
macro-level variables, and/or country dummies. Our results
are presented in Table 4. It is important to note that the
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individual characteristics are binominal variables which
are included as separate independent variables in the
model. The robust results that emerge from this exer-
cise lend evidence to the statistical significance of the
following few determinants: choice of doctor, well-
trained staff, and, in the majority of instances, proximity
of doctor and modern medical equipment. The control
variables (individual and macro-level variables) tend to
maintain the same sign and significance as in the per-
ception of quality exercise
j
.
Likelihood of experiencing an adverse event
We then proceeded with analysing the likelihood of
experiencing a hospital or non-hospital adverse event.
The results of our ordered logit analysis are captured
in Table 5. First, the results suggest that the statistical
significance of the likelihood of experiencing a non-hospital
adverse event is lower compared with the results of
the analysis conducted on the likelihood of experien-
cing a hospital-related adverse event. However, there
are some interesting results associated with individual
socio-demographic characteristics. As in some of the
casesabove,wefindevidencefortheexistenceofa
non-linear relationship between age and the likelihood
of experiencing an adverse event. This relationship,
however, is exhibited only in the context of hospital-
related adverse events (the results are insignificant in
the case of non-hospital care). As in some of the exercises
conducted above, here as well, the poor tend to have
higher expectations for the possibility of experiencing
an adverse event. Interestingly, we find evidence that
in countries with higher inequality, the perceived like-
lihood of experiencing adverse events among respon-
dents tends to be higher. In a way, this finding is a
variation of the poor people-likelihood of experiencing
hospital/non-hospital adverse event nexus, which we
established above.
We then repeated the exercise above for all individual
types of adverse events –hospital infections, incorrect
diagnosis, surgical errors, medication related errors, and
medical device errors. The results of that analysis broadly
confirm our findings and are available upon request.
In addition to the analysis we conducted two more
robustness checks. The first one assessed the likeli-
hood of a person suffering an adverse event contingent
upon their assessment of the quality of healthcare. The
storyline that emerges from this analysis confirms our
main findings from above. The second robustness check
was conducted in order to establish if preferences for
access versus treatment increase the likelihood of be-
ing exposed to an adverse event. Our results did not
find statistically significant evidence for this, whilst
providing evidence for our conclusions from above. As
in the previous case, the results of this analysis are
available upon request
k
.
Additional robustness checks
In addition, we conducted a few overall robustness
checks. In the first one, we ran a probit analysis using
the dichotomised perception of health care quality as a
dependent variable and the individual socio-economic
characteristics as independent variables and then sum-
marised the fitted values on a country by country basis.
Ultimately, we conducted a simple correlation exercise
between the obtained fitted values and the macro-
economic variables used in the analysis above. Our
results are reported in Additional file 3: Panel 1. The
analysis broadly confirms our findings vis-à-vis corruption
and inequality.
We conducted similar robustness checks for the like-
lihood of adverse events variables (both for hospital
andnon-hospital-relatedharm)andtheresultsofour
analysis are presented in Additional file 3: Panels 2 and 3.
As in the case above, our results broadly confirm our
initial conclusions
l
.
Our additional robustness check includes running
correlations of the perception of quality of health care
and individual health care attributes (both obtained as
fitted values from a probit analysis on individual socio-
economic and demographic characteristics). The results
Table 2 Summary of statistics
Variable Obs Mean Std. Dev. Min Max
GDP per capita, PPP (consant USD, 2005) average 2008-2010 26025 26525.14 10055.47 11166.64 70095.39
Gini, average 2008-2010 26025 29.80 3.86 23.30 37.10
Health care expenditure per capita, in USD, average 2008-2010 26025 2829.94 1289.29 814.67 6470.09
Health care expenditure (as % of GDP), average 2008-2010 26025 8.94 1.78 5.55 11.66
Nurses (per 1,000 people), average 2008-2010 26025 7.21 5.35 0.15 23.96
Physicians (per 1,000 people), average 2008-2010 26025 3.39 0.79 2.15 6.11
Transparency international corruption perception index 26025 6.392042 1.794522 3.666667 9.3
Unemployment (in %), average 2008-2010 26025 8.50 3.00 3.57 16.47
Source: World Development Indicators (WDI), Eurostat and Transparency International.
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are presented in Additional file 3: Panel 4. Here we find
evidence for a strong correlation between preferences
for well-trained doctors and cleanliness of health care
facilities on the one hand, and perceived quality of the
health care on the other.
Interaction between quality of health care and likelihood
of adverse events
Our final analysis incorporated the perception of health
care quality and likelihood of occurrence of adverse events.
In other words, in our last exercise we tried to ascertain
whether people with a higher opinion of the health care
system also believe that the likelihood of an adverse
event occurring is lower. In conducting the analysis we
used a similar approach to the one described above –we
plotted the fitted values obtained from a probit analysis on
socio-economic and demographic characteristics for both
perception of health care quality and the likelihood of
adverse events occurring. Our results are presented in
Additional file 1: Charts 1, 2 and 3. This analysis un-
doubtedly confirms the link between the two variables,
i.e. people with better perception of health care quality
also believe that the likelihood of adverse events is
smaller and vice versa, for respondents with a negative
perception of health care quality, the perceived likeli-
hood of adverse events increases.
Discussion
The main aim of this paper is to examine the percep-
tion of quality of health care, the main determinants
of health care quality and to examine the perception
of the likelihood of being harmed by different adverse
events (both hospital and non-hospital-related).
A starting assumption underpinning our analysis was
that both individual and macro-level characteristics
would determine respondents’perceptions of health
care quality as well as the likelihood of being harmed
by specific adverse events. We find evidence that age
and socio-economic status, and to some extent, gender,
act as main determinants of individuals’perception of
health care quality. Moreover, we find robust evidence
for a non-linear impact of age on both the perceived
quality of health care and the perception of being harmed
by an adverse event. In that respect, our research feeds
Table 3 Perceptions of quality of healthcare - ordered probit
(1) (2) (3)
Married 0.0963*** −0.0435* −0.0408
(0.0368) (0.0244) (0.0300)
Divorced 0.00588 −0.0303 −0.0539
(0.0434) (0.0357) (0.0478)
Widow 0.112** −0.0475 −0.0376
(0.0453) (0.0360) (0.0424)
Primary education 0.0285 0.0581 0.0828
(0.0771) (0.0655) (0.0686)
Secondary education 0.124** 0.111** 0.0681
(0.0612) (0.0454) (0.0621)
University education 0.0188 0.0241 0.0295
(0.0553) (0.489) (0.0518)
Female 0.00935 0.0419** 0.0366**
(0.0196) (0.0193) (0.0178)
Urban 0.0435 0.0121 0.0478
(0.0411) (0.0206) (0.0326)
Household size 0.0346** −0.00778 −0.00385
(0.0154) (0.00622) (0.0104)
Poor 0.258*** 0.188*** 0.185***
(0.0419) (0.0441) (0.0455)
Age 0.0170*** 0.0169*** 0.0172***
(0.00363) (0.00349) (0.00347)
Age squared −0.000293*** −0.000209*** −0.000218***
(0.0000373) (0.0000352) (0.0000370)
Asset index −0.325*** −0.0223 −0.0396**
(0.0388) (0.0146) (0.0177)
_cut1 −0.824*** −0.453*** −5.765*
(0.153) (0.0863) (3.264)
_cut2 0.785*** 1.491*** −3.959
(0.168) (0.0822) (3.239)
_cut3 1.793*** 2.694*** −2.838
(0.148) (0.111) (3.217)
Log of GDP per capita (average 2008-2010) −0.404
(0.301)
Total health expenditure
(as % of GDP, average 2008-2010)
−0.00968
(0.0481)
Gini, average 2008-2010 0.0277
(0.0236)
Log of number of doctors per 1000 people
(average 2008-2010)
−0.118
(0.405)
Corruption perception index (average 2008-2010) −0.206***
(0.0551)
Table 3 Perceptions of quality of healthcare - ordered probit
(Continued)
N 25661 25661 25661
Country dummies No yes No
adj. R-sq
Pseudo R-sq 0.039 0.161 0.114
Standard errors in parentheses.
=”*p < 0.1, **p < 0.05, ***p < 0.01”.
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into an emerging body of social sciences research that
finds a U-curve relationship between overall satisfaction
with services and age.
Second and most important, we find evidence that two
macro-level variables play a significant role in determining
the perception of health care quality and the likelihood
Table 4 Determinants of quality of healthcare
Ordered probit Ordered probit Ordered probit Ordered probit OLS OLS OLS OLS
(1) (2) (3) (4) (5) (6) (7) (8)
Doctor_prox −0.181*** −0.165*** −0.989*** −0.0370 −0.133*** −0.120*** −0.0596*** −0.0217
(0.0627) (0.0571) (0.0276) (0.0389) (0.0451) (0.0401) (0.0164) (0.0244)
Choice_doc −0.946* −0.960* −0.0737** −0.736* −0.0723 −0.0722 −0.0466** −0.0474*
(0.0555) (0.0554) (0.0321) (0.0416) (0.0408) (0.0401) (0.0196) (0.0266)
Patient_dig 0.0177 0.00907 0.0222 0.0667 0.00804 0.00653 0.0107 0.0422
(0.0527) (0.0529) (0.0438) (0.441) (0.0404) (0.0398) (0.0270) (0.0297)
Trained −0.255*** −0.240*** −0.116*** −0.119*** −0.188*** −0.174*** −0.0696*** −0.0732***
(0.0492) (0.0479) (0.0280) (0.0268) (0.0360) (0.398) (0.0270) (0.0297)
Clean −0.113 −0.131* 0.00187 0.0178 −0.0826 −0.0942 −0.000917 0.0122
(0.787) (0.0771) (0.0337) (0.0565) (0.0585) (0.0562) (0.0206) (0.0363)
Effective −0.0431 −0.0410 −0.0602* −0.0420 −0.0332 −0.0310 −0.0373* −0.0258
(0.0652) (0.0614) (0.0337) (0.0444) (0.0484) (0.0448) (0.0205) (0.0287)
Hosp_choice −0.108* −0.101* −0.0338 −0.0223 −0.0805* −0.0738 −0.0223 −0.0131
(0.0631) (0.0614) (0.367) (0.0429) (0.0468) (0.0449) (0.0222) (0.0275)
No_harm −0.0875 −0.0959 0.0153 0.0292 −0.663 −0.0710 0.00669 0.0177
(0.697) (0.0677) (0.0299) (0.0428) (0.0513) (0.0491) (0.0184) (0.0274)
No_waitlist −0.0699 −0.0557 0.0549 0.159*** −0.0568 −0.0454 0.0280 0.0940**
(0.0937) (0.0886) (0.0346) (0.0525) (0.0703) (0.0653) (0.0211) (0.0353)
Friendly −0.0735 −0.0711 −000400 −0.0479 −0.0541 −0.0513 −0.0233 −0.0296
(0.0489) (0.0499) (0.0469) (0.0507) (0.0363) (0.0363) (0.0283) (0.0331)
Modern_equip −0.106* −0.109* −0.124*** −0.129*** −0.0794* −0.0803* −0.0718*** −0.0808***
(0.0634) (0.0620) (0.0405) (0.0408) (0.0464) (0.0446) (0.0235) (0.0255)
_cut1 −1.429*** −1.216*** −0.594*** −6.527***
(0.124) (0.156) (0.115) (3.268)
_cut2 0.107 0.347*** 1.359*** −4.704
(0.142) (0.171) (0.109) (3.245)
_cut3 1.069*** 1.327*** 2.566*** −3.575
(0.120) (0.154) (0.141) (3.227)
_cons 2.524*** 2.354*** 1.816*** 5.707**
(0.0963) (0.115) (0.0655) (2.059)
N 25661 25661 25661 25661 25661 25661 25661 25661
R-sq 0.013 0.042 0.318 0.243
adj. R-sq 0.013 0.041 0.317 0.242
Pseudo R-sq 0.006 0.018 0.163 0.120
Individual socio-economic
characterestics
No Yes Yes Yes No Yes Yes Yes
Macro level variables No No No No No No No No
Contry dummies No No Yes No No No Yes No
Standard errors in parentheses.
=”*p < 0.1, **p < 0.05, ***p < 0.01”.
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Table 5 Likelihood of experiencing hospital and non-hospital harm, ordered probit
(1) (2) (3) (4) (5) (6)
Married −0.00422 0.0628 0.09277 −0.0610 0.0303 0.0395
(0.0638) (0.0622) (0.0575) (0.0619) (0.0530) (0.0577)
Divorced 0.0179 0.00655 0.0526 −0.0189 −0.0159 0.0274
(0.0544) (0.0558) (0.0548) (0.0662) (0.0590) (0.0625)
Widow 0.0385 0.103 0.138 −0.0575 0.0192 0.0458
(0.0774) (0.0858) (0.0898) (0.0587) (0.0522) (0.0638)
Primary education −0.183 −0.239*** 0.138 −0.0575 0.0192 0.0458
(0.142) (0.0894) (0.132) (0.143) (0.0958) (0.130)
Secondary education −0.228*** −0.258*** −0.171** −0.255*** −0.313*** −0.198**
(0.0875) (0.0726) (0.0826) (0.0824) (0.0673) (0.0778)
University education −0.180** −0.0384 −0.179** −0.252*** −0.194*** −0.246***
(0.0715) (0.0689) (0.0732) (0.0768) (0.0690) (0.0.0774)
Female −0.161*** −0.187*** −0.179*** −0.113*** −0.133*** −0.131***
(0.0301) (0.0323) (0.0307) (0.0336) (0.0336) (0.0319)
Urban −0.00288 0.00489 0.00433 −0.00810 −0.00817 −0.00217
(0.571) (0.0406) (0.0519) (0.0525) (0.0397) (0.0480)
Household size −0.0155 0.0167 0.0134 −0.0157 0.0114 0.0135
(0.0250) (0.0158) (0.0196) (0.0189) (0.0119) (0.0141)
Poor −0.478*** −0.314*** −0.411*** −0.428*** −0.267*** −0.354***
(0.0567) (0.0585) (0.0547) (0.0683) (0.0683) (0.0683)
Age −0.0144** −0.0182*** −0.0161*** −0.00621 −0.00730 −0.00709
(0.00577) (0.00609) (0.00588) (0.00468) (0.00472) (0.00484)
Age squared 0.000209*** 0.000195*** 0.000165*** 0.000175*** 0.000134*** 0.000121**
(0.0000535) (0.0000565) (0.0000527) (0.0000497) (0.0000478) (0.0000490)
Asset index 0.196*** 0.0110 0.0115 0.228*** 0.0173 0.0160
(0.0568) (0.0270) (0.0387) (0.0562) (0.0274) (0.0380)
_cut1 −11.03*** −11.54*** −14.24*** −10.78*** −11.50*** 9.530**
(1.047) (1.061) (4.211) (1.025) (1.043) (4.284)
_cut2 −2.619*** 3.063*** −5.812 −2.445*** −3.122*** −1.182
(0.211) (0.148) (3.971) (0.157) (0.127) (4.062)
_cut3 −0.336* −0.587*** 3.469 −0.141 −0.656*** 1.178
(0.204) (0.137) (4.003) (0.152) (0.101) (4.080)
_cut4 2.529*** 2.455*** −0.540 2.875*** 2.507*** 4.247
(0.211) (0.155) (4.102) (0.129) (0.118) (4.082)
Log of GDP per capita (average 2008-2010) −0.277 0.150
(0.441) (0.438)
Total health expenditure (as % of GDP, average 2008-2010) −0.0279 −0.00589
(0.0813) (0.0742)
Gini, average 2008-2010 −0.0662*** −0.00589
(0.0197) (0.0207)
Log of number of doctors per 1000 people (average 2008-2010) 0.538 0.221
(0.613) (0.521)
Corruption perception index (average 2008-2010) 0.195** 0.144*
(0.0991) (0.0805)
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of being harmed by hospital and non-hospital care. Our
analysis provides evidence for a positive association
between transparency and the quality of health care. In
other words, in countries with higher public sector
transparency and lower corruption, the general public
seems to be more satisfied with the quality of health
care services. More importantly, the perception of the
likelihood of being harmed by medical errors increases
in countries that are more unequal, which is suggestive
of the possibility of inadequate access to health care,
and especially to quality health care, for lower socio-
economic groups.
As noted previously, there are limitations to our analysis
that mainly stem from a lack of variables that could help
us control for the individual level of utilisation of health
care services and for subjective health care evaluation.
There are, however, ways to deal with these shortcomings
and we believe that we have addressed them. First, we use
a macro-level aggregate proxy for health care utilisation
(total healthcare expenditure as a percentage of GDP). In
addition, while the survey does not contain a measure of
subjective health assessment, given the strong correlation
between age and subjective health assessment, we believe
that age could be used as a proxy for health status.
Conclusions
There is a possibility for our results to be distilled
into policy actions. Improving transparency and fight-
ing corruption across all echelons of the public sector
(but especially in the health care sector) would help
to improve peoples’perception of the overall quality
of health care. Moreover, improving access to health
care, especially access for the poorest socio-economic
groups would help to improve not only their percep-
tion of overall health care quality, but it would also
boost their confidence that they are entitled to good
health care, and more importantly, care that is safe
and free from harm.
Finally, our results also point to the fact that there is no
panacea for an instantaneous improvement in perceptions
of health care quality. Indeed, our analysis suggests that
the perceptions of health care quality and safety are tied
to much more endemic problems, such as corruption,
low transparency and income inequality. This also points
to the impossibility of a successful reform that would
only target the health care system, or, even less so, a
certain sector of the health care system. Reform that is
all-encompassing, that would target all the echelons of the
public sector and that would also promote equitable and
inclusive growth could improve Europe’sgeneralpublic
perception of health care quality and patients’safety.
Endnotes
a
In May 2004, ten new countries became EU members:
Cyprus,CzechRepublic,Slovenia,Slovakia,Poland,
Estonia, Latvia, Lithuania, Malta and Hungary. In 2007,
Bulgaria and Romania also joined.
b
For the purpose of this literature review, the term
“patients”also comprises the general public that uses
healthcare services.
c
A detailed description of the variables used in the model
is provided in Additional file 1: Table A2.
d
Additional file 4: Table A3 provides a detailed account
of the questions used as dependent variables.
e
However, there are proxies that could be used in this
instance, such as, for example, age.
f
The conclusions that stem from this exercise closely
correspond to our findings that rely on the usage of
country dummies. The country dummies suggest a strong
link between quality of care and countries that are more
egalitarian and have a better institutional set up. The results
of this exercise are available upon request.
g
In addition, we employ a secondary robustness check,
i.e. we experiment with using age as a categorical variable.
The results that we obtain broadly correspond to our main
findings. Furthermore, they are available upon request.
h
In order to validate our results, we also conducted
a secondary exercise, i.e. we ran our basic model on indi-
vidual countries’data. The results that emerge from that
analysis confirm our main results vis-à-vis the independ-
ent variables –gender, age and income categories seem to
matter when assessing the quality of healthcare. There are
notable differences (both in terms of sign and significance
across countries) which are picked up by country dummies
in the cross-sectional part of the analysis.
i
These results are available upon request.
j
The results table does not report the coefficients of the
individual nor macro-level variables, but they are available
on request.
k
Results are available upon request.
Table 5 Likelihood of experiencing hospital and non-hospital harm, ordered probit (Continued)
N 25016 25016 25016 24832 24832 24832
Country dummies No Yes No No Yes No
adj. R-sq
Pseudo R-sq 0.010 0.060 0.027 0.011 0.053 0.026
Standard errors in parentheses.
=”*p < 0.1, **p < 0.05, ***p < 0.01”.
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l
In addition, we also conducted secondary robustness
checks, whilst also using more lenient measures of quality
as well as a more lenient measure of the perceived like-
lihood of being affected by adverse events. Even with
these newly established variables, our results strictly
follow our already established relationship between
perception of quality and likelihood of adverse events
and macro-economic variables.
Additional files
Additional file 1: Table A2. Further definition of variables, availability
and source.
Additional file 2: Table A1. Perception of quality of healthcare: OLS
and probit.
Additional file 3: Panel 1. Perception of quality of healthcare and
selected macro variables. Panel 2. Likelihoodof hospital harm and
selected macro variables. Panel 3. Likelihoodof non-hospital harm and
selected macro variables. Panel 4. Perception of quality of healthcare and
attributes of healthcare. Chart 1. Perception of healthcare quality and
adverse events. Chart 2. Perception of quality and healthcare system.
Chart 3. Quality of healthcare system and adverse events.
Additional file 4: Table A3. Questions used as dependent variables in
the analysis.
Abbreviations
EU: European union; GDP: Gross domestic product; HMO: Health
maintenance organization; OECD: Organisation for economic cooperation
and development; OLS: Ordinary least squares; PPP: Purchasing power parity;
UK: United Kingdom.
Competing interests
The authors declare that there are no competing interests.
Authors’contributions
Both authors were involved in the process of inception and drafting of the
manuscript. EM provided the overall framework for the analysis, while ZN
conducted the statistical analysis. ZN provided the write up for the
background, methods and results part of the paper, while EM drafted the
discussion and the conclusion. Both authors were involved in editing of the
paper. Both authors read and approved the final manuscript.
Received: 14 December 2012 Accepted: 18 October 2013
Published: 11 November 2013
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doi:10.1186/1472-6963-13-472
Cite this article as: Nikoloski and Mossialos: Corruption, inequality and
population perception of healthcare quality in Europe. BMC Health
Services Research 2013 13:472.
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