SELF-MEDICATION WITH ANTIBIOTICS: AN ASSOCIATION …
Transcript of SELF-MEDICATION WITH ANTIBIOTICS: AN ASSOCIATION …
MASTERS THESIS
SELF-MEDICATION WITH ANTIBIOTICS: AN ASSOCIATION BETWEEN INFORMATION
PROVISION AND LOWER LEVELS?
ANNA-SOPHIE DE JONG
SUPERVISORS
DR. IOANA VAN DEURZEN (TILBURG UNIVERSITY)
PROF. PETER ACHTERBERG (TILBURG UNIVERSITY)
EXTENDED MASTERS SOCIOLOGY
DECEMBER 2015
TABLE OF CONTENTS Abstract…………………………………………………………………………………..……2
1.1 Introduction……………………………………………………………………………….3
1.2 Results of Eurobarometer 2013………………………………………………….…….6
2. Theory……………………………………………………………………………….….…14
3. Methods…………………………………………………………………………….…..…21
4. Data Analysis……………………………………………………………………….…….26
5. Results…………………………………………………………………………….………29
6. Discussion and conclusions…………………………………………………….…..…..38
7. Bibliography…………………………………………………………………….……..….42
8. Appendix ………………………………………………………………..…...…….……..45
9. Annex…………………………………………………….………………………………..54
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Abstract The current thesis aims to investigate whether there is an association between information
provision of antibiotics and the behaviour of those who self-medicate with antibiotics. Self-
medication is the use of drugs without medical guidance, in response to self-diagnosed
disorders or symptoms, or the continued or sporadic use of a prescribed drug for recurrent or
chronic symptoms of disease (Kunin, 1978). Insight into the behaviour of those who self-
medicate with antibiotics is valuable as this form of antibiotic use contributes to the misuse of
antibiotics, which further contributes to the issue of antibiotic resistance. I analysed data from
the ‘Antimicrobial resistance and causes of non-prudent use of antibiotics in human
medicine’ research project, conducted by the Netherlands Institute for Health Services
Research (NIVEL). A total sample of 1,846 respondents across seven European countries
who self-medicated with antibiotics in the past 18 months were surveyed through a
computer-assisted telephone interview in 2015. Data was analyzed by use of a logistic
regression. The results show the association between information provision and self-
medication to be weak at most, however due to the selective sample the results are not
generalizable. Future research should include data on individuals who have not self-
medicated with antibiotics in order to determine real effects.
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1.1. Introduction
In recent decades, a growing body of evidence has advanced our medical and social
knowledge of antibiotics and the ability of microorganisms to transform and develop immunity
to them. Antibiotic resistance is the resistance of a microorganism to an antibiotic drug that
was originally effective for treatment of infections caused by it (WHO, 2014). Evidence of the
consequences of antibiotic resistance and methods to delay, reduce or avoid it are found in
both medical and social research fields. On the social level, both individual and societal
implications are of potential concern. On the individual level, antibiotic resistance poses the
risk of an increase in the quantity and intensity of complicated treatments due to limited
treatment options (Levy, 2005). On the wider societal level, consequences include avoidable
healthcare costs and new infectious diseases. The European Centre for Disease Prevention
and Control (ECDC) estimates that each year in EU countries, 25,000 deaths are directly
attributable to multi-drug resistant infections and 1.5 billion lost due to extra in-hospital and
outpatient costs and productivity losses due to absence from work and patients who died
from their infection (ECDC, 2009). Combining this with an evident increase in globalization
(Denis et al., 2006), international transmission enables a worldwide threat of emerged
diseases (Grigoryan et al., 2007). While not the focus of this thesis, the severity of the
consequences of antibiotic resistance highlights the importance of identifying and combating
the factors which cause it.
Previous studies have shown that on the national level, the prevalence of antibiotic
resistance is positively correlated with prescribed outpatient drug use (non-hospitalised drug
use, i.e. drug use in primary care) (Goossens et al., 2005; Albrich el al., 2004). Cause for
concern then is the fact that outpatient use accounts for more than two-thirds of antibiotic
sales globally (Llor & Cots, 2009) and it has been found that more than 40% of prescriptions
for antibiotics are more or less inappropriate (WHO, 2014). More significantly, there is
evidence for a correlation between outpatient use and antibiotic resistance in Europe
(Adriaenssens et al., 2011; Goossens, 2005). These figures, however, report levels of
prescribed antibiotics. What is missing from empirical research is information on self-
medication with antibiotics and determinants explaining the variations in levels, while we
know that this is substantial in several countries (Lopez-Vazquez et al., 2012).
Self-medication is the use of drugs without medical guidance, in response to self-diagnosed
disorders or symptoms, or the continued or sporadic use of a prescribed drug for recurrent or
chronic symptoms of disease (Kunin, 1978). Self-medication occurs most frequently through
over-the-counter (OTC) sales, left-over supplies and drugs procured from family or friends
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(Berzanskyte et al., 2006). Although OTC sales of antibiotics are largely illegal, this does
occur (Markovic-Pekovic, 2012). Misuse of antibiotics may increase antibiotic resistance in a
community’s commensal flora (microorganisms within a relationship whereby one benefits
from the other while not benefiting or hurting the other) by exerting a selective pressure on
the skin, gut and upper respiratory tract, favouring bacteria resistant to the antibiotics
(McNulty et al., 2007). Therefore these forms of antibiotic use constitute a primary form of
irrational use of medicine, as antibiotics represent one of the most prescribed drugs
worldwide (Donkor et al., 2012).
In 2011 the European Union put in place a communication strategy in response to the threat
of antimicrobial resistance (EU, 2011). The goals of this strategy are to prevent the spread of
microbial infections, undertake research into effective ways to combat resistance and ensure
the appropriate use of antimicrobials. Communication, education and training form the core
of the strategy. The importance of educational campaigns throughout the EU is argued in
light of widespread public misconceptions regarding the nature and appropriate use of
antibiotics. Also in 2011, The World Health Organisation attributed World Health Day to
increasing awareness of the issue of antibiotic resistance and released a 6 point policy
package addressing how to combat resistance (WHO, 2011). Preventing self-medication is
presented as a core action in tackling resistance by requiring the enforcement of
prescription-only use. Five suggestions are put forward in the area of public education
promotion of antibiotics and their use. Suggestions emphasise the role of prescribers and
dispensers in educating patients on the correct use of antibiotics and proposes the use of
targeted public health campaigns. They also suggest to introduce the correct use of
medicines in health education components of both school curricula and adult education
programs. The European Centre for Disease Prevention and Control also dedicates urgency
to the issue of antibiotic resistance and proper use through the organisation of the annual
European Antibiotic Awareness Day (EAAD) (ECDC, 2005). The EAAD aims to increase
awareness of the public threat of antibiotic resistance and the importance of the correct use
of antibiotics. As part of this strategy, the ECDC aims to monitor both levels of public use of
antibiotics and knowledge about antibiotics.
In light of the theme of education found in the initiatives and research focused on combatting
antibiotic resistance, this research will seek to discover how differences in interaction
between healthcare professionals or media outlets and patients may contribute to levels self-
medication with antibiotics. Specifically, I will investigate whether receiving information on the
use of antibiotics is associated with lower levels of self-medication. The overarching research
question of this thesis is:
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‘How do differences in information provision by healthcare professionals or media outlets to
patients who self-medicate with antibiotics affect the level of self-medication?’.
The purpose of this research question is to determine whether there is an association
between receiving information and lower levels of self-medication. It also extends to
determine which type of information and source of information produces a stronger
relationship. The research will focus on seven European countries: Cyprus, Estonia, Greece,
Hungary, Italy, Romania and Spain. These countries were selected as they were identified by
a special Eurobarometer report on antimicrobial resistance as countries where levels of self-
medication are high and public knowledge of antibiotics is low (Eurobarometer, 2013). The
results will be relevant for the research project ‘Antimicrobial resistance and causes of non-
prudent use of antibiotics in human medicine’ (ARNA), conducted by the Netherlands
Institute for Health Services Research (NIVEL) as interventions to address the non-prudent
use of antibiotics will be developed for the seven countries.
In the next section, I will present some relevant results of this Eurobarometer survey (TNS
Opinion & Social, 2013) to establish a context for the analysis. The selected results highlight
the importance of educating the public on antibiotic use, especially in geographic areas
where levels of self-medication are high and knowledge low - as is the case for the 7
countries under study.
In Chapter 2 I will discuss the theoretical background underlying this research, and as the
arguments build, the hypotheses are presented. A number of theories are utilised to
formulate two hypotheses and one exploratory hypothesis. I will also present a conceptual
model for clarity.
In Chapter 3 I will outline the methods of the research including descriptions of the data
collection, the sample and the questionnaire used.
In Chapter 4 I will describe the statistical analysis including both the operationalization of the
variables and the regression performed.
In Chapter 5 I will present the results of the analysis. This will begin with descriptive statistics
followed by a comparison of the background characteristics of those who self-medicated with
antibiotics and those who did not, in order to determine whether those who self-medicate are
a selective group. I will also present the results of the statistical analysis.
In Chapter 6 I will reflect on the results and provide conclusions of the research and a
discussion of its limitations and implications.
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1.2. Results of Eurobarometer 2013
As part of the ECDC strategy, the Directorate-General for Health and Consumers
commissioned an EU wide survey on antimicrobial resistance published in 2013. The
purpose of the survey is to monitor public knowledge and use of antibiotics. The report
covers the 28 EU Member States (Table 1.1) with a total of 27,680 respondents.
Respondents who represent different social and demographic background were interviewed
face-to-face in their mother tongue.
Respondents were asked to indicate true or false to the following four statements:
- Antibiotics kill viruses. (correct answer: False)
- Antibiotics are effective against colds and flu. (correct answer: False)
- Unnecessary use of antibiotics makes them become ineffective. (correct answer: True)
- Taking antibiotics often has side-effects, such as diarrhoea. (correct answer: True).
According to the findings of this survey, only just over a fifth of Europeans correctly answered
their four questions about antibiotics, while the European average of correct answers is 2.4
out of 4.
While 84 percent know that the unnecessary use of antibiotics makes them become
ineffective, 49 percent are unaware that they are ineffective against viruses (with an
additional 11 percent indicating they don’t know); and 41 percent are unaware that they are
ineffective against colds and flu. This becomes interesting in light of evidence which shows
that self-limiting, virus-caused infections are a leading reason to self-medicate with antibiotics
(Grigotyan et al., 2007; Väänänen et al., 2006). A geographical distinction is evident in the
results; all nine countries in which a majority indicated correctly that antibiotics do not kill
viruses are in northern or western Europe (See Figure 1.1). The highest proportion of correct
responses is from Sweden at 74 percent, while the rest range from 51 to 59 percent. All
countries under study here fall below the EU27 average of 40% correct responses; Hungary
(38%), Estonia (36%), Italy (33%), Spain (29%), Greece (25%), Cyprus (21%), and Romania
coming last at 15%. Education proves to be a significant factor in that on average, only 27
percent of those whose education ended before or at the age of 15 (lowest education level)
gave a correct response, in contrast to 52% of those whose education ended at or before the
age of 20 (highest education level) (Table 2.1). Obtained information and the source of this
information also have an effect. Those who received information from a healthcare
professional answered this question correctly 10 percent more than those who did not
receive information (44% and 34% respectively). In addition, media campaigns prove to be
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particularly effective at raising awareness, as 55% of those who received information from
such a source gave a correct answer.
Table 1.1. Country abbreviations
Abbreviations Countries Abbreviations Countries Abbreviations
Countries included in study
AT Austria
LU Luxembourg CY Cyprus
BE Belgium
MT Malta
EE Estonia
CZ Czech Republic NL The Netherlands EL Greece
BG Bulgaria
PL Poland
ES Spain
DK Denmark
PT Portugal
HU Hungary
DE Germany
RO Romania
IT Italy FR France
SI Slovenia
RO Romania
HR Ireland
SK Slovakia IE Italy
FI Finland
LT Lithuania
SE Sweden LV Latvia
UK The United Kingdom
Figure 1.1. For each of the following statements please tell me whether you think it is true or false. Antibiotics kill viruses.
*Adapted from: Eurobarometer (2013: 25)
0
10
20
30
40
50
60
70
80
90
100
SE FR LU DK
NL FI UK
BE IE HR SI
EU2
7
HU DE EE CZ IT PL
SK ES AT
LV LT EL MT
BG CY
PT
RO
Percentage of correct responses to the statement; 'Antibiotics kill viruses'
KEY
European countries EU average Countries included in present analysis
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Table 2.1. For each of the following statements please tell me whether you think it is true or false. Antibiotics kill viruses. By education, received information, and information source.
EU27 (n= 26,680)
Correct answer Incorrect answer
40% 49%
Education (End of)
15- 27% 58% 16 - 19 37% 51% 20 + 52% 41% Still studying 40% 50% Received information
Yes 52% 41% No 34% 53% Information source
Advice from a professional 44% 50% From media/campaigns 55% 37% Did not receive information 34% 53%
*Adapted from: Eurobarometer (2013: 27).
A similar pattern is observed for the results of those who are aware that antibiotics are not
effective against colds and flu, although knowledge of this is better, with Estonia and Italy
both scoring above the average at 54% and 52% correct responses respectively (Figure 2.1).
These are followed by Spain (44%), Hungary (37%), Greece (34%), Romania (33%), and
Cyprus (24%). Again, the highest scoring countries are mostly northern and western with
Sweden scoring the highest at 77 percent, followed by Denmark, Finland, the Netherlands
and the UK all scoring 70 percent or higher. A similar educational gap as described above is
observed, with 41% of those with the lowest level of education giving the correct response
compared with 63% of those with the highest level of education (Table 3.1). Again, obtained
information proves to be important. 71 percent of those who received information from a
media source and 58 percent of those who received information from a healthcare
professional answered this question correctly, versus 45 percent of those who received no
information.
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Figure 2.1. ‘For each of the following statements, please tell me whether you think it’s true or false. ‘Antibiotics are effective against cold and flu.’’
*Adapted from: Eurobarometer (2013: 29)
Table 3.1. ‘For each of the following statements, please tell me whether you think it’s true or false. ‘Antibiotics are effective against cold and flu.’’ By education, received information, and information source.
EU27 (n= 26,680)
Correct answer Incorrect answer
52% 41%
Education (End of)
15- 41% 50% 16 - 19 51% 41% 20 + 63% 37% Still studying 44% 47% Received information
Yes 65% 30% No 45% 46% Information source
Advice from a professional 58% 38% From media/campaigns 71% 25% Did not receive information 45% 46% *Adapted from: Eurobarometer (2013: 32)
While a vast majority of respondents are aware that antibiotics may become ineffective
following unnecessary use, obtained information shows interesting intersections (Table 4.1).
94% of those who received information from a media source and 85% of those who received
information from a healthcare professional answered this question correctly compared to
80% of those who received no information. 11% of those who did not receive information
0
10
20
30
40
50
60
70
80
90
100
SE DK FI NL
UK
BE
LU IE FR CZ SI HR EE SK IT
EU2
7 ES DE LT LV MT
HU EL PL
RO AT
BG PT
CY
Percentage of correct responses to the statement;
'Antibiotics are effective against cold and flu'
KEY
European countries EU average Countries included in present analysis
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were unable to answer this question compared to only 4% of those who received information.
While all these results regarding information and knowledge of antibiotics do not imply
causation, they do support the principle that propagating information concerning the correct
use of antibiotics is essential in tackling widespread misconceptions regarding appropriate
use.
Figure 3.1. ‘For each of the following statements, please tell me whether you think it’s true or false. ‘Unnecessary use of antibiotics makes them become ineffective.’’
*Adapted from: Eurobarometer (2013: 34)
Table 4.1. ‘For each of the following statements, please tell me whether you think it’s true or false. ‘Unnecessary use of antibiotics makes them become ineffective’’. By education, received information, and information source.
EU27 (n= 26,680)
Correct answer Incorrect answer
84% 8%
Education (End of)
15- 80% 8% 16 - 19 84% 8% 20 + 89% 6% Still studying 83% 8% Received information
Yes 91% 5% No 80% 9% Information source
Advice from a professional 85% 10% From media/campaigns 94% 3% Did not receive information 80% 9% *Adapted from: Eurobarometer (2013: 36).
0
10
20
30
40
50
60
70
80
90
100
SE DK SI NL EL FR CY
CZ FI
MT
UK
LU SK BE ES DE
HR PL IE LT
EU2
7
AT EE PT
LV BG
HU IT RO
Percentage of correct responses to the statement; 'Unnecessary use of antibiotics makes them become ineffective'
KEY
European countries EU average Countries included in present analysis
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Results of the public’s knowledge of the fourth and final statement: ‘Taking antibiotics often
has side-effects, such as diarrhoea’ show disparity from the results of the previous
statements. Although two thirds (66%) gave a correct response, there is clearly more
uncertainty as 19 percent of respondents were unable to give a response. In addition the
geographic divide that is evident in the previous results (better knowledge in the Northern
and Western countries compared to the Southern and Eastern countries) is not as
distinguished in the results to this statement. Three of the five highest scoring countries are
Eastern European countries: Poland (78% correct responses), Estonia (77%) and Slovakia
(75%). Similar to the previous results, those who received information are more likely to give
a correct response than those who did not (73% correct responses versus 62% respectively).
Media campaigns proved to have less of an impact on knowledge of this statement
compared to the previous statement and compared to advice from a healthcare professional.
Results to this statement by education level are not presented in the report.
Figure 4.1. ‘For each of the following statements, please tell me whether you think it’s true or false. ‘Taking antibiotics often has side-effects, such as diarrhoea.’’
*Adapted from: Eurobarometer (2013: 38)
0
10
20
30
40
50
60
70
80
90
100
PL
LU EE FI SK SI EL AT
CY
DK LT BG IT DE
MT
BE
HR FR
EU2
7 LV UK CZ
PT
HU SE NL
ES IE RO
Percentage of correct responses to the statement;'Taking antibiotics often has side-effects, such as diarrhoea'
KEY
European countries EU average Countries included in present analysis
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Table 5.1. ‘For each of the following statements, please tell me whether you think it’s true or false. ‘Taking antibiotics often has side-effects, such as diarrhoea’’. By received information, and information source.
EU27 (n= 26,680)
Correct answer Incorrect answer
66% 15%
Received information
Yes 73% 13% No 62% 16% Information source
Advice from a professional 75% 13% From media/campaigns 72% 13% Did not receive information 62% 16% *Adapted from: Eurobarometer (2013: 40)
While these results prove interesting, they still beg the question why is increased awareness
of antibiotic use and misuse expected to be associated with lower levels of self-medication
with antibiotics, among those who self-medicate? Although a clear correlation is found
between receipt of information and levels of objective knowledge, this is not to imply
causation, particularly considering the positive relationship of some socio-demographic
variables –i.e. education – with both variables. It is therefore impossible to go further to argue
that information provision will be associated with patient behaviour. This expectation comes
from results which show that when information is provided by a healthcare professional,
patients are more likely to change their behaviour – than when it is provided by a media
outlet - in accordance with this information or advice. This point will be expanded upon in the
theory section.
On average, only a third of respondents remembered receiving information about not taking
antibiotics unnecessarily in the last 12 months. Of the 33 percent who received information,
almost a fifth received it from a media source as opposed to only 11 percent from a
healthcare professional. Media sources are most common in France (52%), Belgium (35%)
and Luxembourg (32%), and lowest in Romania (7%) and Hungary and Portugal (both
5%). Variation in advice from professionals ranges from roughly a fifth in Luxembourg,
Romania and Italy to 6 percent in Portugal and Spain and 5 percent in the Netherlands. The
case of Spain is particularly interesting as we can see a decrease of 13 percentage points in
advice from the media and 12 percentage points in advice from a professional since 2009.
Those residing in southern European countries and those with poorer levels of knowledge
about antibiotics are more likely to have their views changed by such information. A pattern
can be drawn from the results in that countries with a higher level of correct knowledge in the
general population also have lower levels of self-medication. In that sense, a possible
method of reducing overuse or misuse of antibiotics could be through educating those who
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use them by informing them of appropriate use and the consequences of misuse. Education
interventions have been found to reduce absolute rates of antibiotic consumption (AHRQ,
2006).
The importance of the source of information is also highlighted in the Eurobarometer (2013)
study, as 80 percent of those who received information from a healthcare professional
reported that they will consult a doctor about the use of antibiotics in the future compared
with only 69 percent of those who received information from media campaigns. While a
striking majority (94%) stated that they would choose to see a medical professional in order
to receive reliable information about antibiotics, there was also a stronger preference to visit
a doctor (87%) than a pharmacist (49%).
These results coincide with the argument that while media campaigns are effective at
disseminating information, healthcare professionals are more effective in transforming
behaviour change and as such, the role of healthcare professionals in educating patients
about the use and misuse of antibiotics is very important if we are to change their behaviour.
This study found no socio-demographic differences on the question of where the
respondents obtained their last course of antibiotics. This is due to an overwhelming majority
on average who indicated that they obtained them with a prescription. Therefore, low
response rates for the self-medicating answers (antibiotics left over from a previous course
or obtained without a prescription) imply that differences in these categories are not
statistically significant. This is one reason to conduct an in-depth analysis of those who did
self-medicate (ARNA had samples of 400 persons per country), in order to determine
differences in this category.
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2. Theory
The use of antibiotics by patients is firmly controlled through a variety of mechanisms
including controlled clinical trials, regulation and release of pharmaceuticals on the
commercial market, and the controlled distribution by physician prescription through licensed
pharmacies. The goal of these successive steps is to increase the likelihood that patients
receive the most appropriate and efficacious medication for specified indications. It is the
final steps of this process which are of concern for this thesis. The drug dispensing and
consumption processes are intimately involved with human factors such as prescribing
practices of the physician, expectations of the patient and their interaction in general.
Arguably these human factors have the ability to distort and negate the effectiveness of the
entire process which precedes the interaction of the patient, physician and medication (Hulka
et al., 1976). More recently, Awad and Aboud (2015) cited the complex interaction of human
factors and relationships which inevitably have an effect on consumption and use patterns,
including inadequate informing of patients by physicians, patients perceptions of the patient-
prescriber communication, patients’ knowledge, expectations and beliefs. They concluded
that these factors perpetuate the misuse of antibiotics.
Many interdependent factors have been shown to produce an increase in the non-prudent
use of antibiotics. Previous studies from across the globe have shown improper use of
antibiotics and noncompliance with the regimen to be strongly associated with public
knowledge and awareness of the subject (Pavyde et al., 2015). In order for self-medicators of
antibiotics to make an informed decision to change their behaviour, they must first be given
some scientific knowledge or incentives to correctly use antibiotics. McNulty et al. (2007)
found that awareness of public campaigns in England among both self-medicators and
prescribed users was associated with better knowledge of antibiotics. This was reflected in
the 42% versus 24% correct responses to statements, particularly the statement that
antibiotics do not cure most coughs and colds. This is highly relevant, as treating throat
symptoms or bronchitis is found to be the top medical as opposed to practical reason for self-
medicating (Grigoryan et al., 2006). McNulty et al. (2007) also found a significant association
between knowledge of antibiotics and being more likely to finish a course of antibiotics.
However, knowledge was also associated with being more likely to take antibiotics without
being advised by a healthcare professional.
There remains the question: why is it expected that an increase in knowledge would result in
actual behaviour change? This argument is derived from the Rational Choice Theory
(Hechter & Kanazawa, 1997). The main assumption of this theory is that people are
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individuals with agency who act rationally in response to the information made available to
them. It is individuals who take action, and according to this theory it is assumed that this
action is based on rationally evaluated, self-maximizing conclusions. Actions are also based
on optimality. Individuals base their actions on both their preferences and the opportunities or
constraints present to them. In this way, individuals do the best they can given their
circumstances (Abell, 2000). However rationality is the most dominant assumption of the
theory. According to this assumption all individuals behave in the manner which is perceived
as the most self-promoting or follow actions which benefit themselves most. Applied to the
context of self-medication with antibiotics: informing the public can rectify ignorance about
antibiotic resistance and proper use. This information will arouse concern which in turn will
materialise into behaviour change which yields the most favourable outcome for the
individual. This argument is logically applied to the context of antibiotic misuse as there are
many fundamental issues about which the public is misinformed (Eurobarometer, 2013). An
explanation of these issues may lead to a change in behaviour as patients evaluate the
benefits of not self-medicating, both directly for themselves (e.g. because antibiotics used
incorrectly may result in adverse side-effects) and / or indirectly through benefiting society
(e.g. if antibiotic resistance were not an urgent issue to be dealt with, more resources would
be available to develop treatments for other illnesses or diseases).
This paradigm of rational action is best exemplified by two theories; the theory of reasoned
action (Ajzen & Fishbein, 1980) and the theory of planned behaviour (Ajzen, 1985). Both
theories explain behaviour through the attitudes towards the behaviour and the intention to
adopt it. Intention is the foremost predictor of behaviour as it is a function of the attitudes
towards the behaviour. These attitudes are determined by individual beliefs concerning the
outcome of such behaviour, including an evaluation of this outcome. This is the rational
calculation. The intention to adopt the behaviour is also determined by subjective norms.
These norms come from the individual’s beliefs of the expectations of their significant others
and the intention to meet said expectations. In addition, both the intention and the behaviour
are determined by the individual’s perceived control over the behaviour. That is, their
perceptions of their own ability to perform the behaviour. (Bartiaux, 2008) The theory of
planned behaviour adds to this with the assumption that only specific attitudes regarding the
behaviour are expected to predict it. By placing beliefs, both behavioural and normative
beliefs, at the beginning of the causal model, the fundamental role of information in
determining behaviour is highlighted. Information has the potential to modify these beliefs
and, as consequence, the attitudes to the behaviour and the intention to behave in a certain
way (Bartiaux, 2008).
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Although not directly a source of self-medication, inappropriate prescribing by a physician
facilitates self-medication through left-overs, especially where antibiotics are dispensed in full
packages as is the case in the seven countries under study. Effective communication
between healthcare professionals and patients is fundamental to limit inappropriate
prescribing and dispensing of antibiotics. Stevenson et al. (1999) found that doctors who
provided information and education to patients were successful in reducing antibiotic
prescriptions. While some GPs may prescribe inappropriately in the belief that patient
satisfaction primarily stems from such an outcome, this has not been found to be the case.
On the contrary, patients were found to be content to leave without a prescription if an
explanation was provided (Stevenson et al. 1999). In addition, previous experience of being
prescribed an antibiotic may give the patient ample confidence to use said antibiotic without
seeking medical advice. This and evidence from Jenkins et al. (2003) that 62% of
consultations resulted in at least one kind of problem such as a patient getting less than what
they wanted, a mismatch in patient and doctor perceptions, unwanted prescriptions,
unnecessary prescriptions, inappropriate prescribing, patient non-adherence or problems
with medication, highlight the importance of effective communication in the consultation
process. If the general public does not have a clear understanding of the potential
consequences of misusing antibiotics, a new social norm for antibiotics as a last resort is
improbable (Pinder et al., 2015).
From this we can derive the first hypothesis;
H1: Of those who self-medicate, the level of self-medication will be lower among those who
have received information than among those who did not receive information.
A second point of interest is whether the type of information received by patients who self-
medicate with antibiotics is associated with their level of self-medication. Although to my
knowledge there is no previous literature detailing which type of information may motivate a
patient to not self-medicate with antibiotics, a hypothesis will be derived based on Protection
Motivation Theory (PMT) (Rogers, 1975) and the usefulness of negativity in conveying
information (Geer, 2006).
The starting assumption is that in health communication, the level of fear arousal may
increase the perceived seriousness of the issue at hand, and the perceived susceptibility to
being adversely affected by said issue. This effect is explained through Protection Motivation
Theory (PMT), based on the works of Lazarus (1966) and Leventhal (1970). PMT argues that
in response to fear-evoking information, individuals exercise either adaptive or maladaptive
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coping responses as the result of two processes. Maladaptive coping responses are those
which pose a health risk to the individual including the absence of a behaviour (e.g. missing
a check-up) and behaviours which may result in consequences (e.g. smoking). In the context
of self-medication with antibiotics, an adaptive coping response would be to only use
antibiotics when they are prescribed while an example of a maladaptive coping response
would be to deny the issue.
The two processes which result in either of these responses are 1) health threat appraisal
and 2) coping appraisal. During the threat appraisal process, factors related to the evaluation
of the threat are assessed. In the context of health behaviour, perceived susceptibility to the
threat and the perceived severity of the threat are estimated. Both evaluations are expected
to reduce the adoption of maladaptive responses. In this sense, fear arousal is beneficial in
that it enhances the perceived severity and susceptibility and thereby increases the
protection motivation. During the coping appraisal process, those factors relevant to the
evaluation of coping responses are appraised. These are both the response efficacy - the
likelihood that carrying out the behaviour will result in a reduction of the threat - and self-
efficacy, taken from Bandura’s social learning theory (1977). Self-efficacy is one’s belief in
one's own ability to successfully adopt or complete the behaviour. According to PMT,
adaptive behaviour is most likely when both response efficacy and self-efficacy are high.
In addition to Protection Motivation Theory, the benefits of utilising negative messages to
convey information with the goal of influencing behaviour have been argued and documented
by Geer (2006). In the past, the dominant view was that positive messages were key in
changing behaviour. This came from the idea that providing alternative solutions to problems
was a constructive way to motivate. However an informed decision on any topic requires
knowledge of both good and bad aspects. Without the knowledge of the bad aspects, we put
ourselves liable to risk. In addition, Geer argues that negative information is additionally
beneficial in that it is more likely to be backed up by evidence. This is because positive
information is more readily accepted by the public whereas negative information demands
evidence to solidify its credibility. In turn, evidence based arguments are more likely to
change behaviour. Applied to the context of self-medication with antibiotics, it could be
argued that when patients who self-medicate receive information on the side-effects of
incorrect antibiotic use, they will be more motivated and therefore more associated with lower
level of self-medication. This negative information is more likely to arouse fear in the patient
resulting in the rational decision to protect themselves by not self-medicating with antibiotics.
Information on side-effects will also be backed up by evidence, certainly more so than
18
general information on antibiotics, specifically the operationalization of general information in
this thesis, as will be discussed in chapter 4.
Additionally, it is also fair to consider that since most self-medication occurs for the medical
reason ‘to treat colds or coughs’, and knowledge of the fact that antibiotics are ineffective
against these ailments is low (Eurobarometer, 2013), the effect of receiving information
regarding side-effects may be the strongest as it includes the ‘information concerning
conditions in which not to take an antibiotic’.
From this hypothesis 2 is derived:
H2: Among patients who self-medicate, the effect of receiving information on ‘side-effects’
will be most associated with lower levels of self-medication.
A third interesting aspect under investigation is whether the source of the information has an
association with lower levels of self-medication with antibiotics. Knowledge of this can
potentially influence policy. It is beneficial to know whether efforts should be spent on
governmental interventions such as campaigns or whether the focus should be on the
communication between the healthcare professional and the patient, such as part of a
healthcare-professional oriented intervention. While it has been shown that media campaigns
are more effective at disseminating information regarding antibiotics, clinicians have been
found to be more effective at changing the behaviour of their patient (Pinder et al., 2015).
Arguably what is important here is how the patients receive the information from their source
and whether they perceive their source as credible or not.
Previous research has repeatedly cited physicians as the most preferred and trusted
sources, ranked by patients (Narhi, 2007; Mayer et al., 2007; Rutten et al., 2005; Hesse et
al., 2005). However although they are preferred, they are not necessarily the most frequently
used source. Patients have also been found to utilize information leaflets and the internet
more than physicians (Narhi, 2007; Hesse et al., 2005). Hesse et al. (2005) found that 50
percent of patients cited physicians as their preferred first point of contact regarding specific
health information, however only 11 percent actually went to their physician first, compared
to 48 percent who sought information online first. This could be an indication that the degree
of distrust of the internet as a source of information is not as strong as previous surveys have
concluded. Perhaps the convenience of the internet outweighs the concerns about the quality
of the information; or perhaps people have found strategies to identify and avoid less
trustworthy information; or perhaps it is a combination of these (Eysenbach, 2008). Genni et
19
al. (2006) argue that patients use the information they find online as a discussion point with
their doctors. In this way, although patients generally find media information trustworthy, they
still seek confirmation from their doctor. Arguably then, it is this professional confirmation
which has a stronger influence over people’s attitudes and consequently their beliefs
concerning antibiotics and antibiotic resistance, than information found directly from
media/internet.
From this a third and final (exploratory) hypothesis is derived:
H3: When information is received from a healthcare professional, levels of self-medication
among those who self-medicate will be lower than when the information is obtained from the
media/internet.
20
*Note: Cells with a dashed border are not tested in the analysis.
Figure 5.1.: Conceptual Model* (Source: Adapted from Ajzen (2006))
21
3. Methods
Data will be used from the ‘Antimicrobial resistance and causes of non-prudent use of
antibiotics in human medicine’ (ARNA) research project, conducted by the Netherlands
Institute for Health Services Research (NIVEL). The project runs for 2 years and consists of a
broad analysis of the whole of the EU followed by an in-depth analysis of seven countries by
use of an additional questionnaire. These seven countries were selected as they were found
by the Eurobarometer (2013) to have high levels of self-medication with antibiotics. These
are: (percentages indicate the share of antibiotics used for self-medication of all antibiotics
collected) Romania (20%), Greece (16%), Hungary (8%), Spain (8%), Italy (5%), Cyprus
(10%), and Estonia (7%) (Eurobarometer, 2013: 12). Although the percentage of self-
medication found in Italy is equivalent to that of the EU27 average (Eurobarometer, 2013:
12), it was included in the ARNA study due to an explicit request for inclusion by the
European Commission, one of the funders of the research project. The EC requested the
inclusion of Italy in order to confirm the findings of the Eurobarometer for Italy which were
suspected to be incorrect.
Data Collection
Data collection was outsourced to the Dutch survey company TNS NIPO. This company has
a database of thousands of screened respondents. For each survey, a random selection is
made from the database. For the current study, the sample was selected using Random
Digital Dialling (RDD) in order to conduct Computer-assisted telephone interviews (CATI).
This type of surveying has been found to be preferable to self-administered surveys in terms
of both quality and quantity of responses (Bowling, 2005). In Cyprus, Estonia, Greece,
Hungary, Italy and Romania, the sample was selected randomly through telephone numbers
which were randomly generated. In Spain, the sample was selected randomly through
national telephone directories. TNS NIPO employed the triple C method to collect the data in
the seven countries. This means that data collection was coordinated from one central
location in Amsterdam in one central database, and each country was assigned its own
Triple C team who conducted the telephone interviews and sent the data back to the central
database. Each country received the same questionnaire, translated into the respective
national language.
Overall, data was collected between December 2014 and February 2015. In Cyprus and
Greece, data collection began in December and ended January, lasting roughly six weeks. In
Estonia and Romania, data collection took place between January and February, also taking
22
roughly six weeks. In the remaining countries - Hungary, Italy and Spain - data collection ran
from December to February and lasted about ten weeks.
A target of 400 self-medicating respondents from each country was set for the ARNA project.
A sample was selected in each country in order to reach this target. If the target was not met
after the whole sample was contacted, the parameters of the sample were extended.
Contacts were stopped once the target of 400 self-medicating respondents was met.
Implications of this method on the results will be discussed in Chapter 6. Difficulties in
collecting 400 self-medicating respondents in Estonia led to the conclusion that a sample 200
for this country would suffice.
Initially, 165,694 contacts were made across all seven countries (Figure 6.1). This initial
sample was based on the incident rates of self-medication found in the Eurobarometer
(2013) report. Based on these levels of self-medication, the costs for the fieldwork were
calculated, including how many contacts should be made in order to reach the target of 400
respondents per country. From the 165,694 initial contacts, 65,103 were successful. That
means that 100,591 contacts were unsuccessful, either the phone number did not work, the
call was unanswered or the respondent refused to participate. Information on the non-
respondents has not been provided by TNS NIPO to date.
The 65,103 respondents were screened by two questions. First they were asked ‘Have you
or your child(ren) taken any antibiotics orally such as tablets, powder or syrup in the last 18
months?’. Those who answered ‘no’ to this question were dismissed from the interview
process. The 29,647 respondents who answered ‘yes’ were screened further with the
question ‘How did you obtain these courses of antibiotics in the last 18 months?’. Those who
answered that they obtained them with a prescription were only asked their sex and age.
Respondents who answered that they obtained the antibiotics without a medical prescription
continued with the full interview. This resulted in a total of 2,601 full interviews consisting of
400 respondents from Cyprus, Greece, Hungary, Italy and Spain, 200 respondents from
Estonia and 401 respondents from Romania. As this sample of self-medicators also included
respondents who answered the interview on behalf of their child(ren), said respondents were
removed from the data for the analysis (Table 5.1). This means that from the 2,601 self-
medicating respondents, 755 gave either the response ‘yes my child(ren) have’ or ‘yes, I and
my children have’. These respondents were excluded from the analysis which results in a
total sample of 1,846 respondents who self-medicated with antibiotics in the past 18 months.
23
Figure 6.1. Flow diagram of data collection
165,694 respondents (Total sample)
CY 1,882
EE 6,312
EL 1,487
ES 6,303
HU 6,676
IT 4,687
RO 2,300
CY 23,410
EE 26,780
EL 28,358
ES 17,182
HU 26,688
IT 31,312
RO 11,964
65,103 respondents (Total interviews)
CY 3,423
EE 16,779
EL 3,330
ES 11,795
HU 17,205
IT 9,313
RO 3,936
29,647 respondents (who used antibiotics with or without prescription)
2,601 respondents (who used antibiotics without medical prescription for their last
course, including children)
CY 400
EE 200
EL 400
ES 400
HU 400
IT 400
RO 401
1,846 respondents (who used antibiotics without medical prescription)
CY 315
EE 164
EL 295
ES 256
HU 261
IT 269
RO 271
24
Table 5.1. Responses to the question ‘Have you or your child(ren) taken any antibiotics orally such as tablets, powder or syrup in the last 18 months?’
Total Cyprus Estonia Greece Hungary Italy Romania Spain
Yes, I have 1,831 (70%) 315 (79%) 164 (82%) 295 (74%) 261 (65%) 269 (67%) 271 (68%) 256 (64%)
Yes, my child(ren) have 313 (12%) 37 (9%) 13 (7%) 54 (14%) 58 (15%) 72 (18%) 35 (9%) 44 (11%)
Yes, my children and I have 442 (17%) 48 (12%) 23 (12%) 50 (13%) 81 (20%) 49 (12%) 91 (23%) 100 (25%)
No 15 (0.6%) 1 (0.25%) 10 (3%) 4 (1%)
Total 2,601 400 200 400 400 400 401 400
Table 6.1. Data collection in the seven European countries.
ABBR. Countries Method Sample selection Number of interviews
Fieldwork dates Population
CY Cyprus Telephone interviews-CATI Random through telephone numbers 400 03/12/2014 16/01/2015 23,410 EE Estonia Telephone interviews-CATI Random through telephone numbers 200 21/01/2015 26/02/2015 26,780 EL Greece Telephone interviews-CATI Random through telephone numbers 400 15/12/2014 20/01/2015 28,358 EE Spain Telephone interviews-CATI Random through national telephone
directories 400 09/12/2014 19/02/2015 17,182
HU Hungary Telephone interviews-CATI Random through telephone numbers 400 16/12/2014 23/02/2015 26,688 IT Italy Telephone interviews-CATI Random through telephone numbers 400 03/12/2014 09/02/2015 31,312 RO Romania Telephone interviews-CATI Random through telephone numbers 401 07/01/0215 20/02/2015 11,964 Total 2,601 165,694
25
Representativeness of the data
In Cyprus, 41% of respondents came from the Capital, Nicosia, while the remaining 59%
were divided between four other smaller cities in the south (25%), southeast (17%),
southwest (7%) and east (10%). In Greece, all 400 participants were sourced from Athens
with roughly a fifth to a quarter from each geographical district (north, south, east and centre
and west). In Hungary, respondents were sourced from seven different geographical regions
with the largest section (29%) coming from Central Hungary and the smallest (9%) coming
from Southern Transdanubia. In Italy, data collection occurred in four regions; one third of
respondents came from the south and islands, one quarter from the north-west and a fifth
from both the centre and north-east region. Data collection was mostly divided by region in
Spain. Respondents ranged from 0.6% from La Rioja to 17.3% in Andalusia and were spread
over 18 regions including the Capital and the Canary Islands. In-depth descriptions of the
location(s) of data collection in Estonia and Romania are not available to date.
Questionnaire
A structured questionnaire of 34 questions was developed in English and translated for each
country. Topics in the questionnaire included:
1. Respondents’ and their children's antibiotic use without a prescription, the source of
said antibiotics, when they were taken and the reason why they were used without a
prescription.
2. Other people’s use of antibiotics without a prescription, including friends, family,
neighbours, partner.
3. Type and dose of antibiotics used
4. Medical reasons for using the antibiotics without a prescription
5. Whether or not information was received on how to prudently use the medication and
if so what information was obtained.
6. Knowledge of antibiotics
7. Experienced positive consequences and negative side-effects.
8. Role of the general practitioners and pharmacists in over-the-counter use of
antibiotics - how they prevent and simulate this.
26
4. Data Analysis
Operationalization
The variables of interest for this study are those concerning the level of self-medication of
respondents and the information they received regarding the use and side-effects of
antibiotics. This includes both type and source of information. In addition, relevant
background variables of the respondents are controlled for. A country indicator is included in
the analysis to determine any variation between the countries.
The level of self-medication with antibiotics is defined as the number of courses the
respondent has used without a prescription in the past 18 months. In the questionnaire,
respondents could indicate ‘1 course’, ‘2 - 3 courses’ or ‘4 or more courses’. Preliminary
analysis showed weak effects when the dependent variable was coded as three categories
(Table 9.2, Appendix). This is due to the distribution of the data: the small n-values of those
indicating ‘2 - 3 courses’ or ‘4 or more courses’ compared to those indicating ‘1 course’ of
self-medicated antibiotics. Therefore, for this analysis, level of self-medication is
dichotomously coded with 0 indicating ‘1 course’ of self-medicated antibiotics in the past 18
months and 1 indicating ‘2 or more courses’.
The three key independent variables are whether the respondent received information, the
type of information received and the source of this information.
Respondents were asked whether or not they had received any information on how to use
the last course of antibiotics that they self-medicated with in the past 18 months. This is
coded dichotomously.
Respondents were asked what information they obtained about how to use the last course
of antibiotics that they self-medicated with. The types of information respondents could
indicate that they received are: i) Complete the full course, ii) Only take the antibiotics over
the prescribed period at the correct dose, iii) Do not give the antibiotics to anyone else, iv)
Return left-over antibiotics to the pharmacy, v) Advice about how to act if the condition
persists or the infection becomes worse, vi) Information about conditions under which the
antibiotics should not be taken, information concerning: vii) Possible allergies, viii) Side-
effects, and ix) Drug interaction, x) Other, and xi) Don’t know / refuse.
These response categories are used to create the three ‘types of information’ variables;
proper use, general information, and side-effects.
The variable proper use is created by grouping the response categories i) Complete the full
course and ii) Only take antibiotics over the prescribed period and at the correct dose.
27
The variable general information is created by grouping the responses iii) Do not give them
to anyone else, iv) Return left-overs to a pharmacy and v) How to act if the condition persists.
The variable side-effects is created by grouping the response categories vi) Information
about when antibiotics should not be taken, vii) Information about allergies, viii) Information
about side-effects and ix) Information about drug interaction.
The ‘Other’ and ‘Don’t know / refuse’ categories are recoded as missing.
Respondents were also asked to indicate the source of this information. Answers were
initially recorded as i) Pharmacy staff, ii) General practitioner, iii) Other healthcare
professional, iv) Internet, v) National campaign, vi) Other media, vii) Other and viii) Don’t
know / refuse.
These responses are grouped according to the two information sources of interest for this
study: Healthcare professional and the Media.
Response categories i) Pharmacy staff, ii) General practitioner and iii) Other healthcare
professional are grouped together to create the variable Healthcare professional.
All the categories iv) Internet, v) National campaign and vi) Other media are grouped
together to create the variable Media.
‘Other’ and ‘Don’t know / refuse’ responses were recoded as missing.
Background variables of interest are Education, Employment status, Health insurance, Self-
reported health, Smoking status and Presence of a longstanding illness or condition.
Education is the second background variable, operationalized as ‘Low’, ‘Medium’ or ‘High’.
Health insurance is the next control variable. Respondents could specify whether they are
covered by ‘Private insurance’, ‘Public insurance’, ‘Community / social insurance’ or ‘Other
health insurance’. These four types are grouped together so that Health insurance is
operationalized as ‘Insured’ or ‘Uninsured’.
Self-reported health was originally recorded as ‘Excellent’, ‘Good’, ‘Fair’, ‘Bad’ or ‘Very bad’.
These categories are grouped to make three; ‘(Very) good’, ‘Fair’ or ‘(Very) bad’ self-reported
health categories.
Presence of a longstanding illness or condition is the final background variable. Here the
respondents could specify whether they suffer from ‘Asthma’, ‘Chronic obstructive pulmonary
disease’, ‘Emphysema’, ‘Diabetes mellitus’, ‘Cardiovascular disease’, ‘Hypertension’ or
‘Other’. These are grouped together to indicate the presence of a chronic illness.
These background variables were selected, as preliminary analysis suggested potential
significance in the analysis while several other background variables were excluded.
28
Although education did not prove to be significant in the preliminary analysis, it was included
in the logistic regression due to the results of the Eurobarometer (2013) which suggested an
intersection of education, information and knowledge of antibiotics.
Analysis
When the dependent variable is dichotomously coded as is the case for this research, a
logistic regression rather than a multiple regression or discriminant analysis is suitable
(Hosmer & Lemeshow, 1989; SPSS, 1989). Therefore, the chosen method of analysis is a
logistic regression, followed by a Poisson regression as a robustness check. In order to
perform the Poisson regression, the dependent variable is changed from a binary variable to
a three category variable. Consequently, level of self-medication is recorded as 1 course, 2-3
courses or 4 or more courses. Although significant variation between the countries is not
expected, the final model of the logistic regression is run separately for each country to give
further explanation to the effects found in the grouped regression model.
The building of the models for the logistic regression analysis is as follows:
Model 1 consists of background variables; area of residence, education, employment status,
health insurance, smoking, perceived health, chronic illness, age and sex and a country
indicator to provide insight into potential variation between the countries. This model will give
an idea of the characteristics of the sample and an initial sense of why respondents may be
more susceptible to self-medication with antibiotics.
In Model 2 the impact of generally receiving information is added. This will test hypothesis 1
by indicating the general effect of receiving information.
In Model 3, the impact of general information is removed and is replaced by the three
different types of information. This will test hypothesis 2 by determining whether the three
types of information received are equal in terms of their effect on self-medication with
antibiotics.
In Model 4 the effect of the source of information is added. If the effects observed in model 3
change or disappear, this can be interpreted as a stronger effect of the source of information,
rather than the type of information obtained. In that case, the source of information would be
considered more important than the type of information in reducing levels of self-medication
with antibiotics. This model will also test the explanatory hypothesis, as it will tell which
source of information produces a stronger effect in reducing levels of self-medication.
All analyses are carried out by the procedures in STATA/SE 13.
29
5. Results
First I will present the results of the descriptive analyses, followed by the results of the
logistic regression. Tables of the preliminary analysis results (multilevel regression),
descriptive results and robustness check results (poisson regression) can be found in the
appendix (Tables 1.2 – 10.2).
Descriptives
From preliminary results, it was found that the variance in levels of self-medication is as
follows: of the entire sample of self-medicators (n= 1,808), 67 percent stated that they self-
medicated with one course of antibiotics in the past 18 months, while 33 percent stated 2 or
more courses. Three quarters (74%) had received advice or information on how to use the
antibiotics while one quarter had not (n= 1,833). Four fifths received advice regarding the
proper use of antibiotics (80%), two fifths had received general information (41%) and two
fifths received information on side-effects (42%) (n= 1,294). A majority (89%) received this
information from a healthcare professional while 5 percent received it from a media outlet (n=
1,351).
Figure 7.1. Respondent’s level of self-medication in past 18 months (n= 1,808).
In terms of sample characteristics, half have achieved a medium level of education (50%),
more than a third achieved a high level of education (36%), while the remaining 14 percent
are lowly educated (n= 1,831). Almost three fifths report (very) good self-perceived health
(58%), one third report fair health while the remaining nine percent report (very) bad health
0
10
20
30
40
50
60
70
80
90
100
1 course 2 + courses
Level of self-medication
30
(n= 1,832). One third of the sample have a chronic illness (35%) or condition while two thirds
do not (n= 1,829).
Almost two thirds of the studied sample are female (65%) while slightly more than two thirds
are male (35%). This is comparable to the sample of those who used antibiotics with a
prescription in the past 18 months, of whom three fifths are female (61%) and two fifths male
(Table 7.1). Of the sample of self-medicators, 30 percent are between the ages of 18 and 40,
half of the respondents are between the ages of 41 and 60 while the remaining 16 percent
are aged 65 or over. This is also comparable to the sample of respondents who did not self-
medicate with antibiotics. Of this sample, one quarter are aged between 18 and 40, half are
between the ages of 41 and 60 while the remaining quarter are 65 years old or older (Table
7.1). From this, it is determined that those who self-medicate with antibiotics are not selective
on the basis of sex or age.
Table 7.1. Comparison of sex and age between those who did not self-medicate with antibiotics and those who did self-medicate.
Self-medicators Prescribed users
N (%) SD Min. Max. N (%) SD Min. Max.
Sex 0 1 0 1
Male 666 (36%) 24,132 (39%)
Female 1,180 (64%) 38,361 (61%)
Age 16.42 15 99 16.27 2 100
0-17 7 (0.4%) 212 (0.3%)
18-40 539 (30%) 14,559 (24%)
41-64 927 (50%) 31,614 (51%)
65+ 364 (20%) 15,624 (25%)
Regression 1.1 Logistic Regression.
To test the three research hypotheses regarding the relationship between receiving
information on the use of antibiotics and respondents’ levels of self-medication, a two
predictor logistic model is fitted to the data. Results of the regression analysis are presented
in Table 8.1. As the interpretation of the logistic coefficient is not as straightforward as in the
case of interpreting a multiple linear regression coefficient, the conventional Beta coefficient
is rewritten in terms of the odds of engaging in high levels of self-medication with antibiotics
(Pyke & Sheridan, 1993). This is defined as the ratio of the probability that high levels of self-
medication will occur, to the probability that it will not. Factors with values greater than one
indicate that the odds of high levels of self-medication with antibiotics are increased, and
factors with values less than one indicate that the odds are decreased.
31
According to model 1, the odds of self-medicating more with antibiotics are positively related
to having a chronic illness (OR: 1.47, 95% CI: 1.16-1.85). In other words, respondents with a
chronic illness are more associated with higher levels of self-medication with antibiotics than
those who do not have a chronic illness (Table 8.1). All other background characteristics
show no significant relationship with levels of self-medication with antibiotics. In addition, little
country variation is found as only the Estonian dummy variable produces a significant
association with lower levels of self-medication with antibiotics (OR: 0.48, 95% CI: 0.30-
0.75).
To test hypothesis 1, the variable of generally receiving information is added to the model
(model 2). When this is done the positive association of having a chronic illness remains
stable. The effect of generally receiving information is surprisingly associated with higher
levels of self-medication with antibiotics. That is, respondents who received information on
how to use the last course of antibiotics that they self-medicated with are more likely to report
higher levels of self-medication. However this effect is only borderline significant (OR: 1.25,
95% CI: 0.99-1.58), meaning, Hypothesis 1: ‘Of those who self-medicate, the level of self-
medication will be lower among those who have received information than among those who
did not receive information.’ is rejected. In addition, country variation remains relatively stable
in model 2 compared to model 1.
In order to test hypothesis 2, the general effect of receiving information is removed from the
model and the three different types of information are added (model 3). When this is done
only one type of information bears a significant relationship to the prediction of respondents’
levels of self-medication: proper use. Respondents who received information regarding the
proper use of antibiotics are associated with lower levels of self-medication (OR: 0.74, 95%
CI: 0.56-0.98). Although not significant at the p<0.05 level - yet borderline significant - the
effect of receiving information regarding side-effects is associated with higher levels of self-
medication with antibiotics. Receiving general information is associated with lower levels of
self-medication, however it is also not significant at the p<0.05 level. This indicates that
receiving either of these types of information is not associated with lower levels of self-
medication among patients who self-medicate. Thus from these results Hypothesis 2 ‘Among
patients who self-medicate, the effect of receiving information on ‘side-effects’ will be most
associated with lower levels of self-medication.’ is rejected. Receiving the more negative
information, side-effects, did not yield a significant result on respondents' level of self-
medication. However further investigation could be warranted on the effect of receiving
information on proper use. Additionally, in model 3 the effect of having a chronic illness on
respondents’ level of self-medication remains constant. The dummy variable for Spain
32
produces a significant association with lower levels of self-medication with antibiotics (OR:
0.58, 95% CI: 0.37-0.91), while the rest of the country variables remain relatively constant.
In order to test the third and final hypothesis, the two sources of information are added to the
model (model 4). Both sources of information are not significant at the p<0.05 level. In other
words, the source of the information on how to use antibiotics does not play a significant role
in the relationship between receiving information and respondents’ level of self-medication. In
addition, both sources produce an odds ratio higher than 1. From these results, hypothesis 3
‘When information is received from a healthcare professional, levels of self-medication
among those who self-medicate will be lower than when the information is obtained from the
media/internet.’ is rejected. It can also be seen from model 4 that the effect of receiving
information regarding the proper use of antibiotics remains constant (OR: 0.74, 95% CI: 0.56-
0.98), as does the effect of having a chronic illness. In addition, the country variation remains
constant.
33
Table 8.1. Logistic regression analysis results.
Model 1 - Basic Model Model 2 - Hypothesis 1 Model 3 - Hypothesis 2 Model 4 - Hypothesis 3
OR 95% CI OR 95% CI OR 95% CI OR 95% CI
Education (ref Low)
Medium 1.1 (0.8-1.52) 1.08 (0.79-1.49) 1.2 (0.82-1.74) 1.2 (0.82-1.75) High 0.95 (0.68-1.33) 0.94 (0.67-1.32) 1.04 (0.69-1.55) 1.03 (0.69-1.55) Perceived health (ref (very) good)
Fair 1.08 (0.86-1.37) 1.09 (0.86-1.38) 1.27 (0.97-1.67) 1.27 (0.97-1.67) (Very) Bad 1.44 (0.98-2.11) 1.46 (0.99-2.14) 1.54 (0.99-2.42) 1.54 (0.99-2.42) Chronic Illness
1.47* (1.16-1.85) 1.46* (1.15-1.84) 1.36* (1.03-1.79) 1.36* (1.03-1.79)
Age (ref 0-17 yrs)
18-40 1.83 (0.35-9.66) 1.87 (0.36-9.91) 4.2 (0.48-36.89) 4.3 (0.49-37.79) 41-64 1.15 (0.22-6.02) 1.17 (0.22-6.18) 2.52 (0.29-22.05) 2.57 (0.29-22.56) 65+ 0.98 (0.18-5.20) 1.01 (0.19-5.40) 1.97 (0.22-17.5) 2.02 (0.23-18.00) Sex (ref female)
Male 1.16 (0.94-1.43) 1.16 (0.94-1.43) 1.11 (0.87-1.42) 1.11 (0.87-1.43) Received Information
1.25 (0.99-1.58)
Type of Information
Proper Use
0.74* (0.56-0.98) 0.74* (0.56-0.98)
General Information
0.87 (0.66-1.17) 0.87 (0.65-1.16)
Side-Effects
1.3 (0.98-1.70) 1.29 (0.98-1.71) Source of Information
Healthcare Professional
1.07 (0.74-1.56)
Media
1.1 (0.63-1.95) Country
(ref Cyprus) Estonia 0.48* (0.30-0.75) 0.47* (0.30-0.74) 0.45* (0.26-0.72) 0.44* (0.26-0.73) Greece 0.86 (0.61-1.22) 0.88 (0.62-1.25) 0.7 (0.46-1.08) 0.7 (0.46-1.08) Hungary 1.32 (0.93-1.87) 1.31 (0.92-1.86) 1.11 (0.74-1.67) 1.11 (0.74-1.68) Italy 0.89 (0.61-1.31) 0.89 (0.60-1.30) 0.88 (0.56-1.39) 0.88 (0.56-1.39) Romania 1.2 (0.85-1.69) 1.19 (0.84-1.68) 1.01 (0.68-1.51) 1.01 (0.68-1.51) Spain 0.8 (0.56-1.15) 0.81 (0.56-1.16) 0.58* (0.37-0.91) 0.58* (0.37-0.91)
N 1,801 1,789 1,324 1,324
* p<0.05
34
Regression 1.2 Logistic Regression by country
To investigate this picture further, models 2 and 4 were run separately for each country.
Results for this analysis are displayed in Table 9.1. The aim of this is not to investigate each
variable separately per country but to use the country analysis to explain the effects found in
the combined analysis. Hypothesis 1, ‘Of those who self-medicate, the level of self-
medication will be lower among those who have received information than among those who
did not receive information.’ was rejected on the basis that the variable indicating that
respondents received any information on the use of antibiotics did not yield a statistically
significant effect on their level of self-medication. When run separately for each country, it
can be seen that only in Cyprus does receiving any information produce a statistically
significant effect on respondents’ level of self-medication (OR: 2.26, 95% CI: 1.21-4.21). In
Cyprus, patients who received any information on how to use antibiotics are associated with
higher levels of self-medication with antibiotics. Only in Spain is receiving any information
associated with lower levels of self-medication with antibiotics, however this effect is not
significant at the p<0.05 level.
Hypothesis 2, ‘Among patients who self-medicate, the effect of receiving information on ‘side-
effects’ will be most associated with lower levels of self-medication.’ was also rejected.
Information regarding the side-effects of antibiotics was found to produce a borderline
significant result. When run by country, it is found that this is driven by a strong effect in
Cyprus and is associated with higher levels of self-medication (OR: 2.42, 95% CI: 1.13-5.20).
Only in Hungary and Spain was receiving information regarding the side-effects of antibiotics
associated with lower levels of self-medication with antibiotics, however both results are not
significant at the p<0.05 level. The effect of receiving information regarding the proper use of
antibiotics was found to be significantly associated with lower levels of self-medication with
antibiotics in the combined analysis. When run individually for each country, this effect does
not prove to be significant at the p<0.05 level in any country, however in Cyprus it is
borderline significant (OR: 0.53, 95% CI: 0.26-1.06). This shows the value of running the
pooled analysis, rather than only separate country analyses.
Hypothesis 3, ‘When information is received from a healthcare professional, levels of self-
medication among those who self-medicate will be lower than when the information is
obtained from the media/internet.’ was also rejected. Neither source yielded a significant
result in the combined analysis. When run separately for each country, a healthcare-
professional source does not yield a significant result in any country. A media source
35
produces a strongly significant result in one country only - Spain - where it is associated with
higher levels of self-medication (OR: 8.86, 95% CI: 1.93-40.58).
36
CYPRUS** ESTONIA*** GREECE**** HUNGARY***** ITALY****** ROMANIA******* SPAIN********
OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI
Education (ref Low)
Medium 3.91* (1.3-11.7) 0.62 (0.24-1.58) 0.7 (0.16-3.04) 1.37 (0.54-3.5) 1.08 (0.46-2.53) 0.92 (0.34-2.48) 0.43 (0.14-1.39) High 2.15 (0.68-6.76) 1 (omitted) 0.73 (0.17-3.18) 1.19 (0.44-3.2) 1.04 (0.38-2.86) 0.79 (0.27-2.3) 0.19* (0.05-0.77) Perceived health (ref (very) good)
Fair 1.24 (0.54-2.87) 1.58 (0.51-4.90) 1.76 (0.72-4.32) 0.94 (0.48-1.86) 2.49 (0.88-7.03) 1.03 (0.53-2.04) 0.94 (0.33-2.66)
(Very) bad 0.24 (0.05-1.09) 2.3 (0.49-10.86) 8.95* (1.91-42.01) 2.2 (0.81-5.99) 3.19 (0.71-14.23) 1.58 (0.47-5.26)
Chronic Illness
1.34 (0.67-2.69) 1.27 (0.42-3.82) 1.29 (0.58-2.84) 1.32 (0.66-2.62) 1.32 (0.57-3.08) 0.77 (0.33-1.81) 2.46* (1.02-5.98)
Age (ref 0-17 yrs)
18-40 - - - - 4.3* (1.34-13.78) 1.06 (0.4-2.82) - - 2.03 (0.68-6.05) - -
41-64 0.37* (0.18-0.73) 1.6 (0.50-5.11) 1.56 (0.54-4.53) 1.07 (0.48-2.39) 0.45 (0.16-1.26) 1.71 (0.64-4.57) 0.33* (0.14-0.78) 65+ 0.82 (0.30-2.22) 0.74 (0.14-3.83) 1 (omitted) 1 (omitted) 0.25* (0.07-0.82) 1 (omitted) 0.34 (0.10-1.12)
Sex (ref female)
Male 0.79 (0.43-1.45) 0.99 (0.37-2.63) 2.88* (1.32-6.26) 1.003 (0.53-1.89) 0.79 (0.33-1.87) 1.38 (0.76-2.51) 1.41 (0.67-3.01)
Received information 2.26* (1.21-4.21) 2.36 (0.73-7.63) 1.09 (0.63-1.90) 1.22 (0.58-2.59) 1.78 (0.98-3.27) 1.04 (0.56-1.95) 0.59 (0.31-1.12)
Type of information
Proper use 0.53 (0.26-1.06) 0.65 (0.19-2.18) 0.8 (0.36-1.78) 1.17 (0.56-2.45) 0.51 (0.20-1.26) 0.78 (0.36-1.69) 0.94 (0.31-2.88)
General information 0.41 (0.15-1.1) 0.7 (0.21-2.36) 1.48 (0.68-3.2) 1.48 (0.77-2.84) 1.6 (0.66-3.86) 0.63 (0.28-1.39) 0.48 (0.14-1.67)
Side effects 2.42* (1.13-5.20) 1.83 (0.56-5.94) 1.03 (0.48-2.19) 0.998 (0.52-1.93) 1.34 (0.6-3.02) 1.66 (0.75-3.66) 0.83 (0.26-2.68) Source of information
Healthcare professional
0.9 (0.29-2.78) 1.54 (0.49-4.82) 0.71 (0.17-3.01) 0.6 (0.29-1.24) 1.67 (0.28-9.75) 1.36 (0.44-4.18) 3.3 (0.73-14.87) Media 1 (omitted) 2.04 (0.43-9.74) 0.19 (0.02-2.21) 1.19 (0.42-3.4) 1 (omitted) 0.28 (0.05-1.49) 8.86* (1.93-40.58)
N 232 123 176 212 173 208 176
* p<0.05 ******* Age dropped (predicts success perfectly) ******** Perceived health !=0 predicts success perfectly. Perceived health dropped.
** media != 0 predicts success perfectly. Media dropped *** Education dropped (predicts success perfectly)
**** Age dropped (predicts success perfectly)
***** Age !=0 predicts failure perfectly. Age dropped
****** Media !=0 predicts failure perfectly. Media dropped
Table 9.1. Logistic regression Models 2 and 4 run by country results.
37
Regression 2. Poisson Regression
Following the logistic regression, a Poisson regression was conducted as a robustness test.
A Poisson regression is chosen due to the distribution of the data. Poisson regressions are
suitable for modelling count data. Those who indicated 1 course of antibiotics in the past 18
months are over-represented compared to those who indicated 2 - 3 courses and again
compared to those who indicated 4 or more courses.
Building the models for the Poisson regression mirrors that of the logistic regression. Results
from the Poisson regression show that the logistic regression is robust and the conclusions
remain the same (Table 10.2, Appendix).
38
6. Discussion and Conclusions
This study aimed to answer the research question: ‘How do differences in information
provision by healthcare professionals or media outlets to patients who self-medicate with
antibiotics affect this level of self-medication?’. Data was used from the ‘Antimicrobial
Resistance and Causes of Non-Prudent Use of Antibiotics in Human Medicine’ research
project conducted by NIVEL. Data was collected in 7 European countries (Cyprus, Estonia,
Greece, Hungary, Italy, Romania and Spain) which were identified by the Eurobarometer
(2013) as countries with high levels of self-medication with antibiotics. This data was deemed
appropriate for this study as the selected countries were also found by the Eurobarometer to
have low levels of knowledge regarding antibiotics. Results from the Eurobarometer (2013)
found an association between receiving information on the use of antibiotics and correct
knowledge of antibiotics. The present research aimed to answer whether receiving
information on antibiotics is also associated with the behaviour of an individual who self-
medicates with antibiotics. In addition, it aimed to investigate whether the specific type of
information and source of information produce a stronger association with this behaviour. In
order to answer the research question two hypotheses and one explanatory hypothesis were
tested. Before discussing the results, a limit of the research is discussed, to provide clarity to
the conclusions drawn.
A limitation of the present research is the nature of the dependent variable. 1 course of self-
medicated antibiotics in the past 18 months is the lowest level respondents could indicate.
Therefore there is no reference to respondents who used antibiotics but did not self-medicate
with them. This sample bias means that the results are not generalizable to whole
populations but can only be applied to a subgroup of antibiotic users, although it was found
that self-medicators are not selective on the basis of sex and age. Strengths of the study
include a large sample size, which was well distributed geographically. In addition, the
method of data collection – computer assisted telephone interviews - has been found to be
optimal in terms of quality and reliability of responses, as opposed to self-administered
surveys (Bowling, 2005).
Hypothesis 1; ‘Of those who self-medicate, the level of self-medication will be lower among
those who have received information than among those who did not receive information’ was
based on rational choice theory, exemplified through the theory of reasoned action (Ajzen &
Fishbein, 1980) and the theory of planned behaviour (Ajzen, 1985). Based on the results of
the logistic regression, this hypothesis was rejected. A possible explanation for this surprising
result is that respondents may have received information but had forgotten it. Arguably,
39
information is better remembered when it has influenced one’s behaviour. If the information
did not have an impact on the respondent’s behaviour, then it is likely that they forgot the
information or forgot receiving information in general. An additional, more concrete
explanation is a statistical one. Only data on respondents who had previously self-medicated
with antibiotics was collected. Therefore, due to the nature of the dependent variable (level of
self-medication) what is being tested in the analysis is not a causal reduction effect but rather
an association. With this in mind, one cannot entirely reject the theory behind the hypothesis
before an analysis is carried out with data on those who did not self-medicate with antibiotics.
Hypothesis 2; ‘Among patients who self-medicate, the effect of receiving information on ‘side-
effects’ will be most associated with lower levels of self-medication.’ was based on Protection
Motivation Theory (Rogers, 1975) and the usefulness of negativity in conveying information
(Geer, 2006). From this, it was expected that negative information would have the strongest
association with lower levels of self-medication. This is because when the severity of an
issue is highlighted, people behave in a rational, protective manner and because negative
information is more often validated by facts than positive information it is therefore more
motivating to change behaviour. Based on the results of the regression analysis, this
hypothesis was also rejected. This is a particularly surprising result considering the
operationalization of the variable side-effects. Information about when antibiotics should not
be taken, allergies, side-effects and drug interaction would theoretically evoke a rational,
behaviour changing thought in the patient. Again the issue of the nature of the dependent
variable should be considered. What was analysed was the respondents’ intensity of self-
medication. Perhaps the results would be different if data on those who did not self-medicate
was included. We cannot completely reject the theory behind the hypothesis because
although evidence for it was not found in this study, perhaps evidence would be found if the
sample was representative.
Although information on the side-effects of antibiotics did not bear an association with
respondents’ level of self-medication, obtaining information on the proper use of antibiotics
was found to be associated with less self-medication. A possible explanation for this can be
given following the arguments of Geer (2006). Drawing on the results of previous studies
concerning methods to inform the public on topics which the common person is not an
expert, Geer argues that simple messages are the most effective. The public are not
interested in spending time learning intricate details but are more receptive to messages
which are straightforward and uncomplicated. Considering the operationalization of the three
types of information in this research, it could be argued that information on the proper use of
antibiotics (complete the full course and only take antibiotics over the prescribed period and
40
at the correct dose) is the most explicit and easiest to understand. Therefore respondents
are more receptive to this type of information and it has a stronger association with their level
of self-medication with antibiotics. In addition, the non-existent effect of obtaining general
information could be explained through the operationalization of this variable. No knowledge
of how to act if the condition persists is unlikely to encourage a patient to self-medicate
further.
The (exploratory) hypothesis 3 ‘When information is received from a healthcare professional,
levels of self-medication among those who self-medicate will be lower than when the
information is obtained from the media/internet.’ was also rejected as neither source yielded
a significant relationship with the respondents’ level of self-medication with antibiotics.
Although a disappointing result, it could be an indication that governments need not invest
time and energy in public media campaigns, and instead, efforts should be focused on the
supply side of self-medication with antibiotics. That is, interventions should be directed
towards pharmacists who allow over-the-counter selling of antibiotics which one would
normally need a medical prescription to obtain. Or perhaps thorough discussion with patients
at pharmacies and in GP offices should be reinforced. The effect of having a chronic illness
or poorer health suggests that this method would be feasible in reducing self-medication as
these persons would have more frequent and regular contact with healthcare professionals
than people with better health.
A possible explanation for the contradictory results found in this study compared to those
found in the Eurobarometer (2013) is that this study assessed the factors that explain some
level of self-medication with antibiotics (i.e. 1 course of antibiotics is the base), while the
Eurobarometer assessed the factors that explain any level of self-medication (i.e. no self-
medication is possible). Consequently, the findings are not directly comparable.The
Eurobarometer also only included respondents who used antibiotics in the past 12 months
while this study included respondents who used antibiotics in the past 18 months. In addition,
the Eurobarometer (2013) study assessed 27 EU countries, while the present study only
focused on 7 EU countries.
Antibiotic resistance continues to be an issue of immediate concern and it is fundamental
that the public is made aware of the basic facts of antibiotics and their use. Therefore
education about antibiotics, specifically regarding the proper use of antibiotics, through
school curricula could be a possible venture for the countries in this research. However
further research is also warranted into the reasons for self-medication with antibiotics.
41
Because this thesis has shown the association between self-medication and information to
be weak at most, other explanations should be sought. Logically, there could be economic,
and cultural factors involved. For instance, if future research finds that people primarily self-
medicate with antibiotics for cost-saving reasons (i.e. to avoid paying for an appointment with
a GP) then public educational interventions would not be the most effective. In this case,
pharmacist based interventions could be more appropriate.
42
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8. Appendix 1 Descriptives tables Table 1.2. Descriptives (Total sample)
Variables Min Max % Valid cases
Level of self-medication (D.V.) 0 1
1,841
1 course
65%
2 + courses
33%
Don't know / refuse
2%
Received information 0 1
1,846
Yes
73%
No
26%
Don't know / refuse
1%
Type of information
1,353
Proper use 0 1 79%
General information 0 1 38%
Side-effects 0 1 41%
Don't know / refuse 0 1 4%
Source of information
1,353
Healthcare professional 0 1 88%
Media 0 1 5%
Don't know / refuse 0 1 0.2%
Age
15 99
1,846
< 17
0.4%
18-40
29%
41-64
50%
65+
20%
Refuse
0.5%
Sex
0 1
1,846
Female
64%
Male
36%
Education 1 3
1,846
Low
14%
Medium
50%
High
36%
Don't know / refuse
0.8%
Chronic Illness 0 1
1,846
Yes
35%
No
65%
Don't know / refuse
0.9%
Perceived health 1 3
1,846
(Very) good
58%
Fair
33%
(Very) bad
9%
Don't know / refuse 1%
46
Table 2.2. Descriptives (Cyprus) Variables
Min Max % Valid cases
Level of self-medication (D.V.) 0 1
315
1 course
65%
2 + courses
34%
Don’t know / refuse
1%
Received information 0 1
315
Yes
75%
No
24%
Don't know / refuse
0.6%
Type of information
237
Proper use 0 1 70%
General information 0 1 11%
Side-effects 0 1 20%
Don't know / refuse 0 1 7%
Source of information
237
Healthcare professional 0 1 92%
Media 0 1 1%
Don't know / refuse 0 1 -
Age
18 85
315
< 17
-
18-40
33%
41-64
49%
65+
18%
Refuse
0.3%
Sex
0 1
315
Female
55%
Male
45%
Education 1 3
315
Low
14%
Medium
45%
High
41%
Don't know / refuse
-
Chronic Illness 0 1
315
Yes
37%
No
63%
Don't know / refuse
0.3%
Perceived health 1 3
315
(Very) good
74%
Fair
19%
(Very) bad
7%
Don't know / refuse -
47
Table 3.2. Descriptives (Estonia)
Variables Min Max % Valid cases
Level of self-medication (D.V.) 0 1
164
1 course
77%
2 + courses
21%
Don't know / refuse
2% Received information 0 1
164
Yes
80%
No
19%
Don't know / refuse
1% Type of information
131
Proper use 0 1 81%
General information 0 1 43%
Side-effects 0 1 44%
Don't know / refuse 0 1 6% Source of information
131
Healthcare professional 0 1 76%
Media 0 1 7%
Don't know / refuse 0 1 -
Age
18 80
164
< 17
18-40
23%
41-64
57%
65+
20%
Sex
0 1
164
Female
60%
Male
40% Education 1 3
164
Low
5%
Medium
55%
High
40%
Don't know / refuse
- Chronic Illness 0 1
164
Yes
41%
No
59%
Don't know / refuse
-
Perceived health 1 3
164
(Very) good
42%
Fair
46%
(Very) bad
12%
Don't know / refuse -
48
Table 4.2. Descriptives (Greece)
Variables Min Max % Valid cases
Level of self-medication (D.V.) 0 1
295
1 course
71%
2 + courses
29%
Don't know / refuse
- Received information 0 1
296
Yes
61%
No
39%
Don't know / refuse
0.3% Type of information
181
Proper use 0 1 73%
General information 0 1 38%
Side-effects 0 1 41%
Don't know / refuse
- Source of information
181
Healthcare professional 0 1 92%
Media 0 1 4%
Don't know / refuse
-
Age
16 87
296
< 17
0.7%
18-40
25%
41-64
54%
65+
19%
Refuse
1% Sex
0 1
296
Female
71%
Male
29% Education 1 3
296
Low
10%
Medium
44%
High
46%
Don't know / refuse
- Chronic Illness 0 1
296
Yes
35%
No
65%
Don't know / refuse
0.3%
Perceived health 1 3
296
(Very) good
72%
Fair
24%
(Very) bad
5%
Don't know / refuse 0.7%
49
Table 5.2. Descriptives (Hungary)
Variables Min Max % Valid cases
Level of self-medication (D.V.) 0 1
261
1 course
56%
2 + courses
42%
Don't know / refuse
2% Received information 0 1
261
Yes
84%
No
14.5%
Don't know / refuse
1.5% Type of information
219
Proper use 0 1 79%
General information 0 1 57%
Side-effects 0 1 57%
Don't know / refuse 0 1 4% Source of information
219
Healthcare professional 0 1 78%
Media 0 1 8%
Don't know / refuse 0 1 -
Age
17 90
261
< 17
0.4%
18-40
29%
41-64
51%
65+
19%
Refuse
0.8% Sex
0 1
261
Female
70%
Male
30% Education 1 3
261
Low
14%
Medium
56%
High
30%
Don't know / refuse
0.4% Chronic Illness 0 1
261
Yes
48%
No
51%
Don't know / refuse
1%
Perceived health 1 3
261
(Very) good
54%
Fair
31%
(Very) bad
14%
Don't know / refuse -
50
Table 6.2. Descriptives (Italy)
Variables Min Max % Valid cases
Level of self-medication (D.V.) 0 1
279
1 course
66%
2 + courses
30%
Don't know / refuse
4% Received information 0 1
279
Yes
65%
No
34%
Don't know / refuse
1% Type of information
182
Proper use 0 1 82%
General information 0 1 27%
Side-effects 0 1 34%
Don't know / refuse
3% Source of information
182
Healthcare professional 0 1 96%
Media 0 1 0.6%
Don't know / refuse
0.6%
Age
18 99
279
< 17
18-40
14%
41-64
53%
65+
33%
Refuse
0.4% Sex
0 1
279
Female
73%
Male
27% Education 1 3
279
Low
28%
Medium
48%
High
20%
Don't know / refuse
4% Chronic Illness 0 1
279
Yes
30%
No
67%
Don't know / refuse
3%
Perceived health 1 3
279
(Very) good
17%
Fair
68%
(Very) bad
11%
Don't know / refuse 4%
51
Table 7.2. Descriptives (Romania)
Variables Min Max % Valid cases
Level of self-medication (D.V.) 0 1
271
1 course
58%
2 + courses
39%
Don't know / refuse
3% Received information 0 1
275
Yes
79%
No
20%
Don't know / refuse
0.3% Type of information
218
Proper use 0 1 81%
General information 0 1 34%
Side-effects 0 1 33%
Don't know / refuse
7% Source of information
218
Healthcare professional 0 1 91%
Media 0 1 6%
Don't know / refuse
0.5%
Age
15 81
275
< 17
1%
18-40
44%
41-64
39%
65+
16%
Refuse
0.7% Sex
0 1
275
Female
59%
Male
41% Education 1 3
275
Low
13%
Medium
48%
High
39%
Don't know / refuse
0.3% Chronic Illness 0 1
275
Yes
28%
No
72%
Don't know / refuse
0.7%
Perceived health 1 3
275
(Very) good
60%
Fair
31%
(Very) bad
9%
Don't know / refuse 0.4%
52
Table 8.2. Descriptives (Spain)
Variables Min Max % Valid cases
Level of self-medication (D.V.) 0 1
256
1 course
70%
2 + courses
29%
Don’t know / refuse
1% Received information 0 1
256
Yes
72%
No
27%
Don’t know / refuse
0.4% Type of information
185
Proper use 0 1 87%
General information 0 1 64%
Side-effects 0 1 60%
Don’t know / refuse
3% Source of information
185
Healthcare professional 0 1 90%
Media 0 1 6%
Don’t know / refuse
-
Age
16 99
256
< 17
0.4%
18-40
34%
41-64
52%
65+
13%
Refuse
0.4% Sex
0 1
256
Female
57%
Male
43% Education 1 3
256
Low
11%
Medium
55%
High
33%
Don’t know / refuse
0.8% Chronic Illness 0 1
256
Yes
26%
No
74%
Don’t know / refuse
0.8%
Perceived health 1 3
256
(Very) good
80%
Fair
17%
(Very) bad
3%
Don’t know / refuse 0.4%
53
Regression Tables Table 9.2. Multilevel logistic regression (Preliminary analysis)
Model 1 Model 2 Model 3 Model 4
Variables Coef. P-Value Coef. P-Value Coef. P-Value Coef. P-Value
Area -0.01 0.489 -0.01 0.609 -0.01 0.511 -0.01 0.494
Education (ref. Low) -0.03 0.153 -0.03 0.164 -0.02 0.411 -0.02 0.422
Employed -0.04 0.212 -0.04 0.182 -0.03 0.459 -0.03 0.451
Health insurance 0.02 0.741 0.01 0.759 -0.04 0.446 -0.04 0.446 Smoker 0.04 0.181 0.05 0.168 0.06 0.155 0.05 0.157
Perceived health (ref. (very) good) 0.06 0.018 0.07 0.011 0.1 0.002 0.1 0.002
Chronic Illness 0.08 0.024 0.08 0.031 0.05 0.199 0.05 0.206
Age (ref. 0-17 yrs) -0.08 0.001 -0.08 0.001 -0.09 0.002 -0.09 0.002
Sex (ref. female) 0.07 0.019 0.08 0.015 0.05 0.137 0.05 0.136
Information
0.06 0.083 Proper use
-0.12 0.004 -0.12 0.004
General information
-0.05 0.224 -0.05 0.227
Side effects
0.05 0.192 0.06 0.18 Healthcare professional
0.001 0.982
Media
-0.04 0.636
N 1,630 1,621 1,198 1,198
Table 10.2. Poisson regression (robustness test)
Model 1 Model 2 Model 3 Model 4
Variables Coef. P-Value Coef. P-Value Coef. P-Value Coef. P-Value
Education (ref. Low) -0.02 0.483 -0.02 0.494 -0.01 0.711 -0.02 0.731 Perceived health (ref. (very) good) 0.05 0.196 0.05 0.174 0.07 0.086 0.07 0.086
Chronic Illness 0.06 0.169 0.06 0.185 0.05 0.361 0.05 0.366
Age (ref. 0-17 yrs) -0.06 0.062 -0.06 0.069 -0.06 0.077 -0.06 0.078
Sex (ref. female) 0.04 0.354 0.04 0.338 0.03 0.566 0.03 0.541
Information
0.05 0.297 Proper use
-0.08 0.158 -0.08 0.147
General information
-0.03 0.639 -0.03 0.622
Side effects
0.04 0.503 0.04 0.481 Healthcare professional
0.03 0.711
Media
0.01 0.959
N 1,769 1,758 1,299 1,299
54
9. Appendix 2. Syntax *Countries: *1=Cyprus *2=Estonia *3=Greece *4=Hungary *5=Italy *6=Romania *7=Spain sort country destring, replace *DROP RESPONDENTS WHO USED AB WITH PRESCRIPTION + CHILDREN drop if v5last==. drop if v3course_2==1 | v3course_2==2 | v3course_2==3 | v3course_2==4 drop if v3course_3==1 | v3course_3==2 | v3course_3==3 | v3course_3==4 **BACKGROUND** *AREA* recode v25area (4=.a) tab v25area summarize v25area *EDUCATION* recode v26edu (4=.a) summarize v26edu tab v26edu *JOB* recode v27job (10=.a) recode v27job (9=.a) tab v27job gen employed=0 replace employed = 1 if v27job==1 | v27job==2 | v27job==3 replace employed = . if v27job==. replace employed = .a if v27job==.a tab employed label define employedlabel 0"no" 1"yes" label values employed employedlabel codebook employed *HEALTH INSURANCE* *Yes, covered by national health insurance* tab v28ins_1 *Yes, covered by private health insurance*
tab v28ins_2 *Yes, covered by community/social healthcare insurance* tab v28ins_3 *Other* tab v28ins_4 *Not insured* tab v28ins_5 *Don't know/Refuse* tab v28ins_6 gen healthinsurance=0 replace healthinsurance = 1 if v28ins_1==1 | v28ins_2==1 | v28ins_3==1 | v28ins_4==1 replace healthinsurance = . if v28ins_1==. tab healthinsurance codebook healthinsurance *SMOKE?** tab v30smoke recode v30smoke (3=.a) gen smoke=0 replace smoke = 1 if v30smoke==1 replace smoke = . if v30smoke==. replace smoke = .a if v30smoke==.a label define smokelabels 0"no" 1"yes" label values smoke smokelabel codebook smoke **PERCEIVED HEALTH?** recode v31health (6=.a) codebook v31health gen Phealth=0 replace Phealth = 1 if v31health==1 | v31health==2 replace Phealth = 2 if v31health==3 replace Phealth = 3 if v31health==4 | v31health==5 replace Phealth = . if v31health==. replace Phealth = .a if v31health==.a label define Phealthlabel 1"(very)good" 2"fair" 3"(very) bad" label values Phealth Phealthlabel codebook Phealth summarize Phealth tab Phealth **CHRONIC DISEASE?** *Asthma* tab v32chro_1
55
codebook v32chro_1 *COPD* tab v32chro_2 *Emphysema* tab v32chro_3 *Diabetes Mellitus* tab v32chro_4 *Cardiovascular disease* tab v32chro_5 *Hypertension* tab v32chro_6 *Other* tab v32chro_7 *No* tab v32chro_8 *Don't know/Refuse* tab v32chro_9 codebook v32chro_9 gen chronic=0 replace chronic = 1 if v32chro_1==1 | v32chro_2==1 | v32chro_3==1 | v32chro_4==1 | v32chro_5==1 | v32chro_6==1 | v32chro_7==1 replace chronic = . if v32chro_1==. replace chronic = .a if v32chro_9==1 tab chronic *AGE* tab v33age_1 *remove 999 years answer* replace v33age_1=. if v33age_1==999 replace v33age_1=. if v33age_1==0 codebook v33age_1 recode v33age_1 (min/17=1)(18/40=2)(41/64=3)(65/max=4) tab v33age_1 codebook v33age_1 *SEX* tab v34sexe_1 recode v34sexe_1 (3=.a) codebook v34sexe_1 gen sex=0 replace sex=0 if v34sexe_1==2 replace sex=1 if v34sexe_1==1 replace sex = .a if v34sexe_1==.a label define sexlabel 0"female" 1"male" label values sex sexlabel tab sex **HOW MANY COURSES W/OMP?** recode v3course_1 (4=.a)
tab v3course_1 by country: tab v3course_1 gen course=0 replace course=1 if v3course_1==2 | v3course_1==3 replace course=0 if v3course_1==1 replace course=. if v3course_1==. label define courselabel 1"2+courses" 0"1 course" label values course courselabel tab course summarize course **ADVICE ABOUT HOW TO USE THE ANTIBIOTIC?** recode v17advice(3=.a) tab v17advice codebook v17advice gen information=0 replace information=0 if v17advice==2 replace information=1 if v17advice==1 replace information=. if v17advice==. replace information=.a if v17advice==.a label define informationlabel 0"no" 1"yes" label values information informationlabel tab information codebook information **WHAT INFORMATION DID YOU OBTAIN?** gen properuse=0 replace properuse = 1 if v19info_1==1 | v19info_2==1 replace properuse = . if v19info_1==. | v19info_2==. tab properuse codebook properuse gen generalinfo=0 replace generalinfo = 1 if v19info_3==1 | v19info_4==1 | v19info_5==1 replace generalinfo = . if v19info_3==. | v19info_4==. | v19info_5==. tab generalinfo gen sideeffects=0 replace sideeffects = 1 if v19info_6==1 | v19info_7==1 | v19info_8==1 | v19info_9==1 replace sideeffects = . if v19info_6==. | v19info_7==. | v19info_8==. | v19info_9==. tab sideeffects
56
**FROM WHO OR WHERE?** gen HCProfesh=0 replace HCProfesh = 1 if v18where_1==1 | v18where_2==1 | v18where_3==1 replace HCProfesh = . if v18where_1==. tab HCProfesh gen media=0 replace media = 1 if v18where_4==1 | v18where_5==1 | v18where_6==1 replace media = . if v18where_4==. tab media codebook media *ANALYSIS 1ST ATTEMPT* *Multivariate logistic regression* *Model 1 - control variables manova v3course_1 = v25area v26edu employed healthinsurance smoke Phealth chronic v33age_1 sex mvreg *Model 2 - received information manova v3course_1 = v25area v26edu employed healthinsurance smoke Phealth chronic v33age_1 sex information mvreg *Model 3 - Replace received info with type of info manova v3course_1 = v25area v26edu employed healthinsurance smoke Phealth chronic v33age_1 sex properuse generalinfo sideeffects mvreg *Model 4 - Add source of info manova v3course_1 = v25area v26edu employed healthinsurance smoke Phealth chronic v33age_1 sex properuse generalinfo sideeffects HCProfesh media mvreg *ANALYSIS 2ND ATTEMPT* *Multi level mixed-effects linear regression *Model 1 mixed v3course_1 c.v25area c.v26edu c.employed c.healthinsurance c.smoke c.Phealth c.chronic c.v33age_1 c.sex || country: *Model 2 mixed v3course_1 c.v25area c.v26edu c.employed c.healthinsurance c.smoke
c.Phealth c.chronic c.v33age_1 c.sex information || country: *Model 3 mixed v3course_1 c.v25area c.v26edu c.employed c.healthinsurance c.smoke c.Phealth c.chronic c.v33age_1 c.sex properuse generalinfo sideeffects || country: *Model 4 mixed v3course_1 c.v25area c.v26edu c.employed c.healthinsurance c.smoke c.Phealth c.chronic c.v33age_1 c.sex properuse generalinfo sideeffects HCProfesh media || country: *Poisson regression *Model 1 mepoisson v3course_1 c.v26edu c.Phealth c.chronic c.v33age_1 c.sex || country: *Model 2 mepoisson v3course_1 c.v26edu c.Phealth c.chronic c.v33age_1 c.sex information || country: *Model 3 mepoisson v3course_1 c.v26edu c.Phealth c.chronic c.v33age_1 c.sex properuse generalinfo sideeffects || country: *Model 4 mepoisson v3course_1 c.v26edu c.Phealth c.chronic c.v33age_1 c.sex properuse generalinfo sideeffects HCProfesh media || country: *ANALYSIS 3RD ATTEMPT* *lOGISTIC REGRESSION WITH DICHOTOMOUS DEPENDENT VARIABLE* *Model 1 - control variables logit course i.v26edu i.Phealth i.chronic i.v33age_1 i.sex country *Model 2 - received information logit course i.v26edu i.Phealth i.chronic i.v33age_1 i.sex information country *Model 3 - Replace received info with type of info logit course i.v26edu i.Phealth i.chronic i.v33age_1 i.sex properuse generalinfo sideeffects country *Model 4 - Add source of info
57
logit course i.v26edu i.Phealth i.chronic i.v33age_1 i.sex properuse generalinfo sideeffects HCProfesh media country *ANALYSIS 4TH ATTEMPT* *BY COUNTRY: lOGISTIC REGRESSION WITH DICHOTOMOUS DEPENDENT VARIABLE* *Model 1 - control variables by country: logit course i.v26edu i.Phealth i.chronic i.v33age_1 i.sex *Model 2 - received information by country: logit course i.v26edu i.Phealth i.chronic i.v33age_1 i.sex information *Model 3 - Replace received info with type of info by country: logit course i.v26edu i.Phealth i.chronic i.v33age_1 i.sex properuse generalinfo sideeffects *Model 4 - Add source of info by country: logit course i.v26edu i.Phealth i.chronic i.v33age_1 i.sex properuse generalinfo sideeffects HCProfesh media *ANALYSIS 5th ATTEMPT* *lOGISTIC REGRESSION WITH DICHOTOMOUS DEPENDENT VARIABLE* *Model 1 - control variables logistic course i.v26edu i.Phealth i.chronic i.v33age_1 i.sex i.country *Model 2 - received information logistic course i.v26edu i.Phealth i.chronic i.v33age_1 i.sex i.information i.country *Model 3 - Replace received info with type of info logistic course i.v26edu i.Phealth i.chronic i.v33age_1 i.sex properuse generalinfo sideeffects i.country *Model 4 - Add source of info logistic course i.v26edu i.Phealth i.chronic i.v33age_1 i.sex properuse generalinfo sideeffects HCProfesh media i.country