Open Access

Inequalities in medicine use in Central Eastern Europe: an empirical investigation of socioeconomic determinants in eight countries

International Journal for Equity in HealthThe official journal of the International Society for Equity in Health201514:124

https://doi.org/10.1186/s12939-015-0261-0

Received: 2 April 2015

Accepted: 29 October 2015

Published: 5 November 2015

Abstract

Background

Equitable access to essential medicines is a major challenge for policy-makers world-wide, including Central and Eastern European countries. Member States of the European Union situated in Central and Eastern Europe have publicly funded pharmaceutical reimbursement systems that should promote accessibility and affordability of, at least essential medicines. However, there is no knowledge whether socioeconomic inequalities exist in these countries. Against this backdrop, this study analyses whether socioeconomic determinants influence the use of prescribed and non-prescribed medicines in eight Central and Eastern European countries (Bulgaria, Czech Republic, Hungary, Latvia, Poland, Romania, Slovenia, Slovakia). Further, the study discusses observed (in)equalities in medicine use in the context of the pharmaceutical policy framework and the implementation in these countries.

Methods

The study is based on cross-sectional data from the first wave of the European Health Interview Survey (2007–2009). Multivariate logistic regression analyses were carried out to determine the association between socioeconomic status (measured by employment status, education, income; controlled for age, gender, health status) and medicine use (prescribed and non-prescribed medicines). This was supplemented by a pharmaceutical policy analysis based on indicators in four policy dimensions (sustainable funding, affordability, availability and accessibility, and rational selection and use of medicines).

Results

Overall, the analysis showed a gradient favouring individuals from higher socioeconomic groups in the consumption of non-prescribed medicines in the eight surveyed countries, and for prescribed medicines in three countries (Latvia, Poland, Romania). The pharmaceutical systems in the eight countries were, to varying degrees, characterized by a lack of (public) funding, thus resulting in high and growing shares of private financing (including co-payments for prescribed medicines), inefficiencies in the selection of medicines into reimbursement and limitations in medicines availability.

Conclusion

Pharmaceutical policies aiming at reducing inequalities in medicine use require not only a consideration of the role of co-payments and other private expenditure but also adequate investment in medicines and transparent and clear processes regarding the inclusion of medicines into reimbursement.

Keywords

MedicinesAccessUseInequalitiesEducationIncomeAffordabilityAvailabilityCo-payments

Background

Ensuring equitable access to essential medicines is a major challenge for policy-makers. Essential medicines are those that satisfy the priority health care needs of the population and should be selected with due regard to disease prevalence, evidence on efficacy and safety, and comparative cost effectiveness. Essential medicines are intended to be available within the context of functioning health systems at all times, in adequate amounts, in the appropriate dosage forms, with assured quality, and at a price that the individual and the community can afford [1]. Countries have the responsibility to provide access to essential medicines while the implementation of the concept of essential medicines is intended to be flexible and adaptable to country specific situations [2].

The World Health Organization (WHO) has been promoting the concept of universal health coverage, arguing that timely access to health services, including medication, requires a well-functioning health financing system [3]. Related to pharmaceuticals, this means that, independently from the ownership of the suppliers (private or public suppliers and dispensaries), those medicines considered as essential in the national context are, at least partially, funded by the state, through a social health insurance system or a national health service.

Such reimbursement systems are in place in all Member States of the European Union (EU) where funding and reimbursement of medicines remains a national competence even if other areas (marketing authorization) have been harmonized [4]. This is also the case of the Central and Eastern European countries (CEECs) that are members of the EU. As of 2015, this is a total of eleven countries: Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovenia and Slovakia (acceding to the EU in 2004); Bulgaria and Romania (joining the EU in 2007); and Croatia which has been an EU Member State since mid-2013.

From the 1990s, the CEECs have changed from highly centralised and hospital-centred Semaskho systems towards social insurance systems with varying degrees of state centeredness and liberal market elements [5, 6]. In terms of pharmaceutical policies, the CEECs departed from the former principle of free medication for the entire population, starting to differentiate between medicines that should either be fully or partially reimbursed by the health system and introducing co-payment schemes for out-patient medicines [710].

In the new millennium, all these countries had positive lists, i.e. lists of medicines that may be prescribed at the expense of a third party payer (corresponding to the essential medicines lists concept). However, there are indications that the lists of (co-)funded medicines in the CEECs were smaller compared to those in the Western, Northern and Southern EU Member States [9, 10]. Further, medicines included in the positive lists were not necessarily 100 % funded since medicines whose therapeutic benefit was assessed to be lower were funded partially, with the remainder to be paid by out-patients in out-patient care [810]. In order to ensure financial sustainability for medicines, the CEECs implemented, to a greater or lesser extent, several of the policy options available: They applied price regulation for medicines, at least for those (partially) funded by the state at ex-factory price (manufacturer price) as well as at wholesale and retail price level (regulating the maximum allowed profits for distributors and dispensaries) [810]. In addition, lower priced generics were more widely used in the CEECs compared to other EU Member States: this might be attributable to a traditionally strong role of local generic industry, but also to demand-side measures such as encouraging doctors to prescribe by International Non-Proprietary Name (INN) and allowing pharmacists to substitute prescribed brands by generics (generic substitution) [11, 12].

Still, investment in health care, including medicines, is known to be lower in the Central and Eastern European region, and several CEECs have been struggling with limited health budgets, making cost-containment a major policy concern [6, 7, 13]. Consequently, out-of-pocket-payments, including informal payments [1417], considerably increased. Respective austerity measures shifted the financial burden from the state to private households, with a potential risk that vulnerable population groups refrain from using health care services, including getting prescriptions filled and purchasing non-prescribed Over-the-Counter (OTC) medicines [18].

At the same time, limited evidence is available regarding inequalities in medicine use. To our best knowledge, the socioeconomic determinants of medicine use in the CEECs have not yet been explored in detail. Exceptions include Geckova et al. [19], studying socioeconomic differences in health, including the use of prescribed and non-prescribed medicines, of adolescents in Slovakia in 1998, or Gorecka et al. [20] analysing as to whether a socioeconomic gradient existed in the use of (prescribed) cardiovascular medicines at district level in the Czech Republic for the years 1997-2000. Though not studying socioeconomic (in)equalities, the study by Baji et al. [21] is also relevant in this context: Concerns for socioeconomic inequalities were raised following the increase in out-of-pocket payments, including co-payments after the 2007 health care reform in Hungary.

Against this background, our study aims to analyse whether socioeconomic determinants influence medicine use, i.e. both use of prescribed medicines and non-prescribed medicines, in eight CEECs (Bulgaria, Czech Republic, Hungary, Latvia, Poland, Romania, Slovenia, Slovakia). Based on these findings, we discuss to what extent observed inequalities can potentially be explained by current pharmaceutical policies (or a lack of respective policies).

This article is thus first to study inequalities in medicine use in CEECs. To this end, we first present a brief review of the literature on inequalities in health care and, more specifically, medicine use, and, introduce our analytical framework. We perform a quantitative analysis of (in)equalities in medicine use based on household survey data from the first wave of the European Health Interview Survey (EHIS). The findings are interpreted in a policy analysis that follows the developed framework and uses information and research on national pharmaceutical systems.

Addressing inequalities in medicine use

Following the international literature, equitable access to and equitable use of health care is generally defined as access to and use of health care that is determined by health care need rather than any other criteria such as socioeconomic and demographic factors (e.g. income, education, sex or age) or the macro level institutional factors (e.g. health insurance coverage or co-payments). For medicines, this would require that health care need and no other potential factor determines access to and use of medicines. The empirical and policy oriented literature dealing with (in)equalities in health care revolves around three major issues: measuring the extent of the inequalities, identifying the factors that determine inequalities and addressing measures that help reduce existing inequalities.

Over the past two decades, there has been a vastly growing literature attempting to measure the existence and the extent of socioeconomic inequalities. This first stream of the literature is widely covering developing as well as developed countries (e.g. [2224]) and has focused on access to general practitioners (GPs) or specialists [25], on access to specific treatments [26], but less on access to hospital care. Existing studies on inequalities in medicine consumption focus on single countries [27] or even regions within countries [28], on specific medicines [29] or specific groups of the population [30], while there is very little cross-country evidence on socioeconomic inequalities in medicines consumption.

The second stream of the literature goes beyond measuring (in)equalities and applies different frameworks to disentangle the factors that determine (in)equalities in access to and use of health care. Much of this work builds on Andersen’s behavioural model [31], i.e. his seminal work on the predictive factors for the use of medical care. While different extensions and adaptations of the original model exist, the approach primarily differentiates between predisposing factors (including demographic factors such as age and gender, socioeconomic factors but also health beliefs or cultural norms), enabling factors (including the financial situation of the user, the means of transportation but also health care system conditions) and the need factor (differentiating between perceived need and evaluated need). Health policies (and pharmaceutical policies) largely fall into the category of contextual enabling factors. In a 2012 review of the application of Andersen’s model in the 1998-2011 period [32], 328 articles have been identified, with more than half referring to the 1995 model [33]. None of the articles identified in this systematic review has dealt with the use of medicines. There are, however, studies on medicine use explicitly or implicitly referring to the ideas of the Andersen model (e.g., [34]).

Another approach to disentangling the factors that determine unequal access to health care services starts from a taxonomic definition of the 5As: availability, accessibility, accommodation, affordability and acceptability [35]. For medicines, this approach has been adapted to include availability, accessibility, acceptability and affordability, with emphasizing quality as a factor relevant in all the four dimensions [36]. In a more recent adaptation for access to health technologies including medicines, for poor populations, availability and affordability have been complemented with architecture and adoption [37]. Bigdeli et al. [38], in an attempt to grasp the complexity of barriers to accessing medicines and to more explicitly address the actors involved, differentiate between the demand side level of individuals, households and communities, health service delivery as the supply side perspective, health sector governance and the national and international context beyond the health sector.

This latter approach puts more emphasis on the health policy perspective. In addition to identifying socioeconomic inequalities in access to and use of health care and in addition to the aforementioned approaches to disentangle the factors that determine existing inequalities, the third stream of the literature on inequalities in health care use deals with the impact of policies on access to and use of medicines or on improving access to health care in general or medicines more specifically. Examples for this body of work are Gemmill et al. [39] analysing the impact of prescription medicines charges on efficiency and equity or Leopold et al. studying the impact of the economic recession on pharmaceutical consumption in eight European countries [40] and the impact of the policy policies on the use of antipsychotic medicines for Finland and Portugal [41]. In general, the more policy oriented literature is broad and often aimed at very specific health policy concerns. But often it lacks a coherent framework for analysis. For our study, we follow a WHO approach for collective action towards equitable access to essential medicines [42], differentiating between four key actions: rational selection and use of essential medicines, affordable prices, sustainable financing, and reliable supply systems.

With a view to the aim of this study, discussing the impact of pharmaceutical policies on (in)equalities in the use of medicines, and in combining the above cited literature, we use four key dimensions for the analysis: (1) sustainable funding, (2) affordability, (3) rational selection and use of medicines and (4) availability and accessibility. As depicted in Table 1, the first two dimensions are dealing with financing, (1) on the macro level, (2) on the micro level, the other two dimension are related to issues of delivery, (3) on the macro level, (4) on the micro level.
Table 1

Pharmaceutical policies and equitable access to medicines: a framework for analysis

 

Macro level

Micro level

FINANCING

Sustainable funding

Affordability

Addressing financial issues on macro levels: the availability of funding for health care in general and the level of pharmaceutical spending more specifically

Addressing financial issues on micro levels: who pays and how much, thus the role of public and private payments for pharmaceuticals

Categories of indicators: type of health care system, total health expenditure, pharmaceutical expenditure

Categories of indicators: private health expenditure, private pharmaceutical expenditure, co-payments, informal payments, medicine prices, VAT on medicines, policies addressing the poor

DELIVERY

Rational selection and use

Availability and accessibility

Addressing delivery on macro levels: which medicines, at least potentially, are made available, rational prescribing, dispensing and use of medicines in the health care system

Addressing delivery on micro levels: the issue of supply meeting demand in terms of volume and reachability given distances and travel resources

Categories of indicators: INN prescribing, generic market, reference price system, prescription monitoring, treatment guidelines

Categories of indicators: number of medicines, pharmacy density, OTC sale

Note: A definition of all the indicators chosen is given in Table 5 which provides a comparative overview for the eight study countries.

Methods

Quantitative analysis

We draw on cross-sectional data from the first wave of the European Health Interview Survey (EHIS), collected between 2006 and 2009 in 19 EU Member States [43]. This study focuses on CEECs. Countries in this region, due to their weaker economic situation compared to Western European countries, risk substantial socioeconomic inequalities. Eight (Bulgaria, Czech Republic, Hungary, Latvia, Poland, Romania, Slovenia, Slovakia) of the ten CEECs that were EU Member States at the time of the EHIS survey were included in our study (see Table 2 for an overview of the dataset); Estonia and Lithuania were excluded due to limited data and non-inclusion in the EHIS survey, respectively. Generally, the data focussed on the non-institutionalized population, but in the Czech Republic also the institutionalized population (living in nursing homes and convents/monasteries) was included in the survey [44]. Since this, however, only concerns less than an estimated 0.5 % of this country’s sample population (Š Daňková 2014, pers. comm., 6 October), we hardly expect this to affect our results. Finally, the EHIS dataset covers the population above age 15, however, we restrict our analysis to adults (aged 18 and over) as it is assumed that the development of an individual socioeconomic status may be expected from around this age onwards.
Table 2

Data sources

 

BG

CZ

HU

LV

PL

RO

SI

SK

Fieldwork

10/2008–11/2008

06/2008–10/2008

09/2009–10/2009

09/2008–12/2008

10/2009–12/2009

05/2008–06/2008

10/2007–11/2007

09/2009–10/2009

Response rate in %

74

56

81

72

72

89

68

66

Sample size (age 15+)

5,661

1,995

5,051

6,458

35,100

18,172

2,188

4,972

Note: BG Bulgaria, CZ Czech Republic, HU Hungary, LV Latvia, PL Poland, RO Romania, SI Slovenia, SK Slovakia

Source: Eurostat [44]

To determine the association between socioeconomic status and pharmaceutical consumption, we carried out multinomial multivariate logistic regression analyses (Stata 13.1). In these models, the probability was estimated of falling into certain categories compared to the reference group. To check the statistical significance of the calculated relative-risk ratios (RRR), z-statistics were used. Only results with p < 0.05 were considered statistically significant and thus discussed further. McFadden’s R2 is reported as measure of model fit.

A detailed description of the variables included in the regression analysis can be found in Table 3. The dependent variable is based on the information on the consumption of non-prescribed medicines (only), consumption of prescribed medicine (only) and consumption of both types of medicines versus no medicine use. For an unbiased gender comparison, use of female-specific medicines (contraceptive pills; hormones for menopause; based on an additional survey question) was excluded from the analysis. As independent variables of interest, three socioeconomic indicators were included [45]. First, employment status was grouped into employed (reference group), retired, unemployed or disabled and in training or at home. Second, the highest level of education completed was measured based on the International Standard Classification of Education (ISCED) 1997 and distinguishes between ISCED ≤ 2, ISCED 3-4 and ISCED 5-6. Third, the income level was based on the range of the monthly net household income against the values of the national deciles’ limits and categorized into quintiles (reference group: first quintile). To control for demographic characteristics, we included age and sex, with the youngest and males as reference groups. Finally, since a socioeconomic gradient in health disadvantaging the poor is also well-established in CEE regions [46], we introduced dummy variables for chronic conditions (no chronic conditions as reference group) and self-assessed health (good as reference group).
Table 3

Variable definitions

Variable

Survey question

Subcategories (as used in the analysis) and reference group

Medicine use

During the past two weeks, have you used any medicines (including dietary supplements such as herbal medicines or vitamins) that were prescribed or recommended for you by a doctor?

• None; reference group

• Non-prescribed

• Prescribed

• Both

During the past two weeks, have you used any medicines or dietary supplement or herbal medicines or vitamins not prescribed or recommended by a doctor?

Employment status

How would you define your current labour status?

• Employed (working for pay or profit); reference group

• Retired (in retirement or early retirement or has given up business)

• Unemployed or disabled (unemployed; permanently disabled)a

• In training or at home (pupil, student, further training, unpaid work experience)

Highest education

What is the highest education leaving certificate, diploma or education degree you have obtained? Please include any vocation training.

• ISCED ≤ 2 (no formal education or below ISCED 1; primary education; lower secondary education); reference group

• ISCED 3-4 (upper secondary education; post-secondary but non-tertiary education)

• ISCED 5-6 (first stage of tertiary education; second stage of tertiary education)

Income quintile

Perhaps you can provide the appropriate range [instead of the household’s total net income per month]. Would you (please look at this card and) tell me which group represents your household’s total net monthly income from all these sources after deductions for income tax, National Insurance etc. Is it …

• First quintile; reference group

• Second quintile

• Third quintile

• Fourth and fifth quintile

Age

Age of the person at the moment of the interview

• 18–29 years

• 30–39

• 40–49

• 50–59

• 60–69

• 70+

Gender

Sex

• Male; reference group

• Female

Chronic conditions

Do you have any longstanding illness or [longstanding] health problem? [By longstanding, I mean illnesses or health problems which have lasted, or are expected to last, for 6 months or more.]

• No; reference group

• Yes

Self-assessed health

How is your health in general? Is it …

• Good (very good, good); reference group

• Poor (very bad, bad, fair)

Note: aAs pointed out by an anonymous reviewers, it can be argued that unemployed and permanently disabled people differ in terms of health and medicine use and should thus be analysed separately. However, as both groups individually make for a small proportion of the sample and thus only very few observations would be left for the subgroup analyses (in the most extreme case 0 observations and in the majority of cases, less than 20 observations, which, for Eurostat confidentially reasons would need to be left blank), it was decided to analyse them as one subgroup.

Source: Data provided by Eurostat [80]; presentation by the authors

An overview of the descriptive information for the individual countries is given in Table 4.
Table 4

Descriptive information

 

BG

CZ

HU

LV

PL

RO

SI

SK

 

n

%

n

%

n

%

n

%

n

%

n

%

n

%

n

%

Medicine use

5,475

100.0

1,866

100.0

4,899

100.0

6,114

100.0

33,416

100.0

17,464

100.0

2,028

100.0

4,737

100.0

 None

2,185

39.9

508

27.2

1,558

31.8

2,161

35.3

7,714

23.1

9,742

55.8

705

34.8

1570

33.1

 Non-prescribed

792

14.5

357

19.1

750

15.3

1,308

21.4

7,036

21.1

1,284

7.4

409

20.2

941

19.9

 Prescribed

1,393

25.4

529

28.3

1,557

31.8

1,381

22.6

7,022

21.0

5,013

28.7

595

29.3

1,051

22.2

 Both

666

12.2

454

24.3

1,031

21.0

1,084

17.7

7,835

23.4

1,351

7.7

318

15.7

1,157

24.4

 Missing

439

8.0

18

1.0

3

0.1

180

2.9

3,809

11.4

74

0.4

1

0.0

18

0.4

Employment status

5,475

100.0

1,866

100.0

4,899

100.0

6,114

100.0

33,416

100.0

17,464

86.5

2,028

85.9

4,737

89.9

 Employed

2,682

49.0

995

53.3

2,462

50.3

3,324

54.4

15,836

47.4

8,261

47.3

1,005

49.6

2,798

59.1

 Retired

1,875

34.2

594

31.8

1,322

27.0

1,551

25.4

9,263

27.7

6,132

35.1

553

27.3

991

20.9

 Unemployed, disabled

631

11.5

104

5.6

740

15.1

602

9.9

4,002

12.0

722

4.1

184

9.1

471

9.9

 In training, at home

287

5.2

173

9.3

375

7.7

637

10.4

4,315

12.9

2,349

13.0

286

14.1

477

10.1

 Missing

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

Highest education

5,475

100.0

1,866

100.0

4,899

100.0

6,114

100.0

33,416

100.0

17,464

100.0

2,028

100.0

4,737

100.0

 ISCED ≤ 2

1,830

33.4

292

15.6

1,289

26.3

1,760

28.8

7,741

23.2

6,381

36.5

903

44.5

525

11.1

 ISCED 3-4

2,754

50.3

1,370

73.4

2,728

55.7

3,155

51.6

20,257

60.6

9,568

54.8

955

47.1

3,311

69.9

 ISCED 5-6

889

16.2

196

10.5

877

17.9

1,196

19.6

5,418

16.2

1,515

8.7

170

8.4

901

19.0

 Missing

2

0.0

8

0.4

5

0.1

3

0.0

0

0.0

0

0.0

0

0.0

0

0.0

Income quintile

5,475

100.0

1,866

100.0

4,899

100.0

6,114

100.0

33,416

100.0

17,464

100.0

2,028

100.0

4,737

100.0

 1

1,384

25.3

189

10.1

1,048

21.4

201

3.3

3,530

10.6

9,592

54.9

260

12.8

559

11.8

 2

577

10.5

217

11.6

890

18.2

2,101

34.4

5,860

17.5

6,190

35.4

358

17.7

947

20.0

 3

787

14.4

383

20.5

1,015

20.7

2,809

45.9

5,915

17.7

595

3.4

281

13.9

1,034

21.8

 4–5

2,452

44.8

683

36.6

1,537

31.4

1,003

16.4

10,884

32.6

171

1.0

611

30.1

1,603

33.8

 Missing

275

5.0

394

21.1

409

8.3

0

0.0

7,227

21.6

916

5.2

518

25.5

594

12.5

Age

5,475

100.0

1,866

100.0

4,899

100.0

6,114

100.0

33,416

100.0

17,464

100.0

2,028

100.0

4,737

100.0

 18–29

767

14.0

344

18.4

841

17.2

1,328

21.7

6,673

20.0

2,717

15.6

420

20.7

1,121

23.7

 30–39

751

13.7

344

18.4

919

18.8

963

15.8

5,261

15.7

2,985

17.1

338

16.7

965

20.4

 40–49

894

16.3

243

13.0

756

15.4

984

16.1

5,361

16.0

2,840

16.3

355

17.5

800

16.9

 50–59

1,061

19.4

320

17.1

933

19.0

972

15.9

6,804

20.4

3,320

19.0

380

18.7

837

17.7

 60–69

948

17.3

326

17.5

732

14.9

882

14.4

4,548

13.6

2,593

14.8

279

13.8

577

12.2

 70+

1,054

19.3

289

15.5

718

14.7

985

16.1

4,769

14.3

3,009

17.2

256

12.6

437

9.2

 Missing

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

Gender

5,475

100.0

1,866

100.0

4,899

100.0

6,114

100.1

33,416

100.0

17,464

100.0

2,028

100.0

4,737

100.0

 Male

2,588

47.3

891

47.7

2,225

45.4

2,692

44.1

15,295

45.8

8,202

47.0

948

46.8

2,271

47.9

 Female

2,887

52.7

975

52.3

2,674

54.6

3,422

56.0

18,121

54.2

9,262

53.0

1,080

53.3

2,466

52.1

 Missing

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

Chronic conditions

5,475

100.0

1,866

100.0

4,899

100.0

6,114

100.0

33,416

100.0

17,464

100.0

2,028

100.0

4,737

100.0

 No

3,020

55.2

951

51.0

1,430

29.2

3,413

55.8

15,705

47.0

11,143

63.8

1,245

61.4

1,957

41.3

 Yes

2,429

44.4

904

48.4

3,467

70.8

2,692

44.0

17,557

52.5

6,206

35.5

779

38.4

2,767

58.4

 Missing

26

0.5

11

0.6

2

0.0

9

0.1

154

0.5

115

0.7

4

0.2

13

0.3

Self-assessed health

5,475

100.0

1,866

100.0

4,899

100.0

6,114

100.0

33,416

100.0

17,464

100.0

2,028

100.0

4,737

100.0

 Good

3,099

56.6

1,161

62.2

2,529

51.6

2,661

43.5

16,287

48.7

11,252

64.4

1,237

61.0

2,975

62.8

 Bad

1,950

35.6

703

37.7

2,369

48.4

3,275

53.6

13,346

39.9

6,204

35.5

789

38.9

1,756

37.1

 Missing

426

7.8

2

0.1

1

0.0

178

2.9

3,783

11.3

8

0.0

2

0.1

6

0.1

Source: Data provided by Eurostat [43]; calculation and presentation by the authors

Pharmaceutical policy analysis

The analysis of pharmaceutical policies as potential co-determinant of inequalities in medicine use builds on an analytical framework based on the WHO approach for collective action [42] (see Addressing inequalities in medicine use and Table 1). We identified four key dimensions for the analysis, (1) sustainable funding, (2) affordability, (3) rational selection and use of medicines and (4) availability and accessibility. For each of the four dimensions, we developed a set of indicators (see Tables 1 and 5).
Table 5

Pharmaceutical policy framework – Indicators for equitable access to essential medicines

Indicator

BG

CZ

HU

LV

PL

RO

SI

SK

Reference year

2008

2008

2009

2008

2009

2008

2007

2009

Sustainable funding

 HC system

SHI

SHI

SHI

SHI

SHI

SHI

SHI

SHI

 TPE in % of THE

33.4 % (0.0 pp)

20.4 % (-4.4 pp)

32.5 % (+1.5 pp)

19.4 % (-2.6 pp)

22.9 % (-4.3 pp)

25.0 % (-3.2 pp)

19.6 % (-1.1 pp)

26.6 % (-3.2 pp)

 TPE share  (CEE-10 comparison)

+9.0 pp

−4.0 pp

+6.7 pp

−5.0 pp

−1.5 pp

+0.6 pp

- 6.0 pp

+3.2 pp

 TPE per capita

€ 109.9 (+50.3 %)

€ 205.6 (+17.1 %)

€ 229.4 (+0.8 %)

€ 134.3 (+66.6 %)

€ 134.5 (+11.7 %)

€ 92.2 (+59.1 %)

€ 264.0 (+12.0 %)

€ 283.5 (+56.6 %)

 TPE/capita  (CEE-10 comparison)

−39.1 %

+14.0 %

+35.3 %

−25.6 %

−16.5 %

−48.9 %

+56.9 %

+47.0 %

Affordability

 Private HE/THE

41.8 % (+6.5 %)

17.5 % (+37.5 %)

34.3 % (+13.5 %)

37.4 % (-12.7 %)

28.2 % (-6.0 %)

18.0 % (-6.2 %)

28.1 % (+4.6 %)

34.3 % (+8.3 %)

 Private HE/THE  (CEE-10 compar.)

+13.6 pp

−10.8 pp

+4.7 pp

+9.2 pp

- 0.1 pp

−10.3 pp

−2.2 pp

+4.0 pp

 Private PE/TPE

81.7 % (+4.8 %)

38.4 % (+56.9 %)

51.6 % (37.7 %)

62.2 % (-9.4 %)

61.4 % (0 %)

55.0 % (+4.8 %)

40.1 % (+2.9 %)

30.2 % (+11.3 %)

 Private PE/TPE  (CEE-10 comp.)

+27.8 pp

−15.5 pp

−2.9 pp

+8.3 pp

+7.8 pp

+1.2 pp

−13.7 pp

−25.2 pp

 Informal pays

Yes, extensive

Yes

Yes

Yes, extensive

Yes

Yes, large scale

No

Yes

 Medicine prices

n.a.

€ 5.09 (-71.4 %)

€ 6.91 (-61.2 %)

€ 4.23 (-76.2 %)

€ 4.54 (-74.5 %)

n.a.

€ 12.34 (-30.8 %)

n.a.

 Co-pays (out-patient)

No PF

PF

PF

No PF

PF for some indications

No PF

No PF

PF

%: 0 %, >25 %, >50 %

No fixed R rates

%: 0 %, 10 %, 30 %; 25 %, 45 %, 75 %

%: 0 %, 10 %, 25 %, 50 %, 25 %

%: 0 %, 30 %, 50 %

%: 0 %, 10 %, 50 %

%: 0 %, 25 %, 75 %

%: no fixed R rates

No D

No D

No D

No D

No D

No D

No D

No D

 Changes in co-pays

No

Yes

Yes, increase

No, but planned (90 % R rate to be abolished)

No since mid-1990-ties

No

No

Yes, decrease in PF in 10/2006

 Co-pay exempt.

None

None

Yes

Yes

Yes

Yes, see comment

Yes

Yes

 De-listings

No, see comment

Not known

Yes, see comment

See comment

Yes

See comment

No

No major delisting

 Co-pays (in-patient)

No co-payment

No co-payment

No co-payment

No co-payment

No co-payment

No co-payment

No co-payment

No co-payment

 VAT on medicines

20 % (20 %)

9 % (19 %)

5 % (20 %)

5 % (18 %), planned increase from 1/2009

7 % (22)

9 % (19 %)

8.5 % (20 %)

10 % (19 %)

 OTC market

22 %

26.8 %

15.8 %

n.a.

25.8 %

22.4 %

6.7 %

25.8 %

Availability and accessibility

 No. of medicines

5,8302006

7,8802006

5,525 2006

n.a.

(8,0892005)

6,7112007

2,7912006

19,693 November 2006

4,4812006

4,1302006

3,1442006

5,7142007

4,2752006

≈4,0002007

n.a.

19,320November 2006

1,598

6,9882006

2,8862006

2,3372007

2,7492006

5,7912007

2,6602006

17,804November 2006

See comment

 

See comment

See comment

See comment

See comment

See comment

See comment

 Pharmacies

4,299/1,7952006

2,520/4,0962007

≈2,500/≈4,0002010

899/2,2092007

10,632/3,5852007

5,660/3,7332007

271/7,3842005

1,523/3,528Oct. 2006

 OTC sale outside pharmacies permitted

Yes

Yes

Yes

No

No

Yes, but pharmacist must be present

Yes

No

Rational selection and use of medicines

 INN prescribing

Yes, indicative

Yes, indicative

Yes, indicative

Yes, indicative

Yes, indicative

Yes, indicative

Yes, indicative

Yes, indicative

 Generic substitution

No, not allowed

Yes, indicative

Yes, indicative

Yes, obligatory

Yes, indicative

Yes, indicative

Yes, indicative

Yes, obligatory

 Ref. price syst.

Yes, ATC 5

Yes, ATC 4 + 5

Yes, ATC 4 + 5

Yes, ATC 3,4 + 5

Yes, ATC 3,4 + 5

Yes, ATC 5

Yes, ATC 4

Yes, ATC 4 + 5

 Generic market

See comment

55 %/35 %2005

42 %/26 %2005

No data

No data

≈70 %/≈40 %e

70 %/38 %2005

65 %/48 %2005

 Prescription monitoring

Yes, but limited

Yes

Yes, but limitations in enforcement

Yes

Yes, but limitations in enforcement

Yes, but limitations in enforcement

Yes

Yes

Indicators:

Reference year: data in this table refer to the year of the household survey unless indicated differently

HC system: indicates the type of health care system: Semaskho system, social health insurance (SHI) or National Health Service (NHS)

TPE in % of THE: Total pharmaceutical expenditure (TPE) in % of total health expenditure (THE) in the year of the survey; in brackets: change in the share of TPE in % of THE in percentage points compared to 3 years earlier

TPE share (CEE-10 comparison): Difference of the share of TPE in % of THE in the surveyed country in 2008 compared to CEE-10 average (Bulgaria, Czech Republic, Estonia, Latvia, Lithuania, Hungary, Poland, Romania, Slovenia, Slovakia) in 2008, expressed in percentage points

TPE per capita: Total pharmaceutical expenditure (TPE) per capita in the year of the survey, in Euro; in brackets: change in TPE per capita compared to 3 years earlier, expressed in per cent)

TPE/capita (CEE-10 comparison): Difference of the TPE per capita in the surveyed country in 2008 compared to CEE-10 average in 2008, expressed in per cent

Private HE/THE: Private health expenditure (HE) in % of total health expenditure (THE) in the year of the survey (unless BG – 2007 data); in brackets: change in the share of private HE in % of THE in per cent compared to 3 years earlier

Private HE/THE (CEE-10 compar.): Difference of the share of private HE in % of THE in the surveyed country in 2008 compared to CEE-10 average in 2008 (unless BG – 2007 data), expressed in percentage points

Private PE/TPE: Private pharmaceutical expenditure (PE) in % of total pharmaceutical expenditure (TPE) in the year of the survey; in brackets: change in the share of private PE in % of TPE in per cent compared to 3 years earlier

Private PE/TPE (CEE-10 comp.): Difference of the share of private PE in % of TPE in the surveyed country in 2008 compared to CEE-10 average in 2008, expressed in percentage points

Informal pays: Indications of informal payments in health care

Medicine prices: Average pharmacy retail price in € per pack in the total out-patient market in 2006; differences in % to average of EU-15

Co-pays (out-patient): Co-payments for medicines in the out-patient sector: PF = prescription fee, % = percentage co-payment rates (for reimbursable medicines different co-payment rates in per cent of the price of product apply, usually linked to the severity of the disease and therapeutic benefit of the medicine), D = deductible (upfront initial out-of-pocket payment up to a fixed amount for a service or over a defined period of time; then the rest of the cost is covered by a public party payer). Percentage co-payments due to the reference price system (see below indicator ‘Ref. pricing syst.’) are not considered.

Changes in co-pays: Changes in co-payments in the years before the survey

Co-pay exempt.: Mechanisms for vulnerable groups (e.g. exemptions, reductions) from co-payments in the out-patient sector

De-listings: Exclusion of medicines from reimbursement in the years before the survey

Co-pays (in-patient): Co-payments for medicines in the in-patient sector

VAT on medicines: Value-added tax (VAT) rate on medicines; in bracket: standard VAT rate

OTC market: Relevance of the Over-the-Counter (OTC) market expressed by the share of sales in total non-prescription market as per cent of total pharmaceutical market sales in the years of the EHIS survey (at consumer price level, unless indicated differently)

No. of medicines: Number of medicines authorized, medicines on the market, and prescription-only medicines. Counted incl. different pharmaceutical forms and dosages, excl. different pack sizes unless indicated differently

Pharmacies: Number of community pharmacies, and inhabitants per pharmacy served

OTC sale outside pharmacies permitted: Information as to whether the sale of Over-the-Counter (OTC) medicines was permitted outside pharmacies, or not

INN prescribing: Information as to whether prescribing by International Non-Proprietary Name (INN) was permitted, and if yes, whether the policy was voluntary (indicative) or mandatory for the prescriber

Generic substitution: Information as to whether the practice of substituting a medicine, whether marketed under a trade name or generic name (branded or unbranded generic), with a less expensive medicine (e.g. branded or unbranded generic), often containing the same active ingredient, was permitted, and if yes, whether the policy was voluntary (indicative) or mandatory for the pharmacist

Ref. price syst.: Information as to whether a reference price system was in place, i.e. a reimbursement policy, in which identical or similar medicines are clustered – the public payer reimburses a defined maximum amount (reference price) for all medicines of the cluster, and patients have to co-pay the remainder up to the pharmacy retail price gross), and its clustering: ATC-5: clustering of same active ingredients or groups of active ingredients, ATC-4: clustering of medicines of the same therapeutic group, clustering of medicines of the same pharmacological subgroup

Generic market: Generic market share (in value and in volume) in the out-patient sector

Prescription monitoring: Information as to whether prescription monitoring was performed

Abbreviations: ATC Anatomical Therapeutic Chemical classification system, D deductible, HE health expenditure, OTC Over-the-Counter medicines, pp percentage points, PE pharmaceutical expenditure, PF prescription fee, R reimbursement, THE total health expenditure, TPE total pharmaceutical expenditure, VAT value-added tax

Notes:

BG:

Private HE / THE and Private HE / THE (CEE-10 compar.): no data for private health expenditure in 2008 available; 2007 data used instead: change indicated for the years 2004-2007

Changes in co-pays: Increase of percentage co-payments for some medicines, treating Parkinson, osteoporosis, Glaucoma, etc. took place after the EHIS survey, in 2010

De-listings: No de-listings reported but constant increase of medicines included in the positive list during 2007

No. of medicines: Different data sources and counting methods for medicines authorized and on the market compared to prescription-only medicines. The latter were counted per brand name, excl. different pharmaceutical forms, dosages and pack sizes. Counted according to that method, the number of the medicines on the market were 4,299, thus resulting in a share of prescription-only medicines of the medicines on the market of around 37 %.

Pharmacies: High regional disparities in the density of pharmacies, concentration in larger cities

Generic market: No data on generic market shares available. 74 % of the medicines on the market are generics.

CZ:

Co-pays (out-patient): Percentage co-payment rates result from application of the reference price system

Changes in co-pays: In 2008 (year of the survey), a prescription fee was introduced, as well as further co-payments in health care (not medicines related) such as a co-payment for visit to a doctor

Pharmacies: High regional disparities in the density of pharmacies, concentration in larger cities

HU:

Co-pays (out-patient): Percentage co-payment rates of 0, 10 and 30 % for out-patient medicines for specific indications, and of 25, 45 and 75 % for all other reimbursable medicines in the out-patient sector

Co-pay exempt.: Exemptions from co-payment for socially disadvantaged persons. 100 % reimbursement for patients with long-term illness, however, the prescription fee had also to be paid by them

De-listings: A few de-listings, and changes in reimbursement categories (pharmaceutical groups were granted a lower percentage reimbursement rate) at large scale since 2006/2007

No. of medicines: Counted per brand name, excl. different pharmaceutical forms, dosages and pack sizes

Pharmacies: In addition to pharmacies, around 370 doctors are also allowed to dispense prescription-only medicines

Prescription monitoring: Limitations particularly related to off-patent medicines: guidelines for cost-effective prescribing are frequently not followed, no audit or feed-back of the social health insurance on generic prescribing by doctors

LV:

De-listings: De-listings were not explicitly reported but took place most probably: since 2005 major changes in the reimbursement list were reported, the principle of a limited number of medicines in the positive list was in place, and regular reimbursement reviews were performed. Medicines were included in the positive list for a period of 2 years, then the marketing authorization holder had to apply for re-inclusion.

No. of medicines: Number of medicines authorized not included since medicines are only counted per trade name

Pharmacies: Including 100 branch pharmacies, availability of pharmacies varied between towns and rural areas

PL:

OTC market: At manufacturer price level; refers to self-medication market

No. of medicines: Data on number of prescription-only medicines includes hospital-only medicines. In 2009, 3,380 medicines (counted including different pharmaceutical forms, dosages and pack sizes) were on the reimbursement list. Data on authorized medicines have to be interpreted with caution due to a different counting method: counted including different dosages, pharmaceutical forms and pack sizes.

Pharmacies: High regional disparities in the density of pharmacies, concentration in larger cities

RO:

Co-pay exempt.: Exemptions from co-payments are made for children, students and pregnant women as well as war veterans and disabled people on low income

De-listings: Quarterly updates of the reimbursement lists; no confirmed information of major de-listings (however number of medicines on the reimbursement list is, in general, rather low)

No. of medicines: 1,247 of the 5,791 prescription-only medicines were hospital-only medicines

Pharmacies: High regional disparities in the density of pharmacies, concentration in larger cities

Generic market: Estimate for the year 2007

SI:

Co-pays (out-patient): Co-payments covered by voluntary insurance that around 90 % of the population concluded

Co-pay exempt.: Exemptions from co-payments for medicines for prevention, for defined social groups (such as people under 18 years) or for the treatment of specific diseases (e.g. HIV/AIDS, diabetes)

De-listings: No major wave of de-listings reported; on the contrary, some new medicines could be included in the positive lists following changes in medicine prices due to change in pricing methodology

OTC market: At wholesale price level, refers to self-medication market

No. of medicines: Counted including different dosages, pharmaceutical forms and pack sizes

Pharmacies: Some OTC medicines were ‘pharmacy-only’, whereas others were permitted to be sold in ‘specialised stores’ outside pharmacies

SK:

No. of medicines: Counted including different pharmaceutical forms, dosages and pack sizes; homeopathic medicines excluded

Sources: Indicator 2: [7, 10, 8189]; Indicators 3-10: [49]; Indicator 11: [1416, 63, 83, 86, 88100]; Indicator 12: [68]; Indicators 13-16, 18, 20, 22-24, 26, 27: [9, 63, 101111]; Indicator 17: [112117]; Indicator 19: [5355]; Indicator 21: pharmacy data and information on disparities: [7, 9, 63, 82, 84, 86, 102, 103, 105, 111, 118, 119], population data: [49]; Indicator 25: RO: [12, 63]

To fill these indicators with country specific information, we drew on several data sources: Background information and data for the policy analysis were taken from the literature, predominantly grey literature. Search terms included ‘medicines’, ‘pharmaceuticals’, ‘equity’, ‘access’ and the name of the country. We focussed on the period around the EHIS survey, and three years before, and also literature with overall relevant information, e.g. related to the transition process, was included. Country reports about national pharmaceutical systems were a key source of information, in particular the PPRI (Pharmaceutical Pricing and Reimbursement Information) Pharma Profiles (available for five of the eight CEECs), the Health Systems in Transition reports by the European Observatory on Health Systems and Policies as well as country reports produced by the Organisation for Economic Co-operation and Development (OECD) and by the Austrian Public Health Institute (references listed below Table 5 and below the additional table [see Additional file 1]). Since some of the reports did not refer to the exact years of the survey, unpublished information from the PPRI network was used as supplementary source [47, 48]. Health and pharmaceutical expenditure data were retrieved from the Eurostat database [49].

Based on the information and data collected for the countries, we produced eight brief country posters [see Additional file 1]) and an overview table (Table 5). Following methods of policy analysis research [50], we used the country specific information and data to understand whether the identified socioeconomic (in)equalities in medicine use were impacted by the existing implementation degree of pharmaceutical policies. In the analysis, we interpreted ‘prescribed medicines’ as medicines funded, at least partially, by state (independently from their prescription status: both prescription-only and non-prescription medicines), whereas ‘non-prescribed medicines’ were considered as non-funded self-medication.

Results

As can be seen from the descriptive information for the individual countries (Table 4), the proportion of people reporting to take no medicine at all ranged between 23.1 % (Poland) and 39.9 % (Bulgaria) in all countries except for Romania, in which 55.8 % reported no medicine consumption. Romania also holds a special position regarding non-prescribed medicine use (7.4 %), which lied between 14.5 % (Bulgaria) and 21.4 % (Latvia) in the remaining countries. Prescribed medicines (only) were taken by around 21.0 % (Poland) and 31.8 % (Hungary) of the surveyed populations.

Socioeconomic (in)equalities in medicine use

Overall, in the regression analyses we find that whenever there is a socioeconomic gradient in medicine consumption to be observed in a country, it will in virtually every case favour the well-off, as shown in Figs. 1 and 2, respectively. The full results including all variables of the country-specific multinomial logistic regression analyses are presented in an additional table [see Additional file 2].
Fig. 1

Socioeconomic determinants of non-prescribed medicine use (versus no medicine use). Note: Graphical illustration of the full results [see Additional file 2] regarding non-prescribed medicine use for socioeconomic indicators; RRR = multivariate relative risk ratio, CI = 95 % confidence interval, grey line: not significant, red line: *** = significant at 0.01 %, ** = significant at 0.1 %, * = significant at 5 %. Results based on less than 20 observations: tightly dashed line and exact numbers omitted; results based on 20-49 observations: widely dashed line and exact numbers in italics. Source: Data provided by Eurostat [43]; calculation and presentation by the authors

Fig. 2

Socioeconomic determinants of prescribed medicine use (versus no medicine use). Note: Graphical illustration of the full results [see Additional file 2] regarding prescribed medicine use for socioeconomic indicators; RRR = multivariate relative risk ratio, CI = 95 % confidence interval, grey line: not significant, red line: *** = significant at 0.01 %, ** = significant at 0.1 %, * = significant at 5 %. Results based on less than 20 observations: tightly dashed line and exact numbers omitted; results based on 20-49 observations: widely dashed line and exact numbers in italics. Source: Data provided by Eurostat [43]; calculation and presentation by the authors

For non-prescribed medicine use (versus no medicine use), individuals with higher education and/or income had a higher chance of taking such medicines in seven of the eight CEECs: In Hungary, for instance, individuals in the highest education group were estimated to be four times more likely to take non-prescribed medicines (RRR = 4.32, 95 % CI = 2.98–6.27) while the relative risk for people in high income households increases by a factor of 1.66 (95 % CI = 1.20–2.31), respectively, given the other variables in the model are held constant. In Poland, on the other hand, the observed gradient was comparably less pronounced (highest education: RRR = 1.79, 95 % CI = 1.55–2.07; highest income: RRR = 1.38, 95 % CI = 1.20–1.58). Only in Bulgaria, neither education nor income played any role for non-prescribed medicine use, but the unemployed and disabled were attributed a lower consumption likelihood.

For prescribed medicines, in contrast, a statistically significant socioeconomic gradient could be identified in three economies only, i.e. Latvia, Poland, Romania: The increased likelihood of the highest education groups in these countries (Latvia: RRR = 2.09, 95 % CI = 1.59–2.73; Poland: RRR = 1.92, 95 % CI = 1.62–2.27; Romania: RRR = 1.73, 95 % CI = 1.19–2.54) is similar in magnitude, whereas in terms of the highest income groups, Latvia showed the most pronounced effect with an RRR close to 2 (RRR = 1.99, 95 % CI = 1.17–3.37).

Regarding the influence of demographic characteristics on medicine use [see Additional file 2], females were more likely to take (any) medicines in general, whereas advanced age was particularly relevant for prescribed pharmaceutical consumption, which is consistent with the literature [51]. Such prescribed medicine use was also highly determined by health characteristics, especially by longstanding illnesses.

Pharmaceutical policies

Details on the pharmaceutical policies in the CEECs are provided in Table 5 and an additional table [see Additional file 1]. Key features are as follows: Health and pharmaceutical expenditure, primarily funded by social health insurance contributions, was low compared to the Western European countries, and the share of health expenditure spent on medicines was rather high. In 2008, the EU-15 (i.e. 15 countries from Western, Northern and Southern Europe that joined the EU before 2004), spent an average of € 424 on medicines, corresponding to around 15.6 % of total health expenditure, whereas average figures equalled to € 180 and 24.4 % in the CEE-10 (the eight surveyed countries plus Estonia and Lithuania) [49, 52]. Within the eight surveyed CEECs, major discrepancies existed both in terms of the share of health expenditure spent on medicines and per capita pharmaceutical expenditure. Pharmaceutical expenditure was particularly low in Romania and Bulgaria, and also in Latvia and Poland. High increases in pharmaceutical expenditure were observed in Latvia (67 % during the three years before the survey), Romania (59 %) and Slovakia (57 %).

Private funding of health care, particularly of medicines (on average 54 % in the CEE-10), was high compared to the EU-15 with 38 % [49, 52], and there was considerable variation between the surveyed CEECs: Bulgaria had the highest share of private pharmaceutical spending (82 %), followed by Latvia (62 %) and Poland (61 %). Apart from Latvia and Poland, the share of private pharmaceutical expenditure had grown in the three years before the EHIS survey, particularly in the Czech Republic (57 %) and Hungary (38 %). Private pharmaceutical expenditure resulted from full out-of-pocket payments in the case of self-medication (non-prescribed, non-reimbursable medicines) and from co-payments of reimbursable medicines (i.e. medicines whose cost were, at least, partially covered by public payers). Market shares for non-prescription and self-medication medicines were higher than in Western European countries [5355]. Whereas no co-payments were applied in the in-patient sector in the CEECs, they were charged for out-patient medicines: Some countries had prescription fees, and all CEECs applied co-payments as percentage of the price for those medicines that displayed a lower therapeutic value. De-listings (i.e. exclusion of medicines from reimbursement) were reported for some CEECs, and they contributed to increased private pharmaceutical expenditure.

The number of authorized medicines and of medicines on the market was usually lower in the CEECs than in the other European countries (for data on Europe see [9]). Except for Slovenia, pharmacy density was higher in the surveyed CEECs than in Western European countries (an average of 3,360 and 5,780 inhabitants per pharmacy in the EU-10 and the EU-15 respectively in 2005, [9]). But major intra-country discrepancies resulting in low accessibility to pharmacies in the rural areas existed in some CEECs (Bulgaria, Czech Republic, Poland, Romania). The sale of (specific) OTC medicines outside pharmacies was permitted in all surveyed CEECs but Latvia, Poland and Slovakia.

Pricing and reimbursement is a national competence in the EU Member States under the condition that overall procedural provisions, as laid down in the EU Transparency Directive [56] such as deadlines for pricing and reimbursement decisions, for instance, are complied with. The CEECs adapted their pricing and reimbursement systems to the ‘acquis communitaire’ before acceding to the EU in 2004 and 2007, respectively [57]. Some CEECs (Czech Republic, Slovenia, Slovakia) undertook additional changes in the years before the EHIS survey, to further strengthen their institutional framework and/or to react to new challenges, such as high-cost medicines [see country profiles in Additional file 1]. At the time of the survey, Poland was fundamentally changing its pharmaceutical pricing and reimbursement system following a European Commission’s infringement procedure related to the EU Transparency Directive that also addressed long decision times. Related to demand-side measures, measures to encourage rational prescribing and use of medicines, including a promotion of generics’ uptake, were in place in the CEECs, but limited enforcement was reported.

Overall, the literature review identified several key challenges and shortcomings in the pharmaceutical systems of the investigated CEECs: In all countries but Slovenia high co-payments and out-of-pocket payments as well a limited enforcement of measures to promote a more rational prescribing and use of medicines (including encouraging generics uptake) were an issue. Underfunding of the pharmaceutical system was reported from Bulgaria, the Czech Republic, Latvia and Romania, and informal payments on a major scale were identified in Bulgaria, Hungary, Latvia, Poland, and Romania. Bulgaria, Poland and Romania were criticized for inefficiencies in procedures and policy coordination. Slovenia was the only country out of the eight CEECs for which the above-mentioned deficits were not reported. However, Slovenia was struggling with limited availability of medicines, and so did Bulgaria, Latvia and Romania.

Discussion

According to the analysis of EHIS survey data higher socioeconomic groups were found to have a comparably higher likelihood to use non-prescribed medicines in all eight surveyed CEECs and to use prescribed medicines in three countries (Latvia, Poland, and Romania). Our findings are partly in line with the very few studies on this issue: Geckova et al. [19] found a pro-rich socioeconomic gradient in non-prescribed medicine use and a pro-poor gradient in prescribed medicine use of adolescents in Slovakia in 1998. Gorecka et al. [20] identified geographic variation in the use of cardiovascular medicines level in the Czech Republic for the years 1997-2000 and attributed this to socioeconomic factors.

The eight CEECs have a social health insurance system in place that should, in principle, ensure coverage of the entire population and would allow linking health related social exclusion to the social policy context which is considered important [57]. The identified socioeconomic inequalities, particularly to prescribed medicine use, might result from deficits in the social insurance systems (issues of non-participation were reported for some CEECs (e.g. Bulgaria: [58]) and from lack of or inefficiencies in specific pharmaceutical policies. In the following, we explore potential factors that are able to contribute to the socioeconomic (in)equalities observed.

Sustainable funding

A major prerequisite for equitable access to medicines is sustainable funding [38, 42]. The comparably low pharmaceutical expenditure in Romania (€ 92 per capita) and Bulgaria (€ 110 per capita), but also Latvia and Poland (around € 135 per capita) points to possible underfunding. Insufficient investment in the field of medicines could be one explanation for the socioeconomic gradient in prescribed medicine use observed in Latvia, Poland and Romania. The more than 50 % increase in per capita pharmaceutical expenditure in Latvia, Romania, Slovakia and Bulgaria in the three years before the survey could be interpreted as countries’ efforts to invest into medicines, but it might also be an indicator that the countries spent more than their economic situation allowed and thus negatively impacted sustainability.

Affordability

Apart from Slovakia, all above mentioned countries had shares of private pharmaceutical expenditure of more than 55 % (Bulgaria as high as 82 %). The high patients’ contributions in Latvia, Romania and Poland are likely a major reason for the non-use of non-prescribed, privately funded medicines, but they might also explain the socioeconomic gradient in prescribed medicine use observed in these countries since patients were frequently required to co-pay for out-patient prescribed medicines. Further co-payments in health care could be additional barriers for patients with a lower socioeconomic background to visit a doctor and get a prescription: In the CEECs co-payments for attending a physician were, generally, in place, supplemented by ‘under-the-table payments’.

Overall economic developments are key drivers for policy-makers to curb publicly financed expenditure, even more with the global financial crisis. The Czech Republic, for instance, introduced co-payments to doctors’ visits and significantly increased the value-added tax (VAT) on medicines in 2008, and the share of the private pharmaceutical spending had grown by 57 % within three years. This could explain the socioeconomic gradient in non-prescribed use, and it might also lead to socioeconomic inequalities in prescribed medicine use though this was not confirmed by the EHIS results on the Czech Republic.

Within the CEECs Latvia, Hungary and Romania were particularly impacted by the global financial crisis [59]. Latvia responded through several cost-containment measures [60, 61]. This probably aggravated existing socioeconomic inequalities whose existence in health care (not medicines) had been identified as extensive in Latvia, also in comparison to the other Baltic countries [62]. In Romania, accessibility to prescribed, reimbursable medicines was strongly limited at the time of the survey due to high co-payments for out-patient medicines, and particularly due to the existence of pharmaceutical budgets for pharmacies: Once a monthly financial threshold had been reached a pharmacy was no longer permitted to fill prescriptions [63]. Thus, patients could not access prescribed medication unless they were willing, and able, to pay for it out-of-pocket. In 2007, after years of galloping growth rates in pharmaceutical expenditure, Hungary launched a reform to drastically curb public pharmaceutical expenditure. As a result, the share of private pharmaceutical expenditure (52 %) was rather high at the time of the survey (2009), and it had considerably grown (by 38 %) within the previous three years. This may cause concerns about limited affordability for lower socioeconomic groups [28]. Despite this austere cost-containment strategy no socioeconomic gradient in prescribed medicine use was found. A possible explanation is provided by a study on Hungary [64] according to which affordability of medicines was not negatively impacted by the cost-containment measures. It was argued that the policies were designed in a way to particularly target market participants (industry, distribution actors) rather than private households. Medicine prices were drastically cut, for instance, but this resulted in lower (percentage) co-payments for the patients.

Medicine prices should be affordable for those who pay for them – public payers and private households. There is lack of knowledge about prices of OTC medicines in general [65], and these prices have not been surveyed in the CEECs either. The socioeconomic gradient in non-prescribed medicine use that was found in all eight CEECs potentially implies that patients with a lower socioeconomic background considered medicines, which had to be paid out-of-pocket, as not affordable. In this context, attention should be paid to the value-added tax (VAT) on medicines. In many low- and middle-income countries world-wide, taxes and duties account for a considerable share of the medicine prices, thus contributing to increasing medicine prices up to an unaffordable level for the majority of the population [66]. In the CEECs, the only relevant tax related to the price of a medicine is the value-added tax. In the CEECs the VAT rates on medicines (amounting to 10 % or less) tended to be lower than the standard VAT rates [9, 67] and lower compared to the other EU countries. Nonetheless, they had risen in some countries (Czech Republic, Poland, Romania, Slovenia) from usually 5 % in 2004 [7] to their current rates of between 7.5 and 10 % since an increase in VAT rates was a common cost-containment policy [60]. While it might not appear high, it might still result in a medicine price of non-funded medicines that lower socioeconomic groups were not able, or not willing, to afford.

Little can be said about whether medicine prices were affordable for public payers in the CEECs given the lack of price studies in this region. One study [68] on 23 European countries that also included six CEECs showed that the pharmacy retail price (i.e. price at pharmacy) in the CEECs, except for Slovenia, was considerably lower than in the other European countries. However, unpublished analyses from the Pharma Price Information service of the Austrian Public Health Institute [69] indicated that at the time of the EHIS survey prices for specific new, high-cost medicines (e.g. cancer medicines, orphan medicines) were comparably high. These medicines were typically fully funded by public payers, and this ensured access to higher-priced medicines for the patients. This might explain why a socioeconomic gradient for prescribed medicine use was found in only three of the eight CEECs included in our study.

Availability and accessibility

Limited medicine availability was an issue in the CEECs. However, availability deficits were apparently not linked to the socioeconomic background of the patients, but targeted the whole population, as in the case of Slovenia and Latvia with a low number of medicines authorized and/or on the market. These countries are considered as ‘small markets’ that might not be sufficiently attractive for the pharmaceutical industry to be supplied [70]. Limited availability also resulted from medicine shortages, i.e. that medicines were not supplied in sufficient quantity to pharmacies and hospitals. This was reported to be a major problem in Romania [71]. Furthermore, we found indications of regional inequalities: Despite the generally high pharmacy density in the CEECs (except for Slovenia), major intra-country discrepancies existed in some CEECs (Bulgaria, Czech Republic, Poland, Romania) and resulted in low accessibility to pharmacies in the rural areas. Regarding the regional variation of OTC retailers, based on the experience from Western European countries about a clustering of these dispensaries in towns with already good accessibility [65], it can be assumed that the easier access to OTC medication was likely to primarily serve well-off people in the urban areas who were willing and able to pay for these non-funded medicines. This would contribute to regional inequalities within countries, but depending on the regional distribution of socioeconomic groups can also contribute to the socioeconomic gradient identified in this paper.

Rational selection and use of medicines

A major need for improving the pricing and reimbursement procedure was reported from Bulgaria and Romania whose processes were considered unclear and intransparent. The design of a pricing and reimbursement system is not simply a technical issue but it can considerably impact accessibility and affordability: Unclear and intransparent processes encourage corruption and an irrational selection of medicines into reimbursement, and this, eventually, can limit sustainable public funding. Cumbersome long processes, as observed for Poland, might result in delays in access to needed medicines for patients.

The CEECs have been criticized for not paying enough attention to promoting a rational use of medicines [57] that aims to ensure that patients receive the appropriate medicine at the right dose and at the time they require. The instruments to encourage rational prescribing existed in the CEECs but were apparently not fully enforced. Knowing this, however, we cannot draw conclusions as to whether the medicine use reported in the EHIS survey was appropriate, or not.

Given the limits in funding by both public payers and private households, promoting the uptake of generics is considered as a recommended policy option [72]. The CEECs already had comparably high generic market shares, since local generic industries played a strong role historically, even before the transition process [73], and the use of prescribed generics was promoted by tools such as INN prescribing and generic substitution. Still, high reluctance versus the quality of generics was reported [12]. Thus, awareness-raising campaigns to inform about generics could be a policy to reduce in the observed socioeconomic gradient in non-prescribed use, by motivating people to use lower priced generics.

Reducing socioeconomic inequalities through pharmaceutical policies?

Summarizing, the policy analysis identified some factors that help explain the observed socioeconomic gradient in non-prescribed medicine use in the eight CEECs and in prescribed medicine use in three of them. These include a high and increasing share of private funding such as co-payments for prescribed out-patient medicines and co-payments beyond medicines (e.g. for attending doctors to get a prescription), limited investment in medicines, and long and intransparent processes regarding the inclusion of medicines into reimbursement. Further deficits such as limited availability of medicines, irrational prescribing and regional variation in the accessibility of pharmacies and OTC retailers are additional factors limiting an equitable access but it could not be determined whether, or not, lower socioeconomic groups are particularly affected. The analysis of the pharmaceutical policies in Latvia, Poland and Romania provides an indications that actually these policies contribute to the observed socioeconomic inequalities in prescribed medicine use in these countries. While the three countries cannot be seen as a very homogenous cluster in terms of pharmaceutical policies, they share an accumulation of policies that do not adequately address inequalities in medicines use. Similarly, for Bulgaria, the policy framework observed would suggest a socioeconomic gradient in prescribed medicine use. This, however, was not confirmed based on the EHIS data. Also, it might have been possible that cost-containment policies would have had a negative impact on equitable access to medicines in the Czech Republic and in Hungary. In this respect, note that the only two other studies [19, 20] on socioeconomic determinants in medicine use in CEECs, focussing on adolescents in Slovakia and on cardiovascular medicines in Czech Regions did identify a socioeconomic gradient. Though pharmaceutical policies were apparently successful in reducing socioeconomic inequalities in medicine use in some CEECs, further factors were likely to have an impact. These might be factors at the individual level of the patients (e.g. adherence to medicines) as well as overall political and economic factors, such as the stability of a health care system, patients’ trust into it and the impact of the transition processes as observed in the CEECs in the 1990-ties. Finally, the non-existence of a socioeconomic gradient in medicine use can still imply any under-use, over-use or misuse of medicines in general, and overall limited availability and accessibility of medicines for the patients of that country.

Limitations

This study does not come without limitations. First, the quantitative analysis is based on self-reported information and hence comes with a risk of measurement errors. However, at the individual country level, where data was collected, several quality control steps were taken [74] and data comparability was also ensured by Eurostat, based e.g. on a standard questionnaire, conceptual guidelines and rationales as well as a standard translation protocol [75, 76]. Data was collected either via a stand-alone survey or as part of another (health or non-health) survey (i.e. national health interview survey, labour force survey, other household survey) based on face-to-face interviews, telephone interviews, self-administered questionnaires (or a combination of these means) [77]. Consistency and integrity checks on the national EHIS data were carried out by Eurostat, and quality reports were also disseminated [74]. Also, household surveys are an acknowledged standard tool in pharmaceutical consumption research according to the WHO [78]. Second, the EHIS household surveys goes beyond the regulatory definition of medicines in national legislations since ‘dietary supplements such as herbal medicines or vitamins’ were part of a question on medicine use (Table 3). To find out if this influences our results, sensitivity analyses were carried out excluding both the use of ‘vitamins, minerals or tonics’ or/and ‘some other type or medicine or supplement’, respectively. While the socioeconomic gradient was generally found to be less pronounced but still statistically significant in these sensitivity analyses, the qualitative conclusions regarding the socioeconomic gradient identified in the main analysis remained the same. Third, interviewees were asked about their use of ‘medicines prescribed or recommended by a doctor’ and ‘medicines not prescribed or recommended by a doctor’ (Table 3). Since we had no information about medicines recommended but not prescribed, we interpreted these two categories of ‘prescribed’ and ‘non-prescribed use’. We based this approach on the assumption that doctors tend to believe that patients expect a prescription [79]. Fourth, the analysis refers to different time periods between 2007 and 2009. We investigated the pharmaceutical policy framework at the time of the EHIS survey at national level, but for comparing national data (e.g. expenditure) versus a European or CEE average a specific year (2008) was taken as reference. This was, in some cases, not the year of the survey. Also, the survey comes with different national sample sizes. This seems especially relevant for Poland, where the analysis is based on a comparatively large study population, and thus even small differences between socioeconomic groups may become statistically significant. Finally, non-use of medicines can have numerous causes. We explored whether the pharmaceutical policy framework impacts, positively or negatively, on socioeconomic (in)equalities. However, as the wealth of literature on adherence rightly points out, there are several other reasons for not using medicines or stopping prescribed medication prematurely. To identify the relative importance of the various factors as co-determinants of the use or non-use of medicines, in-depth investigation for single countries is needed in future research.

Conclusion

Our study confirmed a socioeconomic gradient in medicine use in Central and Eastern Europe: Socioeconomic inequalities in non-prescribed medicine use were found in all eight surveyed CEECs, whereas for prescribed medicines this was only the case in three countries (Latvia, Poland, and Romania). The latter could be an indication that pharmaceutical policies, which typically refer to publicly (co-)funded medicines, had been able to successfully address inequalities in the other CEECs. Increasing public funding for medicines, and reductions in and exemptions from co-payments for vulnerable groups are likely appropriate strategies to ensure more equitable access to medicines. In addition, an increased generics uptake, a more rational selection of medicines into reimbursement and more rational prescribing can contribute to raise resources for public spending.

Our study confirms that pharmaceutical policies, and their implementation and enforcement, are important tools to address socioeconomic inequalities. As a more general conclusion, with improved knowledge about the extent and the character of socioeconomic inequalities in medicine consumption in CEECs as well as in other parts of the world, pharmaceutical policies could be used more effectively as a major tool to improve access to medicines across entire populations.

This study refers to the years 2007-2009. Meanwhile, the CEEC region has been severely affected by the global financial crisis. The economic situation has led to increasing poverty and growing financial pressure for large parts of the populations, which, not least, can lead to changes in medicine consumption. Further, public budgets came under increasing pressure, a pressure that also tightens financial scope for pharmaceutical policies. For future research, it is suggested to follow up this study by analysing pharmaceutical policies in the CEECs during the crisis and exploring their impact on medicine use.

Abbreviations

(T)HE: 

(total) health care expenditure

(T)PE: 

(total) pharmaceutical expenditure

BG: 

Bulgaria

CEE(Cs): 

Central and Eastern European (countries)

CI: 

Confidence interval

CZ: 

Czech Republic

D: 

Deductible

EHIS: 

European Health Interview Survey

EU: 

European Union

GPs: 

General practitioners

HC system: 

Health care system

HU: 

Hungary

INN: 

International Non-Proprietary Name

ISCED: 

International Standard Classification of Education

LV: 

Latvia

OECD: 

Organisation for Economic Co-operation and Development

OTC: 

Over-the-Counter medicines

PE: 

Pharmaceutical expenditure

PF: 

Prescription fee

PL: 

Poland

PP: 

Percentage points

PPRI: 

Pharmaceutical Pricing and Reimbursement Information

R: 

Reimbursement

RO: 

Romania

RRR: 

Relative-risk ratio

SI: 

Slovenia

SK: 

Slovakia

VAT: 

Value-added tax

WHO: 

World Health Organization

Declarations

Acknowledgements

The authors gratefully acknowledge the feedback by the participants of the first ATHEA conference (28 November 2014) in Vienna. The authors are also grateful to the members of the Pharmaceutical Pricing and Reimbursement Information (PPRI) network – representatives of competent authorities for pharmaceutical pricing and reimbursement and public payers from today 45, mainly European countries. Several of them were authors of the PPRI Pharma Profiles and information providers of unpublished data on Central and Eastern European countries, which were fed into the pharmaceutical policy analysis. Finally, the authors gratefully acknowledge the constructive comments made by three anonymous reviewers.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Health Economics, WHO Collaborating Centre for Pricing and Reimbursement Policies, Gesundheit Österreich GmbH (Austrian Public Health Institute)
(2)
Department of Socioeconomics, Institute for Social Policy, Vienna University of Economics and Business
(3)
Department of Health Economics, Centre for Public Health, Medical University of Vienna

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