Activity: Survey Research
Cookiemamma87
Buffel-TheInstitutionalFoundationsofMedicalization_ACross-nationalAnalysisofMentalHealthandUnemployment1.pdf
https://doi.org/10.1177/0022146517716232
Journal of Health and Social Behavior 2017, Vol. 58(3) 272 –290 © American Sociological Association 2017 DOI: 10.1177/0022146517716232 jhsb.sagepub.com
Original Article
As a recent meta-analysis of cross-sectional and longitudinal studies showed, the negative relation- ship between unemployment and mental health is well established (Paul and Moser 2009), but much less is known about how unemployment translates into mental healthcare (MHC) utilization. The few studies that have explored this relationship used the consumption of health services or medication as a proxy for mental health problems (Schmitz 2011; Virtanen et al. 2008). This is a significant limitation, given that MHC and antidepressant use among the unemployed are not exclusively need based (Buffel, Dereuddre, and Bracke 2015; Buffel, van de Straat, and Bracke 2015). Previous research confirms that the unemployed have higher MHC and medication use than expected based on their mental health sta- tus, which indicates the medicalization of unem- ployment (Buffel, Dereuddre, et al. 2015; Buffel, van de Straat, et al. 2015).
An even more striking limitation of existing research into unemployment, health, and MHC uti- lization is the lack of cross-national comparative research (Bambra and Eikemo 2009). It is crucial to understand whether, how, and why unemployment drives cross-national differences in MHC utiliza- tion given the current context of (a) high unemploy- ment rates and healthcare expenditures in many wealthy democracies and (b) austerity policy implementation in many European countries that has led to public expenditure cutbacks.
716232HSBXXX10.1177/0022146517716232Journal of Health and Social BehaviorBuffel et al. research-article2017
1Ghent University, Ghent, Belgium 2Harvard University, Cambridge, MA, USA
Corresponding Author: Veerle Buffel, Department of Sociology, Ghent University, Korte Meer 5. 9000, Ghent, Belgium. E-mail: [email protected]
The Institutional Foundations of Medicalization: A Cross- national Analysis of Mental Health and Unemployment
Veerle Buffel1, Jason Beckfield2, and Piet Bracke1
Abstract In this study, we question (1) whether the relationship between unemployment and mental healthcare use, controlling for mental health status, varies across European countries and (2) whether these differences are patterned by a combination of unemployment and healthcare generosity. We hypothesize that medicalization of unemployment is stronger in countries where a low level of unemployment generosity is combined with a high level of healthcare generosity. A subsample of 36,306 working-age respondents from rounds 64.4 (2005–2006) and 73.2 (2010) of the cross-national survey Eurobarometer was used. Country-specific logistic regression and multilevel analyses, controlling for public disability spending, changes in government spending, economic capacity, and unemployment rate, were performed. We find that unemployment is medicalized, at least to some degree, in the majority of the 24 nations surveyed. Moreover, the medicalization of unemployment varies substantially across countries, corresponding to the combination of the level of unemployment and of healthcare generosity.
Keywords employment status, healthcare generosity, medicalization, mental healthcare use, unemployment generosity
Buffel et al. 273
In this study, we investigate first whether the rela- tionship between unemployment and MHC use var- ies across European countries. Second, we explore whether these differences are patterned by a combi- nation of unemployment policies and healthcare characteristics, including disability benefits. Third, we analyze how levels of generosity in both policy domains shape the relationship between unemploy- ment and MHC use. Using data from before and after the start of the recent economic recession, which sparked austerity policies in many countries, allows us to shed light on the role austerity politics play in connecting unemployment and mental health (Beatty and Fothergill 2015).
BACKGrOUnD Medicalizing Unemployment and the Gaps in Current Medicalization Research Medicalization describes a process by which nonmedical (social) problems—such as unemploy- ment—are defined and/or treated as medical prob- lems (Conrad 1992). Hitherto, the lion’s share of medicalization research has taken a social construc- tivist approach, focusing on the construction of new medical categories and the subsequent expansion of medical jurisdiction (such as with hyperactivity, meno- pause, and alcoholism; Conrad 2005). Such work has developed an extensive conceptual and empirical lit- erature on the process and effects of medicalization, yet the range of institutional contexts considered is rela- tively limited. This limitation has truncated the range of institutional factors that have been considered as poten- tial foundations for medicalization.
Our theoretical contribution focuses on institu- tional foundations for medicalization, drawing insights from comparative political economy research. We thus address several limitations of existing research that has drawn on Conrad’s (1992) medicalization theory. First, we develop a novel technique for measuring medicalization, building on related cross-national comparative work on medicalization (Christiaens and Bracke 2014; Olafsdottir 2007). Our approach measures and interprets MHC utilization beyond its actual need, as an indicator of medicalization that can be com- pared across societies. In other words, if MHC is used more by the unemployed than the employed, and can only partly be ascribed to poorer mental health status, this indicates the medicalization of unemployment.
Medicalization of unemployment will be explored in one specific domain: the use of medical
care in the mental health field. We recognize that the medicalization of unemployment may take many manifestations, such as in national discourses on unemployment as a personal failure. Unemploy- ment may also be medicalized to allow reliance on disability benefits, which are more stable, less stig- matizing, and often more generous than unemploy- ment benefits (Beatty and Fothergill 2015). Labor market inactivity of those who rely on disability benefits for income support is sometimes known as “hidden unemployment”; arguments in the litera- ture over the boundary between the inactive and the disabled prevent precise identification of hidden- unemployment effects (Bratsberg, Fevang, and Roed 2010). Our analysis addresses this by (1) excluding respondents who report being out of the labor market due to disability and (2) controlling for public expenditures on disability benefits.
Second, we contribute to the developing cross- national comparative approach to understanding medicalization (Olafsdottir 2007, 2010). Our inno- vation, in view of the trend toward deinstitutional- ization in the European mental health sector (Hermans, de Witte, and Dom 2012), is to relax the assumption that physicians and hospitals are the (only) key actors in medicalization and recognize that multiple power actors—the pharmaceutical industry, policy makers, and patients or healthcare consumers—also contribute to the process (Clarke and Shim 2011; Conrad 2005).
Third, we advance the integration of medical- ization research across levels of analysis (Conrad 2005). We apply multilevel modeling to evaluate the hypothesis that medicalization, as a cultural transformation that varies across institutional set- tings, shapes the health behavior of individuals, such as consulting medical professionals.
In addition, multilevel analysis of data collected before and after the onset of economic recession and austerity policies allows us to address the rela- tionship between austerity and the medicalization of unemployment. Previous studies (Antonakakis and Collins 2015; Karanikolos et al. 2013; Kondilis et al. 2013; McKee et al. 2012) indicate that cutbacks in government expenditures impact employment and unemployment conditions, health outcomes, and the consumption of health services.
Unemployment Generosity and Its Relation to Medicalizing Unemployment We investigate whether the relationship between unemployment and medical care use is moderated by welfare generosity in unemployment and
274 Journal of Health and Social Behavior 58(3)
healthcare. Our measure of generosity, taken from Scruggs, Jahn, and Kuitto (2014) captures the level of benefit payments and the conditionality and strictness of entitlements that affect the level of cov- erage (e.g., targeted vs. universal benefits). Healthcare generosity is the degree to which health- care is delivered as a social right of citizenship rather than as something to be purchased in the medical marketplace. In line with the work of Scruggs (2014), who builds on work on decommod- ification by Esping-Andersen (1990), we use the term generosity to engage with institutionalist research on welfare-state effects on population health (Beckfield et al. 2015), highlight improve- ments in measurement since Esping-Anderson’s pioneering work, and emphasize its use in more European countries with recent data.
Countries exemplifying a low level of unem- ployment generosity include the United Kingdom (Bambra 2005a) and several eastern European states. The social protection systems for the unem- ployed in these countries are relatively weak, with less generous income replacement rates and strict entitlement criteria, which may increase financial stress and lessen the feeling of self-control (Strandh 2001). For example, the maximum duration of a standard unemployment benefit in the United Kingdom, Estonia, Lithuania, Slovakia, and Czech Republic is only 26 weeks. Thereafter, the unem- ployed in the United Kingdom rely on means-tested benefits, which are known to be highly stigmatizing (Rodriguez, Frongillo, and Chandra 2001). The unemployed are often considered to be responsible for their situation (Bambra and Eikemo 2009), which can stimulate self-blame, perceptions of fail- ure, and social exclusion. All of these factors may be associated with the medicalization of unemploy- ment. As a result, we can expect that unemployment will be more strongly related to MHC use in coun- tries with low levels of unemployment generosity.
Although a consistent negative relationship between unemployment and well-being, health, and mental health has been observed in previous research (Bambra and Eikemo 2009; Strandh 2001; Wulfgramm 2014), in countries with high levels of unemployment generosity (such as some Scandinavian countries; Esping-Andersen 1990), the medicalization of unem- ployment may be weaker. There is strong protection for the unemployed through highly interventionist governments, which value universalism and social equality (Bambra and Eikemo 2009). In the more gen- erous welfare states, the level of benefits is relatively high and benefits are universalist (rather than means tested), which may result in lower stigma. As a
consequence, unemployment may be less stressful and related to reduced feelings of self-blame and per- sonal failure. Unemployment may be considered more of a social problem, which requires a structural solution.
Healthcare Generosity and Its Relation to Medicalizing Unemployment Institutional conditions and welfare policies may also affect the access to and availability of the healthcare resources needed to make the medical- ization of unemployment possible. Therefore, in addition to the role of unemployment generosity, we also explore the role of healthcare generosity.
Although healthcare is a key dimension of all modern welfare states, it is relatively absent from major welfare-state theories (Bambra 2005a). In response to this limitation, Bambra (2005a, 2005b) introduced the concept of healthcare decommodifi- cation. It accounts for the provision of care, the degree to which this provision is independent from the market, and the extent to which an individual’s access is dependent on his or her market position. The indicators included in our healthcare generos- ity measurement assess the financing, provision, and coverage of the private sector and are, there- fore, useful indicators of the varied role of the mar- ket in a healthcare system. The larger the size of the private health sector, in terms of expenditure and consumption, the larger the role of the market and, therefore, the lower the degree of healthcare gener- osity (Bambra 2005b). For example, the United Kingdom has a relatively high level of healthcare generosity because it has a coverage level of 100% combined with only 3.7% private hospital beds (of the total bed stock) and 1.72% private health expen- ditures (of the gross domestic product [GDP]). In contrast, Belgium (99.0% covered) and Germany (89.1% covered) have, respectively, 61.8% and 59.3% private hospital beds and 2.54% and 2.74% private health expenditures and therefore relatively low levels of healthcare generosity. We can expect that in countries with high levels of healthcare gen- erosity, the unemployed will be less constrained in their medical care use.
Based on this theoretical framework, we hypoth- esize that a combination of low unemployment gener- osity and high healthcare generosity—with the United Kingdom as an illustration—will trigger the medicalization of unemployment. In this situation, the unemployed will possibly perceive greater stig- matization of, and individual responsibility for, unemployment. These correlates of mental illness,
Buffel et al. 275
then, may facilitate increased MHC in generous healthcare systems.
Figure 1 shows the countries according to their combined unemployment generosity and healthcare generosity. Typical countries characterized by the inverse combination of above are Belgium and Bulgaria, the first especially regarding its high unemployment generosity level, and the later con- cerning its low healthcare generosity level. Greece is a typical country with low levels on both gener- osity measurements.
DAtA AnD MEtHODS The Eurobarometer Survey This study used data from the Eurobarometer rounds 64.4 (2005–2006) and 73.2 (2010),1 which included information about a general population ages 15 and above in more than 20 European Union member states. To our knowledge, the Eurobarometer is the
only cross-national survey that combines (1) nation- ally representative samples, (2) measurements of mental health status, (3) measurements of MHC uti- lization, (4) employment status, and (5) broad cross- national institutional variation.
The basic sample design used in all countries comprised a multistage, random (probability) sam- ple of individuals within households within an area. Interviews were conducted face-to-face in the national language. To ensure nationally representa- tive samples, poststratification weights were applied to restore specific town size, age, and gen- der distributions for the general population in each country, using the most recent census data. For our purposes, it was appropriate for small countries, such as Belgium, to be weighted the same as large countries, such as Germany (Frohlich et al. 2001). Unweighted analyses yielded more valid estima- tions. We did not weight the samples according to population size, as the population sizes of the sam- pled countries were highly heterogeneous and
Figure 1. Countries Positioned in a two-dimensional Graph of Unemployment Generosity by Healthcare Generosity Note: Unemployment generosity scores (including the replacement rate, qualifying period, duration of benefits payments, waiting period, and level of coverage) are based on the Comparative Welfare Entitlements Dataset 2 (Scruggs, Jahn, and Kuitto 2014) and calculations using Scruggs’ (2014) formula. Healthcare generosity scores (including level of coverage, private health expenditures, private hospital beds, and household out-of-pocket payments) are based on data from Eurostat (2015), the Organisation for Economic Co-operation and Development (2012), and the World Health Organization (2005, 2011), and calculations using Scruggs’ (2014) formula.
276 Journal of Health and Social Behavior 58(3)
because we were interested in the institutional foundations of medicalization.
We limited our subsample to respondents of working age (20–65 years old; N = 37,477 respon- dents). Missingness was no more than approxi- mately 2% for all variables. We omitted 1,171 cases with missing values from the sample. The final sample consisted of 24 European countries and contained information for 36,306 respondents. Descriptive statistics and the sample size per coun- try are provided in Appendix A, in the online ver- sion of this article.
Measures We constructed two dichotomous outcome vari- ables for MHC use: contacting a general practitio- ner (GP) and/or contacting a psychiatrist (each item coded 1 = yes, 0 = no).
The main independent variables were employ- ment status and mental health status. Employment status contained three categories: employed (refer- ence), unemployed, and nonemployed. Mental health was measured with the five-item version of the Mental Health Inventory (MHI-5), a subscale of the SF-36 Version 2 (Ware and Sherbourne 1992). The scale measured depression and anxiety-related complaints and ranged from 1 (good mental health) to 5 (poor mental health). If one or two items were missing, mean substitution was applied. The internal reliability of the MHI-5 scale was good (Cronbach’s alpha = .803).
Age was measured in years. Period was a cate- gorical variable: 2005–2006 (reference) and 2010. To examine within-country differences in the provi- sion of healthcare services, we controlled for the degree of urbanization using the following catego- ries:2 large town (reference), rural area or village, and small or medium-sized town. This could be considered as a proxy for supply, because the avail- ability of medical professionals varies between urban and rural areas (Saxena et al. 2007). We also controlled for marital status (married [reference], divorced, widowed, or single) and education level. Respondents were asked at what age they finished full-time education, and the European Commission provided a standard categorization for the answers: ages up to 15 (reference), 16 to 19, and 20 and above. This corresponds approximately to primary, secondary, and tertiary education.
At the country level, our central variables were the level of unemployment generosity and health- care generosity. To construct the unemployment generosity measurement, we relied on Scruggs’
updated “unemployment generosity measure” (Scruggs and Allan 2006),3 which was an adaptation of Esping-Andersen’s (1990) original measurement. Scruggs used z scores to combine information on five indicators into a single measurement that facili- tates interpretation. The five indicators were the level of benefits paid to the unemployed (replace- ment rate), the qualifying period, duration of bene- fits payments, waiting period before entitlement, and percentage of the working-age population cov- ered by the program (see online Appendix B). More information and this data set, the Comparative Welfare Entitlements Dataset (CWED 2), are avail- able at http://cwed2.org/.
For comparability with Scruggs’s measure- ment, we adapted Bambra’s (2005a, 2005b) mea- surement of the decommodification of healthcare for the construction of our healthcare generosity measurement, using the same z score technique to combine the following indicators:4 Private health expenditure as a percentage of GDP, private hospi- tal beds as a percentage of total bed stock, the cov- erage of the population by the public healthcare system, and household out-of-pocket (OOP) pay- ments as a percentage of the total health expendi- ture.5 The majority of information is available at http://data.euro.who.int/hfadb/; for coverage per- centages, we used data from the Organisation for Economic Co-operation and Development (OECD; 2012).
For both country variables, we used as much data as possible from the periods 2004–2006 and 2009–2010. We used data for the year of the inter- view and the preceding year because respondents were asked whether they had sought professional help in the year before the interview and because of an expected time lag. This also resulted in the best model fit. Both generosity measurements were interval-level variables, which were grand-mean centered.
We included additional macrolevel control vari- ables to guard against residual confounding. The effects of the nature of welfare policies concerning mental health and the MHC use of the unemployed may partly depend on the condition of the country’s labor market and the general economic capacity (GDP per capita) of a country. A short period of income support for unemployed people, for example, may be less associated with high levels of anxiety and insecurity in countries where the unemployment level is low and unemployment tends to be of short duration (Gallie, Kostova, and Kuchar 2001). We can also expect that in countries with low unemploy- ment, it will be less randomly distributed and thus
Buffel et al. 277
more frequently considered a direct or indirect con- sequence of health selection. Unemployment will be more stigmatizing, different from the norm, and treated as an individualized problem (Clark 2003), which can be a trigger for medicalization. Therefore, GDP per capita (Model 3) and unemployment rates (Model 4) were included (information derived from Eurostat 2015, Table 1).
Disability generosity was included in the mod- els as an additional control variable to take partly into account the possibility of “hidden unemploy- ment” via relying on disability benefits. Generosity in terms of disability benefits is often measured by the level of public spending on disability (Börsch- Supan 2007). This information was available from
Eurostat (2015). Although it was not the objective of this study, we could not ignore the current debate about the claim that there is a movement from a passive toward an active welfare state in several European countries (Bonoli 2010). Central to this are active labor market programs (ALMP; Knotz 2012; Strandh 2001). The level of expenditure on ALMP is often used as an indicator for the activa- tion effort of a country (Knotz 2012). While this is an approximate measurement, we included expen- diture on ALMP (as a percentage of GDP) as a con- trol variable in the multilevel analysis (data retrieved from OECD 2015).
Based on the economic crisis literature (Antonakakis and Collins 2015; Karanikolos et al. 2013; Stuckler
Table 1. Country Scores on the Generosity Measurement of Unemployment and Healthcare, and Countries’ national Unemployment rate and GDP.
Group Categorization Country Unemployment
Generositya Healthcare Generosityb
Unemployment ratec
GDP per Capitac
1 High unemployment generosity and high healthcare generosity
Austria 1.42 12.04 5.5 31,450 Denmark 1.60 15.24 5.4 39,400 netherlands 11.94 14.12 5.3 33,100 Sweden 1.08 13.52 7.8 32,250 France 11.24 13.50 9.0 28,300 Ireland 1.64 13.39 7.0 37,500
2 High unemployment generosity and low healthcare generosity
Belgium 13.72 9.45 8.3 30,300 Germany 1.30 9.89 9.7 28,000 Portugal 1.50 9.31 9.1 15,250 Spain 11.04 1.70 12.7 21,900 Latvia 11.35 8.30 13.1 7,200 Bulgaria 11.55 7.46 9.7 3,800
3 Low unemployment generosity and high healthcare generosity
United Kingdom 8.70 15.77 5.7 28,350 Slovenia 9.51 14.79 6.2 15,850 Czech republic 9.73 15.95 7.6 11,900 Finland 9.22 13.10 8.5 31,150 Estonia 8.37 12.94 1.5 9,350 romania 5.68 14.45 7.2 4,750
4 Low unemployment generosity and low healthcare generosity
Greece 7.38 7.39 1.1 19,050 Hungary 7.59 11.62 7.8 8,950 Italy 5.64 11.72 7.8 24,850 Lithuania 7.51 11.62 11.0 7,350 Poland 6.79 11.08 15.0 7,250 Slovakia 7.50 11.49 15.6 9,350
Note: GDP = gross domestic product. aData from the Comparative Welfare Entitlements Dataset 2 (Scruggs, Jahn, and Kuitto 2014) and calculations using Scruggs’ (2014) formula. bData from Eurostat (2015), the Organisation for Economic Co-operation and Development (2012), the World Health Organization (2005, 2011), and calculations using Scruggs’ (2014) formula. cData from Eurostat (2015).
278 Journal of Health and Social Behavior 58(3)
et al. 2009), cuts in government expenditure on domains such as health, unemployment, ALMP, family, and housing were used as proxy of fiscal austerity. Data for general government final con- sumption expenditure as a percentage of GDP were collected from the World Bank’s (2015) World Development Indicators database. In line with Antonakakis and Collins’ (2015) work, we divided general government final consumption expenditure by real GDP, as the expenditure measurement might have been biased during a period when nominal GDP was falling. Because of a time lag of at least one year, information from 2005 for wave 64.4 (2005–2006) and 2009 for wave 73.2 (2010) was used to calculate the mean government expenditure over the periods per country. The operationalization of the change in government expenditure is explained in the next section, as it relates to the sta- tistical procedures used. Country scores on the macro variables are presented in online Appendix C.
Estimation Our analyses consisted of two parts. The first part focused on country-specific differences in the MHC use of the unemployed and how these differences were patterned by unemployment and healthcare generosity levels. In the second part, we looked for general trends regarding the impact of the level of unemployment generosity and healthcare generos- ity on the relationship between employment status and MHC use, while taking several key institutional and macroeconomic factors into account.
In the first part, on the basis of country-specific logistic regressions, we tested the relationship between employment status and MHC use and to what extent this association could be ascribed to mental health. To compare the MHC use of the unemployed with that of the employed between countries, predicted probabilities (PPs) for the unemployed and employed were calculated based on the odds ratios (ORs) resulting from the logistic regression analyses.6 The differences (PPunemployed – PPemployed) between both PPs are presented in Table 2. PPs are preferable to reporting differences in logistic regression coefficients because PPs do not require the assumption that the error variance is identical across countries. First, the PPs were based on the models controlled for age, gender, marital status, education, and period. Second, they were based on the adjusted models including mental health. The results based on Model 2 are presented in a bar chart (Figure 2) and related to country scores on the generosity measurement of unemployment and of healthcare. We categorized the countries into
groups depending on whether a country’s score was above or below the median score of all countries included in the study on the unemployment generos- ity and the healthcare generosity measure (see Figure 1). This resulted in four groups of countries with a specific combination of scores: a relatively high level on both generosity measures, a relatively low level on both measures, and a relatively high score on one measure and a relatively low score on the other (and visa versa). The four groups of coun- tries do not represent a typology, nor are they the results of a cluster analysis.
In the second part, to test whether unemploy- ment and healthcare generosity have a significant moderating effect on the relationship between employment status and MHC use, we performed logistic multilevel analyses that included cross- level interaction effects of employment status and the two generosity measurements. Multilevel anal- ysis enabled us to take the clustering of our data in periods, as well as countries, into account. However, two periods were not enough to use period as a separate level, and thus—like most repeated cross-sectional surveys––we faced a prob- lem of obtaining an adequate number of higher- level units at the period level. Given the cross-national nature of the Eurobarometer, there was a solution to this lack of sufficient repeated waves, as has been described by Fairbrother (2014): considering the clustering of different waves clus- tered within countries. National-level time-series cross-sectional data enable simultaneously model- ing cross-sectional effects that explain between- country differences and longitudinal effects that explain within-country differences over time.
In sum, respondents, as units of the individual level (level 1), were nested within country-years ranging from 2005–2006 to 2010 at the period level (level 2), which were in turn nested within countries (level 3; see online Appendix D). To control for aus- terity measurements, we estimated an additional model that took the average level of government expenditure into account as well as the change in expenditures. To include longitudinal effects at the period level and cross-sectional effects of govern- ment expenditure at the country level in the same model, the longitudinal effects were group-mean centered, and the cross-sectional effects grand-mean centered, as described by Fairbrother (2014).
Seven models per outcome variable were esti- mated: Model 1 included only the individual vari- ables, without controlling for mental health; in Model 2, mental health was added; Model 3 contained the moderation effect of unemployment and healthcare generosity, controlled for GDP; and Models 4, 5, and
Buffel et al. 279
6, each included one additional macro control vari- able—respectively, unemployment rate, public dis- ability spending, and ALMP expenditures. We estimated austerity effects in Model 7.
All models were estimated in the statistical soft- ware package MLwiN using Markov chain Monte Carlo estimation procedures, as this approach has been shown to be robust, particularly when includ- ing cross-level interactions (Stegmueller 2013).
rESULtS The descriptive results, shown in online Appendix A, confirm that the unemployed have significantly
worse mental health than the employed in every European country (tested by one-way ANOVA). Linked to the poorer mental health of the unem- ployed, the percentage of them who contact a GP or psychiatrist for mental health problems is higher than for the employed in most European countries. However, these differences are not significant for several countries, especially with regard to GP con- sultations (tested by chi-square).
The country scores for the unemployment and healthcare generosity measurements are shown in Table 1. We categorize the countries into four groups to simplify the discussion of results. In the first group, the countries score relatively high
Table 2. Mental Health and Mental Healthcare Use by the Unemployed Compared with the Employed per Country (Eurobarometer 2005–2006 and 2010, N = 36,306).
Group Categorization Country
Mental Health
General Practitioner Consultations
Psychiatrist Consultations
ba Model 1b Model 2c Model 1b Model 2c
1 High unemployment generosity and high healthcare generosity
Austria −.471*** .122*** .028 .032** .007 Denmark −.258*** .113*** .077* .054*** .031** netherlands −.354*** .085** .044† .039* .012 Sweden −.269*** −.028 −.052 −.011 −.012 France −.173* .016 −.010 .023* .005 Ireland −.214*** .036 −.005 .006† .002
2 High unemployment generosity and low healthcare generosity
Belgium −.169** .020 .001 .006 −.001 Germany −.253*** .051*** .011 .061*** .024*** Portugal −.322*** .021 −.014 .026* .007 Spain −.170** .046** .025 −.007 −.005* Latvia −.225*** .020 .000 .006** .001 Bulgaria −.256*** .019† .009 −.004 −.002
3 Low unemployment generosity and high healthcare generosity
United Kingdom −.341*** .135*** .071** .015* .008†
Slovenia −.106† .070*** .056** .017 .003 Czech republic −.303*** .031† .004 .012† .001 Finland −.135* .017 .000 .044** .026* Estonia −.371*** .056* .013 .028** .008* romania −.343*** .023 −.007 .011† .003
4 Low unemployment generosity and low healthcare generosity
Greece −.324*** .005 −.008 .002 −.001 Hungary −.197** −.010 −.019 .027* .006 Italy −.118† .030 .016 −.003 −.002 Lithuania −.166** .038* .020 .017* .005 Poland −.218*** −.003 −.015 .004 .001 Slovakia −.169** .014 −.001 .033** .012*
athe estimated coefficient of the unemployed (reference = employed) on mental health (Mental Health Inventory–5), while controlling for age, gender, education, period, and marital status. bthe difference between the predicted probabilities of the unemployed and the employed, while controlling for age, gender, marital status, education, and period. cAlso adjusted for mental health status. †p < .100, *p < .05, **p < .01, ***p < .001 (two tailed).
280 Journal of Health and Social Behavior 58(3)
(above the median) for both dimensions of generos- ity: Austria, Denmark, the Netherlands, Sweden, France, and Ireland. The second group contains countries with relatively high levels of unemploy- ment generosity but low levels of healthcare gener- osity: Belgium Germany, Portugal, Spain, Latvia, and Bulgaria.
The third group has low levels of unemploy- ment generosity combined with relatively high lev- els of healthcare generosity: the United Kingdom, Slovenia, the Czech Republic, Finland, Estonia, and Romania. The fourth group contains countries scoring low for both generosity measurements and contains only southern and eastern European coun- tries: Greece, Hungary, Italy, Lithuania, Poland, and Slovakia. As can be seen in the Table 1, within the groups there are also important differences.
The differences between the PPs of the unem- ployed versus the employed for MHC utilization are shown in Table 2. With regard to GP consulta- tions, in the majority of the countries, the higher likelihood of the unemployed contacting a GP—as
observed in Model 1—is no longer significant when adding mental health status to the model (Model 2). The higher PP of MHC utilization of the unemployed remains significant (controlling for mental health) only in Denmark and the Netherlands (group 1) and in the United Kingdom and Slovenia (group 3).
In more than half of the countries—and dis- persed over the four groups—the unemployed are more likely to consult psychiatrists than the employed (Model 1, Table 2). However, when also controlling for reported mental health (Model 2), the higher probability of psychiatrist consultations for the unemployed remains significant only in Denmark (group 1), Germany (group 2), and Slovakia (group 4). In Spain, characterized by mod- erate unemployment generosity and low healthcare generosity (group 2), the unemployed actually have a significantly lower probability of contacting a psychiatrist. As expected, group 3 has the most countries (the United Kingdom, Finland, and Slovenia) with a significantly higher PP among the
Figure 2. Country-specific Differences in the Predicted Probabilities for Mental Healthcare Use for the Unemployed and the Employed, Adjusted for Individuals’ Mental Health and Other Control Variables. Note: the predicted probabilities are based on the results of the country-specific logistic regression analyses. A subsample of 36,306 working-age respondents from rounds 64.4 (2005–2006) and 73.2 (2010) of the Eurobarometer cross-national survey was used. †p < .10, *p < .05, **p < .01, ***p < .001 (two tailed).
Buffel et al. 281
unemployed, after controlling for reported mental health.
Regarding the multilevel results (Table 3), the likelihood of contacting a GP for mental health problems (OR = 1.491; 95% confidence interval [CI] [1.328, 1.679]) and contacting a psychiatrist (OR = 2.557; 95% CI [2.002, 3.232]) is signifi- cantly higher among the unemployed than the employed (Model 1). After controlling for mental health status, the higher likelihood of contacting a GP for the unemployed (OR = 1.103; 95% CI [.976, 1.244]) is no longer significant (Model 2). For psy- chiatrist consultations, this is only partly the case. However, the unemployed are still more likely than the employed to contact a psychiatrist (OR = 2.557; 95% CI [2.002, 3.232]), controlling for mental health.
Despite the fact that we do not find evidence for medicalization of unemployment via GP consulta- tions in Model 2, the Model 3 results show that in countries with a high level of healthcare generosity (ORinteration term = 1.083; 95% CI [1.031, 1.139]) and/ or unemployment generosity (OR = 1.071; 95% CI [1.007, 1.141]), the unemployed have a higher like- lihood of contacting a GP, controlling for mental health.7 However, additional analyses performed separately for the countries with low and high unemployment rates (results not shown) show that the moderating effect of unemployment generosity is significant only in countries with a lower unem- ployment rate (OR = 1.093; 95% CI [1.007, 1.187]). Healthcare generosity also has a significant effect on GP consultations for mental health problems among the employed (ORemployed = 1.095; 95% CI [1.014, 1.176]), but this positive effect was signifi- cantly lower than that on the MHC use among the unemployed (ORunemployed = 1.095 × 1.083 = 1.186). Contacting a GP, regardless of mental health status, among the employed (OR = 1.095; 95% CI [1.014, 1.176]) is thus also more likely in countries with a higher level of healthcare generosity. The higher likelihood of contacting a psychiatrist among the unemployed compared to the employed is more pronounced in countries with high healthcare gen- erosity (ORinteraction term = 1.081; 95% CI [1.011, 1.156]; see Model 3 of Table 4). Healthcare gener- osity has no significant effect on the psychiatrist consultations of the employed.
These interaction effects with unemployment and the generosity measurements remain similar when the other control variables—unemployment rate, public disability spending, and ALMP spending— are taken into account, respectively, in Models 4, 5, and 6. There were no significant associations
between these macro variables and either GP con- tacts or psychiatrist consultations. In Model 7, where we control for possible austerity effects by including the context and change variable of gov- ernment expenditures, the main results remain sig- nificant. However, a positive change in government expenditures has a positive effect on GP consulta- tions for emotional and psychosocial problems, net of actual mental health status and the average level of government expenditures (OR = 1.027; 95% CI [1.003, 1.052]). This means that in austerity con- texts, cutbacks in government expenditures decrease the likelihood of contacting a GP, control- ling for mental health. We also tested the interaction effect of change in government expenditures and employment status (results not shown), and we found that for psychiatrist consultations, there is an effect only among the employed. In countries with a cut in government expenditures, the likelihood of contacting a psychiatrist has decreased among the employed, irrespective of their mental health status (ORemployed = 1.075; 95% CI [1.011, 1.142]). There is also a period effect on specialized MHC use: the likelihood of contacting a psychiatrist is signifi- cantly lower in 2010 than in 2005–2006, which can also be interpreted as an association that is consis- tent with an austerity or recession effect (OR = .792; 95% CI [.657, .958]).
DISCUSSIOn Before discussing the main findings, we note the key limitations of this study. The first limitation is the divergent timing between measurements of unem- ployment MHC utilization. Employment status indi- cates the situation of respondents at the time of the interview. However, the items concerning care-seek- ing refer to the preceding 12 months, and the period of reference for experiencing depressive feelings is the preceding month. As a result, we cannot use time priority to bolster causal inferences. Accordingly, we addressed threats to causal inference in several other ways. Reverse causality is a concern if individuals with poorer health are more likely to be unemployed. By separating respondents who were inactive due to illness or disability from those who were unem- ployed, we reduce possible reverse causality. Models were also reestimated separately for countries with high and low unemployment rates because we expected selection effects to be more likely in coun- tries with low unemployment rates, as unemploy- ment is less randomly dispersed. However, we did not find evidence consistent with this selection sce- nario. Nevertheless, with the available data, we are
282
T a b
le 3
. Lo
gi st
ic M
ul ti le
ve l A
na ly
si s
o n
G en
er al
P ra
ct it io
ne r
C o ns
ul ta
ti o ns
( Eu
ro ba
ro m
et er
2 00
5– 20
06 a
nd 2
01 0,
N =
3 6,
30 6)
.
V ar
ia bl
e
M o de
l 1 M
o de
l 2 M
o de
l 3 M
o de
l 4 M
o de
l 5 M
o de
l 6 M
o de
l 7
O r
[ C
I] O
r [
C I]
O r
[ C
I] O
r [
C I]
O r
[ C
I] O
r [
C I]
O r
[ C
I]
C o ns
ta nt
.0 81
** *
[. 06
6, .0
99 ]
.0 58
** *
[. 09
5, .0
95 ]
.0 73
** *
[. 05
7, .0
89 ]
.0 72
** *
[. 05
7, .0
90 ]
.0 72
** *
[. 05
8, .0
91 ]
.0 72
** *
[. 05
7, .0
89 ]
.0 67
** *
[. 05
0, .0
89 ]
Em pl
o ym
en t
st at
us (
re fe
re nc
e em
pl o ye
d)
U ne
m pl
o ye
d 1.
49 1*
** [1
.3 28
, 1 .6
79 ]
.9 76
** *
[1 .2
44 , 1
.2 44
] 1.
10 5*
** [. 97
7, 1
.2 47
] 1.
10 3
[. 97
9, 1
.2 44
] 1.
10 5
[. 97
6, 1
.2 47
] 1.
10 7*
** [. 97
9, 1
.2 49
] 1.
10 4*
** [. 97
7, 1
.2 51
]
n o ne
m pl
o ye
d 1.
39 4*
** [1
.2 87
, 1 .5
14 ]
1. 12
2* **
[1 .3
28 , 1
.3 28
] 1.
22 1*
** [1
.1 24
, 1 .3
28 ]
1. 22
2* **
[1 .1
21 , 1
.3 29
] 1.
22 2*
** [1
.1 23
,1 .3
30 ]
1. 22
4* **
[1 .1
24 , 1
.3 32
] 1.
22 1*
** [1
.1 21
, 1 .3
30 ]
A ge
1. 01
4* **
[1 .0
11 , 1
.0 17
] 1.
00 8*
** [1
.0 14
, 1 .0
15 ]
1. 01
1* **
[1 .0
08 , 1
.0 14
] 1.
01 1*
** [1
.0 08
, 1 .0
14 ]
1. 01
1* **
[1 .0
08 , 1
.0 14
] 1.
01 1*
** [1
.0 08
, 1 .0
14 ]
1. 01
1* **
[1 .0
08 , 1
.0 14
] W
o m
en (
re fe
re nc
e m
en )
1. 40
7* **
[1 .3
08 , 1
.5 12
] 1.
17 8*
** [1
.3 72
, 1 .3
72 ]
1. 27
3* **
[1 .1
79 , 1
.3 75
] 1.
27 3*
** [1
.1 80
, 1 .3
75 ]
1. 27
2* **
[1 .1
80 , 1
.3 73
] 1.
27 3*
** [1
.1 80
, 1 .3
73 ]
1. 27
4* **
[1 .1
82 , 1
.3 76
] M
en ta
l h ea
lt h
.3 50
** *
[. 38
5, .3
85 ]
.3 66
** *
[. 34
9, .3
84 ]
.3 66
** *
[. 34
9, .3
84 ]
.3 66
** *
[. 34
9, .3
84 ]
.3 66
** *
[. 34
9, .3
84 ]
.3 66
** *
[. 34
9, .3
84 ]
Pe ri
o d:
2 01
0 (r
ef er
en ce
2 00
5) 1.
18 4
[. 99
7, 1
.3 99
] 1.
11 7
[. 93
9, 1
.3 17
] 1.
10 8
[. 91
7, 1
.3 33
] 1.
11 4
[. 94
3, 1
.3 34
] 1.
11 7
[. 91
6, 1
.3 26
] 1.
12 1
[. 93
1, 1
.3 36
] 1.
20 7*
* [1
.0 00
, 1 .4
55 ]
U ne
m pl
o ym
en t
ge ne
ro si
ty 1.
00 9
[. 92
1, 1
.1 03
] 1.
02 1
[. 93
6, 1
.1 15
] 1.
00 6
[. 90
8, 1
.1 01
] .9
94 [. 90
0, 1
.1 02
] 1.
02 2
[. 93
8, 1
.1 20
] H
ea lt hc
ar e
ge ne
ro si
ty 1.
09 5*
* [1
.0 14
, 1 .1
76 ]
1. 10
8* *
[1 .0
18 , 1
.2 08
] 1.
08 5*
[1 .0
05 , 1
.1 71
] 1.
09 1*
[1 .0
17 , 1
.1 66
] 1.
10 3*
[1 .0
22 , 1
.1 84
] C
ro ss
-l e ve
l in
te a ct
io n
e ff
e ct
s U
ne m
pl o ye
d ×
U ne
m pl
o ym
en t
ge ne
ro si
ty 1.
07 2*
[1 .0
07 , 1
.1 41
] 1.
07 2*
[1 .0
06 , 1
.1 40
] 1.
07 2*
[1 .0
06 , 1
.1 42
] 1.
07 2*
[1 .0
07 , 1
.1 43
] 1.
07 1*
[1 .0
07 , 1
.1 42
] n
o ne
m pl
o ye
d ×
U ne
m pl
o ym
en t
ge ne
ro si
ty 1.
00 9
[. 97
3, 1
.0 48
] 1.
01 0
[. 97
3, 1
.0 49
] 1.
01 0
[. 97
2, 1
.0 49
] 1.
01 0
[. 97
3, 1
.0 50
] 1.
01 0
[. 97
2, 1
.0 49
] U
ne m
pl o ye
d ×
H ea
lt hc
ar e
ge ne
ro si
ty 1.
08 3*
* [1
.0 30
, 1 .1
40 ]
1. 08
3* *
[1 .0
29 , 1
.1 41
] 1.
08 4*
* [1
.0 30
, 1 .1
41 ]
1. 08
3* *
[1 .0
30 , 1
.1 38
] 1.
08 3*
* [1
.0 29
, 1 .1
40 ]
n o ne
m pl
o ye
d ×
H ea
lt hc
ar e
ge ne
ro si
ty .9
97 [. 96
5, 1
.0 31
] .9
97 [. 96
4, 1
.0 31
] .9
98 [. 96
5, 1
.0 32
] .9
98 [. 96
4, 1
.0 32
] .9
98 [. 96
6, 1
.0 34
]
(c on
tin ue
d)
283
V ar
ia bl
e
M o de
l 1 M
o de
l 2 M
o de
l 3 M
o de
l 4 M
o de
l 5 M
o de
l 6 M
o de
l 7
O r
[ C
I] O
r [
C I]
O r
[ C
I] O
r [
C I]
O r
[ C
I] O
r [
C I]
O r
[ C
I]
M a cr
o c
o n
tr o
l va
ri a b
le s
G D
P (×
1 00
0) 1.
03 0
[. 86
8, 1
.2 19
]
U ne
m pl
o ym
en t
ra te
1. 00
7 [. 93
8, 1
.0 78
]
Pu bl
ic e
xp en
di tu
re s
o n
di sa
bi lit
y (×
1 00
) 1.
01 5
[. 96
9, .0
01 ]
A ct
iv e
la bo
r m
ar ke
t pr
o gr
am s
ex pe
nd it ur
es 1.
24 1
[. 74
5, 2
.0 07
]
G o ve
rn m
en t
ex pe
nd it ur
es 1.
00 2
[. 98
8, 1
.0 16
] C
ha ng
e go
ve rm
en t
ex pe
nd it ur
es 1.
02 8*
[1 .0
01 , 1
.0 58
] V
a ri
a n
ce C
o un
tr y
.1 08
[. 03
7, .2
40 ]
.1 55
[. 05
0, .3
43 ]
.1 01
[. 01
0, .2
51 ]
.1 02
[. 00
6, .2
54 ]
.1 06
[. 01
6, .2
54 ]
.1 01
[. 01
7, .2
58 ]
.1 07
[. 00
5, .2
60 ]
C o un
tr y
× P
er io
d .0
58 [. 02
5, .1
40 ]
.0 64
[. 02
8, .1
60 ]
.0 71
[. 03
0, .1
74 ]
.0 72
[. 03
0, .1
96 ]
.0 68
[. 03
0, .1
69 ]
.0 68
[. 02
9, .1
63 .0
60 [. 02
5, .1
77 ]
N ot
e: M
o de
ls c
o nt
ro lle
d fo
r ed
uc at
io n,
m ar
it al
s ta
tu s,
d eg
re e
o f ur
ba ni
za ti o n,
a nd
p er
io d.
t he
m et
ri c
va ri
ab le
s ar
e gr
an d-
m ea
n ce
nt er
ed . t
hr ee
-l ev
el d
es ig
n: in
di vi
du al
s (n
= 3
6, 30
6; le
ve l
1) a
re n
es te
d in
c o un
tr y-
ye ar
s (n
= 4
8; le
ve l 2
), w
hi ch
a re
n es
te d
in c
o un
tr ie
s (n
= 2
4; le
ve l 3
). O
r =
o dd
s ra
ti o ; C
I =
9 5%
c o nf
id en
ce in
te rv
al ; G
D P
= g
ro ss
d o m
es ti c
pr o du
ct .
*p <
.0 5,
* *p
< .0
1, *
** p
< .0
01 (
tw o t
ai le
d) .
T a b
le 3
. (c
o nt
in ue
d)
284
T a b
le 4
. Lo
gi st
ic M
ul ti le
ve l A
na ly
si s
o n
Ps yc
hi at
ri st
C o ns
ul ta
ti o ns
( Eu
ro ba
ro m
et er
2 00
5– 20
06 a
nd 2
01 0,
N =
3 6,
30 6)
.
V ar
ia bl
e
M o de
l 1 M
o de
l 2 M
o de
l 3 M
o de
l 4 M
o de
l 5 M
o de
l 6 M
o de
l 7
O r
[ C
I] O
r [
C I]
O r
[ C
I] O
r [
C I]
O r
[ C
I] O
r [
C I]
O r
[ C
I]
C o ns
ta nt
.0 09
** *
[. 00
6, .0
13 ]
.0 06
** *
[. 00
4, .0
09 ]
.0 06
** *
[. 00
4, .0
09 ]
.0 06
** *
[. 00
4, .0
09 ]
.0 06
** *
[. 00
4, .0
09 ]
.0 06
** *
[. 00
4, .0
09 ]
.0 07
** *
[. 00
4, .0
13 ]
Em pl
o ym
en t
st at
us (
re fe
re nc
e em
pl o ye
d)
U ne
m pl
o ye
d 2.
55 7*
** [2
.0 02
, 3 .2
32 ]
1. 60
5* **
[1 .2
47 , 2
.0 52
] 1.
63 6*
** [1
.2 61
, 2 .1
04 ]
1. 63
6* *
[1 .2
64 , 2
.0 90
] 1.
63 7*
** [1
.2 65
, 2 .0
98 ]
1. 64
2* **
[1 .2
76 , 2
.1 09
] 1.
64 4*
** [1
.2 72
, 2 .1
11 ]
n
o ne
m pl
o ye
d 2.
56 0*
** [2
.1 30
, 3 .0
68 ]
1. 99
8* **
[1 .6
62 , 2
.4 01
] 2.
03 8*
** [1
.6 92
, 2 .4
69 ]
2. 03
8* **
[1 .6
87 , 2
.4 62
] 2.
04 2*
** [1
.6 97
, 2 .4
57 ]
2. 04
2* **
[1 .6
87 , 2
.4 72
] 2.
04 4*
** [1
.6 89
, 2 .4
72 ]
A ge
1. 00
7 [1
.0 00
, 1 .0
13 ]
1. 00
1 [. 99
4, 1
.0 09
] 1.
00 1
[. 99
4, 1
.0 09
] 1.
00 1
[. 99
4, 1
.0 08
] 1.
00 1
[. 99
4, 1
.0 00
] 1.
00 1
[. 99
4, 1
.0 08
] 1.
00 1
[. 99
4, 1
.0 08
] W
o m
en (
re fe
re nc
e m
en )
1. 22
5* *
[1 .0
39 , 1
.4 57
] 1.
04 3
[. 88
1, 1
.2 36
] 1.
04 5
[. 88
2, 1
.2 39
] 1.
04 2
[. 88
1, 1
.2 34
] 1.
04 2
[. 87
8, 1
.2 32
] 1.
04 4
[. 87
8, 1
.2 40
] 1.
04 3
[. 88
3, 1
.2 34
] M
en ta
l h ea
lt h
.2 46
** *
[. 22
3, .2
72 ]
.2 45
** *
[. 22
3, .2
70 ]
.2 45
** *
[. 22
2, .2
69 ]
.2 45
** *
[. 22
2, .2
70 ]
.2 44
** *
[. 22
2, .2
69 ]
.2 45
** *
[. 22
3, .2
70 ]
Pe ri
o d:
2 01
0 (r
ef er
en ce
2 00
5) .8
20 [. 68
5, .9
81 ]*
* .7
92 **
* [. 65
7, .9
58 ]
.7 91
** *
[. 65
1, .9
47 ]
.7 90
** *
[. 64
7, .9
51 ]
.7 91
** *
[. 65
5, .9
60 ]
.7 94
** *
[. 65
6, .9
57 ]
.8 41
[. 68
8, 1
.0 23
] U
ne m
pl o ym
en t
ge ne
ro si
ty 1.
17 1
[. 98
4, 1
.3 94
] 1.
17 1
[. 98
4, 1
.3 94
] 1.
11 5
[. 92
6, 1
.3 20
] 1.
11 5
[. 92
6, 1
.3 20
] 1.
17 1
[. 98
4, 1
.3 94
] H
ea lt hc
ar e
ge ne
ro si
ty 1.
08 4
[. 95
1, 1
.2 39
] 1.
11 7
[. 96
8, 1
.2 93
] 1.
05 0
[. 92
4, 1
.1 91
] 1.
05 4
[. 93
1, 1
.2 00
] 1.
05 8
[. 93
5, 1
.2 07
] C
ro ss
-l e ve
l in
te ra
ct io
n e
ff e ct
s U
ne m
pl o ye
d ×
U ne
m pl
o ym
en t
ge ne
ro si
ty .9
40 [. 82
4, 1
.0 71
] .9
40 [. 82
5, 1
.0 70
] .9
41 [. 82
3, 1
.0 74
] .9
40 [. 82
5, 1
.0 69
] .9
36 [. 82
1, 1
.0 68
] n
o ne
m pl
o ye
d ×
U ne
m pl
o ym
en t
ge ne
ro si
ty .9
23 [. 84
2, 1
.0 12
] .9
22 [. 84
0, 1
.0 10
] .9
23 [. 83
9, 1
.0 13
] .9
23 [. 83
9, 1
.0 10
] .9
20 [. 83
9, 1
.0 11
] U
ne m
pl o ye
d ×
H ea
lt hc
ar e
ge
ne ro
si ty
1. 08
1* [1
.0 11
, 1 .1
56 ]
1. 08
1* [1
.0 11
, 1 .1
56 ]
1. 08
1* [1
.0 11
, 1 .1
56 ]
1. 08
1* [1
.0 11
, 1 .1
56 ]
1. 06
7* [1
.0 09
, 1 .1
49 ]
n o ne
m pl
o ye
d x
H ea
lt hc
ar e
ge
ne ro
si ty
1. 01
5 [. 93
8, 1
.0 96
] 1.
01 7
[. 94
3, 1
.0 97
] 1.
01 6
[. 94
0, 1
.0 99
] 1.
01 7
[. 94
1, 1
.0 99
] 1.
01 5
[. 93
9, 1
.0 94
]
(c on
tin ue
d)
285
V ar
ia bl
e
M o de
l 1 M
o de
l 2 M
o de
l 3 M
o de
l 4 M
o de
l 5 M
o de
l 6 M
o de
l 7
O r
[ C
I] O
r [
C I]
O r
[ C
I] O
r [
C I]
O r
[ C
I] O
r [
C I]
O r
[ C
I]
M a cr
o c
o n
tr o
l va
ri a b
le s
G D
P (×
1 00
0) 1.
00 7
[. 74
8, 1
.3 44
]
U ne
m pl
o ym
en t
ra te
1. 00
5 [. 93
7, 1
.1 86
]
Pu bl
ic e
xp en
di tu
re s
o n
di sa
bi lit
y
(× 1
00 )
1. 03
2 [. 94
0, 1
.1 01
]
A ct
iv e
la bo
r m
ar ke
t pr
o gr
am s
ex pe
nd it ur
es 1.
87 2
[. 90
8, 4
.4 50
]
G o ve
rm en
t ex
pe nd
it ur
es .9
83 [. 95
8, 1
.0 12
] C
ha ng
e go
ve rm
en t
ex pe
nd it ur
es 1.
03 1
[. 99
3, 1
.0 76
] V
a ri
a n
ce
C o un
tr y
.2 45
[. 10
7, .5
39 ]
.4 38
[. 22
2, .9
00 ]
.3 69
[. 17
5, .8
30 ]
.3 54
[. 16
7, .7
87 ]
.3 44
[. 16
1, .7
58 ]
.3 27
[. 15
1, .7
51 ]
.3 45
[. 15
9, .7
30 ]
C o un
tr y
× P
er io
d .0
14 [. 00
1, .1
07 ]
.0 13
[. 00
1, .1
07 ]
.0 15
[. 00
1, .1
00 ]
.0 18
[. 00
1, .1
17 ]
.0 17
[. 00
1, .1
12 ]
.0 16
[. 00
1, .1
06 ]
.0 14
[. 00
1, .1
45 ]
N ot
e: M
o de
ls c
o nt
ro lle
d fo
r ed
uc at
io n,
m ar
it al
s ta
tu s,
d eg
re e
o f ur
ba ni
za ti o n,
a nd
p er
io d.
t he
m et
ri c
va ri
ab le
s ar
e gr
an d-
m ea
n ce
nt er
ed . t
hr ee
-l ev
el d
es ig
n: in
di vi
du al
s (n
= 3
6, 30
6; le
ve l
1) a
re n
es te
d in
c o un
tr y-
ye ar
s (n
= 4
8; le
ve l 2
), w
hi ch
a re
n es
te d
in c
o un
tr ie
s (n
= 2
4; le
ve l 3
). O
r =
o dd
s ra
ti o ; C
I =
9 5%
c o nf
id en
ce in
te rv
al ; G
D P
= g
ro ss
d o m
es ti c
pr o du
ct .
*p <
.0 5,
* *p
< .0
1, *
** p
< .0
01 (
tw o t
ai le
d) .
T a b
le 4
. (c
o nt
in ue
d)
286 Journal of Health and Social Behavior 58(3)
unable to confirm the direction of any causal effects. Based on the meta-analysis by Paul and Moser (2009), which includes information concerning lon- gitudinal studies, we know that the mental health selection effect on unemployment and job searching is relatively weak. Selection bias is possibly also more of a concern when the outcome variable of interest is mental health instead of MHC use, as it is less likely that the use of care has an impact on becoming unemployed, irrespective of mental health.
Two additional limitations are also related to the data source. First, response rates for waves 64.4 and 73.2 of the Eurobarometer are not available. The poststratification weights used in our analyses are the only means of addressing the representativeness of the Eurobarometer surveys. Second, income measurement is omitted from the Eurobarometer surveys we use, which prevents us from assessing the role of household material resources in access- ing MHC.
Bearing in mind these limitations, our study pro- duces three main findings. First, in addition to the fact that unemployment is consistently related to poorer mental and general health (Bambra and Eikemo 2009), we find that in several European countries, unemployment is medicalized to some degree. This medicalization, which we quantify as the remaining association between unemployment and MHC utili- zation, after controlling for reported mental health status, varies substantially across national contexts. In the United Kingdom, for example, the higher special- ized care consumption of the unemployed compared to the employed remains after controlling for differ- ences in mental health between the employed and unemployed. In Spain, the unemployed have a lower likelihood of contacting a psychiatrist regardless of their poorer mental health.
Second, the variation in the extent of medical- ization of unemployment is significantly patterned by a country’s level of unemployment generosity and especially healthcare generosity. The institu- tional approach to welfare-state effects on health (Beckfield et al. 2015; Bergqvist, Yngwe, and Lundberg 2013), whereby unemployment and healthcare generosity are addressed as separate welfare domains (Bambra 2005b; Kasza 2002), allows for this insight. In several countries, policy is generous in one domain but not the other. The combination of low unemployment generosity and high healthcare generosity (as seen in the United Kingdom, Finland, Estonia, the Czech Republic, Romania, and Slovenia) was hypothesized to create the most favorable institutional conditions for
medicalizing unemployment. In line with this hypothesis, we found that in the United Kingdom, Slovenia, Finland, and Estonia, MHC utilization among the unemployed is significantly higher than expected based on their mental health, at least for one type of medical care. Romania and the Czech Republic, however, did not confirm our hypothesis. In Romania, this might be explained by exception- ally low unemployment generosity, which may lead to high financial insecurity among the unemployed. In addition, Romania’s high healthcare generosity is due to a very low provision of private hospitals and low private health expenditure.
Third, by testing the moderating effect of both generosity measurements on the relationship between unemployment and MHC use, a high level of healthcare generosity is highlighted as an impor- tant institutional factor for unemployment medical- ization via medical professionals. Bambra’s (2005a, 2005b) adjusted and updated healthcare generosity measurement accentuates the private-public mix- ture. A large proportion of private health insurance is provided through the workplace (Colombo and Tapay 2004). As a result, in countries with high expenditures on private health insurance and ser- vices, the employed often benefit more than the unemployed, for whom it is more difficult to use private services and to obtain private insurance (Colombo and Tapay 2004). Moreover, more pri- vate (insurance) expenditures, more private service provision, and higher OOP payments increase social inequality in healthcare access, especially by harming the most vulnerable (Bambra, Garthwaite, and Hunter 2014), such as the unemployed. By con- trast, in countries with high healthcare generosity, the structural thresholds for contacting a medical professional are lower, and the access to and avail- ability of medical resources are independent of (or minimally dependent on) an individual’s position in the labor market and/or their economic capital. This may explain why we find that the unemployed in these countries are more likely to utilize MHC services.
With regard to the role of unemployment gener- osity in the relationship between unemployment and GP consultations, the results contradict our expectations that a low level will trigger the medi- calization of unemployment. However, additional analyses show that this is the case only in countries with a relatively low unemployment rate. Therefore, in a context where the chances of (health) selection effects are higher, where unemployment is less ran- domly distributed, and where any social-norm effect of unemployment is virtually absent (Clark
Buffel et al. 287
2003), higher levels of unemployment generosity may strengthen the medicalization of unemploy- ment via GP consultations. Denmark and the Netherlands have this combination of characteris- tics and, for these countries, we find higher primary care use by the unemployed than expected based on their mental health. A possible explanation can be found in the pro-poor distribution of GP consulta- tions when need is taken into account (van Doorslaer, Koolman, and Jones 2004). Both coun- tries also have a flexicurity labor market model characterized by minimal job protection, generous unemployment benefits, and extensive use of ALMP (Heyes 2011). Although we controlled for ALMP expenditures, specific types of programs, especially those that are compulsory and in some sense paternalistic, can act as an explanation for our findings. Previous work (Heggebo 2016) indicates that people with ill health and hence more unstable labor market attachment could be more prone to unemployment in a flexicurity model. Our findings suggest they are also more vulnerable for medical- izing it.
A strength of this study is that we have taken possible austerity effects into account. In countries where there were cutbacks in general government expenditures between 2005 and 2009, the likeli- hood of contacting a GP for psychosocial problems was lower compared to countries without a decrease in government expenditures, controlling for indi- vidual mental health status and the average level of government expenditures. In several countries the GP has a gatekeeper function (Wendt 2014), refer- ring patients to the most adequate care, which makes this finding especially worrisome. Recent studies (Kondilis et al. 2013; McKee et al. 2012) have already warned of austerity effects on health outcomes.
While we found that psychiatrist consultations were reduced in 2010 compared to 2005–2006, only psychiatrist consultations of the employed were less likely in countries with a cut in government expendi- tures. In previous research (Buffel, van de Straat, et al. 2015), we found that the employed are less likely to contact a psychiatrist in countries that have experi- enced a decline in the GDP growth rate. A possible explanation for the findings that the austerity effect and the crisis effect apply only to working people may be that the employed avoid specialized care use for fear of being labeled as sick and thus stigmatized (de Belvis et al. 2012). In slack labor markets, such stigma could result in job loss (Gene-Badia et al. 2012), especially in countries most strongly affected by the economic crisis and austerity policies.
In addition, our models incorporated the level of public disability spending, as studies have already observed that high levels of spending (especially in countries with less generous unemployment bene- fits) can lead to “hidden unemployment.” This can be interpreted as the medicalization of unemploy- ment via relying on disability benefits. Future research is needed to disentangle this part of the medicalization of unemployment, as has been done for “monetizing illness” (O’Brien 2015).
Finally, related to the recent activation debate, some researchers are focusing on the shifting balance between the rights and responsibilities of the unem- ployed and the growing conditionality requirements for unemployment benefits (Knotz 2012). We need to be cautious about our results in this regard, because Knotz (2012) argues that if there is increasing condi- tionality, generosity scores will be less accurate. For example, if there is a reasonably generous unemploy- ment benefits system but the unemployed cannot refuse certain jobs without losing their entitlements, then generosity is not that high because benefit claim- ants are more dependent on the labor market. This may be a possible explanation for why we find con- vincing support for the expected positive relation between healthcare generosity and medicalizing unemployment, and less support for the hypothesized negative relation with unemployment generosity.
SUPPLEMEntAL MAtErIAL The appendices are available in the online version of the article.
ACKnOWLEDGMEntS This paper was presented at the Special Interest Meeting of the European Society of Health and Medical Sociology, Trondheim, Norway, September 3–4, 2015.
nOtES 1. To test the validity of the data, we calculated the
correlation between countries’ unemployment rates derived from the Eurobarometer data and the Eurostat national data (une_rt_a). The correlation is .789 (wave 2010) and .813 (wave 2005–2006). We also compared the Eurobarometer data (2005–2006 to 2010) with waves 2006 to 2012 of the European Social Survey, which also includes information about mental health (but not mental healthcare [MHC] use) and employment status (per country). The correla- tions are relatively high (see online Appendix A).
2. Degree of urbanization was included only in the multilevel analyses, as the country-specific samples were too small to control for this additional variable.
288 Journal of Health and Social Behavior 58(3)
3. For some countries (especially eastern and central European), the unemployment generosity index was not available, only the separate indicators. We calcu- lated the index for these countries based on the for- mula of Scruggs (Scruggs, Jahn, and Kuitto 2014). As a validity check, we did the same for the coun- tries for which we had the unemployment generosity score. The correlation between our calculations and Scruggs’ scores is nearly 1 (r = .987).
4. Bambra’s (2005a, 2005b) measurement is con- structed based only on the initial Organisation for Economic Co-operation and Development coun- tries, similar to the work of Esping-Anderson (Yu 2012). Given we focus only on European countries, there is less variation in the indicator of cover- age, as almost every European country has a full coverage rate (near 100%). In addition Bambra’s measurement could incorrectly reflect the real situation for central and eastern European coun- tries. According to her three indicators of decom- modification, some central and eastern European countries have relatively high scores (see online Appendix C) compared to other countries, such as the Netherlands, Belgium, and Germany. This can mainly be ascribed to the fact that some of them still have very little private healthcare provision in terms of hospital beds (Yu 2012), despite the general trend toward privatization (King, Hamm, and Stuckler 2009). Nevertheless, it is known that their healthcare systems––and especially their MHC services––are less developed (World Health Organization 2005, 2011). The process of privatization in these countries has led to higher out-of-pocket (OOP) payments, which restricts the accessibility of healthcare services for vul- nerable groups, such as the unemployed (King et al. 2009). The proportion of private health insurance spending is not necessarily related to household OOP spending as a proportion of total expenditure on health; for example, in Estonia, Poland, and Hungary, expenditure on private health insurance is almost nonexistent, but OOP payments are relatively high (Quesnel-Vallée et al. 2012). This is a good indicator for assessing the accessi- bility of healthcare services and detecting financial barriers. Further, the increase in OOP payments and patients’ fees is related to greater social inequality healthcare access (Bambra, Garthwaite, and Hunter 2014). Therefore, we add this indicator, measured as the household OOP payments as percentage of the total health expenditure, to Bambra’s healthcare decommodification measurement.
5. Measuring the indicator this way avoids it being an indicator of use, such as our outcome variable.
6. The following formula is used: predicted probability (PP) of the employed = [exp(a + b1X1 + b2X2)] / [1 + exp(a + b1X1 + b2X2)]. To compute the PP, we used the mean score for the metric independent variables and proportions for the categorical variables.
7. Second-order interactions (Unemployment Generos- ity × Healthcare Generosity × Employment Status) are insignificant for the three outcome variables.
rEFErEnCES Antonakakis, Nikolaos, and Alan Collins. 2015. “The
Impact of Fiscal Austerity on Suicide Mortality: Evidence across the ‘Eurozone Periphery.’” Social Science & Medicine 145:63–78.
Bambra, Clare. 2005a. “Cash versus Services: ‘Worlds of Welfare’ and the Decommodification of Cash Benefits and Health Care Services.” Journal of Social Policy 34(2):195–213.
Bambra, Clare. 2005b. “Worlds of Welfare and the Health Care Discrepancy.” Social Policy & Society 4(1):31–41.
Bambra, Clare, and Terje Eikemo. 2009. “Welfare State Regimes, Unemployment and Health: A Comparative Study of the Relationship between Unemployment and Self-reported Health in 23 European Countries.” Journal of Epidemiology and Community Health 63(2):92–98.
Bambra, Clare, Kayleigh Garthwaite, and David Hunter. 2014. “All Things Being Equal: Does It Matter for Equity How You Organize and Pay for Health Care? A Review of the International Evidence.” International Journal of Health Services 44(3):457–77.
Beatty, Christina, and Steve Fothergill. 2015. “Disability Benefits in an Age of Austerity.” Social Policy & Administration 49(2):161–81.
Beckfield, Jason, Clare Bambra, Terje Eikemo, Tim Huijts, Courtney McNamara, and Claus Wendt. 2015. “Towards an Institutional Theory of Welfare State Effects on the Distribution of Population Health.” Social Theory & Health 13(3/4):227–44.
Bergqvist, Kersti, Monica Aberg Yngwe, and Olle Lundberg. 2013. “Understanding the Role of Welfare State Characteristics for Health and Inequalities: An Analytical Review.” BMC Public Health 13(1):1234.
Bonoli, Giuliano. 2010. “The Political Economy of Active Labor-market Policy.” Politics & Society 38(4):435–57.
Börsch-Supan, Axel. 2007. “Work Disability, Health, and Incentive Effects.” Discussion Papers Series. Mannheim, Germany: Mea-Mannheim Research Institute for the Economics of Aging.
Bratsberg, Bernt, Elisabeth Fevang, and Knut Roed. 2010. “Disability in the Welfare State: An Unemployment Problem in Disguise.” IZA Discussion Paper No. 4897. Bonn, Germany: IZA.
Buffel, Veerle, Rozemarijn Dereuddre, and Piet Bracke. 2015. “Medicalization of the Uncertainty? An Empirical Study of the Relationships between Unemployment or Job Insecurity, Professional Care Seeking, and the Consumption of Antidepressants.” European Sociological Review 31(4):446–59.
Buffel, Veerle, Vera van de Straat, and Piet Bracke. 2015. “Employment Status and Mental Health Care
Buffel et al. 289
Use in Times of Economic Contraction: A Repeated Cross-sectional Study in Europe, Using a Three-level Model.” International Journal for Equity in Health 14(1):29.
Christiaens, Wendy, and Piet Bracke. 2014. “Work– Family Conflict, Health Services and Medication Use among Dual-income Couples in Europe.” Sociology of Health & Illness 36(3):319–37.
Clark, Andrew E. 2003. “Unemployment as a Social Norm: Psychological Evidence from Panel Data.” Journal of Labor Economics 21(2):323–51.
Clarke, Adele, and Janet Shim. 2011. “Medicalization and Biomedicalization Revisited: Technoscience and Transformations of Health, Illness and American Medicine.” Pp. 173–99 in Handbook of the Sociologiy of Health, Illness, and Healing: A Blueprint for the 21st Century, Handbooks of Sociology and Social Research, edited by B. A. Pescosolido, J. K. Martin, J. D. McLeod, and A. Rogers. New York: Springer Science + Business Media.
Colombo, Francesca, and Nicole Tapay. 2004. “Private Health Insurance in OECD Countries: The Benefits and Costs for Individuals and Health Systems.” OECD Working Papers. Paris: OECD.
Conrad, Peter. 1992. “Medicalization and Social- control.” Annual Review of Sociology 18(1):209–32.
Conrad, Peter. 2005. “The Shifting Engines of Medicalization.” Journal of Health and Social Behavior 46(1):3–14.
de Belvis, Antonio G, Francesca Ferre, Maria L Specchia, Luca Valerio, Giovanni Fattore, and Walter Ricciardi. 2012. “The Financial Crisis in Italy: Implications for the Healthcare Sector.” Health Policy 106(1):10–16.
Esping-Andersen, Gosta. 1990. The Three Worlds of Welfare Capitalism. Cambridge, UK: Polity Press.
Eurostat. 2015. “Unemployment Rate by Sex and Age Groups: Annual Average, %.” (http://appsso .eurostat.ec.europa.eu/nui/show.do?dataset=une_ rt_a&lang=en).
Fairbrother, Malcolm. 2014. “Two Multilevel Modeling Techniques for Analyzing Comparative Longitudinal Survey Datasets.” Political Science Research and Methods 2(22):119–40.
Frohlich, Norman, K. Cough Carriere, Louise Potvin, and Anne C. Black. 2001. “Assessing Socioeconomic Effects on Different Sized Populations: To Weight or Not to Weight?” Journal of Epidemiology and Community Health 55(12):913–20.
Gallie, Duncan, Dobrinka Kostova, and Pavel Kuchar. 2001. “Social Consequences of Unemployment: An East–West Comparison.” Journal of European Social Policy 11(1):39–54.
Gene-Badia, Joan, Pedro Gallo, Cristina Hernandez- Quevedo, and Sandra Garcia-Armesto. 2012. “Spanish Health Care Cuts: Penny Wise and Pound Foolish?” Health Policy 106(1):23–28.
Heggebo, Kristian. 2016. “Hiring, Employment and Health in Scandinavia: The Danish ‘Flexicurity’ Model in Comparative Perspective.” European Societies 18(5):460–86.
Hermans, Marc H., Nele de Witte, and Geert Dom. 2012. “The State of Psychiatry in Belgium.” International Review of Psychiatry 24(4):286–94.
Heyes, Jason. 2011. “Flexicurity, Employment Protection and the Jobs Crisis.” Work, Employment and Society 25(4):642–57.
Karanikolos, Marina, Philipa Mladovsky, Jonathan Cylus, Sarah Thomson, Sanjay Basu, David Stuckler, Johan P. Mackenbach, and Martin McKee. 2013. “Financial Crisis, Austerity, and Health in Europe.” Lancet 381(9874):1323–31.
Kasza, Gregory. 2002. “The Illusion of Welfare Regimes.” Journal of Social Policy 31(2):271–87.
King, Lawrence, Patric Hamm, and David Stuckler. 2009. “Rapid Large-scale Privatization and Death Rates in Ex-communist Countries: An Analysis of Stress- related and Health System Mechanisms.” International Journal of Health Services 39(3):461–89.
Knotz, Carlo. 2012. “Measuring the ‘New Balance of Rights and Responsibilities’ in Labor Market Policy.” ZeS Working Papers. Bremen, Germany: University of Bremen.
Kondilis, Elias, Stathis Giannakopoulos, Magda Gavana, Ioanna Ierodiakonou, Howard Waitzkin, and Alexis Benos. 2013. “Economic Crisis, Restrictive Policies, and the Population’s Health and Health Care: The Greek Case.” American Journal of Public Health 103(6):973–98 .
McKee, Martin, Marinna Karanikolos, Paul Belcher, and David Stuckler. 2012. “Austerity: A Failed Experiment on the People of Europe.” Clinical Medicine 12(4):346–50.
O’Brien, Rourke L. 2015. “Monetizing Illness: The Influence of Disability Assistance Priming on How We Evaluate the Health Symptoms of Others.” Social Science & Medicine 128:31–35.
Organisation for Economic Co-operation and Development. 2012. Employment and Labour Market Statistics: Labour Force Statistics by Sex and Age. Retrieved May 29, 2015 (http://dx.doi.org/1 .1787/unemp-table2131-en).
Organisation for Economic Co-operation and Development. 2015. Public Expenditureon ALMP (as % of GDP). (https://stats.oecd.org/Index.aspx? DataSetCode=LMPEXP)
Olafsdottir, Sigrun. 2007. Medicalizing Mental Health: A Comparative View of the Public, Private, and Professional Construction of Mental Illness. Dissertation, Sociology, Indiana University, Bloomington.
Olafsdottir, Sigrun. 2010. “Medicalization and Mental Health: The Critique of Medical Expansion, and a Consideration of How Markets, National States, and Citizens Matter.” Pp. 239–61 in The Sage Handbook of Mental Health and Illness, edited by D. Pilgrim, A. Rogers, and B. Pescosolido. London: Sage.
Paul, Karsten I., and Klaus Moser. 2009. “Unemployment Impairs Mental Health: Meta-analyses.” Journal of Vocational Behavior 74(3):264–82.
Quesnel-Vallée, Amélie, Emilie Renahy, Tania Jenkins, and Helen Cerigo. 2012. “Assessing
290 Journal of Health and Social Behavior 58(3)
Barriers to Health Insurance and Threats to Equity in Comparative Perspective: The Health Insurance Access Database.” BMC Health Services Research 12(1):107.
Rodriguez, Eunice, Edward A. Frongillo, and Prakash Chandra. 2001. “Do Social Programs Contribute to Mental Well-being? The Long Term Impact of Unemployment on Depression in the US.” International Journal of Epidemiology 30(1):163–70.
Saxena, Sonia, Graham Thornicroft, Martin Knapp, and Harvey Whiteford. 2007. “Global Mental Health 2: Resources for Mental Health. Scarcity, Inequity, and Inefficiency.” Lancet 370(9590):878–89.
Schmitz, Hendrik. 2011. “Why Are the Unemployed in Worse Health? The Causal Effect of Unemployment on Health.” Labour Economics 18(1):71–78.
Scruggs, Lyle. 2014. “Social Welfare Generosity Scores in CWED 2: A Methodological Genealogy.” CWED Working Paper 1.
Scruggs, Lyle, and Julie Allan. 2006. “Welfare-state Decommodification in 18 OECD Countries: A Replication and Revision.” Journal of European Social Policy 16(1):55–72.
Scruggs, Lyle, Detlev Jahn, and Kati Kuitto. 2014. Comparative Welfare Entitlements Dataset 2. Version 2014–03. Storrs: University of Connecticut. Greifswald, Germany: University of Greifswald.
Stegmueller, Daniel. 2013. “How Many Countries for Multilevel Modeling? A Comparison of Frequentist and Bayesian Approaches.” American Journal of Political Science 57(3):748–61.
Strandh, Mattias. 2001. “State Intervention and Mental Well-being among the Unemployed.” Journal of Social Policy 30(1):57–80.
Stuckler, David, Sanjay Basu, Marc Suhrcke, Adam Coutts, and Martin McKee. 2009. “The Public Health Effect of Economic Crises and Alternative Policy Responses in Europe: An Empirical Analysis.” Lancet 374(9686):315–23.
van Doorslaer, Eddy, Xander Koolman, and Andrew M. Jones. 2004. “Explaining Income-related Inequalities in Doctor Utilisation in Europe.” Health Economics 13(7):629–47.
Virtanen, Marianna, Mika Kivimaki, Jane E. Ferrie, Marko Elovainio, Teija Honkonen, Jaana Pentti, Timo Klaukka, and Jussi Vahtera. 2008. “Temporary Employment and Antidepressant Medication: A Register Linkage Study.” Journal of Psychiatric Research 42(3):221–29.
Ware, John E., and Cathy Donald Sherbourne. 1992. “The MOS 36-item Short-form Health Survey (SF- 36): 1. Conceptual-framework and Item Selection.” Medical Care 30(6):473–83.
Wendt, Claus. 2014. “Changing Healthcare System Types.” Social Policy & Administration 48(7):864–82.
World Bank. 2015. World Development Indicators Database: Government Final Consumption Expen- diture as a % of GDP. (http://data.worldbank .org/data-catalog/world-development-indicators).
World Health Organization. 2005. Mental Health Atlas 2005. Geneva: Author.
World Health Organization. 2011. Mental Health Atlas 2011. Geneva: Author.
Wulfgramm, Melike. 2014. “Life Satisfaction Effects of Unemployment in Europe: The Moderating Influence of Labour Market Policy.” Journal of European Social Policy 24(3):258–72.
Yu, Sam. 2012. “Contribution of Health Care Decommod- ification Index to the Analysis of the Marginalisation of East Asian Countries in Comparative Welfare Studies.” Development and Society 41(2):253–70.
AUtHOr BIOGrAPHIES Veerle Buffel is a postdoctoral researcher in the Department of Sociology at Ghent University. She holds a master’s degree in sociology (2012, Ghent University) and a PhD in health sociology (2016, Ghent University). She is working on her project financed by Bijzonder Onderzoeks Fonds. The project studies the relationship between work status, mental health, and professional healthcare and psychotropic drug use because of mental health problems from a cross-country and cross-time comparative perspective. An institutional approach is used to assess the impact of social policies and character- istics of the healthcare system. In addition, attention is paid to job insecurity and gender, age, and cohort differences.
Jason Beckfield is a professor in the Department of Sociology at Harvard University. His research investigates the institutional causes and consequences of social inequality. Currently he is working on three projects: (1) a book about economic inequality in the European Union; (2) a monograph and a series of journal articles that develop an institutional theory of stratification, with a sub- stantive focus on population health; and (3) collaborative publications, many coauthored with PhD students, that investigate long-term trends in the development of politi- cal economy.
Piet Bracke is a professor in the Department of Sociology at Ghent University. His research focuses on gender issues and the sociology of the family, and social epidemiology from a comparative perspective; mental health and mental health services research are important fields of research too. He is a member of the executive committee of the European Society of Health and Medical Sociology and a member of the Scientific Advisory Board of the European Social Survey.
