Studying the impact of the Eurozone’s Great Recession on health: Methodological choices and challenges

Economics and Human Biology 35 (2019) 162–184

Contents lists available at ScienceDirect

Economics and Human Biology journal homepage: www.elsevier.com/locate/ehb

Studying the impact of the Eurozone’s Great Recession on health: Methodological choices and challenges Kristina Thompsona,* , Johan van Ophemb , Annemarie Wagemakersc a b c

Department of Health Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV Amsterdam, the Netherlands Chair Group Urban Economics, Department of Social Sciences, Wageningen University and Research, Hollandseweg 1, 6706KN Wageningen, the Netherlands Chair Group Health and Society, Department of Social Sciences, Wageningen University and Research, Hollandseweg 1, 6706KN Wageningen, the Netherlands

A R T I C L E I N F O

A B S T R A C T

Article history: Received 9 January 2019 Received in revised form 13 June 2019 Accepted 17 June 2019 Available online 28 June 2019

Europe’s Great Recession provides an opportunity to study the impact of increased financial insecurity on health. A number of studies explored the impact of the Recession on health, but they often reached different conclusions. To understand the root of this debate, we undertook a systematic literature review. Articles were analysed thematically based on: geography, data type, operationalisations of wealth and health, and study design. A critical appraisal was also undertaken. Forty-two studies, published from January 2010 to October 2018, were included in our review. Twenty-six of the forty-two studies found that the Great Recession worsened physical health indicators in the Eurozone. In terms of geography, a large concentration of studies focussed on Spain and Greece, indicating that there may be a gap in understanding the health consequences for EU countries with less severe experiences of the Recession. Regarding data type, nearly all studies used secondary datasets, possibly meaning that studies were constrained by the data available. In terms of operationalisations of wealth and health, a majority of studies used single/simple measures of both, so that these multi-faceted concepts were not fully reflected. Further, fewer than half included studies used panel data, with the remaining studies unable to undertake more causal analyses. The results of the critical appraisal showed that lower-quality studies tended to not find a negative impact of the Recession on health, whereas higher quality studies generally did. In future, we recommend conducting cross-country comparisons, using (inter)nationallyrepresentative panel data conducted over a minimum of a ten-year time horizon, and employing multi-faceted operationalisations of wealth and health. This could provide more common ground across studies, and a clearer indication of whether the Recession impacted health. © 2019 The Author. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Keywords: Great Recession Health Eurozone Wealth Literature review

1. Introduction The amount of money individuals have is a key determinant of health (Deaton, 2013). Within countries, the poorest people tend to have more diseases and disabilities than the richest (Wilkinson, 1997). This typically translates to between five and ten years of shorter life expectancies at birth, and between ten and twenty years fewer years of disability-free living (Mackenbach, 2012). The more money people have, the healthier they tend to be, and the longer they tend to live. The converse also appears to be true, particularly when people experience a loss of financial security relative to their previous position (Bezruchka, 2009; Kondo et al., 2008).

* Corresponding author. E-mail address: [email protected] (K. Thompson).

Perhaps the most compelling piece of evidence for this wealth/ health relationship is the gradient that forms when income is compared against health indicators. This gradient forms between various indicators of socio-economic status (SES) and health across all age groups and all countries studied, along with virtually every health indicator, including morbidity, disability and perceived health status (Wolfe et al., 2012; Kawachi and Berkman, 2000). The wealth/health gradient also indicates that there are two types of influences on health: direct and indirect. Evidence for the direct influence on health comes from the gradient’s slope, which declines with increasing income, so that rising income produces diminishing returns (Kawachi and Berkman, 2000; Wolfe et al., 2012). This points to a material, direct influence of income on health indicators: the ability to buy certain goods and services leads to good health (e.g. Marmot et al., 1991; and Mustard et al., 1997; Subramanian and Kawachi, 2004). This gradient also indicates that wealth influences health through an indirect path: each thousand dollars improves health

https://doi.org/10.1016/j.ehb.2019.06.004 1570-677X/© 2019 The Author. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

K. Thompson et al. / Economics and Human Biology 35 (2019) 162–184

indicators across the entire income spectrum, albeit with these diminishing returns (e.g. Wilkinson and Pickett, 2006; Wamala et al., 1999; Steptoe and Willemsen, 2002). Because of the persistence of the wealth/health gradient even after basic needs are met, it does not appear that directly buying better health is the only way that wealth influences health. This may occur through the mechanism of relative deprivation, or the inability to sustain a lifestyle approved by or accustomed to their reference group (Wagstaff and van Doorslaer, 2000). When individuals feel that they have less income than others in their reference group, they are relatively deprived (Jones and Wildman, 2008). The influence of wealth on health has been studied at length in general contexts (e.g. Martikainen et al., 2003; Eikemo et al., 2008), but is increasingly being studied in periods of pronounced financial strain (e.g. Cutler et al., 2007 examining the Great Depression; Tapia Granados, 2008, examining post-war Japan). Yet, it is far from clear what effect financial strain has on health: in a literature review, Drydakis (2016) found that, while recessions often have a negative effect on mental health, they are often associated with lower mortality rates. The Great Recession in the Eurozone offers a unique moment to study the impacts of financial strain on health, thanks to its widespread and acute reach across countries and (often) socioeconomic strata. In the EU overall, the Great Recession reached a fever pitch in 2011 as a sovereign debt crisis. Eurozone economies particularly Portugal, Ireland, Italy, Greece and Spain - defaulted on debts. As a result, the European Commission, the European Central Bank and the International Monetary Fund offered bail-out programmes and credit agreements. To meet their conditions, recipient countries employed austerity policies, or the “deliberate deflation of domestic wages and prices through cuts to public spending” (Blythe, 2013, p. 41). But austerity measures were not contained to the bail-out countries, and were implemented across Europe. These policies, in tandem with the existing strain of the Recession, had dire consequences for households in the Eurozone. According to Thomson et al. (2014), “many households have faced growing financial pressure and insecurity as a result of collapses in house prices, greater indebtedness, job loss and falling incomes” (p. 20). As unemployment steadily climbed throughout the Eurozone, consumer spending and household savings rates declined dramatically (ibid.). Since the early 2010s, a number of countries, particularly those that were less severely impacted by the Recession, have returned strongly to growth. Yet those that were more acutely affected continue to be hobbled by its effects. For this reason, while there is a relatively clear starting date of the Recession, its end date is contested: some argue that it ended as early as July 2012, when Mario Draghi, then-president of the European Central Bank, promised to do ‘whatever it takes’ to preserve the euro (ECB, 2012). Others point to Ireland exiting its bailout programme in December 2013, or Greece exiting its own in August 2018 (Brunsden and Khan, 2018). For many households, however, the crippling effects of the Recession on employment and income persist at the time of writing (McKee et al., 2017). Already, the Recession’s influence on health in the Eurozone has been the focus of a number of studies (e.g. Drydakis, 2015; Rajmil et al., 2013; Reile et al., 2014). Still, there is some evidence that the quality of existing research may limit the conclusions that can be

163

drawn from their findings. Parmar, Stavropoulou & Ioannidis (2016) found in their literature review that of the 41 studies included, only two (Drydakis, 2015 & Eiríksdóttir et al., 2016) had low risk of bias, with all other studies having high or moderate risk of bias. The authors therefore did not synthesise results, and concluded that there was a need for more observations and methodological improvements in future research. However, the authors also noted that there appeared to be a negative impact of the Recession on mental health, and mixed findings about the influence on self-rated health. The purpose of Parmar et al. (2016) was to determine the Recession’s effects on health, with the review concluding that this was too difficult given the quality of existing studies, and the heterogeneity in study design and analysis. We used this study as a starting point, and aimed to take their research several steps further. First, we did so by identifying the aspects of studies’ methods and orientations that may make their results difficult to synthesise, and of variable quality. Second, we narrowed the focus of our study in terms of health outcomes and geography, in order to better compare like-for-like studies. We therefore elected to focus solely on physical health, versus health behaviours or mental health. We also focussed solely on the Eurozone, where the worst effects of the Recession were felt. Ultimately, with our review, we aimed to indicate how to find common ground among future research. Therefore, we posed the following questions: what methodologies have been used to assess the impact of the Great Recession on health, and to what extent do these methodologies impact studies’ quality? 2. Methods To accomplish this, we conducted a literature review using systematic search methods, and analysed results thematically based on methodological aspects (geography of focus; data choice; operationalization of health and wealth; and study design). Then, we conducted a critical appraisal, in order to assess the validity, relevance and quality of included studies (Sanderson et al., 2007). 2.1. Locating studies To locate relevant studies, the following databases were searched: PubMed, Web of Science and Scopus. These databases were selected because of their general size (Scopus is the largest database of peer reviewed literature) and/or topical relevance (PubMed focuses on life sciences/medical topics, and Web of Science examines cross-disciplinary research). We conducted this search in October 2018. The minimum publication year was January 2010 (as the effects of the Recession on households only became widely evident in 2009). To yield relevant sources, an adapted version of a Population, Intervention/exposure, Comparison, Outcome, (PICO) framework was used (Ecker and Skelly, 2010). A PICO framework, while designed to search for clinical trials, was selected because it has been shown to yield a wider variety of hits with greater sensitivity compared to its counterparts designed for the social sciences (e.g. SPIDER), even for qualitative and nonclinical studies (Methley et al., 2014). Because no comparison group was necessary to understand the Recession’s influence on health, this was excluded.

Table 1 Boolean search terms. Population

Intervention

Outcome

‘Europe*’; Eurozone; EU

‘economic crisis; ‘financial crisis’; ‘Great Recession’; ‘debt crisis’

health; ‘disease*’

164

K. Thompson et al. / Economics and Human Biology 35 (2019) 162–184

The following Boolean search string resulted from these terms: (Europe OR Eurozone OR EU) AND (crisis OR ‘economic crisis’ OR ‘financial crisis’ OR ‘Great Recession’ OR ‘debt crisis’) AND (health OR disease) (Table 1). Snowball sampling was also used; once articles were selected, their bibliographies were scanned for additional relevant sources, which yielded two additional articles. 2.2. Study selection and evaluation We sought to identify articles that were specifically concerned with the Great Recessions impact on health in the Eurozone. To do so, we looked at studies that viewed the Recession’s influence or some other measure of wealth as a predictor, and health as an outcome. We selected three most commonly-used and wellestablished measures of health: disease incidence/prevalence and mortality rates (population-level indicators); and self-rated health (an individual-level indicator). Parrish (2010) recommended these three indicators as the most reliable to measure both the overall level of health of a population, and the distribution of health within and among populations. In a departure from Parmar et al. (2016), we decided to exclude articles predominantly concerned with mental health, healthcare

provision/access and health behaviours. With this narrower focus, we intended to compare like-for-like studies, with the aim of being able to integrate results, as Parmar et al. (2016) was not able to. We elected to include disease incidence/prevalence and mortality, as they are among the most commonly used indicators of physical health. Self-rated health, while more subjective, has been found to be a reliable indicator of future trends in mortality (e.g. Singh-Manoux et al., 2007; Schnittker and Bacak, 2014). Further, the three health indicators we selected are able to be directly measured and are comparatively equally represented across socio-economic strata (Larson and Mercer, 2004). This may not be the case with service use and mental health: Steele et al. (2007) found that higher-income individuals are more likely to use health services and to be diagnosed with mental illnesses. Further, mental and physical health, while heavily intertwined, nonetheless have different aetiologies, with mental illness sometimes viewed as a distal cause of disease (and vice versa) (e.g. Link and Phelan, 1995; Chida and Steptoe, 2008). We therefore believe mental and physical health warrant being studying separately. In practice, this meant only including studies with 50% or more of their health outcomes concerned with disease incidence/prevalence, mortality rates and/or self-rated health.

Fig. 1. Flowchart of the literature selection process. Based on: Moher et al. (2009).

K. Thompson et al. / Economics and Human Biology 35 (2019) 162–184

Beyond the definition of health, there were several inclusion criteria. First, to ensure that the economic climate was similar, included studies were predominantly focussed on the Eurozone. For studies focussing on multiple countries, this meant including those that focussed on the Eurozone, the EU or Europe overall. For studies focussing on a handful of countries, this meant only including those with 50% or more of the countries in the Eurozone. For studies focussing on a single country, that country had to be a Eurozone country. Second, only articles written in 2010 or later were included. This is because the Great Recession’s effects on household only began to be felt in the late 2000s or early 2010s, and changes in health indicators take time to become evident (e.g. Pye et al., 2016). Third, articles had to be written in English, due to the language limitations of the authors. This initial search yielded 930 results from PubMed, 353 from Web of Science, and 574 from Scopus, totalling 1857 articles. Bibliographic information and abstracts were then uploaded to the reference programme Endnote. Once duplicates were removed, 1316 unique papers remained for consideration. Then, we undertook a step-by-step title and abstract search to eliminate articles that did not meet our inclusion criteria (see Fig. 1). To minimise bias in the selection of articles, two of the authors scanned abstracts: one read all abstracts, while a second performed a check of a random selection of 25% of unique abstracts. From this process, 124 articles remained and were read in a full paper analysis. At this stage, articles that, upon further inspection, did not meet the inclusion criteria, and/or were not written in English were excluded. We also decided at this stage to exclude systematic reviews/literature reviews. From here, we generated a final list of included studies. 2.3. Analysis and synthesis The resulting 42 included studies were analysed based on subquestions to help answer the main research question. 1 In what geographies has the Recession’s influence on health been studied? 2 What types of data sources were used? 3 How do existing studies conceptualise health and wealth? 4 What study designs were used? These sub-questions were selected to better understand the methodological choices each study made, ultimately to help identify how studies reached their conclusions. These are largely borrowed from our critical appraisal tool, which pinpoints the defines the ‘study methods’ as: study design, sampling frame, sample size, outcome measures, measurement and response rate (Loney et al., 1998). For our purposes, response rate was less important, as most studies exploit secondary data, so this point was dropped. We also felt it was important understand variations in predictor measures, as we found, from initial reading, that these varied widely. Question 1 and 2 covers sampling frame and sample size. Question 3 covers predictor and outcome measures. Question 4 covers study design. Regarding study design (question 4), we defined ecological studies as those using averaged outcomes for a geographically and temporally defined space. The geography or population is the unit of analysis (Coggon et al., 2009). In panel studies, the same cohort is observed at least twice (Coggon et al., 2009). Cross-sectional studies are defined as those that observe different cohorts at either a single moment in time, or at multiple points in time (Coggon et al., 2009). We further broke

165

down study design into (inter)nationally representative or nonrepresentative samples. To assess whether these methodological choices affected study design, we also included the following questions about reported impact: 5 What were the reported impacts of the Recession on health? 6 What were the magnitude of these impacts? Regarding question 5, results were classified as negative, positive/neutral, or inconclusive/mixed. To answer question 6, we reported the main effect sizes of mortality, disease incidence/ prevalence, and/or self-rated health. The summarized findings of these six questions can be found in Appendix A. 2.4. Critical appraisal To help determine the value, relevance and trustworthiness of included studies, we conducted a critical appraisal (Morrison, 2017). To do so, we created a synthesis of two scoring tools. The first is Loney et al. (1998), which itself was included in a systematic review (Sanderson et al., 2007), and was the most recent tools that was appropriate for cohort, case control and cross-sectional studies, as well as being the most suited for this study’s subject matter. We also used elements from Parmar, Stavropoulou & Ioannidis (2016)’s risk of bias assessment tool. While we initially attempted to replicate these methods precisely, we found that their scoring system was described too opaquely for us to reproduce accurately. This was particularly the case in relation to their item, ‘selection bias’, with studies often not containing the granularity necessary to score them on response rates. Also, with Parmar, Stavropoulou & Ioannidis (2016)’s item, ‘measurement error in health outcome’, studies were scored based on the risk of misreporting or misstatement: most studies did not include the information necessary to evaluate these factors. We therefore elected to borrow elements from Parmar, Stavropoulou & Ioannidis (2016) to tailor Loney et al. (1998)’s tool specifically to the study of the Recession’s impact on health. We included seven items in our critical appraisal tool, with each element being scored as 1 = present; 0 = absent. A score of 6 or 7 indicated low bias, a score of 4 or 5 moderate bias, and a score of 3 or below a high risk of bias. Below, each item in our measurement tool is discussed: 1 Study sample includes the whole population or is a representative random sample (Loney et al., 1998): We included this to assess the generalizability of included studies. 2 Sufficient time horizon (Parmar et al., 2016): This item was absent from Loney et al. (1998). However, we opted to include it because a time comparison (before and after the Recession) was present in nearly all of the included studies, and because it takes time for changes in health to become evident. This is particularly important for studies that did not find an impact of the Recession on health: while it may not be necessary for studies to have a long time horizon (particularly those that found an effect over a few years), it may be that shorter time horizons were not sufficient to show effects of the Recession on health. We considered studies to have a ‘sufficient’ time horizon if it examined a period of at least ten years. Unlike Parmar, Stavropoulou & Ioannidis (2016), we did not include a criterion that the time horizon had to extend to three years after the Recession ended. As discussed elsewhere, it is not clear when the Recession ended (if it has ended at the time of writing). 3 Use of panel data (elaboration of Parmar et al., 2016): In observational research, panel data is considered the ‘gold

166

K. Thompson et al. / Economics and Human Biology 35 (2019) 162–184

Table 2 Results overview – Geography. Geography

Countries

Citation

Europe (12)

EU 17 - EU28

Multiple countries with different experiences of the Recession (2)

Germany, Finland, Portugal, Slovenia, Poland, Czech Republic, Slovakia and Bulgaria; 232 European regions Greece and Sweden; Greece, Finland and Iceland; Greece, Ireland and Poland; Greece and Poland Estonia, Lithuania and Finland

Abebe et al. (2016); Clair et al. (2016); Ferrarini et al. (2014); Heggebø and Elstad (2018); Huijts et al. (2015); Karanikolos et al. (2018); Mackenbach et al. (2018); Nelson and Tøge (2017); Tapia Granados and Ionides (2017); Toffolutti and Suhrcke (2014); Tøge (2016a,Tøge, 2016b; Tøge and Blekesaune (2015) Bartoll and Mari-Dell’Olmo (2016); Baumbach and Gulis (2014)

2-3 countries with different experiences of the Recession (4)

2-3 countries with similar experiences of the Recession (1) 1 country that fared well in Recession Germany (1) 1 country that fared poorly in Spain Recession (13)

1 country that fared poorly in Recession (7) 1 country that fared poorly in Recession (1) 1 country that fared poorly in Recession (1)

4

5

6

7

Faresjö et al. (2013);); Hessel et al. (2014); Tapia Granados and Rodriguez (2015); Vandoros et al. (2013) Reile et al. (2014) Loerbroks et al. (2014)

Italy

Aguilar-Palacio et al. (2018); Arroyo et al. (2015); Barroso et al. (2016); Bartoll et al. (2015); Benmarhnia et al. (2014); Coveney et al. (2016); Fernandez et al. (2015); Lopez Del Amo Gonzalez et al (2018); Maynou et al. (2014); Moya et al. (2015); Regidor et al. (2014); Urabanos-Garrido & Lopez-Valcarcel (2015); Vasquez-Vera et al. (2016) Barlow et al. (2015); Bonovas and Nikolopoulos (2012); Kollia et al. (2016); Vlachadis et al. (2014a), b; Vrachnis et al. (2015); Zavras et al. (2013) Sarti and Zella (2016)

Portugal

Nogueira (2016)

Greece

standard’: using panel data enables more accurate model parameters to be estimated, allowing for omitted variables to be controlled for, and often enables more abstract or latent concepts, such as health, to be measured (Hsiao, 2007). While Parmar et al. (2016) assessed whether studies used ecological data and therefore were prone to ecological fallacy, we extended this to include cross-sectional studies, which also are subject to greater bias than studies using panel data. Unbiased predictor measure (both Loney et al., 1998 and Parmar et al., 2016): This item, along with Item 5, helped to ensure that included studies measured what they intended to measure. We defined being ‘unbiased’ as having a specific measure of the Recession (e.g. income, employment status). This is a departure from both Loney et al. (1998) and Parmar et al. (2016). Loney et al. (1998) left the definition of ‘bias’ vague. Parmar et al. (2016) defined being ‘unbiased’ as the presence of macroeconomic indicators. However, this failed to take into account studies that used individual-level measures as predictors. We therefore opted for a more general measure: if either an individual- or population-level indicator of health was present, a study scored a point on this item. Unbiased outcome measure (both Loney et al., 1998 and Parmar et al., 2016): We specified this item as having an individual- or population-level measure of health. Because this was part of our inclusion criteria, all studies were rated as having an unbiased outcome measure. Again, this item was not precisely specified by Loney et al. (1998). Parmar et al. (2016) rated outcome measures on their likelihood of being validated and/or their chances of being accurately reported. It was not possible to determine this for several studies included in our review, so we opted to not include this. Confidence intervals/subgroup analysis (Loney et al., 1998): With the presence of confidence intervals and/or sub-group analyses, results were more fully interpretable. Study subjects/survey respondents described (Loney et al., 1998): Loney et al. (1998) define their original item about study subjects

as “Are the study subjects and setting described in detail and similar to those of interest to you?” (p. 171). Because a large share of our included studies are based on observational data, we broadened this to include survey respondents. While this itemwas defined vaguely in Loney et al. (1998), we defined this as studies including basic descriptive statistics of their samples. We also excluded several items from Loney et al. (1998). Because of the overlap with Item 1 (study sample includes the whole population or is a random sample), we opted to exclude Loney et al. (1998)’s items ‘unbiased sampling frame’ and ‘adequate sample size’. Also, because nearly all studies used routine sources of data, we excluded Loney et al.(1998)’s item, ‘adequate response rate (70%), refusers described’. In our critical appraisal, all studies were initially assessed separately by two of the authors. When there was disagreement over scoring, we discussed the study in question, and reached a consensus. To see if study quality impacted results, we included studies’ main findings, characterized as negative, positive/neutral, or inconclusive/mixed, alongside their risk of bias scores in Appendix B. 3. Results A summary table of our literature review’s findings can be found in Appendix A. Here, the full references, research questions, data sources, geography, wealth measure, health measure, study design, key findings and effect sizes are presented. Below, results are presented based on: geography, data type, wealth and health measure used, study design and reported impact. Then, the results of the critical appraisal are analysed. 3.1. Geography Clear trends emerged regarding studies’ geographies. A large share of studies (19 out of 42) analysed groups of European countries. Twelve of these studies focussed on Europe overall,

K. Thompson et al. / Economics and Human Biology 35 (2019) 162–184

167

Table 3 Results overview – Data type. Type

Source

Citation

Routine (secondary), representative data

EU-SILC (14)

Routine (secondary), representative data Routine (secondary), representative data Routine (secondary), representative data Routine (secondary), representative data

Eurostat (3)

Abebe et al. (2016); Barlow et al. (2015); Clair et al. (2016); Coveney et al. (2016); Ferrarini et al. (2014); Heggebø and Elstad (2018); Hessel et al. (2014); Huijts et al. (2015); Lopez Del Amo Gonzalez et al. (2018); Nelson and Tøge (2017); Sarti and Zella (2016); Tøge (2016a,Tøge, 2016b; Tøge and Blekesaune (2015); Vandoros et al. (2013) Bartoll and Mari-Dell’Olmo (2016); Baumbach and Gulis (2014); Regidor et al. (2014)

Routine (secondary), representative data Routine (secondary), representative data Routine (secondary), representative data Primary, nonrepresentative data Primary, nonrepresentative data

European Centre for Disease Control (ECDC) (1) Prevention and the European Health for All Database/WHO (4) Nationally or regionally representative cross-sectional data (9) Nationally or regionally representative panel data (1) Census/register data (6) Combination (mortality registers, EUSILC; European Social Survey) (1) Cross-sectional study (1) Panel data (2)

Bonovas and Nikolopoulos (2012) Karanikolos et al. (2018); Tapia Granados and Rodriguez (2015); Tapia Granados and Ionides (2017); Toffolutti and Suhrcke (2014) Aguilar-Palacio et al. (2018); Arroyo et al. (2015); Barroso et al. (2016); Bartoll et al. (2015); Moya et al. (2015); Urbanos-Garrido and Lopez-Valcarcel (2015); Reile et al. (2014); Vasquez-Vera et al. (2016); Zavras et al. (2013) Loerbroks et al. (2014) Benmarhnia et al. (2014);); Maynou et al. (2014); Nogueira (2016); Vlachadis et al. (2014a), b; Vrachnis et al. (2015) Mackenbach et al. (2018) Faresjö et al. (2013) Fernandez et al. (2015); Kollia et al. (2016)

ranging from the EU17 to the EU28. Bartoll and Mari-Dell’Olmo (2016) and Baumbach and Gulis (2014) stood out as the only studies that compared multiple (more than three) European countries that were impacted very differently by the Recession. Four other articles compared two to three countries who experienced different levels of financial insecurity in the wake of the Recession. Reile et al. (2014) was the only included study that analysed several countries assumed to have had similar levels of financial insecurity. The remaining 23 studies examined single countries, or individual cities or regions within single countries. Only one study (Loerbroks et al., 2014) focussed on a country that fared relatively well in terms of financial insecurity, while 22 analysed countries that fared among the worst in the Eurozone. Thirteen studies examined Spain overall or a region in Spain (most commonly Catalonia). Another sizeable share (seven articles) of the included studies concentrated on Greece as a whole, or municipalities within Greece. One study focussed on Italy, and one on Portugal (see Table 2). 3.2. Data type Regarding data type, clear patterns were present. Routine sources of data were used in 39 out of 42 included studies, making it far and away the most common type of data source. These data sources were nationally representative, but were not designed specifically or solely to assess changes in health. Of these data sources, the most commonly-used was the European Statistics on Income and Living Conditions (EU-SILC), used in 14 studies. Other multi-national data sources, such as Eurostat and Prevention and the European Health for All Database/WHO statistics, were used in eight studies. Nine studies used nationally or regionally representative cross-sectional surveys, such as the Spanish National Health Survey. One other (Loerbroks et al., 2014) used nationally or regionally representative panel data that is routinely collected, while six studies used national census data. Mackenbach et al. (2018) was the only study using a combination of data types,

including mortality registers, the EU-SILC, and the European Social Survey. Only three included studies used primary, non-representative data: a cross-sectional study of Swedish and Greek medical students (Faresjö et al., 2013); a longitudinal study of 143 participants from a neighbourhood in Barcelona (Fernandez et al., 2015); and a longitudinal, representative study of Attica, Greece (Kollia et al., 2016) (see Table 3). 3.3. Wealth measures There was a great deal of variety how studies measured financial security (or wealth). In nine studies, there was no specific measure of financial security; rather, a comparison of before the Recession (2008 and earlier) and after (2010 and later) was used to indicate a change in financial security. Faresjö et al. (2013) also did not use a before-after comparison, as it conducted a cross-sectional study of Greek and Swedish participants and assumed that the Greek students were more financially insecure. Among the studies that used wealth indicators, a mix of population- or individual-level ones were used. Regarding population-level measures, macroeconomic indicators (e.g. national unemployment rate; GDP; Gini coefficients, national employment insurance rates, national spending on social insurance programmes) were used in ten studies. One study (Maynou et al., 2016) used neighbourhood-level wealth data. In terms of individual-level wealth measures, employment status was commonly used, with six studies using this. An additional two studies (Clair et al., 2016; Vasquez-Vera et al., 2016) used issues with housing payments as a measure of wealth. Eleven other studies used composite measures of SES (including employment status, occupation type, education, and income). Only two studies (Heggebø and Elstad, 2018; Mackenbach et al., 2018) used a combination of individual- and population-level data to measure wealth. Of the different aspects we compared, wealth varied the most among studies (see Table 4).

168

K. Thompson et al. / Economics and Human Biology 35 (2019) 162–184

Table 4 Results overview – Wealth measure. Type

Citation

None used (implicit with time change) (9)

Arroyo et al. (2015); Benmarhnia et al. (2014); Bonovas and Nikolopoulos (2012); Hessel et al. (2014); Karanikolos et al. (2018); Vandoros et al. (2013); Vlachadis et al. (2014a), b; Vrachnis et al. (2015) Faresjö et al. (2013) Abebe et al. (2016); Bartoll and Mari-Dell’Olmo (2016); Baumbach and Gulis (2014); Coveney et al. (2016); Ferrarini et al. (2014); Moya et al. (2015); Regidor et al (2014); Tapia Granados and Ionides (2017); Tapia Granados and Rodriguez (2015);and Toffolutti and Suhrcke (2014) Maynou et al. (2014) Aguilar-Palacio et al. (2018); Huijts et al. (2015); Tøge and Blekesaune (2015); Nelson and Tøge (2017); Urbanos-Garrido and Lopez-Valcarcel (2015); Loerbroks et al. (2014) Clair et al. (2016); Vasquez-Vera et al. (2016) Barlow et al. (2015); Barroso et al. (2016); Bartoll et al. (2015); Fernandez et al (2015); Kollia et al. (2016); Lopez Del Amo Gonzalez et al (2018); Nogueira (2016); Reile et al. (2014); Sarti and Zella (2016); Tøge (2016a,Tøge, 2016b; Zavras et al. (2013) Heggebø and Elstad (2018); Mackenbach et al. (2018)

None used (implicit with country comparison) (1) Population-level: Macroeconomic statistics (GDP, unemployment rate) (10) Population-level: Neighbourhood-level statistics (1) Individual-level: Employment status and/or job insecurity (6) Individual-level: Issues with housing payments (2) Individual-level: Combined measure (e.g. employment status, occupation type, education, income) (11) Combination: Individual level employment status and population-level economic indicators (2)

3.4. Health measure As with measures of financial security, included studies used both population- and individual-level measures of health. First looking at the former measure, four studies used disease incidence rates. These include infectious disease incidence rates (Bonovas & Nikolopoulous, 2012); cardiovascular disease prevalence (Kollia et al., 2016); asthma incidence (Loerbroks et al., 2014); or a combination (Moya et al., 2015: diabetes, depression, myocardial infection, cancer). Several other studies used overall mortality rates as measures of health, including: Karanikolos et al. (2018); Maynou et al. (2016) Nogueira (2016); and Vlachadis et al. (2014b). One study used cardiovascular mortality rates (Vlachadis et al., 2014a). Vrachnis et al. (2015) used cancer mortality rates. Baumbach and Gulis (2014) and Toffolutti and Suhrcke (2014) examined several mortality rates, including overall mortality, suicide rates and transport mortality rates. Benmarhnia et al. (2014) used mortality rates in individuals over 60 years old. Bartoll and Mari-Dell’Olmo (2016) and Tapia Granados and Ionides (2017) used life expectancy at birth to measure health. Granados & Rodriguez (2015) used a combination of mortality and life expectancy rates. Turning to individual-level measures, the most common of these relied solely on self-rated health. This was used in 21 studies, with nearly all using the same dataset, the EU-SILC. This dataset assessed

health using a single self-assessment question: “How is your health in general? Is it: (1) very good, (2) good, (3) fair, (4) bad, (5) very bad?” Other studies used more elaborate measures of self-rated health. Three studies asked about self-rated physical health and mental health, while one asked about health-related quality of life. A handful of other studies used multiple indicators of health. These included: Bartoll et al. (2015), which examined selfreported health, overweight and obesity and health behaviours; Clair et al. (2016), which used self-rated health and the prevalence of chronic conditions; and Faresjö et al. (2013), which used biomarkers such as cortisol levels in hair and self-reported health. Mackenbach et al. (2018) and Regidor et al. (2014) stand out for using a combination of population and individual measures of health. The former used all-cause mortality, cause-specific mortality and self-rated health, while the later used disease incidence/prevalence rates and self-rated health (see Table 5). 3.5. Study design Study designs fell into three camps: those using ecological, panel and cross-sectional data. We found that the largest share of studies (15) used (inter)nationally representative panel data. A further two studies (Fernandez et al., 2015; Kollia et al., 2015) used nonrepresentative panel data. The next-largest share of studies used

Table 5 Results overview – Health measure. Health measure

Citation

Population measure: disease incidence/prevalence rates (4)

Bonovas and Nikolopoulous (2012); Kollia et al. (2016); Loerbroks et al. (2014); Moya et al. (2015) Baumbach and Gulis (2014); Bartoll and Mari-Dell’Olmo (2016); Benmarhnia et al. (2014); Karanikolos et al. (2018); Maynou et al. (2014); Nogueira (2016); Tapia Granados and Ionides (2017); Tapia Granados and Rodriguez (2015); Toffolutti and Suhrcke (2014); Vlachadis et al. (2014b), a; Vrachnis et al. (2015) Abebe et al. (2016); Aguilar-Palacio et al. (2018); Arroyo et al. (2015); Barlow et al. (2015); Barroso et al. (2016); Coveney et al. (2016); Fernandez et al. (2015); Ferrarini et al. (2014); Heggebø and Elstad (2018); Hessel et al. (2014); Huijts et al. (2015); Lopez Del Amo Gonzalez et al (2018); Nelson and Tøge (2017); Reile et al. (2014); Sarti and Zella (2016); Tøge (2016a,Tøge, 2016b; Tøge and Blekesaune (2015); Urbanos-Garrido and Lopez-Valcarcel (2015); Vandoros et al. (2013); Vasquez-Vera et al. (2016); Zavras et al. (2013); Bartoll et al. (2015); Clair et al. (2016); Faresjö et al. (2013) Mackenbach et al. (2018); Regidor et al. (2014)

Population measure: mortality rates / life expectancy at birth (12)

Individual measure: self-rated health (including mental health and quality of life) (21)

Individual measure: composite measures of individual health (3) Combination: population and individual measures (2)

K. Thompson et al. / Economics and Human Biology 35 (2019) 162–184

169

Table 6 Results overview – Study design. Study design

Representativeness Citation

Ecological

Representative (10) Baumbach and Gulis (2014); Benmarhnia et al. (2014); Bonovas and Nikopoulos (2012); Karanikolos et al. (2018); Tapia Granados and Rodriguez (2015); Tapia Granados and Ionides (2017); Toffolutti and Suhrcke (2014); Vlachadis et al. (2014a), b; Vrachnis et al. (2015) NonMaynou et al. (2014); Nogueira (2016); Regidor et al. (2014) representative (3) Representative (15) Abebe et al. (2016); Barlow et al. (2015); Clair et al. (2016); Coveney et al. (2016); Ferrarini et al. (2014); Heggebø and Elstad (2018); Hessel et al. (2014); Huijts et al. (2015); Lopez Del Amo Gonzalez et al (2018); Loerbroks et al. (2014); Nelson and Tøge (2017); Sarti and Zella (2016); Tøge (2016a,Tøge, 2016b; Tøge and Blekesaune (2015); Vandoros et al. (2013) NonFernandez et al. (2015); Kollia et al. (2015); representative (2) Representative (9) Arroyo et al. (2015); Aguilar-Palacio et al. (2018); Barroso et al. (2016); Bartoll and Mari-Dell’Olmo (2016); Bartoll et al. (2015); Moya et al. (2015); et al. (2014); Urbanos-Garrido and Lopez-Valcarcel (2015); Zavras et al. (2013) NonFaresjö et al. (2013); Vasquez-Vera et al. (2016) representative (2) Representative (1) Mackenbach et al. (2018)

Panel

Cross-sectional

Combination (ecological, panel and cross-sectional)

ecological data, 10 of which were (inter)nationally representative. Maynou et al. (2016); Nogueira (2016) and Regidor et al. (2014) used non-nationally representative ecological data. Additionally, nine studies used (inter)nationally representative cross-sectional data, while two studies (Faresjö et al., 2013; Vasquez-Vera et al., 2016) used non-representative cross-sectional data. Finally, Mackenbach et al. (2018) exploited a mix of ecological, panel and cross-sectional data ( see Table 6). 3.6. Reported impact Overall, the present study found that there is some debate over whether the Great Recession impacted health in Europe. The majority of included studies (26 articles) found that some measure of financial insecurity during the Great Recession worsened health indicators. Eight studies concluded that the crisis had a neutral or even a positive effect on health indicators. Eight studies were inconclusive, or had mixed findings (see Table 7). 3.7. Critical appraisal Of the 42 included studies, 13 had a low risk of bias. At 21 studies, the majority had a moderate risk of bias, and 8 had a high risk of bias. The full critical appraisal can be found in Appendix B. Looking at the individual items comprising the scores, all but five studies (Faresjö et al., 2013; Fernandez et al., 2015; Kollia et al., 2016; Nogueria, 2016; Vasquez-Vera et al., 2016) used representative samples or data from the whole population. Yet, regarding an unbiased time horizon, only six studies (Aguilar-Palacio et al.,

2018; Bartoll et al., 2015; Baumbach and Gulis, 2014; Karanikolos et al., 2018; Mackenbach et al., 2018; Regidor et al., 2014) scored a point. As mentioned above, 18 studies used (representative and non-representative) panel data, and scored a point on this item. In terms of an unbiased predictor indicator, all but 9 studies (Arroyo et al., 2015; Benmarhnia et al., 2014; Bonovas and Nikolopoulos, 2012; Hessel et al., 2014; Karanikolos et al., 2018; Vandoros et al., 2013; Vlachadis et al., 2014a, b; Vrachnis et al., 2015) measured the impact of the Recession on health, and so scored a point on this item. Regarding unbiased outcome measures, all included studies scored on this item (having a health measure was part of our inclusion criteria). Similarly, with confidence intervals and/or subgroup analyses, all but ten studies (Baumbach and Gulis, 2014; Benmarhnia et al., 2014; Bonovas and Nikolopoulos, 2012; Ferrarini et al., 2014; Karanikolos et al., 2018; Regidor et al., 2014; Vlachadis et al., 2014a, b; Vrachnis et al., 2015) scored on this item. Finally, regarding study subjects/survey respondents described, all but thirteen of the included studies (Baumbach and Gulis, 2014; Benmarhnia et al., 2014; Bonovas and Nikolopoulos, 2012; Ferrarini et al., 2014; Karanikolos et al., 2018; Nogueira, 2016; Regidor et al., 2014; Tapia Granados and Ionides, 2017; Toffolutti and Suhrcke, 2014; Tøge and Blekesaune, 2015; Vlachadis et al., 2014a, b; Vrachnis et al., 2015) scored on this item (see Table 8). To understand if studies’ quality impacted their findings, we included studies’ reported impact of the Recession on health alongside their risk of bias score in Appendix B. We found a clear link between the two: out of the eight studies with a high risk of bias, five reported a neutral/positive, or inconclusive/mixed impact

Table 7 Results overview – Reported impact. Reported impact

Citation

Great Recession worsened outcomes Abebe et al. (2016); Aguilar-Palacio et al. (2018); Barlow et al. (2015); Barroso et al. (2016); Bartoll and Mari-Dell’Olmo (2016); (26) Bartoll et al. (2015); Bonovas and Nikopoulos (2012); Clair et al. (2016); Ferrarini et al. (2014); Heggebø and Elstad (2018); Huijts et al. (2015); Kollia et al. (2016); Loerbroeks et al. (2014); Lopez Del Amo Gonzalez et al. (2018); Maynou et al. (2014); Moya et al. (2015); Nelson and Tøge (2017); Nogueira (2015); Reile et al. (2014); Sarti and Zella (2016); Tøge and Blekesaune (2015); UrbanosGarrido and Lopez-Valcarcel (2015); Vandoros et al. (2013); Vasquez-Vera et al. (2016); Vlachadis et al. (2014b); Zavras et al. (2013) Great Recession had neutral/positive Arroyo et al. (2015); Coveney et al. (2016); Fernandez et al. (2015); Tapia Granados and Rodriguez (2015); Tapia Granados and impact (8) Ionides (2017); Regidor et al. (2014); Vlachadis et al. (2014a); Vrachnis et al. (2015) Inconclusive/mixed (8) Baumbach and Gulis (2014); Benmarhnia et al. (2014); Faresjö et al. (2013); Hessel et al. (2014); Karanikolos et al. (2018); Mackenbach et al. (2018); Toffolutti and Suhckre (2014); Tøge (2016a,Tøge, 2016b

170

K. Thompson et al. / Economics and Human Biology 35 (2019) 162–184

Table 8 Critical appraisal summary. Risk of bias

Citation

Low risk of bias: score of 6 or 7 (13)

Abebe et al. (2016); Aguilar-Palacio et al. (2018); Barlow et al. (2015); Bartoll et al. (2015); Clair et al. (2016); Coveney et al. (2016); Heggebø and Elstad (2018); Lopez Del Amo Gonzalez et al. (2018); Nelson and Tøge (2017); Sarti and Zella (2016); Tøge (2016a,Tøge, 2016b; Loerbroks et al. (2014); Mackenbach et al. (2018) Moderate risk of bias: score of 4 Arroyo et al. (2015); Barroso et al. (2016); Bartoll and Mari-Dell’Olmo (2016); Baumbach and Gulis (2014); Fernandez et al. (2015); or 5 (21) Ferrarini et al. (2014); Hessel et al. (2014); Huijts et al. (2015); Kollia et al. (2016); Maynou et al. (2014); Moya et al. (2015); Regidor et al. (2014); Reile et al. (2014); Tapia Granados and Ionides (2017); Tapia Granados and Rodriguez (2015); Toffolutti and Suhckre (2014); Tøge and Blekesaune (2015); Urbanos-Garrido and Lopez-Valcarcel (2015); Vandoros et al. (2013); Vasquez-Vera et al. (2016); Zavras et al. (2013) High risk of bias: score of 1, 2 or 3 Benmarhnia et al. (2014); Bonovas and Nikolopoulos (2012); Faresjö et al. (2013); Karanikolos et al. (2018); Nogueira (2016); Vlachadis (8) et al. (2014a), b; Zavras et al. (2013)

of the Recession on health. Out of the twelve studies with a low risk of bias, only two reported a neutral/positive, or inconclusive/mixed impact of the Recession on health. It appears that lower-quality studies were more likely to not find a negative impact of the Recession on health.

4. Discussion With this literature review, we sought to add to the existing debate on the impact of the Great Recession on physical health in the Eurozone. To do so, we focussed on different methodological choices studies made, helping us to identify why and how different conclusions were reached. Our approach may also have helped to find common ground for future studies on the Recession. We found that studies made divergent methodological choices, some of which may have impacted study quality and generalizability. Further, the Great Recession is extremely recent, and the body of literature focussing on its impact on health continues to grow. For this reason, revisiting this topic several years on from Parmar et al. (2016) was worthwhile unto itself. We also elaborated on their approach by analysing the different methodological choices included studies made, and assessing their quality in a critical appraisal. The findings from both the methodology analysis and the critical appraisal are discussed below. First, we found that an area that likely influenced studies’ conclusions was their geography. For instance, a number of included studies focussed on single countries, which tended to be the hardest-hit. Greece and Spain in particular were the subject of a large share of studies. These countries are, in many ways, exceptional in their experiences of the Recession. The Recession has lasted the longest there, and has had the most corrosive effects. Another common approach was to study Europe as a whole. Yet, this may be problematic, as the constituent countries of the EU often had vastly different experiences of the Recession. This may mean that the Recession’s impacts were masked by viewing the EU (or Eurozone) as a single bloc. It would therefore likely be fruitful to study the way in which the Recession impacted health in different countries, particularly comparing countries that fared well during the Recession with countries that fared poorly. This may help to identify the mechanisms linking financial insecurity and health, inc-

luding geographic inequalities and related socio-economic indicators (e.g. Borrell et al., 2014). This also may help to understand whether the Recession’s impact on health outcomes is related to its severity: in some countries, its influence may be moderate or even positive; in other countries with vastly more negative experiences, the Recession may have negatively impacted health. We also found what appears to be a link between the type of data that studies used and the conclusions that they reached: two of the three articles using primary, non-representative data sources (Faresjö et al., 2013; Fernandez et al., 2015) found that the Recession did not have a detrimental impact on health, while a vast majority of those using representative sources did. It simply may be that studies using non-representative samples lacked the power to register population-level shifts in health (Pye et al., 2016). Even among studies that used representative data, finding data sources that accurately assessed the Recession’s impact may have been a challenge. While routine, secondary datasets achieve the representativeness necessary to register population-level changes in health indicators, they often have the disadvantage of not being designed for a particular study’s purpose, and may not as accurately or completely measure concepts as authors would like (Pye et al., 2016). No study used data both that was nationally representative, and that was designed to specifically measure changes in population wealth and health. To achieve both objectives, new, fit-for-purpose, national-level studies are likely necessary. Also, while included studies used a variety of measures of financial security, few of them used measures that took into account multiple aspects of this concept. Yet, financial security is widely understood in research to be multi-faceted: not only as how much money individuals actually have, but also how much they perceive they have relative to others in society. The level of respect individuals’ occupations afford them likely also plays a role in their understanding of security (Jones and Wildman, 2008). How people perceive their level of wealth has already been found to have a significant impact on their health, with health worse in more unequal societies (McKee et al., 2017; Lago et al., 2018). However, none of the financial security measures employed in existing studies involved subjective assessments of how much money people felt they had. Doing so in future research may be important to fully understanding how the Recession impacted health.

K. Thompson et al. / Economics and Human Biology 35 (2019) 162–184

Further, more context-specific factors may determine which measure of wealth is the most appropriate. For instance, gross domestic product (GDP) may be a very good measure of wealth in some countries. In countries where self-employment rates are extremely high (Greece and Italy’s self-employed workers as a share of the total workforce in 2017 were 29% and 21%, respectively), trends in GDP will more closely mirror trends in households’ income (Eurostat, 2017). However, GDP may be an extremely poor measure of wealth in other contexts. In Spain, where self-employment taxes are relatively high, there is a widespread concealment of self-employment income (Martinez-Lopez, 2013). In this instance, more individual-level measures of wealth would be more appropriate than aggregate measures. We also found that operationalisations of health impacted the conclusions and quality of studies. As mentioned, included studies employed a mix of individual-level and population-level indicators. In the former instance, self-rated health was the most commonly used. Self-rated health likely encapsulates facets of mental health and general well-being, so that what is being measured is somewhat outside the scope of this literature review (which aimed to focus predominantly on physical health). Indeed, Idler et al. (1999) discussed that self-rated health is often heavily influenced by social and mental wellbeing: those who reported more social satisfaction tended to identify themselves as healthier, holding biomedical markers of well-being constant. Still, as previously noted, several studies (e.g. Singh-Manoux et al., 2007; Schnittker and Bacak, 2014) have found that self-rated assessments of health were good short-term indicators of future trends of mortality, as mortality trends take longer to register shifts in health. We opted to include it for this reason. It is also interesting to note that the majority of studies using self-rated health as their health measures found that the Recession had negatively impacted health (although many studies using self-rated health also used the same datasets, namely the EU-SILC). What we found more problematic than the use of self-rated health was the use of population-level measures to proxy individual-level health. This is particularly the case with mortality rates, which may be insufficient to register the relatively recent impact (if any) of the Recession on health. Research has shown that mortality rates are not sensitive to changes in quality of life (Parkin et al., 1987; Lago et al., 2018), and that, where possible, individual-level measures are preferable (Wagstaff & van Doerslaer, 2000). This appears to be reflected in our study: articles that used mortality rates as a measure of health also tended to find that health had not been impacted by the Recession; while studies using more sensitive measures like self-rated health found that the Recession did impact health. It may simply be too close to the Recession for mortality rates to have been impacted. Further, as with financial insecurity measures, few included studies employed multi-faceted or multiple health measures. There are a number of definitions of health used in public health research, but one of the most popular is Huber et al. (2011)’s, which states that health is “the ability to adapt and self-manage in the light of the physical, emotional and social challenges of life” (p. 1). To break this apart bluntly, this would mean examining at least three facets of health to have research aligned with theory. Yet, nearly no studies did so: many solely considered health status to be the presence or absence of disease. Further, few included studies engaged meaningfully with a

171

theoretical definition of health (or wealth), and attempted to derive its operationalisations from this definition. This may be partially due to our study’s inclusion parameters of only including studies that predominantly focussed on physical health. However, there does appear to be a disjunction between the way in which health is understood theoretically and the way in which it is operationalised in research. Deriving operationalisations of health from theoretical definitions (of which Huber et al., 2011, is one of many) may help to better reflect this multifaceted concept. Study design also may have had a sizeable effect on included articles’ findings and conclusions. A sizeable share of included studies used ecological data. While ecological data have the advantages of being representative of large populations and are routinely collected, they also have distinct disadvantages (Sedgwick, 2014). Particularly, ecological studies are prone to the ecological fallacy, whereby analyses at the group level are applied to individuals (Sedgwick, 2014). It may be that the conclusions we can draw from studies using ecological data cannot be meaningfully compared with those using individual-level data. Another large share of studies used cross-sectional data. While it is often done for availability/practicality reasons, analyses using crosssectional data lack the certainty necessary to establish causal relationships. However, the largest share of included studies used panel data, allowing authors to control for individual-level factors that may impact health, and to establish with less error whether a relationship between the Recession and health outcomes exists. It is also worth noting that a majority of studies using panel data found that the Recession worsened health (again, however, it is worth caveating that a majority of these studies used the same dataset). Taken together, there are a number of methodological reasons why there is no clear understanding of the Recession’s impact on health. To facilitate more common ground among future studies, we have formulated several recommendations. First, it appears that examining more geographies, and particularly, comparing countries that had different experiences of the Recession could give a more granular and nuanced understanding of the Recession’s influence on wealth. Second, using nationally representative data sources is necessary to test this relationship. Third, including subjective measures of wealth and health alongside objective ones may help to bring methods in line with theory. Doing so may also help to register short-term changes in health. Fourth, nationally representative panel data should be seen as the ‘gold standard’ for measuring the impact of the Recession on health: this will enable future studies to establish more conclusively whether a relationship exists. Another potential source of disagreement that was out of our initial purview was authors’ perspectives (whether these are led by ideological standpoint or existing research). Authors who have multiple articles included in this literature review tend to have articles that reached similar conclusions. Therefore, authors who have published several times on the subject may have skewed our literature review’s results. Further, our critical appraisal, based on a combination of one validated scale (Loney et al., 1998), with a topically relevant one (Parmar et al., 2016), also provided evidence that the variable quality of studies may be at the root of the debate on the Recession’s impact on health. No article scored a perfect 7 out of 7 points, and only 12 studies scored 6 out of 7, indicating a low risk of bias. Yet, thirty articles scored 5 or below, indicating that a

172

K. Thompson et al. / Economics and Human Biology 35 (2019) 162–184

large share of studies had a moderate or high risk of bias. This points to fundamental issues in reporting. Like Parmar et al. (2016), we were unable to synthesise results, because of the high percentage with a moderate and high risk of bias (along with heterogeneity in study designs, discussed more fully below). Additionally, only seven studies scored on the time horizon item, which required that the observation window be for ten years or greater, to allow for changes in health to register. Initially, we had wanted this item to be both a ten year observation window and being written in 2015 or later (as the more corrosive effects of the Recession on households only became evident in 2009), but, in that instance, only two studies scored on this item. Clearly, there is a need for longer windows of observation and more recent data collection. Perhaps most remarkable about our critical appraisal was the clear link between studies’ risk of bias and their reported impact: studies with a higher risk of bias were more likely to report neutral/positive or inconclusive impacts of the Recession on health. This calls into question whether there is really a debate over the impact of the Recession on health: perhaps the issue is more fundamentally whether these variable findings are due to the variable quality of studies on the topic. This finding, above and beyond others in our study, has clear policy implications: if it is indeed the case that higher-quality studies generally find a negative impact of the Recession on physical health, there is a clear rationale against austerity policies. Eurozone governments would thus have an imperative to spend counter-cyclically on, inter alia, unemployment benefits and healthcare. Overall, while we believe that we have meaningfully added to the discussion on the Recession’s impact on health, our study had several limitations. First, we elected to largely focus on physical health; we wanted to focus on one type of health indicator to be able to find meaningful methodological commonalities and divergences among included studies. However, as discussed elsewhere, health is not solely physical: social and mental/ emotional well-being are also important aspects of health. Also, because physical health tends to take longer to change than mental health, our study results may have been distorted, possibly underestimating the impact of the Recession on health. Still, this deliberate choice may also have been a strength: this gave the literature review a narrower focus and a more similar pool of articles to study, possibly increasing the validity of our findings. Second, our literature review was confined to three databases and articles written in English. Particularly because of the latter point, all relevant articles were not included: 14 studies were excluded because they were not written in English (instead, they were written in either Spanish or Greek). This may mean that the overall conclusions about the Recession’s impact on health were underestimated, as the Recession was worse in these countries. However, our literature review already contains a large share of articles from Spain and Greece, so this was likely not the case. Third, in a similar vein, we selected articles that are predominantly (more than 50%) focussed on the Eurozone. This is somewhat of a limitation, as the Recession impacted households in countries more broadly considered to be part of Europe (partic-

ularly Iceland and the United Kingdom). Indeed, there are a number of studies that are concerned with the Recession’s impact on health in these countries (e.g. Eiríksdóttir et al., 2015 looking at the impact of the Great Recession on cases of hypertensive disorders in Iceland; and Barr, Kinderman & Whitehead, 2015, looking at the impact of the Recession on mental health outcomes in the United Kingdom). Still, we elected to include study populations that would have been subjected to the same (or very similar) macroeconomic policies, and therefore be able to compare them more meaningfully. Fourth, our literature review did not statistically synthesise results from the included studies to understand the extent to which health was impacted by the Recession, as noted elsewhere. This means that our results are based on a blunt counting of articles. In Appendix A, key effect sizes are reported. Yet, the heterogeneity of research questions, study designs and health measures (even within the more narrowed parameters we set) prevented us from synthesising articles more quantitatively. These differences may have had an impact on reported impact of the Recession on health. In future, taking on board the recommendations outlined in our discussion may enable synthesis of articles. Likewise, our critical appraisal tool, while based on an existing, validated one (Loney et al., 1998), is based on a summary score of items. Each item may not have an equal impact on the overall quality of a study. However, Sanderson et al. (2007), a literature review of critical appraisal tool, found that a majority of critical appraisal tools used summary scores (e.g. summary statistics of a study may not impact its overall validity). They note that there is no clear way to weight items in a more nuanced way. Therefore, while we may have inadvertently overemphasized aspects of study’s quality in our critical appraisal, we have done so using the most appropriate methods available to us. 5. Conclusions A number of existing studies have explored the Recession’s impact on health. Yet, it appeared that their quality and heterogeneity were preventing a more unified understanding of this relationship. To help determine if and why this was the case, we conducted a systematic literature review. In this review, we assessed the key methodological choices and risks of bias of the included studies. To ensure that we were comparing like-for-like, we focussed solely on the Eurozone, and on studies using measures of physical health as their outcomes. After reviewing 42 studies meeting our inclusion criteria, we found that, while studies did diverge in terms of their methodological approaches, they varied most significantly in terms of their quality. In particular, we found a link between studies being of higher risk of bias and finding no (or a neutral) impact of the Great Recession on health. With this study, we have more specifically pinpointed the issues with existing research, helping to provide a methodological roadmap for future studies. Appendix A. Literature review synopsis

Research question

Abebe et al. (2016). Individual-level changes in self-rated health before and during the economic crisis in Europe. International Journal for Equity in Health, 15(1), 1. Aguilar-Palacio et al. (2018). Recession, employment and self-rated health: a study on the gender gap. Public Health, 154, 44-50. doi:10.1016/j. puhe.2017.10.013 Arroyo et al. (2015). How the economic recession has changed the likelihood of reporting poor self-rated health in Spain. International Journal for Equity in Health, 14(1), 149.

EU Statistics on Has self-rated health changed since start of the Income and Living Great Recession? Conditions

Barlow et al. (2015). Austerity, precariousness, and the health status of Greek labour market participants: Retrospective cohort analysis of employed and unemployed persons in 2008–2009 and 2010– 2011. Journal of Public Health Policy, 36(4), 452468. Barroso et al. (2016). Health inequalities by socioeconomic characteristics in Spain: the economic crisis effect. International Journal for Equity in Health, 15(1), 62. Bartoll and Mari-Dell’Olmo (2016). Patterns of life expectancy before and during economic recession, 2003–12: a European regions panel approach. The European Journal of Public Health, 26 (5), 783-788.

How strong is the association (if any) between self-rated health and employment status and its evolution over time and between genders? How has self-rated health changed since the start of the Great Recession?

How did self-reported health change from Greece's initial recession (2008-2009) and its austerity programme?

Data source

Geography

Observation period

Wealth measure

Health measure

Study design

Reported impact

Effect size

EU-23

2005-2011

Unemployment rate; GDP

Self-rated health

Mixed-effects ordinal logistic regression models using representative panel data

Self-rated health has declined among workingage populations across Europe, although this was not more pronounced among lower SES people.

0.001 (marginal fixed effect of reporting poor health during the severe recession)

Spanish National Spain Health Survey

2001-2014

Employment status

Self-rated health

Logistic regression models using representative repeat crosssectional data

Unemployment worsened self-rated health for men. Gender disparities in employment reduced over time, and women’s selfrated health improved.

In 2014 (using 2009 as a reference group), the likelihood of men reporting poor selfrated health was 1.05. It was .98 for women.

Spanish National Spain Health Survey

2006-2011

None – data is compared pre and post crisis

Self-rated health

Random effects logistic models using representative cross-sectional data

The financial crisis did not alter the likelihood of reporting ill health in 2011 as compared to 2006.

EU Statistics on Income and Living Conditions

2008-2011

Employment status and Self-rated health education

2006-2012

Professional status and Self-rated health household financial situation of interviewee

2003-2012

Employment rate and gross domestic product

Greece

Spanish National Spain What, if any, are the differences in the effect of Health Survey data socioeconomic characteristics on selfreported health status, pre and post crisis? Is the Great Recession associated with an increase in life expectancy?

Regional life expectancy statistics from Eurostat

232 European regions compared

Multi-variate logistic regression using representative panel data

Binary logit and probit models cross-sectional nationally representative cross-sectional data. Life expectancy at Regression model birth of first differences using nationally representative time series pooled crosssectional data

Descriptive comparison of different covariates’s odds ratios stratified by year: no overall comparison of the Recession’s impact on health. Odds ratio of The economic crisis is associated with a decline in becoming unemployed on the self-reported health both probability of during the Recession and subsequent austerity period, reporting a decline in health in 2008-2009: especially among the 1.17; 2010-2011: 1.61. unemployed.

Descriptive comparison of different covariates pre and post crisis: no single comparison of the Recession’s impact on health. Descriptive There is a negative comparison of association between life different covariates expectancy and unemployment in the lower pre and post crisis: no single comparison of income southern the Recession’s impact Mediterranean, but not in on health. the higher income Mediterranean region and Northern Europe. The economic crisis brought about a slight increase in socioeconomic inequalities in the likelihood of reporting good health.

K. Thompson et al. / Economics and Human Biology 35 (2019) 162–184

Reference

173

174

(Continued) Research question

Bartoll et al. (2015). Health and health behaviours before and during the Great Recession, overall and by socioeconomic status, using data from four repeated crosssectional health surveys in Spain (2001–2012). BMC Public Health, 15(1), 865. Baumbach and Gulis (2014). Impact of financial crisis on selected health outcomes in Europe. The European Journal of Public Health, 24(3), 399-403.

Benmarhnia et al. (2014). Impact of the economic crisis on the health of older persons in Spain: research clues based on an analysis of mortality. SESPAS report 2014. Gaceta Sanitaria, 28, 137141. Bonovas and Nikolopoulos (2012). High-burden epidemics in Greece in the era of economic crisis. Early signs of a public health tragedy. Journal of Preventive Medicine and Hygiene, 53(3). Clair et al. (2016). The impact of the housing crisis on self-reported health in Europe: multilevel longitudinal modelling of 27 EU countries. The European Journal of Public Health, 26 (5), 788-793. Coveney et al. (2016). Health disparities by income in Spain before and after the economic crisis. Health Economics, 25(S2), 141158. Faresjö et al. (2013). Higher perceived stress but lower cortisol levels found among young Greek

Data source

Geography

Observation period

Wealth measure

Spanish National Spain How has the Great Recession changed health Health Survey and health related behaviours in Spain?

2001-2012

Employment status and Self-reported education level health, overweight obesity + health behaviours

Linear probability Socio-economic inequalities in health have increased in models using the wake of the crisis. nationally representative cross-sectional data

Eurostat, 2000What are the effects of 2011 the financial crisis on selected population-level health outcomes?

2000-2010

Unemployment rate and GDP

Overall mortality, suicide and transport mortality

Correlation analysis using nationally representative ecological data

While cause-effect relationships are unclear, suicide mortality increased and transport mortality decreased after the crisis.

Rank correlation coefficient rs between GDP vs total mortality (-0.21); transport mortality (-0.43); suicide mortality (-0.19)

2005-2012

None – pre-and postcrisis comparison.

Mortality rates in individuals 60 years +

Difference-indifference models using nationally representative ecological data

During the crisis, the mortality rate decreased at a slower rate; winter mortality increased; the impact of the crisis has been greater on women than on men.

The death rate increased by 0.04 per 100,000 per month, compared to what would have been expected without the crisis.

Germany, Finland, Portugal, Slovenia, Poland, Czech Republic, Slovakia, Buglaria Spain

Health measure

Study design

Reported impact

Effect size Association between good self-reported health and the Recession: 0.076 (men); 0.096 (women)

How has the crisis changed trends in mortality among the elderly in Spain?

Statistics from the Spanish National Statistics Institute

How did infectious disease incidence rates change in the wake of the Greek financial crisis?

European Centre for Disease Control and Prevention

Greece

Descriptive comparison of the Recession.

None – pre- and postcrisis comparison

Infectious disease incidence rates (e.g. flu, West Nile, non-imported malaria)

Comparison of pre- and postRecession disease incidence rates using nationally representative ecological data

Greece has experienced several large-scale pandemics of infectious diseases since the outbreak of the financial crisis.

Descriptive statistics: no effect sizes reported.

How did housing payment problems during the Great Recession affect selfrated health?

EU Statistics on Income and Living Conditions

EU27

2008-2010

Housing payment problems; disposable income

Self-rated health and prevalence of chronic conditions

Multivariate linear regression and multilevel models using nationally representative panel data

People who had housing arrears experience increased risk of worsening self-reported health, particularly among renters.

Impact of housing arrears on selfreported health: -0.03

How did health disparities by income change during the financial crisis?

EU Statistics on Income and Living Conditions

Spain

2004-2012

Income growth; income Self-rated health inequality; differential income mobility

A decomposition method using sing nationally representative panel data

Change in health inequality, 20092012: -0.00643

How did the financial crisis impact cortisol levels in Greece and Sweden?

The total number of participants in the study was

Greece and 2012 Sweden

Biomarker human hair cortisol levels + perceived

Multivariate linear regression using nonrepresentative

Health inequality began to fall at a faster pace after the crisis began, although those who are healthiest are the most affected (perhaps masking the full extent of the effects) Greek students had significantly lower cortisol levels than Swedish students, although the

None, assumption that Greek students were more financially

Effect of being Swedish: -.457; no effect size for reported stress.

K. Thompson et al. / Economics and Human Biology 35 (2019) 162–184

Reference

n = 114 Swedish and n = 125 Greek students.

adults living in a stressful social environment in comparison with Swedish young adults. PLoS One, 8 (9), e73828.

stress; Selfreported health

cross-sectional data

Greek sample reported higher perceived stress, reported more experience of serious life events, had lower hope for the future, and had widespread symptoms of depression and anxiety. There was no association between having been affected by the crisis and physical health-related quality of life.

Having experienced an economic crisis’ impact on physical health-related quality of life: 3.66 (insignificant)

What is the impact of the crisis in health-related quality of life, while taking into account the possible buffering effect of social support?

Longitudinal study – Social Support and Quality of Life study (143 participants)

Barcelona, Spain

2012

Having experienced a personal economic crisis and perceived social support

Self-assessed health-related quality of life

OLS regression using nonrepresentative panel data

What was the role of unemployment insurance for deteriorating self-rated health in the working age population at the onset of the Great Recession in Europe? Are the negative health consequences of job losses mitigated when the experience is shared (when employment is more widespread)?

EU Statistics on Income and Living Conditions

EU 23

2006-2009

Coverage and net replacement rates of unemployment insurance

Self-rated health

Multilevel logistic conditional change models using nationally representative panel data

Unemployment insurance reduced worsening selfrated ill-health and, particularly, programme coverage is important. This is true for populations and individuals.

Impact of unemployment insurance coverage rate on transitions onto self-rated ill health: -1.519.

EU Statistics on Income and Living Conditions

EU25

2010-2013

National-level average level of unemployment and unemployment trend and individuallevel employment status

Self-rated health

OLS regression models using nationally representative panel data

There is a weak tendency towards less health effects of unemployment in countries where the experience is widely shared.

Descriptive comparison of different countries: no single comparison of the Recession’s impact on health.

How did the financial crisis impact health in two countries heavily hit by the financial crisis, but that had different welfare regimes?

EU Statistics on Income and Living Conditions

2006-2010 Greece, Ireland and Poland

None - measured before Self-rated health and after crisis

The study found that health Poor self-rated health worsened in Greece but not in Greece odds ratio (compared to Poland): in Ireland. 1.216; Ireland: 0.97

Is regaining a job sufficient to reverse the harmful impacts on health of job loss during the Great Recession?

EU Statistics on Income and Living Conditions

EU27

Self-rated health Employment status unemployed who then regained job after a year vs long term unemployed

Difference-indifference models using odds ratios, and using nationally representative panel data Linear regression models using nationally representative panel data

2007-2009

K. Thompson et al. / Economics and Human Biology 35 (2019) 162–184

Fernandez et al. (2015). Effects of the economic crisis and social support on health-related quality of life: first wave of a longitudinal study in Spain. British Journal of General Practice, 65(632), e198-e203. Ferrarini et al. (2014). Unemployment insurance and deteriorating selfrated health in 23 European countries. Journal of Epidemiology and Community Health, 68 (7), 657-662. Heggebø and Elstad (2018). Is it Easier to Be Unemployed When the Experience Is More Widely Shared? Effects of Unemployment on Selfrated Health in 25 European Countries with Diverging Macroeconomic Conditions. European Sociological Review, 34 (1), 22-39. doi:10.1093/ esr/jcx080. Hessel et al. (2014). The differential impact of the financial crisis on health in Ireland and Greece: a quasi-experimental approach. Public Health, 128(10), 911-919. Huijts et al. (2015). The impacts of job loss and job recovery on self-rated health: testing the mediating role of financial strain and income. The European Journal of Public Health, 25(5), 801-806.

strained than Swedish ones

Impact of job loss on Men and women’s health self-rated health: 0.12 appears to suffer equally from job loss but differs in (men); 0.13 (women) recovery. For men, employment recovery was insufficient to alleviate financial strain and associated health consequences, whereas in women regaining employment leads to health recovery.

175

176

(Continued) Research question

Data source

Geography

Observation period

Wealth measure

Health measure

Study design

Reported impact

Effect size

Karanikolos et al. (2018). Amenable mortality in the EU-has the crisis changed its course? European Journal of Public Health, 28 (5), 864-869. doi:10.1093/ eurpub/cky116 Kollia et al. (2016). Exploring the association between low socioeconomic status and cardiovascular disease risk in healthy Greeks, in the years of financial crisis (2002– 2012): The ATTICA study. International Journal of Cardiology, 223, 758-763. Loerbroks et al. (2014). Job insecurity is associated with adult asthma in Germany during Europe's recent economic crisis: a prospective cohort study. Journal of Epidemiology and Community Health, jech-2014. Lopez Del Amo Gonzalez et al. (2018). Long term unemployment, income, poverty, and social public expenditure, and their relationship with selfperceived health in Spain (2007-2011). BMC Public Health, 18(1), 133. doi:10.1186/s12889-0175004-2. Mackenbach et al. (2018). Trends in health inequalities in 27 European countries. Proceedings of the National Academy of Sciences of the United States of America, 115(25), 6440-6445. doi:10.1073/ pnas.1800028115. Maynou et al. (2016). Has the economic crisis widened the intraurban socioeconomic inequalities in mortality? The case of Barcelona, Spain. Journal of Epidemiology and

How did the Recession impact amenable mortality in Europe?

WHO data on mortality

EU28

2005-2012

None – before/after Recession comparison

Amenable mortality

Jointpoint regression models using ecological data

In Estonia, Greece and Slovenia, amenable mortality increased in years after Recession. In all other countries, levels were similar.

Descriptive comparison of different countries: no single comparison of the Recession’s impact on health.

2002-2012

Educational level and annual income

Cardiovascular disease incidence

Multiple logistic regression models using non- (nationally) representative panel data

There is evidence for a consistent reverse relation between SES and the incidence of CVD and for higher CVD risk factors among less privileged individuals.

Comparing low and high SES association with 10-year CVD incidence: 2.7

2009-2011

Job insecurity – measured by respondents rating the likelihood of losing their jobs in the next two years

Asthma incidence

Multivariable Poisson regression using nationally representative panel data

Asthma incidence is more likely with greater job insecurity.

Job insecurity (continuous – no scale given) on the risk of adult asthma: 1.24

Constant multilevel logistic longitudinal models (level 1: year; level 2: individual; level 3: region) using nationally representative panel data

Long and very long term unemployment, available income and poverty were associated to self-perceived bad health in Spain during the financial crisis.

Being unemployed between 24 and 48 months odds ratio of reporting poor health: 1.71.

All-cause mortality; causespecific mortality; self-assessed heatlh; activity limitations

Interrupted timeseries and country-fixed effects analyses using nationally representative ecological data

Contrary to the abstract, there is evidence in the paper that inequalities in self-reported health and activity limitations increased somewhat in the wake of the Recession. Inequalities in mortality rates decreased.

No overall effect sizes reported.

Mortality rates

Spatio-temporal ecological mixed regressions using nonrepresentative panel data

Relative risks from mortality For men, the relative have increased since 2009. risk of mortality in the fifth quintil was 0.985 for the fifth quintile compared to the first for men, and 1.022 for women.

Attica, During 2001– Greece 2002, information from 1528 men (18–87 years old) and 1514 women (18–89 years old) was collected. 10 year follow up Germany The German Is asthma incidence associated with increased Socio-economic Panel job insecurity in Germany?

What was the effect of low socioeconomic status on a 10-year cardiovascular disease incidence, in the years of financial crisis?

What is the joint relationship between long-term unemployment, social deprivation, and regional social public expenditure on one side, and selfperceived health in Spain (2007-2011)?

EU Statistics on Income and Living Conditions

Spain

2007-2011

Long and very long term Self-rated health unemployment, available income, severe material deprivation

What are recent health trends in European countries, paying attention to possible disruptions from the Great Recession?

Register-based mortality data; European Social Survey; EU-SILC

EU17 & EU27

1980-2014

(i) have spatial variations in socioeconomic inequalities in mortality at an intraurban level changed over time? and (ii) as a result of the economic crisis, has the gap between such disparities widened?

National Barcelona, Statistical Spain Institute of Spain population data

Education level and before/after comparison of the Recession (although national income, unemployment, material deprivation, social transfers and health expenditures were controlled for). Neighbourhood wealth

2005-2011

K. Thompson et al. / Economics and Human Biology 35 (2019) 162–184

Reference

Community Health, jech2013. Moya et al. (2015). Social inequality in morbidity, framed within the current economic crisis in Spain. International Journal for Equity in Health, 14(1), 131.

Reile et al. (2014). The recent economic recession and self-rated health in Estonia, Lithuania and Finland: a comparative cross-sectional study in 2004–2010. Journal of Epidemiology and Community Health, jech2014. Sarti and Zella (2016). Changes in the labour market and health inequalities during the years of the recent economic downturn in Italy. Social Science Research, 57, 116-132. Tapia Granados and Ionides (2017). Population health and the economy: Mortality and the Great Recession in Europe. Health Economics, 26(12), e219-e235.

Spanish National Spain Health Surveys and from the European Health Surveys in Spain

2003-2012

GDP and work intensity Disease (diabetes, rate depression, myocardial infection, presence of malignant tumors) prevalence rates

Multivariate logistic regression using nationally representative cross-sectional data

How have health trends changed as a result of employment status during the Great Recession?

EU Statistics on Income and Living Conditions

EU28

2010-2013

Employment status

Self-rated health

Individual fixedeffects regression models using nationally representative panel data

Lisbon, Portugal

2001-2011

Multiple Deprivation score

Mortality

ANOVA using nonrepresentative (for Portugal) ecological data

The ‘newly deprived’ people in Lisbon’s middle classes are experiencing a worse decline in mortality.

Inequality n standardized premature mortality ratio: 66.9 in 2011; 64.3 in 2001.

15 prevalence and incidence rates that result in premature mortality, e.g. cancer and CVD; self-reported health Self-rated health

Joinpoint regression and average annual percent change using nationally representative ecological data

Most mortality rates showed significant downward trends during the recession. Poor selfperceived health also decreased.

All-cause mortality: -1; Self-perceived health: -5.7

Multivariable logistic regression using nationally representative cross-sectional data

The Recession worsened self-rated health and different socio-economic groups were affected differently.

1.1% increase in overall poor self-rated heatlh

Individuals who are unemployed or are in precarious occupation positions are more likely to have worse health in the wake of the crisis

0.23 decrease in health status in 2010 relative to 2007.

Recessions on average have a beneficial effect on population mortality.

All-cause mortality decreased -0.003% as a result of a 1% decrease in the employment rate.

Portuguese Have mortality trends changed as a result of the census data financial crisis, as stratified by SES status?

How have trends in health outcomes changed as a result of the financial crisis in Spain?

Eurostat; national health registries the National Health Survey

Spain

1995-2011

GDP-PPP

How did health change in Estonia and Lithuania compared to Finland in the wake of the crisis?

Cross-sectional surveys from FinBalt Health Monitor project

Estonia, Lithuania and Finland

2004-2010

Education - high/mid/ low; Employment status - employed/ unemployed

How did work trajectory influence self-reported health during the financial crisis?

EU-SILC data for Italy, individuals aged 30 to 60

Italy

2007-2010

Education and occupational changes

2004-2010

National unemployment rate and GDP

European Health EU-27 How did mortality change in the wake of the for All data (WHO) and Great Recession? World Development Indicators (World Bank)

Self-reported health

Multivariate binomial regression and structural equation models using nationally representative panel data Life expectancy at Fixed effects and birth, along with linear regression models using 15 other ecological data indicators of population health

K. Thompson et al. / Economics and Human Biology 35 (2019) 162–184

Nelson and Tøge (2017). Health trends in the wake of the financial crisis Increasing inequalities? Scandinavian Journal of Public Health, 45(18), 2229. doi:10.1177/ 1403494817707088 Nogueira (2016). What is happening to health in the economic downturn? A view of the Lisbon Metropolitan Area, Portugal. Annals of Human Biology, 43(2), 164-168. Regidor et al. (2014). Has health in Spain been declining since the economic crisis?. Journal of Epidemiology and Community Health, 68(3), 280-282.

Education plays a larger role Odds ratios (in 2011; in preventable diseases than reference group: 2003): Diabetes: 0.96 non-preventable ones. (men); 1.00 (women). Myocardial infections: 1.12 (men); 1.02 (women) Malignant tumor 0.93 (men); 0.85 (women). Unemployed at all Unemployed respondents had a larger decline in self- observations’effect on rated health than employed self rated health: -0.037; employed at respondents all observations: -0.027

How does education and region impact the prevalence of preventable vs less preventable diseases?

177

178

(Continued) Reference

Research question

How did austerity Tapia Granados and Rodriguez (2015). Health, policies impact health? economic crisis, and austerity: a comparison of Greece, Finland and Iceland. Health Policy, 119 (7), 941-953.

Geography

Observation period

Wealth measure

Health measure

Study design

WHO data, supplemented by Eurostat.

Greece, Finland and Iceland

1990-2012

GDP

2003-2010

Unemployment rate

Slopes of the linear trend were compared for the years before and after the recession started, using nationally representative ecological data Health and health Linear and dynamic behaviour regression indicators models using nationally representative ecological data "Population health" measures, particularly mortality rates, life-expectancy data and selfreported health.

Reported impact

Effect size

The study found no difference in health between Greece, Finland and Iceland.

Descriptive comparison of different countries: no single comparison of the Recession’s impact on health.

Increase in unemployment rate is associated with decrease in mortality rates, with exception of suicide rate. However, there are SES differences in countries with different levels of social protection. Financial strain is found to be a potential mediator of the individual health effect of unemployment, while neither absolute income, relative income, relative rank, income deprivation nor unemployment benefits are found to be mediators of this relationship. There was a decrease in selfrated health as people entered unemployment.

All-cause mortality decreased 3.4% as a result of a !5 decrease in the unemployment rate.

Becoming unemployed is associated with a -0.039 change in selfrated health.

What are some of the adverse health effects of the Great Recession?

European Health EU23 for All Database and the mortality indicator database

Is the effect of unemployment on selfrated health (SRH) is mediated by income, financial strain and unemployment benefits?

EU Statistics on Income and Living Conditions

EU 28

2008-2011

Income and unemployment

Self-rated health

Fixed effects models using nationally representative panel data

How has self-rated health EU Statistics on Income and changed as a result of Living unemployment? Conditions

EU 28

2008-2011

Employment status

Self-rated health

Fixed effects models using nationally representative panel data

Spanish Health How have the effects of unemployment on health Survey changed in the wake of the crisis?

Spain

2006-2012

Employment status

Self-rated health and self-rated mental health

Difference-indifference models using nationally representative cross-sectional data

Unemployment had a significant impact on selfrated health and self-rated mental health, particularly self-rated mental health.

The impact of longterm unemployment on self-rated health before the Recession: -.28

None, pre- and postcrisis comparison

Self-rated health

Difference-indifference models using nationally representative panel data

Results provide strong evidence of a statistically significant negative effect of the financial crisis on health trends in Greece.

Relative to Polish respondents, Greeks experienced a 1.16 odds-ratio of reporting poor health after the Recession.

Mortgage foreclosure status

Self-rated health (physical and mental)

Prevalence and prevalence ratios using nonrepresentative

People who have experienced mortgage trouble have poorer mental and physical health than the general population. Poor

Prevalence ratio of poor self-rated heatlh among people with housing issues versus

Greece and 2006-2009 Poland (control)

Did health in Greece worsen in the aftermath of the crisis?

EU Statistics on Income and Living Conditions

Are those affected by mortgage issues more likely to have worse health outcomes than

2014 Health Survey of Spain (Catalonia) Catalonia and data collected by People Affected by Mortgages

Becoming unemployed is associated with a -0.035 change in selfrated heatlh.

K. Thompson et al. / Economics and Human Biology 35 (2019) 162–184

Toffolutti and Suhrcke (2014). Assessing the short term health impact of the Great Recession in the European Union: a cross-country panel analysis. Preventive Medicine, 64, 54-62. Tøge (2016a,Tøge, 2016b. Health effects of unemployment in Europe (2008–2011): a longitudinal analysis of income and financial strain as mediating factors. International Journal for Equity in Health, 15(1), 75. Tøge and Blekesaune (2015). Unemployment transitions and self-rated health in Europe: A longitudinal analysis of EU-SILC from 2008 to 2011. Social Science & Medicine, 143, 171-178. Urbanos-Garrido and LopezValcarcel (2015). The influence of the economic crisis on the association between unemployment and health: an empirical analysis for Spain. The European Journal of Health Economics, 16(2), 175-184. Vandoros et al. (2013). Have health trends worsened in Greece as a result of the financial crisis? A quasiexperimental approach. The European Journal of Public Health, ckt020. Vasquez-Vera et al. (2016). Foreclosure and health in Southern Europe: results from the Platform for People Affected by

Data source

Mortgages. Journal of Urban Health, 93(2), 312330.

Vrachnis et al. (2015). Cancer mortality in Greece during the financial crisis. Acta Oncologica, 54(2), 287288. Zavras et al. (2013). Impact of economic crisis and other demographic and socio-economic factors on self-rated health in Greece. The European Journal of Public Health, cks143.

cross-sectional data

Cardiovascular mortality data provided by the Hellenic Statistics Authority

Greece

2004-2012

None – assumed that time passing was indicative of the crisis worsening.

Cardiovascular mortality rates

Chi-square tests using nationally representative ecological data

Mortality data Were mortality rates impacted by the financial provided by the Hellenic crisis? Statistics Authority

Greece

2003-2012

None – assumed that time passing was indicative of the crisis worsening.

Mortality rates

Descriptive analysis of nationally representative ecological data

Were cancer mortality rates impacted by the financial crisis?

Mortality data provided by the Hellenic Statistics Authority

Greece

2004-2012

None – assumed that time passing was indicative of the crisis worsening.

Cancer mortality rates

Descriptive analysis using nationally representative ecological data

Did health in Greece worsen in the aftermath of the crisis?

Cross-sectional, nationally representative surveys (secondary)

Greece

2006-2011

Unemployment and income

Self-rated health

Logistic regression using nationally representative cross-sectional data

Were cardiovascular mortality rates impacted by the financial crisis?

mental health was more prevalent in the early stages of foreclosure, while poor physical health was more evident in later stages of foreclosure. The decline in cardiovascular mortality that Greece was experiencing levelled off (although deaths did not increase).

Age-adjusted mortality rates continued to decline after the advent of the crisis. The 2011-2012 increase in mortality in the age group 55 and up indicates that there may be an austerityrelated link to mortality. Cancer mortality rates continued to decline in the aftermath of the crisis.

In 2011, in this study representing the year of the crisis, self-rated health was worse compared to previous years.

the general population: 3.22

Cardiovascular mortality rates decreased by 15% during the Recession

All-cause mortality was unaffected, declining at a rate of 6.3% during the Recession, compared to 5.7% in the period before the Recession. Cancer mortality rates have declined 4.5% when comparing 2012 with 2008.

The odds ratio of reporting good health in 2011 versus 2006 was 0.88.

K. Thompson et al. / Economics and Human Biology 35 (2019) 162–184

Vlachadis, N., Iliodromiti, Z., Vlachadi, M., Xanthos, T., Ktenas, E., Vrachnis, D., Kornarou, E. & Vrachnis, N. (2014). Cardiovascular mortality and the financial crisis in Greece: Trends and outlook. International Journal of Cardiology, 176(3), 13671368. Vlachadis, N., Vrachnis, N., Ktenas, E., Vlachadi, M., & Kornarou, E. (2014). Mortality and the economic crisis in Greece. The Lancet, 383(9918), 691.

those not affected by mortgage issues?

179

180

K. Thompson et al. / Economics and Human Biology 35 (2019) 162–184

Appendix B. : Critical appraisal

Study

Random sample or whole population

Unbiased time horizon

Use of Unbiased panel predictor measure data

Unbiased outcome measure

Confidence intervals; subgroup analysis

Study subjects/ survey respondents described

Total score

Reported impact

Abebe et al. (2016). Individual-level changes in selfrated health before and during the economic crisis in Europe. International Journal for Equity in Health, 15 (1), 1. Aguilar-Palacio et al. (2018). Recession, employment and self-rated health: a study on the gender gap. Public Health, 154, 44-50. doi:10.1016/j. puhe.2017.10.013 Arroyo et al. (2015). How the economic recession has changed the likelihood of reporting poor self-rated health in Spain. International Journal for Equity in Health, 14(1), 149. Barlow et al. (2015). Austerity, precariousness, and the health status of Greek labour market participants: Retrospective cohort analysis of employed and unemployed persons in 2008-2009 and 2010-2011. Journal of Public Health Policy, 36(4), 452-468. Barroso et al. (2016). Health inequalities by socioeconomic characteristics in Spain: the economic crisis effect. International Journal for Equity in Health, 15(1), 62. Bartoll and Mari-Dell’Olmo (2016). Patterns of life expectancy before and during economic recession, 2003-12: a European regions panel approach. The European Journal of Public Health, 26(5), 783-788. Bartoll et al. (2015). Health and health behaviours before and during the Great Recession, overall and by socioeconomic status, using data from four repeated cross-sectional health surveys in Spain (2001-2012). BMC Public Health, 15(1), 865. Baumbach and Gulis (2014). Impact of financial crisis on selected health outcomes in Europe. The European Journal of Public Health, 24(3), 399-403. Benmarhnia et al. (2014). Impact of the economic crisis on the health of older persons in Spain: research clues based on an analysis of mortality. SESPAS report 2014. Gaceta Sanitaria, 28, 137-141. Bonovas and Nikolopoulos (2012). High-burden epidemics in Greece in the era of economic crisis. Early signs of a public health tragedy. Journal of Preventive Medicine and Hygiene, 53(3). Clair et al. (2016). The impact of the housing crisis on self-reported health in Europe: multilevel longitudinal modelling of 27 EU countries. The European Journal of Public Health, 26(5), 788-793. Coveney et al. (2016). Health disparities by income in Spain before and after the economic crisis. Health Economics, 25(S2), 141-158. Faresjö et al. (2013). Higher perceived stress but lower cortisol levels found among young Greek adults living in a stressful social environment in comparison with Swedish young adults. PLoS One, 8(9), e73828. Fernandez et al. (2015). Effects of the economic crisis and social support on health-related quality of life: first wave of a longitudinal study in Spain. British Journal of General Practice, 65(632), e198-e203. Ferrarini et al. (2014). Unemployment insurance and deteriorating self-rated health in 23 European countries. Journal of Epidemiology and Community Health, 68(7), 657-662. Heggebø and Elstad (2018). Is it Easier to Be Unemployed When the Experience Is More Widely Shared? Effects of Unemployment on Self-rated Health in 25 European Countries with Diverging Macroeconomic Conditions. European Sociological Review, 34(1), 22-39. doi:10.1093/esr/jcx080. Hessel et al. (2014). The differential impact of the financial crisis on health in Ireland and Greece: a

1

0

1

1

1

1

1

6

Negative

1

1

0

1

1

1

1

6

Negative

1

0

0

0

1

1

1

4

Neutral/ positive

1

0

1

1

1

1

1

6

Negative

1

0

0

1

1

1

1

5

Negative

1

0

0

1

1

1

1

5

Negative

1

1

0

1

1

1

1

6

Negative

1

1

0

1

1

0

0

4

Inconclusive/ mixed

1

0

0

0

1

0

0

2

Inconclusive/ mixed

1

0

0

0

1

0

0

2

Negative

1

0

1

1

1

1

1

6

Negative

1

0

1

1

1

1

1

6

Neutral/ positive

0

0

0

0

1

1

1

3

Inconclusive/ mixed

0

0

1

1

1

1

1

5

Neutral/ positive

1

0

1

1

1

0

0

4

Negative

1

0

1

1

1

1

1

6

Negative

1

0

1

0

1

1

1

5

Inconclusive/ mixed

K. Thompson et al. / Economics and Human Biology 35 (2019) 162–184 quasi-experimental approach. Public Health, 128(10), 911-919. Huijts et al. (2015). The impacts of job loss and job recovery on self-rated health: testing the mediating role of financial strain and income. The European Journal of Public Health, 25(5), 801-806. Karanikolos et al. (2018). Amenable mortality in the EU-has the crisis changed its course? Eur J Public Health, 28(5), 864-869. doi:10.1093/eurpub/cky116 YES Kollia et al. (2016). Exploring the association between low socioeconomic status and cardiovascular disease risk in healthy Greeks, in the years of financial crisis (2002–2012): The ATTICA study. International Journal of Cardiology, 223, 758-763. Loerbroks et al. (2014). Job insecurity is associated with adult asthma in Germany during Europe's recent economic crisis: a prospective cohort study. Journal of Epidemiology and Community Health, jech-2014. Lopez Del Amo Gonzalez et al. (2018). Long term unemployment, income, poverty, and social public expenditure, and their relationship with selfperceived health in Spain (2007-2011). BMC Public Health, 18(1), 133. doi:10.1186/s12889-017-5004-2. Mackenbach et al. (2018). Trends in health inequalities in 27 European countries. Proceedings of the National Academy of Sciences of the United States of America, 115(25), 6440-6445. doi:10.1073/ pnas.1800028115 Maynou et al. (2016). Has the economic crisis widened the intraurban socioeconomic inequalities in mortality? The case of Barcelona, Spain. Journal of Epidemiology and Community Health, jech-2013. Moya et al. (2015). Social inequality in morbidity, framed within the current economic crisis in Spain. International Journal for Equity in Health, 14(1), 131. Nelson and Tøge (2017). Health trends in the wake of the financial crisis - Increasing inequalities? Scandinavian Journal of Public Health, 45(18_suppl), 22-29. doi:10.1177/1403494817707088 YES Nogueira (2016). What is happening to health in the economic downturn? A view of the Lisbon Metropolitan Area, Portugal. Annals of Human Biology, 43(2), 164-168. Regidor et al. (2014). Has health in Spain been declining since the economic crisis?. Journal of Epidemiology and Community Health, 68(3), 280-282. Reile et al. (2014). The recent economic recession and self-rated health in Estonia, Lithuania and Finland: a comparative cross-sectional study in 2004–2010. Journal of Epidemiology and Community Health, jech-2014. Sarti and Zella (2016). Changes in the labour market and health inequalities during the years of the recent economic downturn in Italy. Social Science Research, 57, 116-132. Tapia Granados and Ionides (2017). Population health and the economy: Mortality and the Great Recession in Europe. Health Economics, 26(12), e219-e235. Tapia Granados and Rodriguez (2015). Health, economic crisis, and austerity: a comparison of Greece, Finland and Iceland. Health Policy, 119(7), 941-953. Toffolutti and Suhrcke (2014). Assessing the short term health impact of the Great Recession in the European Union: a cross-country panel analysis. Preventive Medicine, 64, 54-62. Tøge (2016a,Tøge, 2016b. Health effects of unemployment in Europe (2008–2011): a longitudinal analysis of income and financial strain as mediating factors. International Journal for Equity in Health, 15(1), 75. Tøge and Blekesaune (2015) Unemployment transitions and self-rated health in Europe: A longitudinal analysis of EU-SILC from 2008 to 2011. Social Science & Medicine, 143, 171-178. Urbanos-Garrido and Lopez-Valcarcel (2015). The influence of the economic crisis on the association between unemployment and health: an empirical

181

1

0

0

1

1

1

1

5

Negative

1

1

0

0

1

0

0

3

Inconclusive/ mixed

0

0

1

1

1

1

1

5

Negative

1

0

1

1

1

1

1

6

Negative

1

0

1

1

1

1

1

6

Negative

1

1

1

1

1

0

0

1

0

0

1

1

1

1

5

Negative

1

0

0

1

1

1

1

5

Negative

1

0

1

1

1

1

1

6

Negative

0

0

0

1

1

1

0

3

Negative

1

1

0

1

1

0

0

4

Neutral/ positive

1

0

0

1

1

1

1

5

Negative

1

0

1

1

1

1

1

6

Negative

1

0

0

1

1

1

0

4

Neutral/ positive

1

1

0

1

1

0

1

4

Neutral/ positive

1

0

0

1

1

1

0

4

Inconclusive/ mixed

1

0

1

1

1

1

1

6

Inconclusive/ mixed

1

0

1

1

1

1

0

5

Negative

1

0

0

1

1

1

1

5

Negative

Inconclusive/ mixed

182

K. Thompson et al. / Economics and Human Biology 35 (2019) 162–184 (Continued) Study

analysis for Spain. The European Journal of Health Economics, 16(2), 175-184. Vandoros et al. (2013). Have health trends worsened in Greece as a result of the financial crisis? A quasiexperimental approach. The European Journal of Public Health, ckt020. Vasquez-Vera et al. (2016). Foreclosure and health in Southern Europe: results from the Platform for People Affected by Mortgages. Journal of Urban Health, 93(2), 312-330. Vlachadis, N., Iliodromiti, Z., Vlachadi, M., Xanthos, T., Ktenas, E., Vrachnis, D., Kornarou, E. & Vrachnis, N. (2014). Cardiovascular mortality and the financial crisis in Greece: Trends and outlook. International Journal of Cardiology, 176(3), 1367-1368. Vlachadis, N., Vrachnis, N., Ktenas, E., Vlachadi, M., & Kornarou, E. (2014). Mortality and the economic crisis in Greece. The Lancet, 383(9918), 691. Vrachnis et al. (2015). Cancer mortality in Greece during the financial crisis. Acta Oncologica, 54(2), 287-288. Zavras et al. (2013). Impact of economic crisis and other demographic and socio-economic factors on selfrated health in Greece. The European Journal of Public Health, cks143.

Random sample or whole population

Unbiased time horizon

Use of Unbiased panel predictor measure data

Unbiased outcome measure

Confidence intervals; subgroup analysis

Study subjects/ survey respondents described

Total score

Reported impact

1

0

1

0

1

1

1

5

Negative

0

0

0

1

1

1

1

4

Negative

1

0

0

0

1

0

0

2

Neutral/ positive

1

0

0

0

1

0

0

2

Negative

1

0

0

0

1

0

0

2

Neutral/ positive

1

0

0

1

1

1

1

5

Negative

References Abebe, D.S., Tøge, A.G., Dahl, E., 2016. Individual-level changes in self-rated health before and during the economic crisis in Europe. Int. J. Equity Health 15 doi:110.1186/s12939-015-0290-8. Aguilar-Palacio, I., Carrera-Lasfuentes, P., Sanchez-Recio, R., Alonso, J.P., Rabanaque, M.J., 2018. Recession, employment and self-rated health: a study on the gender gap. Public Health 154, 44–50. doi:http://dx.doi.org/10.1016/j. puhe.2017.10.013. Arroyo, E., Renart, G., Saez, M., 2015. How the economic recession has changed the likelihood of reporting poor self-rated health in Spain. Int. J. Equity Health 14 doi:14910.1186/s12939-015-0285-5. Barlow, P., Reeves, A., Mckee, M., Stuckler, D., 2015. Austerity, precariousness, and the health status of Greek labour market participants: retrospective cohort analysis of employed and unemployed persons in 2008-2009 and 2010-2011. J. Public Health Policy 36 (4), 452–468. doi:http://dx.doi.org/10.1057/ jphp.2015.25. Barroso, C., Abasolo, I., Caceres-Hernandez, J.J., 2016. Health inequalities by socioeconomic characteristics in Spain: the economic crisis effect. Int. J. Equity Health 15 doi: 6210.1186/s12939-016-0346-4. Bartoll, X., Mari-Dell’Olmo, M., 2016. Patterns of life expectancy before and during economic recession, 2003-12: a European regions panel approach. Eur. J. Public Health 26 (5), 783–788. doi:http://dx.doi.org/10.1093/eurpub/ckw075. Bartoll, X., Toffolutti, V., Malmusi, D., Palència, L., Borrell, C., Suhrcke, M., 2015. Health and health behaviours before and during the Great Recession, overall and by socioeconomic status, using data from four repeated cross-sectional health surveys in Spain (2001–2012). BMC Public Health 15 (1), 865. Baumbach, A., Gulis, G., 2014. Impact of financial crisis on selected health outcomes in Europe. Eur. J. Public Health 24 (3), 399–403. doi:http://dx.doi.org/10.1093/ eurpub/cku042. Benmarhnia, T., Zunzunegui, M.V., Llacer, A., Beland, F., 2014. Impact of the economic crisis on the health of older persons in Spain: research clues based on an analysis of mortality. SESPAS report 2014. Gaceta Sanitaria 28 (Suppl 1), 137– 141. doi:http://dx.doi.org/10.1016/j.gaceta.2014.02.016. Bezruchka, S., 2009. The effect of economic recession on population health. Can. Med. Assoc. J. 181 (5), 281–285. Blythe, M., 2013. The austerity delusion: why a bad idea won over the west. Foreign Aff. 92, 41. Bonovas, S., Nikolopoulos, G., 2012. High-burden epidemics in Greece in the era of economic crisis. Early signs of a public health tragedy. J. Prev. Med. Hyg. 53 (3), 169–171. Borrell, C., Marí-Dell’olmo, M., Palència, L., Gotsens, M., Burström, B.O., DomínguezBerjón, F., Rodríguez-Sanz, M., Dzúrová, D., Gandarillas, A., Hoffmann, R., Kovacs, K., Marinacci, C., Martikainen, P., Pikhart, H., Corman, D., Rosicova, K., Saez, M., Santana, P., Tarkiainen, L., Puigpinós, R., Morrison, J., Pasarín, M.I., Díez, È., 2014.

Socioeconomic inequalities in mortality in 16 European cities. Scand. J. Public Health 42 (3), 245–254. Brunsden, J., Khan, M., 2018. Eurozone creditors reach “historic” deal on Greek debt reliefFinanc. Times . . Retrieved from: https://www.ft.com/content/b1cba7c475a2-11e8-a8c4-408cfba4327c. Chida, Y., Steptoe, A., 2008. Positive psychological well-being and mortality: a quantitative review of prospective observational studies. Psychosom. Med. 70 (7), 741–756. Clair, A., Reeves, A., Loopstra, R., McKee, M., Dorling, D., Stuckler, D., 2016. The impact of the housing crisis on self-reported health in Europe: multilevel longitudinal modelling of 27 EU countries. Eur. J. Public Health 26 (5), 788–793. doi:http:// dx.doi.org/10.1093/eurpub/ckw071. Coggon, D., Barker, D., Rose, G., 2009. Epidemiology for the Uninitiated. John Wiley & Sons, Hoboken. Coveney, M., Garcia-Gomez, P., Van Doorslaer, E., Van Ourti, T., 2016. Health disparities by income in Spain before and after the economic crisis. Health Econ. 25 (Suppl 2), 141–158. doi:http://dx.doi.org/10.1002/hec.3375. Cutler, D.M., Miller, G., Norton, D.M., 2007. Evidence on early-life income and latelife health from America’s Dust Bowl era. Proc. Natl. Acad. Sci. 104 (33), 13244– 13249. Deaton, A., 2013. The Great Escape: Health, Wealth, and the Origins of Inequality. Princeton University Press, Princeton. Drydakis, N., 2015. The effect of unemployment on self-reported health and mental health in Greece from 2008 to 2013: a longitudinal study before and during the financial crisis. Soc. Sci. Med. 128, 43–51. doi:http://dx.doi.org/10.1016/j. socscimed.2014.12.025. Ecker, E.D., Skelly, A.C., 2010. Conducting a winning literature search. Evid. Based Spine Care J. 1 (1), 9–14. doi:http://dx.doi.org/10.1055/s-0028-1100887. Eikemo, T.A., Bambra, C., Joyce, K., Dahl, E., 2008. Welfare state regimes and incomerelated health inequalities: a comparison of 23 European countries. Eur. J. Public Health 18 (6), 593–599. European Central Bank, 2012. “”Speech by Mario Draghi, President of the European Central Bank at the Global Investment Conference in London 26 July 2012.” Retrieved from:. . https://www.ecb.europa.eu/press/key/date/2012/html/ sp120726.en.html. Eurostat, 2017. Taking a Look at Self-employed in the EU Retrieved from:. European Commission. https://ec.europa.eu/eurostat/web/products-eurostat-news/-/ DDN-20170906-1. Faresjö, A., Theodorsson, E., Chatziarzenis, M., Sapouna, V., Claesson, H.P., Koppner, J., Faresjö, T., 2013. Higher perceived stress but lower cortisol levels found among young Greek adults living in a stressful social environment in comparison with Swedish young adults. PLoS One 8 (9) e73828 doi:http://dx. doi.org/10.1371/journal.pone.0073828. Fernandez, A., Garcia-Alonso, J., Royo-Pastor, C., Garrell-Corbera, I., Rengel-Chica, J., Agudo-Ugena, J., Ramos, A., Mendive, J.M., 2015. Effects of the economic crisis

K. Thompson et al. / Economics and Human Biology 35 (2019) 162–184 and social support on health-related quality of life: first wave of a longitudinal study in Spain. Br. J. Gen. Pract. 65 (632), e198–203. doi:http://dx.doi.org/ 10.3399/bjgp15X684025. Ferrarini, T., Nelson, K., Sjöberg, O., 2014. Unemployment insurance and deteriorating self-rated health in 23 European countries. J. Epidemiol. Community Health 68 (7), 657–662. doi:http://dx.doi.org/10.1136/jech-2013203721. Heggebø, K., Elstad, J.I., 2018. Is it easier to Be unemployed when the experience is more widely shared? Effects of unemployment on self-rated health in 25 european countries with diverging macroeconomic conditions. Eur. Sociol. Rev. 34 (1), 22–39. doi:http://dx.doi.org/10.1093/esr/jcx080. Hessel, P., Vandoros, S., Avendano, M., 2014. The differential impact of the financial crisis on health in Ireland and Greece: a quasi-experimental approach. Public Health 128 (10), 911–919. doi:http://dx.doi.org/10.1016/j.puhe.2014.08.004. Huber, M., Knottnerus, J.A., Green, L., van der Horst, H., Jadad, A.R., Kromhout, D., Leonard, B., Lorig, K., Louriero, M.I., van der Meer, J.W.M., Schnabel, P., Smid, H., 2011. How should we define health? Br. Med. J. 343 doi:d416310.1136/bmj. d4163. Hsiao, C., 2007. Panel data analysis—advantages and challenges. Test 16 (1), 1–22. Huijts, T., Reeves, A., McKee, M., Stuckler, D., 2015. The impacts of job loss and job recovery on self-rated health: testing the mediating role of financial strain and income. Eur. J. Public Health 25 (5), 801–806. doi:http://dx.doi.org/10.1093/ eurpub/ckv108. Idler, E.L., Hudson, S.V., Leventhal, H., 1999. The meanings of self-ratings of health: a qualitative and quantitative approach. Res. Aging 21 (3), 458–476. Jones, A.M., Wildman, J., 2008. Health, income and relative deprivation: evidence from the BHPS. J. Health Econ. 27 (2), 308–324. doi:http://dx.doi.org/10.1016/j. jhealeco.2007.05.007. Karanikolos, M., Mackenbach, J.P., Nolte, E., Stuckler, D., McKee, M., 2018. Amenable mortality in the EU-has the crisis changed its course? Eur. J. Public Health 28 (5), 864–869. doi:http://dx.doi.org/10.1093/eurpub/cky116. Kawachi, I., Berkman, L.F., 2000. Social cohesion, social capital and health. In: Berkman, L.F., Kawachi, I. (Eds.), Social Epidemiology. Oxford University Press, Oxford, pp. 174–190. Kollia, N., Panagiotakos, D.B., Georgousopoulou, E., Chrysohoou, C., Tousoulis, D., Stefanadis, C., Papageorgiou, C., Pitsavos, C., 2016. Exploring the association between low socioeconomic status and cardiovascular disease risk in healthy Greeks, in the years of financial crisis (2002-2012): the ATTICA study. Int. J. Cardiol. 223, 758–763. doi:http://dx.doi.org/10.1016/j.ijcard.2016.08.294. Kondo, N., Subramanian, S.V., Kawachi, I., Takeda, Y., Yamagata, Z., 2008. Economic recession and health inequalities in Japan: analysis with a national sample, 1986–2001. J. Epidemiol. Community Health 62 (10), 869–875. Lago, S., Cantarero, D., Rivera, B., Pascual, M., Blazquez-Fernandez, C., Casal, B., Reyes, F., 2018. Socioeconomic status, health inequalities and noncommunicable diseases: a systematic review. Zeitschrift für Gesundheitswissenschaften 26 (1), 1–14. doi:http://dx.doi.org/10.1007/s10389017-0850-z. Larson, C., Mercer, A., 2004. Global health indicators: an overview. Can. Med. Assoc. J. 171 (10), 1199–1200. doi:http://dx.doi.org/10.1503/cmaj.1021409. Link, B.G., Phelan, J., 1995. Social conditions as fundamental causes of disease. J. Health Soc. Behav. 80–94. Loerbroks, A., Bosch, J.A., Douwes, J., Angerer, P., Li, J., 2014. Job insecurity is associated with adult asthma in Germany during Europe’s recent economic crisis: a prospective cohort study. J. Epidemiol. Community Health 68 (12), 1196–1199. doi:http://dx.doi.org/10.1136/jech-2014-204274. Loney, P.L., Chambers, L.W., Bennett, K.J., Roberts, J.G., Stratford, P.W., 1998. Critical appraisal of the health research literature: prevalence or incidence of a health problem. Chronic Dis. Canada 19 (4), 170–176. Lopez Del Amo Gonzalez, M.P., Benitez, V., Martin-Martin, J.J., 2018. Long term unemployment, income, poverty, and social public expenditure, and their relationship with self-perceived health in Spain (2007-2011). BMC Public Health 18 (1), 133. doi:http://dx.doi.org/10.1186/s12889-017-5004-2. Mackenbach, J.P., 2012. The persistence of health inequalities in modern welfare states: the explanation of a paradox. Soc. Sci. Med. 75 (4), 761–769. doi:http:// dx.doi.org/10.1016/j.socscimed.2012.02.031. Mackenbach, J.P., Valverde, J.R., Artnik, B., Bopp, M., Bronnum-Hansen, H., Deboosere, P., Kalediene, R., Kovács, K., Leinsalu, M., Martikainen, P., Menvielle, G., Regidor, E., Rychtaríková, J., Rodriguez-Sanz, M., Vineis, P., White, C., Wojtyniak, B., Hu, Y., Nusselder, W.J., 2018. Trends in health inequalities in 27 European countries. Proc. Natl. Acad. Sci. U. S. A. 115 (25), 6440–6445. doi: http://dx.doi.org/10.1073/pnas.1800028115. Marmot, M.G., Smith, G.D., Stansfeld, S., Patel, C., North, F., Head, J., White, I., Brunner, E., Feeney, A., 1991. Health inequalities among British civil servants: the Whitehall II study. Lancet 337 (8754), 1387–1393. Martikainen, P., Adda, J., Ferrie, J.E., Smith, G.D., Marmot, M., 2003. Effects of income and wealth on GHQ depression and poor self rated health in white collar women and men in the Whitehall II study. J. Epidemiol. Community Health 57 (9), 718– 723. Martinez-Lopez, D., 2013. The underreporting of income by self-employed workers in Spain. SERIEs 4 (4), 353–371. Maynou, L., Saez, M., Lopez-Casasnovas, G., 2016. Has the economic crisis widened the intraurban socioeconomic inequalities in mortality? The case of Barcelona, Spain. J. Epidemiol. Community Health 70 (2), 114–124. doi:http://dx.doi.org/ 10.1136/jech-2013-203447.

183

Mckee, M., Reeves, A., Clair, A., Stuckler, D., 2017. Living on the edge: precariousness and why it matters for health. Arch. Public Health 75 doi:1310.1186/s13690-0170183-y. Methley, A.M., Campbell, S., Chew-Graham, C., McNally, R., Cheraghi-Sohi, S., 2014. PICO, PICOS and SPIDER: a comparison study of specificity and sensitivity in three search tools for qualitative systematic reviews. BMC Health Serv. Res. 14 doi:57910.1186/s12913-014-0579-0. Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G., Group, Prisma, 2009. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 6 (7) e1000097. Morrison, K., 2017. Dissecting the Literature : the Importance of Critical Appraisal Retrieved from. The Royal College of Surgeons. https://www.rcseng.ac.uk/ library-and-publications/library/blog/dissecting-the-literature-theimportance-of-critical-appraisal/. Moya, A.Z., Buffel, V., Yáñez, C.N., Bracke, P., 2015. Social inequality in morbidity, framed within the current economic crisis in Spain. Int. J. Equity Health 14, 131– 151. Mustard, C.A., Derksen, S., Berthelot, J.M., Wolfson, M., Roos, L.L., 1997. Age-specific education and income gradients in morbidity and mortality in a Canadian province. Soc. Sci. Med. 45 (3), 383–397. Nelson, K., Tøge, A.G., 2017. Health trends in the wake of the financial crisis increasing inequalities? Scand. J. Public Health 45 (18_suppl), 22–29. doi:http:// dx.doi.org/10.1177/1403494817707088. Nogueira, H., 2016. What is happening to health in the economic downturn? A view of the Lisbon Metropolitan Area, Portugal. Ann. Hum. Biol. 43 (2), 164–168. doi: http://dx.doi.org/10.3109/03014460.2015.1131846. Parkin, D., McGuire, A., Yule, B., 1987. Aggregate health care expenditures and national income: is health care a luxury good? J. Health Econ. 6 (2), 109–127. Parmar, D., Stavropoulou, C., Ioannidis, J.P.A., 2016. Health outcomes during the 2008 financial crisis in Europe: systematic literature review. Br. Med. J. 354 doi: i458810.1136/bmj.i4588. Parrish, R.G., 2010. Measuring population health outcomes. Prev. Chronic Dis. 7 (4) . Pye, V., Taylor, N., Clay-Williams, R., Braithwaite, J., 2016. When is enough, enough? Understanding and solving your sample size problems in health services research. BMC Res. Notes 9, 90. doi:http://dx.doi.org/10.1186/s13104-016-1893-x. Rajmil, L., Medina-Bustos, A., Fernandez de Sanmamed, M.J., Mompart-Penina, A., 2013. Impact of the economic crisis on children’s health in Catalonia: a beforeafter approach. BMJ Open 3 (8) e003286 doi:http://dx.doi.org/10.1136/ bmjopen-2013-003286. Regidor, E., Barrio, G., Bravo, M.J., de la Fuente, L., 2014. Has health in Spain been declining since the economic crisis? J. Epidemiol. Community Health 68 (3), 280–282. doi:http://dx.doi.org/10.1136/jech-2013-202944. Reile, R., Helakorpi, S., Klumbiene, J., Tekkel, M., Leinsalu, M., 2014. The recent economic recession and self-rated health in Estonia, Lithuania and Finland: a comparative cross-sectional study in 2004-2010. J. Epidemiol. Community Health 68 (11), 1072–1079. doi:http://dx.doi.org/10.1136/jech-2014-204196. Sanderson, S., Tatt, I.D., Higgins, J., 2007. Tools for assessing quality and susceptibility to bias in observational studies in epidemiology: a systematic review and annotated bibliography. Int. J. Epidemiol. 36 (3), 666–676. Sarti, S., Zella, S., 2016. Changes in the labour market and health inequalities during the years of the recent economic downturn in Italy. Soc. Sci. Res. 57, 116–132. doi:http://dx.doi.org/10.1016/j.ssresearch.2015.12.010. Schnittker, J., Bacak, V., 2014. The increasing predictive validity of self-rated health. PLoS One 9 (1) e84933 doi:http://dx.doi.org/10.1371/journal.pone.0084933. Sedgwick, P., 2014. Ecological studies: advantages and disadvantages. Br. Med. J. 348 (4), g2979. Singh-Manoux, A., Dugravot, A., Shipley, M.J., Ferrie, J.E., Martikainen, P., Goldberg, M., Zins, M., 2007. The association between self-rated health and mortality in different socioeconomic groups in the GAZEL cohort study. Int. J. Epidemiol. 36 (6), 1222–1228. Steele, L.S., Dewa, C.S., Lin, E., Lee, K.L., 2007. Education level, income level and mental health services use in Canada: associations and policy implications. Healthcare Policy 3 (1), 96–106. Steptoe, A., Willemsen, G., 2002. Psychophysiological responsivity in coronary heart disease. In: Stansfeld, S., Marmot, M.G. (Eds.), Stress and the Heart. BMJ Books, London, pp. 168–180. Subramanian, S.V., Kawachi, I., 2004. Income inequality and health: what have we learned so far? Epidemiol. Rev. 26 (1), 78–91. Tapia Granados, J.A., 2008. Macroeconomic fluctuations and mortality in postwar Japan. Demography 45, 323–343. Tapia Granados, J.A., Ionides, E.L., 2017. Population health and the economy: mortality and the great recession in Europe. Health Econ. 26 (12), E219–E235. doi:http://dx.doi.org/10.1002/hec.3495. Tapia Granados, J.A., Rodriguez, J.M., 2015. Health, economic crisis, and austerity: a comparison of Greece, Finland and Iceland. Health Policy 119 (7), 941–953. doi: http://dx.doi.org/10.1016/j.healthpol.2015.04.009. Thomson, S., Figueras, J., Evetovits, T., Jowett, M., Mladovsky, P., Maresso, A., Kluge, H., 2014. Economic Crisis, Health Systems and Health in Europe Retrieved from:. World Health Organisation. http://www.euro.who.int/__data/assets/pdf_file/ 0008/257579/Economic-crisis-health-systems-Europe-impact-implicationspolicy.pdf. Toffolutti, V., Suhrcke, M., 2014. Assessing the short term health impact of the Great Recession in the European Union: a cross-country panel analysis. Prev. Med. 64, 54–62. doi:http://dx.doi.org/10.1016/j.ypmed.2014.03.028.

184

K. Thompson et al. / Economics and Human Biology 35 (2019) 162–184

Tøge, A.G., 2016a. Health effects of unemployment in Europe (2008-2011): a longitudinal analysis of income and financial strain as mediating factors. Int. J. Equity Health 15 doi:http://dx.doi.org/10.1186/s12939-016-0360-6. Tøge, A.G., 2016b. Health effects of unemployment in Europe during the great recession: the impact of unemployment generosity. Int. J. Health Serv. 46 (4), 614–641. doi:http://dx.doi.org/10.1177/0020731416664688. Tøge, A.G., Blekesaune, M., 2015. Unemployment transitions and self-rated health in Europe: a longitudinal analysis of EU-SILC from 2008 to 2011. Soc. Sci. Med. 143, 171–178. doi:http://dx.doi.org/10.1016/j.socscimed.2015.08.040. Urbanos-Garrido, R.M., Lopez-Valcarcel, B.G., 2015. The influence of the economic crisis on the association between unemployment and health: an empirical analysis for Spain. Eur. J. Health Econ. 16 (2), 175–184. doi:http://dx.doi.org/ 10.1007/s10198-014-0563-y. Vandoros, S., Hessel, P., Leone, T., Avendano, M., 2013. Have health trends worsened in Greece as a result of the financial crisis? A quasi-experimental approach. Eur. J. Public Health 23 (5), 727–731. doi:http://dx.doi.org/10.1093/eurpub/ckt020. Vasquez-Vera, H., Rodriguez-Sanz, M., Palencia, L., Borrell, C., 2016. Foreclosure and health in Southern Europe: results from the platform for people affected by mortgages. J. Urban Health-Bull. N. Y. Acad. Med. 93 (2), 312–330. doi:http://dx. doi.org/10.1007/s11524-016-0030-4. Vlachadis, N., Iliodromiti, Z., Vlachadi, M., Xanthos, T., Ktenas, E., Vrachnis, D., Kornarou, E., Vrachnis, N., 2014a. Cardiovascular mortality and the financial crisis in Greece: trends and outlook. Int. J. Cardiol. 176 (3), 1367–1368. doi: http://dx.doi.org/10.1016/j.ijcard.2014.07.275.

Vlachadis, N., Vrachnis, N., Ktenas, E., Vlachadi, M., Kornarou, E., 2014b. Mortality and the economic crisis in Greece. Lancet 383 (9918) doi:http://dx.doi.org/ 10.1016/S0140-6736(14)60250-6 691-691. Vrachnis, N., Vlachadis, N., Salakos, N., Vlachadi, M., Iliodromiti, Z., 2015. Cancer mortality in Greece during the financial crisis. Acta Oncol. 54 (2), 287–288. doi: http://dx.doi.org/10.3109/0284186x.2014.958785. Wagstaff, A., Van Doorslaer, E., 2000. Income inequality and health: what does the literature tell us? Annu. Rev. Public Health 21 (1), 543–567. Wamala, S.P., Mittleman, M.A., Schenck-Gustafsson, K., Orth-Gomer, K., 1999. Potential explanations for the educational gradient in coronary heart disease: a population-based case-control study of Swedish women. Am. J. Public Health 89 (3), 315–321. Wilkinson, R.G., 1997. Socioeconomic determinants of health - Health inequalities: Relative or absolute material standards? Br. Med. J. 314 (7080), 591–595. doi: http://dx.doi.org/10.1136/bmj.314.7080.591. Wilkinson, R.G., Pickett, K.E., 2006. Income inequality and population health: a review and explanation of the evidence. Soc. Sci. Med. 62 (7), 1768–1784. Wolfe, B., Evans, W., Seeman, T.E. (Eds.), 2012. The Biological Consequences of Socioeconomic Inequalities. Russell Sage Foundation. Zavras, D., Tsiantou, V., Pavi, E., Mylona, K., Kyriopoulos, J., 2013. Impact of economic crisis and other demographic and socio-economic factors on self-rated health in Greece. Eur. J. Public Health 23 (2), 206–210. doi:http://dx.doi.org/10.1093/ eurpub/cks143.