Media Exposure and Health in Europe: Mediators and Moderators of Media Systems

Traditional media channels are central providers of entertainment, providing an important leisure time activity (Vorderer et al. 2004). According to displacement theory, individuals only have a limited amount of leisure time available, posing constraints on their activities (Dutta-Bergman 2005; Nie and Erbring 2000). Because of time displacement, television exposure is related negatively with social and physical activities (Keim et al. 2004; Putnam 2001; Saelens et al. 2002; Brown and Walsh-Childers 2002). Considering that exposure to television and other types media are all time consuming, people who are exposed to media can spend less time on social and physical activities that are related to health (Huijts 2011; Huijts and Kraaykamp 2012). Therefore, we hypothesize that:

Over the last decade, we expect that the educating role of exposure to internet to have become more dominant: seeking health information has become one of the most common activities on internet and as a result internet has become an increasingly important source of information (Brown and Walsh-Childers 2002; Greenberg et al. 2004). Even though medical researchers have expressed great concerns about the quality of health information on internet (e.g. Morahan-Martin 2004), findings amongst youngsters show that respondents recognize internet information that may not be credible and have strategies to test its reliability (Gray et al. 2005). Since knowledge provided by internet helps people to choose healthier lifestyles (Kenkel 1991), we hypothesize that:

Many efforts have gone into studying the relation between media exposure and health, such as social isolation, and moreover, the mean world syndrome. In this study we examine the possible mediating effect of several determinants, starting with the mediating role of social isolation. We first discuss exposure to traditional media, followed by exposure to contemporary media.

To test our hypotheses, we used individual-level data from the European Social Survey (ESS) round 5, conducted in 2010/2011. Data were collected through face-to-face interviews held with individuals aged 15 and over, residing within private households, regardless of their nationality, citizenship, language or legal status. Samples are representative at a country level and in general response percentages are high; the overall response rate is above 60 percent (European Social Survey 2012). For more information about the data, see http://​www.​europeansocialsu​rvey.​org.

The original dataset contains information on 50,781 respondents across 27 countries: Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Israel, Lithuania, the Netherlands, Norway, Poland, Portugal, Russian Federation, Slovakia, Slovenia, Spain, Sweden, Switzerland, the United Kingdom and Ukraine. Cyprus and Israel were left out of the analyses, for they do not fall into the typology of media systems, and there are no theoretical reasons to assume they do belong to one of the above mentioned types of media systems. Furthermore, only people between 25 and 75 years of age are included in the analyses, for there is possible health selection above the age of 75, and people younger than 25 have often yet to finish their education, possibly resulting in bias. After this age selection and listwise deletion of respondents with missing values on the individual characteristics, we estimated our models with 36,692 respondents across 25 European countries.

To measure general health, covering both physical and psychological health, respondents were asked: “how is your health in general?” The answer categories were: (1) “very bad”, (2) “bad”, (3) “fair”, (4) “good” and (5) “very good”. The categories are considered to be metric. Self-assessed health has been shown to be a reliable and valid measurement of health (Chandola and Jenkinson 2000; Lundberg and Manderbacka 1996). By using this measurement of self-assessed general health, we followed a prominent tradition in epidemiological research (Huijts and Kraaykamp 2011a). For the descriptive statistics (of all variables) we refer to Table 2 in the appendix.

Social isolation was measured with the single question: “How often do you meet socially with friends, relatives or work colleagues?” The answer categories being (1) “every day”, (2) “several times a week”, (3) “once a week”, (4) “several times a month”, (5) “once a month”, (6) “less than once a month” and (7) “never”. Previous studies used this variable to refer to the informal aspect of social capital (e.g. Savelkoul et al. 2011). We assessed the relation between these answer categories and the dependent variable to be linear, as will be shown at the end of this section.

Fear of crime was measured with the questions: “How often, if at all, do you worry about [becoming a victim of violent crime/your home being burgled]”. The answer categories are (1) “never”, (2) “just occasionally”, (3) “some of the time” and (4) “all or most of the time”. Although these items are often used as a part of a larger scale (Eitle and Taylor 2008; Taylor et al. 2009), several previous studies also relied on this selection of two items (Visser et al. 2013). The scale has been composed using Categorical Principle Component Analysis (CATPCA) in the 22nd SPSS edition.

The scale perceived ethnic threat was measured with three questions: “Would you say it is generally bad or good for [country]’s economy that people come to live here from other countries?”; “Would you say that [country]’s cultural life is generally undermined or enriched by people coming to live here from other countries?”; and “Is [country] made a worse or a better place to live by people coming to live here from other countries?” The answer categories ranged from 0 to 10. In line with previous studies (Coenders et al. 2004; Visser et al. 2013), we constructed a scale where we used the mean sum score (Cronbach’s α = 0.865). The scale was reversed, so a higher score meant people perceived more ethnic threat. This scale is equivalent across all countries of the ESS (Coenders et al. 2004); our principal factor analyses also pointed in this direction (lowest communality = 0.457, lowest factor loading = 0.676). The relation between these answer categories and the dependent variable were assessed as linear.

Generalized social distrust was measured with the questions: “Generally speaking, would you say that most people can be trusted, or that you cannot be too careful in dealing with people?”; “Do you think that most people would try to take advantage of you if they got the chance, or would they try to be fair?”; and “Would you say that most of the time people try to be helpful or that they are mostly looking out for themselves?” The answer categories ranged from 0 to 10. These questions are derived from the Rosenberg Trust Scale, which has been shown to be reliable and valid for the ESS countries (Cronbach’s α = 0.789) (Reeskens and Hooghe 2008). Our analyses showed that the factor solutions were comparable across countries. (the lowest communality was 0.159, the lowest factor loading 0.398. The other communalities were all above 0.200, the other factor loadings above 0.400.) We assessed the relation between the answer categories and the dependent variable as linear.

Distrust in institutions was measured using the questions how much confidence people have in politicians, parliament, the legal system, the police, the European Parliament and the United Nations. The answer categories ranged from (0) “no trust at all” to (10) “complete trust”. The scale was constructed by calculating the mean of the sum, and is subsequently reversed. It was reliable (Cronbach’s α = 0.912), valid and equivalent (Zmerli and Newton 2008; Zmerli et al. 2007). Principal factor analyses also showed that the factor solutions were roughly comparable across countries, although in eight countries two factors were found. Restricting the factors to one in these countries still provided sufficiently high communalities and factor loadings (lowest communality = 0.195, lowest factor loading = 0.446). The relation between the answer categories and the dependent variable were assessed as linear.

Feelings of unsafety were measured using the question “how safe do you—or would you—feel walking alone in this area after dark?”, with the answer categories ranging from (1) “very safe” to (4) “very unsafe”. Even though single-item indicators might be less reliable and valid, this straightforward question makes it unlikely for interpretation issues to arise (Visser et al. 2013). We assessed the relation between the answer categories and the dependent variables to be linear, as explained at the end of this section.

In the analyses, we controlled for age (in years), age square, sex (0 = male, 1 = female), educational level, cohabiting status, religiosity and main activity. The respondent’s highest completed level of education was assessed with multiple categories, ranging from (less than) lower secondary to higher tertiary. After testing for linearity, the categories were coded into three categories, namely: (less than) lower secondary, upper secondary and vocational and tertiary education. These categories constituted a linear relation with self-assessed health. The cohabiting status for all respondents was coded by the interviewers, stating if (1) “respondent lives with husband/wife/partner” or whether another situation is at place: (0) “all others”. Religiosity was measured by asking “[a]part from special occasions such as weddings and funerals, about how often do you attend religious services nowadays?” Answer categories ranged from (0) “never” to (7) “every day” and are considered continuous. The main activity of people was measured by asking “which of these descriptions best describes your situation (in the last 7 days)?” The answer categories were: “in paid work”, “in education”, “unemployed and actively looking for a job”, “unemployed, wanting a job but not actively looking for a job”, “permanently sick or disabled”, “retired”, “in community or military service”, “doing housework, looking after children or other persons” and “other”. The two unemployment categories were merged, for reasons of parsimony.

At the contextual level, we added one variable indicating the media system of one’s country. In line with Hallin and Mancini (2004), we distinguished four types of media systems: the polarized pluralist model (France, Greece, Portugal and Spain), the democratic corporatist model (Belgium, Denmark, Finland, Germany, Netherlands, Norway, Sweden and Switzerland) and the liberal model (Ireland and United Kingdom). The former communist model consists of the countries not classified by Hallin and Mancini (2004) (Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Lithuania, Poland, Russia, Slovakia, Slovenia and Ukraine). Cyprus and Israel were left out of the analyses. We consider it important to note that Hallin and Mancini’s classification shows great similarities with the types of welfare state systems by Esping-Andersen (1990).

Finally, following Huijts (2011), we controlled for two macro-level control variables: Gross domestic product (GDP) per capita (in US dollars divided by 1000, adjusted for purchasing power parities) and welfare state expenditure, the percentage of the total health expenditure in a country that is covered by the government (World Health Organization, 2013). GDP per capita was retrieved from the United Nations Economic Commission for Europe (n.d.). Following Huijts (2011), we used the log linear function to account for the influence of very low and high income countries. Incorporating these macro-level controls avoids spurious relationships, for these controls are linked to general health and differ over media systems.

ANOVA-tests for (deviance from) linearity are conducted for the variables which we originally planned to include as metric variables in the analyses. The results of these tests are presented in Table 3 in the appendix. These analyses indicated that most relations are predominantly linear, even though there are also deviations from linearity. The added value of categorization of independent variables is limited (F-value of deviation from linearity differs <10 % of linear F-value), and therefore these will be included as linear indicators. The exceptions are media exposure to radio and newspaper and religious attendance, leading us to include the media exposure indicators as dummies (0 = no exposure, 1 = exposure), and include all religious attendance categories as dummies (reference is never attending religious services). All metric variables are grand-mean centered.

To account for the hierarchical structure of the data, multilevel linear regression analyses with the MIXED procedure were used in SPSS (IBM Corp, Armonk, NY) to produce more reliable parameter estimates and standard errors (Snijders and Bosker 1999). The intra-country correlations for our dependent variable general health indicated that 10.6 % (0.089/(0.089 + 0.749) × 100) of the variance was attributed to the country level, justifying the use of multilevel analysis. After the descriptive analyses, media exposure measures were taken into account in the first model (Table 1). Individual-level controls were added in the second model. In the third model, intermediating variables were added and in the final models the macro-level variables were incorporated. The parameters in these models contain unstandardized regression coefficients. Subsequently, tests for robustness have been conducted with different operationalizations of media exposure in the first robustness-check. In addition, checks for robustness of findings have been conducted in which self-assessed general health and the discussed mediators have been included as nominal or ordinal variables.

The levels of self-assessed general health differ greatly across countries, as can be seen in Fig. 2. The lowest average level of general health is found in Ukraine (3.069), the highest in Greece (4.200). Media exposure varies across countries as well, as shown in Table 4 in the Appendix. People in Switzerland report the lowest level of exposure to television (3.153), in Bulgaria the highest (5.558). Furthermore, people listen the most to the radio in Ireland (90.2 %), and the least in Bulgaria (40.2 %). Reading newspapers is most widespread in Norway, where 95.4 % of the population reads newspapers, followed by the Finnish and Swedish. Reading newspapers is least common in Greece, where 34.1 % reads newspapers. Lastly, internet exposure is especially prevalent in Scandinavia and the Netherlands, while it is less common in Eastern Europe. For the complete list of statistics of media exposure, see Table 4 in the Appendix.

We differentiated between exposure to traditional media (television, radio, and newspapers) versus contemporary media (internet). Two contradicting hypotheses were formulated for traditional media exposure, either emphasizing the displacement (hypothesis 1a), or educator effect (hypothesis 1b) of media exposure.

The results in Model 1 (Table 1) indicate that exposure to television is negatively related to general health, while exposure to the radio and newspapers is positively related to self-assessed general health. These results remain, although change slightly, in Model 2 when controlled for educational attainment, sex, age, age squared, religious attendance, cohabiting status, and main daily activity. Hence, these results indicate that for exposure to television, the displacement effect outweighs the educator effect; while on the contrary, for exposure to radio and newspapers the educator effect outweighs the displacement effect. Therefore, hypothesis 1a and 1b are both partly supported: hypothesis 1a is supported for exposure to television and hypothesis 1b is supported for exposure to radio and newspapers.

Next, exposure to contemporary media is explored. For contemporary media exposure we expected the educator effect to outweigh the displacement effect (hypothesis 1c). In model 2 we see that internet exposure indeed relates positively to general health, clearly supporting hypothesis 1c. There is positive relation between internet use and general health (b = 0.026). We can conclude that more frequent use of internet induces higher levels of general health, supporting hypothesis 1c.

The next step in our study is adding all mediating variables to gain a better understanding of the relation between media exposure and general health. First, the correlations between the mediators, general health and media exposure are discussed. Next, we review the findings in relation to social isolation, separately for traditional and contemporary media. Hereafter, we discuss the mediating role of mean world syndrome.

As can be seen in Table 5 in the Appendix, the mediating variables social isolation and indicators of mean world syndrome are all related to media exposure and general health. Exposure to radio, news papers and internet are negatively linked to these mediators, meaning that a higher level of exposure to these types of media is associated with a lower degree of social isolation and a more positive world view. In contrast, exposure to television is positively related to (more) social isolation and a (more) negative world view. Furthermore, the mediators are negatively associated with general health, indicating that a stronger mean worldview and more social isolation are both associated with a lower level of self-assessed general health.

As previously demonstrated, we found a link between media exposure and self-assessed health. To assess whether these findings are robust against variations in operationalizations, while accounting for small deviances from linearity, measurements on television and internet exposure were categorized in “low exposure” (‘no time at all’, ‘no access’ respectively), “high” (‘more than 3 h a day’, ‘every day’ respectively), and “medium” (the categories in between). The findings can be found in Table 8 in the Appendix. The results show substantial similarities to results discussed above. Both medium and high exposure to internet are related to a higher level of self-assessed general health, and high exposure to television is negatively related to self-assessed health. The parameter of a medium level of exposure to television is non-significantly related to self-assessed general health.

For the next tests of robustness, self-assessed general health is categorized in “bad health” (‘very bad’, ‘bad’ and ‘fair’ health) versus “good health” (‘good’ and ‘very good’ health). Next to the categorization of media exposure discussed above, the mediating variables were categorized as well (below versus above the grand mean). The check for robustness was conducted by using multi-level logistic regression analyses in the statistical package R by means of the glmmPQL procedure in the MASS package. The results in Table 9 in the Appendix again show substantial similarities with previous results; exposure to radio, newspaper, and internet are all positively related to self-assessed general health. The parameters for television exposure are, however, non-significant after taking the individual level controls into account. The mediators remain negatively related to self-assessed general health. Overall, these tests for robustness indicate that the findings for exposure to radio, newspapers, and internet are robust for varying operationalizations of self-assessed general health. However, the results regarding exposure to television and health require some attention, given the more measurement-sensitive results.