Problem Statement

Depression or depressive disorders are classified as a mental disease according to the ICD-11 classification (ICD-11 for Mortality and Morbidity Statistics, 2025). Globally, depression is estimated to have a prevalence of approximately 4%, with women being affected 50% more often than men. Furthermore, it is the fourth most prevalent cause of death among individuals aged 15–29 (World Health Organization, 2023). In the EU, “Spain was the second largest consumer of prescribed anxiolytics […] in 2021” (OECD, 2023, p. 20) with a 6% prevalence of depressive disorders in 2019. A similar gender trend, where females are more affected than men, can also be observed in Spain (OECD, 2023).

From a salutogenic perspective, the question arises: Which social determinants contribute to or impact depression? According to the World Health Organization, social determinants “[…] are the conditions in which people are born, grow, work, live, and age”. These may include working conditions, access to affordable and high-quality healthcare, activities, and age itself (World Health Organization, 2025).

To explore this question, predictor variables (social determinants) will be identified using data from the European Social Survey, Round 11 (2022). The following hypotheses will be tested for Spain.
1) Depression decreases with age. 2) Frequency of personal internet use is associated with depression. 3) Volunteering for non-governmental organisations (NGOs) decreases depression.

Literature

Age as a factor can be examined by analysing depression rates across different age groups. However, this variable may be influenced by several other factors, such as activities, employment, income, or other social determinants. Data on the Spanish population suggests that depression increases with age, showing a left-skewed distribution, with the highest prevalence observed in individuals aged 75–79 (Ministerio de Sanidad, 2021, p. 21).

The relationship between internet usage and depression was studied among older adults in high-income countries (Guo et al., 2025), showing a small reduction in depression (1.4%). Mediating factors such as social interactions, increased physical activity, and access to education helped to mitigate depressive symptoms. Conversely, a cross-sectional study among medical students found that (problematic) internet use led to 27% of students experiencing a depressive episode (Kożybska et al., 2022). These two groups likely differ in their frequency of usage, which will be taken into account in the following analysis.

An umbrella review of Nichol et al. (2024) identified eleven reviews encompassing 41 studies, that examined volunteering as an explanatory variable for depression. 39 of these studies found a positive effect, though moderators such as age and gender influenced the results. Despite the positive effects, volunteering with high empathetic arousals may increase depression. Lorenti et al. (2025) state that volunteering lowers the probability of depression by 5% in the general population and early retired people benefit even more.

dataES = df[df$cntry=="Spain",]
#table(dataES$cntry)

Methods

The statistical analysis was performed by using the software R Studio to examine the social determinants of depression in Spain. Depression was treated as the dependent variable, while potential social determinants from the European Survey 11 (ESS 11) (European Social Survey, 2022) were selected as predictor variables.

The construct was operationalised using a short form of the Depression Scale CES, employed in the ESS 11 (2024). The short version (CES-D8) compromises eight items: 1) Felt depressed, how often past week; 2) Felt everything did as effort, how often past week; 3) Sleep was restless, how often past week; 4) Were happy, how often past week; 5) Felt lonely, how often past week; 6) Enjoyed life, how often past week; 7) Felt sad, how often past week; 8) Could not get going, how often past week. All items, except for four and six, are negatively phrased. These two items needed to be reversed in polarity to ensure uniformity in the results. As the scale is ordinal, ranging from “none or almost none of the time” to “all or almost all of the time”, it was converted to numeric values. Similarly, the predictor variables were converted based on their respective scales. To assess the internal consistency of CES-D8, Cronbach’s alpha was calculated. The scale demonstrated multidimensionality and a fair similarity of items (alpha = 0.861).

The ESS11 questionnaire was examined for relevant social determinants, which were then confirmed by the literature to formulate hypotheses. To analyse these hypotheses, pairwise associations were applied. For all three hypotheses, Pearson’s correlation coefficient was calculated. Furthermore, a multivariate model incorporating structural variables such as age and gender was developed and supplemented with the pre-analysed variables from the hypotheses.

Results

Sample Description

Gender Distribution

table (dataES$gndr)
## 
##   Male Female 
##    875    969

Age Characteristics

mean(dataES$agea, na.rm=T)
## [1] 50.00218
sd(dataES$agea, na.rm=T)
## [1] 18.91651
median(dataES$agea, na.rm=T)
## [1] 50
min(dataES$agea, na.rm=T)
## [1] 16
max(dataES$agea, na.rm=T)
## [1] 90
range(dataES$agea, na.rm=T)
## [1] 16 90
quantile(dataES$agea, c(.25, .5, .75), na.rm=T)
## 25% 50% 75% 
##  35  50  64
dataES$agea_n <- cut(dataES$agea, 
                    breaks = c(15, 24, 34, 44, 54, 64, 74, 84, 90), 
                    labels = c(1, 2, 3, 4, 5, 6, 7, 8),
                    right = TRUE)
hist(dataES$agea, 
     main = "Age Distribution", 
     xlab = "Age in Years", 
     col = "gray",
     breaks=6)

Depression Scale

hist(dataES$CES_D8, 
     main = "Distribution of Mean Item Score", 
     xlab = "CES_D8 Item Score", 
     col = "lightblue",
     breaks=6)

cronbach.alpha(dataES[,c("fltdpr", "flteeff", "slprl", "wrhpp", 
                        "fltlnl", "enjlf", "fltsd", "cldgng")], na.rm=T)
## 
## Cronbach's alpha for the 'dataES[, c("fltdpr", "flteeff", "slprl", "wrhpp", "fltlnl", "enjlf", ' '    "fltsd", "cldgng")]' data-set
## 
## Items: 8
## Sample units: 1844
## alpha: 0.861

Multivariate Models

The first model incorporated structural variables such as age and gender. The intercept was 1.565 when both variables were included in the depression scale. With each additional year of age, the CES-D8 score increased slightly by 0.001 (p = 0.126), indicating no significant effect. However, gender showed a statistically significant effect, with females scoring 0.178 higher than males (p < 0.001). Adding volunteering and internet use to the model, further decreased the estimate for age (-0.0003). Internet use (est. -0.032; p = 0.004) and volunteering (est. -0.053; p = 0.098) negatively correlated with depression. However, the effect of female gender on depression increased (est. 0.18; p < 0.001).

dataES$volunfp_numeric <- ifelse(dataES$volunfp == "Yes", 1, 
                             ifelse(dataES$volunfp == "No", 0, NA))
dataES$netusoft_num = as.numeric(dataES$netusoft)
lm(CES_D8 ~  agea + gndr + netusoft_num + volunfp_numeric, data=dataES) 
## 
## Call:
## lm(formula = CES_D8 ~ agea + gndr + netusoft_num + volunfp_numeric, 
##     data = dataES)
## 
## Coefficients:
##     (Intercept)             agea       gndrFemale     netusoft_num  
##       1.7824459       -0.0003354        0.1801799       -0.0324825  
## volunfp_numeric  
##      -0.0529678
model=lm(CES_D8 ~  agea + gndr + netusoft_num + volunfp_numeric, data=dataES)
summary(model)
## 
## Call:
## lm(formula = CES_D8 ~ agea + gndr + netusoft_num + volunfp_numeric, 
##     data = dataES)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.9026 -0.3965 -0.1025  0.2702  2.3981 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      1.7824459  0.0810084  22.003  < 2e-16 ***
## agea            -0.0003354  0.0008139  -0.412  0.68031    
## gndrFemale       0.1801799  0.0254177   7.089 1.93e-12 ***
## netusoft_num    -0.0324825  0.0113741  -2.856  0.00434 ** 
## volunfp_numeric -0.0529678  0.0320280  -1.654  0.09834 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5416 on 1823 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.03435,    Adjusted R-squared:  0.03223 
## F-statistic: 16.21 on 4 and 1823 DF,  p-value: 4.692e-13