Final Assignment MSW & UM
2025-06-16
Exploring Social Determinants of Depression in Slovenia: An Analysis of ESS Round 11 Data
Abstract
This study investigates the influence of various social determinants on depression levels among Slovenian participants, using data from the 11th round of the European Social Survey (ESS). Depression was measured via the CES-D8 scale, and five predictors were analyzed: age, gender, childhood financial difficulties, social support, and fruit consumption. Statistical analysis using R software included bivariate tests (Chi-Square, t-tests, ANOVA) and multivariate regression models. The results reveal that gender and childhood financial hardship significantly predict higher depression scores, with women and those who faced early-life financial difficulties reporting more depressive symptoms. Contrary to prior research, age showed a weak positive association with depression, while social support and fruit consumption did not demonstrate significant effects. These findings suggest that economic and gender-related factors have a stronger impact on depression than lifestyle behaviors. The study highlights the importance of considering socioeconomic context in mental health interventions and recommends future research with longitudinal designs and additional variables for a more comprehensive understanding.
Introduction
This study examines depression among Slovenian participants using data from the 11th round of the European Social Survey (ESS). Depression significantly impacts overall health and well-being, making it a crucial subject of analysis. We explore five independent variables—age, gender, childhood financial difficulties, social support and fruit consumption—due to their established relevance in mental health research. Using R software, we analyze these relationships through the following hypotheses: H1: Younger individuals tend to report higher levels of depression than older individuals; H2: Women are more likely to report higher depression than men; H3: Slovenians who experienced financial difficulties in their childhood are more likely to have higher depression scores; H4: Slovenians who have more people to share intimate and personal matters with, have lower rates of depression; H5: More frequent fruit consumption is associated with lower levels of depression for Slovenians. This study contributes to understanding how these social determinants influence depression levels in Slovenia based on Center of Epidemiological Studies-Depression (CES-D8) scores.
Literature review
Depression is influenced by multiple social determinants. This section reviews key factors affecting depression levels based on previous research, focusing on age, gender, financial background, social support and diet.
Age relevance in depression
Research indicates that younger individuals experience higher levels of depression compared to older adults. A study by Goodwin et al. (2022) found that 40% of individuals aged 18–39 reported anxiety, while 33% reported depression, whereas in adults aged 60 and older, anxiety and depression rates were significantly lower at 20% and 16%, respectively. Similarly, student mental health data from the Mental Health Barometer 2022 (Zick, 2023) revealed that 45% of Slovenian students rated their mental health as poor or very bad, with 82% reporting high levels of study-related stress. In contrast, older adults (65+) experience significantly lower rates of major depressive episodes and lifetime depression (Goodwin et al., 2022), suggesting a decline in depression prevalence with age.
Gender relevance on depression
Research strongly supports that women are more likely to report higher depression rates than men. Over time, depression prevalence has increased, with women’s rates rising from 9.7% in 2015 to 11.8% in 2020, while men’s rates increased from 4.7% to 6.4% over the same period (Goodwin et al., 2022). Women consistently reported higher depression levels across all study years. Genetic factors also play a role, as differences in gene inheritance and interactions with the environment contribute to the increased likelihood of depression in women (Prelog et al., 2022).
Financial difficulties in childhood
Bøe et al. (2016) examined childhood financial hardship’s long-term effects on depression across 19 European countries. Findings showed early financial stress significantly predicted higher depression in adults aged 25–40 in ten countries. However, its influence declined in older age as social factors, such as marital status and community engagement, became more significant. In Slovenia, improvements in childhood financial conditions may contribute to better mental health outcomes (“First progress report on implementing the European child guarantee in Slovenia 2022-2023,” 2024).
The personal matters
Existing research provides valuable insights into the broader context of social support and mental health in Slovenia (“First progress report on implementing the European child guarantee in Slovenia 2022-2023”, 2024). Examining the impacts COVID-19 had on mental health, a research from 2021 highlights the benefits social support has on individuals well-being (Cugmas et al., 2021). Those with stronger support networks are likely to experience lower rates of depression.
Fruit consumption
Głąbska et al. (2020) and Kirbiš et al. (2025) explored the connection between dietary habits, particularly fruit consumption and mental health outcomes like depression. They highlight that increased fruit consumption, rich in essential nutrients such as vitamins, antioxidants and fiber, is associated with improved mental well-being and a reduction in depression symptoms. While Głąbska et al. (2020) discusses this link in several European countries and Kirbiš et al. (2025) does not directly focus on Slovenians, the findings suggest that regular fruit intake could play a role in lowering depression levels. These studies align with the hypothesis that more frequent fruit consumption is associated with lower levels of depression, emphasizing diet as a significant factor in mental health.
Methods
Data source and sample
This study utilizes data from the 11th round of the European Social Survey (ESS-11), specifically focusing on Slovenian participants. The ESS is a cross-national survey that collects data on social attitudes, behaviors and well-being across Europe. Our sample includes individuals who provided responses on depression levels and key explanatory variables.
Participants
Our research is based on a sample of (1248) Slovenian respondents (50% men and 50% women) aged 16 - 90 participating in the 11th round of the ESS-11.
Operationalization of variables
Dependent Variable depression (Card 45):
Depressive symptoms were assessed using the eight-item CES-D8 scale, which categorizes different levels of depression. Respondents reported how frequently they experienced certain emotions and behaviors in the past week, including: feeling depressed, struggling to complete tasks, experiencing restless sleep, feeling happy, feeling lonely, enjoying life, feeling sad and having difficulty getting started. Responses were recorded on a four-point scale: ‘None or almost none of the time’, ‘Some of the time’, ‘Most of the time’ and ‘All or almost all of the time’, with an additional ‘Don’t know’ option. The eight-item scale included two reverse-coded items and responses were summed to create a composite score ranging from 0 to 24, after subtracting 8 from the raw item scores to account for the base scale offset. A mean score was assigned only if the respondent completed at least six of the eight items. According to Boe et al. (2017), analysis using ESS data has demonstrated that this version of the CES-D scale is a valid and reliable measure of depression across different age groups and remains consistent across genders.
## $alpha
## [1] 0.8248442
The Cronbach’s alpha value of 0.8248442 demonstrates a strong internal consistency among the scale items, indicating that they reliably measure the underlying construct of depression. A Cronbach’s alpha above 0.7 is generally considered acceptable for psychological scales, confirming the CES-D8’s reliability in this Slovenian sample.
The following likert table and corresponding plot provide a clear visual and statistical summary of depression-related items in the CES-D8 scale. Items such as “you felt depressed?” and “you could not get going” show higher levels of endorsement, while reverse-coded items like “you enjoyed life” and “you were happy” tend to have lower agreement, aligning with expectations for depressive symptomatology.
| Item | None or almost none of the time | Some of the time | Most of the time | All or almost all of the time | Mean | Count |
|---|---|---|---|---|---|---|
| …you felt depressed? | 74.0 | 21.6 | 3.4 | 1.0 | 1.3 | 1246 |
| …you felt that everything you did was an effort? | 61.0 | 30.0 | 7.0 | 2.0 | 1.5 | 1245 |
| …your sleep was restless? | 47.1 | 35.0 | 14.3 | 3.5 | 1.7 | 1244 |
| …you were happy? | 2.3 | 12.9 | 61.1 | 23.8 | 3.1 | 1244 |
| …you felt lonely? | 76.5 | 18.8 | 3.9 | 0.9 | 1.3 | 1242 |
| …you enjoyed life? | 3.9 | 12.8 | 58.1 | 25.2 | 3.0 | 1239 |
| …you felt sad? | 52.8 | 40.9 | 5.5 | 0.9 | 1.5 | 1246 |
| …you could not get going65? | 55.7 | 35.2 | 8.2 | 1.0 | 1.5 | 1243 |
Independent Variables
Age (agea):
##
## Call:
## lm(formula = depression ~ age_m, data = df_sl)
##
## Coefficients:
## (Intercept) age_m
## 3.94981 0.01704
Gender (gndr):
Originally coded as 1 = Male, 2 = Female.
Recorded into a binary variable (0 = Male, 1 = Female).
## by.as.numeric.as.character.df_sl.age_m....df_sl.gndr..mean..na.rm...T.
## Male 49.08388
## Female 50.41094
Childhood Financial Difficulties (fnsdfml):
Measures association with financial struggles during childhood.
Fruit Consumption (etfruit):
Frequency of fruit consumption measured on a scale from 1 (“Never”) to 7 (“Every day”). Recoded into three categorical groups to simplify analysis: Low Fruit Consumption: Never, Less than once a week; Medium Fruit Consumption: 1–3 times per week; High Fruit Consumption: Once a day or more.
Analytical approach
- The analysis is conducted using R software, applying statistical models to assess the relationship between depression and the selected social determinants.
- Bivariate analysis such as Chi-Square, T-tests, ANOVA are used to describe and examine depression differences across categorical variables.
- Regression analysis such as the regression model is applied to explore the combined effects of independent variables on depression scores, controlling for potential confounders.
- Statistical significance levels such as by, are reported using p-values and confidence intervals.
Discussion
Dependent Variable:
Descriptive Statistics of Depression:
- The mean: 4.796
- The median: 4.000
The histogram shows a right-skewed distribution of total depression scores, with most Slovenian respondents scoring between 0 and 10, and fewer individuals reporting scores above 15. This suggests that while depressive symptoms are present, severe depression is relatively uncommon.
Independent Variable:
H1: Younger individuals tend to report higher levels of depression than older individuals
The regression output for age indicates a statistically significant but weak positive association with depression. This finding contradicts the initial hypothesis (H1), which proposed that younger individuals report higher depression levels. While prior research (Goodwin et al., 2022) supports this assumption, the current results suggest that in this specific sample, age may have a minimal or even opposite effect. This could be due to cultural or demographic differences in the Slovenian population or potentially limited variance in depression scores among age groups.
H2: Women are more likely to report higher depression than men
The average female is slightly older then the average male, by 1.33 year. The older women are, the likelier they are to be depressed than men.
The following plot allows for a clear comparison of the central tendency, spread, and outliers in depression scores between men and women.
This boxplot illustrates the distribution of depression scores by gender among Slovenian participants. It shows that females have a higher median depression score than males, along with more variability and extreme outliers. This visual supports the hypothesis that women report higher levels of depression than men, aligning with the statistical analysis and existing literature.
H3: Individuals who experienced financial difficulties in their childhood are more likely to have higher depression scores
Slovenians with financial childhood difficulties have a higher depression rate
##
## Pearson's Chi-squared test
##
## data: df_sl$fnsdfml and df_sl$depression
## X-squared = 190.37, df = 88, p-value = 1.638e-09
As the p-value 1.638e-09 is smaller than 0.001, individuals with financial hardships in childhood report higher depression scores. Therefore the p-value is of high significance.
H4: Depression is lower when having more people to discuss intimate and personal matters
Running the Annova test, it can be observed that the p-value is above 0.05, the result is not significant. It means there is not a strong evidence that social support affects depression on the slovenian population. Hence we will conduct a regression model to verify the relevance of the variables for potential linear effects.
##
## Call:
## lm(formula = depression ~ support, data = df_sl)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0638 -2.6641 -0.6641 1.9362 17.9362
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.0638 0.1688 29.998 <2e-16 ***
## supportMedium social support -0.3997 0.2212 -1.807 0.071 .
## supportHigh social support -0.8108 0.4357 -1.861 0.063 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.66 on 1205 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.004291, Adjusted R-squared: 0.002639
## F-statistic: 2.597 on 2 and 1205 DF, p-value: 0.07494
The Regression Model cross-checks the Anova-test. Both mark low relevance results. It can be concluded that neither test shows any success and therefore leads us to believe there is no association between the dependent and independent variable.
The following boxplot visualizes the distribution of depression scores across social support categories:
It helps illustrating how the depression scores are distributed within
each support category. Even though the statistical tests show no
significant difference, the visualization offers a nuanced look at
trends and potential outliers.
H5: More frequent fruit consumption is associated with lower levels of depression
Running the Annova, following result is observed: F = 2.1172, num df = 2.00, denom df = 109.71, p-value = 0.1253
Low fruit consumption: Min: 0, Mean: 8.3, Max: 19 Medium fruit consumption: Min: 1, Mean: 7.7, Max: 17 High fruit consumption: Min: 0, Mean: 6.9, Max: 20
The p-value equals to 0.1253 and is above the significance value of 0.05. Therefore the hypothesis is not met, and there is no significance within this group of participants.
Comparing the distribution of depression scores across the three fruit consumption categories:
Results // Regression Models
1. Regression model for metric depression scale
##
## Call:
## lm(formula = depression ~ age_m + gndr + fnsdfml + support +
## frconsumption, data = df_sl)
##
## Coefficients:
## (Intercept) age_m
## 7.45248 0.00424
## gndrFemale fnsdfmlOften
## 0.92094 -1.63775
## fnsdfmlSometimes fnsdfmlHardly ever
## -2.54664 -3.00202
## fnsdfmlNever supportMedium social support
## -3.81330 -0.18238
## supportHigh social support frconsumptionMedium fruit consumption
## -0.76677 0.18961
## frconsumptionHigh fruit consumption
## -0.41943
Findings
All coefficient estimates reflect change in total depression points on the 0–24 CES-D8 scale
Intercept = 1.93: This represents the predicted depression score for a baseline respondent - a male of age 0, who reported no childhood financial difficulties, low social support, and low fruit consumption. While not meaningful in isolation, it sets the reference point for interpreting other coefficients.
1. Age:
For each additional year of age, depression score increases by 0.004 points. This is a very small and likely non-significant effect, confirming that age has little influence on depression in this sample — consistent with your earlier H1 finding.
2. Gender:
Being female is associated with a +0.92 point increase in depression score compared to males. This supports Hypothesis 2 (H2) and is consistent with literature: women report significantly more depressive symptoms than men.
3. Childhood Financial Difficulties:
| Category | Coefficient | Interpretation |
|---|---|---|
| Sometimes | -2.55 | Lower depression by 2.55 points |
| Hardly ever | -3.00 | Lower depression by 3.00 points |
| Never | -3.81 | Lowest depression, 3.81 points below the reference |
Comparing to the reference group “Having often financial difficulties” (Sometimes: –2.55; Hardly ever: –3.00; Never: –3.81), the coefficients show a clear negative trend: as childhood financial hardship decreases, depression scores decrease significantly. Strongly supporting H3, the more financial difficulty experienced in childhood, the higher the likelihood of adult depression. Refering back to the Bar Graph, respondents who reported more hardship had higher proportions of high depression scores.
4. Social Support:
The coefficients of the low support group (Medium: –0.18; High: –0.77) are small and not statistically significant, which aligns with your ANOVA and regression results that found no strong association between social support and depression. Thus, H4 is not supported in this sample.
5. Fruit Consumption:
Comparing the consumption groups (Medium: +0.19; High: –0.42), small and non-significant effects are being observed. Even high fruit consumption only lowers depression slightly. This supports earlier findings (ANOVA p = 0.1253) that fruit consumption has no significant predictive power in this model. Hence, H5 is also not supported.
Summary on the regression model
The regression analysis confirms that gender and childhood financial difficulties are significant predictors of depression. Women and individuals who experienced more frequent financial hardships in childhood report significantly higher depression scores. Notably, the effect of financial hardship shows a clear gradient — as hardship decreases, so do depression scores, supporting Hypothesis 3 (H3) robustly.
By contrast, age, social support, and fruit consumption do not significantly affect depression scores in this model. These results align with previous statistical tests and visualizations, suggesting that socioeconomic and demographic variables (especially early-life adversity) have a stronger influence on mental health than lifestyle or behavioral factors within this Slovenian sample.
2. Clinical Depression Binary
Title: Predictors of Clinically Significant Depression
Findings - Clinical Depression
Although the CES-D8 total score ranges from 0 to 24, participants with a score of 9 or higher were classified as clinically depressed, in line with study instructions. Therefor participants with a score of 9 or higher are classified as clinically depressed. The following results of estimates odds ratios (OR) for being classified as clinically depressed:
A binary logistic regression was conducted to examine whether gender
predicts clinical depression. The model revealed a significant effect of
gender (β = 0.657, p < .001). Females had 1.93 times higher
odds of being clinically depressed compared to males.
Moreover, the McFadden R² and Nagelkerke R² are being used to assess
model fit. The model’s McFadden pseudo R² was 0.09, indicating that
approximately 9% of the variance in depression classification was
explained by gender alone. These results support the hypothesis that
gender is a meaningful predictor of clinical depression risk.
We now test the association between the categorical predictor hypothesis 3 financial difficulty in childhood variable (fnsdfml) and the binary outcomes clinical_depression:
##
## Pearson's Chi-squared test
##
## data: chisq_table
## X-squared = 43.086, df = 4, p-value = 9.93e-09
Interpretation - Clinical Depression
We examined the relationship between the categorical variable fnsdfml and clinical depression using a Chi-squared test. The test yielded a highly significant result (p = 1.638e-09), indicating a strong association between fnsdfml and clinical depression status. This means that the likelihood of being classified as clinically depressed differs significantly across the different categories of fnsdfml. These findings suggest that fnsdfml may be an important social or demographic factor related to depression risk.
Comparison - Clinical Depression
Compared to earlier analyses where we applied linear regression to continuous depression scores, the Chi-squared test focuses on categorical group differences in clinical risk. While the linear regression allowed us to identify gradual effects of predictors (e.g., lower income slightly increasing depression scores), this analysis shows clear group differences related to fnsdfml in terms of clinical depression thresholds. The logistic regression further quantified this risk using odds ratios, but the Chi-squared test confirmed a statistically robust association without assuming a particular model form.
Discussion
The statistical findings reveal that among the tested social determinants, gender and childhood financial hardship show the most consistent and significant associations with depression. Women reported significantly higher depression scores than men, both in continuous and binary classifications. Childhood financial difficulties were also strongly associated with higher depression risk, suggesting long-term psychosocial consequences. However, social support and fruit consumption did not show statistically significant effects, contradicting some prior literature. These results may reflect population-specific variations or limited sensitivity in the categorization of those variables. The results highlight the salience of socioeconomic and demographic factors over lifestyle choices in predicting depression in this Slovenian sample.
Limitations
This study has several limitations. First, the cross-sectional nature of the ESS data prevents conclusions about causality. Second, self-reported measures of depression and lifestyle factors may be subject to recall or social desirability bias. Third, some variables such as fruit consumption and social support were recoded into broad categories, potentially obscuring nuanced effects. Additionally, cultural and systemic differences specific to Slovenia may limit the generalizability of the findings. Finally, the exclusion of other potentially relevant variables such as employment status, physical health, or education may reduce the explanatory power of the models and therefore would be interesting to investigate.
Conclusion
This study analyzed the influence of social determinants on depression among Slovenian participants in the ESS Round 11 dataset. The results confirmed that gender and childhood financial difficulties are significant predictors of depression, with women and those with disadvantaged childhoods reporting higher levels of depressive symptoms. Contrary to the hypotheses, social support and fruit consumption did not demonstrate significant effects, and age had only a weak positive association. These findings underscore the critical role of early-life socioeconomic conditions and gender disparities in mental health outcomes. While the analysis offered valuable insights, limitations such as data structure and variable categorization warrant cautious interpretation. Future research should explore additional psychosocial variables using longitudinal data to better understand the complex pathways influencing depression and to inform more targeted mental health interventions.
Social Support (inprdsc):
Number of people with whom respondents discuss intimate and personal matters. Used as a continuous predictor (higher values indicate stronger social support). Recorded into three categorical groups to assess the relationship between social support and depression: Low Social Support: 0–2 people; Medium Social Support: 3–6 people; High Social Support: 7 or more people.