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.

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 (α = 0.4862241).

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

The Spanish sample consists of N = (male, female) participants with an average age of 50, minimum age of 16 and a maximum age of 90.

The age distribution of the Spanish population can be seen in the histogram below. The distribution is right skewed with the mean 50 being slightly bigger than 50.

Depression Scale

In the histogram below, the depression scored on the scale CES_D8, divided by gender can be seen. Lower depression scores can be more observed in men than in women. Whereas higher depression scores are predominant in females.


Distribution of answers regarding Depression Level (ESS round 11, Spain)
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
Felt depressed, how often past week. 59.2 33.1 5.6 2.1 1.5 1844
Felt everything did as effort, how often past week. 50.4 37.6 8.2 3.7 1.7 1842
Sleep was restless, how often past week. 45.7 34.8 13.3 6.1 1.8 1843
Were happy, how often past week. 31.8 44.7 20.2 3.3 1.9 1843
Felt lonely, how often past week. 70.5 22.2 4.9 2.3 1.4 1843
Enjoyed life, how often past week. 25.8 43.9 24.0 6.3 2.1 1842
Felt sad, how often past week. 44.4 47.7 5.3 2.6 1.7 1844
Could not get going, how often past week 50.7 40.2 6.4 2.7 1.6 1843


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).

“To ensure that the sample accurately reflects the larger population, sampling is applied to minimize bias and increase survey results reliability and validity of survey results” (survalizer, 2023) Therefore, the regression model was weighted by using the analysis function (anweight) to account the effect for Spain. According to the survey weights’ file of ESS 11, from round nine onwards, the function is already integrated in the main file. In comparison to the unweighted model the population baseline is now higher. Effects of age and internet use were underestimated,i.e. now have a stronger negative effect on depression. Whereas gender (female) has remained relatively stable with a slight increase. Volunteering shows a reduced negative effect after adjustment.

Linear Regression Model
Estimate Std. Error z-value p-value Signifi
(Intercept) 6.595 0.652 10.111 0.000 ***
Age -0.005 0.007 -0.838 0.402
Gender (Female) 1.461 0.201 7.256 0.000 ***
Volunteering for NPO-organisation -0.316 0.092 -3.422 0.001 ***
Frequency Internet Use -0.264 0.253 -1.042 0.297
Significance codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

Predictors of Clinically Significant Depression

The score ranges from 0-24 with a maximum score of 4 per item. Briggs et al. (2018) suggest that a score of 9 and more can be used to identify those with clinically significant symptoms.

Generalised Linear Model
Estimate Std. Error z-value p-value Signif
(Intercept) -1.543 0.373 -4.133 0.000 ***
Age 0.004 0.004 1.147 0.251
Gender (Female) 0.534 0.120 4.460 0.000 ***
Volunteering for NPO-organisation -0.313 0.157 -1.993 0.046
Frequency Internet Use -0.066 0.050 -1.312 0.189
Significance codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

Females have 1.71 times higher odds of clinical-level depression compared to males, holding other variables constant. Each year of age is associated with a 0.4% increase in odds of depression, but this effect is not statistically significant. Volunteers have 27% lower odds of clinical depression compared to non-volunteers, holding other factors constant. More frequent internet use is weakly associated with lower odds of depression, but this result is not statistically significant.

### Depression rate across regions in Spain In addition, it is hypothesised here that depression varies across the 18 regions (comunidades autónomas) in Spain. Regions are for instance Assuming that the initial depression score varies by each region (random intercept) and there is a constant development rate of depression across regions (fixed slope).

Mixed effects model on regional level
Estimate Std. Error t-value
(Intercept) 0.185 0.060 3.054
Age 0.001 0.001 1.172
Gender (Female) 0.085 0.019 4.539
Volunteering for NPO-organisation -0.046 0.024 -1.952
Frequency Internet Use -0.012 0.008 -1.378

The random intercept variance is very small (≈ 0.001), opposing to initially assumed. This suggest very little variation in baseline CES_D8 between regions (NUTS2 codes). Most of the variation is within regions (residual variance ≈ 0.16). The gender female is a strong, significant predictor: women score higher on CES_D8. Whereas age and the frequency of internet use are not significant in this model. Volunteering for NPOs (non-profit organisations) is slightly negatively related to depression. However, it needs to be noted, that the scale with yes or no answer options, is not best transposed here.

References

Briggs, R., Carey, D., O’Halloran, A. M., Kenny, R. A., & Kennelly, S. P. (2018). Validation of the 8-item Centre for Epidemiological Studies Depression Scale in a cohort of community-dwelling older people: Data from The Irish Longitudinal Study on Ageing (TILDA). European Geriatric Medicine, 9(1), 121–126. https://doi.org/10.1007/s41999-017-0016-0

European Social Survey. (2022). ESS Round 11 Source Questionnaire. ESS ERIC Headquarters.

Guo, L., Li, Y., Cheng, K., Zhao, Y., Yin, W., & Liu, Y. (2025). Impact of Internet Usage on Depression Among Older Adults: Comprehensive Study. Journal of Medical Internet Research, 27, e65399. https://doi.org/10.2196/65399

ICD-11 for Mortality and Morbidity Statistics. (2025). Depressive disorders. https://icd.who.int/browse/2025-01/mms/en#1563440232

Kożybska, M., Kurpisz, J., Radlińska, I., Skwirczyńska, E., Serwin, N., Zabielska, P., Kotwas, A., Karakiewicz, B., Lebiecka, Z., Samochowiec, J., & Flaga-Gieruszyńska, K. (2022). Problematic Internet Use, health behaviors, depression and eating disorders: a cross-sectional study among Polish medical school students. Annals of General Psychiatry, 21(1). https://doi.org/10.1186/s12991-022-00384-4

Lorenti, A., Rose, A. de, & Racioppi, F. (2025). Volunteering during early retirement reduces depression. Social Science & Medicine, 367, 117790. https://doi.org/10.1016/j.socscimed.2025.117790

Ministerio de Sanidad. (2021). Salud mental en datos: prevalencia de los problemas de salud y consumo de psicofármacos y fármacos relacionados a partir de los registros clínicos de atención primaria.

Nichol, B., Wilson, R., Rodrigues, A., & Haighton, C. (2024). Exploring the Effects of Volunteering on the Social, Mental, and Physical Health and Well-being of Volunteers: An Umbrella Review. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 35(1), 97–128. https://doi.org/10.1007/s11266-023-00573-z

OECD. (2023). Spain: Country Health Profile in 2023. State of Health in the EU.
https://eurohealthobservatory.who.int/publications/m/spain-country-health-profile-2023

World Health Organization. (2023, March 31). Depressive disorder (depression). https://www.who.int/news-room/fact-sheets/detail/depression

World Health Organization. (2025, January 21). Social determinants of health.
https://www.who.int/health-topics/social-determinants-of-health#tab=tab_1

Declaration of AI-utilisation

Declaration of the use of generative AI and AI-assisted technologies/tools The author(s) declare the utilization of AI-enabled tools to develop the academic work submitted together with this disclaimer and the proof/documentation of use in the appendix. The author(s) assume full responsibility for the content of the said submission and have done due diligence to verify the credibility, authenticity, factuality (or equivalent) of the content. All arguments, findings, interpretations, and conclusions etc. presented in the academic work are those of the author(s). The author(s) will be able to supplement his/her/their submission with earlier/original drafts developed prior to the application of the relevant AI-tool(s) upon requested.

Chat-GPT was used for creation, extension and correction of codes. It also helped interpreting models.

11.07.2025, Jana Drescher