1. INTRODUCTION

In recent decades, mental health has emerged as a critical public health concern globally.
According to Wu et al. (2023), the prevalence of mental health disorders has increased substantially between 1990 and 2019, and projections indicate a continued upward trend in the coming years.
This alarming trajectory underscores the need to deepen our understanding of the social, psychological, and environmental factors that contribute to mental health outcomes across different populations.

This paper aims to contribute to the existing body of mental health research by exploring a set of psychosocial and contextual determinants that might influence depressive symptoms among the Italian population. Specifically, the study investigates whether individuals’ perceived control over their lives, overall satisfaction with life, interpersonal trust, confidence in the health system, and satisfaction with governmental efforts to address climate change are associated with variations in depressive symptomatology.

The analysis is based on data from the European Social Survey (ESS11, 2023), a cross-national survey that provides comparative data on attitudes, beliefs, and behavioral patterns across European countries.
By focusing on the Italian subsample, this research seeks to identify key predictors of mental health and offer insights that drive health interventions and public policy strategies aimed at mitigating the burden of depression in Italy.

2. LITERATURE REVIEW

To contextualize the current study, this section reviews previous research on the psychosocial and institutional factors contributing to depression. Depression is a multifactorial condition influenced by individual, interpersonal, and societal variables.

Nguyen et al. (2020) found that perceived life control mitigates the effects of external stressors more effectively than trust in institutions or religious reliance. Similarly, life satisfaction plays a preventive role against depression, with a moderately bidirectional relationship to perceived control (Zalewska et al., 2021).

Interpersonal trust also emerges as a key protective factor. Martinez et al. (2019) and Zhang (2024) show that strong social ties—especially with family and neighbors—enhance emotional support and resilience.

Trust in healthcare systems further influences mental health outcomes; individuals who perceive healthcare as reliable are more likely to seek support, reducing the risk of worsening symptoms (Ahnquist et al., 2010; Rasanathan, 2024).

Environmental concerns also intersect with mental health. Shen et al. (2024) demonstrate that effective climate policies, such as carbon trading, can positively impact psychological well-being, especially in vulnerable rural populations.

Collectively, these findings underline the need for a holistic and multisectoral approach to understanding depression, emphasizing the interplay of personal agency, social trust, and institutional confidence.

3. HYPOTHESIS

This study hypothesizes that greater perceived life control and satisfaction, along with higher trust in others and societal institutions, are associated with reduced depressive symptoms.

4. METHODOLOGY

1 Following data extraction from the Italian ESS11 sample, the CES-D8 Depression Scale was constructed using 8 variables (d20-d27 variables) related to mood, energy, and emotional states. Below, the process of scale development and reliability testing is outlined.
Happiness, sadness, loneliness, joy and depressive feelings, as well as sleeping habits are combined to create the dependent variable CES_D8 that evaluates personal wellbeing from different points of view.

First of all, creating the CES_D8 Scale requires to check the polarity of the chosen variables.

DataIT$wrhpp <- factor(DataIT$wrhpp, levels = rev(levels(DataIT$wrhpp)))
DataIT$enjlf <- factor(DataIT$enjlf, levels = rev(levels(DataIT$enjlf)))

After having changed the polarity of “happiness” and “joy”, I need to convert the other variables into numeric ones, so that the final scale can be calculated.

DataIT$fltdpr_num <- as.numeric(DataIT$fltdpr)
DataIT$flteeff_num <- as.numeric(DataIT$flteeff) 
DataIT$slprl_num <- as.numeric(DataIT$slprl) 
DataIT$wrhpp_num <- as.numeric(DataIT$wrhpp) 
DataIT$fltlnl_num <- as.numeric(DataIT$fltlnl)
DataIT$enjlf_num <- as.numeric(DataIT$enjlf)
DataIT$fltsd_num <- as.numeric(DataIT$fltsd)
DataIT$cldgng_num <- as.numeric(DataIT$cldgng)

Now that all variables are numeric, the CES_D8 Scale can be computed by doing the rows’ sum.

DataIT$CES_D8 <- rowSums(DataIT[, c("fltdpr_num", "flteeff_num", "slprl_num", "wrhpp_num", "fltlnl_num", "enjlf_num", "fltsd_num", "cldgng_num")])-8

library(knitr)

kable(t(summary(DataIT$CES_D8)), caption = "Summary Statistics for CES-D8 Score", digits = 2)
Summary Statistics for CES-D8 Score
Min. 1st Qu. Median Mean 3rd Qu. Max. NA’s
0 3 5 6.09 8 24 100

At this point, before checking the Scale’s reliability, it would be interesting to understand how people answered to the single items that compose our CES_D8 Scale.
This will clarify the extent to which individuals distinctively experience feelings of depression, loneliness and related emotional states.

plot(likert_table, main = "Likert Plot for CES-D8 Scale", xlab = "Percentage")

The chart reveals that while most individuals rarely feel depressed, a non-negligible minority report experiencing sadness or loneliness frequently.
However, it might be interesting to quantify such answers and further analyse the mean and counts of the survey’s responses to create a numeric, quantifiable overview of such responses.

The following lollipop graph represents indeed the average frequency of each depressive symptom in the Italian sample, based on a 0–3 scale.
Positive feelings like “Enjoyed life” and “Felt happy” have higher mean scores, indicating they occurred more often. In contrast, symptoms like “Felt depressed” and “Felt lonely” have lower averages, suggesting they were less frequently experienced.
However, as will be explained later on in this paper, such less frequently experienced feelings still have significant implications for mental health.

kable(likert_table$results, caption = "Distribution of CES-D8 Scale Responses (Italy)", digits = 2) %>% kable_styling(full_width = F)
Distribution of CES-D8 Scale Responses (Italy)
Item None or almost none of the time Some of the time Most of the time All or almost all of the time Mean Count
Felt depressed 71.1 24.0 4.1 0.8 1.3 2840
Felt everything was an effort 52.6 37.4 7.3 2.7 1.6 2838
Sleep was restless 53.7 38.1 6.0 2.2 1.6 2850
Felt happy 14.8 43.3 34.2 7.7 2.3 2817
Felt lonely 61.8 29.6 5.9 2.7 1.5 2837
Enjoyed life 9.8 29.7 45.7 14.7 2.7 2803
Felt sad 48.3 45.3 4.5 1.9 1.6 2832
Could not get going 57.2 35.4 5.7 1.7 1.5 2825 
ggplot(likert_table$results, aes(x = reorder(Item, Mean), y = Mean)) +
  geom_segment(aes(xend = Item, yend = 0), color = "firebrick") +
  geom_point(size = 4, color = "steelblue") +
  coord_flip() +
  scale_y_continuous(breaks = 0:3, limits = c(0, 3)) +  # <- y-axis is the numeric one
  labs(
    title = "Mean CES-D8 Score per Item",
    x = "Item",
    y = "Mean Score (0–3)"
  ) +
  theme_minimal()


Considering that the average number of individuals reporting happiness is higher than those reporting sadness, it is worthwhile to investigate whether there are gender-based differences in the frequency of negative emotional experiences. Taking into account that this sample comprises 1526 women and 1339 men (i.e. genders proportion is similar) , the aim is to examine whether a higher proportion of women report feeling sad, lonely, and depressed compared to men.

To do so, items “fltdpr” (felt depressed), “fltlnl” (felt lonely), and “fltsd” (felt sad), will be isolated to focus exclusively on the response categories “Most of the time” and “All or almost all of the time.” This allows to capture individuals who experience these negative emotions with greater frequency.

depression_filtered %>%
  kable(
    caption = "High Levels of Depressive Symptoms by Gender (Italy)",
    col.names = c("Symptom", "Gender", "Number of Respondents"),
    align = c("l", "c", "c"),
    format = "markdown",
    digits = 0
  ) %>%
  kable_styling(
    full_width = FALSE,
    bootstrap_options = c("striped", "hover", "condensed", "responsive"),
    font_size = 14
  )
High Levels of Depressive Symptoms by Gender (Italy)
Symptom Gender Number of Respondents
Felt depressed Female 85
Felt depressed Male 55
Felt lonely Female 161
Felt lonely Male 85
Felt sad Female 116
Felt sad Male 65 
ggplot(depression_filtered, aes(x = Item, y = Count, fill = gndr)) +
  geom_bar(stat = "identity", position = "dodge") +
  scale_fill_manual(values = c("Male" = "steelblue", "Female" = "firebrick")) +
  labs(title = "Reported High Levels of Depressive Symptoms by Gender",
       subtitle = '"Most" or "All or almost all of the time" responses only',
       x = "Depressive Symptom",
       y = "Number of Respondents",
       fill = "Gender") +
  theme_minimal()


As confirmed by the table and clearly illustrated in the accompanying graph, in Italy individuals identifying as female tend to experience emotions commonly classified as negative more frequently than their male counterparts. Beyond the potential explanations for this phenomenon, this finding opens up the possibility for future research to explore whether this reported difference corresponds to a genuinely higher prevalence of depressive symptoms among women.

2 To assess the internal consistency of the CES-D8 scale, Cronbach’s Alpha will be computed. A high alpha value indicates reliability in capturing depressive symptomatology.
2.1. To correctly measure the Cronbach’s Alpha, the variables’ values will be checked, as the presence of NA’s could then affect the computation’s reliability.

kable(summary_df, caption = "Summary Statistics of Selected Variables", digits = 3)
Summary Statistics of Selected Variables
Statistic Felt depressed Felt everything was an effort Sleep was restless Felt happy Felt lonely Enjoyed life Felt sad Could not get going
Min Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.0 Min. :1.000
1st Qu. 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:1.0 1st Qu.:1.000
Median Median :1.000 Median :1.000 Median :1.000 Median :2.000 Median :1.000 Median :3.000 Median :2.0 Median :1.000
Mean Mean :1.347 Mean :1.601 Mean :1.567 Mean :2.348 Mean :1.497 Mean :2.654 Mean :1.6 Mean :1.519
3rd Qu. 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:2.0 3rd Qu.:2.000
Max Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.0 Max. :4.000
NA’s NA’s :25 NA’s :27 NA’s :15 NA’s :48 NA’s :28 NA’s :62 NA’s :33 NA’s :40
  1. However, not to undermine the statistical power, is better to replace missing values with the column mean to preserve sample size.
for (col in c("fltdpr_num", "flteeff_num", "slprl_num", "wrhpp_num","fltlnl_num", "enjlf_num", "fltsd_num", "cldgng_num")) {
  DataIT[is.na(DataIT[, col]), col] <- mean(DataIT[, col], na.rm = TRUE)}
  1. Now the Cronbach’s Alpha can be computed.
suppressPackageStartupMessages(library(ltm))

cronbach_alpha <- cronbach.alpha(DataIT[, c("fltdpr_num", "flteeff_num", "slprl_num", "wrhpp_num","fltlnl_num", "enjlf_num","fltsd_num", "cldgng_num")],  na.rm = TRUE)
cronbach_alpha
## 
## Cronbach's alpha for the 'DataIT[, c("fltdpr_num", "flteeff_num", "slprl_num", "wrhpp_num", ' '    "fltlnl_num", "enjlf_num", "fltsd_num", "cldgng_num")]' data-set
## 
## Items: 8
## Sample units: 2865
## alpha: 0.838
cat("Cronbach’s alpha =", round(cronbach_alpha$alpha, 3))
## Cronbach’s alpha = 0.838
  1. Given the independent variables mentioned before, namely the perceived life control (ctrlife), life satisfaction (stflife), trust in people (ppltrst) and satisfaction related to government’s climate action (testji9) and health services (stfhlth), the correlation between independent variables and the dependent-independent one will be computed.
DataIT$ctrlife <- as.numeric(as.character(DataIT$ctrlife)) 
DataIT$stflife <- as.numeric(as.character(DataIT$stflife))
DataIT$ppltrst <- as.numeric(as.character(DataIT$ppltrst))
DataIT$testji9 <- as.numeric(as.character(DataIT$testji9))
DataIT$stfhlth <- as.numeric(as.character(DataIT$stfhlth))

subset <- DataIT[, c("ctrlife", "stflife", "ppltrst", "testji9", "stfhlth", "CES_D8")]
correlation <- cor(subset, use = "complete.obs")
colnames(correlation) <- rownames(correlation) <- c(
  "Perceived Life Control",
  "Life Satisfaction",
  "Trust in People",
  "Satisfaction with Climate Action",
  "Satisfaction with Health Services",
  "CES-D8 Depression Score"
)
library(knitr)
kable(round(correlation, 2), caption = "Correlation Matrix of Key Variables")
Correlation Matrix of Key Variables
Perceived Life Control Life Satisfaction Trust in People Satisfaction with Climate Action Satisfaction with Health Services CES-D8 Depression Score
Perceived Life Control 1.00 0.45 0.21 0.22 0.12 -0.38
Life Satisfaction 0.45 1.00 0.23 0.08 0.19 -0.45
Trust in People 0.21 0.23 1.00 0.05 0.16 -0.17
Satisfaction with Climate Action 0.22 0.08 0.05 1.00 0.04 -0.13
Satisfaction with Health Services 0.12 0.19 0.16 0.04 1.00 -0.11
CES-D8 Depression Score -0.38 -0.45 -0.17 -0.13 -0.11 1.00

This correlation matrix gives a first hint about how variables interact and allows to explore the patters and relationships between variables before proceeding with the multivariate regression analysis.

5. INITIAL RESULTS

In order to understand if the chosen independent variable have a positive or negative influence on depressive symptoms, a multivariate regression model using the lm function will be developed.
This aims to understand how the chosen independent variables actually impact the CES-D8 Scale. Given the literature review and the correlation matrix presented above, it might be reasonable to expect that when independent variables increase by 1 unit, depression decreases.

In other words, a higher perceived control and satisfaction over one’s life, increased trust feelings toward others, as well as a better planned healthcare services and government action in the climate field should help reducing depressive symptoms.

Linear Regression Results: Predictors of CES-D8 Score
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.794 0.819 20.515 0.000
Perceived Life Control -0.521 0.104 -4.994 0.000
Life Satisfaction -0.877 0.101 -8.713 0.000
Trust in People -0.086 0.077 -1.113 0.266
Satisfaction with Climate Action -0.111 0.069 -1.602 0.110
Satisfaction with Health Services -0.027 0.081 -0.340 0.734


The regression model explains approximately 24.89% of the variance in depressive symptoms (CES_D8), indicating a moderate explanatory power (Adjusted R² = 0.2428). The model is statistically significant overall (F = 40.76, p < 1.195737e-71).
Among the predictors, perceived control over life (ctrlife) and life satisfaction (stflife) are the strongest and statistically significant factors negatively associated with depressive symptoms. Specifically, greater control reduces distress by -0.521 units, and higher life satisfaction reduces it by -0.877 units.

However, it might be necessary to weight the regression model. With a Post-Stratification weight sampling and non-response errors should be minimized.

Weighted Linear Regression Results: Predictors of CES-D8 Score
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.848 0.819 20.577 0.000
ctrlife -0.508 0.103 -4.934 0.000
stflife -0.878 0.100 -8.809 0.000
ppltrst -0.070 0.078 -0.893 0.372
testji9 -0.161 0.069 -2.344 0.019
stfhlth -0.002 0.081 -0.030 0.976

6. PREDICTORS AND OUTCOME OF CLINICALLY SIGNIFICANT DEPRESSION

In order to reduce the limitations of this study and provide an objective parameter to evaluate the depressive symptoms, a cut off rule will be employed. This will reveal how many Italian people in this sample can be considered clinically depressed.
Given, the CES-D8 Scale ranges between 0 and 24, Briggs R. et al (2018) found that the reasonable cut off threshold for such a Scale should be >= 9.

In order to do that, a binary variable for clinical depression (1 = yes, 0 = no) will be created. In this way, the final proportion of individuals above vs below the cutoff can be computed.

prop.table(table(DataIT$depression))
## 
##         0         1 
## 0.7649186 0.2350814

Now, given the binary outcome, a logistic regression model GLM can be computed.

modelGLM <- glm(DataIT$depression ~ ctrlife + stflife + ppltrst + testji9 + stfhlth, data = subset, family = binomial(link = "logit"))
kable(glm_table, 
      caption = "Logistic Regression Results: Predictors of Depression", 
      align = "lccccc", 
      format = "markdown", 
      row.names = FALSE)
Logistic Regression Results: Predictors of Depression
Term Odds.Ratio Std..Error X95..CI.Lower X95..CI.Upper p.value
(Intercept) 63.280 0.607 19.804 214.567 0.00e+00
ctrlife 0.714 0.072 0.618 0.820 2.80e-06
stflife 0.634 0.069 0.552 0.725 0.00e+00
ppltrst 0.975 0.056 0.874 1.088 6.50e-01
testji9 0.944 0.051 0.855 1.043 2.57e-01
stfhlth 1.059 0.058 0.945 1.188 3.29e-01 
r_mcfadden<- with(summary(modelGLM), 1 - deviance/null.deviance)
r_nagelkerke<- with(summary(modelGLM), r_mcfadden/(1 - (null.deviance / nrow(modelGLM$data)*log(2))))
r_nagelkerke
## [1] 0.2109136

The logistic regression model indicates that higher levels of perceived control over life (ctrlife) and life satisfaction (stflife) are significantly associated with lower odds of experiencing depressive symptoms. Specifically, the odds ratio for ctrlife is 0.71 (95% CI = [0.62, 0.82]), and for stflife is 0.63 (95% CI = [0.55, 0.72]), with both intervals falling below 1.

Other predictors, such as trust in people (ppltrst), trust in the justice system (testji9), and self-rated health (stfhlth), yield odds ratios close to 0.98, 0.94, and 1.06, respectively, suggesting weaker or statistically non-significant effects.

The model explains approximately 0.211 of the variance in the outcome, as indicated by Nagelkerke’s R².

7. EXPLORING MEDIATION: DOES LIFE SATISFACTION MEDIATE THE EFFECT OF PERCEIVED LIFE CONTROL ON DEPRESSION?

Building on the findings from the previous models, both perceived control over life and life satisfaction emerge as significant predictors of depressive symptoms. This suggests the potential for an indirect relationship between these variables. Accordingly, we propose the following hypothesis:

Life satisfaction mediates the relationship between perceived control over life and depressive symptoms.

To examine this hypothesis, a mediation analysis using the CES-D8 score as the dependent variable will be conducted. The analysis aims to assess whether the influence of perceived control on depressive symptoms is partially or fully transmitted through life satisfaction.

kable(mediation_table, caption = "Mediation Analysis Results: Life Satisfaction as a Mediator",
      align = "lcccc",
      format = "markdown")
Mediation Analysis Results: Life Satisfaction as a Mediator
Effect Estimate X95..CI.Lower X95..CI.Upper p.value
ACME (indirect effect) -0.376 -0.443 -0.319 0
ADE (direct effect) -0.494 -0.615 -0.380 0
Total Effect -0.870 -0.972 -0.767 0
Proportion Mediated 0.432 0.352 0.520 0

As this table makes clear, there is a significant indirect effect of perceived control on depression through life satisfaction. This suggests that individuals who feel more in control of their lives are more satisfied, which in turn is associated with lower depressive symptoms. The direct effect of perceived control also remains significant, indicating partial mediation.

The results supports the idea that life satisfaction partially mediates the relationship between perceived control and depressive symptoms. Both indirect and direct effects are statistically significant, with a substantial mediated proportion of around 43%.

8. CONCLUSIONS

This study analysed data from the European Social Survey (ESS11, 2023) to explore psychosocial determinants of depressive symptoms specifically within the Italian population. Key variables included perceived life control, life satisfaction, interpersonal trust, satisfaction with healthcare services, and attitudes toward governmental climate policies.

In line with previous research (Nguyen et al., 2020; Zalewska et al., 2021), perceived control over life and life satisfaction emerged as the strongest protective factors against depressive symptoms among Italian respondents. Regression models confirmed their negative association with depression, and mediation analysis showed that life satisfaction explained 43% of the effect of life control on depressive symptoms.

Other factors—such as institutional trust, satisfaction with healthcare services, and approval of climate policies—showed weaker or marginal associations in the Italian context. Notably, satisfaction with governmental climate action was borderline significant (p = 0.019), suggesting a possible contextual influence worth further exploration in Italy.

Gender-based analysis revealed that Italian women reported higher levels of sadness, loneliness, and depressive symptoms—consistent with broader epidemiological trends. The CES-D8 scale used to assess depressive symptoms demonstrated strong internal reliability (α = 0.838), with results indicating that approximately 23.5% of Italian respondents may be classified as clinically depressed.

These findings highlight the key role of psychological resources in protecting against depression in Italy and suggest directions for future research, including the roles of economic insecurity, social isolation, and digital connectivity within the Italian setting.

9. REFERENCES

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Nguyen, T.- vy, McPhetres, J., & Deci, E. L. (2020). Beyond God and Government: The Role of Personal Control in Supporting Citizens’ Well-Being in the Face of Changing Economy and Rising Inequality. Social Psychological Bulletin, 15(1), 1-21. https://doi.org/10.32872/spb.2663

Rasanathan, K. (2024). How can health systems under stress achieve universal health coverage and health equity? International Journal for Equity in Health, 23(1). https://doi.org/10.1186/s12939-024-02293-2

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