REFER TO ASSIGNMENT 5 BELOW

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

Depression is a multifactorial condition influenced by individual, interpersonal, and societal variables. 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.

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 preventative 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. METHODOLOGY

1 After extracting data related to the Italian country, I created the CES_D8 Depression Scale.
This scale is based on d20-d27 variables from the ESS11 survey. 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.

Summary Statistics for CES-D8 Score
Min. 1st Qu. Median Mean 3rd Qu. Max. NA’s
8 10 12 12.87 15 32 33

2 In this second step, the Cronbach’s Alpha will be employed. The goal is to check the Scale’s reliability, as this is required step when dealing with composite scores.
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.

Summary Statistics of Selected Variables
Statistic fltdpr_num flteeff_num slprl_num wrhpp_num fltlnl_num enjlf_num fltsd_num cldgng_num
Min Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 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.:1.000 1st Qu.:1.000
Median Median :1.000 Median :1.000 Median :1.000 Median :2.000 Median :1.000 Median :2.000 Median :1.000 Median :1.000
Mean Mean :1.359 Mean :1.591 Mean :1.633 Mean :2.127 Mean :1.286 Mean :2.187 Mean :1.361 Mean :1.363
3rd Qu. 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:1.000 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:2.000
Max Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000
NA’s NA’s :4 NA’s :6 NA’s :8 NA’s :16 NA’s :4 NA’s :17 NA’s :9 NA’s :7
  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.
## 
## 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: 2354
## alpha: 0.803
## Cronbach’s alpha = 0.803
  1. Given the independent variables mentioned before, namely the perceived life control (ctrlife), life satisfaction (stflife), trust in people (ppltrst) and satisfaction related to climate change action (testji9) and health sevices (stfhlth), I am going to compute the correlation between independent variables and the dependent-independent one.
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")
library(knitr)
kable(round(correlation, 2), caption = "Correlation Matrix of Key Variables")
Correlation Matrix of Key Variables
ctrlife stflife ppltrst testji9 stfhlth CES_D8
ctrlife 1.00 0.36 0.19 0.25 0.19 -0.32
stflife 0.36 1.00 0.27 0.06 0.12 -0.52
ppltrst 0.19 0.27 1.00 0.22 0.29 -0.19
testji9 0.25 0.06 0.22 1.00 0.27 -0.07
stfhlth 0.19 0.12 0.29 0.27 1.00 -0.15
CES_D8 -0.32 -0.52 -0.19 -0.07 -0.15 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.

4. 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 independent variables I chose actually impact the CES-D8 Scale. Given the literature review and the correlation matrix presented above, I expect that when my independent variables increase by 1 unit, depression decreases.

In other words, I expect that 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 help reducing depressive symptoms.

Linear Regression Results: Predictors of CES-D8 Score
Estimate Std. Error t value Pr(>|t|)
(Intercept) 25.244 1.053 23.974 0.000
ctrlife -0.366 0.123 -2.980 0.003
stflife -1.103 0.112 -9.856 0.000
ppltrst -0.046 0.077 -0.599 0.549
testji9 0.019 0.061 0.318 0.750
stfhlth -0.102 0.075 -1.360 0.174


The regression model explains approximately 29.42% of the variance in depressive symptoms (CES_D8), indicating a moderate explanatory power (Adjusted R² = 0.2854). The model is statistically significant overall (F = 33.34, p < 2.222026e-79).
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 -0.366 units, and higher life satisfaction reduces it by -1.103 units.

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.

Other variables—trust in people, confidence in government climate action, and health satisfaction—do not show statistically significant effects, indicating weaker associations with depressive symptoms.

5. CONCLUSIONS

This study confirms that perceived life control and life satisfaction are the most significant predictors of lower depressive symptoms among Italians, reinforcing previous findings by Nguyen et al. (2020) and Zalewska et al. (2021).

In contrast, trust in people, healthcare services, and government climate action did not show significant associations with psychological distress, diverging from earlier research (e.g., Estrada et al., 2019; Ahnquist et al., 2010; Shen et al., 2024).

These results suggest that while institutional trust may have indirect effects, subjective well-being and personal agency are more impactful for mental health in this context.

However, the study has limitations: its cross-sectional design restricts causal claims, reliance on self-reported data introduces potential bias, and the model explains only approximately 28.54% of the variance. Possible multicollinearity between life satisfaction and life control may have also affected estimates.

Future research should explore the pathways linking institutional trust and mental health, consider cultural and regional differences, and evaluate interventions aimed at boosting life satisfaction and perceived control.

6. REFERENCES

Ahnquist, J., Wamala, S. P., & Lindström, M. (2010). What has trust in the health-care system got to do with psychological distress? Analyses from the national Swedish survey of public health. International journal for quality in health care : journal of the International Society for Quality in Health Care, 22(4), 250–258. https://doi.org/10.1093/intqhc/mzq024

Martínez, L. M., Estrada, D., & Prada, S. I. (2019). Mental health, interpersonal trust and subjective well-being in a high violence context. SSM - population health, 8, 100423. https://doi.org/10.1016/j.ssmph.2019.100423

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

Round 11 questionnaire and provisional release dates | European Social Survey. (2025, January 6). https://www.europeansocialsurvey.org/news/article/round-11-questionnaire-and-provisional-release-dates

Shen, S. (2024). Green Finance and Health: How Does Implementing Carbon Emissions Trading Affect Mental Health? Advances in Economics, Management and Political Sciences, 44(1), 253–261. https://doi.org/10.54254/2754-1169/44/20232191

Van Damme-Ostapowicz, K., Cybulski, M., Galczyk, M., Krajewska-Kulak, E., Sobolewski, M., & Zalewska, A. (2021). Life satisfaction and depressive symptoms of mentally active older adults in Poland: a cross-sectional study. BMC Geriatrics, 21(1). https://doi.org/10.1186/s12877-021-02405-5

Wu, Y., Wang, L., Tao, M., Cao, H., Yuan, H., Ye, M., Chen, X., Wang, K., & Zhu, C. (2023). Changing trends in the global burden of mental disorders from 1990 to 2019 and predicted levels in 25 years. Epidemiology and psychiatric sciences, 32, e63. https://doi.org/10.1017/S2045796023000756

Zhang, Y. (2024). The road home: intimacy with parents, trust, and depression. Humanities & Social Sciences Communications, 11(1). https://doi.org/10.1057/s41599-024-03433-3

Assignment 2

Continue with first part of Assignment.

Introduction

This analysis explores CES-D8 depression items in Austria (ESS Round 11), grouped as:

We use Likert scales to show responses in percentages.

Likert Analysis – CES-D8 Depression Items (Austria)

Define and Reverse Positive Items

vnames = c("fltdpr", "flteeff", "slprl", "fltlnl", "enjlf", "cldgng", "fltsd", "wrhpp")
likert_df = df[, vnames]

Basic Likert Plot and Table

# Create basic likert object
likert_obj = likert(likert_df)

Append Mean and Count

# Convert to numeric
likert_numeric_df = as.data.frame(lapply(df[, vnames], as.numeric))

# Long version – calculate means
likert_means = c()
for (v in vnames) {
  likert_means[v] = mean(likert_numeric_df[[v]], na.rm = TRUE)
}

# Long version – calculate counts
likert_counts = c()
for (v in vnames) {
  likert_counts[v] = sum(!is.na(likert_numeric_df[[v]]))
}

# Create and enrich table
likert_table = likert_obj$results
likert_table$Mean = round(unlist(likert_means), 3)
likert_table$Count = unlist(likert_counts)

Rename Items and Round Percentages

# Set descriptive labels
likert_table$Item = c(
  "Felt depressed",
  "Everything was an effort",
  "Sleep was restless",
  "Felt lonely",
  "Enjoyed life",
  "Could not get going",
  "Felt sad",
  "Felt happy"
)

# Round percentages and mean
likert_table[, 2:6] = round(likert_table[, 2:6], 1)

Display Formatted Table

kable_styling(
  kable(likert_table, caption = "Distribution of depression-related responses in Austria (ESS11)"),
  bootstrap_options = "striped"
)
Distribution of depression-related responses in Austria (ESS11)
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 64.9 29.1 4.6 1.5 1.4 39981
Everything was an effort 48.4 38.4 9.8 3.4 1.7 39983
Sleep was restless 43.9 39.9 11.6 4.6 1.8 40017
Felt lonely 68.1 24.3 5.3 2.3 1.4 39983
Enjoyed life 5.3 24.8 44.8 25.0 2.9 39878
Could not get going 55.7 36.1 6.2 2.0 1.5 39949
Felt sad 52.5 41.1 4.9 1.6 1.6 39981
Felt happy 4.0 23.5 48.9 23.6 2.9 39890

Replot from Table

# Plot again using only percentage columns
plot(likert(summary = likert_table[, 1:6]))

Assignment 4 - Predictors of Clinically Significant Depression

1. Reverse-code positive items

‘Happy’ and ‘Enjoyed life’ are reversed so that higher values mean more depressive symptoms

DataIT$enjlf_rev <- 5 - DataIT$enjlf_num
DataIT$wrhpp_rev <- 5 - DataIT$wrhpp_num

2. Compute new total CES-D8 score using consistent directionality

DataIT$CES_D8_new <- rowSums(DataIT[, c(
  "fltdpr_num", "flteeff_num", "slprl_num", "fltlnl_num", 
  "cldgng_num", "fltsd_num", "enjlf_rev", "wrhpp_rev"
)], na.rm = TRUE)

3. Visualize distribution to choose clinical cutoff

hist(DataIT$CES_D8_new, 
     breaks = 20, 
     main = "Distribution of CES-D8 Depression Scores", 
     xlab = "Total Score", 
     col = "skyblue", 
     border = "white")

4. Create binary variable for clinically significant depression

Each CES-D8 item is scored from 1 to 4, yielding a total range from 8 to 32. Based on the score distribution, a threshold of 16 was chosen, as it marks the right tail where symptom severity appears clinically significant.

DataIT$clin_dep <- ifelse(DataIT$CES_D8_new >= 16, 1, 0)

Frequency table

Clinical Depression Status (0 = not clinically depressed, 1 = depressed)
Var1 Freq
0 1759
1 595
Proportion of Sample with Clinical Depression
Var1 Freq
0 74.72
1 25.28

5. Logistic regression model

Summary of model

## 
## Call:
## glm(formula = clin_dep ~ ctrlife + stflife + ppltrst + testji9 + 
##     stfhlth, family = binomial(link = "logit"), data = DataIT)
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  3.90459    0.85417   4.571 4.85e-06 ***
## ctrlife     -0.22908    0.09501  -2.411   0.0159 *  
## stflife     -0.39825    0.09033  -4.409 1.04e-05 ***
## ppltrst      0.07401    0.06534   1.133   0.2573    
## testji9     -0.08968    0.05331  -1.682   0.0925 .  
## stfhlth     -0.05142    0.06198  -0.830   0.4067    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 462.21  on 409  degrees of freedom
## Residual deviance: 414.68  on 404  degrees of freedom
##   (1944 observations deleted due to missingness)
## AIC: 426.68
## 
## Number of Fisher Scoring iterations: 4

Odds ratios with confidence intervals

Odds Ratios and 95% Confidence Intervals
OddsRatio CI_lower CI_upper
(Intercept) 49.630 9.643 277.546
ctrlife 0.795 0.660 0.959
stflife 0.671 0.559 0.798
ppltrst 1.077 0.949 1.227
testji9 0.914 0.822 1.014
stfhlth 0.950 0.842 1.074

6. Interpretation:

Eaton et al. (2004) further support the clinical utility of the CES-D and its revised versions (CESD-R), noting its ability to quickly screen for depressive symptoms in under five minutes. Their review highlights the scale’s effectiveness across diverse populations and its strong psychometric properties. This strengthens the justification for using a CES-D-based threshold (such as a score ≥16 on the adapted 8-item version) to identify individuals with clinically significant depression in general population samples. This logistic regression estimates the odds of being clinically depressed (CES-D8 score ≥ 16). The scoring follows ESS guidelines, where each symptom is measured from 1 (none of the time) to 4 (all or almost all of the time). Positive items (happy, enjoyed life) are reversed so that higher total scores reflect worse depressive symptoms. Although CES-D cutoffs are often derived from a proportion of the total score, the observed distribution in our sample revealed that a score of 16 or higher corresponds to the tail end of the scale and is therefore a more empirically supported threshold for identifying clinically significant cases. This threshold is also supported by the original CES-D literature (Radloff, 1977; Lewinsohn et al., 1997), which uses a cutoff score of 16 (out of 60) to flag individuals at risk for clinical depression. These guidelines have demonstrated high sensitivity, specificity, and internal consistency in large-scale epidemiological studies. Higher perceived control and life satisfaction are associated with lower odds of clinical depression, supporting the protective role of these psycho-social factors.

References for this section

Eaton, W. W., Smith, C., Ybarra, M., Muntaner, C., & Tien, A. (2004). Center for Epidemiologic Studies Depression Scale: Review and Revision (CESD and CESD-R). In M. E. Maruish (Ed.), The use of psychological testing for treatment planning and outcomes assessment (3rd ed., Vol. 3, pp. 363–377). Lawrence Erlbaum Associates Publishers. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4868329/

Radloff, L. S. (1977). The CES-D scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1(3), 385–401. https://doi.org/10.1177/014662167700100306

Lewinsohn, P. M., Seeley, J. R., Roberts, R. E., & Allen, N. B. (1997). Center for Epidemiological Studies Depression Scale (CES-D) as a screening instrument for depression among community-residing older adults. Psychology and Aging, 12(2), 277–287. https://doi.org/10.1037/0882-7974.12.2.277

Vilagut, G., Forero, C. G., Barbaglia, G., & Alonso, J. (2016). Screening for Depression in the General Population with the Center for Epidemiologic Studies Depression (CES-D): A Systematic Review with Meta-Analysis. PloS one, 11(5), e0155431. https://doi.org/10.1371/journal.pone.0155431

Assignment 5

1. Histogram of CES-D8 Depression Scores

# Histogram of CES-D8 Scores
ggplot(DataIT, aes(x = CES_D8_new)) +
  geom_histogram(binwidth = 1, fill = "steelblue", color = "white") +
  labs(title = "Distribution of CES-D8 Depression Scores",
       subtitle = "Austria – ESS Round 11",
       x = "CES-D8 Score (Reverse-coded)",
       y = "Frequency",
       caption = "Re Garbagnati Gaia") +
  theme_minimal()

This shows the distribution of depression scores to define a meaningful cutoff for “clinically depressed” individuals (suggested: CES-D8 ≥ 16).

2. Bar Chart: Frequency of Clinical Depression

# Frequency of clinically significant depression
ggplot(DataIT, aes(x = factor(clin_dep))) +
  geom_bar(fill = "deeppink", color = "yellow") +
  labs(title = "Clinical Depression in Austria",
       subtitle = "CES-D8 Threshold ≥ 16",
       x = "Clinical Depression (0 = No, 1 = Yes)",
       y = "Count",
       caption = "Re Garbagnati Gaia") +
  theme_minimal()

This visualizes prevalence of clinically significant depression in the Austrian sample using a binarized version of CES-D8.

3. Odds Ratios for Predictors of Clinical Depression

# Create plot data
plot_df = as.data.frame(odds_table)
plot_df$Predictor = rownames(plot_df)
plot_df = plot_df[plot_df$Predictor != "(Intercept)", ]

# Odds Ratio Plot
ggplot(plot_df, aes(x = OddsRatio, y = reorder(Predictor, OddsRatio))) +
  geom_point(size = 4, color = "steelblue") +
  geom_errorbarh(aes(xmin = CI_lower, xmax = CI_upper), height = 0.2, color = "grey30") +
  geom_vline(xintercept = 1, linetype = "dashed", color = "red") +
  scale_x_log10() +
  labs(title = "Effect of Psychosocial Predictors on Clinical Depression",
       subtitle = "Logistic Regression (CES-D8 ≥ 16)",
       x = "Odds Ratio (log scale)",
       y = "Predictors",
       caption = "Re Garbagnati Gaia") +
  theme_minimal()

This highlights which factors (life control, satisfaction, trust) are protective or risky for clinical depression.