Depression is a prevalent global health issue, experienced by 4-10% of the global population over their lifetime (Chapman et al., 2022). With currently, around 280 million people (3.8%) being affected globally (WHO, 2023), depression ranks among the top contributors to the global health burden (Santomauro et al., 2021). Although adequate and cost-efficient treatments are available across low-, middle-, and high-income countries, and stigma surrounding professional help is decreasing, depression treatment challenges are common occurrences (Ferrari et al., 2024); delayed diagnosis, treatments that are not tailored to individual needs, and inadequate responses to treatment attempts represent such obstacles (Chapman et al., 2022; Ferrari et al., 2024). For instance, in the United Kingdom (UK), 15 30% of individuals still suffer from depression after undergoing two or more treatments. This is relevant, as depression affects various aspects of human’s life, including education, employment, and personal relationships (OECD & European Commission, 2024), while contributing significantly to economic and healthcare costs (Statista, 2024; Vinokur et al., 1996).
H1. The prevalence of depression increases with experienced discrimination based on an individual’s sexuality.
H2. The prevalence of depression increases with experienced discrimination based on an individual’s skin colour or race.
H3. The prevalence of depression decreases with age.
H4. The prevalence of depression among females compared to males is higher.
The present paper aimed to investigate depression in a British population, as 15-30% of individuals do not recover from depression after two or more treatments (Chapman et al., 2022) and therefore a greater understanding of potential contributing factors is crucial for improving recovery outcomes.
Our dependent Variable is “Depression”, which was measured using the Centre for Epidemiological Studies Depression Scale (CES-D8), an 8-item scale.
Discrimination based on the respondent’s sexuality (nominal: “marked”, “not marked”), discrimination based on colour or race (nominal: “marked”, “not marked”), age (ratio scale: 15-90), and gender (nominal: “male”, “female”) were considered as potential factors influencing depression.
To assess the internal consistency of the depression scale, Cronbach’s alpha was calculated, typically ranging from 0 to 1 (Döring & Bortz, 2016), although it can sometimes be negative (Bühner, 2005).
# calculation of Cronbach's alpha (using df_uk) to check internal consistency ("reliability") of depression items
cronbach.alpha(df_uk[,c("d20", "d21", "d22", "d23", "d24", "d25", "d26", "d27")], na.rm=T)##
## Cronbach's alpha for the 'df_uk[, c("d20", "d21", "d22", "d23", "d24", "d25", "d26", "d27")]' data-set
##
## Items: 8
## Sample units: 1684
## alpha: 0.415The degree of agreement between items was high, with an alpha of 0.415, well below the recommended threshold of 0.7 (Hair, 2010), indicating that not all items measure the same underlying construct of depression (Osburn, 2000).
The dataset was subsetted to include only participants from the United Kingdom, resulting in an initial sample size of n=1684. However, the final sample consisted of n= 1635 valid respondents, aged 15 to 90 years, for the depression variable in the UK sample.
This section presents information about the depression score.
# table
kable_styling(
kable(summary_df,
col.names = c("Statistic", "Value"),
caption = "Summary Statistics of Depression Scores")
)| Statistic | Value |
|---|---|
| Minimum | 1.000 |
| Maximum | 4.000 |
| Median | 1.875 |
| Mean | 1.952 |
The depression scale has a range from 1 (lowest depression level) to 4 (highest depression level). Based on the data, we observe the following: At least one individual answered all items with a score of 1 (indicating the lowest possible depression level), and at least one individual answered all items with a score of 4 (indicating the highest possible depression level). The majority of participants report low to moderate depression levels, as indicated by the median (1.875) and mean (1.952) scores, which are relatively low. This suggests that most participants fall within the lower half of the depression scale.
Here is a comprehensive overview of the frequency distribution:
table(df_uk$depression)##
## 1 1.125 1.25 1.375 1.5 1.625 1.75 1.875 2 2.125 2.25 2.375 2.5
## 11 1 6 17 110 178 297 304 221 176 97 72 48
## 2.625 2.75 2.875 3 3.125 3.25 3.375 3.75 4
## 25 29 13 10 6 9 3 1 1# frequency distribution of the new variable (depression)# final model and show extended summary
model = lm(depression ~ dscrsex + female, data=df_uk)
summary(model)##
## Call:
## lm(formula = depression ~ dscrsex + female, data = df_uk)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.98402 -0.23402 -0.03865 0.14098 2.01598
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.91365 0.01259 152.029 < 2e-16 ***
## dscrsexMarked 0.08674 0.05793 1.497 0.135
## female 0.07038 0.01746 4.030 5.83e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3526 on 1632 degrees of freedom
## (49 observations deleted due to missingness)
## Multiple R-squared: 0.0109, Adjusted R-squared: 0.009691
## F-statistic: 8.995 on 2 and 1632 DF, p-value: 0.0001303A linear regression was conducted to predict depression scores based on dscrsexMarked (reported or experienced disrimination because of one’s sex) and female variables. Intercept: is estimated at 1.91365 (p < 2e-16, highly significant).
dscrsexMarked: This variable has a coefficient estimate of 0.08674 but is not statistically significant (p = 0.135), suggesting no strong evidence that this variable predicts depression in this model.
female: The coefficient is 0.07038, statistically significant (p = 5.83e-05), indicating that females tend to have slightly higher depression scores by about 0.07 units compared to the reference group.
The overall model explains a small proportion of variance in depression scores: Multiple R-squared = 0.0109 (about 1.1% variance explained), which means, that there must be additional factors influencing depression that are not included in this model.
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