Table 5. Risk factors of depression with epilepsy (n = 20).
| Author, year | Sample (n) |
Age range studied | Risk factors | Examined factors | Statistical method | Depression scales |
|---|---|---|---|---|---|---|
| Adewuya 2005 [12] | 102 | 12–18 | Frequency of seizures, number of antiepileptic drugs, perception of stigma | Age, gender, level of education/class, age of onset of illness, duration of epilepsy, seizure type, types of AEDs, number of AEDs | Regression analysis | DSM-Ⅳ |
| Alsaadi 2015 [13] | 186 | 18–65 | Age, gender | Marital status, nationality, seizure frequency age, gender, epilepsy classification, number of seizures in the 6 months prior to the clinic visit |
Multi regression mode | PHQ-9 |
| Bosak 2015 [16] | 289 | NR | Age, frequent seizures, use medications |
Age, gender, marital status, education level, occupational activity, use of antidepressant | Logistic regression modeling | BDI |
| Chaka 2018 [18] | 422 | ≥18 | Female, single, perceived stigma, medication adherence, current substance use | Age, gender, ethnicity, marital status, religion, residence, education, occupation, with whom living now | Logistic regression analysis |
PHQ-9 |
| Cianchetti 2018 [19] | 326 | 8–18 | Severity and duration of the epilepsy | Sex, education, epilepsy severity, disease duration, antiepileptic treatment |
Chi-square or Fisher’s exact test | SAFA-D |
| Espinosa 2016 [24] | 220 | 18–79 | Unemployed | Age, sex, education, marital status, and occupational activity, risk factors for epilepsy, age of diagnosis, type of seizures, frequency of seizures, treatment with antiepileptic drugs, and therapeutic response |
A multiple linear regression model | NDDI-E |
| Kui 2014 [30] | 215 | >18 | Employment status, presence of chronic medical illnesses, drug responsiveness | Education, marriage status, employment status, gender, age, age at seizure onset, duration of epilepsy, seizure type, aetiology of epilepsy, epileptic family history, previous status epilepticus, EEG findings, neuroimaging findings outcome of epilepsy, chronic medical illnesses | A binary logistic regression |
DSM-Ⅳ |
| Lee 2018 [32] | 141 | >18 | Higher neuroticism, lower self-esteem, marital status, and lower extroversion |
Gender, age at the first seizure onset, marriage, job, economic class, presence vs. absence of perceived stigma | Stepwise linear regression model | HADS |
| Lopez-Gomez 2005 [34] | 241 | NR | Seizure frequency | Age, gender, marital status, educational degree, or type of economic activity |
A logistic regression model | BDI MADRS |
| Mensah 2006 [35] | 499 | 18–78 | Unemployment | Gender, marital status, or monotherapy or polytherapy antiepileptic medication | A stepwise multiple regression analysis | HADS |
| Milovanović 2014 [36] | 203 | 18–65 | Educational level | Age, educational level, occupational status, marital status, epilepsy history, seizure types, seizure frequency, comorbidity, drug treatment | Hierarchical multiple regression analysis | BDI-II |
| Peterson 2014 [39] | 279 | ≥18 | Employment status, high levels of social stigma, ineffective control of seizures | Gender, employment, marital status, education | Pearson correlations and block recursive regression | HADS |
| Somayajula 2015 [44] | 165 | >16 | Married | Gender, married, unemployment, graduate age | Logistic regression | ICD-10 |
| Stefanello 2011 [45] | 153 | ≥13 | Unemployment, fewer years of schooling, age above 41 | Age, gender, marital status, occupation schooling, economic group | Logistic regression analysis |
HAD |
| Tegegne 2015 [46] | 415 | ≥18 | Using poly-therapy of anticonvulsants, perceived stigma, inability to read or write |
Age, gender, marital status, residence, religion, ethnicity, educational status, occupation, monthly income, frequency of seizure | Logistic regression analysis | HADS |
| Tsegabrhan 2014 [48] | 300 | >18 | Epilepsy‑related perceived stigma, high seizure frequency, low educational status | Age, duration of illness, marital status, educational status, occupation, place of residence, seizure frequency, type of AEDs, epilepsy‑related perception of stigma | Bivariate logistic regression |
BDI‑II |
| Viguera 2018 [49] | 1763 | ≥18 | Age, black race, lower income, lower health-related quality-of-life, higher LSSS score (worse severity) |
Age, gender, race, marital status, household median income, patient-reported health-related quality of life, disease-specific performance scale |
Univariate logistic regression models | PHQ-9 |
| Wang 2018 [50] | 458 | ≥18 | Income, frequent seizures | Gender, marital status, age, income, education, age at seizure onset, polytherapy | NR | C-NDDI-E |
| Yildirim 2018 [51] | 302 | 15–73 | Female, lower education and income levels, never employed, higher seizure frequency |
Gender, marital status, educational level, occupation, income level, seizure frequency, seizure type, medication, family history of epilepsy | A multivariate linear regression |
BDI |
| Zhao 2012 [53] | 140 | 15–71 | Complex partial seizures, number of seizure types | Gender, seizure type, seizure frequency, number of anti-epilepsy drugs | NR | HAMD |