Prevalence of Depression Among Urban and Rural Residents of Saudi Arabia Compared With Other Gulf Cooperation Council Countries: A Systematic Review
Bibliographic record
Abstract
Depression is a leading cause of disability worldwide, with significant variations in prevalence across urban and rural populations. In the Gulf Cooperation Council (GCC) countries, rapid urbanization and socioeconomic changes have introduced new mental health challenges. However, comprehensive data on depression disparities between urban and rural residents remain limited. This systematic review aims to explore depression prevalence in Saudi Arabia and other GCC nations, examining associated factors and regional variations. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a systematic search of PubMed, MEDLINE, PsycINFO, Scopus, and Web of Science for studies published between 2010 and 2024. Studies published between 2010 and 2024 were included if they assessed depression prevalence among adults in urban or rural settings within GCC countries using validated diagnostic tools. Studies were excluded if they focused on narrow subpopulations or lacked clear geographic classification. Data were extracted independently by two reviewers, and study quality was assessed using the Newcastle-Ottawa Scale for observational studies and Assessment of Multiple Systematic Reviews 2 for reviews. Twenty-four studies were included, with 18 from Saudi Arabia, four from the UAE, two from Oman, and one from Qatar. No studies from Bahrain or Kuwait met the inclusion criteria. Prevalence ranged widely: 2.1-77.8% in Saudi Arabia, 2.1-21.1% in the UAE, and 8.1-21.7% in Oman. Rural-specific data were scarce, though indirect evidence suggested higher rates in rural Saudi Arabia (e.g., 62.3% in northern regions). Women, younger adults in Qatar, older adults in Saudi Arabia, and individuals with lower socioeconomic status consistently showed higher depression rates. Stigma and underdiagnosis (74% undetected cases in Saudi Arabia) were key barriers. Depression prevalence in the GCC varies significantly by country, urbanization level, and demographic factors. The lack of rural-specific data and studies from Bahrain and Kuwait highlights critical research gaps. Culturally tailored interventions, improved mental health infrastructure, and anti-stigma campaigns are urgently needed, particularly for women and rural populations. Future research should standardize measurement tools and prioritize disaggregated urban-rural analyses to guide equitable policy-making.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".