Approaches for Measuring Socioeconomic Status in Health Studies in Sub-Saharan Africa: A Scoping Review
Bibliographic record
Abstract
BACKGROUND Socioeconomic status (SES) is essential for determining a person or community’s position about certain social and economic characteristics. This is particularly important in sub-Saharan Africa, where health disparities are pronounced. We conducted a scoping review to explore approaches used in health studies to measure socio-economic status in the sub-Saharan region. METHODS A comprehensive literature search covering January 2012 to June 2024 was conducted in five databases: PubMed, EMBASE, CIHNAL, Web of Science, and African Index Medicus. All studies in sub-Saharan Africa focused on health-related socioeconomic status were included, regardless of study methodology. Three peer reviewers independently evaluated the selected articles according to inclusion and exclusion criteria. Discrepancies between reviewers were resolved through a consensus meeting. The review protocol was registered on the Open Science Framework (OSF, OSF.IO/7NGX3). RESULTS The initial search yielded 19,669 articles. At the end of the screening process, 65 articles were analysed. Cross-sectional studies have been widely used. South Africa (13.4%) and Kenya (11%) were the most represented countries. Maternal, neonatal, and infant/juvenile health was the most covered theme (31%). The review identified 12 categories of SES measurement methods, with the asset-based wealth index being the most widespread (61.9%). Principal component analysis (PCA) is the primary analytical method used to calculate this index (57.7%). CONCLUSIONS This scoping review identified the asset-based wealth index as the most frequently used and provided essential elements for pooling different SES calculation methodologies to reach a consensus. Using SES to improve interventions is important to limit African health disparities.
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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 | 0.069 | 0.211 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.055 | 0.038 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".