Approaches for Measuring Socioeconomic Status in Health Studies in Sub-Saharan Africa: A Scoping Review
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it