The Influence of Between and Within-group Variance on Recurrent Miscarriage in Ghana
Bibliographic record
Abstract
Abstract Background: Recurrent miscarriage is a spontaneous loss of two or more pregnancies. It is a major global reproductive public health problem and the least researched in Ghana. There is an urgent need to move from more individualized and genetic risk factors that may be culturally inappropriate to assess and explore the influence of socio-ecological or contextual factors on recurrent miscarriage in Ghana. This study explored the between and within-group variance of zonal, regional, cluster, and household as well as the influence of selected socio-demographic characteristics on recurrent miscarriage.Methods: We conducted a secondary analysis of the 2017 cross-sectional demographic health survey data (25,062 women) in their reproductive ages (15-49 years) in Ghana. Data were collected from zones (3), regions (10), clusters (900), and households (27000). A logistic regression: Mixed effect model was used. Random effects were used to estimate the between and within groups differences in recurrent miscarriage. The fixed effects of selected socio-demographic variables were also assessed.Results: We found that cluster differences in recurrent miscarriage risk were more prominent compared to zonal, regional, and household. There was higher variability in recurrent miscarriage among women in the same household, cluster, and region compared to those from different households in the same cluster or those from different clusters in the same region and household. In the fixed-effects model, the odds of recurrent miscarriage were 1.54 times (95% CI=1.16-2.03) higher among urban dwellers than rural dwellers. Increasing maternal age was significantly associated with recurrent miscarriage (p=0.0001). There was a direction association between educational level and recurrent miscarriage. Insignificant protective factors of partner support and access to first-trimester antenatal care visits were also found.Conclusion: Differences in recurrent miscarriage risk exist at the contextual level and should be the focus of public health prevention efforts. Our key finding highlights the need to contextualize and localize recurrent miscarriage risk reduction efforts if interventions to reduce recurrent miscarriage in women with a history of unexplained pregnancy loss are to be successful in Ghana.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".