Impact of COVID-19 lockdown on low birthweight in Soweto, South Africa
Bibliographic record
Abstract
BACKGROUND: Pregnant women were indirectly affected by the COVID-19 pandemic owing to heightened stress, fear of mother-to-child transmission of COVID-19 and the disruption of antenatal health services. Increased stress and lack of antenatal healthcare could result in an increase in adverse birth outcomes such as preterm birth or low birthweight. OBJECTIVES: Using a case-control design, to compare the prevalence of low birthweight among infants born before and during the pandemic in Soweto, South Africa. METHOD: Infants born before the pandemic and national lockdown were included in the control group, while infants who were in utero and born during the pandemic were included in the case group. Only infants born ≥37 weeks' gestation with no birth complications were included. Multivariable logistic regression was employed to determine whether the pandemic was associated with an increase in low birthweight. A birthweight <2.5 kg was classified as low birthweight. RESULTS: In total, 199 mother-infant pairs were included in the control group, with 201 mother-infant pairs in the case group. The prevalence of low birthweight was 4% in the control group and 11% in the case group, with those born during the pandemic at a higher risk of being of low birthweight. CONCLUSION: The high prevalence of low birthweight in infants born ≥37 weeks' gestation during the pandemic could result in an increase in child stunting and poor development. Future research should measure early child development and growth in infants born during the pandemic to assess whether there is a need to intervene and provide additional support to minimise the negative effects.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".