Measuring stillbirth and perinatal mortality rates through household surveys: a population-based analysis using an integrated approach to data quality assessment and adjustment with 157 surveys from 53 countries
Bibliographic record
Abstract
BACKGROUND: In most low-income and middle-income countries (LMICs), national surveys are the main data source for stillbirths and perinatal mortality. Data quality issues such as under-reporting and misreporting have greatly limited the usefulness of such data. We aimed to enhance the use of mortality data in surveys by proposing data quality metrics and exploring adjustment procedures to obtain the best possible measure of perinatal mortality. METHODS: We performed a population-based analysis of data from 157 demographic and health surveys (DHSs) from 1990 to 2020, with reproductive calendar and birth history data from 53 LMICs. Pregnancies terminated before 7 months' gestation were excluded. We examined data quality and compared survey values with reference values obtained from a literature review to assess misreporting of the age at early neonatal death, omission and transference of stillbirths, and very early neonatal deaths. Real cohort life-table rates of stillbirth, early neonatal, and perinatal mortality per 1000 births were calculated. The underlying risks of stillbirth and daily deaths were modelled using modified Gompertz-Makeham models. FINDINGS: Data for 2 008 807 pregnancies of ≥7 months' gestational age were extracted from the reproductive calendar for the analysis period. Age heaping at day 7 occurred in most surveys. The median value for the heaping index of deaths at day 7 was 2·05 (IQR 1·36-2·87). The median ratio of stillbirths to deaths on days 0-1 was 1·15 (0·86-1·51). Of the 157 surveys, 23 (15%) were considered to have plausible ratios, 71 (45%) had probable ratios, and 63 (40%) had improbable ratios. The ratio of deaths on days 0-1 to deaths on days 2-6 varied considerably between surveys and 119 surveys (76%) had ratios of less than 2·4, indicative of under-reporting of very early neonatal deaths in most surveys. The fully adjusted model increased the median stillbirth rates from 12·2 (9·4-15·9) to 25·6 (18·0-33·4) per 1000 births, with a median relative increase of 95·0% (56·6-136·6). The median perinatal mortality rate also increased from 32·6 (23·6-38·3) to 44·8 (32·8-58·0) per 1000 births, with a median relative increase of 47·8% (6·9-61·0). INTERPRETATION: A simultaneous focus on stillbirths and early neonatal mortality facilitates a comprehensive assessment of inaccurate reporting in household surveys and allows for better use of surveys in planning and monitoring of efforts to reduce stillbirths and early neonatal mortality. FUNDING: None.
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How this classification was reachedexpand
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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".