Child, maternal, and adult mortality in Sierra Leone: nationally representative mortality survey 2018–20
Bibliographic record
Abstract
BACKGROUND: Sierra Leone's child and maternal mortality rates are among the highest in the world. However, little is known about the causes of premature mortality in the country. To rectify this, the Ministry of Health and Sanitation of Sierra Leone launched the Sierra Leone Sample Registration System (SL-SRS) of births and deaths. Here, we report cause-specific mortality from the first SL-SRS round, representing deaths from 2018 to 2020. METHODS: The Countrywide Mortality Surveillance for Action platform established the SL-SRS, which involved conducting electronic verbal autopsies in 678 randomly selected villages and urban blocks throughout the country. 61 surveyors, in teams of four or five, enrolled people and ascertained deaths of individuals younger than 70 years in 2019-20, capturing verbal autopsies on deaths from 2018 to 2020. Centrally, two trained physicians independently assigned causes of death according to the International Classification of Diseases (tenth edition). SL-SRS death proportions were applied to 5-year mortality averages from the UN World Population Prospects (2019) to derive cause-specific death totals and risks of death nationally and in four Sierra Leone regions, with comparisons made with the Western region where Freetown, the capital, is located. We compared SL-SRS results with the cause-specific mortality estimates for Sierra Leone in the 2019 WHO Global Health Estimates. FINDINGS: Between Sept 1, 2019, and Dec 15, 2020, we enrolled 343 000 people and ascertained 8374 deaths of individuals younger than 70 years. Malaria was the leading cause of death in children and adults, nationally and in each region, representing 22% of deaths under age 70 years in 2020. Other infectious diseases accounted for an additional 16% of deaths. Overall maternal mortality ratio was 510 deaths per 100 000 livebirths (95% CI 483-538), and neonatal mortality rate was 31·1 deaths per 1000 livebirths (95% CI 30·4-31·8), both among the highest rates in the world. Haemorrhage was the major cause of maternal mortality and birth asphyxia or trauma was the major cause of neonatal mortality. Excess deaths were not detected in the months of 2020 corresponding to the peak of the COVID-19 pandemic. Half of the deaths occurred in rural areas and at home. If the Northern, Eastern, and Southern regions of Sierra Leone had the lower death rates observed in the Western region, about 20 000 deaths (just over a quarter of national total deaths in people younger than 70 years) would have been avoided. WHO model-based data vastly underestimated malaria deaths and some specific causes of injury deaths, and substantially overestimated maternal mortality. INTERPRETATION: Over 60% of individuals in Sierra Leone die prematurely, before age 70 years, most from preventable or treatable causes. Nationally representative mortality surveys such as the SL-SRS are of high value in providing reliable cause-of-death information to set public health priorities and target interventions in low-income countries. FUNDING: Bill & Melinda Gates Foundation, Canadian Institutes of Health Research, Queen Elizabeth Scholarship Program.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".