High Child Mortality and Interventions Coverage in the City of Dar es Salaam, Tanzania: Are the Poorest Paying an Urban Penalty?
Bibliographic record
Abstract
The 'urban penalty' in health refers to the loss of a presumed survival advantage due to adverse consequences of urban life. This study investigated the levels and trends in neonatal, post-neonatal and under-5 mortality rate and key determinants of child survival using data from Tanzania Demographic and Health Surveys (TDHS) (2004/05, 2010 and 2015/16), AIDS Indicator Survey (AIS), Malaria Indicator survey (MIS) and health facility data in Tanzania mainland. We compared Dar es Salaam results with other urban and rural areas in Tanzania mainland, and between the poorest and richest wealth tertiles within Dar es Salaam. Under-5 mortality declined by 41% between TDHS 2004/05 and 2015/2016 from 132 to 78 deaths per 1000 live births, with a greater decline in rural areas compared to Dar es Salaam and other urban areas. Neonatal mortality rate was consistently higher in Dar es Salaam during the same period, with the widest gap (> 50%) between Dar es Salaam and rural areas in TDHS 2015/2016. Coverage of maternal, new-born and child health interventions as well as living conditions were generally better in Dar es Salaam than elsewhere. Within the city, neonatal mortality was 63 and 44 per 1000 live births in the poorest 33% and richest 33%, respectively. The poorest had higher rates of stunting, more overcrowding, inadequate sanitation and lower coverage of institutional deliveries and C-section rate, compared to richest tertile. Children in Dar es Salaam do not have improved survival chances compared to rural children, despite better living conditions and higher coverage of essential health interventions. This urban penalty is higher among children of the poorest households which could only partly be explained by the available indicators of coverage of services and living conditions. Further research is urgently needed to understand the reasons for the urban penalty, including quality of care, health behaviours and environmental conditions.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".