Trends and Inequalities in Maternal and Newborn Health Services for Unplanned Settlements of Lusaka City, Zambia
Bibliographic record
Abstract
Living conditions and other factors in urban unplanned settlements present unique challenges for improving maternal and newborn health (MNH), yet MNH inequalities associated with such challenges are not well understood. This study examined trends and inequalities in coverage of MNH services in the last 20 years in unplanned and planned settlements of Lusaka City, Zambia. Geospatial information was used to map Lusaka's settlements and health facilities. Zambia Demographic Health Surveys (ZDHS 2001, 2007, 2013/2014, and 2018) were used to compare antenatal care (ANC), institutional delivery, and Cesarean section (C-section) coverage, and neonatal mortality rates between the poorer 60% and richer 40% households. Health Management Information System (HMIS) data from 2018 to 2021 were used to compute service volumes and coverage rates for ANC1 and ANC4, and institutional delivery and C-sections by facility level and type in planned and unplanned settlements. Although the correlation is not exact, our data analysis showed close alignment; and thus, we opted to use the 60% poorer and 40% richer groups as a proxy for households in unplanned versus planned settlements. Unplanned settlements were serviced by primary centers or first-level hospitals. ZDHS findings show that by 2018, at least one ANC visit and institutional delivery became nearly universal throughout Lusaka, but early and four or more ANC visits, C-sections, and neonatal mortality rates remained worse among poorer than richer women in ZDHS. In HMIS, ANC and institutional delivery volumes were highest in public facilities, especially in unplanned settlements. The volume of C-sections was much greater within facilities in planned than unplanned settlements. Our study exposed persistent gaps in timing and use of ANC and emergency obstetric care between unplanned and planned communities. Closing such gaps requires strengthening outreach early and consistently in pregnancy and increasing emergency obstetric care capacities and referrals to improve access to important MNH services for women and newborns in Lusaka's unplanned settlements.
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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".