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Record W4214652336 · doi:10.23889/ijpds.v7i2.1739

Sociodemographic, living environment and maternal health associations with stillbirth in a tertiary healthcare setting in Kano, Northern Nigeria

2022· article· en· W4214652336 on OpenAlexaff
Rebecca Milton, Fatima Modibbo, David Gillespie, Fatima Ibrahim Alkali, Aisha Mukaddas, F. H. Sa’ad, Fatima Tukur, Rashida Khalid, Murjanatu Bello, Chinago Precious Edwin, Ese Ogudo, Kenneth Iregbu, Lim Jones, Kerenza Hood, Peter Ghazal, Julia Sanders, Brekhna Hassan, Timothy Walsh

Bibliographic record

VenueInternational Journal for Population Data Science · 2022
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsMedicineObstetricsPregnancyHealth facilityObstructed labourObservational studyReferralSocioeconomic statusPediatricsPopulationEnvironmental healthFamily medicineCaesarean sectionHealth services

Abstract

fetched live from OpenAlex

Background Stillbirths are reported as one of the most neglected tragedies in global health, with around 2m stillbirths occurring annually and the majority occurring in low- and middle income countries (LMICs). Many antenatal stillbirths are due to preventable conditions such as maternal infections and non-communicable diseases. Almost half of all stillbirths occur during the intrapartum period, with many linked to obstetric complications. Known risk factors for stillbirths overall include young or advancing maternal age, fetal infection, maternal hypertensive conditions, perinatal asphyxia, history of previous stillbirth, obstetric complications, intrauterine growth restriction and abruptio placenta/placenta praevia. Common non-clinical risk factors include lack of education, socioeconomic deprivation and substandard antenatal care. Methods A single site prospective observational study conducted over three-months was conducted in a tertiary referral hospital in Kano, Nigeria. Eligible participants were mothers presenting at the site in labour and their babies. Demographic and clinical data were collected by paper-based questionnaires. Data were collected on living environment, health and medical history, pregnancy history and pregnancy/birth factors. Each mother answered pre-delivery questions, with potential follow-on questions dependent on birth outcome. Further data points were collected from clinical observations. Photographs were taken of stillborn babies to support data collected and to aid the UK team on classifying degrees of maceration in an attempt to identify antenatal and intrapartum fetal death. Findings Higher odds of stillbirth were associated with low levels of education, a further distance to travel from home to the hospital, living in a shack, maternal hypertension and having had a previous stillbirth after adjusting for all sociodemographic and health features. Higher odds of intrapartum stillbirth included; shoulder presentation, compound presentation and breech presentation compared to cephalic presentation. Other birth related factors associated with higher odds of stillbirth included reported birthing complications, duration of labour being >=18 hours), antepartum haemorrhage, prolonged/obstructed labour, vaginal breech delivery, emergency Caesarean-section delivery, and signs of trauma to the neonate. ConclusionsIdentified risk factors associated with stillbirths are relatively amenable to intervention and a lot of work has been conducted globally, so the development of intervention with sufficient funding should be a relatively rapid process. For collaborations please contact: Email: miltonrl1@cardiff.ac.uk

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.021
GPT teacher head0.333
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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