Cohort Profile: PRECISE-DYAD: a prospective cohort study linking maternal and infant health trajectories in sub-Saharan Africa
Bibliographic record
Abstract
Abstract Purpose The PRECISE-DYAD study is a prospective observational cohort, designed to investigate health outcomes among mother-child pairs (dyads), over the first three years of life in two contexts from sub-Saharan Africa. The primary objective of the study was to explore the effects of selected placenta-related complications, such as pregnancy hypertension, fetal growth restriction, and preterm birth, on 1) child health and development, and 2) women’s health and well-being, including outcomes after stillbirth Participants The PRECISE-DYAD study enrolled women (and their children) originally recruited into the PRECISE regnancy cohort study in The Gambia and Kenya between July 2021 and April 2024. Participants were seen at 6 weeks to 6 months, 12 months, 24 months, and 36 months post-partum. Clinical and health data, including anthropometry and diet were collected for both mothers and children. Mother assessment included a cardiology assessment and collection of data about symptoms of COVID-19 infection. In a subset of participants, mothers were asked about their mental health, their health care costs during and after pregnancy, and experiences of care during labour and childbirth / delivery. Additonally, a personal environmental exposure assessement was performed for a subset of the cohort, by collecting air and water quality data alongside geographical, demographic, and behavioural factors. Child development was assessed using neurodevelopmental assessments, home environment evaluation, and quality of life measures. Biological samples were collected from mothers and children, processed promptly and biobanked locally. Sample data were entered into an OpenSpecimen database and linked to each individual, as well as to their corresponding social determinants and clinical data. Findings to date A total of 2,980 women and 2,909 children completed at least one PRECISE-DYAD study visit. The biorepository contains 108,897 biological samples from mothers and children. Baseline descriptive analysis of the cohort are reported here. Future plans Analysis of data and samples will include biomarker studies, social determinants of health, and epidemiological investigations. These analyses will explore how placenta-related complications and environmental exposures, such as nutrition and air quality, interact to shape maternal health, mental well-being, subsequent pregnancies, and mother-child interaction, as well as child growth and neurodevelopment through early childhood. Additional work will examine the biological pathways linking these exposures to outcomes and the impacts of caring for children with moderate-to-severe disabilities on maternal well-being. Findings will be disseminated through scientific publications, conference presentations, engagement with local stakeholders, and continued community outreach. Strengths and limitations - This is a unique pregnancy-enrolled, population-based cohort with extensive social, clinical, and biological data, including biospecimens, collected across two geographically diverse settings in sub-Saharan Africa. Women were recruited at the time of booking for antenatal care, allowing early identification and longitudinal follow-up of those with placenta-related complications. The integration of PRECISE and PRECISE-DYAD data enables the comprehensive investigation of the drivers and impacts of placental disorders on maternal and child health, and outcomes related to the COVID-19 pandemic. - Data were collected on women’s social and physical environments, including air quality, and water, sanitation, and hygiene (WASH) conditions. In-depth data were also gathered on children, with a focus on neurodevelopmental assessments. Consistent data collection procedures and standardised methodologies were used across both study sites. - Extensive and sustained community engagement, including 108 sensitisation meetings with nearly 4,000 participants, enhanced trust, study understanding, and acceptability. - A limitation of the study is the loss to follow-up of participants who relocated outside of the study area during pregnancy or after the child’s birth, or changed their contact details. - A second limitation is that the Mozambique pregnancy cohort has provided only air quality data through PRECISE-DYAD, and has not been followed up otherwise at this time.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".