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The development of a synthetic dataset of women at risk of readmission following stillbirth deliveries in Uganda

Amhaz, Haneen

<br/><strong>Background:</strong> In 2020, 287,000 mothers died from complications of pregnancy or childbirth; one-third of these deaths (30%) occur during the first 6 weeks after birth. Precision public health approaches leverage risk prediction to identify the most vulnerable patients and inform decisions around use of scarce resources, including the frequency, intensity, and type of postnatal care follow-up visits. However, these approaches may not accurately or precisely predict risk for specific sub-groups of women who are statistically underrepresented in the total population, such as women who experience stillbirths. <br /> <br /><strong>Methods:</strong> We leverage our existing dataset of sociodemographic and clinical variables and health outcomes for mother and baby dyads in Uganda to generate a synthetic dataset to enhance our risk prediction model for identifying women at a high-risk of death or readmission in the 6 weeks after a hospital delivery. <br/> <br /><strong>Data Collection Methods:</strong> The original mom and baby project data were collected at the point of care using encrypted study tablets and these data were then uploaded to a Research Electronic Data Capture (REDCap) database hosted at the BC Children’s Hospital Research Institute (Vancouver, Canada). Following delivery and obtaining informed written consent, trained study nurses collected data grouped according to four periods of care; admission, delivery, discharge, and six-week post-discharge follow up. Data from admission and delivery were captured from the hospital medical record where possible and by direct observation, direct measurement or patient interview when not. Discharge and post-discharge data were collected by observation, measurement or interview. Six-weeks after delivery, field officers contacted every mother and/or caregivers of newborns who survived to discharge to determine vital status, readmission and care seeking for illnesses and routine postnatal care. In-person visits were completed in situations where participants could not be reached by phone. <br/> Mothers who had experienced a stillbirth were filtered from the overall dataset. The synthetic dataset was subsequently based off the stillbirth cohort and evaluated it to ensure its statistical properties were maintained. <br /> <br /><strong>Data Processing Methods:</strong> Synthetic data and evaluation metrics were generated using the synthpop R package. The first variable (column) in the dataset is generated via random sampling with replacement with subsequent variables generated conditioned on all previously synthesized variables using a pre-specified algorithm. We used the classification and regression tree (CART) algorithm as it is non-parametric and compatible with all data types (continuous, categorical, ordinal). Additional setup for generating the synthetic dataset included identifying eligible and relevant variables for synthesis and outlining rules for variables that have branching logic (i.e., variables that are only entered if a previous variable has a specific response). <br/> For evaluation, we used the utility metric recommended by the authors of the synthpop package, the standardized propensity-score mean squared error (pMSE) ratio which measures how easy it is to tell whether a data point comes from the original data or the synthetic dataset. All the standardized pMSE ratios were below 10, which is the suggested cut-off for acceptable utility as proposed by the synthpop authors. Plots were also generated to visually compare the univariate distribution of each variable in the synthetic dataset against the original dataset. <br /> <br /><strong>Ethics Declaration:</strong> Ethics approvals have been obtained from the Makerere University School of Public Health (MakSPH) Institutional Review Board (SPH-2021-177), the Uganda National Council of Science and Technology (UNCST) in Uganda (HS2174ES) and the University of British Columbia in Canada (H21-03709). This study has been registered at clinicaltrials.gov (NCT05730387).<br > <br /><strong>Abbreviations:</strong> <br > JRRH: Jinja Regional Referral Hospital <br > MRRH: Mbarara Regional Referral Hospital<br > PNC: Post-natal care<br > SES: Socio-economic index<br > SpO2: Oxygen saturation<br > <br /><strong>Study Protocol & Supplementary Materials:</strong> <br > <a href = "https://doi.org/10.5683/SP3/EIUHJF">Smart Discharges for Mom & Baby 2.0: A cohort study to develop prognostic algorithms for post-discharge readmission and mortality among mother-infant dyads </a><br >; <br /><strong>NOTE for restricted files:</strong> If you are not yet a CoLab member, please complete our <a href = "https://rc.bcchr.ca/redcap/surveys/?s=EDCYL7AC79">membership application survey</a> to gain access to restricted files within 2 business days. <br />Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at <a href = mailto:sepsiscolab@bccchr.ca>sepsiscolab@bcchr.ca</a> or visit our <a href = "https://wfpiccs.org/pediatric-sepsis-colab/">website</a>.

Dataset

2025-01-21

CC BY-NC-SA 4.0

https://doi.org/10.5683/SP3/W3TBZY

Lacuna Fund; Canadian Insitute for Health Research; Michael Smith Health Research BC; Michael Smith Health Research BC

Dataverse