ArticleBMJ open2019
Does neighborhood socioeconomic status predict the risk of preterm birth? A community-based Canadian cohort study.
Article in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 20 citations in OpenAlex.
- How does high-density built-environment affect the incidence of gestational diabetes mellitus.iScience · 2026Article
- Maternal Sociodemographic Factors, Adiposity, and Pregnancy Outcomes Among Preterm Deliveries Admitted to the Neonatal Intensive Care Unit.Maternal and child health journal · 2025Article
- Engaging, recruiting, and retaining pregnant people from marginalized communities in environmental health cohort studies: a scoping review.BMC public health · 2025Article
- Development and validation of a spontaneous preterm birth risk prediction algorithm based on maternal bioinformatics: A single-center retrospective study.BMC pregnancy and childbirth · 2024Article
- Influence of Neighborhood Socioeconomic Status on Adverse Outcomes in Pregnancy.Maternal and child health journal · 2023Article
- Unsupervised Learning Applied to the Stratification of Preterm Birth Risk in Brazil with Socioeconomic Data.International journal of environmental research and public health · 2022Article
- Machine Learning-Based Prediction Model of Preterm Birth Using Electronic Health Record.Journal of healthcare engineering · 2022Article
- Data-driven risk stratification for preterm birth in Brazil: a population-based study to develop of a machine learning risk assessment approach.Lancet regional health. Americas · 2021Article
- Can the adverse childhood experiences (ACEs) checklist be utilized to predict emergency department visits among children and adolescents?BMC medical research methodology · 2021Article
- Impact of Race and Socioeconomic Status on Outcomes in Patients Hospitalized with COVID-19.Journal of general internal medicine · 2021Article
- Data harmonization and data pooling from cohort studies: a practical approach for data management.International journal of population data science · 2021Article
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study developed and internally validated a predictive model for preterm birth (PTB) to examine the ability of neighbourhood socioeconomic status (SES) to predict PTB.
designCohort study using individual-level data from two community-based prospective pregnancy cohort studies (All Our Families (AOF) and Alberta Pregnancy Outcomes and Nutrition (APrON)) and neighbourhood SES data from the 2011 Canadian census.
settingCalgary, Alberta, Canada.
participantsPregnant women who were <24 weeks of gestation and >15 years old were enrolled in the cohort studies between 2008 and 2012. Overall, 5297 women participated in at least one of these cohorts: 3341 women participated in the AOF study, 2187 women participated in the APrON study and 231 women participated in both studies. Women who participated in both studies were only counted once. PRIMARY AND SECONDARY OUTCOME MEASURES: PTB (delivery prior to 37 weeks of gestation).
resultsThe rates of PTB in the least and most deprived neighbourhoods were 7.54% and 10.64%, respectively. Neighbourhood variation in PTB was 0.20, with an intra-class correlation of 5.72%. Neighbourhood SES, combined with individual-level predictors, predicted PTB with an area under the receiver-operating characteristic curve (AUC) of 0.75. The sensitivity was 91.80% at a low-risk threshold, with a high false-positive rate (71.50%), and the sensitivity was 5.70% at a highest risk threshold, with a low false-positive rate (0.90%). An agreement between the predicted and observed PTB demonstrated modest model calibration. Individual-level predictors alone predicted PTB with an AUC of 0.60.
conclusionAlthough neighbourhood SES combined with individual-level predictors improved the overall prediction of PTB compared with individual-level predictors alone, the detection rate was insufficient for application in clinical or public health practice. A prediction model with better predictive ability is required to effectively find women at high risk of preterm delivery.
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