ArticleFamily medicine and community health2022
Medical, behavioural and social preconception and interconception risk factors among pregnancy planning and recently pregnant Canadian women.
Article in Family medicine and community health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Long-term risk of bowel resection and mortality associated with sleep behaviours and sleep pattern in patients with inflammatory bowel disease: a large-scale prospective cohort study.Family medicine and community health · 2026Article
- Latent profile analysis of fertility intention among women of reproductive age.Frontiers in reproductive health · 2026Article
- Article
- What is the meaning of interpregnancy interval after caesarean section for women planning further pregnancies: a mixed method systematic review protocol.Systematic reviews · 2025Article
- Preconception health risk profiles among women planning pregnancy in northern Ethiopia: a latent class analysis.BMJ open · 2025Article
- Preconception Oral Health Is Associated with Modifiable Health Behaviors.Journal of dental research · 2025Article
- Subthreshold anxiety in Chinese college students: prevalence, gender differences, and correlates.BMC psychology · 2025Article
- Influence of perceived stress on fertility intention among women of childbearing age without children: multiple mediating effect of anxiety, family communication and subjective well-being.Reproductive health · 2024Article
- Interconception care during well-child visits by family physicians in the United States: A cross-sectional study.Journal of general and family medicine · 2024Article
- Impact of Geography and Rurality on Preconception Health Status in the United States.Preventing chronic disease · 2023Article
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Authors and funding
11 authors.
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Abstract
objectivesThe objective of this study is to describe the clustering of medical, behavioural and social preconception and interconception health risk factors and determine demographic factors associated with these risk clusters among Canadian women.
designCross-sectional data were collected via an online questionnaire assessing a range of preconception risk factors. Prevalence of each risk factor and the total number of risk factors present was calculated. Multivariable logistic regression models determined which demographic factors were associated with having greater than the mean number of risk factors. Exploratory factor analysis determined how risk factors clustered, and Spearman's r determined how demographic characteristics related to risk factors within each cluster.
settingCanada.
participantsParticipants were recruited via advertisements on public health websites, social media, parenting webpages and referrals from ongoing studies or existing research datasets. Women were eligible to participate if they could read and understand English, were able to access a telephone or the internet, and were either planning a first pregnancy (preconception) or had ≥1 child in the past 5 years and were thus in the interconception period.
resultsMost women (n=1080) were 34 or older, and were in the interconception period (98%). Most reported risks in only one of the 12 possible risk factor categories (55%), but women reported on average 4 risks each. Common risks were a history of caesarean section (33.1%), miscarriage (27.2%) and high birth weight (13.5%). Just over 40% had fair or poor eating habits, and nearly half were not getting enough physical activity. Three-quarters had a body mass index indicating overweight or obesity. Those without a postsecondary degree (OR 2.35; 95% CI 1.74 to 3.17) and single women (OR 2.22, 95% CI 1.25 to 3.96) had over twice the odds of having more risk factors. Those with two children or more had 60% lower odds of having more risk factors (OR 0.68, 95% CI 0.52 to 0.86). Low education and being born outside Canada were correlated with the greatest number of risk clusters.
conclusionsMany of the common risk factors were behavioural and thus preventable. Understanding which groups of women are prone to certain risk behaviours provides opportunities for researchers and policy-makers to target interventions more efficiently and effectively.
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