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Deleterious and Protective Psychosocial and Stress-Related Factors Predict Risk of Spontaneous Preterm BirthFunding This work was supported by the March of Dimes Prematurity Research Center at Stanford University (D.K.S.) the Bill & Melinda Gates Foundation OPP1189911 (D.K.S.), the Robertson Family Foundation (D.K.S.), the National Institutes of Health R35GM138353 (N.A.), and the Burroughs Wellcome Fund (N.A.).
Objectives The aim of the study was to: (1) Identify (early in pregnancy) psychosocial and stress-related factors that predict risk of spontaneous preterm birth (PTB, gestational age <37 weeks); (2) Investigate whether “protective” factors (e.g., happiness/social support) decrease risk; (3) Use the Dhabhar Quick-Assessment Questionnaire for Stress and Psychosocial Factors (DQAQ-SPF) to rapidly quantify harmful or protective factors that predict increased or decreased risk respectively, of PTB.
Study Design This is a prospective cohort study. Relative risk (RR) analyses investigated association between individual factors and PTB. Machine learning-based interdependency analysis (IDPA) identified factor clusters, strength, and direction of association with PTB. A nonlinear model based on support vector machines was built for predicting PTB and identifying factors that most strongly predicted PTB.
Results Higher levels of deleterious factors were associated with increased RR for PTB: General anxiety (RR = 8.9; 95% confidence interval [CI] = 2.0,39.6), pain (RR = 5.7; CI = 1.7,17.0); tiredness/fatigue (RR = 3.7; CI = 1.09,13.5); perceived risk of birth complications (RR = 4; CI = 1.6,10.01); self-rated health current (RR = 2.6; CI = 1.0,6.7) and previous 3 years (RR = 2.9; CI = 1.1,7.7); and divorce (RR = 2.9; CI = 1.1,7.8). Lower levels of protective factors were also associated with increased RR for PTB: low happiness (RR = 9.1; CI = 1.25,71.5); low support from parents/siblings (RR = 3.5; CI = 0.9,12.9), and father-of-baby (RR = 3; CI = 1.1,9.9). These factors were also components of the clusters identified by the IDPA: perceived risk of birth complications (p < 0.05 after FDR correction), and general anxiety, happiness, tiredness/fatigue, self-rated health, social support, pain, and sleep (p < 0.05 without FDR correction). Supervised analysis of all factors, subject to cross-validation, produced a model highly predictive of PTB (AUROC or area under the receiver operating characteristic = 0.73). Model reduction through forward selection revealed that even a small set of factors (including those identified by RR and IDPA) predicted PTB.
Conclusion These findings represent an important step toward identifying key factors, which can be assessed rapidly before/after conception, to predict risk of PTB, and perhaps other adverse pregnancy outcomes. Quantifying these factors, before, or early in pregnancy, could identify women at risk of delivering preterm, pinpoint mechanisms/targets for intervention, and facilitate the development of interventions to prevent PTB.
Newly designed questionnaire used for rapid quantification of stress and psychosocial factors early during pregnancy.
Deleterious factors predict increased preterm birth (PTB) risk.
Protective factors predict decreased PTB risk.
Keywordschronic stress - psychosocial factors - predicting preterm birth - anxiety - sleep - social support - life events - pregnancy - machine learning
Reproducibility and Data Availability
The data and source code for reproduction of the results are publicly available at https://nalab.stanford.edu/wp-content/uploads/stress-preterm.zip
All authors have approved the manuscript and declared that no competing interests exist. J.A.M. conducted analyses involving demographic data and relative risk with guidance from G.M.S., wrote related methods sections, and reviewed multiple drafts of the manuscript. M.B. conducted the machine learning-based analyses, wrote related methods and results sections and limited portions of the discussion, all with the guidance and supervision from N.A. M.B. and N.A. reviewed and commented on multiple drafts of the manuscript. N.K.P. assisted with interpretation of initial analyses and reviewed the manuscript. C.C.Q. and A.L. facilitated numerous aspects of the overall project and worked with F.S.D. to integrate the Dhabhar Quick-Assessment Questionnaire for Stress and Psychosocial Factors (DQAQ-SPF) into the study. L.K. and I.H.G. reviewed and edited a draft of the manuscript. B.G. and M.S.A. commented on and approved the machine learning-based analyses conducted by M.B. and N.A. G.M.S. and D.K.S., obtained funding, and led, supported, and enabled all aspects of the March of Dimes Prematurity Research Center (PRC) at Stanford University. They reviewed multiple drafts of this manuscript. F.S.D. conceptualized and designed the DQAQ-SPF questionnaire, integrated it into the Stanford Prematurity Research Center project, interpreted results, and wrote the majority of this manuscript.
* These authors contributed equally to this article.
Received: 08 July 2020
Accepted: 02 March 2021
Article published online:
20 May 2021
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