Predicting Time to Subsequent Pregnancy
ABSTRACT Women in poverty may benefit from avoiding closely spaced pregnancies. This study sought to identify predictive factors that could identify women at risk for closely spaced pregnancies.
We studied 20,028 women receiving welfare (cash assistance) from Washington State. Using Cox proportional hazards methods, we estimated the effects of individual- and community-level variables on time from an index birth until a subsequent pregnancy (between June 1992 and December 1999). Prediction models developed in a random half of our data were validated in the other half. Receiver operator characteristic plots appropriate for proportional hazards models were calculated to compare the sensitivity and specificity of each model.
At 5 years of follow-up, the most predictive model contained just individual-level variables (age, education, race, marital status, number of prior pregnancies); the area under the receiver operator characteristic curve was 0.66 (.62-.69). The addition of community-level variables (percent in poverty, with a high school degree or higher, Black, Hispanic, in an urban area; female unemployment rate; income inequality) added little predictive ability. Differences were found between women with different individual- and community-level characteristics, but the results suggest that these factors are not strong predictors of pregnancy spacing.
Individual- and community-level characteristics are associated with interpregnancy intervals; however, we found little evidence that the selected variables predicted pregnancy interval in a useful manner.
- JAMA The Journal of the American Medical Association 05/2006; 295(15):1837-8. DOI:10.1001/jama.295.15.1837 · 30.39 Impact Factor
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ABSTRACT: Pregnancy outcomes in the United States are generally worse than those in most developed countries. Contributing to these adverse outcomes are the relatively high levels of poverty in the United States, a characteristic that is associated with decreased utilization of appropriate prenatal care and delivery services as well as having an increased number of other risk factors. Poor women tend to be more obese, to have more medical conditions, such as hypertension and diabetes, to be more likely to be stressed or depressed, and to smoke cigarettes and use illicit drugs. We present some of the potential mechanisms that explain the association between these characteristics and adverse pregnancy outcomes--focusing on preterm birth.Annals of the New York Academy of Sciences 02/2008; 1136:80-5. DOI:10.1196/annals.1425.016 · 4.31 Impact Factor
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ABSTRACT: Sylhet Division in Bangladesh has the highest proportion of births spaced less than 36 months (46.5%) in Bangladesh. Formative research was conducted on current fertility-related practices in order to explore how to integrate the promotion of healthy fertility practices into a package of maternal and neonatal care interventions. In-depth interviews, focus group discussions and other qualitative methods were utilized with recently delivered women, their families, community health workers and community leaders in Sylhet Division. Mothers of young children generally understood the benefits of both healthy timing and spacing of pregnancies. However, a variety of factors prevent these desired behaviours from becoming actualized, including the roles of women in the provision of children/grandchildren, local understandings of modern contraceptive methods, perceived side effects, lack of communication regarding healthy fertility practices between partners and extended family members, and limited female autonomy. In order to increase families' ability to achieve optimal birth intervals, we propose the promotion and integration of healthy fertility practices into antenatal and newborn care interventions, focusing on providing biomedically correct and culturally appropriate information on modern contraceptive methods to the entire family, while simultaneously encouraging open spousal and family communication patterns regarding timing and spacing of pregnancy.International Health 11/2013; DOI:10.1093/inthealth/iht031 · 1.13 Impact Factor