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Background

This study explores how stress impacts preterm delivery and suggests a threshold model where stress levels influence pregnancy outcomes. Using epidemiological data and statistical models, the research examines the relationship between stress and pregnancy outcomes.

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  1. A Threshold Effect in the Relation of Stressful Life Events and Preterm DeliveryNedra Whitehead, Ph.D.

  2. Background

  3. Biological studies • Suggest stress may affect the timing of delivery through • Premature hormonal stimulation of labor; or • Immunosupression resulting in increased risk of chorioamnionitis • Epidemiological studies • have been inconsistent • Different measures of stress related to preterm delivery in different studies • Some studies find no relationship

  4. A possible reason for inconsistent results: the specified model is not correct • Two types of models have been tested: • Discrete model (Model A in figure) • Women are dichotomized as stressed or not-stressed. • Average risk is the same among all stressed women. • Logistic model (Model B in figure) • Each additional unit of stress causes a linear increase in the log-odds of the outcome

  5. A possible model which has not been tested: • Threshold model (Model C in figure) • Stress does not cause poor pregnancy outcome until a certain level is reached. • Above the threshold, each unit of stress causes a linear increase in the log odds of the outcome

  6. Models of Relationship between Stress and Pregnancy Outcome No threshold (A) Threshold (C) Discrete exposure (B) Stress Level

  7. Methods • Data were collected by the Pregnancy Risk Assessment Monitoring System (PRAMS) • Method described by Ulm1 was used to estimate and test for a threshold effect 1Ulm K. A statistical method for assessing a threshold in epidemiological studies. Stat Med 1991;10:341-9.

  8. PRAMS Pregnancy Risk Assessment Monitoring System • What is PRAMS? • Ongoing state population-based surveillance system • Study population: women who recently delivered a live-born infant • Maternal attitudes, behaviors, and experiences during pregnancy and early infancy • Core and state-specific items

  9. PRAMSSampling and Data Collection Methods • Sampling frame: state birth certificate files • High-risk women are oversampled • States annual sample size: 1600-3000 • Data collected 2-6 months after delivery • Uses Dillman’s2 Total Design Method • Questionnaire mailed 2-3 times • Mail non-responders interviewed by telephone 2 Dillman DA. Mail and telephone surveys: the total design method. 1st ed. New York, NY: John Wiley & Sons, Inc., 1978

  10. States and Response Rates State Years of Data No. Respondents Response Rate Alabama 1992 - 1995 5,646 74.9 Alaska 1990 - 1995 10,142 73.2 Florida 1993 - 1995 6,991 78.6 Georgia 1993 - 1995 5,674 71.7 Indiana 1993 - 1994 5,092 70.9 Maine 1990 - 1995 5,955 81.1 Michigan 1993 - 1995 5,122 79.7 New York* 1993 - 1995 4,014 73.3 Oklahoma 1990 - 1995 10,124 73.1 South Carolina 1993 - 1995 5,881 70.3 West Virginia 1990 - 1995 9,739 79.1 Total 1990 - 1995 74,380 75.0

  11. Analytic Methods • Estimating and testing threshold • Fit a logistic model for each possible value of the threshold from 0 (minimum number of events) to 17 (one less than maximum number of events) • Graph the log-likelihood values by the threshold level for the model

  12. Estimated threshold, , is the threshold value from the model with the maximum likelihood value • To determine if a threshold exists, test • Null hypothesis: H0:  = 0 • Alternative hypothesis: H1:  > 0 • Test statistic: Log-likelihood statistic, • R = -2 (ln L (=0) - ln L( = ) • For constrained parameter, , Pr [R] = 0.5 + the probability from 0 to R of the standard normal distribution

  13. The null hypothesis is rejected if R > 1.64 • 95% C.I. on  includes all values of J which fulfill the condition: D(J) = 2×(ln L() ! ln L(J)) < P21, .95

  14. Other Variables Maternal race Maternal age Marital status SES indicators Unintended pregnancy Pregnancy history Parity Tobacco use Alcohol use Interactions Maternal age Maternal race Marital status Maternal education SES indicators Unintended pregnancy Pregnancy history Parity Other Variables & Interactions in the Regression Model

  15. Risk of Poor Pregnancy Outcome by Number of Life Events 30 25 20 Percent 15 10 5 0 0 1 2 3 4 5 7 8 9 10 11 12 13 14 15 16 17 18 6 Number of Events Preterm (%)

  16. Results - Bivariate comparison • Risk of preterm delivery increased among women who experienced more life events

  17. Number of Events Number of Women Preterm Delivery (%) 0 25,280 7.74 1 15,068 8.80 2 11,999 9.22 3 8,351 9.32 4 5,183 9.93 5 3,231 10.45 6 1,998 12.63 7 1,208 12.67 8 704 12.96 9 410 12.58 10 180 15.07 11 107 14.90 12 43 13.79 13 20 14.12 14 13 17.74 15 6 18.51 16 5 19.50 17 2 28.17 18 19 28.41

  18. Modeling results • Threshold effects • Only among singleton births • Inconsistent by parity and time period • Threshold of 5 for multiparous women from 1990-1993 • Threshold of 2 for primiparous women from 1994-1995 • Association of life events with preterm delivery • Was significant only for the two models with a significant threshold effect • Was weak (OR: 1.06/event, 1.07/event) even when significant

  19. Modeling Results, cont.. • Inconsistencies in results remained when analysis done by state, year of birth and maternal race

  20. Preterm Delivery, Singletons 10 8 6 Difference in Ln-Likelihood 4 2 0 -2 -4 -6 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Threshold Level (Number of Life Events) Primiparas 94-95 Multiparas 90-93 Primiparas 90-93 Multiparas 94-95

  21. Preterm Delivery, Multiple Births 0.5 0 Difference in Log-Likelihood -0.5 -1 -1.5 -2 0 1 2 3 4 5 6 7 8 9 10 Threshold Level Multi 94-95 Multi 90-93

  22. Threshold Effect between Number of Life Events and Preterm Delivery

  23. Conclusions • Threshold model may fit the relation of stress and preterm delivery better than model with out threshold among some women • Results do not support a biological relation between stress and preterm delivery • Biological effect might vary by parity or plurality but is unlikely to vary by time

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