Exploration of Threshold Analysis in the Relation between Stressful Life Events and Preterm Delivery
Nedra Whitehead1,
Holly A. Hill2,
Donna J. Brogan3 and
Cheryl Blackmore-Prince1
1 Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA.
2 Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA.
3 Department of Biostatistics, Rollins School of Public Health, Emory University, Atlanta, GA.
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ABSTRACT
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Biologic evidence suggests that the hormones activated by stress affect gestational length, but the results of epidemiologic investigations are inconsistent. The authors of this paper know of no threshold models that have been studied; these models assume that stress does not affect preterm delivery until a certain amount of stress has been experienced but that each unit of stress above the threshold adds to the risk of preterm delivery. By using standard logistic regression, the authors compared threshold and nonthreshold models of the relation between number of stressful life events and preterm delivery in 11 US states. They used data on 19901995 births from the Pregnancy Risk Assessment Monitoring System. The risk of preterm delivery among multiparas who gave birth in 19901993 increased 7% for each event over five they experienced, but no relation was found for 19941995 births. Among primiparas who gave birth in 19941995, the risk increased 5% for each event over two, but no relation was found for 19901993 births. These results suggest that a threshold model may fit the relation between stress and preterm delivery better than one with no threshold. However, the inconsistent results are difficult to reconcile with a biologic threshold in the relation between stress and preterm delivery.
gestational age; pregnancy; pregnancy outcome; stress, psychosocial
Abbreviations:
PRAMS, Pregnancy Risk Assessment Monitoring System
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INTRODUCTION
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Stress has long been thought to increase a woman's risk of preterm delivery, and the theory is supported by biologic evidence suggesting that the hormones activated in response to stress affect gestational length. Women who deliver preterm have higher mean corticotropin-releasing hormone concentrations at 1620 weeks of gestation than do women who deliver at term (1
). However, epidemiologic investigations are inconsistent and inconclusive. In a recent review, only one of 11 studies found an association between stressful life events and preterm delivery (2
). Methodological flaws may account for some of the inconsistent findings (3
, 4
), but it may be that the relation between stress and preterm delivery has been modeled incorrectly.
Threshold models theorize that stress affects preterm delivery only after a certain amount of stress has been experienced, but, after the threshold level has been achieved, each additional stressor increases the risk of preterm delivery. Biologic mechanisms to accommodate stress exist, and it may be that only unusually high levels of stress increase the risk of preterm delivery. To our knowledge, these models have not been studied.
We tested two hypotheses in this study. We first tested for a relation between number of stressful life events and preterm delivery; then we compared a threshold model of this relation with a linear model. We estimated the most likely threshold level and tested the hypothesis that the threshold model fit better than the linear model.
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MATERIALS AND METHODS
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Study design and population
The data were collected by the Pregnancy Risk Assessment Monitoring System (PRAMS), which has been described in detail elsewhere (5
). In this system, a stratified random sample of women who delivered a liveborn infant is drawn from the state birth certificate file. These women are mailed a self-administered questionnaire 26 months after they deliver, with telephone follow-up for those who do not respond. PRAMS has been approved by institutional review boards at both the federal (Centers for Disease Control and Prevention, Atlanta, Georgia) and state levels. The mailed packet and telephone introductory script contain information on PRAMS, state its purpose and goals, and indicate that participation is voluntary. Informed consent is implied by completing the survey or agreeing to proceed with the interview.
The PRAMS data set contains questionnaire data, selected birth certificate information, and selected information on the timing and mode of data collection. The data are weighted to account for the sampling design, nonresponse, and noncoverage (Centers for Disease Control and Prevention, unpublished data, 1996). The present study includes 70,840 singleton births from 1990 to 1995 in 11 US states (Alabama, Alaska, Florida, Georgia, Indiana, Maine, Michigan, New York (excluding New York City), Oklahoma, South Carolina, and West Virginia), although not all states have data for all 6 years. Response rates (the number of women who completed a questionnaire divided by the number of women sampled) ranged from 70.3 percent to 81.1 percent.
Variables
Stressful life events were measured by using an 18-item subset of the Modified Life Events Inventory (6
). Included were family illness and death, finances and job loss, relationships, physical injury, and legal matters.
Preterm delivery was defined as birth at a gestational age of less than 37 weeks. Information needed to determine gestational age (infant's birth data, date of last menstrual period, and clinical estimate of gestational age) was taken from the birth certificate. The method used to determine the clinical estimate was not available. Gestational age was calculated by using the composite of last menstrual period and clinical estimate described by Alexander et al. (7
). Age at the last menstrual period was used if this age and the clinical estimate of age differed by no more than 13 days, and the clinical estimate of age was used if age at the last menstrual period was unknown or differed from the clinical estimate of age by 14 or more days. If neither age at the last menstrual period nor clinical estimate of age was available, gestational age was calculated from the mother's due date reported on the questionnaire. We discarded observations with implausible birth weightgestational age combinations based on the ranges of birth weight for gestational age reported by Adams et al. (8
). Gestational age was available for 65,051 (91.8 percent) of the infants of study respondents and was determined from the last menstrual period for 53,419 (82.1 percent), clinical estimate for 9,276 (14.3 percent), and delivery due date for 2,356 (3.6 percent) infants.
Women were classified as smokers if they reported on the PRAMS questionnaire that they had smoked during the 3 months before pregnancy; they were considered nonsmokers if they did not. Mother's race and parity were obtained from the infant's birth certificate and pregnancy history (previous pregnancy outcome) from the PRAMS questionnaire.
Analysis
We first determined the relation between number of stressful life events and preterm delivery in the represented population, without adjusting for other factors, by using SUDAAN software (9
). We then used logistic regression and the nonthreshold model (figure 1, model 1) to determine, after adjusting for other factors, whether the number of stressful life events experienced was related to preterm delivery. Finally, we estimated the threshold level (
) at which stressful life events began to affect preterm delivery and tested the hypothesis that the threshold model (figure 1, model 2) fit the relation between stressful life events and preterm delivery better than the nonthreshold model (figure 1, model 1). The method for testing and estimating a threshold was developed by Ulm (10
).

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FIGURE 1. Theoretical logistic regression models examined in a study of the relation between stressful life events and preterm delivery, United States, 19901995. Model 1, nonthreshold; model 2, model incorporating a threshold.
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The threshold was estimated by fitting a logistic model for each possible value of the threshold,
, from 0 to 17. It is unlikely that a threshold level above 11 could be detected, because very few women experienced more than 11 stressful life events. However, fitting models at higher threshold levels allowed the likelihood values to be included when we considered the pattern of results, and it avoided an arbitrary cutoff.
To estimate the threshold value, let j = the threshold model being fit: j = 0, 1, ..., 17; (p - 1) = the number of covariates in the logistic model; x1 = the number of life events a respondent reports; x(j) = the value of x1 equal to j;
(j) = the estimated threshold level in model j (estimated parameters are underlined); and model(j) = the logistic regression model with a threshold parameter equal to
(j) (figure 1). Let
, where yi is the observed value of the out-come and xi is the vector of exposure and covariate values for observation i; and let ln L(j) = ln L(ß0, ß1, ß2,...ßp,
(j)). Then, the model(j) with the maximum ln L(j) is the final threshold model, model(k). The estimated threshold,
estimates
(k), and the estimated regression coefficients for the covariates are ß2,...,ßp for model(k).
The null hypothesis is H0:
= 0, where
is the threshold value and the alternate hypothesis is H1:
> 0. The test statistic is the likelihood ratio statistic: R = -2(ln L(H0) - ln L(H1)), where L(H0) is the log-likelihood value of the model without a threshold (
(j) = 0), and L(H1) is the log-likelihood value of the model with the threshold level (
=
). Because
is constrained by min x1
max x1, the R statistic is not distributed as chi-square with 1 df. Instead, it is approximately distributed as a quasi-one-sided
2 distribution,
, where R = the likelihood ratio statistic, F(1)R = the cumulative probability distribution of R, and f(x) = the probability distribution of the standard normal distribution. The null hypothesis is rejected if the log-likelihood statistic, R, is greater than 1.645 (critical value,
- 0.05). The 95 percent confidence interval on
is calculated by using the deviance, D(
), and includes all values of
that fulfill the condition, D(
) = 2 x(ln L(
) - ln L(
)) < 3.85 (the critical value for
1, 0.952). Confidence intervals determined by this method can be as wide as the entire range of the variable x1 if the log-likelihood values do not differ much for different threshold levels, as narrow as one value if the change in log-likelihood values is abrupt, and noncontinuous if the graph of log-likelihood values is multimodal.
We did not adjust for the PRAMS sample design because the analyses describe relations between variables in groups of persons rather than estimate the prevalence of factors within the population (11
), and the factors used in the sample design are included in the regression model as covariates (except for low birth weight). Therefore, SAS software (12
) was used for the logistic regression modeling analyses.
In our initial analysis, we used data from 1990 through 1993; the analysis was then repeated with 19941995 data to determine whether the results could be replicated. The covariate risk factors included in the regression model (maternal race, income from public aid, smoking status, parity, and pregnancy history) were determined for the model with no threshold, and these covariates were used in the threshold models. We excluded women for whom data were missing on one or more variables in the model. Colinearity was assessed with an SAS software (12
) macro developed by Dan Rosen and Matthew Zack (Centers for Disease Control and Prevention, personal communication, 1997). Primiparas and multiparas were analyzed separately to eliminate colinearity between parity and pregnancy history. We also stratified by number of months from delivery to response and fit the nonthreshold model for each month to assess whether time since delivery affected the results.
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RESULTS
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The characteristics of the sample, respondents, and represented population are described in table 1. During the year before giving birth, 34 percent of the women did not experience any of the stressful life events, and 46 percent experienced more than one such event (table 2). Without controlling for other risk factors, we found that the risk of preterm delivery increased with the number of life events experienced, and there did not seem to be a threshold in the relation of stressful life events to preterm delivery (table 2).
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TABLE 1. Characteristics of total selected sample and respondents, Pregnancy Risk Assessment Monitoring System, United States, 19901995
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TABLE 2. Risk of preterm delivery by number of life events, Pregnancy Risk Assessment Monitoring System, United States, 19901995
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Among multiparous women, number of stressful life events was not linearly related to the log odds of preterm delivery when we used the nonthreshold model and adjusted for confounding factors during either data collection period (tables 3 and 4). Confounding by smoking, maternal race, aid income, and state of residence accounted for most of the apparent bivariate relation between number of stressful life events and preterm delivery. Parity and pregnancy history were weaker confounders of this relation.
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TABLE 3. Final nonthreshold model of association (beta* (standard error)) between number of life events and preterm delivery, Pregnancy Risk Assessment Monitoring System, United States, 19901995
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TABLE 4. Threshold and effect of number of stressful life events on preterm delivery, Pregnancy Risk Assessment Monitoring System, United States, 19901995
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The threshold model results were mixed for multiparous women. For the 19901993 data collection period, the model with a threshold level of five fit better than a linear model (p value < 0.0001) (figure 2, table 4), and a significant relation between stressful life events and preterm delivery was found. A woman's risk of preterm delivery increased 7 percent for each additional stressful life event above five events that she experienced. However, these results were not confirmed for the 19941995 data collection period. While the model with a threshold of five fit slightly better than the linear model, the difference in the models was not significant (p value = 0.2055), and no relation was found between stressful life events and preterm delivery.
For primiparous women, the results for the two models were inconsistent. For the 19901993 data collection period, the nonthreshold model fit slightly better than the threshold model, although there was very little difference between them (figure 2, table 4). No significant relation between stressful life events and preterm delivery was found for either model. For the 19941995 data collection period, number of stressful life events experienced was related to risk of preterm delivery when the nonthreshold model was used (figure 2, table 4). For each event that a woman experienced, her risk of preterm delivery increased by 3 percent. However, the model with a threshold of two fit better than the nonthreshold model (p value < 0.0001), and the relation between life events and preterm delivery was slightly stronger: a 5 percent increase in the risk of preterm delivery for each event over two that a woman experienced.
Nineteen percent of respondents were excluded because of missing data on one or more covariates. This loss should not have greatly biased the results of the study, however, because the distribution of variables in the model and the association between life events and preterm delivery (table 2) were similar for included women and all respondents. The time between delivery and response did not affect the results either. The odds ratio per event differed little between months, and there was no discernible trend by months since delivery. The p values for Hosmer-Lemeshow goodness-of-fit tests (13
) ranged from 0.0013 to 0.1661, but the differences between the number of observed and expected cases were not large; graphically, the relation was linear, with little or no deviation from linear at either tail.
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DISCUSSION
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We found some evidence that stressful life events are related to preterm delivery and that there may be a threshold in this relation. Findings from 19901993 were not confirmed by those from 19941995, however. For multiparous women, the best model had a threshold of five events, but a significant association was found for only 19901993. For primiparous women, there was no association for 19901993 with either model. For 19941995, a significant association was found with both models, but a model with a threshold of two fit significantly better than the nonthreshold model. Even when the effect was statistically significant, it was not large. If the 19901993 results were used, a multiparous woman who experienced all 18 events would have a 90 percent greater risk of preterm delivery than if she had not experienced any of these life events.
This study has several strengths not usually found in studies of stressful life events. The study sample was population based. Some demographic information was available for all sampled women, and there were few demographic differences between sampled women and respondents, reducing the likelihood of nonresponse bias. Because the study is ongoing, we attempted to replicate our results from 19901993 by using 19941995 data collected with the same methodology. The sample size was large enough to ensure precise estimates for both data collection periods, and data were collected on important potential confounders, including maternal race, smoking, and indicators of socio-economic status.
The study also has some limitations. Stress measurement was limited to a single 18-item inventory of negative life events. The inventory included the types of events found on many life event inventories (6
, 14
, 15
), but women may have experienced stressful life events, such as involvement in civil lawsuits, that were not measured. In addition, one study has suggested that events have the strongest effect on preterm delivery soon after they occur (16
), and we had little information on the timing of the events relative to the pregnancy.
Each event counts equally, and it may be that some events are more stressful and should receive more weight. However, Shrout demonstrated that if the weights are positive, differential weighting of the events is unlikely to alter the results much because the weighted sum is strongly correlated with the total number of events (17
).
Events were reported retrospectively, and women may have underreported or forgotten events or reported events that did not occur until after delivery. Women who delivered preterm may have recalled events better than women who delivered at term. The tendency to underreport socially unacceptable behaviors or events was mitigated somewhat because most questionnaires were self-administered, which usually yields higher reporting of sensitive behaviors than telephone or face-to-face interviews (18
20
). We are not aware of any studies that examine pre- and postnatal reporting of stressful life events, but studies that examined agreement between concurrent records and maternal recall of other types of events have been mixed. Researchers have found good agreement overall on previous obstetric history (21
) and gestational age at delivery (22
) but underreporting of short-term illnesses (23
). Our results were not affected by time from delivery to response, suggesting they were not strongly biased by incorrect recall of events. Increased recall by women who delivered preterm does not seem to have strongly affected the results either, given the weak association found between stress and preterm delivery.
We had information on many factors associated with stressful life events and preterm delivery, but unmeasured factors, such as maternal drug use, may have confounded the observed relation. In addition, data were not collected on psychosocial characteristics that may modify the effect of stress.
Our results are in general agreement with other studies of stressful life events and preterm delivery. Other studies that have used nonthreshold models and have controlled for important confounding factors have found no relation between number of life events and risk of preterm delivery or contractions (16
, 24


28
). We found such a relation in only one of four groups of women studied. Berkowitz and Kasl found that risk of preterm delivery increased markedly for women who experienced four or more life events during the first two trimesters of pregnancy (29
). This finding is similar to the threshold levels we found, but they did not control for confounding. In two other studies, experiencing high numbers of life events was not associated with preterm delivery (30
, 31
). Stress score, measured in a variety of ways, has been associated with preterm delivery in some studies (25
, 32
34
) but not others (24
, 27
, 30
, 35
).
One cannot conclude that our results support a biologic threshold in the relation between stress and preterm delivery, because the findings were not replicated in the two data collection periods. Stress may have different biologic effects in primiparous and multiparous women, but, after adjustment for parity, a biologic effect should have been consistent for both data collection periods. Such an effect may have been present but might have been masked by the study limitations discussed above, or the associations present might have been an artifact resulting from the study limitations or chance. In addition, the threshold estimation method has not been widely used. Even though no assumptions about exposure distribution were specified, it may not perform well when the range of exposures is limited and the distribution skewed (10
).
Our results, although inconclusive, suggest that a threshold model of the relation of stress to preterm delivery may fit better in some cases than a nonthreshold model does. Future research in which this method is used with other continuous or ordinal stress measures, especially measures with a wider range of values or less-skewed distribution, would be interesting. Such measures may have more consistent results and might clarify whether a threshold effect does exist in the relation of stress to pregnancy outcome.
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ACKNOWLEDGMENTS
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The authors thank Dr. Owen Devine for his statistical assistance and valuable comments. They also acknowledge the efforts of their collaborators in participating PRAMS states, as represented by the PRAMS Working Group, made up of one representative from each participating state: Alabama: Rhonda Stephens; Alaska: Kathy Perham-Hester; Arkansas: Gina Redford; Colorado: Darci Cherry; Florida: Dr. Richard Hopkins; Georgia: Tonya Johnson; Hawaii: Loretta Fuddy; Illinois: Theresa Sandidge; Louisiana: Suzanne Kim; Maine: Martha Henson; Maryland: Dr. Diana Cheng; Nebraska: Dr. Debora Barnes-Josiah; New Mexico: Dr. Susan Nalder; New York: Michael Medvesky; New York City: Dr. Fabienne Laraque; North Carolina: Dr. Paul Buescher; Ohio: Jo Bouchard; Oklahoma: Richard Lorenz; South Carolina: Kristen Helms; Utah: Lois Bloebaum; Vermont: Margaret Brozicevic; Washington: Linda Lohdefinck; and West Virginia: Melissa Baker.
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NOTES
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Reprint requests to Dr. Nedra Whitehead, 4770 Buford Highway NE, MS K-22, Atlanta, GA 30341-3717 (e-mail: nsw1{at}cdc.gov).
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Received for publication April 5, 2000.
Accepted for publication July 19, 2001.