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ORIGINAL RESEARCH |
1Department of Epidemiology and 2Department of Maternal and Child Health, University of Alabama at Birmingham, Alabama; and 3Department of Obstetrics and Gynecology, Northwestern University, Chicago, Illinois.
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ABSTRACT |
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 ABSTRACT MATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Methods: Singleton deliveries at 20 weeks of gestation or later in the United States from 1989 through 2000 were analyzed. Risk for stillbirth in women with 1–4 (moderate parity, category I), 5–9 (high parity, category II), 10–14 (very high parity, category III), and 15 or more (extremely high parity, category IV) prior live births were computed using logistic regression.
Results: Overall, 27,069,385 births, including 1,206 to extremely high parity mothers, were analyzed. Of the 81,386 stillbirths, 71,623 (2.8/1,000), 9,206 (5.0/1,000), 531 (14.4/1,000), and 26 (21.6/1,000) cases occurred among category I, category II, category III, and category IV gravidas, respectively. With category I as referent category, the odds ratio for stillbirth increased consistently with ascending parity after adjusting for potential confounders: category II (odds ratio [OR] 1.05, 95% confidence interval [CI] 1.02–1.07), category III (OR 1.97, 95% CI 1.81–2.15), and category IV (OR 2.31, 95% CI 1.56–3.42) (P for trend < .001). Among extremely high parity women (category IV), the odds ratio for stillbirth also increased with unit increment in the number of prior live births: 15 (OR 2.72, 95% CI 1.29–5.74), 16 (OR 3.14, 95% CI 1.17–8.41), 17 (OR 6.11, 95% CI 2.56–16.5), and 18 or more prior live births (OR 16.17, 95% CI 8.77–29.82) (P for trend < .001).
Conclusions: The risk for stillbirth is substantially elevated among very high and extremely high parity women, and care providers may consider these groups for targeted periconceptional counseling.
Level of Evidence: II-2
One reason for this inconsistency is the different definitions assigned to parity, which includes prior stillbirths in some cases or only live births in others. Another limitation inherent in previous studies is the lack of sufficient power to offer convincing conclusions regarding findings. Moreover, many of these studies could not control for important confounders such as maternal age, so that the independent contribution of parity to stillbirth could not be inarguably determined. As a result of such shortcomings, it has become imperative to undertake an analysis that is sufficiently powered to yield conclusive evidence regarding the link between parity and stillbirth so that optimal counseling and patient care options based on risk assessment profiles can be offered to these patients.
In this paper, we sought to estimate this association by expanding parity groupings to include liveborn parity of 15 and beyond, a group we denote as extremely high parity women. The rationale for our approach is that, if increasing parity is associated with stillbirth, then the relationship should manifest itself at such an extreme degree of parity in a dose-effect pattern.
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MATERIALS AND METHODS |
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ABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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We selected singleton live births and fetal deaths of 20 weeks of gestation or later for analysis. Because the focus of the study was on deliveries to multiparous women and also because nulliparous women have unique obstetric risk profiles,14,15 we did not include these mothers in the analysis. We defined maternal parity as the total number of live deliveries the mother had experienced, a term we also designate as "liveborn parity" for clarification. For the purpose of this study, we further classified mothers into 4 parity groups: category I (1–4 previous live births), category II (5–9 previous live births), category III (10–14 previous live births), and category IV (>> 15 previous live deliveries). Additionally, we characterized categories I–IV as moderate, high, very high, and extremely high parity, respectively. Previous studies that examined parity subgroups and gradation of risk for adverse birth outcomes provided the basis for defining these parity groupings, except for category IV, for which no previous study existed.7,11,16–20 The reporting of parity status on the birth certificate has been found to have a high degree of accuracy (
99.0%), with a sensitivity of 98.8% and specificity of 99.3%.21
We compared the following sociodemographic characteristics among mothers in the 4 parity groups: maternal age, race, educational level attained, marital status, reported use of tobacco during pregnancy, and adequacy of prenatal care. Adequacy of prenatal care was assessed with the revised graduated index algorithm,22,23 which has been found to be more accurate than several others, especially in describing the level of prenatal care use among groups that are high risk.24 This index assesses the adequacy of care based on the trimester during which prenatal care began, number of visits, and the gestational age of the infant at birth.
The main outcome of interest was occurrence of stillbirth, which we defined as intrauterine fetal death at 20 weeks or more of gestation. The interval between the first day of the last menstrual period (LMP) and the date of birth was used to compute gestational age in completed weeks. Records missing the date of the LMP were imputed by the National Center for Health Statistics when there was a valid month and year. Clinical estimate of gestation was used in the computation of gestational age in cases where the date of the LMP was not reported or where the LMP date was inconsistent with the birth weight.25,26 Approximately 4–5% of the gestational ages during the period were based on clinical estimate of gestation.
Because stillbirth is a heterogeneous concept, we further classified the type of stillbirth based on gestational age and fetal growth into term stillbirth (at >> 37 completed gestational weeks), preterm stillbirth (at < 37 gestational weeks), small for gestational age (SGA) stillbirth (at < 10th percentile of birth weight for gestational age), and preterm-SGA stillbirth. Other investigators have variably used a similar classification approach.28,29 In classifying neonates as small for gestational age, we used population-based national reference curves for singletons.30
We computed stillbirth rates by dividing the number of stillbirths by the sum of live births and stillbirths and multiplying by 1,000. Because a woman might have more than one live birth during the period of study, we matched siblings by using 4 distinct maternal characteristics: maternal place of birth, maternal race, ethnicity, and the computed maternal ages in subsequent years within the study period, as previously reported.31 A variant of this algorithm has been previously validated and found by us and other investigators to be accurate.31,32 We used odds ratio to approximate relative risks, and we applied the generalized estimating equation framework33 to control for intracluster correlations among siblings by using the PROC GENMOD in SAS 9.1 (SAS, Cary, NC). The generalized estimating equation method is a statistical technique that accounts for dependence of observations and was applied in this paper based on the following assumptions:
We assessed goodness-of-fit of models using the –2 log likelihood ratio test, and we estimated the significance of main effects using the Wald test.35 We tested for trend using the 
2 statistic.35 Using the maternal characteristics as covariates, we also generated propensity scores36 for each individual and included these scores in the multivariable models. The propensity score in this case represented each mother’s probability, on the basis of her covariate values, of belonging to a specific parity subgroup. We applied this "quasi-randomized experiment" technique to reduce bias and increase the precision of our estimates.37 We included the scores directly into the model without prior matching into subgroups because previous reports have found both approaches to be similarly effective in minimizing biases.37 The results with the propensity scores differed slightly from those without, but we still report in this paper those estimates from models that included the propensity scores.
We conducted power analysis using the following assumptions:
Accordingly, we needed a sample size of 12,067, of which 1,097 would be in the extremely high parity group (category IV), to detect a relative risk of 2.5 using the category I mothers as referent. All tests of hypothesis were 2-tailed, with a type 1 error rate fixed at 5%. This study was approved by the Institutional Review Board for Human Use at the University of Alabama at Birmingham.
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RESULTS |
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A total of 81,386 stillbirths were documented, yielding an overall stillbirth rate of 3.0 per 1,000. Of these, 71,623, 9,206, 531, and 26 cases occurred among gravidas of category I, category II, category III, and category IV, respectively. Figure 2 shows the crude stillbirth rates per 1,000 by parity status. The numbers on top of the bar charts represent absolute risk estimates. Stillbirth rates rose consistently with increase in parity in a dose-dependent pattern (P < .001). The independent relationship between parity levels and risk for stillbirth is summarized in Table 2. In model I, adjusted estimates were generated without accounting for maternal complications of pregnancy, whereas model II controlled for pregnancy complications as well because they could also be associated with high parity and are also known causes of stillbirth (confounders). The maternal complications considered included maternal diabetes, chronic and pregnancy-associated hypertension, eclampsia, cardiac disease, placental abruption, and placenta previa. The adjusted odds ratios from both models were similar. In comparison with women of category I, the risk difference rose from 5% (category II) to more than 100% among extremely high parity mothers.
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Results of analysis of stillbirth subgroups by parity status are summarized in Figure 3. The frequencies of the stillbirth subtypes within each specific parity subpopulation are displayed on top of the corresponding bars. For any given stillbirth subtype, the frequency increased consistently with increase in parity. It is also apparent from Figure 3 that the dominant (most frequent) stillbirth subtype was preterm stillbirth, regardless of parity category. Except for extremely high parity women, the least frequent stillbirth subtype was SGA. Among extremely high parity women, the most uncommon subtype was the combined preterm-SGA stillbirth.
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It was also of interest to estimate subtype-specific rates for stillbirth, which represent the proportion of a specific fetal subgroup that died in utero. Term stillbirth rate, for instance, denotes the fraction of all babies born at term that were stillborn. Hence, it is a measure of group or subtype vulnerability, and in this respect, it differs from the frequency of a stillbirth subtype described in the previous paragraph. Although preterm stillbirth was the most common subtype (Fig. 3), the highest rate for stillbirth was among babies that were both preterm and SGA, regardless of maternal parity status (Table 3). Subtype-specific stillbirth rates also increased consistently with rising parity in a dose-effect pattern (P for trend < .001) (Table 3).
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The results of subanalysis on extremely high parity mothers (category IV) are summarized in Table 4. We examined the incidence of stillbirth for each unit increment in parity up to mothers with 18 or more liveborn children. We could not subcategorize beyond 18 because of very small numbers. The adjusted risk ratio for stillbirth increased substantially from 2.72 (for extremely high parity women with 15 previous liveborn infants) to 16.17 among extremely high parity women with 18 or more previous live deliveries, an increase of about 500% in absolute terms.
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DISCUSSION |
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Earlier investigators have advanced the phenomenon of "maternal depletion syndrome" as a mechanism that could explain adverse pregnancy outcomes among women with short interpregnancy intervals and high parity.39 According to this hypothesis, too frequent pregnancies impose undue nutritional stress on the mother, leading to poorer nutritional status in successive pregnancies and higher-than-expected probability for adverse fetal outcomes. It is noteworthy that the concept of "maternal depletion syndrome" is used primarily in the context of developing nations where food supply is a problem and undernutrition is common (Costello CA. Maternal and child health in rural Uganda: the role of nutrition [dissertation]. Philadelphia (PA): University of Pennsylvania; 1986). It is however, debatable whether that could explain our findings because our study participants come from a developed setting where overnutrition rather than undernutrition is the main public health concern.45,46
A different, albeit related, mechanism may explain these unique results from a developed nation. We speculate that, with successive pregnancies, a threshold is reached at which "uterine exhaustion" sets in because of overuse of the organ, leading to lowered uterine efficiency in performing its main role of nurturing the fetus. Poor maternal-fetal exchange of nutrients, despite adequate nutrient storage at the uterine level, compromises normal fetal growth and development, and in extreme cases fetal demise ensues. We further speculate that, among extremely high parity women, the level of uterine inefficiency may be so marked that the underlying uterine feature may be better described by the term "uterine overexhaustion," which represents a more severe degree of "uterine exhaustion." This could explain the substantially elevated risk for stillbirth among extremely high parity women in our study, as well as the dose-effect pattern among parous women in general, and within the subgroups of extremely high parity mothers, in particular. However, it must be emphasized that these explanations represent speculations, and other possible etiologic pathways (eg, increased risk of abruptions due to previous cesarean deliveries, increased risk of lethal anomalies, or other factors unrelated to any uterine physiology) are candidate areas for future investigation.
The relationship between stillbirth and parity could also have been influenced by maternal age because women with more than a dozen liveborn children are more likely to be advanced in age, and the risk for stillbirth and other negative fetal outcomes are heightened among older mothers. In our analysis, we included maternal age in all the adjusted models to account for its confounding effects so that the estimates being reported are unlikely to be explained by advanced maternal age. The ample sample size permitted us to control for several other confounding characteristics, which previous investigators who examined birth outcomes among high parity women were not able to do because of a lack of sufficient sample size.
The risk of stillbirth has been estimated to increase 2- to 10-fold in women with a prior stillbirth,50,51 and repeated pregnancies among women might be indicative of attempts to have a successful live birth after previous fetal losses.52 Unfortunately, specific information on prior fetal loss was not available in the data set used for this analysis. Although this could explain the occurrence of shortened pregnancy intervals, it is very unlikely that repeated fetal loss represents a motivating factor for women to have additional pregnancies after prior successful live births of up to and beyond 15 children.
One limitation of this study is our inability to provide information on causes of stillbirths because the relevant data as indicated on birth certificates are not considered reliable. In a study carried out in 5 states, Kirby53 reported that approximately 30–45% of the reported causes of stillbirth from vital records were not valid or useful. Further, National Center for Health Statistics does not provide underlying cause of death on its public-use data files. Therefore, we did not assess causes of stillbirth to avoid providing erroneous results. Similarly, we were unable to account for the contribution of shorter interpregnancy intervals to the occurrence of stillbirth because the data set for this study did not provide specific information in this regard. It is also worth mentioning that the algorithm used in grouping sibling clusters born to the same woman should not be interpreted as being perfect because there was no gold standard (namely, matched set using unique identifiers, such as social security numbers of mothers) to compare it with. It only represents an attempt at arriving at an estimate that minimizes biases and improves the precision of our computation. Another shortcoming of the data is the inability to identify consecutive pregnancies and their outcomes. This makes it impossible to determine the sequence of live births or stillbirths in the different parity groups.
Certain unique features of this study deserve to be highlighted. Very little is known about parity and stillbirth at the population level, and an analysis such as ours that bases its results on population-based data has the advantage of minimizing biases due to selection as well as being more generalizable than otherwise. This is also a large study that bears sufficient power to show any existing linkage between parity and stillbirth, if one exists. The absolute risk estimates show that the susceptibility to stillbirth almost doubles among women with 5–9 previous live births (compared with those with 1–4). For women with 10–14 previous live births, the risk increases 5-fold, and for those with 15 or more previous live births, the risk increase is 8-fold. These important leaps in risk differences underscore the importance of the association between parity status and stillbirth and the need to include parity-based considerations in performing risk assessment of patients. The findings presented here will potentially improve our understanding of the link between parity in its extreme and the risk of fetal demise. This information could also prove useful to care providers in counseling pregnant mothers of high parity periconceptionally.
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Footnotes |
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This study was supported by a Young Clinical Scientist Award to the second author (H.M.S.) by the Flight Attendant Medical Research Institute.
Address reprint requests to: Hamisu Salihu, MD, PhD, Department of Maternal and Child Health, University of Alabama at Birmingham, 1665 University Boulevard, Room 320, Birmingham, AL 35294; e-mail: hsalihu{at}uab.edu.
doi:10.1097/01.AOG.0000165825.65203.69
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REFERENCES |
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ABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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2. U.S. Department of Health and Human Services. Healthy people 2010. 2nd ed. Understanding and improving health and Objectives for improving health (2 vols). Washington, DC: U.S. Department of Health and Human Services, 2000.
3. Schram ELR. The problem of the grand multipara. Am J Obstet Gynecol 1954;67:253–62.[Medline]
4. James WH. Stillbirth and birth order. Ann Hum Genet 1968;32:151–62.[Medline]
5. Nelson JH, Sandmeyer MW. A study of 812 grand multiparas. Am J Obstet Gynecol 1958;75:1262–6.[Medline]
6. Seidman DS, Dollberg S, Stevenson DK, Gale R. The effects of parity and socioeconomic status on obstetric and neonatal outcome. Arch Gynecol Obstet 1991;249:119–27.[Medline]
7. Roman H, Robillard P, Verspyck E, Hulsey TC, Marpeau L, Barau G. Obstetric and neonatal outcomes in grand multiparity. Obstet Gynecol 2004;103:1294–9.
8. Oron T, Sheiner E, Shoham-Vardi I, Mazor M, Katz M, Hallak M. Risk factors for antepartum fetal death. Reprod Med 2001;46:825–30.
9. Chang A, Larkin P, Esler EJ, Condie R, Morrison J. The obstetric performance of the grand multipara. Med J Aust 1977;1:330–2.[Medline]
10. Brunner J, Melander E, Krook-Brandt M, Thomassen PA. Grand multiparity as an obstetric risk factor: a prospective case-control study. Eur J Obstet Gynecol Reprod Biol 1992;47:201–5.[Medline]
11. Goldman GA, Kaplan B, Neri A, Hecht-Resnick R, Harel L, Ovadia J. The grand multipara. Eur J Obstet Gynecol Reprod Biol 1995;61:105–10.[Medline]
12. Martin J, Curtin S, Saulnier M, Mousavi J. Development of the Matched Multiple Birth File. In: 1995–1998 Matched Multiple Birth Dataset. NCHS CD-ROM series 21, no. 13a. Hyattsville (MD): National Center for Health Statistics; 2003.
13. National Center for Health Statistics. 1995–1998 linked birth/infant death data set. Vital Statistics of the United States: Quality Control Procedures. Hyattsville (MD): U.S. Department of Health and Human Services, Centers for Disease Control and Prevention; 2000.
14. Salihu HM, Aliyu MH, Rouse DJ, Kirby RS, Alexander GR. The association of parity with mortality outcomes among triplets. Am J Obstet Gynecol 2004;190:784–9.[Medline]
15. Bai J, Wong F, Bauman A, Mohsin M. Parity and pregnancy outcomes. Am J Obstet Gynecol 2002;186:274–8.[Medline]
16. Kumari AS, Badrinath P. Extreme grandmultiparity: is it an obstetric risk factor? Eur J Obstet Gynecol Reprod Biol 2002;101:22–5.[Medline]
17. Babinszki A, Kerenyi T, Torok O, Grazi V, Lapinski R, Berkowitz RL. Perinatal outcome in grand and great grand multiparity: effects of parity on obstetric risk factors. Am J Obstet Gynecol 1999;181:669–74.[Medline]
18. Rizk DE, Khalfan M, Ezimokhai M. Obstetric outcome in grand multipara in the United Arab Emirates: a case control study. Arch Gynecol Obstet 2001;264:194–8.[Medline]
19. Toohey JS, Keegan JR, Morgan M, Francis J, Task S, deVeciana M. The "dangerous multipara": fact or fiction? Am J Obstet Gynecol 1995;172:683–6.[Medline]
20. Aziz FA. Pregnancy and labor of grand multiparous Sudanese women. Int J Gynaecol Obstet 1980;18:144–6.[Medline]
21. DiGiuseppe DL, Aron DC, Ranbom L, Harper DL, Rosenthal GE. Reliability of birth certificate data: a multi-hospital comparison to medical records information. Matern Child Health J 2002;6:169–79.[Medline]
22. Alexander GR, Kotelchuck M. Quantifying the adequacy of prenatal care: a comparison of indices. Public Health Rep 1996;111:408–18.[Medline]
23. Alexander GR, Cornely DA. Prenatal care utilization: its measurement and relationship to pregnancy outcome. Am J Prev Med 1987;3:243–53.[Medline]
24. Kogan MD, Martin JA, Alexander GR, Kotelchuck M, Ventura SJ, Frigoletto FD. The changing pattern of prenatal care utilization in the United States, 1981–1995, using different prenatal care indices. JAMA 1998;279:1623–8.
25. Ventura SJ, Martin JA, Mathews TJ. Births: final data for 1997. Natl Vital Stat Rep 1999;47:1–96.[Medline]
26. Taffel S, Johnson D, Heuser R. A method of imputing length of gestation on birth certificates. Vital Health Stat 2 1982;93:1–11.
27. Little RE, Weinberg CR. Risk factors for antepartum and intrapartum stillbirth. Am J Epidemiol 1993;137:1177–89.
28. Stephansson O, Dickman PW, Johansson AL, Cnattingius S. The influence of socioeconomic status on stillbirth risk in Sweden. Int J Epidemiol 2001;30:1296–1301.
29. Surkan PJ, Stephansson O, Dickman PW, Cnattingius S. Previous preterm and small-for-gestational age births and the subsequent risk of stillbirth. N Engl J Med 2004;350:777–85.
30. Alexander GR, Kogan M, Martin J, Papiernik E. What are the fetal growth patterns of singletons, twins, and triplets in the United States? Clin Obstet Gynecol 1998;41:114–25.[Medline]
31. Salihu HM, Alexander MR, Shumpert NM, Pierre-Louis BJ, Alexander GR. Infant mortality among twins born to teenagers in the United States: black-white disparity. J Reprod Med 2003;48:257–67.[Medline]
32. Pollack H, Lantz PM, Frohna JG. Maternal smoking and adverse birth outcomes among singletons and twins. Am J Public Health 2000;90:395–400.
33. Zeger SL, Liang KY. Longitudinal data analysis for discrete and continuous outcomes. Biometrics 1986;42:121–30.[Medline]
34. White HA. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 1980;48:817–38.
35. Clayton D, Hills M. Statistical models in epidemiology. Oxford (UK): Oxford University Press; 1993.
36. Rosenbaum P, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika 1983;70:41–55.
37. D’Agostino RB Jr. Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat Med 1998;17:2265–81.[Medline]
38. Salihu HM, Kinniburgh, Aliyu MH, Kirby RS, Alexander GR. Racial disparity in stillbirth among singleton, twin, and triplet gestations in the United States. Obstet Gynecol 2004;104:734–40.
39. Winkvist A, Rasmussen KM, Habicht JP. A new definition of maternal depletion syndrome. Am J Public Health 1992;82:691–4.
40. DaVanzo J, Habicht JP, Butz WP. Assessing socioeconomic correlates of birth weight in Peninsular Malaysia: ethnic differences and changes over time. Soc Sci Med 1984;18:387–402.
41. DeSweemer C. The influence of child spacing on child survival. Popul Stud 1984;38:47–72.
42. Hobcraft J, McDonald JW, Rutstein S. Child spacing effects on infant and early child mortality. Popul Index 1983;49:585–618.
43. Winikoff B. The effects of birth spacing on child and maternal health. Stud Fam Plann 1983;14:231–45.[Medline]
44. Merchant K, Martorell R. Frequent reproductive cycling: does it lead to nutritional depletion of mothers? Prog Food Nutr Sci 1988;12:339–69.[Medline]
45. Stein CJ, Colditz GA. The epidemic of obesity. J Clin Endocrinol Metab 2004;89:2522–5.
46. Flegal KM, Carroll MD, Ogden CL, Johnson CL. Prevalence and trends in obesity among U.S. adults, 1999–2000. JAMA 2002;288:1723–7.
47. Fretts RC, Schmittdiel J, McLean FH, Usher RH, Goldman MB. Increased maternal age and the risk of fetal death. N Engl J Med 1995;333:953–7.
48. Cnattingius S, Forman MR, Berendes HW, Isotalo L. Delayed childbearing and risk of adverse perinatal outcome. JAMA 1992;268:886–90.[Abstract]
49. Salihu HM, Shumpert MN, Slay M, Kirby RS, Alexander GR. Childbearing beyond maternal age 50 and fetal outcomes in the United States. Obstet Gynecol 2003;102:1006–14.
50. Greenwood R, Samms-Vaughan M, Golding J, Ashley D. Past obstetric history and risk of perinatal death in Jamaica. Paediatr Perinat Epidemiol 1994;8 suppl 1:40–53.
51. Samueloff A, Xenakis EM, Berkus MD, Huff RW, Langer O. Recurrent stillbirth: significance and characteristics. J Reprod Med 1993;38:883–6.[Medline]
52. Fortney JA, Higgins JE. The effect of birth interval on perinatal survival and birth weight. Public Health 1984;98:73–83.[Medline]
53. Kirby RS. The coding of underlying cause of death from fetal death certificates: issues and policy considerations. Am J Public Health 1993;83:1088–91.
This article has been cited by other articles:
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  How Much Does Parity Matter? Obstet. Gynecol., September 1, 2005; 106(3): 444 - 445. [Full Text] [PDF]
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