1 Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Durham, NC.
2 Biometry Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Durham, NC.
Received for publication February 5, 2004; accepted for publication March 29, 2004.
Birth is a dangerous event. Of all infant deaths in the first year, one third happen on the first day and 10 percent in the first hour (1). There is a similar pattern among fetal deaths, with the highest risk just prior to delivery. Given this cluster of deaths around birth, epidemiologists have commonly combined stillbirths and deaths in the newborn period into a category called perinatal mortality. This grouping avoids the practical difficulty of distinguishing between a stillbirth and a death soon after birth (2). It also implicitly assumes that the causes of death are not different for the two. While this may be true for the peak of deaths around delivery, it is probably less true for the fetal deaths that occur well before labor and the neonatal deaths that occur days or weeks after birth.
The gap between the causes of stillbirths and the causes of neonatal deaths may be widening. Fetal and infant mortality rates are reaching unprecedented low levels in developed countries, which probably changes the underlying mix of causative factors. At the same time (and not entirely unrelated), intensive fetal monitoring and aggressive obstetric intervention are altering the natural patterns of mortality. Kramer et al. (3) have argued that fetal and neonatal mortality are increasingly distinct entities that should be studied separately. Such a strategy opens new questions about competing categorizations and how interventions aimed at reducing one outcome might increase the other.
Against this background, two papers in this issue of the Journal (4, 5) raise an interesting, related question: how should epidemiologists define and analyze gestational-age-specific mortality? This question can apply not just to fetal mortality but to perinatal and neonatal mortality as well.
Cheung (4) sets the stage with a review of the options for fetal mortality. The numerator and the denominator are both open to question. The customary definition of gestational-age-specific fetal (stillbirth) mortality has been the number of stillbirths at a given gestational age divided by all births at that gestational age. By this formula, fetal mortality falls sharply with advancing gestational age and then rises slightly at the longest gestations.
In 1987, Yudkin et al. (6) suggested an alternative definition of fetal mortality that uses the same numerator (number of stillbirths at a given gestational age) but a denominator of all births plus all fetuses still in utero. These authors argued that the group at risk of stillbirth (the denominator) should contain everyone at risk at the given gestational age, including the fetuses yet unborn. Doing so recognizes that fetuses may die before the onset of labor; therefore, all fetuses have some risk at each gestational age regardless of whether labor occurs. When this formula is used, fetal mortality is very low until the last gestational-age stratum, at which time it rises.
A third option for estimating gestational-age-specific mortality was introduced by Feldman (7) in 1992. He accepted Yudkin et al.s proposed denominator but suggested a change in the numerator, suggesting that stillbirth risk be calculated as all stillbirths occurring at a given gestational age or later divided by all fetuses at risk at the given gestational age (including the continuing pregnancies). This is not literally the risk of mortality at a given gestational age but rather the cumulative risk of stillbirths yet to occur in the cohort of fetuses surviving to a given gestational age.
As Cheung points out, all of these formulations are "correct" in the sense that they define a measurable and interpretable aspect of fetal mortality. For example, the first measure could be used to describe the underlying health or vitality of fetuses that survive to a given gestational age and then are put through the ordeal of labor and delivery. The deeper question is, which formulations are the most informative? Which best enable epidemiologists to answer important etiologic questions? Which will enhance the ability of obstetricians to assess risks and make good clinical decisions? Cheung argues that Yudkin et al.s general approach is the least useful, in that it produces a misleading pattern of mortality that rises with gestational age. Cheung prefers Feldmans definition because it better reflects the risk that remains for a fetus rather than a short-term risk across the upcoming week of gestation.
In this issue of the Journal, Platt et al. (5) argue strongly for a survival model based on Yudkin et al.s approach. Platt et al. combine stillbirth and neonatal mortality in the model, and they treat the estimated gestational time (the presumed time since conception) as the primary time scale for a survival analysis. The event of birth is included simply as a time-dependent predictor.
The notion of including everyone at risk in a combined and parsimonious analysis has much appeal. The generalized Cox model that Platt et al. apply does serve to remove the seemingly paradoxical crossover of mortality curves for Blacks versus Whites. This approach also seems to resolve concerns that stillbirth and neonatal mortality can be competing outcomes and that the study of either risk separately might distort the inference. Nonetheless, Platt et al.s approach raises further questions. One practical problem with applying survival analytic methods based on gestational time is that the actual time of fetal death is usually not known. All that is known is that death preceded the time of delivery. This uncertainty may be remedied by careful clinical monitoring of pregnancies, where daily movement patterns are recorded by the mother. However, such monitoring may itself influence clinical intervention in pregnancies that show a slowing of fetal activity.
The model proposed by Platt et al. is quite general in that the relative hazards need not be constant across gestational time. However, it is problematic to assume that birth can be entered simply as a time-dependent dichotomy. Labor and delivery impose a kind of fitness test that is most stringent immediately after birth. The 38-week infant born yesterday has a higher risk than the 36-week infant born 2 weeks ago. To assume that they both have the same risk ignores the peak of mortality near birth and its steep decline thereafter. Biologically, two time scales have strong effects on risk. At the time of birth, an individuals time axis diverges into two paths, one being time since conception and the other based on time since birth. Both of these time scales are relevant to the infants risk. Time since birth cannot simply be collapsed into a dichotomous indicator and still capture the complex effects of the two time scales.
There is no obvious way to model survival to take both time scales fully into account. The predictive value of the model would presumably be improved by entering the actual time since birth as a covariate, with a value of zero prior to birth. Effects of this continuous time covariate could, for example, be included as spline functions. One would presumably also have to include gestational age at birth in some time-dependent way.
Another problem arises when applying Platt et al.s model to any etiologic factor that works through preterm delivery on the causal pathway to mortality. If one of the ways that a risk factor increases perinatal mortality is by causing early birth, then a model that adjusts in any way for the time of birth could bias the inference. The authors examples of ethnicity and smoking could well be such factors. When assessing etiology, it might be best to ignore the timing of birth altogether and simply model mortality as a function of time since conception.
It is curious that discussions of gestational-age-specific risk have given so little attention to its theoretical or biologic underpinnings. What are the specific questions we want to answer? What are the crucial biologic processes we need to identify? What are the etiologic pathways that we assume are at work? Ironically, the neglect of these questions may be the by-product of data that are too freely available (in the form of vital statistics). Investigators of perinatal risk have not been held to the standards of rigorous thinking required when proposing original data collection.
To their credit, Kramer et al. touch on some of these basic issues (for example, the changing pattern of the timing of fetal deaths) in arguing for a distinction between fetal and neonatal mortality. But such discussion needs to go further. There are at least two fundamentally distinct mechanisms by which an exposure might lead to fetal death. An exposure might trigger premature labor and delivery, inflicting the ordeal of labor on an immature but otherwise healthy fetus that dies in the process. Alternately, an exposure might work by damaging the fetus without precipitating labor. A carefully constructed analysis might distinguish these two scenarios. For example, the latter could be identified by a model for time to birth that shows no difference between the exposed and unexposed, combined with a model for risk based on two time scales that finds a difference.
Of course, reality may be even more complex. Early delivery may be provoked by fetal distress or perhaps be triggered independently by the exposure. Then, both models would detect a difference. Whatever analytic approach is taken, it should begin with a clear articulation of the question being asked and the biologic mechanisms presumably at work. No new statistical tools can be considered defensible without a thorough exploration of their biologic underpinnings.
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