Epidemiology Branch, National Institute of Environmental Health Sciences, Durham NC 27709, USA. E-mail: WILCOX{at}NIEHS.NIH.GOV
Abstract
Birthweight is one of the most accessible and most misunderstood variables in epidemiology. A baby's weight at birth is strongly associated with mortality risk during the first year and, to a lesser degree, with developmental problems in childhood and the risk of various diseases in adulthood. Epidemiological analyses often regard birthweight as on the causal pathway to these health outcomes. Under this assumption of causality, birthweight is used to explain variations in infant mortality and later morbidity, and is also used as an intermediate health endpoint in itself. Evidence presented here suggests the link between birthweight and health outcomes may not be causal. Methods of analysis that assume causality are unreliable at best, and biased at worst. The category of low birthweight in particular is uninformative and seldom justified. The main utility of the birthweight distribution is to provide an estimate of the proportion of small preterm births in a population (although even this requires special analytical methods). While the ordinary approaches to birthweight are not well grounded, the links between birthweight and a range of health outcomes may nonetheless reflect the workings of biological mechanisms with implications for human health.
Keywords Birthweight, fetal growth, gestational age, low birthweight, intrauterine growth retardation, small for gestational age, infant mortality, analytical methods
Accepted 30 July 2001
There are thousands of research papers on birthweight, with hundreds more appearing every year. Why is birthweight such a popular topic? One factor is that birthweight data are precisely recorded, free (through vital statistics), and available in vast numbers. A second factor is that birthweight is an extremely powerful predictor of an individual baby's survival. In general, the lower the weight, the higher a baby's risk of infant mortality.1 A third factor is that, on a population level, mean birthweight is associated with infant mortality. Groups with lower mean birthweight often have higher infant mortality (e.g. the infants of mothers who smoke, or of mothers with lower socioeconomic status).2,3 Finally, birthweight is associated with health outcomes later in life. Asthma, low IQ, and hypertension have all been reported to be more common among those who were small at birth.46
The strong association of birthweight with infant mortality is the usual focus of birthweight research, with the assumption that birthweight is a major determinant of infant survival. Researchers track population trends in birthweight, assuming these to have implications for infant mortality trends.7 In the US, interventions to increase birthweight are recommended as a strategy to improve infant mortality.810
This commentary questions the causal role of birthweight in its association with health outcomes. The structure of this paper is as follows. The first section reviews the history of research on birthweight as a cause of infant mortality. The second describes the basic features of birthweight and its association with infant mortality. The third section proposes an alternative hypothesis of a non-causal association between birthweight and health outcomes. Under this alternative, birthweight is of limited importance as an end-point in itself, and is inconsequential in the analysis of infant mortality or other outcomes. The fourth and final section discusses implications for epidemiological research.
A Short History of Birthweight
For most of the previous century, birthweight has been treated as a dichotomy. Low birthweight (LBW) is the category of babies weighing less than 2500 g at birth, and normal birthweight is all the rest. For many years, the presumed reason for babies to be born LBW was their preterm delivery. Indeed, the terms LBW and premature were used interchangeably in the scientific literature from the 1920s to the 1960s.
However, not all small babies are premature, and not all premature babies are small. An accumulation of epidemiological data during the 1950s and 1960s finally made this distinction clear. In 1961, the World Health Organization recommended that LBW no longer be used as the official definition of prematurity.11 By the 1970s, most researchers were complying, although as late as 1977 a book on LBW was titled The Epidemiology of Prematurity.12 Perinatal epidemiologists now avoid the word premature altogether, preferring the label preterm for a baby born too early.
As researchers began to recognize that LBW and preterm are not synonymous, they faced an uncomfortable new problem. Term babies born at less than 2500 g nonetheless have a high risk of mortality. What accounts for this risk, if not preterm delivery?
This gap was filled by the invention of a new diseaseintrauterine growth retardation (IUGR). The usual definition of IUGR is small for gestational age (SGA), the lightest 10% in each gestational age stratum. Under the percentile definition, the vast majority of IUGR babies are born at term. (This is simply a function of definition: under a percentile formula, the category of IUGR contains the same small per cent of preterm births as is present in the general population.) Taken as a whole, IUGR babies correspond closely with the set of LBW babies at term, and provides these LBW babies with a diagnosis'. Thus, the creation of an entity called IUGR effectively preserved LBW as a group of babies with preventable ailments. Small babies who are not preterm are growth retarded.
This convenient solution to the problem of term LBW infants led to the rapid acceptance of the concept of IUGR during the 1970s. According to PubMed, the number of papers about IUGR swelled between 1970 and 1979 from a handful to more than 200 a year. In fact, this was not a new research area but a shift within LBW research from one label (prematurity) to two (preterm and IUGR).
Popular assumptions about LBW
The dichotomization of birthweight is deeply entrenched in public health research. Why have researchers been so determined to cling to this strategy? This practice rests on several assumptions about LBW.
LBW causes infant mortality
In the first year of life, LBW babies are typically 20 or more times more likely to die than heavier babies.13 The sheer strength of this association with mortality is regarded as evidence of its causality.
The per cent LBW in a population is an indicator of infant risk
Infant death is rare (at least in developed countries), so researchers need a more prevalent surrogate indicator of perinatal risk. Low birthweight serves this purpose nicely. Furthermore, under this assumption, the causes of LBW themselves become topics of investigation.
LBW is preventable
If LBW is caused by either preterm delivery or fetal growth retardation, then LBW is presumably completely preventable. Thus, LBW provides a target for interventions to improve infant survival. The prevention of LBW is an explicit part of US public health policy to decrease infant mortality.8
While these assumptions about LBW are generally accepted, not all aspects of LBW neatly fit into them. For example, groups with a larger per cent of LBW babies do not invariably have the greater risk. A well-known example is the comparison of female and male babies.14 But the most telling contradiction is the low birthweight paradox.
The LBW paradox
Populations with a higher per cent of LBW often have higher rates of infant mortality. This supports the notion that LBW is a useful surrogate of population risk. However, there is an odd thing about LBW babies in high-risk populationsthey usually have lower mortality than LBW babies in better-off populations. This is the LBW paradox, and its history is entwined with one of the most famous controversies in the history of epidemiology: the debate over the causal role of cigarette smoking.
In the 1950s, researchers found that mothers who smoked had smaller babies. By the 1960s, there was evidence that babies of these mothers also had higher infant mortality. But the effect of mother's smoking on infant mortality came with a strange twist. The LBW babies born to mothers who smoked had lower mortality than the LBW babies of mothers who did not smoke. If a baby was born LBW, it seemed an advantage to have a mother who smoked.
These data on the survival of LBW babies provoked a controversy. Yerushalmy was a prominent epidemiologist (and smoker) who defended smoking. One of Yerushalmy's weapons was precisely this observation of better survival among LBW babies born to smokers. He argued that if the survival of these LBW babies was improved by their mothers' smoking, then cigarettes could not be an agent causing them harm. In Yerushalmy's mind, the LBW paradox called into question the causal role of maternal smoking on infant mortality as a whole.15
Brian MacMahon rebutted Yerushalmy with a novel argument. MacMahon proposed that a mother's smoking lowered birthweight without affecting the baby's risk.16 If an exposed baby is smaller but has no corresponding change in its capacity to survive, then the exposed baby's mortality at its new (lighter) weight would be the same as an unexposed baby at the heavier weight. In other words, the smaller infant of a smoking mother might have better survival than other babies at the same weight because the exposed baby still carried the lower risk of its (unachieved) heavier weight. (This argument is discussed in greater detail below.)
MacMahon's insight was subtle, profound, and unappreciated. It was not his argument that ultimately defeated Yerushalmy, but rather the sheer weight of evidence against smoking. Meanwhile, the LBW paradox among the small babies of smoking mothers persists to this day.
The LBW paradox is not unique to smoking. It is also found among babies born at high altitude compared to low altitude,2 African-American babies compared with white babies,17 twins compared with singletons,18 US births compared with Norwegian births19 and many other examples. Researchers have tried to explain this paradox as due to confounding by gestational age, physiological differences, or specific diseases, but no explanation has withstood testing (e.g. ref. 20). As MacMahon realized, the answer does not lie in confounding but rather in the deeper assumptions brought to the analysis of birthweight. In order to lay the groundwork for re-examining these assumptions, the following section describes the basic epidemiological features of birthweightfeatures often neglected in the emphasis on birthweight as a dichotomy
The Epidemiology of Birthweight
Frequency distribution
The frequency distribution of birthweight is strikingly Normal (or bell-shaped), with an extended lower tail. The bar graph in Figure 1 shows the observed distribution of weights for 400 000 births. The Normal component of the birthweight distribution, called the predominant distribution is indicated by the solid line.14 The predominant distribution (defined by its mean and standard deviation [SD]) comprises the vast majority of births. The remainder of the birthweight distribution is the residual distribution. This residual comprises all births in the lower tail of the curve that falls outside the predominant distribution. In a typical population, 2 to 5% of births are in the residual distribution. Special statistical methods are needed to estimate the predominant and residual distributions (see below). A small excess of large births is less often found in the upper tail of the birthweight distribution. (Methods have been developed to assess both tails of the distribution simultaneously,21 although a residual in the upper tail has little impact on infant mortality.)
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It follows that virtually all births in the residual distribution are preterm. However, not all preterm births are in the residual distributionjust the small ones, which also happen to be the ones at highest risk. Populations with a larger per cent of births in the residual distribution would be expected to have a greater number of small preterm births.
Thus, the predominant distribution and the residual distribution of birthweight provide indirect information about aspects of gestational age without actually requiring gestational-age data. The predominant distribution closely approximates the weight distribution of term births. The residual distribution estimates the per cent of births that are small and preterm. No other approach to birthweight (certainly not a fixed criterion such as 2500 g) provides this glimpse into a population's gestational-age characteristics. A statistical programme for estimating these two components of the birthweight distribution is available on the internet in a user-friendly format (http://eb.niehs.nih.gov/bwt).
Independence of the two components
The predominant and residual distributions of birthweight are independent of one another. An exposure that affects fetal growth does not necessarily affect the risk of preterm delivery. (The mean of the predominant distribution can change without affecting the per cent of births in the residual distribution.) Conversely, a factor that increases the risk of preterm delivery would not necessarily change the average weight of babies delivered at term. (The per cent in the residual distribution can change without affecting the predominant distribution.) In order to understand birthweight as an epidemiological endpoint, it is essential to grasp this functional independence of the two components of the birthweight distribution.
Implications for infant mortality
When comparing populations of births, a difference in the per cent in the residual suggests a difference in the per cent of small preterm births. Since these are the very babies at highest risk, a population with more babies in the residual distribution will have higher infant mortality (all else being equal).
In contrast, if two populations of babies have different predominant distributions, there is no predictable difference in their infant mortality. Populations with lighter babies do not necessarily have worse mortality. For example, the predominant distribution of Mexican-American babies is shifted to lower weights compared to US white babies, but Mexican-American babies have the better overall survival.13,23 The mean or SD of the predominant distribution are not reliable indicators of infant mortality.
Reconsidering LBW
How do the two components of the birthweight distribution relate to LBW? Babies less than 2500 g include the whole residual distribution plus the lower tail of the predominant distribution (Figure 1). An increase in residual births (which suggests a health problem) will increase the per cent of LBW. However, the per cent LBW also increases with a decrease in the mean of the predominant distribution, or with an increase in the SD. Such changes in the weight distribution of term births may or may not be associated with changes in mortality. This is why, on a population level, the per cent of LBW is an unreliable marker of perinatal risk.
Birthweight-specific mortality
Birthweight by itself would not have caught the attention of epidemiologists were it not for its association with infant mortality. The relation of mortality to birthweight has a highly distinctive pattern (Figure 2). Mortality ranges more than 100-fold across the spectrum of birthweights. (The Figure shows mortality on a log scale in order to accommodate this huge range.) The reverse-J pattern of weight-specific mortality is found in all populations, and occurs with fetal mortality (stillbirths) and with neonatal or infant mortality.1 While high mortality among small babies is one of the chief justifications for studying LBW, note the continuous rise of mortality with lower weight. The mortality curve provides no particular justification for 2500 g as the criterion for risk.
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Mean weight and optimum weight
One essential feature of weight-specific mortality is not observable in Figure 2. This feature becomes apparent only when weight-specific mortality is considered in relation to the distribution of birthweights from which the rates are derived (Figure 3
). Mean birthweight is several hundred grams lower than optimum birthweight (i.e. the weight with lowest mortality).27 This difference is maintained across populations and over time, so that as the average birthweight varies, so does optimum weight.28
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An Alternative Hypothesis for the Relation of Birthweight to Infant Mortality
Epidemiologists generally assume that since small babies have high risk, there should logically be an increase in infant mortality with a reduction in mean birthweight. This is not necessarily true.
The effect of altitude
Infant mortality rates are similar in the US as a whole and in the state of Colorado. Most people in Colorado live at high altitudes, and high altitude produces smaller babies. The shift of Colorado birthweights to lower weights is clearly seen in Figure 4 (reprinted from ref. 2). This Figure also shows the curves of weight-specific mortality for Colorado and the US. The two curves intersect. Mortality rates are lower in Colorado for small babies, and higher for large babies. There is no obvious biological explanation for why small babies should do better in Colorado and larger babies should do worse.
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With this adjustment, the two weight distributions correspond nearly exactly, as do the two mortality curves (Figure 5). (The residual distributions are magnified in the inset box for easier inspection.) The simplest explanation for the convergence of mortality curves is that altitude affects birthweight but not mortality. The two mortality curves are essentially the same curve, with the one in Colorado carried along with the shift in birthweight. For babies weighing less than the optimum weight, this shift gives the appearance of lower mortality at any given birthweight. For babies heavier than the optimum weight, the shift gives the appearance of higher mortality. In fact, the birthweight distribution and its accompanying mortality curve have shifted without any change in the survival of individual babies. In this example, fetal growth retardation (on the population level) has no effect on mortality.
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Now imagine a more complicated but plausible scenario. What if a factor decreases birthweight and also increases infant mortality? The same analytical approach can be applied. In the process, we can discover the underlying sense behind the LBW paradox.
The effect of smoking
Mothers who smoke have smaller babies. Their babies, as a whole, have higher infant mortality. If we look at the birthweight and mortality curves for smokers and non-smokers (Figure 6, reprinted from ref. 2), the initial picture is similar to Colorado and the US (Figure 4
), if more exaggerated. There are two distinct distributions of birthweight, and the two mortality curves intersect. Small babies have lower mortality if their mothers smoke. This is the paradox by which Yerushalmy defended smoking.
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As discussed earlier, the intersection of weight-specific mortality curves is not uncommon. It can be found in nearly any setting where populations have different mean birthweights. In each case, the underlying difference in weight-specific mortality can be revealed by adjustment to a relative scale of birthweight.
Beyond infant mortality
These conclusions extend to any health endpoint associated with birthweight. For example, Liu and colleagues recently published an analysis of cerebral palsy and its association with birthweight in twins and singletons.31 The LBW paradox was present in their data. Twins had higher rates of cerebral palsy overall, but LBW twins had lower rates of cerebral palsy than LBW singletons. The authors resolved this paradox by adjusting birthweight to the Normal distribution of weight in singletons and twins. After adjustment, the increased risk among twins for cerebral palsy was apparent in every stratum of birthweight.
Implications for Analysis of Birthweight and Related Outcomes
The problem with LBW
The characteristics of the birthweight distribution and its relation to weight-specific mortality provide a foundation for assessing the earlier assumptions about LBW. Is per cent LBW a good surrogate indictor of a population's infant risk? No, because LBW is easily affected by changes in the predominant distribution which are not reliable indicators of risk. Altitude produces more LBW babies, but this does not lead to an increase in infant deaths.2 Another example is Mexican-American babies. Babies born of Mexican mothers in the US have a predominant distribution of birthweights shifted to lower weights than non-Hispanic whites.23 This causes Mexican-Americans to have more LBW babies than non-Hispanic whites. However, Mexican-Americans have lower infant mortality.13 Low birthweight would identify Mexican-Americans as a group at higher risk for infant mortality, but they are not. In this example, the difference in per cent of LBW merely reflects harmless differences in the predominant distribution.
Are LBW births really preventable? Preterm delivery is preventable in principle, and preterm births comprise a major portion of LBW. But what about the lower end of the Normal distribution of births? How can these births be prevented? One option might be to increase the mean or reduce the SD until little of the distribution falls below 2500 g. But if the mortality curve automatically shifts with the birthweight distribution, this strategy is of dubious value. Another alternative would be to change the fundamental Normal distribution of birthweight (for example, by truncating its lower tail). This seems infeasible. Elimination of LBW is neither practical nor necessary in order to achieve the lowest possible rates of infant mortality.
Alternatives to LBW
The arguments above suggest that LBW is muddled as an endpoint, and unreliable as a predictor of population risk. The fact that these uses of LBW are time-honoured is hardly a defence. What alternatives are available? The answer depends on the purpose of the investigator. If the aim is to assess perinatal health through some convenient surrogate, there are several options depending on the type of data available.
When only birthweight is available
If birthweight is the only type of data at hand, the residual distribution should be estimated. The per cent of births in the residual distribution is preferable to LBW as an indicator of perinatal health. The residual provides an estimate of the number of small preterm birthsthe babies at highest risk. On-line software makes this estimate easy to carry out (http://eb.niehs.nih.gov/bwt).
When birthweight and gestational data are both available
The proportion of preterm births in the population should be examined directly whenever possible. The residual distribution of birthweight is informative, but it is not as good as actual information on preterm delivery. (This of course assumes that the gestational data are of good quality, which is not always the case.) Once the per cent of preterm births is known, the analysis of birthweight can be simplified by restricting the sample to term births. Among term births, the influence of gestational age is minor and can be ignored. The mean and SD of birthweights among term births provide a way to compare fetal growth across groups. The comparison of fetal growth patterns may be interesting in its own right (for example, in understanding the biological effect of a specific exposure), but fetal growth on the population level should not be regarded as a marker of perinatal health.
What about the fetal growth curve?
The pattern of mean birthweights across strata of gestational age has been used to describe the course of intrauterine fetal growth.32 The assumptions necessary to justify the use of cross-sectional birth data to describe longitudinal growth are dubious at best. At a given gestational age, births are not a random sample of all intrauterine fetuses. This is especially true of births delivered preterm.33 The use of birth data to describe intrauterine growth patterns is unsound and should be avoided.34
What about IUGR (or SGA) as an epidemiologic endpoint?
The use of a weight percentile to define fetal growth retardation has several logical problems. When an external factor (for example, altitude) acts to retard fetal growth, it acts on all babies, not just the small ones. A 9-lb baby can therefore be just as growth retarded as a 5-lb baby when compared with their unaffected weight. Under this scenario, there is no logic in singling out the smallest 10% of babies as the ones who are growth retarded.35 On a more clinical level, IUGR defined by percentile corresponds poorly with medical signs of fetal growth retardation.36 Furthermore, IUGR has the unfortunate property of mixing preterm and term births (just as LBW does). If an investigator wishes to summarize intrauterine growth in a population, there is no simpler or more direct endpoint than the mean weight of term births.
The analysis of birthweight becomes even more complicated when birthweight is not the endpoint in itself, but is treated as an intermediate variable. An example is the analysis of infant mortality stratified by birthweight. Such analysis is sometimes done without taking into account the corresponding birthweight distributions.37,38 This is risky because meaningless differences in weight-specific mortality may be taken as real (as in Figure 4) or important differences may be missed (as in Figure 6
). The comparison of US mortality curves in Figure 2
is informative only because the US birthweight distribution has changed so little over the last half-century.
Adjustments of weight-specific mortality can be made using a z-scale, based on the mean and SD of the predominant distribution. A cruder but serviceable method is to compare mortality rates by percentiles of birthweight.39 The percentile approach may be slightly distorted when study populations differ in their proportion of residual births, but this is probably a minor problem. A method has also been proposed to adjust mortality to a z-scale while controlling for multiple confounding variables.40
All these special methods for adjusting to a relative scale of birthweight serve only to underscore one central point. Whatever method is used, excess relative risk tends to be uniform across adjusted birthweights. Despite the huge mortality gradient by birthweight within a population, mortality differences between populations are generally independent of birthweight.
The unimportance of birthweight
When comparing two populations, the only difference in birthweight that directly affects mortality is a difference in the residual distribution (i.e. a difference in the rate of small preterm births). When infant mortality is higher in one population than another, the mortality difference must be due either to a difference in small preterm births or to differences in weight-specific mortality that are independent of birthweight. This demonstrates the central importance of preterm delivery in infant mortality, and the unimportance of birthweight.
By extension, any analysis of birthweight in relation to associated outcomes must be approached with caution. The most innocent routines of epidemiological analysis are problematic when birthweight is used as an intermediate variable. For example, when analysing infant mortality, epidemiologists often attempt to remove the effects of birthweight by direct or indirect standardization, or by logistic regression. This is presumably done to isolate the mortality effects of factors operating other than through birthweight. As Robins and Greenland have described, this general strategy is unwise.41 In the specific case of birthweight, the ordinary adjustments of mortality by birthweight implicitly assume that weight-specific differences in mortality are uniform across strata of absolute birthweight. Since weight-specific mortality rates usually intersect under the very conditions that provoke adjustment (i.e. when there are different distributions of birthweight), ordinary birthweight adjustment is nearly always unjustifiable. Furthermore, results of such adjustment have been shown to be biased.42
The relation of birthweight to health outcomes in adults
There has been a resurgence of interest in the associations between birthweight and diseases of adulthoodfor example, cardiovascular diseases, diabetes, certain cancers, and impairments of hearing or vision.6,43 It is fascinating to find that, when weight-specific data are available, the risks of later endpoints seem to echo the same reverse-J-shaped pattern seen with infant mortality.43
Barker has promulgated the hypothesis that fetal nutrition explains these associations. Fetal nutrition determines fetal growth, fetal growth determines birthweight, and therefore the associations of birthweight with adult diseases demonstrate the impact of fetal nutrition on adult health.6 However, if (as has been suggested here) the association of birthweight with infant mortality is not causal, there must be similar doubts about birthweight's causal association with diseases in adulthood. Alternative explanations are beginning to emerge, with hypotheses regarding shared genetic mechanisms for fetal growth and later disease.4446
Biological mechanisms that link birthweight to illness or mortality are of great interest, even if they are not causal. Why is infant mortality so strongly related to birthweight, regardless of gestational age? What are the biological underpinnings of the relationship between birthweight and cerebral palsy, or adult hypertension? Perhaps there are metabolism or growth genes that determine fetal size (in some dynamic competition with the maternal system), and that go on to regulate physical development in ways that affect later risk of disease. Such hypotheses offer rich opportunities for further investigation.
In summary, birthweight is strongly associated with a range of health outcomes. These associations have understandably led to an emphasis on birthweight as an epidemiological endpoint in itself. However, this emphasis is misplaced. Birthweight offers little information about population health. Analyses that adjust the effects of birthweight on health outcomes by ordinary means are unsound. Even so, the association of birthweight with so diverse a spectrum of health outcomes is a genuinely fascinating phenomenon. Despite the thousands of papers on birthweight published in past decades, there may be no subject in all of epidemiology more ready for creativeperhaps even revolutionaryinsights.
Acknowledgments
The ideas in this commentary have benefited greatly from discussions with my colleagues over the years, most particularly Ian Russell, Pierre Buekens, Rolv Skjaerven, Rolv Terje Lie, and Clarice Weinberg. Matthew Longnecker, Christine Parks, and David Savitz provided helpful comments on the manuscript. Rolv Skjaerven provided the data in Figure 1. D Robert McConnaughey generated the graphs.
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