1 Department of Epidemiology and Biostatistics, School of Medicine, Case Western Reserve University, Cleveland, OH.
2 Department of Population and Family Health Sciences, School of Hygiene and Public Health, The Johns Hopkins University, Baltimore, MD.
3 National School of Public Health at Medunsa, Medunsa, South Africa.
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ABSTRACT |
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anthropometry; birth weight; body constitution; body weights and measures; pregnancy outcome; smoking
Abbreviations: BBR, brain:body weight ratio; CI, confidence interval.
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INTRODUCTION |
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MATERIALS AND METHODS |
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At the first prenatal care visit, each woman is interviewed, and information on smoking habits is recorded using check-boxes categorizing smoking into nondaily smoking (i.e., 0 or <1 cigarette per day), moderate smoking (19 cigarettes per day), and heavy smoking (10 cigarettes per day). In the 19911992 cohort used in this study, similar smoking data were collected at the 32-week visit for approximately 15 percent of the women. This was not a random sample but rather a convenience sample of women whom health care providers chose to question about smoking at the 32-week visit. Data on maternal age, height, parity, and prepregnancy weight are also collected at the first prenatal care visit. At birth, data from infant anthropometric measurements are collected by standardized methods. Beginning in 1990, the collection of head circumference data was standardized to the pediatric method of measurement, that is, across the forehead and around the largest part of the crown. Table 1 describes how the subjects included in our analysis differed from the rest of the population in terms of the variables used in this study. Unfortunately, information on socioeconomic status and maternal use of alcohol, drugs, and coffee was not available in this data set.
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Of the 206,258 births remaining at this point, data on maternal height and prepregnancy weight were available for approximately 40 percent and data on smoking in late pregnancy were available for approximately 18 percent, leaving a total of 15,185 births for study. We carried out 2 tests to compare the distributions of important maternal and infant variables among these 15,185 births with the distributions among those births that were excluded because of missing data.
The outcome variables were birth weight (in grams), crown-heel length (in centimeters), head circumference (in centimeters), and two measures of infant body proportionality: ponderal index and BBR. Table 1 shows the percentage distributions of the population studied and of the subjects excluded for each outcome variable. Ponderal index is defined as 100 x [birth weight (g)/crown-heel length (cm)3]. A lower ponderal index indicates a longer, thinner infant, while a higher ponderal index indicates a shorter and/or fatter infant. Typical values of ponderal index in this data set were between 2.6 and 2.9, with the range extending from 2.0 through 3.5. BBR is an indicator of head-to-body proportionality, and is defined as 100 x the ratio of the infant's estimated brain weight to its birth weight (in other words, the percentage of the infant's birth weight that is estimated to reside in the brain). Brain weight was estimated from the formula brain weight (g) = 0.037 x head circumference (cm)2.57, which is derived from the National Institute of Neurological and Communicative Disorders and Stroke's Collaborative Perinatal Project (25). Thus, the value for each infant's BBR was calculated from the values of its birth weight and head circumference using the formula BBR = 100 x [0.037 x head circumference (cm)2.57]/birth weight (g). A higher BBR indicates a higher proportion of birth weight residing in the brain, while a lower BBR indicates a lower percentage of birth weight residing in the brain. Typical BBR values for healthy full term infants are 910 percent, and the range of values seen in this data set extended from 7 percent to 14 percent.
Each of the five infant measurements was regressed on maternal smoking status and other potential confounders using multivariate linear regression. For the outcomes of birth weight, crown-heel length, and head circumference, the other predictor variables included infant gender, gestational age, parity, maternal age, maternal height, and maternal body mass index (weight (kg)/height (m)2). For the two infant proportionality outcomes, the predictors, in addition to smoking, included the variables listed above plus the infant's birth weight z score. Birth weight z score was included because the degree of growth retardation, measured by birth weight z score, was found to strongly confound the effect of smoking on body proportionality in earlier studies (13, 14
). The more growth-retarded an infant is by weight, the lower the ponderal index tends to be (26
) and the higher the BBR. This aspect of our study sought to examine the effect of a particular growth-retarding influence, namely smoking, on body proportionality. Thus, to compare the body proportions of infants who have had different intrauterine environments and therefore different reasons for growth retardation, it is necessary to control for the birth weight factor as well as for gestational age. If one were not to control for birth weight in these analyses, smokers' infants, who are known to be growth-retarded in comparison with nonsmokers' infants, would appear to be thinner and to have proportionately larger heads compared with the full-sized infants of nonsmokers. Instead of comparing growth-retarded infants to full-sized infants (which is what comparing smokers' infants to nonsmokers' infants amounts to if one doesn't control for the degree of growth retardation), our study sought to compare the proportions of infants with the same degree of growth retardation who differed in only one aspect of their exposure, i.e., whether or not their mothers smoked during pregnancy.
All of these predictor and outcome variables were coded continuously except for infant gender, which is a dichotomous variable. Each continuous variable except for parity was centered around the population mean so that the regression intercepts would refer to the mean value of the outcome variable at the population mean of each predictor variable. Parity was expressed as actual parity minus 1 (e.g., a parity of 1 was coded as 0, a parity of 2 was coded as 1, etc.) so that the regression intercepts would refer to the mean values of the outcome variables among first births. The value of each infant's birth weight z score was calculated relative to that of other infants of the same 1-week gestational-age group and sex, using the standard formula: birth weight z score = (birth weight mean birth weight)/standard deviation of birth weight. Birth weight z score was included as a predictor in these regressions because it is known that both of the proportionality indicators are highly correlated with birth weight z score, and that it confounds the effect of smoking on these outcomes (13, 14
).
The women were divided into four categories depending on their smoking status at the first prenatal care visit and the 32-week prenatal care visit: nondaily smokers (n = 9,802), women who smoked at the moderate level (19 cigarettes per day) at the first visit and continued to smoke at the 32-week visit (n = 2,340), women who were heavy smokers (10 cigarettes per day) at the first visit and who continued to smoke at the 32-week visit (n = 2,097), and women who smoked one or more cigarettes per day at the first visit and were nondaily smokers by the 32-week visit (n = 946). For the regression analyses, the three smoking categories were coded as three indicator variables compared with the control group of nondaily smokers.
Regression variables were selected and parameters were calculated using PROC STEPWISE and PROC REG in the Statistical Analysis System (SAS Institute, Cary, North Carolina). In each regression, the variables representing each of the three smoking categories were retained in the model. Other variables were selected using a backward selection procedure which employed the F test and eliminated at each step the variable with the lowest p value. When all of the variables were significant at least at the 0.05 level, the stepwise elimination procedure was ended. Once the main effects were thus selected, interactions between each of the smoking categories and each of the other main predictors were tested using the same procedure. In the final model for each outcome, the interaction terms for a confounder with all three smoking categories were included even if only one of them was significant.
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RESULTS |
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Table 2 presents the intercepts and smoking parameter estimates for the regression analyses of the five anthropometric outcome variables. (Covariates included in the model are given in the footnotes to table 2.) Each regression intercept refers to the main value of the outcome variable among nonsmokers' infants who also have the reference values for the other predictors in the modelnamely, female gender, parity of 1, maternal age of 28.2 years, maternal height of 166.2 cm, and maternal body mass index of 23.4. The mean birth weight was 3,459 g (95 percent confidence interval (CI): 3,444, 3,474). Smoking at the lower level throughout pregnancy was associated with a decrease in birth weight of 136 g (95 percent CI: -109, -162). Smoking at the higher level throughout pregnancy was associated with a decrease in birth weight of 175 g (95 percent CI: -146, -203). For infants of women who stopped smoking some time between the first prenatal care visit and the 32nd week of pregnancy, the birth weight differences compared with infants of nonsmokers were not statistically significant.
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The regression intercept value for head circumference was 34.43 cm (95 percent CI: 34.39, 34.47). Continued smoking at the lower level was associated with a statistically significant decrease in head circumference of 0.37 cm (95 percent CI: -0.31, -0.43), while continued smoking at the higher level was associated with a statistically significant decrease in head circumference of 0.41 cm (95 percent CI: -0.35, -0.47). No effect on head circumference was observed among the infants of mothers who stopped smoking.
The ponderal index regression controlled for the infant's birth weight z score as well as for the factors controlled for in the other regression analyses (table 2 footnotes). The mean ponderal index value for nondaily smokers' infants was 2.803 (95 percent CI: 2.795, 2.811). Continued smoking throughout pregnancy was associated with an increase in ponderal index of 0.029 for moderate smoking (95 percent CI: 0.017, 0.041) and 0.040 for heavy smoking (95 percent CI: 0.026, 0.054). Infants of smokers who stopped smoking had a statistically significant increase in ponderal index of 0.027 (95 percent CI: 0.009, 0.045) compared with the infants of nonsmokers of the same birth weight and gestational age. In other words, when smokers' infants were compared with nonsmokers' infants who nevertheless had a similar degree of growth (as measured by birth weight z score), the smokers' infants had a higher ponderal index, being presumably shorter for the same birth weight. Some of this smoking effect was evident even when the mothers had stopped smoking early in pregnancy.
As we did in the ponderal index regression analysis, the regression of BBR controlled for infant birth weight z score and other covariates shown in the relevant footnote of table 2. The mean BBR in this group was 9.456 (95 percent CI: 9.427, 9.485). Continued smoking throughout pregnancy was associated with decreases in BBR of 0.074 for moderate smoking (95 percent CI: -0.031, -0.117) and 0.046 for heavy smoking (95 percent CI: -0.001, -0.091). The infants of women who stopped smoking between the first prenatal care visit and week 32 of pregnancy were statistically indistinguishable from the infants of nondaily smokers in terms of their BBRs. In other words, when we compared smokers' infants with nonsmokers' infants with the same gestational age and birth weight, the smokers' infants had disproportionately smaller heads (and presumably made up the weight in some other body dimension). However, the infants of women who stopped smoking before week 32 of pregnancy were no different in their BBRs from nonsmokers' infants of the same birth weight and gestational age.
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DISCUSSION |
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Regarding the two indicators of body proportionality, BBR and ponderal index, continued smoking throughout pregnancy reduced BBR and increased ponderal index. The BBR represents the percentage of birth weight that is estimated to reside in the brain, based on the measure of head circumference (14). Choosing a reference birth weight of 3,500 g, we calculated that continued moderate smoking resulted in an average brain weight deficit of 2.59 g, corresponding to the regression coefficient of 0.074 for continued moderate smoking. We calculated that continued heavy smoking resulted in an average brain weight deficit of 1.61 g. However, quitting smoking at the point the women in our study did was sufficient to completely eliminate the smoking-associated deficits in BBR.
The BBR regression results appear to suggest that the effect of moderate smoking on BBR is greater than the effect of heavy smoking. However, in the regression analyses, the infants of moderate smokers and the infants of heavy smokers were not necessarily compared with nonsmokers' infants in the same way. Because birth weight z score was a predictor in the regression analyses, each group of smokers' infants was compared with nonsmokers' infants with the same degree of growth retardation. For example, infants of moderate smokers may be only moderately growth-retarded, and it appears from the regression results that they have a significant reduction in BBR compared with other such moderately growth-retarded infants. The infants of heavy smokers also showed a significant reduction in BBR compared with nonsmokers' infants with a degree of growth retardation equal to their own. The infants of heavy smokers tended to be more growth-retarded than the infants of moderate smokers, and thus they would have been compared with more severely growth-retarded infants of nonsmokers. Thus, the gap in BBR between moderate smokers' infants and similarly growth-retarded nonsmokers' infants and the gap in BBR between heavy smokers and other (possibly more severely growth-retarded) nonsmokers' infants need not follow a dose-response pattern. However, the point remains that, regardless of their birth weight z score, infants with smoking exposure had lower mean BBRs than similarly growth-retarded infants who did not have smoking among their prenatal exposures, and the effect was not seen when the mothers stopped smoking before week 32 of pregnancy.
Regarding ponderal index, the infants of women who continued to smoke throughout pregnancy had elevated ponderal indices compared with the infants of nonsmokers of the same age and weight, which suggests an excess reduction in crown-heel length compared with birth weight. Stopping smoking at the point the women in our study did was not sufficient to eliminate the smoking-associated increase in ponderal index. Thus, it appears that the length stunting, which had already occurred due to smoking during the early period of pregnancy, was not overcome even when the mother was a nondaily smoker by week 32.
Regarding the generalizability of this study, one should first note that the data were drawn from a very homogeneous population. While there are likely to have been some socioeconomic differences between smokers and nonsmokers in this population for which our analysis could not account, these may not be as large as one would find in a more ethnically and socially diverse population. This is a strength in that the smoking-associated effects seen here are more likely to be truly due to smoking than results one might find in a more diverse population. In a more diverse population, one would be more likely to see confounding in both directions, possibly either hiding or else seeming to exacerbate the effect of smoking. A second point to note is that it is clear from table 1 that the subpopulation for which both 32-week smoking status and maternal body mass index data were available was similar to but not entirely representative of the whole population, despite the fact that births occurring before 32 weeks' gestation were not considered. The infants studied were less likely to have extreme values in either infant dimensions or maternal predictors than were infants in the population as a whole. In a regression analysis, this is a strength in that it reduces the likelihood that points with extreme values have unduly influenced the results. However, it also means that the results should not be extrapolated beyond the values present in the data set.
In summary, our findings suggest that early to midpregnancy smoking cessation prevents deficits in infant birth weight, head circumference, and BBR. However, this study suggests that even smoking cessation in early to midpregnancy may not be sufficient to eliminate the effects of smoking in early pregnancy on the infant's crown-heel length and ponderal index at birth.
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ACKNOWLEDGMENTS |
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NOTES |
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REFERENCES |
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