Maternal active smoking has been associated with a number of adverse developmental and reproductive endpoints. 1–4 Infants of women who smoke during pregnancy are estimated to have twice the risk of low birth weight or an average weight decrement of 150–200 gm at birth, compared with those of non-smokers. The decrement in birth weight appears to be due primarily to intra-uterine growth retardation, and to a lesser extent to preterm birth. 5–9 Because of these relations, exposure to environmental tobacco smoke (ETS) has been of increasing concern. 10,11 Persons exposed to environmental tobacco smoke are subjected to most of the same constituents as those contained in mainstream smoke, but the pattern and amounts of exposure differ. 12 Studies of environmental tobacco smoke exposure tend to show a decrement in mean birth weight of a small magnitude and suggest a slight increase in the risk of low birth weight. 11,13–26 Many studies, however, were based on only crude measures of ETS exposure, such as paternal smoking. Fewer studies have examined ETS exposure and preterm birth, some of which found moderately increased risks. 13,22,27
Some studies of maternal smoking or ETS exposure have shown greater effects in older women. 27–29 Furthermore, a few studies have indicated that blacks have higher levels of cotinine, a nicotine metabolite, than do whites at the same reported smoking level, suggesting possible differences in metabolism. 30–32 Age and race may thus modify effects of tobacco smoke exposure.
This report has several objectives. The primary one is to examine the association of ETS exposure (or passive smoking) with birth weight and gestational age in a prospective study conducted during the first trimester of pregnancy, with ETS exposure reported at home and work. Our second objective is to estimate the effects of active smoking using a non-smoke-exposed comparison group, instead of including possibly ETS-exposed women in the comparison as is done in most studies of active smoking. Another objective is to examine these associations among demographic sub-groups defined by maternal age and race.
Subjects and Methods
Study methods have been published elsewhere, 33 but are described briefly below.
Subject Recruitment and Endpoints
Pregnant women were recruited during 1990–1991 from a large pre-paid health plan (Kaiser Permanente Medical Care Program) in three regions of California at the time they called to make their first prenatal appointment. A prospective study design was used to obtain information on exposures early in pregnancy to avoid problems of reporting bias. Eligibility criteria included age 18 years or older, 12 weeks gestation or less and English or Spanish speaking. Telephone interviews were completed within a few weeks of initial contact for 5,342 women; 18% of the total refused, about 10% were ineligible and 3% could not be re-contacted. The median gestational age at interview was 8 weeks.
Pregnancy outcomes were ascertained primarily from computerized hospital admission files as well as by abstraction of medical records for pregnancy losses. Less than 1% of outcomes could not be determined. We limited this analysis of birth weight and preterm delivery to singleton live births. Birth weight was abstracted from the birth certificate for 4,454 births, which excludes 103 (2.3%) that could not be linked. We calculated gestational age using the date of birth as reported on the birth certificate and the 1st day of the last menstrual period as ascertained at interview preferentially, to maintain dating used throughout the interview. These were compared with gestational age as reported on the birth certificate, and in 48 births we substituted the certificate gestational age to correct an improbable calculated gestational age.
We defined low birth weight (LBW) as weight less than 2,500 gm. We examined the usual classification of preterm delivery as less than 37 weeks’ gestation, as well as an earlier cut-off at less than 35 weeks (“very preterm”) to identify infants at even higher risk of neonatal morbidity and mortality. 34 We defined small for gestational age (SGA) as birth weight less than the 10th percentile for gestational week. For this purpose, we calculated a standard from the weight distribution of the over 2 million singleton births that occurred in California during years comparable with this study (1990–93) separately for males and females, for each gestational week from 24–44.
The interview asked about a number of topics including demographics (eg age, race, education); reproductive history; job characteristics; physical and psychosocial stress; and water, tobacco, caffeine, and alcohol consumption. Most of the consumption questions (including amount smoked) were asked about two time periods: the week before interview (referred to here as “during” the first trimester of pregnancy) and the week at last menstrual period (“before” pregnancy). The smoking status of the infant’s father was also ascertained for the same two time periods. The number of hours per day that non-smoking respondents were near other people smoking both at home and at work, since the last menstrual period, was also asked. We summed these two variables to estimate total daily ETS exposure. These ETS variables were examined only among women who reported not smoking both before and during pregnancy. Thus, women who reported smoking at one of the time intervals, but not the other, were excluded (N = 351) to avoid potential misclassification. (Infants of these women did not generally appear at increased risk for any adverse outcome.) We created a categorical variable with six levels of smoke exposure (excluding an additional four women missing one of the smoking variables) as follows:
1. No smoke exposure (reference): none or <0.5 hours ETS exposure/day
2. Moderate ETS: 0.5–6.5 hours ETS/day
3. High ETS: ≥7 hours ETS/day
Among smokers irrespective of ETS exposure:
4. Low smoking: <5 cigarettes/day at interview
5. Moderate smoking: 5–10 cigarettes/day at interview
6. Heavy smoking: >10 cigarettes/day at interview
We calculated crude rates for the four categorical endpoints as well as rate ratios and adjusted odds ratios and their 95% confidence limits (95% CL) by smoke exposure, relative to non-exposed. We assessed a variety of potential confounders, identified from the literature and univariate analyses, in logistic regression models (ie the “full” model) for each endpoint. These included: maternal age, pre-pregnancy body mass index (BMI = weight/height 2), parity and prior pregnancy loss, race, education, marital status, employment status (and hours worked), stressful life events and social support (assessed using modifications of standard instruments), 35 and caffeinated and alcoholic beverage consumption during the first trimester. We used the change in estimate method 36 to identify covariates whose exclusion from the full models changed the adjusted odds ratio for high environmental tobacco smoke or heavy smoking by 10% or more, to create a “reduced” model. The variables that met this criterion were pregnancy history for all endpoints, race for small for gestational age, stressful life events for preterm delivery, and BMI and education for very preterm delivery. We included all five in the reduced models used to calculate the adjusted odds ratios (AOR) presented, unless otherwise indicated. Potential effect modifiers of a priori interest from previous smoking studies were maternal age and race, assessed by stratification and separate models.
We determined mean birth weight for each exposure category, and we calculated the difference from the non-tobacco-exposed group. We used multivariate regression models to control for all the variables in the full model noted above. In addition we added gestational age to some models to examine effects on weight, controlling for the influence of age at delivery.
The mothers of these live births were on average 27.8 years old, with most (90%) under 35 years. About two thirds of the women were white, one quarter were nulliparous, and most were married (83.4%). Over three quarters of the women worked during the first trimester and 60% had some education past the high school level (Table 1). In this cohort of women receiving prenatal care at a health maintenance organization, 3.2% of infants had low birth weight, 6.9% were small for gestational age, and 6.1% were born preterm, 40% of which were very preterm (or 2.4% of total). Of those with low birth weight, 65% were born preterm.
About two thirds of the women reported no smoke exposure, 18% were non-smokers with some ETS exposure (or 21% of non-smokers reported ETS), and about 11% smoked during the first trimester. As shown in Table 1, reported exposure to any form of tobacco smoke decreased with increasing age and education. Asians were the least likely to report any smoke exposure whereas blacks were most likely to report exposure, particularly to ETS. Women who were not married were more likely to be exposed to either form of tobacco smoke, and women who worked were more likely to be exposed to ETS. Consumers of alcohol or caffeinated beverages were more likely to smoke, but they were not more likely to report ETS exposure.
Mean Birth Weight
Infants of active smokers had reductions in crude mean birth weight on the order of 100–200 gm, with a dose response trend. These weight decrements were greater in the adjusted models (Table 2). Adding gestational age to the model still yielded large decrements in weight for all smoking categories. Mean birth weight varied little by ETS exposure (Table 2). When we examined home and work ETS exposures separately, we found a slight weight decrement with high exposure at home, but the confidence intervals were wide. Examining an even higher ETS exposure level, infants of the 28 women reporting 12 hours or more per day had a weight decrement of −128 gm (SE 101) or −88 gm (SE 103) when adjusted. Had we used paternal smoking status as the ETS exposure variable, the adjusted difference in mean birth weight would be about −32 gm (95% CL = −81, 18).
Stratifying by race, infants of Asians and Pacific Islanders who reported high ETS exposure (≥7 hours/day) had crude birth weight reductions of up to 500 gm. Infants of exposed blacks had about a 100 gm reduction, Hispanics had little reduction and exposed whites had somewhat increased weights, compared with unexposed women of the same race. (A weight reduction of about 100 gm was seen among white infants at the higher ETS exposure level of ≥12 hours/day.) Because of small numbers in some of these categories, we compared whites with all other races. The non-whites had much greater absolute and relative crude weight decrements than whites with both high ETS exposure and heavier smoking (Table 3). Including gestational age in the models reduced the magnitude of the weight decrements more for non-whites than for whites. Stratifying by maternal age revealed a greater weight reduction with heavier smoking among older women (Table 3), which was magnified at an even greater smoking level (>20 cigarettes/day, data not shown). This age modification was not as apparent with high ETS exposure (Table 3).
Low Birth Weight, Preterm Delivery and Small for Gestational Age
Increasing amount smoked was associated with an increased risk in low birth weight and small for gestational age; infants of heavy smokers had adjusted risks 2.6–4.5 times those of unexposed women (Table 4). The risk of preterm birth was not greatly increased by smoking, but the risk of very preterm birth was more than doubled among heavier smokers.
High ETS exposure was associated with an increased risk of LBW on the order of that of low-moderate smoking, which was changed little by adjustment (Table 4). The adjusted odds ratio for any ETS exposure (≥1 hr/day) and LBW was 1.1 (95% CL = 0.71, 1.7). The increased risk from high ETS exposure appeared more strongly related to preterm delivery, particularly very preterm, than to growth retardation (Table 4). The adjusted odds ratio for any ETS exposure and preterm delivery was 1.2 (95% CL = 0.90, 1.7). For both low birth weight and preterm birth, risks were somewhat greater with ETS exposure at home rather than work.
Stratifying by race (Table 5) indicated that the risk of LBW from high ETS exposure was much greater in non-whites than in whites. This finding was not consistent for active smoking, but when we examined individual race categories, we found that blacks and Hispanics who were heavier smokers had crude rates of LBW that were six times those of non-exposed based on very small numbers. There was a pattern of excess risk of preterm and very preterm birth with both high ETS exposure and heavy smoking in non-whites (Table 5).
Stratifying by maternal age (Table 5) indicated little age modification of the association of SGA with heavier active smoking. The risks of LBW associated with active smoking were greater among older women, but there was little difference in the association with high ETS exposure by age. The associations of preterm and very preterm birth with both active and passive smoke exposure tended to be greater in older women.
Our strong finding of a decrement in birth weight with active smoking is consistent with the literature and was not affected materially by the exclusion of ETS-exposed women from the comparison group. Earlier findings regarding ETS and mean birth weight have varied, with some studies reporting decrements of 30–100 gm and others showing little effect. 10,11 ETS exposure has been defined in a variety of ways in these studies and effects may be missed if high exposure is not examined, as suggested here. It is highly likely that the proportion of ETS-exposed women that were highly exposed was greater in studies conducted earlier and in areas such as Europe and Asia where smoking is more pervasive than in California. 15,17–19,21–22,24–26
Offering further validation of our data, we confirmed the consistent findings of an excess risk of low birth weight, and particularly small for gestational age, with heavier smoking. 1–4 While smoking is thought to be more strongly associated with SGA, there is sufficient evidence to indicate it is moderately associated with preterm delivery. 8,9,34,37–39 In support of our finding of a doubled rate of very preterm birth (<35 weeks) among heavy smokers, other studies have reported a greater association of smoking with delivery before 33 weeks gestation than with moderate preterm delivery (33–36 weeks). 37,38
High ETS exposure was moderately related to low birth weight and particularly to very preterm birth, but not to small for gestational age. Other studies of ETS exposure and low birth weight or small for gestational age have found varying results from no effect up to about a doubling of risk. 10,11,13,14,16,17,19,20,23,25 A meta-analysis of studies published by 1995 reported pooled adjusted odds ratios from 1.1–1.4 for these endpoints. 11 Studies of preterm delivery and ETS exposure are fewer and several have found moderate associations, but none has examined earlier preterm births. 13,22,27
An effect of ETS on very preterm delivery that is of a similar magnitude as that of heavy smoking appears inconsistent. Non-smokers exposed to ETS generally have levels of cotinine, a metabolite of nicotine, that are 1–2 levels of magnitude lower than active smokers, suggesting that effects of ETS exposure should be correspondingly lower than those of active smoking. This theory presupposes a linear relationship, however, which may not be appropriate. 10,15,18 Furthermore, ETS is composed of hundreds of compounds contained in exhaled mainstream, as well as sidestream, smoke of which others besides nicotine are probable toxicants of interest (eg carbon monoxide, cadmium, polycyclic aromatic hydrocarbons). 10 These compounds are not all present in the same relative ratio as nicotine in mainstream versus sidestream smoke, 12 nor in aged ETS, so that a linear trend of effects by cotinine level might not be expected. ETS was not associated with SGA, as smoking is, but SGA has a different etiology than preterm delivery. A recent study of breast cancer similarly found an odds ratio for passive smoking that was of the same magnitude as that for active smoking. 40
The associations we found with high ETS exposure appeared limited to non-whites for the most part, which was not explained by a differential exposure distribution within this high category. There was generally a similar pattern of race-modification for heavy smoke exposure, but the number of non-whites in this group was small and estimates less stable. A previous U.S. study 19 reported that ETS and LBW were more strongly associated in non-whites than in whites. Several other studies 31,41–43 found few racial differences in the effects of active smoking on low birth weight or small for gestational age, but mean birth weight does not appear to have been examined in this way. There is some evidence that blacks metabolize nicotine differently than whites. 30–32 Data on other specific ethnic groups is not readily available, so our preliminary data suggesting some differential effects by ethnicity (including Hispanics and Asians) is of interest.
Our finding of generally greater effects of heavy smoking among older mothers did not appear to be explained by a differential distribution of smoking within this high category and is supported by several other studies. 28,29,44 Such an effect may be due to cumulative exposure over years or perhaps to diminished reserves to compensate for the toxic effects of smoking among older mothers. A recent U.S. study 27 found some age modification of ETS exposure with both LBW and preterm delivery in a low income population, but two other studies did not and we found it only for preterm birth. 18,45
Although our ascertainment of ETS exposure was more detailed than in many previous studies, it was based on self-report of hours exposed and did not include exposure outside home or work. The associations of low birth weight and preterm birth with high ETS exposure were not dependent on the cutpoint selected, as similar increases in risk were seen at 5 or more hours/day. Decreases in mean birth weight, however, were not observed until ETS exposure levels were much greater, highlighting possible difficulties in across-study comparisons of self-reported exposure. Another limitation was that exposure was assessed during the first trimester of pregnancy. Many pregnant women have already changed their smoking habits by this time, 46 but in an interview we conducted during the third trimester (or post-natally) on a sub-set of this sample, about one third of first-trimester smokers did not report any smoking in the last 3 months of pregnancy; some of this may reflect retrospective reporting errors. Nevertheless, the associations we found based on first trimester smoking may be underestimated (eg a conservative bias) if quitting later decreases risk. It is unlikely that many women started smoking later in pregnancy, so associations with ETS exposure examined among non-smokers should not be greatly affected. We did not ask about ETS exposure in the third trimester re-interview and there is little other data on the consistency of ETS exposure during pregnancy, so some misclassification may exist.
Another possible source of error is misclassification of outcome. We did not have information from medical records to verify gestational age. As several authors 47,48 have recommended updating fetal growth curves and California has an unusual racial distribution (more Hispanic births), we calculated a new standard for California. The weight-for-age cutpoints were similar to, but consistently slightly higher than, recent U.S. and Canadian standards. 47,48 This HMO-based population had generally low risks of the adverse outcomes examined, perhaps due to early, routine prenatal care and/or lack of extreme socioeconomic disadvantages, which limited the power for sub-group analyses at higher smoke exposure levels. Although we were able to examine many potential confounders, including some on stress, exertion, and socioeconomic factors, residual confounding may influence the results. We had no data on history of other diseases, particularly sexually transmitted diseases, which may play a role in preterm delivery, or on the specific causes of preterm birth.
The strengths of this study are many, including the prospective design, nearly complete follow-up of all pregnancy outcomes, and a population with equal access to medical care that should decrease possible confounding effects of socio-demographic variables on exposure status.
We thank Kirsten Waller for her work in assigning gestational age and final pregnancy outcome. Financial support from the Packard Family Foundation allowed completion of data collection. We also acknowledge the contributions of the Kaiser clinics involved in patient recruitment and investigators at Kaiser Division of Research, including Catherine Schaefer and Robert Hiatt.
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