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Gestational Age at Birth and Risk of Autism Spectrum Disorders in Alberta, Canada
Gestational Age at Birth and Risk of Autism Spectrum Disorders in Alberta, Canada Anna Leavey, PhD1,2, Lonnie Zwaigenbaum, MD, FRCPC3, Karyn Heavner, PhD2, and Igor Burstyn, PhD2 Objective To examine the association between autism spectrum disorders (ASD) and each completed week of gestation using a graphical method of presenting results at all possible categorizations of gestational age (GA).
Study design The risk of ASD in a total of 218 110 singleton live births with complete data from Alberta, Canada between 1998 and 2004 was examined through linkage to health insurance records. The relative risk of developing ASD according to the 21 dichotomizations of shorter gestation (GA #23 weeks vs >23 weeks to #43 weeks vs >43 weeks, in 1-week increments) was calculated using log-binomial regression and adjusted for fetal sex, socioeconomic status, and birth year. Results We observed a gradual increased risk of ASD with shorter gestation. Cutoffs only between 29 and 40 weeks clearly denoted an elevated risk of developing ASD compared with longer gestation, and the risk increased with earlier GA cutoff. The results were not affected by sex or measures of fetal growth. Conclusion Our data confirm the role of shortened gestation in ASD risk. We warn against the use of prespecified or a data-driven GA cutoff, however; instead, we recommend systematically examining all plausible cutoffs for GA to avoid overstating the homogeneity of risk in children on either side of a given cutoff, as well as to increase the comparability of studies. (J Pediatr 2013;162:361-8).
A
utism spectrum disorders (ASD) include a range of behaviorally defined neurodevelopmental conditions characterized by impairments in social interaction and communication, as well as restricted and repetitive patterns of behavior and interests.1 ASD symptoms are heterogeneous, and little is known about their etiology.2 Genetic predisposition,3-5 maternal autoimmune disorders,6 parental age,7 and male sex8,9 have all been associated with an increased risk of ASD. Increasingly, other obstetric and environmental factors occurring early in life have been implicated.4,10-13 However, with the exception of child’s sex and rare mutations,14 no single factor has shown such a consistent association as to imply causality.15 Preterm birth (defined as less than 37 completed weeks of gestation16) has been identified as an obstetric risk factor associated with an elevated risk of ASD. In the US, the rate of singleton preterm births rose from 9.7% to 10.8% between 1990 and 2004, largely accounted for by rising numbers of late-preterm births, between 34 and 36 weeks gestation (representing 71.2% of the total preterm population).17 Canada has also witnessed an increase in preterm births.18,19 Even though prematurity is responsible for more than one-third of perinatal mortality,17 especially in those born at <28 weeks gestation,20 it is neither a defined disease nor a syndrome.21 Although <6% of preterm births occur before 28 weeks gestation,17 these extreme preterm children bear the brunt of the neurodevelopmental and physiological impairments, including an elevated risk of ASD.22 Many previous studies have examined the association between gestational age (GA) and ASD. Gillberg and Gillberg23 compared the week-by-week frequency distribution of GA (35-42 weeks) in 25 children with autism with that of 25 controls born in the same obstetric department and were of the same sex and matched “as closely as possible on time of birth” (likely within a year). They observed increased association with autism for those children born preterm and postterm. Their results also indicated a more heterogeneously distributed GA in the autism group. Although other studies have examined wider ranges of GAs in relation to ASD,9,11 most dichotomized their data, conventionally at 37 weeks gestation.12,24-26 Other dichotomies have specified 38 weeks27 or 36 weeks.15,28 Several studies have selected cutoffs that depict established convenFrom the Aerosol and Air Quality Research Laboratory, Department of Energy, Environmental and Chemical tions for different stages of late- or early-preterm gestation.29-31 Still other studies Engineering, Washington University in St. Louis, St. have focused exclusively on preterm and extremely preterm birth (eg, <26 Louis, MO; Department of Environmental and Occupational Health, School of Public Health, Drexel weeks,32-34 23-30 weeks,35 or <28 weeks36). Although the majority of these studUniversity, Philadelphia, PA; and Department of Pediatrics, Faculty of Medicine and Dentistry, University ies found a greater prevalence of ASD for shorter gestational periods, given the of Alberta, Edmonton, Alberta, Canada diversity of cutoffs used and the varying associations with ASD for each cutoff, I.B. and L.Z. are supported by a Population Health Investigator award and a Health Scholar award, respeccomparisons among studies (and future meta-analysis) are difficult, and the 1
2
3
ASD GA RR SES
Autism spectrum disorder Gestational age Relative risk Socioeconomic status
tively, from the Alberta Heritage Foundation for Medical Research. This study is based on data supplied by Alberta Health and Wellness (AHW) and the Alberta Perinatal Health Program (APHP). The interpretation and conclusions contained herein are those of the researchers and do not necessarily represent the views of the Government of Alberta, AHW, or the APHP. The authors declare no conflicts of interest. 0022-3476/$ - see front matter. Copyright ª 2013 Mosby Inc. All rights reserved. http://dx.doi.org/10.1016/j.jpeds.2012.07.040
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possibility of publication bias in situ exists.37 It is also noteworthy that conventional a priori GA cutoffs for prematurity used in ASD epidemiology do not necessarily reflect etiologically relevant categorizations, possibly undermining the ability to detect important associations. Evidence to date supports the hypothesis that an individual’s risk of ASD is influenced by the duration of gestation. However, the conventional method of dichotomizing a continuous variable according to some predefined cutoff point has been criticized by some for its somewhat arbitrary selection of cutoffs, creating groups with wide ranges of actual GA (eg, up to 3 months apart); lack of transparent reasons for selection of the particular cutoffs; difficulty facilitating between-study comparisons; and loss of potentially important information through factorization of an inherently continuous variable.38,39 It should be acknowledged that when only a limited sample size is studied, as is often the case with rare conditions such as ASD, some selection of dichotomization is intuitively justified, typically one informed by the literature on the boundary of “the highest-risk group.” Heavner et al38 suggested at least providing risk/effect estimates for a plausible range of dichotomizations, allowing readers to obtain the maximum information from the study and to more accurately compare results with other studies. We examined the association between GA at birth and the risk of ASD for each observed GA cutoff for which an association has been reported in the literature. If more children are born late preterm but the risk of developing neurodevelopmental problems increases for early preterms,35 then distinguishing between these different GA bands is vital to gaining a more comprehensive understanding of the doseresponse relationship between prematurity and risk of ASD. Although Gillberg and Gillberg attempted a similar study almost 3 decades ago,23 their analysis was based on a much smaller sample that did not encompass the wide range of GAs included in our analysis.
Methods A total of 273 343 singleton live births were recorded between January 1, 1998, and December 31, 2004, in Alberta, Canada. All births were identified using delivery records completed at the time of hospital admission as part of routine clinical care, or by the attending registered midwife in the event of a home birth. These delivery records also contain information on GA, recorded at the time of delivery by the care provider, along with mother’s age, child’s sex, birth date, and other antenatal and perinatal risk factors. (See Burstyn et al24 for detailed description and individual associations with the risk of ASD.) All birth records are held and maintained by the Alberta Perinatal Health Program, which also maintains quality control of all data entries. Any indeterminable errors were replaced with missing values by the Alberta Perinatal Health Program, thereby ensuring internal accuracy and consistency. Follow-up of those children identified by their individual Personal Health Number and matched to the delivery records held by the Alberta Perinatal Health Program and the 362
Vol. 162, No. 2 health insurance data held by Alberta Health and Wellness continued until March 31, 2008, ensuring at least 3 years of follow-up from birth. In Alberta’s universal health care insurance system, all residents are served by physicians and hospitals that bill the government for their services, with the fee linked to specific diagnostic codes from the International Statistical Classification of Diseases and Related Health Problems, 9th revision. The specific diagnostic codes listed with the physician billing record indicating “ASD claim” were 299.0 and 299.8. Those children who could not be identified by health insurance records or had died or were no longer a resident of Alberta during follow-up, those with unrecorded sex or GA data, and those not born within 23-43 weeks gestation were excluded from our analysis. Of the 4 ASD outcome definitions described by Burstyn et al,24 the most liberal (ie, at least one ASD claim by any physician, as defined earlier) was used for subsequent analysis. This definition was selected because a methodology comparable to ours was shown to have reasonable sensitivity (60%) and specificity (85%),40 and because our previous work with this dataset showed that choice of case definition did not affect the association between ASD and the risk factors relevant to current analysis: sex, maternal socioeconomic status (SES), and birth year.24 If the case definition does not affect observed associations with essential covariates, then it is reasonable to expect that analyses of association (although not enumeration of disease burden) based on the alternative definitions would be equivalent. Of course, the more liberal definition also increases the power of statistical analyses, an important consideration for a relatively rare condition such as ASD. GA was examined for each completed week of gestation between 23 and 43 weeks and compared with all later weeks (up to and including 43 weeks). Thus, infants born at <24 weeks were compared with all births of $24 weeks gestation, infants born at <25 weeks were compared with all births of $25 weeks gestation, and so on. The relative risk (RR) (with associated 95% CI) of developing ASD according to the week-by-week dichotomization of GA was calculated using log-binomial regression. Several risk factors were included in the model owing to their association with ASD risk observed in previous analyses of this cohort and the potential for confounding,24 including sex (given the preponderance of ASD in males compared with females), maternal health coverage (eg, aboriginal group, welfare recipient, low-income subsidy, which reflect SES and access to medical care), and birth year (to control for potential ASD diagnosis owing to duration of follow-up). Birth year and SES were included in the models not because of potential confounding, but rather to control for the likelihood of being diagnosed. Nonetheless, there are reasons to be wary of cofounding as well. There is an established relationship between prematurity and year in the source population (ie, our data).41 An association between prematurity and survival and SES in Canada (ie, the source population) is also well established.42 Sex is one of the few established strong risk factors for ASD and is associated with Leavey et al
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February 2013 prematurity.43 In the end, none of the adjustments affected the results (data not shown). The analysis was conducted for the whole cohort and then repeated for each sex separately (in which case sex was not included as a confounder). Although birth weight and weight for date (z-scores) were computed using an improved population-based Canadian reference for birth weight according to GA,44 we focused on GA to remove the possibility of erroneously including infants that were just small at birth and not premature. All analyses were performed using SAS 9.1 (SAS Institute, Cary, North Carolina). The SAS macro used to create curves that display the relationship between RR and cutoff for GA is available from the corresponding author on request. The 95% CIs on proportion for each sex and completed GA were calculated taking into account correction for continuity following the recommendations of Newcombe and Robert,45 and implemented using an online calculator (http://faculty.vassar.edu/lowry/prop1. html). The study protocol was approved by the University of Alberta’s Health Research Ethics Board.
as the infants included in the analysis (details not shown). The Table illustrates the distributions of week-by-week GA between 23 and 43 completed weeks and of ASD prevalence by sex. Although approximately 5000 more boys than girls were born, the distribution of births by GA was similar for each sex. In total, 17 830 (8.2%) preterm live births occurred between 23 and 36 completed weeks of gestation, of which 54% were males. The majority of these preterm births (13 108; 73.5%) occurred during late preterm (34-36 weeks), and only 657 of these (3.7%) would have been classified as extremely preterm (<28 weeks). Of this cohort, 1135 (0.52%) were diagnosed with ASD, including 949 males (0.85% of live-born males) and 186 females (0.17% of live-born females), for a male-to-female ratio of 4.9:1. The prevalence of ASD among those born extremely preterm was 1.22% (n = 8, all of whom were males, indicating an exteme sex ratio that likely arose by chance, as indicated by the wide 95% CI), compared with 0.61% (n = 80) for those born late-preterm, and 0.50% (n = 847) for those born full-term. The ASD prevalence rate of those born postterm mirrored that of full-term, at 0.50%. Figure 1 presents the RR of developing ASD for both sexes combined, adjusted for maternal SES plus child’s sex and birth year. A gradually increasing risk of ASD with shorter gestation was observed. There does not appear to be a step function in risk, just a smooth progression of increased risk for each decreasing week of gestation. This is especially apparent for cutoffs between 29 and 37 weeks. The analysis was repeated for each sex separately. The resulting RR curve for males follows the same pattern as for both sexes combined (Figure 2), owing to the preponderance of males with ASD in this cohort. Although the curve for females
Results Out of 273 343 singleton live births, 25 970 could not be identified by Alberta Health and Wellness, 28 421 had died or lost residence during follow-up, 66 had missing sex information, 444 had no recorded GA, and 332 were born outside of the specified 23- to 43-week range. These infants were excluded from the analysis, leaving a subsequent cohort of 218 110, with approximately equal numbers of males (51.1%) and females. The 28 421 infants excluded because of incomplete follow-up had a similar distribution of all studied risk factors Table. Week-by-week distribution of GA and ASD by sex Female
Male Prevalence
Completed week of gestation
Total children at risk
Number with ASD
Estimate
23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 Total
19 39 62 91 104 117 142 230 286 438 660 1052 1644 3318 7223 17 336 27 117 30 177 15 010 879 16 106 560
0 0 0 0 0 0 1 1 0 1 1 6 3 6 11 40 45 54 17 0 0 186
0 0 0 0 0 0 0.0070 0.0043 0 0.0023 0.0015 0.0057 0.0018 0.0018 0.0015 0.0023 0.0017 0.0018 0.0011 0 0 0.0017
Prevalence
95% CI*
Total children at risk
Number with ASD
Estimate
95% CI*
0-0.21 0-0.11 0-0.07 0-0.05 0-0.04 0-0.04 0.0004-0.04 0.002-0.03 0-0.02 0.0001-0.01 0.0001-0.01 0.0023-0.01 0.0005-0.01 0.0007-0.004 0.0008-0.003 0.002-0.003 0.001-0.002 0.001-0.002 0.0007-0.002 0-0.005 0-0.24 0.0015-0.0020
24 41 63 116 98 152 169 254 342 486 789 1222 1952 3920 8068 18 680 27 830 30 931 15 402 990 21 111 550
1 0 2 3 2 5 4 5 1 11 7 12 15 38 77 154 225 241 133 12 1 949
0.042 0.000 0.032 0.026 0.020 0.033 0.024 0.020 0.003 0.023 0.009 0.010 0.008 0.010 0.010 0.008 0.008 0.008 0.009 0.012 0.048 0.0085
0.002-0.23 0-0.11 0.006-0.12 0.007-0.08 0.004-0.08 0.01-0.08 0.01-0.06 0.007-0.05 0.0001-0.02 0.01-0.04 0.004-0.02 0.005-0.02 0.005-0.01 0.007-0.01 0.008-0.01 0.007-0.01 0.007-0.01 0.007-0.01 0.07-0.01 0.07-0.02 0.003-0.26 0.008-0.009
*95% CIs with continuity correction (see Methods).
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Figure 1. RR of ASD diagnosis by GA, both sexes (n = 318 1100). The middle line is the point estimate of RR compared with longer GA; the lower and upper lines represent boundaries of 95% CIs. The cutoff was changed from 24 to 42 in increments of 1 week; RR (adjusted for SES and birth-cohort) and 95% CI plotted.
(Figure 3) differs in terms of size and prevalence; for example, fewer females overall were diagnosed with ASD, and no females born at <29 weeks GA or >42 weeks GA were diagnosed with ASD, compared with 26 (2.3%) of males born in those periods. Overall, an increasing risk of ASD was linked with a decreasing number of completed weeks in the womb, a trend especially noticeable between 35 and 38 weeks gestation. Analysis by birth weight–adjusted z-scores did not reveal any marked differences in any of the aforementioned results (data not shown). Formal tests of interaction of GA with sex showed that GA was related to the risk of ASD to the same extent in boy and girls (details not shown).
Discussion In our population-based cohort, we observed an association between decreased length of gestation and increased risk of ASD. The prevalence of singleton live preterm births (<37 weeks gestation) in our study cohort was 8.2%, and although this rate is slightly higher than the 1996 reported prevalence for Canada of 7.1%,18 results are likely commensurate given the trend toward increasing rates of preterm birth in Canada each year.18,19 Although some previous studies have examined the prevalence of ASD exclusively in preterm populations29,31,32,35,36 or the frequency of preterm birth in a population of children already diagnosed with an ASD,30 the majority of studies have compared GA and the frequency of ASD in previously selected cohorts.9,11,25,27,46 Preterm birth is most commonly defined as birth at <37 weeks GA; 364
thus, cohorts are divided based on this cutoff. However, previous studies on the association of ASD with GA are difficult to compare, because it is not known whether the differences in ASD rates and length of gestation are related to differences in study samples/methods or in GA cutoffs. Some previous studies have used RR to examine the significance of GA on ASD outcome, for example, <37 weeks and <33 weeks GA,11 between 23 and 28 weeks and <31 weeks GA,46 and <35 weeks GA.9 Again, however, results were presented for only a handful of selected cutoffs, leaving the reader to wonder whether these cutoffs were the only ones examined or the only ones for which significance was observed, and also precluding observation of any potential trends in the data. Many possible explanations for the causal associations between GA and ASD risk have been advanced,46,47 and others have questioned whether any associations are indeed causal.9,11,15,25 For example, prenatal exposure to a stressful event continues to be linked to an increased risk of ASD,5,48 in which case stress may be the primary risk factor in ASD, with lower GA intermediate along the causal pathway toward ASD. Preterm labor may also result from fetal abnormalities (including those related to genetic differences, which may have shared vulnerability) or complications of pregnancy. Conversely, prenatal delivery itself may adversely affect the developing central nervous system, increasing the risk of ASD. Despite some uncertainty within the literature regarding the causal association between GA and ASD, our methodological approach confirms an association between GA and ASD across a wide range of gestational durations. Leavey et al
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February 2013
Figure 2. RR of ASD diagnosis by GA, males only (n = 111 550). The middle line is the point estimate of RR compared with longer GA; the lower and upper lines represent boundaries of 95% CIs. The cutoff was changed from 24 to 42 in increments of 1 week; RR (adjusted for SES and birth-cohort) and 95% CI plotted.
Although a positive association between shortened gestation and increased ASD risk has been well documented in the literature, attempts to compare our results with those reported in other studies and to compare results among studies were hindered by the wide range of cutoffs depicting GA, as well as the different statistical methodologies applied in many of the studies. A general lack of clarity regarding the selection of cutoffs persist: whether they were based on a priori hypothesis or post hoc examination, whether the cutoffs presented were the only ones examined, or whether others were examined and deemed unimportant. Heavner et al38 raised concerns about such arbitrary approaches to cutoff selection, because artificial boundaries may cause a misguided interpretation of the data, thus distorting the reader’s understanding of the exposure–outcome relationship, with possible implications for future study designs or health policy. This type of categorization also may prevent readers from drawing their own conclusions, forcing them to interpret the results based on the GA cutpoint selected by the investigator. Moreover, findings that support an investigator’s hypothesis may be reported disproportionately, resulting in a publication bias within the literature. Our present results are presented as a curve informing the ASD risk in infants born at various GA cutoffs between 23 and 43 weeks relative to the risk in those born at later weeks. This method has been advocated as a means of minimizing the potential loss of information inherent to simple dichot-
omization,38 thus allowing us to evaluate the difference in risk conferred by different lengths of gestation. This method also increases transparency, permitting readers to identify possible thresholds of effect and to select the GA cutoff in which they are interested. This facilitates interstudy comparisons and elucidates any possible trends, thereby “maximizing the utility.of epidemiological studies.”38 For example, it is only because we analyzed and presented our results as a RR curve for all GA cutoffs that we were able to demonstrate the trend that each additional week that a fetus remains in utero (up to 42 weeks) reduces the risk of a later diagnosis of ASD. This is a potentially important observation that other studies are generally unable to make. It also suggests that at least 3 categories of GA may be appropriate in studies of ASD, with effects of both shortened and prolonged gestation examined. Like most general population studies of ASD, we had limited data on females, especially those with very short and prolonged GAs, limiting the conclusions that we can draw about this group. Even a large study like ours, including the entire population of Alberta (population >3 million), was underpowered for GA <29-30 weeks. This suggests that studies seeking to understand the risk of ASD in children with very short gestation need to be designed to draw on larger source populations. We excluded some originally identified singleton live births from our analysis owing to the lack of information on all covariates required for the present study. It should
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Figure 3. RR of ASD diagnosis by GA, females only (n = 106 560). The middle line is the point estimate of RR compared with longer GA; the lower and upper lines represent boundaries of 95% CIs. The cutoff was changed from 24 to 42 in increments of 1 week; RR (adjusted for SES and birth-cohort) and 95% CI plotted.
be noted that both sex-specific and overall rates of ASD were identical. This is not surprising, because in the currently selected dataset, only 2 ASD cases were removed compared with previous analysis24; thus, we believe that there is no important selection bias from this source. Despite the tendency for increased errors in GA on birth certificates compared with birth weights,31 and although uncertainty persists regarding the inconsistent methods used to ascertain GA,21 many advocate the use of GA instead of birth weight to assess prematurity, because low birth weight may be indicative of restricted fetal growth, not necessarily of preterm birth. Thus, using GA may minimize potential confounding by factors that might have restricted fetal growth.33,36,46,49 In addition, delivery records (which were used in the present study to obtain GA) have been reported to be a more reliable source than birth certificates, on which mistakes are more frequent.15 However, despite every effort to ensure reliability, the methods used to determine GA are not known, and thus some records may be more accurate than others. And although the liberal definition of at least one ASD claim by any physician used in the present study did not alter the association with ASD and the other risk factors (see Methods), the diagnostic criteria used by each physician to identify ASD is also not known. What is more, the etiology of any given preterm birth, and whether the different etiologies alter ASD risk,36 were not assessed. 366
This study highlights a new way of examining the association between GA and ASD risk, offering increased transparency and more information, facilitating comparisons with other studies and illuminating severity and overall trends. Our findings demonstrate a decreased risk of ASD for every additional week that a fetus remains in utero, with results most informative for cutoffs between 29 to 40 weeks (but plausibly applicable to the full range of GAs). There appears to be no step function in risk, but rather a smooth progression of increased risk with shortened duration of gestation. GA may be just one of the risk factors indicative of a suboptimal pregnancy, and further work to differentiate between the causes of preterm labor and birth and subsequent ASD is suggested across the full range of GAs. In short, our data confirm the role of shortened gestation in the risk of ASD. We warn against the use of prespecified GA cutoffs, however; instead, we recommend systematically examining all plausible cutoffs for GA to avoid overstating the homogeneity of risk in children on either side of a given cutoff, and to increase comparability among studies. Future studies presenting data as a risk curve may provide important insights where dichotomization otherwise needlessly obscures (or distorts) evidence contained in the data. n We thank Alberta Health and Wellness and the Alberta Perinatal Health Program for supplying and linking the data. We also thank Leavey et al
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February 2013 Alex Marinov, Wendy Mitchell, Nancy Piche, Nancy Bott, and Sharon Zhang for assistance with development of dataset used for the project. Submitted for publication Feb 8, 2012; last revision received May 7, 2012; accepted Jul 17, 2012. Reprint requests: Igor Burstyn, PhD, Department of Environmental and Occupational Health, School of Public Health, Drexel University, 1505 Race Street, Room 1332, Philadelphia, PA 19102. E-mail: [email protected]
References 1. American Psychological Association. Disorders usually first diagnosed in infancy, childhood, or adolescence. In DSM-IV-TR: Diagnostic and Statistical Manual of Mental Disorders 2001. Available from: http:// psychiatryonline.org/content.aspx?bookid=22§ionid=1888402#7549. Accessed November 11, 2011. 2. Levy SE, Mandell DS, Schultz RT. Autism. Lancet 2009;7:1627-38. 3. Abrahams BS, Geschwind DH. Advances in autism genetics: on the threshold of a new neurobiology. Nature 2008;9:341-56. 4. Hallmayer J, Cleveland S, Torres A, Phillips J, Cohen B, Torigoe T, et al. Genetic heritability and shared environmental factors among twin pairs with autism. Arch Gen Psychiatry 2011;68:1095-102. 5. Newschaffer CJ, Croen LA, Daniels J, Giarelli E, Grether JK, Levy SE, et al. The epidemiology of autism spectrum disorders. Annu Rev Public Health 2007;28:21.1-21.24. 6. Comi AM, Zimmerman AW, Frye VH, Law PA, Peeden JN. Familial clustering of autoimmune disorders and evaluation of medical risk factors in autism. J Child Neurol 1999;14:388-94. 7. Croen LA, Najjar DV, Fireman B, Grether JK. Maternal and paternal age and risk of autism spectrum disorders. Arch Pediatr Adolesc Med 2007; 161:334-40. 8. Fombonne E, Zakarian R, Bennett A, Meng L, McLean-Heywood D. Pervasive developmental disorders in Montreal, Quebec, Canada: prevalence and links with immunizations. Pediatrics 2006;118:e139-50. 9. Larsson HG, Eaton WW, Madsen KM, Vestergaard M, Olesen AV, Agerbo E, et al. Risk factors for autism: perinatal factors, parental psychiatric history, and socioeconomic status. Am J Epidemiol 2005;161:91625. 10. Bryson SE, Smith IM, Eastwood D. Obstetrical suboptimality in autistic children. J Am Acad Child Adolesc Psychiatry 1988;27:418-22. 11. Eaton WW, Mortensen PB, Thomsen PH, Frydenberg M. Obstetric complications and risk for severe psychopathology in childhood. J Autism Devel Disord 2001;31:279-85. 12. Juul-Dam N, Townsend J, Courchesne E. Prenatal, perinatal, and neonatal factors in autism, pervasive developmental disorder-not otherwise specified, and the general population. Pediatrics 2001;107:e63-8. 13. Nelson KB. Prenatal and perinatal factors in the etiology of autism. Pediatrics 1991;87:761-6. 14. Hughes JR. A review of recent reports on autism: 1000 studies published in 2007. Epilepsy Behav 2008;13:425-37. 15. Bilder D, Pinborough-Zimmerman J, Miller J, McMahon W. Prenatal, perinatal, and neonatal factors associated with autism spectrum disorders. Pediatrics 2009;123:1293-300. 16. Beck S, Wojdyla D, Say L, Betran AP, Merialdi M, Requejo JH, et al. The worldwide incidence of preterm birth: a systematic review of maternal mortality and morbidity. Bull World Health Organ 2010;88:1-38. 17. Martin JA, Hamilton BE, Sutton PD, Ventura SJ, Menacker F, Kirmeyer S, et al. Births: final data for 2004. National Vital Statistics Reports 2006 Available from: http://www.cdc.gov/nchs/data/nvsr/nvsr55/nvsr55_01. pdf. Accessed October 27, 2011. 18. Public Health Agency of Canada. Measuring up, a health surveillance update on Canadian children and youth: preterm birth, 1999. Available from: http://www.phac-aspc.gc.ca/publicat/meas-haut/mu_d-eng.php. Accessed November 17, 2011. 19. Joseph KS, Kramer M, Marcoux S, Ohlsson A, Wen SW, Allen A, et al. Determinants of preterm birth rates in Canada from 1981 through 1983 and from 1992 through 1994. N Engl J Med 1998;339:1434-9.
20. Callaghan WM, MacDorman MF, Rasmussen SA, Qin C, Lackritz EM. The contribution of preterm birth to infant mortality rates in the United States. Pediatrics 2006;118:1566-73. 21. Behrman RE, Butler AS, eds; Institute of Medicine. Preterm birth: causes, consequences, and prevention. Washington, DC: National Academies Press; 2007. 22. Limperopoulos C. Autism spectrum disorders in survivors of extreme prematurity. Clin Perinatol 2009;36:791-805. 23. Gillberg C, Gillberg IC. Infantile autism: a total population study of reduced optimality in the pre-, peri- and neonatal period. J Autism Dev Disord 1983;13:153-66. 24. Burstyn I, Sithole F, Zwaigenbaum L. Autism spectrum disorders, maternal characteristics and obstetric complications among singletons born in Alberta, Canada. Chronic Dis Canada 2010;30:125-34. 25. Brimacombe M, Ming X, Lamendola M. Prenatal and birth complications in autism. Matern Child Health J 2007;11:73-9. 26. Sugie Y, Sugie H, Fukuda T, Ito M. Neonatal factors in infants with autistic disorder and typically developing infants. Autism 2005;9:487-94. 27. Tuchman RF, Rapin I, Shinnar S. Autistic and dysphasic children, I: clinical characteristics. Pediatrics 1991;88:1211-8. 28. Hultman CM, Sparen P, Cnattingius S. Perinatal risk factors for infantile autism. Epidemiology 2002;13:417-23. 29. Wier ML, Odouli R, Grether JK, Croen LA. Congenital anomalies associated with autism spectrum disorders. Dev Med Child Neurol 2006;48:500-7. 30. Schieve LA, Baio J, Rice CE, Durkin M, Kirby RS, Drews-Botsch C, et al. Risk for cognitive deficit in a population-based sample of US children with autism spectrum disorders: variation by perinatal health factors. Disabil Health J 2010;3:202-12. 31. Schendel D, Bhasin TK. Birth weight and gestational age characteristics of children with autism, including a comparison with other developmental disabilities. Pediatrics 2008;121:1155-64. 32. Johnson S, Hollis C, Kochhar P, Hennessy E, Wolke D, Marlow N. Autism spectrum disorders in extremely preterm children. J Pediatr 2010; 156:525-31. 33. Johnson S, Hollis C, Kochhar P, Hennessy E, Wolke D, Marlow N. Psychiatric disorders in extremely preterm children: longitudinal finding at age 11 years in the EPICure Study. J Am Acad Child Adolesc Psychiatry 2010;49:453-63. 34. Msall ME. Central nervous system connectivity after extreme prematurity: understanding autistic spectrum disorder. J Pediatr 2010;156:519-20. 35. Limperopoulos C, Bassan H, Sullivan NR, Soul JS, Robertson RL, Moore M, et al. Positive screening for autism in ex-preterm infants: prevalence and risk factors. Pediatrics 2008;121:758-65. 36. O’Shea TM, Allred EN, Dammann O, Hirtz D, Kuban KCK, Paneth N, et al. The ELGAN study of the brain and related disorders in extremely low gestational age newborns. Early Hum Dev 2009;85:719-25. 37. Phillips CV. Publication bias in situ. BMC Med Res Methodol 2004;4: 20-30. 38. Heavner KK, Phillips CV, Burstyn I, Hare W. Dichotomization: 2 2 (2 2 2.) categories: infinite possibilities. BMC Med Res Methodol 2010;10:59-70. 39. Naggara O, Raymond J, Guilbert F, Roy D, Weill A, Altman DG. Analysis by categorizing or dichotomizing continuous variables is inadvisable: an example from the natural history of unruptured aneurysms. AJNR Am J Neuroradiol 2011;32:437-40. 40. Dodds L, Spence A, Shea S, Fell DB, Armson BA, Allen AC, et al. Validity of autism diagnoses using administrative health data. Chronic Dis Canada 2009;29:102-7. 41. Reproductive Health Report Working Group. Alberta Reproductive Health: Pregnancies and births, 2004. Available from: http://www. health.alberta.ca/documents/Reproductive-Health-2004.pdf. Accessed April 27, 2012. 42. Canadian Institute for Health Information. Reducing gaps in health: a focus on socioeconomic status in urban Canada, 2008. Available from: http://secure.cihi.ca/cihiweb/products/Reducing_Gaps_in_Health_ Report_EN_081009.pdf. Accessed April 27, 2012. 43. Aibar L, Puertas A, Valverde M, Carrillo MP, Montoya F. Fetal sex and perinatal outcomes. J Perinat Med 2012;40:271-6.
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44. Kramer MS, Platt RW, Wen SW, Joseph KS, Allen A, Abrahamowicz M, et al. A New and improved population-based canadian reference for birth weight for gestational age. Pediatrics 2001;108:e35-42. 45. Newcombe Robert G. Two-sided confidence intervals for the single proportion: comparison of seven methods. Stat Med 1998;17:857-72. 46. Moster D, Lie RT, Markestad T. Long-term medical and social consequences of preterm birth. N Engl J Med 2008;359:262-73.
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Vol. 162, No. 2 47. Kolevzon A, Gross R, Reichenberg A. Prenatal and perinatal risk factors for autism: a review and integration of findings. Arch Pediatr Adolesc Med 2007;161:326-33. 48. Kinney DK, Munir KM, Crowley DJ, Miller AM. Review: Prenatal stress and risk for autism. Neurosci and Biobehavioral Rev 2008;32:1519-32. 49. Goldenberg RL, Culhane JF, Iams JD, Romero R. Epidemiology and causes of preterm birth. Lancet 2008;371:75-84.
Leavey et al
Study design The risk of ASD in a total of 218 110 singleton live births with complete data from Alberta, Canada between 1998 and 2004 was examined through linkage to health insurance records. The relative risk of developing ASD according to the 21 dichotomizations of shorter gestation (GA #23 weeks vs >23 weeks to #43 weeks vs >43 weeks, in 1-week increments) was calculated using log-binomial regression and adjusted for fetal sex, socioeconomic status, and birth year. Results We observed a gradual increased risk of ASD with shorter gestation. Cutoffs only between 29 and 40 weeks clearly denoted an elevated risk of developing ASD compared with longer gestation, and the risk increased with earlier GA cutoff. The results were not affected by sex or measures of fetal growth. Conclusion Our data confirm the role of shortened gestation in ASD risk. We warn against the use of prespecified or a data-driven GA cutoff, however; instead, we recommend systematically examining all plausible cutoffs for GA to avoid overstating the homogeneity of risk in children on either side of a given cutoff, as well as to increase the comparability of studies. (J Pediatr 2013;162:361-8).
A
utism spectrum disorders (ASD) include a range of behaviorally defined neurodevelopmental conditions characterized by impairments in social interaction and communication, as well as restricted and repetitive patterns of behavior and interests.1 ASD symptoms are heterogeneous, and little is known about their etiology.2 Genetic predisposition,3-5 maternal autoimmune disorders,6 parental age,7 and male sex8,9 have all been associated with an increased risk of ASD. Increasingly, other obstetric and environmental factors occurring early in life have been implicated.4,10-13 However, with the exception of child’s sex and rare mutations,14 no single factor has shown such a consistent association as to imply causality.15 Preterm birth (defined as less than 37 completed weeks of gestation16) has been identified as an obstetric risk factor associated with an elevated risk of ASD. In the US, the rate of singleton preterm births rose from 9.7% to 10.8% between 1990 and 2004, largely accounted for by rising numbers of late-preterm births, between 34 and 36 weeks gestation (representing 71.2% of the total preterm population).17 Canada has also witnessed an increase in preterm births.18,19 Even though prematurity is responsible for more than one-third of perinatal mortality,17 especially in those born at <28 weeks gestation,20 it is neither a defined disease nor a syndrome.21 Although <6% of preterm births occur before 28 weeks gestation,17 these extreme preterm children bear the brunt of the neurodevelopmental and physiological impairments, including an elevated risk of ASD.22 Many previous studies have examined the association between gestational age (GA) and ASD. Gillberg and Gillberg23 compared the week-by-week frequency distribution of GA (35-42 weeks) in 25 children with autism with that of 25 controls born in the same obstetric department and were of the same sex and matched “as closely as possible on time of birth” (likely within a year). They observed increased association with autism for those children born preterm and postterm. Their results also indicated a more heterogeneously distributed GA in the autism group. Although other studies have examined wider ranges of GAs in relation to ASD,9,11 most dichotomized their data, conventionally at 37 weeks gestation.12,24-26 Other dichotomies have specified 38 weeks27 or 36 weeks.15,28 Several studies have selected cutoffs that depict established convenFrom the Aerosol and Air Quality Research Laboratory, Department of Energy, Environmental and Chemical tions for different stages of late- or early-preterm gestation.29-31 Still other studies Engineering, Washington University in St. Louis, St. have focused exclusively on preterm and extremely preterm birth (eg, <26 Louis, MO; Department of Environmental and Occupational Health, School of Public Health, Drexel weeks,32-34 23-30 weeks,35 or <28 weeks36). Although the majority of these studUniversity, Philadelphia, PA; and Department of Pediatrics, Faculty of Medicine and Dentistry, University ies found a greater prevalence of ASD for shorter gestational periods, given the of Alberta, Edmonton, Alberta, Canada diversity of cutoffs used and the varying associations with ASD for each cutoff, I.B. and L.Z. are supported by a Population Health Investigator award and a Health Scholar award, respeccomparisons among studies (and future meta-analysis) are difficult, and the 1
2
3
ASD GA RR SES
Autism spectrum disorder Gestational age Relative risk Socioeconomic status
tively, from the Alberta Heritage Foundation for Medical Research. This study is based on data supplied by Alberta Health and Wellness (AHW) and the Alberta Perinatal Health Program (APHP). The interpretation and conclusions contained herein are those of the researchers and do not necessarily represent the views of the Government of Alberta, AHW, or the APHP. The authors declare no conflicts of interest. 0022-3476/$ - see front matter. Copyright ª 2013 Mosby Inc. All rights reserved. http://dx.doi.org/10.1016/j.jpeds.2012.07.040
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possibility of publication bias in situ exists.37 It is also noteworthy that conventional a priori GA cutoffs for prematurity used in ASD epidemiology do not necessarily reflect etiologically relevant categorizations, possibly undermining the ability to detect important associations. Evidence to date supports the hypothesis that an individual’s risk of ASD is influenced by the duration of gestation. However, the conventional method of dichotomizing a continuous variable according to some predefined cutoff point has been criticized by some for its somewhat arbitrary selection of cutoffs, creating groups with wide ranges of actual GA (eg, up to 3 months apart); lack of transparent reasons for selection of the particular cutoffs; difficulty facilitating between-study comparisons; and loss of potentially important information through factorization of an inherently continuous variable.38,39 It should be acknowledged that when only a limited sample size is studied, as is often the case with rare conditions such as ASD, some selection of dichotomization is intuitively justified, typically one informed by the literature on the boundary of “the highest-risk group.” Heavner et al38 suggested at least providing risk/effect estimates for a plausible range of dichotomizations, allowing readers to obtain the maximum information from the study and to more accurately compare results with other studies. We examined the association between GA at birth and the risk of ASD for each observed GA cutoff for which an association has been reported in the literature. If more children are born late preterm but the risk of developing neurodevelopmental problems increases for early preterms,35 then distinguishing between these different GA bands is vital to gaining a more comprehensive understanding of the doseresponse relationship between prematurity and risk of ASD. Although Gillberg and Gillberg attempted a similar study almost 3 decades ago,23 their analysis was based on a much smaller sample that did not encompass the wide range of GAs included in our analysis.
Methods A total of 273 343 singleton live births were recorded between January 1, 1998, and December 31, 2004, in Alberta, Canada. All births were identified using delivery records completed at the time of hospital admission as part of routine clinical care, or by the attending registered midwife in the event of a home birth. These delivery records also contain information on GA, recorded at the time of delivery by the care provider, along with mother’s age, child’s sex, birth date, and other antenatal and perinatal risk factors. (See Burstyn et al24 for detailed description and individual associations with the risk of ASD.) All birth records are held and maintained by the Alberta Perinatal Health Program, which also maintains quality control of all data entries. Any indeterminable errors were replaced with missing values by the Alberta Perinatal Health Program, thereby ensuring internal accuracy and consistency. Follow-up of those children identified by their individual Personal Health Number and matched to the delivery records held by the Alberta Perinatal Health Program and the 362
Vol. 162, No. 2 health insurance data held by Alberta Health and Wellness continued until March 31, 2008, ensuring at least 3 years of follow-up from birth. In Alberta’s universal health care insurance system, all residents are served by physicians and hospitals that bill the government for their services, with the fee linked to specific diagnostic codes from the International Statistical Classification of Diseases and Related Health Problems, 9th revision. The specific diagnostic codes listed with the physician billing record indicating “ASD claim” were 299.0 and 299.8. Those children who could not be identified by health insurance records or had died or were no longer a resident of Alberta during follow-up, those with unrecorded sex or GA data, and those not born within 23-43 weeks gestation were excluded from our analysis. Of the 4 ASD outcome definitions described by Burstyn et al,24 the most liberal (ie, at least one ASD claim by any physician, as defined earlier) was used for subsequent analysis. This definition was selected because a methodology comparable to ours was shown to have reasonable sensitivity (60%) and specificity (85%),40 and because our previous work with this dataset showed that choice of case definition did not affect the association between ASD and the risk factors relevant to current analysis: sex, maternal socioeconomic status (SES), and birth year.24 If the case definition does not affect observed associations with essential covariates, then it is reasonable to expect that analyses of association (although not enumeration of disease burden) based on the alternative definitions would be equivalent. Of course, the more liberal definition also increases the power of statistical analyses, an important consideration for a relatively rare condition such as ASD. GA was examined for each completed week of gestation between 23 and 43 weeks and compared with all later weeks (up to and including 43 weeks). Thus, infants born at <24 weeks were compared with all births of $24 weeks gestation, infants born at <25 weeks were compared with all births of $25 weeks gestation, and so on. The relative risk (RR) (with associated 95% CI) of developing ASD according to the week-by-week dichotomization of GA was calculated using log-binomial regression. Several risk factors were included in the model owing to their association with ASD risk observed in previous analyses of this cohort and the potential for confounding,24 including sex (given the preponderance of ASD in males compared with females), maternal health coverage (eg, aboriginal group, welfare recipient, low-income subsidy, which reflect SES and access to medical care), and birth year (to control for potential ASD diagnosis owing to duration of follow-up). Birth year and SES were included in the models not because of potential confounding, but rather to control for the likelihood of being diagnosed. Nonetheless, there are reasons to be wary of cofounding as well. There is an established relationship between prematurity and year in the source population (ie, our data).41 An association between prematurity and survival and SES in Canada (ie, the source population) is also well established.42 Sex is one of the few established strong risk factors for ASD and is associated with Leavey et al
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February 2013 prematurity.43 In the end, none of the adjustments affected the results (data not shown). The analysis was conducted for the whole cohort and then repeated for each sex separately (in which case sex was not included as a confounder). Although birth weight and weight for date (z-scores) were computed using an improved population-based Canadian reference for birth weight according to GA,44 we focused on GA to remove the possibility of erroneously including infants that were just small at birth and not premature. All analyses were performed using SAS 9.1 (SAS Institute, Cary, North Carolina). The SAS macro used to create curves that display the relationship between RR and cutoff for GA is available from the corresponding author on request. The 95% CIs on proportion for each sex and completed GA were calculated taking into account correction for continuity following the recommendations of Newcombe and Robert,45 and implemented using an online calculator (http://faculty.vassar.edu/lowry/prop1. html). The study protocol was approved by the University of Alberta’s Health Research Ethics Board.
as the infants included in the analysis (details not shown). The Table illustrates the distributions of week-by-week GA between 23 and 43 completed weeks and of ASD prevalence by sex. Although approximately 5000 more boys than girls were born, the distribution of births by GA was similar for each sex. In total, 17 830 (8.2%) preterm live births occurred between 23 and 36 completed weeks of gestation, of which 54% were males. The majority of these preterm births (13 108; 73.5%) occurred during late preterm (34-36 weeks), and only 657 of these (3.7%) would have been classified as extremely preterm (<28 weeks). Of this cohort, 1135 (0.52%) were diagnosed with ASD, including 949 males (0.85% of live-born males) and 186 females (0.17% of live-born females), for a male-to-female ratio of 4.9:1. The prevalence of ASD among those born extremely preterm was 1.22% (n = 8, all of whom were males, indicating an exteme sex ratio that likely arose by chance, as indicated by the wide 95% CI), compared with 0.61% (n = 80) for those born late-preterm, and 0.50% (n = 847) for those born full-term. The ASD prevalence rate of those born postterm mirrored that of full-term, at 0.50%. Figure 1 presents the RR of developing ASD for both sexes combined, adjusted for maternal SES plus child’s sex and birth year. A gradually increasing risk of ASD with shorter gestation was observed. There does not appear to be a step function in risk, just a smooth progression of increased risk for each decreasing week of gestation. This is especially apparent for cutoffs between 29 and 37 weeks. The analysis was repeated for each sex separately. The resulting RR curve for males follows the same pattern as for both sexes combined (Figure 2), owing to the preponderance of males with ASD in this cohort. Although the curve for females
Results Out of 273 343 singleton live births, 25 970 could not be identified by Alberta Health and Wellness, 28 421 had died or lost residence during follow-up, 66 had missing sex information, 444 had no recorded GA, and 332 were born outside of the specified 23- to 43-week range. These infants were excluded from the analysis, leaving a subsequent cohort of 218 110, with approximately equal numbers of males (51.1%) and females. The 28 421 infants excluded because of incomplete follow-up had a similar distribution of all studied risk factors Table. Week-by-week distribution of GA and ASD by sex Female
Male Prevalence
Completed week of gestation
Total children at risk
Number with ASD
Estimate
23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 Total
19 39 62 91 104 117 142 230 286 438 660 1052 1644 3318 7223 17 336 27 117 30 177 15 010 879 16 106 560
0 0 0 0 0 0 1 1 0 1 1 6 3 6 11 40 45 54 17 0 0 186
0 0 0 0 0 0 0.0070 0.0043 0 0.0023 0.0015 0.0057 0.0018 0.0018 0.0015 0.0023 0.0017 0.0018 0.0011 0 0 0.0017
Prevalence
95% CI*
Total children at risk
Number with ASD
Estimate
95% CI*
0-0.21 0-0.11 0-0.07 0-0.05 0-0.04 0-0.04 0.0004-0.04 0.002-0.03 0-0.02 0.0001-0.01 0.0001-0.01 0.0023-0.01 0.0005-0.01 0.0007-0.004 0.0008-0.003 0.002-0.003 0.001-0.002 0.001-0.002 0.0007-0.002 0-0.005 0-0.24 0.0015-0.0020
24 41 63 116 98 152 169 254 342 486 789 1222 1952 3920 8068 18 680 27 830 30 931 15 402 990 21 111 550
1 0 2 3 2 5 4 5 1 11 7 12 15 38 77 154 225 241 133 12 1 949
0.042 0.000 0.032 0.026 0.020 0.033 0.024 0.020 0.003 0.023 0.009 0.010 0.008 0.010 0.010 0.008 0.008 0.008 0.009 0.012 0.048 0.0085
0.002-0.23 0-0.11 0.006-0.12 0.007-0.08 0.004-0.08 0.01-0.08 0.01-0.06 0.007-0.05 0.0001-0.02 0.01-0.04 0.004-0.02 0.005-0.02 0.005-0.01 0.007-0.01 0.008-0.01 0.007-0.01 0.007-0.01 0.007-0.01 0.07-0.01 0.07-0.02 0.003-0.26 0.008-0.009
*95% CIs with continuity correction (see Methods).
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Figure 1. RR of ASD diagnosis by GA, both sexes (n = 318 1100). The middle line is the point estimate of RR compared with longer GA; the lower and upper lines represent boundaries of 95% CIs. The cutoff was changed from 24 to 42 in increments of 1 week; RR (adjusted for SES and birth-cohort) and 95% CI plotted.
(Figure 3) differs in terms of size and prevalence; for example, fewer females overall were diagnosed with ASD, and no females born at <29 weeks GA or >42 weeks GA were diagnosed with ASD, compared with 26 (2.3%) of males born in those periods. Overall, an increasing risk of ASD was linked with a decreasing number of completed weeks in the womb, a trend especially noticeable between 35 and 38 weeks gestation. Analysis by birth weight–adjusted z-scores did not reveal any marked differences in any of the aforementioned results (data not shown). Formal tests of interaction of GA with sex showed that GA was related to the risk of ASD to the same extent in boy and girls (details not shown).
Discussion In our population-based cohort, we observed an association between decreased length of gestation and increased risk of ASD. The prevalence of singleton live preterm births (<37 weeks gestation) in our study cohort was 8.2%, and although this rate is slightly higher than the 1996 reported prevalence for Canada of 7.1%,18 results are likely commensurate given the trend toward increasing rates of preterm birth in Canada each year.18,19 Although some previous studies have examined the prevalence of ASD exclusively in preterm populations29,31,32,35,36 or the frequency of preterm birth in a population of children already diagnosed with an ASD,30 the majority of studies have compared GA and the frequency of ASD in previously selected cohorts.9,11,25,27,46 Preterm birth is most commonly defined as birth at <37 weeks GA; 364
thus, cohorts are divided based on this cutoff. However, previous studies on the association of ASD with GA are difficult to compare, because it is not known whether the differences in ASD rates and length of gestation are related to differences in study samples/methods or in GA cutoffs. Some previous studies have used RR to examine the significance of GA on ASD outcome, for example, <37 weeks and <33 weeks GA,11 between 23 and 28 weeks and <31 weeks GA,46 and <35 weeks GA.9 Again, however, results were presented for only a handful of selected cutoffs, leaving the reader to wonder whether these cutoffs were the only ones examined or the only ones for which significance was observed, and also precluding observation of any potential trends in the data. Many possible explanations for the causal associations between GA and ASD risk have been advanced,46,47 and others have questioned whether any associations are indeed causal.9,11,15,25 For example, prenatal exposure to a stressful event continues to be linked to an increased risk of ASD,5,48 in which case stress may be the primary risk factor in ASD, with lower GA intermediate along the causal pathway toward ASD. Preterm labor may also result from fetal abnormalities (including those related to genetic differences, which may have shared vulnerability) or complications of pregnancy. Conversely, prenatal delivery itself may adversely affect the developing central nervous system, increasing the risk of ASD. Despite some uncertainty within the literature regarding the causal association between GA and ASD, our methodological approach confirms an association between GA and ASD across a wide range of gestational durations. Leavey et al
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Figure 2. RR of ASD diagnosis by GA, males only (n = 111 550). The middle line is the point estimate of RR compared with longer GA; the lower and upper lines represent boundaries of 95% CIs. The cutoff was changed from 24 to 42 in increments of 1 week; RR (adjusted for SES and birth-cohort) and 95% CI plotted.
Although a positive association between shortened gestation and increased ASD risk has been well documented in the literature, attempts to compare our results with those reported in other studies and to compare results among studies were hindered by the wide range of cutoffs depicting GA, as well as the different statistical methodologies applied in many of the studies. A general lack of clarity regarding the selection of cutoffs persist: whether they were based on a priori hypothesis or post hoc examination, whether the cutoffs presented were the only ones examined, or whether others were examined and deemed unimportant. Heavner et al38 raised concerns about such arbitrary approaches to cutoff selection, because artificial boundaries may cause a misguided interpretation of the data, thus distorting the reader’s understanding of the exposure–outcome relationship, with possible implications for future study designs or health policy. This type of categorization also may prevent readers from drawing their own conclusions, forcing them to interpret the results based on the GA cutpoint selected by the investigator. Moreover, findings that support an investigator’s hypothesis may be reported disproportionately, resulting in a publication bias within the literature. Our present results are presented as a curve informing the ASD risk in infants born at various GA cutoffs between 23 and 43 weeks relative to the risk in those born at later weeks. This method has been advocated as a means of minimizing the potential loss of information inherent to simple dichot-
omization,38 thus allowing us to evaluate the difference in risk conferred by different lengths of gestation. This method also increases transparency, permitting readers to identify possible thresholds of effect and to select the GA cutoff in which they are interested. This facilitates interstudy comparisons and elucidates any possible trends, thereby “maximizing the utility.of epidemiological studies.”38 For example, it is only because we analyzed and presented our results as a RR curve for all GA cutoffs that we were able to demonstrate the trend that each additional week that a fetus remains in utero (up to 42 weeks) reduces the risk of a later diagnosis of ASD. This is a potentially important observation that other studies are generally unable to make. It also suggests that at least 3 categories of GA may be appropriate in studies of ASD, with effects of both shortened and prolonged gestation examined. Like most general population studies of ASD, we had limited data on females, especially those with very short and prolonged GAs, limiting the conclusions that we can draw about this group. Even a large study like ours, including the entire population of Alberta (population >3 million), was underpowered for GA <29-30 weeks. This suggests that studies seeking to understand the risk of ASD in children with very short gestation need to be designed to draw on larger source populations. We excluded some originally identified singleton live births from our analysis owing to the lack of information on all covariates required for the present study. It should
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Figure 3. RR of ASD diagnosis by GA, females only (n = 106 560). The middle line is the point estimate of RR compared with longer GA; the lower and upper lines represent boundaries of 95% CIs. The cutoff was changed from 24 to 42 in increments of 1 week; RR (adjusted for SES and birth-cohort) and 95% CI plotted.
be noted that both sex-specific and overall rates of ASD were identical. This is not surprising, because in the currently selected dataset, only 2 ASD cases were removed compared with previous analysis24; thus, we believe that there is no important selection bias from this source. Despite the tendency for increased errors in GA on birth certificates compared with birth weights,31 and although uncertainty persists regarding the inconsistent methods used to ascertain GA,21 many advocate the use of GA instead of birth weight to assess prematurity, because low birth weight may be indicative of restricted fetal growth, not necessarily of preterm birth. Thus, using GA may minimize potential confounding by factors that might have restricted fetal growth.33,36,46,49 In addition, delivery records (which were used in the present study to obtain GA) have been reported to be a more reliable source than birth certificates, on which mistakes are more frequent.15 However, despite every effort to ensure reliability, the methods used to determine GA are not known, and thus some records may be more accurate than others. And although the liberal definition of at least one ASD claim by any physician used in the present study did not alter the association with ASD and the other risk factors (see Methods), the diagnostic criteria used by each physician to identify ASD is also not known. What is more, the etiology of any given preterm birth, and whether the different etiologies alter ASD risk,36 were not assessed. 366
This study highlights a new way of examining the association between GA and ASD risk, offering increased transparency and more information, facilitating comparisons with other studies and illuminating severity and overall trends. Our findings demonstrate a decreased risk of ASD for every additional week that a fetus remains in utero, with results most informative for cutoffs between 29 to 40 weeks (but plausibly applicable to the full range of GAs). There appears to be no step function in risk, but rather a smooth progression of increased risk with shortened duration of gestation. GA may be just one of the risk factors indicative of a suboptimal pregnancy, and further work to differentiate between the causes of preterm labor and birth and subsequent ASD is suggested across the full range of GAs. In short, our data confirm the role of shortened gestation in the risk of ASD. We warn against the use of prespecified GA cutoffs, however; instead, we recommend systematically examining all plausible cutoffs for GA to avoid overstating the homogeneity of risk in children on either side of a given cutoff, and to increase comparability among studies. Future studies presenting data as a risk curve may provide important insights where dichotomization otherwise needlessly obscures (or distorts) evidence contained in the data. n We thank Alberta Health and Wellness and the Alberta Perinatal Health Program for supplying and linking the data. We also thank Leavey et al
ORIGINAL ARTICLES
February 2013 Alex Marinov, Wendy Mitchell, Nancy Piche, Nancy Bott, and Sharon Zhang for assistance with development of dataset used for the project. Submitted for publication Feb 8, 2012; last revision received May 7, 2012; accepted Jul 17, 2012. Reprint requests: Igor Burstyn, PhD, Department of Environmental and Occupational Health, School of Public Health, Drexel University, 1505 Race Street, Room 1332, Philadelphia, PA 19102. E-mail: [email protected]
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