semester project

Prenatal and Infant Exposure to Thimerosal From Vaccines and Immunoglobulins and Risk of Autism

WHAT’S KNOWN ON THIS SUBJECT: Most previous research has not revealed an increased risk of autism associated with receipt of thimerosal-containing vaccines. Evidence is limited, however, on the timing of vaccination, especially prenatal exposure, and associations with different subtypes of autism.

WHAT THIS STUDY ADDS: This study revealed no increased risk of ASD associated with receipt of thimerosal-containing vaccines. No increased risk was found for subtypes of ASD, including ASD with regression, and prenatal exposure was not associated with a risk of ASD.

abstract OBJECTIVE: Exposure to thimerosal, a mercury-containing preserva- tive that is used in vaccines and immunoglobulin preparations, has been hypothesized to be associated with increased risk of autism spec- trum disorder (ASD). This study was designed to examine relationships between prenatal and infant ethylmercury exposure from thimerosal- containing vaccines and/or immunoglobulin preparations and ASD and 2 ASD subcategories: autistic disorder (AD) and ASD with regression.

METHODS: A case-control study was conducted in 3 managed care organizations (MCOs) of 256 children with ASD and 752 controls matched by birth year, gender, andMCO. ASD diagnoses were validated through standardized in-person evaluations. Exposure to thimerosal in vaccines and immunoglobulin preparations was determined from electronic immunization registries, medical charts, and parent inter- views. Information on potential confounding factorswas obtained from the interviews andmedical charts. We used conditional logistic regres- sion to assess associations between ASD, AD, and ASD with regression and exposure to ethylmercury during prenatal, birth-to-1 month, birth- to-7-month, and birth-to-20-month periods.

RESULTS: There were no findings of increased risk for any of the 3 ASD outcomes. The adjusted odds ratios (95% confidence intervals) for ASD associated with a 2-SD increase in ethylmercury exposure were 1.12 (0.83–1.51) for prenatal exposure, 0.88 (0.62–1.26) for exposure from birth to 1 month, 0.60 (0.36–0.99) for exposure from birth to 7 months, and 0.60 (0.32–0.97) for exposure from birth to 20 months.

CONCLUSIONS: In our study of MCO members, prenatal and early-life exposure to ethylmercury from thimerosal-containing vaccines and immunoglobulin preparations was not related to increased risk of ASDs. Pediatrics 2010;126:656–664

AUTHORS: Cristofer S. Price, ScM,a William W. Thompson, PhD,b Barbara Goodson, PhD,a Eric S. Weintraub, MPH,c

Lisa A. Croen, PhD,d Virginia L. Hinrichsen, MS, MPH,e

Michael Marcy, MD,f Anne Robertson, PhD,a Eileen Eriksen, MPH,f Edwin Lewis, MPH,d Pilar Bernal, MD,g

David Shay, MD, MPH,h Robert L. Davis, MD, MPH,i and Frank DeStefano, MD, MPHc

aAbt Associates Inc, Cambridge, Massachusetts; bNational Center for Chronic Disease Prevention and Health Promotion, cImmunization Safety Office, and hInfluenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia; dDivision of Research, Kaiser Permanente Northern California, Oakland, California; gDepartment of Psychiatry and Behavioral Sciences, Kaiser Permanente ASD Center San Jose Northern California Region, Stanford University, Palo Alto, California; eDepartment of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, Massachusetts; fSouthern California Kaiser Permanente, and Center for Vaccine Research, University of California, Los Angeles, California; and iCenter for Health Research Southeast, Kaiser Permanente, Atlanta, Georgia

KEY WORDS thimerosal, mercury, vaccines, immunoglobulins, autism

ABBREVIATIONS CDC—Centers for Disease Control and Prevention MCO—managed care organization ASD—autism spectrum disorder TCI—thimerosal-containing injection AD—autistic disorder ADI-R—Autism Diagnostic Interview-Revised ADOS—Autism Diagnostic Observation Schedule SCQ—Social Communication Questionnaire OR—odds ratio Hib—Haemophilus influenzae type b

The views in this article are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention.

www.pediatrics.org/cgi/doi/10.1542/peds.2010-0309

doi:10.1542/peds.2010-0309

Accepted for publication Jun 9, 2010

Address correspondence to Frank DeStefano, MD, MPH, Immunization Safety Office, MS D-26, Centers for Disease Control and Prevention, Atlanta, GA 30333. E-mail: [email protected]

PEDIATRICS (ISSN Numbers: Print, 0031-4005; Online, 1098-4275).

Copyright © 2010 by the American Academy of Pediatrics

FINANCIAL DISCLOSURE: Dr Marcy received honoraria for speaking for Merck and GlaxoSmithKline within the last 5 years and grant support for studies on Gardasil and ProQuad from Merck within the last 5 years; Mr Lewis received grant support from Medimmune, Sanofi Pasteur, Chiron, Wyeth, Merck, and GlaxoSmithKline; and Dr Bernal received research funding from the CDC, the National Institute of Mental Health, Health Resources and Service Administration, and Autism Speaks. The other authors have no financial relationships relevant to this article to disclose.

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Thimerosal has been used as a preser- vative in vaccines since the 1930s.1 It is 49.6%mercury byweight and ismetab- olized into ethylmercury and thiosa- licylate.2 In 1999, the US Food and Drug Administration estimated that infants who were immunized according to the recommended schedule might have received amounts of ethylmercury that exceed Environmental Protection Agency limits for exposure to methyl- mercury.1 As a precautionary mea- sure, the US Public Health Service and the American Academy of Pediatrics urged vaccine manufacturers to re- move thimerosal from all infant vaccines as soon as practical and recommended that studies be con- ducted to investigate the risks associ- ated with ethylmercury exposure from thimerosal-containing vaccines.3 In re- sponse, the Centers for Disease Con- trol and Prevention (CDC) planned studies to examine potential links be- tween ethylmercury exposure and de- velopmental outcomes. The first, a screening analysis that used com- puterized databases from 3 large managed care organizations (MCOs), examined relationships between eth- ylmercury exposure from childhood vaccines and several neurodevelop- mental conditions. No significant as- sociations with autism spectrum dis- order (ASD) were found.4 Two subsequent CDC-sponsored studies examined neuropsychological out- comes, but ASD was not assessed in either of them.5,6

Our current study was designed to examine the relationships be- tween ethylmercury exposure from thimerosal-containing injections (TCIs), which include thimerosal-containing vaccines and immunoglobulin prepara- tions, and any of 3 ASD outcomes: ASD; autistic disorder (AD); and ASD with re- gression. We used state-of-the-art assessment tools to confirm ASD out-

comes and evaluated both prenatal and postnatal exposure.

METHODS

We performed a case-control study in 3 MCOs that participate in the CDC’s Vac- cine Safety Datalink.7–9 The institu- tional review boards of the 3 MCOs, CDC, and Abt Associates Inc approved the study. The study protocol was de- veloped before data collection in con- sultation with a panel of external consultants that included autism advo- cates and experts in autism, child de- velopment, toxicology, epidemiology, biostatistics, and vaccine safety. All subgroup analyses and interaction tests were specified in the study proto- col before data collection.

Data sources included MCO comput- erized data files, abstraction of mater- nal and child medical charts, and standardized telephone interviews with the children’s biological mothers. Case-children underwent standard- ized in-person assessments to verify case status. Additional details regard- ing study design, analyses, and results can be found in technical reports avail- able online.10,11

Study Population

Children from each MCO were eligible to participate if they were born be- tween January 1, 1994, and December 31, 1999; had been continuously en- rolled in the MCO from birth until their second birthday and were currently enrolled at the time of sample selec- tion; and lived within 60 miles of a study assessment clinic. Children were 6 to 13 years old at the time of data collection. Children had to have lived with their biological mother since birth, and their family had to be fluent in English. Parents provided written consent to participate in the study. Children were excluded if they had the following medical conditions with known links to ASD traits: fragile X

syndrome; tuberous sclerosis; Rett syndrome; congenital rubella syn- drome; or Angelman syndrome. Re- cruitment was attempted for all eligi- ble case-children within the MCO populations. Control children were randomly selected from the MCO pop- ulations to match case-children within matching strata defined by birth year, gender, and MCO.

Case Enrollment and Verification

Potential case-children were identified by searching the MCO computerized records for relevant ASD International Classification of Diseases, Ninth Revi- sion, codes (299.0-ASD or 299.8-PDD NOS), supplemented by text-string searches at 1 MCO, and text strings and autism registries at another. Mothers of case-children were administered the Autism Diagnostic Interview-Revised (ADI-R),12 and case- children were directly assessed by us- ing the Autism Diagnostic Observation Schedule (ADOS).13

ASD consists of qualitative abnormali- ties in reciprocal social interactions and communication and restrictive, repetitive, and stereotyped patterns of behavior. Children who meet study cri- teria for ASD had ADOS scores that in- dicated abnormalities in all 3 areas and had ADI-R scores that indicated ab- normalities in reciprocal social inter- actions and either communication or patterns of behavior. Children who met study criteria for ADwere a subset of ASD children who had higher scores on all 3 areas of the ADOS, had ADI-R scores that indicated abnormalities in all 3 areas, and had onset at younger than 36 months. Using items from the ADI-R, ASDwith regression was defined as the subset of case-children with ASD who reported loss of previously acquired language skills after acquisi- tion. For additional details on case- ascertainment criteria, see the techni- cal report.10

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Assessors were trained and as- sessed for reliability using proce- dures developed by Dr Catherine Lord, 1 of the developers of the ADOS and ADI-R instruments. Assessors were blinded with respect to the thimerosal exposure status of the child and mother.

Controls

To reduce the likelihood that the control group included children with undiag- nosedASD, the lifetime formof theSocial Communication Questionnaire (SCQ)14

was administered as part of the mater- nal interview for children who had indi- cations of neurodevelopmental difficul- ties.10 Seven control-group children with SCQ scores higher than 15 were ex- cluded from the analyses (C. Lord, PhD, personal verbal communication, 2004).

Sample

Physician consent was required be- fore families could be recruited. Con- sent was requested for all case- children who met the eligibility requirements that could be ascer- tained from MCO records, before re- cruitment and eligibility calls, and for a randomly selected sample of controls that were matched to case-children within birth year, gender, and MCO matching strata. This sampling stage resulted in a pool of controls with phy- sician consent (Fig 1). As case-children were confirmed as eligible and en- rolled as study participants, random samples of matched controls were se- lected for recruitment from the pool of controls. The targeted control to case ratio was 3 to 1 within each matching stratum. Controls who were matched to case-children who later did not meet the study’s clinical assessment criteria for ASD were excluded from the analyses.

Ethylmercury Exposure From TCIs

Children’s histories of TCI receipts were obtained from computerized im-

munization records and abstracted medical charts. Mercury content of the TCIs was determined by linking the manufacturer, lot number, and year of receipt information to published da- ta15–17 and manufacturer records. Ma- ternal receipt of immunoglobulins, tet- anus toxoids, and diphtheria-tetanus during pregnancy was primarily as- certained from medical charts (81 re- ceipts) and less often from maternal interviews (6 receipts). Maternal re- ceipt of flu vaccine during pregnancy was infrequently recorded in medical charts (2 receipts) and primarily came frommaternal report (36 receipts). We defined postnatal exposure as micro- grams of ethylmercury divided by the weight of the child (in kilograms) at the time of administration of each TCI. Exposures were summed over the time periods of interest. Prenatal exposure was defined as the cumulative ethyl- mercury amount (in micrograms) of all TCIs received by the mother during her pregnancy with the child.

Covariates

Covariates tested for inclusion in the statistical models were child and fam- ily characteristics (maternal and pa- ternal age at birth of child, maternal education level, family income, single- parent status, birth order, twin/triplet, breastfeeding duration); maternal ex- posures during pregnancy (exposure to mercury from fish, from cosmetics or medicines, or from dental fillings; use of tobacco, alcohol, or illegal drugs; use of folic acid or valproic acid; viral infections; lead exposure); child birth conditions (birth weight, Apgar score, birth asphyxia, respiratory dis- tress syndrome, hyperbilirubinemia); early-childhood health conditions (anemia, lead exposure, pica, enceph- alitis); andmaternal health care–seek- ing behavior (Kotelchuck prenatal care index, cholesterol and Papanico- laou test screenings).

Statistical Analysis

We used the SAS 9.1 (SAS Institute Inc, Cary, NC) PHReg procedure to fit condi- tional logistic regressionmodels18 that accounted for matching within strata defined by birth year, gender, and MCO to estimate the odds ratios (ORs) for ASD outcomes associated with in- creases in ethylmercury exposure for 4 different periods: prenatal; birth to 1 month; birth to 7 months; and birth to 20 months. Models were fit with and without covariates. Covariates were retained in the final models if they sat- isfied a change-in-estimate19 criterion evaluated by dropping terms that re- sulted in a �10% change in exposure coefficients relative to a full model with all potential covariates.

All tests were 2-tailed, and statistical significance was set at P � .05. To fa- cilitate interpretation of results, we present ORs in 2 forms. The first is the OR associated with an increase of 1 unit of exposure, in which 1 unit equals 1 �g of ethylmercury for prenatal ex- posure or 1 �g of ethylmercury per kilogram of body weight for postnatal exposure. The second, which is used as an indication of the difference between low and high exposure, is the OR for a difference in exposure equal to 2 SDs for each particular exposure measure of interest. A 2 SD increase in exposure can be thought of as roughly the differ- ence between the 10th and 90th per- centiles on these measures. For the measure of prenatal ethylmercury ex- posure, 2 SDs is equal to 16.34 �g or a little more than the amount in typical Rhogam injections in use during the years included in our study. Two SDs of thebirth-to-1-monthmeasure is 4.08�g/ kg, and 2 SDs for the birth-to-7-month and the birth-to-20-month measures are 15.56 and 17.82�g/kg, respectively.

For the ASD outcome, for each 2 SD increase in mercury received in the prenatal, birth-to-1-month, birth-to-7- month, and birth-to-20 month periods,

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posthoc calculations indicate that the study had �80% power to detect ORs of 1.5, 1.7, 2.1, and 2.2, respectively.

In addition, by adding model terms to test for interactions, we examined whether the effect of postnatal

thimerosal exposure on the risk of the 3 ASD outcomes was modified by the gender of the child, concurrent

AD (n = 187)

AD (n = 187)

AD (n = 187)

ASD with Regression ASD with

Regression ASD with

regression (n = 49)

Physician consent not obtainedb

(n = 31)

Physician consent not obtainedb

(n = 31)

Physician consent not obtainedb

(n = 31)

Ineligibled

(n = 103)

Unlocated (n = 27)

Refused (n = 255)

Ineligibled

(n = 103)

Unlocated (n = 27)

Refused (n = 255)

Ineligibled

(n = 103)

Unlocated (n = 27)

Refused (n = 255)

Ineligibled

(n = 103)

Unlocated (n = 27)

Refused (n = 255)

Ineligibled

(n = 103)

Unlocated (n = 27)

Refused (n = 255)

Ineligibled

(n = 316)

Unlocated (n = 467)

Refused (n = 1203)

Did not complete clinical

assessment (n = 65)

Did not complete clinical

assessment (n = 65)

Did not complete clinical

assessment (n = 65)

Did not meet criteria

for ASD (n = 65)

Did not meet criteria

for ASD (n = 65)

Did not meet criteria

for ASD (n = 65)

Potential controls (n = 70,801)

Potential controls (n = 70,801)

Potential controls (n = 70 801)

Random sample (n = 5,028)

Random sample (n = 5,028)

Random sample (n = 5028)

Available control poolc

(n = 4,854)

Available control poolc

(n = 4,854)

Available control poolc (n = 4854)

Recruitment attempted (n = 2,760)

Recruitment attempted (n = 2,760)

Recruitment attempted (n = 2760)

Parent interview

completed (n = 774)

Parent intervrr iew

completed (n = 774)

Parent interview

completed (n = 774)

Controls (n = 762) Controls (n = 762)

Controls (n = 762)

Controls (n = 752) Controls (n = 752)

Controls (n = 752)

Controls (n = 724) Controls (n = 724)

Controls (n = 724)

Controls (n = 652) Controls (n = 652)

Controls (n = 652)

No ASD cases in matching stratumf

(n = 10)

No ASD cases in matching stratumf

(n = 10)

No ASD cases in matching stratumf

(n = 10)

No ASD cases in matching stratum

(n = 38)

No ASD cases in matching stratum

(n = 38)

No AD cases in matching stratum

(n = 38)

No ASD w/reg cases in matching

stratum (n = 110)

No ASD w/w reg cases in matching

stratum (n = 110)

No ASD w/reg cases in matching

stratum (n = 110)

Physician consent not obtainedb

(n = 174)

Physician consent not obtainedb

(n = 174)

Physician consent not obtainedb

(n = 174)

Ineligiblee

(n = 12) Ineligiblee

(n = 12) Ineligiblee

(n = 12)

Potential casesa

(n = 802) Potential casesa

(n = 802) Potential casesa

(n = 802)

Recruitment attempted (n = 771)

Recruitment attempted (n = 771)

Recruitment attempted (n = 771)

Parent interview

completed (n = 386)

Parent intervrr iew

completed (n = 386)

Parent interview

completed (n = 386)

Clinical Assessment Completed

(n = 321)

Clinical Assessment Completed

(n = 321)

Clinical assessment completed (n = 321)

Met study criteria for ASD

(n = 256)

Met study criteria for ASD

(n = 256)

Met study criteria for ASD

(n = 256) MatchedMatched

MatchedMatched

MatchedMatched

Matched Matched

Ma tch ed

Ma tch ed

FIGURE 1 Sample flow diagram. a Potential case-children had a diagnosis of ASD in their medical charts (see text for eligibility criteria). b Before recruitment, physician consent was required. c Physician consent was obtained for 4854 potential controls. From this group, random samples of controls (totaling 2760) were drawn, as needed, to match participating case-children within birth year, gender, and MCO matching strata. d Ineligibility was determined during recruitment or eligibility calls. e Ineligibility was determined from information obtained from parent interview, SCQ, or medical chart abstraction. f Controls were matched to case-children by birth year, gender, and MCO. If there were no potential case-children who met study criteria for ASD within a birth year, gender, and MCO matching stratum, the controls in that stratum could not be used in the analysis.

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antibiotic use, or prenatal thimero- sal exposure.

RESULTS

Characteristics of the Children

Of 771 potential case-children and 2760 controls selected for recruit- ment, 103 case-children (13.4%) and 316 controls (11.4%) were found to be ineligible (Fig 1). Among the 668 case-children and 2444 controls re- maining, 321 case-children (48.1%) and 774 controls (31.7%) participated in all phases of the study. Reasons for nonparticipation included inability to locate (cases: n� 27 [4.0%]; controls: n � 467 [19.1%]), refusal to partici- pate (cases: n� 255 [38.2%]; controls: n � 1203 [49.2%]), and difficulty scheduling or completing the clinical assessment (cases: n � 65 [9.7%]). Ninety-four control mothers and 14 case-mothers participated in a refusal survey. Among controlmothers, lack of time (62%) and distrust or ambiva- lence toward research (23%) were stated as primary reasons for nonpar- ticipation. For case-mothers, the pri- mary reasons were lack of time (50%), belief that child was ineligible (14%), and maternal health (14%). Among the 774 control participants, 12 (1.6%) were excluded because the analysis of their medical charts and parent inter- view data revealed they had exclusion- ary conditions. In addition, 10 controls were not included in the analysis be- cause there were no case-children who met study criteria for ASD within the relevant birth year, gender, and MCO matching strata (Fig 1).

Of the 321 potential case-children who participated in standardized assess- ments, 256 (79.8%) met study criteria for ASD (Fig 1). Among those who met criteria for ASD, 187 (73%) met the stricter criteria for AD, and 49 (19%) met criteria for ASD with regression.

Children were 6 to 13 years old at the time of data collection, 85% were male,

and 7% had low birth weight (Table 1). Maternal age, maternal education, ma- ternal marital status, and paternal age were similar for case-children and controls.

Relationships of ASD Outcomes to Ethylmercury Exposure

On average, case-children and control children had similar cumulative ethyl-

TABLE 1 Characteristics of Study Participants

Characteristic Children With ASD (N� 256), %

Controls (N� 752), %

Pa

MCO MCO-A 4 4 .49 MCO-B 43 46 MCO-C 54 49 Child’s year of birth 1994 14 16 .90 1995 15 15 1996 16 18 1997 21 19 1998 19 17 1999 14 14 Child’s age at time of interview/assessment, y 6 10 5 .04b

7 21 18 8 17 18 9 21 19 10 14 17 11 14 17 12 2 5 13 0.4 0.3

Gender Female 13 15 .37 Male 87 85 Birth weight, g

�1000 2 0.3 .16 1000–1499 0.4 1 1500–2499 7 5 2500–3999 76 79 �4000 16 15

Biological mother’s age at birth of child, y �20 2 1 .27 20–24 5 9 25–29 23 23 30–34 36 36 �35 35 30

Biological father’s age at birth of child, y �20 1 1 .65 20–29 20 24 30–39 60 56 40–49 17 17 �49 2 2

Mother’s education level No diploma 3 3 .75 High school graduate 15 15 Some college 19 22 College graduate 63 60 Single parent No 82 85 .29 Yes 18 15

Percentages of cases and controls were not exactly identical on matching variables (birth year, gender, MCO) because we did not always get exactly 3 matched controls per case within each matching stratum. a P for �2 test of independence between row (characteristic of study participant) and column (ASD case versus control). b Recruitment of controls lagged behind case-children so that controls could be recruited to match case-children who had agreed to participate within birth year, gender, andMCOmatching strata. The lagged recruitment meant that controls were an average of 3 months older than case-children at the time of interview/assessment.

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mercury exposures at the end of each exposure period (Table 2). Variation among children’s exposure amounts was attributable to variation in mer- cury content of TCIs (eg, Haemophilus influenzae type b [Hib] vaccines in use at the time contained 0, 12.5, or 25 �g of ethylmercury), use of combined ver- sus separate vaccines (eg, separate receipts of diphtheria-tetanus toxoids- pertussis and Hib vaccines could re- sult in twice the mercury exposure as receipt of a combined diphtheria- tetanus toxoids-pertussis-Hib vaccine) and variation in the number of TCIs received.

Exposure to ethylmercury from TCIs prenatally or in the first month of life was not significantly associated with any of the ASD outcomes (Table 3). The prenatal and birth-to-1-month results were similar even when adjusted for other covariates. In the adjusted anal- yses, however, increased cumulative exposures in the age ranges frombirth

to 7 months and birth to 20 months were both associated with decreased risk of all 3 ASD outcomes.

We found no significant differences in exposure effects between boys and girls for any of the ASD outcomes, no evidence that higher prenatal expo- sure exacerbated the effects of post- natal exposure, and no evidence that concurrent ethylmercury exposure and antimicrobial use was associated with risk of ASDs (for full model re- sults, see the technical report).10

DISCUSSION

We found no evidence that increasing ethylmercury exposure from TCIs was associated with increased risk of ASD, AD, or ASD with regression. The unad- justed model results showed no signif- icant associations between exposure and risk of ASD or AD. In the covariate adjusted models, we found that an in- crease in ethylmercury exposure in 2 of the 4 exposure time periods evalu-

ated was associated with decreased risk of each of the 3 ASD outcomes. We are not aware of a biological mecha- nism that would lead to this result. Analyses to explore potential explana- tions are presented in the technical re- port.10,11 For example, there were no significant differences between case- children and controls in the numbers of vaccines received up to ages 7 or 20 months. Case-children were more likely to have received thimerosal-free or combined Hib vaccines than con- trols and more likely to have received thimerosal-free hepatitis B vaccines, resulting in the slightly lower cumula- tive exposure amounts. Knowledge that a child had ASD was not likely to have influenced choice of vaccines be- cause none of the case-children had ASD diagnoses by 7 months old, and few had diagnoses by 20 months. There was no significant association between having an older autistic sib- ling and exposure levels. In addition,

TABLE 2 Cumulative Exposure to Ethylmercury According to Exposure Period

Case/Control Comparison/Exposure Period Cumulative Exposure Amount, �g

Case-Children Controls

Mean Minimum Maximum Mean Minimum Maximum

Case-children with ASD (n� 256) vs controls (n� 752) Prenatal 2.70 0 74.00 2.35 0 100.00a

Birth to 1 mo (28 d) 9.01 0 45.00 8.99 0 50.00b

Birth to 7 mo (214 d) 101.13 0 190.83d 103.54 0 187.50c

Birth to 20 mo (609 d) 133.58 0 300.00 137.00 0 262.50 Case-children with AD (n� 187) vs controls (n� 724) Prenatal 2.96 0 62.75 2.28 0 100.00 Birth to 1 mo (28 d) 9.40 0 45.00 9.01 0 50.00 Birth to 7 mo (214 d) 101.42 0 190.83 104.65 0 187.50 Birth to 20 mo (609 d) 134.64 0 253.33 138.54 0 262.50 Case-children with ASD with regression (n� 49) vs controls (n� 652) Prenatal 3.34 0 25.00 1.86 0 37.75 Birth to 1 mo (28 d) 9.08 0 45.00 8.92 0 50.00 Birth to 7 mo (214 d) 101.09 0 190.83 103.28 0 187.50 Birth to 20 mo (609 d) 140.12 0 253.33 136.80 0 262.50

Ethylmercury from thimerosal-containing vaccines and immunoglobulins. For descriptive purposes, the postnatal exposure amounts shown here were not divided by weight at time of vaccine receipt. Most vaccines in use at the time that case-children were infants contained 0, 12.5, or 25 �g of ethylmercury per dose. Among case-children with ASD, mean prenatal ethylmercury exposure was 2.70 and ranged from 0 to 74�g of ethylmercury from thimerosal-containing vaccines and immunoglobulins received by the mother during her pregnancy with the study child. a Maximum from maternal receipt of 2 immunoglobulins during pregnancy, each containing 50 �g of ethylmercury. b Maximum from child receipt of hepatitis B immunoglobulin (25 �g) and hepatitis B vaccine (12.5 �g) at birth and hepatitis B vaccine (12.5 �g) at 28 days of age. c Maximum from child receipt of 3 hepatitis B (12.5 �g), 3 diphtheria-tetanus-acellular pertussis (25 �g), and 3 Hib (25 �g) vaccines in first 7 months. d Maximum from child receipt of 2 hepatitis B (12.5 �g), 1 rabies (20 �g), 3 diphtheria-tetanus toxoids-pertussis (24.27, 23.28, and 23.28 �g), and 3 Hib (25 �g) vaccines in first 7 months.

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there was no substantive difference in the association between thimerosal exposure and risk for ASD among chil- drenwith an older autistic sibling com- pared with children without an older autistic sibling, nor did we find that ex- cluding children with older autistic siblings qualitatively changed our results.

Sensitivity analyses that assessed the effects of potentially influential obser- vations and potential sources of bias are presented in the technical report.11

For example, results from fitting mod- els separately to data from the 2 larg- est MCOs showed that the exposure es- timates in both were similar to the overall results. We found no evidence that the results were sensitive to ex- treme exposure amounts, extreme re- sidual values, or were being driven by a few unusual individuals. We further

determined that modeling exposure measures as linear terms was appro- priate. Use of postnatal exposure vari- ables that were not divided by the child’s weight at the time of vaccine receipt did not change our findings. Ex- clusion of low birth weight children from the analyses resulted in only a slight attenuation of exposure effects toward 0.

Our study’s primary limitations are those inherent in observational stud- ies. Specifically, althoughwewere able to control for many potential con- founders, there is no way of knowing whether a critical confounder was omitted, and the relatively low re- sponse rates suggest a potential for selection bias to influence the results. However, analysis of ethylmercury ex- posure levels of the entire selected sample, as assessed through the use

of computerized MCO records, indi- cated no significant differences among participant case-children, nonpartici- pant case-children, participant con- trols, and nonparticipant controls in cumulative exposure amounts at ages 1, 7, or 20months, suggesting that self- selection did not bias the results.11 In addition, all study children were MCO members for their first 2 years of life, and were members of the same MCOs 6 to 13 years later, at the time of sam- ple selection. Although unlikely, if there were a relationship between a family’s decision to leave or remain in the MCO and exposure level that dif- fered according to case/control sta- tus, then the results could be biased.

Reporting bias can also be a concern with case-control studies, particularly because of differential recall of expo- sures by case-children compared with

TABLE 3 Association Between Thimerosal Exposure and Autism Outcomes

Exposure Measure Unadjusted Model Results (No Covariates) Covariate Adjusted Model Results

1-U Difference in Exposure, OR (95% CLs)a

2-SD Difference in Exposure, OR (95% CLs)b

1-U Difference in Exposure, OR (95% CLs)a

2-SD Difference in Exposure, OR (95% CLs)b

Case-children with ASD (n� 256) vs controls (n� 752)

Prenatal 1.007 (0.990, 1.025) 1.125 (0.846, 1.495) 1.007 (0.988, 1.026) 1.119 (0.827, 1.513) Birth to 1 mo (28 d) 0.973 (0.898, 1.054) 0.894 (0.644, 1.240) 0.970 (0.889, 1.059) 0.883 (0.617, 1.264) Birth to 7 mo (214 d) 0.992 (0.966, 1.020) 0.887 (0.582, 1.351) 0.967 (0.937, 0.999)c 0.597 (0.360, 0.990)c

Birth to 20 mo (609 d) 0.991 (0.965, 1.016) 0.862 (0.533, 1.336) 0.968 (0.938, 0.998)c 0.598 (0.317, 0.971)c

Case-children with AD (n� 187) vs controls (n� 724) Prenatal 1.010 (0.991, 1.030) 1.179 (0.862, 1.614) 1.011 (0.990, 1.032) 1.196 (0.855, 1.674) Birth to 1 mo (28 d) 1.010 (0.927, 1.100) 1.040 (0.732, 1.478) 1.029 (0.935, 1.132) 1.123 (0.759, 1.661) Birth to 7 mo (214 d) 0.991 (0.962, 1.022) 0.875 (0.545, 1.404) 0.958 (0.924, 0.994)c 0.516 (0.290, 0.916)c

Birth to 20 mo (609 d) 0.992 (0.964, 1.021) 0.884 (0.520, 1.449) 0.962 (0.928, 0.996)c 0.544 (0.265, 0.938)c

Case-children with ASD with regression (n� 49) vs controls (n� 652) Prenatal 1.031 (0.993, 1.072) 1.656 (0.885, 3.095) 1.039 (0.997, 1.083) 1.860 (0.945, 3.660) Birth to 1 mo (28 d) 0.938 (0.794, 1.108) 0.769 (0.390, 1.519) 0.901 (0.761, 1.067) 0.653 (0.327, 1.303) Birth to 7 mo (214 d) 0.936 (0.880, 0.994)c 0.355 (0.138, 0.915)c 0.906 (0.848, 0.968)c 0.214 (0.076, 0.600)c

Birth to 20 mo (609 d) 0.953 (0.900, 1.009) 0.473 (0.154, 1.170) 0.925 (0.869, 0.985)c 0.297 (0.081, 0.764)c

Covariates for ASDmodels: birth weight, maternal age, birth order, breastfeeding duration, family income, maternal health care–seeking behavior (Kotelchuck inadequacy of prenatal care, use of cholesterol screening, use of Papanicolaou test screening), maternal exposures during pregnancy with study child (alcohol use, folic acid use, viral infection, lead exposure), and early childhood health conditions (anemia between 6 and 30months of age; pica before 3 years of age). CLs indicates confidence limits. Covariates for ADmodels: birth weight, maternal age, birth order, breastfeeding duration, family income, maternal health care–seeking behavior (Kotelchuck inadequacy of prenatal care, use of cholesterol screening, use of Papanicolaou test screening), maternal exposures during pregnancy with study child (folic acid use), and early childhood health conditions (anemia between 6 and 30 months of age; pica before 3 years of age). Covariates for ASD with regression models: birth weight, maternal age, family income, maternal education level, maternal exposures during pregnancy with study child (alcohol use). a OR for autism associated with a 1 U increase in exposure. For prenatal exposure, 1 U� 1 �g of ethylmercury. For postnatal exposure, 1 U� 1 �g of ethylmercury per 1 kg of body weight at time of vaccine or immunoglobulin receipt. b OR for autism associatedwith an increase in exposure equal to 2 SD units of the exposuremeasure. For themeasure of prenatal exposure, 2 SDs� 16.34�g. Two SDs of the birth-to-1-month measure is 4.08 �g/kg U. Similarly, 2 SDs of birth-to-7-month and birth-to-20-month exposures are 15.56 �g/kg and 17.82 �g/kg. c 95% CLs for the OR does not include 1.000.

662 PRICE et al by guest on March 23, 2017Downloaded from

controls. For measures of prenatal ex- posure, we used information obtained from the maternal interview on vacci- nation and immunoglobulin exposures during pregnancy. However, we at- tempted to minimize the effects of re- call bias by also using information re- corded in maternal medical charts.

ASDs are behaviorally defined and therefore difficult to diagnose defini- tively. Among the strengths of our study was the use of state-of-the-art assessment tools to validate the ASD diagnoses in children’s medical charts and the use of the SCQ assessment tool to exclude children with potentially un- diagnosed ASDs from the control group. Additional strengths were that measures of childhood exposure to ethylmercury from TCIs were derived from computerized and medical chart data sources and were therefore not susceptible to recall bias, and the col- lection of extensive information re- garding potential confounding factors.

Given that a large-scale prospective randomized trial is not ethically feasi- ble, no single study can definitively es- tablish or disprove the hypothesis that thimerosal exposure increases the risk of ASDs. Our study adds to the growing base of epidemiologic studies that have been conducted to investi- gate the hypothesis. In 2004 the immu- nization safety review committee of the Institute of Medicine20 published a review of the research evidence concerning relationships between thimerosal-containing vaccines and ASDs. The committee discussed the strengths and limitations of each study reviewed and concluded that the evidence available at that time did not demonstrate a link between thimerosal-containing vaccines and ASDs. Subsequently, 2 ecological stud- ies have found that the prevalence of ASDs continued to increase after the removal of thimerosal from childhood vaccines that began in 1999,21,22 and 2

studies of prenatal exposure via maternal receipt of thimerosal- containing immunoglobulin prepara- tions during pregnancy did not find as- sociations with ASDs.23,24

CONCLUSION

The results of our study of MCO mem- bers do not support the hypothesis that ethylmercury exposure fromTCIs admin- isteredprenatally orduring infancy is re- lated to increased risk of ASDs.

ACKNOWLEDGMENTS This work was supported by a contract from the CDC to America’s Health In- surance Plans and via America’s Health Insurance Plans subcontracts to Abt Associates Inc; Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School; Southern California Kaiser Per- manente, and Center for Vaccine Re- search, University of California Los An- geles; and Division of Research, Kaiser Permanente Northern California.

We thank clinical assessors Meg Man- ning, Seton Lindsay, Rachel Hundley, Mary Goyer Shapiro, Thomas Craw- ford, Liza Stevens, Anh Weber, Susan Bassett, Candace Wollard Bivona, Stephany Cox, Pegeen Cronin, James Earhart, Angela Geissbuhler, Elizabeth Lizaola, and Nuri Reyes; Natacha Ak- shoomoff, PhD (quality control clini- cian) from the School of Medicine, Uni- versity of California San Diego; Ellen Hanson (clinical quality control man- ager), Cathleen Yoshida (program- mer), Roxana Odouli (project man- ager), medical chart abstractors Darmell Brown, Martha Estrada, Jes- sica Locke, Sandy Bauska, Margarita Magallon, and Cat Magallon, and clinic coordinator Victoria M. Heffernan from the Division of Research, Kaiser Permanente Northern California; Tracy A. Lieu (principal investigator), Xian- Jie Yu (programmer/analyst), and Ru- pak Datta (recruiter andmedical chart abstractor) from the Department of

Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School; Joanne L. Mitchell (medical co- ordinator) and Deborah Samet (coor- dinator) from Harvard Vanguard Medical Associates, Developmental Consultation Service (Somerville, MA); Emily London (coordinator), Dorothy Yungman (autism management pro- gram coordinator), Linda Ford (re- cruitment), Tynesha Brown (recruit- ment and clinic coordinator), Daisy Gonzalez (recruitment and clinic man- agement), Norma Kirk (recruitment and clinic management), Zendi Solano (medical chart abstractor), Oliver De- laCruz (medical chart abstractor), Jerri McIlhagga (medical chart ab- stractor), Dotty Carmichael (adminis- trative support), Monica Marshall (ad- ministrative support), and Kathryn Lee (clinic coordinator) from the UCLA Cen- ter for Vaccine Research and Southern California Kaiser Permanente; Douglas Frazier (provision of information on thimerosal content of immunoglobulin preparations) from the US Food and Drug Administration (Bethesda, MD); Robert Chen, James Baggs, Fred Mur- phy, John Iskander, and Marshalyn Yeargin-Allsopp (for administrative and technical support) from the CDC; Kevin Fahey (for administrative and technical support) from America’s Health Insurance Plans (Washington, DC); Stephen Kennedy (project quality assurance advisor), Patty Connor (field director), Carter Smith (design phase support), Gerrie Stewart (de- sign phase support), Amanda Parsad (data analysis), Laura Simpson (data analysis), Julie Williams (data analy- sis), Yeqin He (data analysis), Bulbul Kaul (data analysis), Melanie Brown- Lyons (data management), Brenda Ro- driguez (survey management), and Mi- chael Harnett (survey management) from Abt Associates Inc; and external expert consultants David S. Baskin (De- partment of Neurosurgery, Methodist Neurological Institute); Sallie Bernard

ARTICLE

PEDIATRICS Volume 126, Number 4, October 2010 663 by guest on March 23, 2017Downloaded from

(SafeMinds); Philip W. Davidson (Strong Center for Developmental Dis- abilities Golisano Children’s Hospital, Strong University of Rochester School of Medicine and Dentistry); Irving Gottesman (Departments of Psychiatry and Psychology, University of Minne- sota Medical School); Catherine Lord

(Autism and Communication Disor- ders Center, Center for Human Growth and Development, University of Michi- gan); Thomas Saari (professor of pedi- atrics [Emeritus], Department of Pedi- atrics, Division of Pediatric Infectious Diseases, University of Wisconsin School of Medicine and Public Health);

Penelope H. Dennehy, MD (director of pediatric infectious diseases, Hasbro Children’s Hospital, and vice chair for academic affairs, Department of Pedi- atrics, and professor of pediatrics, Warren Alpert Medical School of Brown University); and Andy Shih (Au- tism Speaks).

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