Reports of rising prevalence of autism spectrum disorders (ASD), along with their profound personal and societal burden, emphasize the need of methodologically sound studies to explore their causes and consequences. We here present the design of a large intergenerational resource for ASD research, along with population-based prevalence estimates of ASD and their diagnostic validity.
The Stockholm Youth Cohort is a record-linkage study comprising all individuals aged 0–17 years, ever resident in Stockholm County in 2001–2007 (N = 589,114). ASD cases (N = 5,100) were identified using a multisource approach, involving registers covering all pathways to ASD diagnosis and care, and categorized according to co-morbid intellectual disability. Prospectively recorded information on potential determinants and consequences of ASD were retrieved from national and regional health and administrative registers. Case ascertainment was validated through case-note review, and cross validation with co-existing cases in a national twin study.
The 2007 year prevalence of ASD in all children and young people was 11.5 per 1,000 (95% confidence interval 11.2–11.8), with a co-morbid intellectual disability recorded in 42.6% (41.0–44.2) of cases. We found 96.0% (92.0–98.4) of reviewed case-notes being consistent with a diagnosis of ASD, and confirmed ASD in 85.2% (66.2–95.8) of affected twins.
Findings from this contemporary study accords with recently reported prevalence estimates from Western countries at around 1%, based on valid case ascertainment. The Stockholm Youth Cohort, in light of the availability of extensive information from Sweden's registers, constitutes an important resource for ASD research. On-going work, including collection of biological samples, will enrich the study further.
Citation: Idring S, Rai D, Dal H, Dalman C, Sturm H, Zander E, et al. (2012) Autism Spectrum Disorders in the Stockholm Youth Cohort: Design, Prevalence and Validity. PLoS ONE 7(7): e41280. doi:10.1371/journal.pone.0041280
Editor: Valerie W. Hu, The George Washington University, United States of America
Received: February 21, 2012; Accepted: June 19, 2012; Published: July 20, 2012
Copyright: © 2012 Idring et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The source of funding is Stockholm County Council, Centre for Psychiatric Research, http://www.webbhotell.sll.se/Psykiatriforskning/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders characterized by qualitative impairments in social interaction, communication and restricted and stereotyped patterns of interests and behaviours. ASD are classified as pervasive developmental disorders in current classification systems, ICD-10 and DSM-IV-TR, which include autistic disorder, Asperger disorder, pervasive developmental disorders not otherwise specified, childhood disintegrative disorder, and Rett disorder , .
The apparent prevalence of autism spectrum disorders (ASD) has risen sharply over the past two decades , recently estimated from approximately 1% in large studies ascertaining cases from populations with identified needs or symptoms that may be associated with ASD in the US and the UK ,  to 2.6% in a South Korean study actively screening for ASD in a general population sample . Although changes in diagnostic practices and wider recognition may explain part of this rise, a true increase in incidence of ASD has not been ruled out . Recent Scandinavian reports are inconsistent, with ASD prevalence ranging from 0.13 to 1.2% . Furthermore, Scandinavian studies have frequently ascertained ASD cases via health care registries , , , . This approach may underestimate the prevalence of ASD, since affected children require social and educational interventions more often than health care.
Although ASD are heritable disorders, environmental factors are considered increasingly important in their etiology , . However, the causes of ASD remains poorly understood since studies are still comparatively few and often hampered by methodological limitations such as small samples and lack of prospective data on exposures or confounders.
In light of an appearingly rising prevalence along with the profound individual and societal burden of ASD, there is an urgent need of large, prospective, population-based studies exploring modifiable risk factors. The Stockholm Youth Cohort (SYC), a register-based total population study, was established as such a resource for ASD research. We here describe the design of the SYC, including completed and ongoing data collection, and present findings from studies on the validity of our case ascertainment. Furthermore, we estimate the prevalence of ASD in Stockholm County and discuss opportunities for future research.
Study population and design
The Stockholm Youth Cohort is a record-linkage study comprising all children aged 0 through 17 years resident in Stockholm County at any time in 2001 through 2007 (total N = 589,114), identified through the Register of Total Population (provided by Statistics Sweden). The primary key for register linkage was the unique personal identification number assigned to each Swedish citizen at birth or upon arrival in Sweden for immigrants . These numbers are recorded in all contacts with health care, social and administrative services, enabling complete and accurate register linkage. Register linkage was conducted by Statistics Sweden, which also replaced personal identification numbers with unique SYC identification numbers to maintain individual anonymity. To maximize the possibility of being registered with a diagnosis of ASD, all children not residing within Stockholm County for at least four years were excluded (N = 144,960). Thus, the final study population for the present report includes 444,154 individuals.
All ASD related services, including diagnosis and follow-up health, special educational and social care are provided by services run by, or contracted with the Stockholm County Council and available free of charge. Referrals for diagnostic evaluation of suspected ASD are commonly made by child healthcare centres, whose health- and developmental surveillance program engages 99.8% of all preschool children . Developmental surveillance is performed by specially trained child healthcare centre nurses at regular intervals (1, 2, 6, 10–12, 18, 36, 48 and 60 months of age), with examination by a paediatrician at key ages (2, 6, 10–12 months) and in case of developmental deviation or according to need at other age intervals. Speech abilities and language comprehension are evaluated by nurses at 36 and 48 months, and examination of sight and hearing is made at 48 months. The purpose of developmental surveillance is to ensure timely discovery of developmental problems such as cerebral paresis, speech disorders, ASD, intellectual disabilities and ADHD . Referrals for diagnostic evaluation of suspected ASD may also be requested by parents through general practitioners, paediatricians, child psychiatrists, speech therapists, or by schools as well as other health or social care agencies. Diagnostic evaluations are made by multi-professional teams, typically consisting of at least a psychologist and a medical doctor at child paediatric or mental health services, according to the DSM- or ICD classification system . Habilitation services are offered to all children with a diagnosed ASD, and include multimodal interventions such as special education, parental education programs, school based interventions and staff training, occupational therapy, social care, or other services as relevant.
ASD case status as of December 31, 2007, was ascertained using four national and regional registers, covering all the pathways of ASD diagnosis and care in Stockholm County that we were aware of. These included (with the respective case proportion ascertained from each source in parentheses): 1) the Habilitation Register (67.7%), 2) Clinical Database for Child and Adolescent Psychiatry in Stockholm (58.3%), 3) the VAL database (44.4%), and 4) the National Patient Register (14.1%). ASD was defined as a recorded diagnosis of ICD-9 (299) or ICD-10 (F84) in 3) or 4), and DSM-IV (299) in 2), or in case of Habilitation Register, through registration as a service recipient in these specialist centers, where a formal ASD diagnosis is a pre-requisite before referral. The registers used for case ascertainment are further described in an overview of record-linkages to national and regional registers in the SYC (Table 1). Since the information of DSM-IV subcategories was not readily available in all registers, we divided children with ASD into two groups based on the presence of co-morbid intellectual disability (defined as IQ<70 and functional impairment by international and Swedish norms). Intellectual disability was ascertained as a recorded diagnosis of 317–319 or F70-79 according to ICD-9 and ICD-10, respectively, and 317–319 according to DSM-IV , , , and supplemented using the Habilitation Register (Table 1), which categorizes service recipients as having autism with or without intellectual disability.
Validation of case ascertainment
We used two validation procedures to assess our ASD case ascertainment.
In a case note validation study, we drew a random sample of 100 ASD cases without, and 100 ASD cases with, co-morbid intellectual disability. After ethical approval, we requested de-identification of these cases by Statistics Sweden, and requested their case-notes from the relevant clinical unit responsible for their care. Using this process, we were able to retrieve case-notes for 177 cases (88.5%). The remaining 23 notes were either missing or had non-responding clinical units. Case-noteswere scrutinized according to a case validation survey constructed by a child psychiatrist and neuropediatrician (H.S., S.I.) and a learning disability psychiatrist (D.R.). The survey covered documented diagnoses, age at diagnosis, evaluation procedures (according to current County Council guidelines , i.e. parental interviews, child observation, psychometric and diagnostic tests and interviews, medical examination, and complementary assessments), and information on any referral to health and/or community services related to ASD with or without intellectual disability. Two clinical experts, a child psychiatrist (S.I.) and a neuropediatrician reviewed these case notes. Of the 177 notes studied, 74 were assessed independently by both assessors, blinded of each other. The criteria used to determine final case status were 1) a case-note documented diagnosis of ASD with or without intellectual disability according to ICD-9, ICD-10 and DSM-IV (see above) and at least one the remaining criteria, 2) documented evidence of use of a structured diagnostic process, 3) evidence of referral to health- and/or community services related to ASD with or without intellectual disability.
We also cross validated the SYC cases against information from a national population-based study of twins (the Child and Adolescent Twin Study in Sweden- CATSS) . In CATSS, ASD was assessed via parental report and a comprehensive screening interview for neuro-developmental disorders (A-TAC), which is considered a reliable and valid screening tool for ASD , . We identified all twins co-occurring in SYC and CATSS, and estimated the proportion of SYC twins confirmed as ASD cases according to CATSS.
Other data collection
Using national personal identity numbers assigned to all Swedish nationals, first degree relatives (adoptive and biological parents, and siblings) have been identified through the Multigenerational Register . Additional data available in the SYC include perinatal and social characteristics, somatic and mental disorders, legal drug use, sick-leave, disability pension, education and scholastic achievements, and crime convictions. This information has been retrieved via record linkage to national and regional health data and administrative registers, for both the index population and their relatives (Table 1).
The Clopper-Pearson method was used to calculate the 95% confidence intervals (CI) around proportions. Results by sex and age were compared using the Chi2 statistic, and its associated degrees of freedom used to calculate the p-value. Inter-rater reliability was calculated as a kappa (Κ) coefficient  of concordance of final case status (overall and by presence of co-morbid intellectual disability) between the two reviewers. IBM SPSS version 19 was used for all statistical analyses.
The study was approved by the Regional Ethical Review Board in Stockholm. In accordance with their decision and Swedish regulations, we did not obtain informed consent from participants involved in the study.
Table 2 describes the year 2007 distribution of the Stockholm Youth Cohort study population by case status, according to sex and age. Overall, 5,100 individuals with a recorded diagnosis of ASD were identified, of which 2,172 (42.6%, 95% CI 41.0–44.2) had a registered co-morbid intellectual disability.
Age-specific prevalence of ASD with and without intellectual disability
Age-specific prevalence of ASD with and without intellectual disability is presented overall and by sex in Table 2 and Figures 1a–c. The overall prevalence of ASD in 2007 was 11.5 per 1,000 (11.2–11.8), ranging from 6.5 per 1,000 among children aged 4–6 years to 14.6 per 1,000 among those aged 13–17 years. The proportion of ASD cases with co-morbid intellectual disability was 35.5% (29.4–41.6), 47.9% (45.3–50.5) and 41.0% (38.9–43.1) among children aged 4–6, 7–12 years and 13+ years, respectively. The male: female prevalence ratio for ASD overall was 2.6∶1, which was similar for ASD with and without intellectual disability (2.7∶1 versus 2.5∶1). This ratio decreased with age (from 5.1∶1 at age 8, to 1.9∶1 at age 18), especially for ASD without intellectual disability.
Points indicate the observed prevalence (cases per 1,000) for each age (4–23 years). An empirical mode decomposition smoothing curve is superimposed. A) ASD prevalence with or without co-morbid intellectual disability B) ASD prevalence by gender C) ASD prevalence by gender with or without co-morbid intellectual disability.
Validation of case ascertainment
Of the 177 randomly selected case-notes that were scrutinized, 170 (96.0%; 92.0–98.4) were consistent with a diagnosis of ASD. Among the 7 non-confirmed ASD cases (6 boys and 1 girl), 2 presented with autistic traits, 1 had a registered diagnosis of attention/hyperactivity disorder (ADHD), 1 had a registered diagnosis of ADHD and speech and language disorder, and 1 had ADHD but had also received services at a Habilitation centre targeted at Asperger's disorder. The remaining 2 non-confirmed ASD cases had a registered diagnosis of language delay and hereditary muscle atrophy, respectively.
When assessing the validity of the cases according to their co-morbid intellectual disability status, a higher proportion of cases without (77 out of 87, 88.5%), than those with (68 out of 90, 75.6%), co-morbid intellectual disability was confirmed. Of the 22 non-confirmed ASD cases with intellectual disability, 17 had ASD but not any documented intellectual disability. Inter rater agreement on ASD status was attained in 71 of the 74 cases reviewed by both raters (corresponding to a Κ of 0.91). The proportion of confirmed cases was significantly lower in boys than in girls (p<0.05) for ASD with intellectual disability (Table 3). Children aged 7–12 years seemingly had a higher portion of confirmed cases than other age groups, but differences were not statistically significant.
Where such information was available (n = 148), the median age at diagnosis was 8.0 years for ASD overall (range 1–19, interquartile range [IQR] 8.0). For cases without and with intellectual disability (n = 80 and 68), the corresponding ages were 11.5 (range 4–19, IQR 6.0) and 6.0 (range 1–17, IQR 4.0) years. Girls (n = 48) were older than boys (n = 100) at diagnostic assessment (median age 11.0 as compared to 8.0 years).
In the validation sample, 54.2% of the cases were found to be diagnostically evaluated within mental health services, 35.9% within pediatric clinics and 9.8% in other settings (i.e. outside Stockholm County Council). A majority of cases were evaluated according to current regional practice guidelines . All evaluations included patient history by parental report, 95.9% comprised child observation in naturalistic settings, and 92.6% included an assessment of the child's intellectual ability using standardized intelligence tests. Diagnostic evaluations were performed by multi-disciplinary teams in 95.0% of the validation sample; complementary assessments as part of the diagnostic evaluation procedure by a speech therapist were made for 38.0%, physiotherapist 19.0%, occupational therapist 14.9%, and pedagogical assessment 18.2% of cases. Assessment of intellectual ability was performed using a standardized neuropsychological evaluation based on the child's age (such as different versions of the Wechsler scales, Sniders-Oomen Nonverbal Intelligence Test and Leiter).
A total of 27 twins with of ASD were identified in SYC and, according to our cross-validation against diagnostic information in the CATSS, 23 (85.2%, 66.2–95.8) of these had an ASD confirmed in the CATSS. A total of 27 (1.0%, 0.7–1.4) of the non-case twins in our study (n = 2721) received an ASD diagnosis in CATSS.
In this paper the SYC is presented as a resource for population-based research on ASD. Using a multisource ascertainment in Stockholm County, 5100 cases of ASD were found in the SYC. The 2007 year prevalence of ASD in all children was 1.2%, with a recorded intellectual disability in 43% of cases. Among randomly reviewed ASD cases in a validation study, 96% were consistent with a diagnosis of ASD.
The 2007 year prevalence of ASD varied across age groups in the SYC, being lower amongst the oldest and youngest children. Truncation, i.e. key registers used for case ascertainment only being started in 1997 and 2001, respectively, may have deflated the observed ASD prevalence among older children. The lower rates among young children may instead be explained by reluctance among clinicians to label children with ASD already at very young ages, and/or by long diagnostic evaluation lead times . This notion is supported by our finding of median age at ASD diagnosis of 8 years. This age corresponds to findings from a Danish study of a similar age group (4–26 years), reporting a mean age at ASD diagnosis of 8.9 years . Despite a possible underascertainment of ASD among pre-school aged children, ASD prevalence rates in the SYC are somewhat higher compared to a recent population-based estimate of autistic disorder among a large population of 6-year olds in California largely diagnosed by 5 years of age , although our case definition is different. As expected, ASD cases with co-morbid intellectual disability were diagnosed at an earlier age than those without intellectual disability . Further in agreement with previous findings, girls in SYC were diagnosed at a later age than boys , .
Our results are comparable to those from recent Scandinavian studies ascertaining ASD cases through active screening and diagnostic evaluation in general populations , , , which reported ASD prevalence estimates of 0.8–1.2%, however from small samples , or studies possibly prone to participation bias . Our results are also similar to the 1.2% and 0.9% prevalence estimates, reported by large UK and US studies, respectively, using active screening and diagnostic evaluations (through clinical assessment or case-note review) in populations with identified special education needs, symptoms associated with ASD or co-morbid conditions , . A lower prevalence (0.6%) was, however, reported from another UK study of comparable design . Studies like these may overlook affected children in the general population who, due to various barriers to care, remain unidentified , . Indeed, studies employing active screening and diagnostic evaluations in population-based samples have found higher rates of ASD, including recent reports from the UK and South Korea where prevalence estimates approached 2% and 3%, respectively , . These findings, however, also call for replication as estimates may be biased by non-participation , .
It is notable that cases ascertained using the National Patient Register, which predominantly covers hospital admissions, used in several epidemiological studies of ASD in Sweden , , comprised only 14% of our case sample. Similarly, case ascertainment based solely on psychiatric care may have resulted in somewhat lower prevalence estimates at 0.5% and 0.7% in the Swedish city of Gothenburg and in Denmark, respectively , .Correspondingly, more ASD cases were found in comparative age groups in the SYC than in a recent study solely ascertaining cases from specialized autism habilitation services  illustrating the advantage of our multiple source case ascertainment strategy.
Although the male dominance in ASD occurrence in our data is in line with one of the most consistent findings in autism, our overall male: female ratio was of a smaller magnitude than the commonly reported 4∶1 ratio . Among children younger than 13 years in SYC, however, the sex ratio was in agreement with some studies , , , and higher than in two Japanese studies , . Although the underlying mechanisms for the skewed sex ratio in ASD are still unknown , a relative under-ascertainment, and/or delayed diagnosis, of ASD in females may contribute , . This reasoning is underpinned by our findings of a decreasing male: female ratio with increasing age, especially in ASD without intellectual disability.
Proportion of ASD cases with Intellectual Disability
ASD is reported to presents with co-morbid intellectual disability in 25 to 70% of cases . The proportion of ASD cases with intellectual disability in our study was comparable to, or somewhat lower, than that in recent studies identifying ASD through screening and diagnostic evaluations , , . Kim et al, however, reported lower rates of co-morbid disability than us from their South Korean study in a general-population sample screened for ASD , indicating that children with ASD and normal IQ may not be identified by healthcare, special educational or social support services. The proportion of ASD cases with co-morbid intellectual disability peaked in 7–12 year olds, probably since intellectual disabilities are often diagnosed at this age .
Validation of ASD cases
Through our clinical case-note review, we found 96.0% of scrutinized case-notesto be consistent with a diagnosis of ASD. Furthermore, diagnostic procedures were in accordance with practice guidelines  in almost all evaluations. Studies using Danish and Finnish health care registers ,  have achieved similar results. Our cross-validation against the CATSS ,  also generated high ASD confirmation rates (85.2%), as well as evidence that very few of the non-case twins in our study received an autism diagnosis in the CATSS. We found a higher proportion of confirmed cases with intellectual disability among girls than boys, which may be due to a later age of diagnosis in girls. The median age of diagnosis in the validation sample was relatively high considering the systematic developmental surveillance system in Sweden. There is a possibility that age at diagnosis may have been affected by previous tradition within the Swedish child- and adolescent mental health services to avoid diagnostic labelling of young children, and/or long waiting times for diagnostic evaluation, as well as its process being performed during a long time period . Likewise, a high age at ASD diagnosis was found in neighbouring Denmark  which offers a comparable social service system, and in the UK where age at diagnosis was found to be 9.6 years in cases with Asperger's syndrome .
Prevalence estimates in our study were based on ascertainment of previously diagnosed and identified cases only and may be conservative, since previous findings suggest that 40% of ASD cases may go unidentified , . There is a possibility that young children remain unidentified in the SYC considering the relatively high age at diagnosis found in our validation study. Studies based on routine service use generally report lower ASD rates than those where active systems of screening and diagnostic evaluations were set up for research purposes , . Nonetheless, the well-developed ASD diagnostic and care pathways in Stockholm County, including universal developmental screening at child healthcare centres, is likely to have enabled identification of a large proportion of children with at least clinically important ASD. However, since outcome misclassification cannot be ruled out, findings from this study should be interpreted with this potential bias in mind. It can be hypothesized that such misclassification is likely to be independent from exposure, and hence lead to underestimation of true associations rather than false positive findings. Since information on DSM or ICD subcategories was not readily available in all registers, we could not ASD according to these classifications. However, as boundaries between subcategories are not clearly supported in literature , we instead defined ASD according to DSM-V work group recommendations , and used an empirically supported division based on co-morbid intellectual disability . Another limitation is that our case validation was based on case-note review rather than direct clinical assessment.
During recent years, a number of impressive large-scale epidemiological studies of environmental exposures, comprising biological samples have been initiated , , , , . While offering important opportunities for ASD research, potential drawbacks of these studies include case ascertainment through routine health service use only , selection bias through non-participation , , , retrospective data collection , , attrition and limited generalizability to the general population . Moreover, the tracking of ASD outcomes requires several years of follow-up, meaning that assembled biological samples will not yield reportable findings until years later in recently initiated prospective studies. The SYC was therefore set up to complement other endeavours, taking advantage of Sweden's rich system of total population registers containing prospectively collected data, and national biobanks containing biological samples from both neonates and pregnant women. Relatives have been identified via the Swedish Multi-Generation Register , and various early life determinants of ASD can be studied with control for unmeasured confounding by shared familial factors through appropriate designs such as sibling control analyses . Record linkage to the Medical Birth Register  enables etiological research into pre- and perinatal exposures, whereas data from other health care registers with diagnostic information allows for research on morbidity and mortality outcomes in ASD. Scholastic and other social consequences of ASD can be studied through record linkage to National School Registers , Integrated Database for Labour Market Research , Social Insurance Registers and National Convictions Register . In order to explore further associations on ASD and environmental exposures and confirm previous findings suggesting a potential association between ASD and hazardous air pollutants , there is a possibility to assign exposure levels of air pollutant concentrations to ASD cases by census tracts of birth residence. Perinatal exposure to potential neurodevelopmental toxicants  can also be analyzed through linkage to data on parental occupation from the 1990 census. At present, we are in the process of collecting stored biological samples for a case-control sample nested in the SYC. These include routinely collected neonatal blood spots (gathered for the purpose of screening for phenylketonuria since 1975) and maternal blood samples from antenatal health care (collected during first trisemester of pregnancy for the purpose of screening for congenital infections), stored by the Swedish Biobank. These specimens can be examined for biomarkers of relevance to ASD etiology – for example within domains of infectious agents, inflammation or steroid hormones. Lastly, means of collecting anthropometric and developmental data from existing child healthcare centre records health are currently explored.
Findings from this total population cohort in Stockholm County confirm recent reports of ASD prevalence at around 1%, based on valid case ascertainment. The Stockholm Youth Cohort study design, through its exhaustive case ascertainment and subtyping of ASD by intellectual disability, provides a potent platform for further research of these complex disorders. Linkage with a large range of registers, potential for access to biological samples and developmental data opens opportunities to investigate the etiology and outcome trajectories of ASD.
The authors thank Anna Shchokina, M.D., for contributions to the case validation study.
Conceived and designed the experiments: SI DR HD CD HS EZ BL ES CM. Performed the experiments: SI HD. Analyzed the data: SI DR HD BL CM. Wrote the paper: SI DR HD CD HS EZ BL ES CM.
- 1. International Statistical Classification of Diseases and Related Health Problems 10th revision (2010) World Health Organisation. Accessed 2011 Dec 13.
- 2. American Psychiatric Association (1994) Diagnostic and Statistical Manual of Mental Disorders (4th edition) (DSM-IV). Washington DC.: American Psychiatric Association. 866 p.
- 3. Fombonne E (2009) Epidemiology of pervasive developmental disorders. Pediatr Res 65: 591–598.
- 4. Prevalence of autism spectrum disorders-Autism and Developmental Disabilities Monitoring Network, 14 sites, United States (2008) MMWR Surveill Summ 61: 1–19.
- 5. Baird G, Simonoff E, Pickles A, Chandler S, Loucas T, et al. (2006) Prevalence of disorders of the autism spectrum in a population cohort of children in South Thames: the Special Needs and Autism Project (SNAP). Lancet 368: 210–215.
- 6. Kim YS, Leventhal BL, Koh YJ, Fombonne E, Laska E, et al. (2011) Prevalence of autism spectrum disorders in a total population sample. Am J Psychiatry 168: 904–912.
- 7. Williams JG, Higgins JP, Brayne CE (2006) Systematic review of prevalence studies of autism spectrum disorders. Arch Dis Child 91: 8–15.
- 8. Gillberg C, Cederlund M, Lamberg K, Zeijlon L (2006) Brief report: “The Autism Epidemic” J Autism Dev Disord 36: 429–435. The registered prevalence of autism in a Swedish urban area.
- 9. Hultman CM, Sandin S, Levine SZ, Lichtenstein P, Reichenberg A (2011) Advancing paternal age and risk of autism: new evidence from a population-based study and a meta-analysis of epidemiological studies. Mol Psychiatry 16: 1203–12.
- 10. Hultman CM, Sparen P, Cnattingius S (2002) Perinatal risk factors for infantile autism. Epidemiology 13: 417–423.
- 11. Parner ET, Thorsen P, Dixon G, de Klerk N, Leonard H, et al. (2011) A Comparison of Autism Prevalence Trends in Denmark and Western Australia. J Autism Dev Disord 41: 1601–8.
- 12. Hallmayer J, Cleveland S, Torres A, Phillips J, Cohen B, et al. (2011) Genetic Heritability and Shared Environmental Factors Among Twin Pairs With Autism. Arch Gen Psychiatry 68: 1095–102.
- 13. Newschaffer CJ, Croen LA, Daniels J, Giarelli E, Grether JK, et al. (2007) The epidemiology of autism spectrum disorders. Annu Rev Public Health 28: 235–258.
- 14. Ludvigsson JF, Otterblad-Olausson P, Pettersson BU, Ekbom A (2009) The Swedish personal identity number: possibilities and pitfalls in healthcare and medical research. Eur J Epidemiol 24: 659–667.
- 15. Stockholm Läns Landsting (2011) Barnhälsovårds årsrapport 2010. Stockholm. Accessed 2011 Dec 13.
- 16. Stockholm Läns Landsting (2011) Metodbok Barnhälsovården. Stockholm Läns Landsting. Accessed 2011 Dec 13.
- 17. Axén M BA, Huslid E, Nordin V, Nylander L, Walch M (2010) Regionalt Vårdprogram: ADHD, lindrig utvecklingsstörning och autismspektrumtillstånd hos barn, ungdomar och vuxna. Stockholm Läns Landsting. Accessed 2012 Jun 27.
- 18. World Health Organisation (1978) International Classification of diseases, 9th edition. Geneva: World Health Organisation.
- 19. Lichtenstein P, Carlstrom E, Rastam M, Gillberg C, Anckarsater H (2010) The genetics of autism spectrum disorders and related neuropsychiatric disorders in childhood. Am J Psychiatry 167: 1357–1363.
- 20. Hansson SL, Svanstrom Rojvall A, Rastam M, Gillberg C, Gillberg C, et al. (2005) Psychiatric telephone interview with parents for screening of childhood autism – tics, attention-deficit hyperactivity disorder and other comorbidities (A-TAC): preliminary reliability and validity. Br J Psychiatry 187: 262–267.
- 21. Larson T, Anckarsater H, Gillberg C, Stahlberg O, Carlstrom E, et al. (2010) The autism–tics, AD/HD and other comorbidities inventory (A-TAC): further validation of a telephone interview for epidemiological research. BMC Psychiatry 10: 1.
- 22. Statistics Sweden. Accessed 2012 Jun 27.
- 23. Cohen J (1968) Weighted kappa: nominal scale agreement with provision for scaled disagreement or partial credit. Psychol Bull 70: 213–220.
- 24. Stockholm Läns Landsting (2004) Väntetider till utredning och behandling inom SLL för personer med neuropsykiatriska problem. Stockholm Läns Landsting. Report 22/2004 (RK 200409-98) Report 22/2004. (RK 200409-98).
- 25. Atladottir HO, Thorsen P, Schendel DE, Ostergaard L, Lemcke S, et al. (2010) Association of hospitalization for infection in childhood with diagnosis of autism spectrum disorders: a Danish cohort study. Arch Pediatr Adolesc Med 164: 470–477.
- 26. King MD, Bearman PS (2011) Socioeconomic Status and the Increased Prevalence of Autism in California. Am Sociol Rev 76: 320–346.
- 27. Shattuck PT, Durkin M, Maenner M, Newschaffer C, Mandell DS, et al. (2009) Timing of identification among children with an autism spectrum disorder: findings from a population-based surveillance study. J Am Acad Child Adolesc Psychiatry 48: 474–483.
- 28. Mandell DS, Novak MM, Zubritsky CD (2005) Factors associated with age of diagnosis among children with autism spectrum disorders. Pediatrics 116: 1480–1486.
- 29. Kadesjo B, Gillberg C, Hagberg B (1999) Brief report: autism and Asperger syndrome in seven-year-old children: a total population study. J Autism Dev Disord 29: 327–331.
- 30. Mattila ML, Kielinen M, Linna SL, Jussila K, Ebeling H, et al. (2011) Autism spectrum disorders according to DSM-IV-TR and comparison with DSM-5 draft criteria: an epidemiological study. J Am Acad Child Adolesc Psychiatry 50: 583–592 e511.
- 31. Posserud M, Lundervold AJ, Lie SA, Gillberg C (2009) The prevalence of autism spectrum disorders: impact of diagnostic instrument and non-response bias. Soc Psychiatry Psychiatr Epidemiol 45: 319–327.
- 32. Williams E, Thomas K, Sidebotham H, Emond A (2008) Prevalence and characteristics of autistic spectrum disorders in the ALSPAC cohort. Dev Med Child Neurol 50: 672–677.
- 33. Fernell E, Gillberg C (2010) Autism spectrum disorder diagnoses in Stockholm preschoolers. Res Dev Disabil 31: 680–685.
- 34. Honda H, Shimizu Y, Imai M, Nitto Y (2005) Cumulative incidence of childhood autism: a total population study of better accuracy and precision. Dev Med Child Neurol 47: 10–18.
- 35. Kawamura Y, Takahashi O, Ishii T (2008) Reevaluating the incidence of pervasive developmental disorders: impact of elevated rates of detection through implementation of an integrated system of screening in Toyota, Japan. Psychiatry Clin Neurosci 62: 152–159.
- 36. Baron-Cohen S, Lombardo MV, Auyeung B, Ashwin E, Chakrabarti B, et al. (2011) Why are autism spectrum conditions more prevalent in males? PLoS Biol 9: e1001081.
- 37. Giarelli E, Wiggins LD, Rice CE, Levy SE, Kirby RS, et al. (2010) Sex differences in the evaluation and diagnosis of autism spectrum disorders among children. Disabil Health J 3: 107–116.
- 38. Maulik PK, Mascarenhas MN, Mathers CD, Dua T, Saxena S (2011) Prevalence of intellectual disability: a meta-analysis of population-based studies. Res Dev Disabil 32: 419–436.
- 39. Lampi KM, Sourander A, Gissler M, Niemela S, Rehnstrom K, et al. (2010) Brief report: validity of Finnish registry-based diagnoses of autism with the ADI-R. Acta Paediatr 99: 1425–1428.
- 40. Lauritsen MB, Jorgensen M, Madsen KM, Lemcke S, Toft S, et al. (2010) Validity of childhood autism in the Danish Psychiatric Central Register: findings from a cohort sample born 1990–1999. J Autism Dev Disord 40: 139–148.
- 41. Baron-Cohen S, Scott FJ, Allison C, Williams J, Bolton P, et al. (2009) Prevalence of autism-spectrum conditions: UK school-based population study. Br J Psychiatry 194: 500–509.
- 42. Barbaresi WJ, Colligan RC, Weaver AL, Katusic SK (2009) The incidence of clinically diagnosed versus research-identified autism in Olmsted County, Minnesota, 1976–1997: results from a retrospective, population-based study. J Autism Dev Disord 39: 464–470.
- 43. Association AP Proposed revision: Autism Spectrum Disorder. DSM5 development [serial online]. Available: www.dsm5.org Accessed 2011, Dec 13.
- 44. Szatmari P, White J, Merikangas KR (2007) The use of genetic epidemiology to guide classification in child and adult psychopathology. Int Rev Psychiatry 19: 483–496.
- 45. Hertz-Picciotto I, Croen LA, Hansen R, Jones CR, van de Water J, et al. (2006) The CHARGE study: an epidemiologic investigation of genetic and environmental factors contributing to autism. Environ Health Perspect 114: 1119–1125.
- 46. Lampi KM, Banerjee PN, Gissler M, Hinkka-Yli-Salomaki S, Huttunen J, et al. (2011) Finnish Prenatal Study of Autism and Autism Spectrum Disorders (FIPS-A): overview and design. J Autism Dev Disord 41: 1090–1096.
- 47. Landrigan PJ, Trasande L, Thorpe LE, Gwynn C, Lioy PJ, et al. (2006) The National Children's Study: a 21-year prospective study of 100,000 American children. Pediatrics 118: 2173–2186.
- 48. Schendel DE, Diguiseppi C, Croen LA, Fallin MD, Reed PL, et al. (2012) The Study to Explore Early Development (SEED): A Multisite Epidemiologic Study of Autism by the Centers for Autism and Developmental Disabilities Research and Epidemiology (CADDRE) Network. J Autism Dev Disord. Feb 17 [Epub ahead of print].
- 49. Stoltenberg C, Schjolberg S, Bresnahan M, Hornig M, Hirtz D, et al. (2010) The Autism Birth Cohort: a paradigm for gene-environment-timing research. Mol Psychiatry 15: 676–680.
- 50. D'Onofrio BM, Singh AL, Iliadou A, Lambe M, Hultman CM, et al. (2010) Familial confounding of the association between maternal smoking during pregnancy and offspring criminality: a population-based study in Sweden. Arch Gen Psychiatry 67: 529–538.
- 51. The National Bureau of Health and Welfare (2003) The Swedish Medical Birth Register. A summary of content and Quality. Accessed 2011 Dec 13.
- 52. Statistiska centralbyrån (2004) Educational attainment of the population. Statistiska meddelanden Serie UF. Stockholm: Statistiska centralbyrån. Available: http://www.scb.se/statistik/UF/UF0506/2005A01/UF0506_2005A01_SM_UF37SM0501.pdf, Accessed 2012 Jun 27.
- 53. Statistics Sweden (2009) Integrated Database for Labour Market Research. Statistics Sweden. Accessed 2012 Jun 27.
- 54. Brottsförebyggande Rådet (2011) Kriminalstatistik. 2010. Stockholm: BRÅ. 280 p.
- 55. Windham GC, Zhang L, Gunier R, Croen LA, Grether JK (2006) Autism spectrum disorders in relation to distribution of hazardous air pollutants in the San Francisco bay area. Environ Health Perspect 114: 1438–1444.
- 56. Landrigan PJ (2010) What causes autism? Exploring the environmental contribution. Curr Opin Pediatr 22: 219–225.
- 57. Sverige. Statistiska centralbyrån. Avdelningen för befolknings- och välfärdsstatistik (2008) Multi-generation register 2007 : a description of contents and quality. Örebro: Population and welfare department, Statistics Sweden. 101 p.
- 58. Statistics Sweden. The Censuses in Sweden (Folk- och Bostadsräkningar i Sverige). Accessed 2011 Dec 13.
- 59. Statistiska centralbyrån (2008) Table on Population of Sweden 2007. Örebro: Statistiska centralbyrån. 444 p.
- 60. Cnattingius S, Ericson A, Gunnarskog J, Kallen B (1990) A quality study of a medical birth registry. Scand J Soc Med 18: 143–148.
- 61. Socialstyrelsen. Epidemiologiskt centrum, Sverige. Socialstyrelsen. Epidemiologiskt centrum (2007) In-patient diseases in Sweden 1987–2005. Stockholm: Socialstyrelsen. 68 p.
- 62. Barlow L, Westergren K, Holmberg L, Talback M (2009) The completeness of the Swedish Cancer Register: a sample survey for year 1998. Acta Oncol 48: 27–33.
- 63. Johansson LA, Westerling R (2000) Comparing Swedish hospital discharge records with death certificates: implications for mortality statistics. Int J Epidemiol 29: 495–502.
- 64. de Faire U, Friberg L, Lorich U, Lundman T (1976) A validation of cause-of-death certification in 1,156 deaths. Acta Med Scand 200: 223–228.
- 65. Wettermark B, Hammar N, Fored CM, Leimanis A, Otterblad Olausson P, et al. (2007) The new Swedish Prescribed Drug Register–opportunities for pharmacoepidemiological research and experience from the first six months. Pharmacoepidemiol Drug Saf 16: 726–735.