Cumulative incidence proportions are shown for each birth cohort, 1990 to 2003, as it ages from 1 to 9 years (Fig. 2). With each successive year of births, cumulative incidence increased, and this was true at every age above 2 years. For instance, at age 6 years, the cumulative incidence of autism was 8.9 in the 1990 birth cohort, 22.2 in the 1994 birth cohort, and 40.3 in the 1998 birth cohort. Increasing trends were observed for all races, all categories of mother’s or father’s age, both sexes, all education levels of parents, and any payment method for delivery of child (data not shown). Age-specific incidence rates (Fig. 3) indicate the steepest rise over time in children aged 3 years, followed by 2-year olds, 4-year olds, and successively older ages. The proportional shift in age at DDS report of autism is shown explicitly in Figure 4: Children are appearing at California’s Regional Centers at younger ages, even as total diagnoses have been increasing. For example, the proportion diagnosed by the fifth birthday rose from 54% for 1990 births to 61% for 1996 births.
Based on administrative data from the California DDS Regional Center system, the annual number of new cases of autism has continued to rise in California, especially among preschool aged children. These trends are generally consistent with a recent analysis of the state’s Client Developmental Evaluation Record data.13 However, because we used data from the Early Start Reports for younger children in addition to the data on children aged 3 years and older, and because we used the true age of the child rather than a crude estimate based on date of record archival, our rates for 2- and 3-year olds are more accurate. Our analysis shows substantially higher rates of autism among 3-year olds than those previously published.
Changes in Definitions and Ascertainment
The rise in autism incidence has occurred during a time of diagnostic and legislative changes affecting the definition of autism and the availability of services for developmental disabilities. In the late 1980s, a revision of Diagnostic and Statistical Manual of Mental Disorders (DSM-III) denoted DSM-III-R, expanded the definition of autism. However, this expansion was considered “overly broad,”17 and the 1994 implementation of the ICD-101 was more restrictive and considered appropriately reflective of clinicians’ practices.17 The DSM-IV criteria matched closely with the ICD-10, but used a more compact set of qualifying behaviors.2,17 A study in northern Finland compared the prevalence proportions obtained using a Kanner definition to those obtained from ICD-10 and DSM-IV criteria in approximately 1000 children and adolescents with autism or other developmental or psychiatric conditions.18 The application of 2 sets of criteria to the entire sample indicated a 2.2-fold higher prevalence, using the more recent diagnostic criteria (12.2 vs. 5.6 per 10,000 individuals). A meta-analysis of 37 studies of autism prevalence found a 3.6-fold higher risk from DSM-IV or ICD-10 criteria versus other criteria, but this figure would have been confounded by the year of study.19 Thus, an expanded definition potentially increased the numbers in California who met the criteria for autism and may have been partially responsible for changes from the early 1990s to 2006, as it can take years for practitioners, as a group, to adopt new diagnostic standards.
In the third quarter of 2003, the state eligibility criteria changed from having a condition that results in “a major impairment of cognitive and/or social functioning” to additionally having “significant functional limitations, as determined by the regional center, in 3 or more of the following areas of major life activity, as appropriate to the individual’s age: receptive and expressive language, learning, self-care, mobility, self-direction, capacity for independent living, and economic self-sufficiency.” Despite these additional qualifying criteria, the autism incidence continued to rise in preschool age children. It has, however, leveled off above age 4 years, possibly reflecting fewer severe functional limitations20 in those diagnosed at a later age.
The inclusion of milder cases has been suggested as an explanation for the increase in autism. Neither Asperger’s syndrome nor “pervasive developmental disorders not otherwise specified” qualify under the category of autism in the DDS system. In the Childhood Autism Risks from Genetics and the Environment study, which enrolls children from 20 California counties, 64% of the cases of autism recorded in the state system for 2- to 5-year olds were confirmed by 2 standardized, research-reliable instruments (Autism Diagnostic Observation Schedule21 and Autism Diagnostic Inventory22), 87% were confirmed by at least one of the instruments, and 98% met the criteria for autism spectrum disorders based on at least one of the instruments.23 Some children not meeting the criteria for full syndrome autism may have met the criteria previously but improved through treatment before the Childhood Autism Risks from Genetics and the Environment study evaluation. These confirmation proportions are only from recent years; in the most extreme scenario, if all cases in the early years met the criteria on both instruments, these data might suggest a 56% rise (100%/64%) due to a trend toward providing services to milder cases.
Age at Diagnosis
A shift toward younger age at diagnosis was clear but not huge: 12% more children were diagnosed before age 5 years in the 1996 birth cohort (the most recent with 10 years of follow-up) in comparison with those in the 1990 cohort. Extrapolation into the later birth cohorts (eg, 2002) would suggest a 24% rise in the proportion of diagnoses by age 5. No corresponding decline in diagnoses occurred in school-aged children, and only in the last few years has the rate of diagnosis at ages 5–9 years begun to level off. Calculating cumulative incidence to an older age (such as age 10 years) minimizes the effect of decreasing age of diagnosis because diagnoses above this cut-off point are infrequent. Shifts from above to below this cut-off point would tend to be minimal and affect primarily mild cases, thus likely playing a minor role in our analysis.
Inappropriate use of prevalence and other measures of occurrence can engender the problem of noncomparability. Prevalence data or incidence data in earlier years in the younger ages will miss patients who have not yet been diagnosed. Also, if increasing awareness is a major factor in time trends, an unknown number of undiagnosed cases in the oldest age groups will disproportionately affect rates or proportions in the earlier years. Jick and Kaye24 calculated annual incidence rates in 24- to 59-month-old children in the large General Practice Research Database from the United Kingdom within a defined 7-year birth cohort, but as noted elsewhere,25 the earliest year of the analysis included only 2-year olds, and the most recent births were only 3 years old in the later years used for the trend analysis. This meant different opportunities for a diagnosis across time. A later analysis of this database reported a peak in 1999 with a leveling off after 2000,26 a pattern quite different from our population incidence findings of a continued rise throughout the same period and well beyond. In any case, because of the relatively young cut-off (59 months), their estimates could also have been influenced by a change in age at diagnosis. However, the common practice of calculating prevalence over a broad age range can also produce biased comparisons across time. In general, cumulative incidence is a more valid measure than prevalence for assessment of time trends; it avoids the problem of noncomparability across years due to changing age at diagnosis when calculated to an age beyond which diagnoses are rare.
A simulation study by Wazana et al27 suggests that an apparent increase of as much as 28-fold could be explained by the combination of 3 artifacts: a change in case definition, a decline in age at diagnosis, and better ascertainment. Several problems with this analysis detract from its validity and applicability. First, the data they use for the decline in mean age at diagnosis are based on noncomparable cohorts. Specifically, they rely on a reported analysis of DDS data used follow-up to a specific calendar date rather than to a specific age. This inflates the decline in age at diagnosis because children from recent birth cohorts are too young for calculation of diagnosed at older ages. Using longer follow-up and equivalent follow-up periods, we recalculated the mean age at diagnosis for birth cohorts from 1990 to 1996 to be 5.23, 5.16, 5.12, 5.18, 5.02, 4.90, 4.83, a 10-fold smaller shift (0.14 years between 1991 and 1994) than what was assumed in the simulation study (1.6 years). This shift is evident from our Figure 4. Secondly, the extremely large increases found in the simulation are observed only in the analysis of cumulative incidence to age 4 years (labeled “prevalence” by the authors).27 When the simulation is carried out to age 12 years, the magnitude of the explained increase is much less. By this age, the impact of age at diagnosis is largely eliminated, and the magnitude of artifactual increases that result from the other 2 assumptions (change in definition and more efficient ascertainment) combine to a 2.4-fold increase. This prediction is much smaller than the actual increases in autism rates in the California DDS data, even if we assume, as Wazana et al did, that all clinicians were using DSM-III in the early period (unlikely, given that DSM-III-R had already been adopted) and all clinicians were using DSM-IV at the end of our study period.
Our birth cohort analysis assumed that out-migration was independent of whether a child developed autism. If out-migration were differential, the population incidence rates or cumulative incidence proportions could be slightly under- or overestimated. In either case, out-migration would not have affected overall trends unless the differential also varied substantially over time. Domestic out-migration from California is low—about 1.4% per year among children aged 0 to 10 years.
Programmatic and financial changes implemented in this time period could have affected access to state-funded services. In the 1980s, services for persons with developmental disabilities became Medicaid reimbursable. From the early 1990s to 2006, State of California funding for family services for persons with developmental disabilities rose from about $72 to over $400 million, and total spending for individual, family, and community services increased from $2.8 to $4.9 billion.28 Although these figures include funds for cerebral palsy, epilepsy, and mental retardation, the population prevalence proportions of these other conditions have remained stable.10 In 1986, state legislation in California mandated preschool programs for 3- to 5-year olds who have disabilities or who are at risk. Implementation of the Early Start program was initiated in 1993, reaching statewide coverage by about 1995. The federal Education of the Handicapped Act was amended in 1990 to include children with autism.29
Comparison of DDS Rates With Rates From Other Populations
A review of studies completed between 1998 and 2001 from several countries concluded that the prevalence of autism is about 13 per 10,000 individuals and for pervasive developmental disorders more generally, 37 per 10,000 individuals.4 Data from educational systems or administrative databases alone tend to indicate lower proportions, whereas recent investigations using intensive screening or multiple ascertainment sources obtain higher figures: a range of 58 to 67 cases of pervasive developmental disorder per 10,000 individuals.3,30–32 The CDC’s Autism and Developmental Disabilities Monitoring network found prevalence of autism spectrum disorders (ASD), defined as autistic disorder, Asperger’s syndrome, and pervasive developmental disorder not otherwise specified, at age 8 years ranging from 45 to 99 per 10,000 individuals across 6 sites.33 The CDC use of a single age likely produced data more comparable with our cumulative incidence measure, and by age 8 years, also avoided much of the bias associated with changing age at diagnosis. By comparison, the cumulative incidence of autism (not the broader category of ASDs) through age 9 years, based on the California State data, was about 30 per 10,000 individuals for the 1995 California birth cohort, and will certainly exceed 40 per 10,000 individuals for the 2000 and 2001 birth cohorts. These figures are higher than most published estimates for autism alone but may be inflated by inclusion of some ASD cases.
However, because the State of California does not perform active autism surveillance, these figures underestimate the true autism incidence. Although many children with autism enter the state system well beyond the age of 3 years (when symptoms are by definition already present), some may never enter. These include those who receive services through the educational system, those whose families can afford private providers, those whose parents are undocumented immigrants (who are eligible for services but may fear contact with state agencies) or whose parents are mentally or physically ill, and others. Underrepresentation of this type, however, is unlikely to have changed so as to explain the observed long-term trends: an 8-fold rise in annual new cases in 16 years, and a 7-fold increase in cumulative incidence over 11 birth cohorts.
One strength of this study relative to many other analyses of time trends is the reliance on a single administrative database with consistent study methodology over a 12- 15-year period covering a well-defined geographic region. Another is our use of cumulative incidence to a fixed follow-up age, beyond which few diagnoses occur, to compare birth cohorts. The data presented here would not be subject to varying study methods, but would be affected by community awareness, which has grown in the general public as well as among health providers. Also, the availability of early treatment programs has sparked hope for improvement of those affected, which could also contribute to the increased numbers seeking services. The increased funding of services may have attracted more families to the Regional Centers. However, the demand for services for persons with autism has tended to outstrip supply, suggesting that the rise is not simply artifact fueled by federal and state funding. Similarly, although state funding for Regional Centers increased during 1992–1997 concurrently as the federal government raised the ceiling on Medicaid eligibility numbers, increases in autism incidence continued well beyond this period.
In summary, the incidence of autism rose 7- to 8-fold in California from the early 1990s through the present. Quantitative analysis of the changes in diagnostic criteria, the inclusion of milder cases, and an earlier age at diagnosis during this period suggests that these factors probably contribute 2.2-, 1.56-, and 1.24-fold increases in autism, respectively, and hence cannot fully explain the magnitude of the rise in autism. Differential migration also likely played a minor role, if any. Wider awareness, greater motivation of parents to seek services as a result of expanding treatment options, and increased funding may each have contributed, but documentation or quantification of these effects is lacking. With no evidence of a leveling off, the possibility of a true increase in incidence deserves serious consideration. One approach to this question would be a rigorous investigation to determine incidence or prevalence in 20- to 30-year olds. If there has been no true increase and no individuals who were cured or outgrew their diagnosis, then the application to adults of criteria equivalent to those being used today in children should find, for each previously identified autism case, 4 to 8 undiagnosed cases. Whatever the final determination with regard to overlooked cases of autism in the past, the current occurrence of autism, a seriously disabling disorder in young children, at rates of greater than 30 per 10,000 individuals—and still rising in California—is a major public health and educational concern.
The authors thank the Data Extraction Unit of the California Department of Developmental Services for providing data files.
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