Validating an administrative data-based case definition for identifying children and youth with autism spectrum disorder for surveillance purposes

Heterogeneity in Autism Spectrum Disorder Case-Finding Algorithms in United States Health Administrative Database Analyses

Strengthening systems of care to meet the needs of individuals with autism spectrum disorder (ASD) is of growing importance. Administrative data provide advantages for research and planning purposes, including large sample sizes and the ability to identify enrollment in insurance coverage and service utilization of individuals with ASD. Researchers have employed varying strategies to identify individuals with ASD in administrative data. Differences in these strategies can limit the comparability of results across studies. This review describes implications of the varying strategies that have been employed to identify individuals with ASD in US claims databases, with consideration of the strengths and limitations of each approach.

Accuracy of administrative database algorithms for autism spectrum disorder, attention-deficit/hyperactivity disorder and fetal alcohol spectrum disorder case ascertainment: a systematic review

INTRODUCTION

The purpose of this study was to perform a systematic review to assess the validity of administrative database algorithms used to identify cases of autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD) and fetal alcohol spectrum disorder (FASD).

METHODS

MEDLINE, Embase, Global Health and PsycInfo were searched for studies that validated algorithms for the identification of ASD, ADHD and FASD in administrative databases published between 1995 and 2021 in English or French. The grey literature and reference lists of included studies were also searched. Two reviewers independently screened the literature, extracted relevant information, conducted reporting quality, risk of bias and applicability assessments, and synthesized the evidence qualitatively. PROSPERO CRD42019146941.

RESULTS

Out of 48 articles assessed at full-text level, 14 were included in the review. No studies were found for FASD. Despite potential sources of bias and significant between-study heterogeneity, results suggested that increasing the number of ASD diagnostic codes required from a single data source increased specificity and positive predictive value at the expense of sensitivity. The best-performing algorithms for the identification of ASD were based on a combination of data sources, with physician claims database being the single best source. One study found that education data might improve the identification of ASD (i.e. higher sensitivity) in school-aged children when combined with physician claims data; however, additional studies including cases without ASD are required to fully evaluate the diagnostic accuracy of such algorithms. For ADHD, there was not enough information to assess the impact of number of diagnostic codes or additional data sources on algorithm accuracy.

CONCLUSION

There is some evidence to suggest that cases of ASD and ADHD can be identified using administrative data; however, studies that assessed the ability of algorithms to discriminate reliably between cases with and without the condition of interest were lacking. No evidence exists for FASD. Methodologically higher-quality studies are needed to understand the full potential of using administrative data for the identification of these conditions.

Self-reported COVID-19 vaccination acceptance and hesitancy among autistic adults

Identifying factors associated with COVID-19 vaccination acceptance among vulnerable groups, including autistic individuals, can increase vaccination rates and support public health. The purpose of this study was to determine differences among autistic adults who reported COVID-19 vaccination acceptance from those who did not. In this study we describe COVID-19 vaccination status and self-reported preferences among autistic adults and identify related factors. Vaccine accepters were more likely to report increased loneliness during COVID-19, lived in more populous counties (p = 0.02), and lived in counties won by President Biden in the 2020 US presidential election (p < 0.001). Positive correlations were found between desire to protect others, concern about contracting COVID-19, and trusting vaccine safety (p < 0.001). Concern about vaccine safety was common among the vaccine hesitant, while lack of concern about COVID-19 overall was not. Identifying health promotion strategies based on self-reported, lived experiences about COVID-19 among vulnerable groups is key for public health impact.

The prevalence and incidence of early-onset dementia among adults with autism spectrum disorder

The prevalence and incidence of early-onset dementia among adults with autism spectrum disorder (ASD) is currently unknown. In this case-control study, the prevalence and incidence of early-onset dementia in individuals with ASD was examined during 2008-2012 using Medicaid Analytic eXtract files. Participants were 30-64 year-old adults who were Medicaid beneficiaries and had either a diagnosis of ASD only (n = 12,648), a diagnosis of ASD with co-occurring intellectual disability (ID) (n = 26,168), a diagnosis of ID without ASD (n = 406,570), or no ASD nor ID diagnoses (n = 798,828). The 5-year prevalence of dementia was 4.04% among adults with ASD only, and 5.22% for those with ASD and co-occurring ID. This prevalence was higher compared to the prevalence of dementia in individuals with no ASD and no ID (0.97%), but lower compared to individuals with ID only (7.10%). Risk factors associated with the increased prevalence in the general population were similarly associated with the increased risk of dementia in individuals with ASD. Even after adjusting for these risk factors, compared to the general population, dementia was found to occur more frequently in individuals with ASD only (adjusted hazard ratio, 1.96; 95% CI, 1.69-2.28), as well as individuals with ASD and co-occurring ID (adjusted hazard ratio, 2.89; 95% CI, 2.62-3.17). In conclusion, adults with ASD under the age of 65 were approximately 2.6 times more likely to be diagnosed with dementia compared to the general population in our study. LAY SUMMARY: It is unclear whether adults diagnosed with autism spectrum disorder (ASD) are at higher risk of being diagnosed with early-onset dementia compared to those who are not on the autism spectrum. In this study we examined for the first time the nationwide prevalence and incidence of Alzheimer's Disease and other types of dementia in ASD in a sample of adults with ASD aged 30-64 years who were enrolled in Medicaid, the largest insurer of behavioral health services in the US. Medicaid claims data, which include information on the diagnoses that beneficiaries receive, suggested that the adults with ASD were approximately 2.6 times more likely to be diagnosed with early-onset Alzheimer's disease and related dementias compared to the general population.

Assessing the validity of administrative health data for the identification of children and youth with autism spectrum disorder in Ontario

Population‐level identification of children and youth with ASD is essential for surveillance and planning for required services. The objective of this study was to develop and validate an algorithm for the identification of children and youth with ASD using administrative health data. In this retrospective validation study, we linked an electronic medical record (EMR)‐based reference standard, consisting 10,000 individuals aged 1–24 years, including 112 confirmed ASD cases to Ontario administrative health data, for the testing of multiple case‐finding algorithms. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and corresponding 95% confidence intervals (CI) were calculated for each algorithm. The optimal algorithm was validated in three external cohorts representing family practice, education, and specialized clinical settings. The optimal algorithm included an ASD diagnostic code for a single hospital discharge or emergency department visit or outpatient surgery, or three ASD physician billing codes in 3 years. This algorithm's sensitivity was 50.0% (95%CI 40.7–88.7%), specificity 99.6% (99.4–99.7), PPV 56.6% (46.8–66.3), and NPV 99.4% (99.3–99.6). The results of this study illustrate limitations and need for cautious interpretation when using administrative health data alone for the identification of children and youth with ASD.

Lay Summary

We tested algorithms (set of rules) to identify young people with ASD using routinely collected administrative health data. Even the best algorithm misses more than half of those in Ontario with ASD. To understand this better, we tested how well the algorithm worked in different settings (family practice, education, and specialized clinics). The identification of individuals with ASD at a population level is essential for planning for support services and the allocation of resources. Autism Res 2021, 14: 1037–1045. © 2021 The Authors. Autism Research published by International Society for Autism Research published by Wiley Periodicals LLC.

Identifying Children and Youth With Autism Spectrum Disorder in Electronic Medical Records: Examining Health System Utilization and Comorbidities

Autism spectrum disorder (ASD) is a neurodevelopmental disorder requiring significant health and educational resources for affected individuals. A reference standard for ASD was generated from an existing population‐based cohort of 10,000 children and youth aged 1–24 years who were randomly selected for chart abstraction from 29,256 patients from 119 family physicians. We developed and validated an algorithm to identify children and youth with ASD within an electronic medical record system (N = 80,237, aged 1–24 years) in order to examine the prevalence of comorbidities and quantify health system utilization within the cohort. We identified 1,062 children and youth with ASD representing a prevalence of 1.32%. Compared to individuals without ASD, those with ASD had a higher prevalence of asthma, were more likely to visit a specialist, undergo surgery, and be hospitalized for psychiatric reasons. Children and youth with ASD in Ontario have complex health system needs, illustrated through a significant burden of comorbidities and increased health system utilization.

Lay Summary

Our paper generates population‐based estimates of health system use by children and youth with ASD, who have a higher burden of comorbidities than the general population. We developed a case‐finding algorithm and applied it in electronic medical records to create a cohort of children and youth with ASD, thereby generating an important resource to further study the health care needs of individuals with ASD.

Autism spectrum disorder/Takiwātanga: An Integrated Data Infrastructure-based approach to autism spectrum disorder research in New Zealand

New Zealand has few estimates of the prevalence of autism spectrum disorder and no national registry. The use of administrative data sources is expanding and could be useful in autism spectrum disorder research. However, the extent to which autism spectrum disorder can be captured in these data sources is unknown. In this study, we utilised three linked administrative health data sources from the Integrated Data Infrastructure to identify cases of autism spectrum disorder among New Zealand children and young people. We then investigated the extent to which a range of mental health, neurodevelopmental and related problems co-occur with autism spectrum disorder. In total, 9555 unique individuals aged 0–24 with autism spectrum disorder were identified. The identification rate for 8-year-olds was 1 in 102. Co-occurring mental health or related problems were noted in 68% of the autism spectrum disorder group. The most common co-occurring conditions were intellectual disability, disruptive behaviours and emotional problems. Although data from the Integrated Data Infrastructure may currently undercount cases of autism spectrum disorder, they could be useful for monitoring service and treatment-related trends, types of co-occurring conditions and for examining social outcomes. With further refinement, the Integrated Data Infrastructure could prove valuable for informing the national incidence and prevalence of autism spectrum disorder and the long-term effectiveness of clinical guidelines and interventions for this group.

Identification of Pediatric Autism Spectrum Disorder Cases Using Health Administrative Data

Administrative data are frequently used to identify Autism Spectrum Disorder (ASD) cases in epidemiological studies. However, validation studies on this mode of case ascertainment have lacked access to high-quality clinical diagnostic data and have not followed published reporting guidelines. We report on the diagnostic accuracy of using readily available health administrative data for pediatric ASD case ascertainment. The validation cohort included almost all the ASD-positive children born in British Columbia, Canada from April 1, 2000 to December 31, 2009 and consisted of 8,670 children in total. 4,079 ASD-positive and 2,787 ASD-negative children were identified using Autism Diagnostic Observation Schedule (ADOS) and Autism Diagnostic Interview-Revised (ADI-R) assessments done through the British Columbia Autism Assessment Network (BCAAN). An additional 1,804 ADOS/ADI-R assessed ASD-positive children were identified using Ministry of Education records. This prospectively collected clinical data (the diagnostic gold standard) was then linked to each child's physician billing and hospital discharge data. The diagnostic accuracy of 11 algorithms that used the administrative data to assign ASD case status was assessed. For all algorithms, high positive predictive values (PPVs) were observed alongside low values for other measures of diagnostic accuracy illustrating that PPVs alone are not an adequate measure of diagnostic accuracy. We show that British Columbia's health administrative data cannot reliably be used to discriminate between children with ASD and children with other developmental disorders. Utilizing these data may result in misclassification bias. Methodologically sound, region-specific validation studies are needed to support the use of administrative data for ASD case ascertainment. Autism Res 2020, 13: 456-463. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Health administrative data are frequently used to identify Autism Spectrum Disorder (ASD) cases for research purposes. However, previous validation studies on this sort of case identification have lacked access to high-quality clinical diagnostic data and have not followed published reporting guidelines. We show that British Columbia's health administrative data cannot reliably be used to discriminate between children with ASD and children with other developmental disorders.

Data Linkage: Canadian and Australian Perspectives on a Valuable Methodology for Intellectual and Developmental Disability Research

Data linkage holds great promise for generating new information about people with intellectual and developmental disabilities (IDD) as a population, yet few centers have developed the infrastructure to utilize this methodology. Two examples, from Canada and Australia, describe their efforts in building data linkage capabilities, and how linked databases can be used to identify persons with IDD and used for population-based research. The value of data linkage is illustrated through new estimates of prevalence of IDD; health service utilization patterns; associations with sociodemographic characteristics, and with physical and mental health conditions (e.g., chronic diseases, injury, fertility, and depression); and findings on equity in medical treatments. Examples are provided of findings used for governmental policy and program planning.

Prenatal antibiotics exposure and the risk of autism spectrum disorders: A population-based cohort study

Background

Prenatal antibiotic exposure induces changes in infants’ gut microbiota composition and is suggested as a possible contributor in the development of autism spectrum disorders (ASD). In this study, we examined the association between prenatal antibiotic exposure and the risk of ASD.

Methods

This was a population-based cohort study utilizing the Manitoba Population Research Data Repository. The cohort included 214 834 children born in Manitoba, Canada between April 1, 1998 and March 31, 2016. Exposure was defined as having filled one or more antibiotic prescription during pregnancy. The outcome was autism spectrum disorder diagnosis. Multivariable Cox proportional hazards regression was used to estimate the risk of developing ASD in the overall cohort and in a sibling cohort.

Results

Of all subjects, 80 750 (37.6%) were exposed to antibiotics prenatally. During follow-up, 2965 children received an ASD diagnosis. Compared to children who were not exposed to antibiotics prenatally, those who were exposed had a higher risk of ASD: (adjusted HR 1.10 [95% CI 1.01, 1.19]). The association was observed in those exposed to antibiotics in the second or third trimester (HR 1.11 [95% CI 1.01, 1.23] and 1.17 [95% CI 1.06, 1.30], respectively). In the siblings’ cohort, ASD risk estimate remained unchanged (adjusted HR 1.08 [95% CI 0.90, 1.30], although it was not statistically significant.

Conclusions

Prenatal antibiotic exposure is associated with a small increase in the risk of ASD. Given the potential of residual confounding beyond what it was controlled through our study design and because of possible confounding by indication, such a small risk increase in the population is not expected to be clinically significant.

Needles in the haystack: Using open-text fields to identify persons with intellectual and developmental disabilities in administrative home care data

BACKGROUND

Use of administrative health data to study populations of interest is becoming more common. Identifying individuals with intellectual and developmental disabilities (IDD) in existing databases can be challenging due to inconsistent definitions and terminologies of IDD over time and across sectors, and the inability to rely on etiologies of IDD as they are frequently unknown.

AIMS

To identify diagnoses related to IDD in an administrative database and create a cohort of persons with IDD.

METHODS

Open-text diagnostic entries related to IDD were identified in an Ontario home care database (2003-2015) and coded as being either acceptable (e.g. Down syndrome) or ambiguous (e.g. intellectually challenged). The cognitive and functional skills of the resulting groups were compared using logistic regressions and standardized differences, and their age distributions were compared to that of the general home care population.

RESULTS

Just under 1% of the home care population had a diagnostic entry related to IDD. Ambiguous terms were most commonly used (61%), and this group tended to be older and less impaired than the group with more acceptable terms used to describe their IDD.

CONCLUSIONS

Open-text diagnostic variables in administrative health records can be used to identify and study individuals with IDD.

IMPLICATIONS

Future work is needed to educate assessors on the importance of using standard, accepted terminology when recording diagnoses related to IDD.