Validity of claims‐based algorithms to identify neurodevelopmental disorders in children

Abstract

Purpose To validate healthcare claim‐based algorithms for neurodevelopmental disorders (NDD) in children using medical records as the reference. Methods Using a clinical data warehouse of patients receiving outpatient or inpatient care at two hospitals in Boston, we identified children (≤14 years between 2010 and 2014) with at least one of the following NDDs according to claims‐based algorithms: autism spectrum disorder/pervasive developmental disorder (ASD), attention deficit disorder/other hyperkinetic syndromes of childhood (ADHD), learning disability, speech/language disorder, developmental coordination disorder (DCD), intellectual disability, and behavioral disorder. Fifty cases per outcome were randomly sampled and their medical records were independently reviewed by two physicians to adjudicate the outcome presence. Positive predictive values (PPVs) and 95% confidence intervals (CIs) were calculated. Results PPVs were 94% (95% CI, 83%–99%) for ASD, 88% (76%–95%) for ADHD, 98% (89%–100%) for learning disability, 98% (89%–100%) for speech/language disorder, 82% (69%–91%) for intellectual disability, and 92% (81%–98%) for behavioral disorder. A total of 19 of the 50 algorithm‐based cases of DCD were confirmed as severe coordination disorders with functional impairment, with a PPV of 38% (25%–53%). Among the 31 false‐positive cases of DCD were 7 children with coordination deficits that did not persist throughout childhood, 7 with visual‐motor integration deficits, 12 with coordination issues due to an underlying medical condition and 5 with ADHD and at least one other severe NDD. Conclusions PPVs were generally high (range: 82%–98%), suggesting that claims‐based algorithms can be used to study NDDs. For DCD, additional criteria are needed to improve the classification of true cases.

Neurodevelopmental Disorders Among Publicly or Privately Insured Children in the United States

“…Outcomes of interest were the most common NDDs: (1) ASD, (2) attention-deficit/hyperactivity disorder and other hyperkinetic syndromes of childhood (ADHD), (3) learning disability, (4) developmental speech or language disorder, (5) developmental coordination disorder (DCD), (6) intellectual disability, (7) behavioral disorder (including disturbance of conduct and disturbance of emotions), and (8) any NDD (defined based on the presence of any of the specific 7 preceding disorders) (eTable 1 in the Supplement). The presence of the individual outcomes was assessed using validated claims-based algorithms …”

Association of Neuraxial Labor Analgesia for Vaginal Childbirth With Risk of Autism Spectrum Disorder

“…This algorithm has been shown to identify the outcome of ASD with a high positive predictive value based on medical record review (94% [95% CI, 83%-99%]). 14 …”

“…Children with ASD were identified based on the presence of at least 2 medical encounters with a documented diagnosis of ASD ( ICD-9 codes 299.x, pervasive developmental disorders, excluding 299.1x, childhood disintegrative disorder) at 1 year or older. This algorithm has been shown to identify the outcome of ASD with a high positive predictive value based on medical record review (94% [95% CI, 83%-99%]) …”

Association of Antipsychotic Drug Exposure in Pregnancy With Risk of Neurodevelopmental Disorders

“…Despite the heterogeneity of NDD, a composite outcome was considered given the frequent co-occurrence of these disorders and the potential for shared mechanisms across NDDs (eg, the neurotoxic effects of medications on the developing brain). The presence of individual NDDs was defined using validated algorithms that have been shown to identify the outcomes with generally high positive predictive values 27 and to generate event rates consistent with US statistics. 28 Covariates A broad range of potential (proxies for) confounders was considered, including: treatment indications, lifestyle behaviors, proxies for severity of underlying mental health-related illnesses, exposure to other psychotropics and nonpsychotropic medications, demographic factors, other maternal comorbidities, 29 and county-level measures of socioeconomic status (SES; data available for MAX only; details in the Table and eTable 3 and eFigure 2 in the Supplement).…”

“…The strengths of our study were the following: (1) the use of mother-child linked birth cohorts of publicly and privately insured individuals nested in nationally representative data sources; (2) the large study size that enabled the evaluation of individual antipsychotic drugs, different exposure windows, and specific NDDs; (3) use of validated outcome definitions 27 ; and (4) careful attention to a broad range of potential confounding variables and extensive sensitivity analyses to evaluate the robustness of the findings to exposure and outcome misclassification, residual confounding, and selection bias.…”