Dataset Information

Brief Report: Impact of COVID-19 in Individuals with Autism Spectrum Disorders: Analysis of a National Private Claims Insurance Database.

ABSTRACT: The COVID-19 pandemic continues to have a detrimental impact on individuals with disabilities. Data from FAIR Health's FH® NPIC (National Private Insurance Claims) database, one of the nation's largest databases of private insurance claim records, were analyzed to understand the experiences of individuals with ASD in the COVID-19 pandemic. Multivariate logistic regression models revealed that individuals with ASD + ID were nine times more likely to be hospitalized following COVID-19 infection (OR = 9.3; 95% CI: 6.9-12.5) and were nearly six times more likely to have an elevated length of hospital stay (OR = 5.9; 95% CI: 3.5-10.1) compared to those without ASD + ID. These findings point to the need for prioritizing access to vaccines to prevent COVID-19 infection and morbidities. This is the first study to illustrate a higher likelihood of hospitalization and elevated length of hospital stay from COVID-19 in individuals with ASD and other comorbidities.

SUBMITTER: Karpur A

PROVIDER: S-EPMC8153091 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Brief Report: Impact of COVID-19 in Individuals with Autism Spectrum Disorders: Analysis of a National Private Claims Insurance Database.

Karpur Arun A Vasudevan Vijay V Shih Andy A Frazier Thomas T

Journal of autism and developmental disorders 20210526 5

The COVID-19 pandemic continues to have a detrimental impact on individuals with disabilities. Data from FAIR Health's FH® NPIC (National Private Insurance Claims) database, one of the nation's largest databases of private insurance claim records, were analyzed to understand the experiences of individuals with ASD in the COVID-19 pandemic. Multivariate logistic regression models revealed that individuals with ASD + ID were nine times more likely to be hospitalized following COVID-19 infection (O ...[more]

PMID: 34041682

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Project description:ImportanceAlthough there is no pharmacological treatment for autism spectrum disorder (ASD) itself, behavioral and pharmacological therapies have been used to address its symptoms and common comorbidities. A better understanding of the medications used to manage comorbid conditions in this growing population is critical; however, most previous efforts have been limited in size, duration, and lack of broad representation.ObjectiveTo use a nationally representative database to uncover trends in the prevalence of co-occurring conditions and medication use in the management of symptoms and comorbidities over time among US individuals with ASD.Design, setting, and participantsThis retrospective, population-based cohort study mined a nationwide, managed health plan claims database containing more than 86 million unique members. Data from January 1, 2014, to December 31, 2019, were used to analyze prescription frequency and diagnoses of comorbidities. A total of 26 722 individuals with ASD who had been prescribed at least 1 of 24 medications most commonly prescribed to treat ASD symptoms or comorbidities during the 6-year study period were included in the analysis.ExposuresDiagnosis codes for ASD based on International Classification of Diseases, Ninth Revision, and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision.Main outcomes and measuresQuantitative estimates of prescription frequency for the 24 most commonly prescribed medications among the study cohort and the most common comorbidities associated with each medication in this population.ResultsAmong the 26 722 individuals with ASD included in the analysis (77.7% male; mean [SD] age, 14.45 [9.40] years), polypharmacy was common, ranging from 28.6% to 31.5%. Individuals' prescription regimens changed frequently within medication classes, rather than between classes. The prescription frequency of a specific medication varied considerably, depending on the coexisting diagnosis of a given comorbidity. Of the 24 medications assessed, 15 were associated with at least a 15% prevalence of a mood disorder, and 11 were associated with at least a 15% prevalence of attention-deficit/hyperactivity disorder. For patients taking antipsychotics, the 2 most common comorbidities were combined type attention-deficit/hyperactivity disorder (11.6%-17.8%) and anxiety disorder (13.1%-30.1%).Conclusions and relevanceThis study demonstrated considerable variability and transiency in the use of prescription medications by US clinicians to manage symptoms and comorbidities associated with ASD. These findings support the importance of early and ongoing surveillance of patients with ASD and co-occurring conditions and offer clinicians insight on the targeted therapies most commonly used to manage co-occurring conditions. Future research and policy efforts are critical to assess the extent to which pharmacological management of comorbidities affects quality of life and functioning in patients with ASD while continuing to optimize clinical guidelines, to ensure effective care for this growing population.

Project description:ImportanceAlthough a few studies have shown that mental health disorders (MHDs) are strongly associated with the 5-year survival and recurrence rates in patients with head and neck cancer (HNC), none have been replicated in a large-scale study.ObjectiveTo describe the prevalence of MHDs in patients with HNC and the potential associations with survival and recurrence using a large insurance claims database.Design, setting, and participantsThis retrospective cohort study assessed data queried from the MarketScan database from January 1, 2005, through December 31, 2014, for 52 641 patients with a diagnosis of HNC. To exclude patients with a preexisting HNC diagnosis or those with incomplete data, patients were included if they were in the database for at least 12 months before the index diagnosis and continuously enrolled. Data were analyzed from February 20, 2017, through January 22, 2019.Main outcomes and measuresTo compare the frequency of MHDs before and after diagnosis of HNC, χ2 tests for independence were used. Adjusted adds ratios (aORs) were obtained using multivariable logistic regression by comparing the prevalence of MHDs in patients with oral cavity cancer and those with other cancer sites in the head and neck.ResultsAmong the 52 641 patients included in the analysis (mean [SD] age, 51.31 [9.79] years), men (58.5%), patients aged 55 to 64 years (46.6%), and those from the South (40.3%) were most commonly affected by HNC. Oral cavity cancers (40.4%) were the most common type, followed by cancers of the oropharynx (19.2%) and larynx (15.5%). Of the various cancer sites, the OR for MHD prevalence was significantly increased in patients with cancers of the trachea compared with the oral cavity (2.11; 95% CI, 1.87-2.38). The prevalence of MHDs in patients with HNC increased to 29.9% compared with 20.6% before the cancer diagnosis. Specifically, women (adjusted OR, 1.58; 95% CI, 1.49-1.67) and patients with a history of tobacco use (adjusted OR, 1.42; 95% CI, 1.34-1.50) and alcohol use (adjusted OR, 1.56; 95% CI, 1.38-1.76) had significantly higher odds of MHDs after the diagnosis of HNC.Conclusions and relevanceAlthough the baseline MHD prevalence of 20.6% before the cancer diagnosis was close to the national average (17.9% according to the National Survey on Drug Use and Health), results of this study showed that it increased to 29.9% after the cancer diagnosis. Women and patients with a history of tobacco and alcohol use were most susceptible to being diagnosed with an MHD. There is an association between patients with HNC and an increased prevalence of MHDs after treatment compared with the general population.

Dataset Information

Brief Report: Impact of COVID-19 in Individuals with Autism Spectrum Disorders: Analysis of a National Private Claims Insurance Database.

Publications

Brief Report: Impact of COVID-19 in Individuals with Autism Spectrum Disorders: Analysis of a National Private Claims Insurance Database.

Similar Datasets

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