Data On Race, Ethnicity, And Language Largely Incomplete For Managed Care Plan Members

Ng, Judy; Ye, Faye; Ward, L. Monique; Haffer, Samuel C.; Scholle, Sarah Hudson

doi:10.1377/hlthaff.2016.1044

Cited by 47 publications

(39 citation statements)

References 6 publications

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“…Further, a substantial proportion of private claims data do not report on race/ethnicity, making it challenging to examine disparities in experiences (Ng et al, 2017). Factors that indicate social and economic vulnerability, including living situation, would likely present more nuanced and policy-relevant findings.…”

Section: Discussionmentioning

confidence: 99%

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

Karpur

Vasudevan

Shih

et al. 2021

J Autism Dev Disord

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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.

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Section: Discussionmentioning

confidence: 99%

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

Karpur

Vasudevan

Shih

et al. 2021

J Autism Dev Disord

View full text Add to dashboard Cite

show abstract

“…An additional limitation is that condition identification was based on the ICD-10 diagnostic codes that may miss some individuals. Further, a substantial proportion of private claims data do not report on race/ethnicity, making it difficult to examine disparities in experiences (Ng, Ye, Ward, Haffer, & Scholle, 2017). Factors indicative of social and economic vulnerability, including living situation, would likely present more nuanced and policy-relevant findings.…”

Section: Discussionmentioning

confidence: 99%

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

Karpur

Vasudevan

Shih

et al. 2021

Preprint

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

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“…- Missing data : If communicated sources such as patient-reported data are incomplete (missing or inaccessible), algorithms (that only use available data) may correctly misinterpret available data ( 144 ). Algorithms could thus be a bad choice for people with missing data ( 145 ) [people with low socioeconomic status ( 146 ) or those with psychosocial issues ( 147 ) for instance].…”

Section: Project Designmentioning

confidence: 99%

An Iterative and Collaborative End-to-End Methodology Applied to Digital Mental Health

Boulos

Mendes²,

Delmas³

et al. 2021

Front. Psychiatry

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Artificial intelligence (AI) algorithms together with advances in data storage have recently made it possible to better characterize, predict, prevent, and treat a range of psychiatric illnesses. Amid the rapidly growing number of biological devices and the exponential accumulation of data in the mental health sector, the upcoming years are facing a need to homogenize research and development processes in academia as well as in the private sector and to centralize data into federalizing platforms. This has become even more important in light of the current global pandemic. Here, we propose an end-to-end methodology that optimizes and homogenizes digital research processes. Each step of the process is elaborated from project conception to knowledge extraction, with a focus on data analysis. The methodology is based on iterative processes, thus allowing an adaptation to the rate at which digital technologies evolve. The methodology also advocates for interdisciplinary (from mathematics to psychology) and intersectoral (from academia to the industry) collaborations to merge the gap between fundamental and applied research. We also pinpoint the ethical challenges and technical and human biases (from data recorded to the end user) associated with digital mental health. In conclusion, our work provides guidelines for upcoming digital mental health studies, which will accompany the translation of fundamental mental health research to digital technologies.

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Data On Race, Ethnicity, And Language Largely Incomplete For Managed Care Plan Members

Cited by 47 publications

References 6 publications

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

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

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

An Iterative and Collaborative End-to-End Methodology Applied to Digital Mental Health

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