Making a Case for an Autism-Specific Multimorbidity Index: A Comparative Cohort Study.

Area of Science:

• Medical research
• Public health
• Autism research

Background:

• Autistic individuals face healthcare disparities and unique health conditions.
• Existing multimorbidity indices may not accurately predict health outcomes in autistic populations.
• Understanding specific mortality risks for autistic adults is crucial for targeted interventions.

Purpose of the Study:

• To evaluate general multimorbidity indices for predicting COVID-19 mortality in autistic adults.
• To develop and validate a novel, autism-specific multimorbidity index (ASD-MI).
• To inform the development of tailored healthcare tools for neurodivergent populations.

Main Methods:

• Utilized data from the CVD-COVID-UK/COVID-IMPACT Consortium, including 1,027 autistic adults hospitalized with COVID-19.
• Employed logistic regression with fivefold cross-validation to identify predictors for the ASD-MI.
• Compared the predictive performance (AUC, pseudo-R2) of the ASD-MI against the general population Quan Index.

Main Results:

• The ASD-MI, using diabetes, coronary heart disease, and thyroid disorders, achieved an AUC of 0.872, outperforming the Quan Index (0.828).
• The ASD-MI demonstrated a better model fit (pseudo-R2 0.25) compared to the Quan Index (0.20).
• Identified specific comorbidities contributing to COVID-19 mortality risk in autistic adults.

Conclusions:

• Tailored multimorbidity indices, like the ASD-MI, are essential for accurately predicting mortality risks in autistic individuals.
• Current general indices are insufficient for this population, underscoring the need for autism-specific healthcare tools.
• Further research is required to refine autism-specific indices and address health disparities in neurodivergent populations.