Developing an autism-specific mortality risk index using data from Medicare-enrolled autistic older adults

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High risk for premature mortality is one of the most pressing issues faced by the growing population of aging autistic adults. Autistic adults are disproportionately more likely to have chronic conditions, leading to increased risk for mortality compared to the general population. However, one major barrier to identifying those at greatest risk for mortality is the absence of accurate predictive tools for this population. Our objective is to establish a novel, machine-learning derived mortality risk index for autistic older adults. We will leverage our team’s unique expertise in autism aging research, population-level administrative data analysis, and machine learning to achieve our specific aims: (Aim 1) identify comorbidities and geriatric complaints that differentially influence time- to-mortality for autistic and non-autistic older adults; (Aim 2) compare existing mortality risk indices to a novel, autism-specific index for predicting autistic older adults’ risk of mortality; (Exploratory Aim 3) determine the distribution of mortality risk among autistic older adults in local healthcare systems as a precursor for prospective studies. We will achieve these aims through the synergistic use of national administrative billing data and local electronic health records data. In Aim 1, we will apply a machine learning technique called “logic forest” in an innovative way to identify specific comorbidities and age-related conditions, or combinations thereof, that differentially influence time-to-mortality among autistic and non-autistic older adults using the most recent nine years of national Medicare data. In Aim 2, we will apply a stochastic hill climbing optimization technique, a type of machine learning, to national Medicare data to develop an algorithm-based index that quantifies autistic older adults’ risk of mortality based on comorbidities and demographic characteristics. We will compare the predictive validity of our novel autism-specific algorithm-based index to the Charlson and Elixhauser comorbidity indices, the gold-standards of mortality risk measurement among the general population. Last, in Exploratory Aim 3 we will obtain sample size estimates for prospective studies by quantifying mortality risk among aging autistic adults in two large healthcare systems using the Charlson, Elixhauser, and our novel autism-specific mortality risk indices. Findings of this study will have practical applications for researchers to identify participants for prospective observational and intervention studies and clinicians to identify high-risk cases for special management and intervention. This study is responsive to NOT-AG-21-020 in that we will analyze existing Medicare claims data to examine “subgroups of older adults with special needs” and “health outcomes in complex multimorbid older adults”. Further, this study is aligned with the NIA’s Strategic Plan as we seek to “understand disparities related to aging and [inform] strategies to improve the health status of older adults” on the autism spectrum. This project will have a high public health impact yielding critical new information about mortality risk among a historically understudied aging population that can ultimately be used to improve life-expectancy among autistic people.