TY - JOUR
T1 - Alzheimer`s disease mortality in the United States
T2 - Cross-sectional analysis of county-level socio-environmental factors
AU - Salerno, Pedro RVO
AU - Dong, Weichuan
AU - Motairek, Issam
AU - Makhlouf, Mohamed HE
AU - Saifudeen, Mehlam
AU - Moorthy, Skanda
AU - Dalton, Jarrod E.
AU - Perzynski, Adam T.
AU - Rajagopalan, Sanjay
AU - Al-Kindi, Sadeer
N1 - Publisher Copyright:
© 2023 Elsevier B.V.
PY - 2023/12
Y1 - 2023/12
N2 - Background: Geographical disparities in mortality among Alzheimer`s disease (AD) patients have been reported and complex sociodemographic and environmental determinants of health (SEDH) may be contributing to this variation. Therefore, we aimed to explore high-risk SEDH factors possibly associated with all-cause mortality in AD across US counties using machine learning (ML) methods. Methods: We performed a cross-sectional analysis of individuals ≥65 years with any underlying cause of death but with AD in the multiple causes of death certificate (ICD-10,G30) between 2016 and 2020. Outcomes were defined as age-adjusted all-cause mortality rates (per 100,000 people). We analyzed 50 county-level SEDH and Classification and Regression Trees (CART) was used to identify specific county-level clusters. Random Forest, another ML technique, evaluated variable importance. CART`s performance was validated using a “hold-out” set of counties. Results: Overall, 714,568 individuals with AD died due to any cause across 2,409 counties during 2016–2020. CART identified 9 county clusters associated with an 80.1% relative increase of mortality across the spectrum. Furthermore, 7 SEDH variables were identified by CART to drive the categorization of clusters, including High School Completion (%), annual Particulate Matter 2.5 Level in Air, live births with Low Birthweight (%), Population under 18 years (%), annual Median Household Income in US dollars ($), population with Food Insecurity (%), and houses with Severe Housing Cost Burden (%). Conclusion: ML can aid in the assimilation of intricate SEDH exposures associated with mortality among older population with AD, providing opportunities for optimized interventions and resource allocation to reduce mortality among this population.
AB - Background: Geographical disparities in mortality among Alzheimer`s disease (AD) patients have been reported and complex sociodemographic and environmental determinants of health (SEDH) may be contributing to this variation. Therefore, we aimed to explore high-risk SEDH factors possibly associated with all-cause mortality in AD across US counties using machine learning (ML) methods. Methods: We performed a cross-sectional analysis of individuals ≥65 years with any underlying cause of death but with AD in the multiple causes of death certificate (ICD-10,G30) between 2016 and 2020. Outcomes were defined as age-adjusted all-cause mortality rates (per 100,000 people). We analyzed 50 county-level SEDH and Classification and Regression Trees (CART) was used to identify specific county-level clusters. Random Forest, another ML technique, evaluated variable importance. CART`s performance was validated using a “hold-out” set of counties. Results: Overall, 714,568 individuals with AD died due to any cause across 2,409 counties during 2016–2020. CART identified 9 county clusters associated with an 80.1% relative increase of mortality across the spectrum. Furthermore, 7 SEDH variables were identified by CART to drive the categorization of clusters, including High School Completion (%), annual Particulate Matter 2.5 Level in Air, live births with Low Birthweight (%), Population under 18 years (%), annual Median Household Income in US dollars ($), population with Food Insecurity (%), and houses with Severe Housing Cost Burden (%). Conclusion: ML can aid in the assimilation of intricate SEDH exposures associated with mortality among older population with AD, providing opportunities for optimized interventions and resource allocation to reduce mortality among this population.
KW - Alzheimer's disease
KW - Machine learning
KW - Public health
UR - http://www.scopus.com/inward/record.url?scp=85164728496&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85164728496&partnerID=8YFLogxK
U2 - 10.1016/j.archger.2023.105121
DO - 10.1016/j.archger.2023.105121
M3 - Article
C2 - 37437363
AN - SCOPUS:85164728496
SN - 0167-4943
VL - 115
JO - Archives of Gerontology and Geriatrics
JF - Archives of Gerontology and Geriatrics
M1 - 105121
ER -