TY - GEN
T1 - Explaining Health Risk Behaviors in the U.S. with Social Deprivation at Local and Regional Levels
AU - Gokhale, Swapna S.
AU - Lebakula, Viswadeep
AU - Peluso, Alina
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Health risk behaviors are precursors to many chronic health outcomes, and hence, they pose a challenge to public health. Social deprivation undoubtedly creates circumstances that limit access to healthy habits. Moreover, broad regional effects (weather patterns, political ideology, social norms), and local characteristics (cultural notions and barriers, urban places) also influence lifestyle choices and must be accounted for to truly understand the impact of social deprivation on risky behaviors. This research fills the knowledge gap in epidemiological modeling of health risk behaviors by leveraging machine learning to find associations between social deprivation and health risk behaviors, when adjusted by regional and local effects. Four health risk behaviors, namely, binge drinking, smoking, lack of sleep, and lack of physical activity from the CDC PLACES project are considered in a single framework to understand and compare the interplay between local/regional characteristics and seven measures of social deprivation. Our results indicate that local and/or regional factors rise to the top for three out of four risk behaviors (binge drinking, smoking and lack of sleep) out-competing social deprivation measures. Un-entangling the geographical effects reveals that poverty, educational attainment and non-employment are the three deprivation measures most significantly associated with all four health risk factors. The research thus indicates that public health policies to promote healthy lifestyle behaviors must seek to remedy social deprivation, but using socially and culturally sensitive interventions.
AB - Health risk behaviors are precursors to many chronic health outcomes, and hence, they pose a challenge to public health. Social deprivation undoubtedly creates circumstances that limit access to healthy habits. Moreover, broad regional effects (weather patterns, political ideology, social norms), and local characteristics (cultural notions and barriers, urban places) also influence lifestyle choices and must be accounted for to truly understand the impact of social deprivation on risky behaviors. This research fills the knowledge gap in epidemiological modeling of health risk behaviors by leveraging machine learning to find associations between social deprivation and health risk behaviors, when adjusted by regional and local effects. Four health risk behaviors, namely, binge drinking, smoking, lack of sleep, and lack of physical activity from the CDC PLACES project are considered in a single framework to understand and compare the interplay between local/regional characteristics and seven measures of social deprivation. Our results indicate that local and/or regional factors rise to the top for three out of four risk behaviors (binge drinking, smoking and lack of sleep) out-competing social deprivation measures. Un-entangling the geographical effects reveals that poverty, educational attainment and non-employment are the three deprivation measures most significantly associated with all four health risk factors. The research thus indicates that public health policies to promote healthy lifestyle behaviors must seek to remedy social deprivation, but using socially and culturally sensitive interventions.
KW - Local effects
KW - Machine learning
KW - PLACES
KW - Public health
KW - Regional effects
KW - Risk behaviors
KW - Social deprivation
UR - http://www.scopus.com/inward/record.url?scp=85204053408&partnerID=8YFLogxK
U2 - 10.1109/COMPSAC61105.2024.00294
DO - 10.1109/COMPSAC61105.2024.00294
M3 - Conference contribution
AN - SCOPUS:85204053408
T3 - Proceedings - 2024 IEEE 48th Annual Computers, Software, and Applications Conference, COMPSAC 2024
SP - 1856
EP - 1864
BT - Proceedings - 2024 IEEE 48th Annual Computers, Software, and Applications Conference, COMPSAC 2024
A2 - Shahriar, Hossain
A2 - Ohsaki, Hiroyuki
A2 - Sharmin, Moushumi
A2 - Towey, Dave
A2 - Majumder, AKM Jahangir Alam
A2 - Hori, Yoshiaki
A2 - Yang, Ji-Jiang
A2 - Takemoto, Michiharu
A2 - Sakib, Nazmus
A2 - Banno, Ryohei
A2 - Ahamed, Sheikh Iqbal
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 48th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2024
Y2 - 2 July 2024 through 4 July 2024
ER -