학술논문
Incorporating social determinants of health into transmission modeling of COVID-19 vaccine in the US: a scoping review
Document Type
article
Author
Khanh N.C. Duong; Danielle T. Nguyen; Warittakorn Kategeaw; Xi Liang; Win Khaing; Lindsay D. Visnovsky; Sajesh K. Veettil; Mary M. McFarland; Richard E. Nelson; Barbara E. Jones; Andrew T. Pavia; Emma Coates; Karim Khader; Jay Love; George G. Vega Yon; Yue Zhang; Tina Willson; Egenia Dorsan; Damon J.A. Toth; Makoto M. Jones; Matthew H. Samore; Nathorn Chaiyakunapruk
Source
The Lancet Regional Health. Americas, Vol 35, Iss , Pp 100806- (2024)
Subject
Language
English
ISSN
2667-193X
Abstract
Summary: During COVID-19 in the US, social determinants of health (SDH) have driven health disparities. However, the use of SDH in COVID-19 vaccine modeling is unclear. This review aimed to summarize the current landscape of incorporating SDH into COVID-19 vaccine transmission modeling in the US. Medline and Embase were searched up to October 2022. We included studies that used transmission modeling to assess the effects of COVID-19 vaccine strategies in the US. Studies’ characteristics, factors incorporated into models, and approaches to incorporate these factors were extracted. Ninety-two studies were included. Of these, 11 studies incorporated SDH factors (alone or combined with demographic factors). Various sets of SDH factors were integrated, with occupation being the most common (8 studies), followed by geographical location (5 studies). The results show that few studies incorporate SDHs into their models, highlighting the need for research on SDH impact and approaches to incorporating SDH into modeling. Funding: This research was funded by the Centers for Disease Control and Prevention (CDC).