Incorporating Social Determinants of Health Into Transmission Modeling of COVID-19 Vaccine in the US: A Scoping Review

Speaker(s)

Duong K1, Nguyen DT1, Kategaew W1, Liang X1, Khaing W2, Visnovsky LD3, Veettil SK4, McFarland MM5, Nelson RE3, Jones B6, Pavia A7, Coates E8, Khader K3, Love J3, Yon GV3, Zhang Y3, Willson T3, Dorsan E3, Toth D3, Jones MM3, Samore M3, Chaiyakunapruk N9
1Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, UT, USA, 2Department of Pharmacotherapy, College of Pharmacy, University of Utah, Bangkok, 10, Thailand, 3Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA, 4School of Pharmacy, International Medical University, Kuala Lumpur, Kuala Lumpur, Malaysia, 5Spencer S. Eccles Health Sciences Library, University of Utah, Salt Lake City, UT, USA, 6Division of Pulmonary & Critical Care, Spencer Fox Eccles School of Medicine, University of Utah, salt lake city, UT, USA, 7Division of Pediatric Infectious Diseases, Spencer Fox Eccles School of Medicine ,University of Utah, Salt Lake City, UT, USA, 8Department of Mathematics & Statistics, McMaster University, Hamilton, ON, Canada, 9College of Pharmacy, University of Utah, Salt Lake City, UT, USA

Presentation Documents

OBJECTIVES: Existing evidence during COVID-19 in the US indicated that social determinants of health (SDH) were significantly associated with higher case numbers, worse outcomes, and reduced vaccine accessibility. However, it is unclear how SDH factors were used in COVID-19 vaccine mathematical modeling. This scoping review aimed to summarize the current landscape of how SDH factors are incorporated into the transmission modeling of the COVID-19 vaccine in the US.

METHODS: Medline and Embase were searched from the inception to October 2022. We included studies that used mathematical transmission modeling to assess the effects of the COVID-19 vaccine or vaccine strategies in the US. Data related to characteristics of studies, factors incorporated into the models, types of models, and approaches to incorporate these factors were extracted. Factors incorporated into models were classified into demographic factors (including age, gender, race, ethnicity, and comorbidities) and SDH factors (including occupation, geographical location, and living conditions). A qualitative synthesis was performed. The study screening and data extraction were independently conducted by two reviewers in the review team.

RESULTS: A total of 92 studies were included. Of these, 27 studies incorporated demographic factors alone, 11 studies incorporated SDH factors (alone or in combination with demographic factors), and 9 studies incorporated both demographic and SDH factors. There were varying sets of SDH factors integrated into the model. The most common factor was occupation (8 studies), followed by geographical location (5 studies), and living conditions (2 studies).

CONCLUSIONS: A few transmission modeling studies of COVID-19 vaccine in the US incorporated SDH factors. Additionally, we observed variations in which SDH factors were incorporated in these models. These findings highlight the need for future research to investigate SDH impact and standardized approaches to selecting and incorporating SDHs into modeling.

Code

EPH123

Topic

Epidemiology & Public Health, Health Policy & Regulatory, Methodological & Statistical Research, Study Approaches

Topic Subcategory

Health Disparities & Equity, Literature Review & Synthesis, Public Health

Disease

Infectious Disease (non-vaccine), Vaccines