Browsing by Author "Essien, Inih J."

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    Methods to Adjust for Confounding in Test-Negative Design COVID-19 Effectiveness Studies: Simulation Study
    (JMIR, 2025-01-27) Rowley, Elizabeth A. K.; Mitchell, Patrick K.; Yang, Duck-Hye; Lewis, Ned; Dixon, Brian E.; Vazquez-Benitez, Gabriela; Fadel, William F.; Essien, Inih J.; Naleway, Allison L.; Stenehjem, Edward; Ong, Toan C.; Gaglani, Manjusha; Natarajan, Karthik; Embi, Peter; Wiegand, Ryan E.; Link-Gelles, Ruth; Tenforde, Mark W.; Fireman, Bruce; Health Policy and Management, Richard M. Fairbanks School of Public Health
    Background: Real-world COVID-19 vaccine effectiveness (VE) studies are investigating exposures of increasing complexity accounting for time since vaccination. These studies require methods that adjust for the confounding that arises when morbidities and demographics are associated with vaccination and the risk of outcome events. Methods based on propensity scores (PS) are well-suited to this when the exposure is dichotomous, but present challenges when the exposure is multinomial. Objective: This simulation study aimed to investigate alternative methods to adjust for confounding in VE studies that have a test-negative design. Methods: Adjustment for a disease risk score (DRS) is compared with multivariable logistic regression. Both stratification on the DRS and direct covariate adjustment of the DRS are examined. Multivariable logistic regression with all the covariates and with a limited subset of key covariates is considered. The performance of VE estimators is evaluated across a multinomial vaccination exposure in simulated datasets. Results: Bias in VE estimates from multivariable models ranged from -5.3% to 6.1% across 4 levels of vaccination. Standard errors of VE estimates were unbiased, and 95% coverage probabilities were attained in most scenarios. The lowest coverage in the multivariable scenarios was 93.7% (95% CI 92.2%-95.2%) and occurred in the multivariable model with key covariates, while the highest coverage in the multivariable scenarios was 95.3% (95% CI 94.0%-96.6%) and occurred in the multivariable model with all covariates. Bias in VE estimates from DRS-adjusted models was low, ranging from -2.2% to 4.2%. However, the DRS-adjusted models underestimated the standard errors of VE estimates, with coverage sometimes below the 95% level. The lowest coverage in the DRS scenarios was 87.8% (95% CI 85.8%-89.8%) and occurred in the direct adjustment for the DRS model. The highest coverage in the DRS scenarios was 94.8% (95% CI 93.4%-96.2%) and occurred in the model that stratified on DRS. Although variation in the performance of VE estimates occurred across modeling strategies, variation in performance was also present across exposure groups. Conclusions: Overall, models using a DRS to adjust for confounding performed adequately but not as well as the multivariable models that adjusted for covariates individually.
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    Protection of Two and Three mRNA Vaccine Doses Against Severe Outcomes Among Adults Hospitalized With COVID-19-VISION Network, August 2021 to March 2022
    (Oxford, 2023-04-15) DeSilva, Malini B.; Mitchell, Patrick K.; Klein, Nicola P.; Dixon, Brian E.; Tenforde, Mark W.; Thompson, Mark G.; Naleway, Allison L.; Grannis, Shaun G.; Ong, Toan C.; Natarajan, Karthik; Reese, Sarah E.; Zerbo, Ousseny; Kharbanda, Anupam B.; Patel, Palak; Stenehjem, Edward; Raiyani, Chandni; Irving, Stephanie A.; Fadel, William F.; Rao, Suchitra; Han, Jungmi; Reynolds, Sue; Davis, Jonathan M.; Lewis, Ned; McEvoy, Charlene; Dickerson, Monica; Dascomb, Kristin; Valvi, Nimish R.; Barron, Michelle A.; Goddard, Kristin; Vazquez-Benitez, Gabriela; Grisel, Nancy; Mamwala, Mufaddal; Embi, Peter J.; Fireman, Bruce; Essien, Inih J.; Griggs, Eric P.; Arndorfer, Julie; Gaglani, Manjusha; Biostatistics and Health Data Science, School of Medicine
    Background We assessed coronavirus disease 2019 (COVID-19) vaccination impact on illness severity among adults hospitalized with COVID-19, August 2021–March 2022. Methods We evaluated differences in intensive care unit (ICU) admission, in-hospital death, and length of stay among vaccinated (2 or 3 mRNA vaccine doses) versus unvaccinated patients aged ≥18 years hospitalized for ≥24 hours with COVID-19–like illness and positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) molecular testing. We calculated odds ratios (ORs) for ICU admission and death and subdistribution hazard ratios (SHR) for time to hospital discharge adjusted for age, geographic region, calendar time, and local virus circulation. Results We included 27 149 SARS-CoV-2–positive hospitalizations. During both Delta- and Omicron-predominant periods, protection against ICU admission was strongest among 3-dose vaccinees compared with unvaccinated patients (Delta OR, 0.52 [95% CI, .28–.96]; Omicron OR, 0.69 [95% CI, .54–.87]). During both periods, risk of in-hospital death was lower among vaccinated compared with unvaccinated patients but ORs overlapped across vaccination strata. We observed SHR >1 across all vaccination strata in both periods indicating faster discharge for vaccinated patients. Conclusions COVID-19 vaccination was associated with lower rates of ICU admission and in-hospital death in both Delta and Omicron periods compared with being unvaccinated.