Browsing by Author "Embi, Peter"

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    Effectiveness of a Third Dose of mRNA Vaccines Against COVID-19–Associated Emergency Department and Urgent Care Encounters and Hospitalizations Among Adults During Periods of Delta and Omicron Variant Predominance — VISION Network, 10 States, August 2021–January 2022
    (U.S. Department of Health & Human Services, 2022-01-28) Thompson, Mark G.; Natarajan, Karthik; Irving, Stephanie A.; Rowley, Elizabeth A.; Griggs, Eric P.; Gaglani, Manjusha; Klein, Nicola P.; Grannis, Shaun J.; DeSilva, Malini B.; Stenehjem, Edward; Reese, Sarah E.; Dickerson, Monica; Naleway, Allison L.; Han, Jungmi; Konatham, Deepika; McEvoy, Charlene; Rao, Suchitra; Dixon, Brian E.; Dascomb, Kristin; Lewis, Ned; Levy, Matthew E.; Patel, Palak; Liao, I-Chia; Kharbanda, Anupam B.; Barron, Michelle A.; Fadel, William F.; Grisel, Nancy; Goddard, Kristin; Yang, Duck-Hye; Wondimu, Mehiret H.; Murthy, Kempapura; Valvi, Nimish R.; Arndorfer, Julie; Fireman, Bruce; Dunne, Margaret M.; Embi, Peter; Azziz-Baumgartner, Eduardo; Zerbo, Ousseny; Bozio, Catherine H.; Reynolds, Sue; Ferdinands, Jill; Williams, Jeremiah; Link-Gelles, Ruth; Schrag, Stephanie J.; Verani, Jennifer R.; Ball, Sarah; Ong, Toan C.; Family Medicine, School of Medicine
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    Foundations for Studying Clinical Workflow: Development of a Composite Inter-Observer Reliability Assessment for Workflow Time Studies
    (American Medical Informatics Association, 2019) Lopetegui, Marcelo; Yen, Po-Yin; Embi, Peter; Payne, Philip; Medicine, School of Medicine
    The ability to understand and measure the complexity of clinical workflow provides hospital managers and researchers with the necessary knowledge to assess some of the most critical issues in healthcare. Given the protagonist role of workflow time studies on influencing decision makers, major efforts are being conducted to address existing methodological inconsistencies of the technique. Among major concerns, the lack of a standardized methodology to ensure the reliability of human observers stands as a priority. In this paper, we highlight the limitations of the current Inter-Observer Reliability Assessments, and propose a novel composite score to systematically conduct them. The composite score is composed of a) the overall agreement based on Kappa that evaluates the naming agreement on virtually created one-seconds tasks, providing a global assessment of the agreement over time, b) a naming agreement based on Kappa, requiring an observation pairing approach based on time-overlap, c) a duration agreement based on the concordance correlation coefficient, that provides means to evaluate the correlation concerning tasks duration, d) a timing agreement, based on descriptive statistics of the gaps between timestamps of same-task classes, and e) a sequence agreement based on the Needleman-Wunsch sequence alignment algorithm. We hereby provide a first step towards standardized reliability reporting in workflow time studies. This new composite IORA protocol is intended to empower workflow researchers with a standardized and comprehensive method for validating observers' reliability and, in turn, the validity of their data and results.
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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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    SARS-CoV-2 Infection, Hospitalization, and Death in Vaccinated and Infected Individuals by Age Groups in Indiana, 2021‒2022
    (American Public Health Association, 2023) Tu, Wanzhu; Zhang, Pengyue; Roberts, Anna; Allen, Katie S.; Williams, Jennifer; Embi, Peter; Grannis, Shaun; Biostatistics and Health Data Science, School of Medicine
    Objectives: To assess the effectiveness of vaccine-induced immunity against new infections, all-cause emergency department (ED) and hospital visits, and mortality in Indiana. Methods: Combining statewide testing and immunization data with patient medical records, we matched individuals who received at least 1 dose of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines with individuals with previous SARS-CoV-2 infection on index date, age, gender, race/ethnicity, zip code, and clinical diagnoses. We compared the cumulative incidence of infection, all-cause ED visits, hospitalizations, and mortality. Results: We matched 267 847 pairs of individuals. Six months after the index date, the incidence of SARS-CoV-2 infection was significantly higher in vaccine recipients (6.7%) than the previously infected (2.9%). All-cause mortality in the vaccinated, however, was 37% lower than that of the previously infected. The rates of all-cause ED visits and hospitalizations were 24% and 37% lower in the vaccinated than in the previously infected. Conclusions: The significantly lower rates of all-cause ED visits, hospitalizations, and mortality in the vaccinated highlight the real-world benefits of vaccination. The data raise questions about the wisdom of reliance on natural immunity when safe and effective vaccines are available.
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    Team Science to maximize rapid collection and analyses of biosamples from patients with Covid-19
    (Cambridge, 2021) Moe, Sharon M.; Patz, Brooke; Liu, Yunlong; Orschell, Christie; Yu, Andy; Denne, Scott; Embi, Peter; Foroud, Tatiana; Medicine, School of Medicine
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    What can we learn about SARS-CoV-2 prevalence from testing and hospital data?
    (Cornell University, 2020-08-01) Sacks, Daniel W.; Menachemi, Nir; Embi, Peter; Wing, Coady; Kelley School of Business - Indianapolis
    Measuring the prevalence of active SARS-CoV-2 infections is difficult because tests are conducted on a small and non-random segment of the population. But people admitted to the hospital for non-COVID reasons are tested at very high rates, even though they do not appear to be at elevated risk of infection. This sub-population may provide valuable evidence on prevalence in the general population. We estimate upper and lower bounds on the prevalence of the virus in the general population and the population of non-COVID hospital patients under weak assumptions on who gets tested, using Indiana data on hospital inpatient records linked to SARS-CoV-2 virological tests. The non-COVID hospital population is tested fifty times as often as the general population. By mid-June, we estimate that prevalence was between 0.01 and 4.1 percent in the general population and between 0.6 to 2.6 percent in the non-COVID hospital population. We provide and test conditions under which this non-COVID hospitalization bound is valid for the general population. The combination of clinical testing data and hospital records may contain much more information about the state of the epidemic than has been previously appreciated. The bounds we calculate for Indiana could be constructed at relatively low cost in many other states.