Background We investigated whether we could use influenza data to develop prediction models for COVID-19 to increase the speed at which prediction models can reliably be developed and validated early in a pandemic. We developed COVID-19 Estimated Risk (COVER) scores that quantify a patient’s risk of hospital admission with pneumonia (COVER-H), hospitalization with pneumonia requiring intensive services or death (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis using historical data from patients with influenza or flu-like symptoms and tested this in COVID-19 patients. Methods We analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries containing data collected on or before 4/27/2020. We used a 2-step process to develop 3 scores using historical data from patients with influenza or flu-like symptoms any time prior to 2020. The first step was to create a data-driven model using LASSO regularized logistic regression, the covariates of which were used to develop aggregate covariates for the second step where the COVER scores were developed using a smaller set of features. These 3 COVER scores were then externally validated on patients with 1) influenza or flu-like symptoms and 2) confirmed or suspected COVID-19 diagnosis across 5 databases from South Korea, Spain, and the United States. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death, and iii) death in the 30 days after index date. Results Overall, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated which patients would experience any of our three outcomes. The models achieved good performance in influenza and COVID-19 cohorts. For COVID-19 the AUC ranges were, COVER-H: 0.69–0.81, COVER-I: 0.73–0.91, and COVER-F: 0.72–0.90. Calibration varied across the validations with some of the COVID-19 validations being less well calibrated than the influenza validations. Conclusions This research demonstrated the utility of using a proxy disease to develop a prediction model. The 3 COVER models with 9-predictors that were developed using influenza data perform well for COVID-19 patients for predicting hospitalization, intensive services, and fatality. The scores showed good discriminatory performance which transferred well to the COVID-19 population. There was some miscalibration in the COVID-19 validations, which is potentially due to the difference in symptom severity between the two diseases. A possible solution for this is to recalibrate the models in each location before use.
More than 1,300 Canadians are diagnosed with cervical cancer annually, which is nearly preventable through human papillomavirus (HPV) immunization. Across Canada, coverage rates remain below the 90% target set out by the Action Plan for the Elimination of Cervical Cancer in Canada (2020–2030). To support this Plan, the Canadian Partnership Against Cancer has commissioned the Urban Public Health Network (UPHN) to coordinate a quality improvement project with Canada’s school-based HPV immunization programs. In Alberta, the UPHN partnered with Alberta Health Services (AHS) for this work. This study has one overarching research question: what are parent/guardian and program stakeholder perceived barriers, enablers and opportunities to immunization for youth as part of the school-based HPV immunization program in Alberta? This study uses a mixed-methods sequential explanatory design. A survey will be emailed to a sample of Albertans with children aged 11–17 years. Questions will be based on a Conceptual Framework of Access to Health Care. Subsequent qualitative work will explore the survey’s findings. Parents/guardians identifying as vaccine hesitant in the survey will be invited to participate in virtual, semi-structured, in-depth interviews. Stakeholders of the school-based immunization program will be purposively sampled from AHS’ five health zones for virtual focus groups. Quantitative data will be analyzed using SAS Studio 3.6 to carry out descriptive statistics and, using logistic regression, investigate if Framework constructs are associated with parents’/guardians’ decision to immunize their children. Qualitative data will be analyzed using NVivo 12 to conduct template thematic analysis guided by the Framework. Study results will provide insights for Alberta’s public health practitioners to make evidence-informed decisions when tailoring the school-based HPV immunization program to increase uptake in vaccine hesitant populations. Findings will contribute to the national study, which will culminate in recommendations to increase HPV immunization uptake nationally and progress towards the 90% coverage target.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.