Biostatistics Commons^™

Determining Diagnosis Date Of Diabetes Using Structured Electronic Health Record (Ehr) Data: The Search For Diabetes In Youth Study, Kristin M. Lenoir, Lynne E. Wagenknecht, Jasmin Divers, Ramon Casanova, Dana Dabelea, Sharon Saydah, Catherine Pihoker, Angela D. Liese Ph.D., Debra Standiford, Richard Hamman, Brian J. Wells, The Search For Diabetes In Youth Study Group

Faculty Publications

Background Disease surveillance of diabetes among youth has relied mainly upon manual chart review. However, increasingly available structured electronic health record (EHR) data have been shown to yield accurate determinations of diabetes status and type. Validated algorithms to determine date of diabetes diagnosis are lacking. The objective of this work is to validate two EHR-based algorithms to determine date of diagnosis of diabetes. Methods A rule-based ICD-10 algorithm identified youth with diabetes from structured EHR data over the period of 2009 through 2017 within three children’s hospitals that participate in the SEARCH for Diabetes in Youth Study: Cincinnati Children’s Hospital, …

Recognition And Screening For Chagas Disease In The Usa, Rachel Marcus, Andres F. Henao-Martinez, Melissa Nolan Ph.D., Mph, Elizabeth Livingston, Stephen A. Klotz, Robert H. Gilman, Monica Miranda-Schaeubinger, Sheba Meymandi

Faculty Publications

Chagas disease (CD), caused by the protozoan Trypanosoma cruzi, is a public health concern, mainly among countries in South and Central America. However, despite the large number of immigrants from endemic countries living in the USA, awareness of CD is poor in the medical community, and therefore it is significantly underdiagnosed. To avoid the catastrophic cardiac complications of CD and to prevent maternal–fetal transmission, widespread educational programs highlighting the need for diagnosis are urgently needed.

Sars-Cov-2 Transmission In Alberta, British Columbia, And Ontario, Canada, December 25, 2019, To December 1, 2020, Isaac Chun-Hai Fung, Yuen Wai Hung, Sylvia Ofori, Kamalich Muniz-Rodriguez, Po-Ying Lai, Gerardo Chowell

Department of Biostatistics, Epidemiology, and Environmental Health Sciences Faculty Publications

Objective:

This study aimed to investigate coronavirus disease (COVID-19) epidemiology in Alberta, British Columbia, and Ontario, Canada.

Methods:

Using data through December 1, 2020, we estimated time-varying reproduction number, R t , using EpiEstim package in R, and calculated incidence rate ratios (IRR) across the 3 provinces.

Results:

In Ontario, 76% (92 745/121 745) of cases were in Toronto, Peel, York, Ottawa, and Durham; in Alberta, 82% (49 878/61 169) in Calgary and Edmonton; in British Columbia, 90% (31 142/34 699) in Fraser and Vancouver Coastal. Across 3 provinces, R t dropped to ≤ 1 after April. …