Gender and sex bias in COVID-19 epidemiological data through the lens of causality
- Natalia Díaz RodríguezRuta Binkyte Raja Chatila
- 1 January 2023
Medicine
The paper outlines how non-causal models can motivate discriminatory policies such as biased allocation of the limited resources in intensive care units (ICUs) and considers causal knowledge and causal-based techniques to compliment the collection and analysis of observational big-data.
1087-P: A Randomized Pragmatic Real-World Clinical Trial Comparing Insulin Glargine 300 U/mL (Gla-300) with Standard of Care First-Generation Basal Insulins (SoC-BIs) in Insulin-Naïve Patients with…
- L. MeneghiniJ. GillA. DauchyAndrius BaceviciusJodi StrongT. Bailey
- 1 June 2019
Medicine
The 12-month analysis of the ACHIEVE Control study translated the findings of improved clinical outcomes for Gla-300 vs. SoC-BIs previously seen in randomized controlled trials to a broader real-world population managed in a usual care setting.
Insulin glargine 300 U/mL versus first‐generation basal insulin analogues in insulin‐naïve adults with type 2 diabetes: 12‐month outcomes of ACHIEVE Control, a prospective, randomized, pragmatic…
- L. MeneghiniL. Blonde T. Bailey
- 15 June 2020
Medicine
To report the effectiveness and safety of insulin glargine 300 U/mL (Gla‐300) versus standard‐of‐care basal insulin analogues (SOC‐BI) at 12 months in the ACHIEVE Control trial, which is a…
Questioning causality on sex, gender and COVID-19, and identifying bias in large-scale data-driven analyses: the Bias Priority Recommendations and Bias Catalog for Pandemics
- Natalia Díaz RodríguezRuta Binkyt.e-Sadauskien.e Raja Chatila
- 29 April 2021
Sociology, Medicine
An encyclopedia-like reference guide, the Bias Catalog for Pandemics (BCP), is compiled, to provide definitions and emphasize realistic examples of bias in general, and within the COVID-19 pandemic context, to raise awareness on the dimensionality of such foreseen impacts.