Related Experiment Video
Updated: Aug 27, 2025
14:43
A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting
Published on: January 12, 2018
12.0K
Understanding Racial Disparities in COVID-19-Related Complications: Protocol for a Mixed Methods Study
Jessica L Harding1,2,3, Shivani A Patel4, Teaniese Davis5
1Department of Surgery, Emory University, Atlanta, GA, United States.
JMIR Research Protocols
|September 27, 2022
Summary
This study investigates racial disparities in COVID-19 severity among Black and white patients using mixed methods. Findings will inform strategies to reduce health inequities and improve care for Black communities.
Area of Science:
- Health Services Research
- Epidemiology
- Health Disparities
Background:
- COVID-19 has exacerbated existing health disparities for Black communities in the U.S.
- Social determinants of health (SDOH) are implicated, but large-scale studies on underlying drivers are limited.
Purpose of the Study:
- To elucidate the mechanisms driving racial disparities in severe COVID-19 outcomes.
- To identify, quantify, and contextualize factors contributing to these disparities.
Main Methods:
- Convergent parallel mixed methods approach using electronic medical records (EMR) and patient surveys.
- Quantitative cohorts from Kaiser Permanente Georgia (KPGA) members (Jan 2020-Sep 2021).
- Qualitative semistructured interviews to capture patient experiences and healthcare interactions.
Main Results:
- 31,500 COVID-19 cases in EMR cohort; 482 completed surveys; 20 interviews conducted (as of Aug 2022).
- Data cleaning and coding complete; analysis underway.
- Results anticipated December 2022.
Conclusions:
- Pilot study offers insights into mechanisms of racial disparities in COVID-19 complications.
- Quantitative and qualitative data will generate hypotheses and inform multilevel strategies.
- Integrating qualitative findings enhances strategy efficacy, adoption, and sustainability.
Related Concept Videos
01:25Strategies for Assessing and Addressing Confounding
146
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
146
01:27Confounding in Epidemiological Studies
239
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
239
01:29Bias in Epidemiological Studies
534
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
534
01:25Statistical Methods for Analyzing Epidemiological Data
493
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
493
01:20Comparing the Survival Analysis of Two or More Groups
256
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
256
01:18Steps in Outbreak Investigation
171
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
171