Related Experiment Video

Updated: Aug 5, 2026

Real-World M3-BREATHE: Toward Multimodal Mobile Monitoring of Behaviour, Respiration, and Exposures for Treatment and Health Evaluation
09:50

Real-World M3-BREATHE: Toward Multimodal Mobile Monitoring of Behaviour, Respiration, and Exposures for Treatment and Health Evaluation

Published on: June 5, 2026

Comorbidity Exposure-Window Definitions and Multidimensional Disparities in Long COVID Risk: Evidence from a U.S.

Yewen Chen, Zhetao Chen, Ge Yang

    Medrxiv : the Preprint Server for Health Sciences
    |July 30, 2026
    PubMed
    Summary

    Defining comorbidities before SARS-CoV-2 infection, not Long COVID (LC) diagnosis, significantly impacts risk estimates. This methodological choice is crucial for accurate LC epidemiology and identifying high-risk populations.

    Related Concept Videos

    Factors Affecting Illness01:18

    Factors Affecting Illness

    When a person's physical, emotional, intellectual, social development or spiritual functioning is compromised, this deviation from a healthy normal state is called illness. Illness creates stress that in turn harms individuals. Irritation, anger, denial, hopelessness, and fear are behavioral and emotional changes an individual experiences in the phases of illness. A variety of factors influence a person's health and well-being.
    For instance, risk factors are connected to illness, disability,...
    Prevalence and Incidence01:08

    Prevalence and Incidence

    In statistical epidemiology and health sciences, two essential metrics—prevalence and incidence—are fundamental for understanding disease dynamics within a population. These measures enable public health officials, epidemiologists, and researchers to assess the burden of diseases, allocate resources effectively, and design impactful public health policies and interventions.
    Prevalence indicates the proportion of individuals in a population who have a specific disease or health condition at a...
    Bias in Epidemiological Studies01:29

    Bias in Epidemiological Studies

    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:
    Pharmacokinetic–Pharmacodynamic Relationship: Exposure, Response and Effect01:26

    Pharmacokinetic–Pharmacodynamic Relationship: Exposure, Response and Effect

    The pharmacokinetic-pharmacodynamic (PK-PD) relationship describes the intricate link between drug exposure, efficacy, and toxicity, forming the foundation for optimal dosing regimens. This relationship uses mathematical modeling to characterize drug concentration-effect dynamics, ensuring precise therapeutic outcomes.Exposure represents the pharmacokinetic aspect of the PK-PD relationship, denoting the drug amount that elicits a biological response. It is typically quantified by administered...
    Dimensions of Health and Illness01:21

    Dimensions of Health and Illness

    The factors influencing the health-illness continuum can be internal or external and may or may not be under conscious control. They are related to the following eight human dimensions, and each dimension is interrelated to one other.
    Causality in Epidemiology01:21

    Causality in Epidemiology

    Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...