Related Experiment Video
Updated: Jun 2, 2025
06:55
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
14.4K
Epidemiological methods in transition: Minimizing biases in classical and digital approaches
Sara Mesquita1,2, Lília Perfeito1, Daniela Paolotti3
1Social Physics and Complexity (SPAC) Lab, LIP-Laboratory for Instrumentation and Experimental Particle Physics, Lisboa, Portugal.
PLOS Digital Health
|January 13, 2025
Summary
Digital Epidemiology uses diverse data to track diseases, facing challenges with data biases. Shifting focus to data types over sources can improve methods and reduce health inequities.
Area of Science:
- Epidemiology
- Public Health
- Data Science
Background:
- Epidemiology and Public Health increasingly utilize diverse data sources beyond traditional health systems.
- The COVID-19 pandemic significantly impacted the scope and nature of Digital Epidemiology.
- Data generated outside clinical settings present unique technical and bias-correction challenges.
Purpose of the Study:
- To review the evolution of Digital Epidemiology before and after the COVID-19 pandemic.
- To analyze the technical challenges and biases associated with non-traditional data sources in epidemiology.
- To propose a data-type-centric definition for enhancing the operational utility of Digital Epidemiology.
Main Methods:
- Review of Digital Epidemiology practices and literature.
- Statistical perspective on data bias and correction.
- Analysis of data-source versus data-type definitions.
Main Results:
- Digital Epidemiology has expanded in scope and nature post-COVID-19.
- External data sources introduce significant, hard-to-correct biases.
- A data-type focus offers clearer methodological insights than a data-source focus.
Conclusions:
- Reframing Digital Epidemiology around data types can address methodological gaps.
- Understanding and mitigating biases in diverse data is crucial.
- Strategic use of Digital Epidemiology can help reduce health inequities.
Related Concept Videos
01:29Bias in Epidemiological Studies
149
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:
149
01:25Strategies for Assessing and Addressing Confounding
82
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...
82
01:26Introduction to Epidemiology
637
Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...
637
01:20Study Designs in Epidemiology
178
Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
178
01:25Statistical Methods for Analyzing Epidemiological Data
296
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:
296
01:27Confounding in Epidemiological Studies
139
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...
139