Related Experiment Video
Updated: Jun 9, 2026
06:34
Identification of Rare Bacterial Pathogens by 16S rRNA Gene Sequencing and MALDI-TOF MS
Published on: July 11, 2016
Rare diseases - avoiding misperceptions and establishing realities: the need for reliable epidemiological data
Stephen C Groft1, Manuel Posada de la Paz
1Office of Rare Diseases Research, National Institutes of Health, Bethesda, MD 20892-7518, USA. sg18b@nih.gov
Advances in Experimental Medicine and Biology
|September 9, 2010
Summary
Reliable epidemiological data is lacking for rare diseases, hindering progress. Global collaboration and research infrastructure are crucial for advancing rare disease understanding and patient care.
Area of Science:
- Epidemiology
- Rare Diseases
- Global Health
Background:
- The rare disease community lacks reliable epidemiological data on prevalence and incidence.
- This data gap hinders understanding and progress in diagnosis, prevention, and treatment.
- Addressing myths with realities requires global efforts and improved data.
Purpose of the Study:
- To highlight the critical need for reliable epidemiological data in the rare disease community.
- To advocate for global partnerships and research infrastructure development.
- To emphasize the importance of accessible information for patients and families.
Main Methods:
- The abstract discusses the need for global efforts and infrastructure.
- It emphasizes worldwide partnerships with Patient Advocacy Groups.
- It calls for a global research infrastructure with qualified investigators and common protocols.
Main Results:
- The absence of reliable epidemiological data is a significant challenge.
- Global partnerships and research infrastructure are identified as key requirements.
- Improved access to information is essential for patient care.
Conclusions:
- Strengthening global partnerships and research infrastructure is vital for rare disease advancement.
- Epidemiological data is crucial for guiding further clinical studies.
- Enhanced information dissemination will improve the lives of patients and families.
Related Concept Videos
01:29Bias 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:
01:26Introduction to Epidemiology
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,...
01:26Principles of Disease Surveillance
Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
01:25Statistical Methods for Analyzing Epidemiological Data
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:
01:27Confounding in Epidemiological Studies
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 phenomenon...
01:28Infectious Diseases and Their Occurrence
Infectious diseases appear in populations through various transmission patterns, influenced by pathogen characteristics, population immunity, environmental conditions, and social behavior. Understanding these patterns is essential for effective public health surveillance and intervention. These categories—sporadic, outbreak, epidemic, pandemic, and endemic—help frame the nature and scope of disease events.Sporadic diseases occur irregularly and infrequently, without a predictable temporal or...