Related Experiment Video
Updated: Apr 12, 2026
11:10
Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3
Published on: December 27, 2010
12.9K
Improving cohort coverage estimation using a data triangulation framework: the Swiss HIV Cohort Study example
Jessy J Duran Ramirez1,2, Roger D Kouyos1,2, Irene Abela1,2
1Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich.
AIDS (London, England)
|April 10, 2026
Summary
A data triangulation framework improved estimation of Swiss HIV Cohort Study (SHCS) coverage. While broadly representative, the SHCS needs strategies to include underrepresented groups for continued research generalizability.
Area of Science:
- Epidemiology
- Public Health
- Biostatistics
Background:
- The Swiss HIV Cohort Study (SHCS) is crucial for HIV research in Switzerland.
- Accurate estimation of cohort coverage and representativeness is vital for generalizability.
- Data triangulation offers a novel approach to validate cohort data.
Purpose of the Study:
- To enhance the estimation of cohort coverage within the SHCS.
- To assess the representativeness of the SHCS using multiple data sources.
- To implement a data triangulation framework for ongoing monitoring.
Main Methods:
- Retrospective longitudinal analysis of SHCS data (1985-2023).
- Triangulation of SHCS data with national HIV/AIDS surveillance, ART sales data, and literature.
- Assessment of temporal trends and demographic representativeness by sex, age, HIV acquisition mode, and region.
Main Results:
- Over 38 years, mean SHCS coverage was 62.4% for HIV diagnoses, 74.0% for AIDS, and 64.9% for ART uptake.
- Coverage of HIV diagnoses declined recently, with observed geographical heterogeneity.
- While broadly representative, females, older adults, and individuals with heterosexually acquired HIV were underrepresented.
Conclusions:
- Data triangulation is a practical method for monitoring cohort coverage and representativeness.
- Tailored strategies are necessary to improve the inclusion of underrepresented subgroups in the SHCS.
- Sustained monitoring ensures research generalizability and informs clinical care and public health responses.
Keywords:
HIV epidemicHIV surveillancecohort coveragecohort studydata triangulationrepresentativenessMore Related Videos
Related Concept Videos
01:29Bias in Epidemiological Studies
1.7K
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:
1.7K
01:16Stratified Sampling Method
16.4K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
To choose a stratified sample, divide the population into groups called strata and then take a...
16.4K
01:25Statistical Methods for Analyzing Epidemiological Data
1.2K
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:
1.2K
01:26Longitudinal Studies
691
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
691
01:27Confounding in Epidemiological Studies
1.1K
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...
1.1K
01:20Study Designs in Epidemiology
1.6K
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...
1.6K