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The problem of comparative analysis of birth prevalence of congenital cardiovascular malformations
1Department of Epidemiology and Preventive Medicine, University of Maryland School of Medicine, Baltimore.
Insights
Comparing congenital cardiovascular malformation (CCVM) birth prevalences across countries is challenging. This study explores biases in data from Hungary, USA, and Belgium to improve international epidemiologic data comparability.
Area of Science:
- Epidemiology
- Public Health
- Cardiovascular Science
Background:
- International comparisons of congenital cardiovascular malformation (CCVM) birth prevalences are frequently published.
- Meaningful intercountry comparisons face challenges due to differential circumstances.
Purpose of the Study:
- To explore the feasibility of meaningful intercountry comparisons of CCVM birth prevalences.
- To identify and highlight factors that influence data comparability across different regions.
Main Methods:
- Analysis of CCVM data from three Hungarian studies, one USA study, and one Belgian study.
- Exploration of differential perinatal and diagnostic circumstances as sources of bias.
- Identification of four main domains of bias: study population, case ascertainment, CCVM categorization, and diagnostic definitions.
Main Results:
- Presented data for selected CCVMs from diverse international studies.
- Highlighted unique features within the data that impact comparability.
- Identified key areas of bias affecting intercountry comparisons of CCVM prevalence.
Conclusions:
- Differential perinatal and diagnostic circumstances significantly bias CCVM prevalence data.
- A descriptive framework is proposed to enhance the comparability of epidemiologic data.
- Improved comparability is crucial for accurate international CCVM surveillance and research.
Abstract:
A number of published papers have dealt with the comparison of birth prevalences of congenital cardiovascular malformation (CCVMs). The feasibility of meaningful intercountry comparison was explored during the visiting fellowships to Dr Andrew Czeizel of Charlotte Ferencz and Dr Francine Lys. Data from three Hungarian studies, one USA and one Belgian study are presented here for selected CCVMs. Differential perinatal and diagnostic circumstances which lead to possible causes of bias involve four main domains: study population, ascertainment of cases, categorization of CCVMs and diagnostic definitions. Unique features are highlighted as a descriptive framework which will promote the comparability of epidemiologic data from various regions.