Accuracy of information on the underlying cause of death: An analysis in Colombia during the COVID-19 pandemic in 2021
Contact us if these videos are not relevant.
Contact us if these videos are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.The accuracy of original death certificates in Colombia was adequate, with 74% concordance to a gold standard. Improvements in training for health professionals are recommended to enhance the death certification process.
Area Of Science
- Public Health
- Epidemiology
- Vital Statistics
Background
- Accurate death certification is crucial for public health surveillance and resource allocation.
- Previous studies have highlighted variability in the accuracy of cause-of-death (CoD) reporting globally.
- Colombia's National Department of Statistics provides original death certificates for analysis.
Purpose Of The Study
- To estimate the accuracy of underlying Cause of Death (CoD) on original death certificates in Colombia for 2021.
- To compare original certificates against a gold standard derived from clinical records and interviews.
- To identify specific areas of overestimation and underestimation in reported causes of death.
Main Methods
- A two-stage stratified random sample of 776 deaths from 326,833 original certificates in Colombia.
- A gold standard CoD was determined using medical records and interviews with relatives/witnesses.
- Analysis included concordance rates, false positives/negatives, and kappa values for quality evaluation.
Main Results
- Overall concordance between original and gold standard certificates was 74%.
- High agreement was observed for COVID-19 codes (kappa=0.84) and neoplasms (kappa=0.84).
- Overestimation occurred for circulatory, pregnancy-related, poorly defined, and respiratory diseases; underestimation for genitourinary diseases.
Conclusions
- The concordance level for death certificates in Colombia was adequate.
- Recommendations include enhancing training programs for health professionals to improve death certification accuracy.
- Specific attention may be needed for certain disease categories like circulatory and respiratory conditions.
Contact us if these videos are not relevant.
Contact us if these videos are not relevant.
Related Concept Videos
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:
Selection Bias: This occurs when the study population is not...
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...
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:
Descriptive Statistics: These provide basic...
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...
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...