Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Statistical Methods for Analyzing Epidemiological Data01:25

Statistical 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:
Bias in Epidemiological Studies01:29

Bias 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:
Applications of Life Tables01:22

Applications of Life Tables

Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
Principles of Disease Surveillance01:26

Principles 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...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Temporal trends in acute coronary syndrome among women and association with socioeconomic factors-evidence from a middle-income country.

Frontiers in global women's health·2026
Same author

Linking diabetic retinal changes with the occurrence of anxiety and depression in working population.

Open medicine (Warsaw, Poland)·2026
Same author

A Comparative Analysis of Differences in Salivary hBD-2 Levels and Their Correlation with Dental Caries and Unstimulated Saliva pH in Children with Primary and Permanent Dentition.

Diagnostics (Basel, Switzerland)·2026
Same author

Exploring Medical Students' Perceptions Regarding ChatGPT and AI Studying at the University of Niš: A Study on Usage, Attitudes, and Linguistic Influence-Single-Centered Study in Serbia-A Paradoxical Ally?

Journal of medical education and curricular development·2025
Same author

Serum Hepcidin as a Biomarker of Subclinical Atherosclerosis in Peritoneal Dialysis: A Cross-Sectional Study.

Journal of clinical medicine·2025
Same author

Psychometric Evaluation of the Serbian Version of the Southampton Dupuytren's Scoring Scheme in Patients with Dupuytren's Contracture.

Journal of clinical medicine·2025

Related Experiment Video

Updated: May 24, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
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

Leveraging ANACONDA for Data Quality Assessment in National Mortality Databases: The Serbian Case Study.

Marija Anđelković Apostolović1,2, Aleksandra Ignjatović1,2, Miodrag Stojanović1,2

  • 1Medical Faculty, University of Niš, Serbia, Medical statistics and informatics.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary

This study improved Serbian mortality data quality using the ANACONDA framework. Algorithmic auditing enhanced vital statistics performance and reduced garbage codes, supporting better public health policy.

Keywords:
ANACONDACRVSSerbiagarbage codesmortality data qualitypublic health informatics

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Related Experiment Videos

Last Updated: May 24, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
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

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Area of Science:

  • Public Health
  • Health Informatics
  • Demography

Background:

  • Reliable mortality statistics are crucial for public health policy, Sustainable Development Goal (SDG) monitoring, and health system evaluation.
  • Civil Registration and Vital Statistics (CRVS) systems are fundamental for collecting cause-of-death (CoD) data.
  • Assessing and improving CoD data quality is essential for accurate health intelligence.

Purpose of the Study:

  • To apply the World Health Organization's ANACONDA framework to assess and improve the quality of Serbian mortality data.
  • To evaluate trends in cause-of-death data quality from 2005 to 2019 using standardized algorithmic metrics.
  • To demonstrate the utility of algorithmic auditing in strengthening national mortality data systems.

Main Methods:

  • Utilized the ANACONDA (v5.0) framework for algorithmic quality assessment of mortality data.
  • Analyzed 1.54 million death records from Serbia (2005-2019) coded using the International Classification of Diseases, 10th Revision (ICD-10).
  • Measured key quality indicators including garbage codes (GC), high-impact GC (Levels 1-3), and the Vital Statistics Performance Index for Quality (VSPI(Q)).

Main Results:

  • The Vital Statistics Performance Index for Quality (VSPI(Q)) improved from medium to high quality over the study period.
  • Overall garbage codes (GC) decreased from 47.3% to 40.5% in the analyzed mortality data.
  • The application of algorithmic auditing revealed significant improvements in cause-of-death data quality.

Conclusions:

  • Algorithmic quality auditing, as implemented by the ANACONDA framework, effectively strengthens national mortality intelligence.
  • Improvements in data quality support more robust evidence-based public health policy and data-driven health governance.
  • The study highlights the successful application of standardized metrics to enhance vital statistics systems.