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

Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

1.6K
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:  
1.6K
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

1.0K
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...
1.0K
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

11.4K
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...
11.4K
Prevalence and Incidence01:08

Prevalence and Incidence

2.3K
In statistical epidemiology and health sciences, two essential metrics—prevalence and incidence—are fundamental for understanding disease dynamics within a population. These measures enable public health officials, epidemiologists, and researchers to assess the burden of diseases, allocate resources effectively, and design impactful public health policies and interventions.
Prevalence indicates the proportion of individuals in a population who have a specific disease or health...
2.3K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical 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
Regression Toward the Mean01:52

Regression Toward the Mean

7.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
7.3K

You might also read

Related Articles

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

Sort by
Same author

Inconsistent Reporting of Cigarette Smoking Among European High-School Students: Prevalence and Impact on Daily Use Estimates Across 48 Countries and Regions.

Substance use & misuse·2026
Same author

How Inconsistent Reporting Affects Bullying Victimization Estimates: A Data-Adjusted Replication Study in 29 Low- and Middle-Income Countries.

Journal of adolescence·2026
Same author

Inconsistent Reporting of Alcohol Use Among Adolescents: Implications for Survey Validity.

Annals of the New York Academy of Sciences·2025
Same author

Psychometric Evaluation and Sociodemographic Measurement Invariance of the WHO-5 Well-Being Index among Adolescents in Luxembourg.

Journal of personality assessment·2025
Same author

The Pitfalls of Measuring Burnout Prevalence: μετά the Meta-Analysis, What Then?

United European gastroenterology journal·2025
Same author

Inconsistent Reporting Affects the Link Between Substance Use and Suicide Attempt in Adolescents.

Substance use & misuse·2025

Related Experiment Video

Updated: Mar 31, 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

15.5K

Enhancing prevalence estimation by addressing inconsistent reporting: insights from a cross-national, data-adjusted

Romain Brisson1

  • 1Centre for Childhood and Youth Research, University of Luxembourg, 11, Porte des Sciences, L-4366 Esch-sur-Alzette, Luxembourg.

Alcohol and Alcoholism (Oxford, Oxfordshire)
|March 30, 2026
PubMed
Summary

Inconsistent reporting of adolescent alcohol use is common in low- and middle-income countries (LMICs). Adjusting for this inconsistency significantly impacts prevalence estimates, highlighting the need for data validation in public health research.

Keywords:
adolescentsalcohol useinconsistent reportingreplication

More Related Videos

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.8K

Related Experiment Videos

Last Updated: Mar 31, 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

15.5K
Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.8K

Area of Science:

  • Global Health
  • Adolescent Health
  • Substance Use Research

Background:

  • Evidence on adolescent alcohol use in low- and middle-income countries (LMICs) is limited.
  • Existing research often fails to address inconsistent data reporting, impacting reliability.

Purpose of the Study:

  • To assess the effect of inconsistent reporting on adolescent alcohol use prevalence estimates in 57 LMICs.
  • To analyze how data adjustments influence findings on alcohol use and acquisition patterns.

Main Methods:

  • Examined 37 datasets from LMICs for logical inconsistencies in reporting.
  • Calculated prevalence estimates using both unadjusted and adjusted data (excluding inconsistent reporters).

Main Results:

  • Inconsistent reporting was prevalent (9.6% on average), with significant variation across datasets.
  • Excluding inconsistent reporters led to an average -8.9% change in prevalence, with substantial heterogeneity observed.
  • Adjustments altered age- and sex-related patterns and significantly impacted alcohol acquisition source data.

Conclusions:

  • Inconsistent reporting is a common issue in LMIC adolescent alcohol use studies, similar to high-income countries.
  • The impact of inconsistent reporting on prevalence estimates is substantial, heterogeneous, and unpredictable.
  • Implementing consistency checks is crucial for improving the validity of research informing public health decisions.