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 Experiment Videos

GUILD: GUidance for Information about Linking Data sets.

Ruth Gilbert1, Rosemary Lafferty1, Gareth Hagger-Johnson1

  • 1Administrative Data Research Centre for England, University College London Great Ormond Street Institute of Child Health, London, UK.

Journal of Public Health (Oxford, England)
|April 4, 2017
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Intellectual and physical disability, household living arrangements and COVID-19 outcomes in Scotland: protocol for a retrospective cohort study.

BMJ open·2026
Same author

Intellectual and Physical Disability and Risk of COVID-19 Infection, Hospitalisation, and Mortality: A National Cohort of 3.7 Million Adults in Scotland.

Journal of epidemiology and global health·2026
Same author

Whole population cohorts vs sampled comparators designs for evaluating health and educational outcomes of children with inborn rare conditions: a simulation study.

Journal of clinical epidemiology·2026
Same author

Response to "Public health advice on sun exposure and sunbed user should not be disregarded" and its discussion of our paper "Ultraviolet light exposure is associated with lower mortality".

The British journal of dermatology·2026
Same author

Strategies for ascertaining timing of address changes in administrative data.

International journal of population data science·2026
Same author

Data Resource Profile: Maternity Services Dataset (MSDS).

International journal of population data science·2026
Same journal

Marginalising prevention during Health Devolution in Greater Manchester? A qualitative study of the perceptions and experiences of public health professionals.

Journal of public health (Oxford, England)·2026
Same journal

Smoking prevalence in Italy from 1993 to 2024: a trend analysis framed by public health policies and emerging consumption behaviors.

Journal of public health (Oxford, England)·2026
Same journal

The exposure of adolescent girls with/without disabilities to discrimination: cross-sectional analyses of nationally representative surveys undertaken in 37 countries.

Journal of public health (Oxford, England)·2026
Same journal

Classifying healthcare facilities as predictors of COVID-19 mortality rates in US counties (2020-2021).

Journal of public health (Oxford, England)·2026
Same journal

How conditions within immigration removal centres in the United Kingdom affect the mental wellbeing of detainees: a scoping review.

Journal of public health (Oxford, England)·2026
Same journal

Double burden of malnutrition and body composition among ethnic minority children aged 5-16 years in Tra Vinh, Vietnam: a community-based study.

Journal of public health (Oxford, England)·2026
See all related articles

Data linkage errors can bias results, disproportionately affecting disadvantaged groups. New guidance aims to improve transparency and accuracy in data linkage for better policy and public health evidence.

Area of Science:

  • Data Science
  • Public Health Research
  • Administrative Data Analysis

Background:

  • Record linkage of administrative and survey data is vital for policy and service evidence generation.
  • Data processing errors during linkage can introduce biases, compromising research validity.
  • Lack of transparency regarding linkage processes hinders bias assessment and mitigation.

Framework:

  • Develop comprehensive guidance for data providers, linkers, and analysts on essential linkage information.
  • Establish clear reporting standards for data linkage processes.
  • Promote awareness of potential biases and their impact on disadvantaged populations.

Implementation:

  • Collaborative effort involving researchers and government data experts.
  • Guidance extends beyond research reports to cover the entire linkage pathway.

Related Experiment Videos

  • Focus on improving information availability at each linkage step.

    • Implications:

    • Enhance transparency, reproducibility, and accuracy of data linkage.
    • Improve the validity of analyses and interpretation of results from linked data.
    • Mitigate bias in evidence generation, particularly for public health initiatives affecting vulnerable groups.