ReviewEpidemiology and health2018
Population data science: advancing the safe use of population data for public benefit.
Review in Epidemiology and health, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- The Road to Hell Winds On: The High Administrative Burden of Maintaining Linked National Health Data.International journal of population data science · 2026Article
- Review
- Interdisciplinary data science to advance environmental health research and improve birth outcomes.Environmental research · 2021Article
- Considerations for an integrated population health databank in Africa: lessons from global best practices.Wellcome open research · 2021Article
- Age-Dependent and Seasonal Changes in Menstrual Cycle Length and Body Temperature Based on Big Data.Obstetrics and gynecology · 2020Article
- Panel 4: Recent advances in understanding the natural history of the otitis media microbiome and its response to environmental pressures.International journal of pediatric otorhinolaryngology · 2020Review
- The Centre for Data and Knowledge Integration for Health (CIDACS): Linking Health and Social Data in Brazil.International journal of population data science · 2019Article
- Public Views on Models for Accessing Genomic and Health Data for Research: Mixed Methods Study.Journal of medical Internet research · 2019Article
- Exploring barriers and solutions in advancing cross-centre population data science.International journal of population data science · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The value of using population data to answer important questions for individual and societal benefit has never been greater. Governments and research funders world-wide are recognizing this potential and making major investments in data-intensive initiatives. However, there are challenges to overcome so that safe, socially-acceptable data sharing can be achieved. This paper outlines the field of population data science, the International Population Data Linkage Network (IPDLN), and their roles in advancing data-intensive research. We provide an overview of core concepts and major challenges for data-intensive research, with a particular focus on ethical, legal, and societal implications (ELSI). Using international case studies, we show how challenges can be addressed and lessons learned in advancing the safe, socially-acceptable use of population data for public benefit. Based on the case studies, we discuss the common ELSI principles in operation, we illustrate examples of a data scrutiny panel and a consumer panel, and we propose a set of ELSI-based recommendations to inform new and developing data-intensive initiatives.We conclude that although there are many ELSI issues to be overcome, there has never been a better time or more potential to leverage the benefits of population data for public benefit. A variety of initiatives, with different operating models, have pioneered the way in addressing many challenges. However, the work is not static, as the ELSI environment is constantly evolving, thus requiring continual mutual learning and improvement via the IPDLN and beyond.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.