ArticleEpidemiology (Cambridge, Mass.)2015
Commentary: Epidemiology in the era of big data.
Article in Epidemiology (Cambridge, Mass.), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 1 of them a synthesis that pooled it.
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
71 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Area-level racial prejudice and health: A systematic review.Health psychology : official journal of the Division of Health Psychology, American Psychological Association · 2022Pooled it
- Predicting the Future of Aesthetic Surgery: An Artificial Intelligence Framework for Global Publication Forecasting.Aesthetic surgery journal · 2026Observational
- AI-assisted forecasting in microsurgery: A dual-component framework for global publication trends.PloS one · 2026Article
- Complex methods for complex data: key considerations for interpretable and actionable results in exposome research.European journal of epidemiology · 2025Article
- Modern sources of controls in case-control studies.American journal of epidemiology · 2025Article
- Harvard Electroencephalography Database: A comprehensive clinical electroencephalographic resource from four Boston hospitals.Epilepsia · 2025Article
- Enhancing Clinical Data Infrastructure for AI Research: Comparative Evaluation of Data Management Architectures.Journal of medical Internet research · 2025Article
- Augmenting electronic health record data with social and environmental determinant of health measures to understand regional factors associated with asthma exacerbations.PLOS digital health · 2025Article
- Adverse COVID-19 outcomes in American Veterans with age-related macular degeneration: a case-control study.BMJ open · 2023Article
- Maternal and child nutrition programme of investigation within the 100 Million Brazilian Cohort: study protocol.BMJ open · 2023Article
- Use of Danish National Somatic Cell Count Data to Assess the Need for Dry-Off Treatment in Holstein Dairy Cattle.Animals : an open access journal from MDPI · 2023Article
- Human Capital and Productivity in Plastic Surgery Research Across Nations.Aesthetic plastic surgery · 2023Article
- Article
- Decolonizing Epidemiological Research: A Critical Perspective.Avicenna journal of medicine · 2023Review
- Article
- Reproducibility and Scientific Integrity of Big Data Research in Urban Public Health and Digital Epidemiology: A Call to Action.International journal of environmental research and public health · 2023Article
- Linking Ambient NO2 Pollution Measures with Electronic Health Record Data to Study Asthma Exacerbations.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- To scrape or not to scrape, this is dilemma. The post-API scenario and implications on digital research.Frontiers in sociology · 2023Article
- Big data in corneal diseases and cataract: Current applications and future directions.Frontiers in big data · 2023Review
- Big data, machine learning, and population health: predicting cognitive outcomes in childhood.Pediatric research · 2023Review
11 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Big Data has increasingly been promoted as a revolutionary development in the future of science, including epidemiology. However, the definition and implications of Big Data for epidemiology remain unclear. We here provide a working definition of Big Data predicated on the so-called "three V's": variety, volume, and velocity. From this definition, we argue that Big Data has evolutionary and revolutionary implications for identifying and intervening on the determinants of population health. We suggest that as more sources of diverse data become publicly available, the ability to combine and refine these data to yield valid answers to epidemiologic questions will be invaluable. We conclude that while epidemiology as practiced today will continue to be practiced in the Big Data future, a component of our field's future value lies in integrating subject matter knowledge with increased technical savvy. Our training programs and our visions for future public health interventions should reflect this future.
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.