ArticleJournal of biomedical informatics2016
Computing disease incidence, prevalence and comorbidity from electronic medical records.
Article in Journal of biomedical informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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
25 citing papers in PubMed.
- Trial
- Population differences in wearable device wear time: Rescuing data to address biases and advance health equity.medRxiv : the preprint server for health sciences · 2026Article
- Blood Pressure in Young Adults in Iowa.Journal of clinical hypertension (Greenwich, Conn.) · 2025Observational
- The epidemiology of hereditary spastic paraplegia and associated common mental health outcomes in England and Northern Ireland.Orphanet journal of rare diseases · 2025Article
- The burden of chronic pain in transgender and gender diverse populations: Evidence from a large US clinical database.European journal of pain (London, England) · 2025Article
- Article
- Algorithms for the identification of prevalent diabetes in the All of Us Research Program validated using polygenic scores.Scientific reports · 2024Article
- Conservative Versus Operative Treatment of Proximal Humerus Fractures in Older Individuals.Deutsches Arzteblatt international · 2024Article
- Demographics, Trends, and Clinical Characteristics of HIV Pre-Exposure Prophylaxis Recipients and People Newly Diagnosed with HIV from Large Electronic Health Records in Florida.AIDS patient care and STDs · 2024Article
- Subclinical hypothyroidism in Wales from 2000 to 2021: A descriptive cohort study based on electronic health records.PloS one · 2024Article
- The impact of varying the number and selection of conditions on estimated multimorbidity prevalence: A cross-sectional study using a large, primary care population dataset.PLoS medicine · 2023Article
- Supermarket Proximity and Risk of Hypertension, Diabetes, and CKD: A Retrospective Cohort Study.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2023Article
- Network-medicine framework for studying disease trajectories in U.S. veterans.Scientific reports · 2022Article
- Comparing medical history data derived from electronic health records and survey answers in the All of Us Research Program.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Medical Service Utilization and Direct Medical Cost of Stroke in Urban China.International journal of health policy and management · 2022Article
- Describing a complex primary health care population to support future decision support initiatives.International journal of population data science · 2022Article
- The incidence of psoriasis in Chile: an analysis of the National Waiting List Repository.Clinical and experimental dermatology · 2021Article
- Importance of Geospatial Heterogeneity in Chronic Disease Burden for Policy Planning in an Urban Setting Using a Case Study of Singapore.International journal of environmental research and public health · 2021Article
- Augmenting the Clinical Data Sources for Enigmatic Diseases: A Cross-Sectional Study of Self-Tracking Data and Clinical Documentation in Endometriosis.Applied clinical informatics · 2020Observational
- Article
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
Electronic medical records (EMR) represent a convenient source of coded medical data, but disease patterns found in EMRs may be biased when compared to surveys based on sampling. In this communication we draw attention to complications that arise when using EMR data to calculate disease prevalence, incidence, age of onset, and disease comorbidity. We review known solutions to these problems and identify challenges for future work.
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.