ArticleEGEMS (Washington, DC)2016
Design of the New York City Macroscope: Innovations in Population Health Surveillance Using Electronic Health Records.
Article in EGEMS (Washington, DC), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 4 of them syntheses 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
32 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Data Harmonization Methods and Data Quality Assessment for Integrating Routinely Collected Data Across Multiple Sources: A Systematic Review.Advances in health information science and practice · 2026Pooled it
- Electronic health record data quality assessment and tools: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023Pooled it
- Digital Data Sources and Their Impact on People's Health: A Systematic Review of Systematic Reviews.Frontiers in public health · 2021Pooled it
- Electronic health record-based disease surveillance systems: A systematic literature review on challenges and solutions.Journal of the American Medical Informatics Association : JAMIA · 2020Pooled it
- Implementation of a surveillance and monitoring system for chronic disease: A scoping review: Running title: Chronic disease surveillance and monitoring system.AIMS public health · 2026Review
- Chronic Disease Monitoring: Methodology for Classification Error and Self-Selection Bias Correction in Clinical Laboratory Data.Healthcare (Basel, Switzerland) · 2025Article
- Blood Pressure in Young Adults in Iowa.Journal of clinical hypertension (Greenwich, Conn.) · 2025Observational
- Electronic Health Records for Population Health Management: Comparison of Electronic Health Record-Derived Hypertension Prevalence Measures Against Established Survey Data.Online journal of public health informatics · 2024Article
- Characterizing opioid overdose hotspots for place-based overdose prevention and treatment interventions: A geo-spatial analysis of Rhode Island, USA.The International journal on drug policy · 2024Article
- Facilitation of team-based care to improve HTN management and outcomes: a protocol for a randomized stepped wedge trial.BMC health services research · 2023Article
- Utilization of Electronic Health Records for Chronic Disease Surveillance: A Systematic Literature Review.Cureus · 2023Review
- Small-area estimation for public health surveillance using electronic health record data: reducing the impact of underrepresentation.BMC public health · 2022Article
- Addressing Hypertension Outcomes Using Telehealth and Population Health Managers: Adaptations and Implementation Considerations.Current hypertension reports · 2022Review
- Establishing a National Cardiovascular Disease Surveillance System in the United States Using Electronic Health Record Data: Key Strengths and Limitations.Journal of the American Heart Association · 2022Article
- Using inpatient electronic medical records to study influenza for pandemic preparedness.Influenza and other respiratory viruses · 2022Article
- RiskScape: A Data Visualization and Aggregation Platform for Public Health Surveillance Using Routine Electronic Health Record Data.American journal of public health · 2021Article
- The Cosmos Collaborative: A Vendor-Facilitated Electronic Health Record Data Aggregation Platform.ACI open · 2021Article
- Defining Major Depressive Disorder Cohorts Using the EHR: Multiple Phenotypes Based on ICD-9 Codes and Medication Orders.Neurology, psychiatry, and brain research · 2020Article
- Prevalence estimation by joint use of big data and health survey: a demonstration study using electronic health records in New York city.BMC medical research methodology · 2020Article
- A step closer to nationwide electronic health record-based chronic disease surveillance: characterizing asthma prevalence and emergency department utilization from 100 million patient records through a novel multisite collaboration.Journal of the American Medical Informatics Association : JAMIA · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
introductionElectronic health records (EHRs) have the potential to offer real-time, inexpensive standardized health data about chronic health conditions. Despite rapid expansion, EHR data evaluations for chronic disease surveillance have been limited. We present design and methods for the New York City (NYC) Macroscope, an EHR-based chronic disease surveillance system. This methods report is the first in a three part series describing the development and validation of the NYC Macroscope. This report describes in detail the infrastructure underlying the NYC Macroscope; indicator definitions; design decisions that were made to maximize data quality; characteristics of the population sampled; completeness of data collected; and lessons learned from doing this work. The second report describes the methods used to evaluate the validity and robustness of NYC Macroscope prevalence estimates; presents validation results for estimates of obesity, smoking, depression and influenza vaccination; and discusses the implications of our findings for NYC and for other jurisdictions embarking on similar work. The third report applies the same validation methods to metabolic outcomes, including the prevalence, treatment and control of diabetes, hypertension and hyperlipidemia.
methodsWe designed the NYC Macroscope for comparison to a local "gold standard," the 2013-14 NYC Health and Nutrition Examination Survey, and the telephonic 2013 Community Health Survey. NYC Macroscope indicators covered prevalence, treatment, and control of diabetes, hypertension, and hyperlipidemia; and prevalence of influenza vaccination, obesity, depression and smoking. Indicators were stratified by age, sex, and neighborhood poverty, and weighted to the in-care NYC population and limited to primary care patients. Indicator queries were distributed to a virtual network of primary care practices; 392 practices and 716,076 adult patients were retained in the final sample.
findingsThe NYC Macroscope covered 10% of primary care providers and 15% of all adult patients in NYC in 2013 (8-47% of patients by neighborhood). Data completeness varied by domain from 98% for blood pressure among patients with hypertension to 33% for depression screening. DISCUSSION: Design and validation efforts undertaken by NYC are described here to provide one potential blueprint for leveraging EHRs for population health monitoring. To replicate a model like NYC Macroscope, jurisdictions should establish buy-in; build informatics capacity; use standard, simple case defnitions; establish documentation quality thresholds; restrict to primary care providers; and weight the sample to a target population.
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.