Evidence map›Paper›PMID 28338425›Full record

ArticlePopulation health management2017

Assessing the Reliability of Performing Citywide Chronic Disease Surveillance Using Emergency Department Data from Sentinel Hospitals.

David C Lee, Jordan L Swartz, Christian A Koziatek, Andrew J Vinson, Jessica K Athens, Stella S Yi

Abstract read
In one paragraph

Article in Population health management, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

David C Lee1 Ronald O. Perelman Department of Emergency Medicine, New York University School of Medicine , New York, New York.
Jordan L Swartz1 Ronald O. Perelman Department of Emergency Medicine, New York University School of Medicine , New York, New York.
Christian A Koziatek1 Ronald O. Perelman Department of Emergency Medicine, New York University School of Medicine , New York, New York.
Andrew J Vinson1 Ronald O. Perelman Department of Emergency Medicine, New York University School of Medicine , New York, New York.
Jessica K Athens2 Department of Population Health, New York University School of Medicine , New York, New York.
Stella S Yi2 Department of Population Health, New York University School of Medicine , New York, New York.

Funding

Using Geographically Targeted and Community-Based Methods to Identify Factors Associated with Micro-Level Disparities in Diabetic Outcomes and Enhance Monitoring of Glycemic Control Among Black MenK23DK110316 · NIDDK · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI LEE, DAVID C · 2016 to 2020
$982k
NIDDK NIH HHS K23 DK110316
6 · The paper itself

Abstract

Given the inequalities in the distribution of disease burden, geographically detailed methods of disease surveillance are needed to identify local hot spots of chronic disease. However, few data sources include the patient-level addresses needed to perform these studies. Given that individual hospitals would have access to this geographically granular data, this study assessed the reliability of estimating chronic disease prevalence using emergency department surveillance at specific hospitals. Neighborhood-level diabetes, hypertension, and asthma prevalence were estimated using emergency claims data from each individual hospital in New York City from 2009-2012. Estimates were compared to prevalence obtained from a traditional health survey. A multivariable analysis also was performed to identify which individual hospitals were more accurate at estimating citywide disease prevalence. Among 52 hospitals, variation was found in the accuracy of disease prevalence estimates using emergency department surveillance. Estimates at some hospitals, such as NYU Langone Medical Center, had strong correlations for all diseases studied (diabetes: 0.81, hypertension: 0.84, and asthma: 0.84). Hospitals with patient populations geographically distributed throughout New York City had better accuracy in estimating citywide disease prevalence. For diabetes and hypertension, hospitals with racial/ethnic patient distributions similar to Census estimates and higher fidelity of diagnosis coding also had more accurate prevalence estimates. This study demonstrated how citywide chronic disease surveillance can be performed using emergency data from specific sentinel hospitals. The findings may provide an alternative means of mapping chronic disease burden by using existing data, which may be critical in regions without resources for geographically detailed health surveillance.

Indexed as

Sentinel SurveillanceAdolescentAdultAgedChronic DiseaseEmergency Service, HospitalFemaleGeographic Information SystemsHospitalsHumansMaleMiddle AgedNew York CityPrevalenceTreatment OutcomeYoung Adultadministrative claims datachronic disease prevalenceemergency department surveillancegeographic information systems

Identifiers

PMID28338425
PMCPMC5709695

What OpenQuestion holds

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Registered trials

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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.