Evidence map›Paper›PMID 35308901›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2021

The Addition of United States Census-Tract Data Does Not Improve the Prediction of Substance Misuse.

Daniel To, Cara Joyce, Sujay Kulshrestha, Brihat Sharma, Dmitry Dligach, Matthew Churpek, Majid Afshar

Abstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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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

2 citing papers in PubMed.

  1. Review
  2. Machine learning approaches for electronic health records phenotyping: a methodical review.Journal of the American Medical Informatics Association : JAMIA · 2023
    Article
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

7 authors.

Daniel ToStritch School of Medicine, Loyola University Chicago, Maywood, IL.
Cara JoyceDepartment of Public Health, Stritch School of Medicine, Loyola University Chicago, Maywood, IL.
Sujay KulshresthaDepartment of Surgery, Loyola University Medical Center, Maywood, IL.
Brihat SharmaDepartment of Psychiatry and Behavioral Sciences, Rush University Medical Center, Chicago, IL.
Dmitry DligachDepartment of Public Health, Stritch School of Medicine, Loyola University Chicago, Maywood, IL.
Matthew ChurpekDepartment of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.
Majid AfsharDepartment of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.

Funding

Data Driven Strategies for Substance Misuse Identification in Hospitalized PatientsR01DA051464 · NIDA · UNIVERSITY OF WISCONSIN-MADISON · PI Majid Afshar · 2020 to 2026
$4.5M
CHANGE OF GRANTEE INSTITUTION 1 K23 AA024503 Alcohol, Burn-Injury, and Acute Respiratory Distress SyndromeK23AA024503 · NIAAA · UNIVERSITY OF WISCONSIN-MADISON · PI AFSHAR, MAJID · 2016 to 2020
$948k
NIAAA NIH HHS K23 AA024503NIDA NIH HHS R01 DA051464
6 · The paper itself

Abstract

Predictors from the structured data in the electronic health record (EHR) have previously been used for case-identification in substance misuse. We aim to examine the added benefit from census-tract data, a proxy for socioeconomic status, to improve identification. A cohort of 186,611 hospitalizations was derived between 2007 and 2017. Reference labels included alcohol misuse only, opioid misuse only, and both alcohol and opioid misuse. Baseline models were created using 24 EHR variables, and enhanced models were created with the addition of 48 census-tract variables from the United States American Community Survey. The absolute net reclassification index (NRI) was applied to measure the benefit in adding census-tract variables to baseline models. The baseline models already had good calibration and discrimination. Adding census-tract variables provided negligible improvement to sensitivity and specificity and NRI was less than 1% across substance groups. Our results show the census-tract added minimal value to prediction models.

Indexed as

CensusesOpioid-Related DisordersCohort StudiesElectronic Health RecordsHumansSocial ClassUnited States

Identifiers

PMID35308901
PMCPMC8861711

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.