Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Building A Longitudinal Cohort From 9‐1‐1 to 1‐Year Using Existing Data Sources, Probabilistic Linkage, and Multiple Imputation: A Validation Study

3 Jul 2018

Abstract excerpt

OBJECTIVE: The objective was to describe and validate construction of a population-based, longitudinal cohort of injured older adults from 9-1-1 call to 1-year follow-up using existing data sources, probabilistic linkage, and multiple imputation. METHODS: This was a descriptive cohort study conducted in seven counties in Oregon and Washington from January 1, 2011, through December 31, 2011, with follow-up through...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Chronic Disease Management Strategies
Share to Data-Driven Disease Surveillance
Share to Global Cancer Incidence and Screening

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 29969840
DOI: 10.1111/acem.13512

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Building A Longitudinal Cohort From 9‐1‐1 to 1‐Year Using Existing Data Sources, Probabilistic Linkage, and Multiple Imputation: A Validation StudyPMID 29969840Read ↗

Patient-Level Stroke Outcomes Following Linkage of a Statewide Stroke Registry to Administrative Claims DataSemantic proximity Read ↗

Disparities in Life Expectancy of Pacific Northwest American Indians and Alaska Natives: Analysis of Linkage-Corrected Life TablesSemantic proximity Read ↗

Assessing record linkage between health care and Vital Statistics databases using deterministic methodsSemantic proximity Read ↗

Validity of Deterministic Record Linkage Using Multiple Indirect Personal IdentifiersSemantic proximity Read ↗

Data linkage multiplies research insights across diverse healthcare sectorsSemantic proximity Read ↗

Comparing record linkage software programs and algorithms using real-world dataSemantic proximity Read ↗

TRAUMA: A Machine Learning–Based Record Linkage Method for Health DatabasesSemantic proximity Read ↗

Comparison of Vital Statistics Definitions of Suicide against a Coroner Reference Standard: A Population-Based Linkage StudySemantic proximity Read ↗

Challenges and adaptions in longitudinal data linkage to track patient health service use and care pathways after Emergency Department presentation: an exemplarSemantic proximity Read ↗

Assessing data linkage quality in cohort studiesSemantic proximity Read ↗

Validation of a Hierarchical Deterministic Record-Linkage Algorithm Using Data From 2 Different Cohorts of Human Immunodeficiency Virus-Infected Persons and Mortality Databases in BrazilSemantic proximity Read ↗

Linkage of National Congenital Heart Disease Audit data to hospital, critical care and mortality national datasets to enable research focused on quality improvementSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.