Evidence map›Paper›PMID 38317268›Full record

ArticleDiagnostic and prognostic research2024

A study protocol for a predictive model to assess population-based avoidable hospitalization risk: Avoidable Hospitalization Population Risk Prediction Tool (AvHPoRT).

Laura C Rosella, Mackenzie Hurst, Meghan O'Neill, Lief Pagalan, Lori Diemert, Kathy Kornas, Andy Hong, Stacey Fisher, Douglas G Manuel

Abstract read
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In one paragraph

Article in Diagnostic and prognostic research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. 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

9 authors.

Laura C RosellaDalla Lana School of Public Health, University of Toronto, 155 College Street, Health Sciences Building 6th Floor, Toronto, ON, M5T 3M7, Canada. laura.rosella@utoronto.ca.ORCID http://orcid.org/0000-0003-4867-869X
Mackenzie HurstDalla Lana School of Public Health, University of Toronto, 155 College Street, Health Sciences Building 6th Floor, Toronto, ON, M5T 3M7, Canada.
Meghan O'NeillDalla Lana School of Public Health, University of Toronto, 155 College Street, Health Sciences Building 6th Floor, Toronto, ON, M5T 3M7, Canada.
Lief PagalanDalla Lana School of Public Health, University of Toronto, 155 College Street, Health Sciences Building 6th Floor, Toronto, ON, M5T 3M7, Canada.
Lori DiemertDalla Lana School of Public Health, University of Toronto, 155 College Street, Health Sciences Building 6th Floor, Toronto, ON, M5T 3M7, Canada.
Kathy KornasDalla Lana School of Public Health, University of Toronto, 155 College Street, Health Sciences Building 6th Floor, Toronto, ON, M5T 3M7, Canada.
Andy HongPEAK Urban Research Programme, Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK.
Stacey FisherDalla Lana School of Public Health, University of Toronto, 155 College Street, Health Sciences Building 6th Floor, Toronto, ON, M5T 3M7, Canada.
Douglas G ManuelOttawa Hospital Research Institute, Ottawa, Canada.

Funding

Canada Research Chairs 72051628Institute of Population and Public Health 72056684
6 · The paper itself

Abstract

introductionAvoidable hospitalizations are considered preventable given effective and timely primary care management and are an important indicator of health system performance. The ability to predict avoidable hospitalizations at the population level represents a significant advantage for health system decision-makers that could facilitate proactive intervention for ambulatory care-sensitive conditions (ACSCs). The aim of this study is to develop and validate the Avoidable Hospitalization Population Risk Tool (AvHPoRT) that will predict the 5-year risk of first avoidable hospitalization for seven ACSCs using self-reported, routinely collected population health survey data. METHODS AND ANALYSIS: The derivation cohort will consist of respondents to the first 3 cycles (2000/01, 2003/04, 2005/06) of the Canadian Community Health Survey (CCHS) who are 18-74 years of age at survey administration and a hold-out data set will be used for external validation. Outcome information on avoidable hospitalizations for 5 years following the CCHS interview will be assessed through data linkage to the Discharge Abstract Database (1999/2000-2017/2018) for an estimated sample size of 394,600. Candidate predictor variables will include demographic characteristics, socioeconomic status, self-perceived health measures, health behaviors, chronic conditions, and area-based measures. Sex-specific algorithms will be developed using Weibull accelerated failure time survival models. The model will be validated both using split set cross-validation and external temporal validation split using cycles 2000-2006 compared to 2007-2012. We will assess measures of overall predictive performance (Nagelkerke R ETHICS AND DISSEMINATION: This study was approved by the University of Toronto Research Ethics Board. The predictive algorithm and findings from this work will be disseminated at scientific meetings and in peer-reviewed publications.

Indexed as

Ambulatory care sensitive conditionsAvoidable hospitalizationPopulation levelPrediction modelStudy protocolSurvival analysis

Identifiers

PMID38317268
PMCPMC10845544

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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.