Evidence map›Paper›PMID 32496565›Full record

ArticleJAMA network open2020

Development and Validation of the Chronic Disease Population Risk Tool (CDPoRT) to Predict Incidence of Adult Chronic Disease.

Ryan Ng, Rinku Sutradhar, Kathy Kornas, Walter P Wodchis, Joykrishna Sarkar, Randall Fransoo, Laura C Rosella

Open access · goldAbstract readValidation Study
In one paragraph

Article in JAMA network open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
3.4field-weighted citation impact, top 7% of its field
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

16 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.

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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

7 authors at 4 institutions in 1 country.

Ryan NgDalla Lana School of Public Health, Division of Epidemiology, University of Toronto, Toronto, Ontario, Canada.
Rinku SutradharICES, Toronto, Ontario, Canada.
Kathy KornasDalla Lana School of Public Health, Division of Epidemiology, University of Toronto, Toronto, Ontario, Canada.
Walter P WodchisICES, Toronto, Ontario, Canada.
Joykrishna SarkarManitoba Centre for Health Policy, University of Manitoba, Winnipeg, Manitoba, Canada.
Randall FransooManitoba Centre for Health Policy, University of Manitoba, Winnipeg, Manitoba, Canada.
Laura C RosellaDalla Lana School of Public Health, Division of Epidemiology, University of Toronto, Toronto, Ontario, Canada.
University of Toronto · CAManitoba Health · CAInstitute for Clinical Evaluative Sciences · CATrillium Health Centre · CA

Funding

CIHR FRN14180
6 · The paper itself

Abstract

Importance: Predicting chronic disease incidence for the population provides a comprehensive picture to health policy makers of their jurisdictions' overall future chronic disease burden. However, no population-based risk algorithm exists for estimating the risk of first major chronic disease. Objective: To develop and validate the Chronic Disease Population Risk Tool (CDPoRT), a population risk algorithm that predicts the 10-year incidence of the first major chronic disease in the adult population. Design, Setting, and Participants: In this cohort study, CDPoRT was developed and validated with 6 cycles of the Canadian Community Health Survey, linked to administrative data from January 2000 to December 2014. Development and internal validation (bootstrap and split sample) of CDPoRT occurred in Ontario, Canada, from June 2018 to April 2019 followed by external validation in Manitoba from May 2019 to July 2019. The study cohorts included 133 991 adults (≥20 years) representative of the Ontario and Manitoba populations who did not have a history of major chronic disease. Exposures: Predictors were routinely collected risk factors from the Canadian Community Health Survey, such as sociodemographic factors (eg, age), modifiable lifestyle risk factors (ie, alcohol consumption, cigarette smoking, unhealthy diet, and physical inactivity), and other health-related factors (eg, body mass index). Main Outcomes and Measures: Six major chronic diseases were considered, as follows: congestive heart failure, chronic obstructive pulmonary disease, diabetes, myocardial infarction, lung cancer, and stroke. Sex-specific CDPoRT algorithms were developed with a Weibull model. Model performance was evaluated with measures of overall predictive performance (eg, Brier score), discrimination (eg, Harrell C index), and calibration (eg, calibration curves). Results: The Ontario cohort (n = 118 747) was younger (mean [SD] age, 45.6 [16.1] vs 46.3 [16.4] years), had more immigrants (23 808 [20.0%] vs 1417 [10.7%]), and had a lower mean (SD) body mass index (26.9 [5.1] vs 27.7 [5.4]) than the Manitoba cohort (n = 13 244). During development, the full and parsimonious CDPoRT models had similar Brier scores (women, 0.087; men, 0.091), Harrell C index values (women, 0.779; men, 0.783), and calibration curves. A simple version consisting of cigarette smoking, age, and body mass index performed slightly worse than the other versions (eg, Brier score for women, 0.088; for men, 0.092). Internal validation showed consistent performance across models, and CDPoRT performed well during external validation. For example, the female parsimonious version had C index values for bootstrap, split sample, and external validation of 0.778, 0.776, and 0.752, respectively. Conclusions and Relevance: In this study, CDPoRT provided accurate, population-based risk estimates for the first major chronic disease.

Indexed as

AlgorithmsAdultAgedAged, 80 and overAge FactorsBody Mass IndexChronic DiseaseCigarette SmokingCohort StudiesDiabetes MellitusFemaleForecastingHealth SurveysHeart FailureHumansIncidence

Identifiers

PMID32496565
PMCPMC7273197
OpenAlexW3031084765

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.