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A study protocol for a predictive algorithm to assess population-based premature mortality risk: Premature Mortality Population Risk Tool (PreMPoRT)

BACKGROUND: Premature mortality is an important population health indicator used to assess health system functioning and to identify areas in need of health system intervention. Predicting the future incidence of premature mortality in the population can facilitate initiatives that promote equitable...

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Autores principales: Rosella, Laura C., O’Neill, Meghan, Fisher, Stacey, Hurst, Mackenzie, Diemert, Lori, Kornas, Kathy, Hong, Andy, Manuel, Douglas G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7640636/
https://www.ncbi.nlm.nih.gov/pubmed/33292834
http://dx.doi.org/10.1186/s41512-020-00086-z
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author Rosella, Laura C.
O’Neill, Meghan
Fisher, Stacey
Hurst, Mackenzie
Diemert, Lori
Kornas, Kathy
Hong, Andy
Manuel, Douglas G.
author_facet Rosella, Laura C.
O’Neill, Meghan
Fisher, Stacey
Hurst, Mackenzie
Diemert, Lori
Kornas, Kathy
Hong, Andy
Manuel, Douglas G.
author_sort Rosella, Laura C.
collection PubMed
description BACKGROUND: Premature mortality is an important population health indicator used to assess health system functioning and to identify areas in need of health system intervention. Predicting the future incidence of premature mortality in the population can facilitate initiatives that promote equitable health policies and effective delivery of public health services. This study protocol proposes the development and validation of the Premature Mortality Risk Prediction Tool (PreMPoRT) that will predict the incidence of premature mortality using large population-based community health surveys and multivariable modeling approaches. METHODS: PreMPoRT will be developed and validated using various training, validation, and test data sets generated from the six cycles of the Canadian Community Health Survey (CCHS) linked to the Canadian Vital Statistics Database from 2000 to 2017. Population-level risk factor information on demographic characteristics, health behaviors, area level measures, and other health-related factors will be used to develop PreMPoRT and to predict the incidence of premature mortality, defined as death prior to age 75, over a 5-year period. Sex-specific Weibull accelerated failure time models will be developed using a Canadian provincial derivation cohort consisting of approximately 500,000 individuals, with approximately equal proportion of males and females, and about 12,000 events of premature mortality. External validation will be performed using separate linked files (CCHS cycles 2007–2008, 2009–2010, and 2011–2012) from the development cohort (CCHS cycles 2000–2001, 2003–2004, and 2005–2006) to check the robustness of the prediction model. Measures of overall predictive performance (e.g., Nagelkerke’s R(2)), calibration (e.g., calibration plots), and discrimination (e.g., Harrell’s concordance statistic) will be assessed, including calibration within defined subgroups of importance to knowledge users and policymakers. DISCUSSION: Using routinely collected risk factor information, we anticipate that PreMPoRT will produce population-based estimates of premature mortality and will be used to inform population strategies for prevention.
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spelling pubmed-76406362020-11-04 A study protocol for a predictive algorithm to assess population-based premature mortality risk: Premature Mortality Population Risk Tool (PreMPoRT) Rosella, Laura C. O’Neill, Meghan Fisher, Stacey Hurst, Mackenzie Diemert, Lori Kornas, Kathy Hong, Andy Manuel, Douglas G. Diagn Progn Res Protocol BACKGROUND: Premature mortality is an important population health indicator used to assess health system functioning and to identify areas in need of health system intervention. Predicting the future incidence of premature mortality in the population can facilitate initiatives that promote equitable health policies and effective delivery of public health services. This study protocol proposes the development and validation of the Premature Mortality Risk Prediction Tool (PreMPoRT) that will predict the incidence of premature mortality using large population-based community health surveys and multivariable modeling approaches. METHODS: PreMPoRT will be developed and validated using various training, validation, and test data sets generated from the six cycles of the Canadian Community Health Survey (CCHS) linked to the Canadian Vital Statistics Database from 2000 to 2017. Population-level risk factor information on demographic characteristics, health behaviors, area level measures, and other health-related factors will be used to develop PreMPoRT and to predict the incidence of premature mortality, defined as death prior to age 75, over a 5-year period. Sex-specific Weibull accelerated failure time models will be developed using a Canadian provincial derivation cohort consisting of approximately 500,000 individuals, with approximately equal proportion of males and females, and about 12,000 events of premature mortality. External validation will be performed using separate linked files (CCHS cycles 2007–2008, 2009–2010, and 2011–2012) from the development cohort (CCHS cycles 2000–2001, 2003–2004, and 2005–2006) to check the robustness of the prediction model. Measures of overall predictive performance (e.g., Nagelkerke’s R(2)), calibration (e.g., calibration plots), and discrimination (e.g., Harrell’s concordance statistic) will be assessed, including calibration within defined subgroups of importance to knowledge users and policymakers. DISCUSSION: Using routinely collected risk factor information, we anticipate that PreMPoRT will produce population-based estimates of premature mortality and will be used to inform population strategies for prevention. BioMed Central 2020-11-04 /pmc/articles/PMC7640636/ /pubmed/33292834 http://dx.doi.org/10.1186/s41512-020-00086-z Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Protocol
Rosella, Laura C.
O’Neill, Meghan
Fisher, Stacey
Hurst, Mackenzie
Diemert, Lori
Kornas, Kathy
Hong, Andy
Manuel, Douglas G.
A study protocol for a predictive algorithm to assess population-based premature mortality risk: Premature Mortality Population Risk Tool (PreMPoRT)
title A study protocol for a predictive algorithm to assess population-based premature mortality risk: Premature Mortality Population Risk Tool (PreMPoRT)
title_full A study protocol for a predictive algorithm to assess population-based premature mortality risk: Premature Mortality Population Risk Tool (PreMPoRT)
title_fullStr A study protocol for a predictive algorithm to assess population-based premature mortality risk: Premature Mortality Population Risk Tool (PreMPoRT)
title_full_unstemmed A study protocol for a predictive algorithm to assess population-based premature mortality risk: Premature Mortality Population Risk Tool (PreMPoRT)
title_short A study protocol for a predictive algorithm to assess population-based premature mortality risk: Premature Mortality Population Risk Tool (PreMPoRT)
title_sort study protocol for a predictive algorithm to assess population-based premature mortality risk: premature mortality population risk tool (premport)
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7640636/
https://www.ncbi.nlm.nih.gov/pubmed/33292834
http://dx.doi.org/10.1186/s41512-020-00086-z
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