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The predictive power of geographic health care utilization for unintentional fatal fall rates

BACKGROUND: Falls are the leading cause of fatal and nonfatal injuries among adults over 65 years old. The increase in fall mortality rates is likely multifactorial. With a lack of key drivers identified to explain rising rates of death from falls, accurate predictive modelling can be challenging, h...

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Autores principales: Crowson, Matthew Gordon, Beyea, Jason A., Cottrell, Justin, Karmali, Faisal, Lampasona, Giovanni, Saunders, James E., Lewis, Richard F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8848674/
https://www.ncbi.nlm.nih.gov/pubmed/35172791
http://dx.doi.org/10.1186/s12889-022-12731-x
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author Crowson, Matthew Gordon
Beyea, Jason A.
Cottrell, Justin
Karmali, Faisal
Lampasona, Giovanni
Saunders, James E.
Lewis, Richard F.
author_facet Crowson, Matthew Gordon
Beyea, Jason A.
Cottrell, Justin
Karmali, Faisal
Lampasona, Giovanni
Saunders, James E.
Lewis, Richard F.
author_sort Crowson, Matthew Gordon
collection PubMed
description BACKGROUND: Falls are the leading cause of fatal and nonfatal injuries among adults over 65 years old. The increase in fall mortality rates is likely multifactorial. With a lack of key drivers identified to explain rising rates of death from falls, accurate predictive modelling can be challenging, hindering evidence-based health resource and policy efforts. The objective of this work is to examine the predictive power of geographic utilization and longitudinal trends in mortality from unintentional falls amongst different demographic and geographic strata. METHODS: This is a nationwide, retrospective cohort study using the United States Centers for Disease Control (CDC) Web-based Injury Statistics Query and Reporting System (WISQARS) database. The exposure was death from an unintentional fall as determined by the CDC. Outcomes included aggregate and trend crude and age-adjusted death rates. Health care utilization, reimbursement, and cost metrics were also compared. RESULTS: Over 2001 to 2018, 465,486 total deaths due to unintentional falls were recorded with crude and age-adjusted rates of 8.42 and 7.76 per 100,000 population respectively. Comparing age-adjusted rates, males had a significantly higher age-adjusted death rate (9.89 vs. 6.17; p <  0.00001), but both male and female annual age-adjusted mortality rates are expected to rise (Male: + 0.25 rate/year, R(2)= 0.98; Female: + 0.22 rate/year, R(2)= 0.99). There were significant increases in death rates commensurate with increasing age, with the adults aged 85 years or older having the highest aggregate (201.1 per 100,000) and trending death rates (+ 8.75 deaths per 100,000/year, R(2)= 0.99). Machine learning algorithms using health care utilization data were accurate in predicting geographic age-adjusted death rates. CONCLUSIONS: Machine learning models have high accuracy in predicting geographic age-adjusted mortality rates from health care utilization data. In the United States from 2001 through 2018, adults aged 85+ years carried the highest death rate from unintentional falls and this rate is forecasted to accelerate. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-022-12731-x.
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spelling pubmed-88486742022-02-18 The predictive power of geographic health care utilization for unintentional fatal fall rates Crowson, Matthew Gordon Beyea, Jason A. Cottrell, Justin Karmali, Faisal Lampasona, Giovanni Saunders, James E. Lewis, Richard F. BMC Public Health Research Article BACKGROUND: Falls are the leading cause of fatal and nonfatal injuries among adults over 65 years old. The increase in fall mortality rates is likely multifactorial. With a lack of key drivers identified to explain rising rates of death from falls, accurate predictive modelling can be challenging, hindering evidence-based health resource and policy efforts. The objective of this work is to examine the predictive power of geographic utilization and longitudinal trends in mortality from unintentional falls amongst different demographic and geographic strata. METHODS: This is a nationwide, retrospective cohort study using the United States Centers for Disease Control (CDC) Web-based Injury Statistics Query and Reporting System (WISQARS) database. The exposure was death from an unintentional fall as determined by the CDC. Outcomes included aggregate and trend crude and age-adjusted death rates. Health care utilization, reimbursement, and cost metrics were also compared. RESULTS: Over 2001 to 2018, 465,486 total deaths due to unintentional falls were recorded with crude and age-adjusted rates of 8.42 and 7.76 per 100,000 population respectively. Comparing age-adjusted rates, males had a significantly higher age-adjusted death rate (9.89 vs. 6.17; p <  0.00001), but both male and female annual age-adjusted mortality rates are expected to rise (Male: + 0.25 rate/year, R(2)= 0.98; Female: + 0.22 rate/year, R(2)= 0.99). There were significant increases in death rates commensurate with increasing age, with the adults aged 85 years or older having the highest aggregate (201.1 per 100,000) and trending death rates (+ 8.75 deaths per 100,000/year, R(2)= 0.99). Machine learning algorithms using health care utilization data were accurate in predicting geographic age-adjusted death rates. CONCLUSIONS: Machine learning models have high accuracy in predicting geographic age-adjusted mortality rates from health care utilization data. In the United States from 2001 through 2018, adults aged 85+ years carried the highest death rate from unintentional falls and this rate is forecasted to accelerate. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-022-12731-x. BioMed Central 2022-02-16 /pmc/articles/PMC8848674/ /pubmed/35172791 http://dx.doi.org/10.1186/s12889-022-12731-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Crowson, Matthew Gordon
Beyea, Jason A.
Cottrell, Justin
Karmali, Faisal
Lampasona, Giovanni
Saunders, James E.
Lewis, Richard F.
The predictive power of geographic health care utilization for unintentional fatal fall rates
title The predictive power of geographic health care utilization for unintentional fatal fall rates
title_full The predictive power of geographic health care utilization for unintentional fatal fall rates
title_fullStr The predictive power of geographic health care utilization for unintentional fatal fall rates
title_full_unstemmed The predictive power of geographic health care utilization for unintentional fatal fall rates
title_short The predictive power of geographic health care utilization for unintentional fatal fall rates
title_sort predictive power of geographic health care utilization for unintentional fatal fall rates
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8848674/
https://www.ncbi.nlm.nih.gov/pubmed/35172791
http://dx.doi.org/10.1186/s12889-022-12731-x
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