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. 2022 Feb 16;22(1):328.
doi: 10.1186/s12889-022-12731-x.

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

Matthew Gordon Crowson  1   2 , Jason A Beyea  3   4 , Justin Cottrell  5 , Faisal Karmali  6   7 , Giovanni Lampasona  8 , James E Saunders  9 , Richard F Lewis  6   7
Affiliations

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

Matthew Gordon Crowson et al. BMC Public Health. .

Abstract

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, R2= 0.98; Female: + 0.22 rate/year, R2= 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, R2= 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.

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Conflict of interest statement

The authors do not have financial and non-financial competing interests relevant to this work.

Figures

Fig. 1
Fig. 1
Annual United States age-adjusted falls mortality rate per 100,000 population over 2001–2018
Fig. 2
Fig. 2
Death rates from falls in adults over 2001–2018. Error bars reflect standard error. Multiple paired comparisons between age strata were completed using Wilcoxon tests with adjusted p-values. ns: p > 0.05, *: p < = 0.05, **: p < = 0.01, ***: p < = 0.001, ****: p < = 0.0001
See this image and copyright information in PMC

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