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

Matthew Gordon Crowson,Jason A Beyea,Faisal Karmali,Giovanni Lampasona,Richard F Lewis,James E Saunders,Justin Cottrell

doi:10.1186/s12889-022-12731-x

Abstract

BackgroundFalls 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.MethodsThis 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.ResultsOver 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.ConclusionsMachine 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.

Highlights

Falls are the leading cause of fatal and nonfatal injuries among adults over 65 years old
This study investigates the predictive power of machine learning models trained on geographic healthcare utilization intensity metrics for producing accurate predictions of age-adjusted death rates derived from the Dartmouth Atlas Project
Data sources The Centers for Disease Control (CDC) Web-based Injury Statistics Query and Reporting System (WISQARS) database was mined for mortality event data attributed to an ‘unintentional fall’ mechanism

Summary

Introduction

Falls are the leading cause of fatal and nonfatal injuries among adults over 65 years old. In a cohort from the United States in 2014, 28.7% of adults aged 65 years or older reported falling at least once in the past year which resulted in approximately 29 million fall events [1]. Of those adults who fell, 37.5% required medical care as a result of the fall [1]. Analogous studies from other countries such as China and the Netherlands have reported a high risk of fall and subsequent mortality in older adults [2, 3]. The economic burden for direct medical cost of injuries and deaths from falls is substantial with annual costs eclipsing billions of dollars [13, 14]

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