Using floating catchment area (FCA) metrics to predict health care utilization patterns

Paul L Delamater,Ashton M Shortridge,Rachel C Kilcoyne

doi:10.1186/s12913-019-3969-5

Abstract

BackgroundFloating Catchment Area (FCA) metrics provide a comprehensive measure of potential spatial accessibility to health care services and are often used to identify geographic disparities in health care access. An unexplored aspect of FCA metrics is whether they can be useful in predicting where people actually seek care. This research addresses this question by examining the utility of FCA metrics for predicting patient utilization patterns, the flows of patients from their residences to facilities.MethodsUsing more than one million inpatient hospital visits in Michigan, we calculated expected utilization patterns from Zip Codes to hospitals using four FCA metrics and two traditional metrics (simple distance and a Huff model) and compared them to observed utilization patterns. Because all of the accessibility metrics rely on the specification of a distance decay function and its associated parameters, we conducted a sensitivity analysis to evaluate their effects on prediction accuracy.ResultsWe found that the Three Step FCA (3SFCA) and Modified Two Step FCA (M2SFCA) were the most effective metrics for predicting utilization patterns, correctly predicting the destination hospital for nearly 74% of hospital visits in Michigan. These two metrics were also the least sensitive to changes to the distance decay functions and parameter settings.ConclusionsOverall, this research demonstrates that FCA metrics can provide reasonable predictions of patient utilization patterns and FCA utilization models could be considered as a substitute when utilization pattern data are unavailable.

Highlights

Floating Catchment Area (FCA) metrics provide a comprehensive measure of potential spatial accessibility to health care services and are often used to identify geographic disparities in health care access
The results of the analysis demonstrate that the disaggregated information contained within FCA metrics serves as a viable model for predicting geographic utilization patterns when this information is unavailable, as is often the case
We attempted to use as little auxiliary information as possible to mimic the conditions researchers would generally face when attempting to estimate utilization patterns from publicly-available facility data. Another data-related limitation is that three hospitals were excluded from the analysis, as was one Zip Code, due to lack of data; the missing data from these facilities and Zip Code represent a miniscule fraction of the statewide hospitalization and we do not believe this had any effect on our results. Another limitation is that our analysis demonstrated that the predictive accuracy of the FCA metrics was sensitive to the particular metric, distance decay function, and the decay function parameters

Summary

Introduction

Floating Catchment Area (FCA) metrics provide a comprehensive measure of potential spatial accessibility to health care services and are often used to identify geographic disparities in health care access. An unexplored aspect of FCA metrics is whether they can be useful in predicting where people seek care. Much of the recent geographic research regarding access to health care has focused on examinations of potential access to services, rather than on realized access or utilization of health care services [1]. The Floating Catchment Area (FCA) family of metrics simultaneously integrate the three essential components required to measure potential spatial accessibility: supply of services, potential demand for services, and distance separating supply and demand locations. Much of the recent FCA-related research has focused on methodological improvements to the metrics (e.g., [6,7,8]) or using the metrics to map and identify disparities in health care accessibility (e.g., [9,10,11])

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