Using floating catchment area (FCA) metrics to predict health care utilization patterns
Abstract Background 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 predi...
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| Format: | Article |
| Language: | English |
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BMC
2019-03-01
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| Series: | BMC Health Services Research |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12913-019-3969-5 |
| _version_ | 1811273452588367872 |
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| author | Paul L. Delamater Ashton M. Shortridge Rachel C. Kilcoyne |
| author_facet | Paul L. Delamater Ashton M. Shortridge Rachel C. Kilcoyne |
| author_sort | Paul L. Delamater |
| collection | DOAJ |
| description | Abstract Background 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 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. Methods Using 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. Results We 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. Conclusions Overall, 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. |
| first_indexed | 2024-04-12T22:59:25Z |
| format | Article |
| id | doaj.art-aefdf9e8194d466bb33c6b9f7ed2c05f |
| institution | Directory Open Access Journal |
| issn | 1472-6963 |
| language | English |
| last_indexed | 2024-04-12T22:59:25Z |
| publishDate | 2019-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Health Services Research |
| spelling | doaj.art-aefdf9e8194d466bb33c6b9f7ed2c05f2022-12-22T03:13:06ZengBMCBMC Health Services Research1472-69632019-03-0119111410.1186/s12913-019-3969-5Using floating catchment area (FCA) metrics to predict health care utilization patternsPaul L. Delamater0Ashton M. Shortridge1Rachel C. Kilcoyne2Department of Geography and the Carolina Population Center, University of North Carolina at Chapel HillDepartment of Geography, Environment, and Spatial Sciences, Michigan State UniversityDepartment of Geography and Geoinformation Science, George Mason UniversityAbstract Background 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 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. Methods Using 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. Results We 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. Conclusions Overall, 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.http://link.springer.com/article/10.1186/s12913-019-3969-5Spatial accessibilityAccess to health careHealth care useUtilization patternsHospitalizationsFloating catchment areas |
| spellingShingle | Paul L. Delamater Ashton M. Shortridge Rachel C. Kilcoyne Using floating catchment area (FCA) metrics to predict health care utilization patterns BMC Health Services Research Spatial accessibility Access to health care Health care use Utilization patterns Hospitalizations Floating catchment areas |
| title | Using floating catchment area (FCA) metrics to predict health care utilization patterns |
| title_full | Using floating catchment area (FCA) metrics to predict health care utilization patterns |
| title_fullStr | Using floating catchment area (FCA) metrics to predict health care utilization patterns |
| title_full_unstemmed | Using floating catchment area (FCA) metrics to predict health care utilization patterns |
| title_short | Using floating catchment area (FCA) metrics to predict health care utilization patterns |
| title_sort | using floating catchment area fca metrics to predict health care utilization patterns |
| topic | Spatial accessibility Access to health care Health care use Utilization patterns Hospitalizations Floating catchment areas |
| url | http://link.springer.com/article/10.1186/s12913-019-3969-5 |
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