ArticleBMC health services research2019
Using floating catchment area (FCA) metrics to predict health care utilization patterns.
Article in BMC health services research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Gravity models for potential spatial healthcare access measurement: a systematic methodological review.International journal of health geographics · 2023Pooled it
- Pooled it
- Optimizing Territorial Healthcare Networks with a Capacity-Constrained Hub-And-Spoke Allocation Algorithm: The Province of L'Aquila Case Study.Healthcare (Basel, Switzerland) · 2026Article
- Hospitals alike, hospitals different: spatial clustering of inequality around Boston's hospitals.Frontiers in sociology · 2026Article
- A scoping review of the methods used to estimate health facility catchment populations for child health indicators in sub-Saharan Africa.Population health metrics · 2025Article
- Spatial analysis of prehospital emergency medical services accessibility: a comparative evaluation of the GAUSS-probability two-step floating catchment area model in Handan City.Frontiers in public health · 2025Article
- Analysing the intra and interregional components of spatial accessibility gravity model to capture the level of equity in the distribution of hospital services in Italy: do they influence patient mobility?BMC health services research · 2024Article
- Spatiotemporal trends and geographic disparities in spatial accessibility to maternal and child health services in Nanning, China: impact of two-child policies.BMC health services research · 2024Article
- Spatial Accessibility to Primary Care in Metropolitan France: Results Using the SCALE Spatial Accessibility Index for All Regions.International journal of environmental research and public health · 2024Article
- Transit and Fairness: Exploring Spatial Equity in Accra's Public Transport System.African transport studies · 2024Article
- Applying a zero-corrected, gravity model estimator reduces bias due to heterogeneity in healthcare utilization in community-scale, passive surveillance datasets of endemic diseases.Scientific reports · 2023Article
- Spatial evaluation of healthcare accessibility across archipelagic communities of Maluku Province, Indonesia.PLOS global public health · 2023Article
- Women's Access to Kidney Transplantation in France: A Mixed Methods Research Protocol.International journal of environmental research and public health · 2022Article
- Can diverse population characteristics be leveraged in a machine learning pipeline to predict resource intensive healthcare utilization among hospital service areas?BMC health services research · 2022Article
- An External Patient Healthcare Index (EPHI) for Simulating Spatial Tendencies in Healthcare Seeking Behavior.Frontiers in public health · 2022Article
- Efficiency of fine scale and spatial regression in modelling associations between healthcare service spatial accessibility and their utilization.International journal of health geographics · 2021Article
- Trap of weights: The reuse of weights in the floating catchment area (FCA) methods to measuring accessibility.F1000Research · 2021Article
- Spatial accessibility of general inpatient care in Germany: an analysis of surgery, internal medicine and neurology.Scientific reports · 2020Article
- Prediction of hospital visits for the general inpatient care using floating catchment area methods: a reconceptualization of spatial accessibility.International journal of health geographics · 2020Article
Corrections and comments
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Authors and funding
3 authors.
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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.
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