Prediction of hospital visits for the general inpatient care using floating catchment area methods: a reconceptualization of spatial accessibility

The adequate allocation of inpatient care resources requires assumptions about the need for health care and how this need will be met. However, in current practice, these assumptions are often based on outdated methods (e.g. Hill-Burton Formula). This study evaluated floating catchment area (FCA) me...

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Bibliographic Details
Published in:International journal of health geographics 2020-07, Vol.19 (1), p.29-11, Article 29
Main Authors: Bauer, J, Klingelhöfer, D, Maier, W, Schwettmann, L, Groneberg, D A
Format: Article
Language:English
Subjects:
Accessibility
Analysis
Attraction
Catchment Area, Health
Catchment areas
Congestive heart failure
Correlation analysis
Emergency medical services
Epilepsy
Error analysis
Error correction
Fibrillation
Floating catchment area
Flutter
Germany
Health care
Health care access
Health care policy
Health insurance
Health planning
Health Services Accessibility
Heart
Hospital visits
Hospitals
Humanitarianism
Humans
Inpatients
Internal medicine
Medical care quality
Medicine
Methods
Need
Neurology
Patient care
Population
Prediction
Spatial accessibility
Surgery
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Summary:The adequate allocation of inpatient care resources requires assumptions about the need for health care and how this need will be met. However, in current practice, these assumptions are often based on outdated methods (e.g. Hill-Burton Formula). This study evaluated floating catchment area (FCA) methods, which have been applied as measures of spatial accessibility, focusing on their ability to predict the need for health care in the inpatient sector in Germany. We tested three FCA methods (enhanced (E2SFCA), modified (M2SFCA) and integrated (iFCA)) for their accuracy in predicting hospital visits regarding six medical diagnoses (atrial flutter/fibrillation, heart failure, femoral fracture, gonarthrosis, stroke, and epilepsy) on national level in Germany. We further used the closest provider approach for benchmark purposes. The predicted visits were compared with the actual visits for all six diagnoses using a correlation analysis and a maximum error from the actual visits of ± 5%, ± 10% and ± 15%. The analysis of 229 million distances between hospitals and population locations revealed a high and significant correlation of predicted with actual visits for all three FCA methods across all six diagnoses up to ρ = 0.79 (p 
ISSN:1476-072X
1476-072X
DOI:10.1186/s12942-020-00223-3