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

Abstract

Background: 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 However, in current practice, these assumptions are often based on outdated methods (e.g. Hill-Burton Formula).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. Methods: 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%. Results: 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 < 0.001). Overall, all FCA methods showed a substantially higher correlation with actual hospital visits compared to the closest provider approach (up to ρ = 0.51; p < 0.001). Allowing a 5% error of the absolute values, the analysis revealed up to 13.4% correctly predicted hospital visits using the FCA methods (15% error: up to 32.5% correctly predicted hospital). Finally, the potential of the FCA methods could be revealed by using the actual hospital visits as the measure of hospital attractiveness, which returned very strong correlations with the actual hospital visits up to ρ = 0.99 (p < 0.001). Conclusion: We were able to demonstrate the impact of FCA measures regarding the prediction of hospital visits in non-emergency settings, and their superiority over commonly used methods (i.e. closest provider). However, hospital beds were inadequate as the measure of hospital attractiveness resulting in low accuracy of predicted hospital visits. More reliable measures must be integrated within the proposed methods. Still, this study strengthens the possibilities of FCA methods in health care planning beyond their original application in measuring spatial accessibility.

Measuring and validating spatial accessibility to Covid-19 vaccination sites: a case study in England

“…As shown in Panel 1, the Pseudo R-squared is 0.311, meaning that 31.1% of the variation of the logit of vaccine uptake can be explained by the predictors. This 𝑅 𝑝 2 is comparable to a previous study (Bauer et al 2020), in which the 𝑅 2 values between FCA accessibility scores and actual hospital visits due to six medical diagnoses fall into a range between 0.132 and 0.473. The low explained variance can be explained by the omittance of important variables, such as the supply capacity of vaccine sites.…”

Differences in Accessibility of Public Health Facilities in Hierarchical Municipalities and the Spatial Pattern Characteristics of Their Services in Doumen District, China

“…The similarity lies in the relatively high accessibility of high-level and large-scale health service facilities. The difference lies in the suggestion by relevant studies of public health facilities that the level of health facilities is negatively correlated with the spatial difference of their accessibility, i.e., the higher the level of health facilities, the smaller the spatial difference of their accessibility; however, this study suggests that the level of health facilities is positively correlated with the spatial difference of their accessibility, i.e., the higher the level of health facilities, the greater the spatial difference of their accessibility [47][48][49].…”

Disparities in Spatial Access to Emergency Surgical Services in the US

“…We also used bed number as a measure of hospital capacity, which is the most commonly used measure in E2SFCA models of hospital access; however, it does not account for average hospital bed occupancy. [45][46][47] Second, the presence of surgical capabilities did not necessarily equate to continuous service availability by surgeons, particularly at small rural hospitals that frequently experience workforce shortages and high staff turnover. [48][49][50][51] The results of this study, therefore, provided an optimistic view of potential spatial access to surgical care in the US.…”