Key result
Spatio-temporal modeling accurately predicts municipality-level OHCA, explaining ~90% of yearly variability.
Why the study?
Out-of-hospital cardiac arrest rates and occurrences at municipality level with temporal and spatial heterogeneity were not well characterized, limiting resource planning.
Observational (n=2,344)
Effect estimate: 89% (2017) and 90% (2018) of yearly variability explained
A spatio-temporal statistical model using Integrated Nested Laplace Approximation can accurately predict OHCA risk at the municipality level to guide resource allocation.
May guide municipal OHCA resource planning; leaves open prospective validation of predictions.
AIMS: To determine the out-of-hospital cardiac arrest (OHCA) rates and occurrences at municipality level through a novel statistical model accounting for temporal and spatial heterogeneity, space-time interactions and demographic features. We also aimed to predict OHCAs rates and number at municipality level for the upcoming years estimating the related resources requirement. METHODS: All the consecutive OHCAs of presumed cardiac origin occurred from 2005 until 2018 in Canton Ticino region were included. We implemented an Integrated Nested Laplace Approximation statistical method for estimation and prediction of municipality OHCA rates, number of events and related uncertainties, using age and sex municipality compositions. Comparisons between predicted and real OHCA maps validated our model, whilst comparisons between estimated OHCA rates in different yeas and municipalities identified significantly different OHCA rates over space and time. Longer-time predicted OHCA maps provided Bayesian predictions of OHCA coverages in varying stressful conditions. RESULTS: 2344 OHCAs were analyzed. OHCA incidence either progressively reduced or continuously increased over time in 6.8% of municipalities despite an overall stable spatio-temporal distribution of OHCAs. The predicted number of OHCAs accounts for 89% (2017) and 90% (2018) of the yearly variability of observed OHCAs with prediction error ≤1OHCA for each year in most municipalities. An increase in OHCAs number with a decline in the Automatic External Defibrillator availability per OHCA at region was estimated. CONCLUSIONS: Our method enables prediction of OHCA risk at municipality level with high accuracy, providing a novel approach to estimate resource allocation and anticipate gaps in demand in upcoming years.
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Auricchio et al. (2020) conducted an observational in Out-of-hospital cardiac arrest (OHCA) (n=2,344). Spatio-temporal prediction model (INLA) was evaluated on Prediction of OHCA rates and number of events at municipality level (89% (2017) and 90% (2018) of yearly variability explained). The spatio-temporal prediction model accurately estimated the number of out-of-hospital cardiac arrests, accounting for 89% and 90% of the yearly variability in 2017 and 2018, respectively, with a prediction error of ≤1 event in most municipalities.
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