Performance of the French national hospital discharge database algorithm to identify hospitalised Lyme borreliosis cases, France, 2017-2018
Abstract
BACKGROUND: In France, surveillance of Lyme borreliosis (LB) is based on general practitioners of a sentinel network and the national hospital discharge database (PMSI). Given the known limitations of the PMSI for epidemiological surveillance, we assessed the performance of its algorithm in identifying hospitalised LB cases in three university hospitals, in terms of sensitivity and positive predictive value (PPV). METHODS: We identified patients hospitalised during 2017-2018 with positive laboratory results for LB from hospital laboratory databases. Simultaneously, we screened in the PMSI LB hospitalised patients via the algorithm based on ICD-10 codes. Classifications were made by applying the EUCALB criteria. Confirmed and probable LB cases were classified as "LB+". We then calculated sensitivity (proportion of LB+ hospitalised cases identified by the PMSI algorithm) and PPV (proportion of LB+ cases among patients identified as LB by the PMSI algorithm). RESULTS: Among 541 patients with positive laboratory results, 54 were classified as LB+. The PMSI identified 62 cases, of which 38 were LB+. Overall sensitivity was 62%, varying by site (40-79%). Sensitivity was highest for paediatric cases (83%), Lyme arthritis (83%), and neuroborreliosis ( 61%). PPV was 61%, reaching 96% for neuroborreliosis and 83% for Lyme arthritis. CONCLUSION: The PMSI algorithm showed moderate sensitivity and PPV for identifying hospitalised LB cases, with higher performance for neuroborreliosis and Lyme arthritis. Our findings support focusing PMSI-based surveillance on neuroborreliosis and arthritis. Improvement of the PMSI algorithm is necessary but it remains a valid tool for assessing the burden and trends over time at the national and regional levels.
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BibTeX
@article{septfons2026performance,
title = {{Performance of the French national hospital discharge database algorithm to identify hospitalised Lyme borreliosis cases, France, 2017-2018}},
author = {Alexandra Septfons and Leslie Grammatico-Guillon and Jean Capsec and Christophe Garborit and Adrien Lemaignen and Philippe Lanotte and Solène Patrat-Delon and Pierre Tattevin and Alexandre Scanff and Florian Bouchet-Crivat and Brigitte Degeilh and Yves Hansmann and Claire Sauvage and Amandine Woerly and Benoît Jaulhac and Thierry Blanchon and Alice Raffetin and Julie Figoni and Jean-Claude Desenclos and Henriette de Valk},
journal = {BMC Infectious Diseases},
year = {2026},
month = may,
doi = {10.1186/s12879-026-13508-y},
url = {https://hal.science/hal-05657664v1},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Performance of the French national hospital discharge database algorithm to identify hospitalised Lyme borreliosis cases, France, 2017-2018
AU - Alexandra Septfons
AU - Leslie Grammatico-Guillon
AU - Jean Capsec
AU - Christophe Garborit
AU - Adrien Lemaignen
AU - Philippe Lanotte
AU - Solène Patrat-Delon
AU - Pierre Tattevin
AU - Alexandre Scanff
AU - Florian Bouchet-Crivat
AU - Brigitte Degeilh
AU - Yves Hansmann
AU - Claire Sauvage
AU - Amandine Woerly
AU - Benoît Jaulhac
AU - Thierry Blanchon
AU - Alice Raffetin
AU - Julie Figoni
AU - Jean-Claude Desenclos
AU - Henriette de Valk
PY - 2026
DA - 2026/05/11
JO - BMC Infectious Diseases
DO - 10.1186/s12879-026-13508-y
UR - https://hal.science/hal-05657664v1
AB - BACKGROUND: In France, surveillance of Lyme borreliosis (LB) is based on general practitioners of a sentinel network and the national hospital discharge database (PMSI). Given the known limitations of the PMSI for epidemiological surveillance, we assessed the performance of its algorithm in identifying hospitalised LB cases in three university hospitals, in terms of sensitivity and positive predictive value (PPV). METHODS: We identified patients hospitalised during 2017-2018 with positive laboratory results for LB from hospital laboratory databases. Simultaneously, we screened in the PMSI LB hospitalised patients via the algorithm based on ICD-10 codes. Classifications were made by applying the EUCALB criteria. Confirmed and probable LB cases were classified as "LB+". We then calculated sensitivity (proportion of LB+ hospitalised cases identified by the PMSI algorithm) and PPV (proportion of LB+ cases among patients identified as LB by the PMSI algorithm). RESULTS: Among 541 patients with positive laboratory results, 54 were classified as LB+. The PMSI identified 62 cases, of which 38 were LB+. Overall sensitivity was 62%, varying by site (40-79%). Sensitivity was highest for paediatric cases (83%), Lyme arthritis (83%), and neuroborreliosis ( 61%). PPV was 61%, reaching 96% for neuroborreliosis and 83% for Lyme arthritis. CONCLUSION: The PMSI algorithm showed moderate sensitivity and PPV for identifying hospitalised LB cases, with higher performance for neuroborreliosis and Lyme arthritis. Our findings support focusing PMSI-based surveillance on neuroborreliosis and arthritis. Improvement of the PMSI algorithm is necessary but it remains a valid tool for assessing the burden and trends over time at the national and regional levels.
SN - 1471-2334
ER -