Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association
August 25, 2026 · via Europe PMC
Spatial and seasonal patterns of pediatric idiopathic nephrotic syndrome in France.
Parmentier C, Bayer F, Merieau E, +5 more, Hogan J.
AI summary — for doctors, generated from the abstract only
Nationwide French data show pediatric idiopathic nephrotic syndrome incidence of about 2.5 per 100,000 person-years with geographic clustering and seasonal patterns.
What was studied
A nationwide cohort study using the French National Health Data System (SNDS) to identify incident pediatric idiopathic nephrotic syndrome, validated against the NEPHROVIR cohort.
Key findings
- 4,475 incident cases (62% boys; median age 6 years)
- Case identification showed 84% sensitivity versus NEPHROVIR
- Mean annual incidence 2.49 per 100,000 person-years (95% CI 2.41-2.56); 3.05 in boys vs 1.93 in girls
- High-risk cluster in the Paris area (SIR 1.59); other cluster details and p-values not fully captured in the abstract
- Consistent seasonal patterns observed
Clinical relevance
These population data suggest environmental or infectious factors may contribute to childhood nephrotic syndrome onset, which may inform research and service planning rather than individual management.
Who should care
Limitations
Administrative claims-based case identification (84% sensitivity) may miss cases; ecological spatial analysis cannot identify causal exposures.
Childhood nephrotic syndrome clusters by place and season in France, hinting at environmental triggers.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
Background and hypothesis
Idiopathic nephrotic syndrome (INS) is the most frequent glomerular disease in children, yet robust population-based data remain limited, particularly at the national level. We hypothesized that nationwide health data could provide reliable estimates of INS incidence and help characterize its spatial and temporal patterns.
Methods
We conducted a nationwide cohort study using the French National Health Data System (SNDS), which covers nearly the entire population and captures hospital, emergency, and day-care activity. Children and adolescents < 20 years with an in-hospital diagnosis of nephrotic syndrome between 2013 and 2023 were identified and validated through oral corticosteroid (OCS) dispensing. A 7-year wash-out period excluded prevalent cases. We estimated national incidence, identified spatial clusters using SaTScan, and assessed seasonality using Seasonal-Trend Decomposition (STL).
Results
The analytic cohort included 4 475 incident INS cases (62% boys; median age, 6 years). External validation against the NEPHROVIR cohort showed 84% sensitivity with comparable age and sex distributions. The national mean annual incidence was 2.49 per 100 000 person-years (95% CI, 2.41-2.56), higher in boys (3.05) than in girls (1.93). Two significant high-risk clusters were identified: the Paris area (standardized incidence ratio [SIR], 1.59; P < 0.001) and south-western France (SIR, 1.34; P < 0.001). These clusters were consistent across sensitivity analyses. STL analysis revealed a reproducible bimodal seasonal pattern with peaks in winter and late spring.
Conclusions
This nationwide study provides robust estimates of pediatric INS incidence in France and identifies geographic clustering and consistent seasonal patterns, suggesting a potential role for environmental and infectious factors.
DOI
10.1093/ndt/gfag193
PMID
42640586
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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