Systemic sclerosis & Raynaud's
Geographic Clustering of Systemic Sclerosis in Areas of Environmental Pollution
Authors' conclusion
SSc exhibits nonrandom, clustered distributions in a US Medicare beneficiary cohort composed of 179,188 individuals from 2014 to 2018.
Study context
- Population
- Medicare beneficiaries with an systemic sclerosis (SSc) or SSc-related diagnosis code, US zip codes with beneficiary counts >11, 2014–2018
- Participants
- 179,188 beneficiaries
- Outcome
- Clustered or dispersed geographic patterns of SSc and SSc-related diagnosis code incidence across the US
Methodological detailsgeospatial analysis, global and local Moran's I, Getis Ord Gi hot spot analysis
- Geospatial analysis
- Global and local Moran's I
- Getis Ord Gi hot spot analysis
Main finding
As reported in the abstract
Spatial clustering of SSc (global Moran's I)
Global Moran's I, cohort 1 (clustered pattern)
0.588
Global Moran's I, cohort 2 (clustered pattern)
0.521
Observational data: associations, not effects.
Further findings
Local clusters (local Moran's I)
- Clusters of disease in Mississippi, New York, Wisconsin and Michigan, among others
High-incidence zip codes and Superfund sites
- Some zip codes with high disease incidences were home to at least one Superfund site
Original publication
Geographic Clustering of Systemic Sclerosis in Areas of Environmental Pollution. Arthritis Care Res (Hoboken). Published 28 February 2025. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →