Feline sporotrichosis hotspots during a period of declining notifications: A surveillance-sensitive analysis in hyperendemic Rio de Janeiro.

In hyperendemic settings, a reduction in notification curves may reflect that surveillance capacity is deteriorating rather than indicating epidemiological improvement; this distinction has direct consequences for disease control. Rio de Janeiro, Brazil, where cat-transmitted sporotrichosis has persisted for decades, provides a suitable setting to examine whether a decrease in reported cases is accompanied by persistent spatiotemporal clustering. In this ecological study, laboratory-confirmed notifications of feline sporotrichosis from 2017 to 2024 and stratified by administrative region were analyzed. Notification rates were calculated using estimated feline populations. Temporal patterns were investigated using generalized additive models, spatiotemporal clusters were identified using SaTScan, and ecological associations with the Social Progress Index were assessed using geographically weighted regression. In total, 14,885 feline cases were reported, with an average notification of 13.07 cases per 1000 estimated cats per year. Although the number of notifications decreased at the municipal level, the highest average rates remained concentrated in Santa Cruz, Guaratiba, São Cristóvão, and Campo Grande, and model-identified high-risk clusters persisted in the western, northern, and central regions. Lower values of the Social Progress Index were generally associated with a larger number of notifications, although local associations were spatially heterogeneous and do not reflect causal relationships. These findings demonstrate that declining municipal notifications coexisted with hotspots and heterogeneous reported risk patterns, suggesting that aggregate trends may obscure local disease persistence and surveillance gaps. In long-standing hyperendemic settings, territorial surveillance, sustained diagnostic access, and spatially targeted One Health strategies are necessary to translate notification data into actionable epidemiological intelligence.