Abstract:

Objective To investigate the epidemiological characteristics and spatiotemporal clustering of scarlet fever in Chengdu area for evidences to formulate scientific prevention and control measures for this acute respiratory infection. Methods Surveillance data of scarlet fever reported at each subdistrict/township of urban areas, counties, communities in Chengdu City from 2017 to 2023 were obtained from the Chinese Disease Prevention and Control Information System. The epidemiological profile and temporal, spatial and population distribution were characterized, and spatial autocorrelation and spatiotemporal scan analyses were performed to identify clustering patterns. Results Between 2017 and 2023, a total of 4 433 scarlet fever cases were reported in Chengdu area. The annual incidence ranged from 1.41/100 000 to 7.50/100 000, and overall, the prevalence tended to decline (χ²_trend=1 089.79, P<0.01). Seasonal peaks occurred in April-June (n=1 655; 37.33%) and November-January (n=1 336; 30.14%). The incidence was dominated by children aged 3-8 years (n=3 837; 86.56%). The reported cases were 2 669 for males and 1 764 for females, and the annually reported cases were higher in males than in females (n=2 669; 2.08/100 000 vs. n=1 764; 1.38/100 000). Qionglai City had the highest average annual incidence (13.68/100 000), whereas the most cases were from Xindu District (n=1 034; 23.33%). Spatial autocorrelation analysis revealed a significant spatial positive correlation in 2017-2019 and 2021-2023 (Moran’s I>0, Z>1.960, P<0.05), and that the hotspots were involved in the subdistricts/townships of Xindu, Qionglai, Shuangliu, and Longquanyi. Spatiotemporal scan analysis identified one primary cluster (Xindu District, January 2017-January 2019) and six secondary clusters. Conclusion The incidence of scarlet fever in Chengdu City exhibited a fluctuating downward trend with notable spatiotemporal clustering. Our findings suggest that surveillance, early warning, publicity and education should be intensified in key areas and key populations during the higher prevalence seasons to mitigate the disease burden.

Key words: Scarlet fever, Epidemic characteristics, Spatial autocorrelation, Spatiotemporal scan, Chengdu City