TY - JOUR
T1 - Scarlet fever and meteorological exposures in Jiangsu, China
T2 - a time-stratified case-crossover study
AU - Wang, Kai
AU - Liu, Wendong
AU - Zhu, Hongfei
AU - Tang, Yifan
AU - Ji, Hong
AU - Wang, Ying
AU - Zhu, Liguo
AU - Ling, Chengxiu
N1 - Publisher Copyright:
© The Author(s) 2025.
PY - 2025/12
Y1 - 2025/12
N2 - Background: There has been an increasing interest in the association between meteorological risk factors and scarlet fever risk. However, the associations between individual-level exposure to meteorological factors and the scarlet fever risk remain poorly understood. Methods: We collected 36, 912 scarlet fever cases in Jiangsu Province, China (2005–2023) from the Nationwide Notifiable Infectious Diseases Reporting Information System. These data were then paired with daily meteorological factors, including temperature, relative humidity, solar radiation, wind speed, surface pressure, and total precipitation, sourced from the ERA5-Land dataset. A time-stratified case-crossover design was applied using conditional logistic regression combined with distributed lag non-linear models to examine both linear and non-linear associations, while adjusting for public holidays and recent outbreaks. Subgroup analyses by age, gender, period, and season were conducted to assess potential heterogeneity. Effect estimates for individual exposures are expressed as odds ratios (ORs), and interactions were evaluated using the relative excess odds due to interaction (REOI), attributable proportion (AP), and the synergy index (S). Results: Significant linear associations (ORs per one-unit increment) were observed for all meteorological variables except wind speed with a 2–5 day lag, peaking at 3 days. Positive associations were found for solar radiation (OR = 1.009, 95% CI: 1.005–1.012) per MJ/ and surface pressure (OR = 1.088, 95% CI: 1.057–1.120) per kPa, and negative associations for temperature (OR = 0.991, 95% CI: 0.986–0.995) per C, relative humidity (OR = 0.995, 95% CI: 0.994–0.996) per %, and total precipitation (OR = 0.994, 95% CI: 0.990–0.997) per mm. All exposures exhibited complex non-linear effects. Temperature showed a reversed U-shaped curve with peak risk at 15–19C and fluctuating risk at extreme cold (below −5C). Relative humidity displayed an M-shaped curve, peaking at 56–86% and dropping near 39% (OR 0.97). Solar radiation and surface pressure showed overall declining trends, with elevated risks at lower levels (OR = 1.03 at 1.08 MJ/m; OR = 1.01 at 99.82 kPa) and fluctuations across their central ranges. Total precipitation and wind speed showed rising trends, with ORs >1 above 15.1 mm and 5.0 m/s. Stronger associations were observed among individuals aged 6 years (e.g., temperature OR = 0.988, 95% CI: 0.983–0.994; surface pressure OR = 1.117, 95% CI: 1.077–1.159) and during the post-COVID-19 period (2020–2023), particularly for temperature (OR = 0.989, 95% CI: 0.980–0.999), solar radiation (OR = 1.014, 95% CI: 1.005–1.023), and surface pressure (OR = 1.134, 95% CI: 1.060–1.212). Conclusions: Our study suggests significant associations between meteorological factors and scarlet fever risk, with non-linear exposure-response patterns. We also provide some insights into the mechanisms and targeted prevention strategies for vulnerable individuals, especially in the post-COVID-19 period.
AB - Background: There has been an increasing interest in the association between meteorological risk factors and scarlet fever risk. However, the associations between individual-level exposure to meteorological factors and the scarlet fever risk remain poorly understood. Methods: We collected 36, 912 scarlet fever cases in Jiangsu Province, China (2005–2023) from the Nationwide Notifiable Infectious Diseases Reporting Information System. These data were then paired with daily meteorological factors, including temperature, relative humidity, solar radiation, wind speed, surface pressure, and total precipitation, sourced from the ERA5-Land dataset. A time-stratified case-crossover design was applied using conditional logistic regression combined with distributed lag non-linear models to examine both linear and non-linear associations, while adjusting for public holidays and recent outbreaks. Subgroup analyses by age, gender, period, and season were conducted to assess potential heterogeneity. Effect estimates for individual exposures are expressed as odds ratios (ORs), and interactions were evaluated using the relative excess odds due to interaction (REOI), attributable proportion (AP), and the synergy index (S). Results: Significant linear associations (ORs per one-unit increment) were observed for all meteorological variables except wind speed with a 2–5 day lag, peaking at 3 days. Positive associations were found for solar radiation (OR = 1.009, 95% CI: 1.005–1.012) per MJ/ and surface pressure (OR = 1.088, 95% CI: 1.057–1.120) per kPa, and negative associations for temperature (OR = 0.991, 95% CI: 0.986–0.995) per C, relative humidity (OR = 0.995, 95% CI: 0.994–0.996) per %, and total precipitation (OR = 0.994, 95% CI: 0.990–0.997) per mm. All exposures exhibited complex non-linear effects. Temperature showed a reversed U-shaped curve with peak risk at 15–19C and fluctuating risk at extreme cold (below −5C). Relative humidity displayed an M-shaped curve, peaking at 56–86% and dropping near 39% (OR 0.97). Solar radiation and surface pressure showed overall declining trends, with elevated risks at lower levels (OR = 1.03 at 1.08 MJ/m; OR = 1.01 at 99.82 kPa) and fluctuations across their central ranges. Total precipitation and wind speed showed rising trends, with ORs >1 above 15.1 mm and 5.0 m/s. Stronger associations were observed among individuals aged 6 years (e.g., temperature OR = 0.988, 95% CI: 0.983–0.994; surface pressure OR = 1.117, 95% CI: 1.077–1.159) and during the post-COVID-19 period (2020–2023), particularly for temperature (OR = 0.989, 95% CI: 0.980–0.999), solar radiation (OR = 1.014, 95% CI: 1.005–1.023), and surface pressure (OR = 1.134, 95% CI: 1.060–1.212). Conclusions: Our study suggests significant associations between meteorological factors and scarlet fever risk, with non-linear exposure-response patterns. We also provide some insights into the mechanisms and targeted prevention strategies for vulnerable individuals, especially in the post-COVID-19 period.
KW - Distributed lag nonlinear model
KW - Meteorological exposure
KW - Outbreak effect
KW - Scarlet fever
KW - Time-stratified case-crossover design
UR - https://www.scopus.com/pages/publications/105022230457
U2 - 10.1186/s12889-025-25366-5
DO - 10.1186/s12889-025-25366-5
M3 - Article
C2 - 41257746
AN - SCOPUS:105022230457
SN - 1471-2458
VL - 25
JO - BMC Public Health
JF - BMC Public Health
IS - 1
M1 - 4055
ER -