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/<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_25366_Article_IEq1.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="8" /> </InlineMediaObject> <EquationSource Format="TEX">\(^2\)</EquationSource> </InlineEquation> 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 <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_25366_Article_IEq2.gif" Format="GIF" Height="7" Rendition="HTML" Resolution="72" Type="Linedraw" Width="9" /> </InlineMediaObject> <EquationSource Format="TEX">\(^{\circ }\)</EquationSource> </InlineEquation>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 15–19<InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_25366_Article_IEq2.gif" Format="GIF" Height="7" Rendition="HTML" Resolution="72" Type="Linedraw" Width="9" /> </InlineMediaObject> <EquationSource Format="TEX">\(^{\circ }\)</EquationSource> </InlineEquation>C and fluctuating risk at extreme cold (below −5<InlineEquation ID="IEq4"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_25366_Article_IEq2.gif" Format="GIF" Height="7" Rendition="HTML" Resolution="72" Type="Linedraw" Width="9" /> </InlineMediaObject> <EquationSource Format="TEX">\(^{\circ }\)</EquationSource> </InlineEquation>C). Relative humidity displayed an M-shaped curve, peaking at 56–86% and dropping near 39% (OR <InlineEquation ID="IEq5"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_25366_Article_IEq5.gif" Format="GIF" Height="6" Rendition="HTML" Resolution="72" Type="Linedraw" Width="14" /> </InlineMediaObject> <EquationSource Format="TEX">\(=\)</EquationSource> </InlineEquation> 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<InlineEquation ID="IEq6"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_25366_Article_IEq1.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="8" /> </InlineMediaObject> <EquationSource Format="TEX">\(^2\)</EquationSource> </InlineEquation>; 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 <InlineEquation ID="IEq7"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_25366_Article_IEq7.gif" Format="GIF" Height="15" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">\(\ge\)</EquationSource> </InlineEquation> 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.

Scarlet fever and meteorological exposures in Jiangsu, China: a time-stratified case-crossover study

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2025年11月19日

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Kai Wang,
Wendong Liu,
Hongfei Zhu,
Yifan Tang,
Hong Ji,
Ying Wang,
Liguo Zhu,
Chengxiu Ling

摘要

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/ \(^2\) 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 \(^{\circ }\) 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 15–19 \(^{\circ }\) C and fluctuating risk at extreme cold (below −5 \(^{\circ }\) C). 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 \(^2\) ; 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 \(\ge\) 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.