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. 2024 Jun 5:10:e52221.
doi: 10.2196/52221.

Epidemic Characteristics and Meteorological Risk Factors of Hemorrhagic Fever With Renal Syndrome in 151 Cities in China From 2015 to 2021: Retrospective Analysis

Yizhe Luo #  1   2 , Longyao Zhang #  3 , Yameng Xu  1   2 , Qiyuan Kuai  2 , Wenhao Li  2 , Yifan Wu  2 , Licheng Liu  4 , Jiarong Ren  5 , Lingling Zhang  6 , Qiufang Shi #  3 , Xiaobo Liu #  5   7   8 , Weilong Tan #  1   2
Affiliations

Epidemic Characteristics and Meteorological Risk Factors of Hemorrhagic Fever With Renal Syndrome in 151 Cities in China From 2015 to 2021: Retrospective Analysis

Yizhe Luo et al. JMIR Public Health Surveill. .

Abstract

Background: Hemorrhagic fever with renal syndrome (HFRS) continues to pose a significant public health threat to the population in China. Previous epidemiological evidence indicates that HFRS is climate sensitive and influenced by meteorological factors. However, past studies either focused on too-narrow geographical regions or investigated time periods that were too early. There is an urgent need for a comprehensive analysis to interpret the epidemiological patterns of meteorological factors affecting the incidence of HFRS across diverse climate zones.

Objective: In this study, we aimed to describe the overall epidemic characteristics of HFRS and explore the linkage between monthly HFRS cases and meteorological factors at different climate levels in China.

Methods: The reported HFRS cases and meteorological data were collected from 151 cities in China during the period from 2015 to 2021. We conducted a 3-stage analysis, adopting a distributed lag nonlinear model and a generalized additive model to estimate the interactions and marginal effects of meteorological factors on HFRS.

Results: This study included a total of 63,180 cases of HFRS; the epidemic trends showed seasonal fluctuations, with patterns varying across different climate zones. Temperature had the greatest impact on the incidence of HFRS, with the maximum hysteresis effects being at 1 month (-19 ºC; relative risk [RR] 1.64, 95% CI 1.24-2.15) in the midtemperate zone, 0 months (28 ºC; RR 3.15, 95% CI 2.13-4.65) in the warm-temperate zone, and 0 months (4 ºC; RR 1.72, 95% CI 1.31-2.25) in the subtropical zone. Interactions were discovered between the average temperature, relative humidity, and precipitation in different temperature zones. Moreover, the influence of precipitation and relative humidity on the incidence of HFRS had different characteristics under different temperature layers. The hysteresis effect of meteorological factors did not end after an epidemic season, but gradually weakened in the following 1 or 2 seasons.

Conclusions: Weather variability, especially low temperature, plays an important role in epidemics of HFRS in China. A long hysteresis effect indicates the necessity of continuous intervention following an HFRS epidemic. This finding can help public health departments guide the prevention and control of HFRS and develop strategies to cope with the impacts of climate change in specific regions.

Keywords: China; HFRS; climate change; distributed lag nonlinear model; hemorrhagic fever with renal syndrome; meteorological factors.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Distribution of cases of hemorrhagic fever with renal syndrome in China from 2015 to 2021.
Figure 2
Figure 2
Summary of cumulative exposure-response curves of hemorrhagic fever with renal syndrome incidence for meteorological factors with a lag of 0-6 months in 3 selected temperature zones from 2015 to 2021. The y-axis represents the relative risk of each variable. The x-axis represents the range of observations for each variable. The blue lines represent means estimated by the distributed lag nonlinear model, and the shaded areas represent the 95% CI. MTZ: midtemperate zone; SZ: subtropical zone; WTZ: warm temperate zone.
Figure 3
Figure 3
Lag-specific effects of meteorological factors on hemorrhagic fever with renal syndrome infection in different climate zones from 2015 to 2021. The y-axis represents the relative risk of each variable. The x-axis represents the range of observations for each variable. The purple lines represent means estimated by the distributed lag nonlinear model, and the shaded areas represent the 95% CI. MTZ: midtemperate zone; SZ: subtropical zone; WTZ: warm temperate zone.
Figure 4
Figure 4
Meta-analysis of meteorological factors on hemorrhagic fever with renal syndrome in different climate zones from 2015 to 2021. “All” represents data from all 151 cities, covering all temperature zones. MTZ: midtemperate zone; SZ: subtropical zone; WTZ: warm temperate zone.
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