Climate and socio-economic factors drive the spatio-temporal dynamics of HFRS in Northeastern China

Wang, Yanding; Wei, Xianyu; Xiao, Xuyang; Yin, Wenwu; He, Junyu; Ren, Zhoupeng; Li, Zhiqiang; Yang, Meitao; Tong, Shilu; Guo, Yuming; Zhang, Wenyi; Wang, Yong

doi:10.1016/j.onehlt.2022.100466

Cited by 10 publications

(25 citation statements)

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“…Based on the previous studies, we used meteorological factors with a lag of 1-6 months [28][29][30] because meteorological factors showed a delayed effect on the risk of HFRS. The map is shown in Figure 1, and the mechanism route in this study is shown in Figure S1 in Multimedia Appendix 1.…”

Section: Methodsmentioning

confidence: 99%

The Spatiotemporal Pattern and Its Determinants of Hemorrhagic Fever With Renal Syndrome in Northeastern China: Spatiotemporal Analysis

Wang¹,

Wei²,

Jia³

et al. 2023

JMIR Public Health Surveill

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Background Hemorrhagic fever with renal syndrome (HFRS) is a significant zoonotic disease mainly transmitted by rodents. However, the determinants of its spatiotemporal patterns in Northeast China remain unclear. Objective This study aimed to investigate the spatiotemporal dynamics and epidemiological characteristics of HFRS and detect the meteorological effect of the HFRS epidemic in Northeastern China. Methods The HFRS cases of Northeastern China were collected from the Chinese Center for Disease Control and Prevention, and meteorological data were collected from the National Basic Geographic Information Center. Times series analyses, wavelet analysis, Geodetector model, and SARIMA model were performed to identify the epidemiological characteristics, periodical fluctuation, and meteorological effect of HFRS in Northeastern China. Results A total of 52,655 HFRS cases were reported in Northeastern China from 2006 to 2020, and most patients with HFRS (n=36,558, 69.43%) were aged between 30-59 years. HFRS occurred most frequently in June and November and had a significant 4- to 6-month periodicity. The explanatory power of the meteorological factors to HFRS varies from 0.15 ≤ q ≤ 0.01. In Heilongjiang province, mean temperature with a 4-month lag, mean ground temperature with a 4-month lag, and mean pressure with a 5-month lag had the most explanatory power on HFRS. In Liaoning province, mean temperature with a 1-month lag, mean ground temperature with a 1-month lag, and mean wind speed with a 4-month lag were found to have an effect on HFRS, but in Jilin province, the most important meteorological factors for HFRS were precipitation with a 6-month lag and maximum evaporation with a 5-month lag. The interaction analysis of meteorological factors mostly showed nonlinear enhancement. The SARIMA model predicted that 8,343 cases of HFRS are expected to occur in Northeastern China. Conclusions HFRS showed significant inequality in epidemic and meteorological effects in Northeastern China, and eastern prefecture-level cities presented a high risk of epidemic. This study quantifies the hysteresis effects of different meteorological factors and prompts us to focus on the influence of ground temperature and precipitation on HFRS transmission in future studies, which could assist local health authorities in developing HFRS-climate surveillance, prevention, and control strategies targeting high-risk populations in China.

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Section: Methodsmentioning

confidence: 99%

The Spatiotemporal Pattern and Its Determinants of Hemorrhagic Fever With Renal Syndrome in Northeastern China: Spatiotemporal Analysis

Wang¹,

Wei²,

Jia³

et al. 2023

JMIR Public Health Surveill

Self Cite

View full text Add to dashboard Cite

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“…Growing evidence associates climatic parameters with HFRS 11 , 15 , 16 . However, the asymmetric relationships in both long and short terms have been underexplored.…”

Section: Discussionmentioning

confidence: 99%

“…There has been a decline in HFRS cases in Shandong during 2004–2019 (AAPC = − 9.568%), aligning with the broader trend observed across China 4 . Nonetheless, a few cities, such as Daqing, Songyuan, and Tonghua, have seen a minor uptick in recent years 11 . Government initiatives like vaccination, rodent control, public awareness campaigns, and environmental management have been crucial in driving this decline 4 .…”

Section: Discussionmentioning

confidence: 99%

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Asymmetric impact of climatic parameters on hemorrhagic fever with renal syndrome in Shandong using a nonlinear autoregressive distributed lag model

Wang,

Liang,

Qing

et al. 2024

Sci Rep

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Hemorrhagic fever with renal syndrome (HFRS) poses a major threat in Shandong. This study aimed to investigate the long- and short-term asymmetric effects of meteorological factors on HFRS and establish an early forecasting system using autoregressive distributed lag (ARDL) and nonlinear ARDL (NARDL) models. Between 2004 and 2019, HFRS exhibited a declining trend (average annual percentage change = − 9.568%, 95% CI − 16.165 to − 2.451%) with a bimodal seasonality. A long-term asymmetric influence of aggregate precipitation (AP) (Wald long-run asymmetry [WLR] = − 2.697, P = 0.008) and aggregate sunshine hours (ASH) (WLR = 2.561, P = 0.011) on HFRS was observed. Additionally, a short-term asymmetric impact of AP (Wald short-run symmetry [WSR] = − 2.419, P = 0.017), ASH (WSR = 2.075, P = 0.04), mean wind velocity (MWV) (WSR = − 4.594, P < 0.001), and mean relative humidity (MRH) (WSR = − 2.515, P = 0.013) on HFRS was identified. Also, HFRS demonstrated notable variations in response to positive and negative changes in ∆MRH(−), ∆AP(+), ∆MWV(+), and ∆ASH(−) at 0–2 month delays over the short term. In terms of forecasting, the NARDL model demonstrated lower error rates compared to ARDL. Meteorological parameters have substantial long- and short-term asymmetric and/or symmetric impacts on HFRS. Merging NARDL model with meteorological factors can enhance early warning systems and support proactive measures to mitigate the disease's impact.

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“…Although the northern region possesses a strong medical base and high urbanization levels, the less comfortable public health conditions are due to colder and drier weather [35]. Southern provinces, on the other hand, have well-established public health infrastructure owing to a warm and humid climate with abundant forest cover [53]. As a result, the overall CCD level is higher in the East and South.…”

Section: The Spatial Distribution Of the Coupling Coordination Betwee...mentioning

confidence: 99%

Coupling analysis of public health resource allocation and economic growth with spatial-temporal variation: a case study in China 2017- 2021

Mao,

Xie,

Lin

et al. 2023

Preprint

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Background China currently faces a formidable challenge regarding the inadequate and uneven allocation of resources in the public health sector. The increasing impact of economic and health crises stemming from public health emergencies highlights the crucial relationship between public health resource allocation (PHRA) and economic growth (EG). This study sought to investigate the Coupling Coordination Degree (CCD) and the spatiotemporal variations between PHRA and EG, expecting to provide guidance for future public health and economic resource allocation and development planning.Methods By establishing a comprehensive evaluation-indexing system, we applied the polar, entropy, and linear weighting methods to calculate the comprehensive indices of PHRA and EG from 2017 to 2021. Subsequently, we empirically evaluated the CCD using the CCM model. Trend surface analysis and exploratory spatial data analysis were employed to assess the spatiotemporal variations of the CCD.Results The comprehensive indices for PHRA, EG, and the CCD exhibited a gradual upward trend from 2017 to 2021, although most provinces remained in the basically balanced or moderate unbalanced stage. In the eastern region, coordinated development was hindered primarily due to lagging HPAG, while the central and western regions faced challenges due to lower levels of EG. Furthermore, we observed significant spatial heterogeneity in the CCD, reflecting a stable pattern of "high in the East and low in the West, high in the South and low in the North", with a growing gap between southern and northern regions. Additionally, the CCD transitioned from a random distribution to spatial clustering, with hot and cold spot areas centered around the Yangtze Plain and the Qinghai-Tibet Plateau. Notably, areas with hotspots exhibited remarkable spatial diffusion effects.Conclusion The PHRA and EG have not achieved a high-quality coordination stage and remain out of sync across provinces. Moreover, substantial disparities and reinforced spatial agglomeration patterns were observed in the CCD. Consequently, it is imperative to implement additional administrative measures to promote both PHRA and EG. Tailored policies considering regional characteristics should also be implemented to bridge development gaps between regions, considering the spatial differences and correlations among them to optimize the spatial linkage environment.

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Climate and socio-economic factors drive the spatio-temporal dynamics of HFRS in Northeastern China

Cited by 10 publications

References 43 publications

The Spatiotemporal Pattern and Its Determinants of Hemorrhagic Fever With Renal Syndrome in Northeastern China: Spatiotemporal Analysis

The Spatiotemporal Pattern and Its Determinants of Hemorrhagic Fever With Renal Syndrome in Northeastern China: Spatiotemporal Analysis

Asymmetric impact of climatic parameters on hemorrhagic fever with renal syndrome in Shandong using a nonlinear autoregressive distributed lag model

Coupling analysis of public health resource allocation and economic growth with spatial-temporal variation: a case study in China 2017- 2021

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