1
|
Wang Y, Liang Z, Qing S, Xi Y, Xu C, Lin F. Asymmetric impact of climatic parameters on hemorrhagic fever with renal syndrome in Shandong using a nonlinear autoregressive distributed lag model. Sci Rep 2024; 14:9739. [PMID: 38679612 PMCID: PMC11056385 DOI: 10.1038/s41598-024-58023-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 03/25/2024] [Indexed: 05/01/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Yongbin Wang
- Department of Epidemiology and Health Statistics, School of Public Health, The First Affiliated Hospital of Xinxiang Medical University, No. 601 Jinsui Road, Hongqi District, Xinxiang, Henan Province, 453003, People's Republic of China.
| | - Ziyue Liang
- Department of Epidemiology and Health Statistics, School of Public Health, The First Affiliated Hospital of Xinxiang Medical University, No. 601 Jinsui Road, Hongqi District, Xinxiang, Henan Province, 453003, People's Republic of China
| | - Siyu Qing
- Department of Epidemiology and Health Statistics, School of Public Health, The First Affiliated Hospital of Xinxiang Medical University, No. 601 Jinsui Road, Hongqi District, Xinxiang, Henan Province, 453003, People's Republic of China
| | - Yue Xi
- Department of Epidemiology and Health Statistics, School of Public Health, The First Affiliated Hospital of Xinxiang Medical University, No. 601 Jinsui Road, Hongqi District, Xinxiang, Henan Province, 453003, People's Republic of China
| | - Chunjie Xu
- Beijing Key Laboratory of Antimicrobial Agents/Laboratory of Pharmacology, Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100050, China
| | - Fei Lin
- Department of Epidemiology and Health Statistics, School of Public Health, The First Affiliated Hospital of Xinxiang Medical University, No. 601 Jinsui Road, Hongqi District, Xinxiang, Henan Province, 453003, People's Republic of China.
| |
Collapse
|
2
|
Neves JMM, Belo VS, Catita CMS, de Oliveira BFA, Horta MAP. Modeling the Climatic Suitability of COVID-19 Cases in Brazil. Trop Med Infect Dis 2023; 8:tropicalmed8040198. [PMID: 37104323 PMCID: PMC10142792 DOI: 10.3390/tropicalmed8040198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 03/23/2023] [Accepted: 03/26/2023] [Indexed: 03/31/2023] Open
Abstract
Studies have shown that climate may affect the distribution of coronavirus disease (COVID-19) and its incidence and fatality rates. Here, we applied an ensemble niche modeling approach to project the climatic suitability of COVID-19 cases in Brazil. We estimated the cumulative incidence, mortality rate, and fatality rate of COVID-19 between 2020 and 2021. Seven statistical algorithms (MAXENT, MARS, RF, FDA, CTA, GAM, and GLM) were selected to model the climate suitability for COVID-19 cases from diverse climate data, including temperature, precipitation, and humidity. The annual temperature range and precipitation seasonality showed a relatively high contribution to the models, partially explaining the distribution of COVID-19 cases in Brazil based on the climatic suitability of the territory. We observed a high probability of climatic suitability for high incidence in the North and South regions and a high probability of mortality and fatality rates in the Midwest and Southeast regions. Despite the social, viral, and human aspects regulating COVID-19 cases and death distribution, we suggest that climate may play an important role as a co-factor in the spread of cases. In Brazil, there are regions with a high probability that climatic suitability will contribute to the high incidence and fatality rates of COVID-19 in 2020 and 2021.
Collapse
|
3
|
Rittweger J, Gilardi L, Baltruweit M, Dally S, Erbertseder T, Mittag U, Naeem M, Schmid M, Schmitz MT, Wüst S, Dech S, Jordan J, Antoni T, Bittner M. Temperature and particulate matter as environmental factors associated with seasonality of influenza incidence - an approach using Earth observation-based modeling in a health insurance cohort study from Baden-Württemberg (Germany). Environ Health 2022; 21:131. [PMID: 36527040 PMCID: PMC9755806 DOI: 10.1186/s12940-022-00927-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 10/21/2022] [Indexed: 05/04/2023]
Abstract
BACKGROUND Influenza seasonality has been frequently studied, but its mechanisms are not clear. Urban in-situ studies have linked influenza to meteorological or pollutant stressors. Few studies have investigated rural and less polluted areas in temperate climate zones. OBJECTIVES We examined influences of medium-term residential exposure to fine particulate matter (PM2.5), NO2, SO2, air temperature and precipitation on influenza incidence. METHODS To obtain complete spatial coverage of Baden-Württemberg, we modeled environmental exposure from data of the Copernicus Atmosphere Monitoring Service and of the Copernicus Climate Change Service. We computed spatiotemporal aggregates to reflect quarterly mean values at post-code level. Moreover, we prepared health insurance data to yield influenza incidence between January 2010 and December 2018. We used generalized additive models, with Gaussian Markov random field smoothers for spatial input, whilst using or not using quarter as temporal input. RESULTS In the 3.85 million cohort, 513,404 influenza cases occurred over the 9-year period, with 53.6% occurring in quarter 1 (January to March), and 10.2%, 9.4% and 26.8% in quarters 2, 3 and 4, respectively. Statistical modeling yielded highly significant effects of air temperature, precipitation, PM2.5 and NO2. Computation of stressor-specific gains revealed up to 3499 infections per 100,000 AOK clients per year that are attributable to lowering ambient mean air temperature from 18.71 °C to 2.01 °C. Stressor specific gains were also substantial for fine particulate matter, yielding up to 502 attributable infections per 100,000 clients per year for an increase from 7.49 μg/m3 to 15.98 μg/m3. CONCLUSIONS Whilst strong statistical association of temperature with other stressors makes it difficult to distinguish between direct and mediated temperature effects, results confirm genuine effects by fine particulate matter on influenza infections for both rural and urban areas in a temperate climate. Future studies should attempt to further establish the mediating mechanisms to inform public health policies.
Collapse
Affiliation(s)
- Jörn Rittweger
- Institute of Aerospace Medicine, German Aerospace Center (DLR), 51147, Cologne, Germany.
- Department of Pediatrics and Adolescent Medicine, University Hospital Cologne, Cologne, Germany.
| | - Lorenza Gilardi
- German Remote Sensing Data Center, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
| | - Maxana Baltruweit
- Allgemeine Ortskrankenkasse Baden-Württemberg (AOK-BW), Stuttgart, Germany
| | - Simon Dally
- Allgemeine Ortskrankenkasse Baden-Württemberg (AOK-BW), Stuttgart, Germany
| | - Thilo Erbertseder
- German Remote Sensing Data Center, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
| | - Uwe Mittag
- Institute of Aerospace Medicine, German Aerospace Center (DLR), 51147, Cologne, Germany
| | - Muhammad Naeem
- Kohat University of Science and Technology, Kohat, Pakistan
| | - Matthias Schmid
- Institute of Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, Bonn, Germany
| | - Marie-Therese Schmitz
- Institute of Aerospace Medicine, German Aerospace Center (DLR), 51147, Cologne, Germany
- Institute of Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, Bonn, Germany
| | - Sabine Wüst
- German Remote Sensing Data Center, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
| | - Stefan Dech
- German Remote Sensing Data Center, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
| | - Jens Jordan
- Institute of Aerospace Medicine, German Aerospace Center (DLR), 51147, Cologne, Germany
- Medical Faculty, University of Cologne, Cologne, Germany
| | - Tobias Antoni
- Allgemeine Ortskrankenkasse Baden-Württemberg (AOK-BW), Stuttgart, Germany
| | - Michael Bittner
- German Remote Sensing Data Center, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
| |
Collapse
|
4
|
Baek K, Choi J, Park JT, Kwak K. Influence of temperature and precipitation on the incidence of hepatitis A in Seoul, Republic of Korea: a time series analysis using distributed lag linear and non-linear model. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2022; 66:1725-1736. [PMID: 35829753 DOI: 10.1007/s00484-022-02313-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 03/21/2022] [Accepted: 06/06/2022] [Indexed: 06/15/2023]
Abstract
This study aimed to analyze the association between temperature and precipitation and the incidence of hepatitis A in Seoul, Korea, as meteorological factors may have different effects on specific diseases depending on the lifestyle in each region. Weekly cases of hepatitis A, weekly mean daily precipitation, and temperature data from 2016 to 2020 were analyzed. Quasi-Poisson-generalized linear models with time variable adjusted by spline function were used considering 0-6-week lags. The association of each variable and hepatitis A incidence was assessed by the single lag and the constrained distributed lag model. Multivariable distributed lag linear and non-linear models were used to develop models with significant independent variables. Weekly mean of daily mean temperature (Tmean) and maximum temperature (Tmax) were negatively associated with hepatitis A in the 6-week lag. Precipitation was negatively associated with hepatitis A in the 5- and 6-week lags. The multivariable model showed the negative association of Tmax, precipitation and hepatitis A in the 5- and 6-week lags. In the non-linear models, the incidence rate ratio (IRR) was the highest at a Tmax of 11 °C and decreased thereafter. IRR was the highest at 12 mm of precipitation and showed decrease pattern to 25 mm and then gradually increased in the 5- and 6-week lags. Identifying the impact of climate factors on hepatitis A incidence would help in the development of strategies to prevent diseases and indirectly estimate the impact of climate change on hepatitis A epidemiology.
Collapse
Affiliation(s)
- Kiook Baek
- Department of Occupational and Environmental Medicine, Yeungnam University Hospital, Daegu, Republic of Korea
| | - Jonghyuk Choi
- Department of Preventive Medicine, College of Medicine, Dankook University, Cheonan, Republic of Korea
| | - Jong-Tae Park
- Department of Occupational and Environmental Medicine, Korea University Ansan Hospital, Ansan, Republic of Korea
| | - Kyeongmin Kwak
- Department of Occupational and Environmental Medicine, Korea University Ansan Hospital, Ansan, Republic of Korea.
| |
Collapse
|