1
|
Yin Y, Lai M, Lu K, Jiang X, Chen Z, Li T, Wang L, Zhang Y, Peng Z. Association between ambient temperature and influenza prevalence: A nationwide time-series analysis in 201 Chinese cities from 2013 to 2018. ENVIRONMENT INTERNATIONAL 2024; 189:108783. [PMID: 38823156 DOI: 10.1016/j.envint.2024.108783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 05/20/2024] [Accepted: 05/27/2024] [Indexed: 06/03/2024]
Abstract
BACKGROUND Temperature affects influenza transmission; however, currently, limited evidence exists about its effect in China at the national and city levels as well as how temperature can be integrated into influenza interventions. METHODS Meteorological, pollutant, and influenza data from 201 cities in mainland China between 2013 and 2018 were analyzed at both the city and national levels to investigate the relationship between temperature and influenza prevalence. We examined the impact of temperature on the time-varying reproduction number (Rt) using generalized additive quasi-Poisson regression models combined with the distributed lag nonlinear model. Threshold temperatures were determined for seven regions based on the early warning threshold of serious influenza outbreaks, set at Rt = 1.2. A multivariate random-effects meta-analysis was employed to assess region-specific associations. The excess risk (ER) index was defined to investigate the correlation between Rt and temperature, modified based on seasonal and regional characteristics. RESULTS At the national level and in the central, northern, northwestern, and southern regions, temperature was found to be negatively correlated with relative risk, whereas the shapes of the data curves for the eastern, southwestern, and northeastern regions were not well defined. Low temperatures had an observable effect on influenza prevalence; however, the effects of high temperatures were not obvious. At an Rt of 1.2, the threshold temperatures for reaching a warning for serious influenza outbreaks were - 24.3 °C in the northeastern region, 16.6 °C in the northwestern region, and between 1℃ and 10 °C in other regions. CONCLUSION The study findings revealed that temperature had a varying effect on influenza transmission trends (Rt) across different regions in China. By identifying region-specific temperature thresholds at Rt = 1.2, more effective early warning systems for influenza outbreaks could be tailored. These findings emphasize the significance of the region-specific adaptation of influenza prevention and control measures.
Collapse
Affiliation(s)
- Yi Yin
- School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Miao Lai
- School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Kailai Lu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xin Jiang
- School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Ziying Chen
- School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Tiantian Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Liping Wang
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Division of Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yanping Zhang
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Division of Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zhihang Peng
- School of Public Health, Nanjing Medical University, Nanjing 211166, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Division of Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China.
| |
Collapse
|
2
|
Zhang JL, Wang YC, Lee YL, Yang CY, Chen PS. Airborne Influenza Virus in Daycare Centers. Viruses 2024; 16:822. [PMID: 38932115 PMCID: PMC11209538 DOI: 10.3390/v16060822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 05/18/2024] [Accepted: 05/18/2024] [Indexed: 06/28/2024] Open
Abstract
In this study, we investigated the concentration of airborne influenza virus in daycare centers and influencing factors, such as common cold prevalence, air pollutants, and meteorological factors. A total of 209 air samples were collected from daycare centers in Kaohsiung and the influenza virus was analyzed using real-time quantitative polymerase chain reaction. Air pollutants and metrological factors were measured using real-time monitoring equipment. Winter had the highest positive rates of airborne influenza virus and the highest prevalence of the common cold, followed by summer and autumn. The concentration of CO was significantly positively correlated with airborne influenza virus. Daycare center A, with natural ventilation and air condition systems, had a higher concentration of airborne influenza A virus, airborne fungi, and airborne bacteria, as well as a higher prevalence of the common cold, than daycare center B, with a mechanical ventilation system and air purifiers, while the concentrations of CO2, CO, and UFPs in daycare center A were lower than those in daycare center B. We successfully detected airborne influenza virus in daycare centers, demonstrating that aerosol sampling for influenza can provide novel epidemiological insights and inform the management of influenza in daycare centers.
Collapse
Affiliation(s)
- Jia Lin Zhang
- Department of Public Health, College of Health Science, Kaohsiung Medical University, Kaohsiung 80708, Taiwan; (J.L.Z.)
| | - Yu-Chun Wang
- Department of Environmental Engineering, College of Engineering, Chung Yuan Christian University, Taoyuan 32023, Taiwan;
| | - Yi Lien Lee
- Department of Public Health, College of Health Science, Kaohsiung Medical University, Kaohsiung 80708, Taiwan; (J.L.Z.)
- Soil and Groundwater Remediation Division, CPC Corporation, Kaohsiung 811251, Taiwan
| | - Chun-Yuh Yang
- Department of Public Health, College of Health Science, Kaohsiung Medical University, Kaohsiung 80708, Taiwan; (J.L.Z.)
| | - Pei-Shih Chen
- Department of Public Health, College of Health Science, Kaohsiung Medical University, Kaohsiung 80708, Taiwan; (J.L.Z.)
- Institute of Environmental Engineering, College of Engineering, National Sun Yat-Sen University, Kaohsiung 804201, Taiwan
- Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung 80756, Taiwan
- Research Center for Precision Environmental Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
- Institute of Wildlife Conservation, College of Veterinary Medicine, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan
| |
Collapse
|
3
|
Islam A, Munro S, Hassan MM, Epstein JH, Klaassen M. The role of vaccination and environmental factors on outbreaks of high pathogenicity avian influenza H5N1 in Bangladesh. One Health 2023; 17:100655. [PMID: 38116452 PMCID: PMC10728328 DOI: 10.1016/j.onehlt.2023.100655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Accepted: 11/08/2023] [Indexed: 12/21/2023] Open
Abstract
High Pathogenicity Avian Influenza (HPAI) H5N1 outbreaks continue to wreak havoc on the global poultry industry and threaten the health of wild bird populations, with sporadic spillover in humans and other mammals, resulting in widespread calls to vaccinate poultry. Bangladesh has been vaccinating poultry since 2012, presenting a prime opportunity to study the effects of vaccination on HPAI H5N1circulation in both poultry and wild birds. We investigated the efficacy of vaccinating commercial poultry against HPAI H5N1 along with climatic and socio-economic factors considered potential drivers of HPAI H5N1 outbreak risk in Bangladesh. Using a multivariate modeling approach, we estimated that the rate of outbreaks was 18 times higher before compared to after vaccination, with winter months having a three times higher chance of outbreaks than summer months. Variables resulting in small but significant increases in outbreak rate were relatively low ambient temperatures for the time of year, literacy rate, chicken and duck density, crop density, and presence of highways; this may be attributable to low temperatures supporting viral survival outside the host, higher literacy driving reporting rate, density of the host reservoir, and spread of the virus through increased connectivity. Despite the substantial impact of vaccination on outbreaks, we note that HPAI H5N1 is still enzootic in Bangladesh; vaccinated poultry flocks have high rates of H5N1 prevalence, and spillover to wild birds has increased. Vaccination in Bangladesh thus bears the risk of supporting "silent spread," where the vaccine only provides protection against disease and not also infection. Our findings underscore that poultry vaccination can be part of holistic HPAI mitigation strategies when accompanied by monitoring to avoid silent spread.
Collapse
Affiliation(s)
- Ariful Islam
- Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, Melbourne, Victoria, Australia
- EcoHealth Alliance, New York, NY 10018, USA
| | | | - Mohammad Mahmudul Hassan
- Queensland Alliance for One Health Sciences, School of Veterinary Science, University of Queensland, Brisbane, QLD, Australia
- Faculty of Veterinary Medicine, Chattogram Veterinary and Animal Sciences University, Chattogram 4225, Bangladesh
| | | | - Marcel Klaassen
- Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, Melbourne, Victoria, Australia
| |
Collapse
|
4
|
Islam A, Amin E, Islam S, Hossain ME, Al Mamun A, Sahabuddin M, Samad MA, Shirin T, Rahman MZ, Hassan MM. Annual trading patterns and risk factors of avian influenza A/H5 and A/H9 virus circulation in turkey birds ( Meleagris gallopavo) at live bird markets in Dhaka city, Bangladesh. Front Vet Sci 2023; 10:1148615. [PMID: 37470075 PMCID: PMC10352991 DOI: 10.3389/fvets.2023.1148615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 05/12/2023] [Indexed: 07/21/2023] Open
Abstract
The impacts of the avian influenza virus (AIV) on farmed poultry and wild birds affect human health, livelihoods, food security, and international trade. The movement patterns of turkey birds from farms to live bird markets (LBMs) and infection of AIV are poorly understood in Bangladesh. Thus, we conducted weekly longitudinal surveillance in LBMs to understand the trading patterns, temporal trends, and risk factors of AIV circulation in turkey birds. We sampled a total of 423 turkeys from two LBMs in Dhaka between May 2018 and September 2019. We tested the swab samples for the AIV matrix gene (M-gene) followed by H5, H7, and H9 subtypes using real-time reverse transcriptase-polymerase chain reaction (rRT-PCR). We used exploratory analysis to investigate trading patterns, annual cyclic trends of AIV and its subtypes, and a generalized estimating equation (GEE) logistic model to determine the factors that influence the infection of H5 and H9 in turkeys. Furthermore, we conducted an observational study and informal interviews with traders and vendors to record turkey trading patterns, demand, and supply and turkey handling practices in LBM. We found that all trade routes of turkey birds to northern Dhaka are unidirectional and originate from the northwestern and southern regions of Bangladesh. The number of trades from the source district to Dhaka depends on the turkey density. The median distance that turkey was traded from its source district to Dhaka was 188 km (Q1 = 165, Q3 = 210, IQR = 45.5). We observed seasonal variation in the median and average distance of turkey. The qualitative findings revealed that turkey farming initially became reasonably profitable in 2018 and at the beginning of 2019. However, the fall in demand and production in the middle of 2019 may be related to unstable market pricing, high feed costs, a shortfall of adequate marketing facilities, poor consumer knowledge, and a lack of advertising. The overall prevalence of AIV, H5, and H9 subtypes in turkeys was 31% (95% CI: 26.6-35.4), 16.3% (95% CI: 12.8-19.8), and 10.2% (95% CI: 7.3-13.1) respectively. None of the samples were positive for H7. The circulation of AIV and H9 across the annual cycle showed no seasonality, whereas the circulation of H5 showed significant seasonality. The GEE revealed that detection of AIV increases in retail vendor business (OR: 1.71; 95% CI: 1.12-2.62) and the bird's health status is sick (OR: 10.77; 95% CI: 4.31-26.94) or dead (OR: 11.33; 95% CI: 4.30-29.89). We also observed that winter season (OR: 5.83; 95% CI: 2.80-12.14) than summer season, dead birds (OR: 61.71; 95% CI: 25.78-147.75) and sick birds (OR 8.33; 95% CI: 3.36-20.64) compared to healthy birds has a higher risk of H5 infection in turkeys. This study revealed that the turkeys movements vary by time and season from the farm to the LBM. This surveillance indicated year-round circulation of AIV with H5 and H9 subtypes in turkey birds in LBMs. The seasonality and health condition of birds influence H5 infection in birds. The trading pattern of turkey may play a role in the transmission of AIV viruses in the birds. The selling of sick turkeys infected with H5 and H9 highlights the possibility of virus transmission to other species of birds sold at LBMs and to people.
Collapse
Affiliation(s)
- Ariful Islam
- EcoHealth Alliance, New York, NY, United States
- Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, Geelong, VIC, Australia
| | - Emama Amin
- Institute of Epidemiology, Disease Control and Research (IEDCR), Dhaka, Bangladesh
| | - Shariful Islam
- Institute of Epidemiology, Disease Control and Research (IEDCR), Dhaka, Bangladesh
| | - Mohammad Enayet Hossain
- One Health Laboratory, International Centre for Diarrheal Diseases Research, Bangladesh (icddr,b), Dhaka, Bangladesh
| | - Abdullah Al Mamun
- Institute of Epidemiology, Disease Control and Research (IEDCR), Dhaka, Bangladesh
| | - Md. Sahabuddin
- One Health Laboratory, International Centre for Diarrheal Diseases Research, Bangladesh (icddr,b), Dhaka, Bangladesh
| | - Mohammed Abdus Samad
- National Reference Laboratory for Avian Influenza, Bangladesh Livestock Research Institute (BLRI), Savar, Bangladesh
| | - Tahmina Shirin
- Institute of Epidemiology, Disease Control and Research (IEDCR), Dhaka, Bangladesh
| | - Mohammed Ziaur Rahman
- One Health Laboratory, International Centre for Diarrheal Diseases Research, Bangladesh (icddr,b), Dhaka, Bangladesh
| | - Mohammad Mahmudul Hassan
- Queensland Alliance for One Health Sciences, School of Veterinary Science, The University of Queensland, Brisbane, QLD, Australia
- Faculty of Veterinary Medicine, Chattogram Veterinary and Animal Sciences University, Chattogram, Bangladesh
| |
Collapse
|
5
|
Wang X, Cai J, Liu X, Wang B, Yan L, Liu R, Nie Y, Wang Y, Zhang X, Zhang X. Impact of PM 2.5 and ozone on incidence of influenza in Shijiazhuang, China: a time-series study. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:10426-10443. [PMID: 36076137 PMCID: PMC9458314 DOI: 10.1007/s11356-022-22814-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 08/27/2022] [Indexed: 05/03/2023]
Abstract
Most of the studies are focused on influenza and meteorological factors for influenza. There are still few studies focused on the relationship between pollution factors and influenza, and the results are not consistent. This study conducted distributed lag nonlinear model and attributable risk on the relationship between influenza and pollution factors, aiming to quantify the association and provide a basis for the prevention of influenza and the formulation of relevant policies. Environmental data in Shijiazhuang from 2014 to 2019, as well as the data on hospital-confirmed influenza, were collected. When the concentration of PM2.5 was the highest (621 μg/m3), the relative risk was the highest (RR: 2.39, 95% CI: 1.10-5.17). For extremely high concentration PM2.5 (348 μg/m3), analysis of cumulative lag effect showed statistical significance from cumulative lag0-1 to lag0-6 day, and the minimum cumulative lag effect appeared in lag0-2 (RR: 0.760, 95% CI: 0.655-0.882). In terms of ozone, the RR value was 2.28(1.19,4.38), when O3 concentration was 310 μg/m3, and the RR was 1.65(1.26,2.15), when O3 concentration was 0 μg/m3. The RR of this lag effect increased with the increase of lag days, and reached the maximum at lag0-7 days, RR and 95% CI of slightly low concentration and extremely high concentration were 1.217(1.108,1.337) and 1.440(1.012,2.047), respectively. Stratified analysis showed that there was little difference in gender, but in different age groups, the cumulative lag effect of these two pollutants on influenza was significantly different. Our study found a non-linear relationship between two pollutants and influenza; slightly low concentrations were more associated with contaminant-related influenza. Health workers should encourage patients to get the influenza vaccine and wear masks when going out during flu seasons.
Collapse
Affiliation(s)
- Xue Wang
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, 361 Zhongshan East Road, Shijiazhuang, 050017, China
| | - Jianning Cai
- The Department of Epidemic Treating and Preventing, Center for Disease Prevention and Control of Shijiazhuang City, Shijiazhuang, China
| | - Xuehui Liu
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, 361 Zhongshan East Road, Shijiazhuang, 050017, China
| | - Binhao Wang
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, 361 Zhongshan East Road, Shijiazhuang, 050017, China
| | - Lina Yan
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, 361 Zhongshan East Road, Shijiazhuang, 050017, China
| | - Ran Liu
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, 361 Zhongshan East Road, Shijiazhuang, 050017, China
| | - Yaxiong Nie
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, 361 Zhongshan East Road, Shijiazhuang, 050017, China
| | - Yameng Wang
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, 361 Zhongshan East Road, Shijiazhuang, 050017, China
| | - Xinzhu Zhang
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, 361 Zhongshan East Road, Shijiazhuang, 050017, China
| | - Xiaolin Zhang
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, 361 Zhongshan East Road, Shijiazhuang, 050017, China.
| |
Collapse
|
6
|
Zhu H, Chen S, Lu W, Chen K, Feng Y, Xie Z, Zhang Z, Li L, Ou J, Chen G. Study on the influence of meteorological factors on influenza in different regions and predictions based on an LSTM algorithm. BMC Public Health 2022; 22:2335. [PMID: 36514013 PMCID: PMC9745690 DOI: 10.1186/s12889-022-14299-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 09/26/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Influenza epidemics pose a threat to human health. It has been reported that meteorological factors (MFs) are associated with influenza. This study aimed to explore the similarities and differences between the influences of more comprehensive MFs on influenza in cities with different economic, geographical and climatic characteristics in Fujian Province. Then, the information was used to predict the daily number of cases of influenza in various cities based on MFs to provide bases for early warning systems and outbreak prevention. METHOD Distributed lag nonlinear models (DLNMs) were used to analyse the influence of MFs on influenza in different regions of Fujian Province from 2010 to 2021. Long short-term memory (LSTM) was used to train and model daily cases of influenza in 2010-2018, 2010-2019, and 2010-2020 based on meteorological daily values. Daily cases of influenza in 2019, 2020 and 2021 were predicted. The root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) were used to quantify the accuracy of model predictions. RESULTS The cumulative effect of low and high values of air pressure (PRS), air temperature (TEM), air temperature difference (TEMD) and sunshine duration (SSD) on the risk of influenza was obvious. Low (< 979 hPa), medium (983 to 987 hPa) and high (> 112 hPa) PRS were associated with a higher risk of influenza in women, children aged 0 to 12 years, and rural populations. Low (< 9 °C) and high (> 23 °C) TEM were risk factors for influenza in four cities. Wind speed (WIN) had a more significant effect on the risk of influenza in the ≥ 60-year-old group. Low (< 40%) and high (> 80%) relative humidity (RHU) in Fuzhou and Xiamen had a significant effect on influenza. When PRS was between 1005-1015 hPa, RHU > 60%, PRE was low, TEM was between 10-20 °C, and WIN was low, the interaction between different MFs and influenza was most obvious. The RMSE, MAE, MAPE, and SMAPE evaluation indices of the predictions in 2019, 2020 and 2021 were low, and the prediction accuracy was high. CONCLUSION All eight MFs studied had an impact on influenza in four cities, but there were similarities and differences. The LSTM model, combined with these eight MFs, was highly accurate in predicting the daily cases of influenza. These MFs and prediction models could be incorporated into the influenza early warning and prediction system of each city and used as a reference to formulate prevention strategies for relevant departments.
Collapse
Affiliation(s)
- Hansong Zhu
- Emergency Response and Epidemic Management Institute, Fujian Center for Disease Control and Prevention, Fuzhou, 350012 Fujian China ,Fujian Provincial Key Laboratory of Zoonosis Research, Fuzhou, 350012 Fujian China ,grid.256112.30000 0004 1797 9307The practice base on the school of public health Fujian Medical University, Fuzhou, 350012 Fujian China
| | - Si Chen
- Climate Assessment Office of Fujian Climate Center, Fuzhou, 350007 Fujian China
| | - Wen Lu
- grid.415108.90000 0004 1757 9178Shengli Clinical Medical College of Fujian Medical University, Department of Health Management of Fujian Provincial Hospital, Fuzhou, 350001 Fujian China
| | - Kaizhi Chen
- grid.411604.60000 0001 0130 6528College of Computer and Data Science, Fuzhou University, Fuzhou, 350108 Fujian China
| | - Yulin Feng
- grid.256112.30000 0004 1797 9307School of Public Health, Fujian Medical University, Fujian 350108 Fuzhou, China
| | - Zhonghang Xie
- Emergency Response and Epidemic Management Institute, Fujian Center for Disease Control and Prevention, Fuzhou, 350012 Fujian China ,Fujian Provincial Key Laboratory of Zoonosis Research, Fuzhou, 350012 Fujian China ,grid.256112.30000 0004 1797 9307The practice base on the school of public health Fujian Medical University, Fuzhou, 350012 Fujian China
| | - Zhifang Zhang
- Fujian Provincial Key Laboratory of Zoonosis Research, Fuzhou, 350012 Fujian China ,Science and Technology Information and Management, Fujian Center for Disease Control and Prevention, Fuzhou, 350012 Fujian China
| | - Lingfang Li
- Emergency Response and Epidemic Management Institute, Fujian Center for Disease Control and Prevention, Fuzhou, 350012 Fujian China ,Fujian Provincial Key Laboratory of Zoonosis Research, Fuzhou, 350012 Fujian China
| | - Jianming Ou
- Emergency Response and Epidemic Management Institute, Fujian Center for Disease Control and Prevention, Fuzhou, 350012 Fujian China ,Fujian Provincial Key Laboratory of Zoonosis Research, Fuzhou, 350012 Fujian China ,grid.256112.30000 0004 1797 9307The practice base on the school of public health Fujian Medical University, Fuzhou, 350012 Fujian China
| | - Guangmin Chen
- Emergency Response and Epidemic Management Institute, Fujian Center for Disease Control and Prevention, Fuzhou, 350012 Fujian China ,Fujian Provincial Key Laboratory of Zoonosis Research, Fuzhou, 350012 Fujian China ,grid.256112.30000 0004 1797 9307The practice base on the school of public health Fujian Medical University, Fuzhou, 350012 Fujian China
| |
Collapse
|
7
|
Zhang R, Peng Z, Meng Y, Song H, Wang S, Bi P, Li D, Zhao X, Yao X, Li Y. Temperature and influenza transmission: Risk assessment and attributable burden estimation among 30 cities in China. ENVIRONMENTAL RESEARCH 2022; 215:114343. [PMID: 36115415 DOI: 10.1016/j.envres.2022.114343] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 08/19/2022] [Accepted: 09/11/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Many studies have explored the epidemiological characteristics of influenza. However, most previous studies were conducted in a specific region without a national picture which is important to develop targeted strategies and measures on influenza control and prevention. OBJECTIVES To explore the association between ambient temperature and incidence of influenza, to estimate the attributable risk from temperature in 30 Chinese cities with different climatic characteristics for a national picture, and to identify the vulnerable populations for national preventative policy development. METHODS Daily meteorological and influenza incidence data from the 30 Chinese cities over the period 2016-19 were collected. We estimated the city-specific association between daily mean temperature and influenza incidence using a distributed lag non-linear model and evaluated the pooled effects using multivariate meta-analysis. The attributable fractions compared with reference temperature were calculated. Stratified analyses were performed by region, sex and age. RESULTS Overall, an N-shape relationship between temperature and influenza incidence was found in China. The cumulative relative risk of the peak risk temperature (5.1 °C) was 2.13 (95%CI: 1.41, 3.22). And 60% (95%eCI: 54.3%, 64.3%) of influenza incidence was attributed to ambient temperature during the days with sensitive temperatures (1.6°C-14.4 °C). The ranges of sensitive temperatures and the attributable disease burden due to temperatures varied for different populations and regions. The residents in South China and the children aged ≤5 and 6-17 years had higher fractions attributable to sensitive temperatures. CONCLUSIONS Tailored preventions targeting on most vulnerable populations and regions should be developed to reduce influenza burden from sensitive temperatures.
Collapse
Affiliation(s)
- Rui Zhang
- Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zhibin Peng
- Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yujie Meng
- Chinese Center for Disease Control and Prevention, Beijing, China
| | - Hejia Song
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Songwang Wang
- Chinese Center for Disease Control and Prevention, Beijing, China
| | - Peng Bi
- School of Public Health, The University of Adelaide, South Australia, Australia
| | - Dan Li
- Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xiang Zhao
- National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xiaoyuan Yao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yonghong Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
| |
Collapse
|
8
|
Zhang JL, Chen ZY, Lin SL, King CC, Chen CC, Chen PS. Airborne Avian Influenza Virus in Ambient Air in the Winter Habitats of Migratory Birds. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:15365-15376. [PMID: 36288568 DOI: 10.1021/acs.est.2c04528] [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] [Indexed: 06/16/2023]
Abstract
Outbreaks of avian influenza virus (AIV) have raised public concerns recently. Airborne AIV has been evaluated in live poultry markets and case farms; however, no study has discussed airborne AIV in ambient air in the winter habitats of migratory birds. Therefore, this study aimed to evaluate airborne AIV, specifically H5, H7, and H9, in a critical winter habitat of migratory birds and assess the factors influencing airborne AIV transmission in ambient air to provide novel insights into the epidemiology of avian influenza. A total of 357 ambient air samples were collected in the Aogu Wetland, Taiwan, Republic of China, between October 2017 and December 2019 and analyzed using quantitative real-time polymerase chain reaction. The effects of environmental factors including air pollutants, meteorological factors, and the species of the observed migratory birds on the concentration of airborne AIV were also analyzed. To our knowledge, this is the first study to investigate the relationship between airborne AIV in ambient air and the influence factors in the winter habitats of migratory birds, demonstrating the benefits of environmental sampling for infectious disease epidemiology. The positive rate of airborne H7 (12%) was higher than that of H5 (8%) and H9 (10%). The daily mean temperature and daily maximum temperature had a significant negative correlation with influenza A, H7, and H9. Cold air masses and bird migration were significantly associated with airborne H9 and H7, respectively. In addition, we observed a significant correlation between AIV and the number of pintails, common teals, Indian spot-billed ducks, northern shovelers, Eurasian wigeons, tufted ducks, pied avocets, black-faced spoonbills, and great cormorants. In conclusion, we demonstrated the potential for alternative surveillance approaches (monitoring bird species) as an indicator for influenza-related risks and identified cold air masses and the presence of specific bird species as potential drivers of the presence and/or the airborne concentration of AIV.
Collapse
Affiliation(s)
- Jia Lin Zhang
- Department of Public Health, College of Health Science, Kaohsiung Medical University, Kaohsiung City807, Taiwan, Republic of China
| | - Zi-Yu Chen
- Department of Public Health, College of Health Science, Kaohsiung Medical University, Kaohsiung City807, Taiwan, Republic of China
| | - Si-Ling Lin
- Department of Public Health, College of Health Science, Kaohsiung Medical University, Kaohsiung City807, Taiwan, Republic of China
| | - Chwan-Chuen King
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei City106, Taiwan, Republic of China
| | - Chen-Chih Chen
- Animal Biologics Pilot Production Center, National Pingtung University of Science and Technology, Pingtung City912, Taiwan, Republic of China
- Research Center for Animal Biologics, National Pingtung University of Science and Technology, Pingtung City912, Taiwan, Republic of China
- Institute of Wildlife Conservation, College of Veterinary Medicine, National Pingtung University of Science and Technology, Pingtung City912, Taiwan, Republic of China
| | - Pei-Shih Chen
- Department of Public Health, College of Health Science, Kaohsiung Medical University, Kaohsiung City807, Taiwan, Republic of China
- Institute of Environmental Engineering, College of Engineering, National Sun Yat-Sen University, Kaohsiung City807, Taiwan, Republic of China
- Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung City807, Taiwan, Republic of China
- Research Center for Precision Environmental Medicine, Kaohsiung Medical University, Kaohsiung City807, Taiwan, Republic of China
| |
Collapse
|
9
|
Chien LC, Chen LWA, Lin RT. Lagged meteorological impacts on COVID-19 incidence among high-risk counties in the United States-a spatiotemporal analysis. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2022; 32:774-781. [PMID: 34211113 PMCID: PMC8247626 DOI: 10.1038/s41370-021-00356-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Revised: 06/16/2021] [Accepted: 06/17/2021] [Indexed: 05/16/2023]
Abstract
BACKGROUND The associations between meteorological factors and coronavirus disease 2019 (COVID-19) have been discussed globally; however, because of short study periods, the lack of considering lagged effects, and different study areas, results from the literature were diverse and even contradictory. OBJECTIVE The primary purpose of this study is to conduct more reliable research to evaluate the lagged meteorological impacts on COVID-19 incidence by considering a relatively long study period and diversified high-risk areas in the United States. METHODS This study adopted the distributed lagged nonlinear model with a spatial function to analyze COVID-19 incidence predicted by multiple meteorological measures from March to October of 2020 across 203 high-risk counties in the United States. The estimated spatial function was further smoothed within the entire continental United States by the biharmonic spline interpolation. RESULTS Our findings suggest that the maximum temperature, minimum relative humidity, and precipitation were the best meteorological predictors. Most significantly positive associations were found from 3 to 11 lagged days in lower levels of each selected meteorological factor. In particular, a significantly positive association appeared in minimum relative humidity higher than 88.36% at 5-day lag. The spatial analysis also shows excessive risks in the north-central United States. SIGNIFICANCE The research findings can contribute to the implementation of early warning surveillance of COVID-19 by using weather forecasting for up to two weeks in high-risk counties.
Collapse
Affiliation(s)
- Lung-Chang Chien
- Department of Epidemiology and Biostatistics, School of Public Health, University of Nevada, Las Vegas, Las Vegas, NV, USA
| | - L-W Antony Chen
- Department of Environmental and Occupational Health, School of Public Health, University of Nevada, Las Vegas, Las Vegas, NV, USA
| | - Ro-Ting Lin
- Department of Occupational Safety and Health, College of Public Health, China Medical University, Taichung, Taiwan.
| |
Collapse
|
10
|
Wang J, Zhang L, Lei R, Li P, Li S. Effects and Interaction of Meteorological Parameters on Influenza Incidence During 2010-2019 in Lanzhou, China. Front Public Health 2022; 10:833710. [PMID: 35273941 PMCID: PMC8902077 DOI: 10.3389/fpubh.2022.833710] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 01/10/2022] [Indexed: 11/13/2022] Open
Abstract
Background Influenza is a seasonal infectious disease, and meteorological parameters critically influence the incidence of influenza. However, the meteorological parameters linked to influenza occurrence in semi-arid areas are not studied in detail. This study aimed to clarify the impact of meteorological parameters on influenza incidence during 2010-2019 in Lanzhou. The results are expected to facilitate the optimization of influenza-related public health policies by the local healthcare departments. Methods Descriptive data related to influenza incidence and meteorology during 2010-2019 in Lanzhou were analyzed. The exposure-response relationship between the risk of influenza occurrence and meteorological parameters was explored according to the distributed lag no-linear model (DLNM) with Poisson distribution. The response surface model and stratified model were used to estimate the interactive effect between relative humidity (RH) and other meteorological parameters on influenza incidence. Results A total of 6701 cases of influenza were reported during 2010-2019. DLNM results showed that the risk of influenza would gradually increase as the weekly mean average ambient temperature (AT), RH, and absolute humidity (AH) decrease at lag 3 weeks when they were lower than 12.16°C, 51.38%, and 5.24 g/m3, respectively. The low Tem (at 5th percentile, P5) had the greatest effect on influenza incidence; the greatest estimated relative risk (RR) was 4.54 (95%CI: 3.19-6.46) at cumulative lag 2 weeks. The largest estimates of RRs for low RH (P5) and AH (P5) were 4.81 (95%CI: 3.82-6.05) and 4.17 (95%CI: 3.30-5.28) at cumulative lag 3 weeks, respectively. An increase in AT by 1°C led to an estimates of percent change (95%CI) of 3.12% (-4.75% to -1.46%) decrease in the weekly influenza case counts in a low RH environment. In addition, RH showed significant interaction with AT and AP on influenza incidence but not with wind speed. Conclusion This study indicated that low AT, low humidity (RH and AH), and high air pressure (AP) increased the risk of influenza. Moreover, the interactive effect of low RH with low AT and high AP can aggravate the incidence of influenza.
Collapse
Affiliation(s)
- Jinyu Wang
- School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Ling Zhang
- Institute of Occupational Health and Environmental Health, School of Public Health, Lanzhou University, Lanzhou, China
| | - Ruoyi Lei
- Institute of Occupational Health and Environmental Health, School of Public Health, Lanzhou University, Lanzhou, China
| | - Pu Li
- The Second People's Hospital of Baiyin, Baiyin, China
| | - Sheng Li
- The First People's Hospital of Lanzhou, Lanzhou, China
| |
Collapse
|
11
|
Joung YH, Jang TS, Kim JK. Association among sentinel surveillance, meteorological factors, and infectious disease in Gwangju, Korea. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:17561-17569. [PMID: 34669138 PMCID: PMC8527811 DOI: 10.1007/s11356-021-17085-2] [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: 06/24/2021] [Accepted: 10/13/2021] [Indexed: 05/13/2023]
Abstract
The outbreak of new infectious diseases is threatening human survival. Transmission of such diseases is determined by several factors, with climate being a very important factor. This study was conducted to assess the correlation between the occurrence of infectious diseases and climatic factors using data from the Sentinel Surveillance System and meteorological data from Gwangju, Jeollanam-do, Republic of Korea. The climate of Gwangju from June to September is humid, with this city having the highest average temperature, whereas that from December to February is cold and dry. Infection rates of Salmonella (temperature: r = 0.710**; relative humidity: r = 0.669**), E. coli (r = 0.617**; r = 0.626**), rotavirus (r = - 0.408**; r = - 0.618**), norovirus (r = - 0.463**; r = - 0.316**), influenza virus (r = - 0.726**; r = - 0.672**), coronavirus (r = - 0.684**; r = - 0.408**), and coxsackievirus (r = 0.654**; r = 0.548**) have been shown to have a high correlation with seasonal changes, specifically in these meteorological factors. Pathogens showing distinct seasonality in the occurrence of infection were observed, and there was a high correlation with the climate characteristics of Gwangju. In particular, viral diseases show strong seasonality, and further research on this matter is needed. Due to the current COVID-19 pandemic, quarantine and prevention have become important to block the spread of infectious diseases. For this purpose, studies that predict infectivity through various types of data related to infection are important.
Collapse
Affiliation(s)
- You Hyun Joung
- Department of Medical Laser, Dankook University Graduate School of Medicine, Cheonan-Si, Chungnam, Republic of Korea
| | - Tae Su Jang
- Department of Preventive Medicine, Dankook University College of Medicine, Cheonan-Si, Chungnam, Republic of Korea
| | - Jae Kyung Kim
- Department of Biomedical Laboratory Science, Dankook University College of Health Sciences, 119, Dandae-ro, Dongnam-gu, Cheonan-si, Chungnam, 31116, Republic of Korea.
| |
Collapse
|
12
|
Chen C, Zhang X, Jiang D, Yan D, Guan Z, Zhou Y, Liu X, Huang C, Ding C, Lan L, Huang X, Li L, Yang S. Associations between Temperature and Influenza Activity: A National Time Series Study in China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182010846. [PMID: 34682590 PMCID: PMC8535740 DOI: 10.3390/ijerph182010846] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Revised: 10/10/2021] [Accepted: 10/12/2021] [Indexed: 01/03/2023]
Abstract
Previous studies have reported that temperature is the main meteorological factor associated with influenza activity. This study used generalized additive models (GAMs) to explore the relationship between temperature and influenza activity in China. From the national perspective, the average temperature (AT) had an approximately negative linear correlation with the incidence of influenza, as well as a positive rate of influenza H1N1 virus (A/H1N1). Every degree that the monthly AT rose, the influenza cases decreased by 2.49% (95%CI: 1.24%–3.72%). The risk of influenza cases reached a peak at −5.35 °C with RRs of 2.14 (95%CI: 1.38–3.33) and the monthly AT in the range of −5.35 °C to 18.31 °C had significant effects on the incidence of influenza. Every degree that the weekly AT rose, the positive rate of A/H1N1 decreased by 5.28% (95%CI: 0.35%–9.96%). The risk of A/H1N1 reached a peak at −3.14 °C with RRs of 4.88 (95%CI: 1.01–23.75) and the weekly AT in the range of −3.14 °C to 17.25 °C had significant effects on the incidence of influenza. Our study found that AT is negatively associated with influenza activity, especially for A/H1N1. These findings indicate that temperature could be integrated into the current influenza surveillance system to develop early warning systems to better predict and prepare for the risks of influenza.
Collapse
Affiliation(s)
- Can Chen
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
| | - Xiaobao Zhang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
| | - Daixi Jiang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
| | - Danying Yan
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
| | - Zhou Guan
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
| | - Yuqing Zhou
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
| | - Xiaoxiao Liu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
| | - Chenyang Huang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
| | - Cheng Ding
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
| | - Lei Lan
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
| | - Xihui Huang
- Subject Teaching (English), College of Foreign Languages, Fujian Normal University, Fujian 350117, China;
| | - Lanjuan Li
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
- Correspondence: (L.L.); (S.Y.); Tel.: +86-13605705640 (S.Y.)
| | - Shigui Yang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China; (C.C.); (X.Z.); (D.J.); (D.Y.); (Z.G.); (Y.Z.); (X.L.); (C.H.); (C.D.); (L.L.)
- Correspondence: (L.L.); (S.Y.); Tel.: +86-13605705640 (S.Y.)
| |
Collapse
|
13
|
Li X, Chen D, Zhang Y, Xue X, Zhang S, Chen M, Liu X, Ding G. Analysis of spatial-temporal distribution of notifiable respiratory infectious diseases in Shandong Province, China during 2005-2014. BMC Public Health 2021; 21:1597. [PMID: 34461855 PMCID: PMC8403828 DOI: 10.1186/s12889-021-11627-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 08/12/2021] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Little comprehensive information on overall epidemic trend of notifiable respiratory infectious diseases is available in Shandong Province, China. This study aimed to determine the spatiotemporal distribution and epidemic characteristics of notifiable respiratory infectious diseases. METHODS Time series was firstly performed to describe the temporal distribution feature of notifiable respiratory infectious diseases during 2005-2014 in Shandong Province. GIS Natural Breaks (Jenks) was applied to divide the average annual incidence of notifiable respiratory infectious diseases into five grades. Spatial empirical Bayesian smoothed risk maps and excess risk maps were further used to investigate spatial patterns of notifiable respiratory infectious diseases. Global and local Moran's I statistics were used to measure the spatial autocorrelation. Spatial-temporal scanning was used to detect spatiotemporal clusters and identify high-risk locations. RESULTS A total of 537,506 cases of notifiable respiratory infectious diseases were reported in Shandong Province during 2005-2014. The morbidity of notifiable respiratory infectious diseases had obvious seasonality with high morbidity in winter and spring. Local Moran's I analysis showed that there were 5, 23, 24, 4, 20, 8, 14, 10 and 7 high-risk counties determined for influenza A (H1N1), measles, tuberculosis, meningococcal meningitis, pertussis, scarlet fever, influenza, mumps and rubella, respectively. The spatial-temporal clustering analysis determined that the most likely cluster of influenza A (H1N1), measles, tuberculosis, meningococcal meningitis, pertussis, scarlet fever, influenza, mumps and rubella included 74, 66, 58, 56, 22, 64, 2, 75 and 56 counties, and the time frame was November 2009, March 2008, January 2007, February 2005, July 2007, December 2011, November 2009, June 2012 and May 2005, respectively. CONCLUSIONS There were obvious spatiotemporal clusters of notifiable respiratory infectious diseases in Shandong during 2005-2014. More attention should be paid to the epidemiological and spatiotemporal characteristics of notifiable respiratory infectious diseases to establish new strategies for its control.
Collapse
Affiliation(s)
- Xiaomei Li
- School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, No.619 Changcheng Road, Taian, 271016, Shandong Province, China
| | - Dongzhen Chen
- School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, No.619 Changcheng Road, Taian, 271016, Shandong Province, China.,Liaocheng Center for Disease Control and Prevention, Liaocheng, 252100, Shandong Province, China
| | - Yan Zhang
- Guiqian International General Hospital, Guiyang, 550018, Guizhou Province, China
| | - Xiaojia Xue
- Qingdao Municipal Center for Disease Control & Prevention, Qingdao, 266033, Shandong Province, China
| | - Shengyang Zhang
- Shandong Center for Disease control and Prevention, Jinan, 250014, Shandong Province, China
| | - Meng Chen
- Jining Center for Disease Control and Prevention, Qingdao, 272113, Shandong Province, China
| | - Xuena Liu
- School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, No.619 Changcheng Road, Taian, 271016, Shandong Province, China.
| | - Guoyong Ding
- School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, No.619 Changcheng Road, Taian, 271016, Shandong Province, China.
| |
Collapse
|