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Coalson JE, Anderson EJ, Santos EM, Madera Garcia V, Romine JK, Luzingu JK, Dominguez B, Richard DM, Little AC, Hayden MH, Ernst KC. The Complex Epidemiological Relationship between Flooding Events and Human Outbreaks of Mosquito-Borne Diseases: A Scoping Review. ENVIRONMENTAL HEALTH PERSPECTIVES 2021; 129:96002. [PMID: 34582261 PMCID: PMC8478154 DOI: 10.1289/ehp8887] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 08/10/2021] [Accepted: 08/19/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Climate change is expected to increase the frequency of flooding events. Although rainfall is highly correlated with mosquito-borne diseases (MBD) in humans, less research focuses on understanding the impact of flooding events on disease incidence. This lack of research presents a significant gap in climate change-driven disease forecasting. OBJECTIVES We conducted a scoping review to assess the strength of evidence regarding the potential relationship between flooding and MBD and to determine knowledge gaps. METHODS PubMed, Embase, and Web of Science were searched through 31 December 2020 and supplemented with review of citations in relevant publications. Studies on rainfall were included only if the operationalization allowed for distinction of unusually heavy rainfall events. Data were abstracted by disease (dengue, malaria, or other) and stratified by post-event timing of disease assessment. Studies that conducted statistical testing were summarized in detail. RESULTS From 3,008 initial results, we included 131 relevant studies (dengue n = 45 , malaria n = 61 , other MBD n = 49 ). Dengue studies indicated short-term (< 1 month ) decreases and subsequent (1-4 month) increases in incidence. Malaria studies indicated post-event incidence increases, but the results were mixed, and the temporal pattern was less clear. Statistical evidence was limited for other MBD, though findings suggest that human outbreaks of Murray Valley encephalitis, Ross River virus, Barmah Forest virus, Rift Valley fever, and Japanese encephalitis may follow flooding. DISCUSSION Flooding is generally associated with increased incidence of MBD, potentially following a brief decrease in incidence for some diseases. Methodological inconsistencies significantly limit direct comparison and generalizability of study results. Regions with established MBD and weather surveillance should be leveraged to conduct multisite research to a) standardize the quantification of relevant flooding, b) study nonlinear relationships between rainfall and disease, c) report outcomes at multiple lag periods, and d) investigate interacting factors that modify the likelihood and severity of outbreaks across different settings. https://doi.org/10.1289/EHP8887.
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Affiliation(s)
- Jenna E. Coalson
- Center for Insect Science, University of Arizona, Tucson, Arizona, USA
| | | | - Ellen M. Santos
- Department of Epidemiology and Biostatistics, University of Arizona Mel and Enid Zuckerman College of Public Health, Tucson, Arizona, USA
| | - Valerie Madera Garcia
- Department of Epidemiology and Biostatistics, University of Arizona Mel and Enid Zuckerman College of Public Health, Tucson, Arizona, USA
| | - James K. Romine
- Department of Epidemiology and Biostatistics, University of Arizona Mel and Enid Zuckerman College of Public Health, Tucson, Arizona, USA
| | - Joy K. Luzingu
- Department of Epidemiology and Biostatistics, University of Arizona Mel and Enid Zuckerman College of Public Health, Tucson, Arizona, USA
| | - Brian Dominguez
- Department of Epidemiology and Biostatistics, University of Arizona Mel and Enid Zuckerman College of Public Health, Tucson, Arizona, USA
| | - Danielle M. Richard
- Department of Epidemiology and Biostatistics, University of Arizona Mel and Enid Zuckerman College of Public Health, Tucson, Arizona, USA
| | - Ashley C. Little
- Department of Epidemiology and Biostatistics, University of Arizona Mel and Enid Zuckerman College of Public Health, Tucson, Arizona, USA
| | - Mary H. Hayden
- National Institute for Human Resilience, University of Colorado Colorado Springs, Colorado Springs, Colorado, USA
| | - Kacey C. Ernst
- Department of Epidemiology and Biostatistics, University of Arizona Mel and Enid Zuckerman College of Public Health, Tucson, Arizona, USA
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Suchowiecki K, Reid SP, Simon GL, Firestein GS, Chang A. Persistent Joint Pain Following Arthropod Virus Infections. Curr Rheumatol Rep 2021; 23:26. [PMID: 33847834 PMCID: PMC8042844 DOI: 10.1007/s11926-021-00987-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/20/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE OF REVIEW Persistent joint pain is a common manifestation of arthropod-borne viral infections and can cause long-term disability. We review the epidemiology, pathophysiology, diagnosis, and management of arthritogenic alphavirus infection. RECENT FINDINGS The global re-emergence of alphaviral outbreaks has led to an increase in virus-induced arthralgia and arthritis. Alphaviruses, including Chikungunya, O'nyong'nyong, Sindbis, Barmah Forest, Ross River, and Mayaro viruses, are associated with acute and/or chronic rheumatic symptoms. Identification of Mxra8 as a viral entry receptor in the alphaviral replication pathway creates opportunities for treatment and prevention. Recent evidence suggesting virus does not persist in synovial fluid during chronic chikungunya infection indicates that immunomodulators may be given safely. The etiology of persistent joint pain after alphavirus infection is still poorly understood. New diagnostic tools along and evidence-based treatment could significantly improve morbidity and long-term disability.
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Affiliation(s)
- Karol Suchowiecki
- Department of Medicine, George Washington University, 2150 Pennsylvania Ave Suite 5-416, Washington, DC 20037 USA
| | - St. Patrick Reid
- Department of Pathology and Microbiology, 985900 Nebraska Medical Center, Omaha, NE 68198-5900 USA
| | - Gary L. Simon
- Department of Medicine, George Washington University, 2150 Pennsylvania Ave Suite 5-416, Washington, DC 20037 USA
| | - Gary S. Firestein
- UC San Diego Health Sciences, 9500 Gilman Drive #0602, La Jolla, CA 92093 USA
| | - Aileen Chang
- Department of Medicine, George Washington University, 2150 Pennsylvania Ave Suite 5-416, Washington, DC 20037 USA
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Murphy AK, Clennon JA, Vazquez-Prokopec G, Jansen CC, Frentiu FD, Hafner LM, Hu W, Devine GJ. Spatial and temporal patterns of Ross River virus in south east Queensland, Australia: identification of hot spots at the rural-urban interface. BMC Infect Dis 2020; 20:722. [PMID: 33008314 PMCID: PMC7530966 DOI: 10.1186/s12879-020-05411-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Accepted: 09/10/2020] [Indexed: 12/02/2022] Open
Abstract
Background Ross River virus (RRV) is responsible for the most common vector-borne disease of humans reported in Australia. The virus circulates in enzootic cycles between multiple species of mosquitoes, wildlife reservoir hosts and humans. Public health concern about RRV is increasing due to rising incidence rates in Australian urban centres, along with increased circulation in Pacific Island countries. Australia experienced its largest recorded outbreak of 9544 cases in 2015, with the majority reported from south east Queensland (SEQ). This study examined potential links between disease patterns and transmission pathways of RRV. Methods The spatial and temporal distribution of notified RRV cases, and associated epidemiological features in SEQ, were analysed for the period 2001–2016. This included fine-scale analysis of disease patterns across the suburbs of the capital city of Brisbane, and those of 8 adjacent Local Government Areas, and host spot analyses to identify locations with significantly high incidence. Results The mean annual incidence rate for the region was 41/100,000 with a consistent seasonal peak in cases between February and May. The highest RRV incidence was in adults aged from 30 to 64 years (mean incidence rate: 59/100,000), and females had higher incidence rates than males (mean incidence rates: 44/100,000 and 34/100,000, respectively). Spatial patterns of disease were heterogeneous between years, and there was a wide distribution of disease across both urban and rural areas of SEQ. Overall, the highest incidence rates were reported from predominantly rural suburbs to the north of Brisbane City, with significant hot spots located in peri-urban suburbs where residential, agricultural and conserved natural land use types intersect. Conclusions Although RRV is endemic across all of SEQ, transmission is most concentrated in areas where urban and peri-urban environments intersect. The drivers of RRV transmission across rural-urban landscapes should be prioritised for further investigation, including identification of specific vectors and hosts that mediate human spillover.
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Affiliation(s)
- Amanda K Murphy
- Mosquito Control Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Australia. .,School of Biomedical Sciences, Faculty of Health, and Institute for Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia.
| | - Julie A Clennon
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, USA
| | | | - Cassie C Jansen
- Communicable Diseases Branch, Queensland Health, Herston, Australia
| | - Francesca D Frentiu
- School of Biomedical Sciences, Faculty of Health, and Institute for Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia
| | - Louise M Hafner
- School of Biomedical Sciences, Faculty of Health, and Institute for Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia
| | - Wenbiao Hu
- School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia
| | - Gregor J Devine
- Mosquito Control Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Australia
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Madzokere ET, Hallgren W, Sahin O, Webster JA, Webb CE, Mackey B, Herrero LJ. Integrating statistical and mechanistic approaches with biotic and environmental variables improves model predictions of the impact of climate and land-use changes on future mosquito-vector abundance, diversity and distributions in Australia. Parasit Vectors 2020; 13:484. [PMID: 32967711 PMCID: PMC7510059 DOI: 10.1186/s13071-020-04360-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 09/11/2020] [Indexed: 02/07/2023] Open
Abstract
Changes to Australia's climate and land-use patterns could result in expanded spatial and temporal distributions of endemic mosquito vectors including Aedes and Culex species that transmit medically important arboviruses. Climate and land-use changes greatly influence the suitability of habitats for mosquitoes and their behaviors such as mating, feeding and oviposition. Changes in these behaviors in turn determine future species-specific mosquito diversity, distribution and abundance. In this review, we discuss climate and land-use change factors that influence shifts in mosquito distribution ranges. We also discuss the predictive and epidemiological merits of incorporating these factors into a novel integrated statistical (SSDM) and mechanistic species distribution modelling (MSDM) framework. One potentially significant merit of integrated modelling is an improvement in the future surveillance and control of medically relevant endemic mosquito vectors such as Aedes vigilax and Culex annulirostris, implicated in the transmission of many arboviruses such as Ross River virus and Barmah Forest virus, and exotic mosquito vectors such as Aedes aegypti and Aedes albopictus. We conducted a focused literature search to explore the merits of integrating SSDMs and MSDMs with biotic and environmental variables to better predict the future range of endemic mosquito vectors. We show that an integrated framework utilising both SSDMs and MSDMs can improve future mosquito-vector species distribution projections in Australia. We recommend consideration of climate and environmental change projections in the process of developing land-use plans as this directly impacts mosquito-vector distribution and larvae abundance. We also urge laboratory, field-based researchers and modellers to combine these modelling approaches. Having many different variations of integrated (SDM) modelling frameworks could help to enhance the management of endemic mosquitoes in Australia. Enhanced mosquito management measures could in turn lead to lower arbovirus spread and disease notification rates.
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Affiliation(s)
- Eugene T. Madzokere
- Institute for Glycomics, Griffith University, Gold Coast Campus, Southport, QLD 4215 Australia
| | - Willow Hallgren
- Environmental Futures Research Institute, Griffith School of Environment, Gold Coast campus, Griffith University, Gold Coast, QLD 4222 Australia
| | - Oz Sahin
- Cities Research Institute, Gold Coast campus, Griffith University, Gold Coast, QLD 4222 Australia
| | - Julie A. Webster
- QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006 Australia
| | - Cameron E. Webb
- Department of Medical Entomology, NSW Health Pathology, ICPMR, Westmead Hospital, Westmead, NSW 2145 Australia
- Marie Bashir Institute of Infectious Diseases and Biosecurity, University of Sydney, Sydney, NSW 2006 Australia
| | - Brendan Mackey
- Griffith Climate Change Response Program, Griffith School of Environment, Gold Coast campus, Griffith University, Gold Coast, QLD 4222 Australia
| | - Lara J. Herrero
- Institute for Glycomics, Griffith University, Gold Coast Campus, Southport, QLD 4215 Australia
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Qian W, Viennet E, Glass K, Harley D. Epidemiological models for predicting Ross River virus in Australia: A systematic review. PLoS Negl Trop Dis 2020; 14:e0008621. [PMID: 32970673 PMCID: PMC7537878 DOI: 10.1371/journal.pntd.0008621] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 10/06/2020] [Accepted: 07/20/2020] [Indexed: 01/18/2023] Open
Abstract
Ross River virus (RRV) is the most common and widespread arbovirus in Australia. Epidemiological models of RRV increase understanding of RRV transmission and help provide early warning of outbreaks to reduce incidence. However, RRV predictive models have not been systematically reviewed, analysed, and compared. The hypothesis of this systematic review was that summarising the epidemiological models applied to predict RRV disease and analysing model performance could elucidate drivers of RRV incidence and transmission patterns. We performed a systematic literature search in PubMed, EMBASE, Web of Science, Cochrane Library, and Scopus for studies of RRV using population-based data, incorporating at least one epidemiological model and analysing the association between exposures and RRV disease. Forty-three articles, all of high or medium quality, were included. Twenty-two (51.2%) used generalised linear models and 11 (25.6%) used time-series models. Climate and weather data were used in 27 (62.8%) and mosquito abundance or related data were used in 14 (32.6%) articles as model covariates. A total of 140 models were included across the articles. Rainfall (69 models, 49.3%), temperature (66, 47.1%) and tide height (45, 32.1%) were the three most commonly used exposures. Ten (23.3%) studies published data related to model performance. This review summarises current knowledge of RRV modelling and reveals a research gap in comparing predictive methods. To improve predictive accuracy, new methods for forecasting, such as non-linear mixed models and machine learning approaches, warrant investigation.
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Affiliation(s)
- Wei Qian
- Mater Research Institute‐University of Queensland (MRI‐UQ), Brisbane, Queensland, Australia
| | - Elvina Viennet
- Research and Development, Australian Red Cross Lifeblood, Brisbane, Queensland, Australia
- Institute for Health and Biomedical Innovation, School of Biomedical Sciences, Queensland University of Technology (QUT), Queensland, Australia
| | - Kathryn Glass
- Research School of Population Health, Australian National University, Acton, Australian Capital Territory, Australia
| | - David Harley
- Mater Research Institute‐University of Queensland (MRI‐UQ), Brisbane, Queensland, Australia
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Beggs PJ, Zhang Y, Bambrick H, Berry HL, Linnenluecke MK, Trueck S, Bi P, Boylan SM, Green D, Guo Y, Hanigan IC, Johnston FH, Madden DL, Malik A, Morgan GG, Perkins-Kirkpatrick S, Rychetnik L, Stevenson M, Watts N, Capon AG. The 2019 report of the MJA-Lancet Countdown on health and climate change: a turbulent year with mixed progress. Med J Aust 2019; 211:490-491.e21. [PMID: 31722443 DOI: 10.5694/mja2.50405] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The MJA-Lancet Countdown on health and climate change was established in 2017 and produced its first Australian national assessment in 2018. It examined 41 indicators across five broad domains: climate change impacts, exposures and vulnerability; adaptation, planning and resilience for health; mitigation actions and health co-benefits; economics and finance; and public and political engagement. It found that, overall, Australia is vulnerable to the impacts of climate change on health, and that policy inaction in this regard threatens Australian lives. In this report we present the 2019 update. We track progress on health and climate change in Australia across the same five broad domains and many of the same indicators as in 2018. A number of new indicators are introduced this year, including one focused on wildfire exposure, and another on engagement in health and climate change in the corporate sector. Several of the previously reported indicators are not included this year, either due to their discontinuation by the parent project, the Lancet Countdown, or because insufficient new data were available for us to meaningfully provide an update to the indicator. In a year marked by an Australian federal election in which climate change featured prominently, we find mixed progress on health and climate change in this country. There has been progress in renewable energy generation, including substantial employment increases in this sector. There has also been some progress at state and local government level. However, there continues to be no engagement on health and climate change in the Australian federal Parliament, and Australia performs poorly across many of the indicators in comparison to other developed countries; for example, it is one of the world's largest net exporters of coal and its electricity generation from low carbon sources is low. We also find significantly increasing exposure of Australians to heatwaves and, in most states and territories, continuing elevated suicide rates at higher temperatures. We conclude that Australia remains at significant risk of declines in health due to climate change, and that substantial and sustained national action is urgently required in order to prevent this.
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Affiliation(s)
| | | | | | | | | | | | - Peng Bi
- University of Adelaide, Adelaide, SA
| | | | - Donna Green
- Climate Change Research Centre, UNSW, Sydney, NSW
| | | | | | - Fay H Johnston
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS
| | | | | | - Geoffrey G Morgan
- University Centre for Rural Health, University of Sydney, Lismore, NSW
| | | | - Lucie Rychetnik
- Menzies Centre for Health Policy, University of Sydney, Sydney, NSW
| | | | - Nick Watts
- Institute of Global Health, University College London, London, UK
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