Abstract:The primary vector of the dengue fever virus, the Aedes aegypti mosquito, is distributed across the tropical and sub-tropical latitudes; however, the area at risk of infection has been expanding steadily. This study aimed to identify the industries most vulnerable to the effects of dengue fever by 2030. The assessment was done by considering the international supply chain, with aspects such as the labor intensity, and the relevant geographical and socioeconomic aspects being taken into account. In addition, mu… Show more
“…One of the strongest predictors of dengue in our final specification is “Share of labour force with secondary education”. As previously noted, this is consistent with other findings by [ 58 , 59 ] as dengue tends to affect more those working in labour-intensive industries, such as agriculture or fishing.…”
Section: Resultssupporting
confidence: 93%
“…This result also could be an indicator of more advance and urbanized regions vs agricultural and less developed regions. It is reported that dengue tends to affect more those working in labour-intensive industries, such as agriculture or fishing [ 58 , 59 ]. Fig.…”
Background
This study examines the impact of climate, socio-economic and demographic factors on the incidence of dengue in regions of the United States and Mexico. We select factors shown to predict dengue at a local level and test whether the association can be generalized to the regional or state level. In addition, we assess how different indicators perform compared to per capita gross domestic product (GDP), an indicator that is commonly used to predict the future distribution of dengue.
Methods
A unique spatial-temporal dataset was created by collating information from a variety of data sources to perform empirical analyses at the regional level. Relevant regions for the analysis were selected based on their receptivity and vulnerability to dengue. A conceptual framework was elaborated to guide variable selection. The relationship between the incidence of dengue and the climate, socio-economic and demographic factors was modelled via a Generalized Additive Model (GAM), which also accounted for the spatial and temporal auto-correlation.
Results
The socio-economic indicator (representing household income, education of the labour force, life expectancy at birth, and housing overcrowding), as well as more extensive access to broadband are associated with a drop in the incidence of dengue; by contrast, population growth and inter-regional migration are associated with higher incidence, after taking climate into account. An ageing population is also a predictor of higher incidence, but the relationship is concave and flattens at high rates. The rate of active physicians is associated with higher incidence, most likely because of more accurate reporting. If focusing on Mexico only, results remain broadly similar, however, workforce education was a better predictor of a drop in the incidence of dengue than household income.
Conclusions
Two lessons can be drawn from this study: first, while higher GDP is generally associated with a drop in the incidence of dengue, a more granular analysis reveals that the crucial factors are a rise in education (with fewer jobs in the primary sector) and better access to information or technological infrastructure. Secondly, factors that were shown to have an impact of dengue at the local level are also good predictors at the regional level. These indices may help us better understand factors responsible for the global distribution of dengue and also, given a warming climate, may help us to better predict vulnerable populations on a larger scale.
“…One of the strongest predictors of dengue in our final specification is “Share of labour force with secondary education”. As previously noted, this is consistent with other findings by [ 58 , 59 ] as dengue tends to affect more those working in labour-intensive industries, such as agriculture or fishing.…”
Section: Resultssupporting
confidence: 93%
“…This result also could be an indicator of more advance and urbanized regions vs agricultural and less developed regions. It is reported that dengue tends to affect more those working in labour-intensive industries, such as agriculture or fishing [ 58 , 59 ]. Fig.…”
Background
This study examines the impact of climate, socio-economic and demographic factors on the incidence of dengue in regions of the United States and Mexico. We select factors shown to predict dengue at a local level and test whether the association can be generalized to the regional or state level. In addition, we assess how different indicators perform compared to per capita gross domestic product (GDP), an indicator that is commonly used to predict the future distribution of dengue.
Methods
A unique spatial-temporal dataset was created by collating information from a variety of data sources to perform empirical analyses at the regional level. Relevant regions for the analysis were selected based on their receptivity and vulnerability to dengue. A conceptual framework was elaborated to guide variable selection. The relationship between the incidence of dengue and the climate, socio-economic and demographic factors was modelled via a Generalized Additive Model (GAM), which also accounted for the spatial and temporal auto-correlation.
Results
The socio-economic indicator (representing household income, education of the labour force, life expectancy at birth, and housing overcrowding), as well as more extensive access to broadband are associated with a drop in the incidence of dengue; by contrast, population growth and inter-regional migration are associated with higher incidence, after taking climate into account. An ageing population is also a predictor of higher incidence, but the relationship is concave and flattens at high rates. The rate of active physicians is associated with higher incidence, most likely because of more accurate reporting. If focusing on Mexico only, results remain broadly similar, however, workforce education was a better predictor of a drop in the incidence of dengue than household income.
Conclusions
Two lessons can be drawn from this study: first, while higher GDP is generally associated with a drop in the incidence of dengue, a more granular analysis reveals that the crucial factors are a rise in education (with fewer jobs in the primary sector) and better access to information or technological infrastructure. Secondly, factors that were shown to have an impact of dengue at the local level are also good predictors at the regional level. These indices may help us better understand factors responsible for the global distribution of dengue and also, given a warming climate, may help us to better predict vulnerable populations on a larger scale.
“…One used heat stress records measured on different workplaces to discuss the current and future projections of the risk [97]. The last paper focused on a non-heat climate change impact, the risk of dengue among supply chain workers, using methods to estimate the population at risk of dengue infection in 2030 [98].…”
Section: Observational Categorymentioning
confidence: 99%
“…The articles also discussed other impacts of climate change besides temperature variation. Such as solar radiation and its effect on workers' health [177]; the development of allergies and other diseases related to air pollution [174,182]; the increase in vectorborne diseases, such as dengue [28,98]; climatic variations such as changes in humidity characteristics that worsen workers' exposure and absorption to dangerous substances such as pesticides [176]; changes in rain patterns and a consequent increase in insectborne diseases [136]; extreme weather events such as floods and their consequences for the health and safety of workers [173].…”
BACKGROUND: The working population is exposed daily to unavoidable climatic conditions due to their occupational settings. Effects of the weather such as rain, heat, and air pollution may increase the risk of diseases, injuries, accidents, and even death during labor. OBJECTIVE: This paper aims to summarize the impacts of climate change on workers’ health, safety and performance, identifying the risks, affected workplaces and the range of methodological approaches used to assess this problem. METHODS: A thorough systematic mapping was conducted in seven scientific international databases: Emerald, IEEE Xplore, Science Direct, Scielo, Scopus, SpringerLink, and Web of Science. Three research questions guided the extraction process resulting in 170 articles regarding the impacts of climate change on occupational health and safety. RESULTS: We found an accentuated trend in observational studies applying primary and secondary data collection. Many studies focused on the association between rising temperatures and occupational hazards, mainly in outdoor working settings such as agriculture. The variation of temperature was the most investigated impact of climate change. CONCLUSIONS: We established a knowledge base on how to explore the impacts of climate change on workers’ well-being and health. Researchers and policymakers benefit from this review, which explores the suitable methods found in the literature and highlights the most recurring risks and their consequences to occupational health and safety.
“…Dengue fever, dengue haemorrhagic fever, and mild acute febrile illness can all be caused by the dengue virus (Chaturvedi et al, 2000 ). Patients may experience headaches, high temperatures, joint pain, vomiting, and myalgia during the initial febrile phase (Nakano, 2018 ). Dengue shock syndrome, which includes plasma leakage, coagulation problems, and enhanced vascular fragility, is one of the most significant clinical symptoms (Bhatt et al, 2021 ).…”
Dengue fever, which is spread by
Aedes
mosquitoes, has claimed many lives in Kerala, with the Thiruvananthapuram district bearing the brunt of the toll. This study aims to demarcate the dengue risk zones in Thiruvananthapuram district using the analytical hierarchy process (AHP) and the fuzzy-AHP (F-AHP) methods. For the risk modelling, geo-environmental factors (normalized difference vegetation index, land surface temperature, topographic wetness index, land use/land cover types, elevation, normalized difference built-up index) and demographic factors (household density, population density) have been utilized. The ArcGIS 10.8 and ERDAS Imagine 8.4 software tools have been used to derive the risk zone maps. The area of the risk maps is classified into five zones. The dengue risk zone maps were validated using dengue case data collected from the Integrated Disease Surveillance Programme portal. From the receiver operating characteristic (ROC) curve analysis and the area under the ROC curve (AUC) values, it is proved that the F-AHP method (AUC value of 0.971) has comparatively more prediction capability than the AHP method (AUC value of 0.954) in demarcating the dengue risk zones. Also, based on the comparison of the risk zone map with actual case data, it was confirmed that around 82.87% of the dengue cases occurred in the very high and high-risk zones, thus proving the efficacy of the model. According to the dengue risk map prepared using the F-AHP model, 9.09% of the area of Thiruvananthapuram district is categorized as very high risk. The prepared dengue risk maps will be helpful for decision-makers, staff with the health, and disaster management departments in adopting effective measures to prevent the risks of dengue spread and thereby minimize loss of life.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
