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As a result of changes in climatic conditions and greater resistance to insecticides, many regions across the globe, including Colombia, have been facing a resurgence of vector-borne diseases, and dengue fever in particular. Timely information on both (1) the spatial distribution of the disease, and (2) prevailing vulnerabilities of the population...
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... an estimated population of 2.3 million, Cali is currently the third largest city in Colombia. The city is administratively organized in 22 communes, which are divided into 340 neighborhoods (Figure 1). Communes are neighborhood groupings based on homogeneous demographic and socioeconomic charac- teristics. ...Citations
... Use of factors was found to be more common because factors offer an easy and broad spectrum for selecting indicators. Some studies using the MOVE framework exclude the exposure dimension (Bizimana et al. 2015;Hagenlocher et al. 2013). These studies consider vulnerability to be a predisposition of a population or system to be adversely affected by a hazard event. ...
... Index-based assessment was the key analytical approach for quantitative vulnerability assessment. Some studies used multivariate analyses such as principal component analysis (Bizimana et al. 2015;Hagenlocher et al. 2013) to group indicators, to check their robustness. Studies have used different aggregation equations for quantifying vulnerability (Bizimana et al. 2015;Hamidi et al. 2020). ...
Conceptual frameworks are vital for identifying relevant components, dimensions and indicators to assess vulnerability to natural hazards and climatic change. Given the fact that vulnerability is applied and used in various disciplines and by multiple schools of thought, several conceptual frameworks to assess and conceptualise vulnerability have been developed. Even though these frameworks have been widely cited in research, the range and context of application and contextual use of such frameworks have rarely been explored. This paper provides a systematic review of the MOVE (Methods for the Improvement of Vulnerability Assessment in Europe) framework. Bibliometric and systematic analyses were performed to better understand who and how the MOVE framework has been taken up by other researchers. The MOVE framework has been widely cited in different research fields. Several studies directly used the framework for assessing vulnerability both in terms of its factors and the different thematic dimensions of vulnerability (e.g. social, physical, ecological). Some studies have used it as a basis for developing context-specific studies of vulnerability and risk assessment frameworks. Finally, we also discuss critiques of the MOVE framework that can provide direction for future vulnerability assessments.
Contribution
Critique of the MOVE framework can be helpful in further improvement and development of a multi-hazard holistic framework that would be flexible enough to support multiple theoretical perspectives in disaster risk and climate change discourses.
... An epidemic/pandemic disease is not a local phenomenon. Hence, our goal was not to estimate the epidemic/pandemic risk, but the socio-economic vulnerability of municipalities focusing on the residents to better identify people/groups in vulnerable situations [24]. In the adapted framework, hazard, risk, and vulnerability are connected but rest in different dimensions. ...
... An epidemic/pandemic disease is not a local phenomenon. Hence, our goal was not to estimate the epidemic/pandemic risk, but the socio-economic vulnerability of municipalities focusing on the residents to be er identify people/groups in vulnerable situations [24]. In the adapted framework, hazard, risk, and vulnerability are connected but rest in different dimensions. ...
The outbreaks of a new pandemic in 2019 let humankind face a new type of challenge. People and groups in vulnerable situations were especially affected. Increasing urbanization, climate change, and global travel raise the likelihood of pandemics. COVID-19 has shown that sustainable and well-planned pandemic management is necessary, which also includes and identifies people in vulnerable situations. In this study, a socioeconomic vulnerability assessment (VA) for supporting improved pandemic/epidemic risk management at the municipality level in Austria was conducted. The VA provides a holistic overview of the vulnerability under pre-event conditions in Austria, which can be used to support pandemic management. Therefore, we calculated a composite indicator with expert-based weighting. The necessary indicators were defined through a literature review and an expert consortium consisting of practical and scientific members. As a result, an interactive map containing the vulnerability index (VI) for each municipality was created, making it possible to also assess underlying vulnerable factors to support decision-making. The applicability of the VA was shown in the relationship between a high VI in a municipality and a high number of deaths. A limiting factor to the VA was the missing data for health indicators for the whole of Austria. Hence, we provide a list with recommendations on which data should be collected to improve the VA in the future.
... Rural northern areas likely experienced higher exposure due to socioeconomic disadvantages. These findings aligned with previous research [24,33,34] that highlighted the significance of socioeconomic risk factors in explaining the variation of Aedes aegypti mosquito densities and the risks of mosquito-borne diseases in Colombia. Likewise, municipalities with a high probability of having suitable zones for the Aedes aegypti mosquito, are largely consistent with the high-risk municipalities of ZIKV, DENV and CHIKV reported in the smoothed maps [35]. ...
Co-circulation of diseases is a public health concern phenomenon as it often informs of population cross-exposure, susceptibility, and cross-protection dynamics. While it commonly occurs, spatial analysis predominately focuses on understanding the individual character of the involved diseases, often neglecting any contributions of co-circulating conditions. This study explores the spatial dynamics and interactions among Zika virus (ZIKV), Dengue virus (DENV), and Chikungunya virus (CHIKV) within the context of co-circulation in the Andean region of Colombia, a tropical and subtropical area highly affected by mosquito-borne diseases. We used Poisson cokriging, a geostatistical method tailored to handle count data (cases), account for the heterogeneous distribution of the population and integrate auxiliary information in the form of count variables. Our results show that Poisson cokriging effectively mitigates the impact of highly variable population densities in the Andean region, producing refined and denoised risk estimates. Likewise, by incorporating information from co-infection, we improved the individual risk estimates and refined the identification of high-risk areas. Our findings show that disease hotspots primarily emerge in municipalities characterized by high rates of co-circulation, coupled with suboptimal water coverage, hygiene conditions, and crowded living environments. We anticipate that the outcomes of this study will contribute to a better understanding of disease co-circulation. In the context of the mosquito-borne disease syndemics in the Andean region, it may offer insights for evidence-based public health strategies.
... Cluster and Grouping Analysis identified statistically significant locations of social characteristics as related to attendance at the follow-up visit. Other studies have used similar cluster analysis techniques [18,[21][22][23][36][37][38][39] and research is being conducted to develop new methods of cluster analysis [22]. As with all studies that use GWR, it is important to review the results to identify places where the regression equations have lower results (e.g. ...
Stroke is the leading cause of major disability and the fifth leading cause of death in the United States. Stroke incidence across the U.S. is not uniform where the southeastern states, known as the “Stroke Belt”, have historically higher rates. Importantly, while the national average death rate due to stroke has been declining, the death rate in the Stroke Belt (from 2013 to 2015) increased 4.2% overall and 5.8% within the Hispanic population. Healthcare interventions have been designed to improve acute stroke care, but they are less prevalent in addressing post-acute care needs of stroke survivors. Therefore, this chapter will describe the results of a recent study that investigated patterns in post-stroke care using a sequence of geospatial statistics. Through this investigation, the reader will learn the sequence of Geographic Information System (GIS) techniques appropriate to use when studying complex spatial patterns.
... The systematic review followed the Preferred Reporting Items for Systematic Reviews and Metaanalyses Guidelines (PRISMA) (20). The search was initiated through three electronic databases: PubMed, Scopus and ScienceDirect. ...
... Social predictors, such as education level, unemployment and poverty rates, sanitation, and access to clean water, are used to assess environmental conditions and hygiene. These data were found in 6 studies (10,11,(17)(18)(19)(20). ...
... Logistic regression, multinomial, general linear, and general additive models are common approaches used to calculate risk levels and create maps. The ecological niche is also commonly used to model environmental suitability for dengue cases and can cover a wide and diverse area, such as a country scale (10,11,15,(18)(19)(20)22,24,29). Index ...
Dengue is a global health problem. There has been an increase in dengue cases more than 15 times over the last two decades. Therefore, effective tools for surveillance, prevention, and control are needed. This review aimed to provide a systematic overview of the predictors and modeling approaches to generate dengue risk maps. Studies references is a Systematic Review that follows the guidelines for systematic reviews from PRISMA. Researchers searched electronic databases such as PubMed, Scopus, and ScienceDirect. Keywords based on Population, Intervention, Comparison, Outcome (PICO) formulation. Studies were organized by inclusion and exclusion criteria and evaluated using an evidence-based critical assessment checklist adapted for a cross-sectional study using the Newcastle Ottawa scale. Various predictors and models were used to create a dengue risk map, and no specific pattern was identified in the combination of predictors or models. The most widely and commonly used predictors for demographic and socioeconomic categories are land cover, age, education, housing conditions, and income level. Environmental categories are rainfall and temperature, which are significant predictors. Generally, the model is divided into statistical and expert-based approaches. Most available dengue risk maps are based on descriptive and retrospective data. Despite the limitations, the risk map facilitates decision-making in public health. Mobile devices can be optimized to describe dengue transmission dynamics through human movement from dengue serological profile data.
... Las vastas regiones tropicales y subtropicales y la costa (439 km) en este estado fronterizo promueven altos niveles de comercio exterior y turismo, que fomentan el movimiento humano, que se incrementa aún más por la migración intensiva de los países centroamericanos (Cuddehe, 2009). Por otro lado, se ha informado que el calentamiento global tiene un fuerte efecto con la expansión del dengue (Hagenlocher, Delmelle, Casas & Kienberger, 2013). 205 La Tabla 1 está de acuerdo con Prasith, Keosavanh & Arima (2013), ya que se observó un exceso de casos en jóvenes entre (10-29 años). ...
... Retrospective descriptive is research conducted with the main aim of making a picture or description of a situation objectively by looking back. Although some studies (8, 14,15,21,23,24,34,36,39,45) did not use DHF data as a reference, the majority of them used secondary data on DHF cases from health surveillance system surveys. Aside from reported DHF cases, critical predictors for model generation and DHF risk maps included variables from a variety of categories, including population, demography, socioeconomic, climatology, environmental, entomological, capacity, and epidemiological data. ...
... Three studies used precipitation, temperature, and humidity (26, 29,35). Predominantly used predictors were precipitation and temperature (2,6,7,9,16,22,27,31,33,34,39,40,41,43), and eleven studies used only one rain parameter, namely 1,13,16,19,23,24,28,30,32,34,36. Thirteen studies used climatological stations to interpolate the spatial distribution of temperature and eight studies used remote sensing to analyze Land Surface Temperature (LST) (13,19,23,24,28,30,31,34). They used remotely sensed data on climatic variables to address the lack of routinely collected data from meteorological stations. ...
... Predominantly used predictors were precipitation and temperature (2,6,7,9,16,22,27,31,33,34,39,40,41,43), and eleven studies used only one rain parameter, namely 1,13,16,19,23,24,28,30,32,34,36. Thirteen studies used climatological stations to interpolate the spatial distribution of temperature and eight studies used remote sensing to analyze Land Surface Temperature (LST) (13,19,23,24,28,30,31,34). They used remotely sensed data on climatic variables to address the lack of routinely collected data from meteorological stations. ...
This review aims to provide a comprehensive overview of the important predictors, and additionally spatial modeling tools capable of producing Dengue Hemorrhagic Fever (DHF) risk maps. A literature search was conducted in PubMed, Scopus, Science Direct, and Google Scholar for studies reporting DHF risk factors. The Preferred Reporting Items for Systematic Reviews (PRISMA) 2020 statement is used to report this scoping review. It lasted from January 2011 to August of 2022. Initially 1329 articles were found, after inclusion and exclusion criteria, 45 manuscripts were selected. A variety of models and techniques were used to identify DHF risk areas with an arrangement of various multiple-criteria decision-making, statistical, and Machine Learning technique. We found that There was no pattern of predictor use associated with particular approaches; instead, a wide range of predictors was used to create DHF risk maps. Predictors are various variables or factors that are considered when assessing the likelihood or intensity of DHF outbreaks in a specific area in the context of DHF risk mapping. These predictors can include climatology factors (e.g., temperature, rainfall, humidity), socio-economic indicators (e.g., population density, urbanization level), environmental factors (land-use, elevation) and other relevant factors (e.g., mosquito abundance, previous DHF cases). The spatial model of DHF risk is a valuable tool for public health authorities, policymakers, and communities to identify areas at higher risk of dengue transmission, but its limitations underscore the importance of complementing it with other approaches and considering contextual factors for a more holistic assessment of DHF outbreaks. It enables targeted interventions, such as vector control measures and public awareness campaigns, to be implemented in high-risk areas, ultimately helping to mitigate the impact of dengue outbreaks and protect public health.
... The city might become more vulnerable to DF in the future as elderly people are deemed a high-risk group for developing severe dengue infection (Xiao et al., 2016). In addition, the low socioeconomic level may be associated with unsanitary and poor living conditions which can promote mosquito reproduction directly and increase the DF risk indirectly (Hagenlocher et al., 2013). Thirdly, Hong Kong is a global transit hub for international travellers and is geographically close to hyperendemic areas in Southeast Asia. ...
Dengue fever, a mosquito-borne fatal disease, brings a huge health burden in tropical regions. With global warming, rapid urbanization and the expansion of mosquitoes, dengue fever is expected to spread to many subtropical regions, leading to increased potential health risks on local populations. So far, limited studies assessed the dengue fever risk spatially for subtropical non-endemic regions hindering the development of related public health management. Therefore, we proposed a spatial hazard-exposure-vulnerability assessment framework for mapping the dengue fever risk in Hong Kong. Firstly, the spatial distribution of the habitat suitability for Aedes albopictus, the mosquito proxy for the dengue fever hazard, was predicted using a species distribution model (e.g., MaxEnt) relying on a list of variables related to local climate, urban morphology, and landscape metrics. Secondly, the spatial autocorrelation between high dengue hazard and high human population exposure in urban areas was measured. Finally, the dengue fever risk was assessed at community scale by integrating the results of vulnerability analysis basing on census data. This approach allowed the identification of 17 high-risk spots within Hong Kong. The landscape metrics about land utilities and vegetations, and urban morphological characteristics are the influential factors on the spatial distribution of dengue vector. In addition, the underlying factors behind each hot spot were investigated, and specific suggestions for dengue prevention were proposed accordingly. The findings provide a useful reference for developing local dengue fever risk prevention measures, with the proposed method easily exportable to other high-density cities within subtropical Asia and elsewhere.
... Benefit-of-the-doubt Socioeconomic (Ogneva-Himmelberger et al., 2013) and social exclusion (Libório et al., 2022a(Libório et al., , 2022b(Libório et al., , 2022c) Factor analysis Intra-urban deprivation (Oyebanji, 1984), travel attitudes and reasons for location choice (Ettema & Nieuwenhuis, 2017), assessment of oil and gas geopolitical influence (Gu & Wang, 2015), urban spheres of influence (Wang et al., 2011), environments of disadvantage (Pacione, 2004a), target-regional and target-country cultural variation (Slangen, 2016), social capital (Kemeny & Cooke, 2017), marginalization of youth in the labor market (Bauder & Sharpe, 2000), disadvantaged live in rural areas (Pacione, 2004b), and Vulnerability to COVID-19 (Fall et al., 2022) PCA Neighborhood socio-demographic characteristics (Wang & Lindsey, 2019), risk and resilience (González et al., 2018), vulnerability to dengue fever (Hagenlocher et al., 2013), intraregional agricultural characteristics (Su et al., 2020), agricultural development (Halder, 2021), material well-being (Sinha & Basu, 2022), settlement development (Kallingal & Mohammed Firoz, 2022), family living conditions (Das et al., 2021a(Das et al., , 2021b, urban spatial inequality (Rabiei-Dastjerdi & Matthews, 2021), and access to services and basic urban amenities (Das et al., 2021a(Das et al., , 2021b ...
Social phenomena are multidimensional and dependent on geographic space. Numerous methods are capable of representing multidimensional social phenomena through a composite indicator. Among these methods, principal component analysis (PCA) is the most used when considering the geographical perspective. However, the composite indicators built by the method are sensitive to outliers and dependent on the input data, implying informational loss and specific eigenvectors that make multi-space–time comparisons impossible. This research proposes a new method to overcome these problems: the Robust Multispace PCA. The method incorporates the following innovations. The sub-indicators are weighted according to their conceptual importance in the multidimensional phenomenon. The non-compensatory aggregation of these sub-indicators guarantees the function of the weights as of relative importance. Aggregating indicators in dimensions balances the weight structure of dimensions in the composite indicator. A new scale transformation function that eliminates outliers and allows multispatial comparison reduces by 1.52 times the informational loss of the composite indicator of social exclusion in eight cities' urban areas. The Robust Multispace-PCA has a high potential for appropriation by researchers and policymakers, as it is easy to follow, offers more informative and accurate representations of multidimensional social phenomena, and favors the development of policies at multiple geographic scales.
... Clinical dengue case incidence is a proxy for transmission risk [26], however, there are many asymptomatic cases, which are responsible for silent and cryptic transmission of arboviruses. In order to take this aspect into account, the accuracy of risk models can be improved by including, besides case data, other factors influencing transmission, such as population density, entomological infestation and environmental and social characteristics [17,[27][28][29][30][31]. Such multicomponent approaches for dengue risk mapping can be based on indices or on models [27]. ...
... Several vulnerability frameworks have been developed and later adapted for health risk assessment. For example, the European research project MOVE (Methods for the Improvement of Vulnerability Assessment in Europe) [33] created a framework for the study of climate change that has been adapted to assess socioeconomic vulnerability to dengue fever in Cali, Colombia [30]. In Brazil, the Health Vulnerability Index (HVI) developed by Geren-cia de Epidemiologia Informaçao (GEEPI, 2013) Belo Horizonte and the ArboAlvo model have been used for the study of dengue and other arboviral diseases [34,35]. ...
... Evidence on the use of disease risk mapping to identify areas of higher vulnerability for dengue transmission with the aim of guiding preventive actions is less extensive and still shows research gaps [83]. In Colombia, vulnerable areas were identified using sociodemographic data [30], while in Argentina and Brazil, multicomponent models have been automated and implemented using software tools [84,85]. ...
To better guide dengue prevention and control efforts, the use of routinely collected data to develop risk maps is proposed. For this purpose, dengue experts identified indicators representative of entomological, epidemiological and demographic risks, hereafter called components, by using surveillance data aggregated at the level of Consejos Populares (CPs) in two municipalities of Cuba (Santiago de Cuba and Cienfuegos) in the period of 2010–2015. Two vulnerability models (one with equally weighted components and one with data-derived weights using Principal Component Analysis), and three incidence-based risk models were built to construct risk maps. The correlation between the two vulnerability models was high (tau > 0.89). The single-component and multicomponent incidence-based models were also highly correlated (tau ≥ 0.9). However, the agreement between the vulnerability- and the incidence-based risk maps was below 0.6 in the setting with a prolonged history of dengue transmission. This may suggest that an incidence-based approach does not fully reflect the complexity of vulnerability for future transmission. The small difference between single- and multicomponent incidence maps indicates that in a setting with a narrow availability of data, simpler models can be used. Nevertheless, the generalized linear mixed multicomponent model provides information of covariate-adjusted and spatially smoothed relative risks of disease transmission, which can be important for the prospective evaluation of an intervention strategy. In conclusion, caution is needed when interpreting risk maps, as the results vary depending on the importance given to the components involved in disease transmission. The multicomponent vulnerability mapping needs to be prospectively validated based on an intervention trial targeting high-risk areas.
