Spatial pattern analysis of the impact of community food environments on foetal macrosomia, preterm births and low birth weight.

Community food environments (CFEs) have a strong impact on child health and nutrition and this impact is currently negative in many areas. In the Republic of Argentina, there is a lack of research evaluating CFEs regionally and comprehensively by tools based on geographic information systems (GIS). This study aimed to characterize the spatial patterns of CFEs, through variables associated with its three dimensions (political, individual and environmental), and their association with the spatial distribution in urban localities in Argentina. CFEs were assessed in 657 localities with ≥5,000 inhabitants. Data on births and CFEs were obtained from nationally available open-source data and through remote sensing. The spatial distribution and presence of clusters were assessed using hotspot analysis, purely spatial analysis (SaTScan), Moran's Index, semivariograms and spatially restrained multivariate clustering. Clusters of low risk for LBW, macrosomia, and preterm births were observed in the central-east part of the country, while high-risk clusters identified in the North, Centre and South. In the central-eastern region, low-risk clusters were found coinciding with hotspots of public policy coverage, high night-time light, social security coverage and complete secondary education of the household head in areas with low risk for negative outcomes of the birth variables studied, with the opposite with regard to households with unsatisfied basic needs and predominant land use classes in peri-urban areas of crops and herbaceous cover. These results show that the exploration of spatial patterns of CFEs is a necessary preliminary step before developing explanatory models and generating novel findings valuable for decision-making.

Landscape service flow dynamics in the metropolitan area of Córdoba (Argentina).

Human decisions, policies, and management strategies play an important role in structuring landscape patterns in a metropolitan area. Land-use/land-cover (LULC) changes can be considered probably the most important factor affecting the environment and the maintenance of landscape service flow. In particular, processes such as agricultural intensification, deforestation, urbanization and industrialization affect landscape heterogeneity in terms of composition and configuration. However, the multifunctional aspect of LULC as well as traditional agricultural practices can contribute to the maintenance of landscape service flow. This research aims to analyze and assess: (1) LULC dynamics and change from 1988 to 2019 within the metropolitan area of Córdoba (Argentina); (2) the effect of this change on landscape composition and configuration; (3) the flow of landscape services from 1988 to 2019, with the identification of hot-spots of landscape service provision. To analyze LULC dynamics and change within the study area, three Landsat images were utilized, while change detection analysis has been performed to identify the areas most affected by changes, the spatial distribution of change and the change trajectories of LULC classes in terms of landscape composition and configuration. Finally, the valuation of landscape service flow has been carried out by placing an economic value on the LULC classes, through the use of proxies. LULC pattern change has resulted in the expansion of extensive agriculture. The total variation from 1988 to 2019 has highlighted a significant reduction of Horticulture, Forests, and Grasslands, which have been converted into other classes (Urban and Extensive Agriculture). This conversion of LULC classes has had profound effects on landscape service flow, which guarantees the well-being of local communities. This research has contributed to the knowledge of where the hot-spots of landscape service' provision are located by helping landscape managers to identify suitable local policies able to preserve them, thus avoiding their loss, and enhancing landscape integrity, functionality, and resilience.

Operational satellite-based temporal modelling of Aedes population in Argentina.

Aedes aegypti is a vector for Chikungunya, Dengue and Zika viruses in Latin America and is therefore a large public health problem for the region. For this reason, several inter-institutional and multidisciplinary efforts have been made to support vector control actions through the use of geospatial technologies. This study presents the development of an operational system for the application of free access to remotely sensed products capable of assessing the oviposition activity of Ae. aegypti in all of Argentina's northern region with the specific aim to improve the current Argentine National Dengue risk system. Temporal modelling implemented includes remotely sensed variables like the normalized difference vegetation index, the normalized difference water index, day and night land surface temperature and precipitation data available from NASA's tropical rainfall measuring mission and global precipitation measurement. As a training data set, four years of weekly mosquito oviposition data from four different cities in Argentina were used. A series of satellite-generated variables was built, downloading and resampling the these products both spatially and temporally. From an initial set of 41 variables chosen based on the correlation between these products and the oviposition series, a subset of 11 variables were preserved to develop temporal forecasting models of oviposition using a lineal multivariate method in the four cities. Subsequently, a general model was generated using data from the cities. Finally, in order to obtain a model that could be broadly used, an extrapolation method using the concept of environmental distance was developed. Although the system was oriented towards the surveillance of dengue fever, the methodology could also be applied to other relevant vector-borne diseases as well as other geographical regions in Latin America.

Determining fuel moisture thresholds to assess wildfire hazard: A contribution to an operational early warning system.

Fuel moisture content (FMC) is an important fuel property for assessing wildfire hazard, since it influences fuel flammability and fire behavior. The relationship between FMC and fire activity differs among land covers and seems to be a property of each ecosystem. Our objectives were to analyze pre-fire FMC among different land covers and to propose a wildfire hazard classification for the Sierras Chicas in the Chaco Serrano subregion (Argentina), by analyzing pre-fire FMC distributions observed for grasslands, shrublands and forests and using percentiles to establish thresholds. For this purpose, we used a fire database derived from Landsat imagery (30 m) and derived FMC maps every 8 days from 2002 to 2016 using MODIS reflectance products and empirical equations of FMC. Our results indicated that higher FMC constrains the extent of wildfires, whereas at lower FMC there are other factors affecting their size. Extreme and high fire hazard thresholds for grasslands were established at FMC of 55% and 67% respectively, at 72% and 105% for forests and at 106% and 121% for shrublands. Our FMC thresholds were sensitive to detect extreme fire hazard conditions during years with high fire activity in comparison to average conditions. The differences in the distributions of pre-fire FMC among land covers and between ecosystems highlighted the need to locally determine land cover-specific FMC thresholds to assess wildfire hazard. Our wildfire hazard classification applied to FMC maps in an operational framework will contribute to improving early warning systems in the Sierras Chicas. However, moisture alone is not sufficient to represent true fire hazard in Chaco forests and the combination with other variables would provide better hazard assessments. These operational wildfire hazard maps will help to better allocation of fire protective resources to minimize negative impact on people, property and ecosystems. To the best of our knowledge, this is the first study analyzing pre-fire FMC over several fire seasons in a non-Mediterranean ecosystem, aiming at assessing wildfire hazard.

Downscaling incidence risk mapping for a Colombian malaria endemic region.

OBJECTIVE: To map at a fine spatial scale, the risk of malaria incidence for the important endemic region is Urabá-Bajo Cauca and Alto Sinú, NW Colombia, using a new modelling framework based on GIS and remotely sensed environmental data. METHODS: The association between environmental and topographic variables obtained from remote sensors and the annual parasite incidence (API) for the years 2013-2015 was calculated using multiple regression analysis; subsequently, a model was constructed to estimate the API and to project it to the entire endemic region in order to design the risk map. The model was validated by relating the obtained API values with the presence of the three main Colombian malaria vectors, Anopheles darlingi, Anopheles albimanus and Anopheles nuneztovari. RESULTS: Temperature and Normalized Difference Water Index (NDWI) showed a significant correlation with the observed API. The risk map of malaria incidence showed that the zones at higher risk in the Urabá-Bajo Cauca and Alto Sinú region were located south-east of the region, while the northern area presented the lowest malaria risk. A method was generated to estimate the API for small urban centres, instead of the used reports at the municipality level. CONCLUSIONS: These results provide evidence of the utility of risk maps to identify environmentally vulnerable areas at a fine spatial resolution in the Urabá-Bajo Cauca and Alto Sinú region. This information contributes to the implementation of vector control interventions at the microgeographic scale at areas of high malaria risk.

Urban environmental clustering to assess the spatial dynamics of Aedes aegypti breeding sites.

The identification of Aedes aegypti breeding hotspots in urban areas is crucial for the rational design of control strategies against this disease vector. Remote sensing and geographic information systems offer valuable tools for mapping habitat suitability of a given area. However, predicting species occurrences by means of probability distribution maps based on transversal entomological surveys has limited utility for local authorities. The aim of the present study was to carefully examine the temporal evolution of the number of houses infested with immature stages of Ae. aegypti in each individual neighbourhood and to explore the value of producing environmental clusters generated with information provided by remotely sensed variables to explain the observed differential temporal behaviour. Entomological surveys were conducted between 2011 and 2013 throughout a small town in Argentina registering the number of houses with containers harbouring immature stages of Ae. aegypti. A SPOT 5 satellite image was used to obtain land cover variables, which were subsequently submitted to k-means partitioning for grouping neighbourhoods into four environmental clusters. Finally, a generalized linear model was fitted showing that the number of houses found to be positive for Ae. aegypti was jointly affected by the interaction between environmental clusters and the year of sampling. Moreover, the number of positive houses in one of the clusters was 9.5 times higher (P<0.005, SE=0.37) in 2013 than in 2012, but we did not observe any other statistically significant increases.

Temporal Dynamics and Spatial Patterns of Aedes aegypti Breeding Sites, in the Context of a Dengue Control Program in Tartagal (Salta Province, Argentina).

BACKGROUND: Since 2009, Fundación Mundo Sano has implemented an Aedes aegypti Surveillance and Control Program in Tartagal city (Salta Province, Argentina). The purpose of this study was to analyze temporal dynamics of Ae. aegypti breeding sites spatial distribution, during five years of samplings, and the effect of control actions over vector population dynamics. METHODOLOGY/PRINCIPAL FINDINGS: Seasonal entomological (larval) samplings were conducted in 17,815 fixed sites in Tartagal urban area between 2009 and 2014. Based on information of breeding sites abundance, from satellite remote sensing data (RS), and by the use of Geographic Information Systems (GIS), spatial analysis (hotspots and cluster analysis) and predictive model (MaxEnt) were performed. Spatial analysis showed a distribution pattern with the highest breeding densities registered in city outskirts. The model indicated that 75% of Ae. aegypti distribution is explained by 3 variables: bare soil coverage percentage (44.9%), urbanization coverage percentage(13.5%) and water distribution (11.6%). CONCLUSIONS/SIGNIFICANCE: This results have called attention to the way entomological field data and information from geospatial origin (RS/GIS) are used to infer scenarios which could then be applied in epidemiological surveillance programs and in the determination of dengue control strategies. Predictive maps development constructed with Ae. aegypti systematic spatiotemporal data, in Tartagal city, would allow public health workers to identify and target high-risk areas with appropriate and timely control measures. These tools could help decision-makers to improve health system responses and preventive measures related to vector control.

Spatial patterns of high Aedes aegypti oviposition activity in northwestern Argentina.

BACKGROUND: In Argentina, dengue has affected mainly the Northern provinces, including Salta. The objective of this study was to analyze the spatial patterns of high Aedes aegypti oviposition activity in San Ramón de la Nueva Orán, northwestern Argentina. The location of clusters as hot spot areas should help control programs to identify priority areas and allocate their resources more effectively. METHODOLOGY: Oviposition activity was detected in Orán City (Salta province) using ovitraps, weekly replaced (October 2005-2007). Spatial autocorrelation was measured with Moran's Index and depicted through cluster maps to identify hot spots. Total egg numbers were spatially interpolated and a classified map with Ae. aegypti high oviposition activity areas was performed. Potential breeding and resting (PBR) sites were geo-referenced. A logistic regression analysis of interpolated egg numbers and PBR location was performed to generate a predictive mapping of mosquito oviposition activity. PRINCIPAL FINDINGS: Both cluster maps and predictive map were consistent, identifying in central and southern areas of the city high Ae. aegypti oviposition activity. A logistic regression model was successfully developed to predict Ae. aegypti oviposition activity based on distance to PBR sites, with tire dumps having the strongest association with mosquito oviposition activity. A predictive map reflecting probability of oviposition activity was produced. The predictive map delimitated an area of maximum probability of Ae. aegypti oviposition activity in the south of Orán city where tire dumps predominate. The overall fit of the model was acceptable (ROC=0.77), obtaining 99% of sensitivity and 75.29% of specificity. CONCLUSIONS: Distance to tire dumps is inversely associated with high mosquito activity, allowing us to identify hot spots. These methodologies are useful for prevention, surveillance, and control of tropical vector borne diseases and might assist National Health Ministry to focus resources more effectively.

Oviposición diaria de Aedes aegypti en Orán, Salta, Argentina / Daily oviposition activity of Aedes aegypti in Orán, Argentina / Oviposição diária de Aedes aegypti em Orán, Salta (Argentina)

El objetivo del estudio fue determinar los horarios de máxima actividad de oviposición de Aedes aegypti en la ciudad de Orán (noroeste argentino). Se realizaron muestreos quincenales entre noviembre de 2006 y febrero de 2007 (primavera-verano). La ciudad fue dividida en tres zonas (norte, centro, sur), donde fueron seleccionadas al azar las viviendas para colocar dos ovitrampas en el exterior de las mismas. Las ovitrampas fueron reemplazadas cada cuatro horas, desde la mañana (8h) hasta el atardecer (20h). Se registró la mayor cantidad de huevos entre las 16 y 20 h (81 por ciento). Estos datos aportan al conocimiento del vector y, por lo tanto, de la posibilidad de control, pudiendo realizarse las fumigaciones en la franja horaria con mayor actividad de oviposición.

The study aimed to determinate the maximum daily peak of Aedes aegypti oviposition in the city of Oran, northwestern Argentina. Biweekly samplings were taken between November 2006 and February 2007 (spring-summer). The city was divided into three areas (north, center, and south) and households were randomly selected. Two ovitraps were placed outdoors in the selected houses. Ovitraps were replaced every four hours, from morning (8 a.m.) to late afternoon (8 p.m.). The largest number of eggs was recorded between 4 p.m. and 8 p.m. (81 percent). These findings enhance our understanding of the vector and thus its control such as spraying during the hours of peak oviposition activity.

O objetivo do estudo foi determinar os horários de máxima atividade de oviposição de Aedes aegypti na cidade de Orán (noroeste argentino). Foram realizadas coletas quinzenais, entre novembro de 2006 e fevereiro de 2007 (primavera-verão). A cidade foi dividida em três zonas (norte, centro, sul); foram escolhidos aleatoriamente dez domicílios em cada zona e duas ovitrampas foram instaladas no exterior de cada domicílio. As ovitrampas eram trocadas a cada quatro horas, cobrindo desde a manhã (8h) até a noite (20h). A maior quantidade de ovos (81 por cento) foi registrada entre as 16h e 20h. Esses dados aportam conhecimento do vetor e, portanto, a seu controle, como, por exemplo, realizar a fumegação na faixa horária de maior atividade de oviposição.

Daily oviposition activity of Aedes aegypti in Orán, Argentina.

The study aimed to determinate the maximum daily peak of Aedes aegypti oviposition in the city of Oran, northwestern Argentina. Biweekly samplings were taken between November 2006 and February 2007 (spring-summer). The city was divided into three areas (north, center, and south) and households were randomly selected. Two ovitraps were placed outdoors in the selected houses. Ovitraps were replaced every four hours, from morning (8 a.m.) to late afternoon (8 p.m.). The largest number of eggs was recorded between 4 p.m. and 8 p.m. (81%). These findings enhance our understanding of the vector and thus its control such as spraying during the hours of peak oviposition activity.