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Background: Intestinal infectious diseases are a global concern in terms of morbidity, and they are closely linked to socioeconomic variables such as quality of life, weather and access to healthcare services. Despite progress in spatial analysis tools and geographic information systems in epidemiology, studies in Ecuador that evaluate temporal trends, specific geographic groups, and their correlation with socioeconomic variables are lacking. The absence of such information makes it challenging to formulate public health policies. This study sought to identify the spatial and temporal patterns of these diseases in Ecuador, along with their correlation with socioeconomic variables. Methods: In Ecuador, the study was carried out in a continental territory, focusing on data related to intestinal infectious diseases collected from the National Institute of Statistics and Census (Instituto Nacional de Estadística y Censos) during the period from 2014 to 2019. This study involved spatial and temporal analyses using tools such as the global Moran's index and Local Indicators of Spatial Association to identify spatial clustering patterns and autocorrelation. Additionally, correlations between morbidity rates and socioeconomic variables were examined. Results: During the investigated period, Ecuador registered 209,668 cases of these diseases. Notable variations in case numbers were identified, with a 9.2% increase in 2019 compared to the previous year. The most impacted group was children under 5 years old, and the highest rates were centered in the southern and southwestern regions of the country, with Limón Indanza and Chunchi being the cantons with the highest rates, notably showing a significant increase in Limón Indanza. Additionally, there were significant correlations between morbidity rates and socioeconomic variables, school dropout rates, low birth weight, and access to water services. Conclusion: This study emphasizes the importance of considering socioeconomic variables when addressing these diseases in Ecuador. Understanding these correlations and geospatial trends can guide the development of health policies and specific intervention programs to reduce the incidence in identified high-risk areas. More specific research is needed to understand the underlying causes of variability in morbidity and develop effective prevention strategies.

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AbstractExplaining diversity in tropical forests remains a challenge in community ecology. Theory tells us that species differences can stabilize communities by reducing competition, while species similarities can promote diversity by reducing fitness differences and thus prolonging the time to competitive exclusion. Combined, these processes may lead to clustering of species such that species are niche differentiated across clusters and share a niche within each cluster. Here, we characterize this partial niche differentiation in a tropical forest in Panama by measuring spatial clustering of woody plants and relating these clusters to local soil conditions. We find that species were spatially clustered and the clusters were associated with specific concentrations of soil nutrients, reflecting the existence of nutrient niches. Species were almost twice as likely to recruit in their own nutrient niche. A decision tree algorithm showed that local soil conditions correctly predicted the niche of the trees with up to 85% accuracy. Iron, zinc, phosphorus, manganese, and soil pH were among the best predictors of species clusters.

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Rabies, a globally distributed and highly lethal zoonotic neglected tropical disease, has a significant impact in South America. In Ecuador, animal rabies cases are primarily linked to livestock, and hematophagous bats play a crucial role in disease transmission. This study aims to identify temporal trends, spatial patterns, and risk factors for animal rabies in Ecuador between 2014 and 2019. Epidemiological survey reports from the official Animal Rabies Surveillance Program of the Phyto and Zoosanitary Regulation and Control Agency of Ecuador (AGROCALIDAD) were used. The Animal Rabies Surveillance Program from AGROCALIDAD consists of an official passive surveillance program that receives reports from farmers or individuals (both trained or untrained) who have observed animals with neurological clinical signs and lesions compatible with bat bites, or who have seen or captured bats on their farms or houses. Once this report is made, AGROCALIDAD personnel is sent for field inspection, having to confirm the suspicion of rabies based on farm conditions and compatibility of signs. AGROCALIDAD personnel collect samples from all suspicious animals, which are further processed and analyzed using the Direct Fluorescent Antibody (DFA) test for rabies confirmatory diagnosis. In this case, study data comprised 846 bovine farms (with intra-farm sample sizes ranging from 1 to 16 samples) located in different ecoregions of Ecuador; out of these, 397 (46.93%) farms tested positive for animal rabies, revealing six statistically significant spatial clusters. Among these clusters, three high-risk areas were identified in the southeast of Ecuador. Seasonality was confirmed by the Ljung-Box test for both the number of cases (p < 0.001) and the positivity rate (p < 0.001). The Pacific Coastal lowlands and Sierra regions showed a lower risk of positivity compared to Amazonia (OR = 0.529; 95% CI = 0.318 - 0.883; p = 0.015 and OR = 0.633; 95% CI = 0.410 - 0.977; p = 0.039, respectively). The breeding of non-bovine animal species demonstrated a lower risk of positivity to animal rabies when compared to bovine (OR = 0.145; 95% CI = 0.062 - 0.339; p < 0.001). Similarly, older animals exhibited a lower risk (OR = 0.974; 95% CI = 0.967 - 0.981; p < 0.001). Rainfall during the rainy season was also found to decrease the risk of positivity to animal rabies (OR = 0.996; 95% CI = 0.995 - 0.998; p < 0.001). This study underscores the significance of strengthening the national surveillance program for the prevention and control of animal rabies in Ecuador and other countries facing similar epidemiological, social, and geographical circumstances.

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Objectives: Our aim was to test if machine learning algorithms can predict cancer mortality (CM) at an ecological level and use these results to identify statistically significant spatial clusters of excess cancer mortality (eCM). Methods: Age-standardized CM was extracted from the official databases of Brazil. Predictive features included sociodemographic and health coverage variables. Machine learning algorithms were selected and trained with 70% of the data, and the performance was tested with the remaining 30%. Clusters of eCM were identified using SatScan. Additionally, separate analyses were performed for the 10 most frequent cancer types. Results: The gradient boosting trees algorithm presented the highest coefficient of determination (R 2 = 0.66). For total cancer, all algorithms overlapped in the region of Bagé (27% eCM). For esophageal cancer, all algorithms overlapped in west Rio Grande do Sul (48%-96% eCM). The most significant cluster for stomach cancer was in Macapá (82% eCM). The most important variables were the percentage of the white population and residents with computers. Conclusion: We found consistent and well-defined geographic regions in Brazil with significantly higher than expected cancer mortality.

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CONTEXT: Studies that analyze the temporal trend and spatial clustering of medical education indicators are scarce, especially in developing countries such as Brazil. This analysis is essential to subsidize more equitable policies for the medical workforce in the states and regions of Brazil. Thus, this study aimed to analyze the temporal trend and identify spatial clusters of medical education indicators in Brazil disaggregated by public and private education, states, and regions. METHODS: A time-series ecological study was conducted using data from the Higher Education Census of the Ministry of Education from 2010 to 2021. The study analyzed vacancy density indicators of active and former students/100,000 population, disaggregated by public and private education, 27 states, and 5 regions in Brazil. Prais-Winsten regression was used for trend analyses of indicators. Hot Spot Analysis (Getis-Ord Gi*) was used to identify spatial clusters of indicators. RESULTS: The number of medical schools increased by 102.2% between 2010 and 2021. A total of 366 medical schools offered 54,870 vacancies at the end of 2021. Vacancy density and active and former students increased significantly in the period, but this increase was greater in private institutions. Most states and regions showed an increasing trend in the indicators, with higher increase percentages in private than in public schools. Hot spot spaces changed over time, concentrated in the southeast, center-west, and north at the end of 2021. Medical education remains uneven in Brazil, with a low provision in regions with low socioeconomic development, academic structure, and health services, represented by regions in the north and northeast. CONCLUSIONS: There is a growing trend in medical education indicators in Brazil, especially in the private sector. Spatial clusters were found predominantly in the southeast, center-west, and north. These results indicate the need for more equitable medical education planning between the regions.

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OBJECTIVE: This study aimed to investigate the spatial clusters of high and low COVID-19 vaccination rates among children and adolescents across Brazilian municipalities and their relationship to social determinants of health. STUDY DESIGN: This is a nationwide population-based ecological study. METHODS: We have obtained for each of the 5570 Brazilian municipalities data on the COVID-19 vaccination rate of children and adolescents by August 16, 2022, the Gini index, the social vulnerability index and the municipal human development index. A Bayesian empirical local model was used to identify fluctuations in the COVID-19 vaccination rates. Spatial clusters were identified using scan spatial statistic tests. The relationship among COVID-19 vaccination rates and social determinants of health was explored by using multiple linear regression models. RESULTS: Overall, 52.1% of children aged 5-11 years and 72.8% of adolescents aged 12-17 years have been fully vaccinated against COVID-19 in Brazil by mid-August 2022. There was spatial dependence on the smoothed rates for both children (I Moran 0.66; P < 0.001) and adolescent (I Moran 0.65; P < 0.001) groups. The lowest rates occurred in municipalities in the North and Northeast regions. Municipalities with a higher Gini Index, higher social vulnerability index and lower municipal human development index were more likely to have a lower COVID-19 vaccination rate for both children and adolescent groups. CONCLUSION: COVID-19 vaccination of children and adolescents was heterogeneously distributed, with spatial clusters of the lowest vaccination rates occurring mainly in municipalities with marked socio-economic disparities and social vulnerability, especially in the North and Northeast regions.

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The infant mortality rate (IMR) is still a key indicator in a middle-income country such as Ecuador where a slightly increase up to 11.75 deaths per thousand life births has been observed in 2019. The purpose of this study is to propose and apply a prioritization method that combines clusters detection (Local Indicators of Spatial Association, LISA) and a monotonic statistic depicting time trend over 10 years (Mann-Kendall) at municipal level. Annual national databases (2010 to 2019) of live births and general deaths are downloaded from National Institute of Statistics and Censuses (INEC). The results allow identifying a slight increase in the IMR at the national level from 9.85‰ in 2014 to 11.75‰ in 2019, neonatal mortality accounted for 60% of the IMR in the last year. The LISA analysis allowed observing that the high-high clusters are mainly concentrated in the central highlands. At the local level, Piñas, Cuenca, Ibarra and Babahoyo registered the highest growth trends (0.7,1). The combination of techniques made it possible to identify eight priority counties, half of them pertaining to the highlands region, two to the coastal region and two to the Amazon region. To keep infant mortality at a low level is necessary to prioritize critical areas where public allocation of funds should be concentrated and formulation of policies.

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This research is framed in the area of biomathematics and contributes to the epidemiological surveillance entities in Colombia to clarify how breast cancer mortality rate (BCM) is spatially distributed in relation to the forest area index (FA) and circulating vehicle index (CV). In this regard, the World Health Organization has highlighted the scarce generation of knowledge that relates mortality from tumor diseases to environmental factors. Quantitative methods based on geospatial data science are used with cross-sectional information from the 2018 census; it's found that the BCM in Colombia is not spatially randomly distributed, but follows cluster aggregation patterns. Under multivariate modeling methods, the research provides sufficient statistical evidence in terms of not rejecting the hypothesis that if a spatial unit has high FA and low CV, then it has significant advantages in terms of lower BCM.

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Equine infectious anemia (EIA) is a viral infection, caused by a lentivirus of the Retroviridae family, Orthoretrovirinael subfamily and its occurrence generates significant economic losses due to culling of positive animals as a measure of infection control. The objective of this work was to determine the prevalence of horses positive for equine infectious anemia virus (EIAV) and to identify the occurrence of areas with higher densities of cases in the states of Paraíba (PB), Pernambuco (PE), Rio Grande do Norte (RN) and Ceará (CE), Northeast region of Brazil, during the rainy (May and June) and dry (October and November) periods of 2017 and 2018. Serum samples from 6,566 horses from the states of PB, PE, RN and CE, Brazil, provided by the Laboratório Veterinária Diagnóstico - Ltda., were used. Serological diagnosis of EIA was performed using indirect enzyme-linked immunosorbent assay (ELISA) as a screening test and agar gel immunodiffusion test (AGID) as a confirmatory test. The apparent prevalence was obtained by dividing the number of seroreactive animals by the total number of animals, while the true prevalence was estimated by adjusting the apparent prevalence, considering the sensitivity (100%) and specificity (98.6%) of the diagnostic protocol used. For the construction of Kernel estimates, the Quartic function was used. In the dry season, of the 1,564 animals sampled, 28 were serologically positive, of which 19 belonged to the state of Ceará, 7 to Paraíba and 2 to Rio Grande do Norte. In 2018, it was observed that, during the rainy season, 26 of the 1,635 horses were seroreactive, with 19 cases resulting from Ceará, 4 from Paraíba and 3 from Pernambuco. In the dry season, 32 of the 1,526 animals were seroreactive to EIAV, of which 26 were from Ceará, 3 from Paraíba, 1 from Rio Grande do Norte and 2 from Pernambuco. In the dry period of 2017, the CE had a real prevalence of 1.22% (95% CI = 0.05 - 2.99%). In 2018, during the rainy season, prevalences of 0.03% (95% CI = 0 - 1.18%) were identified in CE and 1.69% (95% CI = 0 - 8.38%) in PE. Regarding the 2018 dry period, a prevalence of 1.32% (95% CI = 0.26 - 2.84%) was found in the state of CE. In both dry and rainy periods of 2017, the presence of spatial clusters of animals positive for EIA was observed, mainly in the border areas among the states of CE, PE, PB and RN. In 2018, there was a variation in the distribution of areas with higher densities of cases between the rainy and dry periods. The state of CE had the highest prevalence of positive animals and the presence of areas with higher densities of EIA cases in both climatic periods, in the years 2017 and 2018. In some municipalities of the CE, important sporting events of agglomeration of animals take place, which can favor the transmission of EIAV by facilitating the contact of infected and susceptible animals. Population density may be a factor associated with the higher prevalence observed in this region, as it has the second largest herd among the states studied. Higher densities indirectly contribute to the occurrence of infectious diseases, as they favor the contact of infected and susceptible animals. The occurrence of higher densities of cases in the border areas of the states of PE, RN, CE, and PB may be related to the greater movement of animals in these regions, favoring the indirect contact of infected horses with susceptible ones. The observed results demonstrate the circulation of the EIAV in four states in the Northeast region of Brazil.(AU)

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[This corrects the article DOI: 10.3389/fvets.2020.00562.].

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Cryptosporidium parvum, a major cause of diarrhea in calves, is of concern given its zoonotic potential. Numerous outbreaks of human cryptosporidiosis caused by C. parvum genetic subtypes are reported yearly worldwide, with livestock or water being frequently identified sources of infection. Although cryptosporidiosis has been reported from human patients in Uruguay, particularly children, epidemiologic information is scant and the role of cattle as reservoirs of zoonotic subtypes of C. parvum has not been explored. In this study, we aimed to (a)-identify C. parvum subtypes infecting dairy calves in Uruguay (including potentially zoonotic subtypes), (b)-assess their association with calf diarrhea, (c)-evaluate their spatial clustering, and (d)-assess the distance of infected calves to surface watercourses draining the farmlands and determine whether these watercourses flow into public water treatment plants. Feces of 255 calves that had tested positive for Cryptosporidium spp. by antigen ELISA were selected. Samples had been collected from 29 dairy farms in seven Uruguayan departments where dairy farming is concentrated and represented 170 diarrheic and 85 non-diarrheic calves. Selected samples were processed by nested PCRs targeting the 18S rRNA and gp60 genes followed by sequencing to identify C. parvum subtypes. Of seven C. parvum subtypes detected in 166 calves, five (identified in 143 calves on 28/29 farms) had been identified in humans elsewhere and have zoonotic potential. Subtype IIaA15G2R1 was the most frequent (53.6%; 89/166), followed by IIaA20G1R1 (24.1%; 40/166), IIaA22G1R1 (11.4%; 19/166), IIaA23G1R1 (3.6%; 6/166), IIaA17G2R1 (3%; 5/166), IIaA21G1R1 (2.4%; 4/166), and IIaA16G1R1 (1.8%; 3/166). There were no significant differences in the proportions of diarrheic and non-diarrheic calves infected with any of the C. parvum subtypes. Two spatial clusters were detected, one of which overlapped with Uruguay's capital city and its main water treatment plant (Aguas Corrientes), harvesting surface water to supply ~1,700,000 people. Infected calves on all farms were within 20-900 m of a natural surface watercourse draining the farmland, 10 of which flowed into six water treatment plants located 9-108 km downstream. Four watercourses flowed downstream into Aguas Corrientes. Calves are reservoirs of zoonotic C. parvum subtypes in Uruguay and pose a public health risk.

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This study examines the spatial structure of children with cleft lip and palate (CLP) and its association with polluted areas in the Monterrey Metropolitan Area (MMA). The Nearest Neighbor Index (NNI) and the Spatial Statistical Scan (SaTScan) determined that the CLP cases are agglomerated in spatial clusters distributed in different areas of the city, some of them grouping up to 12 cases of CLP in a radius of 1.2 km. The application of the interpolation by empirical Bayesian kriging (EBK) and the inverse distance weighted (IDW) method showed that 95% of the cases have a spatial interaction with values of particulate matter (PM10) of more than 50 points. The study also shows that 83% of the cases interacted with around 2000 annual tons of greenhouse gases. This study may contribute to other investigations applying techniques for the identification of environmental and genetic factors possibly associated with congenital malformations and for determining the influence of contaminating substances in the incidence of these diseases, particularly CLP.

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The growing number of cargo trucks on highway crashes in recent years due to the increase in freight movement in Chile motivates this study to identify the formation of persistent crash clusters on highway Ruta 5 (R5). Two spatial statistical methods (Moran's I and Getis-Ord Gi*) were used to determine whether crashes on this highway showed spatial clustering over time from a global and local perspective. Globally, recurrent crash clusters are spatially correlated on vertical curves and straight highway sections on northern R5 with different truck types and with the tractor-trailer units during rainy days on southern R5. The local spatial autocorrelation results suggest that the contributing causes related to the loss of control of the vehicle, the fatigue and imprudence of the driver, and crashes involving tractor units with trailer tend to cause persistent rollover crash clusters throughout R5. Overall, clustering of crash attributes with high values (i.e., hot spots) occurring on highway locations with vertical curves and on cloudy days predominated in the northern R5, and the largest number of recurrent hot spots occurred on sunny days along southern R5. A hot spot spatial co-occurrence analysis was further performed to identify the strong relationships between the studied crash attributes, and the crash and injury types as outcomes. The indication of high risk for the clustering of cargo trucks on highways crashes provides a basis for improving highway safety and reduce the associated social and economic costs.

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