Universidad de Buenos Aires, Facultad de Ciencias Veterinarias, Cátedra de Parasitología y Enfermedades Parasitarias, Av. San Martín 5285 (1417DSM), Buenos Aires, Argentina; CONICET - Universidad de Buenos Aires, Facultad de Ciencias Veterinarias, Instituto de Investigaciones en Producción Animal (INPA), Buenos Aires, Argentina. Electronic address: mribicich@fvet.uba.ar.
Universidad de Buenos Aires, Facultad de Ciencias Veterinarias, Cátedra de Parasitología y Enfermedades Parasitarias, Av. San Martín 5285 (1417DSM), Buenos Aires, Argentina; CONICET - Universidad de Buenos Aires, Facultad de Ciencias Veterinarias, Instituto de Investigaciones en Producción Animal (INPA), Buenos Aires, Argentina.
Universidad de Buenos Aires, Facultad de Ciencias Veterinarias, Cátedra de Parasitología y Enfermedades Parasitarias, Av. San Martín 5285 (1417DSM), Buenos Aires, Argentina; Servicio Nacional de Sanidad Animal, SENASA, Argentina.
Universidad de Buenos Aires, Facultad de Ciencias Veterinarias, Cátedra de Parasitología y Enfermedades Parasitarias, Av. San Martín 5285 (1417DSM), Buenos Aires, Argentina; CONICET - Universidad de Buenos Aires, Facultad de Ciencias Veterinarias, Instituto de Investigaciones en Producción Animal (INPA), Buenos Aires, Argentina.
Universidad de Buenos Aires, Facultad de Ciencias Veterinarias, Cátedra de Parasitología y Enfermedades Parasitarias, Av. San Martín 5285 (1417DSM), Buenos Aires, Argentina; CONICET - Universidad de Buenos Aires, Facultad de Ciencias Veterinarias, Instituto de Investigaciones en Producción Animal (INPA), Buenos Aires, Argentina.
Universidad de Buenos Aires, Facultad de Ciencias Veterinarias, Cátedra de Parasitología y Enfermedades Parasitarias, Av. San Martín 5285 (1417DSM), Buenos Aires, Argentina; CONICET - Universidad de Buenos Aires, Facultad de Ciencias Veterinarias, Instituto de Investigaciones en Producción Animal (INPA), Buenos Aires, Argentina.
Department of Preventive Medicine and Public Health, University of Santiago de Compostela, Santiago de Compostela, Spain.
Department of Public Health, Health Promotion and Disease Prevention Research Group (Grupo de Investigación Promoción de la Salud y Prevención de la Enfermedad - GIPSPE), Universidad de Caldas, Manizales, Colombia.
Postgraduate Program in Animal Science in the Tropics, Federal University of Bahia, Salvador, Bahia, CEP: 40170-110, Brazil. Electronic address: franklinrietcorrea@gmail.com.
Universidad Católica de Salta. Facultad de ciencias agrarias y veterinarias; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET); Área de Sanidad Animal "Dr. Bernardo Jorge Carrillo"-Instituto de Investigación Animal Chaco Semiárido (Sede Salta) CIAP-INTITUTO NACIONAL DE TECNOLOGIA AGROPECUARIA. (INTA).
Plataforma de Investigación en Salud Animal (PSA), Instituto Nacional de Investigación Agropecuaria (INIA), Estación Experimental del Norte, Tacuarembó, 45000, Uruguay.
Laboratorio de Anatomía Comparada y Evolución de los Vertebrados, Museo Argentino de Ciencias Naturales "Bernardino Rivadavia" (CONICET); Av. Ángel Gallardo 470, C1405DJR Ciudad Autónoma de, Buenos Aires, Argentina. nicochimento@hotmail.com.
Laboratorio de Anatomía Comparada y Evolución de los Vertebrados, Museo Argentino de Ciencias Naturales "Bernardino Rivadavia" (CONICET); Av. Ángel Gallardo 470, C1405DJR Ciudad Autónoma de, Buenos Aires, Argentina.
Fundación de Historia Natural "Félix de Azara", Departamento de Ciencias Naturales y Antropología, CEBBAD - Universidad Maimónides, Hidalgo 767, C1405BDB, Buenos Aires, Argentina.
Laboratorio de Anatomía Comparada y Evolución de los Vertebrados, Museo Argentino de Ciencias Naturales "Bernardino Rivadavia" (CONICET); Av. Ángel Gallardo 470, C1405DJR Ciudad Autónoma de, Buenos Aires, Argentina.
Spatially referenced data arise in many fields, including imaging, ecology, public health, and marketing. Although principled smoothing or interpolation is paramount for many practitioners, regression...
The study contextualises the spatial heterogeneity and associated drivers of open defecation (OD) in India....
The present study involved a secondary cross-sectional survey data from the fifth round of the National Family Health Survey conducted during 2019-2021 in India. We mapped the spatial heterogeneity of...
The study was conducted in India and included 636 699 sampled households within 36 states and union territories covering 707 districts of India....
The outcome measure was the prevalence of OD....
The prevalence of OD was almost 20%, with hot spots primarily located in the north-central belts of the country. The rural-urban (26% vs 6%), illiterate-higher educated (32% vs 4%) and poor-rich (52% ...
The practice of OD is concentrated in the north-central belt of India and is particularly among the poor, illiterate and socially backward groups. Policy measures should be taken to improve sanitation...
The World Health Organization (WHO) encourages breastfeeding to begin within the first hour after birth in order to save children's lives. In Ethiopia, different studies are done on the prevalence and...
A cross-sectional study was undertaken using the nationally representative 2016 Ethiopian Demographic and Health Survey (EDHS) dataset. Global Moran's I statistic was used to measure whether delayed b...
A total weighted sample of 4169 children of aged 0 to 23 months was included in this study. Delayed initiation of breastfeeding was spatially varies across the country with a global Moran's I value of...
In Ethiopia initiation of breastfeeding varies geographically across region. A significant hotspot was identified in the Amhara, Afar, and Tigray regions. The GWR analysis revealed that orthodox relig...
In developing countries, the death probability of a child and mother is more significant than in developed countries; these inequalities in health outcomes are unfair. The present study encompasses a ...
This study used micro-level household datasets from multiple indicator cluster surveys (MICS) to estimate the DMI. To find out how different the DMI scores were, the inequality ratio and slope were us...
The inequality ratio for DMI showed that the upper decile districts are 16 times more prone to mortalities than districts in the lower decile, and the districts of Baluchistan depicted extreme spatial...
The findings reveal a significant disparity in DMI and spatial relationships among all mortalities in Pakistan's districts. Additionally, socioeconomic, environmental, health, and housing variables ha...
Intensive human activities caused massive socio-economic and land-use changes that directly or indirectly resulted in excessive accumulation of heavy metals in agricultural soils. The goal of our stud...
This study explored the environmental determinants of different months on snail density measured in April at different types of snail habitats (marshlands, inner embankments, and hills) by considering...
Suicide mortality remains a global health concern, and community characteristics affect regional variations in suicide. This study investigated spatially clustered patterns of suicide mortality rates ...
Suicide mortality rates were estimated by sex, age group, and district, using the 2021 Cause of Death Statistics in South Korea from the MicroData Integrated Service. Community-determinant data for 20...
Suicide mortality rates were significantly higher among men (40.64 per 100,000) and adults over the age of 65 years (43.18 per 100,000). The male suicide mortality rates exhibited strong spatial depen...
Community cultural and structural factors exacerbate regional disparities in suicide among men. The influencing factors exhibit differential effects and significance depending on the community, highli...
To explore the complex spatial pattern between the incidence of hand, foot, and mouth disease (HFMD) and meteorological factors [average temperature (AT), average relative humidity (ARH), average air ...
This study investigated the relationship between demographic, healthcare, and socio-economic factors, and COVID-19 incidence rate per 100,000 population in Thailand at the province level between Janua...
Diabetes and its complications represent a significant public health burden in the United States. Some communities have disproportionately high risks of the disease. Identification of these disparitie...
Behavioral Risk Factor Surveillance System data for 2013 and 2016 were provided by the Florida Department of Health. Tests for equality of proportions were used to identify counties with significant c...
There was a small but significant increase in the prevalence of diabetes in Florida (10.1% in 2013 to 10.4% in 2016), and statistically significant increases in prevalence occurred in 61% (41/67) of c...
The persistent geographic disparities of diabetes prevalence and temporal increases identified in this study are concerning. There is evidence that the impacts of the determinants on diabetes risk var...