RESUMEN
Cutaneous Leishmaniasis (CL) is a vector-borne disease caused by a protozoan of the genus Leishmania and is considered one of the most important neglected tropical diseases. The Brazilian Amazon Forest harbors one of the highest diversity of Leishmania parasites and vectors and is one of the main focuses of the disease in the Americas. Previous studies showed that some types of anthropogenic disturbances have affected the abundance and distribution of CL vectors and hosts; however, few studies have thoroughly investigated the influence of different classes of land cover and land-use changes on the disease transmission risk. Here, we quantify the effect of land use and land-cover changes on the incidence of CL in all municipalities within the Brazilian Amazon Forest, from 2001 to 2017. We used a structured spatiotemporal Bayesian model to assess the effect of forest cover, agriculture, livestock, extractivism, and- deforestation on CL incidence, accounting for confounding variables such as population, climate, socioeconomic, and spatiotemporal random effects. We found that the increased risk of CL was associated with deforestation, especially modulated by a positive interaction between forest cover and livestock. Landscapes with ongoing deforestation for extensive cattle ranching are typically found in municipalities within the Amazon Frontier, where a high relative risk for CL was also identified. These findings provide valuable insights into developing effective public health policies and land-use planning to ensure healthier landscapes for people.
Asunto(s)
Teorema de Bayes , Conservación de los Recursos Naturales , Bosques , Leishmaniasis Cutánea , Brasil/epidemiología , Leishmaniasis Cutánea/epidemiología , Incidencia , Animales , Agricultura , Humanos , Análisis Espacio-TemporalRESUMEN
OBJECTIVES: This study aimed to spatiotemporally analyze the profile of influenza-like illness (ILI) outbreaks in the state of São Paulo, Brazil, between 2020 and 2022. STUDY DESIGN: This was a cross-sectional retrospective study. METHODS: Outbreaks of ILI with final diagnoses of COVID-19, influenza, or other respiratory viruses (ORVs) recorded between January 2020 and November 2022, obtained from the Notifiable Diseases Information System (SINAN NET) Outbreak module, were analyzed. Kernel density estimates and Getis-Ord Gi∗ statistics were performed to identify spatial clusters. RESULTS: A total of 13,314 ILI outbreaks were identified, involving 130,568 cases and 2649 deaths. Of these, 104,399 (80%) were confirmed as COVID-19, 15,861 (12%) were confirmed as ORV, and 10,308 (8%) were confirmed as influenza. The year 2021 had the highest number of outbreaks and cases. Schools recorded the most outbreaks and cases, followed by long-term care facilities for older adults (LTCs). The highest average number of cases per outbreak and the highest attack rates occurred at social gatherings and prisons. Prisoners were three times more likely to contract COVID-19 during outbreaks than people in other institutions. The highest hospitalization and mortality rates for all virus types occurred in the LTC group. The occurrence and intensity of outbreaks were highly heterogeneous among the different institutions after the introduction of new SARS-CoV-2 variants in the state. CONCLUSIONS: ILI outbreaks were not randomly distributed; they clustered in specific areas. Transmissibility varied among different institutions with different responses to the COVID-19 pandemic. These results can be used as a basis for prioritizing actions and allocating resources during future pandemics.