Spotted Fever in the Morphoclimatic Domains of Minas Gerais State, Brazil - de Carvalho Nunes, Emília; de Moura-Martiniano, Nicole Oliveira; de Lima Duré, Ana Íris; de Melo Iani, Felipe Campos; de Oliveira, Stefan Vilges; de Mello, Flávio Luis; Gazêta, Gilberto Salles (Frontiers in Tropical Diseases, 2022)
In Brazil, the tick-borne rickettsiosis known as Spotted Fever (SF) has been recorded from 59% of the Federative Units, however, the knowledge of the epidemiology and dynamics of human infection remains incipient in certain areas, complicating appropriate public health actions to inform the general population and control the disease. Here, we improved the interpretation of epidemiological information of SF cases recorded for an important endemic area. A descriptive epidemiological study was carried out based on records in the SINAN (Notifiable Diseases Information System) SF case databases. Data analysis was performed using Python programming language, Pandas library and Qgis map making. To evaluate the sociodemographic, clinical, assistance, laboratory and epidemiological characteristics, simple and relative nominal values of occurrences, means and standard deviations, and molecular analyzes were performed to identify the bioagent present in biological samples collected during each case investigation. Of the 298 confirmed cases, 98 resulted in death, the number of cases increased from 2011, and the disease scenario had 32.8% lethality. Overall, 207 cases involved men, and lethality was higher in this group. The most affected age group was 30 to 59 years old. The majority of patients reported having had contact with animals such as ticks, capybara and domestic animals such as dogs and cats. The results corroborate existing studies in areas of severe SF cases in Brazil. Despite reports of SF cases from the Cerrado Biome, analyses show that serious cases occur in anthropized areas of the Atlantic Forest biome, and in a transition area between this and the Cerrado. Complex, longitudinal, multidisciplinary studies, with an eco-epidemiological focus, should be carried out to allow the construction of algorithms capable of predicting, in time and space, the risk factors associated with severe cases and deaths from SF, with the aim of avoiding their expansion.