Literature DB >> 32244954

Dengue Surveillance System in Brazil: A Qualitative Study in the Federal District.

Marco Angelo1,2, Walter Massa Ramalho2, Helen Gurgel3, Nayara Belle1,3, Eva Pilot1,4.   

Abstract

Dengue's increasing trends raise concerns over global health and pose a challenge to the Brazilian health system, highlighting the necessity of a strong surveillance system to reduce morbidity, mortality, and the economic burden of this disease. Although the Brazilian surveillance system reports more dengue cases than any other country, recent studies suggest that non-reported cases are the majority. The aim of the study is to explore the strengths and weaknesses of the Brazilian surveillance system, particularly looking at the functioning of data collection and reporting. This was done through qualitative semi-structured interviews with 17 experts in dengue surveillance, supported by quantitative data from the official notification system. To select the interviewees, purposive and theoretical sampling were used. Data were analyzed through thematic analysis. The research highlighted that a lack of human and technological resources in healthcare units and surveillance departments slows down the notification process and data analysis. Due to a lack of integration in the private sector, the surveillance system fails to detect the socioeconomic profile of the patients. Investments in public healthcare, human and technological resources for surveillance and better integration in the private healthcare system, and vector surveillance may improve dengue surveillance.

Entities:  

Keywords:  dengue; health geography; health information; infectious diseases; public health; qualitative research; surveillance; tropical diseases; underreporting; urban health

Year:  2020        PMID: 32244954     DOI: 10.3390/ijerph17062062

Source DB:  PubMed          Journal:  Int J Environ Res Public Health        ISSN: 1660-4601            Impact factor:   3.390


  2 in total

1.  COVID-19 prevention measures reduce dengue spread in Yunnan Province, China, but do not reduce established outbreak.

Authors:  Z Y Sheng; M Li; R Yang; Y H Liu; X X Yin; J R Mao; Heidi E Brown; J An; H N Zhou; P G Wang
Journal:  Emerg Microbes Infect       Date:  2022-12       Impact factor: 7.163

2.  Improving Dengue Forecasts by Using Geospatial Big Data Analysis in Google Earth Engine and the Historical Dengue Information-Aided Long Short Term Memory Modeling.

Authors:  Zhichao Li; Helen Gurgel; Lei Xu; Linsheng Yang; Jinwei Dong
Journal:  Biology (Basel)       Date:  2022-01-21
  2 in total

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