BACKGROUND The number of malaria cases in Roraima nearly tripled from 2016 to 2018. The capital, Boa Vista, considered a low-risk area for malaria transmission, reported an increasing number of autochthonous and imported cases. OBJECTIVES This study describes a spatial analysis on malaria cases in an urban region of Boa Vista, which sought to identify the autochthonous and imported cases and associated them with Anopheles habitats and the potential risk of local transmission. METHODS In a cross-sectional study at the Polyclinic Cosme e Silva, 520 individuals were interviewed and diagnosed with malaria by microscopic examination. Using a global positional system, the locations of malaria cases by type and origin and the breeding sites of anopheline vectors were mapped and the risk of malaria transmission was evaluated by spatial point pattern analysis. FINDINGS Malaria was detected in 57.5% of the individuals and there was a disproportionate number of imported cases (90.6%) linked to Brazilian coming from gold mining sites in Venezuela and Guyana. MAIN CONCLUSIONS The increase in imported malaria cases circulating in the west region of Boa Vista, where there are positive breeding sites for the main vectors, may represent a potential condition for increased autochthonous malaria transmission in this space.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7350773 | PMC |
http://dx.doi.org/10.1590/0074-02760200043 | DOI Listing |
Epidemiol Serv Saude
January 2025
Universidade de BrasÃlia, BrasÃlia, DF, Brazil.
Objective: To evaluate opportunity for vaccination in children born alive in Londrina, up to 6 months old and the relationship between socioeconomic stratum and vaccination regularity.
Method: Population survey study based on a retrospective cohort of children born in 2017 and 2018 that identified vaccines not administered in a given session. Vaccination regularity was compared between socioeconomic strata using Pearson's chi-square test.
Epidemiol Serv Saude
January 2025
Universidade de BrasÃlia, BrasÃlia, DF, Brazil.
Objective: To estimate and compare vaccination coverage among children born in 2017-2018 in São Paulo and Campinas, according to the Vaccination Coverage Survey (ICV 2020) and the National Immunization Program Information System (SI-PNI).
Methods: ICV 2020 analyzed vaccination card records. Coverage was calculated and compared to doses recorded on the SI-PNI, divided by the target population.
Epidemiol Serv Saude
January 2025
Universidade de BrasÃlia, BrasÃlia, DF, Brazil.
Objective: To estimate vaccination coverage and analyze factors associated with full vaccination among children up to 15 months old in the city of Natal-RN, Brazil.
Methods: Population-based survey with data recorded on children's vaccination cards and interviews conducted in 2020 and 2021. Analysis of factors associated with complete vaccination was performed by calculating prevalence ratios (PR) and 95% confidence intervals (95%CI) using Poisson regression.
Epidemiol Serv Saude
January 2025
Universidade de BrasÃlia, BrasÃlia, DF, Brazil.
Objective: To describe the polio vaccination status in 26 state capitals, the Federal District, and 12 municipalities in Brazil, among children born between 2017 and 2018.
Methods: This was a population-based household survey conducted from 2020 to 2022, which assessed polio vaccination coverage in children, considering valid, administered, and timely doses by municipality.
Results: Data were collected from 37,801 children.
Epidemiol Serv Saude
January 2025
Universidade de BrasÃlia, BrasÃlia, DF, Brazil.
Objective: To analyze vaccination coverage and factors associated with incomplete vaccination in inland municipalities of Northeastern Brazil.
Methods: This was a household survey using cluster sampling conducted in Vitória da Conquista, Bahia state, Caruaru, Pernambuco state, Sobral, Ceará state and Imperatriz, Maranhão state between 2020 and 2022. Vaccination coverage by valid doses and vaccine hesitancy were analyzed, with the odds ratio (OR) estimated and adjusted using logistic regression.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!