Spatial analysis of areas at risk for schistosomiasis in the Alto Tietê Basin, São Paulo, Brazil

We analyzed the spatial relation between incomplete vaccine coverage for children and the distance from vaccination services. This was a cross-sectional study of children from 13 to 35 months of age from the cities of São Luís (Maranhão State) and Ribeirão Preto (São Paulo State), Brazil, and from basic health units (UBS, in Portuguese). The sample consisted of 2,744 children from São Luís and 3,325 from Ribeirão Preto. Data about incomplete vaccine coverage for children were obtained from the BRISA birth cohorts. Data about the quality of UBS vaccination services were obtained from the first cycle of the Brazilian National Program for Improvement of Access and Quality of Basic Care (PMAQ-AB, in Portuguese). For the spatial analysis, we determined the distance between the residence of the children (with and without a complete vaccine calendar) and the vaccination services of the UBS (classified according to number of structural items). Incomplete vaccine coverage was more pronounced in São Luís, with greater percentages for human rotavirus and triple viral vaccines, with the latter being the least available. In Ribeirão Preto, incomplete BCG vaccine coverage was more pronounced, with the tetravalent vaccine being the least available. Children from the two cities showed similarities: most of them had adult mothers with 9 to 11 years of schooling and did not reside with siblings in the household. They also showed differences: in São Luís, most mothers belonged to the economic class C, while in Ribeirão Preto they belong to the A and B classes. In the two cities with different socioeconomic conditions, complete vaccine coverage seemed not to depend on the location or quality of the vaccination service. Although São Luís showed a better structure of the services, incomplete vaccine coverage was higher compared to Ribeirão Preto.

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PREVALENCE OF THE HEPATITIS C VIRUS AMONG UNIVERSITY EMPLOYEES IN SÃO PAULO, SOUTHEASTERN BRAZIL: predictive factors and geoprocessing spatial analysis

Arquivos de Gastroenterologia ◽

10.1590/s0004-28032015000100003 ◽

2015 ◽

Vol 52 (1) ◽

pp. 9-13 ◽

Cited By ~ 5

Author(s):

Cássio Vieira de OLIVEIRA ◽

Walnei Fernandes BARBOSA ◽

Liciana Vaz de Arruda SILVEIRA ◽

Juliana MENEZES ◽

Flávia Souza MACHADO ◽

...

Keyword(s):

Risk Factors ◽

Hepatitis C Virus ◽

Hepatitis C ◽

Spatial Analysis ◽

Sao Paulo ◽

Hcv Infection ◽

Southeastern Brazil ◽

São Paulo ◽

University Employees ◽

Factors Associated

Background There are limited studies on the prevalence and risk factors associated with hepatitis C virus (HCV) infection. Objective Identify the prevalence and risk factors for HCV infection in university employees of the state of São Paulo, Brazil. Methods Digital serological tests for anti-HCV have been performed in 3153 volunteers. For the application of digital testing was necessary to withdraw a drop of blood through a needlestick. The positive cases were performed for genotyping and RNA. Chi-square and Fisher’s exact test were used, with P-value <0.05 indicating statistical significance. Univariate and multivariate logistic regression were also used. Results Prevalence of anti-HCV was 0.7%. The risk factors associated with HCV infection were: age >40 years, blood transfusion, injectable drugs, inhalable drugs (InDU), injectable Gluconergam®, glass syringes, tattoos, hemodialysis and sexual promiscuity. Age (P=0.01, OR 5.6, CI 1.4 to 22.8), InDU (P<0.0001, OR=96.8, CI 24.1 to 388.2), Gluconergam® (P=0.0009, OR=44.4, CI 4.7 to 412.7) and hemodialysis (P=0.0004, OR=90.1, CI 7.5 – 407.1) were independent predictors. Spatial analysis of the prevalence with socioeconomic indices, Gross Domestic Product and Human Development Index by the geoprocessing technique showed no positive correlation. Conclusions The prevalence of HCV infection was 0.7%. The independent risk factors for HCV infection were age, InDU, Gluconergan® and hemodialysis. There was no spatial correlation of HCV prevalence with local economic factors.

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Spatial analysis of pneumococcal meningitis in São Paulo in the pre- and post-immunization era

Revista de Saúde Pública ◽

10.11606/s1518-8787.2019053001183 ◽

2019 ◽

Vol 53 ◽

pp. 59

Author(s):

Danise Senna Oliveira ◽

Francisco Chiaravalloti Neto ◽

Thiago Santos Mota ◽

Daniel Brito de Araujo ◽

Ana Marli Christovam Sartori

Keyword(s):

Spatial Analysis ◽

High Risk ◽

Vaccination Coverage ◽

Pneumococcal Meningitis ◽

Incidence Rates ◽

Sao Paulo ◽

São Paulo ◽

Post Vaccination ◽

Socioeconomic Variables ◽

Vaccination Period

OBJECTIVE: To analyze the pneumococcal meningitis incidence rates in the State of São Paulo, Brazil, by age group, municipalities and micro-regions, as well as the spatial distribution of pneumococcal meningitis incidence rates among children under 5 years old in the pre- (2005–2009) and post-vaccination (2011–2013) periods and its associations with socioeconomic variables and vaccination coverage. METHODS: The data source was the Brazilian Notifiable Diseases Information System. For the pre- and post-vaccination periods, thematic maps were built for pneumococcal meningitis incidence in under-5 children, by São Paulo state micro-regions, vaccination coverage and socioeconomic variables, using QGIS 2.6.1 software. Scan statistics performed by the SatScan 9.2 software were used to analyze spatial and spatiotemporal clusters in São Paulo municipalities and micro-regions. A Bayesian inference for latent Gaussian model with zero-inflated Poisson model through the integrated nested Laplace approximation was used in the spatial analysis to evaluate associations between pneumococcal meningitis incidence rates and socioeconomic variables of interest in São Paulo micro-regions. RESULTS: From 2005 to 2013, 3,963 pneumococcal meningitis cases were reported in São Paulo. Under-5 children were the most affected in the whole period. In the post-vaccination period, pneumococcal meningitis incidence rates decreased among this population, particularly among infants (from 4.17/100,000 in 2005 to 2.54/100,000 in 2013). Two clusters were found in pre-vaccination – one of low risk for pneumococcal meningitis, in the northwest of the state (OR = 0.45, p = 0.0003); and another of high risk in the southeast (OR = 1.62, p = 0.0000). In the post-vaccination period, only a high-risk cluster remained, in the southeast (RR = 1.97, p = 0.0570). In Bayesian analysis, wealth was the only variable positively associated to pneumococcal meningitis (RR = 1.026, 95%CI 1.002–1.052). CONCLUSIONS: Pneumococcal meningitis is probably underdiagnosed and underreported in São Paulo. Differentiated rates of pneumococcal meningitis diagnosis and reporting in each microregion, according to the São Paulo Index of Social Responsibility, might explain our results.

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O VALUE-AT-RISK NA VOLATILIDADE DOS PREÇOS À VISTA DO BOI GORDO NO ESTADO DE SÃO PAULO

Agronomia: Jornadas Científicas - Volume 2 ◽

10.37885/200400115 ◽

2020 ◽

pp. 102-112

Author(s):

Carlos Alberto Gonçalves Da Silva

Keyword(s):

At Risk ◽

Value At Risk ◽

Sao Paulo ◽

São Paulo

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Spatial analysis of the COVID-19 distribution pattern in São Paulo State, Brazil

Ciência & Saúde Coletiva ◽

10.1590/1413-81232020259.17082020 ◽

2020 ◽

Vol 25 (9) ◽

pp. 3377-3384

Author(s):

Franciel Eduardo Rex ◽

Cléber Augusto de Souza Borges ◽

Pâmela Suélen Käfer

Keyword(s):

Spatial Analysis ◽

Spatial Pattern ◽

Kernel Density ◽

Sao Paulo ◽

Control Measures ◽

Metropolitan Region ◽

São Paulo ◽

Paulo State ◽

First Case ◽

São Paulo State

Abstract At the end of 2019, the outbreak of COVID-19 was reported in Wuhan, China. The outbreak spread quickly to several countries, becoming a public health emergency of international interest. Without a vaccine or antiviral drugs, control measures are necessary to understand the evolution of cases. Here, we report through spatial analysis the spatial pattern of the COVID-19 outbreak. The study site was the State of São Paulo, Brazil, where the first case of the disease was confirmed. We applied the Kernel Density to generate surfaces that indicate where there is higher density of cases and, consequently, greater risk of confirming new cases. The spatial pattern of COVID-19 pandemic could be observed in São Paulo State, in which its metropolitan region standed out with the greatest cases, being classified as a hotspot. In addition, the main highways and airports that connect the capital to the cities with the highest population density were classified as medium density areas by the Kernel Density method.It indicates a gradual expansion from the capital to the interior. Therefore, spatial analyses are fundamental to understand the spread of the virus and its association with other spatial data can be essential to guide control measures.

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