Análise espacial das anomalias congênitas do sistema nervoso

Lima, Luciana Moura Mendes de; Melo, Ana Cláudia Oliveira de; Vianna, Rodrigo Pinheiro de Tolêdo; Moraes, Ronei Marcos de

doi:10.1590/1414-462x201900030313

Cited by 12 publications

(12 citation statements)

References 16 publications

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“…The reasons for the geographic differences in prevalence both in Maranhão and in Brazil may be the consequences of population (genetic, ancestry) and environmental differences 11,12,29 , and should be further investigated. However, when they are put in clusters that occurred in specific years and do not repeat in later years, a possible explanation is that, in rare events and in small populations, the additional birth of a single child with CA can impact the frequency of the year in question.…”

Section: Discussionmentioning

confidence: 99%

Prevalence of congenital anomalies at birth among live births in the state of Maranhão from 2001 to 2016: temporal and spatial analysis

Reis

Barbian

Cardoso-dos-Santos

et al. 2021

Rev. bras. epidemiol.

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Objectives: To analyze the prevalence at birth and the spatial and temporal distribution of congenital anomalies (CAs) among live births in the state of Maranhão in 2001 to 2016. To describe demographic, gestational and neonatal variables of interest. Methods: Ecological, population-based study, using secondary data from the Live Birth Information System (SINASC). Annual prevalence of total and per-group CAs was calculated. Spatial analyzes were based on the Local Indicators of Spatial Association (LISA) and the Moran I Index, and interactive maps were generated. Demographic, gestational and neonatal variables of interest available from SINASC were described in the group of newborns with CAs. Results: 1,831,830 live births, 6,110 with CAs (33.4/10,000) were included. Higher frequencies occurred in more recent years. Spatial clusters have been observed in specific years. The prevalence of newborns with CAs was different between categories of variables considered as risk factors for this outcome. Conclusion: The prevalence at birth of total CAs was lower than expected for major human defects (3%). The temporal peak of records in 2015/2016 is probably related to the increase in CAs caused by gestational infection by the Zika virus. The spatial clusters were probably due to variations at random due to the small number of births as they are not repeated in other years. Studies like this are the basis for the establishment of CA surveillance programs.

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Section: Discussionmentioning

confidence: 99%

Prevalence of congenital anomalies at birth among live births in the state of Maranhão from 2001 to 2016: temporal and spatial analysis

Reis

Barbian

Cardoso-dos-Santos

et al. 2021

Rev. bras. epidemiol.

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“…Let V ( a i ) be a random variable that refers to the number of occurrences of an epidemiological event in a limited period of time within each geo-object in a i , expressed as v 1 , v 2 ,…, v n . Lastly, let X ( a i ) be the population under risk for that epidemiological event within each geo-object of a a i , denoted by x 1 , x 2 ,…, x n [34, 35]. The SIR for each geo-object a i can be presented as in the following equation [34, 35]:that is, the SIR of the geo-object a i is given by the incidence ratios of the occurrence of the event in that geo-object with respect to the geographic region.…”

Section: Methodsmentioning

confidence: 99%

“…Lastly, let X ( a i ) be the population under risk for that epidemiological event within each geo-object of a a i , denoted by x 1 , x 2 ,…, x n [34, 35]. The SIR for each geo-object a i can be presented as in the following equation [34, 35]:that is, the SIR of the geo-object a i is given by the incidence ratios of the occurrence of the event in that geo-object with respect to the geographic region. Table 1 shows the interpretation of the SIR according to its classification.…”

Section: Methodsmentioning

confidence: 99%

A new combination rule for Spatial Decision Support Systems for epidemiology

et al. 2019

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BackgroundDecision making in the health area usually involves several factors, options and data. In addition, it should take into account technological, social and spatial aspects, among others. Decision making methodologies need to address this set of information , and there is a small group of them with focus on epidemiological purposes, in particular Spatial Decision Support Systems (SDSS).MethodsMakes uses a Multiple Criteria Decision Making (MCDM) method as a combining rule of results from a set of SDSS, where each one of them analyzes specific aspects of a complex problem. Specifically, each geo-object of the geographic region is processed, according to its own spatial information, by an SDSS using spatial and non-spatial data, inferential statistics and spatial and spatio-temporal analysis, which are then grouped together by a fuzzy rule-based system that will produce a georeferenced map. This means that, each SDSS provides an initial evaluation for each variable of the problem. The results are combined by the weighted linear combination (WLC) as a criterion in a MCDM problem, producing a final decision map about the priority levels for fight against a disease. In fact, the WLC works as a combining rule for those initial evaluations in a weighted manner, more than a MCDM, i.e., it combines those initial evaluations in order to build the final decision map.ResultsAn example of using this new approach with real epidemiological data of tuberculosis in a Brazilian municipality is provided. As a result, the new approach provides a final map with four priority levels: “non-priority”, “non-priority tendency”, “priority tendency” and “priority”, for the fight against diseases.ConclusionThe new approach may help public managers in the planning and direction of health actions, in the reorganization of public services, especially with regard to their levels of priorities.

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“…De acordo com Lima et al (2019), em seu estudo sobre Análise espacial das anomalias congênitas do sistema nervosa, ao identificar aglomerados espaciais, estes auxiliam na escolha de áreas prioritárias para o cuidado à saúde, podendo essa metodologia ser aplicada a problemas de vigilância similares, e para outros agravos.…”

Section: Introductionunclassified