Dataset Information

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

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.

SUBMITTER: Angelo M

PROVIDER: S-EPMC7142734 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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

Angelo Marco M Ramalho Walter Massa WM Gurgel Helen H Belle Nayara N Pilot Eva E

International journal of environmental research and public health 20200320 6

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 sy ...[more]

PMID: 32244954

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Project description:Background: This article provides an assessment of the sustainability of Russian regions' financial systems. The study is based on the methods of generalization and synthesis, correlation-regression analysis, and multivariate classification. Since the structure of the regional financial system is complex, several works are devoted to studying its sustainability issues. The relevance of the study topic is confirmed by the lack of a systematic approach to assessing the integral index of sustainability and the possibility of using various tools in determining the complex indicator. Methods: This methodology with application of mathematical statistics methods makes it possible to assess the financial system sustainability in four sectors, to include the leading indicators in the assessment, and to identify regions with extreme values of debt burden indicators. The method was tested for the regions of the Northwestern Federal District (NWFD) for the period 2010 - 2019 to classify the regions according to three levels of debt sustainability. Data collection from the 1 st January to 30 th April 2022 included statistical data from government open internet sources, sectors studied relate to government, and municipal budgets in the NWFD. Authors analyzed regional debt sustainability indicators and identified themes in the field of sustainability studies for the NWFD. Results: An increased level of financial system sustainability was observed among the NWFD regions in the corporative and personal finance sectors, indicating a significant contribution of businesses and households to maintaining the balance and sustainability of the financial system in Russia as a whole. The results of the study also identified that the NWFD regions belong to three clusters: cluster 1 - high debt sustainability; cluster 2 - medium debt sustainability; and cluster 3 - low debt sustainability. Conclusions: The study results allowed the identification of regions with a constantly high level of debt, financial, and corporative sustainability.

Project description:Efficient and accurate dengue risk prediction is an important basis for dengue prevention and control, which faces challenges, such as downloading and processing multi-source data to generate risk predictors and consuming significant time and computational resources to train and validate models locally. In this context, this study proposed a framework for dengue risk prediction by integrating big geospatial data cloud computing based on Google Earth Engine (GEE) platform and artificial intelligence modeling on the Google Colab platform. It enables defining the epidemiological calendar, delineating the predominant area of dengue transmission in cities, generating the data of risk predictors, and defining multi-date ahead prediction scenarios. We implemented the experiments based on weekly dengue cases during 2013-2020 in the Federal District and Fortaleza, Brazil to evaluate the performance of the proposed framework. Four predictors were considered, including total rainfall (Rsum), mean temperature (Tmean), mean relative humidity (RHmean), and mean normalized difference vegetation index (NDVImean). Three models (i.e., random forest (RF), long-short term memory (LSTM), and LSTM with attention mechanism (LSTM-ATT)), and two modeling scenarios (i.e., modeling with or without dengue cases) were set to implement 1- to 4-week ahead predictions. A total of 24 models were built, and the results showed in general that LSTM and LSTM-ATT models outperformed RF models; modeling could benefit from using historical dengue cases as one of the predictors, and it makes the predicted curve fluctuation more stable compared with that only using climate and environmental factors; attention mechanism could further improve the performance of LSTM models. This study provides implications for future dengue risk prediction in terms of the effectiveness of GEE-based big geospatial data processing for risk predictor generation and Google Colab-based risk modeling and presents the benefits of using historical dengue data as one of the input features and the attention mechanism for LSTM modeling.

Project description:BackgroundThe concept of severe maternal morbidity (SMM)-a potentially life-threatening condition during pregnancy, childbirth or after termination of pregnancy-can be used as a quality indicator of the health care provided to mothers and children. The aim of this study was to investigate the SMM rate and the main factors associated with this condition among women living in the Federal District, Brazil.MethodsWe conducted a cross-sectional population-based sample survey using a structured questionnaire about the sociodemographic characteristics of the participants' families. The data investigated included receipt of financial aid from the Federal Government, age, race, maternal educational level, prenatal care, mode of delivery, and serious complications during pregnancy and postpartum (SMM). 1042 mothers of children up to 1 year old were interviewed, representing a weighted estimated population of 36,724 mothers. The sample was representative of the whole Federal District state.ResultsMothers were between 19 and 34 years old (69%), most of them were brown or black (59.7%), and they had more than 9 years of education (81.2%). Prenatal care was adequate for 91.9% of them, the most common mode of delivery was Cesarean section (61.3%), and most deliveries took place in public hospitals (57.3%). The prevalence of low birth weight (< 2,500 g) was 8.1%. We found 2072 events of SMM in 2060 mothers (SMM rate: 5.6%). There was an association between higher occurrence of SMM and older age (OR: 1.40; 1.26-1.56), lower maternal educational level (OR: 3.29; 2.78-3.90), and inadequate prenatal care (OR: 1.28; 1.09-1.51). Receipt of financial aid was also associated to increased risk for SMM (OR: 1.31; 1.16-1.48). Cesarean section and low birth weight reduced the risk of SMM (decrease of 49.0% and 46.0%, respectively).ConclusionsThe SMM rate in the Federal District was positively associated with higher maternal age, lower maternal educational level, inadequate prenatal care, and government financial aid program. Conversely, SMM was inversely associated with Cesarean delivery and low birth weight. This study showed that specific demographic groups are at higher risk for SMM. Therefore, actions should be focused primarily on those groups for greater effectiveness at reducing maternal mortality and providing better quality of maternal health care.

Dataset Information

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

Publications

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

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