Epidemiological data accessibility in Brazil

Coelho, Flávio Codeço; Codeço, Cláudia Torres; Cruz, Oswaldo Gonçalves; Camargo, Sabrina; Bliman, Pierre‐Alexandre

doi:10.1016/s1473-3099(16)30007-x

Cited by 7 publications

(9 citation statements)

References 6 publications

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“…There are many initiatives to make relevant data sources public, such as temperature and maps. However, the availability and accessibility of health data is still a challenge in Brazil [ 124 ] and worldwide [ 27 ]. Efforts that aim to use data from social networks [ 125 ], news, searches [ 126 , 127 , 128 , 129 , 130 ], crowdsourcing and volunteered geographic information [ 131 ] for modeling epidemics and health surveillance are interesting and show promising results.…”

Section: Discussionmentioning

confidence: 99%

DengueME: A Tool for the Modeling and Simulation of Dengue Spatiotemporal Dynamics

Lima

Lana

Carneiro

et al. 2016

IJERPH

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The prevention and control of dengue are great public health challenges for many countries, particularly since 2015, as other arboviruses have been observed to interact significantly with dengue virus. Different approaches and methodologies have been proposed and discussed by the research community. An important tool widely used is modeling and simulation, which help us to understand epidemic dynamics and create scenarios to support planning and decision making processes. With this aim, we proposed and developed DengueME, a collaborative open source platform to simulate dengue disease and its vector’s dynamics. It supports compartmental and individual-based models, implemented over a GIS database, that represent Aedes aegypti population dynamics, human demography, human mobility, urban landscape and dengue transmission mediated by human and mosquito encounters. A user-friendly graphical interface was developed to facilitate model configuration and data input, and a library of models was developed to support teaching-learning activities. DengueME was applied in study cases and evaluated by specialists. Other improvements will be made in future work, to enhance its extensibility and usability.

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Section: Discussionmentioning

confidence: 99%

DengueME: A Tool for the Modeling and Simulation of Dengue Spatiotemporal Dynamics

Lima

Lana

Carneiro

et al. 2016

IJERPH

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“…System on Reportable Diseases (SINAN) for collecting and processing data on mandatory communicable diseases. [7] Event notification processes, however, are sensitive to incomplete and/or incorrect data, resulting in the inaccurate evaluation of epidemiological studies performed on these data. [8] A number of studies have reported difficulties in collecting reliable data for many communicable diseases, compromising its epidemiological monitoring.…”

Section: National Unified Public Health System (Sus) Including the Bmentioning

confidence: 99%

“…[5-6] In this context, Brazil has been employing information systems developed for its National Unified Public Health System (SUS), including the Brazilian National Information System on Reportable Diseases (SINAN) for collecting and processing data on mandatory communicable diseases. [7]…”

Section: Introductionmentioning

confidence: 99%

“…Sites performing tele-dermatological examinations in the State of Santa Catarina and number of tele-dermatoscopic examinations performedGound TruthAs a ground truth to compare our epidemiological data against we chose the data on leprosy registered on the Brazilian National Information System on Reportable Diseases (SINAN) [7]. Figure 5. shows a comparison between the 528 leprosy cases reported on SINAN and the 114 leprosy cases identified through tele-dermatology during the period 2015-2017.…”

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confidence: 99%

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Integrating Online Georeferenced Epidemiological Analysis and Visualization into a Telemedicine Infrastructure – First Results

Wangenheim

Savaris

Borgatto

et al. 2019

Preprint

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With the objective to perform a first evaluation of the impact of the integration of a graphic spatial epidemiology tool that allows quasi-realtime georeferenced data visualization into a telemedicine infrastructure, this work presents GISTelemed, an online module specialized on indexing structured and semi-structured data, as well as querying the indexed content using structured and free-text search. We evaluated GISTelemed accordingly to the guidelines published by the US Centers for Disease Control and Prevention and input provided by a questionnaire customized according to AdEQUATE (questionnAire for Evaluation of QUAlity in TElemedicine systems). 39 healthcare professionals from 13 municipalities participated in the evaluation. We analyzed data from questionnaires using descriptive statistics, being Lernability and Comfort the characteristics that received the best evaluation. Quantitative evaluation based upon leprosis cases detected through tele-dermatology showed a sensitivity and PPV of respectively 77.2% and 95.3%. 22.8% of the cases detected were un-notified cases. Results from our case study show a good evaluation regarding the perceived software quality. We conclude that the integration of spatial epidemiology tools to the STT/SC system, besides enabling visualization of data in maps, allowed users to analyze the evolution of morbidities and their co-occurrences.

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“…Finally, finding epidemiological data interfaces or data within an interface is often a time-consuming and error-prone task. For example, the Zika virus epidemic has resulted in increased global attention for Brazil, but it has not resulted in a single easy-to-understand machine-readable interface [29]. Until just recently, Brazil's Ministry of Health maintained two separate lists of mosquito-borne illness epidemiological bulletins [30,31].…”

Section: Interface Challengesmentioning

confidence: 99%

Epidemiological Data Challenges: Planning for a More Robust Future Through Data Standards

Fairchild

Tasseff

Khalsa

et al. 2018

Front. Public Health

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Accessible epidemiological data are of great value for emergency preparedness and response, understanding disease progression through a population, and building statistical and mechanistic disease models that enable forecasting. The status quo, however, renders acquiring and using such data difficult in practice. In many cases, a primary way of obtaining epidemiological data is through the internet, but the methods by which the data are presented to the public often differ drastically among institutions. As a result, there is a strong need for better data sharing practices. This paper identifies, in detail and with examples, the three key challenges one encounters when attempting to acquire and use epidemiological data: (1) interfaces, (2) data formatting, and (3) reporting. These challenges are used to provide suggestions and guidance for improvement as these systems evolve in the future. If these suggested data and interface recommendations were adhered to, epidemiological and public health analysis, modeling, and informatics work would be significantly streamlined, which can in turn yield better public health decision-making capabilities.

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Epidemiological data accessibility in Brazil

Abstract: PAHO. Neurological syndrome, congenital malformations, and Zika virus infection. Implications for public health in the Americas-Epidemiological Alert. 2015. http://www.paho. org/hq/index.php?option=com_content& view=category&layout=blog&id=1218&Item id=2291 (accessed Feb 13, 2016).

Cited by 7 publications

References 6 publications

DengueME: A Tool for the Modeling and Simulation of Dengue Spatiotemporal Dynamics

DengueME: A Tool for the Modeling and Simulation of Dengue Spatiotemporal Dynamics

Integrating Online Georeferenced Epidemiological Analysis and Visualization into a Telemedicine Infrastructure – First Results

Epidemiological Data Challenges: Planning for a More Robust Future Through Data Standards

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