Malaria-VisAnalytics: a tool for visual exploratory analysis of Brazilian public malaria data.

BACKGROUND: Data integration and visualisation techniques have been widely used in scientific research to allow the exploitation of large volumes of data and support highly complex or long-lasting research questions. Integration allows data from different sources to be aggregated into a single database comprising variables of interest for different types of studies. Visualisation allows large and complex data sets to be manipulated and interpreted in a more intuitive way. METHODS: Integration and visualisation techniques were applied in a malaria surveillance ecosystem to build an integrated database comprising notifications, deaths, vector control and climate data. This database is accessed through Malaria-VisAnalytics, a visual mining platform for descriptive and predictive analysis supporting decision and policy-making by governmental and health agents. RESULTS: Experimental and validation results have proved that the visual exploration and interaction mechanisms allow effective surveillance for rapid action in suspected outbreaks, as well as support a set of different research questions over integrated malaria electronic health records. CONCLUSION: The integrated database and the visual mining platform (Malaria-VisAnalytics) allow different types of users to explore malaria-related data in a user-friendly interface. Summary data and key insights can be obtained through different techniques and dimensions. The case study on Manaus can serve as a reference for future replication in other municipalities. Finally, both the database and the visual mining platform can be extended with new data sources and functionalities to accommodate more complex scenarios (such as real-time data capture and analysis).

Prioritizing nurturing care at the municipal and district level with the Brazilian Early Childhood Friendly Municipal Index (IMAPI).

The Brazilian Early Childhood Friendly Municipal Index (IMAPI) is a population-based approach to monitor the nurturing care environment for early childhood development (ECD) using routine information system data. It is unknown whether IMAPI can be applied to document metropolitan urban territorial differences in nurturing care environments. We used Brasilia, Brazil's capital with a large metropolitan population of 2,881,854 inhabitants divided into 31 districts, as a case study to examine whether disaggregation of nurturing care data can inform a more equitable prioritization for ECD in metropolitan areas. IMAPI scores were estimated at the municipal level (IMAPI-M, 31 indicators) and at the district level (IMAPI-D, 29 indicators). We developed a quantitative prioritization process for indicators in each IMAPI analysis, and those selected were jointly mapped in the socioecological model for the role of indicators in relation to the enabling environment for nurturing care. Out of 28 common nurturing care indicators across IMAPI analysis, only four were prioritized in both analyses: one from the Adequate nutrition, two from the Opportunities for early learning, and one from the Responsive caregiving domains. These four indicators were mapped as enabling policies, supportive services, and caregivers' capabilities (socioecological model) and Effort, Coverage, and Quality (indicator's role). In conclusion, the different levels of nurturing care data disaggregation in the IMAPI can better inform decision-making than each one individually, especially in metropolitan areas where municipalities and districts within metropolitan areas have relative decision-making autonomy.

Nurturing care indicators for the Brazilian Early Childhood Friendly Municipal Index (IMAPI).

The Nurturing Care Framework (NCF) calls for establishing a global monitoring and accountability systems for early childhood development (ECD). Major gaps to build low-cost and large-scale ECD monitoring systems at the local level remain. In this manuscript, we describe the process of selecting nurturing care indicators at the municipal level from existing routine information systems to develop the Brazilian Early Childhood Friendly Index (IMAPI). Three methodological steps developed through a participatory decision-making process were followed. First, a literature review identified potential indicators to translate the NCF domains. Four technical panels composed of stakeholders from federal, state and municipal levels were consulted to identify data sources, their availability at the municipal level and the strengths and weakness of each potential indicator. Second, national and international ECD experts participated in two surveys to score, following a SMART approach, the expected performance of each nurturing care indicator. This information was used to develop analytical weights for each indicator. Third, informed by strengths and weaknesses pointed out in the previous steps, the IMAPI team reached consensus on 31 nurturing care indicators across the five NCF domains (Good health [n = 14], Adequate nutrition [4], Responsive caregiving [1], Opportunities for early learning [7] and Security and safety [4]). IMAPI represents the first attempt to select nurturing care indicators at the municipal level using data from existing routine information systems.

Sociodemographic inequities in nurturing care for early childhood development across Brazilian municipalities.

Providing an enabling nurturing care environment for early childhood development (ECD) that cuts across the five domains of the Nurturing Care Framework (i.e., good health, adequate nutrition, opportunities for early learning, security and safety and responsive caregiving) has become a global priority. Brazil is home to approximately 18.5 million children under 5 years of age, of which 13% are at risk of poor development due to socio-economic inequalities. We explored whether the Early Childhood Friendly Municipal Index (IMAPI) can detect inequities in nurturing care ECD environments across the 5570 Brazilian municipalities. We examined the validity of the IMAPI scores and conducted descriptive analyses for assessing sociodemographic inequities by nurturing care domains and between and within regions. The strong correlations between school achievement (positive) and socially vulnerable children (negative) confirmed the IMAPI as a multidimensional nurturing care indicator. Low IMAPI scores were more frequent in the North (72.7%) and Northeast (63.3%) regions and in small (47.7%) and medium (43.3%) size municipalities. Conversely, high IMAPI scores were more frequent in the more prosperous South (52.9%) and Southeast (41.2%) regions and in metropolitan areas (41.2%). The security and safety domain had the lowest mean differences (MDs) among Brazilian regions (MD = 5) and population size (MD = 3). Between-region analyses confirmed inequities between the North/Northeast and South/Southeast. The biggest within-region inequity gaps were found in the Northeast (from -22 to 15) and the North (-21 to 19). The IMAPI distinguished the nurturing care ECD environments across Brazilian municipalities and can inform equitable and intersectoral multilevel decision making.

Nurturing care indicators for the Brazilian Early Childhood Friendly Municipal Index (IMAPI)

The Nurturing Care Framework (NCF) calls for establishing a global monitoring and accountability systems for early childhood development (ECD). Major gaps to build low-cost and large-scale ECD monitoring systems at the local level remain. In this manuscript, we describe the process of selecting nurturing care indicators at the municipal level from existing routine information systems to develop the Brazilian Early Childhood Friendly Index (IMAPI). Three methodological steps developed through a participatory decision-making process were followed. First, a literature review identified potential indicators to translate the NCF domains. Four technical panels composed of stakeholders from federal, state and municipal levels were consulted to identify data sources, their availability at the municipal level and the strengths and weakness of each potential indicator. Second, national and international ECD experts participated in two surveys to score, following a SMART approach, the expected performance of each nurturing care indicator. This information was used to develop analytical weights for each indicator. Third, informed by strengths and weaknesses pointed out in the previous steps, the IMAPI team reached consensus on 31 nurturing care indicators across the five NCF domains (Good health [n = 14], Adequate nutrition [4], Responsive caregiving [1], Opportunities for early learning [7] and Security and safety [4]). IMAPI represents the first attempt to select nurturing care indicators at the municipal level using data from existing routine information systems.

Social distancing and living conditions in the pandemic COVID-19 in Salvador-Bahia, Brazil. / Distanciamento social e condições de vida na pandemia COVID-19 em Salvador-Bahia, Brasil.

In the current scenario of the COVID-19 pandemic, Brazilian states and municipalities have adopted social distancing measures as a strategy to reduce the number of cases and control the disease. These measures affect populations and territories differently. This study aims to analyze the trend of social distancing in this pandemic and its relationship with the context of living conditions in Salvador, Bahia, Brazil. An ecological study with spatial distribution was conducted. The municipality's Social Distancing Index and the Living Conditions Index were calculated. Global and Local Moran Indices were employed to assess the degree of spatial dependence and autocorrelation. Fluctuations were observed in the social distancing levels during the analyzed period, with higher distancing percentages in neighborhoods with more favorable living conditions. The analysis and interpretation of COVID-19 containment measures, such as social distancing, should consider the profile of local vulnerability of each territory for the correct dimensioning of pandemic mitigation strategies from the perspective of developing social actions enabling greater adherence of the most impoverished populations.

Distanciamento social e condições de vida na pandemia COVID-19 em Salvador-Bahia, Brasil / Social distancing and living conditions in the pandemic COVID-19 in Salvador-Bahia, Brazil

Resumo Na pandemia COVID-19, estados e municípios brasileiros adotaram medidas de distanciamento social como estratégia para reduzir o número de casos e o controle da doença. Estas medidas atingem de forma diferenciada estratos populacionais e território. O objetivo deste estudo foi analisar a evolução do distanciamento social adotadas para o controle da pandemia COVID-19 e sua relação com as condições de vida da população do município de Salvador, Bahia. Foi conduzido um estudo ecológico utilizando agregados espaciais. Calculou-se o Índice de Isolamento Social do município e o Índice de Condição de Vida. O Índice de Moran Global e Local foram usados para avaliar o grau de dependência e autocorrelação espacial. Observou-se oscilações nos índices de isolamento social durante o período analisado, com maiores percentuais de isolamento nos bairros com condições de vida mais favoráveis. A análise e a interpretação das medidas de contenção da Covid-19, a exemplo do distanciamento social, deve considerar o perfil de vulnerabilidade de cada território visando a monitorar o correto dimensionamento das estratégias de mitigação da pandemia, na perspectiva de desenvolver ações sociais capazes de possibilitar maior adesão das populações mais desfavorecidas.

Abstract In the current scenario of the COVID-19 pandemic, Brazilian states and municipalities have adopted social distancing measures as a strategy to reduce the number of cases and control the disease. These measures affect populations and territories differently. This study aims to analyze the trend of social distancing in this pandemic and its relationship with the context of living conditions in Salvador, Bahia, Brazil. An ecological study with spatial distribution was conducted. The municipality's Social Distancing Index and the Living Conditions Index were calculated. Global and Local Moran Indices were employed to assess the degree of spatial dependence and autocorrelation. Fluctuations were observed in the social distancing levels during the analyzed period, with higher distancing percentages in neighborhoods with more favorable living conditions. The analysis and interpretation of COVID-19 containment measures, such as social distancing, should consider the profile of local vulnerability of each territory for the correct dimensioning of pandemic mitigation strategies from the perspective of developing social actions enabling greater adherence of the most impoverished populations.