Automated Author ProfileValdenir Bandeira Soares
Valdenir Bandeira Soares
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 0.6 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Abstract The scope of this research was to identify and characterize spatial units of epidemiological relevance in the state of Rio de Janeiro, through the highest concentrations of cutaneous leishmaniasis (CL) from 1980 to 2012, considering the geographical aspects. SUCAM, FUNASA and SINAN databases were consulted. A method of adjustment of spatially referenced data for demarcation of the regions with the highest concentrations of cases called circuits and poles was applied. These were superimposed on the socioenvironmental indicator maps. Of the total cases registered in the period, 87% occurred in the municipalities located in the resulting circuits and poles. The variations in the occurrence of cases in the different circuits and poles were not related to the socioenvironmental indicators. The identification of the circuits and poles can subsidize the state CL program of the prioritization of strategies of prevention and control actions and the optimization of the resources of the program. These regions, which are more stable than the localities, allow surveillance and control operations in locations with many cases and in other locations in the identified risk area, because they have the same characteristics as those already affected.
Authors
- Valdenir Bandeira Soares ;
- Sabroza, Paulo Chasgastelles ;
- Waldemir Paixão Vargas ;
- Souza-Santos, Reinaldo ;
- Valdés, Ana Cecília De Oliveira ;
- Sobral, Andréa
Abstract The scope of this research was to identify and characterize spatial units of epidemiological relevance in the state of Rio de Janeiro, through the highest concentrations of cutaneous leishmaniasis (CL) from 1980 to 2012, considering the geographical aspects. SUCAM, FUNASA and SINAN databases were consulted. A method of adjustment of spatially referenced data for demarcation of the regions with the highest concentrations of cases called circuits and poles was applied. These were superimposed on the socioenvironmental indicator maps. Of the total cases registered in the period, 87% occurred in the municipalities located in the resulting circuits and poles. The variations in the occurrence of cases in the different circuits and poles were not related to the socioenvironmental indicators. The identification of the circuits and poles can subsidize the state CL program of the prioritization of strategies of prevention and control actions and the optimization of the resources of the program. These regions, which are more stable than the localities, allow surveillance and control operations in locations with many cases and in other locations in the identified risk area, because they have the same characteristics as those already affected.
Authors
- Valdenir Bandeira Soares ;
- Sabroza, Paulo Chasgastelles ;
- Waldemir Paixão Vargas ;
- Souza-Santos, Reinaldo ;
- Valdés, Ana Cecília De Oliveira ;
- Sobral, Andréa