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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Modelling the impact of social protection on tuberculosis: the S-PROTECT project
|
|---|---|
| Published in |
BMC Public Health, June 2018
|
| DOI | 10.1186/s12889-018-5539-x |
| Pubmed ID | |
| Authors |
D. Boccia, W. Rudgard, S. Shrestha, K. Lönnroth, P. Eckhoff, J. Golub, M. Sanchez, E. Maciel, D. Rasella, P. Shete, D. Pedrazzoli, R. Houben, S. Chang, D. Dowdy |
| Abstract |
Tackling the social determinants of Tuberculosis (TB) through social protection is a key element of the post-2015 End TB Strategy. However, evidence informing policies are still scarce. Mathematical modelling has the potential to contribute to fill this knowledge gap, but existing models are inadequate. The S-PROTECT consortium aimed to develop an innovative mathematical modelling approach to better understand the role of social protection to improve TB care, prevention and control. S-PROTECT used a three-steps approach: 1) the development of a conceptual framework; 2) the extraction from this framework of three high-priority mechanistic pathways amenable for modelling; 3) the development of a revised version of a standard TB transmission model able to capture the structure of these pathways. As a test case we used the Bolsa Familia Programme (BFP), the Brazilian conditional cash transfer scheme. Assessing one of these pathways, we estimated that BFP can reduce TB prevalence by 4% by improving households income and thus their nutritional status. When looking at the direct impact via malnutrition (not income mediated) the impact was 33%. This variation was due to limited data availability, uncertainties on data transformation and the pathway approach taken. These results are preliminary and only aim to serve as illustrative example of the methodological challenges encountered in this first modelling attempt, nonetheless they suggest the potential added value of integrating TB standard of care with social protection strategies. Results are to be confirmed with further analysis. However, by developing a generalizable modelling framework, S-PROTECT proved that the modelling of social protection is complex, but doable and allowed to draw the research road map for the future in this field. |
X Demographics
The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 5 | 63% |
| Brazil | 1 | 13% |
| United Kingdom | 1 | 13% |
| Unknown | 1 | 13% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 3 | 38% |
| Members of the public | 3 | 38% |
| Science communicators (journalists, bloggers, editors) | 1 | 13% |
| Scientists | 1 | 13% |
Mendeley readers
The data shown below were compiled from readership statistics for 117 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 117 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 25 | 21% |
| Researcher | 17 | 15% |
| Student > Ph. D. Student | 9 | 8% |
| Student > Doctoral Student | 6 | 5% |
| Professor | 4 | 3% |
| Other | 15 | 13% |
| Unknown | 41 | 35% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 18 | 15% |
| Social Sciences | 13 | 11% |
| Nursing and Health Professions | 10 | 9% |
| Mathematics | 4 | 3% |
| Psychology | 3 | 3% |
| Other | 19 | 16% |
| Unknown | 50 | 43% |
Attention Score in Context
This research output has an Altmetric Attention Score of 16. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 February 2019.
All research outputs
#2,093,770
of 23,978,283 outputs
Outputs from BMC Public Health
#2,338
of 15,645 outputs
Outputs of similar age
#44,430
of 332,124 outputs
Outputs of similar age from BMC Public Health
#77
of 321 outputs
Altmetric has tracked 23,978,283 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 15,645 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.3. This one has done well, scoring higher than 85% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 332,124 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 321 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.