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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A novel Bayesian geospatial method for estimating tuberculosis incidence reveals many missed TB cases in Ethiopia
|
|---|---|
| Published in |
BMC Infectious Diseases, October 2017
|
| DOI | 10.1186/s12879-017-2759-0 |
| Pubmed ID | |
| Authors |
Debebe Shaweno, James M. Trauer, Justin T. Denholm, Emma S. McBryde |
| Abstract |
Reported tuberculosis (TB) incidence globally continues to be heavily influenced by expert opinion of case detection rates and ecological estimates of disease duration. Both approaches are recognised as having substantial variability and inaccuracy, leading to uncertainty in true TB incidence and other such derived statistics. We developed Bayesian binomial mixture geospatial models to estimate TB incidence and case detection rate (CDR) in Ethiopia. In these models the underlying true incidence was formulated as a partially observed Markovian process following a mixed Poisson distribution and the detected (observed) TB cases as a binomial distribution, conditional on CDR and true incidence. The models use notification data from multiple areas over several years and account for the existence of undetected TB cases and variability in true underlying incidence and CDR. Deviance information criteria (DIC) were used to select the best performing model. A geospatial model was the best fitting approach. This model estimated that TB incidence in Sheka Zone increased from 198 (95% Credible Interval (CrI) 187, 233) per 100,000 population in 2010 to 232 (95% CrI 212, 253) per 100,000 population in 2014. The model revealed a wide discrepancy between the estimated incidence rate and notification rate, with the estimated incidence ranging from 1.4 (in 2014) to 1.7 (in 2010) times the notification rate (CDR of 71% and 60% respectively). Population density and TB incidence in neighbouring locations (spatial lag) predicted the underlying TB incidence, while health facility availability predicted higher CDR. Our model estimated trends in underlying TB incidence while accounting for undetected cases and revealed significant discrepancies between incidence and notification rates in rural Ethiopia. This approach provides an alternative approach to estimating incidence, entirely independent of the methods involved in current estimates and is feasible to perform from routinely collected surveillance data. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Kenya | 1 | 33% |
| Venezuela, Bolivarian Republic of | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 75 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 12 | 16% |
| Researcher | 9 | 12% |
| Student > Ph. D. Student | 5 | 7% |
| Lecturer | 4 | 5% |
| Student > Doctoral Student | 4 | 5% |
| Other | 14 | 19% |
| Unknown | 27 | 36% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 15 | 20% |
| Nursing and Health Professions | 7 | 9% |
| Agricultural and Biological Sciences | 4 | 5% |
| Mathematics | 3 | 4% |
| Biochemistry, Genetics and Molecular Biology | 3 | 4% |
| Other | 11 | 15% |
| Unknown | 32 | 43% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 09 October 2017.
All research outputs
#14,486,104
of 23,302,246 outputs
Outputs from BMC Infectious Diseases
#3,828
of 7,804 outputs
Outputs of similar age
#178,695
of 323,685 outputs
Outputs of similar age from BMC Infectious Diseases
#72
of 136 outputs
Altmetric has tracked 23,302,246 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,804 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.3. This one has gotten more attention than average, scoring higher than 50% 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 323,685 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 136 others from the same source and published within six weeks on either side of this one. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.