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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Comparing tariff and medical assistant assigned causes of death from verbal autopsy interviews in Matlab, Bangladesh: implications for a health and demographic surveillance system
|
|---|---|
| Published in |
Population Health Metrics, June 2018
|
| DOI | 10.1186/s12963-018-0169-1 |
| Pubmed ID | |
| Authors |
Riley H. Hazard, Nurul Alam, Hafizur Rahman Chowdhury, Tim Adair, Saidul Alam, Peter Kim Streatfield, Ian Douglas Riley, Alan D. Lopez |
| Abstract |
Deaths in developing countries often occur outside health facilities, making it extremely difficult to gather reliable cause of death (COD) information. Automated COD assignment using a verbal autopsy instrument (VAI) has been proposed as a reliable and cost-effective alternative to traditional physician-certified verbal autopsy, but its performance is still being evaluated. The purpose of this study was to compare the similarity of diagnosis by Medical Assistants (MA) in the Matlab Health and Demographic Surveillance System (HDSS) with the SmartVA Analyze 1.2 (Tariff 2.0) diagnosis. This study took place between January 2011 and April 2014 in Matlab, Bangladesh. MA with 3 years of medical training assigned COD to Matlab residents by reviewing the information collected using the Population Health Metrics Research Consortium (PHMRC) long-form VAI. Smart VA Analyze 1.2 automatically assigned COD using the same questionnaire. COD agreement and cause-specific mortality fractions (CSMFs) were compared for MA and Tariff. Of the 4969 verbal autopsy cases reviewed, 4328 were adults, 296 were children, and 345 were neonates. Cohen's kappa was 0.38 (0.36, 0.40) for adults, 0.43 (0.38, 0.49) for children, and 0.27 (0.22, 0.33) for neonates. For adults, the top two COD for MA were stroke (29.6%) and ischemic heart diseases (IHD) (14.2%) and for Tariff these were stroke (32.0%) and IHD (14.0%). For children, the top two COD for MA were drowning (33.5%) and pneumonia (13.2%) and for Tariff these were also drowning (36.8%) and pneumonia (12.4%). For neonates, the top two COD for MA were birth asphyxia (41.2%) and meningitis/sepsis (22.3%) and for Tariff these were birth asphyxia (37.0%) and preterm delivery (30.9%). The CSMFs for Tariff and MA showed very close agreement across all age categories but some differences were observed for neonate preterm delivery and meningitis/sepsis. Given the known advantages of automated methods over physician certified verbal autopsy, the SmartVA software, incorporating the shortened VAI questionnaire and Tariff 2.0, could serve as a cost-effective alternative to Matlab MA to routinely collect and analyze verbal autopsy data in a HDSS to generate essential population level COD data for planning. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Bangladesh | 1 | 50% |
| United States | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 2 | 100% |
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 | 10 | 13% |
| Researcher | 9 | 12% |
| Student > Bachelor | 7 | 9% |
| Other | 6 | 8% |
| Student > Postgraduate | 6 | 8% |
| Other | 12 | 16% |
| Unknown | 25 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 22 | 29% |
| Nursing and Health Professions | 5 | 7% |
| Social Sciences | 4 | 5% |
| Biochemistry, Genetics and Molecular Biology | 2 | 3% |
| Computer Science | 2 | 3% |
| Other | 10 | 13% |
| Unknown | 30 | 40% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. 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 08 July 2018.
All research outputs
#3,793,335
of 23,092,602 outputs
Outputs from Population Health Metrics
#105
of 392 outputs
Outputs of similar age
#74,123
of 329,163 outputs
Outputs of similar age from Population Health Metrics
#4
of 5 outputs
Altmetric has tracked 23,092,602 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 392 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.9. This one has gotten more attention than average, scoring higher than 72% 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 329,163 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 77% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one.