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The quality and diagnostic value of open narratives in verbal autopsy: a mixed-methods analysis of partnered interviews from Malawi

Overview of attention for article published in BMC Medical Research Methodology, February 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

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14 tweeters

Citations

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7 Dimensions

Readers on

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54 Mendeley
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Title
The quality and diagnostic value of open narratives in verbal autopsy: a mixed-methods analysis of partnered interviews from Malawi
Published in
BMC Medical Research Methodology, February 2016
DOI 10.1186/s12874-016-0115-5
Pubmed ID
Authors

C. King, C. Zamawe, M. Banda, N. Bar-Zeev, J. Beard, J. Bird, A. Costello, P. Kazembe, D. Osrin, E. Fottrell

Abstract

Verbal autopsy (VA), the process of interviewing a deceased's family or caregiver about signs and symptoms leading up to death, employs tools that ask a series of closed questions and can include an open narrative where respondents give an unprompted account of events preceding death. The extent to which an individual interviewer, who generally does not interpret the data, affects the quality of this data, and therefore the assigned cause of death, is poorly documented. We aimed to examine inter-interviewer reliability of open narrative and closed question data gathered during VA interviews. During the introduction of VA data collection, as part of a larger study in Mchinji district, Malawi, we conducted partner interviews whereby two interviewers independently recorded open narrative and closed questions during the same interview. Closed questions were collected using a smartphone application (mobile-InterVA) and open narratives using pen and paper. We used mixed methods of analysis to evaluate the differences between recorded responses to open narratives and closed questions, causes of death assigned, and additional information gathered by open narrative. Eighteen partner interviews were conducted, with complete data for 11 pairs. Comparing closed questions between interviewers, the median number of differences was 1 (IQR: 0.5-3.5) of an average 65 answered; mean inter-interviewer concordance was 92 % (IQR: 92-99 %). Discrepancies in open narratives were summarized in five categories: demographics, history and care-seeking, diagnoses and symptoms, treatment and cultural. Most discrepancies were seen in the reporting of diagnoses and symptoms (e.g., malaria diagnosis); only one pair demonstrated no clear differences. The average number of clinical symptoms reported was 9 in open narratives and 20 in the closed questions. Open narratives contained additional information on health seeking and social issues surrounding deaths, which closed questions did not gather. The information gleaned during open narratives was subject to inter-interviewer variability and contained a limited number of symptom indicators, suggesting that their use for assigning cause of death is questionable. However, they contained rich information on care-seeking, healthcare provision and social factors in the lead-up to death, which may be a valuable source of information for promoting accountable health services.

Twitter Demographics

The data shown below were collected from the profiles of 14 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Malawi 1 2%
Unknown 53 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 19 35%
Student > Ph. D. Student 8 15%
Researcher 7 13%
Student > Bachelor 5 9%
Lecturer 3 6%
Other 8 15%
Unknown 4 7%
Readers by discipline Count As %
Medicine and Dentistry 17 31%
Social Sciences 8 15%
Nursing and Health Professions 5 9%
Computer Science 4 7%
Economics, Econometrics and Finance 3 6%
Other 10 19%
Unknown 7 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 11 February 2016.
All research outputs
#1,203,811
of 11,252,870 outputs
Outputs from BMC Medical Research Methodology
#177
of 957 outputs
Outputs of similar age
#51,729
of 345,273 outputs
Outputs of similar age from BMC Medical Research Methodology
#9
of 31 outputs
Altmetric has tracked 11,252,870 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 957 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.8. This one has done well, scoring higher than 81% 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 345,273 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 84% of its contemporaries.
We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.