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  • Ability of verbal autopsy data to detect deaths due to uncontrolled hyperglycaemia: testing existing methods and development and validation of a novel weighted score

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Diabetes and endocrinology
Original research
Ability of verbal autopsy data to detect deaths due to uncontrolled hyperglycaemia: testing existing methods and development and validation of a novel weighted score
  1. Sarah Blackstock1,
  2. Miles D Witham2,3,4,
  3. Alisha N Wade3,
  4. Amelia Crampin5,6,
  5. David Beran7,
  6. Graham D Ogle8,
  7. Justine I Davies3,9,10
  1. 1 Department of Paediatric Rheumatology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, London, UK
  2. 2 Department of Aging and Health, School of Medicine, University of Dundee, Dundee, UK
  3. 3 MRC/Wits Rural Public Health and Health Transitions Research Unit, University of the Witwatersrand School of Public Health, Johannesburg, South Africa
  4. 4 AGE Research Group, NIHR Newcastle Biomedical Research Centre, Newcastle University and Newcastle upon Tyne Hospitals Trust, Newcastle, UK
  5. 5 Department of Population Health, London School of Hygiene and Tropical Medicine, London, UK
  6. 6 Malawi Epidemiology and Intervention Research Unit, Malawi Epidemiology and Intervention Research Unit, Lilongwe, Malawi
  7. 7 Division of Tropical and Humanitarian Medicine, University of Geneva and Geneva University Hospitals, Geneva, Switzerland
  8. 8 Life for a Child Program, Diabetes NSW, Glebe, New South Wales, Australia
  9. 9 Centre for Global Health, King's College London, London, UK
  10. 10 Institute for Applied Health Research, Birmingham University, Birmingham, UK
  1. Correspondence to Professor Justine I Davies; j.davies.6{at}bham.ac.uk

Abstract

Objectives Verbal autopsy (VA) is a useful tool to ascertain cause of death where no other mechanisms exist. We aimed to assess the utility of VA data to ascertain deaths due to uncontrolled hyperglycaemia and to develop a weighted score (WS) to specifically identify cases. Cases were identified by a study or site physician with training in diabetes. These diagnoses were also compared with diagnoses produced by a standard computer algorithm (InterVA-4).

Setting This study was done using VA data from the Health and Demographic Survey sites in Agincourt in rural South Africa. Validation of the WS was done using VA data from Karonga in Malawi.

Participants All deaths from ages 1 to 49 years between 1992 and 2015 and between 2002 and 2016 from Agincourt and Karonga, respectively. There were 8699 relevant deaths in Agincourt and 1663 in Karonga.

Results Of the Agincourt deaths, there were 77 study physician classified cases and 58 computer algorithm classified cases. Agreement between study physician classified cases and computer algorithm classified cases was poor (Cohen’s kappa 0.14). Our WS produced a receiver operator curve with area under the curve of 0.952 (95% CI 0.920 to 0.985). However, positive predictive value (PPV) was below 50% when the WS was applied to the development set and the score was dominated by the necessity for a premortem diagnosis of diabetes. Independent validation showed the WS performed reasonably against site physician classified cases with sensitivity of 86%, specificity of 99%, PPV of 60% and negative predictive value of 99%.

Conclusion Our results suggest that widely used VA methodologies may be missing deaths due to uncontrolled hyperglycaemia. Our WS may offer improved ability to detect deaths due to uncontrolled hyperglycaemia in large populations studies where no other means exist.

  • epidemiology
  • diabetes & endocrinology
  • health informatics

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

http://dx.doi.org/10.1136/bmjopen-2018-026331

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    Footnotes

    • Twitter @drjackoids

    • Contributors JID conceived the idea. JID, MDW, GDO, DB, SB, ANW and AC input into the development of the idea. MDW, JID and SB did the analyses. SB, ANW and AC reviewed VA data. JID, MDW, GDO, DB, SB, ANW and AC contributed to writing and approving the manuscript.

    • Funding ANW is supported by the Fogarty International Center of the National Institutes of Health under Award Number K43TW010698. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.SB received a travel grant from the Dowager Countess Eleanor Peel Trust for this study.

    • Competing interests None declared.

    • Patient consent for publication Not required.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data availability statement Data are available from MRC/Wits Rural Public Health and Health Transitions Research Unit or MEIRU upon reasonable request.

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    © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.