Well-functioning civil registration and vital statistics
(CRVS) systems provide policymakers and stakeholders
with accurate and timely information regarding the
number of births, deaths, and specific causes of mortality
within a population. Ideally, this information is used to
guide the effective delivery of health and social development
programs [1]. These systems can also facilitate efforts
to promote equity and justice by demonstrating the
needs of the poorest and most vulnerable groups

Boyce, Ross M.

Reyes, Raquel

Carolina Digital Repository

COMMENTARY Open AccessImplementing and scaling verbal autopsies:into the unknownRoss M. Boyce1* and Raquel Reyes2Please see related article: http://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-020-01520-1.Keywords: Verbal autopsy, Routine data, Civil registration, Vital statistics, Global burden of diseaseBackgroundWell-functioning civil registration and vital statistics(CRVS) systems provide policymakers and stakeholderswith accurate and timely information regarding thenumber of births, deaths, and specific causes of mortalitywithin a population. Ideally, this information is used toguide the effective delivery of health and social develop-ment programs [1]. These systems can also facilitate ef-forts to promote equity and justice by demonstrating theneeds of the poorest and most vulnerable groups.However, many low and middle-income countries(LMICs) – those with the greatest potential benefit fromsuch information – depend on facility-based systemsthat generate data only on the subset of the populationthat reaches a health facility [2]. In Uganda, for example,recent national surveys suggest that less than one-thirdof children under 5 years of age, and less than one-quarter of deaths, are registered with civil authorities,with the lowest rates seen among poor and rural respon-dents. Failing to capture these vital events creates an “in-visible population,” which exacerbates already starkdivides between rich and poor, urban and rural [3].While civil registration with high and representativecoverage remains the long-term goal, investment incomplementary approaches is urgently needed. This willnot only accelerate the design, implementation, andevaluation of cost-effective interventions, but also helpto achieve the Sustainable Development Goal (SDG) of100% birth and 80% death registration [2, 4, 5]. Verbalautopsies (VA), which involve interviews with next ofkin or caregivers of the deceased, have been adopted asa practical means of determining the cause of death inareas where CRVS systems are weak [6, 7]. While imper-fect, there is ample evidence that VA-based algorithmsyield cause of death estimates comparable to othermethods [8, 9].How good is “good enough?”The article by Hazard et al. [10] describes the implemen-tation and scale-up of SmartVA, an automated verbalautopsy data collection tool, across multiple countriesand contexts. The effort required to roll out this ambi-tious program, which is supported by Bloomberg Philan-thropies’ Data for Health (D4H) Initiative, represents asizeable achievement in itself and we should not over-look its potential impact on long-term public health.The authors report estimated cause-specific mortalityfractions (CSMF) derived from more than 55,000events, occurring in four countries analyzed using theSmartVA-Analyze tool. Myanmar and Bangladesh ac-count for about 90% of deaths. It should be highlightedthat most of these events would have not otherwisebeen captured by existing CRVS systems. Had thesedeaths been documented, many would have not beenassigned a cause of death, let alone an accurate one.These results thus represent a major step towardsachieving both the SDG targets, as well as more equit-able representation in routine data.Operational successes aside, these findings raise someimportant questions. For example, what is the appropri-ate comparator, if any, that we should be using to valid-ate and monitor VA-based CRVS systems? Undoubtedly,the data produced by VA is better than that whichexisted prior to implementation (i.e. no data), but is thisgood enough? In the short-term, the answer is probably“yes.” For many areas of the world, simply having accessto rigorously collected and systematically analyzed data,even if imperfect, may accelerate the delivery of needed© The Author(s). 2020 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.* Correspondence: roboyce@med.unc.edu1Division of Infectious Diseases, University of North Carolina at Chapel Hill,130 Mason Farm Road, Chapel Hill, NC 27599, USAFull list of author information is available at the end of the articleBoyce and Reyes BMC Medicine           (2020) 18:53 https://doi.org/10.1186/s12916-020-01527-8services, advance the effective allocation of limited re-sources, and help to engage poor and rural communitiesin policymaking. Over the longer term, however, the an-swer is less clear and highly context-dependent. VA-based interventions are still considered a stop-gap meas-ure, filling a void until more rigorous, traditional CRVSsystems are established. Identifying the time points atwhich transition from VA-based to traditional CRVSsystems should occur will be a key challenge.In the interim, what should be done when VA analysisis unable to determine a cause of death, such as in ap-proximately 18% of events recorded in Myanmar andPakistan? Here, the SmartVA-Analyze software redistrib-uted these deaths based on demographic covariates andcountry-specific patterns of mortality, derived from theGlobal Burden of Disease (GBD) study. This approachlikely biases the results towards the comparator, which –in this case – is the very same GBD study. As the au-thors note, the comparison was not intended for valid-ation, but rather as a convenient (and perhaps the onlyavailable) measure of plausibility.Should we, however, redistribute deaths at all?While the GBD study data probably represents thebest available data to inform redistribution, its uncer-tainty is likely to be greatest in the places where VA-based CRVS systems are most needed. Notably, inPapua New Guinea and the Philippines, there werefewer deaths with unknown cause (6 and 5%, respect-ively), and the results of the VA analysis differed inimportant ways from the GBD estimates. Therefore,are we doing a disservice to poor and rural popula-tions by assigning attribution to their deaths usingdata that is more likely to be derived from popula-tions with more resources and better access to care?Is this just another form of invisibility, albeit occur-ring in a much smaller proportion of deaths than be-fore? Ideally, deaths of underdetermined cause shouldprompt further investigation, especially if clustered inspace or time. These deaths might be prioritized forphysician review, as was done in the Philippines, as ameans of more effectively applying limited resources.ConclusionsThe findings presented in this article provide further evi-dence that implementation of VA-based tools to aug-ment CRVS systems in LMICs is feasible, acceptable,and effective. Context-specific experience should guideiterative development and refinement of data collectionsoftware, protocols, and analysis algorithms, with par-ticular attention being paid to addressing unknowncauses of death. However, this should not delay the im-plementation of an evidence-based strategy to overcomea critical, yet largely neglected issue of social justice andpublic health.AcknowledgementsNot applicable.Authors’ contributionsRMB drafted the initial manuscript. RR significantly contributed to the revisedmanuscript. Both authors read and approved the final manuscript.FundingNo specific funding was received for the work described herein. RMB issupported by the National Institutes of Health (grant number K23AI141764).Availability of data and materialsNot applicable.Ethics approval and consent to participateNot applicable.Consent for publicationNot applicable.Competing interestsThe authors declare that they have no competing interests.Author details1Division of Infectious Diseases, University of North Carolina at Chapel Hill,130 Mason Farm Road, Chapel Hill, NC 27599, USA. 2Division of HospitalMedicine, University of North Carolina at Chapel Hill, 101 Manning Drive, CB#7085, Chapel Hill, NC 27599, USA.Received: 10 February 2020 Accepted: 11 February 2020References1. Mikkelsen L, Phillips DE, AbouZahr C, Setel PW, de Savigny D, Lozano R, et al. Aglobal assessment of civil registration and vital statistics systems: monitoringdata quality and progress. Lancet. 2015;386:1395–406.2. Setel PW, Macfarlane SB, Szreter S, Mikkelsen L, Jha P, Stout S, et al. Ascandal of invisibility: making everyone count by counting everyone. Lancet.2007;370:1569–77.3. Uganda Bureau of Statistics (UBOS), International Community Foundation(ICF). Uganda Demographic and Health Survey 2016. Kampala and Rockville:UBOS, ICF; 2018.4. United Nations. The Sustainable Development Agenda. http://www.un.org/sustainabledevelopment/development-agenda/. Accessed 05 Feb 2020.5. AbouZahr C, Bratschi MW, Munoz DC, Santon R, Richards N, Riley I, et al.How can we accelerate progress on civil registration and vital statistics? BullWorld Health Organ. 2018;96:226–A.6. Fottrell E, Byass P. Verbal autopsy: methods in transition. Epidemiol Rev.2010;32:38–55.7. Nichols EK, Byass P, Chandramohan D, Clark SJ, Flaxman AD, Jakob R, et al.The WHO 2016 verbal autopsy instrument: an international standardsuitable for automated analysis by InterVA, InSilicoVA, and Tariff 2.0. PLoSMed. 2018;15:e1002486.8. Byass P, Herbst K, Fottrell E, Ali MM, Odhiambo F, Amek N, et al. Comparingverbal autopsy cause of death findings as determined by physician codingand probabilistic modelling: a public health analysis of 54 000 deaths inAfrica and Asia. J Global Health. 2015;5:010402.9. Flaxman AD, Joseph JC, Murray CJL, Riley ID, Lopez AD. Performance ofInSilicoVA for assigning causes of death to verbal autopsies: multisite validationstudy using clinical diagnostic gold standards. BMC Med. 2018;16:56.10. Hazard RH, Buddhika MPK Hart JD, Chowdhury HR, Firth S, Joshi R, et al.Automated verbal autopsy: From research to routine use in civil registrationand vital statistics systems. BMC Med. 2020. https://doi.org/10.1186/s12916-020-01520-1.Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.Boyce and Reyes BMC Medicine           (2020) 18:53 Page 2 of 2

Implementing and scaling verbal autopsies: into the unknown

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Implementing and scaling verbal autopsies: into the unknown

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