https://orcid.org/0000-0002-8505-3368
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ABSTRACT
Background
Recognising that the causes of over half the world’s deaths pass unrecorded, the World Health Organization (WHO) leads development of Verbal Autopsy (VA): a method to understand causes of death in otherwise unregistered populations. Recently, VA has been developed for use outside research environments, supporting countries and communities to recognise and act on their own health priorities. We developed the Circumstances of Mortality Categories (COMCATs) system within VA to provide complementary circumstantial categorisations of deaths.
Objectives
Refine the COMCAT system to (a) support large-scale population assessment and (b) inform public health decision-making.
Methods
We analysed VA data for 7,980 deaths from two South African Health and Socio-Demographic Surveillance Systems (HDSS) from 2012 to 2019: the Agincourt HDSS in Mpumalanga and the Africa Health Research Institute HDSS in KwaZulu-Natal. We assessed the COMCAT system’s reliability (consistency over time and similar conditions), validity (the extent to which COMCATs capture a sufficient range of key circumstances and events at and around time of death) and relevance (for public health decision-making).
Results
Plausible results were reliably produced, with ‘emergencies’, ‘recognition, ‘accessing care’ and ‘perceived quality’ characterising the majority of avoidable deaths. We identified gaps and developed an additional COMCAT ‘referral’, which accounted for a significant proportion of deaths in sub-group analysis. To support decision-making, data that establish an impetus for action, that can be operationalised into interventions and that capture deaths outside facilities are important.
Conclusions
COMCAT is a pragmatic, scalable approach enhancing functionality of VA providing basic information, not available from other sources, on care seeking and utilisation at and around time of death. Continued development with stakeholders in health systems, civil registration, community and research environments will further strengthen the tool to capture social and health systems drivers of avoidable deaths and promote use in practice settings.
Responsible Editor
Stig Wall
Responsible Editor
Stig Wall
Acknowledgments
The authors acknowledge the South African Population Research Infrastructure Network (SAPRIN), the African Health Research Institute (AHRI) and the MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt) for their support. The authors gratefully acknowledge Chodwizadziwa Kabudula, Daniel Mahlangu, Dickman Gareta and Siyabonga Nxumalofrom from the Agincourt and AHRI HDSSs who supported with data, and individuals who supported the development and maintenance of the OpenVA software, particularly Dr Jason Thomas from Ohio State University who assisted in the development of the ‘referral’ COMCAT. The authors would also like to thank Professor Sam Clark for support and advice.
Data availability
Data are available from the respective HDSS repositories.
Disclosure statement
The content is solely the responsibility of the authors.
Ethics and consent
The research was a secondary analysis of previously collected VA data, which has been approved by the Biomedical Research Committee (BREC) of the South African Medical Research Council (SAMRC). Thus, the ethical clearance was not required. Within SAPRIN, each Node submits a protocol to their local Human Research Ethics Committee (HREC) based on a core protocol adapted to local needs. In all HDSS nodes, informed consent is conducted by trained fieldworkers using node-specific consent forms in the language spoken locally. Respondents are informed of the purpose and confidentiality of the interview, their right to refuse participation or withdraw from the study.
Paper context
Social determinants are recognised as the fundamental causes of avoid-able mortality. Systematic, consistent and scalable attribution of circumstantial categorisations of deaths has not been developed or used, however. The COMCAT system enhances functionality of VA to promote analysis of circumstances as a routine analytic component in addition to medical cause for service organisation and delivery. InterVA-5 is the first model that generates COMCAT output in VA processing, in parallel to independently generating probable medical causes of death.
Supplementary material
Supplemental data for this article can be accessed here.
‘A few authors have argued that the political legitimacy and technical validity of global health estimates would be improved if estimation processes worked from the bottom up’ (49)
Peter was one of the few. His work addressed a grand moral and empirical challenge: ‘the unequal world of heath data’. Through his mentorship, he encouraged and supported colleagues across the board to embrace difficult questions around material and data poverty. Peter was a unique role model. He led by example through long-standing, far-reaching, critical contributions to building health information systems and capacity internationally in health and sociodemographic surveillance systems (HDSS) processes and infrastructure in LMICs. All of it to understand preventable, ‘invisible’ mortality at scale.
Embedded in HDSS settings in the INDEPTH Network, an international network of HDSSs, we collaborated from the early 2000s to extend verbal autopsy (VA) to complement data on biomedical causes with information on ‘social causes of death’. This work combined data on how deaths occur (in terms of biomedical processes like organ failure and blood loss), with information on why they happened (in terms of circumstantial drivers and root causes), combining numbers representing burden of disease with information on the human experience of that burden. The work culminated in an extension to the international VA standard in 2012, updating it to routinely collect information on critical limiting circumstances and events at and around the time of death.
Our collaboration endured. We progressed ideas about how to further extend and, critically, use information on bio-social causes of death in new partnerships in South Africa with Mpumalanga Department of Health. Together with a founding member of the INDEPTH Network, the MRC/Wits-Agincourt HDSS, we built multi-sectoral collaborations between government and non-government agencies, researchers and rural communities to coproduce evidence including with VA on local health concerns, and act on these data in cooperative, multi-agency partnerships (www.vapar.org). This has been a major contribution to realising the potential of the method outside research settings, in policy, planning and practice.
Peter had a profound influence on this research, which has included the training of many postgraduates who have gone onto build careers in HDSS and elsewhere in global public health. Peter taught us not only the importance of combining different methodological perspectives, but also that good science is working collaboratively and inclusively, with decency and integrity, to have faith and to be fearless! We are deeply privileged to have worked with Peter, he was internationally renowned for his work measuring global burden of disease and was a strong advocate of cooperation in science. Peter was a mentor and colleague to so many of us in the global health community, a true gentleman, dear friend and brilliant scholar. He will be deeply missed.
FOOTNOTE From source (68)
HDSS bring additional data to bear: ‘all-cause mortality rates, cause-specific mortality fractions and rates, and life table probabilities, and fertility and migration rates. Key outputs include population and household characteristics (including social-economic status), disease burden, use of health services, and exposure to environmental risk factors. The associated data on environment, education, immunisation status, housing, access to water, sanitation, and energy provide deep explanatory contexts to the observed dynamics in health and demographic outcomes. In an HDSS, an initial baseline census is conducted of all households and residents in the defined geographic area; data is updated subsequently several times a year through enumeration update rounds. Data can fully complement the Health Management Information System through population-based data that is independent of the use of healthcare services, and can therefore be used for health planning.’
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Notes on contributors
Eilidh Cowan
LD, KK and MV conceived of the study. LD, JP, KH, GG and EF designed the analysis. EC constructed the harmonised naming and coding system supporting use with interpretation tools such as InterVA-5 and InSilico, led the analysis and updated the InterVA-5 model. GG, JS, MV and JP provided guidance on the utility of the method in health systems. Key inputs were made by JD on analytical aspects.