Performance of a safe and dignified burial intervention during an Ebola epidemic in the eastern Democratic Republic of the Congo, 2018–2019

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Abstract

Background

A protracted Ebola Virus Disease (EVD) epidemic in the eastern Ituri, North and South Kivu provinces of the Democratic Republic of Congo (DRC) caused 3470 confirmed and probable cases between July 2018 and April 2020. During the epidemic, the International Federation of Red Cross and Red Crescent Societies (IFRC) supported the DRC Red Cross and other local actors to offer safe and dignified burials (SDB) for suspected and confirmed EVD cases, so as to reduce transmission associated with infectious dead bodies. We conducted a retrospective cohort study of the SDB service’s performance in order to inform future applications of this intervention.

Methods

We analysed data on individual SDB responses to quantify performance based on key indicators and against pre-specified service standards. Specifically, we defined SDB timeliness as response within 24 h and success as all components of the service being implemented. Combining the database with other information sources, we also fit generalised linear mixed binomial models to explore factors associated with unsuccessful SDB.

Results

Out of 14,624 requests for SDB, 99% were responded to, 89% within 24 h. Overall, 61% of SDBs were successful, somewhat below target (80%), with failures clustered during a high-insecurity period. Factors associated with increased odds of unsuccessful SDB included reported community and/or family nonacceptance, insecurity and suspensions of the EVD response, low health facility coverage and high coverage of radio and telephony. Burials supported by mobile Civil Protection (local authorities) and/or static, community-based ‘harm reduction’ teams were associated with lower odds of failure.

Conclusions

A large-scale, timely and moderately performant SDB service proved feasible during the challenging eastern DRC EVD response. Burial teams that are managed by community actors and operate locally, and supported rather than owned by the Red Cross or other humanitarian organisations, are a promising modality of delivering this pillar of EVD control.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12916-023-03194-x.

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Most cited references 28

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The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies.

Erik von Elm, Douglas G. Altman, Matthias Egger … (2008)

Much of biomedical research is observational. The reporting of such research is often inadequate, which hampers the assessment of its strengths and weaknesses and of a study's generalizability. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Initiative developed recommendations on what should be included in an accurate and complete report of an observational study. We defined the scope of the recommendations to cover three main study designs: cohort, case-control, and cross-sectional studies. We convened a 2-day workshop in September 2004, with methodologists, researchers, and journal editors to draft a checklist of items. This list was subsequently revised during several meetings of the coordinating group and in e-mail discussions with the larger group of STROBE contributors, taking into account empirical evidence and methodological considerations. The workshop and the subsequent iterative process of consultation and revision resulted in a checklist of 22 items (the STROBE Statement) that relate to the title, abstract, introduction, methods, results, and discussion sections of articles. Eighteen items are common to all three study designs and four are specific for cohort, case-control, or cross-sectional studies. A detailed Explanation and Elaboration document is published separately and is freely available on the web sites of PLoS Medicine, Annals of Internal Medicine, and Epidemiology. We hope that the STROBE Statement will contribute to improving the quality of reporting of observational studies.

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The table 2 fallacy: presenting and interpreting confounder and modifier coefficients.

Sander Greenland, Daniel Westreich (2013)

It is common to present multiple adjusted effect estimates from a single model in a single table. For example, a table might show odds ratios for one or more exposures and also for several confounders from a single logistic regression. This can lead to mistaken interpretations of these estimates. We use causal diagrams to display the sources of the problems. Presentation of exposure and confounder effect estimates from a single model may lead to several interpretative difficulties, inviting confusion of direct-effect estimates with total-effect estimates for covariates in the model. These effect estimates may also be confounded even though the effect estimate for the main exposure is not confounded. Interpretation of these effect estimates is further complicated by heterogeneity (variation, modification) of the exposure effect measure across covariate levels. We offer suggestions to limit potential misunderstandings when multiple effect estimates are presented, including precise distinction between total and direct effect measures from a single model, and use of multiple models tailored to yield total-effect estimates for covariates.

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Is Open Access

Studying complexity in health services research: desperately seeking an overdue paradigm shift

Trisha Greenhalgh, Chrysanthi Papoutsi (2018)

Complexity is much talked about but sub-optimally studied in health services research. Although the significance of the complex system as an analytic lens is increasingly recognised, many researchers are still using methods that assume a closed system in which predictive studies in general, and controlled experiments in particular, are possible and preferred. We argue that in open systems characterised by dynamically changing inter-relationships and tensions, conventional research designs predicated on linearity and predictability must be augmented by the study of how we can best deal with uncertainty, unpredictability and emergent causality. Accordingly, the study of complexity in health services and systems requires new standards of research quality, namely (for example) rich theorising, generative learning, and pragmatic adaptation to changing contexts. This framing of complexity-informed health services research provides a backdrop for a new collection of empirical studies. Each of the initial five papers in this collection illustrates, in different ways, the value of theoretically grounded, methodologically pluralistic, flexible and adaptive study designs. We propose an agenda for future research and invite researchers to contribute to this on-going series.

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Author and article information

Contributors

Francesco Checchi:

ORCID: http://orcid.org/0000-0001-9030-5382

francesco.checchi@lshtm.ac.uk

Journal

Journal ID (nlm-ta): BMC Med

Journal ID (iso-abbrev): BMC Med

Title: BMC Medicine

Publisher: BioMed Central (London )

ISSN (Electronic): 1741-7015

Publication date (Electronic): 5 December 2023

Publication date PMC-release: 5 December 2023

Publication date Collection: 2023

Volume: 21

Electronic Location Identifier: 484

Affiliations

[1 ]Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, ( https://ror.org/00a0jsq62) London, UK

[2 ]International Federation of Red Cross and Red Crescent Societies, ( https://ror.org/040at4140) Geneva, Switzerland

[3 ]Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, ( https://ror.org/00a0jsq62) London, UK

[4 ]UK Public Health Rapid Support Team, London School of Hygiene & Tropical Medicine, ( https://ror.org/00a0jsq62) London, UK

[5 ]Red Cross Society of the Democratic Republic of Congo, Kinshasa, Democratic Republic of the Congo

Author information

Francesco Checchi http://orcid.org/0000-0001-9030-5382

Article

Publisher ID: 3194

DOI: 10.1186/s12916-023-03194-x

PMC ID: 10696665

PubMed ID: 38049815

SO-VID: 46a84581-a8a1-4368-80e7-805ae9b050c0

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.

History

Date received : 25 March 2023

Date accepted : 23 November 2023

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100012056, Enhancing Learning and Research for Humanitarian Assistance;

Award ID: Research for Health in Humanitarian Crises (R2HC) programme

Award ID: grant number 43670

Award Recipient : Francesco Checchi

Custom metadata

ScienceOpen disciplines: Medicine

Keywords: ebola,epidemic,burial,dead body,performance,intervention,evaluation,democratic republic of congo

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ScienceOpen disciplines: Medicine

Keywords: ebola, epidemic, burial, dead body, performance, intervention, evaluation, democratic republic of congo

Performance of a safe and dignified burial intervention during an Ebola epidemic in the eastern Democratic Republic of the Congo, 2018–2019

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AIP Publishing: Coronavirus

Most cited references 28

The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies.

The table 2 fallacy: presenting and interpreting confounder and modifier coefficients.

Studying complexity in health services research: desperately seeking an overdue paradigm shift

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