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

The geographic scope and timeframe of analysis were North Kivu, Ituri and South Kivu provinces (population ≈ 6,300,000) from 6 August 2018 (date of first recorded SDB dispatch alert) until 10 October 2019, comprising about 91% of all confirmed or probable EVD cases during the epidemic. All SDB alerts received during this period were eligible for analysis.

Figure 1 represents the intended SDB process as a flowchart. Once a death was reported, the ‘Alerts System’ was activated. For EBOV-positive deaths in an Ebola treatment centre (ETC), a dispatch alert was sent directly to the SDB team whereas for suspected cases in the community and in other health facilities, the MoH and WHO Case Investigation Team was first notified in order to determine whether the decedent met the case definition of a suspected EVD case (Additional file 1, Table S1). If the decedent was determined to be a suspect case, a dispatch alert was sent to the closest SDB sub-coordination hub (geographical base within which different SDB teams were based), who dispatched a team to the decedent’s location. The team’s expected composition is detailed in Additional file 1, Table S2.

Once on site, the SDB team was expected to perform the following core tasks: collection of an oral swab specimen for EBOV testing, if not already taken; securing of the body by disinfection with 0.5% chlorine and placement in a leak-proof body bag; decontamination of the dwelling where the deceased resided with 0.5% chlorine; decontamination of the deceased’s belongings with 0.5% chlorine (if not possible, these were to be destroyed safely or placed in the grave); and burial of the body at a site agreed with relatives in a grave at least two metres deep covered by at least one and a half metres of earth to protect it from wild animals. For suspected cases, SDB was to be performed prior to obtaining EBOV test results if consent was provided by the family; if not, the body was to be taken to a mortuary to await results. During burial, family representatives were invited to join the SDB team in securing the body. In order to improve acceptance and inclusivity, the Red Cross employed community engagement and accountability personnel who trained volunteers and liaised with community and religious leaders as well as the family to assist with any feasible modifications to the standard SDB. These staff also analysed community feedback information to identify improvements to SDB services (the use of community feedback information generated by the Red Cross during the epidemic is the subject of a separate paper [6]).

The IFRC maintained an individual, standardised Excel database of all SDB ‘alerts’ received (i.e. requests by a community, a health facility or other actors to deploy a team to assist with burial): this database recorded details of the decedent’s demographic profile, dates (day, time) of alert and completion of different SDB steps, whether key activities in the SDB package were successfully accomplished, and reasons for unsuccessful/incomplete burial deployments. Because the dataset does not distinguish mobile from static (CEHRB) SDBs supported by Civil Protection teams, our analysis henceforth considers three SDB actors: (i) Red Cross mobile teams, (ii) Red Cross-supported CEHRB and (iii) Civil Protection mobile or CEHRB teams.

After applying range and consistency checks, we analysed the SDB database to compute a set of key performance indicators of intervention fidelity and performance (Table 1). The targets in Table 1 were set by the Red Cross team as aspirational benchmarks of success and were not tied to quantitative epidemiological control requirements.

We developed a causal framework of domains and factors potentially determining SDB performance (Additional file 1, Fig. S1). The framework was based on the authors’ knowledge of EVD and associated response interventions, the context and published literature. We then sourced data that would capture as many of the domains in the framework as possible. Most explanatory variables were sourced from key humanitarian websites and EVD response actors in DRC, but some (e.g. lack of community acceptance) were contained within the SDB dataset itself. Explanatory and population datasets we identified are described in Additional file 1 (Table S3): we classified these within (i) distal (gender; age; density of health facilities; road network; mining activity); (ii) intermediate (stage of the epidemic locally; recent EVD incidence; deaths due to insecurity events; attacks against the EVD response; suspensions of EVD services; availability of a nearby EVD treatment centre; number of phone networks; number of radio frequencies); and (iii) proximal (type of SDB team; setting of death; community and/or family nonacceptance of the SDB) levels of causality. Where appropriate, we divided the variable of interest by population to compute a rate and created categories out of continuous variables. We expected that some of the variables would mainly function as proxies of the causal domains of interest, rather than direct measures: for example, mining activity may capture differences in overall economic livelihood as well as movement patterns, in turn affecting EBOV transmission.

We modelled the binomial probability of unsuccessful SDB with a logit link function, defined as per Table 1, as a function of any of the above explanatory variables. We fitted a generalised linear mixed model of the log odds of SDB failure, specifying the sub-coordination hub as a random effect, since we assumed observations within each hub to be correlated. We firstly computed univariate associations, screening out variables if they had an association with p-value ≥ 0.20. We next fitted a model composed of the remaining distal variables, retaining any that were significant at p-value < 0.05, heavily influenced the coefficient of any of the other associations or improved the model’s goodness-of-fit based on the Akaike Information Criterion values of models with and without the variable; we then added intermediate level variables, retaining variables based on the above rules, and lastly proximal variables. We considered potential effect modifications but none were evident. We also observed model diagnostics including the distribution of residuals and their correlation with observations and fitted values. As this was an observational study, we include the Strengthening the Reporting of OBservational studies in Epidemiology (STROBE) checklist [23] in Additional file 1.

A total of 14,624 dispatch alerts were recorded in the Red Cross SDB database between 6 August 2018 and 10 October 2019 (Table 2). Of these, 79% originated from North Kivu, 21% from Ituri and 0.01% from South Kivu. Beni health zone experienced the highest number of alerts (2851, 20%), followed by Katwa (1592, 11%), Bunia (1,251, 9%) and Butembo (1216, 8%); see Additional file 1, Table S4 for data by health zone. ETCs and patient transit centres accounted for 9% of all alerts (1,160 and 135, respectively), while the majority of alerts were from community settings, including health facilities, households and other sites. SDB responses increased in volume throughout the epidemic period, even as reported cases declined (Fig. 2).

About half of responses (n = 7627, 53%) were conducted by Civil Protection teams, including mobile or static CEHRB (Fig. 2). Among the 6755 SDBs supported by the Red Cross, the CEHRB approach was adopted for 841 alerts (13%). Overall, 52% (7740) of alerts were for male decedents. More than half of alerts (57%) were for individuals aged ≥ 18 years old but a considerable number were for infants (Table 2). Only 7% (1003) of alerts featured a positive EBOV test result.

Table 3 summarises key performance indicators by setting of death and EVD status. Excluding false alerts, 1% (161) of dispatch alerts were recorded as not responded to by either a mobile or CEHRB team. The main recorded reasons for these instances of non-response were community and/or family nonacceptance (39%) and insecurity (32%). Logistical issues (21%) included no team being available (8%), a lack of supply materials (body bags, coffins or personal protective equipment) and/or transport (8%), and inaccessibility of locations (5%).

Community and/or family nonacceptance once the team actually arrived on site was recorded for 13% of SDB responses. The proportion of suspected or confirmed EVD deaths receiving SDBs (61%) was lower than the 80% target. However, this target was surpassed (91%) if only confirmed cases are considered.

Considering community responses only, collection of oral swabs was near target at 94% completion; securing of the body and disinfection of dwellings featured lower completion (78% and 76% for suspected and confirmed cases, respectively, increasing to 94% and 88% if considering EBOV+ deaths only). Recorded reasons for SDB failure at community level included, as above, community and/or family nonacceptance, insecurity and logistical issues (data not shown).

In terms of timing, 89% of SDB responses were completed within 24 h, exceeding the target of 80%, while almost all (99.9%) were completed within 72 h. In both Ituri and North Kivu, from December 2018 onwards the proportion of timely SDBs increased, but both provinces experienced large monthly fluctuations in the proportion of successful SDBs throughout 2019 (Fig. 3). Overall, the proportion of all alerts successfully responded to (66%) fell short of the 80% target.

A total of 17 variables ranging from proximal to distal were assessed for their association with SDB failure, 12 of which were carried into multivariate analysis. The final model (Table 4) included the following explanatory variables: gender, number of health facilities per population, stage in the epidemic, presence of any confirmed EVD cases during the previous 6 weeks, occurrence of any suspensions in the EVD response during the previous 6 weeks, number of deaths due to insecurity events per population during the previous 2 weeks, presence of an operational EVD treatment centre, number of mobile phone networks, number of radio frequencies with reception, type of SDB team, occurrence of community and/or family nonacceptance of the SDB and setting in which the death occurred.

At the distal level, reduced health facility coverage per population was associated with higher odds of SDB failure (odds ratio [OR] 1.57, 95% confidence interval [95%CI] 1.07 to 2.30 comparing the lowest- and highest-coverage HZs; p = 0.021). At the intermediate level, odds of failure were higher when the local epidemic was ongoing (OR 2.14, 95%CI 1.35 to 3.38; p = 0.001) or ended (OR 3.60, 95%CI 2.32 to 5.58, p < 0.001), compared to pre-epidemic (namely the period before the first ascertained EVD case in a given HZ, during which the SDB service responded only to alerts for decedents who were EBOV- or had unknown infection status); however, the local occurrence of confirmed cases in the previous month and a half was associated with about half the odds of failure (OR 0.49, 95%CI 0.40 to 0.60; p < 0.001). Suspensions of EVD response activities, as well as insecurity, were associated with 1.61 (95%CI 1.43 to 1.82; p < 0.001) and 1.73 (OR 1.47 to 2.03; p < 0.001) times higher odds of failure respectively, as were the presence of an EVD treatment facility locally (OR 3.59, 95%CI 2.92 to 4.42, p < 0.001), and a high coverage of mobile telephony (OR 1.53, 95%CI 1.10 to 2.12; p = 0.011) and radio (OR 7.43, 95%CI 5.77 to 9.58, comparing the highest- and lowest-coverage HZs, p < 0.001). At the proximal level, SDBs carried out by Red Cross-supported local community members under the static CEHRB programme or mainly supported by Civil Protection teams featured 0.25 (95%CI 0.18 to 0.35; p < 0.001) and 0.61 (95%CI 0.54 to 0.70; p < 0.001) times lower odds of failure than other SDBs, respectively. Any community and/or family nonacceptance was strongly associated with SDB failure (OR 40.20, 95%CI 32.06 to 50.39; p < 0.001). Lastly, SDBs for deaths in hospitals and in communities had markedly higher odds of failure (7.84, 95%CI 5.73 to 10.73; p < 0.001 and 11.18, 95%CI 8.15 to 15.34; p < 0.001, respectively) compared to deaths in EVD treatment or isolation centres.

We also fitted a model to SDB data restricted to known EBOV + decedents (n = 1003; see Additional file 1, Table S5). Some variables, including epidemic stage and SDB team type, were not featured in this model due to data sparsity. The associations with communication means (phone and radio network availability) were not evident in this model, but strong associations of SDB failure with EVD response suspensions, community and/or family nonacceptance and setting of death remained.

The DRC remains affected by crisis conditions. During the main epidemic year of 2018, 1.8 million people were displaced by violence and 12.8 million were in need of humanitarian assistance [24]. With > 100 active armed groups, the eastern DRC is a challenging environment in which to conduct epidemic responses [8]. In such a context, the National Red Cross Society, supported by the IFRC, can leverage its large volunteer network and mandate to support epidemic-affected communities. The Red Cross-led SDB service became available within the first days of the epidemic declaration and achieved a remarkable volume of activity over the 14-month period we analysed. The service’s scale-up was however somewhat delayed, potentially missing opportunities to contain transmission clusters during the first 2–3 months of the outbreak. Conversely, towards the end of the epidemic, the introduction of more specific case definitions or criteria for requesting SDB might have improved efficiency.

SDB teams almost ubiquitously responded to alerts and met timeliness targets. The proportion of successful SDB responses was below target, though low performance was mainly circumscribed to a period from February to May 2019: this period saw high-profile killings of EVD responders, attacks against treatment centres [25, 26] and increased insecurity in health zones reporting EBOV transmission, particularly in Ituri [27], as shown in Fig. 3. Direct threats against Red Cross international staff also occurred during this period, resulting in temporary evacuations from North Kivu. Performance was also lower when SDB teams responded to suspected, rather than confirmed EVD deaths. Despite these reductions in service performance, we find in a separate paper (in preparation) that SDB was associated with substantial reductions in EBOV transmission.

We identified notable risk factors for SDB failure. Plausibly, local insecurity and interruptions to the EVD response were associated with higher odds of failure. Insecurity constrained the EVD response generally and, specifically, reduced SDB access to communities [28]. Burial supported by Civil Protection teams (mobile or CEHRB), and/or by CEHRB teams supported by the Red Cross, was associated with a lower odds of failure, suggesting the benefit of empowering local authorities and localising services to affected communities themselves (note that the true proportion of CEHRB SDBs includes an unknown number supported by Civil Protection). Strong community engagement has been shown to contribute to the effectiveness of EVD response in the West African epidemic [29, 30]. On the other hand, Civil Protection teams, unlike the Red Cross, sometimes travelled with armed escorts and may have contributed to ‘militarising’ the EVD response, with consequences for trust and acceptance [31].

Generally, SDB teams encountered limited community and/or family nonacceptance, but its occurrence was associated with a very high odds of failure, as noted in the West Africa epidemic and more broadly for EVD response interventions in DRC [32‐35]. In the DRC, the Red Cross has engaged with affected communities through a Community Engagement and Accountability approach [36], whereby community volunteers routinely collect information regarding local knowledge, attitudes and practices in order to systematically respond to needs and concerns and adapt EVD response programming to meet communities’ expressed needs. These volunteers accompany SDB teams, liaising with families, community and religious leaders to safely adjust burial procedures based on local customs and preferences. In West Africa, a similar model of mediation, where implemented, increased SDB acceptance [37].

We found that the presence of communication infrastructure (radio, mobile network), as well as community exposure to EVD (health zones being in the midst or past their epidemic; a nearby EVD treatment centre) were associated with higher SDB failure odds. Communication means should in theory facilitate dissemination of response information and healthy behaviours [38]. In this scenario, however, increased communication may have facilitated the spread of negative views of the EVD response, information on inappropriate practices among responders, or conspiracy theories. The presence of communication infrastructure may have had a particularly marked effect once the epidemic had reached a given HZ, explaining why pre-epidemic SDBs were more successful: however, this effect modification was not evident statistically. Moreover, the above risk factors may be proxies rather than direct measures of the infrastructure causal domain. Generally, our and other studies’ findings need to be interpreted with reference to a context of chronic disenfranchisement and inadequate provision of health and other essential services, which time-bound, outbreak-focussed community engagement activities might not sufficiently address [8].

This was a retrospective evaluation, featuring no direct observation of SDB activities and relying principally on monitoring data not originally collected for research purposes. While we observed no patterns suggesting data fabrication, some SDB teams may have felt an incentive to overstate successful activities. Instances of CEHRB burial were anecdotally subject to greater-than-average data completeness and timeliness problems, which may have resulted in under-reporting of failed or untimely alert responses, and thus an over-estimation of the programme’s performance. The SDB dataset contained some ambiguities. EVD status was not ascertained or communicated to the SDB database for a large proportion of deaths in the community: these decedents were considered suspect cases and thus included in our analysis, but some may in fact not have met the suspect case definition, potentially biasing our estimates of key performance indicators. Findings could have been strengthened through qualitative exploration of the perceptions of EVD-affected communities and SDB staff. Separately, we are analysing community feedback data collected by Red Cross volunteers to shed light on this. Lastly, our risk factor analysis was limited by the explanatory and confounder variables that we were able to source: the model would have been strengthened by other variables for which we were unable to find appropriate and complete data: hidden confounding may therefore be present in our analysis. Specifically, the extent to which community engagement and feedback during specific SDB instances were used to adapt the service locally or generally could have been an important factor behind SDB success, and would have thus confounded the other associations. Separately the limited quality of data may have introduced random or non-random error (the former is likely, and would generally bias estimates of association towards non-significance). Generally, exploratory risk factor models with multiple adjustment are subject to the well-described ‘Table 2 fallacy’ [39], and should be interpreted with caution so as, in this case, identify factors that should be evaluated further and considered carefully in future interventions.

While we were able to analyse SDB performance when the service was called upon, we were unable to quantify its coverage (proportion of EBOV+ decedents for whom a SDB was performed). The coverage numerator (EBOV+ SDB burials) is unclear due to the many SDB instances for which no EBOV serostatus was recorded in the database after sample collection (Table 3). The denominator contains an unknown number of EVD deaths that escaped case ascertainment (at least one report [40] suggests a substantial fraction of undetected cases). Because burial at the community level mostly could not be delayed while awaiting results of EBOV testing, the SDB service targeted all decedents that community members may have suspected as EVD cases, meaning about four times as many SDBs were performed as all known EVD cases in the epidemic. If a crude measure of coverage is adopted, whereby any decedent during the epidemic should have received SDB, then over the entire population of HZs affected by the epidemic over the 14 months of the analysis period, and assuming a crude annual death rate of about 10 per 1000 for DRC [41], some 66,000 deaths would have been expected, meaning SDB coverage would have been ≈ 20%: this is likely to be a gross underestimate of the true coverage, as it is plausible that true EVD cases would have been much more likely than at random to have been reached by the SDB service, since the latter was only triggered by cases meeting the EVD suspect definition.

Generally, our study evaluates only one aspect of the SDB service and omits dimensions such as its feasibility, fidelity to different components, acceptability, equity and cost-effectiveness, some of which would be illuminated by a more comprehensive implementation science approach to evaluation adopting epidemiological, social science and health economics methods [42].

This evaluation was enabled by collection of systematic, quality monitoring data on SDBs by the IFRC and partners, reinforcing the benefit of such programmatic data collection. Our evidence shows that a large-scale, timely and moderately performant SDB service was feasible despite the challenging circumstances of the EVD response in eastern DRC. Failed SDBs due to the inability to secure the body or supervise burials suggest weaknesses in community engagement. The considerable number of SDBs for which EVD status remained unclear indicates a gap in testing or reporting of results. Community and/or family nonacceptance, while infrequent, is a key barrier to SDB effectiveness: an acceptance approach emphasising cooperation and with communities and other stakeholders should be pursued [17, 43]. Critically, the apparent success of localised SDB implemented by static teams within affected communities (the CEHRB programme) suggests this model should be relied upon and documented further in future EVD epidemics, particularly where accessibility is a constraint for mobile teams. Its potential benefits, however, should be carefully weighed against risks (insufficient burial safety, infection of SDB performers and family members). Lastly, our findings suggest that the availability of communication means and access to news may not necessarily improve community sentiment about epidemic responses, and trust in control interventions or their implementing actors: as noted by others [8, 34], solutions to this are likely to involve a better understanding of how information flows, engagement with communities through consultative processes based on active social listening, and empowering people themselves to be the main bearers of helpful knowledge.