<i>Notes from the Field</i>: Outbreak of <i>Vibrio cholerae</i> Associated with Attending a Funeral — Chegutu District, Zimbabwe, 2018

McAteer, Jarred B.; Danda, Sydney; Nhende, Tonderai; Manamike, Paul; Parayiwa, Tonderai; Tarupihwa, Andrew; Tapfumanei, Ottias; Manangazira, Portia; Mhlanga, Gibson; Garone, Daniela; Martinsen, Andrea; Aubert, Rachael D.; Davis, William W.; Narra, Rupa; Balachandra, Shirish; Barr, Beth A. Tippett; Mintz, Eric D.

doi:10.15585/mmwr.mm6719a6

Cited by 9 publications

(5 citation statements)

References 6 publications

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“…Funeral attendance and contact with symptomatic diarrhoea patients, variables with p-values between 0.1 and 0.05 in our study, are epidemiologically plausible risk factors for cholera infection and in line with other research [ 31 , 32 ]. Contact with a person showing symptoms of diarrhoea might have also functioned as a proxy of other unexplored risk factors in our study like shared contaminated water sources.…”

Section: Discussionsupporting

confidence: 93%

Sachet water consumption as a risk factor for cholera in urban settings: Findings from a case control study in Kinshasa, Democratic Republic of the Congo during the 2017–2018 outbreak

et al. 2021

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Background Behavioural risk factors for cholera are well established in rural and semi-urban contexts, but not in densely populated mega-cities in Sub-Saharan Africa. In November 2017, a cholera epidemic occurred in Kinshasa, the Democratic Republic of the Congo, where no outbreak had been recorded for nearly a decade. During this outbreak, we investigated context-specific risk factors for cholera in an urban setting among a population that is not frequently exposed to cholera. Methodology/Principal findings We recruited 390 participants from three affected health zones of Kinshasa into a 1:1 matched case control study. Cases were identified from cholera treatment centre admission records, while controls were recruited from the vicinity of the cases’ place of residence. We used standardized case report forms for the collection of socio-demographic and behavioural risk factors. We used augmented backward elimination in a conditional logistic regression model to identify risk factors. The consumption of sachet water was strongly associated with the risk of being a cholera case (p-value 0.019), which increased with increasing frequency of consumption from rarely (OR 2.2, 95% CI 0.9–5.2) to often (OR 4.0, 95% CI 1.6–9.9) to very often (OR 4.1, 95% CI 1.0–16.7). Overall, more than 80% of all participants reported consumption of this type of drinking water. The risk factors funeral attendance and contact with someone suffering from diarrhoea showed a p-value of 0.09 and 0.08, respectively. No socio-demographic characteristics were associated with the risk of cholera. Conclusions/Significance Drinking water consumption from sachets, which are sold informally on the streets in most Sub-Saharan African cities, are an overlooked route of infection in urban cholera outbreaks. Outbreak response measures need to acknowledge context-specific risk factors to remain a valuable tool in the efforts to achieve national and regional targets to reduce the burden of cholera in Sub-Saharan Africa.

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Section: Discussionsupporting

confidence: 93%

Sachet water consumption as a risk factor for cholera in urban settings: Findings from a case control study in Kinshasa, Democratic Republic of the Congo during the 2017–2018 outbreak

et al. 2021

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“…Long delays from symptom onset of the primary case to response (~ 2 weeks) were observed in 29/67 (43.2%) outbreaks for which a response date was available. These appeared to be related to poor sensitivity of the formal surveillance system due to the remote locations of outbreaks [ 63 ] (Papua New Guinea, ID 52); insecurity posed by armed conflict (Somalia, 2008, ID 54; South Sudan, 2008, ID 57; Yemen, 2011, ID 72); reliance on laboratory confirmation to declare an outbreak before initiating a comprehensive response (Iraq, 2008, 2015, ID 33, 34, South Sudan, 2014, ID 58); assuring government declaration and mobilization of non-governmental actors (CAR, 2011, ID 16); a less effective local response which required reinforcement by capacity from the national level or other partners (Congo, 2018, ID 25; Ethiopia, 2015, ID 28; Guinea-Bissau, 2008, ID 31; South Sudan, 2017, ID 61; Uganda, 2015, ID 68; Zimbabwe, 2018, ID 79) [ 113 ]; and missed superspreading events (e.g., a funeral in Zimbabwe, 2018, ID 79) [ 136 ].…”

Section: Resultsmentioning

confidence: 99%

Early detection of cholera epidemics to support control in fragile states: estimation of delays and potential epidemic sizes

Ratnayake

Finger²,

Edmunds

et al. 2020

BMC Med

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Background Cholera epidemics continue to challenge disease control, particularly in fragile and conflict-affected states. Rapid detection and response to small cholera clusters is key for efficient control before an epidemic propagates. To understand the capacity for early response in fragile states, we investigated delays in outbreak detection, investigation, response, and laboratory confirmation, and we estimated epidemic sizes. We assessed predictors of delays, and annual changes in response time. Methods We compiled a list of cholera outbreaks in fragile and conflict-affected states from 2008 to 2019. We searched for peer-reviewed articles and epidemiological reports. We evaluated delays from the dates of symptom onset of the primary case, and the earliest dates of outbreak detection, investigation, response, and confirmation. Information on how the outbreak was alerted was summarized. A branching process model was used to estimate epidemic size at each delay. Regression models were used to investigate the association between predictors and delays to response. Results Seventy-six outbreaks from 34 countries were included. Median delays spanned 1–2 weeks: from symptom onset of the primary case to presentation at the health facility (5 days, IQR 5–5), detection (5 days, IQR 5–6), investigation (7 days, IQR 5.8–13.3), response (10 days, IQR 7–18), and confirmation (11 days, IQR 7–16). In the model simulation, the median delay to response (10 days) with 3 seed cases led to a median epidemic size of 12 cases (upper range, 47) and 8% of outbreaks ≥ 20 cases (increasing to 32% with a 30-day delay to response). Increased outbreak size at detection (10 seed cases) and a 10-day median delay to response resulted in an epidemic size of 34 cases (upper range 67 cases) and < 1% of outbreaks < 20 cases. We estimated an annual global decrease in delay to response of 5.2% (95% CI 0.5–9.6, p = 0.03). Outbreaks signaled by immediate alerts were associated with a reduction in delay to response of 39.3% (95% CI 5.7–61.0, p = 0.03). Conclusions From 2008 to 2019, median delays from symptom onset of the primary case to case presentation and to response were 5 days and 10 days, respectively. Our model simulations suggest that depending on the outbreak size (3 versus 10 seed cases), in 8 to 99% of scenarios, a 10-day delay to response would result in large clusters that would be difficult to contain. Improving the delay to response involves rethinking the integration at local levels of event-based detection, rapid diagnostic testing for cluster validation, and integrated alert, investigation, and response.

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“…However, in this study and previous studies (Mukandavire et al, 2011;Mukandavire et al, 2013) vaccination coverage estimates were based on direct vaccine protection and would potentially reduce if herd protection of cholera vaccines was taken into account (Ali et al, 2005). In the context of the Budiriro and Glen View cholera outbreak, the required vaccination coverage needed to achieve outbreak control could be further reduced with population immunity (Nelson et al, 2009) from recent sporadic cholera outbreaks (McAteer et al, 2018).…”

Section: Discussionmentioning

confidence: 84%

Stemming cholera tides in Zimbabwe through mass vaccination

Mukandavire¹,

Manangazira²,

Nyabadza³

et al. 2020

International Journal of Infectious Diseases

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Background: In 2018, Zimbabwe declared another major cholera outbreak a decade after recording one of the worst cholera outbreaks in Africa. Methods: A mathematical model for cholera was used to estimate the magnitude of the cholera outbreak and vaccination coverage using cholera cases reported data. A Markov chain Monte Carlo method based on a Bayesian framework was used to fit the model in order to estimate the basic reproductive number and required vaccination coverage for cholera control. Results: The results showed that the outbreak had a basic reproductive number of 1.82 (95% credible interval [CrI] 1.53-2.11) and required vaccination coverage of at least 58% (95% Crl 45-68%) to be contained using an oral cholera vaccine of 78% efficacy. Sensitivity analysis demonstrated that a vaccine with at least 55% efficacy was sufficient to contain the outbreak but at higher coverage of 75% (95% Crl 58-88%). However, high-efficacy vaccines would greatly reduce the required coverage, with 100% efficacy vaccine reducing coverage to 45% (95% Crl 35-53%). Conclusions: These findings reinforce the crucial need for oral cholera vaccines to control cholera in Zimbabwe, considering that the decay of water reticulation and sewerage infrastructure is unlikely to be effectively addressed in the coming years.

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Notes from the Field: Outbreak of Vibrio cholerae Associated with Attending a Funeral — Chegutu District, Zimbabwe, 2018

Cited by 9 publications

References 6 publications

Sachet water consumption as a risk factor for cholera in urban settings: Findings from a case control study in Kinshasa, Democratic Republic of the Congo during the 2017–2018 outbreak

Sachet water consumption as a risk factor for cholera in urban settings: Findings from a case control study in Kinshasa, Democratic Republic of the Congo during the 2017–2018 outbreak

Early detection of cholera epidemics to support control in fragile states: estimation of delays and potential epidemic sizes

Stemming cholera tides in Zimbabwe through mass vaccination

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