Highly targeted spatiotemporal interventions against cholera epidemics, 2000–19: a scoping review

Ratnayake, Ruwan; Finger, Flavio; Azman, Andrew S.; Lantagne, Daniele; Funk, Sebastian; Edmunds, W. John; Checchi, Francesco

doi:10.1016/s1473-3099(20)30479-5

Cited by 27 publications

(31 citation statements)

References 89 publications

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“…Inadequate preparedness and poor access to case management drives increased mortality. The rapid detection and control of small outbreaks is therefore key for efficient control before an epidemic propagates [9].…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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“…A recent scoping review reported evidence of some interventions CATI activities such as antibiotic chemoprophylaxis, single-dose OCV, intensive hygiene promotion, and point-of-use water treatment to rapidly limit cholera transmission in the household and its high-risk radius. [36] Several measures of CATI effectiveness were identified in our study, including timeliness, completeness, coverage, and implementation cost. While these criteria provided information on strength of CATIs implementation, they did not measure impact in terms of reduced incidence of cholera.…”

Section: Discussionmentioning

confidence: 99%

“…A recent review reported a 100m radius around the case as appropriate but proposed further study of implementation feasibility in urban settings. [36] Additionally, a study in Kinshasa, DRC reported the use of a targeted grid approach delivering WASH interventions to contain the outbreak in urban settings. [37] The feasibility of using a certain number of households as compared to a ring may depend on settings.…”

Section: Discussionmentioning

confidence: 99%

Case-area targeted preventive interventions to interrupt cholera transmission: Current implementation practices and lessons learned

et al. 2021

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Background Cholera is a major cause of mortality and morbidity in low-resource and humanitarian settings. It is transmitted by fecal-oral route, and the infection risk is higher to those living in and near cholera cases. Rapid identification of cholera cases and implementation of measures to prevent subsequent transmission around cases may be an efficient strategy to reduce the size and scale of cholera outbreaks. Methodology/Principle findings We investigated implementation of cholera case-area targeted interventions (CATIs) using systematic reviews and case studies. We identified 11 peer-reviewed and eight grey literature articles documenting CATIs and completed 30 key informant interviews in case studies in Democratic Republic of Congo, Haiti, Yemen, and Zimbabwe. We documented 15 outbreaks in 11 countries where CATIs were used. The team composition and the interventions varied, with water, sanitation, and hygiene interventions implemented more commonly than those of health. Alert systems triggering interventions were diverse ranging from suspected cholera cases to culture confirmed cases. Selection of high-risk households around the case household was inconsistent and ranged from only one case to approximately 100 surrounding households with different methods of selecting them. Coordination among actors and integration between sectors were consistently reported as challenging. Delays in sharing case information impeded rapid implementation of this approach, while evaluation of the effectiveness of interventions varied. Conclusions/Significance CATIs appear effective in reducing cholera outbreaks, but there is limited and context specific evidence of their effectiveness in reducing the incidence of cholera cases and lack of guidance for their consistent implementation. We propose to 1) use uniform cholera case definitions considering a local capacity to trigger alert; 2) evaluate the effectiveness of individual or sets of interventions to interrupt cholera, and establish a set of evidence-based interventions; 3) establish criteria to select high-risk households; and 4) improve coordination and data sharing amongst actors and facilitate integration among sectors to strengthen CATI approaches in cholera outbreaks.

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“…Localised spatiotemporal interventions have been trialled for several conditions, such as cholera, and have been broadly successful. 2 Real-time surveillance tools can provide an important and timely input into targeting them.…”

Section: Discussionmentioning

confidence: 99%

“…For example, Ratnayake et al review the use of case area-targeted intervention (CATI) for cholera, which entails targeting a range of measures at small areas (50–100 m), including chemoprophylaxis, water treatment and vaccination, in response to the early detection of an outbreak. 2 However, the effective use of CATI requires both reliable early cluster detection and a good understanding of the spatial and temporal distribution of cases and clusters in order to delineate ‘case-areas’.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal analysis of the first wave of COVID-19 hospitalisations in Birmingham, UK

et al. 2021

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ObjectivesTo evaluate the spatiotemporal distribution of the incidence of COVID-19 hospitalisations in Birmingham, UK during the first wave of the pandemic to support the design of public health disease control policies.DesignA geospatial statistical model was estimated as part of a real-time disease surveillance system to predict local daily incidence of COVID-19.ParticipantsAll hospitalisations for COVID-19 to University Hospitals Birmingham NHS Foundation Trust between 1 February 2020 and 30 September 2020.Outcome measuresPredictions of the incidence and cumulative incidence of COVID-19 hospitalisations in local areas, its weekly change and identification of predictive covariates.ResultsPeak hospitalisations occurred in the first and second weeks of April 2020 with significant variation in incidence and incidence rate ratios across the city. Population age, ethnicity and socioeconomic deprivation were strong predictors of local incidence. Hospitalisations demonstrated strong day of the week effects with fewer hospitalisations (10%–20% less) at the weekend. There was low temporal correlation in unexplained variance. By day 50 at the end of the first lockdown period, the top 2.5% of small areas had experienced five times as many cases per 10 000 population as the bottom 2.5%.ConclusionsLocal demographic factors were strong predictors of relative levels of incidence and can be used to target local areas for disease control measures. The real-time disease surveillance system provides a useful complement to other surveillance approaches by producing real-time, quantitative and probabilistic summaries of key outcomes at fine spatial resolution to inform disease control programmes.

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Highly targeted spatiotemporal interventions against cholera epidemics, 2000–19: a scoping review

Cited by 27 publications

References 89 publications

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

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

Case-area targeted preventive interventions to interrupt cholera transmission: Current implementation practices and lessons learned

Spatiotemporal analysis of the first wave of COVID-19 hospitalisations in Birmingham, UK

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