Mitigating Cholera in the Aftermath of Cyclone Idai

Risks of infectious disease hospitalisations in the aftermath of tropical cyclones: a multi-country time-series study

BACKGROUND

The proportion of intense tropical cyclones is expected to increase in a changing climate. However, there is currently no consistent and comprehensive assessment of infectious disease risk following tropical cyclone exposure across countries and over decades. We aimed to explore the tropical cyclone-associated hospitalisation risks and burden for cause-specific infectious diseases on a multi-country scale.

METHODS

Hospitalisation records for infectious diseases were collected from six countries and territories (Canada, South Korea, New Zealand, Taiwan, Thailand, and Viet Nam) during various periods between 2000 and 2019. The days with tropical cyclone-associated maximum sustained windspeeds of 34 knots or higher derived from a parametric wind field model were considered as tropical cyclone exposure days. The association of monthly infectious diseases hospitalisations and tropical cyclone exposure days was first examined at location level using a distributed lag non-linear quasi-Poisson regression model, and then pooled using a random-effects meta-analysis. The tropical cyclone-attributable number and fraction of infectious disease hospitalisations were also calculated.

FINDINGS

Overall, 2·2 million people who were hospitalised for infectious diseases in 179 locations that had at least one tropical cyclone exposure day in the six countries and territories were included in the analysis. The elevated hospitalisation risks for infectious diseases associated with tropical cyclones tended to dissipate 2 months after the tropical cyclone exposure. Overall, each additional tropical cyclone day was associated with a 9% (cumulative relative risk 1·09 [95% CI 1·05-1·14]) increase in hospitalisations for all-cause infectious diseases, 13% (1·13 [1·05-1·21]) for intestinal infectious diseases, 14% (1·14 [1·05-1·23]) for sepsis, and 22% (1·22 [1·03-1·46]) for dengue during the 2 months after a tropical cyclone. Associations of tropical cyclones with hospitalisations for tuberculosis and malaria were not significant. In total, 0·72% (95% CI 0·40-1·01) of the hospitalisations for all-cause infectious diseases, 0·33% (0·15-0·49) for intestinal infectious diseases, 1·31% (0·57-1·95) for sepsis, and 0·63% (0·10-1·04) for dengue were attributable to tropical cyclone exposures. The attributable burdens were higher among young populations (aged ≤19 years) and male individuals compared with their counterparts, especially for intestinal infectious diseases. The heterogeneous spatiotemporal pattern was further revealed at the country and territory level-tropical cyclone-attributable fractions showed a decreasing trend in South Korea during the study period but an increasing trend in Viet Nam, Taiwan, and New Zealand.

INTERPRETATION

Tropical cyclones were associated with persistent elevated hospitalisation risks of infectious diseases (particularly sepsis and intestinal infectious diseases). Targeted interventions should be formulated for different populations, regions, and causes of infectious diseases based on evidence on tropical cyclone epidemiology to respond to the increasing risk and burden.

FUNDING

Australian Research Council, Australian National Health, and Medical Research Council.

Long-lasting household damage from Cyclone Idai increases malaria risk in rural western Mozambique

Cyclone Idai in 2019 was one of the worst tropical cyclones recorded in the Southern Hemisphere. The storm caused catastrophic damage and led to a humanitarian crisis in Mozambique. The affected population suffered a cholera epidemic on top of housing and infrastructure damage and loss of life. The housing and infrastructure damage sustained during Cyclone Idai still has not been addressed in all affected communities. This is of grave concern because storm damage results in poor housing conditions which are known to increase the risk of malaria. Mozambique has the 4th highest malaria prevalence in sub-Saharan Africa and is struggling to control malaria in most of the country. We conducted a community-based cross-sectional survey in Sussundenga Village, Manica Province, Mozambique in December 2019-February 2020. We found that most participants (64%) lived in households that sustained damage during Cyclone Idai. The overall malaria prevalence was 31% measured by rapid diagnostic test (RDT). When controlling for confounding variables, the odds of malaria infection was nearly threefold higher in participants who lived in households damaged by Cyclone Idai nearly a year after the storm. This highlights the need for long-term disaster response to improve the efficiency and success of malaria control efforts.

The Chronic Cholera Situation in Africa: Why Are African Countries Unable to Tame the Well-Known Lion?

Seven years to the Global Taskforce on Cholera Control's target of reducing cholera cases and deaths by 90% by 2030, Africa continues to experience a high incidence of the disease. In the last 20 years, more than 2.6 million cases and 60 000 deaths of the disease have been recorded, mostly in sub-Saharan Africa. Case Fatality Ratio remains consistently above the WHO-recommended 1% with a yearly average of 2.2%. Between 1 January 2022 and 16 July 2023, fourteen African countries reported 213 443 cases and 3951 deaths (CFR, 1.9%) of the disease. In this perspective article, based on available literature and the authors' field experiences in Africa, we discuss the underlying reasons for the sustained transmission of the disease. We posit that in addition to the well-known risk factors for the disease, the chronic cholera situation in Africa is due to the poor socioeconomic development status, weak household and community resilience, low literacy levels, weak capacity of African countries to implement the 2005 International Health Regulation and the pervasively weak health system on the continent. Stemming this tide requires good leadership, partnership, political commitment, and equity in access to health services, water, and sanitation. Therefore, we recommend that African governments and stakeholders recognize and approach cholera prevention and control from the long-term development lens and leverage the current cholera emergency preparedness and response efforts on the continent to strengthen the affected countries' health, water, and sanitation systems. We call on international organizations such as WHO and the Africa Centres for Diseases Control to support African governments in scaling up research and innovations aimed at better characterizing the epidemiology of cholera and developing evidence-based, context-specific, and innovative strategies for its prevention and control. These recommendations require long-term multisectoral and multidisciplinary approaches.

A High-Resolution Earth Observations and Machine Learning-Based Approach to Forecast Waterborne Disease Risk in Post-Disaster Settings

Responding to infrastructural damage in the aftermath of natural disasters at a national, regional, and local level poses a significant challenge. Damage to road networks, clean water supply, and sanitation infrastructures, as well as social amenities like schools and hospitals, exacerbates the circumstances. As safe water sources are destroyed or mixed with contaminated water during a disaster, the risk of a waterborne disease outbreak is elevated in those disaster-affected locations. A country such as Haiti, where a large quantity of the population is deprived of safe water and basic sanitation facilities, would suffer more in post-disaster scenarios. Early warning of waterborne diseases like cholera would be of great help for humanitarian aid, and the management of disease outbreak perspectives. The challenging task in disease forecasting is to identify the suitable variables that would better predict a potential outbreak. In this study, we developed five (5) models including a machine learning approach, to identify and determine the impact of the environmental and social variables that play a significant role in post-disaster cholera outbreaks. We implemented the model setup with cholera outbreak data in Haiti after the landfall of Hurricane Matthew in October 2016. Our results demonstrate that adding high-resolution data in combination with appropriate social and environmental variables is helpful for better cholera forecasting in a post-disaster scenario. In addition, using a machine learning approach in combination with existing statistical or mechanistic models provides important insights into the selection of variables and identification of cholera risk hotspots, which can address the shortcomings of existing approaches.

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

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.

Modelling geographical accessibility to support disaster response and rehabilitation of a healthcare system: an impact analysis of Cyclones Idai and Kenneth in Mozambique

OBJECTIVES

Modelling and assessing the loss of geographical accessibility is key to support disaster response and rehabilitation of the healthcare system. The aim of this study was therefore to estimate postdisaster travel times to functional health facilities and analyse losses in accessibility coverage after Cyclones Idai and Kenneth in Mozambique in 2019.

SETTING

We modelled travel time of vulnerable population to the nearest functional health facility in two cyclone-affected regions in Mozambique. Modelling was done using AccessMod V.5.6.30, where roads, rivers, lakes, flood extent, topography and land cover datasets were overlaid with health facility coordinates and high-resolution population data to obtain accessibility coverage estimates under different travel scenarios.

OUTCOME MEASURES

Travel time to functional health facilities and accessibility coverage estimates were used to identify spatial differences between predisaster and postdisaster geographical accessibility.

RESULTS

We found that accessibility coverage decreased in the cyclone-affected districts, as a result of reduced travel speeds, barriers to movement, road constraints and non-functional health facilities. In Idai-affected districts, accessibility coverage decreased from 78.8% to 52.5%, implying that 136 941 children under 5 years of age were no longer able to reach the nearest facility within 2 hours travel time. In Kenneth-affected districts, accessibility coverage decreased from 82.2% to 71.5%, corresponding to 14 330 children under 5 years of age having to travel >2 hours to reach the nearest facility. Damage to transport networks and reduced travel speeds resulted in the most substantial accessibility coverage losses in both Idai-affected and Kenneth-affected districts.

CONCLUSIONS

Postdisaster accessibility modelling can increase our understanding of spatial differences in geographical access to care in the direct aftermath of a disaster and can inform targeting and prioritisation of limited resources. Our results reflect opportunities for integrating accessibility modelling in early disaster response, and to inform discussions on health system recovery, mitigation and preparedness.