The impact of social and environmental extremes on cholera time varying reproduction number in Nigeria

Charnley, Gina E C; Yennan, Sebastian; Ochu, Chinwe Lucia; Kelman, Ilan; Gaythorpe, Katy A. M.; Murray, Kris A.

doi:10.1101/2022.03.21.22272693

Cited by 3 publications

(7 citation statements)

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“…Specific to India, epidemiological models and serosurvey-based estimation procedures have indicated 5×–10× under-reporting for deaths and 30×–40× under-reporting for cases 65–68. Finally, the time-varying reproduction numbers and any summaries thereof such as the iR can be impacted by geographical diversity and environmental factors, as has been evidenced by studies both in context of prepandemic infectious diseases and COVID-19 32 69 70. Odisha, in particular, has districts that are located at varying distances from the Indian Ocean and experiences differing patterns of weather which may impact the pandemic growth differently.…”

Section: Discussionmentioning

confidence: 99%

Role of multiresolution vulnerability indices in COVID-19 spread in India: a Bayesian model-based analysis

et al. 2022

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ObjectivesCOVID-19 has differentially affected countries, with health infrastructure and other related vulnerability indicators playing a role in determining the extent of its spread. Vulnerability of a geographical region to COVID-19 has been a topic of interest, particularly in low-income and middle-income countries like India to assess its multifactorial impact on incidence, prevalence or mortality. This study aims to construct a statistical analysis pipeline to compute such vulnerability indices and investigate their association with metrics of the pandemic growth.DesignUsing publicly reported observational socioeconomic, demographic, health-based and epidemiological data from Indian national surveys, we compute contextual COVID-19 Vulnerability Indices (cVIs) across multiple thematic resolutions for different geographical and spatial administrative regions. These cVIs are then used in Bayesian regression models to assess their impact on indicators of the spread of COVID-19.SettingThis study uses district-level indicators and case counts data for the state of Odisha, India.Primary outcome measureWe use instantaneous R (temporal average of estimated time-varying reproduction number for COVID-19) as the primary outcome variable in our models.ResultsOur observational study, focussing on 30 districts of Odisha, identified housing and hygiene conditions, COVID-19 preparedness and epidemiological factors as important indicators associated with COVID-19 vulnerability.ConclusionHaving succeeded in containing COVID-19 to a reasonable level during the first wave, the second wave of COVID-19 made greater inroads into the hinterlands and peripheral districts of Odisha, burdening the already deficient public health system in these areas, as identified by the cVIs. Improved understanding of the factors driving COVID-19 vulnerability will help policy makers prioritise resources and regions, leading to more effective mitigation strategies for the present and future.

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Section: Discussionmentioning

confidence: 99%

Role of multiresolution vulnerability indices in COVID-19 spread in India: a Bayesian model-based analysis

et al. 2022

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“…Despite the limitations of conflict and cholera data, the data used here are to the highest standard currently available and has been used by several other studies, making the research comparable ( 11,14,16 ). Creating partnerships with those working on the ground and exploring more sensitive data options is an area of future research ( 58 ). Additional methods to account for data limitations included setting both the event and the exposure to a binary outcome to reduce the impacts of severity and using a weekly instead of daily temporal scale to account for delays.…”

Section: Discussionmentioning

confidence: 99%

Using self-controlled case series to understand the relationship between conflict and cholera in Nigeria and the Democratic Republic of Congo

Charnley

Jean

Kelman

et al. 2021

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BackgroundCholera outbreaks contribute significantly to diarrhoeal disease mortality, especially in low-income countries. Cholera outbreaks have several social and environmental risk factors and extreme conditions can act as catalysts for outbreaks. A social extreme with known links to infectious disease outbreaks is conflict, causing disruption to services, loss of income and displacement.MethodsHere, we explored this relationship in Nigeria and the Democratic Republic of Congo (DRC), by fitting publicly available cholera and conflict data to conditional logistic regression models. We used the self-controlled case series method in a novel application, to understand if an exposure period of excess risk (conflict), increased the relative incidence of cholera. We also used a sensitivity analysis to understand potential lag effects.ResultsWe found that conflict and cholera had a strong positive relationship, especially in the first week after the event, at a national and sub-national level. Conflict increased the risk of cholera in Nigeria by 3.6 times and 2.6 times for the DRC. Conflict was attributed to 19.7% and 12.3% of cholera outbreaks in Nigeria and the DRC, respectively. This was higher for some states/provinces, with a maximum increased risk of 7.5 times.ConclusionThe results found that several states/provinces with the strongest positive relationship were also areas of high reported conflict or were neighbouring states/provinces, suggesting a possible spill-over effect. Our results help highlight the importance of rapid and sufficient assistance during social extremes and the need for pre-existing vulnerabilities such as poverty and access to healthcare to be addressed. In fragile states, conflict resolution should be a top priority to avoid excess risk for both cholera and other health and social implications.FundingNatural Environmental Research Council, UK Medical Research Council, and the Department for International Development.

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“…We first illustrate the current situation in terms of historical cholera and environmental and social risk factors. We then project cholera occurrence and transmission to 2070 both nationally and sub-nationally (administrative level 1) with varying degrees of global change using two models [11,12]. Relatively few studies have projected cholera burden, most of which have taken a climate change focus [13][14][15] and even fewer studies have evaluated the likelihood of meeting the 2030 GTFCC targets using forecasting tools [16,17].…”

Section: Figmentioning

confidence: 99%

“…The sub-national projections were predicted using the best fit model from Charnley et al [12]. In summary, sub-national cholera surveillance data were acquired from the Nigeria Centre for Disease Control.…”

Section: Projectionsmentioning

confidence: 99%

“…To understand if Nigeria is on track to meet these 2030 targets, we project cholera transmission (using cholera time-varying reproduction number (R)) to 2070 both nationally and sub-nationally (administrative level 1). The projections are based on scenarios of varying degrees of global change using a random forest model, proved robust at predicting R in previous studies [11]. Relatively few studies have projected cholera burden, most of which have taken a climate change focus [12][13][14] and even fewer studies have evaluated the likelihood of meeting the 2030 GTFCC targets using model projections [15,16].…”

Section: Figmentioning

confidence: 99%

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Cholera past and future in Nigeria: are the Global Task Force on Cholera Control’s 2030 targets achievable?

Charnley

Yennan

Ochu

et al. 2022

Preprint

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Background Understanding and continually assessing the achievability of global health targets is key to reducing disease burden and mortality. The Global Task Force on Cholera Control (GTFCC) Roadmap aims to reduce cholera deaths by 90% and eliminate the disease in twenty countries by 2030. The Roadmap has three axes focusing on reporting, response and coordination. Here, we assess the achievability of the GTFCC targets in Nigeria and identify where the three axes could be strengthened to reach and exceed these goals. Methodology/Principal Findings First, historical data were analysed for cholera death rates and a range of well-established cholera risk factors (particularly those included in the best fit models), using correlation, regression, autocorrelation function and AutoRegressive Integrated Moving Averages. Next, future scenario datasets were created (based on varying degrees of socioeconomic development and emission reductions) and used to project future cholera outbreak occurrence and transmission to 2070. The two models used were a generalised linear model and a random forest model. The historical data highlighted the significant cholera burden in Nigeria and its correlation with sanitation access. The projections suggest that significant reductions in cholera cases could be achieved by 2030, particularly in the more developed southern states, but increases in cases remain a possibility. Meeting the 2030 target, nationally, currently looks unachievable and we propose a new 2050 target focusing on reducing regional inequities. Conclusion/Significance The 2030 targets could potentially be reached by 2030 in some parts of Nigeria, but more effort is needed to reach these targets at a national level, particularly through access and incentives to cholera testing, sanitation expansion, poverty alleviation and urban planning. The results highlight the importance of and how modelling studies can be used to inform cholera policy and the potential for this to be applied in other contexts.

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The impact of social and environmental extremes on cholera time varying reproduction number in Nigeria

Cited by 3 publications

References 66 publications

Role of multiresolution vulnerability indices in COVID-19 spread in India: a Bayesian model-based analysis

Role of multiresolution vulnerability indices in COVID-19 spread in India: a Bayesian model-based analysis

Using self-controlled case series to understand the relationship between conflict and cholera in Nigeria and the Democratic Republic of Congo

Cholera past and future in Nigeria: are the Global Task Force on Cholera Control’s 2030 targets achievable?

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