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

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 Using cholera surveillance data from Nigeria, cholera incidence was calculated and used to model time-varying reproduction number (R). A best fit random forest model was identified using R as the outcome variable and several environmental and social covariates were considered in the model, using random forest variable importance and correlation clustering. Future scenarios were created (based on varying degrees of socioeconomic development and emission reductions) and used to project future cholera transmission, nationally and sub-nationally to 2070. 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 unlikely and we propose a new 2050 target focusing on reducing regional inequities, while still advocating for cholera elimination being achieved as soon as possible. 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

Large Outbreak of Neisseria meningitidis Serogroup C - Nigeria, December 2016-June 2017.

On February 16, 2017, the Ministry of Health in Zamfara State, in northwestern Nigeria, notified the Nigeria Centre for Disease Control (NCDC) of an increased number of suspected cerebrospinal meningitis (meningitis) cases reported from four local government areas (LGAs). Meningitis cases were subsequently also reported from Katsina, Kebbi, Niger, and Sokoto states, all of which share borders with Zamfara State, and from Yobe State in northeastern Nigeria. On April 3, 2017, NCDC activated an Emergency Operations Center (EOC) to coordinate rapid development and implementation of a national meningitis emergency outbreak response plan. After the outbreak was reported, surveillance activities for meningitis cases were enhanced, including retrospective searches for previously unreported cases, implementation of intensified new case finding, and strengthened laboratory confirmation. A total of 14,518 suspected meningitis cases were reported for the period December 13, 2016-June 15, 2017. Among 1,339 cases with laboratory testing, 433 (32%) were positive for bacterial pathogens, including 358 (82.7%) confirmed cases of Neisseria meningitidis serogroup C. In response, approximately 2.1 million persons aged 2-29 years were vaccinated with meningococcal serogroup C-containing vaccines in Katsina, Sokoto, Yobe, and Zamfara states during April-May 2017. The outbreak was declared over on June 15, 2017, after high-quality surveillance yielded no evidence of outbreak-linked cases for 2 consecutive weeks. Routine high-quality surveillance, including a strong laboratory system to test specimens from persons with suspected meningitis, is critical to rapidly detect and confirm future outbreaks and inform decisions regarding response vaccination

COVID-19 mortality rate and its associated factors during the first and second waves in Nigeria

COVID-19 mortality rate has not been formally assessed in Nigeria. Thus, we aimed to address this gap and identify associated mortality risk factors during the first and second waves in Nigeria. This was a retrospective analysis of national surveillance data from all 37 States in Nigeria between February 27, 2020, and April 3, 2021. The outcome variable was mortality amongst persons who tested positive for SARS-CoV-2 by Reverse-Transcriptase Polymerase Chain Reaction. Incidence rates of COVID-19 mortality was calculated by dividing the number of deaths by total person-time (in days) contributed by the entire study population and presented per 100,000 person-days with 95% Confidence Intervals (95% CI). Adjusted negative binomial regression was used to identify factors associated with COVID-19 mortality. Findings are presented as adjusted Incidence Rate Ratios (aIRR) with 95% CI. The first wave included 65,790 COVID-19 patients, of whom 994 (1∙51%) died; the second wave included 91,089 patients, of whom 513 (0∙56%) died. The incidence rate of COVID-19 mortality was higher in the first wave [54∙25 (95% CI: 50∙98–57∙73)] than in the second wave [19∙19 (17∙60–20∙93)]. Factors independently associated with increased risk of COVID-19 mortality in both waves were: age ≥45 years, male gender [first wave aIRR 1∙65 (1∙35–2∙02) and second wave 1∙52 (1∙11–2∙06)], being symptomatic [aIRR 3∙17 (2∙59–3∙89) and 3∙04 (2∙20–4∙21)], and being hospitalised [aIRR 4∙19 (3∙26–5∙39) and 7∙84 (4∙90–12∙54)]. Relative to South-West, residency in the South-South and North-West was associated with an increased risk of COVID-19 mortality in both waves. In conclusion, the rate of COVID-19 mortality in Nigeria was higher in the first wave than in the second wave, suggesting an improvement in public health response and clinical care in the second wave. However, this needs to be interpreted with caution given the inherent limitations of the country’s surveillance system during the study

Epidemiology, diagnostics and factors associated with mortality during a cholera epidemic in Nigeria, October 2020-October 2021: a retrospective analysis of national surveillance data.

OBJECTIVES: Nigeria reported an upsurge in cholera cases in October 2020, which then transitioned into a large, disseminated epidemic for most of 2021. This study aimed to describe the epidemiology, diagnostic performance of rapid diagnostic test (RDT) kits and the factors associated with mortality during the epidemic. DESIGN: A retrospective analysis of national surveillance data. SETTING: 33 of 37 states (including the Federal Capital Territory) in Nigeria. PARTICIPANTS: Persons who met cholera case definition (a person of any age with acute watery diarrhoea, with or without vomiting) between October 2020 and October 2021 within the Nigeria Centre for Disease Control surveillance data. OUTCOME MEASURES: Attack rate (AR; per 100 000 persons), case fatality rate (CFR; %) and accuracy of RDT performance compared with culture using area under the receiver operating characteristic curve (AUROC). Additionally, individual factors associated with cholera deaths and hospitalisation were presented as adjusted OR with 95% CIs. RESULTS: Overall, 93 598 cholera cases and 3298 deaths (CFR: 3.5%) were reported across 33 of 37 states in Nigeria within the study period. The proportions of cholera cases were higher in men aged 5-14 years and women aged 25-44 years. The overall AR was 46.5 per 100 000 persons. The North-West region recorded the highest AR with 102 per 100 000. Older age, male gender, residency in the North-Central region and severe dehydration significantly increased the odds of cholera deaths. The cholera RDT had excellent diagnostic accuracy (AUROC=0.91; 95% CI 0.87 to 0.96). CONCLUSIONS: Cholera remains a serious public health threat in Nigeria with a high mortality rate. Thus, we recommend making RDT kits more widely accessible for improved surveillance and prompt case management across the country

Healthcare workers knowledge of cholera multi-stranded interventions and its determining factors in North-East Nigeria: planning and policy implications

Abstract Background Healthcare workers’ (HCWs) knowledge of multi-stranded cholera interventions (including case management, water, sanitation, and hygiene (WASH), surveillance/laboratory methods, coordination, and vaccination) is crucial to the implementation of these interventions in healthcare facilities, especially in conflict-affected settings where cholera burden is particularly high. We aimed to assess Nigerian HCWs’ knowledge of cholera interventions and identify the associated factors. Methods We conducted a cross-sectional study using a structured interviewer-administered questionnaire with HCWs from 120 healthcare facilities in Adamawa and Bauchi States, North-East Nigeria. A knowledge score was created by assigning a point for each correct response. HCWs’ knowledge of cholera interventions, calculated as a score, was recoded for ease of interpretation as follows: 0–50 (low); 51–70 (moderate); ≥ 71 (high). Additionally, we defined the inadequacy of HCWs’ knowledge of cholera interventions based on a policy-relevant threshold of equal or lesser than 75 scores for an intervention. Multivariable logistic regression was used to identify the factors associated with the adequacy of knowledge score. Results Overall, 490 HCWs participated in the study (254 in Adamawa and 236 in Bauchi), with a mean age of 35.5 years. HCWs’ knowledge score was high for surveillance/laboratory methods, moderate for case management, WASH, and vaccination, and low for coordination. HCWs’ knowledge of coordination improved with higher cadre, working in urban- or peri-urban-based healthcare facilities, and secondary education; cholera case management and vaccination knowledge improved with post-secondary education, working in Bauchi State and urban areas, previous training in cholera case management and response to a cholera outbreak—working in peri-urban areas had a negative effect. HCWs’ knowledge of surveillance/laboratory methods improved with a higher cadre, 1-year duration in current position, secondary or post-secondary education, previous training in cholera case management and response to a cholera outbreak. However, HCWs’ current position had both positive and negative impacts on their WASH knowledge. Conclusions HCWs in both study locations recorded a considerable knowledge of multi-stranded cholera interventions. While HCWs’ demographic characteristics appeared irrelevant in determining their knowledge of cholera interventions, geographic location and experiences from the current position, training and involvement in cholera outbreak response played a significant role

Number of confirmed cholera cases by sex and age group for 2018 and 2019.

Number of confirmed cholera cases by sex and age group for 2018 and 2019.</p

The cholera risk assessment in Kano State, Nigeria: A historical review, mapping of hotspots and evaluation of contextual factors.

Nigeria is endemic for cholera since 1970, and Kano State report outbreaks annually with high case fatality ratios ranging from 4.98%/2010 to 5.10%/2018 over the last decade. However, interventions focused on cholera prevention and control have been hampered by a lack of understanding of hotspot Local Government Areas (LGAs) that trigger and sustain yearly outbreaks. The goal of this study was to identify and categorize cholera hotspots in Kano State to inform a national plan for disease control and elimination in the State. We obtained LGA level confirmed and suspected cholera data from 2010 to 2019 from the Nigeria Centre for Disease Control (NCDC) and Kano State Ministry of Health. Data on inland waterbodies and population numbers were obtained from online sources and NCDC, respectively. Clusters (hotspots) were identified using SaTScan through a retrospective analysis of the data for the ten-year period using a Poisson discrete space-time scan statistic. We also used a method newly proposed by the Global Task Force on Cholera Control (GTFCC) to identify and rank hotspots based on two epidemiological indicators including mean annual incidence per 100 000 population of reported cases and the persistence of cholera for the study period. In the ten-year period, 16,461 cholera cases were reported with a case fatality ratio of 3.32% and a mean annual incidence rate of 13.4 cases per 100 000 population. Between 2010 and 2019, the most severe cholera exacerbations occurred in 2014 and 2018 with annual incidence rates of 58.01 and 21.52 cases per 100 000 inhabitants, respectively. Compared to 2017, reported cases and deaths increased by 214.56% and 406.67% in 2018. The geographic distribution of outbreaks revealed considerable spatial heterogeneity with the widest in 2014. Space-time clustering analysis identified 18 out of 44 LGAs as high risk for cholera (hotspots) involving both urban and rural LGAs. Cholera clustered around water bodies, and the relative risk of having cholera inside the hotspot LGA were 1.02 to 3.30 times higher than elsewhere in the State. A total of 4,894,144 inhabitants were in these hotspots LGAs. Of these, six LGAs with a total population of 1.665 million had a relative risk greater than 2 compared to the state as a whole. The SaTScan (statistical) and GTFCC methods were in agreement in hotspots identification. This study identified cholera hotspots LGAs in Kano State from 2010-2019. Hotspots appeared in both urban and rural settings. Focusing control strategies on these hotspots will facilitate control and eliminate cholera from the State

Number of confirmed cholera cases by state for 2018 and 2019, grey indicates states that had no reported confirmed cases [44].

Number of confirmed cholera cases by state for 2018 and 2019, grey indicates states that had no reported confirmed cases [44].</p

Single predictor partial dependency plots for the covariates in the best fit model.

Showing the relationships between A, monthly conflict events, B, access to sanitation, C, Palmers Drought Severity Index (PDSI) and D, Multidimensional poverty Index (MPI) and R. (TIFF)</p

Historical temporal trends between the best fit model covariates and the R thresholds (R = >1, R <1).

The mean and standard error for the four covariates included in the best fit model for the full dataset split by month and R threshold. (TIFF)</p