Pathogens that Cause Illness Clinically Indistinguishable from Lassa Fever, Nigeria, 2018.

During the 2018 Lassa fever outbreak in Nigeria, samples from patients with suspected Lassa fever but negative Lassa virus PCR results were processed through custom gene expression array cards and metagenomic sequencing. Results demonstrated no single etiology, but bacterial and viral pathogens (including mixed co-infections) were detected.

Use of Surveillance Outbreak Response Management and Analysis System for Human Monkeypox Outbreak, Nigeria, 2017-2019.

In November 2017, the mobile digital Surveillance Outbreak Response Management and Analysis System was deployed in 30 districts in Nigeria in response to an outbreak of monkeypox. Adaptation and activation of the system took 14 days, and its use improved timeliness, completeness, and overall capacity of the response.

Predictors of Lassa fever diagnosis in suspected cases reporting to health facilities in Nigeria.

Lassa fever (LF) remains endemic in Nigeria with the country reporting the highest incidence and mortality globally. Recent national data suggests increasing incidence and expanding geographic spread. Predictors of LF case positivity in Nigeria have been sparsely studied. We thus sought to determine the sociodemographic and clinical determinants of LF positivity amongst suspected cases presenting to health facilities from 2018 to 2021. A secondary analysis of the national LF surveillance data between January 2018 and December 2021. Socio-demographic and clinical data of 20,027 suspected LF cases were analysed using frequencies and Chi-square statistics with significant p-value set at p < 0.05. The outcome variable was LF case status (positive or negative). Predictors of LF case positivity were assessed using multiple logistic regression models with 95% confidence intervals (CI). Case positivity rate (CPR) for the four years was 15.8% with higher odds of positivity among age group 40-49 years (aOR = 1.40; 95% CI 1.21-1.62), males (aOR = 1.11; 95% CI 1.03-1.20), those with formal education (aOR = 1.33; 95% CI 1.13-1.56), artisans (aOR = 1.70; 95% CI 1.28-2.27), religious leaders (aOR = 1.62; 95% CI 1.04-2.52), farmers (aOR = 1.48; 95% CI 1.21-1.81), and symptomatic individuals (aOR = 2.36; 95% CI 2.09-2.68). Being a health worker (aOR = 0.69; 95% CI 0.53-0.91), a teacher (aOR = 0.69; 95% CI 0.53-0.89) and cases reporting in the 3rd quarter (aOR = 0.79; 95% CI 0.69-0.92) had lower odds. In a sex-disaggregated analysis, female farmers had higher odds of positivity (aOR = 2.43; 95% CI 1.76-3.38; p < 0.001) than male farmers (aOR = 1.52; 95% CI 1.19-1.96; p < 0.01). Fever (aOR = 2.39; 95% CI 2.00-2.84) and gastrointestinal (GI) symptoms (aOR = 2.15; 95% CI 1.94-2.37) had the highest odds among symptoms. Combination of fever and GI symptoms (aOR = 2.15; 95% CI 1.50-3.10), fever and neurological symptoms (aOR = 6.37; 95% CI 1.49-27.16), fever and musculo-skeletal symptoms (aOR = 2.95; 95% CI 1.37-6.33), fever and cardiopulmonary symptoms (aOR = 1.81; 95% CI 1.24-2.64), and cardiopulmonary and general symptoms (aOR = 1.50; 95% CI 1.19-1.89) were also predictive. Cumulative LF CPR appears high with clearly identified predictors. Targeted interventions with heightened index of suspicion for sociodemographic categories predictive of LF in suspected cases are recommended. Ethnographic and further epidemiological studies could aid better understanding of these associations.

Adapting public health response through lessons learnt: Nigeria's experience from Lassa fever and COVID-19.

Response to public health emergencies requires continued adaptation and innovation. The Nigeria Centre for Disease Control (NCDC) is the country's public health institute with the mandate to protect the health of Nigerians. Achieving such mandate in resource-limited settings with divergent demographic characteristics of the citizens, necessitates the readiness to learn from experience and to develop policies and activities in line with lessons learnt and best practices. This practice paper describes the initiatives of the NCDC towards adapting its public health response activities by establishing learning systems across its structure. The paper informs on some of the steps taken by the Centre regarding learning from the Lassa fever outbreak and the COVID-19 pandemic in Nigeria. It concludes that commitment and investments are key requirements for learning and adapting public health responses to achieve success with combating infectious diseases.

Epidemiological trends of Lassa fever in Nigeria, 2018-2021.

BACKGROUND: Lassa fever is a viral haemorrhagic fever endemic in Nigeria. Improved surveillance and testing capacity have revealed in an increased number of reported cases and apparent geographic spread of Lassa fever in Nigeria. We described the recent four-year trend of Lassa fever in Nigeria to improve understanding of its epidemiology and inform the design of appropriate interventions. METHODS: We analysed the national surveillance data on Lassa fever maintained by the Nigeria Centre for Diseases Control (NCDC) and described trends, sociodemographic, geographic distribution, and clinical outcomes. We compared cases, positivity, and clinical outcomes in the period January 2018 to December 2021. RESULTS: We found Lassa fever to be reported throughout the year with more than half the cases reported within the first quarter of the year, a recent increase in numbers and geographic spread of the virus, and male and adult (>18 years) preponderance. Case fatality rates were worse in males, the under-five and elderly, during off-peak periods, and among low reporting states. CONCLUSION: Lassa fever is endemic in Nigeria with a recent increase in numbers and geographical distribution. Sustaining improved surveillance, enhanced laboratory diagnosis and improved case management capacity during off-peak periods should remain a priority. Attention should be paid to the very young and elderly during outbreaks. Further research efforts should identify and address specific factors that determine poor clinical outcomes.

Analysis of sociodemographic and clinical factors associated with Lassa fever disease and mortality in Nigeria.

Over past decades, there has been increasing geographical spread of Lassa fever (LF) cases across Nigeria and other countries in West Africa. This increase has been associated with significant morbidity and mortality despite increasing focus on the disease by both local and international scientists. Many of these studies on LF have been limited to few specialised centres in the country. This study was done to identify sociodemographic and clinical predictors of LF disease and related deaths across Nigeria. We analysed retrospective surveillance data on suspected LF cases collected during January-June 2018 and 2019. Multivariable logistic regression analyses were used to identify the factors independently associated with laboratory-confirmed LF diagnosis, and with LF-related deaths. There were confirmed 815 of 1991 suspected LF cases with complete records during this period. Of these, 724/815 confirmed cases had known clinical outcomes, of whom 100 died. LF confirmation was associated with presentation of gastrointestinal tract (aOR 3.47, 95% CI: 2.79-4.32), ear, nose and throat (aOR 2.73, 95% CI: 1.80-4.15), general systemic (aOR 2.12, 95% CI: 1.65-2.70) and chest/respiratory (aOR 1.71, 95% CI: 1.28-2.29) symptoms. Other factors were being male (aOR 1.32, 95% CI: 1.06-1.63), doing business/trading (aOR 2.16, 95% CI: 1.47-3.16) and farming (aOR 1.73, 95% CI: 1.12-2.68). Factors associated with LF mortality were a one-year increase in age (aOR 1.03, 95% CI: 1.01-1.04), bleeding (aOR 2.07, 95% CI: 1.07-4.00), and central nervous manifestations (aOR 5.02, 95% CI: 3.12-10.16). Diverse factors were associated with both LF disease and related death. A closer look at patterns of clinical variables would be helpful to support early detection and management of cases. The findings would also be useful for planning preparedness and response interventions against LF in the country and region.

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.

Assessing the capacity of symptom scores to predict COVID-19 positivity in Nigeria: a national derivation and validation cohort study.

OBJECTIVES: This study aimed to develop and validate a symptom prediction tool for COVID-19 test positivity in Nigeria. DESIGN: Predictive modelling study. SETTING: All Nigeria States and the Federal Capital Territory. PARTICIPANTS: A cohort of 43 221 individuals within the national COVID-19 surveillance dataset from 27 February to 27 August 2020. Complete dataset was randomly split into two equal halves: derivation and validation datasets. Using the derivation dataset (n=21 477), backward multivariable logistic regression approach was used to identify symptoms positively associated with COVID-19 positivity (by real-time PCR) in children (≤17 years), adults (18-64 years) and elderly (≥65 years) patients separately. OUTCOME MEASURES: Weighted statistical and clinical scores based on beta regression coefficients and clinicians' judgements, respectively. Using the validation dataset (n=21 744), area under the receiver operating characteristic curve (AUROC) values were used to assess the predictive capacity of individual symptoms, unweighted score and the two weighted scores. RESULTS: Overall, 27.6% of children (4415/15 988), 34.6% of adults (9154/26 441) and 40.0% of elderly (317/792) that had been tested were positive for COVID-19. Best individual symptom predictor of COVID-19 positivity was loss of smell in children (AUROC 0.56, 95% CI 0.55 to 0.56), either fever or cough in adults (AUROC 0.57, 95% CI 0.56 to 0.58) and difficulty in breathing in the elderly (AUROC 0.53, 95% CI 0.48 to 0.58) patients. In children, adults and the elderly patients, all scoring approaches showed similar predictive performance. CONCLUSIONS: The predictive capacity of various symptom scores for COVID-19 positivity was poor overall. However, the findings could serve as an advocacy tool for more investments in resources for capacity strengthening of molecular testing for COVID-19 in Nigeria.

Epidemiological comparison of the first and second waves of the COVID-19 pandemic in Nigeria, February 2020-April 2021.

BACKGROUND: With reports of surges in COVID-19 case numbers across over 50 countries, country-level epidemiological analysis is required to inform context-appropriate response strategies for containment and mitigation of the outbreak. We aimed to compare the epidemiological features of the first and second waves of COVID-19 in Nigeria. METHODS: We conducted a retrospective analysis of the Surveillance Outbreak Response Management and Analysis System data of the first and second epidemiological waves, which were between 27 February and 24 October 2020, and 25 October 2020 to 3 April 2021, respectively. Descriptive statistical measures including frequencies and percentages, test positivity rate (TPR), cumulative incidence (CI) and case fatality rates (CFRs) were compared. A p value of <0.05 was considered statistically significant. All statistical analyses were carried out in STATA V.13. RESULTS: There were 802 143 tests recorded during the study period (362 550 and 439 593 in the first and second waves, respectively). Of these, 66 121 (18.2%) and 91 644 (20.8%) tested positive in the first and second waves, respectively. There was a 21.3% increase in the number of tests conducted in the second wave with TPR increasing by 14.3%. CI during the first and second waves were 30.3/100 000 and 42.0/100 000 respectively. During the second wave, confirmed COVID-19 cases increased among females and people 30 years old or younger and decreased among urban residents and individuals with travel history within 14 days of sample collection (p value <0.001). Most confirmed cases were asymptomatic at diagnosis during both waves: 74.9% in the first wave; 79.7% in the second wave. CFR decreased during the second wave (0.7%) compared with the first wave (1.8%). CONCLUSION: Nigeria experienced a larger but less severe second wave of COVID-19. Continued implementation of public health and social measures is needed to mitigate the resurgence of another wave.

The Surveillance Outbreak Response Management and Analysis System (SORMAS): Digital Health Global Goods Maturity Assessment.

BACKGROUND: Digital health is a dynamic field that has been generating a large number of tools; many of these tools do not have the level of maturity required to function in a sustainable model. It is in this context that the concept of global goods maturity is gaining importance. Digital Square developed a global good maturity model (GGMM) for digital health tools, which engages the digital health community to identify areas of investment for global goods. The Surveillance Outbreak Response Management and Analysis System (SORMAS) is an open-source mobile and web application software that we developed to enable health workers to notify health departments about new cases of epidemic-prone diseases, detect outbreaks, and simultaneously manage outbreak response. OBJECTIVE: The objective of this study was to evaluate the maturity of SORMAS using Digital Square's GGMM and to describe the applicability of the GGMM on the use case of SORMAS and identify opportunities for system improvements. METHODS: We evaluated SORMAS using the GGMM version 1.0 indicators to measure its development. SORMAS was scored based on all the GGMM indicator scores. We described how we used the GGMM to guide the development of SORMAS during the study period. GGMM contains 15 subindicators grouped into the following core indicators: (1) global utility, (2) community support, and (3) software maturity. RESULTS: The assessment of SORMAS through the GGMM from November 2017 to October 2019 resulted in full completion of all subscores (10/30, (33%) in 2017; 21/30, (70%) in 2018; and 30/30, (100%) in 2019). SORMAS reached the full score of the GGMM for digital health software tools by accomplishing all 10 points for each of the 3 indicators on global utility, community support, and software maturity. CONCLUSIONS: To our knowledge, SORMAS is the first electronic health tool for disease surveillance, and also the first outbreak response management tool, that has achieved a 100% score. Although some conceptual changes would allow for further improvements to the system, the GGMM already has a robust supportive effect on developing software toward global goods maturity.

Assessing the Concepts and Designs of 58 Mobile Apps for the Management of the 2014-2015 West Africa Ebola Outbreak: Systematic Review.

BACKGROUND: The use of mobile phone information technology (IT) in the health sector has received much attention especially during the 2014-2015 Ebola virus disease (EVD) outbreak. mHealth can be attributed to a major improvement in EVD control, but there lacks an overview of what kinds of tools were available and used based on the functionalities they offer. OBJECTIVE: We aimed to conduct a systematic review of mHealth tools in the context of the recent EVD outbreak to identify the most promising approaches and guide further mHealth developments for infectious disease control. METHODS: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we searched for all reports on mHealth tools developed in the context of the 2014-2015 EVD outbreak published between January 1, 2014 and December 31, 2015 on Google Scholar, MEDLINE, CAB Abstracts (Global Health), POPLINE, and Web of Science in any language using the search strategy: ("outbreak" OR "epidemic") AND ("mobile phone" OR "smartphone" OR "smart phone" OR "mobile phone" OR "tablet" OR "mHealth") AND ("Ebola" OR "EVD" OR "VHF" OR "Ebola virus disease" OR "viral hemorrhagic fever") AND ("2014" OR "2015"). The relevant publications were selected by 2 independent reviewers who applied a standardized data extraction form on the tools' functionalities. RESULTS: We identified 1220 publications through the search strategy, of which 6.31% (77/1220) were original publications reporting on 58 specific mHealth tools in the context of the EVD outbreak. Of these, 62% (34/55) offered functionalities for surveillance, 22% (10/45) for case management, 18% (7/38) for contact tracing, and 6% (3/51) for laboratory data management. Only 3 tools, namely Community Care, Sense Ebola Followup, and Surveillance and Outbreak Response Management and Analysis System supported all four of these functionalities. CONCLUSIONS: Among the 58 identified tools related to EVD management in 2014 and 2015, only 3 appeared to contain all 4 key functionalities relevant for the response to EVD outbreaks and may be most promising for further development.