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.

The One Health approach to incident management of the 2019 Lassa fever outbreak response in Nigeria.

Globally, effective emergency response to disease outbreaks is usually affected by weak coordination. However, coordination using an incident management system (IMS) in line with a One Health approach involving human, environment, and animal health with collaborations between government and non-governmental agencies result in improved response outcome for zoonotic diseases such as Lassa fever (LF). We provide an overview of the 2019 LF outbreak response in Nigeria using the IMS and One Health approach. The response was coordinated via ten Emergency Operation Centre (EOC) response pillars. Cardinal response activities included activation of EOC, development of an incident action plan, deployment of One Health rapid response teams to support affected states, mid-outbreak review and after-action review meetings. Between 1st January and 29th December 2019, of the 5057 people tested for LF, 833 were confirmed positive from 23 States, across 86 Local Government Areas. Of the 833 confirmed cases, 650 (78%) were from hotspot States of Edo (36%), Ondo (26%) and Ebonyi (16%). Those in the age-group 21-40 years (47%) were mostly affected, with a male to female ratio of 1:1. Twenty healthcare workers were affected. Two LF naïve states Kebbi and Zamfara, reported confirmed cases for the first time during this period. The outbreak peaked earlier in the year compared to previous years, and the emergency phase of the outbreak was declared over by epidemiological week 17 based on low national threshold composite indicators over a period of six consecutive weeks. Multisectoral and multidisciplinary strategic One Health EOC coordination at all levels facilitated the swift containment of Nigeria's large LF outbreak in 2019. It is therefore imperative to embrace One Health approach embedded within the EOC to holistically address the increasing LF incidence in Nigeria.

The return on investment of implementing a continuous monitoring system in general medical-surgical units.

OBJECTIVES: To evaluate the cost savings attributable to the implementation of a continuous monitoring system in a medical-surgical unit and to determine the return on investment associated with its implementation. DESIGN: Return on investment analysis. SETTING: A 316-bed community hospital. PATIENTS: Medicine, surgery, or trauma patients admitted or transferred to a 33-bed medical-surgical unit. INTERVENTIONS: Each bed was equipped with a monitoring unit, with data collected and compared in a 9-month preimplementation period to a 9-month postimplementation period. MEASUREMENTS AND MAIN RESULTS: Two models were constructed: a base case model (A) in which we estimated the total cost savings of intervention effects and a conservative model (B) in which we only included the direct variable cost component for the final day of length of stay and treatment of pressure ulcers. In the 5-year return on investment model, the monitoring system saved between $3,268,000 (conservative model B) and $9,089,000 (base model A), given an 80% prospective reimbursement rate. A net benefit of between $2,687,000 ($658,000 annualized) and $8,508,000 ($2,085,000 annualized) was reported, with the hospital breaking even on the investment after 0.5 and 0.75 of a year, respectively. The average net benefit of implementing the system ranged from $224 per patient (model B) to $710 per patient (model A) per year. A multiway sensitivity analyses was performed using the most and least favorable conditions for all variables. In the case of the most favorable conditions, the analysis yielded a net benefit of $3,823,000 (model B) and $10,599,000 (model A), and for the least favorable conditions, a net benefit of $715,000 (model B) and $3,386,000 (model A). The return on investment for the sensitivity analysis ranged from 127.1% (25.4% annualized) (model B) to 601.7% (120.3% annualized) (model A) for the least favorable conditions and from 627.5% (125.5% annualized) (model B) to 1739.7% (347.9% annualized) (model A) for the most favorable conditions. CONCLUSIONS: Implementation of this monitoring system was associated with a highly positive return on investment. The magnitude and timing of these expected gains to the investment costs may justify the accelerated adoption of this system across remaining inpatient non-ICU wards of the community hospital.

Community acquired bacteremia in young children from central Nigeria--a pilot study.

BACKGROUND: Reports of the etiology of bacteremia in children from Nigeria are sparse and have been confounded by wide spread non-prescription antibiotic use and suboptimal laboratory culture techniques. We aimed to determine causative agents and underlying predisposing conditions of bacteremia in Nigerian children using data arising during the introduction of an automated blood culture system accessed by 7 hospitals and clinics in the Abuja area. METHODS: Between September 2008 and November 2009, we enrolled children with clinically suspected bacteremia at rural and urban clinical facilities in Abuja or within the Federal Capital Territory of Nigeria. Blood was cultured using an automated system with antibiotic removing device. We documented clinical features in all children and tested for prior antibiotic use in a random sample of sera from children from each site. RESULTS: 969 children aged 2 months-5 years were evaluated. Mean age was 21±15.2 months. All children were not systematically screened but there were 59 (6%) children with established diagnosis of sickle cell disease and 42 (4.3%) with HIV infection. Overall, 212 (20.7%) had a positive blood culture but in only 105 (10.8%) were these considered to be clinically significant. Three agents, Staphylococcus aureus (20.9%), Salmonella typhi (20.9%) and Acinetobacter (12.3%) accounted for over half of the positive cultures. Streptococcus pneumoniae and non-typhi Salmonellae each accounted for 7.6%. Although not the leading cause of bacteremia, Streptococcus pneumoniae was the single leading cause of all deaths that occurred during hospitalization and after hospital discharge. CONCLUSION: S. typhi is a significant cause of vaccine-preventable morbidity while S. pneumoniae may be a leading cause of mortality in this setting. This observation contrasts with reports from most other African countries where non-typhi Salmonellae are predominant in young children. Expanded surveillance is required to confirm the preliminary observations from this pilot study to inform implementation of appropriate public health control measures.