BackgroundDrug-Resistant tuberculosis (DR-TB) is estimated to cause about 10% of all TB related deaths. There is dearth of data on determinants of DR-TB mortality in Nigeria. Death among DR-TB treated cohorts in Nigeria from 2010 to 2013 was 30%, 29%, 15% and 13% respectively. Our objective was to identify factors affecting survival among DR-TB patients in northern Nigeria.MethodsDemographic and clinical data of all DR-TB patients enrolled in Kano, Katsina and Bauchi states of Nigeria between 1st February 2015 and 30th November 2016 was used. Survival analysis was done using Kaplan-Meier and multiple regression with Cox proportional hazard modeling.ResultsMean time to death during treatment is 19.2 weeks and 3.9 weeks among those awaiting treatment. Death was recorded among 38 of the 147 DR-TB patients assessed. HIV co-infection significantly increased probability of mortality, with an adjusted hazard ratio (aHR) of 2.35, 95% CI: 1.05–5.29, p = 0.038. Treatment delay showed significant negative association with survival (p = 0.000), not starting treatment significantly reduced probability of survival with an aHR of 7.98, 95% CI: 2.83–22.51, p = 0.000. Adjusted hazard ratios for patients started on treatment more than eight weeks after detection or within two to four weeks after detection, was beneficial though not statistically significant with respective p-values of 0.056 and 0.092. The model of care (facility vs. community-based) did not significantly influence survival.ConclusionBoth HIV co-infected DR-TB patients and DR-TB patients that fail to start treatment immediately after diagnosis are at significant risk of mortality. Our study showed no significant difference in mortality based on models of care. The study highlights the need to address programmatic and operational issues pertaining to treatment delays and strengthening DR-TB/HIV co-management as key strategies to reduce mortality.

Skin diseases are common worldwide, though prevalence rates in rural areas are difficult to estimate, and are primarily based on hospital studies rather than community-based studies. Primary health care providers in rural areas often lack sufficient knowledge about skin diseases, which contributes to poor skin management and subsequently causes considerable morbidity. This study looked at the performance of first-line health care providers in the management of common skin disease, using an algorithmic approach with a flowchart with diagnostic steps. As a reference standard, two dermatologists independently validated the diagnoses and treatment choices made by the providers. The performance of the algorithm was calculated in terms of the sensitivity, specificity, positive predictive value (PPV), and negative predictive value for each skin disease of the algorithm. A total of 19 patent medicine vendors and 12 traditional healers from Kano State in Nigeria diagnosed 4,147 patients with suspected skin symptoms. The most common skin disease was tinea capitis (59.2%), and it was found predominantly among boys below 15 years of age. Together, patent medicine vendors and traditional healers had 82% of the cases correctly diagnosed, and in 82% they prescribed the correct treatment. The sensitivities varied for each skin disease from 94.8% for tinea capitis to 7.1% for contact dermatitis. The specificities varied between 87.0% and 98.6%. Except for tinea capitis, lower PPVs were found for the various skin diseases when compared to earlier studies. In spite of the observed low sensitivities and low PPVs for several diseases, the algorithm seems to offer an improvement in management of common skin diseases at the peripheral level. With adaptations in training, further refinement of the algorithm and refresher training, predictive values and sensitivities can be increased.

Background Coronavirus disease 2019 (COVID-19) has emerged as an important cause of morbidity and mortality worldwide. The aim of this study is to identify the clinical predictors of mortality among patients with COVID-19 pneumonia during first and second waves in a treatment center in northwestern Nigeria. Methods This was a retrospective cohort study of 195 patients hospitalized with COVID-19 between April 2020 to March 2021 at a designated COVID-19 isolation center in Kano State, Northwest Nigeria. Data were summarized using frequencies and percentages. Unadjusted odds ratios and 95% confidence intervals and p-values were obtained. To determine independent determinants of mortality, we performed a stepwise multivariate logistic regression model. Results Of 195 patients studied, 21(10.77%) patients died. Males comprised 158 (81.03%) of the study population. In the adjusted stepwise logistic regression analysis, age>64 years (OR = 9.476, 95% CI: 2.181–41.165), second wave of the pandemic (OR = 49.340, 95% CI:6.222–391.247), cardiac complications (OR = 24.984, 95% CI: 3.618–172.508), hypertension (OR = 5.831, 95% CI:1.413–24.065) and lowest systolic blood pressure while on admission greater than or equal to 90mmHg were independent predictors of mortality (OR = 0.111, 95%CI: 0.021–0.581). Conclusion Strategies targeted to prioritize needed care to patients with identified factors that predict mortality might improve patient outcome.

Background and Introduction: COVID-19 has affected almost 180 million people around the world, causing the death of about 5 million persons, as of November 16, 2021. The disease presents with a plethora of pulmonary and extrapulmonary symptoms of varying severity. After an exhaustive review of the literature, we found no data on the mild and moderate COVID-19 disease phenotypes in Northern Nigeria. Our objective is to describe the clinical characteristics of non-severe COVID -19 disease phenotypes in Kano State. Methods: This is a retrospective cohort study at the COVID-19 Isolation Center of Muhammad Buhari Specialist Hospital Kano, Nigeria. We included all patients admitted from May 2020 to December 2020. Patients’ medical records were assessed and evaluated to describe the clinical characteristics at presentation. We explored time to discharge between patients aged ≤ 50 years old versus those >50. We applied the Kaplan-Meier product-limit estimator to generate cumulative probabilities of discharge over time and used the Log-rank test to determine differences between the two age groups. We applied Cox Proportional Hazards to identify predictors of time to discharge among the patients in the study. The study variables comprised of time of viral clearance and time to discharge as outcome variables, while main exposure variables included, age, sex, occupation, mode of exposure, presence of co-morbidity, and duration of hospitalization. Results: A total of 187 COVID-19 patients were reviewed. The commonest symptoms were fever, breathing difficulty, and dry cough. There was no recorded death. Contact with a confirmed COVID-19 positive person was the source of infection in 167(89.3%) of patients. We noted faster time to viral clearance in patients on lopinavir compared to those on chloroquine (Log-rank test p-value = 0.048). There were no significant differences in time to discharge between younger (< 50 years) versus older patients (≥ 50 years) [24 days vs. 26 days respectively; Log-rank test p-value = 0.082]. Age, sex, and source of infection did not appear to be predictors of infection phenotype. Conclusion and Implications for Translation: The findings of this study have a bearing on the surveillance and diagnosis of COVID-19 in Nigeria. While the plethora of clinical features may not be limited to infection with the SARS-CoV-2 virus, healthcare practitioners should consider these symptom clusters in addition to cognate contact and travel history when confronted with a suspected COVID-19 infection.   Copyright © 2022 Maiyaki et al. Published by Global Health and Education Projects, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution License CC BY 4.0.