Background: Knowledge, attitude, and practice (KAP) on dengue are hypothetical constructs which are substantiated through the set of composed questions. KAP on dengue is one of the most economical and effective methods used to curtail the incidence of dengue fever because of the absence of efficient vaccines with considerable contraindications and certified chemoprophylaxis for the infection in the affected regions of the world. Methods: A cross-sectional study was carried out with the use of a validated structured bilingual questionnaire specifically to assess the KAP on dengue among population in Selangor, Malaysia. Exploratory factor analysis was used to examine the dimensionality and interdependence among the items of the questionnaire and partial least square path analysis (PLS-path analysis) was used to explore the weights of the indicative sub-constructs on the main constructs. Results: Items on knowledge about dengue fever and vectors were factored into six sub-constructs, items on attitudes towards dengue were factored into four sub-constructs and items on preventive practices against dengue were also factored into four sub-constructs. PLS-path analysis revealed the exact sub-constructs which had a low and negative impact on the main hypothetical constructs. Conclusion: Visualization of the structural system of KAP on dengue was presented. The results from this novel application gave an insight into the exact KAP on dengue that is insufficient among respondents. Also, this result can be of benefit to the planning of future community health campaigns and mediation strategies to reduce the risk of dengue virus infection.
This work aims to explore the epidemiological status of coronavirus disease (COVID-19) and assemblage of various plant species that have prophylactic or therapeutic potentials on the disease. Epidemiological data were obtained from various health authorities worldwide and articles (totaling 103) published in standard journals from 2002-2020 on medicinal plants used in treating the disease and similar diseases. Epidemiological records of COVID-19 regional epidemic in Africa as of 29 th July, 2020 indicated South Africa as the epicenter of the disease; its continental index case was in Egypt on 14 th February, 2020. This was transmitted via an individual with traveling history from highly COVID burdened nations. Recent records revealed that the new cases of the disease have started trending locally with a person to person contact especially among those without travel history. There were about 874,036 cases in Africa with about 18,498 deaths recorded within the time frame of this study. The age groups mostly affected were 20-49 years with males' frequency marginally surpassing that of females. Seventy-five medicinal plant species from 41 families were recorded. Identified plants are indigenous to both the tropical and subtropical regions. Their medicinal potentials for treating human viral diseases are well described in Africa. Family Lamiaceae have the highest number of plant species (14.6%) used in managing COVID-19 and other related diseases. Asteraceae (12.3%) and Apiaceae (9.7%) families ranked second and third, respectively. Further studies on these plants with promising anti-SARS-CoV 2 properties on different experimental models for subsequent development of nutraceuticals and herbal medicine is imperative.
Haemoglobin concentration is a clinical indicator that examines or measures the presence or otherwise of an anaemia in an individual subject particularly due to iron deficiency. Normal Haemoglobin distributions vary with age, sex, life style, race/ethnicity, socio-economic status, regional difference, drug related issues and clinical characteristics including guidelines, and protocols. Issues arising from the recent spikes in the number of new-borns who were anaemic have attracted attention globally and these are of great concern particularly in Sub-Sahara Africa in which Nigeria has huge share of statistics. Hence, this study examined haemoglobin levels in under 5 children in Nigeria and the set of factors driven it from various statistical approaches. These models were: linear regression model (LM), Linear mixed model (LMM) and multilevel model (MM). This study used dataset of children included in the Nigeria Malaria Indicator Survey, 2015. The results of LM, identified two significant predictors of under 5 children haemoglobin level, also only three predictors were significant under LMM. The random effect of household number under LMM setting had higher variability than the state as random effect. In MM with state and household number as random effects, area of residence of the child, head of household wealth index, and the age of the child were all significant. The estimates of MM produced smaller standard errors compared to LMM. This implies that multilevel model is more competitive than other models considered in this study. Therefore, it could be applied to predict the haemoglobin level of under 5 children in Nigeria.
Coronavirus disease (COVID-19) is a deadly global pandemic caused by a virus of the family coronaviridae. It is an infectious disease which affects respiratory systems and causes people to experience mild to moderate symptoms and sometimes severe cases of the disease which usually resulted into death especially among those patients with other comorbidity conditions and elderly with immunosenescence effects. Nigeria registered its index case of COVID-19 on 27th February 2020. Subsequently, the number of reported cases were on increasing trend. Numerous studies on modelling the sporadic increase cases or spread of SARS-COV-2 using different methodologies were documented in literature. However, issues relating to over-dispersed problem and the presence of autocorrelation were not well handled in such methods. In this present study, the modelling of the spread of SARS-CoV-2 in Nigeria was done using a Negative Binomial Autoregressive model. Study data were collected on a daily basis from the update released by the Nigeria Centre for Disease Control from 1st April, 2020 to 29th May, 2021. The results showed that the number of confirmed SARS-CoV-2 cases increased comparatively between April-2020 to June-2020. However, the number of reported cases dropped steadily between July-2020 to Nov-2020. The data were over-dispersed and the presence of autocorrelation was observed. The results revealed that among the four NBAR estimated candidate models, NBAR (1) returned the lowest Akaike Information Criterion. Thus, NBAR (1) is the most parsimonious NBAR model for the data. Therefore, NBAR (1) can be used in predicting daily cases of SARS-CoV-2 in Nigeria
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