Deaths In Nigeria Research Articles

MODELING NOVEL COVID-19 PANDEMIC IN NIGERIA USING COUNT DATA REGRESSION MODELS

This study aimed to model COVID-19 daily cases in Nigeria, focusing on confirmed, active, critical, recovered, and death cases using count data regression models. Three count data regression models-Poisson regression, Negative Binomial regression, and Generalized Poisson regression were applied to predict COVID-19 related deaths based on the mentioned variables. Secondary data from the Nigeria Centre for Disease Control (NCDC) between February 29, 2020, and October 19, 2020, were used. The study found that Poisson Regression could not handle over-dispersion inherent in the data. Consequently, Negative Binomial Regression and Generalized Poisson Regression were considered, with Generalized Poisson Regression identified as the best model through performance criteria such as -2 log likelihood (-2logL), Akaike information criterion (AIC), and Bayesian information criterion (BIC). The study revealed positive and significant impacts of confirmed, active, and critical cases on COVID-19 related deaths, while recovered cases had a negative effect. Recommendations included increased attention to confirmed, active, and critical cases by relevant authorities to mitigate COVID-19-related deaths in Nigeria.

The Lassa fever cases and mortality in Nigeria: Quantile Regression versus Machine Learning Models

Introduction. Lassa fever (LF) is caused by the Lassa fever virus (LFV). It is endemic in West Africa, of which 25% of the infections are ascribed to Nigeria. This disease affects mostly the productive age and hence a proper understanding of the dynamics of this disease will help in formulating policies that would help in curbing the spread of LF. Objectives. The objective of this study is to compare the performance of quantile regression models with that of Machine Learning models. Methods. Data between between 7th January 2018 2018 and 17th December, 2022 on suspected cases, confirmed cases and deaths resulting from LF were retrieved from the Nigeria Centre for Disease Control (NCDC). The data obtained were fitted to quantile regression models (QRM) at 25%, 50% and 75% as well as to Machine learning models. The response variable being confirmed cases and mortality due to Lassa fever in Nigeria while the independent variables were total confirmed cases, the week, month and year. Results. Result showed that the highest monthly mean confirmed cases (56) and mortality (9) from LF were reported in February. The first quarter of the year reported the highest cases of both confirmed cases and deaths in Nigeria. Result also revealed that for the confirmed cases, quantile regression at 50% outperformed the best of the MLM, Gaussian-matern5/2 GPR (RMSE= 10.3393 versus 11.615), while for mortality, the medium Gaussian SVM (RMSE =1.6441 versus 1.8352) outperformed QRM. Conclusion. Quantile regression model at 50% better captured the dynamics of the confirmed cases of LF in Nigeria while the medium Gaussian SVM better captured the mortality of LF in Nigeria. Among the features selected, confirmed cases was found to be the most important feature that drive its mortality with the implication that as the confirmed cases of Lassa fever increases, is a significant increase in its mortality. This therefore necessitates a need for a better intervention measures that will help curb Lassa fever mortality as a result of the increase in the confirmed cases. There is also a need for promotion of good community hygiene which could include; discouraging rodents from entering homes and putting food in rodent proof containers to avoid contamination to help hart the spread of Lassa fever in Nigeria.

Lassa fever cases and mortality in Nigeria: Quantile Regression vs. Machine Learning Models.

Lassa fever (LF) is caused by the Lassa fever virus (LFV). It is endemic in West Africa, of which % of the infections are ascribed to Nigeria. This disease affects mostly the productive age and hence a proper understanding of the dynamics of this disease will help in formulating policies that would help in curbing the spread of LF. The objective of this study is to compare the performance of quantile regression models with that of Machine Learning models in. Data between between 7th January 2018 2018 and 17th December, 2022 on suspected cases, confirmed cases and deaths resulting from LF were retrieved from the Nigeria Centre for Disease Control (NCDC). The data obtained were fitted to quantile regression models (QRM) at 25, 50 and 75% as well as to Machine learning models. The response variable being confirmed cases and mortality due to Lassa fever in Nigeria while the independent variables were total confirmed cases, the week, month and year. Result showed that the highest monthly mean confirmed cases (56) and mortality (9) from LF were reported in February. The first quarter of the year reported the highest cases of both confirmed cases and deaths in Nigeria. Result also revealed that for the confirmed cases, quantile regression at 50% outperformed the best of the MLM, Gaussian-matern5/2 GPR (RMSE=10.3393 vs. 11.615), while for mortality, the medium Gaussian SVM (RMSE=1.6441 vs. 1.8352) outperformed QRM. Quantile regression model at 50% better captured the dynamics of the confirmed cases of LF in Nigeria while the medium Gaussian SVM better captured the mortality of LF in Nigeria. Among the features selected, confirmed cases was found to be the most important feature that drive its mortality with the implication that as the confirmed cases of Lassa fever increases, is a significant increase in its mortality. This therefore necessitates a need for a better intervention measures that will help curb Lassa fever mortality as a result of the increase in the confirmed cases. There is also a need for promotion of good community hygiene which could include; discouraging rodents from entering homes and putting food in rodent proof containers to avoid contamination to help hart the spread of Lassa fever in Nigeria.

The TB Surge intervention: an optimized approach to TB case-finding in Nigeria

INTRODUCTION: TB remains one of the leading causes of death in Nigeria, and despite progress in treatment coverage, a 56% gap in national case notifications remains. This gap is attributable in part to underdiagnosis due to missed cases in health facilities. The TB Surge intervention presented an opportunity to address barriers to optimal case detection in public health facilities.METHODS: KNCV Nigeria implemented the TB Surge intervention under the USAID-funded TB-LON Project in 1,041 public facilities from June 2020 to September 2022. Trained ad-hoc staff screened hospital attendees, linked identified presumptive TB cases to diagnosis and confirmed TB cases to treatment. Data were reported using the Commcare application. Robust project monitoring was used to address gaps.RESULTS: Of a total of 12,195,874 hospital attendees screened for TB, 729,369 identified as presumptive TB were tested and 65,029 TB cases were diagnosed; 8% of the TB cases were children. Overall TB yield was 9%. Medical ward service delivery point had the highest TB yield of 21%. The number needed to test was 11 and the number needed to screen was 188.CONCLUSION: The TB Surge intervention was of strategic importance in addressing missed cases and barriers to prompt TB diagnosis in health facilities.

Demographics and Clinical Characteristics of Burn Injuries in Nigeria: A Tertiary Hospital Cohort.

Burn injuries account for 4.8% of trauma deaths in Nigeria and annually for 20,000 deaths. This study was to catalogue the demographics of patient who presented at a referral center in Nigeria. In a retrospective study, we reviewed the records of all burn patients who presented over a 36-month period (January 2018 to December 2020). The clinical and demographic data was extracted using a self-designed questionnaire. Information obtained included biodata, etiology, time of presentation, first aid used, TBSA, length of hospital stay (LOHS), and outcome which was analyzed with SPSS version 28. A total of 112 burn patients presented at our facility, 87 male and 25 female (m: f = 3.5:1). Forty-four percent were children 10 years old and below. Though scalding with hot liquids was the commonest cause of injury in children, flame burn was the commonest etiology (57.1%). Inhalational injuries occurred in 14.2%. Raw eggs and pap were used as first aid by 23%. Most patients were discharged home, and 19.6% died. The prevalence of burn injuries remains high in developing countries. Most causes of burns are preventable. Dangerous traditional practices add to patient morbidity. Education on effective prevention strategies is important in reducing morbidity and mortality.

Healthcare Workers’ Knowledge and Use of Safe Childbirth Checklist Tool in Tertiary Hospitals in Rivers State

Globally, maternal mortality is an indicator to monitor maternal health services. In 2010, the WHO indicated that maternal deaths in Nigeria contributed to 14% of global maternal deaths. In 2018 Nigeria’s maternal mortality rate was 512 deaths per 100,000 live births. The WHO and other key partners developed the Safe Childbirth Checklist (SCC) as a facility-based tool to serve as reminder to help health workers adhere to evidence-based practices to ensure safety of the mother and baby. SCC has reportedly been adopted for use in other countries like Brazil and Cameroon. The objective of this study was to assess the Doctors and Nurses’ Knowledge and Use of SCC in public tertiary hospitals in Rivers State. Descriptive cross-sectional study was used. Structured questionnaires were self-administered to 207 respondents (35 nurses and 67 doctors at RSUTH, 49 nurses and 56 doctors at UPTH) working in obstetrics and gynaecology department. Chi-square was used to test the hypothesis. Descriptive statistics was used to assess the responses of the respondents. Responses obtained at Teaching Hospital A showed that 80% of the Nurses/Midwives have no knowledge of SCC tool, 100% of the House Officers have not seen the tool; 90% of the Resident Doctors have not also used SCC tool. At Teaching Hospital B, 80% of the Nurses have no knowledge of SCC tool; 20% of the Nurses had seen it earlier. The study showed evidence that SCC tool is not in use in the two tertiary hospitals in Rivers State. Null hypothesis was accepted.

Comparative Study of the Gompertz and Logistic Growth Models on the Prevalence and Fatality of Covid-19 Pandemic in Nigeria

This study models the prevalence and fatality of the Covid-19 pandemic in Nigeria from February 2020 to July 2022. It is a comparative study of two prominent models: The Gompertz and Logistic population growth models. The data for this study was obtained from the website of Our World in Data, OWID (https//www.ourworldindata.org). The Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) were employed to compare the performance of the models, and the number of iterations before convergence and convergence tolerance for each model was also put into consideration. The study revealed that the Gompertz population growth model provides a better fit compared to the logistic growth in modelling the cumulative covid-19 cases and cumulative covid-19-related deaths in Nigeria. From the models, we obtained important features of the pandemic, such as the growth rate and asymptotes.

Challenges and recommendations for addressing under-five pneumonia morbidity and mortality in Nigeria.

Pneumonia is a severe infection and one of the most common causes of mortality among children under five years of age, when not appropriately managed. Infection of the lungs by bacteria, viruses, or fungi and consequent inflammation may lead to cough and difficult breathing. Some of the key predisposing factors are malnutrition and air pollution. WHO reports that Africa has the highest burden of global child mortality, and 16% of all deaths in pneumonia, were children under five years of age in 2016. This study aimed to explore how health providers perceive pneumonia as a cause of under-five mortality in Nigeria. A qualitative study design with in-depth interviews and focus group discussions was used to explore and understand nurses and pediatricians' views regarding the pneumonia situation, vaccinations, and preventive suggestions to reduce under five pneumonia deaths in Nigeria. Two themes and four categories emerged: participant's anxiety over the situation, their views on impediments, current policies and strategies, and suggestions on addressing severe pneumonia. The results from this study highlight contextual issues playing major roles in pneumonia mortality among children in Nigeria, which will need approaches on several levels to address them.

Modeling the Effect of Population Density and Some Related Factors on Covid-19 Pandemic in Nigeria: An Application of Count Data Regression

Aim: Nigeria's population density and other factors like confirmed, admitted, and discharged cases have adversely impacted health behaviors and the management of the COVID-19 pandemic. This study aims to investigate how population, population density, confirmed, admitted, and discharged cases affect the prevalence of the COVID-19 pandemic in the 36 states of Nigeria, including the FCT. Method: The number of COVID-19-related deaths, confirmed, admitted, and discharged individuals, from June 20, 2021, to December 31, 2022, were extracted from the Nigeria Centre for Disease Control (NCDC) online database, while data set on the Nigeria population and density were collected from Nigeria’s National Population Commission (NPC) website. Three count data regression techniques; Poisson, Negative Binomial, and Generalized Poisson Regression models were employed to analyze these count data. Result: It was found that the number of admitted patients has a significant negative impact on COVID-19, whereas the number of confirmed laboratory COVID-19 cases has a significant positive effect on the number of deaths related to COVID-19. Additionally, the result showed that Nigeria's COVID-19 death rate is negatively impacted by discharged cases, population, and population density. Conclusion: It is inferred that the Generalized Poisson Regression model is the most suitable count data regression model for over-dispersion and is the best model for predicting the number of COVID-19-related deaths in Nigeria between June 20, 2021, and December 31, 2022.

SURVIVAL ANALYSIS OF BREAST CANCER PATIENTS IN KANO STATE

Cancer is a deadly malignant disease and is prevalent in Sub Saharan Africa. Specifically, breast cancer is now the most popular cancer and the second leading cause of death in Nigeria among women. This research is aimed at assessing the length of life of patients and prognostic factors associated with survival of breast cancer patients. Research subjects were breast cancer patients who went to Aminu Kano Teaching hospital (AKTH), patient’s follow-up data were obtained from the medical records and pathological variables were obtained from the department of Pathology from 2018- 2022. The survival analysis was performed using Kaplan Meier, log rank and Cox proportional regression model(semi-parametric). The Kaplan Meier results reveals the overall survival to be 0.75(75%). The median survival time is approximately 44 months, that is the probability that half of the patients have died. From the log-rank test, the survival times significantly differ across groups of Tumor stages, Age groups, Lymph node stages and Treatment Types. Results for the Cox proportional hazards model shows that, Treatment type and Tumor stage breast cancer were the risk factor for death in breast cancer patients. It was found that the hazard of death is twice or more for patients with a tumor characterized as tumor stage IV compared to other tumor stages. The hazard of death for patients on Radiotherapy treatment was 1.4 times as patients on Chemotherapy or Hormonal-therapy treatment. The Proportional Hazard assumption was assessed and all the variables meets the assumption.

Time series analysis and forecasting of cholera disease using discrete wavelet transform and seasonal autoregressive integrated moving average model

PurposeCholera is among the leading causes of death in Nigeria. The main predictors of cholera transmission remain the lack of access to potable water and good sanitary conditions. Cholera is also linked to weather variables such as maximum temperatures, high Rainfall, and humidity. The relationship between cholera cases and weather variables depends on location, time, or season; hence, it is a time series dataset. This research aims to enhance the seasonal autoregressive integrated moving average (SARIMA) model by incorporating the discrete wavelet transform (DWT). MethodsThis research proposed a novel approach to forecasting cholera using the SARIMA model by incorporating DWT as a dimensionality reduction technique and a K-means clustering algorithm for outlier detection. The enhanced model is termed the "Enhanced seasonal autoregressive integrated moving average" (ESARIMA). DWT is a good dimensionality reduction technique for time series data and extracts the best features for forecasting to have better prediction accuracy and minimal error. ResultThe results show that ESARIMA (accuracy = 97%, RSS = 0.502) outperformed the existing model, SARIMA (accuracy = 91.61%, RSS = 0.60). ConclusionNigeria's weekly and monthly cholera outbreaks exhibit stochastic seasonal time series behavior that becomes stationary after the first seasonal differencing; hence, it could be forecasted with specific time series models.

Abstract P223: Estimated Health Benefits, Costs, and Cost-Effectiveness of Eliminating Industrial Trans-Fatty Acids in Nigeria

Objectives: To estimate the potential health gains, costs, and cost-effectiveness of a mandatory limit on industrial trans fatty acids (iTFA) in the Nigerian food supply. Methods: We used Markov cohort models to estimate the effect on ischemic heart disease (IHD) burden, costs, and cost-effectiveness of a mandatory iTFA-limit (≤2% of all fats) for foods in Nigeria. Data on demographics, IHD epidemiology, and trans-fatty acid intake were derived from the 2019 Global Burden of Disease Study. We calculated the IHD burden attributable to iTFA by comparing the current trans-fatty acid intake to counterfactual settings with complete elimination of iTFA intake. Policy implementation costs (including government costs for legislation and monitoring, and industry costs for product reformulation), avoided IHD events and deaths, health-adjusted life years (HALYs) gained, and healthcare costs saved were estimated over 10 years and lifetime of the Nigerian population. Incremental cost-effectiveness ratios using net costs (i.e., implementation costs minus healthcare cost savings) and HALYs gained (both discounted at 3%) were used to assess cost-effectiveness. Results: Over the first 10 years, elimination of iTFA intake was estimated to prevent 9,996 (95% uncertainty interval: 8,870; 11,118): IHD deaths and 66,569 (58,862; 74,083) IHD events, and to save 90 million USD (78; 102) in total healthcare costs. The corresponding estimates over the lifetime were 259,934 (228,736; 290,191), 479,308 (95% UI: 420,472; 538,177), and ~518 (450; 587). Policy implementation costs (government plus industry) were estimated as 18 million USD (12; 25) over the first 10 years, and 27 million USD (20; 35) over the population lifetime. The intervention was estimated to be cost-saving. Findings were robust across several deterministic sensitivity analyses (Figure). Conclusions: Our findings support legislating a mandatory limit of iTFAs as a cost-saving strategy to avert substantial numbers of IHD events and deaths in Nigeria.

Modelling modifiable factors associated with the probability of human rabies deaths among self-reported victims of dog bites in Abuja, Nigeria.

Canine-mediated rabies kills tens of thousands of people annually in lesser-developed communities of Asia, Africa, and the Americas, primarily through bites from infected dogs. Multiple rabies outbreaks have been associated with human deaths in Nigeria. However, the lack of quality data on human rabies hinders advocacy and resource allocation for effective prevention and control. We obtained 20 years of dog bite surveillance data across 19 major hospitals in Abuja, incorporating modifiable and environmental covariates. To overcome the challenge of missing information, we used a Bayesian approach with expert-solicited prior information to jointly model missing covariate data and the additive effects of the covariates on the predicted probability of human death after rabies virus exposure. Only 1155 cases of dog bites were recorded throughout the study period, out of which 4.2% (N = 49) died of rabies. The odds for risk of human death were predicted to decrease among individuals who were bitten by owned dogs compared to those bitten by free-roaming dogs. Similarly, there was a predicted decrease in the probability of human death among victims bitten by vaccinated dogs compared to those bitten by unvaccinated dogs. The odds for the risk of human death after bitten individuals received rabies prophylaxis were predicted to decrease compared to no prophylaxis. We demonstrate the practical application of a regularised Bayesian approach to model sparse dog bite surveillance data to uncover risk factors for human rabies, with broader applications in other endemic rabies settings with similar profiles. The low reporting observed in this study underscores the need for community engagement and investment in surveillance to increase data availability. Better data on bite cases will help to estimate the burden of rabies in Nigeria and would be important to plan effective prevention and control of this disease.

Prediction of Covid-19 New Cases and New Deaths in Nigeria Using Polynomial Regression Model

The Coronavirus disease 2019 (COVID-2019) is a global pandemic and Nigeria is not left out in being affected. This study is focused on the analysis prediction of the spread of Covid-19 in Nigeria, applying statistical model (Polynomial Regression) and available data from the NCDC. The study is an insight into the spread of Covid-19 in Nigeria in order to establish a suitable prediction model, which can be applied as a decision-supportive tool for assigning health interventions and mitigating the spread of the Covid-19 infection. The polynomial regression models are prominent tools in predicting the impact of certain factors on COVID19 outbreak and taking the necessary measures to respond to this crisis. The objective of this study is to implement a predictive model in forecasting the near future number of new death and positive COVID-19 cases expected in Nigeria following the present trend. A polynomial regression model prediction on the epidemiological data obtained from Nigerian Centre for Disease Control to predict the epidemiological trend of the prevalence and incidence of COVID-2019. In this study, we examined the inadequacies involved in the manual method of predicting new death and COVID-19 case to enable government to make decision in reducing the death and spread of COVID-19 in Nigeria. The methodology we adopted in this study is the Object-Oriented Design Methodology (OODM) which combines data and processes (methods) into single entities called objects while Microsoft Visual Basic is our programming language of implementation. The new system will be used in generation of reports and results to create, process, and validate a model that can be used to forecast future outcomes of new death and COVID-19 case. The system was implemented using VB.net programming language and SQLite because it is not a server side application, which needs a standard database management system. Keywords: Prediction, COVID-19, Coronavirus, Pandemic, Morbidity, Polynomial regression, Prominent, Predictors, Nigerian Centre for Disease Control (NCDC).

Analyzing Maternal Mortality in Nigeria: A Qualitative Study Approach using the Three Phases of Delay

Background: Nigeria has a Maternal Mortality Ratio of 814 per 100,000 births. Only 30% of births in Nigeria occur in health facilities. A proven method to prevent maternal deaths is to provide emergency obstetric care and promote hospital birth. Application of the Three Phases of Delay Model to hospital births in Nigeria directed a community needs assessment and may influence local and regional health promotion efforts with the goal of preventing maternal deaths. This study aimed to analyze maternal deaths in Nigeria, employing a qualitative approach with the Three Phases of Delay model. Subjects and Method: This was a qualitative study grounded in theory research that employed focus groups and key informant interviews in Cross River State, Nigeria. A stratified random sampling of local government areas was followed by a random selection of wards and a purposive selection of key informants and focus group participants. In total 26 key informants and 100 focus group, discussion participants were selected across the wards in accordance with local customs. All responses were recorded digitally and transcribed verbatim. All key informant interviews and all but two focus groups were conducted in English. Data were collected in December 2016, over a four-week period. The transcripts were analyzed using Atlas TI to designate codes and to compile quotes by theme. Results: Application of the Three Phases of Delay Model to hospital births in Cross River State, Nigeria found significant points of delay at all levels of the Delay Model. The most prevalent of the delays described by the focus groups and key informants were delays in reaching the point of care and delay in receiving quality care at the health facility. Conclusion: Identifying the influences on delay can be employed to develop and plan local and regional health promotion efforts with the goal of preventing maternal death. Keywords: maternal mortality, childbirths, pregnancy, delay model, health facility. Correspondence: Precious Chidozie Azubuike. Department of Public Health, College of Medical Sciences, University of Calabar, PMB 1115, Calabar, Nigeria. Email: azubuikepreciousc@gmail.com. Phone: +234810- 6353021. Journal of Health Promotion and Behavior (2023), 08(01): 22-33 DOI: https://doi.org/10.26911/thejhpb.2023.08.01.04

Fears, Deaths, Mourning, and Burials in Times of COVID-19 Pandemic in Nigeria

Extant studies on deaths in Nigeria have been conducted without much critical focus on how the public responded to emergency politics or the government’s interventions during pandemics. Fundamentally, this is the gap this research aims to fill. This study focuses on pain events, grieves, and mourning following COVID-19-related deaths. Thus, this study analyzes the intersections between COVID-19 and death discourse by exposing and interrogating the variances, ambiguities, ambivalences, corollaries, and paradoxes amid convoluted public health conversations. This research employed both primary and secondary sources. Primary sources include African beliefs, newspapers reports of past and current pandemics, and radio, television, and social media narratives. Secondary sources include reviews of existing literature on deaths and pandemics. Historical analysis is used in this study, identifying two categories of dead bodies created during the COVID-19 pandemic. The first category is Pandemic Dead Bodies (PDBs) and the second category is Non-Pandemic Dead Bodies (NPDBs). Many concomitants characterizing Pandemic Dead Bodies including stigmatization, apathy, otherization, ambiguities, genderization, demographication, politicization, contestations, and weaponization are interrogated from socio-historical perspectives. Heightening the stress of grieving families are issues around deaths, as burials are postponed or held within the restrictions of National Centre for Disease Control (NCDC) COVID-19 protocols, often with the presence of limited family members of the deceased. Thus, the pains and grieves are not just about the loss of loved ones, but the inability to give them a befitting burial, since Nigerians love to celebrate the liminality of their loved ones into eternity. Likewise, anticipatory grief became more accentuated and aggravated in Nigeria regarding the manner of announcing the demise of some popular politicians who died of COVID-19. Fundamentally, these problematic encumbrances, nuances, and intrigues concerning deaths during the COVID-19 pandemic are historicized. This study concludes that pains, grief, sorrow, death, and burial are historically constituted and configured regarding social, economic, political, cultural, and environmental interactions.

Childhood Diarrhea Prevalence and Uptake of Oral Rehydration Solution and Zinc Treatment in Nigeria.

Given the disproportionate burden of childhood diarrhea deaths in Nigeria, this study assessed the prevalence and predictors of the disease and the uptake of ORS and zinc supplementation as treatments in a population-based national survey. Cross-sectional data from the 2018 Nigeria Demographic Health Survey were used. A log-Poisson regression was used to estimate the relative risks (RR) for the individual-level predictors of childhood diarrhea and the uptake of ORS and zinc treatments. A total of 30,713 children under 5 years of age were included in the survey. The period prevalence of reported diarrhea in the last two weeks was 12.9% (95% CI: 12.5%, 13.3%). Among the children with diarrhea, the proportion who received ORS was 39.7% (95% CI:38.2%, 41.3%), while 29.1% of them received zinc supplements (95% CI:27.7%, 30.5%), and 21.8% of them received both the ORS and zinc treatments as recommended. Children under 6 months of age with diarrhea had a significantly lower likelihood of being given ORS or zinc when they were compared to the older children. The institutional delivery of them, maternal employment, and improved water sources were also independent predictors of the uptake of ORS and zinc treatments for diarrhea (p-values < 0.05). Interventions to prevent childhood diarrhea and improve the coverage of ORS and zinc treatments may reduce the large burden of childhood diarrhea deaths in Nigeria.

Determinants of care-seeking behaviour for fever, acute respiratory infection and diarrhoea among children under five in Nigeria

BackgroundDespite available, inexpensive and effective treatments, malaria, diarrhoea, and pneumonia still contribute the majority of the global burden of childhood morbidity and mortality. Nigeria has the highest absolute numbers of child deaths worldwide. Appropriate care-seeking is important for prompt diagnosis, appropriate and timely treatment, and prevention of complications. The objective of this cross-sectional study was to examine the prevalence of and factors associated with appropriate care-seeking for childhood illnesses.MethodsWe used the most recent Nigeria Demographic and Health Survey (2018) to assess the prevalence of appropriate care-seeking among mothers of children under five with symptoms of common childhood illnesses. For diarrhoea, we defined appropriate care-seeking as seeking care from a formal health provider. For fever and acute respiratory infection (ARI), appropriate care-seeking was defined as seeking care from a formal provider the day of or after symptom onset. Multivariate logistic regression was carried out to assess factors associated with optimal care-seeking for each illness.ResultsAt least 25% of parents did not seek any care for children with fever or ARI; this figure was over one third for diarrhoea. Only 15% and 13% of caregivers showed appropriate care-seeking for their children with fever and ARI respectively, and 27% of mothers sought care from a formal provider for diarrhoea. Predictors of appropriate care-seeking varied according to childhood illness. Previous facility delivery was the only risk factor associated with increased odds of appropriate care-seeking for all three illnesses; other risk factors varied between illnesses.ConclusionOverall, care-seeking for childhood illnesses was suboptimal among caregivers in Nigeria. Interventions to increase caregivers’ awareness of the importance of appropriate care-seeking are needed alongside quality of care interventions that reinforce people’s trust in formal health facilities, to improve timely care-seeking and ultimately reduce the high burden of child deaths in Nigeria.

Comparative Study of the Error Trend and Seasonal Exponential Smoothing and ARIMA Model Using COVID-19 Death Rate in Nigeria

Background: COVID-19 has claimed the lives of millions of people in Nigeria and around the world during the last two years. It is a recognized global health crisis of our day, as well as a persistent threat to the earth. The goal of this study was to examine the trend and fit an Error Trend and Seasonal (ETS) exponential smoothing and Autoregressive Integrated Moving Average (ARIMA) model to Nigeria's COVID-19 daily fatalities.Methods: A dataset of daily COVID-19 confirmed fatality cases was used in the investigation. Data was acquired from the Nigerian Centre for Disease Control (NCDC) web database between the 10th of July 2020 and the 2nd of December 2021. The ARIMA model and twelve (12) ETS exponential smoothing techniques were investigated using a dataset of COVID-19 pandemic deaths in Nigeria. The ARIMA and ETS exponential smoothing algorithms were evaluated using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Hannan Quinn Information Criterion (HQC), and Average Mean Squared Error (AMSE) selection criteria.Result: The ARIMA (0,1,0) model was the best time series modeling for the coronavirus (COVID-19) epidemic in Nigeria since it had the lowest AIC=2863.51, BIC=2866.90, HQ = 2866.90, and AMSE = 0.55471 values.Conclusion: The ARIMA (0,1,0) model is preferred above the other thirteen (13) competing models based on daily confirmed COVID-19 deaths in Nigeria. This research would assist the Nigerian government in better understanding the pestilence's evolution pattern and providing adequate provisions, prompt mediation, and treatment to prevent additional deaths caused by the virus.

Challenges and lessons from a school-based human papillomavirus (HPV) vaccination program for adolescent girls in a rural Nigerian community

BackgroundOver 80% of new cervical cancer cases occur in women living in low- and middle-income countries. It is the second highest cause of female cancer deaths in Nigeria. School based vaccination programs are an effective strategy for delivering the HPV vaccine to adolescent girls. This study aims to understand the challenges to implementing school-based HPV vaccination programs, particularly in a remote rural setting where vaccine hesitancy is high.MethodsA 22- item interviewer administered questionnaire was used to evaluate HPV knowledge and willingness to get the HPV vaccinate among 100 female secondary school students as part of an HPV vaccination pilot in a rural community in Kebbi State, Nigeria. Additionally, semi-structured interviews were used to assess community knowledge and attitudes on cervical cancer and HPV vaccination. Data collected were analyzed thematically to understand challenges and generate lessons for vaccine delivery in the study setting.ResultsKnowledge of HPV and cervical cancer among junior secondary school aged girls was fair with a mean score of 66.05%. For senior secondary school aged girls, the knowledge score ranged from 70 to 100% with a mean of 96.25% indicating good knowledge of HPV and cervical cancer. All participants (n = 100) received the first vaccine dose but due to COVID-19, 33 participants were not able to complete the vaccine dosage within the recommended 6-month schedule. Of the parents who provided consent, none could afford the vaccine out of pocket. Challenges to vaccine delivery included operational costs exacerbated by lack of adequate health workforce and infrastructure in the study setting.ConclusionAn exploration of sociocultural perspectives and contextual realities is crucial to understanding the complexities of HPV vaccine introduction from the perspective of the target audience, and the local community. Strategies for introducing the HPV vaccine should address community concerns through effective communication, appropriate delivery, and targeted advocacy to make the vaccination program locally relevant. While school-based HPV immunization programs have been shown to be successful, adequate design, planning and monitoring is important. Additionally, considerations must be made to account for the high operational cost of vaccine delivery in rural, hard to reach areas where human resources and infrastructure are limited.