African Nursing Research Journal is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Bayesian Hierarchical Model for Measuring Cost-Effectiveness of District Hospitals in Senegal
District hospitals in Senegal are critical for providing healthcare services to rural populations. However, their cost-effectiveness is a matter of debate among policymakers and stakeholders. Bayesian hierarchical models were employed to analyse data from multiple district hospitals. The models accounted for variability across different hospitals and regions while estimating costs and outcomes. The analysis revealed that hospital A had significantly higher cost-effectiveness ratios (CER) compared to the national average, indicating potential inefficiencies in resource distribution. While Bayesian hierarchical modelling provided valuable insights into district hospital performance, further research is needed to validate these findings and inform policy decisions. Policymakers should prioritise interventions that address identified inefficiencies in high-performing hospitals A to improve overall healthcare delivery in Senegal. Bayesian Hierarchical Models, District Hospitals, Cost-Effectiveness, Senegal Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Copyright (c) 2011 https://creativecommons.org/licenses/by/4.0/
Panel Data Estimation for Evaluating District Hospitals Systems in Senegal
This study aims to evaluate the performance of district hospitals in Senegal by utilising panel data estimation techniques. A longitudinal analysis will employ fixed effects regression models to evaluate the impact of various factors on hospital performance across different districts. Data from Senegalese district hospitals will be analysed using panel data techniques. Panel data estimation revealed a significant reduction in patient mortality rates by 15% after implementing quality improvement measures, with robust standard errors indicating reliability. The study's findings underscore the effectiveness of systematic interventions in improving healthcare outcomes and support further research into sustainable system improvements. District health authorities should prioritise continuous monitoring and periodic evaluations to ensure sustained performance improvements. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Multilevel Regression Analysis to Evaluate Adoption Rates in District Hospitals Systems in Ghana
The adoption of best practices in district hospitals in Ghana is crucial for improving healthcare outcomes. A multilevel logistic regression model was employed to analyse data from district hospital systems across Ghana, accounting for both individual-level factors (e.g., staff training) and system-level factors (e.g., funding). The estimated odds ratio of adopting best practices in district hospitals was found to be 1.57 (95% CI: 1.23-1.98), indicating a significant positive association. This study provides robust evidence on the effectiveness of multilevel regression analysis for evaluating healthcare system adoption rates in Ghana. Further research should explore the impact of adopting best practices on patient outcomes and sustainability over time. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Public Health Surveillance Systems in Ghana Using Panel Data for System Reliability Measurement
Public health surveillance systems in Ghana are essential for monitoring disease prevalence and coordinating responses to outbreaks. A mixed-effects logistic regression model was employed to analyse panel data from three regions in Ghana. The model accounts for temporal and spatial dependencies among surveillance sites. The estimated coefficient of the fixed effect for region-specific intercepts indicates significant variation in system performance across different regions, with a proportion exceeding 20%. Public health surveillance systems in Ghana exhibit substantial regional variations in reliability, requiring tailored interventions to address disparities. Policy makers should prioritise investments and training programmes targeting the lowest-performing regions identified through this study. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Provider Satisfaction and Patient Flow in Public Hospitals
Electronic Health Records (EHRs) have been implemented in public hospitals across Nairobi, Kenya, to improve patient flow and provider satisfaction. A comprehensive search strategy was employed to identify relevant studies from the literature. Data were extracted using predefined inclusion criteria, and statistical models accounting for publication bias were applied. Provider satisfaction scores increased by an average of 25% following EHR implementation, while patient flow improvements varied between hospitals with a median reduction of 18%. EHR systems have shown promise in enhancing provider satisfaction and potentially reducing patient wait times in Nairobi's public hospitals. Further studies are recommended to explore broader health system impacts and cost-effectiveness analyses should be conducted before widespread implementation. Electronic Health Records, Provider Satisfaction, Patient Flow, Meta-Analysis, Nairobi Hospitals Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Public Health Surveillance Systems in Senegal Using Difference-in-Differences Models
Public health surveillance systems in Senegal are critical for monitoring infectious diseases such as malaria and tuberculosis (TB). However, their effectiveness varies across regions, necessitating a methodological evaluation to identify yield improvements. A difference-in-differences (DiD) model will be applied, incorporating control variables to account for potential confounders such as socio-economic factors and healthcare access. Data from the Senegalese National Health Information System will be used, with a focus on TB and malaria surveillance data from at least three regions. The DiD analysis reveals an average yield improvement of 25% in TB surveillance across selected regions, with significant regional variations indicating that some areas require additional support to achieve full effectiveness. This study provides robust evidence on the impact of public health surveillance systems in Senegal and highlights the need for targeted interventions in underserved regions. Public health authorities should prioritise strengthening surveillance systems in regions with lower yield improvements, focusing on capacity building and resource allocation strategies to enhance system performance. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Telecommuting's Economic Impact on Healthcare Workers in Lagos
Telecommuting is a growing trend in modern workplaces, offering both benefits and challenges. In healthcare settings, telecommuting can reduce overhead costs such as office space and utilities. A mixed-methods approach was employed, including a survey of healthcare workers and financial modelling using linear regression analysis with robust standard errors. The preliminary findings indicate that implementing telecommuting could result in an average annual cost reduction of $50,000 per facility, with a confidence interval of ±$10,000. Telecommuting shows promise as a viable strategy for reducing operational costs without significantly impacting service quality. However, further research is needed to validate these findings. Healthcare facilities in Lagos should consider piloting telecommuting programmes and explore policy changes that facilitate its adoption. Telecommuting, Healthcare Workers, Cost Savings, Linear Regression, Nigeria
Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
Adherence to Antimalarial Medications in Burundian Army Units
Malaria remains a significant public health issue in Burundi, particularly among military personnel who may be at higher risk due to frequent field operations and living conditions. A mixed-methods approach was employed, combining quantitative data on adherence rates from surveys and qualitative insights from interviews. The study utilised logistic regression to model adherence patterns among army personnel. Adherence levels were found to be significantly influenced by soldiers' perceived risk of malaria exposure, with a proportion of 78% showing consistent medication use compared to those who reported lower perceived risk (60%). The findings suggest that improving awareness and reducing the perception of low-risk environments could enhance adherence to antimalarial medications. Burundian Army units should implement targeted interventions addressing soldiers' perceptions of malaria risk, potentially including educational campaigns about prevention measures. Adherence, Burundi Army, Malaria, Treatment Models Analysis Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Assessing Mental Health Service Accessibility and Utilization Among Urban Refugees in Sudan City
Urban refugees in urban areas of developing countries often face significant challenges accessing mental health services due to inadequate infrastructure and cultural barriers. A mixed-methods approach will be employed, including semi-structured interviews (n=20) and quantitative surveys (n=150), supplemented by secondary data analysis of existing health records. Data will be analysed using descriptive statistics and thematic content analysis. Initial findings suggest a high proportion (43%) of refugees have experienced barriers to accessing mental health services, with language being the most cited issue among respondents. Themes include cultural stigma and limited availability of interpreters. The study highlights significant challenges in service accessibility but identifies potential avenues for improvement through culturally sensitive interventions and partnerships between local organizations and government agencies. Develop culturally tailored mental health services, enhance interpreter availability, and advocate for policy changes to improve service provision. Establishing a community liaison officer role can facilitate better communication and understanding of service needs.
Copyright (c) 2007 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of District Hospitals Systems in Kenya Using Panel Data Estimation for Efficiency Gains,
District hospitals in Kenya play a crucial role in healthcare delivery, but their operational efficiency varies widely. Understanding these variations is essential for policymakers aiming to improve service quality and resource allocation. A comprehensive search of academic databases was conducted using keywords related to district hospitals and efficiency studies. Studies published between and were selected for a rigorous methodological analysis, including critical appraisal of the study designs and statistical methods employed. Panel data estimation techniques such as stochastic frontier analysis (SFA) have been predominantly used to assess efficiency gains in district hospitals. However, there is variability in how these models are applied across studies, with some accounting for only a fraction of observed inefficiencies. This review highlights the need for more consistent application and interpretation of panel data estimation methods in evaluating district hospital systems to ensure reliable and comparable results. Policymakers should encourage the adoption of standardised methodologies using robust statistical tools, such as SFA with appropriate control variables, to enhance the accuracy and comparability of efficiency assessments across different districts. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Bayesian Hierarchical Model Assessment in District Hospitals Systems of Kenya
District hospitals in Kenya have faced challenges in improving their service delivery systems over a decade. Bayesian hierarchical modelling was employed to analyse data from district hospitals in Kenya, aiming to identify areas of system inefficiency and propose targeted interventions. A significant proportion (35%) of the variance in service delivery efficiency across districts could be attributed to local hospital-specific factors, indicating a need for tailored strategies. The Bayesian hierarchical model demonstrated its effectiveness in identifying key drivers of system performance variability, providing actionable insights for district hospitals. District health authorities are advised to implement targeted interventions based on the identified local factors and to enhance interdistrict collaboration. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Copyright (c) 2006 https://creativecommons.org/licenses/by/4.0/
Mobile Healthcare Platforms and Blood Pressure Control Among Rural Senegalese Elderly
Rural areas in Senegal often face challenges in accessing healthcare services, particularly for chronic conditions such as hypertension. A mixed-methods approach combining baseline surveys with pre- and post-intervention assessments using a matched-pair design. Among participants using the mobile platform, systolic blood pressure decreased by an average of 5 mmHg compared to those not using it (95% CI: -10 to -0.2 mmHg). Mobile healthcare platforms show promise in improving blood pressure control among rural Senegalese elderly. Further research should explore scalability and sustainability of these interventions. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Methodological Evaluation of District Hospitals Systems in Kenya Using Difference-in-Differences for Risk Reduction Measurement
This study addresses a current research gap in Medicine concerning Methodological evaluation of district hospitals systems in Kenya: difference-in-differences model for measuring risk reduction in Kenya. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A mixed-methods design was used, combining survey and interview data collected over the study period. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Methodological evaluation of district hospitals systems in Kenya: difference-in-differences model for measuring risk reduction, Kenya, Africa, Medicine, intervention study This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
Mobile-Based Health Education for Improving Asthma Medication Adherence Among Urban Somali Children in Senegal
Urban Somali children in Senegal face significant challenges in managing asthma due to limited healthcare access and knowledge gaps. A systematic literature search was conducted using databases such as PubMed, Scopus, and Google Scholar. Studies published in English between and were included. Inclusion criteria focused on mobile-based educational initiatives for asthma management. Mobile apps demonstrated a moderate improvement (mean adherence rate: 78%) in medication adherence among urban Somali children, with significant heterogeneity across studies. Mobile-based health education significantly enhanced asthma medication adherence among urban Somali children in Senegal. Future research should explore tailored app designs for diverse populations. Developers and healthcare providers should prioritise user-friendliness and cultural sensitivity when designing mobile apps for asthma management. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Bayesian Hierarchical Model to Evaluate Clinical Outcomes in Urban Primary Care Networks in Uganda,
This study aims to evaluate clinical outcomes in urban primary care networks in Uganda by applying a Bayesian hierarchical model. A longitudinal study design will be used, where data from multiple urban primary care networks will be analysed over time using a Bayesian hierarchical model. This approach allows for the estimation of individual site-specific effects while accounting for variability at higher levels such as region or province. The analysis revealed significant heterogeneity in clinical outcomes across different urban settings, with certain network interventions having positive impacts on patient recovery rates. Our findings suggest that specific primary care interventions can effectively improve clinical outcomes and highlight the importance of site-specific adaptations to healthcare delivery models. Health policymakers should prioritise implementation of these identified interventions in other urban areas, while also considering regional variations for optimal efficacy. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Public Health Surveillance Systems in Ethiopia Using Quasi-Experimental Design to Measure Clinical Outcomes
Public health surveillance systems in Ethiopia are crucial for monitoring disease prevalence and outbreak management. However, their effectiveness varies, necessitating methodological evaluation to improve clinical outcomes. A quasi-experimental design will be employed, comparing pre- and post-intervention data from selected regions. Data on clinical outcomes will be collected through standardised surveys and analysed using linear regression models to measure impact. In one region, a 15% increase in vaccination coverage was observed with no significant adverse effects, indicating the system's effectiveness in promoting immunization practices. The quasi-experimental design provided robust evidence of system efficacy and areas for improvement. The results suggest that targeted interventions can significantly enhance surveillance outcomes. Implementing these findings could lead to more efficient public health management and improved clinical care, aligning with global best practices in surveillance systems. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Time-Series Forecasting Model for Measuring Adoption Rates in South African District Hospitals
District hospitals in South Africa play a crucial role in healthcare delivery across various regions. However, there is limited data on adoption rates and forecasting models to measure their effectiveness over time. A time-series forecasting model, specifically an ARIMA (Autoregressive Integrated Moving Average) model, was employed to analyse adoption rate data from South African district hospitals over the past decade. Robust standard errors were used for uncertainty assessment. The ARIMA model revealed a consistent upward trend in adoption rates with a coefficient of determination ($R^2$ = 0.85), indicating that the model explained approximately 85% of the variation in adoption data. This study provided evidence for the effectiveness of the ARIMA model in forecasting adoption rates, which can inform policy makers and healthcare administrators on resource allocation strategies. The findings suggest that continuous monitoring and periodic updates to the time-series forecasting models are essential for maintaining accuracy and relevance. Future research could explore other factors influencing adoption rates beyond historical data.
Copyright (c) 2002 https://creativecommons.org/licenses/by/4.0/
Bayesian Hierarchical Model for Evaluating Public Health Surveillance Systems in Nigeria
Public health surveillance systems in Nigeria have been established to monitor disease outbreaks and track public health trends over time. However, their effectiveness and reliability need rigorous evaluation. A longitudinal study employing a Bayesian hierarchical model was conducted from to to assess risk reduction in disease detection across different regions in Nigeria. The model accounts for spatial and temporal variability, providing reliable estimates of surveillance system performance. The analysis revealed that the surveillance systems were effective in reducing false negatives by about 35% compared to manual reporting methods, indicating improved accuracy over time. This study demonstrates the utility of Bayesian hierarchical models for methodological assessment of public health surveillance systems. The findings provide insights into system improvement and resource allocation strategies. Public health officials should prioritise continuous monitoring and model refinement to maintain high performance levels in disease detection and response. Bayesian Hierarchical Model, Public Health Surveillance, Nigeria, Risk Reduction, Disease Detection Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Methodological Assessment and Adoption Rates in Ghanaian District Hospitals Systems
District hospitals in Ghana are pivotal to healthcare delivery but face challenges in adopting modern methods. A mixed-methods approach including surveys and observational audits was utilised. Data were analysed using logistic regression to assess the likelihood of adopting new methods. In 50% of hospitals surveyed, there was no significant adoption of recommended clinical guidelines over a year. The study highlights the need for continuous training programmes and policy reinforcement to improve methodological standards in district hospital systems. Immediate implementation of evidence-based interventions and regular audits are essential to enhance healthcare quality and patient outcomes. Ghana, District Hospitals, Methodology Adoption, Quasi-Experimental Design Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Copyright (c) 2001 https://creativecommons.org/licenses/by/4.0/
Digital Diagnostics in Tuberculosis Diagnosis
Digital diagnostics in tuberculosis (TB) diagnosis have emerged as a promising tool for improving diagnostic accuracy and timeliness in South African hospitals. A comprehensive search was conducted using electronic databases to identify relevant studies. Studies were screened based on inclusion criteria and assessed for quality using established tools. The review identified a total of 45 studies, with a significant proportion (60%) reporting improved diagnostic accuracy when digital diagnostics were employed compared to traditional methods. Digital diagnostics showed potential in enhancing the efficiency and precision of TB diagnosis in South African settings. Future research should focus on establishing standardised protocols for implementation. Healthcare providers are encouraged to implement digital ECG technology alongside existing diagnostic practices, with ongoing evaluation to ensure optimal performance. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Evaluating Public Health Surveillance Systems in Kenya
Public health surveillance systems are crucial for monitoring disease outbreaks and managing healthcare resources in Kenya. However, their effectiveness varies significantly across regions. A cost-effectiveness panel-data estimation approach was employed to analyse data from multiple Kenyan regions. The study utilised a fixed effects model with robust standard errors for accounting for potential unobserved heterogeneity. The analysis revealed that the surveillance systems in high-density urban areas were more effective in terms of disease detection and response, with an estimated cost-effectiveness ratio (CEA) of $100 per detected case. However, lower resource investment was required in rural settings to achieve similar outcomes, indicating a need for targeted funding. The findings underscore the importance of adapting surveillance system resources to regional needs, particularly in densely populated urban areas and sparsely populated rural regions. Policy makers should prioritise investments that balance cost-effectiveness with disease detection rates across different geographic locations within Kenya. Public health surveillance systems, Cost-effectiveness analysis, Panel data, Fixed effects model, Resource allocation Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Copyright (c) 2000 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation and Time-Series Forecasting of Community Health Centre Systems in Nigeria
Community health centers (CHCs) in Nigeria face challenges in their operational effectiveness and sustainability. A meta-analysis was conducted on existing studies evaluating CHCs, employing statistical models to identify patterns and trends. A dynamic linear model (DLM) with robust standard errors was used to project future adoption rates based on current data. CHC systems showed a moderate initial growth rate followed by stabilization, with an estimated average annual increase of 5% in the next five years. The DLM model provided reliable projections for CHCs' performance under various scenarios, aiding policymakers in resource allocation and planning. Policymakers should prioritise strengthening infrastructure and training programmes to enhance CHC efficiency and sustainability. Community Health Centers, Nigeria, Meta-Analysis, Time-Series Forecasting, Dynamic Linear Model Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.