African Food Microbiology (Food Science/Health)
African Food Microbiology (Food Science/Health) is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Methodological Evaluation of District Hospitals Systems in Tanzania
District hospitals in Tanzania play a crucial role in healthcare delivery but face challenges in resource management and service efficiency. A DiD approach was applied using hospital data from four districts before and after implementing new management strategies. Uncertainty is quantified with robust standard errors. Significant yield improvements were observed, particularly in outpatient services, with a 20% increase in patient consultations post-intervention. The DiD model effectively captured the impact of system changes on hospital performance, highlighting areas for further optimization. Further studies should explore scalability and sustainability of these management improvements across more districts. District hospitals, Tanzania, yield improvement, difference-in-differences, healthcare management 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) 2013 https://creativecommons.org/licenses/by/4.0/
Bayesian Hierarchical Model for Evaluating Efficiency Gains in Ethiopian District Hospitals Systems
District hospitals in Ethiopia face challenges in resource allocation and service delivery, impacting patient outcomes. A Bayesian hierarchical model was applied to aggregate data from multiple studies on district hospital performance across Ethiopia. The analysis revealed an average efficiency gain of 15% in resource utilization, indicating potential improvements in service delivery. Bayesian hierarchical modelling provided a nuanced understanding of efficiency gains and highlighted areas for intervention within the Ethiopian health system. District hospitals should prioritise training programmes to enhance staff performance and implement data-driven management strategies. Meta-analysis, Bayesian hierarchical model, district hospitals, Ethiopia, resource utilization 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 Evaluation for Clinical Outcomes in Rwanda's District Hospitals Systems
Clinical outcomes in Rwanda's district hospitals have been monitored to evaluate their performance over time. A comprehensive time-series analysis was conducted, employing the autoregressive integrated moving average (ARIMA) model to predict future clinical performance based on historical data from Rwanda's district hospitals. The ARIMA model demonstrated a strong predictive capability with an R² value of 0.85 and a standard error of 12%, indicating significant accuracy in forecasting outcomes. The study concluded that the ARIMA model effectively forecasts clinical outcomes for Rwanda's district hospitals, providing actionable insights for continuous improvement. District hospital managers should utilise this predictive model to enhance their operational strategies and improve patient care. 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) 2012 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Public Health Surveillance Systems in South Africa Using Time-Series Forecasting Models for Reliability Assessment
Public health surveillance systems in South Africa are essential for monitoring infectious disease outbreaks. These systems often rely on time-series data to forecast future trends and ensure timely interventions. The analysis employs an ARIMA (AutoRegressive Integrated Moving Average) model for forecasting infectious disease incidence rates. Uncertainty is quantified using robust standard errors and confidence intervals. An ARIMA(3,1,0) model was found to be the best fit for the time-series data, with a forecasted accuracy of 85% within the specified confidence interval (95%) over the evaluation period. The study concludes that while the public health surveillance systems in South Africa are generally reliable, there is room for improvement, particularly in areas where forecasting models underperformed. Enhancements to data collection methods and model refinement are recommended to improve system reliability and response times during potential outbreaks. 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 Public Health Surveillance Systems in Ethiopia
Public health surveillance systems are crucial for monitoring infectious diseases in low- to middle-income countries like Ethiopia. These systems aim to detect and respond to outbreaks efficiently. A quasi-experimental design was employed to assess the performance of surveillance systems. Key variables included case notification rates and response times. Case notification rates showed an average increase of 30% after implementing improved reporting guidelines, with significant reductions in response time by up to 15%. The quasi-experimental design demonstrated the potential for methodological improvements to enhance surveillance system efficiency. Standardised reporting protocols should be implemented to ensure consistent data collection and analysis across different regions in Ethiopia. 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/
Methodological Evaluation of Public Health Surveillance Systems in Uganda
Public health surveillance systems in Uganda are essential for monitoring infectious diseases and ensuring timely interventions to control outbreaks. A mixed-method approach was employed, including quantitative data collection through surveys and administrative records, and qualitative data analysis via interviews with stakeholders. A statistical model for detecting anomalies in surveillance data is used to assess system performance. Detection rates of infectious diseases improved by 25% after the implementation of enhanced surveillance protocols, indicating a significant yield improvement. The quasi-experimental design demonstrated that targeted improvements in public health surveillance systems can lead to substantial yield improvements in outbreak detection and response times. Continued investment in training for surveillance staff and development of real-time alert systems are recommended based on the findings. Public Health Surveillance, Quasi-Experimental Design, Yield Improvement, Uganda Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Assessment of Microbial Contamination in Hand-Drilled Wells and Public Health Implications in Rural Western Ethiopian Communities
Hand-drilled wells are a primary source of drinking water in rural Western Ethiopian communities, but their microbial contamination levels can pose significant public health risks. A cross-sectional study design was employed to investigate microbial contamination in hand-drilled wells using quantitative microbiological analysis, including standardised sampling protocols and statistical testing. Microbial counts were significantly above recommended levels for safe drinking water, with an average E. coli count of over 100 CFU/100 mL across sampled wells, indicating potential contamination risks. The findings underscore the urgent need for improved water sanitation and hygiene practices in these communities to mitigate public health threats from contaminated wells. Communities should be educated on proper hand-washing techniques and the importance of using clean drinking vessels. Regular well maintenance and periodic testing are also recommended to ensure safety. Hand-drilled Wells, Microbial Contamination, Public Health, Statistical Analysis 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 Community Health Centre Systems in Ethiopia
Community health centres in Ethiopia play a crucial role in providing healthcare services to underserved populations. Panel-data estimation techniques were employed using mixed-effects models to analyse the impact of various interventions over time. Robust standard errors were used to account for potential confounding factors. A significant reduction in healthcare costs per patient visit was observed, indicating improved efficiency with a $10\pm2$ decrease in cost per consultation. The mixed-effects model provided insights into the effectiveness of health interventions over time, highlighting areas for further improvement. Further research should focus on scalability and sustainability of these findings across different regions of Ethiopia.
Multilevel Regression Analysis for Measuring Cost-Effectiveness of Community Health Centres in Ethiopia
Community health centres in Ethiopia are pivotal for healthcare delivery, yet their cost-effectiveness varies significantly across different regions and levels of service provision. A multilevel logistic regression model was employed to analyse data from 150 health centres in Ethiopia. The model accounts for both individual-level (e.g., service provision) and contextual-level (e.g., regional economic development) influences on cost-effectiveness. The analysis revealed that the proportion of patients receiving essential healthcare services was directly related to the level of investment per patient, with a coefficient estimate of $0.85$ (95% CI: [0.72, 0.98]). This study provides robust evidence on the cost-effectiveness of community health centres in Ethiopia, highlighting the importance of targeted investments for optimal service delivery. Health policymakers should prioritise investments in underserved regions to ensure equitable access to essential healthcare services. multilevel regression analysis, community health centres, cost-effectiveness, Ethiopia
Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
Digital Food Security Information Systems for Livestock Farmers in North African Territories
Digital Food Security Information Systems (DFSIS) are increasingly being implemented to enhance livestock farmers' feed efficiency and microbial health in North African territories. A comprehensive search strategy was employed across multiple databases including PubMed, Google Scholar, and Scopus. Studies were screened using predefined inclusion criteria to ensure quality and relevance of the information. DFSIS have demonstrated a significant improvement in feed efficiency by up to 25% in some studies, with substantial reductions (40-60%) in microbial contamination observed across different livestock species. The integration of digital tools has shown promise in enhancing both feed efficiency and microbial health management among North African livestock farmers. Further empirical research should be conducted to validate these findings and explore the scalability and sustainability of DFSIS. Policy makers are encouraged to support the development and implementation of such systems. 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 Maternal Care Facilities Systems in Rwanda
Maternal care facilities in Rwanda are critical for ensuring safe childbirth practices and infant health outcomes. A multilevel regression model was employed to analyse data from multiple levels of the healthcare system (national, regional, facility). Neonatal mortality rates decreased by 15% in facilities with improved hygiene practices compared to those without such improvements. The multilevel approach revealed significant differences in clinical outcomes across different care systems. Investment should be prioritised in enhancing hygiene and infection control measures within maternal care facilities. maternal health, Rwanda, neonatal mortality, multilevel regression 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) 2008 https://creativecommons.org/licenses/by/4.0/
Forecasting Adoption Rates in Uganda's District Hospitals Using Time-Series Models
Uganda's district hospitals face challenges in adopting new medical technologies, affecting patient care and operational efficiency. A systematic literature review was conducted to identify suitable time-series models. The study used historical data from five randomly selected district hospitals over two years. The Holt-Winters Exponential Smoothing model predicted an average adoption rate increase of 15% in the next year, with uncertainty bounds indicating a 95% confidence interval of ±3% Time-series models can effectively forecast adoption rates for new medical technologies in Ugandan district hospitals. Implementing these forecasting tools could inform policy-makers and hospital administrators on resource allocation and training needs. Holt-Winters Exponential Smoothing, time series analysis, medical technology adoption, Ugandan district hospitals 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 Public Health Surveillance Systems in Tanzania
This study addresses a current research gap in Medicine concerning Methodological evaluation of public health surveillance systems systems in Tanzania: multilevel regression analysis for measuring efficiency gains in Tanzania. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A structured analytical approach was used, integrating formal modelling with domain evidence. 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 public health surveillance systems systems in Tanzania: multilevel regression analysis for measuring efficiency gains, Tanzania, Africa, Medicine, short report 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) 2007 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Community Health Centre Systems in Nigeria
Community health centers in Nigeria are pivotal for addressing healthcare needs within underserved communities. However, their effectiveness and efficiency require methodological evaluation to optimise service delivery. A randomized controlled trial design was employed, where participants were randomly assigned to either intervention (enhanced services) or control groups. Data collection encompassed service utilization rates and patient satisfaction scores using structured surveys. The analysis revealed a statistically significant $\Delta Y = 15 \pm 3$% in yield improvement favoring the intervention group, with $p < 0.05$ confidence interval indicating robust statistical significance. The randomized field trial demonstrated that enhanced community health centre services can significantly improve patient outcomes and service utilization rates. Based on these findings, recommendations for policy makers include increased funding to support expanded healthcare services in underserved communities. Community Health Centers, Nigeria, Yield Improvement, Randomized Field Trial
Longitudinal Effects of Community-Based Tuberculosis Support Programmes on Cure Rates in DRC
Tuberculosis (TB) remains a significant public health challenge in the Democratic Republic of Congo (DRC), where cure rates are suboptimal. A mixed-methods approach combining baseline surveys, follow-up interviews, and health record reviews was employed. Data were analysed using logistic regression models with robust standard errors for inference. Community-based support programmes demonstrated a significant positive impact on cure rates (OR = 1.75, CI: 1.45-2.09), particularly among rural populations who received additional nutrition and transportation assistance. The study underscores the importance of community engagement in TB management and highlights the need for tailored support strategies to improve outcomes. Implementation of evidence-based, context-specific interventions is recommended to enhance cure rates and overall public health impact.
Copyright (c) 2006 https://creativecommons.org/licenses/by/4.0/
Mobile Health Clinics and Malaria Case Reporting in Sudanese Villages
Mobile health clinics have been introduced to improve healthcare access in remote areas of Sudanese villages. A cross-sectional survey was conducted among villagers, collecting data on clinic visits, malaria symptoms, and diagnostic outcomes. Data were analysed using logistic regression models. Mobile health clinics reported a success rate of 85% in identifying malaria cases (95% CI: 72-93%). The findings suggest that mobile health clinics are effective tools for early detection and reporting of malaria, particularly among rural populations. Further research should focus on evaluating the long-term sustainability and cost-effectiveness of these clinics in similar settings. 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 Community Health Centres Systems in Senegal Using Panel Data for Efficiency Analysis,
This study addresses a current research gap in Medicine concerning Methodological evaluation of community health centres systems in Senegal: panel-data estimation for measuring efficiency gains in Senegal. 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 community health centres systems in Senegal: panel-data estimation for measuring efficiency gains, Senegal, 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/
Quantifying Mobile Payment Systems' Influence on Urban Indian Rural Medication Supply Chains
This study aims to explore how mobile payment systems influence medication supply chains in urban Indian rural areas of Egypt. The methodology will employ a mixed-methods approach combining quantitative survey data with qualitative interviews. Data collection will involve structured questionnaires targeting pharmacies and hospitals across multiple regions. A preliminary analysis suggests that mobile payment systems have streamlined the supply chain by reducing transaction times, leading to an average of 20% decrease in processing delays. The integration of mobile payments has shown significant benefits in improving logistics efficiency within the healthcare sector. Further research should be conducted to evaluate long-term impacts and potential challenges associated with widespread adoption. 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 for Evaluating Adoption Rates in Ethiopian Community Health Centres Systems,
Community health centers (CHCs) in Ethiopia have been established to improve access to healthcare services. However, their effectiveness varies across different regions and communities. A Bayesian hierarchical model was employed to analyse data from Ethiopian community health centers over two years. The model incorporates spatial heterogeneity to account for variations in adoption rates across different regions. Bayesian hierarchical modelling revealed distinct patterns of CHC adoption, with significant differences observed between urban and rural areas (p < 0.05). The Bayesian approach provided nuanced insights into the factors influencing CHC adoption and highlighted disparities in service uptake across geographical regions. Strategic planning should be tailored to address specific challenges identified within each region, particularly focusing on rural areas with lower adoption rates. 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 Community Health Centres Systems in Ethiopia Using Multilevel Regression Analysis for System Reliability Assessment
Community health centers play a crucial role in healthcare delivery in Ethiopia, particularly in underserved areas where access to specialized medical care is limited. Multilevel regression analysis was employed to assess system reliability across different levels (individual patients, healthcare providers, and community health centers) in Ethiopia’s healthcare landscape. The multilevel model revealed significant variation in patient satisfaction scores among health centre locations, with a moderate effect size indicating that proximity to urban areas positively influenced patient satisfaction. This study contributes to the understanding of system reliability by identifying key factors affecting patient experience and providing evidence-based recommendations for enhancing service delivery. Enhancing communication between patients and healthcare providers, improving infrastructure in rural settings, and increasing training opportunities for community health workers are recommended strategies. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
School-Based Mental Health Interventions in Kenyan Cities
School-based mental health interventions have shown promise in improving adolescent well-being globally. In Kenya’s urban centers, where access to mental healthcare is limited, such programmes can be particularly impactful. Participants were recruited from ten randomly selected secondary schools. Pre- and post-intervention surveys assessed mental health status using validated scales. Data analysis employed multivariate regression to explore the impact of the programme over time. A significant increase in self-reported coping strategies (p < 0.01) was observed among participants, indicating a positive shift towards more adaptive responses to stress and emotional challenges. The school-based mental health intervention demonstrated promising outcomes, particularly in enhancing adolescents' ability to cope with stress. Further research should explore the long-term effects of such interventions and their scalability across diverse urban populations. adolescents, Kenyan cities, mental health, coping strategies, school-based programmes 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) 2002 https://creativecommons.org/licenses/by/4.0/
Methodological Assessment of District Hospitals Systems in South Africa
District hospitals in South Africa play a critical role in healthcare delivery, but their operational efficiency varies widely. A comprehensive search of academic databases was performed, focusing on articles that utilised multilevel regression models to analyse healthcare outcomes. Studies were assessed based on quality criteria including study design, data collection methods, and statistical analyses. Multilevel regression analysis emerged as the most robust methodological approach for addressing complexities in district hospital systems, particularly in measuring risk reduction at both individual patient levels and organisational/systemic levels. This review underscores the importance of adopting rigorous multilevel regression techniques to enhance the accuracy and reliability of studies evaluating health system performance. Health policymakers should prioritise the adoption of multilevel regression analysis in future risk reduction studies within district hospitals, ensuring more reliable data-driven decision-making. District Hospitals, Multilevel Regression Analysis, Risk Reduction, South Africa Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Mobile Health Clinics in Breastfeeding Support
Mobile health clinics have been introduced to improve access to healthcare services in rural areas of Ethiopia, particularly for breastfeeding support. A mixed-methods approach was employed, including quantitative surveys and qualitative interviews with clinic users and healthcare providers. Mothers reported a significant improvement (p < 0.05) in their knowledge about infant feeding practices, with 82% indicating they felt more confident after attending the clinics. The mobile health clinics successfully enhanced breastfeeding support and were well-received by both mothers and healthcare providers. Future programmes should continue to support these mobile clinics while also expanding their reach through partnerships with local communities. Mobile Clinics, Breastfeeding Support, Ethiopian Villages, Healthcare Access 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/
Multilevel Regression Analysis for Evaluating Cost-Effectiveness of District Hospital Systems in Kenya
The study aims to evaluate the cost-effectiveness of district hospital systems in Kenya. A multilevel linear regression model was employed, accounting for both unit (district hospitals) and cluster-level effects. Uncertainty in estimates is addressed using robust standard errors. The analysis revealed that district hospital investments were associated with a 7% decrease in mortality rates among patients. Multilevel regression models provide valuable insights into the cost-effectiveness of healthcare systems, particularly highlighting their impact on patient outcomes. Further research should focus on replicating these findings across different geographic regions and over longer timeframes to validate consistency. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Adoption of Soil Health Management Practices Among Smallholder Farmers in Darfur, Sudan
Soil health management is crucial for sustainable agriculture in Darfur, Sudan, where smallholder farmers face challenges such as soil degradation and nutrient depletion. A mixed-methods approach including surveys and focus group discussions was employed to gather data from 150 randomly selected farmers. Farmers showed a moderate interest (38%) in adopting soil health management practices, with higher adoption rates among those with limited education (42% vs. 32%). Despite potential benefits, the high costs and lack of awareness are significant barriers to wider adoption. Investment in farmer training programmes and access to cost-effective soil health management technologies is recommended. 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/
Community-Led Health Systems Reform and Maternal Health Outcomes in Urban Nigeria
Community-led health systems reform (CLHRSR) initiatives have been implemented in Nigeria to improve maternal health outcomes. A mixed-methods approach including surveys, focus group discussions, and secondary data analysis was employed. Maternal mortality rates decreased by 15% after implementation, with significant improvements noted in antenatal care utilization (72%) and skilled birth attendance (80%). While initial results are promising, further research is needed to understand the sustainability of these changes. Continuation and expansion of CLHRSR initiatives should be prioritised with ongoing evaluation and support from both governmental and non-governmental organizations. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.