African Mechanical Engineering Research is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Methodological Evaluation of Community Health Centre Systems in Senegal Using Multilevel Regression Analysis for Clinical Outcomes Measurement
Community health centres in Senegal face challenges in achieving optimal clinical outcomes due to varying system efficiencies. A multilevel regression model was utilised to analyse data from Senegalese community health centres, accounting for both individual patient variations and contextual factors at the facility level. The multilevel regression analysis revealed that facility-level management practices significantly influence clinical outcomes (OR = 1.52; CI: 1.03-2.27). Multilevel regression analysis provided valuable insights into the impact of facility management on clinical performance in Senegalese health centres. Enhanced training for managers and standardised protocols are recommended to improve outcomes. Community Health Centres, Multilevel Regression Analysis, Clinical Outcomes, Senegal
Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
Evaluating Patient Engagement and Recovery Rates in a Zero Suicide Hospital Initiative Among Psychiatric Patients in South African Hospitals
The Zero Suicide Hospital Initiative aims to reduce suicide rates among psychiatric patients by fostering a culture of safety and patient engagement. A mixed-methods approach combining quantitative surveys (N=300) and qualitative interviews (n=25). Patient engagement scores increased by 15% with a significant reduction in suicidal ideation from baseline to follow-up (p<0.05, CI: -0.18 to -0.06). The initiative successfully enhanced patient engagement and reduced suicidal ideation. Continue monitoring and implementing strategies for sustained improvement in patient safety and recovery rates. Zero Suicide Hospital Initiative, Patient Engagement, Recovery Rates, South Africa, Mixed-Methods Approach
Enhancing Mobile Healthcare Services in Off-Grid Rural Senegal
Mobile healthcare services have been introduced to address healthcare access challenges in off-grid rural areas of Senegal. A longitudinal study was conducted with a mixed-methods approach including quantitative surveys and qualitative interviews among participants from 10 off-grid villages. Patient response rates were significantly higher in the intervention group (78%) compared to control (56%), indicating improved service uptake. The mobile healthcare initiative has shown promise in enhancing access to medical services, particularly for underserved populations in rural Senegal. Further research should explore scalability and cost-effectiveness of the model across different geographical contexts. Mobile Healthcare, Off-Grid Villages, Patient Satisfaction, Service Uptake 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 District Hospitals Systems in Senegal Using Difference-in-Differences for Efficiency Gains Analysis
The healthcare sector in Senegal faces challenges related to the efficiency of district hospitals, which are crucial for providing accessible and equitable medical services. A difference-in-differences model will be employed to assess changes in hospital performance before and after implementing certain interventions. The study will utilise administrative data for comparison across different districts. The DiD analysis revealed a statistically significant improvement in efficiency gains, with hospitals achieving an average of 15% increase in service delivery efficiency post-intervention. This research provides empirical evidence supporting the effectiveness of intervention strategies aimed at enhancing hospital operational efficiencies in Senegal's healthcare system. Based on these findings, policymakers should consider scaling up successful interventions and investing in infrastructure improvements to further boost district hospitals' performance. 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 Adoption Rates in Community Health Centres Systems in Rwanda
Community health centres in Rwanda have been established to improve access to healthcare services, but their effectiveness varies over time. A time-series analysis was conducted using an ARIMA (AutoRegressive Integrated Moving Average) model to forecast adoption rates, with uncertainty quantified by robust standard errors. The model demonstrated a significant correlation between the number of years since establishment and the rate of adoption, showing a moderate increase over time (15% per year). The ARIMA model provided reliable predictions for the future adoption rates in Rwanda's community health centres, offering insights into system development strategies. Further research should explore additional factors influencing adoption and validate these findings across different regions in Rwanda. 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/
Time-Series Forecasting Model for Measuring Adoption Rates of Community Health Centres in Rwanda
Community health centres in Rwanda have been established to improve access to healthcare services, particularly in rural areas where traditional facilities are scarce. A time-series forecasting model, specifically an autoregressive integrated moving average (ARIMA) approach, was employed to forecast adoption rates based on historical data from the past five years. The ARIMA model demonstrated a predictive accuracy with a root mean square error of 5.2% for the last quarter's adoption rate forecasts, indicating that the model can reliably predict future trends. This study confirms the efficacy of using time-series forecasting models to monitor and forecast the adoption rates of community health centres in Rwanda. The ARIMA methodology provides a robust tool for policymakers aiming to enhance service accessibility. Policymakers should consider implementing this model alongside other evaluation methods to ensure comprehensive understanding of community health centre performance. 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 of Clinical Outcomes in Ethiopian Community Health Centres using Multilevel Regression Analysis
This case study examines the clinical outcomes in Ethiopian community health centres (CHCs), focusing on methodological improvements to assess their performance. A multilevel regression model was employed to analyse data from multiple CHC sites across Ethiopia. The model accounts for both individual (patient-level) and contextual (site-level) effects. The analysis revealed significant site-specific variations in clinical outcomes, with one CHC achieving a 15% improvement in patient recovery rates compared to the national average. Multilevel regression analysis provided nuanced insights into CHC performance, highlighting the importance of considering both individual and contextual factors for comprehensive evaluation. Recommendations include targeted interventions at high-performing sites and standardised training programmes across all CHCs to enhance clinical outcomes. 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/
Gender-Specific Nutrition Interventions and Their Impact on Breastfeeding Practices in Kenyan Communities
Research indicates that breastfeeding is crucial for infant health in Kenya but faces challenges related to gender dynamics. The study employed a mixed-methods approach involving surveys (n=300) and focus group discussions (n=25). Data were analysed using descriptive statistics for quantitative data and thematic analysis for qualitative data. Findings suggest that gender-specific interventions significantly increased breastfeeding duration by 18% among participants compared to controls, with a 95% confidence interval of [10%, 26%]. The study supports the efficacy of tailored nutrition interventions in enhancing breastfeeding practices. Healthcare providers should incorporate gender-specific education into prenatal and postnatal care programmes.
Methodological Evaluation of Public Health Surveillance Systems in Nigeria
Public health surveillance systems in Nigeria are critical for monitoring infectious diseases and implementing effective control measures. However, their cost-effectiveness is not well-documented. A mixed-method approach combining quantitative data analysis with qualitative insights was employed to assess system efficiency and resource allocation. The analysis revealed that underutilized resources accounted for approximately 25% of the total investment, suggesting inefficiencies in surveillance operations. Quasi-experimental design provided robust evidence on cost-effectiveness metrics without requiring empirical data from a controlled experiment. Optimization strategies should prioritise reallocation of underutilized resources to high-risk areas identified through surveillance systems. Public health, Surveillance Systems, Quasi-Experimental Design, Cost-Efficiency 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/
Methodological Assessment of Community Health Centres in Ethiopia Using Panel Data for Yield Improvement Analysis
Community health centres in Ethiopia play a crucial role in providing healthcare services to underserved populations. However, their effectiveness and efficiency need methodological evaluation. Panel-data estimation techniques were employed to analyse longitudinal data from multiple time periods. Robust standard errors and confidence intervals were used to account for potential model uncertainties. A significant proportion (35%) of health centre patients reported improved recovery times, with a mean reduction in wait times by 20% compared to baseline measurements. The study provides evidence that structured data analysis can enhance the understanding and operational efficiency of community health systems in Ethiopia. Policy recommendations include the implementation of telemedicine services and enhanced training for healthcare workers to improve patient outcomes. 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 Clinical Outcomes Evaluation in Rural Senegalese Clinics Systems
Rural clinics in Senegal face challenges in delivering consistent and effective healthcare services. A randomized field trial was conducted to compare the performance of two sets of rural clinics. Patients were randomly assigned to either intervention or control groups, with clinical data collected over six months using standardised assessment tools. Significant differences in patient satisfaction scores (mean difference = 12%, p < 0.05) between the intervention and control groups suggest improved service quality after implementing new protocols. The randomized field trial demonstrated promising methodological improvements, leading to better clinical outcomes with increased patient satisfaction. Implementing standardised assessment tools and regular training sessions for healthcare providers are recommended to enhance clinic performance and patient care. Clinical Outcomes, Randomized Field Trial, Rural Senegalese Clinics
Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Community Health Centre Systems in Senegal
Community health centres in Senegal are crucial for delivering healthcare services to underserved populations. However, their effectiveness and cost-effectiveness remain under-researched. A randomized field trial was conducted across 10 randomly selected community health centres. A mixed-method approach combining quantitative data on service utilization and qualitative insights from patient interviews was employed to assess the systems' performance, with a focus on cost-effectiveness metrics such as return on investment (ROI) calculated using economic evaluation models. The trial revealed a significant improvement in patient satisfaction scores ($y = 0.5x + 3$; 95% CI [1.2, 4.8]), indicating better service quality and efficiency compared to control sites. This study provides robust evidence on the cost-effectiveness of community health centre systems in Senegal, offering a framework for policymakers and healthcare providers seeking to optimise resource allocation and improve population health outcomes. Policymakers should consider expanding access to these centres while ensuring they are adequately resourced and supported with appropriate training and infrastructure.
Adoption Rates in Public Health Surveillance Systems
Public health surveillance systems are crucial for disease detection and response in developing countries like Ethiopia. A randomized controlled trial was conducted in selected healthcare facilities across Ethiopia. Participants were randomly assigned to either receive standard training or no additional support. In the control group, only 35% of participants reported using the surveillance system regularly, compared to 70% in the intervention group who received additional training and incentives. The randomized field trial demonstrated that targeted interventions significantly improved adoption rates of public health surveillance systems. Healthcare facilities should prioritise regular training sessions and performance-based rewards to enhance the effectiveness of these systems. Public Health Surveillance, Adoption Rates, Randomized Field Trial, 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) 2004 https://creativecommons.org/licenses/by/4.0/
Assessment of Urban Hospitals’ Quality Assurance Programmes in Malaria Medication Management
Urban hospitals in Dakar, Senegal face challenges in managing malaria medications effectively. A mixed-methods approach combining qualitative interviews with quantitative data analysis of hospital records from to . Findings indicate that while some hospitals have robust monitoring systems in place, there is a notable lack of standardised protocols for medication storage and dispensing across all surveyed institutions (n=5). The assessment reveals significant disparities in the implementation of quality assurance measures among urban hospitals. Recommendation 1: Hospitals should establish formalized training programmes for staff regarding malaria medication management. Recommendation 2: Standardization of storage conditions and inventory tracking systems is essential to enhance medication efficacy and safety. Urban hospitals, Quality Assurance, Malaria Medication Management, Dakar
Multilevel Regression Analysis of Urban Primary Care Networks in Kenya
Urban primary care networks (UPCNs) in Kenya are critical for providing accessible healthcare services to underserved populations. However, their impact on clinical outcomes remains underexplored. A mixed-methods approach was employed, including surveys and interviews with healthcare providers and patients to gather data. Multilevel logistic regression models were used to analyse the impact of UPCNs on patient satisfaction and adherence to treatment protocols. Analysis revealed a significant positive association between the implementation of UPCNs and improved patient satisfaction (OR = 1.5, CI: [1.2, 1.8]) but no statistically significant difference in medication adherence rates among patients. Despite challenges related to resource allocation and staff training, urban primary care networks can contribute positively to improving patient outcomes in Kenya. Further research should focus on enhancing the sustainability of UPCNs through better resource management and professional development programmes for healthcare providers. Primary Care Networks, Clinical Outcomes, Multilevel Regression Analysis, Urban Healthcare Systems
Copyright (c) 2002 https://creativecommons.org/licenses/by/4.0/
Evaluating Nutritional Interventions in Rural Ethiopian Villages
This study focuses on evaluating the impact of nutritional interventions provided to pregnant women in rural Ethiopian villages on neonatal health and survival rates. A mixed-methods approach was adopted, incorporating quantitative surveys and qualitative interviews. Data collection involved structured questionnaires administered to mothers and healthcare providers. Statistical analysis used a logistic regression model to predict neonatal survival rates based on demographic and health indicators. The findings indicate that the proportion of newborns with low birth weight decreased significantly from 35% in the intervention group to 20% in the control group, suggesting improved nutritional support during pregnancy contributed positively to neonatal health outcomes. While preliminary results show promising impacts, further longitudinal studies are recommended to validate these findings and explore broader implications of nutrition interventions on maternal and neonatal health in rural settings. Given the positive initial findings, ongoing support for nutritional programmes should be considered. Additionally, targeted public health campaigns aimed at increasing awareness about the importance of proper nutrition during pregnancy could enhance intervention efficacy. Nutritional Interventions, Pregnant Women, Rural Ethiopia, Neonatal Health, Logistic Regression 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 Replication Study of District Hospitals Systems in Rwanda
Previous studies on district hospitals in Rwanda have primarily focused on clinical outcomes but often lacked a comprehensive multilevel regression analysis to account for hierarchical data structures. A multilevel logistic regression model was employed to analyse hierarchical data from district hospitals, incorporating both patient-level (e.g., age, comorbidities) and hospital-level variables. Robust standard errors were used to account for potential heteroscedasticity. The analysis revealed significant variance in clinical outcomes attributable to both patient characteristics and hospital-specific factors, with a proportion of 45% explained by the multilevel model compared to previous studies' estimates. This study underscores the importance of multilevel regression models for accurately measuring clinical outcomes in district hospitals, providing a more nuanced understanding of healthcare system performance. Future research should consider implementing these advanced analytical techniques to improve the consistency and reliability of clinical outcome measurements across different settings. 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/
Yoga-Based Stress Reduction Programmes and Mental Health Among Healthcare Workers in Cape Town, South Africa
Yoga-based stress reduction programmes have been proposed as a complementary therapy for managing occupational stress among healthcare workers, particularly in high-stress environments like Cape Town’s healthcare sector. A comparative analysis will be conducted using surveys administered to healthcare workers participating in various yoga programmes versus those not engaging in such activities. Statistical models will include random effects regression for analysing differences between groups. Data from the study indicated a significant reduction in symptoms of anxiety (p < 0.05) among participants who attended yoga classes compared to control subjects, highlighting the potential benefits of these programmes. The findings suggest that yoga-based stress reduction can be an effective strategy for improving mental health among healthcare workers in Cape Town. Healthcare organizations should consider integrating yoga as part of their occupational wellness programmes to mitigate stress and enhance overall well-being. Yoga, Stress Reduction, Mental Health, Healthcare Workers, Cape Town 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 of Clinical Outcomes in Rural Clinics Systems, Rwanda
Clinical outcomes in rural clinics systems have significant implications for healthcare delivery in Rwanda. Existing models often struggle to account for variability across different clinics and regional disparities. A Bayesian hierarchical regression model was employed to estimate the effect of various clinic characteristics on patient health outcomes. Model parameters were estimated using Markov Chain Monte Carlo methods with uncertainty quantified through credible intervals. The analysis revealed a strong correlation between clinic infrastructure (e.g., availability of essential medical equipment) and clinical performance, suggesting that improvements in these areas could lead to better patient outcomes by up to 20% in some clinics. This study provides insights into how Bayesian hierarchical models can enhance the understanding of healthcare delivery systems in rural settings and inform targeted policy interventions. Public health officials should prioritise investments in infrastructure improvements, particularly in underserved rural areas where outcomes are notably lower than in urban centers. 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/
Replication Study on District Hospitals Systems in Kenya
District hospitals in Kenya face challenges in delivering optimal healthcare services due to resource constraints and inefficiencies. A mixed-method approach was employed, including quantitative data collection via surveys and observational assessments. The study used a two-stage cluster randomization design to ensure representativeness of the sample across districts. The analysis revealed that implementing targeted training programmes for healthcare staff led to an average yield improvement in patient treatment outcomes by 15% (95% CI: 7-23%). This replication study underscores the effectiveness of specific intervention strategies in enhancing hospital performance and service quality. District health authorities should prioritise implementation of evidence-based training programmes to address resource limitations and operational inefficiencies. district hospitals, randomized field trial, yield improvement, healthcare system evaluation Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.