African Hydrology Research (Earth Science focus)
African Hydrology Research (Earth Science focus) is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Eco-Friendly Packaging Solutions for Medical Supplies in East Africa
Medical supplies in East Africa often use non-biodegradable packaging that leads to environmental degradation. In Equatorial Guinea, this issue is particularly pressing due to limited waste management infrastructure. A mixed-methods approach combining surveys with case studies was employed. Quantitative data on user preferences were collected from a sample of healthcare facilities, while qualitative insights were gathered through interviews with stakeholders. The adoption rate for biodegradable packaging solutions among surveyed facilities is 35%, indicating room for improvement despite growing environmental awareness. While initial uptake is positive, the current low rates suggest a need for enhanced policy support and further education on the benefits of eco-friendly materials. Implementing targeted educational campaigns and offering financial incentives to healthcare providers could accelerate the adoption of sustainable packaging solutions. Eco-Friendly Packaging, Medical Supplies, East Africa, Equatorial Guinea, Waste 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/
Methodological Evaluation of Emergency Care Units in Nigeria
Emergency care units (ECUs) in Nigeria are crucial for managing acute medical emergencies effectively. A systematic literature search was conducted using databases such as PubMed, Scopus, and Web of Science. Studies published between and were included based on specific criteria related to ECU functionality and patient outcomes. The analysis revealed a significant improvement in patient survival rates (p < 0.05) when ECUs utilised an evidence-based triage system, with a proportion of 78% showing better clinical outcomes compared to non-ECU hospitals. ECU systems in Nigeria have demonstrated promising clinical outcomes but require further methodological validation and standardization for widespread adoption. Standardised training programmes and regular audits should be implemented to ensure consistent quality of care across all ECUs. Data sharing initiatives between facilities can facilitate benchmarking and continuous improvement. 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 Rwanda
Public health surveillance systems in Rwanda are crucial for monitoring disease outbreaks efficiently. However, their effectiveness varies widely across different regions and requires methodological refinement. A randomized controlled trial was conducted in two districts of Rwanda to compare traditional passive surveillance methods with an enhanced active surveillance approach. Clinical data from patients suspected of having infectious diseases were collected using standardised protocols over six months. In the intervention district, a higher proportion (75%) of clinical cases were detected compared to the control area (60%), demonstrating improved system sensitivity and specificity in identifying infections. The enhanced active surveillance approach significantly improved case detection rates without an increase in false positives, suggesting its potential for broader implementation within Rwanda’s public health infrastructure. Immediate replication of this study across all districts to ensure consistent performance and further refinement based on findings 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) 2012 https://creativecommons.org/licenses/by/4.0/
Sustainable Water Supply Solutions for Rural Zimbabwean Communities
Rural communities in Zimbabwe face challenges with sustainable water supply solutions, impacting health outcomes. A mixed-methods approach combining qualitative interviews and quantitative surveys to evaluate the effectiveness of interventions. Community participation was strongly correlated with improved hygiene practices (p<0.05), indicating significant engagement impacts. Engaged communities demonstrated better health outcomes, highlighting the importance of community involvement in water supply solutions. Promote collaborative models that involve local stakeholders for sustainable and effective water supply initiatives. Water Supply Solutions, Community Engagement, Health Outcomes, Sustainable Practices 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 Centre Systems in South Africa
Community health centres in South Africa are underutilized due to inadequate healthcare delivery systems. A randomized field trial was conducted with a sample size of 200 participants across five randomly selected communities. The primary outcome measure was the number of patients seen per month, compared to pre-intervention data. The intervention increased patient visits by an average of 15% (95% CI: 7-23%) in the trial areas. Community health centres significantly improved clinical outcomes when adequately supported and staffed. Allocate additional resources to community health centres, particularly for training and supplies. 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/
Bayesian Hierarchical Model for Evaluating Cost-Effectiveness in Public Health Surveillance Systems in Nigeria
Public health surveillance systems are crucial for monitoring infectious diseases in Nigeria. However, their cost-effectiveness varies significantly across different regions and healthcare settings. A Bayesian hierarchical model was developed to assess the cost-effectiveness of public health surveillance systems in Nigeria. The model accounts for variability across different regions and incorporates uncertainty through robust standard errors. The analysis revealed that certain regions benefited more from surveillance efforts, with a higher proportion (25%) demonstrating significant cost savings compared to others. The Bayesian hierarchical model provided insights into the optimal allocation of resources for enhanced surveillance effectiveness in Nigeria. Public health authorities should prioritise investments in regions where surveillance can yield substantial cost savings and improve outbreak response times. Bayesian Hierarchical Model, Public Health Surveillance, Cost-Effectiveness Analysis, Nigeria 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 Centre Systems in Rwanda Using Quasi-Experimental Design to Measure Efficiency Gains
Community health centres in Rwanda have been established to improve healthcare accessibility and quality in rural areas. However, their efficiency remains a subject of interest for policymakers and researchers. A mixed-method approach combining quantitative data analysis with qualitative insights was employed. The study utilised an interrupted time series analysis (ITS) model for measuring efficiency gains over time. The ITS model showed significant improvements in service delivery metrics, particularly in the reduction of patient wait times by 25% within a year post-reform implementation. The quasi-experimental design proved effective in detecting efficiency gains without requiring additional resources or external funding. Based on the findings, recommendations include continuous monitoring and periodic re-evaluations to sustain improvements. community health centres, Rwanda, quasi-experimental design, efficiency gains, interrupted time series 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/
Adoption of Antibiotic Resistance Screening Kits in Northern Ghanaian Primary Healthcare Facilities
Antibiotic resistance is a critical public health issue affecting both developed and developing countries. In northern Ghanaian primary healthcare facilities, antibiotic misuse and overuse contribute to rising rates of resistant bacterial infections. A cross-sectional study was conducted, reviewing records from to . Data were collected on facility policies and procedures related to antibiotic use and resistance screening. Only 35% of facilities implemented routine antibiotic resistance testing protocols, with a significant variation in practice between urban and rural settings. The lower adoption rate highlights gaps in healthcare infrastructure and training for proper antibiotic stewardship. Healthcare providers should prioritise the integration of routine antibiotic resistance screening into their practices to mitigate the spread of resistant infections. 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 Community Health Centre Systems in South Africa
Community health centres (CHCs) in South Africa play a crucial role in healthcare delivery, particularly in underserved areas. The effectiveness of these services is influenced by various factors including resource allocation and patient management practices. The study employs rigorous statistical methods such as fixed effects models (FE) and random effects models (RE), considering potential sources of heterogeneity across regions. Panel data analysis is utilised to account for both time-invariant and time-varying variables affecting health outcomes. One specific finding highlights a significant improvement in patient recovery rates by 15% when CHCs implement structured follow-up protocols, as evidenced by robust standard errors of the estimated coefficients. The findings underscore the importance of adopting systematic approaches for resource allocation and management within CHC settings to enhance service efficacy. Health policymakers should prioritise investment in data collection systems and training programmes aimed at improving patient adherence to treatment protocols. 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 Panel Data to Measure Adoption Rates
Public health surveillance systems in Senegal have been implemented to track infectious diseases, but their effectiveness varies across different regions and over time. The review will employ a meta-analytic approach to aggregate findings from various studies, employing mixed-effects models to account for temporal and spatial variation. A key finding is that the implementation rate of digital health platforms varied significantly by region, with urban areas showing adoption rates up to 75% compared to rural regions at 30%. The panel data analysis revealed substantial heterogeneity in the effectiveness of public health surveillance systems across Senegal. Further research should focus on improving digital infrastructure and training for healthcare workers in underserved areas to enhance overall system 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.
Bayesian Hierarchical Model in Ghanaian District Hospitals
Bayesian hierarchical models have been increasingly applied in various fields to analyse complex data hierarchically. A Bayesian hierarchical model was developed and implemented to assess clinical outcomes across different hospital districts. Uncertainty quantification was conducted using robust standard errors. The analysis revealed significant variability in patient recovery rates between districts, with a notable difference in the 20% range for certain medical conditions. Bayesian hierarchical models provide a nuanced approach to evaluating clinical outcomes and system performance in district hospitals. The model's ability to account for spatial variation is particularly useful. The findings suggest that targeted interventions should be implemented based on the identified disparities, aiming to improve patient recovery rates across all districts. 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/
Bayesian Hierarchical Model for Evaluating Efficiency Gains in Senegalese District Hospitals Systems,
This study aims to evaluate efficiency gains in Senegalese district hospitals systems over a single year. Bayesian hierarchical models will be used to analyse data from district hospitals, incorporating uncertainty through robust standard errors. Efficiency gains varied across districts, with some showing significant improvements in service delivery efficiency. The findings suggest that targeted interventions could enhance hospital performance and resource allocation. Policy recommendations include focusing on areas of identified inefficiencies and promoting best practices through training programmes. Bayesian Hierarchical Models, Hospital Efficiency, District Hospitals, Senegal Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Cost-Effectiveness Analysis of Community Health Centre Systems in Tanzania Using Difference-in-Differences Methodology
Community health centres (CHCs) in Tanzania are pivotal for delivering healthcare services to underserved populations. However, their effectiveness and cost-effectiveness need systematic evaluation. The analysis employs a DiD model with robust standard errors to account for potential confounding variables. The study uses administrative data from to as the primary dataset. CHCs in Tanzania showed an average improvement of 15% in patient outcomes compared to pre-intervention levels, indicating positive health impact. The DiD method provided a nuanced understanding of CHC performance and cost-effectiveness. The results suggest that CHCs are efficient in delivering healthcare services with lower costs than traditional models. Further research should focus on sustainability strategies to maintain the benefits observed, particularly in resource-limited settings. Community Health Centres, DiD Methodology, Cost-Effectiveness Analysis, Tanzania 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/
Urban Ghanaian Slums Asthma Education Programme Effectiveness Over Three Months
Urban Ghanaian slums face significant health challenges, particularly asthma prevalence among adolescents. Participants were randomized into intervention (education workshops) and control groups. Pre- and post-intervention assessments included symptom frequency, lung function tests, and parent-reported health status. The education programme led to a statistically significant decrease in asthma symptoms by 25% (p < 0.01), with a moderate effect size measured using Cohen's d. The educational intervention was effective in reducing adolescent asthma symptoms, and the cost-effectiveness analysis showed it was more efficient than existing programmes in urban slums. Future studies should explore long-term effects and scalability of this intervention in other urban 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 Assessment of Emergency Care Units in Uganda
Emergency care units (ECUs) in Uganda are critical for managing acute health crises, yet their effectiveness remains underexplored. A cluster-randomised trial was conducted with 10 ECU sites across Uganda. Clinical data were collected on patient demographics, treatment outcomes, and resource utilisation over six months. ECUs treated 2345 patients predominantly aged 18-60 years (mean age: 32.5 ± 11.9). The mortality rate was 5% with a 95% confidence interval of (3.5%, 7.5%). ECUs in Uganda are effective at reducing mortality among young to middle-aged patients, though further investment is needed in resource allocation and training. Enhanced training for staff, increased availability of essential medical supplies, and improved referral pathways should be prioritised. 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 Evaluation of Community Health Centres Systems in Kenya Using Multilevel Regression Analysis for Clinical Outcomes Assessment
Community health centres (CHCs) play a critical role in healthcare delivery in Kenya, particularly in underserved areas where access to specialized medical care is limited. A comprehensive search strategy was employed, including electronic databases such as PubMed and Embase. Studies were included if they utilised multilevel regression analysis to measure clinical outcomes within CHC settings. Multilevel regression models showed significant variability in the effectiveness of interventions across different levels (e.g., individual patient effects vs. community-level factors). This review highlights the importance of adopting consistent and robust multilevel regression methods for accurate clinical outcome assessment at CHCs. Standardised protocols should be developed to ensure consistency in data collection and analysis across different CHC settings in Kenya. 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 Uganda
Community health centres (CHCs) play a crucial role in healthcare delivery in Uganda, yet their operational efficiency remains underexplored. A mixed-methods approach was employed, including a regression discontinuity design (RDD) for efficiency analysis. Data from - were used to identify treatment effects. CHCs exhibited significant improvement in patient throughput by 35% after implementing telemedicine services, with an estimated effect size of 0.45 standard deviations (SD). The quasi-experimental design provided robust evidence for the effectiveness of telemedicine in enhancing CHC efficiency. Telemedicine should be scaled up to all CHCs to further improve service delivery and patient outcomes. Community Health Centres, Efficiency Gains, Regression Discontinuity Design, Telemedicine 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/
Methodological Evaluation of District Hospitals Systems in Rwanda Using Time-Series Forecasting Models for Yield Improvement Analysis
District hospitals in Rwanda play a critical role in healthcare delivery but face challenges in managing resources efficiently. A systematic review of literature was conducted to assess the effectiveness of time-series forecasting models for predicting health system yields in Rwanda's district hospitals. The study analysed data from various sources including government reports and academic journals. The analysis revealed a significant fluctuation in medical resource utilization, with an average forecast error margin of ±5% when using ARIMA (AutoRegressive Integrated Moving Average) models for yield improvement. Time-series forecasting models can be effective tools for improving the efficiency and predictability of district hospital operations in Rwanda. Future research should explore more sophisticated machine learning techniques to reduce errors further. District hospitals are encouraged to implement data-driven management strategies, which may include regular model updates based on new data inputs and enhanced training for staff in forecasting methods. 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/
Bayesian Hierarchical Model for Assessing System Reliability in Community Health Centres, Kenya
Bayesian hierarchical models have been increasingly applied in various fields to assess system reliability, including healthcare systems. This study focuses on community health centres (CHCs) in Kenya, aiming to enhance understanding of their operational performance. A Bayesian hierarchical model was employed, incorporating data from multiple CHCs across Kenya. The model accounts for both fixed effects (e.g., geographical location) and random effects (e.g., variability within regions). Uncertainty in the model predictions is quantified using robust standard errors. The analysis revealed significant variation in system reliability among different regions of Kenya, with a notable proportion (35%) of CHCs failing to meet established service standards. This suggests substantial room for improvement in infrastructure and operational practices. The Bayesian hierarchical model provides valuable insights into the performance of CHC systems in Kenya, highlighting regional disparities that can inform targeted interventions aimed at improving system reliability. Based on the findings, a multi-faceted approach is recommended, including investment in infrastructure upgrades and training for staff to ensure better service delivery across all regions. 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 Rwanda Using a Difference-in-Differences Approach to Assess Clinical Outcomes
Public health surveillance systems in Rwanda are critical for monitoring disease prevalence and guiding interventions to improve clinical outcomes. However, their effectiveness remains under scrutiny, necessitating methodological evaluations. A difference-in-differences approach was employed to analyse pre- and post-intervention data. Data from existing surveillance systems were utilised, with robust standard errors accounting for potential confounders. The analysis revealed a significant improvement in the proportion of patients receiving timely treatment (from 40% to 65%) following the introduction of new protocols. This study provides evidence that the public health surveillance system significantly enhanced clinical outcomes, justifying its continued implementation and further refinement. Public health authorities should prioritise ongoing evaluation and potential enhancements based on this research's findings. public health surveillance, difference-in-differences, clinical outcomes, 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) 2002 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Rural Clinics Systems in Ethiopia Using Multilevel Regression Analysis for Clinical Outcomes Assessment
Rural clinics in Ethiopia are critical for providing essential healthcare services to underserved communities. However, their performance and impact vary significantly across different regions and settings. Multilevel regression models were employed to analyse data collected from rural clinics across Ethiopia. The model accounts for both clinic-level (e.g., staffing levels, equipment availability) and district-level (e.g., socioeconomic factors, community engagement) influences on clinical outcomes. The multilevel regression analysis revealed that patient recovery rates in clinics with higher staff-to-patient ratios were significantly improved by approximately 15%, indicating a positive impact of adequate staffing levels on clinical performance. This study provides robust evidence supporting the importance of resource allocation and staff management in improving healthcare delivery within rural clinics in Ethiopia. Based on the findings, policy recommendations include enhancing training programmes for clinic personnel and implementing strategies to increase patient access to preventive health check-ups. 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 Tech Models for Malaria Vector Control in Kigali
Malaria remains a significant public health concern in Rwanda, particularly in urban areas like Kigali. Effective malaria vector control strategies are essential for reducing mosquito populations and mitigating disease transmission. A randomized controlled trial was conducted, comparing traditional insecticide-treated bed nets (ITNs) with a mobile app-based vector control intervention. Mosquito populations were monitored weekly using electrocuting traps. The mobile health tech model resulted in an average reduction of 25% in mosquito population compared to ITN use alone, with no significant side effects observed. Mobile technology can be a viable and effective tool for malaria vector control, providing substantial benefits over traditional methods. Further research should explore scalability and cost-effectiveness of the mobile tech intervention across different urban settings in Rwanda. Malaria Vector Control, Mobile Health Tech, Mosquito Population Reduction, Kigali, 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) 2001 https://creativecommons.org/licenses/by/4.0/
Longitudinal Weight Gain Control in School-Based Nutrition Interventions for Childhood Obesity Prevention Among Secondary School Students in Lagos, Nigeria
Childhood obesity is a significant public health issue in Lagos, Nigeria, where secondary school students are at risk due to sedentary lifestyles and poor dietary habits. A longitudinal cohort study was conducted with a sample size of 500 students from 20 secondary schools. Participants were followed annually for six years, and data on dietary intake, physical activity levels, and weight changes were collected using standardised questionnaires and anthropometric measurements. Over the course of six years, participants showed an average longitudinal reduction in weight gain by 15% compared to a control group. The intervention had a positive impact on students' nutritional knowledge scores (mean increase from baseline of 20% with a confidence interval of [18%, 22%]). The school-based nutrition interventions significantly reduced longitudinal weight gain in secondary school students and improved their dietary literacy. Further research should explore the long-term effects of these interventions on obesity-related health outcomes, including metabolic markers and cardiovascular risk factors. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Geographic Terms: African Methodological: Longitudinal Theoretical: Obesity Prevention Relevant to Topic: Nutrition Intervention Thematic: Childhood Health
Methodological Evaluation of Community Health Centre Systems in Tanzania
This study examines community health centre systems in Tanzania to evaluate their operational efficiency over time. A longitudinal panel data approach will be employed using mixed-effects models to estimate the efficiency gains. Panel data from multiple years will be analysed to track changes in service delivery and resource allocation. Efficiency scores for health centres varied significantly, with some showing a 10% improvement over three years due to enhanced management practices and technology integration. The mixed-effects models revealed that specific interventions like telemedicine adoption led to notable efficiency improvements, supporting the need for tailored policy adjustments. Policy recommendations include increased funding for training programmes for health centre managers and the promotion of digital healthcare solutions. 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 of Public Health Surveillance Systems in South Africa Using Difference-in-Differences Modelling for System Reliability Assessment
Public health surveillance systems in South Africa are crucial for monitoring outbreaks of infectious diseases such as influenza and tuberculosis (TB). These systems rely on timely data collection and analysis to inform public health interventions. A DiD model will be applied to historical data from two provinces: Gauteng and KwaZulu-Natal. The model will estimate the impact of a new reporting standard on surveillance accuracy, using province-specific pre- and post-intervention trends as control variables. The analysis indicates that the new reporting standard improved the detection rate of TB cases by 15% in Gauteng, with a confidence interval of ±3.0%. The DiD model demonstrated its effectiveness in evaluating system reliability, providing actionable insights for enhancing public health surveillance in South Africa. Future research should expand the DiD application to additional provinces and incorporate real-time data sources to further validate 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.