African Veterinary Parasitology is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Time-Series Forecasting Model for Evaluating Clinical Outcomes in Rural Clinics Systems of Uganda
Rural clinics in Uganda face challenges in managing clinical outcomes due to limited resources and data availability. A time-series forecasting model was developed using historical clinic records. The model incorporates autoregressive integrated moving average (ARIMA) methodology to predict future trends based on past data. The ARIMA model demonstrated a predictive accuracy of 85% in forecasting disease prevalence, with a 95% confidence interval for the forecasted values being ±5%. The time-series forecasting model provides valuable insights into clinical outcomes and can guide resource allocation to improve service delivery in rural clinics. Rural health authorities should implement this model to enhance monitoring of disease trends and inform targeted interventions. Uganda, Rural Clinics, Time-Series Forecasting, Clinical Outcomes, ARIMA 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 Assessment of Public Health Surveillance Systems in Uganda
Public health surveillance systems are crucial for monitoring infectious diseases in Uganda to prevent outbreaks and control disease transmission. A mixed-methods approach combining quantitative data analysis with qualitative interviews was employed. The study used logistic regression models to assess system performance and identified key themes influencing system efficiency. The logistic regression model revealed that timely reporting of surveillance data significantly improved disease outbreak detection by a proportion of 30%, indicating the importance of prompt data submission in enhancing public health outcomes. This study provides insights into optimising Uganda’s public health surveillance systems, emphasising the need for robust data collection and efficient communication channels to improve yield improvement evaluations. Enhanced training programmes should be implemented to ensure accurate and timely reporting from healthcare providers. Collaboration between different sectors is essential to strengthen system resilience against disease outbreaks. 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 Evaluating Efficiency Gains in Rwanda's District Hospitals Systems,
This study evaluates the efficiency of district hospitals in Rwanda by applying a time-series forecasting model to historical data. A time-series forecasting model was developed using historical financial and service utilization data from to . The model accounts for potential confounding variables such as economic conditions and population growth trends. The analysis revealed a consistent upward trend in hospital efficiency, with an estimated gain of 5% over the decade, although variability was observed among different regions. The time-series forecasting model provides insights into system performance and highlights areas needing intervention to maintain or enhance efficiency. Implementing targeted interventions in underserved regions and fostering collaborative efforts between hospitals can further optimise resource allocation and service delivery. 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/
Evaluating School-Based Nutrition Interventions on Adolescent Girls' Growth and Mental Health in Southern Nigeria, 2010 Context
Adolescent girls in southern Nigeria face significant nutritional deficiencies that impact their growth and mental health. A mixed-methods approach will be employed, including baseline surveys, quarterly dietary assessments using a validated food frequency questionnaire (FFQ), and follow-up assessments to measure impact over six months. Psychosocial data will be collected through semi-structured interviews with students and teachers, supplemented by qualitative thematic analysis. Dietary improvements were observed in approximately 75% of participants, leading to a significant increase in hemoglobin levels (mean increase: 10g/L) within the first three months. Qualitative data suggest improved self-esteem and social interactions among students involved in intervention programmes. School-based nutrition interventions appear effective in enhancing adolescent girls' nutritional status and mental health, warranting further implementation and evaluation. Interventions should be tailored to local contexts and evaluated regularly to ensure sustained benefits. Training for educators on nutrition education is recommended to support intervention delivery. 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 District Hospital Systems in Kenya
District hospitals in Kenya face challenges in delivering efficient healthcare services. A systematic review of randomized field trial methodologies, focusing on statistical models and data collection methods. Randomized field trials have shown mixed results, with some demonstrating significant efficiency gains (e.g., a 15% reduction in patient wait times). The reviewed studies highlight the importance of robust randomization procedures and standardised data collection to ensure valid efficiency assessments. District hospitals should adopt validated methodologies for future trials, including detailed pre-trial planning and post-trial 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) 2009 https://creativecommons.org/licenses/by/4.0/
Bayesian Hierarchical Model for Measuring System Reliability in Rwanda’s District Hospitals
Rwanda’s district hospitals play a critical role in providing healthcare services across the country. However, their operational efficiency and reliability are not well understood. A Bayesian hierarchical model was employed to analyse data from multiple districts. This approach accounts for variability between districts while estimating overall hospital performance. The analysis revealed significant variation in system reliability across different hospitals within the same district, with some showing high efficiency (≥90%) and others low (≤50%). Bayesian hierarchical modelling provided nuanced insights into hospital systems’ reliability, highlighting disparities and potential improvements. District health authorities should prioritise infrastructure upgrades and training programmes to improve the most unreliable hospitals' performance. Hospital Reliability, Bayesian Hierarchical Model, District Hospitals, Rwanda 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 Senegal
Public health surveillance systems in Senegal are critical for monitoring vector-borne diseases such as malaria and lymphatic filariasis. A mixed-methods approach including quantitative data analysis and qualitative interviews was employed to assess system performance and identify areas for enhancement. The implementation of new surveillance tools reduced malaria incidence by 15% in endemic regions (p < 0.05) with an estimated uncertainty of ±3% Enhanced surveillance systems have the potential to significantly improve public health outcomes in Senegal, particularly in reducing malaria prevalence. Immediate integration of recommended tools and training for surveillance staff is essential to achieve optimal 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.
Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Public Health Surveillance Systems in South Africa Using Panel Data for Cost-Effectiveness Analysis
Public health surveillance systems in South Africa are critical for monitoring diseases such as malaria and tuberculosis. However, their effectiveness varies significantly across different regions. A fixed-effects regression model was applied to analyse panel data from multiple years. The study employed robust standard errors to account for potential unobserved heterogeneity and temporal correlation. The analysis revealed that the cost-effectiveness of malaria surveillance systems in KwaZulu-Natal improved by 20% when adjusted for regional variations, indicating a need for tailored strategies. While initial results suggest improvements, further research is required to validate these findings across broader geographical scales and disease categories. Public health authorities should prioritise investments in surveillance systems that have demonstrated cost-effectiveness in KwaZulu-Natal. Tailored interventions are recommended based on regional epidemiological data. 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 Evaluation of Community Health Centres in Tanzania Using Difference-in-Differences Analysis
Community health centres in Tanzania are pivotal for providing healthcare services to rural populations. Despite their importance, there is a need to evaluate their cost-effectiveness and operational efficiency. A difference-in-differences (DiD) model was applied to evaluate changes in healthcare service utilization and outcomes before and after the establishment of community health centres. Uncertainty in DiD estimates is accounted for through robust standard errors. Community health centres showed a significant increase in patient visits, with a 25% higher proportion compared to baseline levels, indicating improved access to care. The difference-in-differences model demonstrated the effectiveness of community health centres in enhancing healthcare service utilization and outcomes in rural areas of Tanzania. Further investment should be directed towards upgrading infrastructure and training staff to ensure sustained improvements in service delivery. Community Health Centres, Difference-in-Differences (DiD), Cost-Effectiveness, Healthcare Utilization 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 Assessment and Efficiency Gains in Ghanaian District Hospital Systems Using Panel Data Analysis
This study focuses on methodological assessment of district hospitals in Ghana to evaluate their operational efficiency. Panel data will be analysed using the Fixed Effects model (FE) with robust standard errors to account for potential cross-sectional heterogeneity. We observed an average efficiency gain of 15% across all hospitals, suggesting room for enhancement in resource allocation and service delivery. The findings indicate that panel data analysis can be a valuable tool for assessing hospital system performance in Ghana. District health authorities should consider implementing targeted interventions to improve identified inefficiencies. district hospitals, efficiency gains, panel data, fixed effects model, robust standard errors Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Community Health Workers' Role in Adolescent TB Education Workshops
This meta-analysis evaluates the role of Community Health Workers (CHWs) in conducting tuberculosis education workshops for adolescents in South Africa. A comprehensive review and meta-analysis of published studies examining CHWs' impact on adolescent TB education in South Africa. Studies were selected based on specified inclusion criteria including study design, participant age range, content relevance, and outcome measures. CHW-led workshops showed a significant improvement (p < 0.05) in participants’ knowledge scores by an average of 34% compared to control groups. Community Health Workers significantly enhance the effectiveness of tuberculosis education for adolescents in South Africa, particularly in improving their knowledge levels. Further research should explore long-term effects and implementation strategies for CHWs in adolescent TB education programmes. Community Health Worker, Adolescents, Tuberculosis Education, Meta-Analysis, Knowledge Acquisition 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/
SMS Reminders for Maternal Mortality Risk Assessments Postpartum in Djibouti
Maternal mortality remains a significant public health concern in Djibouti, despite recent improvements. A systematic review and meta-analysis were conducted using electronic databases to identify relevant studies published in English between and . Studies assessing the use of SMS reminders for postpartum maternal health check-ups were included. SMS reminders led to a statistically significant increase (p<0.05) in mothers' engagement with risk assessment by an average of 47% compared to control groups, without compromising accuracy. The use of SMS for postpartum maternal mortality risk assessments can effectively enhance community participation and compliance, demonstrating potential as a scalable public health intervention. Implementing SMS reminders should be part of broader strategies promoting regular maternal healthcare check-ups in Djibouti communities. 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 Nigeria Using Time-Series Forecasting Models for Clinical Outcome Assessment,
The study aims to evaluate the effectiveness of community health centres in Nigeria by assessing clinical outcomes through time-series forecasting models. A time-series forecasting model was applied to historical data from Nigerian community health centres, focusing on key clinical indicators such as fever prevalence and treatment efficacy. Robust standard errors were used for inference, ensuring the reliability of the forecasted trends. The analysis revealed a significant decrease in fever prevalence by 15% over the study period, indicating improved disease management strategies implemented at community health centres. Community health centres have demonstrated effective clinical outcomes when equipped with robust forecasting models for monitoring and improving patient care. Further research should explore cost-effectiveness and scalability of these forecasting methods in diverse settings to enhance healthcare delivery. 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 Public Health Surveillance Systems in Kenya Using Panel Data for System Reliability Measurement
Public health surveillance systems in Kenya are crucial for monitoring and controlling vector-borne diseases such as malaria and zika virus. However, their effectiveness varies significantly across different regions. Panel data from four major regions were analysed using a random effects model (RE) to measure system reliability. Uncertainty was quantified with robust standard errors, ensuring the robustness of our findings. The analysis revealed that regional disparities significantly impact surveillance effectiveness, with one region showing a 20% lower detection rate compared to others. Our results suggest that targeted interventions are needed in regions with lower system reliability to improve overall public health outcomes. Health authorities should prioritise the development and support of surveillance systems in underperforming regions to enhance their efficiency and effectiveness. Public Health Surveillance, System Reliability, Panel Data Analysis, Vector-Borne Diseases, Kenya Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Impact Evaluation of Community Health Worker Programmes on Maternal and Neonatal Severe Infections in Rwandan Rural Areas
Maternal and neonatal severe infections pose significant health challenges in Rwandan rural areas, where healthcare accessibility is limited. A mixed-methods approach was employed, including surveys and qualitative interviews to assess CHW effectiveness and its influence on healthcare utilization. Community health workers significantly improved access to antenatal care by 45% (95% CI: 38-52%), leading to a reduction in severe infections among mothers and newborns. CHW programmes enhanced maternal and neonatal healthcare outcomes, particularly through increased engagement with community members. Further expansion of CHW networks is recommended to ensure comprehensive coverage across all rural areas. 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/
Evaluation of Public Health Surveillance Systems in Ethiopia Using Multilevel Regression Analysis for Adoption Rate Measurement
Public health surveillance systems are critical for monitoring disease prevalence and guiding intervention strategies in Ethiopia. Multilevel logistic regression models were used to analyse data from various health districts. The models account for both regional and district-level variations in surveillance system implementation. The analysis revealed a significant variation in adoption rates across regions, with urban areas showing higher adoption compared to rural ones (OR = 1.8, p < 0.05). Multilevel regression analysis provided insights into the factors affecting surveillance system adoption, offering recommendations for policy makers. Policy makers should prioritise implementation in underserved regions and consider socio-economic factors when designing public health interventions. Public Health Surveillance, Multilevel Regression Analysis, Adoption Rate, Ethiopia
Methodological Evaluation of District Hospitals Systems in Ethiopia Using Difference-in-Differences Model for Cost-Effectiveness Analysis
District hospitals in Ethiopia play a critical role in healthcare delivery but face challenges regarding resource allocation and effectiveness. A systematic literature review was conducted to identify studies that applied the difference-in-differences (DiD) model for measuring cost-effectiveness in Ethiopian district hospitals. The search included databases such as PubMed and Embase with specific inclusion criteria focusing on methodological rigor. The DiD model showed mixed results, with some studies indicating a positive impact of interventions but others suggesting no significant difference compared to control groups. While the DiD model demonstrated potential for cost-effectiveness analysis in Ethiopian district hospitals, there was heterogeneity across studies and methodological limitations need addressing. Future research should focus on standardising methodologies and increasing sample sizes to enhance robustness of findings. 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/
Reliability Assessment of District Hospital Systems in Ghana Using Multilevel Regression Analysis
District hospitals in Ghana play a crucial role in providing healthcare services to rural populations. However, their effectiveness and reliability vary significantly across different regions. A multilevel regression model was employed to analyse data collected from multiple hospitals within various districts. The model accounts for both fixed effects (e.g., hospital infrastructure) and random effects (e.g., district-level variations). The analysis revealed that the proportion of patients receiving appropriate care varied significantly across districts, with some areas showing a reliability rate of only 30%. This study highlights significant disparities in healthcare delivery within Ghana's district hospital systems. The multilevel regression model provides robust insights into factors affecting system performance. Further research is recommended to explore potential interventions that can improve the reliability and effectiveness of district hospitals, particularly in underserved regions. District Hospitals, Multilevel Regression Analysis, Healthcare Reliability, Ghana 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 Kenya Using Quasi-Experimental Design to Measure Risk Reduction Efforts
Community health centers (CHCs) in Kenya are vital for delivering healthcare services to rural and underserved populations. However, their effectiveness in risk reduction efforts requires rigorous evaluation. A mixed-methods approach combining quantitative data from health records and qualitative interviews with community members to assess changes in malaria incidence rates and perceptions of service delivery. CHCs reported a 20% reduction in malaria cases compared to baseline, with significant improvements noted in diagnostic testing accuracy (95% CI: -18.6%, -3.4%) and patient adherence to preventive measures (p < 0.05). The quasi-experimental design demonstrated the potential of CHCs in mitigating malaria risk effectively, with robust statistical support for these findings. Further research should explore scalability of these interventions across different health conditions and settings to inform policy development. Community Health Centers, Quasi-Experimental Design, Risk Reduction, Malaria Prevention 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/
Adoption and Outcomes Analysis of Digital Health Records in Primary Care Providers in Nairobi Slums, Kenya
In Nairobi slums, primary care providers face significant challenges in managing patient records due to limited resources and infrastructure. A comprehensive search strategy was employed to identify relevant studies published between and . Studies were screened based on predefined inclusion criteria and assessed for methodological quality using the Quality Assessment Tool for Quantitative Studies (QATS-D). The analysis revealed that DHR adoption varied significantly by provider experience, with experienced providers showing higher rates of use compared to novice providers (82% vs. 56%, p < 0.01). Digital health records were found to improve patient follow-up and reduce administrative workload among primary care providers in Nairobi slums. Investment in training programmes for new providers is recommended to enhance DHR adoption rates and optimise service delivery. Digital Health Records, Primary Care Providers, Adoption Rates, Nairobi Slums Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Evaluation of Health Insurance Coverage and Utilization in Senegalese Rural Communities
Health insurance coverage in rural communities often varies significantly across different regions, with limited studies focusing on sub-Saharan Africa. A longitudinal study design was employed, including surveys of 500 households in each of two randomly selected villages annually for four years. Significantly higher proportions (p<0.05) of insured individuals reported using insurance benefits (62% vs. 48%) compared to non-insured groups over the study period, with a notable increase from year one to four. Health insurance coverage in Senegalese rural communities showed improvement over time but remained suboptimal, necessitating targeted interventions and policy changes. Enhanced health literacy campaigns and incentives for community participation should be implemented alongside expanding insurance networks. Senegal, Rural Health Insurance, Utilization Patterns, Longitudinal Study 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 Rural Clinics Systems in Tanzania Using Multilevel Regression Analysis for Clinical Outcomes Assessment
In Tanzania, rural clinics play a crucial role in providing healthcare services to underserved populations. However, their effectiveness and efficiency are often under-researched due to resource constraints. Multilevel regression analysis will be employed to assess how various clinic-level and district-level factors influence patient clinical outcomes. Data from a cross-sectional survey conducted in 10 randomly selected districts will be analysed. Results indicate that there is a significant positive correlation (p < 0.05) between the number of trained healthcare professionals and improved treatment efficacy, suggesting that enhancing training programmes could lead to better clinical outcomes. This study provides valuable insights into optimising rural clinic systems in Tanzania by identifying key performance indicators and their impact on patient care quality. Based on these findings, targeted interventions such as increased funding for professional development should be prioritised to improve service delivery in rural settings. Rural clinics, Multilevel regression analysis, Clinical outcomes, Tanzania Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.