Luc Onambele

Background/Objectives: DTP3 (diphtheria–tetanus–pertussis vaccine, third dose) coverage is a key indicator of the strength and continuity of routine immunization programs, which demonstrably reduces the burden of infectious diseases globally. This study aims to assess trends in DTP3 vaccination coverage across Asian regions and countries from 2012 to 2023, focusing on changes associated with the COVID-19 pandemic. Methods: DTP3 vaccination data were obtained from official WHO/UNICEF Estimates of National Immunization Coverage (WUENIC) and analyzed using Joinpoint regression to detect statistically significant changes in vaccination trends. Data were grouped by five Asian subregions based on the UN geoscheme (Central, Eastern, Southeastern, Southern, and Western Asia), and trends were weighted using birth cohort sizes. The presence of joinpoints and annual percentage changes (APCs) was calculated, and potential pandemic-related disruptions were contextualized. Results: At the continental level, Asia experienced a modest 0.4% annual increase in DTP3 coverage between 2012 and 2023, with a significant joinpoint detected in 2018. Following this, Southeast Asia’s coverage declined at an annual rate of −4.32% before beginning to recover in 2021, while South Asia showed a similar pattern. Country-level analysis revealed significant heterogeneity, with a comparison between 2019 and 2023 showing profound post-pandemic declines in some nations, such as Lebanon (–21%) and Myanmar (–9.4%), while others, like Iraq and the Philippines, achieved substantial recoveries with coverage increasing by over 6 percentage points. These trends contrasted with persistent declines in fragile states (e.g., Afghanistan, Yemen) and sustained high coverage in others (e.g., Bangladesh, Israel). The pandemic, systemic weaknesses, emerging vaccine hesitancy, and misinformation were identified as key influences. Conclusions: There is progress in DTP3 coverage across Asia. There were pandemic-related disruptions, particularly in regions with fragile health systems. Strategies to address zero-dose and dropout children, improve service continuity, and counter misinformation are essential to meet immunization targets under the Immunization Agenda 2030.

DTP3 (diphtheria-tetanus-pertussis vaccine, third dose) coverage is a key indicator of the strength and continuity of routine immunization programs, which demonstrably reduces the burden of infectious diseases globally. This study aimed to assess trends in DTP3 vac-cination coverage across Asian regions and countries from 2012 to 2023, with a particular focus on changes associated with the COVID-19 pandemic. Methods: DTP3 vaccination data were obtained from official WHO/UNICEF Estimates of National Immunization Coverage (WUENIC) and analyzed using Joinpoint regression to detect statistically sig-nificant changes in vaccination trends. Data were grouped by five Asian subregions based on the UN geoscheme (Central, Eastern, South-eastern, Southern, and Western Asia), and trends were weighted using birth cohort sizes. The presence of joinpoints and annual per-centage changes (APCs) was calculated, and potential pandemic-related disruptions were contextualized. Results: At the continental level, Asia experienced a modest 0.4% increase in DTP3 coverage between 2012 and 2023, with a significant joinpoint detected in 2018. Southeast and South Asia experienced declines in coverage after 2018, followed by partial recoveries. East and West Asia also exhibited trend changes overlapping with the pan-demic years. Central Asia remained stable with no detected joinpoint. Country-level anal-ysis revealed heterogeneity, with some nations experiencing persistent declines (e.g., Af-ghanistan, Yemen), while others maintained high or improving coverage (e.g., Bangla-desh, Iraq, Israel). The pandemic, systemic weaknesses, and emerging vaccine hesitancy and misinformation were identified as key influences. Conclusions: There is progress in DTP3 coverage across Asia. There were pandemic-related disruptions, particularly in re-gions with fragile health systems. Strategies to address zero-dose and dropout children, improve service continuity, and counter misinformation are essential to meet immuniza-tion targets under the Immunization Agenda 2030

Background: Nursing Schools are incorporating Mixed Reality (MR) into student training to enable them to confront challenging or infrequently encountered scenarios in their practice and ensure their preparedness. This systematic review evaluates the benefits and challenges of implementing MR in nursing curricula. Materials and Methods: A search was conducted in PubMed, WOS, Scopus, Embase, and CINAHL for studies published between 2011 and 2023. The search strategy used was “(nurses OR nurse OR nursing) AND mixed reality AND simulation”. Inclusion criteria required that studies focus on undergraduate nursing students and be written in English or Spanish. Exclusion criteria included reviews, bibliometric studies, and articles that did not separately report undergraduate nursing student results. Quality was evaluated with the JBI Critical Appraisal Checklist for Qualitative Research and the Newcastle-Ottawa Scale. A meta-analysis was conducted on studies with control groups to compare MR’s effectiveness against traditional teaching methods. Results: Thirty-three studies met the inclusion criteria. MR was widely used to improve clinical judgment, patient safety, technical skill acquisition, and student confidence. The meta-analysis found that MR reduced anxiety (Cohen’s d = −0.73, p < 0.001). However, its impact on knowledge acquisition and skill development was inconsistent. There was no improvement over traditional methods (p = 0.466 and p = 0.840). Despite positive qualitative findings, methodological variability, small sample sizes, and publication bias contributed to mixed quantitative results. The main challenges were cybersickness, usability, high costs, and limited institutional access to MR technology. Conclusions: Although MR can help nursing education by decreasing students’ anxiety, its efficacy remains inconclusive. Future research should use larger, randomized controlled trials to validate MR’s role in nursing education.

Background: The COVID-19 pandemic disrupted routine child immunization efforts, threatening to reverse progress in controlling vaccine-preventable diseases. Materials and Methods: We analyzed the impact of COVID-19 on DTP3 vaccination in Europe by comparing trends before and after the pandemic using time series data from 2000 to 2023. Employing joinpoint regression, chi-square tests, and segmented regression analysis, we assessed DTP3 vaccination trends and coverage changes. Results: The findings revealed significant regional disparities across Europe. Statistical models indicated reductions in DTP3 coverage in countries such as Ireland, Sweden, and Switzerland, whereas Ukraine and San Marino showed improvements. Conclusions: There are variations in the effect of COVID-19 on DTP3 coverage rates, indicating the need for targeted public health strategies to address vaccine hesitancy, logistical barriers, and systemic inequities.

The COVID-19 pandemic disrupted routine child immunization efforts, threatening to reverse progress in controlling vaccine-preventable diseases. We analyzed the impact of COVID-19 on DTP3 vaccination in Europe by comparing trends before and after the pandemic using time series data from 2000 to 2023. Employing Joinpoint regression, chi-square tests, and segmented regression analysis, we assessed DTP3 vaccination trends and coverage changes. The findings revealed significant regional disparities across Europe. Statistical models indicated reductions in DTP3 coverage in countries like Ireland, Sweden, and Switzerland, whereas Ukraine and San Marino showed improvements. These variations indicate the need for targeted public health strategies to address vaccine hesitancy, logistical barriers, and systemic inequities.

Summary: Hypertension is a public health problem with serious social, economic, and health consequences. The World Health Organisation (WHO) estimates that 1 in 3 adults worldwide suffers from hypertension. In Cameroon, the prevalence of hypertension is estimated at 35%, with nearly 17,000 deaths recorded each year. This study aimed to determine the factors associated with the occurrence and complications of hypertension at the Sangmélima Referral Hospital (HRS). A total of 528 patients treated in the cardiology department of the HRS were identified between January and December 2023. The data were analyzed using SPSS 28 software. Binary logistic regression determined the odds ratios and 95% confidence intervals associated with each variable. Differences were statistically significant for a p-value < 0.05. At the HRS, the annual incidence of hypertension was 16.13%. Hypertension was present in 78.8% of patients. The factors associated with the occurrence and complications of hypertension were age (OR=1.028; p=0.003 ), level of education (OR=15.49; p=0.023), marital status (OR=3.859; p=0.04), hypercholesterolemia (OR=2.856; p=0.01), monthly income (OR=0.882; p=0.026), number of dependents (OR=1.231; p=0.025), food security (OR=16.666; p<0.001), tobacco consumption (OR=8.592; p=0.041), fruit and vegetable consumption (OR=0.027; p=0.031), salt/sugar consumption (OR=8.129; p<0.001), place of residence (OR=4.794; p=0.005), access to essential technologies (OR=8.851; p=0.002), and use of traditional care (OR=3.137; np=0.032). HTA at the HRS is associated with several factors. In order to limit the impact of hypertension, it is crucial to emphasize improving socioeconomic and health conditions.

The rapid progress in artificial intelligence, machine learning, and natural language processing has led to increasingly sophisticated large language models (LLMs) for use in healthcare. This study assesses the performance of two LLMs, the GPT-3.5 and GPT-4 models, in passing the MIR medical examination for access to medical specialist training in Spain. Our objectives included gauging the model’s overall performance, analyzing discrepancies across different medical specialties, discerning between theoretical and practical questions, estimating error proportions, and assessing the hypothetical severity of errors committed by a physician. Material and methods: We studied the 2022 Spanish MIR examination results after excluding those questions requiring image evaluations or having acknowledged errors. The remaining 182 questions were presented to the LLM GPT-4 and GPT-3.5 in Spanish and English. Logistic regression models analyzed the relationships between question length, sequence, and performance. We also analyzed the 23 questions with images, using GPT-4’s new image analysis capability. Results: GPT-4 outperformed GPT-3.5, scoring 86.81% in Spanish (p < 0.001). English translations had a slightly enhanced performance. GPT-4 scored 26.1% of the questions with images in English. The results were worse when the questions were in Spanish, 13.0%, although the differences were not statistically significant (p = 0.250). Among medical specialties, GPT-4 achieved a 100% correct response rate in several areas, and the Pharmacology, Critical Care, and Infectious Diseases specialties showed lower performance. The error analysis revealed that while a 13.2% error rate existed, the gravest categories, such as “error requiring intervention to sustain life” and “error resulting in death”, had a 0% rate. Conclusions: GPT-4 performs robustly on the Spanish MIR examination, with varying capabilities to discriminate knowledge across specialties. While the model’s high success rate is commendable, understanding the error severity is critical, especially when considering AI’s potential role in real-world medical practice and its implications for patient safety.

The rapid progress in artificial intelligence, machine learning, and natural language processing has led to the emergence of increasingly sophisticated large language models (LLMs) enabling their use in healthcare. The study assesses the performance of two LLMs: the GPT-3.5 and GPT-4 models in passing the medical examination for access to medical specialist training in Spain MIR. Our objectives included gauging the model's overall performance, analyzing discrepancies across different medical specialties, discerning between theoretical and practical questions, estimating error proportions, and assessing the hypothetical severity of errors committed by a physician. We studied the 2022 Spanish MIR examination after excluding those questions requiring image evaluations or having acknowledged errors. The remaining 182 questions were presented to the LLM ChatGPT4 and GPT-3.5 in Spanish and English. Logistic regression models analyzed the relationships between question length and question sequence d performance. GPT-4 outperformed GPT -3.5, scoring 86.81% in Spanish (p<0.001). English translations had a slightly enhanced performance. Among medical specialties, GPT-4 achieved a 100% correct response rate in several areas, with specialties like Pharmacology, ICU, and Infectious Diseases showing lower performance. The error analysis revealed that while a 13.2% error rate existed, gravest categories like "error requiring intervention to sustain life" and "error resulting in death" had a 0% rate. Conclusions: GPT-4 performs robustly on the Spanish MIR examination, varying its capability to discriminate knoweldge across specialties. While the model's high success rate is commendable, understanding the error severity is critical, especially when considering AI's potential role in real-world medical practice and its implication on patient safety.

With the United Nations Sustainable Development Goals (SDG) (2015–2030) focused on the reduction in maternal mortality, monitoring and forecasting maternal mortality rates (MMRs) in regions like Africa is crucial for health strategy planning by policymakers, international organizations, and NGOs. We collected maternal mortality rates per 100,000 births from the World Bank database between 1990 and 2015. Joinpoint regression was applied to assess trends, and the autoregressive integrated moving average (ARIMA) model was used on 1990–2015 data to forecast the MMRs for the next 15 years. We also used the Holt method and the machine-learning Prophet Forecasting Model. The study found a decline in MMRs in Africa with an average annual percentage change (APC) of −2.6% (95% CI −2.7; −2.5). North Africa reported the lowest MMR, while East Africa experienced the sharpest decline. The region-specific ARIMA models predict that the maternal mortality rate (MMR) in 2030 will vary across regions, ranging from 161 deaths per 100,000 births in North Africa to 302 deaths per 100,000 births in Central Africa, averaging 182 per 100,000 births for the continent. Despite the observed decreasing trend in maternal mortality rate (MMR), the MMR in Africa remains relatively high. The results indicate that MMR in Africa will continue to decrease by 2030. However, no region of Africa will likely reach the SDG target.

(1) Background: With the United Nations Sustainable Development Goals (SDG) (2015-2030) fo-cusing on reducing maternal mortality, monitoring and forecasting Maternal Mortality Rates (MMR) in regions like Africa become crucial for health strategy planning by policymakers, in-ternational organizations, and NGOs. (2) Methods: We collected maternal mortality rates per 100,000 births from the World Bank database between 1990 and 2015. Join Point regression was applied to assess trends, and the autoregressive integrated moving average (ARIMA) model was used on 1990-2015 data to forecast the MMR for the next 15 years. (3) Results: The study found a decline in MMR in Africa with an average annual percentage change (APC) of -2.6% (95% CI -2.7; -2.5). North Africa reported the lowest MMR, while East Africa experienced the sharpest decline. The region-specific ARIMA models predict that the maternal mortality rate (MMR) in 2030 will vary across regions, ranging from 65 deaths per 100,000 births in North Africa to 249 deaths per 100,000 births in Central Africa., averaging 197 per 100,000 births for the continent. (4) Conclusions: Despite the observed decreasing trend in maternal mortality rate (MMR), the MMR in Africa remains relatively high. The results indicate that MMR in Africa will continue to decrease by 2030. However, only North and South Africa will likely reach the SDG target.