BACKGROUND AND OBJECTIVE

Evidence base on employment-related medical examinations is highly variable. The aim of this study is to build expert agreement on the appropriate medical and laboratory tests in major industries in Quezon City.

An initial scoping review of local, national, and international policies on employment-related diagnostic testing was done. The determination of industries at the selected study site (Quezon City) was accomplished through Pareto analysis. Interviews of key informants and representatives, and a consensus-building process through an expert panel were carried out by the Philippine College of Occupational Medicine (PCOM) Quezon City Chapter. Data gathered was used in the study and analyzed. An initial list of medical tests and diagnostics was drafted and the Modified RAND appropriateness method was used as the choice of mixed methods consensus-building process by an expert panel.

Regulations often vary significantly within settings and implementers, particularly the scope, content, and procedures for medical examinations of workers. History taking (including occupational history) and physical examination are the cornerstones of the screening process. CBC and chest x-ray were deemed appropriate screening laboratory tests for asymptomatic preemployment examination, while only chest x-rays were used during periodic examinations. Additional tests for medical surveillance should be based on job demands and specific exposure. For specific chemical exposure, standard references for medical requirements, like from Occupational Safety and Health Administration (OSHA), can be referred to.

A shift in how occupational health and safety measures are implemented is needed, particularly in the pre-employment and periodic examination practices, for more relevant screening while preventing unnecessary and low-yield testing and reducing costs for the employer and the employee. An occupational screening checklist/questionnaire based on the results of the study that includes appropriate clinical history-taking, review of systems (ROS), physical examination, and laboratories must be devised, which is to be followed by training in the proper conduction of these medical assessments.

OBJECTIVE

Given that rabies remains endemic in the Philippines despite government interventions and the pandemicrelated restrictions have hampered its surveillance, this study aimed to estimate the effect of human population, anti-rabies vaccination efforts, and COVID-19 situation on the spread of rabies cases in the districts of Davao City, Philippines.

A retrospective study of the canine records at Davao City Veterinarians’ Office was done from January 2018 to June 2021. Monthly rabies cases were ascertained, and the effect of the human population, COVID-19 season, and vaccination ratio on rabies cases was estimated using panel regression models adjusting for confounding factors.

The reporting of rabies cases was lower during COVID-19 than during the non-COVID-19 season, with an IRR of 0.52 [95% confidence interval (CI): 0.33–0.82]. Furthermore, rabies cases increased by 2.23% (95% CI: 0.60–3.89) per 1% increase in vaccination ratio. Additionally, high-population districts recorded more rabies cases than low-population districts.

Consistency in monitoring rabies cases during the pandemic is suggested as a roadmap for future program initiatives. Vaccination efforts should be reinforced to increase rabies awareness and ensure early response to emerging diseases. Moreover, highpopulated districts should be prioritized in implementing rabies control interventions to gain optimal development.

BACKGROUND AND OBJECTIVE

Rabies continues to be a challenge in Davao City despite the efforts of the city’s local government to vaccinate primarily the non-stray dog population. Meanwhile, studies have shown that time-dependent vaccination strategy is considered a prime factor for a cost-effective rabies control strategy. Hence, this study aims to provide information that will determine the optimal vaccination strategy targeted to the stray dog population that minimizes the rabies-infected dog population and vaccination costs using optimal control theory (OCT).

OCT is used to identify the optimal level of key rabies control, i.e., vaccination. Here, OCT was applied to a modified Susceptible-Exposed-Infectious-Vaccinated (SEIV) compartmental model. The study's key parameters were derived from published articles on rabies in Davao City and similar regions, along with the city's rabies reports.

The findings revealed that while rabies remains endemic in the city, it is possible to reduce the number of cases through consistent implementation of vaccination programs to the exposed and susceptible dog populations. Nevertheless, the feasibility of these findings relies to the effective targeting of vaccine coverage for the dog population. From the simulations performed, the exposed dog population (i.e., pre-rabid dogs) was able to reach zero observation when the transmission rate (?) is 0.001 for all values of anti-rabies vaccine coverages for exposed (?) and susceptible (b) dog populations and ? = 0.01 only when ? = 0.7 and b = 0.7, ? = 0.7 and b = 0.5, and ? = 0.5 and b = 0.7. Consequently, the number of infectious dogs will thereby decrease. Moreover, a nonlinear correspondence was also observed in all scenarios between the vaccination rate and the number of rabies-exposed dogs such that the reduction in the incidence of rabies cases becomes apparent only when the vaccination rate is at least 0.9995.

In high rabies transmissibility scenarios, a time-dependent vaccination strategy demonstrated a reduction in the number of rabies-infected dogs. However, this approach involves a trade-off, limiting the period during which monthly vaccinations can be relaxed. Consequently, a robust and timely vaccination program for dogs is crucial to manage high rabies transmission rates. Lastly, the model simulation underscores the importance of initiating monthly vaccinations.

Purpose: Metabolic diseases such as diabetes mellitus may play a role in the development and progression of prostate cancer (PC); however, this association remains to be explored in the context of specific PC stages. The objective of this study was to systematically review the evidence for an association between diabetes and overall, early, or advanced PC risk. Materials and Methods: A systematic review with meta-analysis was performed (MEDLINE, EMBASE, and CINAHL) from inception until September 2023. Cohort and case-control studies that assessed PC risk in adult males (≥18 years) associated with type 2 diabetes mellitus or diabetes (if there was no distinction between diabetes type) were included. The Newcastle-Ottawa Scale (NOS) was used to assess study bias; those with NOS<7 were excluded. Evidence certainty was assessed with the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) method. Results: Thirty-four studies (n=26 cohorts and n=8 case-controls) were included. Of these, 32 assessed diabetes and all PC stages combined, 12 included early PC stages, and 15 included advanced PC stages. Our meta-analysis showed diabetes had a protective effect against early PC development (n=11, risk ratio [RR]=0.71; 95% confidence interval [CI]=0.61–0.83, I2=84%) but no association was found for combined (n=21, RR=0.95; 95% CI=0.79–1.13, I2=99%) or advanced PC stages (n=15, RR=0.96; 95% CI=0.77–1.18, I2=98%) at diagnosis. According to GRADE, the evidence certainty was very low. Conclusions Diabetes may be protective against early PC stages, yet evidence linking diabetes to risk across all stages, and advanced PC specifically, is less conclusive. High heterogeneity may partially explain discrepancy in findings and was mostly associated with study design, method used for PC diagnosis, and risk measures. Our results may aid risk stratification of males with diabetes and inform new approaches for PC screening in this group, especially considering the reduced sensitivity of prostate-specific antigen values for those with diabetes.

The International Severe Asthma Registry (ISAR) was established in 2017 to advance the understanding of severe asthma and its management, thereby improving patient care worldwide. As the first global registry for adults with severe asthma, ISAR enabled individual registries to standardize and pool their data, creating a comprehensive, harmonized dataset with sufficient statistical power to address key research questions and knowledge gaps. Today, ISAR is the largest repository of real-world data on severe asthma, curating data on nearly 35,000 patients from 28 countries worldwide, and has become a leading contributor to severe asthma research. Research using ISAR data has provided valuable insights on the characteristics of severe asthma, its burdens and risk factors, real-world treatment effectiveness, and barriers to specialist care, which are collectively informing improved asthma management. Besides changing clinical thinking via research, ISAR aims to advance real-world practice through initiatives that improve registry data quality and severe asthma care. In 2024, ISAR refined essential research variables to enhance data quality and launched a web-based data acquisition and reporting system (QISAR), which integrates data collection with clinical consultations and enables longitudinal data tracking at patient, center, and population levels. Quality improvement priorities include collecting standardized data during consultations and tracking and optimizing patient journeys via QISAR and integrating primary/secondary care pathways to expedite specialist severe asthma management and facilitate clinical trial recruitment. ISAR envisions a future in which timely specialist referral and initiation of biologic therapy can obviate long-term systemic corticosteroid use and enable more patients to achieve remission.

BACKGROUND

The relationship between pandemics and climate change has emerged as a critical area of study, particularly underscored by the COVID-19 pandemic, which exposed vulnerabilities in global health systems and environmental governance. Although direct evidence linking climate change to the spread of COVID-19 remains limited, rising global temperatures and ecosystem disruptions have intensified human–wildlife interactions, increasing the risk of zoonotic disease emergence.

This study aims to synthesize existing research on the interconnections between climate change and emerging infectious diseases, identify key knowledge gaps, and provide insights to guide integrated health and environmental policy development.

A systematic literature review was conducted using peer-reviewed articles published within the past two decades. Relevant studies were identified through scientific databases, focusing on evidence linking climate variability, ecosystem shifts, and zoonotic transmission dynamics.

Findings indicate that climate-induced changes—such as rising temperatures, altered precipitation patterns, and habitat disruption—affect vector ecology and wildlife migration, facilitating conditions for pathogen spillover. However, existing research remains fragmented, with limited longitudinal analyses and region-specific data to quantify these associations.

The interconnectedness of human health, environmental health, and biodiversity underscores the need for a holistic One Health approach. Strengthening interdisciplinary collaboration and integrating climate resilience into public health strategies are vital to addressing the multifaceted challenges posed by climate change and emerging pandemics.

Postpartum hemorrhage (PPH) remains a significant contributor to maternal mortality and morbidity worldwide, with approximately 14 million women affected annually and 70,000 resulting deaths. Despite advances in health care, PPH continues to pose challenges even in developed settings. Apart from mortality, PPH leads to various adverse outcomes and morbidity. Recently, there has been a surge in interest in using artificial intelligence (AI), including machine learning and deep learning, across many areas of health care. This article explores the application of AI in tackling PPH, including predictive modeling and risk stratification. Some studies have shown promising results in predicting PPH. However, external validation of these models is crucial and frequently lacking, with barriers including differences in cohort characteristics and variations in outcome measurement methods. Most of the existing research has taken place in well-resourced health care settings, and there is a lack of models applicable to resource-limited settings where the need is arguably greatest. Incorporating uterine contractility metrics and radiomics into predictive models offers new avenues for enhancing prediction accuracy. Beyond risk prediction, AI has also been explored in other aspects of PPH management, including blood product management and early detection using wearable devices. In conclusion, while AI presents exciting opportunities for PPH prediction and management, challenges such as model validation, clinical translation, and applicability in diverse health care settings remain. Further research, particularly in low-and middle-income countries, is necessary to realize the full potential of AI for addressing the global burden of PPH.

Purpose: Metabolic diseases such as diabetes mellitus may play a role in the development and progression of prostate cancer (PC); however, this association remains to be explored in the context of specific PC stages. The objective of this study was to systematically review the evidence for an association between diabetes and overall, early, or advanced PC risk. Materials and Methods: A systematic review with meta-analysis was performed (MEDLINE, EMBASE, and CINAHL) from inception until September 2023. Cohort and case-control studies that assessed PC risk in adult males (≥18 years) associated with type 2 diabetes mellitus or diabetes (if there was no distinction between diabetes type) were included. The Newcastle-Ottawa Scale (NOS) was used to assess study bias; those with NOS<7 were excluded. Evidence certainty was assessed with the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) method. Results: Thirty-four studies (n=26 cohorts and n=8 case-controls) were included. Of these, 32 assessed diabetes and all PC stages combined, 12 included early PC stages, and 15 included advanced PC stages. Our meta-analysis showed diabetes had a protective effect against early PC development (n=11, risk ratio [RR]=0.71; 95% confidence interval [CI]=0.61–0.83, I2=84%) but no association was found for combined (n=21, RR=0.95; 95% CI=0.79–1.13, I2=99%) or advanced PC stages (n=15, RR=0.96; 95% CI=0.77–1.18, I2=98%) at diagnosis. According to GRADE, the evidence certainty was very low. Conclusions Diabetes may be protective against early PC stages, yet evidence linking diabetes to risk across all stages, and advanced PC specifically, is less conclusive. High heterogeneity may partially explain discrepancy in findings and was mostly associated with study design, method used for PC diagnosis, and risk measures. Our results may aid risk stratification of males with diabetes and inform new approaches for PC screening in this group, especially considering the reduced sensitivity of prostate-specific antigen values for those with diabetes.

The International Severe Asthma Registry (ISAR) was established in 2017 to advance the understanding of severe asthma and its management, thereby improving patient care worldwide. As the first global registry for adults with severe asthma, ISAR enabled individual registries to standardize and pool their data, creating a comprehensive, harmonized dataset with sufficient statistical power to address key research questions and knowledge gaps. Today, ISAR is the largest repository of real-world data on severe asthma, curating data on nearly 35,000 patients from 28 countries worldwide, and has become a leading contributor to severe asthma research. Research using ISAR data has provided valuable insights on the characteristics of severe asthma, its burdens and risk factors, real-world treatment effectiveness, and barriers to specialist care, which are collectively informing improved asthma management. Besides changing clinical thinking via research, ISAR aims to advance real-world practice through initiatives that improve registry data quality and severe asthma care. In 2024, ISAR refined essential research variables to enhance data quality and launched a web-based data acquisition and reporting system (QISAR), which integrates data collection with clinical consultations and enables longitudinal data tracking at patient, center, and population levels. Quality improvement priorities include collecting standardized data during consultations and tracking and optimizing patient journeys via QISAR and integrating primary/secondary care pathways to expedite specialist severe asthma management and facilitate clinical trial recruitment. ISAR envisions a future in which timely specialist referral and initiation of biologic therapy can obviate long-term systemic corticosteroid use and enable more patients to achieve remission.