Objective: To investigate COVID-19 mortality in Philippine provinces from 2020 to 2023. Methods: Crude mortality rate (CMR), age-standardized mortality rate (ASMR) and age-specific mortality rate were computed for 84 areas (82 provinces and 2 cities) using COVID-19 surveillance data from the Philippine Department of Health, which captured data about confirmed deaths occurring between 20 January 2020 and 9 May 2023. Provinces were ranked by their ASMR. A correlation analysis was conducted to identify possible predictors of COVID-19 mortality. Among the factors investigated were the incidence of poverty, population density, proportion of the population considered elderly (aged >=65 years), hospital bed density and COVID-19 testing rates. Results: Eight of the 10 provinces that had the highest COVID-19 ASMRs were located in the Luzon island group. The province with the highest ASMR was Benguet in Northern Luzon (207.83 deaths/100 000 population), and the lowest rate was in Tawi-Tawi in Southwestern Mindanao (2.22 deaths/100 000 population). The incidence of poverty was negatively correlated with COVID-19 mortality, while hospital bed density and COVID-19 testing rates were positively correlated with CMRs and ASMRs. Discussion: This analysis provides a starting point for investigating COVID-19 mortality in Philippine provinces. The ranking of provinces by their ASMR is useful for directing future epidemiological investigations and, coupled with the results of the correlation analysis, provides insight into the factors that may have impacted COVID-19 mortality in the Philippines. Our analysis suggests that COVID-19 mortality patterns can partly be explained by the streetlight effect and factors linked to the availability of and access to health care.

Background: Among the different criteria, the General Weighted Average Grade (PMGWAG) holds the biggest bearing on admission for the UP College of Medicine. However, GWAs are not comparable across different courses, different batches, different UP units and different schools. An Adjustment Factor is necessary to make PMGWAGs comparable and to level the playing field. Objectives: This study covering a 24-year period aimed to compare various proposed Admission Adjusted Factors of %PMGWAG (Pre-Med GWAG) in terms of Pearson's Correlation, Linear Regression Models and Mean Differences with %MGWAG (Medical GWAG), Class Rank and Board Rating as Outcome variables. Methodology: Various proposed Adjustment Factors were applied to %PMGWAG of medical students from Class 1990 to Class 2014 and Pearson's Correlation, Linear Regression Models and Mean Differences with %MGWAG, Class Rank and Board Rating were derived and analyzed. Results: Adjustment Factor A3 as applied to %PMGWAG correlates best with Board Rating and Class Rank while Adjustment Factor A6 with %MGWAG. On Linear Regression, A3 likewise bested other Adjustment Factors in predicting %MGWAG and %Board Rating while A6 on predicting Class Ranking. Among the various adjustments, A3 exerted the most impact on the outcome variables, based on mean differences. Conclusion The A3 Adjustment Factor is the preferred and most ideal among the various proposed adjustment factors. Its application on %PMGWAG, correlated best with, most predictive of and most influential to %MGWAG, Board Rating and Class Rating.
Education, Medical ; Academic Performance