Using an Online Calculator to Describe Excess Mortality in the Philippines During the COVID-19 Pandemic

Objective Excess mortality is an indicator of the impact of the coronavirus disease (COVID-19) pandemic. This study aims to describe excess mortality in the Philippines from January 2020 to December 2021 using an online all-cause mortality and excess mortality calculator. Methods All-cause mortality datasets from 2015 to 2021 from the Philippine Statistics Authority were obtained and analysed using the World Health Organization Western Pacific Regional Office All-Cause Mortality Calculator. Expected mortality, excess mortality and P-scores were obtained using two models, 5-year averages and negative binomial regression, for total deaths and by administrative region. Results Reported national all-cause mortality exceeded the expected mortality in August 2020 and from January to November 2021, peaking in September 2021 at 104 per 100 000. Total excess mortality using negative binomial regression was -13 900 deaths in 2020 and 212 000 deaths in 2021, peaking in September 2021. P-scores were -2% in 2020 and 33% in 2021, again peaking in September 2021 at 114%. Reported COVID-19 deaths accounted for 20% of excess deaths in 2021. In 2020, consistently high P-scores were recorded in the National Capital Region from July to September and in the Bangsamoro Autonomous Region in Muslim Mindanao from June to July. In 2021, most regions recorded high P-scores from June to October. Discussion Tracking excess mortality using a robust, accessible and standardized online tool provided a comprehensive assessment of the direct and indirect impacts of the COVID-19 pandemic in the Philippines. Furthermore, analysis by administrative region highlighted the key regions disproportionately affected by the pandemic, information that may not have been fully captured from routine COVID-19 surveillance

Factors affecting vaccine hesitancy among families with children 2 years old and younger in two urban communities in Manila, Philippines

Objective: The study aimed to determine the factors that influence vaccine hesitancy among parents and caregivers ofchildren 2 years old and younger in selected urban communities in Manila, Philippines. Methodology: The study used a cross-sectional study design with a modified questionnaire adapted from the SAGEWorking Group on Vaccine Hesitancy. Self-administered surveys were conducted in two highly urbanized barangays(smallest administrative divisions) in Manila, Philippines. Results: The survey was completed by 110 respondents, comprised mostly of 20–39-year-old mothers. Most respondents(95.5%) believed that vaccines are protective however vaccine hesitancy rates among the respondents reached 36.4%.Respondents who believed in the protective nature of vaccines were less likely to report vaccine hesitancy and were ninetimes less likely to refuse vaccination for their children because of negative media exposure. The main reasons identifiedfor vaccine hesitancy were exposure to negative media information and concerns about vaccine safety. The main negativemedia information identified by the respondents was related to the dengue vaccine, Dengvaxia®. Health-care workers andpolitical leaders were the main supporters of vaccination in the community. Discussion: The recent events surrounding the Dengvaxia® controversy contributed to a decrease in vaccine confidence.The role of mass media in vaccine hesitancy was highlighted in this study, supporting previous evidence that vaccinehesitantparents tend to be more susceptible to media reports. The lack of association between sociodemographic factorsand vaccine hesitancy implies that the determinants of vaccine hesitancy can be highly varied depending on context andsetting

Using Machine Learning To Create a Decision Tree Model To Predict Outcomes of COVID-19 Cases in the Philippines

Objective: The aim of this study was to create a decision tree model with machine learning to predict the outcomes of COVID-19 cases from data publicly available in the Philippine Department of Health (DOH) COVID Data Drop. Methods: The study design was a cross-sectional records review of the DOH COVID Data Drop for 25 August 2020. Resolved cases that had either recovered or died were used as the final data set. Machine learning processes were used to generate, train and validate a decision tree model. Results: A list of 132 939 resolved COVID-19 cases was used. The notification rates and case fatality rates were higher among males (145.67 per 100 000 and 2.46%, respectively). Most COVID-19 cases were clustered among people of working age, and older cases had higher case fatality rates. The majority of cases were from the National Capital Region (590.20 per 100 000), and the highest case fatality rate (5.83%) was observed in Region VII. The decision tree model prioritized age and history of hospital admission as predictors of mortality. The model had high accuracy (81.42%), sensitivity (81.65%), specificity (81.41%) and area under the curve (0.876) but a poor F-score (16.74%). Discussion: The model predicted higher case fatality rates among older people. For cases aged \u3e51 years, a history of hospital admission increased the probability of COVID-19-related death. We recommend that more comprehensive primary COVID-19 data sets be used to create more robust prognostic models

BP-Taking Competency and its Association with Professional Factors Among Barangay Health Workers in Pasig City Using a Self-Developed OSCE Tool

Background Barangay health workers (BHWs) are crucial in the implementation of the Primary Health Care Approach; and regular assessment and improvement of their skills in taking blood pressures (BP) provide for more reliable and inclusive health monitoring in Philippine communities. Objective The study aimed to determine the association between professional factors (i.e.; past experience and training; compensation and incentives; and feedback and praise) and the level of competency in BP-taking of BHWs in selected barangays in Pasig City using a self-developed Objective Structured Clinical Examination (OSCE) tool. Methodology An OSCE tool to measure BP-taking competency was created and validated. The level of competency in BP-taking of BHWs was determined by direct observation using the developed OSCE tool. The association of professional factors and OSCE scores were analyzed using a stepwise linear regression model. Results The developed OSCE tool showed good content validity indexes (I-CVI\u3e0.70; S-CVI\u3e0.70) and high scale percentage agreement (0.930). A total of 97 BHWs participated in the study; and their average OSCE score was 17.567 out of 32 (54.897%). Stepwise linear regression showed significant positive association (p Conclusion Previous literature validates the results that experience influences skills; albeit marginal in this study. Positive attitudes toward monetary compensation appear to be a stronger predictor of higher OSCE scores as compared to actual salary and even nonsalaryincentives; suggesting an altruistic component in BHW performance of duties. We recommend that further studies investigate nonsalary factors that affect BHW performance