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 data sets 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.

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 > 51 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.

Work Preferences in Rural Health Job Posting Among Medical Interns in a Lower Middle-Income Country-a Discrete Choice Experiment.

Introduction: Timely empirical evidence is important in the success of health systems, and such evidence is necessary for informed policy making to address inequity in the health workforce. Literature is ripe with incentives that affect recruitment and retention of physicians in rural and remote areas, but such data in still lacking in the Philippine setting. Discrete choice experiment is one methodology utilized by the World Health Organization which provides both qualitative and quantitative information to aid policy makers in health human resource management. Methods: The study utilized a discrete choice experiment involving three phases: 1) identification of incentives and levels using key informant interviews and focus group discussions, 2) selection of scenarios utilizing an experimental design, and 3) administration of survey based on WHO guidelines. Conditional logistic regression, point estimates, and correlational analyses were done using Stata. Results: There is significant association between type of background and considerations for rural practice among the respondents based on Pearson's correlation (p < 0.01). The respondents put more value into non-wage rural job posting incentives than small to modest base salary increases. The high willingness to pay for the presence of supervision, relative location of work areas from families, and status of workplace infrastructure/equipment or supplies suggest the importance of workplace conditions to attract rural health physicians. Combinations of wage and non-wage incentives may be necessary to provide for the most cost-efficient increases in rural job post uptake rates based on post-estimate calculations. Conclusion: Philippine medical interns and young doctors value non-wage incentives in considering rural health job postings. Rural health job postings with these incentives are predicted to significantly increase recruitment in rural health job posts, particularly when combinations of wage and high-impact non-wage incentives are considered.