COVID-19 mortality in the Philippines: province-level ecological analysis, 2020–2023

Authors

  • Jimuel Celeste Jr System Modeling and Simulation Laboratory, Department of Computer Science, College of Engineering, University of the Philippines Diliman, Quezon City, Philippines; Center for Informatics, University of San Agustin, Iloilo City, Philippines https://orcid.org/0000-0003-0224-4124
  • Jesus Emmanuel Sevilleja Mental Health Research Unit, Office for Special Concerns, National Center for Mental Health, Mandaluyong City, Philippines; Hospital Epidemiology Surveillance Unit, Public Health Unit, National Center for Mental Health, Mandaluyong City, Philippines https://orcid.org/0000-0002-4980-2173
  • Vena Pearl Bongolan System Modeling and Simulation Laboratory, Department of Computer Science, College of Engineering, University of the Philippines Diliman, Quezon City, Philippines https://orcid.org/0000-0001-9056-8529
  • Roselle Leah Rivera Department of Women and Development Studies, College of Social Work and Community Development, University of the Philippines Diliman, Quezon City, Philippines https://orcid.org/0000-0001-8277-3661
  • Salvador Eugenio Caoili Biomedical Innovations Research for Translational Health Science (BIRTHS) Laboratory, Department of Biochemistry and Molecular Biology, College of Medicine, University of the Philippines Manila, Manila, Philippines https://orcid.org/0000-0002-3210-3763
  • Romulo de Castro Center for Informatics, University of San Agustin, Iloilo City, Philippines; Health Informatics Program, Institute of Health Sciences and Nursing, Far Eastern University, Manila, Philippines https://orcid.org/0000-0001-9300-258X

DOI:

https://doi.org/10.5365/wpsar.2026.17.1.1128

Keywords:

COVID-19, age-standardized mortality rates, streetlight effect, Philippines

Abstract

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.

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Published

25-03-2026

How to Cite

1.
Celeste Jr J, Sevilleja JE, Bongolan VP, Rivera RL, Caoili SE, de Castro R. COVID-19 mortality in the Philippines: province-level ecological analysis, 2020–2023. Western Pac Surveill Response J [Internet]. 2026 Mar. 25 [cited 2026 Mar. 28];17(1). Available from: https://ojs.wpro.who.int/ojs/index.php/wpsar/article/view/1128

Issue

Section

Original Research