tetano
Editor, Senior Moderator
Western Pac Surveill Response J
. 2026 Mar 25;17(1):1-12.
doi: 10.5365/wpsar.2026.17.1.1128. eCollection 2026 Jan-Mar.
COVID-19 mortality in the Philippines: province-level ecological analysis, 2020-2023
Jimuel Celeste Jr[SUP] 1 2 [/SUP], Jesus Emmanuel Sevilleja[SUP] 3 4 [/SUP], Vena Pearl Bongolan[SUP] 1 [/SUP], Roselle Leah Rivera[SUP] 5 [/SUP], Salvador Eugenio Caoili[SUP] 6 [/SUP], Romulo de Castro[SUP] 2 7 [/SUP]
Affiliations
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.
. 2026 Mar 25;17(1):1-12.
doi: 10.5365/wpsar.2026.17.1.1128. eCollection 2026 Jan-Mar.
COVID-19 mortality in the Philippines: province-level ecological analysis, 2020-2023
Jimuel Celeste Jr[SUP] 1 2 [/SUP], Jesus Emmanuel Sevilleja[SUP] 3 4 [/SUP], Vena Pearl Bongolan[SUP] 1 [/SUP], Roselle Leah Rivera[SUP] 5 [/SUP], Salvador Eugenio Caoili[SUP] 6 [/SUP], Romulo de Castro[SUP] 2 7 [/SUP]
Affiliations
- PMID: 41959695
- PMCID: PMC13056467
- DOI: 10.5365/wpsar.2026.17.1.1128
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.