To estimate county of residence of Filipinx healthcare workers who died of COVID-19, we retrieved data from the Kanlungan website during the month of December 2020.22 In deciding who to include on the website, the AF3IRM team that established the Kanlungan website set two standards in data collection. First, the team found at least one source explicitly stating that the fallen healthcare worker was of Philippine ancestry; this was mostly media articles or obituaries sharing the life stories of the deceased. In a few cases, the confirmation came directly from the deceased healthcare worker's family member who submitted a tribute. Second, the team required a minimum of two sources to identify and announce fallen healthcare workers. We retrieved 86 US tributes from Kanlungan, but only 81 of them had information on county of residence. In total, 45 US counties with at least one reported tribute to a Filipinx healthcare worker who died of COVID-19 were identified for analysis and will hereafter be referred to as “Kanlungan counties.” Mortality data by county, race, and ethnicity came from the National Center for Health Statistics (NCHS).24 Updated weekly, this dataset is based on vital statistics data for use in conducting public health surveillance in near real time to provide provisional mortality estimates based on data received and processed by a specified cutoff date, before data are finalized and publicly released.25 We used the data released on December 30, 2020, which included provisional COVID-19 death counts from February 1, 2020 to December 26, 2020—during the height of the pandemic and prior to COVID-19 vaccines being available—for counties with at least 100 total COVID-19 deaths. During this time period, 501 counties (15.9% of the total 3,142 counties in all 50 states and Washington DC)26 met this criterion. Data on COVID-19 deaths were available for six major racial/ethnic groups: Non-Hispanic White, Non-Hispanic Black, Non-Hispanic Native Hawaiian or Other Pacific Islander, Non-Hispanic American Indian or Alaska Native, Non-Hispanic Asian (hereafter referred to as Asian American), and Hispanic. People with more than one race, and those with unknown race were included in the “Other” category. NCHS suppressed county-level data by race and ethnicity if death counts are less than 10. In total, 133 US counties reported COVID-19 mortality data for Asian Americans. These data were used to calculate the percentage of all COVID-19 decedents in the county who were Asian American. We used data from the 2018 American Community Survey (ACS) five-year estimates, downloaded from the Integrated Public Use Microdata Series (IPUMS) to create county-level population demographic variables.27 IPUMS is publicly available, and the database integrates samples using ACS data from 2000 to the present using a high degree of precision.27 We applied survey weights to calculate the following variables at the county-level: median age among Asian Americans, average income to poverty ratio among Asian Americans, the percentage of the county population that is Filipinx, and the percentage of healthcare workers in the county who are Filipinx. Healthcare workers encompassed all healthcare practitioners, technical occupations, and healthcare service occupations, including nurse practitioners, physicians, surgeons, dentists, physical therapists, home health aides, personal care aides, and other medical technicians and healthcare support workers. County-level data were available for 107 out of the 133 counties (80.5%) that had NCHS data on the distribution of COVID-19 deaths among Asian Americans, and 96 counties (72.2%) with Asian American healthcare workforce data. The ACS 2018 five-year estimates were also the source of county-level percentage of the Asian American population (alone or in combination) who are Filipinx.8 In addition, the ACS provided county-level population counts26 to calculate population density (people per 1,000 people per square mile), estimated by dividing the total population by the county area, then dividing by 1,000 people. The county area was calculated in ArcGIS 10.7.1 using the county boundary shapefile and projected to Albers equal area conic (for counties in the US contiguous states), Hawai’i Albers Equal Area Conic (for Hawai’i counties), and Alaska Albers Equal Area Conic (for Alaska counties).20

Philippines PT: Volume: Regional Checkless data was reported at 0.000 Number in Mar 2020. This stayed constant from the previous number of 0.000 Number for Feb 2020. Philippines PT: Volume: Regional Checkless data is updated monthly, averaging 0.000 Number from Jan 2018 (Median) to Mar 2020, with 27 observations. The data reached an all-time high of 3.000 Number in Dec 2018 and a record low of 0.000 Number in Mar 2020. Philippines PT: Volume: Regional Checkless data remains active status in CEIC and is reported by Bangko Sentral ng Pilipinas. The data is categorized under Global Database’s Philippines – Table PH.KA010: Payment System Statistics. [COVID-19-IMPACT]

This comprehensive synthesis of severe COVID-19 risk factors specific to the Asia-Pacific (APAC) region addresses gaps in previous global studies, which often overlook regional demographic, epidemiological, and healthcare system variations. Three databases (PubMed, Ovid MedLine, Scopus) and two preprint platforms (BioRxiv, MedRxiv) were searched between December 1, 2019, and March 31, 2023. English-language publications from 11 APAC countries/regions (Australia, Hong Kong, Japan, Macau, New Zealand, Philippines, Singapore, South Korea, Taiwan, Thailand and Vietnam) reporting conditions associated with severe COVID-19 outcomes in adults (aged ≥16 years) were included. Of 295 publications screened, 123 met inclusion criteria, mostly from South Korea (n = 68) and Japan (n = 23). Common risk factors included older age, male sex, obesity, diabetes, heart failure, renal disease, and dementia. Less commonly hypertension, chronic obstructive pulmonary disease, cardio-and cerebrovascular disease, immunocompromise, autoimmune disorders, and mental illness were reported. To date, no prior region-specific synthesis of risk factors for severe COVID-19 outcomes across the APAC region has been identified. The findings support the development of tailored vaccination strategies and public health interventions at both national and regional levels, helping ensure high-risk populations are prioritized in ongoing COVID-19 prevention and management efforts.

Philippines PT: Value: Regional Checkless data was reported at 0.000 PHP mn in Mar 2020. This stayed constant from the previous number of 0.000 PHP mn for Feb 2020. Philippines PT: Value: Regional Checkless data is updated monthly, averaging 0.000 PHP mn from Jan 2019 (Median) to Mar 2020, with 15 observations. The data reached an all-time high of 0.071 PHP mn in Jul 2019 and a record low of 0.000 PHP mn in Mar 2020. Philippines PT: Value: Regional Checkless data remains active status in CEIC and is reported by Bangko Sentral ng Pilipinas. The data is categorized under Global Database’s Philippines – Table PH.KA010: Payment System Statistics. [COVID-19-IMPACT]

Philippines Unemployment: Male: Age: 15 Years & Over data was reported at 1,290.000 Person th in Jan 2025. This records an increase from the previous number of 1,152.000 Person th for Oct 2024. Philippines Unemployment: Male: Age: 15 Years & Over data is updated quarterly, averaging 1,707.000 Person th from Jul 2004 (Median) to Jan 2025, with 83 observations. The data reached an all-time high of 4,841.000 Person th in Apr 2020 and a record low of 1,152.000 Person th in Oct 2024. Philippines Unemployment: Male: Age: 15 Years & Over data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.G033: Labour Force Survey: Unemployment: by Region, Age and Class: Quarterly. [COVID-19-IMPACT]

Philippines Unemployment: Female: Age: 15 Years & Over data was reported at 875.000 Person th in Jan 2025. This records an increase from the previous number of 814.000 Person th for Oct 2024. Philippines Unemployment: Female: Age: 15 Years & Over data is updated quarterly, averaging 1,019.000 Person th from Jul 2004 (Median) to Jan 2025, with 83 observations. The data reached an all-time high of 2,387.000 Person th in Apr 2020 and a record low of 735.000 Person th in Oct 2016. Philippines Unemployment: Female: Age: 15 Years & Over data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.G033: Labour Force Survey: Unemployment: by Region, Age and Class: Quarterly. [COVID-19-IMPACT]

Philippines PT: Value: Bank's (Regional) Withdrawals (IRIS) data was reported at 264,444.973 PHP mn in Mar 2020. This records an increase from the previous number of 125,888.929 PHP mn for Feb 2020. Philippines PT: Value: Bank's (Regional) Withdrawals (IRIS) data is updated monthly, averaging 69,194.433 PHP mn from Jan 2019 (Median) to Mar 2020, with 15 observations. The data reached an all-time high of 264,444.973 PHP mn in Mar 2020 and a record low of 0.038 PHP mn in Jun 2019. Philippines PT: Value: Bank's (Regional) Withdrawals (IRIS) data remains active status in CEIC and is reported by Bangko Sentral ng Pilipinas. The data is categorized under Global Database’s Philippines – Table PH.KA010: Payment System Statistics. [COVID-19-IMPACT]

Vietnam (FDI) Foreign Direct Investment: Year to Date: Registered Cap: Addtl: Philippines data was reported at 0.030 USD mn in Dec 2018. This stayed constant from the previous number of 0.030 USD mn for Nov 2018. Vietnam (FDI) Foreign Direct Investment: Year to Date: Registered Cap: Addtl: Philippines data is updated monthly, averaging 4.000 USD mn from Nov 2013 (Median) to Dec 2018, with 16 observations. The data reached an all-time high of 9.070 USD mn in Dec 2014 and a record low of 0.030 USD mn in Dec 2018. Vietnam (FDI) Foreign Direct Investment: Year to Date: Registered Cap: Addtl: Philippines data remains active status in CEIC and is reported by Foreign Investment Agency. The data is categorized under Global Database’s Vietnam – Table VN.O014: Foreign Direct Investment: Inflow: ytd: By Country or Region. [COVID-19-IMPACT]

While COVID-19 spreads aggressively and rapidly across the globe, many societies have also witnessed the spread of other viral phenomena like misinformation, conspiracy theories, and general mass suspicions about what is really going on. This study investigates how exposure to and trust in information sources, and anxiety and depression, are associated with conspiracy and misinformation beliefs in eight countries/regions (Belgium, Canada, England, Philippines, Hong Kong, New Zealand, United States, Switzerland) during the COVID-19 pandemic. Data were collected in an online survey fielded from May 29, 2020 to June 12, 2020, resulting in a multinational representative sample of 8,806 adult respondents. Results indicate that greater exposure to traditional media (television, radio, newspapers) is associated with lower conspiracy and misinformation beliefs, while exposure to politicians and digital media and personal contacts are associated with greater conspiracy and misinformation beliefs. Exposure to health experts is associated with lower conspiracy beliefs only. Higher feelings of depression are also associated with greater conspiracy and misinformation beliefs. We also found relevant group- and country differences. We discuss the implications of these results.

Vietnam (FDI) Foreign Direct Investment: Year to Date: Registered Cap: Total: Philippines data was reported at 4.119 USD mn in Apr 2020. This records an increase from the previous number of 3.676 USD mn for Mar 2020. Vietnam (FDI) Foreign Direct Investment: Year to Date: Registered Cap: Total: Philippines data is updated monthly, averaging 5.450 USD mn from Nov 2013 (Median) to Apr 2020, with 65 observations. The data reached an all-time high of 54.675 USD mn in Dec 2016 and a record low of 0.009 USD mn in Jan 2019. Vietnam (FDI) Foreign Direct Investment: Year to Date: Registered Cap: Total: Philippines data remains active status in CEIC and is reported by Foreign Investment Agency. The data is categorized under Global Database’s Vietnam – Table VN.O014: Foreign Direct Investment: Inflow: ytd: By Country or Region. [COVID-19-IMPACT]

Philippines PT: Volume: Bank's (Regional) Withdrawals data was reported at 2,294.000 Number in Mar 2020. This records an increase from the previous number of 1,409.000 Number for Feb 2020. Philippines PT: Volume: Bank's (Regional) Withdrawals data is updated monthly, averaging 1,153.000 Number from Jan 2014 (Median) to Mar 2020, with 75 observations. The data reached an all-time high of 2,294.000 Number in Mar 2020 and a record low of 354.000 Number in Jan 2015. Philippines PT: Volume: Bank's (Regional) Withdrawals data remains active status in CEIC and is reported by Bangko Sentral ng Pilipinas. The data is categorized under Global Database’s Philippines – Table PH.KA010: Payment System Statistics. [COVID-19-IMPACT]