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• Volume 10, Issue 11
• The Philippine COVID-19 Outcomes: a Retrospective study Of Neurological manifestations and Associated symptoms (The Philippine CORONA study): a protocol study
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• http://orcid.org/0000-0001-5621-1833 Adrian I Espiritu 1 , 2 ,
• http://orcid.org/0000-0003-1135-6400 Marie Charmaine C Sy 1 ,
• http://orcid.org/0000-0002-1241-8805 Veeda Michelle M Anlacan 1 ,
• http://orcid.org/0000-0001-5317-7369 Roland Dominic G Jamora 1
• 1 Department of Neurosciences , College of Medicine and Philippine General Hospital, University of the Philippines Manila , Manila , Philippines
• 2 Department of Clinical Epidemiology, College of Medicine , University of the Philippines Manila , Manila , Philippines
• Correspondence to Dr Adrian I Espiritu; aiespiritu{at}up.edu.ph

Introduction The SARS-CoV-2, virus that caused the COVID-19 global pandemic, possesses a neuroinvasive potential. Patients with COVID-19 infection present with neurological signs and symptoms aside from the usual respiratory affectation. Moreover, COVID-19 is associated with several neurological diseases and complications, which may eventually affect clinical outcomes.

Objectives The Philippine COVID-19 Outcomes: a Retrospective study Of Neurological manifestations and Associated symptoms (The Philippine CORONA) study investigators will conduct a nationwide, multicentre study involving 37 institutions that aims to determine the neurological manifestations and factors associated with clinical outcomes in COVID-19 infection.

Methodology and analysis This is a retrospective cohort study (comparative between patients with and without neurological manifestations) via medical chart review involving adult patients with COVID-19 infection. Sample size was determined at 1342 patients. Demographic, clinical and neurological profiles will be obtained and summarised using descriptive statistics. Student’s t-test for two independent samples and χ 2 test will be used to determine differences between distributions. HRs and 95% CI will be used as an outcome measure. Kaplan-Meier curves will be constructed to plot the time to onset of mortality (survival), respiratory failure, intensive care unit (ICU) admission, duration of ventilator dependence, length of ICU stay and length of hospital stay. The log-rank test will be employed to compare the Kaplan-Meier curves. Stratified analysis will be performed to identify confounders and effects modifiers. To compute for adjusted HR with 95% CI, crude HR of outcomes will be adjusted according to the prespecified possible confounders. Cox proportional regression models will be used to determine significant factors of outcomes. Testing for goodness of fit will also be done using Hosmer-Lemeshow test. Subgroup analysis will be performed for proven prespecified effect modifiers. The effects of missing data and outliers will also be evaluated in this study.

Ethics and dissemination This protocol was approved by the Single Joint Research Ethics Board of the Philippine Department of Health (SJREB-2020–24) and the institutional review board of the different study sites. The dissemination of results will be conducted through scientific/medical conferences and through journal publication. The lay versions of the results may be provided on request.

Trial registration number NCT04386083 .

• adult neurology
• epidemiology

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/bmjopen-2020-040944

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Strengths and limitations of this study

The Philippine COVID-19 Outcomes: a Retrospective study Of Neurological manifestations and Associated symptoms Study is a nationwide, multicentre, retrospective, cohort study with 37 Philippine sites.

Full spectrum of neurological manifestations of COVID-19 will be collected.

Retrospective gathering of data offers virtually no risk of COVID-19 infection to data collectors.

Data from COVID-19 patients who did not go to the hospital are unobtainable.

Recoding bias is inherent due to the retrospective nature of the study.

Introduction

The COVID-19 has been identified as the cause of an outbreak of respiratory illness in Wuhan, Hubei Province, China, in December 2019. 1 The COVID-19 pandemic has reached the Philippines with most of its cases found in the National Capital Region (NCR). 2 The major clinical features of COVID-19 include fever, cough, shortness of breath, myalgia, headache and diarrhoea. 3 The outcomes of this disease lead to prolonged hospital stay, intensive care unit (ICU) admission, dependence on invasive mechanical ventilation, respiratory failure and mortality. 4 The specific pathogen that causes this clinical syndrome has been named SARS-CoV-2, which is phylogenetically similar to SARS-CoV. 4 Like the SARS-CoV strain, SARS-CoV-2 may possess a similar neuroinvasive potential. 5

A study on cases with COVID-19 found that about 36.4% of patients displayed neurological manifestations of the central nervous system (CNS) and peripheral nervous system (PNS). 6 The associated spectrum of symptoms and signs were substantially broad such as altered mental status, headache, cognitive impairment, agitation, dysexecutive syndrome, seizures, corticospinal tract signs, dysgeusia, extraocular movement abnormalities and myalgia. 7–12 Several reports were published on neurological disorders associated with patients with COVID-19, including cerebrovascular disorders, encephalopathy, hypoxic brain injury, frequent convulsive seizures and inflammatory CNS syndromes like encephalitis, meningitis, acute disseminated encephalomyelitis and Guillain-Barre syndrome. 7–16 However, the estimates of the occurrences of these manifestations were based on studies with a relatively small sample size. Furthermore, the current description of COVID-19 neurological features are hampered to some extent by exceedingly variable reporting; thus, defining causality between this infection and certain neurological manifestations is crucial since this may lead to considerable complications. 17 An Italian observational study protocol on neurological manifestations has also been published to further document and corroborate these findings. 18

Epidemiological data on the proportions and spectrum of non-respiratory symptoms and complications may be essential to increase the recognition of clinicians of the possibility of COVID-19 infection in the presence of other symptoms, particularly neurological manifestations. With this information, the probabilities of diagnosing COVID-19 disease may be strengthened depending on the presence of certain neurological manifestations. Furthermore, knowledge of other unrecognised symptoms and complications may allow early diagnosis that may permit early institution of personal protective equipment and proper contact precautions. Lastly, the presence of neurological manifestations may be used for estimating the risk of certain important clinical outcomes for better and well-informed clinical decisions in patients with COVID-19 disease.

To address this lack of important information in the overall management of patients with COVID-19, we organised a research study entitled ‘The Philippine COVID-19 Outcomes: a Retrospective study Of Neurological manifestations and Associated symptoms (The Philippine CORONA Study)’.

This quantitative, retrospective cohort, multicentre study aims: (1) to determine the demographic, clinical and neurological profile of patients with COVID-19 disease in the Philippines; (2) to determine the frequency of neurological symptoms and new-onset neurological disorders/complications in patients with COVID-19 disease; (3) to determine the neurological manifestations that are significant factors of mortality, respiratory failure, duration of ventilator dependence, ICU admission, length of ICU stay and length of hospital stay among patients with COVID-19 disease; (4) to determine if there is significant difference between COVID-19 patients with neurological manifestations compared with those COVID-19 patients without neurological manifestations in terms of mortality, respiratory failure, duration of ventilator dependence, ICU admission, length of ICU stay and length of hospital stay; and (5) to determine the likelihood of mortality, respiratory failure and ICU admission, including the likelihood of longer duration of ventilator dependence and length of ICU and hospital stay in COVID-19 patients with neurological manifestations compared with those without neurological manifestations.

Scope, limitations and delimitations

The study will include confirmed cases of COVID-19 from the 37 participating institutions in the Philippines. Every country has its own healthcare system, whose level of development and strategies ultimately affect patient outcomes. Thus, the results of this study cannot be accurately generalised to other settings. In addition, patients with ages ≤18 years will be excluded in from this study. These younger patients may have different characteristics and outcomes; therefore, yielded estimates for adults in this study may not be applicable to this population subgroup. Moreover, this study will collect data from the patient records of patients with COVID-19; thus, data from patients with mild symptoms who did not go to the hospital and those who had spontaneous resolution of symptoms despite true infection with COVID-19 are unobtainable.

Methodology

To improve the quality of reporting of this study, the guidelines issued by the Strengthening the Reporting of Observational Studies in Epidemiology Initiative will be followed. 19

Study design

The study will be conducted using a retrospective cohort (comparative) design (see figure 1 ).

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Schematic diagram of the study flow.

Study sites and duration

We will conduct a nationwide, multicentre study involving 37 institutions in the Philippines (see figure 2 ). Most of these study sites can be found in the NCR, which remains to be the epicentre of the COVID-19 pandemic. 2 We will collect data for 6 months after institutional review board approval for every site.

Location of 37 study sites of the Philippine CORONA study.

Patient selection and cohort description

The cases will be identified using the designated COVID-19 censuses of all the participating centres. A total enumeration of patients with confirmed COVID-19 disease will be done in this study.

The cases identified should satisfy the following inclusion criteria: (A) adult patients at least 19 years of age; (B) cases confirmed by testing approved patient samples (ie, nasal swab, sputum and bronchoalveolar lavage fluid) employing real-time reverse transcription PCR (rRT-PCR) 20 from COVID-19 testing centres accredited by the Department of Health (DOH) of the Philippines, with clinical symptoms and signs attributable to COVID-19 disease (ie, respiratory as well as non-respiratory clinical signs and symptoms) 21 ; and (C) cases with disposition (ie, discharged stable/recovered, home/discharged against medical advice, transferred to other hospital or died) at the end of the study period. Cases with conditions or diseases caused by other organisms (ie, bacteria, other viruses, fungi and so on) or caused by other pathologies unrelated to COVID-19 disease (ie, trauma) will be excluded.

The first cohort will involve patients with confirmed COVID-19 infection who presented with any neurological manifestation/s (ie, symptoms or complications/disorder). The comparator cohort will compose of patients with confirmed COVID-19 infection without neurological manifestation/s.

Sample size calculation

We looked into the mortality outcome measure for the purposes of sample size computation. Following the cohort study of Khaledifar et al , 22 the sample size was calculated using the following parameters: two-sided 95% significance level (1 – α); 80% power (1 – β); unexposed/exposed ratio of 1; 5% of unexposed with outcome (case fatality rate from COVID19-Philippines Dashboard Tracker (PH) 23 as of 8 April 2020); and assumed risk ratio 2 (to see a two-fold increase in risk of mortality when neurological symptoms are present).

When these values were plugged in to the formula for cohort studies, 24 a minimum sample size of 1118 is required. To account for possible incomplete data, the sample was adjusted for 20% more. This means that the total sample size required is 1342 patients, which will be gathered from the participating centres.

Data collection

We formulated an electronic data collection form using Epi Info Software (V.7.2.2.16). The forms will be pilot-tested, and a formal data collection workshop will be conducted to ensure collection accuracy. The data will be obtained from the review of the medical records.

The following pertinent data will be obtained: (A) demographic data; (B) other clinical profile data/comorbidities; (C) neurological history; (D) date of illness onset; (E) respiratory and constitutional symptoms associated with COVID-19; (F) COVID-19 disease severity 25 at nadir; (G) data if neurological manifestation/s were present at onset prior to respiratory symptoms and the specific neurological manifestation/s present at onset; (H) neurological symptoms; (i) date of neurological symptom onset; (J) new-onset neurological disorders or complications; (K) date of new neurological disorder or complication onset; (L) imaging done; (M) cerebrospinal fluid analysis; (N) electrophysiological studies; (O) treatment given; (P) antibiotics given; (Q) neurological interventions given; (R) date of mortality and cause/s of mortality; (S) date of respiratory failure onset, date of mechanical ventilator cessation and cause/s of respiratory failure; (T) date of first day of ICU admission, date of discharge from ICU and indication/s for ICU admission; (U) other neurological outcomes at discharge; (V) date of hospital discharge; and (W) final disposition. See table 1 for the summary of the data to be collected for this study.

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Data to be collected in this study

Main outcomes considered

The following patient outcomes will be considered for this study:

Mortality (binary outcome): defined as the patients with confirmed COVID-19 who died.

Respiratory failure (binary outcome): defined as the patients with confirmed COVID-19 who experienced clinical symptoms and signs of respiratory insufficiency. Clinically, this condition may manifest as tachypnoea/sign of increased work of breathing (ie, respiratory rate of ≥22), abnormal blood gases (ie, hypoxaemia as evidenced by partial pressure of oxygen (PaO 2 ) <60 or hypercapnia by partial pressure of carbon dioxide of >45), or requiring oxygen supplementation (ie, PaO 2 <60 or ratio of PaO 2 /fraction of inspired oxygen (P/F ratio)) <300).

Duration of ventilator dependence (continuous outcome): defined as the number of days from initiation of assisted ventilation to cessation of mechanical ventilator use.

ICU admission (binary outcome): defined as the patients with confirmed COVID-19 admitted to an ICU or ICU-comparable setting.

Length of ICU stay (continuous outcome): defined as the number of days admitted in the ICU or ICU-comparable setting.

Length of hospital stay (continuous outcome): defined as the number of days from admission to discharge.

Data analysis plan

Statistical analysis will be performed using Stata V.7.2.2.16.

Demographic, clinical and neurological profiles will be summarised using descriptive statistics, in which categorical variables will be expressed as frequencies with corresponding percentages, and continuous variables will be pooled using means (SD).

Student’s t-test for two independent samples and χ 2 test will be used to determine differences between distributions.

HRs and 95% CI will be used as an outcome measure. Kaplan-Meier curves will be constructed to plot the time to onset of mortality (survival), respiratory failure, ICU admission, duration of ventilator dependence (recategorised binary form), length of ICU stay (recategorised binary form) and length of hospital stay (recategorised binary form). Log-rank test will be employed to compare the Kaplan-Meier curves. Stratified analysis will be performed to identify confounders and effects modifiers. To compute for adjusted HR with 95% CI, crude HR of outcomes at discrete time points will be adjusted for prespecified possible confounders such as age, history of cardiovascular or cerebrovascular disease, hypertension, diabetes mellitus, and respiratory disease, COVID-19 disease severity at nadir, and other significant confounding factors.

Cox proportional regression models will be used to determine significant factors of outcomes. Testing for goodness of fit will be done using Hosmer-Lemeshow test. Likelihood ratio tests and other information criteria (Akaike Information Criterion or Bayesian Information Criterion) will be used to refine the final model. Statistical significance will be considered if the 95% CI of HR or adjusted HR did not include the number one. A p value <0.05 (two tailed) is set for other analyses.

Subgroup analyses will be performed for proven prespecified effect modifiers. The following variables will be considered for subgroup analyses: age (19–64 years vs ≥65 years), sex, body mass index (<18.5 vs 18.5–22.9 vs ≥23 kg/m 2 ), with history of cardiovascular or cerebrovascular disease (presence or absence), hypertension (presence or absence), diabetes mellitus (presence or absence), respiratory disease (presence or absence), smoking status (smoker or non-smoker) and COVID-19 disease severity (mild, severe or critical disease).

The effects of missing data will be explored. All efforts will be exerted to minimise missing and spurious data. Validity of the submitted electronic data collection will be monitored and reviewed weekly to prevent missing or inaccurate input of data. Multiple imputations will be performed for missing data when possible. To check for robustness of results, analysis done for patients with complete data will be compared with the analysis with the imputed data.

The effects of outliers will also be assessed. Outliers will be assessed by z-score or boxplot. A cut-off of 3 SD from the mean can also be used. To check for robustness of results, analysis done with outliers will be compared with the analysis without the outliers.

Study organisational structure

A steering committee (AIE, MCCS, VMMA and RDGJ) was formed to direct and provide appropriate scientific, technical and methodological assistance to study site investigators and collaborators (see figure 3 ). Central administrative coordination, data management, administrative support, documentation of progress reports, data analyses and interpretation and journal publication are the main responsibilities of the steering committee. Study site investigators and collaborators are responsible for the proper collection and recording of data including the duty to maintain the confidentiality of information and the privacy of all identified patients for all the phases of the research processes.

Organisational structure of oversight of the Philippine CORONA Study.

This section is highlighted as part of the required formatting amendments by the Journal.

Ethics and dissemination

This research will adhere to the Philippine National Ethical Guidelines for Health and Health-related Research 2017. 26 This study is an observational, cohort study and will not allocate any type of intervention. The medical records of the identified patients will be reviewed retrospectively. To protect the privacy of the participant, the data collection forms will not contain any information (ie, names and institutional patient number) that could determine the identity of the patients. A sequential code will be recorded for each patient in the following format: AAA-BBB where AAA will pertain to the three-digit code randomly assigned to each study site; BBB will pertain to the sequential case number assigned by each study site. Each participating centre will designate a password-protected laptop for data collection; the password is known only to the study site.

This protocol was approved by the following institutional review boards: Single Joint Research Ethics Board of the DOH, Philippines (SJREB-2020-24); Asian Hospital and Medical Center, Muntinlupa City (2020- 010-A); Baguio General Hospital and Medical Center (BGHMC), Baguio City (BGHMC-ERC-2020-13); Cagayan Valley Medical Center (CVMC), Tuguegarao City; Capitol Medical Center, Quezon City; Cardinal Santos Medical Center (CSMC), San Juan City (CSMC REC 2020-020); Chong Hua Hospital, Cebu City (IRB 2420–04); De La Salle Medical and Health Sciences Institute (DLSMHSI), Cavite (2020-23-02-A); East Avenue Medical Center (EAMC), Quezon City (EAMC IERB 2020-38); Jose R. Reyes Memorial Medical Center, Manila; Jose B. Lingad Memorial Regional Hospital, San Fernando, Pampanga; Dr. Jose N. Rodriguez Memorial Hospital, Caloocan City; Lung Center of the Philippines (LCP), Quezon City (LCP-CT-010–2020); Manila Doctors Hospital, Manila (MDH IRB 2020-006); Makati Medical Center, Makati City (MMC IRB 2020–054); Manila Medical Center, Manila (MMERC 2020-09); Northern Mindanao Medical Center, Cagayan de Oro City (025-2020); Quirino Memorial Medical Center (QMMC), Quezon City (QMMC REB GCS 2020-28); Ospital ng Makati, Makati City; University of the Philippines – Philippine General Hospital (UP-PGH), Manila (2020-314-01 SJREB); Philippine Heart Center, Quezon City; Research Institute for Tropical Medicine, Muntinlupa City (RITM IRB 2020-16); San Lazaro Hospital, Manila; San Juan De Dios Educational Foundation Inc – Hospital, Pasay City (SJRIB 2020-0006); Southern Isabela Medical Center, Santiago City (2020-03); Southern Philippines Medical Center (SPMC), Davao City (P20062001); St. Luke’s Medical Center, Quezon City (SL-20116); St. Luke’s Medical Center, Bonifacio Global City, Taguig City (SL-20116); Southern Philippines Medical Center, Davao City; The Medical City, Pasig City; University of Santo Tomas Hospital, Manila (UST-REC-2020-04-071-MD); University of the East Ramon Magsaysay Memorial Medical Center, Inc, Quezon City (0835/E/2020/063); Veterans Memorial Medical Center (VMMC), Quezon City (VMMC-2020-025) and Vicente Sotto Memorial Medical Center, Cebu City (VSMMC-REC-O-2020–048).

The dissemination of results will be conducted through scientific/medical conferences and through journal publication. Only the aggregate results of the study shall be disseminated. The lay versions of the results may be provided on request.

Protocol registration and technical review approval

This protocol was registered in the ClinicalTrials.gov website. It has received technical review board approvals from the Department of Neurosciences, Philippine General Hospital and College of Medicine, University of the Philippines Manila, from the Cardinal Santos Medical Center (San Juan City) and from the Research Center for Clinical Epidemiology and Biostatistics, De La Salle Medical and Health Sciences Institute (Dasmariñas, Cavite).

Acknowledgments

We would like to thank Almira Abigail Doreen O Apor, MD, of the Department of Neurosciences, Philippine General Hospital, Philippines, for illustrating figure 2 for this publication.

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VMMA and RDGJ are joint senior authors.

AIE and MCCS are joint first authors.

Twitter @neuroaidz, @JamoraRoland

Collaborators The Philippine CORONA Study Group Collaborators: Maritoni C Abbariao, Joshua Emmanuel E Abejero, Ryndell G Alava, Robert A Barja, Dante P Bornales, Maria Teresa A Cañete, Ma. Alma E Carandang-Concepcion, Joseree-Ann S Catindig, Maria Epifania V Collantes, Evram V Corral, Ma. Lourdes P Corrales-Joson, Romulus Emmanuel H Cruz, Marita B Dantes, Ma. Caridad V Desquitado, Cid Czarina E Diesta, Carissa Paz C Dioquino, Maritzie R Eribal, Romulo U Esagunde, Rosalina B Espiritu-Picar, Valmarie S Estrada, Manolo Kristoffer C Flores, Dan Neftalie A Juangco, Muktader A Kalbi, Annabelle Y Lao-Reyes, Lina C Laxamana, Corina Maria Socorro A Macalintal, Maria Victoria G Manuel, Jennifer Justice F Manzano, Ma. Socorro C Martinez, Generaldo D Maylem, Marc Conrad C Molina, Marietta C Olaivar, Marissa T Ong, Arnold Angelo M Pineda, Joanne B Robles, Artemio A Roxas Jr, Jo Ann R Soliven, Arturo F Surdilla, Noreen Jhoanna C Tangcuangco-Trinidad, Rosalia A Teleg, Jarungchai Anton S Vatanagul and Maricar P Yumul.

Contributors All authors conceived the idea and wrote the initial drafts and revisions of the protocol. All authors made substantial contributions in this protocol for intellectual content.

Funding Philippine Neurological Association (Grant/Award Number: N/A). Expanded Hospital Research Office, Philippine General Hospital (Grant/Award Number: N/A).

Disclaimer Our funding sources had no role in the design of the protocol, and will not be involved during the methodological execution, data analyses and interpretation and decision to submit or to publish the study results.

Map disclaimer The depiction of boundaries on the map(s) in this article does not imply the expression of any opinion whatsoever on the part of BMJ (or any member of its group) concerning the legal status of any country, territory, jurisdiction or area or of its authorities. The map(s) are provided without any warranty of any kind, either express or implied.

Competing interests None declared.

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Patient consent for publication Not required.

Provenance and peer review Not commissioned; externally peer reviewed.

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• Published: 04 April 2022

Economic losses from COVID-19 cases in the Philippines: a dynamic model of health and economic policy trade-offs

• Elvira P. de Lara-Tuprio 1 ,
• Maria Regina Justina E. Estuar 2 ,
• Joselito T. Sescon 3 ,
• Cymon Kayle Lubangco   ORCID: orcid.org/0000-0002-1292-4687 3 ,
• Rolly Czar Joseph T. Castillo 3 ,
• Timothy Robin Y. Teng 1 ,
• Lenard Paulo V. Tamayo 2 ,
• Jay Michael R. Macalalag 4 &
• Gerome M. Vedeja 3  

Humanities and Social Sciences Communications volume  9 , Article number:  111 ( 2022 ) Cite this article

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The COVID-19 pandemic forced governments globally to impose lockdown measures and mobility restrictions to curb the transmission of the virus. As economies slowly reopen, governments face a trade-off between implementing economic recovery and health policy measures to control the spread of the virus and to ensure it will not overwhelm the health system. We developed a mathematical model that measures the economic losses due to the spread of the disease and due to different lockdown policies. This is done by extending the subnational SEIR model to include two differential equations that capture economic losses due to COVID-19 infection and due to the lockdown measures imposed by the Philippine government. We then proceed to assess the trade-off policy space between health and economic measures faced by the Philippine government. The study simulates the cumulative economic losses for 3 months in 8 scenarios across 5 regions in the country, including the National Capital Region (NCR), to capture the trade-off mechanism. These scenarios present the various combinations of either retaining or easing lockdown policies in these regions. Per region, the trade-off policy space was assessed through minimising the 3-month cumulative economic losses subject to the constraint that the average health-care utilisation rate (HCUR) consistently falls below 70%, which is the threshold set by the government before declaring that the health system capacity is at high risk. The study finds that in NCR, a policy trade-off exists where the minimum cumulative economic losses comprise 10.66% of its Gross Regional Domestic Product. Meanwhile, for regions that are non-adjacent to NCR, a policy that hinges on trade-off analysis does not apply. Nevertheless, for all simulated regions, it is recommended to improve and expand the capacity of the health system to broaden the policy space for the government in easing lockdown measures.

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Introduction.

The Philippine population of 110 million comprises a relatively young population. On May 22, 2021, the number of confirmed COVID-19 cases reported in the country is 1,171,403 with 55,531 active cases, 1,096,109 who recovered, and 19,763 who died. As a consequence of the pandemic, the real gross domestic product (GDP) contracted by 9.6% year-on-year in 2020—the sharpest decline since the Philippine Statistical Agency (PSA) started collecting data on annual growth rates in 1946 (Bangko Sentral ng Pilipinas, 2021 ). The strictest lockdown imposed from March to April 2020 had the most severe repercussions to the economy, but restrictions soon after have generally eased on economic activities all over the country. However, schools at all levels remain closed and minimum restrictions are still imposed in business operations particularly in customer accommodation capacity in service establishments.

The government is poised for a calibrated reopening of business, mass transportation, and the relaxation of age group restrictions. The government expects a strong recovery before the end of 2021, when enough vaccines have been rolled out against COVID-19. However, the economic recovery plan and growth targets at the end of the year are put in doubt with the first quarter of 2021 growth rate of GDP at -4.2%. This is exacerbated by the surge of cases in March 2021 that took the National Capital Region (NCR) and contiguous provinces by surprise, straining the hospital bed capacity of the region beyond its limits. The government had to reinforce stricter lockdown measures and curfew hours to stem the rapid spread of the virus. The country’s economic development authority proposes to ensure hospitals have enough capacity to allow the resumption of social and economic activities (National Economic and Development Authority, 2020 ). This is justified by pointing out that the majority of COVID-19 cases are mild and asymptomatic.

Efforts in monitoring and mitigating the spread of COVID-19 requires understanding the behaviour of the disease through the development of localised disease models operationalized as an ICT tool accessible to policymakers. FASSSTER is a scenario-based disease surveillance and modelling platform designed to accommodate multiple sources of data as input allowing for a variety of disease models and analytics to generate meaningful information to its stakeholders (FASSSTER, 2020 ). FASSSTER’s module on COVID-19 currently provides information and forecasts from national down to city/municipality level that are used for decision-making by individual local government units (LGUs) and also by key government agencies in charge of the pandemic response.

In this paper, we develop a mathematical model that measures the economic losses due to the spread of the disease and due to different lockdown policies to contain the disease. This is done by extending the FASSSTER subnational Susceptible-Exposed-Infectious-Recovered (SEIR) model to include two differential equations that capture economic losses due to COVID-19 infection and due to the lockdown measures imposed by the Philippine government. We then proceed to assess the trade-off policy space faced by the Philippine government given the policy that health-care utilisation rate must not be more than 70%, which is the threshold set by the government before declaring that the health system capacity is at high risk.

We simulate the cumulative economic losses for 3 months in 8 scenarios across 5 regions in the country, including the National Capital Region (NCR) to capture the trade-off mechanism. These 8 scenarios present the various combinations of either retaining or easing lockdown policies in these regions. Per region, the trade-off policy space was assessed through minimising the 3-month cumulative economic losses subject to the constraint that the average health-care utilisation rate (HCUR) consistently falls below 70%. The study finds that in NCR, a policy trade-off exists where the minimum economic losses below the 70% average HCUR comprise 10.66% of its Gross Regional Domestic Product. Meanwhile, for regions that are non-adjacent to NCR, a policy that hinges on trade-off analysis does not apply. Nevertheless, for all simulated regions, it is recommended to improve and expand the capacity of the health system to broaden the policy space for the government in easing lockdown measures.

The sections of the paper proceed as follows: the first section reviews the literature, the second section explains the FASSSTER SEIR model, the third section discusses the economic dynamic model, the fourth section specifically explains the parameters used in the economic model, the fifth section briefly lays out the policy trade-off model, the sixth discusses the methods used in implementing the model, the seventh section presents the results of the simulations, the eighth section discusses and interprets the results, and the final section presents the conclusion.

Review of related literature

Overview of the economic shocks of pandemics.

The onslaught of the Coronavirus Disease 2019 (COVID-19) pandemic since 2020 has disrupted lifestyles and livelihoods as governments restrict mobility and economic activity in their respective countries. Unfortunately, this caused a –3.36% decline in the 2020 global economy (World Bank, 2022 ), which will have pushed 71 million people into extreme poverty (World Bank, 2020 ; 2021 ).

As an economic phenomenon, pandemics may be classified under the typologies of disaster economics. Particularly, a pandemic’s impacts may be classified according to the following (Benson and Clay, 2004 ; Noy et al., 2020 ; Keogh-Brown et al., 2010 ; 2020 ; McKibbin and Fernando, 2020 ; Verikios et al., 2012 ): (a) direct impacts, where pandemics cause direct labour supply shocks due to mortality and infection; (b) indirect impacts on productivity, firm revenue, household income, and other welfare effects, and; (c) macroeconomic impacts of a pandemic.

For most pandemic scenarios, social distancing and various forms of lockdowns imposed by countries around the world had led to substantial disruptions in the supply-side of the economy with mandatory business closures (Maital and Barzani, 2020 ; Keogh-Brown et al., 2010 ). Social distancing will have contracted labour supply as well, thus contributing to contractions in the macroeconomy (Geard et al., 2020 ; Keogh-Brown et al., 2010 ). Thus, in general, the literature points to a pandemic’s impacts on the supply- and demand-side, as well as the displacement of labour supply; thus, resulting in lower incomes (Genoni et al., 2020 ; Hupkau et al., 2020 ; United Nations Development Programme, 2021 ). Often, these shocks result from the lockdown measures; thus, a case of a trade-off condition between economic losses and the number of COVID-19 casualties.

Static simulations for the economic impacts of a pandemic

The typologies above are evident in the analyses and simulations on welfare and macroeconomic losses related to a pandemic. For instance, computable general equilibrium (CGE) and microsimulation analyses for the 2009 H1N1 pandemic and the COVID-19 pandemic showed increases in inequities, welfare losses, and macroeconomic losses due to lockdown and public prevention strategies (Cereda et al., 2020 ; Keogh-Brown et al., 2020 ; Keogh-Brown et al., 2010 ). Public prevention-related labour losses also comprised at most 25% of the losses in GDP in contrast with health-related losses, which comprised only at most 17% of the losses in GDP.

Amidst the COVID-19 pandemic in Ghana, Amewu et al. ( 2020 ) find in a social accounting matrix-based analysis that the industry and services sectors will have declined by 26.8% and 33.1%, respectively. Other studies investigate the effects of the pandemic on other severely hit sectors such as the tourism sector. Pham et al. ( 2021 ) note that a reduction in tourism demand in Australia will have caused a reduction in income of tourism labourers. Meanwhile, in a static CGE-microsimulation model by Laborde, Martin, and Vos ( 2021 ), they show that as the global GDP will have contracted by 5% following the reduction in labour supply, this will have increased global poverty by 20%, global rural poverty by 15%, poverty in sub-Saharan Africa by 23%, and in South Asia by 15%.

However, due to the static nature of these analyses, the clear trade-off between economic and health costs under various lockdown scenarios is a policy message that remains unexplored, as the simulations above only explicitly tackle a pandemic’s macroeconomic effects. This gap is mostly due to these studies’ usage of static SAM- and CGE-based analyses.

Dynamic simulations for the economic impacts of a pandemic

An obvious advantage of dynamic models over static approaches in estimating the economic losses from the pandemic is the capacity to provide forward-looking insights that have practical use in policymaking. Epidemiological models based on systems of differential equations explicitly model disease spread and recovery as movements of population across different compartments. These compartmental models are useful in forecasting the number of infected individuals, critically ill patients, death toll, among others, and thus are valuable in determining the appropriate intervention to control epidemics.

To date, the Susceptible-Infectious-Recovered (SIR) and SEIR models are among the most popular compartmental models used to study the spread of diseases. In recent years, COVID-19 has become an important subject of more recent mathematical modelling studies. Many of these studies deal with both application and refinement of both SIR and SEIR to allow scenario-building, conduct evaluation of containment measures, and improve forecasts. These include the integration of geographical heterogeneities, the differentiation between isolated and non-isolated cases, and the integration of interventions such as reducing contact rate and isolation of active cases (Anand et al., 2020 ; Chen et al., 2020 ; Hou et al., 2020 ; Peng et al., 2020 ; Reno et al., 2020 ).

Typical epidemiological models may provide insight on the optimal lockdown measure to reduce the transmissibility of a virus. However, there is a need to derive calculations on economic impacts from the COVID-19 case projections to arrive at a conclusion on the optimal frontier from the trade-off between health and economic losses. In Goldsztejn, Schwartzman and Nehorai ( 2020 ), an economic model that measures lost economic productivity due to the pandemic, disease containment measures and economic policies is integrated into an SEIR model. The hybrid model generates important insight on the trade-offs between short-term economic gains in terms of productivity, and the continuous spread of the disease, which in turn informs policymakers on the appropriate containment policies to be implemented.

This approach was further improved by solving an optimal control of multiple group SIR model to find the best way to implement a lockdown (Acemoglu et al., 2020 ). Noting the trade-offs between economic outcomes and spread of disease implied in lockdown policies, Acemoglu et al. ( 2020 ) find that targeted lockdown yields the best result in terms of economic losses and saving lives. However, Acemoglu et al. ( 2020 ) only determine the optimal lockdown policy and their trade-off analysis through COVID-associated fatalities. Kashyap et al. ( 2020 ) note that hospitalisations may be better indicators for lockdown and, as a corollary, reopening policies.

Gaps in the literature

With the recency of the pandemic, there is an increasing but limited scholarship in terms of jointly analysing the losses brought about by the pandemic on health and the economy. On top of this, the literature clearly has gaps in terms of having a trade-off model that captures the context of low- and middle-income countries. Devising a trade-off model for said countries is an imperative given the structural and capability differences of these countries from developed ones in terms of responding to the pandemic. Furthermore, the literature has not explicitly looked into the trade-off between economic losses and health-care system capacities, both at a national and a subnational level.

With this, the paper aims to fill these gaps with the following. Firstly, we extend FASSSTER’s subnational SEIR model to capture the associated economic losses given various lockdown scenarios at a regional level. Then, we construct an optimal policy decision trade-off between the health system and the economy in the Philippines’ case at a regional level. From there, we analyse the policy implications across the different regions given the results of the simulations.

The FASSSTER SEIR model

The FASSSTER model for COVID-19 uses a compartmental model to describe the dynamics of disease transmission in a community, and it is expressed as a system of ordinary differential equations (Estadilla et al., 2021 ):

where β  =  β 0 (1– λ ), \(\alpha _a = \frac{c}{\tau }\) , \(\alpha _s = \frac{{1 - c}}{\tau }\) , and N ( t ) =  S ( t ) =  E ( t ) +  I a ( t ) +  I s ( t ) +  C ( t ) +  R ( t ).

The six compartments used to divide the entire population, namely, susceptible ( S ), exposed ( E ), infectious but asymptomatic ( I a ), infectious and symptomatic ( I s ), confirmed ( C ), and recovered ( R ), indicate the status of the individuals in relation to the disease. Compartment S consists of individuals who have not been infected with COVID-19 but may acquire the disease once exposed to infectious individuals. Compartment E consists of individuals who have been infected, but not yet capable of transmitting the disease to others. The infectious members of the population are split into two compartments, I a and I s , based on the presence of disease symptoms. These individuals may eventually transition to compartment C once they have been detected, in which case they will be quarantined and receive treatment. The individuals in the C compartment are commonly referred to as active cases. Finally, recovered individuals who have tested negative or have undergone the required number of days in isolation will move out to the R compartment. Given that there had only been rare instances of reinfection (Gousseff et al., 2020 ), the FASSSTER model assumes that recovered individuals have developed immunity from the disease. A description of the model parameters can be found in Supplementary Table S1 .

The model has several nonnegative parameters that govern the movement of individuals along the different compartments. The parameter β represents the effective transmission rate, and it is expressed as a product of the disease transmission rate β 0 and reduction factor 1 −  λ . The rate β 0 is derived from an assumed reproduction number R 0 , which varies depending on the region. The parameter λ reflects the effect of mobility restrictions such as lockdowns and compliance of the members of the population to minimum health standards (such as social distancing, wearing of face masks etc.). In addition, the parameter ψ captures the relative infectiousness of asymptomatic individuals in relation to those who exhibit symptoms.

The incubation period τ and fraction of asymptomatic cases c are used to derive the transfer rates α α and α s from the exposed compartment to I a and I s compartments, respectively. Among those who are infectious and asymptomatic, a portion of them is considered pre-symptomatic, and hence will eventually develop symptoms of the disease; this is reflected in the parameter ω. The respective detection rates δ a and δ s of asymptomatic and symptomatic infectious individuals indicate the movement from the undetected infectious compartment to the confirmed compartment. These parameters capture the entire health system capacity to prevent-detect-isolate-treat-reintegrate (PDITR) COVID-19 cases; hence, they will henceforth be referred to as HSC parameters. The recoveries of infectious asymptomatic individuals and among the active cases occur at the corresponding rates θ and r . Death rates due to the disease, on the other hand, are given by ∈ I and ∈ T for the infectious symptomatic and confirmed cases, respectively.

Aside from the aforementioned parameters, the model also utilises parameters not associated with the COVID-19 disease, such as the recruitment rate A into the susceptible population. This parameter represents the birth rate of the population and is assumed to be constant. In addition, a natural death rate per unit of time is applied to all compartments in the model, incorporating the effect of non-COVID-19 related deaths in the entire population.

Economic dynamic model

The trade-off model aims to account for the incurred economic losses following the rise and fall of the number of COVID-19 cases in the country and the implementation of various lockdown measures. The model variables are estimated per day based on the SEIR model estimate of daily cases and are defined as follows. Let Y E ( t ) be the economic loss due to COVID-19 infections (hospitalisation, isolation, and death of infected individuals) and Y E ( t ) be the economic loss due to the implemented lockdown at time t . The dynamics of each economic variable through time is described by an ordinary differential equation. Since each equation depends only on the values of the state variables of the epidemiological model, then it is possible to obtain a closed form solution.

Economic loss due to COVID-19 infections (hospitalisation, isolation, and health)

The economic loss due to hospitalisation, isolation, and death Y E is described by the following differential equation:

where z  = annual gross value added of each worker (assumed constant for all future years and for all ages), w  = daily gross value added, ι i  = % population with ages 0–14 ( i  = 1), and labour force with ages 15–34 ( i  = 2), 35–49 ( i  = 3) and 50–64 ( i  = 4), s r  = social discount rate, κ  = employed to population ratio, T i  = average remaining productive years for people in age bracket i , i  = 1, 2, 3, 4, and T 5  = average age of deaths from 0–14 years old age group. Note that the above formulation assumes that the young population 0–14 years old will start working at age 15, and that they will work for T 1 −15 years.

Solving Eq. ( 7 ), we obtain for t  ≥ 0,

In this equation, the terms on the right-hand side are labelled as (A), (B), and (C). Term (A) is the present value of all future gross value added of 0–14 years old who died due to COVID-19 at time t . Similarly, term (B) is the present value of all future gross value added of people in the labour force who died due to COVID-19 at time t . Term (C) represents the total gross value added lost at time t due to sickness and isolation.

The discounting factors and the population age group shares in (A) and (B) can be simplified further into K 1 and K 2 , where \(K_1 = \iota _1\left( {\frac{{\left( {s_r + 1} \right)^{T_1 + T_5 - 13} - \left( {s_r + 1} \right)}}{{s_r\left( {s_r + 1} \right)^{T_1 + 1}}}} \right)\) and \(K_2 = \mathop {\sum}\nolimits_{i = 2}^4 {\iota _i\left( {\frac{{\left( {s_r + 1} \right)^{T_i + 2} - \left( {s_r + 1} \right)}}{{s_r\left( {s_r + 1} \right)^{T_i + 1}}}} \right)}\) . By letting L 1  = z( K 1  +  K 2 ) ∈ I  +  κw (1 –  ∈ I ) and L 2  = z( K 1  +  K 2 ) ∈ T  +  κw (1 –  ∈ T ), we have:

Economic losses due to lockdown policies

Equation ( 7 ) measures the losses due mainly to sickness and death from COVID-19. The values depend on the number of detected and undetected infected individuals, C and I s . The other losses sustained by the other part of the population are due to their inability to earn because of lockdown policies. This is what the next variable Y L represents, whose dynamics is given by the differential equation

where φ  = the displacement rate, and κ and w are as defined previously.

Solving the differential equation, then

Note that [ S ( t ) +  E ( t ) +  I a ( t ) +  R ( t )] is the rest of the population at time t , i.e., other than the active and infectious symptomatic cases. Multiplying this by κ and the displacement rate φ yields the number of employed people in this population who are displaced due to the lockdown policy. Thus, κwφ [ S ( t ) +  E ( t ) +  I a ( t ) +  R ( t )] is the total foregone income due to the lockdown policy.

Economic model parameters

The values of the parameters were derived from a variety of sources. The parameters for employment and gross value added were computed based on the data from the Philippine Statistics Authority ( 2021 , 2020 , 2019a , 2019b ), the Department of Health’s Epidemiology Bureau (DOH-EB) ( 2020 ), the Department of Trade and Industry (DTI) ( 2020a , 2020b ) and the National Economic Development Authority (NEDA) ( 2016 ) (See Supplementary Tables S2 and S3 for the summary of economic parameters).

Parameters determined from related literature

We used the number of deaths from the data of the DOH-EB ( 2020 ) to disaggregate the long-term economic costs of the COVID-related deaths into age groups. Specifically, the COVID-related deaths were divided according to the following age groups: (a) below 15 years old, (b) 15 to 34 years old, (c) 35 to 49 years old, and (d) 50 to 64 years old. The average remaining years for these groups were computed directly from the average age of death of the respective cluster. Finally, we used the social discount rate as determined by NEDA ( 2016 ) to get the present value of the stream of foregone incomes of those who died from the disease.

Parameters estimated from local data

The foregone value added due to labour displacement was estimated as the amount due to workers in a geographic area who were unable to work as a result of strict lockdown measures. It was expected to contribute to the total value added in a given year if the area they reside or work in has not been locked down.

The employed to population ratio κ i for each region i was computed as

where e i was total employment in region i , and Pi was the total population in the region. Both e i and Pi were obtained from the quarterly labour force survey and the census, respectively (Philippine Statistics Authority, 2020 , 2019a , 2019b ).

The annual gross value added per worker z i for region i was computed as

where g ji was the share of sector j in total gross value added of region i , GVA ji was the gross value added of sector j in region i (Philippine Statistics Authority, 2021 ), and e ji was the number of employed persons in sector j of region i . If individuals worked for an average of 22.5 days for each month for 12 months in a year, then the daily gross value added per worker in region i was given by

Apart from this, labour displacement rates were calculated at regional level. The rates are differentiated by economic reopening scenarios from March 2020 to September 2020, from October 2020 to February 2021, and from March 2021 onwards (Department of Trade and Industry, 2020a , 2020b , 2021 ). These were used to simulate the graduate reopening of the economy. From the country’s labour force survey, each representative observation j in a region i is designated with a numerical value in accordance with the percentage operating capacity of the sector where j works in. Given the probability weights p ji , the displacement rate φ i for region i was calculated by

where x ji served as the variable representing the maximum operating capacity designated for j ’s sector of work.

Policy trade-off model

The trade-off between economic losses and health measures gives the optimal policy subject to a socially determined constraint. From the literature, it was pointed out that the optimal policy option would be what minimises total economic losses subject to the number of deaths at a given time (Acemoglu et al., 2020 ). However, for the Philippines’ case, lockdown restrictions are decided based on the intensive care unit and health-care utilisation rate (HCUR). The health system is said to reach its critical levels if the HCUR breaches 70% of the total available bed capacity in intensive care units. Once breached, policymakers would opt to implement stricter quarantine measures.

Given these, a policy mix of various quarantine restrictions may be chosen for as long as it provides the lowest amount of economic losses subject to the constraint that the HCUR threshold is not breached. Since economic losses are adequately captured by the sum of infection-related and lockdown-related losses, Y E ( t ) +  Y L ( t ), then policy option must satisfy the constrained minimisation below:

where the objective function is evaluated from the initial time value t 0 to T .

The COVID-19 case information data including the date, location transformed into the Philippine Standard Geographic Code (PSGC), case count, and date reported were used as input to the model. Imputation using predictive mean matching uses the mice package in the R programming language. It was performed to address data gaps including the date of onset, date of specimen collection, date of admission, date of result, and date of recovery. Population data was obtained from the country’s Census of Population and Housing of 2015. The scripts to implement the FASSSTER SEIR model were developed using core packages in R including optimParallel for parameter estimation and deSolve for solving the ordinary differential equations. The output of the model is fitted to historical data by finding the best value of the parameter lambda using the L-BFGS-B method under the optim function and the MSE as measure of fitness (Byrd et al., 1995 ). The best value of lambda is obtained by performing parameter fitting with several bootstraps for each region, having at least 50 iterations until a correlation threshold of at least 90% is achieved. The output generated from the code execution contains values of the different compartments at each point in time. From these, the economic variables Y E ( t ) and Y L ( t ) were evaluated using the formulas in Eq. ( 7 ) and ( 8 ) in their simplified forms, and the parameter and displacement rate values corresponding to the implemented lockdown scenario (Fig. 1 ).

The different population states are represented by the compartments labelled as susceptible (S), exposed (E), infectious but asymptomatic ( I a ), infectious and symptomatic ( I s ), confirmed (C), and recovered (R).

We simulate the economic losses and health-care utilisation capacity (HCUR) for the National Capital Region (NCR), Ilocos Region, Western Visayas, Soccsksargen, and for the Davao Region by implementing various combinations of lockdown restrictions for three months to capture one quarter of economic losses for these regions. The National Capital Region accounts for about half of the Philippines’ gross domestic product, while the inclusion of other regions aim to represent the various areas of the country. The policy easing simulations use the four lockdown policies that the Philippines uses, as seen in Table 1 .

Simulations for the National Capital Region

Table 2 shows the sequence of lockdown measures implemented for the NCR. Each lockdown measure is assumed to be implemented for one month. Two sets of simulations are implemented for the region. The first set assumes a health systems capacity (HSC) for the region at 17.99% (A), while the second is at 21.93% (B). A higher HSC means an improvement in testing and isolation strategies for the regions of concern.

From the sequence of lockdown measures in Table 2 , Fig. 2 shows the plot of the average HCUR as well as the corresponding total economic losses for the two sets of simulations for one quarter. For the scenario at 17.99% HSC (A), the highest loss is recorded at 16.58% of the annual gross regional domestic product (GRDP) while the lowest loss is at 12.19% of its GRDP. Lower average HCUR corresponds to more stringent scenarios starting with Scenario 1. Furthermore, under the scenarios with 21.93% HSC (B), losses and average HCUR are generally lower. Scenarios 1 to 4 from this set lie below the 70% threshold of the HCUR, with the lowest economic loss simulated to be at 9.11% of the GRDP.

These include the set of trade-off decisions under a health system capacity equal to 17.99%, and another set equal to 21.93% (Source of basic data: Authors’ calculations).

Overall, the trend below shows a parabolic shape. The trend begins with an initial decrease in economic losses as restrictions loosen, but this comes at the expense of increasing HCUR. This is then followed by an increasing trend in losses as restrictions are further loosened. Notably, the subsequent marginal increases in losses in the simulation with 21.93% HSC are smaller relative to the marginal increases under the 17.99% HSC.

Simulations for the Regions Outside of NCR

Table 2 also shows the lockdown sequence for the Ilocos, Western Visayas, Soccsksargen, and Davao regions. The sequence begins with Level III only. Meanwhile, the lowest lockdown measure simulated for the regions is Level I. Two sets of simulations with differing health system capacities for each scenario are done as well.

With this lockdown sequence, Fig. 3 shows the panel of scatter plot between the average HCUR and total economic losses as percentage of the respective GRDP, with both parameters covering one quarter. Similar to the case of the NCR, the average HCUR for the simulations with higher health system capacity (B) is lower than the simulations with lower health system capacity (A). However, unlike in NCR, the regions’ simulations do not exhibit a parabolic shape.

These include trade-offs for a Ilocos Region, b Western Visayas Region, c Soccsksargen Region, and d Davao Region (Source of basic data: Authors’ calculations).

Discussion and interpretation

The hypothetical simulations above clearly capture the losses associated with the pandemic and the corresponding lockdown interventions by the Philippine government. The trend of the simulations clearly shows the differences in the policy considerations for the National Capital Region (NCR) and the four other regions outside of NCR. Specifically, the parabolic trend of the former suggests an optimal strategy that can be attained through a trade-off policy even with the absence of any constraint in finding the said optimal strategy. This trend is borne from the countervailing effects between the economic losses due to COVID-19 infection ( Y E ) and the losses from the lockdown measures ( Y L ) implemented for the region. Specifically, Fig. 4(a), (b) show the composition of economic losses across all scenarios for the NCR simulation under a lower and higher health system capacity (HSC), respectively.

These include losses under a HSC = 17.99% and b HSC = 21.93% in the National Capital Region (Source of basic data: Authors’ calculations).

In both panels of Fig. 4 , as quarantine measures loosen, economic losses from infections ( Y E ) tend to increase while the converse holds for economic losses due to quarantine restrictions ( Y L ). The results are intuitive as loosening restrictions may lead to increased mobility, and therefore increased exposure and infections from the virus. In fact, economic losses from infections ( Y E ) take up about half of the economic losses for the region in Scenario 7A, Fig. 4(a) .

While the same trends can be observed for the scenarios with higher HSC at 21.93%, the economic losses from infections ( Y E ) do not overtake the losses simulated from lockdown restrictions ( Y L ) as seen in Fig. 4(b) . This may explain the slower upward trend of economic losses in Fig. 2 at HSC = 21.93%.

The output of the simulation for the Davao region shows that the economic losses from COVID-19 infections ( Y E ) remain low even as the lockdown restrictions ease down. At the same time, economic losses from lockdown restrictions ( Y L ) show a steady decline with less stringent lockdown measures. Overall, the region experiences a decreasing trend in total economic losses even as the least stringent lockdown measure is implemented for a longer period. This pattern is similar with the regions of Ilocos, Western Visayas, and Soccsksarkgen.

The results of the simulations from Figs. 2 and 3 also demonstrate differing levels of economic losses and health-care utilisation between the two sets of scenarios for NCR and the four other regions. Clearly, lower economic losses and health-care utilisation rates were recorded for the scenarios with higher HSC. Specifically, lower total economic losses can be attributed to a slower marginal increase in losses from infections ( Y E ) as seen in Fig. 4(b) . Thus, even while easing restrictions, economic losses may be tempered with an improvement in the health system.

With the above analysis, the policy trade-off as a constrained minimisation problem of economic losses subject to HCUR above appears to apply in NCR but not in regions outside of NCR. The latter is better off in enhancing prevention, detection, isolation, treatment, and reintegration (PDITR) strategy combined with targeted small area lockdowns, if necessary, without risking any increases in economic losses. But, in all scenarios and anywhere, the enhancement of the HSC through improved PDITR strategies remains vital to avoid having to deal with local infection surges and outbreaks. This also avoids forcing local authorities in a policy bind between health and economic measures to implement. Enhancing PDITR in congested urban centres (i.e., NCR) is difficult especially with the surge in new daily cases. People are forced to defy social distance rules and other minimum health standards in public transportation and in their workplaces that help spread the virus.

We extended the FASSSTER subnational SEIR model to include two differential equations that capture economic losses due to COVID-19 infection and due to the lockdown measures, respectively. The extended model aims to account for the incurred economic losses following the rise and fall of the number of active COVID-19 cases in the country and the implementation of various lockdown measures. In simulating eight different scenarios in each of the five selected regions in the country, we found a tight policy choice in the case of the National Capital Region (NCR) but not in the cases of four other regions far from NCR. This clearly demonstrates the difficult policy decision in the case of NCR in minimising economic losses given the constraint of its intensive care unit (ICU) bed capacity.

On the other hand, the regions far from the NCR have wider policy space towards economic reopening and recovery. However, in all scenarios, the primary significance of improving the health system capacity (HSC) to detect and control the spread of the disease remains in order to widen the trade-off policy space between public health and economic measures.

The policy trade-off simulation results imply different policy approaches in each region. This is also to consider the archipelagic nature of the country and the simultaneous concentration of economic output and COVID-19 cases in NCR and contiguous provinces compared to the rest of the country. Each local region in the country merits exploration of different policy combinations in economic and health measures depending on the number of active COVID-19 cases, strategic importance of economic activities and output specific in the area, the geographic spread of the local population and their places of work, and considering local health system capacities. However, we would like to caution that the actual number of cases could diverge from the results of our simulations. This is because the parameters of the model must be updated regularly driven generally by the behaviour of the population and the likely presence of variants of COVID-19. Given the constant variability of COVID-19 data, we recommend a shorter period of model projections from one to two months at the most.

In summary, this paper showed how mathematical modelling can be used to inform policymakers on the economic impact of lockdown policies and make decisions among the available policy options, taking into consideration the economic and health trade-offs of these policies. The proposed methodology provides a tool for enhanced policy decisions in other countries during the COVID-19 pandemic or similar circumstances in the future.

Data availability

The raw datasets used in this study are publicly available at the Department of Health COVID-19 Tracker Website: https://doh.gov.ph/covid19tracker . Datasets will be made available upon request after completing request form and signing non-disclosure agreement. Code and scripts will be made available upon request after completing request form and signing non-disclosure agreement.

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Acknowledgements

We thank Dr. Geoffrey M. Ducanes, Associate Professor, Ateneo de Manila University Department of Economics, for giving us valuable comments in the course of developing the economic model, and Mr. Jerome Patrick D. Cruz, current Ph.D. student, Massachusetts Institute of Technology, for initiating and leading the economic team in FASSSTER in the beginning of the project for their pitches in improving the model. We also thank Mr. John Carlo Pangyarihan for typesetting the manuscript. The project is supported by the Philippine Council for Health Research and Development, United Nations Development Programme and the Epidemiology Bureau of the Department of Health.

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Elvira P. de Lara-Tuprio & Timothy Robin Y. Teng

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Maria Regina Justina E. Estuar & Lenard Paulo V. Tamayo

Department of Economics, Ateneo de Manila University, Quezon City, Philippines

Joselito T. Sescon, Cymon Kayle Lubangco, Rolly Czar Joseph T. Castillo & Gerome M. Vedeja

Department of Mathematics, Caraga State University, Butuan City, Philippines

Jay Michael R. Macalalag

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All authors contributed to the study conception and design. Model conceptualization, data collection and analysis were performed by EPdL-T, MRJEE, JTS, CKL, CJTC, TRYT, LPT, JMRM, and GMV. All authors commented on previous versions of the manuscript, and read and approved the final manuscript.

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de Lara-Tuprio, E.P., Estuar, M.R.J.E., Sescon, J.T. et al. Economic losses from COVID-19 cases in the Philippines: a dynamic model of health and economic policy trade-offs. Humanit Soc Sci Commun 9 , 111 (2022). https://doi.org/10.1057/s41599-022-01125-4

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DOI : https://doi.org/10.1057/s41599-022-01125-4

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Philippines: Coronavirus Pandemic Country Profile

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Confirmed cases.

• What is the daily number of confirmed cases?
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Philippines: What is the daily number of confirmed cases?

Related charts:.

Which world regions have the most daily confirmed cases?

This chart shows the number of confirmed COVID-19 cases per day . This is shown as the seven-day rolling average.

What is important to note about these case figures?

• The reported case figures on a given date do not necessarily show the number of new cases on that day – this is due to delays in reporting.
• The number of confirmed cases is lower than the true number of infections – this is due to limited testing. In a separate post we discuss how models of COVID-19 help us estimate the true number of infections .

→ We provide more detail on these points in our page on Cases of COVID-19 .

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Philippines: Daily confirmed cases: how do they compare to other countries?

Differences in the population size between different countries are often large. To compare countries, it is insightful to look at the number of confirmed cases per million people – this is what the chart shows.

Keep in mind that in countries that do very little testing the actual number of cases can be much higher than the number of confirmed cases shown here.

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Philippines: What is the cumulative number of confirmed cases?

Which world regions have the most cumulative confirmed cases?

How do the number of tests compare to the number of confirmed COVID-19 cases?

The previous charts looked at the number of confirmed cases per day – this chart shows the cumulative number of confirmed cases since the beginning of the COVID-19 pandemic.

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Philippines: Cumulative confirmed cases: how do they compare to other countries?

This chart shows the cumulative number of confirmed cases per million people.

Philippines: Biweekly cases : where are confirmed cases increasing or falling?

Why is it useful to look at biweekly changes in confirmed cases.

For all global data sources on the pandemic, daily data does not necessarily refer to the number of new confirmed cases on that day – but to the cases  reported  on that day.

Since reporting can vary significantly from day to day – irrespectively of any actual variation of cases – it is helpful to look at a longer time span that is less affected by the daily variation in reporting. This provides a clearer picture of where the pandemic is accelerating, staying the same, or reducing.

The first map here provides figures on the number of confirmed cases in the last two weeks. To enable comparisons across countries it is expressed per million people of the population.

And the second map shows the percentage change (growth rate) over this period: blue are all those countries in which the case count in the last two weeks was lower than in the two weeks before. In red countries the case count has increased.

What is the weekly number of confirmed cases?

What is the weekly change (growth rate) in confirmed cases?

Philippines: Global cases in comparison: how are cases changing across the world?

In our page on COVID-19 cases , we provide charts and maps on how the number and change in cases compare across the world.

Confirmed deaths

• What is the daily number of confirmed deaths?
• Daily confirmed deaths: how do they compare to other countries?
• What is the cumulative number of confirmed deaths?
• Cumulative confirmed deaths: how do they compare to other countries?
• Biweekly deaths : where are confirmed deaths increasing or falling?
• Global deaths in comparison: how are deaths changing across the world?

Philippines: What is the daily number of confirmed deaths?

Which world regions have the most daily confirmed deaths?

This chart shows t he number of confirmed COVID-19 deaths per day .

Three points on confirmed death figures to keep in mind

All three points are true for all currently available international data sources on COVID-19 deaths:

• The actual death toll from COVID-19 is likely to be higher than the number of confirmed deaths – this is due to limited testing and challenges in the attribution of the cause of death. The difference between confirmed deaths and actual deaths varies by country.
• How COVID-19 deaths are determined and recorded may differ between countries.
• The death figures on a given date do not necessarily show the number of new deaths on that day, but the deaths  reported  on that day. Since reporting can vary significantly from day to day – irrespectively of any actual variation of deaths – it is helpful to view the seven-day rolling average of the daily figures as we do in the chart here.

→ We provide more detail on these three points in our page on Deaths from COVID-19 .

Philippines: Daily confirmed deaths: how do they compare to other countries?

This chart shows the daily confirmed deaths per million people of a country’s population.

Why adjust for the size of the population?

Differences in the population size between countries are often large, and the COVID-19 death count in more populous countries tends to be higher . Because of this it can be insightful to know how the number of confirmed deaths in a country compares to the number of people who live there, especially when comparing across countries.

For instance, if 1,000 people died in Iceland, out of a population of about 340,000, that would have a far bigger impact than the same number dying in the United States, with its population of 331 million. 1 This difference in impact is clear when comparing deaths per million people of each country’s population – in this example it would be roughly 3 deaths/million people in the US compared to a staggering 2,941 deaths/million people in Iceland.

Philippines: What is the cumulative number of confirmed deaths?

Which world regions have the most cumulative confirmed deaths?

The previous charts looked at the number of confirmed deaths per day – this chart shows the cumulative number of confirmed deaths since the beginning of the COVID-19 pandemic.

Philippines: Cumulative confirmed deaths: how do they compare to other countries?

This chart shows the cumulative number of confirmed deaths per million people.

Philippines: Biweekly deaths : where are confirmed deaths increasing or falling?

Why is it useful to look at biweekly changes in deaths.

For all global data sources on the pandemic, daily data does not necessarily refer to deaths on that day – but to the deaths  reported  on that day.

Since reporting can vary significantly from day to day – irrespectively of any actual variation of deaths – it is helpful to look at a longer time span that is less affected by the daily variation in reporting. This provides a clearer picture of where the pandemic is accelerating, staying the same, or reducing.

The first map here provides figures on the number of confirmed deaths in the last two weeks. To enable comparisons across countries it is expressed per million people of the population.

And the second map shows the percentage change (growth rate) over this period: blue are all those countries in which the death count in the last two weeks was lower than in the two weeks before. In red countries the death count has increased.

What is the weekly number of confirmed deaths?

What is the weekly change (growth rate) in confirmed deaths?

Philippines: Global deaths in comparison: how are deaths changing across the world?

In our page on COVID-19 deaths , we provide charts and maps on how the number and change in deaths compare across the world.

• How many COVID-19 vaccine doses are administered daily ?
• How many COVID-19 vaccine doses have been administered in total ?
• What share of the population has received  at least one dose  of the COVID-19 vaccine?
• What share of the population has  completed the initial vaccination protocol ?
• Global vaccinations in comparison: which countries are vaccinating most rapidly?

Philippines: How many COVID-19 vaccine doses are administered daily ?

How many vaccine doses are administered each day (not population adjusted)?

This chart shows the daily number of COVID-19 vaccine doses administered per 100 people in a given population . This is shown as the rolling seven-day average. Note that this is counted as a single dose, and may not equal the total number of people vaccinated, depending on the specific dose regime (e.g., people receive multiple doses).

Philippines: How many COVID-19 vaccine doses have been administered in total ?

How many vaccine doses have been administered in total (not population adjusted)?

This chart shows the total number of COVID-19 vaccine doses administered per 100 people within a given population. Note that this is counted as a single dose, and may not equal the total number of people vaccinated, depending on the specific dose regime as several available COVID vaccines require multiple doses.

Philippines: What share of the population has received  at least one dose  of the COVID-19 vaccine?

How many people have received at least one vaccine dose?

This chart shows the share of the total population that has received at least one dose of the COVID-19 vaccine. This may not equal the share with a complete initial protocol if the vaccine requires two doses. If a person receives the first dose of a 2-dose vaccine, this metric goes up by 1. If they receive the second dose, the metric stays the same.

Philippines: What share of the population has  completed the initial vaccination protocol ?

How many people have completed the initial vaccination protocol?

The following chart shows the share of the total population that has completed the initial vaccination protocol. If a person receives the first dose of a 2-dose vaccine, this metric stays the same. If they receive the second dose, the metric goes up by 1.

This data is only available for countries which report the breakdown of doses administered by first and second doses.

Philippines: Global vaccinations in comparison: which countries are vaccinating most rapidly?

In our page on COVID-19 vaccinations, we provide maps and charts on how the number of people vaccinated compares across the world.

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Philippines: The positive rate

Here we show the share of reported tests returning a positive result – known as the positive rate.

The positive rate can be a good metric for how adequately countries are testing because it can indicate the level of testing relative to the size of the outbreak. To be able to properly monitor and control the spread of the virus, countries with more widespread outbreaks need to do more testing.

It can also be helpful to think of the positive rate the other way around:

How many tests have countries done for each confirmed case in total across the outbreak?

Philippines: The scale of testing compared to the scale of the outbreak

How do daily tests and daily new confirmed cases compare when not adjusted for population ?

This scatter chart provides another way of seeing the extent of testing relative to the scale of the outbreak in different countries.

The chart shows the daily number of tests (vertical axis) against the daily number of new confirmed cases (horizontal axis), both per million people.

Philippines: How many tests are performed each day ?

This chart shows the number of  daily  tests per thousand people. Because the number of tests is often volatile from day to day, we show the figures as a seven-day rolling average.

What is counted as a test?

The number of tests does not refer to the same thing in each country – one difference is that some countries report the number of people tested, while others report the number of tests (which can be higher if the same person is tested more than once). And other countries report their testing data in a way that leaves it unclear what the test count refers to exactly.

We indicate the differences in the chart and explain them in detail in our accompanying  source descriptions .

Philippines: Global testing in comparison: how is testing changing across the world?

In our page on COVID-19 testing , we provide charts and maps on how the number and change in tests compare across the world.

Case fatality rate

• What does the data on deaths and cases tell us about the mortality risk of COVID-19?
• The case fatality rate
• Learn in more detail about the mortality risk of COVID-19

Philippines: What does the data on deaths and cases tell us about the mortality risk of COVID-19?

To understand the risks and respond appropriately we would also want to know the mortality risk of COVID-19 – the likelihood that someone who is infected with the disease will die from it.

We look into this question in more detail on our page about the mortality risk of COVID-19 , where we explain that this requires us to know – or estimate – the number of total cases and the final number of deaths for a given infected population.

Because these are not known , we discuss what the current data on confirmed deaths and cases can and can not tell us about the risk of death. This chart shows both those metrics.

Philippines: The case fatality rate

Related chart:.

How do the cumulative number of confirmed deaths and cases compare?

The case fatality rate is simply the ratio of the two metrics shown in the chart above.

The case fatality rate is the number of confirmed deaths divided by the number of confirmed cases.

This chart here plots the CFR calculated in just that way. 

During an outbreak – and especially when the total number of cases is not known – one has to be very careful in interpreting the CFR . We wrote a  detailed explainer  on what can and can not be said based on current CFR figures.

Philippines: Learn in more detail about the mortality risk of COVID-19

Learn what we know about the mortality risk of COVID-19 and explore the data used to calculate it.

Government Responses

• Government Stringency Index

To understand how governments have responded to the pandemic, we rely on data from the Oxford Coronavirus Government Response Tracker  (OxCGRT), which is published and managed by researchers at the Blavatnik School of Government at the University of Oxford.

This tracker collects publicly available information on 17 indicators of government responses, spanning containment and closure policies (such as school closures and restrictions in movement); economic policies; and health system policies (such as testing regimes).

How have countries responded to the pandemic?

Travel bans, stay-at-home restrictions, school closures – how have countries responded to the pandemic? Explore the data on all policy measures.

Philippines: Government Stringency Index

The chart here shows how governmental response has changed over time. It shows the Government Stringency Index – a composite measure of the strictness of policy responses.

The index on any given day is calculated as the mean score of nine policy measures, each taking a value between 0 and 100. See the authors’  full description  of how this index is calculated.

A higher score indicates a stricter government response (i.e. 100 = strictest response).

The OxCGRT project calculates this index using nine specific measures, including:

• school and workplace closures;
• restrictions on public gatherings;
• transport restrictions;
• and stay-at-home requirements.

You can see all of these separately on our page on policy responses . There you can also compare these responses in countries across the world.

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Open Access

Peer-reviewed

Research Article

COVID-19 vaccine hesitancy and confidence in the Philippines and Malaysia: A cross-sectional study of sociodemographic factors and digital health literacy

Roles Conceptualization, Data curation, Formal analysis, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Clinical Informatics Research Unit, Faculty of Medicine, University of Southampton, Southampton, United Kingdom

Roles Data curation, Methodology, Project administration, Resources, Validation, Writing – original draft, Writing – review & editing

Affiliations Department of Community Medicine, International Medical School, Management and Science University, Shah Alam, Malaysia, Department of Community Medicine, Faculty of Medicine, Asia Metropolitan University, Johor Bahru, Malaysia, Global Public Health, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway, Malaysia

Roles Conceptualization, Data curation, Validation, Writing – original draft, Writing – review & editing

Affiliation Department of Psychiatry, Faculty of Medicine, University of Cyberjaya, Cyberjaya, Malaysia

Roles Conceptualization, Writing – original draft, Writing – review & editing

Affiliation Department: School of Criminal Justice Education, Institution: J.H. Cerilles State College, Caridad, Dumingag, Zamboanga del Sur, Philippines

Roles Conceptualization, Data curation, Supervision, Validation, Writing – original draft, Writing – review & editing

Affiliations Global Public Health, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway, Malaysia, South East Asia Community Observatory (SEACO), Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway, Malaysia

• Ken Brackstone, 
• Roy R. Marzo, 
• Rafidah Bahari, 
• Michael G. Head, 
• Mark E. Patalinghug, 

• Published: October 19, 2022
• https://doi.org/10.1371/journal.pgph.0000742
• Peer Review
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With the emergence of the highly transmissible Omicron variant, large-scale vaccination coverage is crucial to the national and global pandemic response, especially in populous Southeast Asian countries such as the Philippines and Malaysia where new information is often received digitally. The main aims of this research were to determine levels of hesitancy and confidence in COVID-19 vaccines among general adults in the Philippines and Malaysia, and to identify individual, behavioural, or environmental predictors significantly associated with these outcomes. Data from an internet-based cross-sectional survey of 2558 participants from the Philippines ( N = 1002) and Malaysia ( N = 1556) were analysed. Results showed that Filipino (56.6%) participants exhibited higher COVID-19 hesitancy than Malaysians (22.9%; p < 0.001). However, there were no significant differences in ratings of confidence between Filipino (45.9%) and Malaysian (49.2%) participants ( p = 0.105). Predictors associated with vaccine hesitancy among Filipino participants included women (OR, 1.50, 95% CI, 1.03–1.83; p = 0.030) and rural dwellers (OR, 1.44, 95% CI, 1.07–1.94; p = 0.016). Among Malaysian participants, vaccine hesitancy was associated with women (OR, 1.50, 95% CI, 1.14–1.99; p = 0.004), social media use (OR, 11.76, 95% CI, 5.71–24.19; p < 0.001), and online information-seeking behaviours (OR, 2.48, 95% CI, 1.72–3.58; p < 0.001). Predictors associated with vaccine confidence among Filipino participants included subjective social status (OR, 1.13, 95% CI, 1.54–1.22; p < 0.001), whereas vaccine confidence among Malaysian participants was associated with higher education (OR, 1.30, 95% CI, 1.03–1.66; p < 0.028) and negatively associated with rural dwellers (OR, 0.64, 95% CI, 0.47–0.87; p = 0.005) and online information-seeking behaviours (OR, 0.42, 95% CI, 0.31–0.57; p < 0.001). Efforts should focus on creating effective interventions to decrease vaccination hesitancy, increase confidence, and bolster the uptake of COVID-19 vaccination, particularly in light of the Dengvaxia crisis in the Philippines.

Citation: Brackstone K, Marzo RR, Bahari R, Head MG, Patalinghug ME, Su TT (2022) COVID-19 vaccine hesitancy and confidence in the Philippines and Malaysia: A cross-sectional study of sociodemographic factors and digital health literacy. PLOS Glob Public Health 2(10): e0000742. https://doi.org/10.1371/journal.pgph.0000742

Editor: Nnodimele Onuigbo Atulomah, Babcock University, NIGERIA

Received: June 12, 2022; Accepted: September 20, 2022; Published: October 19, 2022

Copyright: © 2022 Brackstone et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data are available on the OSF repository: https://osf.io/ncwjq/ .

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

While many high-income settings have achieved relatively high coverage with their COVID-19 vaccination campaigns, almost 32.1% of the world’s population have not received a single dose of any COVID-19 vaccine as of July 2022 [ 1 ]. The Philippines and Malaysia are among two of the most populous countries in Southeast Asia with an estimated population of 110 million and 32 million people, respectively. To date, Malaysia has seen over 4.6 million cases with a mortality rate of 0.77%, while approximately 3.7 million cases of COVID-19 were detected in the Philippines with a mortality rate of 1.60% [ 2 ]. Malaysia is doing considerably well with their vaccination efforts, with 84.8% of the population currently considered fully vaccinated as of July 2022. However, vaccination campaigns in the Philippines have been more difficult, with 65.6% of the population fully vaccinated [ 3 ]. With the emergence of the highly transmissible Omicron variant across the world [ 4 ], large-scale vaccination coverage remains fundamental to the national and global pandemic response. Regular scientific assessments of factors that may impede the success of COVID-19 vaccination coverage will be critical as vaccination campaigns continue in these nations.

A key factor for the success of vaccination campaigns is people’s willingness to be vaccinated once doses become accessible to them personally. Vaccine hesitancy is defined by the World Health Organization (WHO) as the delay in the acceptance, or blunt refusal of, vaccines. In fact, vaccine hesitancy was described by the WHO as one of the top 10 threats to global health in 2019 [ 5 ]. Conversely, vaccine confidence relates to individuals’ beliefs that vaccines are effective and safe. In general, a loss of trust in health authorities is a key determinant of vaccine confidence, with misconceptions about vaccine safety being among the most common reasons for low confidence in vaccines [ 6 ].

Previously, vaccination in Southeast Asia has been associated with mistrust and fear, particularly in the Philippines, who are still suffering the consequences of the Dengvaxia (dengue) vaccine controversy in 2017 [ 7 ]. Studies suggest that this highly political mainstream event, in which anti-vaccination campaigns linked dengue vaccines with autism spectrum disorder and with corrupt schemes of pharmaceutical companies, continue to erode the population’s trust in vaccines. For example, a survey conducted on over 30,000 Filipinos in early 2021 showed that 41% of respondents would refuse the COVID-19 vaccine once it became available, whereas Malaysia reported 27% hesitancy [ 8 ]. Researchers predict that the controversy surrounding Dengvaxia may have prompted severe medical mistrust and subsequently weakened the public’s attitudes toward vaccines [ 7 , 9 ]. However, there may be many additional factors that weaken confidence in vaccines. For example, incompatibility with religious beliefs is one key driver of weakened confidence in vaccines [ 10 , 11 ], whereas living in urbanised (vs. rural) areas predicts COVID-19 vaccine hesitancy in some countries [ 12 – 14 ], possibly due to being more connected to the internet and social media and being more exposed to COVID-19-related misinformation.

Other predictors of vaccine hesitancy and confidence may include digital health literacy–one’s ability to seek, find, understand, and appraise health information from digital resources–and social media use. Research has shown that beliefs in available information is integral to perceptions of the vaccine safety and effectiveness [ 15 – 17 ]. Previous studies, for example, have associated higher vaccine hesitancy with misinformation about the virus and vaccines, particularly if they relied on social media as a key source of information [ 18 , 19 ]. Social Cognitive Theory (SCT) is a widely accepted theory which may explain individual behaviors, including digital health literacy [ 20 ]. SCT consists of three factors–environmental, personal, and behavioural–and any two of these components interact with each other and influence the third. As such, SCT can assist in establishing a link between one’s behaviour (e.g., information-seeking–one form of digital health literacy) and environmental factors (e.g., availability of information online), which may interact to promote medical mistrust and influence vaccine hesitancy and confidence (personal) [ 21 ]. Thus, health behaviours are often influenced by social systems as well as personal behaviours.

Although vaccine hesitancy and confidence are related concepts (e.g., people who express low confidence in vaccines are more likely to be vaccine-hesitant [ 6 ]), they are also distinct [ 22 ]. Thus, the main aims of this research were to determine levels of hesitancy and confidence in COVID-19 vaccines among general adults in the Philippines and Malaysia, and to identify behavioural or environmental predictors that are significantly associated with both outcomes. Thus, developing a deeper understanding of the factors associated with vaccine hesitancy and confidence will provide insight into how specific population groups may respond to health threats and public health control measures.

Design, subjects, and procedure

This was an internet-based cross-sectional survey conducted from May 2021 to September 2021 in the Philippines and Malaysia. Snowball sampling methods were used for the data collection using social media, including research networks of universities, hospitals, friends, and relatives. Filipino and Malaysian residents aged 18 years or older were invited to take part. The inclusion criteria for participants’ eligibility included 18 years or older, and an understanding of the English language. All invited participants consented to the online survey before completion. Consented participants could only respond to questions once using a single account. The voluntary survey contained a series of questions which assessed sociodemographic variables, social media use, digital literacy skills in health, and attitudes toward the COVID-19 vaccine.

Ethical approval

The study received ethical approval from Asia Metropolitan University’s Medical Research and Ethics Committee (Ref: AMU/FOM/MREC 0320210018). All participants provided informed consent. All study information was written and provided on the first page of the online questionnaire, and participants indicated consent by selecting the agreement box and proceeding to the survey.

Demographics.

Filipino and Malaysian participants indicated their age category (18–24, 25–34, or 35–44), gender (man, woman), community type (rural, urban), educational level (no formal education, primary, secondary, tertiary), employment (unemployed, part-time, full-time), religion (Christian, Buddhism, Muslim, Hinduism, Other, None), income (1 = very insufficient ; 4 = very sufficient ; M = 1.84, SD = 0.81), whether they were permanently impaired by a health problem (no vs. yes), and whether they were social media users (no vs. yes).

Subjective social status.

Participant then rated their own perceived social status using the MacArthur Scale of Subjective Social Status scale [ 23 ]. Participants viewed a drawing of a ladder with 10 rungs, and read that the ladder represented where people stand in society. They read that the top of the ladder consists of people who are best off, have the most money, highest education, and best jobs, and those at the bottom of the ladder consists of people who are worst off, have the least money, lowest education, and worst or no jobs. Using a validated single-item measure, participants placed an ‘X’ on the rung that best represented where they think they stood on the ladder (1 = lowest ; 10 = highest; M = 6.23, SD = 1.86).

Vaccine confidence and hesitancy.

Participants were also asked about their perceived level of confidence in the COVID-19 vaccine (“I am completely confident that the COVID-19 vaccine is safe,” 1 = strongly disagree ; 7 = strongly agree; M = 4.57, SD = 1.48). Then, participants were asked about their level of hesitancy to the COVID-19 vaccine (“I think everyone should be vaccinated according to the national vaccination schedule”; no, I don’t know, yes). These questions were adapted from the World Health Organization, Regional Office for Europe survey [ 24 ]. The tool underwent evaluation by multidisciplinary panel of experts for necessity, clarity, and relevance.

Digital health literacy.

Finally, participants completed the Digital Health Literacy Instrument (DHLI) [ 25 ], which was adapted in the context of the COVID-HL Network. The scale measures one’s ability to seek, find, understand, and appraise health information from digital resources. A total of 12 items (three per each dimension) were asked, and answers were recorded on a four-point Likert scale (1 = very difficult ; 4 = very easy; α = .92; M = 2.15, SD = 0.59). While the original DHLI is comprised of 7 subscales, we used the following four domains, including: (1) information searching or using appropriate strategies to look for information (e.g., “When you search the internet for information on coronavirus virus or related topics, how easy or difficult is it for you to find the exact information you are looking for?”; α = .87; M = 2.15, SD = 0.65), (2) adding self-generated content to online-based platforms (e.g., “When typing a message on a forum or social media such as Facebook or Twitter about the coronavirus a related topic, how easy or difficult is it for you to express your opinion, thought, or feelings in writing?”; α = .74; M = 2.15, SD = 0.65), (3) evaluating reliability of online information (e.g., “When you search the internet for information on the coronavirus or related topics, how easy or difficult is it for you to decide whether the information is reliable or not?”; α = .86; M = 2.20, SD = 0.69), and (4) determining relevance of online information (e.g., “When you search the internet for information on the coronavirus or related topics, how easy or difficult is it for you to use the information you found to make decisions about your health [e.g., protective measures, hygiene regulations, transmission routes, risks and their prevention?”]; α = .87; M = 2.09, SD = 0.68). The reliability statistics for the overall DHL score was 0.92, while the alpha coefficients for the four subscales ranged from 0.74 to 0.87, suggesting acceptable to good internal consistency.

Data analysis

Data were examined for errors, cleaned, and exported into IBM SPSS Statistics 28 for further analysis. All hypotheses were tested at a significance level of 0.05. χ 2 tests were conducted for group differences of categorical variables, and Mann-Whitney tests for continuous variables. Subgroup analyses were performed for Filipino and Malaysian participants.

COVID-19 vaccine hesitancy and confidence were treated as separate dependent variables in a logistic regression model providing the strictest test of potential associations with COVID-19 vaccine hesitancy and confidence among Filipino and Malaysian participants. Low vaccine confidence was operationalised by dichotomising participants’ responses to the statement: “I am completely confident that the COVID-19 vaccine is safe” into those who disagreed or neither agreed nor disagreed (1–4), whereas high vaccine confidence was operationalised by dichotomising participants’ responses into those who agreed to some extent (5–7). Vaccine hesitancy was operationalised by dichotomising responses to the statement: “I think everyone should be vaccinated according to the National vaccination schedule” into those indicating ‘no’ or ‘I don’t know,’ whereas no vaccine hesitancy was operationalized by dichotomising participants’ response into those who indicated ‘yes.’

Independent variables were: age (18–24 vs. 25–34 vs. 35–44 [ref]), gender (women vs. men [ref]), community type (rural vs. urban [ref]), educational level (tertiary vs. secondary or less [ref]), employment (employed to some degree vs. unemployed [ref]), religion (Philippines: Christianity vs. Islam [ref]; Malaysia: Christianity vs. Buddhism vs. Hinduism vs. Islam [ref]), income (low (1–2) vs. high (3–4 [ref])), whether they were permanently impaired by a health problem (yes vs. no [ref]), whether they were social media users [yes vs. no [ref]), their perceived ranking on the MacArthur Scale of Subjective Social Status (continuous variable), and finally the four domains of the DHLI scale (all continuous variables).

A total of 2558 participants completed the online survey. Table 1 shows descriptive statistics of participants from the Philippines ( N = 1002) vs. Malaysia ( N = 1556). Filipino (vs. Malaysian) participants indicated higher rates of education ( p < 0.001), but were more likely to be unemployed ( p < 0.001). Further, Filipino (vs. Malaysian) participants were also more likely to indicate lower income ( p < 0.001) and rate themselves lower on subjective social status ( p < 0.001). Malaysian (vs. Filipino) participants were more likely to live in urban areas ( p < 0.001). Most notably, Filipino participants (56.6%) indicated higher prevalence of COVID-19 vaccine hesitancy compared to Malaysian participants (22.9%; p < 0.001). However, there were no significant differences between Filipino (45.9%) and Malaysian (49.2%) participants in ratings of vaccine confidence ( p = 0.105). Malaysian (vs. Filipino) participants were also more likely to report using social media (96.6 vs. 89.8%; < 0.001).

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Values are presented as percent (n) or means ± SD.

https://doi.org/10.1371/journal.pgph.0000742.t001

Table 2 shows significant predictors of vaccine hesitancy in both Filipino and Malaysian samples. Among Filipino participants, multivariate logistic regression analyses revealed that factors associated with higher vaccine hesitancy included women (OR, 1.51, 95% CI, 1.14–2.00; p = 0.004), residing in a rural community (OR, 1.45, 95% CI, 1.07–1.95; p = 0.015), and having lower income (OR, 1.62, 95% CI, 1.20–2.19; p = 0.001). Among Malaysian participants, women (OR, 1.51, 95% CI, 1.14–2.00; p = 0.004), being aged 25–34 (vs. 18–24; OR, 1.52, 95% CI, 1.48–2.21; p = 0.027), Christians (OR, 2.45, 95% CI, 1.66–3.62; p < 0.001), completing tertiary education (OR, 2.17, 95% CI, 1.63–2.88; p < 0.001), social media use (OR, 11.59, 95% CI, 5.63–23.84; p < 0.001), and information-seeking behaviours (OR, 2.50, 95% CI, 1.74–3.61; p < 0.001) were predictors of higher vaccine hesitancy, whereas having a health impairment (OR, 0.49, 95% CI, 0.30–0.78; p = 0.003) and higher self-reported ratings on subjective social status (OR, 0.82, 95% CI, 0.75–0.89; p < 0.001) were associated with lower vaccine hesitancy.

https://doi.org/10.1371/journal.pgph.0000742.t002

Table 3 shows significant predictors of vaccine confidence in both Filipino and Malaysian samples. Factors positively associated with higher vaccine confidence among Filipino participants included higher self-reported ratings on subjective social status (OR, 1.16, 95% CI, 1.07–1.25; p < 0.001), whereas factors associated with lower vaccine confidence included women (OR, 0.72, 95% CI, 0.54–0.96; p = 0.026) and information-seeking behaviours (OR, 0.63, 95% CI, 0.49–0.81; p < 0.001). Among Malaysian participants, factors positively associated with higher vaccine confidence included women (OR, 1.27, 95% CI, 1.18–1.60; p = 0.035), completing tertiary education (OR, 1.31, 95% CI, 1.03–1.66; p = 0.026), and higher self-reported ratings on subjective social status (OR, 1.08, 95% CI, 1.00–1.16; p = 0.036). Factors negatively associated with lower vaccine confidence included residing in a rural community (OR, 0.63, 95% CI, 0.47–0.87; p = 0.004), Christians (OR, 0.50, 95% CI, 1.20–2.24; p < 0.001), Buddhists (OR, 0.15., 95% CI, 0.10–0.22; p < 0.001), Hindus (OR, 0.24., 95% CI, 0.17–0.34; p = 0.004), information-seeking behaviours (OR, 0.42, 95% CI, 0.31–0.58; p < 0.001), and determining relevance of online information (OR, 0.68, 95% CI, 0.51–0.92; p = 0.013).

https://doi.org/10.1371/journal.pgph.0000742.t003

Malaysia and the Philippines are among the most populous countries in Southeast Asia. While the economic impact of the COVID-19 pandemic has been permanent in the Philippines, it has been shown thus far to be temporary in Malaysia [ 26 ]. Between January and October 2020, around 30,000 Malaysians had been infected by the virus with a mortality rate of 0.79%, while approximately 380,000 cases of COVID-19 were detected in the Philippines with a mortality rate of 1.9% [ 2 ]. Further, 61.8% of Malaysians had completed their vaccination up until September 2021, while the percentage of completed vaccinations during the same period in the Philippines was only 19.2% [ 27 ]. Vaccine uptake is likely to be a key determining factor in the outcome of a pandemic. Knowledge around factors which predict vaccine hesitancy and confidence is of the utmost important in order to improve vaccination rates. Thus, the core aims of this research were to determine levels of hesitancy and confidence in COVID-19 vaccines among general adults in the Philippines and Malaysia, and to identify behavioural or environmental predictors that are significantly associated with these outcomes.

First, while there were no significant differences in ratings of confidence in the COVID-19 vaccine between Filipino and Malaysian participants, Filipino (compared to Malaysian) participants expressed greater vaccine hesitancy. This may be a consequence of previous vaccine scares in the years leading up to the pandemic, including the Dengvaxia controversy in 2016 [ 7 , 9 ]. Systematic reviews demonstrated that, by the end of 2020, the highest vaccine acceptance was in China, Malaysia, and Indonesia [ 28 , 29 ]. The authors postulated that this elevated awareness was due to being among the first countries affected by the virus, hence resulting in greater confidence in vaccines [ 28 ].

Next, this study shows that women expressed greater vaccine hesitancy in both countries. The evidence base shows mixed findings, with other studies reporting higher hesitancy in women [ 30 ] or in men [ 31 ]. In some countries, the gender gap is not as substantial as others. In a large global study conducted in countries such as Russia and the United States, it was found that there is greater gender gap in vaccine hesitancy among men and women compared to countries such as Nepal and Sierra Leone [ 32 , 33 ]. Unsurprisingly, what drives this hesitancy is the inclusion of pregnant women, where studies have consistently demonstrated that this population is more hesitant toward vaccination due to concerns for their babies [ 34 ]. Hence, after taking all consideration into account, gender differences in vaccine hesitancy cannot be supported with certainty. This also emphasises the need for tailored health promotion towards the key populations at risk.

There are clear differences in predictors of vaccine hesitancy in the Philippines and Malaysia. However, when results for both countries were combined, women, urban dwellers, those of Christian faith, those with higher educational attainment, higher self-reported social class, social media use, and information-seeking tendencies remained as predictors of hesitancy. Urban-dwellers and individuals with more years of education have previously been demonstrated as predictors for vaccine hesitancy [ 35 ], but contradictory results have also previously been shown [ 36 , 37 ]. Urban residents are typically more connected to the internet and social media and, thus, may be more exposed to vaccine-related misinformation than rural inhabitants who have fewer sources of information available to them [ 12 – 14 ]. Nevertheless, reports have shown higher vaccine refusals among those with strong religious beliefs such as the Amish Community in the United States and the Orthodox Protestants in the Netherlands [ 38 ], as well as some Muslim groups in Pakistan [ 18 ].

Frequent social media use is the only strong predictor for vaccine hesitancy in this study, followed by information-seeking behaviours. Research has identified that the safety and effectiveness of the vaccine is the primary concern that people have, including beliefs in available information [ 15 – 17 ]. Unfortunately, high internet literacy is a double-edged sword, since participants in this study preferred to seek information through social media, and thus may have been exposed to inaccurate information regarding COVID-19 vaccine. Previous studies have associated higher vaccine hesitancy with misinformation about the virus and vaccines [ 18 ], particularly if they relied heavily on social media as a key source of vaccine-related information [ 19 ]. A 2022 systematic review discovered that high social media use is the main driver of vaccine hesitancy across all countries around the globe, and is especially prominent in Asia [ 39 ]. Furthermore, vaccine acceptance and uptake improved among those who obtained their information from healthcare providers compared to relatives or the internet [ 40 ].

In terms of vaccine confidence, our findings show that those with higher subjective social status have higher confidence in vaccination, consistent with previous studies describing how those with a higher income had expressed willingness to pay for their COVID-19 vaccination if necessary [ 32 , 41 , 42 ]. Further, those of Christian, Buddhist, and Hindu faiths, as well as those with a tendency to seek out information, were associated with lower vaccine confidence. This is in keeping with the previous findings demonstrating that strong religious convictions are often tied to mistrust of authorities and beliefs about the cause of the COVID-19 pandemic, which is fuelled by social media [ 43 ]. Furthermore, concern on the permissibility of these vaccines in their religion reduces its acceptability [ 10 ]. However, it is interesting to note that, while the majority in Malaysia are Muslims, it did not reduce the rate of vaccine acceptance and confidence in the country.

These findings have important implications for health authorities and governments in areas focusing on improving vaccination uptake. Misinformation about vaccination greatly hampers vaccination efforts. Thus, not only is it important to understand how specific population groups are influenced by digital platforms such as social media, but it is imperative to provide the right information driven by governmental and non-governmental organisations [ 39 ]. This could be achieved by having community-specific public education and role modelling from local health and public officials, which has been shown to increase public trust [ 44 ]. Since the primary reason for hesitancy is concern about the safety of vaccines, it is crucial that education programmes stress the effectiveness and importance of COVID-19 vaccinations [ 45 ]. Participants in this study coped with the pandemic by seeking out new information, but they sought information from social media when information from the authorities was lacking or were viewed as untrustworthy, which may have contained erroneous information. One way to deter this is to empower information-technology companies to monitor vaccine-related materials on social media, remove false information, and create correct and responsible content [ 44 ].

Furthermore, behavioural change techniques have been found to be useful in stressing the consequences of rejecting the vaccine on physical and mental health [ 46 ]. The most effective “nudging” interventions included offering incentives for parents and healthcare workers, providing salient information, and employing trusted figures to deliver this information [ 47 ]. Finally, since religious concerns have been prominent in reducing vaccine confidence and increasing hesitancy in this study, it is important to tailor messages to include information related to religion, and the use of religious leaders to spread these messages [ 48 ]. These are all important factors for increasing uptake of the COVID-19 vaccine, but also may be relevant in acceptability of routine immunisations as countries look to transition towards a post-pandemic delivery of healthcare.

A limitation of this study includes its cross-sectional design and the heterogeneity among participants, which meant that temporal changes in attitudes toward COVID-19 vaccines across time were not captured. Further, the need for internet access among Filipino and Malaysian participants limited the representativeness of the sample population. Thus, certain demographic were under-represented, including Filipino and Malaysian individuals over the age of 45, and people of lower socio-economic status. The surveys were also implemented in English, which may have limited the participation of target participants who were not fluent in English. In addition, due to space limitations, vaccine hesitancy and confidence were each captured using one item, which raises concerns of the items’ validity and reliability. Finally, not all independent variables were accounted for, including medical mistrust [ 49 ], vaccine knowledge [ 50 ], and specific social media platforms used [ 11 ]. We also did not assess whether participants had received any doses of the COVID-19 vaccine previously. Future research should include more important predictors to build a broader picture of vaccine-related hesitancy and confidence in the Philippines and Malaysia, and more items should be utilised to tap into these concepts more comprehensively. Despite these limitations, the core strength of this study relates to its relatively large number of participants from both countries, and its comprehensive analysis of predictors to provide as a starting point going forward.

Conclusions

The main aims of this research were to determine levels of hesitancy and confidence in COVID-19 vaccines among unvaccinated individuals in the Philippines and Malaysia, and to identify predictors significantly associated with these outcomes. Predictors of vaccine hesitancy in this study included the use of social media, information-seeking, and Christianity. Higher socioeconomic status positively predicted vaccine confidence. However, being Christian, Buddhist or Hindu, and the tendency to seek information online, were predictors of hesitancy. Efforts to improve uptake of COVID-19 vaccination must be centred upon providing accurate information to specific communities using local authorities, health services and other locally-trusted voices (such as religious leaders), and for the masses through social media. Further studies should focus on the development of locally-tailored health promotion strategies to improve vaccination confidence and increase the uptake of vaccination–especially in light of the Dengvaxia crisis in the Philippines.

Supporting information

S1 file. inclusivity in global research questionnaire..

https://doi.org/10.1371/journal.pgph.0000742.s001

• 1. Ritchie H, Mathieu E, Rodés-Guirao L, Appel C, Giattino C, Ortiz-Ospina E, et al. Coronavirus pandemic. Published online at OurWorldinData.org. Available from https://ourworldindata.org/covid-vaccinations .
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• Feature stories /

100 days of COVID-19 in the Philippines: How WHO supported the Philippine response

Exactly 100 days have passed since the first confirmed COVID-19 case was announced in the Philippines on 30 January 2020, with a 38-year old female from Wuhan testing positive for the novel coronavirus. On the same day, on the other side of the world at the WHO headquarters in Geneva, WHO activated the highest level of alert by declaring COVID-19 as a public health emergency of international concern. The Philippine government mounted a multi-sectoral response to the COVID-19, through the Interagency Task Force (IATF) on Emerging Infectious Diseases chaired by the Department of Health (DOH). Through the National Action Plan (NAP) on COVID-19, the government aims to contain the spread of COVID-19 and mitigate its socioeconomic impacts. The Philippines implemented various actions including a community quarantine in Metro Manila which expanded to Luzon as well as other parts of the country; expanded its testing capacity from one national reference laboratory with the Research Institute of Tropical Medicine (RITM) to 23 licensed testing labs across the country; worked towards ensuring that its health care system can handle surge capacity, including for financing of services and management of cases needing isolation, quarantine and hospitalization; and addressed the social and economic impact to the community including by providing social amelioration to low income families. The World Health Organization (WHO) has been working with Ministries of Health worldwide to prepare and respond to COVID-19. In the Philippines, WHO country office in the Philippines and its partners have been working with the Department of Health and subnational authorities to respond to the pandemic. The country level response is done with support from the WHO regional office and headquarters.

Surveillance

Surveillance is a critical component and is used to detect cases of COVID-19 as well as to understand the disease dynamics and trends and identify hotspots of disease transmission. The Department of Health included COVID-19 in the list of nationally notifiable diseases early in the outbreak to ensure that information was being collected to guide appropriate response actions. Existing surveillance systems were capitalized upon to speed up identification of cases as well as identify unusual clusters. Laboratory confirmation is a critical component of the surveillance system but cannot be the only sources of information. The non-specific symptoms and the novel nature of the disease means that the DOH, with support from WHO, are looking at all available information sources to guide response decision making. WHO also provided technical assistance to selected local government units to strengthen field surveillance for timely data for action at the local level.

Contact tracing

Infection prevention and control

Laboratory and therapeutics access

Clinical care

Non-pharmaceutical interventions and mental health

Risk communication and community engagement

Effective communication and engagement with communities is essential for people to understand the situation, know the situation and practice protective measures to protect their health, their family and the larger community. WHO supported and amplified DOH messaging by releasing various communication materials on the risk of COVID-19 and how people can protect themselves through social media and traditional media. WHO also worked with partners such as UNICEF and OCHA in reaching vulnerable groups, getting their feedback and understanding their information needs.

Logistics support

With lots of moving equipment and supplies required for COVID-19, logistics support is an important part of the response. WHO provided technical support to the DOH in the recalibration of PPE requirements by using WHO projection tools, provided cost estimates, and advised on streamlining the distribution flow of PPEs and other essential supplies. WHO also supported DOH in the development of a commodities dashboard that provides real-time PPE stocks at the facility level, as well as assisted in building an information system for tracking essential COVID-19 commodities.

Subnational operations support

Responding to outbreaks in high risk areas

Moving forward with the response

Much more needs to be done to break the chain of COVID-19 transmission. Some of the challenges that the Philippines continues to face are containing transmission of infection, mitigating the impact in high risks communities and confined settings, as well as ensuring the uniform enforcement of non-pharmaceutical interventions that are already in place. The continuation of the community quarantine will have substantial social and economic impact and thus a heightened effort to control  transmission of infections through rigorous contact tracing, isolation of cases, quarantine of contacts while ensuring timely and adequate treatment to save lives will continue to be the primary public health measure. In addition, while the government is exerting all its efforts in this current situation, it also needs to prepare its health systems for surge capacity in the event that a wide-scale community transmission occurs.

In the next few days, the government will carefully consider the next steps, especially on deciding whether or not the communty quarantine will be lifted or levels of quarantine will be differentiated based on the situation of provinces. WHO strongly recommends that when the government considers adjusting public health and social measures in the context of COVID-19 the following requirements must be in place:

• COVID-19 transmission is controlled through two complementary approaches – breaking chains of transmission by detecting, isolating, testing and treating cases and quarantining contacts and monitoring hot spots of disease circulation
• Sufficient public health workforce and health system capacities are in place
• Outbreak risks in high-vulnerability settings are minimized
• Preventive measures are established in workplaces
• Capacity to manage the risk of exporting and importing cases from communities with high risks of transmission
• Communities are fully engaged

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Psychological impact of COVID-19 pandemic in the Philippines

Affiliations.

• 1 Department of Physiology, College of Medicine, University of the Philippines Manila, Taft Avenue, Manila 1000, Philippines; Philippine One Health University Network. Electronic address: [email protected].
• 2 Department of Pediatrics, College of Medicine, University of the Philippines Manila, Taft Avenue, Manila 1000, Philippines. Electronic address: [email protected].
• 3 Department of Psychiatry and Behavioral Medicine, College of Medicine, University of the Philippines Manila, Taft Avenue, Manila 1000, Philippines.
• 4 School of Statistics, University of the Philippines Diliman, Philippines.
• 5 South East Asia One Health University Network. Electronic address: [email protected].
• 6 Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Kent Ridge 119228, Singapore. Electronic address: [email protected].
• PMID: 32861839
• PMCID: PMC7444468
• DOI: 10.1016/j.jad.2020.08.043

Background: The 2019 coronavirus disease (COVID-19) pandemic poses a threat to societies' mental health. This study examined the prevalence of psychiatric symptoms and identified the factors contributing to psychological impact in the Philippines.

Methods: A total of 1879 completed online surveys were gathered from March 28-April 12, 2020. Collected data included socio-demographics, health status, contact history, COVID-19 knowledge and concerns, precautionary measures, information needs, the Depression, Anxiety and Stress Scales (DASS-21) and the Impact of Events Scale-Revised (IES-R) ratings.

Results: The IES-R mean score was 19.57 (SD=13.12) while the DASS-21 mean score was 25.94 (SD=20.59). In total, 16.3% of respondents rated the psychological impact of the outbreak as moderate-to-severe; 16.9% reported moderate-to-severe depressive symptoms; 28.8% had moderate-to-severe anxiety levels; and 13.4% had moderate-to-severe stress levels. Female gender; youth age; single status; students; specific symptoms; recent imposed quarantine; prolonged home-stay; and reports of poor health status, unnecessary worry, concerns for family members, and discrimination were significantly associated with greater psychological impact of the pandemic and higher levels of stress, anxiety and depression (p<0.05). Adequate health information, having grown-up children, perception of good health status and confidence in doctors' abilities were significantly associated with lesser psychological impact of the pandemic and lower levels of stress, anxiety and depression (p<0.05).

Limitations: An English online survey was used.

Conclusion: During the early phase of the pandemic in the Philippines, one-fourth of respondents reported moderate-to-severe anxiety and one-sixth reported moderate-to-severe depression and psychological impact. The factors identified can be used to devise effective psychological support strategies.

Keywords: Anxiety; COVID-19; Depression; Philippines; Psychological impact; Stress.

Copyright © 2020 Elsevier Ltd. All rights reserved.

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Conflict of interest statement

The authors declare that there is no conflict of interest regarding the publication of this paper.

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• Published: 21 September 2021

Local government responses for COVID-19 management in the Philippines

• Dylan Antonio S. Talabis 1 , 2 ,
• Ariel L. Babierra 1 , 2 ,
• Christian Alvin H. Buhat 1 , 2 ,
• Destiny S. Lutero 1 , 2 ,
• Kemuel M. Quindala III 1 , 2 &
• Jomar F. Rabajante 1 , 2 , 3  

BMC Public Health volume  21 , Article number:  1711 ( 2021 ) Cite this article

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Responses of subnational government units are crucial in the containment of the spread of pathogens in a country. To mitigate the impact of the COVID-19 pandemic, the Philippine national government through its Inter-Agency Task Force on Emerging Infectious Diseases outlined different quarantine measures wherein each level has a corresponding degree of rigidity from keeping only the essential businesses open to allowing all establishments to operate at a certain capacity. Other measures also involve prohibiting individuals at a certain age bracket from going outside of their homes. The local government units (LGUs)–municipalities and provinces–can adopt any of these measures depending on the extent of the pandemic in their locality. The purpose is to keep the number of infections and mortality at bay while minimizing the economic impact of the pandemic. Some LGUs have demonstrated a remarkable response to the COVID-19 pandemic. The purpose of this study is to identify notable non-pharmaceutical interventions of these outlying LGUs in the country using quantitative methods.

Data were taken from public databases such as Philippine Department of Health, Philippine Statistics Authority Census, and Google Community Mobility Reports. These are normalized using Z-transform. For each locality, infection and mortality data (dataset Y ) were compared to the economic, health, and demographic data (dataset X ) using Euclidean metric d =( x − y ) 2 , where x ∈ X and y ∈ Y . If a data pair ( x , y ) exceeds, by two standard deviations, the mean of the Euclidean metric values between the sets X and Y , the pair is assumed to be a ‘good’ outlier.

Our results showed that cluster of cities and provinces in Central Luzon (Region III), CALABARZON (Region IV-A), the National Capital Region (NCR), and Central Visayas (Region VII) are the ‘good’ outliers with respect to factors such as working population, population density, ICU beds, doctors on quarantine, number of frontliners and gross regional domestic product. Among metropolitan cities, Davao was a ‘good’ outlier with respect to demographic factors.

Conclusions

Strict border control, early implementation of lockdowns, establishment of quarantine facilities, effective communication to the public, and monitoring efforts were the defining factors that helped these LGUs curtail the harm that was brought by the pandemic. If these policies are to be standardized, it would help any country’s preparedness for future health emergencies.

Peer Review reports

Introduction

Since the emergence of the COVID-19 pandemic, the number of cases have already reached 82 million worldwide at the end of 2020. In the Philippines, the number of cases exceeded 473,000. As countries around the world face the continuing threat of the COVID-19 pandemic, national governments and health ministries formulate, implement and revise health policies and standards based on recommendations by world health organization (WHO), experiences of other countries, and on-the-ground experiences. Early health measures were primarily aimed at preventing and reducing transmission in populations at risk. These measures differ in scale and speed among countries, as some countries have more resources and are more prepared in terms of healthcare capacity and availability of stringent policies [ 1 , 2 ].

During the first months of the pandemic, several countries struggled to find tolerable, if not the most effective, measures to ‘flatten’ the COVID-19 epidemic curve so that health facilities will not be overwhelmed [ 3 , 4 ]. In responding to the threat of the pandemic, public health policies included epidemiological and socio-economic factors. The success or failure of these policies exposed the strengths or weaknesses of governments as well as the range of inequalities in the society [ 5 , 6 ].

As national governments implemented large-scale ‘blanket’ policies to control the pandemic, local government units (LGUs) have to consider granular policies as well as real-time interventions to address differences in the local COVID-19 transmission dynamics due to heterogeneity and diversity in communities. Some policies in place, such as voluntary physical distancing, wearing of face masks and face shields, mass testing, and school closures, could be effective in one locality but not in another [ 7 – 9 ]. Subnational governments like LGUs are confronted with a health crisis that have economic, social and fiscal impact. While urban areas have been hot spots of the COVID-19 pandemic, there are health facilities that are already well in placed as compared to less developed and deprived rural communities [ 10 ]. The importance of local narratives in addressing subnational concerns are apparent from published experiences in the United States [ 11 ], China [ 12 , 13 ], and India [ 14 ].

In the Philippines, the Inter-Agency Task Force on Emerging Infectious Diseases (IATF) was convened by the national government in January 2020 to monitor a viral outbreak in Wuhan, China. The first case of local transmission of COVID-19 was confirmed on March 7, 2020. Following this, on March 8, the entire country was placed under a State of Public Health Emergency. By March 25, the IATF released a National Action Plan to control the spread of COVID-19. A community quarantine was initially put in place for the national capital region (NCR) starting March 13, 2020 and it was expanded to the whole island of Luzon by March 17. The initial quarantine was extended up to April 30 [ 5 , 15 ]. Several quarantine protocols were then implemented based on evaluation of IATF:

Community Quarantine (CQ) refers to restrictions in mobility between quarantined areas.

In Enhanced Community Quarantine (ECQ), strict home quarantine is implemented and movement of residents is limited to access essential goods and services. Public transportation is suspended. Only economic activities related to essential and utility services are allowed. There is heightened presence of uniformed personnel to enforce community quarantine protocols.

Modified Enhanced Community Quarantine (MECQ) is implemented as a transition phase between ECQ and GCQ. Strict home quarantine and suspension of public transportation are still in place. Mobility restrictions are relaxed for work-related activities. Government offices operates under a skeleton workforce. Manufacturing facilities are allowed to operate with up to 50% of the workforce. Transportation services are only allowed for essential goods and services.

In General Community Quarantine (GCQ), individuals from less susceptible age groups and without health risks are allowed to move within quarantined zones. Public transportation can operate at reduced vehicle capacity observing physical distancing. Government offices may be at full work capacity or under alternative work arrangements. Up to 50% of the workforce in industries (except for leisure and amusement) are allowed to work.

Modified General Community Quarantine (MGCQ) refers to the transition phase between GCQ and the New Normal. All persons are allowed outside their residences. Socio-economic activities are allowed with minimum public health standard.

LGUs are tasked to adopt, coordinate, and implement guidelines concerning COVID-19 in accordance with provincial and local quarantine protocols released by the national government [ 16 ].

In this study, we identified economic and demographic factors that are correlated with epidemiological metrics related to COVID-19, specifically to the number of infected cases and number of deaths [ 17 , 18 ]. At the regional, provincial, and city levels, we investigated the localities that differ with the other localities, and determined the possible reasons why they are outliers compared to the average practices of the others.

We categorized the data into economic, health, and demographic components (See Table  1 ). In the economic setting, we considered the number of people employed and the number of work hours. The number of health facilities provides an insight into the health system of a locality. Population and population density, as well as age distribution and mobility, were used as the demographic indicators. The data (as of November 10, 2020) from these seven factors were analyzed and compared to the number of deaths and cumulative cases in cities, provinces or regions in the Philippines to determine the outlier.

The Philippine government’s administrative structure and the availability of the data affected its range for each factor. Regional data were obtained for the economic component. For the health and demographic components, data from cities and provinces were retrieved from the sources. Due to the NCR exhibiting the highest figures in all key components, an investigation was conducted to identify an outlier among its cities. The z -transform

where x is the actual data, μ is the mean and σ is the standard deviation were applied to normalize the dataset. Two sets of normalized data X and Y were compared by assigning to each pair ( x , y ), where x ∈ X and y ∈ Y , its Euclidean metric d given by d =( x − y ) 2 . Here, the Y ’s are the number of COVID-19 cases and deaths, and X ’s are the other demographic indicators. Since 95% of the data fall within two standard deviations from the mean, this will be the threshold in determining an outlier. This means that if a data pair ( x , y ) exceeds, by two standard deviations, the mean of the Euclidean metric values between the sets X and Y , the pair is assumed to be an outlier.

To identify a good outlier, a bias computation was performed. In this procedure, Y represents the normalized data set for the number of deaths or the number of cases while X represents the normalized data set for every factor that were considered in this study. The bias is computed using the metric

for all x in X and y in Y . To categorize a city, province, or region as a good outlier, the bias corresponding to this locality must exceed two standard deviations from the mean of all the bias computations between the sets X and Y .

Results and discussion

The data used were the reported COVID-19 cases and deaths in the Philippines as of November 10, 2020 which is 240 days since community lockdowns were implemented in the country. Figure  1 shows the different lockdowns implemented per province since March 15. It can be seen that ECQ was implemented in Luzon and major cities in the country in the first few weeks since March 15, and slowly eased into either GCQ or MGCQ as time progressed. By August, the most stringent lockdown was MECQ in the National Capital Region (NCR) and some nearby provinces. Places under MECQ on September were Iloilo City, Bacolod City, and Lanao del Sur, with the last province as the lone community to be placed under MECQ the month after. By November 1, 2020, communities were either placed under GCQ or MGCQ.

COVID-19 community quarantines in Regions III, IVA and VII

Comparison of economic, health, and demographic components and COVID-19 parameters

The economic, health and demographic components were compared to COVID-19 cases and deaths. These comparisons were done for different community levels (regional, provincial, city/metropolitan) (See Tables  2 , 3 , and 4 ). Figure  2 summarizes the correlation of components to COVID-19 cases and deaths at the regional level. In all components, correlations with other parameters to both COVID-19 cases and deaths are close. Every component except Residential Mobility and GRDP have slightly higher correlation coefficient for COVID-19 cases as compared to COVID-19 deaths.

Correlation of components to COVID-19 cases and deaths at the regional level

Among the components, the number of ICU beds component has the highest correlation with COVID-19 parameters. This makes sense as this is one of the first-degree measures of COVID-19 transmission. Population density comes in second, followed by mean hours worked and working population, which are all related to how developed the region is economy-wise. Regions having larger population density also have a huge working population and longer working hours [ 24 ]. Thus, having a huge population density implies high chance of having contact with each other [ 25 , 26 ]. Another component with high correlation to the cases and deaths is the number of doctors on quarantine, which can be looked at two ways; (i) huge infection rate in the region which is the reason the doctors got exposed or are on quarantine, and (ii) lots of doctors on quarantine which resulted to less frontliners taking care of the infected individuals. All definitions of mobility and the GDP are not strongly correlated to any of the COVID-19 measures.

In each data set, outliers were identified depending on their distance from the mean. For simplicity, we denote components that are compared with COVID-19 cases by (C) and with COVID-19 deaths by (D). The summary of outliers among regions in the Philippines is shown in Figs.  3 and 4 . Data is classified according to groups of component. In each outlier region, non-pharmaceutical interventions (NPI) implemented and their timing are identified.

Outliers among regions in the Philippines with respect to COVID-19 cases

Outliers among regions in the Philippines with respect to COVID-19 deaths

Region III is an outlier in terms of working population (C) and the number of ICU beds (C) (see Fig.  5 and Table  5 ). This means that considering the working population of the region, the number of COVID-19 infections are better than that of other regions. Same goes with the number of ICU beds in relation to COVID-19 deaths. Region III is comprised of Aurora, Bataan, Nueva Ecija, Pampanga, Tarlac, Zambales, and Bulacan. This good performance might be attributed to their performance especially on their programs against COVID-19. As early as March 2020, the region had been under a community lockdown together with other regions in Luzon. Being the closest to NCR, Bulacan has been the most likely to have high number of COVID-19 cases in the region. But the province responded by opening infection control centers which offer free healthcare, meals, and rooms for moderate-severe COVID-19 patients [ 27 ]. They have also implemented strict monitoring of entry-exit borders, organization of provincial task force and incident command center, establishment of provincial quarantine facilities for returning overseas Filipino workers, mandated municipal quarantine facilities for asymptomatic cases, and mass testing, among others [ 27 ]. Most of which have been proven effective in reducing the number of COVID-19 cases and deaths [ 28 ].

Outliers among the provinces in Luzon with respect to COVID-19 cases and deaths

Outliers among the provinces in Visayas with respect to COVID-19 cases and deaths

Outliers among the provinces in Mindanao with respect to COVID-19 cases and deaths

Region IV-A is an outlier in terms of population and working population (D) and doctors on quarantine (D) (see Fig.  5 and Table  5 ). Considering their population and working population, the COVID-19 death statistics show better results compared to other regions. Same goes with the number of doctors in the region which are in quarantine in relation to the reported COVID-19 deaths. This shows that the region is doing well in terms of decreasing the COVID-19 fatalities compared to other regions in terms of populations and doctors on quarantine. Region IV-A is comprised of Batangas, Cavite, Laguna, Quezon, and Rizal. Same with Region III, they have been under the community lockdown since March of last year. Provinces of the region such as Rizal have been proactive in responding to the epidemic as they have already suspended classes and distributed face masks even before the nationwide lockdown [ 29 ]. Despite being hit by natural calamities, the region still continue ramping up the response to the pandemic through cash assistance, first aid kits, and spreading awareness [ 30 ].

An interesting result is that NCR, the center of the country and the most densely populated, is a good outlier in terms of GRDP (C) and GRDP (D). Cities in the region launched various programs in order to combat the disease. They have launched mass testings with Quezon City, Taguig City, and Caloocan City starting as early as April 2020. Pasig City started an on-the-go market called Jeepalengke. Navotas, Malabon, and Caloocan recorded the lowest attack rate of the virus. Caloocan city had good strategies for zoning, isolation and even in finding ways to be more effective and efficient. Other programs also include color-coded quarantine pass, and quarantine bands. It is also possible that NCR may just have a very high GRDP compared to other regions. A breakdown of the outliers within NCR can be seen in Fig.  8 .

Outliers in the national capital region with respect to COVID-19 cases and deaths

Region VII is also an outlier in terms of population density (D) and frontliners (D) (see Fig.  6 and Table  5 ). This means that given the population density and the number of frontliners in the region, their COVID-related deaths in the region is better than the rest of the country. This region consists of four provinces (Cebu, Bohol, Negros Oriental, and Siquijor) and three highly urbanized cities (Cebu City, Lapu-Lapu City, and Mandaue City), referred to as metropolitan Cebu. This significant decline may be explained by how the local government responded after they were placed in stricter community quarantine measures despite the rest of the country easing in to more lenient measures. Due to the longer and stricter quarantine in Cebu, the lockdown had a greater impact here than in other areas where restrictions were eased earlier [ 31 ]. Dumaguete was one of the destinations of the first COVID case in the Philippines [ 32 ], their local government was able to keep infections at bay early on. Siquijor was also COVID-19-free for 6 months [ 33 ]. The compounded efforts of the different provinces in the region can account for the region being identified as an outlier.

Among the metropolitan cities, Davao came out as a good outlier in terms of population (C) and working population (C) (see Figs.  7 , 9 , and Table  5 ). This result may be attributed to their early campaign on consistent communication of COVID-19-related concerns to the public [ 34 ]. They were also able to set up transportation for essential workers early on [ 35 ].

Outliers among metropolitan areas in the Philippines with respect to COVID-19 cases and deaths

This study identified outliers in each data group and determined the NPIs implemented in the locality. Economic, health and demographic components were used to identify these outliers. For the regional data, three regions in Luzon and one in Visayas were identified as outliers. Apart from the minimum IATF recommended NPIs, various NPIs were implemented by different regions in containing the spread of COVID-19 in their areas. Some of these NPIs were also implemented in other localities yet these other localities did not come out as outliers. This means that one practice cannot be the sole explanation in determining an outlier. The compounding effects of practices and their timing of implementation are seen to have influenced the results. A deeper analysis of daily data for different trends in the epidemic curve is considered for future research.

Correlation tables, outliers and community quarantine timeline

Availability of data and materials.

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

JFR is supported by the Abdus Salam International Centre for Theoretical Physics Associateship Scheme.

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Dylan Antonio S. Talabis, Ariel L. Babierra, Christian Alvin H. Buhat, Destiny S. Lutero, Kemuel M. Quindala III & Jomar F. Rabajante

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S. Talabis, D.A., Babierra, A.L., H. Buhat, C.A. et al. Local government responses for COVID-19 management in the Philippines. BMC Public Health 21 , 1711 (2021). https://doi.org/10.1186/s12889-021-11746-0

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ORIGINAL RESEARCH article

Impact of the covid-19 pandemic on physical and mental health in lower and upper middle-income asian countries: a comparison between the philippines and china.

• 1 College of Medicine, University of the Philippines Manila, Manila, Philippines
• 2 Faculty of Education, Institute of Cognitive Neuroscience, Huaibei Normal University, Huaibei, China
• 3 Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
• 4 Southeast Asia One Health University Network, Chiang Mai, Thailand
• 5 Department of Psychological Medicine, National University Health System, Singapore, Singapore
• 6 Institute of Health Innovation and Technology (iHealthtech), National University of Singapore, Singapore, Singapore

Objective: The differences between the physical and mental health of people living in a lower-middle-income country (LMIC) and upper-middle-income country (UMIC) during the COVID-19 pandemic was unknown. This study aimed to compare the levels of psychological impact and mental health between people from the Philippines (LMIC) and China (UMIC) and correlate mental health parameters with variables relating to physical symptoms and knowledge about COVID-19.

Methods: The survey collected information on demographic data, physical symptoms, contact history, and knowledge about COVID-19. The psychological impact was assessed using the Impact of Event Scale-Revised (IES-R), and mental health status was assessed by the Depression, Anxiety, and Stress Scale (DASS-21).

Findings: The study population included 849 participants from 71 cities in the Philippines and 861 participants from 159 cities in China. Filipino (LMIC) respondents reported significantly higher levels of depression, anxiety, and stress than Chinese (UMIC) during the COVID-19 ( p < 0.01) while only Chinese respondents' IES-R scores were above the cut-off for PTSD symptoms. Filipino respondents were more likely to report physical symptoms resembling COVID-19 infection ( p < 0.05), recent use of but with lower confidence on medical services ( p < 0.01), recent direct and indirect contact with COVID ( p < 0.01), concerns about family members contracting COVID-19 ( p < 0.001), dissatisfaction with health information ( p < 0.001). In contrast, Chinese respondents requested more health information about COVID-19. For the Philippines, student status, low confidence in doctors, dissatisfaction with health information, long daily duration spent on health information, worries about family members contracting COVID-19, ostracization, and unnecessary worries about COVID-19 were associated with adverse mental health. Physical symptoms and poor self-rated health were associated with adverse mental health in both countries ( p < 0.05).

Conclusion: The findings of this study suggest the need for widely available COVID-19 testing in MIC to alleviate the adverse mental health in people who present with symptoms. A health education and literacy campaign is required in the Philippines to enhance the satisfaction of health information.

Introduction

The World Health Organization (WHO) declared coronavirus disease 2019 (COVID-19) to be a Public Health Emergency of International Concern on January 30 ( 1 ) and a pandemic on March 11, 2020 ( 2 ). COVID-19 predominantly presents with respiratory symptoms (cough, sneezing, and sore throat), along with fever, fatigue and myalgia. It is thought to spread through droplets, contaminated surfaces, and asymptomatic individuals ( 3 ). By the end of April, over 3 million people have been infected globally ( 4 ).

The first country to identify the novel virus as the cause of the pandemic was China. The authorities responded with unprecedented restrictions on movement. The response included stopping public transport before Chinese New Year, an annual event that sees workers' mass emigration to their hometowns, and a lockdown of whole cities and regions ( 1 ). Two new hospitals specifically designed for COVID-19 patients were rapidly built in Wuhan. Such measures help slow the transmission of COVID-19 in China. As of May 2, there are 83,959 confirmed cases and 4,637 deaths from the virus in China ( 4 ). The Philippines was also affected early by the current crisis. The first case was suspected on January 22, and the country reported the first death from COVID-19 outside of mainland China ( 5 ). Similar to China, the Philippines implemented lockdowns in Manila. Other measures included the closure of schools and allowing arrests for non-compliance with measures ( 6 ). At the beginning of May, the Philippines recorded 8,772 cases and 579 deaths ( 4 ).

China was one of the more severely affected countries in Asia in the early stage of pandemic ( 7 ) while the Philippines is still experiencing an upward trend in the COVID-19 cases ( 6 ). The gross national income (GNI) per capita of the Philippines and China are USD 3,830 and 9,460, respectively, were classified with lower (LMIC) and upper-middle-income countries (UMIC) by the Worldbank ( 8 ). During the COVID-19 pandemic, five high-income countries (HIC), including the United States, Italy, the United Kingdom, Spain, and France, account for 70% of global deaths ( 9 ). The HIC faced the following challenges: (1) the lack of personal protection equipment (PPE) for healthcare workers; (2) the delay in response strategy; (3) an overstretched healthcare system with the shortage of hospital beds, and (4) a large number of death cases from nursing homes ( 10 ). The COVID-19 crisis threatens to hit lower and middle-income countries due to lockdown excessively and economic recession ( 11 ). A systematic review on mental health in LMIC in Asia and Africa found that LMIC: (1) do not have enough mental health professionals; (2) the negative economic impact led to an exacerbation of mental issues; (3) there was a scarcity of COVID-19 related mental health research in Asian LMIC ( 12 ). This systematic review could not compare participants from different middle-income countries because each study used different questionnaires. During the previous Severe Acute Respiratory Syndrome (SARS) epidemic, the promotion of protective personal health practices to reduce transmission of the SARS virus was found to reduce the anxiety levels in the community ( 13 ).

Before COVID-19, previous studies found that stress might be a modifiable risk factor for depression in LMICs ( 14 ) and UMICs ( 15 – 17 ). Another study involving thirty countries found that unmodifiable risk factors for depression included female gender, and depression became more common in 2004 to 2014 compared to previous periods ( 18 ). Further, there were cultural differences in terms of patient-doctor relationship and attitudes toward healthcare systems before the COVID-19 pandemic. In China, <20% of the general public and medical professionals view the doctor and patient relationship as harmonious ( 19 ). In contrast, Filipino seemed to have more trust and be compliant to doctors' recommendations ( 20 ). Patient satisfaction was more important than hospital quality improvement to maintain patient loyalty to the Chinese healthcare system ( 21 ). For Filipinos, improvement in the quality of healthcare service was found to improve patients' satisfaction ( 22 ).

Based on the above studies, we have the following research questions: (1) whether COVID-19 pandemic could be an important stressor and risk factor for depression for the people living in LMIC and UMIC ( 23 ), (2) Are physical symptoms that resemble COVID-19 infection and other concerns be risk factors for adverse mental health? (3) Are knowledge of COVID-19 and health information protective factors for mental health? (4) Would there be any cultural differences in attitudes toward doctors and healthcare systems during the pandemic between China and the Philippines? We hypothesized that UMIC (China) would have better physical and mental health than LMIC (the Philippines). The aims of this study were (a) to compare the physical and mental health between citizens from an LMIC (the Philippines) and UMIC (China); (b) to correlate psychological impact, depression, anxiety, and stress scores with variables relating to physical symptoms, knowledge, and concerns about COVID-19 in people living in the Philippines (LMIC) and China (UMIC).

Study Design and Study Population

We conducted a cross-cultural and quantitative study to compare Filipinos' physical and mental health with Chinese during the COVID-19 pandemic. The study was conducted from February 28 to March 1 in China and March 28 to April 7, 2020 in the Philippines, when the number of COVID-19 daily reported cases increased in both countries. The Chinese participants were recruited from 159 cities and 27 provinces. The Filipino participants, on the other hand, were recruited from 71 cities and 40 provinces representing the Luzon, Visayas, and Mindanao archipelago. A respondent-driven recruitment strategy was utilized in both countries. The recruitment started with a set of initial respondents who were associated with the Huaibei Normal University of China and the University of the Philippines Manila; who referred other participants by email and social network; these in turn refer other participants across different cities in China and the Philippines.

As both Chinese and Filipino governments recommended that the public minimize face-to-face interaction and isolate themselves during the study period, new respondents were electronically invited by existing study respondents. The respondents completed the questionnaires through an online survey platform (“SurveyStar,” Changsha Ranxing Science and Technology in China and Survey Monkey Online Survey in the Philippines). The Institutional Review Board of the University of Philippines Manila Research Ethics Board (UPMREB 2020-198-01) and Huaibei Normal University (China) approved the research proposal (HBU-IRB-2020-002). All respondents provided informed or implied consent. The collected data were anonymous and treated as confidential.

This study used the National University of Singapore COVID-19 questionnaire, and its psychometric properties had been established in the initial phase of the COVID-19 epidemic ( 24 ). The National University of Singapore COVID-19 questionnaire consisted of questions that covered several areas: (1) demographic data; (2) physical symptoms related to COVID-19 in the past 14 days; (3) contact history with COVID-19 in the past 14 days; and (4) knowledge and concerns about COVID-19.

Demographic data about age, gender, education, household size, marital status, parental status, and residential city in the past 14 days were collected. Physical symptoms related to COVID-19 included breathing difficulty, chills, coryza, cough, dizziness, fever, headache, myalgia, sore throat, nausea, vomiting, and diarrhea. Respondents also rated their physical health status and stated their history of chronic medical illness. In the past 14 days, health service utilization variables included consultation with a doctor in the clinic, being quarantined by the health authority, recent testing for COVID-19 and medical insurance coverage. Knowledge and concerns related to COVID-19 included knowledge about the routes of transmission, level of confidence in diagnosis, source, and level of satisfaction of health information about COVID-19, the likelihood of contracting and surviving COVID-19 and the number of hours spent on viewing information about COVID-19 per day.

The psychological impact of COVID-19 was measured using the Impact of Event Scale-Revised (IES-R). The IES-R is a self-administered questionnaire that has been well-validated in the European and Asian population for determining the extent of psychological impact after exposure to a traumatic event (i.e., the COVID-19 pandemic) within one week of exposure ( 25 , 26 ). This 22-item questionnaire, composed of three subscales, aims to measure the mean avoidance, intrusion, and hyperarousal ( 27 ). The total IES-R score is divided into 0–23 (normal), 24–32 (mild psychological impact), 33–36 (moderate psychological impact) and >37 (severe psychological impact) ( 28 ). The total IES-R score > 24 suggests the presence of post-traumatic stress disorder (PTSD) symptoms ( 29 ).

The respondents' mental health status was measured using the Depression, Anxiety, and Stress Scale (DASS-21) and the calculation of scores was based on a previous Asian study ( 30 ). DASS has been demonstrated to be a reliable and valid measure in assessing mental health in Filipinos ( 31 – 33 ) and Chinese ( 34 , 35 ). IES-R and DASS-21 were previously used in research related to the COVID-19 epidemic ( 26 , 36 – 38 ).

Statistical Analysis

Descriptive statistics were calculated for demographic characteristics, physical symptom, and health service utilization variables, contact history variables, knowledge and concern variables, precautionary measure variables, and additional health information variables. To analyze the differences in the levels of psychological impact, levels of depression, anxiety and stress, the independent sample t -test was used to compare the mean score between the Filipino (LMIC) and Chinese (UMIC) respondents. The chi-squared test was used to analyze the differences in categorical variables between the two samples. We used linear regressions to calculate the univariate associations between independent and dependent variables, including the IES-S score and DASS stress, anxiety, and depression subscale scores for the Filipino and Chinese respondents separately with adjustment for age, marital status, and education levels. All tests were two-tailed, with a significance level of p < 0.05. Statistical analysis was performed on SPSS Statistic 21.0.

Demographic Characteristics and Their Association With Psychological Impact and Adverse Mental Health Status

We received 849 responses from the Philippines and 861 responses from China for 1,710 individual respondents from both countries. The majority of Filipino respondents were women (71.0%), age between 22 and 30 years (26.6%), having a household size of 3–5 people (53.4%), high educational attainment (91.4% with a bachelor or higher degree), and married (68.9%). Similarly, the majority of Chinese respondents were women (75%), having a household size of 3–5 people (80.4%) and high educational attainment (91.4% with a bachelor or higher degree). There was a significantly higher proportion of Chinese respondents who had children younger than 16 years ( p < 0.001) and student status ( p < 0.001; See Table 1 ).

Table 1 . Comparison of demographic characteristics between Filipino (LMIC) and Chinese (UMIC) respondents ( N = 1,710).

For Filipino respondents, the male gender and having a child were protective factors significantly associated with the lower score of IES-R ( p < 0.05) and depression ( p < 0.001), respectively. Single status was significantly associated with depression ( p < 0.05), and student status was associated with higher IES-R, stress and depression scores ( p < 0.01) (see Table 2 ). For Chinese respondents, the male gender was significantly associated with a lower score of IES-R but higher DASS depression scores ( p < 0.01). Notwithstanding, there were other differences between Filipino and China respondents. Chinese respondents who stayed in a household with 3–5 people ( p < 0.05) and more than 6 people ( p < 0.05) were significantly associated with a higher score of IES-R as compared to respondents who stayed alone.

Table 2 . Comparison of the association between demographic variables and the psychological impact as well as adverse mental health status between Filipino (LMIC) and Chinese (UMIC) respondents ( n = 1,710).

Comparison Between the Filipino (LMIC) and Chinese (UMIC) Respondents and Their Mental Health Status

Figure 1 compares the mean scores of DASS-stress, anxiety, and depression subscales and IES-R scores between the Filipino and Chinese respondents. For the DASS-stress subscale, Filipino respondents reported significantly higher stress ( p < 0.001), anxiety ( p < 0.01), and depression ( p < 0.01) than Chinese (UMIC). For IES-R, Filipino (LMIC) had significantly lower scores than Chinese ( p < 0.001). The mean IES-R scores of Chinese were higher than 24 points, indicating the presence of PTSD symptoms in Chinese respondents only.

Figure 1 . Comparison of the mean scores of DASS-stress, anxiety and depression subscales, and IES-R scores between Filipino and Chinese respondents.

Physical Symptoms, Health Status, and Its Association With Psychological Impact and Adverse Mental Health Status

There were significant differences between Filipino (LMIC) and Chinese (UMIC) respondents regarding physical symptoms resembling COVID-19 and health status. There was a significantly higher proportion of Filipino respondents who reported headache ( p < 0.001), myalgia ( p < 0.001), cough ( p < 0.001), breathing difficulty ( p < 0.001), dizziness ( p < 0.05), coryza ( p < 0.001), sore throat ( p < 0.001), nausea and vomiting ( p < 0.001), recent consultation with a doctor ( p < 0.01), recent hospitalization ( p < 0.001), chronic illness ( p < 0.001), direct ( p < 0.001), and indirect ( p < 0.001) contact with a confirmed diagnosis of COVID-19 as compared to Chinese (see Supplementary Table 1 ). Significantly more Chinese respondents were under quarantine ( p < 0.001).

Linear regression showed that headache, myalgia, cough, dizziness, coryza as well as poor self-rated physical health were significantly associated with higher IES-R scores, DASS-21 stress, anxiety, and depression subscale scores in both countries after adjustment for confounding factors ( p < 0.05; see Table 3 ). Furthermore, breathing difficulty, sore throat, and gastrointestinal symptoms were significantly associated with higher DASS-21 stress, anxiety and depression subscale scores in both countries ( p < 0.05). Chills were significantly associated with higher DASS-21 stress and depression scores ( p < 0.01) in both countries. Recent quarantine was associated with higher DASS-21 subscale scores in Chinese respondents only ( p < 0.05).

Table 3 . Association between physical health status and contact history and the perceived impact of COVID-19 outbreak as well as adverse mental health status during the epidemic after adjustment for age, gender, and marital status ( n = 1,710).

Perception, Knowledge, and Concerns About COVID-19 and Its Association With Psychological Impact and Adverse Mental Health Status

Filipino (LMIC) and Chinese (UMIC) respondents held significantly different perceptions in terms of knowledge and concerns related to COVID-19 (see Supplementary Table 2 ). For the routes of transmission, there were significantly more Filipino respondents who agreed that droplets transmitted the COVID-19 ( p < 0.001) and contact via contaminated objects ( p < 0.001), but significantly more Chinese agreed with the airborne transmission ( p < 0.001). For the detection and risk of contracting COVID-19, there were significantly more Filipino who were not confident about their doctor's ability to diagnose COVID-19 ( p < 0.001). There were significantly more Filipino respondents who were worried about their family members contracting COVID-19 ( p < 0.001). For health information, there were significantly more Filipino who were unsatisfied with the amount of health information ( p < 0.001) and spent more than three hours per day on the news related to COVID-19 ( p < 0.001). There were significantly more Chinese respondents who felt ostracized by other countries ( p < 0.001).

Linear regression analysis after adjustment of confounding factors showed that the Filipino and Chinese respondents showed different findings (see Table 4 ). Chinese respondents who reported a very low perceived likelihood of contracting COVID-19 were significantly associated with lower DASS depression scores ( p < 0.05). There were similarities between the two countries. Filipino and Chinese respondents who perceived a very high likelihood of survival were significantly associated with lower DASS-21 depression scores ( p < 0.05). Regarding the level of confidence in the doctor's ability to diagnose COVID-19, both Filipino and Chinese respondents who were very confident in their doctors were significantly associated with lower DASS-21 depression scores ( p < 0.01). Filipino and Chinese respondents who were satisfied with health information were significantly associated with lower DASS-21 anxiety and depression scores ( p < 0.01). Chinese and Filipino respondents who were worried about their family members contracting COVID-19 were associated with higher IES-R and DASS-21 subscale scores ( p < 0.05). In contrast, only Filipino respondents who spent <1 h per day monitoring COVID-19 information was significantly associated with lower IES-R and DASS-21 stress and anxiety scores ( p < 0.05). Filipino respondents who felt ostracized were associated with higher IES-R and stress scores ( p < 0.05).

Table 4 . Comparison of association of knowledge and concerns related to COVID-19 with mental health status after adjustment for age, gender, and marital status ( N = 1,710).

Health Information About COVID-19 and Its Association With Psychological Impact and Adverse Mental Health Status

Filipino (LMIC) and Chinese (UMIC) respondents held significantly different views on the information required about COVID-19. There were significantly more Chinese respondents who needed information on the symptoms related to COVID-19, prevention methods, management and treatment methods, regular information updates, more personalized information, the effectiveness of drugs and vaccines, number of infected by geographical locations, travel advice and transmission methods as compared to Filipino ( p < 0.01; See Supplementary Table 3 ). In contrast, there were significantly more Filipino respondents who needed information on other countries' strategies and responses than Chinese ( p < 0.001).

Information on management methods and transmission methods were significantly associated with higher IES-R scores in Chinese respondents ( p < 0.05; see Table 5 ). Travel advice, local transmission data, and other countries' responses were significantly associated with lower DASS-21 stress and depression scores in Chinese respondents only ( p < 0.05). There was only one significant association observed in Filipino respondents; information on transmission methods was significantly associated with lower DASS-21 depression scores ( p < 0.05).

Table 5 . Comparison of the association between information needs about COVID-19 and the psychological impact as well as adverse mental health status between Filipino (LMIC) and Chinese (UMIC) participants after adjustment for age, gender, and marital status ( N = 1,710).

To our best knowledge, this is the first study that compared the physical and mental health as well as knowledge, attitude and belief about COVID-19 between citizens from an LMIC (The Philippines) and UMIC (China). Filipino respondents reported significantly higher levels of depression, anxiety and stress than Chinese during the COVID-19, but only the mean IES-R scores of Chinese respondents were above the cut-off scores for PTSD symptoms. Filipino respondents were more likely to report physical symptoms resembling COVID-19 infection, recent use of medical services with lower confidence, recent direct, and indirect contact with COVID, concerns about family members contracting COVID-19 and dissatisfaction with health information. In contrast, Chinese respondents requested more health information about COVID-19 and were more likely to stay at home for more than 20–24 h per day. For the Filipino, student status, low confidence in doctors, unsatisfaction of health information, long hours spent on health information, worries about family members contracting COVID-19, ostracization, unnecessary worries about COVID-19 were associated with adverse mental health.

The most important implication of the present study is to understand the challenges faced by a sample of people from an LMIC (The Philippines) compared to a sample of people from a UMIC (China) in Asia. As physical symptoms resembling COVID-19 infection (e.g., headache, myalgia, dizziness, and coryza) were associated with adverse mental health in both countries, this association could be due to lack of confidence in healthcare system and lack of testing for coronavirus. Previous research demonstrated that adverse mental health such as depression could affect the immune system and lead to physical symptoms such as malaise and other somatic symptoms ( 39 , 40 ). Based on our findings, the strategic approach to safeguard physical and mental health for middle-income countries would be cost-effective and widely available testing for people present with COVID-19 symptoms, providing a high quality of health information about COVID-19 by health authorities.

Students were afraid that confinement and learning online would hinder their progress in their studies ( 41 ). This may explain why students from the Philippines reported higher levels of IES-R and depression scores. Schools and colleges should evaluate the blended implementation of online and face-to-face learning to optimize educational outcomes when local spread is under control. As a significantly higher proportion of Filipino respondents lack confidence in their doctors, health authorities should ensure adequate training and develop hospital facilities to isolate COVID-19 cases and prevent COVID-19 spread among healthcare workers and patients ( 42 ). Besides, our study found that Filipino respondents were dissatisfied with health information. In contrast, Chinese respondents demanded more health information related to COVID-19. The difference could be due to stronger public health campaign launched by the Chinese government including national health education campaigns, a health QR (Quick Response) code system and community engagement that effectively curtailed the spread of COVID-19 ( 43 ). The high expectation for health information could be explained by high education attainment of participants as about 91.4 and 87.6% of participants from China and the Philippines have a university education.

Furthermore, the governments must employ communication experts to craft information, education, and messaging materials that are target-appropriate to each level of understanding in the community. That the Chinese Government rapidly deployed medical personnel and treated COVID-19 patients at rapidly-built hospitals ( 44 ) is in itself a confidence-building measure. Nevertheless, recent quarantine was associated with higher DASS-21 subscale scores in Chinese respondents only. It could be due to stricter control and monitoring of movements imposed by the Chinese government during the lockdown ( 45 ). Chinese respondents who stayed with more than three family members were associated with higher IES-R scores. The high IES-R scores could be due to worries of the spread of COVID-19 to family members and overcrowded home environment during the lockdown. The Philippines also converted sports arena into quarantine/isolation areas for COVID-19 patients with mild symptoms. These prompt actions helped restore public confidence in the healthcare system ( 46 ). A recent study reported that cultural factors, demand pressure for information, the ease of information dissemination via social networks, marketing incentives, and the poor legal regulation of online contents are the main reasons for misinformation dissemination during the COVID-19 pandemic ( 47 ). Bastani and Bahrami ( 47 ) recommended the engagement of health professionals and authorities on social media during the pandemic and the improvement of public health literacy to counteract misinformation.

Chinese respondents were more likely to feel ostracized and Filipino respondents associated ostracization with adverse mental health. Recently, the editor-in-chief of The Lancet , Richard Horton, expressed concern of discrimination of a country or particular ethnic group, saying that while it is important to understand the origin and inter-species transmission of the coronavirus, it was both unhelpful and unscientific to point to a country as the origin of the Covid-19 pandemic, as such accusation could be highly stigmatizing and discriminatory ( 48 ). The global co-operation involves an exchange of expertise, adopting effective prevention strategies, sharing resources, and technologies among UMIC and LIMC to form a united front on tackling the COVID-19 pandemic remains a work in progress.

Strengths and Limitations

The main strength of this study lay in the fact that we performed in-depth analysis and studied the relationship between physical and mental outcomes and other variables related to COVID-19 in the Philippines and China. However, there are several limitations to be considered when interpreting the results. Although the Philippines is a LMIC and China is a UMIC, the findings cannot be generalized to other LIMCs and UMICs. Another limitation was the potential risk of sampling bias. This bias could be due to the online administration of questionnaires, and the majority of respondents from both countries were respondents with good educational attainment and internet access. We could not reach out to potential respondents without internet access (e.g., those who stayed in the countryside or remote areas). Further, our findings may not be generalizable to other middle-income countries.

During the COVID-19 pandemic, Filipinos (LMIC) respondents reported significantly higher levels of depression, anxiety and stress than Chinese (UMIC). Filipino respondents were more likely to report physical symptoms resembling COVID-19 infection, recent use of medical services with lower confidence, recent direct and indirect contact with COVID, concerns about family members contracting COVID-19 and dissatisfaction with health information than Chinese. For the current COVID-19 and future pandemic, Middle income countries need to adopt the strategic approach to safeguard physical and mental health by establishing cost-effective and widely available testing for people who present with COVID-19 symptoms; provision of high quality and accurate health information about COVID-19 by health authorities. Our findings urge middle income countries to prevent ostracization of a particular ethnic group, learn from each other, and unite to address the challenge of the COVID-19 pandemic and safeguard physical and mental health.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

Ethical review and approval was required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin. The Institutional Review Board of the University of Philippines Manila Research Ethics Board (UPMREB 2020- 198-01) and Huaibei Normal University (China) approved the research proposal (HBU-IRB-2020-002).

Author Contributions

Concept and design: CW, MT, CT, RP, VK, and RH. Acquisition, analysis, and interpretation of data: CW, MT, CT, RP, LX, CHa, XW, YT, and VK. Drafting of the manuscript: CW, MT, CT, RH, and JA. Critical revision of the manuscript: MT, CT, CHo, and JA. Statistical analysis: CW, PR, RP, LX, XW, and YT. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyt.2020.568929/full#supplementary-material

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Keywords: anxiety, China, COVID-19, depression, middle-income, knowledge, precaution, Philippines

Citation: Tee M, Wang C, Tee C, Pan R, Reyes PW, Wan X, Anlacan J, Tan Y, Xu L, Harijanto C, Kuruchittham V, Ho C and Ho R (2021) Impact of the COVID-19 Pandemic on Physical and Mental Health in Lower and Upper Middle-Income Asian Countries: A Comparison Between the Philippines and China. Front. Psychiatry 11:568929. doi: 10.3389/fpsyt.2020.568929

Received: 02 June 2020; Accepted: 22 December 2020; Published: 09 February 2021.

Reviewed by:

Copyright © 2021 Tee, Wang, Tee, Pan, Reyes, Wan, Anlacan, Tan, Xu, Harijanto, Kuruchittham, Ho and Ho. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Cuiyan Wang, wcy@chnu.edu.cn

† These authors share first authorship

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Stanford University

COVID-19 in the Philippines – at a Glance

• Marjorie Pajaron

This is the third installment in our series, “Stories in a Time of Pandemic,” in which APARC alumni across Asia share their perspectives on the responses to and implications of COVID-19 in their communities. In part 1 and part 2 of the series, we feature observations from our alumni in China, Mongolia, Myanmar, and Singapore.

The first case of COVID-19 in the Philippines was reported on January 30, 2020, and local transmission was confirmed on March 7, 2020. As of May 21, the number of cases of COVID-19 has risen to 13,434 and the number of deaths attributed to the virus increased to 846, according to the Philippine Department of Health COVID-19 Case Tracker . It is quite alarming that among the ASEAN countries, the Philippines had the second-highest mortality due to COVID-19, next to Indonesia (as per May 5 date by the WHO COVID-19 Dashboard ). This could be attributed to several factors, including whether the country’s health system can handle the overwhelming demand for health care due to the COVID-19 crisis and how effective the government’s response is in stemming the spread of this new pathogen. Inherent in the death statistics is the capacity of a country to conduct COVID-19 tests, which means that there should be a sufficient number of test kits available and that the health workers are properly trained to conduct the tests, trace the contacts, and isolate identified individuals.

The President of the Philippines imposed a total lockdown called enhanced community quarantine (ECQ) for the entire island of Luzon, which encompasses eight administrative regions, including the national capital region, from March 15 to April 30. Other parts of the country have also been under some degree of quarantine at different periods since the appearance of local transmission. Executive Order 112 , signed on April 30, 2020, was issued to further extend the ECQ in identified high-risk areas and a general community quarantine (GCQ) in the rest of the country. The inter-agency task force for the management of emerging infectious diseases defines ECQ as the implementation of temporary restrictions on the mobility of people, strict regulations of industries, and a heightened presence of uniformed personnel. GCQ is, in a nutshell, a less strict version of ECQ.

The Philippines has faced a lot of challenges during this crisis. First, the health system lacks adequate surge capacity to safely handle a nationwide outbreak of COVID-19 due to shortages of personal protective equipment (PPE), mechanical ventilators, and hospitals with ICUs and isolation beds (see this World Bank report and this Rappler article ). More importantly, the insufficient number of health workers , especially in areas outside the metropolitan, is a major concern. Nonetheless, the Department of Health has worked hard to meet the surge in demand due to COVID-19, including partnering with the private sector to repurpose structures and providing data to the public to ensure transparency and accountability. As in other countries, the health workers and those with frontline responsibilities have truly been the new heroes or “bayani” with their tireless efforts and sacrifices. 

Another challenge pertains to the adverse economic impact of COVID-19. The Philippines has a relatively large informal sector and the income of many families depends on daily transactions with no formal job or social security. This has prompted the government to extend cash or in-kind support to vulnerable populations – a response that has posed several challenges, particularly related to the who/what/how framework. First, the Philippine government had to properly identify those in need (who). Second, it had to ensure that sufficient resources can be allocated to the identified groups (what). And third, it had to distribute aid in an efficient, timely, and equitable way (how). The government's social welfare efforts to provide for the vulnerable groups have mixed results: at times, the distribution of aid is organized and efficient, at other times insufficient and disorderly (see these CNN Philippines reports of April 7 and April 30 ).

COVID-19 in the Philippines – How Filipinos Have Coped

There has been a strong spirit of “bayanihan” or collectivism in the country amidst the COVID-19 crisis. People are volunteering, distributing goods to vulnerable groups, or donating PPE to those with frontline duties. Some enterprises also rose to the occasion by repurposing their businesses to meet the local demand for medical products and PPE.

Different individuals have coped differently: some have welcomed the work hiatus that the quarantine has afforded them, some connected more with friends and family, others become more productive working from home. Staying healthy and being mindful are also factors that contribute to remaining calm and rational in this time of national distress.

Despite the challenges, we will continue to face, especially once the quarantine has eased and the new normal is in effect, we can say that Filipinos have also learned some valuable lessons amid this crisis. For one, Filipinos have become more mindful of the importance of good sanitation and non-pharmaceutical public health measures in mitigating the transmission of the virus. Most Filipinos have also become more proactive in their approach, keeping social distance, wearing masks, and practicing proper handwashing, among others. Furthermore, this crisis has redefined and created new heroes who rose to the challenge – from those staying at home to avoid the further spread of the virus to those on the frontline who have dedicated their time and effort to combat the pandemic, to government and business leaders who have served the country sincerely during this crisis.

Perhaps there really is a silver lining in every cloud.

Lessons from Mongolia’s COVID-19 Containment Strategy

Stories in a time of pandemic: aparc alumni share their experiences.

Exploring Perspectives of Parents, Teachers, and Learners in Implementing Modular Instruction Amidst the COVID-19 Pandemic: A Case Study in the Philippine Basic Education

• August 2024

• Cebu Technological University

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Silent Killer: How Fibrin Drives COVID-19’s Hidden Damage and Long-Term Brain Fog

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Coagulopathy, the formation of small blood clots that go on to wreck respiratory and neurologic havoc, has long been a clinical hallmark of COVID, and now it's oft-ignored Long COVID. A new study suggests that fibrin, a key component of blood clots, plays a role. 

Fibrin provides structure to a blood clot and is derived from fibrinogen, a soluble blood protein when the coagulation cascade is activated. If you think of a blood clot as nature’s way of plugging a leak, fibrin deposition is frequently found where there is damage to the walls of blood vessels and the vessels making up the blood-brain barrier. Fibrin serves as a plug and a signal for a greater inflammatory and immune response.  

Given the unique clinical presentation of clotting in COVID compared to other respiratory viruses, the researchers hypothesized that COVID directly binds to fibrinogen, promoting blood clot formation and altering clot structure and function. They found that the spike protein of the virus binds to fibrinogen and fibrin at specific binding sites, suggesting that the virus might contribute to abnormal clotting by interacting with fibrinogen. 

They found that the spike protein altered the structure of clots, making them denser and more resistant to the body’s natural means of removing clots, a process called fibrinolysis. Additionally, the spike protein enhanced the inflammatory signals from fibrin, increasing oxidative forces (reactive oxygen species or ROS) released from macrophages, a first responder of the immune system. 

In converting fibrinogen to fibrin, the spike's binding site (epitope) is exposed. Therapeutically, having identified binding regions, the research found that antibodies could disrupt and reduce these pro-inflammatory effects implicated in acute and long COVID. Among the inflammatory effects reduced by blocking the actions of fibrinogen was the deposition of collagen in the lungs, which creates a barrier to oxygen passage and helps to explain the refractory response to supplemental oxygen we have seen in patients.

Fibrin also suppresses natural killer (NK) cells, which are called "natural killers" because they can recognize and kill stressed cells without prior exposure to a particular pathogen, making them critical first responders. The suppression of NK cell activity results in enhancing viral persistence and lung inflammation. 

In additional studies in mice, the researchers found that this fibrin-dependent inflammatory response occurs independently of the active virus, suggesting a potential mechanism for persistent symptoms in Long COVID. [1] Therapeutically, in their mouse model, the use of a monoclonal antibody targeting the fibrin epitope, in addition to reducing the lung’s inflammatory response, reduced neuroinflammation (associated with long COVID’s brain fog). There were reductions in fibrin deposition and microglial reactivity “leading to improved neuronal survival and reduced white-matter injury.” Microglia are the primary immune cells of the central nervous system. 

To summarize: 

• Coagulopathy in COVID-19 is a primary driver of thrombo-inflammation and neuropathology rather than a consequence of systemic inflammation.
• Fibrin plays a causal immunomodulatory role in promoting hyperinflammation, neuropathological alterations, and increased viral load in COVID-19 by modulating NK cells, macrophages, and microglia.
• Elevated fibrinogen levels and BBB permeability in COVID-19 contribute to neuropathology, and targeting fibrin may offer a dual mechanism of action by inhibiting fibrin-spike interactions and exerting anti-inflammatory effects. A fibrin-targeting antibody effectively blocks many pathological effects of fibrin, providing neuroprotection and reducing thrombo-inflammation.

Their findings have limitations, including how they measured changes in brain tissue, the use of mouse models, and the fact that our inflammatory response may have more than one pathway that results in COVID-19’s deleterious effects. For Long COVID, the fibrin-targeted antibody does not interfere with normal clotting, acting solely on fibrin's inflammatory responses, making it a candidate to protect against pulmonary and cognitive impairment; that will, of course, require clinical trials. 

And there you have it—the silent saboteur behind the lingering specter of Long COVID. Fibrin is not just a bystander in the aftermath of COVID-19; it's a key player driving the chronic symptoms that continue to baffle patients and clinicians alike. The discovery that the virus’s spike protein meddles with fibrin, transforming it into a resilient, inflammatory force, opens a new frontier in the fight against the pandemic’s long tail. The research, though groundbreaking, is still in its early days, confined to animal models, and the complexities of human biology could introduce new challenges.

But if the science holds, targeting fibrin could offer a two-for-one punch against the clotting and inflammation that underpin much of the damage COVID-19 leaves in its wake. For the millions grappling with the enduring effects of Long COVID, this could be a glimmer of hope—a chance to reclaim their lives. 

[1] The inquisitive with a conspiratorial bent might link these inflammatory responses in the absence of infection to deaths felt to be due to the COVID vaccines, which employ the spike as antigenic stimulus. The researchers note that most hematologic changes are triggered by the vaccine vector (an adenovirus) and that “COVID-19 RNA vaccines lead to small amounts of spike protein accumulating locally and within draining lymph nodes where the immune response is initiated, and the protein is eliminated.” 

Source: Fibrin drives thrombo-inflammation and neuropathology in COVID-19 Nature DOI: 10.1038/s41586-024-07873-4

• Fibrin and COVID-19
• COVID-19 brain fog
• Long COVID research
• Thrombo-inflammation in COVID-19
• Neurological damage in COVID-19
• Long COVID Treatment
• Fibrin-targeting therapy
• COVID-19 and coagulopathy
• Neuroinflammation and COVID-19
• Monoclonal antibody treatment COVID-19

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Chuck Dinerstein, MD, MBA

Director of Medicine

Dr. Charles Dinerstein, M.D., MBA, FACS is Director of Medicine at the American Council on Science and Health. He has over 25 years of experience as a vascular surgeon.

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Psychosocial Health of the Badjao People During COVID-19 in Jolo, Philippines: An Exploratory Study

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Background : The Badjao, a nomadic maritime group in Southeast Asia, face heightened vulnerability during public health crises due to their reliance on maritime livelihoods, limited healthcare access, and historical marginalization—challenges exacerbated by the COVID-19 pandemic. However, there is a significant research gap in understanding their unique needs and vulnerabilities especially its specific impacts on their health and well-being due to pandemic.

Methods : This study explores the psychological and social health of the Badjao and the factors affecting theme during the COVID-19 pandemic using an exploratory qualitative design. Guided interviews were conducted through focus-group discussions with 10 purposively selected Badjao respondents from Barangay Bus-Bus, Jolo-Sulu, Philippines. Data were analyzed using thematic analysis.

Results : The study identified five key themes related to the psychosocial impact of COVID-19 on the Badjao community. These included Feelings and Apprehension, with anxiety over contracting the virus and financial instability; Responses to the Pandemic, marked by fear of death, job loss, and hospitalization concerns; Coping Mechanisms, where the community relied on traditional remedies, social support, and quarantine adherence; Social Status, highlighting food insecurity and disrupted social interactions; and Factors Influencing Psychosocial Health, focusing on the importance of support systems and access to accurate information and resources.

Conclusion : The pandemic has significantly disrupted the social and economic stability of the Badjao community, exacerbating their existing vulnerabilities. They need a culturally sensitive interventions that address both their immediate and long-term needs. Collaboration with local government units and stakeholders are crucial in supporting the resilience and well-being of the Badjao in future crises.

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• Aragasi, N. A., & Pangandaman, H. K. (2021). Coping Style, Anxiety Level, Organizational Support, and Work Commitment of Educators During the COVID-19 Pandemic in the Philippines: A Mixed-Methods Study. Belitung Nursing Journal, 7(4), 267-276. https://doi.org/10.33546/bnj.1393
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• Han, W. J., & Hart, J. (2021). Job precarity and economic prospects during the COVID-19 public health crisis. Social Science Quarterly, 102(5), 2394-2411. https://doi.org/10.1111/ssqu.13031
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• Hwang, T. J., Rabheru, K., Peisah, C., Reichman, W., & Ikeda, M. (2020). Loneliness and social isolation during the COVID-19 pandemic. International Psychogeriatrics, 32(10), 1217-1220. https://doi.org/10.1017/s1041610220000988
• Inayat, S., Azhar, K., & Aziz, F. (2022). Burnout and Psychological Distress Among Pakistani Nurses Providing Care to COVID‐19 Patients: A Cross‐sectional Study. International Nursing Review, 69(4), 529-537. https://doi.org/10.1111/inr.12750
• Khetan, A. K., Yusuf, S., Lopez-Jaramillo, P., Szuba, A., Orlandini, A., Mat-Nasir, N., Oguz, A., Gupta, R., Avezum, Á., Rosnah, I., Poirier, P., Teo, K. K., Wielgosz, A., Lear, S. A., Palileo-Villanueva, L. M., Serón, P., Chifamba, J., Rangarajan, S., Mushtaha, M., . . . Leong, D. P. (2022). Variations in the financial impact of the COVID-19 pandemic across 5 continents: A cross-sectional, individual level analysis. EClinicalMedicine, 44, 101284. https://doi.org/10.1016/j.eclinm.2022.101284
• Leong, S., Eom, K., Ishii, K., Aichberger, M. C., Fetz, K., Müller, T. S., Kim, H. S., & Sherman, D. K. (2022). Individual costs and community benefits: Collectivism and individuals' compliance with public health interventions. PLoS One, 17(11), e0275388. https://doi.org/10.1371/journal.pone.0275388
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• Naeem, M., Ozuem, W., Howell, K., & Ranfagni, S. (2023). A Step-by-Step Process of Thematic Analysis to Develop a Conceptual Model in Qualitative Research. International Journal of Qualitative Methods, 22. https://doi.org/10.1177/16094069231205789
• Ness, M. M., Saylor, J., Di Fusco, L. A., & Evans, K. (2021). Healthcare providers' challenges during the coronavirus disease (COVID-19) pandemic: A qualitative approach. Nursing & Health Sciences, 23(2), 389-397. https://doi.org/10.1111/nhs.12820
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• Petrișor, C., Breazu, C., Doroftei, M., Mărieș, I., & Popescu, C. A. (2021). Association of Moral Distress With Anxiety, Depression, and an Intention to Leave Among Nurses Working in Intensive Care Units During the COVID-19 Pandemic. Healthcare, 9(10), 1377. https://doi.org/10.3390/healthcare9101377
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Covid-19 and the impact on families with autistic children

The Autism Centre for Education and Research

2020 saw the education of children around the world change as a consequence of the Covid-19 pandemic. Through our exclusion research we were already hearing about the increasing numbers of parents choosing to home educate their children but with the pandemic, this was suddenly forced upon most families in the UK

Summary of the research

We produced a set of videos, which you can find links to below, to capture the personal experiences of some families. The films cover various topics:

• Autism, Covid-19 lockdown and school support
• Positives from the Covid-19 lockdown
• Struggles during lockdown and managing home education
• Transitioning back to school after lockdown
• Learning points for teachers post-lockdown

As well as the videos, we also produced a factsheet for teachers called The Good, the Bad and the Helpful to summarise what families told us. This has a number of recommendations and resources that teaching staff can use. Please feel free to share it and/or print it out.

Thank you to all those parents who contributed to this research. Particular thanks go to those parents who gave up additional time for us to film them and for sharing their personal experiences on camera.

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Ozempic and Wegovy weight-loss shots may slow aging, research reveals

Studies show individuals who took these drugs had a lower mortality rate from all causes, including alzheimer's disease and even cancer; additionally, the studies highlight a significant reduction in the risk of death from covid-19 among those using these medications.

Summary of the latest findings on weight-loss injections:

• Those who took the medication had a lower mortality rate from all causes, not just from cardiovascular diseases.
• The drug consistently reduced the risk of adverse cardiovascular outcomes.
• Participants who took the medication had a lower mortality rate from infections compared to the placebo group.
• The drug improved symptoms of heart failure and reduced inflammation levels in the body, regardless of weight loss.
• Those who used the medication were equally likely to contract COVID-19 but had a lower risk of dying from the disease.

Dramatic reduction in COVID-19 mortality risk

'an important and effective treatment for heart disease'.

• Title & authors

Neri, Rico S. B., and Mark A. N. Polinar. "Sustainable Practices Amid the Covid-19 Pandemic of A Construction Company in Cebu, Philippines." International Journal of Multidisciplinary: Applied Business and Education Research , vol. 5, no. 1, 2024, pp. 29-37, doi: 10.11594/ijmaber.05.01.04 .

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The construction industry in Cebu City is growing, but sustainability is essential, especially during times of crisis like the COVID-19 pandemic. Some businesses have not enhanced their sustainable practices to survive the new normal. The main objective of this study is to explore the triple bottom line of a construction company in Cebu City, Philippines. The study employed a descriptive-correlational research design, and the sample comprised 58 rank-and-file employees selected through a simple random sampling method. The collected data were analyzed, tabulated, and interpreted using various statistical techniques, namely weighted mean, and Pearson correlation coefficient. The study found that the organization placed a high level of importance on social and economic sustainability, while environmental sustainability was considered moderately significant. The relationship between these three variables was examined, and it was determined that they were positively correlated. The study's conclusion emphasized that balancing economic growth, social inclusion, and environmental preservation is crucial for long-term sustainability. Therefore, prioritizing these aspects can improve a company's performance and contribute to its long-term sustainability.

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