JPH JMIR Public Health Surveill JMIR Public Health and Surveillance 2369-2960 JMIR Publications Toronto, Canada v10i1e54383 39137034 10.2196/54383 Original Paper Original Paper Social Vulnerability and Compliance With World Health Organization Advice on Protective Behaviors Against COVID-19 in African and Asia Pacific Countries: Factor Analysis to Develop a Social Vulnerability Index Mavragani Amaryllis Trzebinski Wojciech Conti Pio Pongutta Suladda PhD 1
International Health Policy Program Ministry of Public Health Muang Nonthaburi, Thailand 66 25902376 66 25902385 suladda@ihpp.thaigov.net
https://orcid.org/0000-0002-6130-2235 Tangcharoensathien Viroj PhD 1 https://orcid.org/0000-0003-3235-0091 Leung Kathy PhD 2 3 4 https://orcid.org/0000-0003-4777-388X Larson Heidi J PhD 5 6 7 https://orcid.org/0000-0002-8477-7583 Lin Leesa PhD 2 3 5 https://orcid.org/0000-0003-4123-4762 International Health Policy Program Ministry of Public Health Muang Nonthaburi Thailand WHO Collaborating Centre for Infectious Disease Epidemiology and Control School of Public Health, Li Ka Shing Faculty of Medicine The University of Hong Kong Hong Kong SAR China Laboratory of Data Discovery for Health Limited (D24H) Hong Kong Science Park Hong Kong SAR China The University of Hong Kong Shenzhen Hospital Shenzhen China Department of Infectious Disease Epidemiology London School of Hygiene and Tropical Medicine London United Kingdom Centre for the Evaluation of Vaccination Vaccine & Infectious Disease Institute University of Antwerp Antwerp Belgium Department of Health Metrics Sciences University of Washington Seattle, WA United States Corresponding Author: Suladda Pongutta suladda@ihpp.thaigov.net 2024 13 8 2024 10 e54383 8 11 2023 13 5 2024 3 6 2024 1 7 2024 ©Suladda Pongutta, Viroj Tangcharoensathien, Kathy Leung, Heidi J Larson, Leesa Lin. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 13.08.2024. 2024

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on https://publichealth.jmir.org, as well as this copyright and license information must be included.

 Background

COVID-19 protective behaviors are key interventions advised by the World Health Organization (WHO) to prevent COVID-19 transmission. However, achieving compliance with this advice is often challenging, particularly among socially vulnerable groups.

 Objective

We developed a social vulnerability index (SVI) to predict individuals’ propensity to adhere to the WHO advice on protective behaviors against COVID-19 and identify changes in social vulnerability as Omicron evolved in African countries between January 2022 and August 2022 and Asia Pacific countries between August 2021 and June 2022.

 Methods

In African countries, baseline data were collected from 14 countries (n=15,375) during the first Omicron wave, and follow-up data were collected from 7 countries (n=7179) after the wave. In Asia Pacific countries, baseline data were collected from 14 countries (n=12,866) before the first Omicron wave, and follow-up data were collected from 9 countries (n=8737) after the wave. Countries’ socioeconomic and health profiles were retrieved from relevant databases. To construct the SVI for each of the 4 data sets, variables associated with COVID-19 protective behaviors were included in a factor analysis using polychoric correlation with varimax rotation. Influential factors were adjusted for cardinality, summed, and min-max normalized from 0 to 1 (most to least vulnerable). Scores for compliance with the WHO advice were calculated using individuals’ self-reported protective behaviors against COVID-19. Multiple linear regression analyses were used to assess the associations between the SVI and scores for compliance to WHO advice to validate the index.

 Results

In Africa, factors contributing to social vulnerability included literacy and media use, trust in health care workers and government, and country income and infrastructure. In Asia Pacific, social vulnerability was determined by literacy, country income and infrastructure, and population density. The index was associated with compliance with the WHO advice in both time points in African countries but only during the follow-up period in Asia Pacific countries. At baseline, the index values in African countries ranged from 0.00 to 0.31 in 13 countries, with 1 country having an index value of 1.00. The index values in Asia Pacific countries ranged from 0.00 to 0.23 in 12 countries, with 2 countries having index values of 0.79 and 1.00. During the follow-up phase, the index values decreased in 6 of 7 African countries and the 2 most vulnerable Asia Pacific countries. The index values of the least vulnerable countries remained unchanged in both regions.

 Conclusions

In both regions, significant inequalities in social vulnerability to compliance with WHO advice were observed at baseline, and the gaps became larger after the first Omicron wave. Understanding the dimensions that influence social vulnerability to protective behaviors against COVID-19 may underpin targeted interventions to enhance compliance with WHO recommendations and mitigate the impact of future pandemics among vulnerable groups.

 social vulnerability Index COVID-19 protective behavior Africa Western Pacific social vulnerability African countries Western countries predict predicts prediction propensity baseline data Factor analysis polychoric varimax rotation protective behaviour behaviours preventive precautionary linear regression predictability Omicron sociodemographic media use communication health risk perception socioeconomic Introduction

SARS-CoV-2 infections can still pose a significant threat, especially for individuals with underlying health conditions and older adults [1], and 45% of those recovering from COVID-19, regardless of severity, experience sequelae (eg, fatigue, breathlessness, impaired sleep, reduction in grey matter brain thickness, other impairments to daily activities) [2,3]. Although immunity against COVID-19 has developed in a significant proportion of the global population and the impact of the disease has diminished, it is unlikely to be completely eradicated in the near future [4,5]. Furthermore, resurgences are possible [6] due to waning immunity against COVID-19 and the evolution of new variants [7,8].

The World Health Organization (WHO) recommended personal protective behaviors to prevent COVID-19 infection, including receiving the full COVID-19 vaccine course, social distancing, maintaining proper indoor ventilation, wearing a mask if at risk, regular handwashing, covering coughs and sneezes, and staying home when feeling unwell [9,10]. These protective behaviors played an important role in minimizing COVID-19 transmission, morbidity, and mortality [11,12], with failure to follow the WHO advice contributing to the spread of SARS-CoV-2 [13]. However, adherence to these social measures has often been challenging for many due to various factors, including vaccine hesitancy, economic disruption, and unemployment [13-16].

The capabilities (which include the abilities, qualities, and willingness) of individuals to comply with the WHO advice necessitate a collaborative effort between governments and individuals. Governments must ensure universal access to the necessary infrastructure and health services needed for COVID-19 control, including ensuring access to vaccines, masks, and hand sanitizers and adapting public spaces to facilitate safe distancing between individuals [17]. Additionally, these measures should be accompanied by effective public communication strategies to promote vaccine uptake and other personal protective behaviors and to address misinformation [17]. Individuals, on the other hand, are required to accept COVID-19 vaccines and to adapt their lifestyles or health behaviors to protect themselves from serious illnesses due to COVID-19 and limit its spread [9]. Inequitable capabilities to comply with this advice vary between countries and across individuals in a country [18-20]. For example, low- and middle-income countries may face challenges accessing vaccines and other health facilities [19,21], while maintaining social distancing and access to clean water are not often possible for many low-income groups [18,20,22]. Individual beliefs and perceptions regarding COVID-19 also vary [23,24].

Evidence shows that vulnerable populations were disproportionately affected by the pandemic [25], so identifying vulnerable populations who have limited ability to protect themselves from infection is crucial to ensure equitable health outcomes [26]. Individuals’ positions within their physical and social environments can determine their ability to cope with hazards or harm, reflecting their level of social vulnerability [27]. A social vulnerability index (SVI) is a composite index that integrates the contributions of diverse social determinants that determine people’s vulnerability to a certain threat, and it is widely used as a tool to identify vulnerable populations susceptible to and most affected by specific threats [28]. An SVI can be advantageous, as it simplifies complex data, facilitates cross-country and temporal comparisons, and permits weighting of the most important dimensions [29].

COVID-19 community vulnerability indices have been developed in some countries, for example, in Africa [30], the United States [31], Kenya [32], and the United Kingdom [33]. These indices are defined by community- and country-level metrics, rather than individual-level metrics. However, these indices are unlikely to reflect individuals’ capabilities to comply with the WHO advice because they do not include variables that influence behaviors, such as individuals’ socioeconomic status, perceptions and attitudes toward COVID-19, and trust in governments [24,34-36]. Furthermore, the variables used for these indices are often constant or change only slightly over time, making these indices unlikely to capture the dynamic behavioral responses of individuals during the different waves of COVID-19, which is essential information for developing an effective response.

Of all COVID-19 variants, Omicron has been responsible for the largest number of confirmed COVID-19 cases worldwide [37]. Globally, the WHO Western Pacific region reported the highest number of confirmed cases from Omicron, while the WHO Africa region had the lowest [37,38]. In the WHO Africa region, Omicron was identified as the fourth wave of COVID-19 in mid-November 2021. The number of confirmed cases reached its peak between December 2021 and January 2022, before declining to baseline in April 2022 [37,39]. In the WHO Western Pacific region, Omicron cases exhibited fluctuating patterns throughout 2022, with the first wave occurring between January and April, the second wave occurring between July and September, and the third wave occurring in December [37,38]. Understanding the patterns of and changes in social vulnerability that predict an individual’s capability to comply with the WHO advice in these countries will provide beneficial information to guide COVID-19 prevention, especially among vulnerable populations.

In this study, we developed an SVI to predict individuals’ propensity to adhere to the WHO advice on protective behaviors against COVID-19 and to identify changes in social vulnerability as Omicron evolved in African countries between January 2022 and August 2022 and Asia Pacific countries between August 2021 and June 2022.

 Methods Conceptual Framework of Social Vulnerability to Compliance With WHO Advice on COVID-19 Protective Behaviors

An SVI should encompass the influences of multiple key factors of a threat or hazard to identify vulnerable groups who are most likely to be affected by the threat or hazard [40]. Current literature indicates that COVID-19 protective behaviors are associated with sociodemographic status (eg, sex, age, income, education, occupation), media use and communication, personal health risk perception, trust in the government, trust in health personnel, socioeconomic status (eg, gross domestic product [GDP] per capita), and access to public health infrastructure (eg, information system, cold chain in vaccine transport, medical facilities per capita, health care access and quality) [21,24,34,35,41-44]. Characteristics of both households and populations, such as access to handwashing facilities and urban population density, have also been used as indicators for an SVI for COVID-19 morbidity and mortality [32].

In our SVI construction models, we included factors associated with COVID-19 protective behaviors, morbidity, and mortality. Figure 1 illustrates the framework used to guide the analysis of this study.

Conceptual framework to construct a social vulnerability index that reflects compliance with World Health Organization advice on COVID-19 protective behaviors. GDP: gross domestic product.

 Variables and Data Sources Variables and Data Sources for SVI Construction

Individual-level variables used to analyze the SVI for African countries were sourced from 2 nationally representative surveys conducted by the Vaccine Confidence Project in January 2022 (baseline, during the first wave of Omicron) and August 2022 (follow-up, after the first wave of Omicron). Baseline data were collected from 14 countries (n=15,375): Cameroon, Cote d'Ivoire, Democratic Republic of the Congo, Ghana, Kenya, Liberia, Mali, Niger, Nigeria, Senegal, Sierra Leone, South Africa, South Sudan, and Uganda. Follow-up data, weighted by age and gender, were collected from 7 countries (n=7179): Cameroon, Democratic Republic of the Congo, Kenya, Nigeria, Senegal, South Africa, and Uganda. These variables encompassed age, gender, education, employment status, urbanicity, trust in the government, trust in health personnel, COVID-19 risk perception, and media use and communication. National-level variables used in this analysis included GDP per capita in 2021 for both waves [45], population density in 2022 for both waves [46], the percentage of the population with basic handwashing facilities at home (latest updated) [47], nursing and midwifery personnel per 10,000 population (latest updated) [48], and health-adjusted life expectancy (HALE) at birth (latest updated) [49].

Variables at the individual level available for the analysis of the SVI for Asia Pacific countries were obtained from 2 nationally representative surveys carried out by the Vaccine Confidence Project from June 2021 to August 2021 (baseline, before the first wave of Omicron) and May 2022 to June 2022 (follow-up, after the first wave of Omicron). Baseline data were collected from 14 countries (n=12,866): China, Cambodia, Vietnam, Lao People's Democratic Republic, Japan, South Korea, Malaysia, Philippines, Mongolia, Fiji, Papua New Guinea, Solomon Islands, Tonga, and Vanuatu. Follow-up data (weighted by age and gender) were collected from 9 countries (n=8737): Cambodia, Vietnam, Lao People's Democratic Republic, Japan, South Korea, Malaysia, Philippines, Mongolia, and Papua New Guinea. Variables used in the SVI construction included age, gender, education, employment status, and COVID-19 risk perception. Variables at the national level used in this analysis were GDP per capita in 2020 for baseline and in 2021 for follow-up [45], population density in 2021 for baseline and in 2022 for follow-up [46], nursing and midwifery personnel per 10,000 population (latest updated) [48], and HALE at birth (latest updated) [49]. Variables included in the SVI construction and sources of data are presented in Table S1 in Multimedia Appendix 1.

 Variables and Data Sources for Computation of the Score for Compliance With WHO Advice on COVID-19 Protective Behavior

We computed scores, which reflect individuals’ capabilities to comply with the WHO advice, using the COVID-19 protective behavior data from the Vaccine Confidence Project’s surveys carried out in the African and Asia Pacific countries listed in the previous section. We identified relative changes in the scores between baseline and follow-up. Variables used in the score computation included doses of COVID-19 vaccines, keeping a safe distance from others, maintaining proper indoor ventilation, wearing a mask if at risk, regular handwashing, covering coughs and sneezes, and staying home when feeling unwell. Each variable was assigned a score of 0 to 3, from least improved (0, indicating negative change or no improvement) to most improved (3, indicating greatest positive change or most improvement). Scores for all variables were summed to obtain the score for compliance with WHO recommendations on COVID-19 protective behavior for each individual. Table 1 illustrates the measurements for COVID-19 protective behaviors and computation of the scores. Since the variables at baseline and during follow-up in Asia Pacific countries were slightly different, the scores were normalized to enable the comparison between the 2 waves.

Variables used to compute compliance with World Health Organization advice on COVID-19 protective behavior score in African countries (AFCs) and Asia Pacific countries (APCs).

Variable Score range Codes AFCs APCs
Baselinea Follow-upa Baselinea Follow- upb
1. Willingness to accept COVID-19 vaccines and have had COVID-19 0 to 3 0=Definitely don’t accept COVID-19 vaccines/lean toward don’t; 1=leaning toward accepting COVID-19 vaccines; 2=Definitely accept; 3=Have had COVID-19 vaccine No No Yes No
2. Number of doses of COVID-19 vaccines received 0 to 3 0=0 dose; 1=1 dose; 2=2 doses; 3=>2 doses Yes Yes No Yes
3. Wearing a face mask 0 to 3 0=Not at all/less regularly than before the COVID-19 pandemic; 1=About the same; 2=A little more regularly; 3=A lot more regularly Yes Yes Yes Yes (merged questions 3 and 4)
4. Covering your mouth and nose when sneezing/coughing 0 to 3 0 = Not at all/less regularly than before the COVID-19 pandemic; 1=About the same; 2=A little more regularly; 3=A lot more regularly Yes Yes Yes Yes (merged questions 3 and 4)
5. Washing my hands 0 to 3 0=Not at all/less regularly than before the COVID-19 pandemic; 1=About the same; 2=A little more regularly; 3=A lot more regularly Yes Yes Yes Yes
6. Having guests in your house 0 to 3 3=Not at all; 2=Less regularly than before the COVID-19 pandemic; 1=About the same; 0=A little more regularly/a lot more regularly Yes Yes Yes Yes (merged questions 6 and 7)
7. Gathering socially in large groups 0 to 3 3=Not at all; 2=Less regularly than before the COVID-19 pandemic; 1=About the same; 0=A little more regularly/a lot more regularly Yes Yes Yes Yes (merged questions 6 and 7)
8. Staying home when feeling unwell 0 to 3 0=Not at all/less regularly than before the COVID-19 pandemic; 1=About the same; 2=A little more regularly; 3=A lot more regularly No No No Yes
9. Keeping physical distance from others 0 to 3 0=Not at all/less regularly than before the COVID-19 pandemic; 1=About the same; 2=A little more regularly; 3=A lot more regularly Yes Yes Yes No
10. Ensuring that spaces I share with others are well ventilated 0 to 3 0=Not at all/less regularly than before the COVID-19 pandemic; 1=About the same; 2=A little more regularly; 3=A lot more regularly No No No Yes

aTotal score range: 0-21.

bTotal score range: 0-18.

 Management of Missing Data

The percentage of missing data for most variables at the individual level ranged from 0.2% to 3.7%. Single imputation was used to address missing values for education, employment status, urbanicity, trust in the government, trust in health personnel, COVID-19 risk perception, media use and communication, and health behaviors, whereby missing values were replaced with the most frequent value for that variable.

 Analysis

Data analysis was conducted according to the practical guide for social vulnerability assessment by the United Nations Development Programme [28] and the variable reduction approach for SVI construction [29]. Variables potentially influencing COVID-19 protective behaviors were included in separate factor analysis models using polychoric correlation with varimax rotation approach with a loading cutoff of 0.3 [50] to identify influential factors and specific variable domains (see results in Figure S1 in Multimedia Appendix 1). Influential factors with an eigenvalue of more than 1 were selected, adjusted for cardinality, summed (equal weights), and min-max normalized to develop a composite index of individual social vulnerability for each region at baseline and follow-up. A t test was used to test the difference between mean SVI at baseline and mean SVI during follow-up in each country.

For each wave of data collection, multiple linear regression analysis was used to assess the association between the index as the independent variable and compliance with WHO advice on COVID-19 protective behavior score as the dependent variable, which was adjusted for sociodemographic characteristics that were excluded from the construction of the SVI and country clustering effects. The analyses were conducted using STATA 17. The SVIs of compliance with the WHO advice were spatially presented using Excel365 (Microsoft Corp).

 Ethical Considerations

The surveys were approved by the Institutional Review Board at the London School of Hygiene and Tropical Medicine (LSHTM 26636) and the Human Research Ethics Committee of the University of Hong Kong (EA230420). Participants were informed of voluntary participation, the privacy and confidentiality protection policy, and the right to withdraw from the survey at any time. Written consent was obtained from all participants, and the use of the data for this study was allowed. This study was performed according to the principles of the Declaration of Helsinki.

 Results Characteristics of Individuals and Countries Included in the SVI Construction

Table 2 presents the characteristics of the indicators included in the factor analysis models for constructing the SVI. Almost all the characteristics of the African and Asia Pacific countries were different, except for the proportion of men to women and the proportion of participants who did not believe in the threat of COVID-19; however, the proportion of participants with a high concern of being infected by COVID-19 was slightly lower in African countries. In African countries, there was a higher proportion of younger individuals and a higher proportion of participants with master’s or higher degrees but a lower proportion of participants with bachelor’s degrees, a lower proportion of participants working full time, lower GDP per capita, fewer nurses and midwives per 10,000 population, and lower population density compared with Asia Pacific countries. In African countries, the proportion of participants having high trust in the government and health care workers decreased, and the information sources individuals had high access to (everyday use >40%) were television, radio, and word of mouth in the local community.

Characteristics of individuals and countries included in the social vulnerability index construction.

Indicators African countries Asia Pacific countries
Baseline Follow-up Baseline Follow-up
Countries, n 14 7 14 9
Participants, n 15,375 7179 12,866 8737
Age (years), n (%)
18-24 4251 (27.6) 2092 (29.1) 2405 (18.7) 1632 (18.7)
25-34 4923 (32) 2368 (33) 3476 (27) 2158 (24.7)
35-44 2980 (19.4) 1384 (19.3) 2835 (22) 1793 (20.5)
45-54 1656 (10.8) 758 (10.6) 2081 (16.2) 1407 (16.1)
≥55 1565 (10.2) 577 (8) 2069 (16.1) 1747 (20)
Gender (male), n (%) 7672 (49.9) 3597 (50.1) 6471 (50.3) 4427 (50.7)
Education, n (%)
No formal education 3039 (19.8) 584 (8.1) 588 (4.5) 329 (3.8)
Primary education 2934 (19.1) 1207 (16.8) 1403 (10.9) 898 (10.3)
Secondary education 5678 (36.9) 3056 (42.6) 5392 (41.4) 3525 (40.4)
Vocational postsecondary education and other 567 (3.7) 195 (2.7) 1076 (8.4) 715 (8.2)
Bachelor 988 (6.4) 691 (9.6) 4066 (31.6) 2905 (33.3)
Master and PhD 2167 (14.1) 1446 (20.1) 412 (3.2) 357 (4.1)
Employment status, n (%)
Unemployed (no income) 2998 (19.5) 1545 (21.5) 1303 (10.1) 841 (9.6)
Retired, student, stay-at-home parent (no own income from work) 4261 (27.7) 1812 (25.3) 3123 (24.3) 2093 (24)
Working part time, self-employed, jobs other than working full time (irregular income) 2971 (19.3) 1487 (20.7) 1647 (12.8) 1663 (19)
Working full time (regular income) 5145 (33.5) 2335 (32.5) 6793 (52.8) 4140 (47.4)
Area of residence (urban), n (%) 7597 (49.4) 3930 (54.7) N/Aa N/A
Trust in the government, n (%)
Not at all 3032 (19.7) 1679 (23.4) N/A N/A
Not much 2064 (13.4) 1087 (15.2) N/A N/A
Somewhat 2859 (18.6) 1890 (26.3) N/A N/A
A lot 7420 (48.3) 2523 (35.1) N/A N/A
Trust in health care personnel (%)
Not at all 1017 (6.6) 484 (6.7) N/A N/A
Not much 1303 (8.47) 637 (8.9) N/A N/A
Somewhat 3103 (20.2) 2078 (29) N/A N/A
A lot 9952 (64.7) 3980 (55.4) N/A N/A
COVID-19 risk perception, n (%)
A. What level of threat does COVID-19 pose to you?
No threat 3063 (19.9) 1141 (15.9) N/A N/A
Very low threat 1026 (6.7) 470 (6.5) N/A N/A
Fairly low threat 680 (4.4) 442 (6.2) N/A N/A
Moderate threat 1070 (6.9) 786 (11) N/A N/A
Fairly high threat 1401 (9.1) 890 (12.4) N/A N/A
Very high threat 8135 (52.9) 3450 (48) N/A N/A
B. How concerned are you about getting COVID-19?
Not at all 2692 (17.5) 1234 (17.2) N/A N/A
A little 2652 (17.2) 1484 (20.7) N/A N/A
Moderate 1920 (12.5) 1087 (15.1) N/A N/A
Very 8111 (52.8) 3374 (47) N/A N/A
C. How strongly do you agree or disagree that the threat from COVID-19 is exaggerated?
Strongly disagree N/A N/A 2411 (18.7) 1256 (14.4)
Disagree N/A N/A 4971 (38.6) 2763 (31.6)
Agree N/A N/A 3612 (28.1) 3804 (43.5)
Strongly agree N/A N/A 1872 (14.6) 914 (10.5)
Media use and communication, n (%)
Television
Never 3639 (23.7) 983 (13.7) N/A N/A
Less than once a month 813 (5.3) 323 (4.5) N/A N/A
At least once a month 957 (6.2) 443 (6.2) N/A N/A
At least once a week 2522 (16.4) 1154 (16.1) N/A N/A
Every day 7444 (48.4) 4276 (59.6) N/A N/A
Radio
Never 2580 (16.8) 1115 (15.5) N/A N/A
Less than once a month 917 (6) 464 (6.5) N/A N/A
At least once a month 1107 (7.2) 578 (8.1) N/A N/A
At least once a week 3056 (19.9) 1393 (19.4) N/A N/A
Every day 7715 (50.2) 3629 (50.5) N/A N/A
Printed media
Never 8894 (57.8) 3300 (45.9) N/A N/A
Less than once a month 1405 (9.1) 793 (11) N/A N/A
At least once a month 1414 (9.2) 877 (12.2) N/A N/A
At least once a week 1943 (12.6) 1173 (16.3) N/A N/A
Every day 1719 (11.2) 1036 (14.4) N/A N/A
Word of mouth in the local community
Never 2042 (13.3) 630 (8.8) N/A N/A
Less than once a month 801 (5.2) 361 (5) N/A N/A
At least once a month 1106 (7.2) 579 (8.1) N/A N/A
At least once a week 2903 (18.9) 1352 (18.8) N/A N/A
Every day 8523 (55.4) 4257 (59.3) N/A N/A
Social media
Never 6268 (40.8) 2080 (29) N/A N/A
Less than once a month 702 (4.6) 299 (4.2) N/A N/A
At least once a month 695 (4.5) 392 (5.5) N/A N/A
At least once a week 1953 (12.7) 1056 (14.7) N/A N/A
Every day 5757 (37.4) 3352 (46.7) N/A N/A
Internet
Never 6954 (45.2) 2234 (31.1) N/A N/A
Less than once a month 744 (4.8) 381 (5.3) N/A N/A
At least once a month 835 (5.4) 518 (7.2) N/A N/A
At least once a week 1997 (13) 1116 (15.6) N/A N/A
Every day 4845 (31.5) 2930 (40.8) N/A N/A
Population using the internet (%), range Not included Not included 11-98 11-98
GDPb per capita (US $), range 486.95-5741.64 577.20-7055 1577.91-39,918.17 1625.20-39,312.70
Nursing and midwifery personnel per 10,000 population, range 2.23-49.74 3.63-49.74 4.53-119.5 4.53-119.5
HALEc at birth (years), range 52.9-59.4 54.1-59.4 57.1-74.1 57.1-74.1
Population with basic handwashing facilities at home (%), range 3.0-44.0 19.2-44.4 N/A N/A
Population density (per km2), range 17.0-234.3 43.7-239.9 2.2-531.5 2.2-531.3

aN/A: not applicable.

bGDP: gross domestic product.

cHALE: health-adjusted life expectancy.

 Social Vulnerability Index (SVI) SVI in African Countries

The factor analysis of baseline data for African countries identified 3 influential factors that explained 88.9% of the total variance shared among the included variables. Factor 1 was the main contributor of total variance (43.4%), followed by factor 2 (29.5%) and factor 3 (16%). Factor 1 was defined by indicators related to literacy and media use (ie, age, education, urbanicity, and media use [TV, print media, social media, internet]). Factor 2 encompassed indicators relevant to trust including trust in the government and trust in health care workers. Factor 3 included indicators related to country income and infrastructure, which consisted of GDP per capita, population with basic handwashing facilities at home, HALE, and nursing and midwifery personnel per 10,000 population. Age had a negative correlation with factor 1, while all other indicators had positive correlations.

Follow-up data from African countries identified 3 factors that explained 89.8% of total variance. Similar to the baseline, factor 1 contributed most to total variance (44.4%), followed by factor 2 (27.7%) and factor 3 (17.8%). Overall, these factors included indicators similar to those identified from baseline data, except that COVID-19 risk perception was additionally included in factor 2 and HALE was excluded from factor 3. Age was the only indicator with a negative correlation with factor 1 (see Figure 2). Factor loadings are presented in Figure S1 in Multimedia Appendix 1.

The mean SVIs in African countries at baseline ranged from 0.00 (extremely vulnerable) to 1.00 (least vulnerable). Most African countries (13 of 14 countries) were extremely vulnerable, with mean SVIs ranging from 0.00 to 0.31. When comparing the periods during and after Omicron’s peak, mean SVIs further decreased in 6 of 7 extremely vulnerable countries, while mean SVIs in the least vulnerable country remained the same (see Table 3 and Figure 3).

Factors and correlated indicators identified by factor analyses at baseline and follow-up in Africa and Asia Pacific. GDP: gross domestic product; +: positive loading; -: negative loading.

Social vulnerability index (SVI) of compliance with World Health Organization advice on COVID-19 protective behaviors and changing patterns of the SVI in African and Asia Pacific countries at baseline and follow-up.

Country Baseline Follow-up Change, % P valuea
Survey respondents, n SVIb, mean (SD) Survey respondents, n SVIb, mean (SD)
Africa
Cameroon 1058 0.18 (0) 1008 0.19 (0) –5.56 <.001
Cote d’Ivoire 1129 0.31 (0) N/Ac N/A N/A N/A
Democratic Republic of the Congo 1190 0.01 (0) 1021 0 (0) –100 <.001
Ghana 1094 0.29 (0) N/A N/A N/A N/A
Kenya 1030 0.24 (0) 1078 0.27 (0) –4.17 <.001
Liberia 1011 0.03 (0) N/A N/A N/A N/A
Mali 1152 0.06 (0) N/A N/A N/A N/A
Niger 1032 0.02 (0) N/A N/A N/A N/A
Nigeria 1024 0.24 (0) 1008 0.29 (0) –4.17 <.001
Senegal 1139 0.18 (0) 1001 0.18 (0) –11.11 <.001
Sierra Leone 1104 0 (0) N/A N/A N/A N/A
South Africa 1171 1.00 (0) 982 1.00 (0) 0 .38
South Sudan 1219 0.09 (0) N/A N/A N/A N/A
Uganda 1022 0.06 (0) 1081 0.06 (0) –16.67 <.001
All African countries 15,375 0.20 (0.25) 7179 0.28 (0.30) N/A <.001
Asia Pacific
China 2000 0.23 (0) N/A N/A N/A N/A
Cambodia 1000 0 (0) 1000 0 (0) –34.22 <.001
Vietnam 1044 0.05 (0) 1003 0.06 (0) 7.23 <.001
Lao People's Democratic Republic 1000 0.03 (0) 1000 0.02 (0) –8.76 <.001
Japan 1040 1.00 (0) 1066 1.00 (0) 0 .001
South Korea 1133 0.79 (0) 1155 0.89 (0) 12.62 <.001
Malaysia 1000 0.22 (0) 1000 0.25 (0) 12.34 <.001
Philippines 1000 0.05 (0) 1000 0.05 (0) 9.87 <.001
Mongolia 1000 0.06 (0) 1000 0.08 (0) 20.25 <.001
Fiji 521 0.09 (0) N/A N/A N/A N/A
Papua New Guinea 552 0.02 (0) 513 0.03 (0) 25.07 <.001
Solomon Islands 527 0.02 (0) N/A N/A N/A N/A
Tonga 517 0.08 (0) N/A N/A N/A N/A
Vanuatu 532 0.03 (0) N/A N/A N/A N/A
All Western Pacific countries 12,866 0.23 (0.31) 8737 0.29 (0.38) N/A <.001

aSignificance set at P<.05.

b0 represents the most vulnerable, and 1 represents the least vulnerable in the region.

cN/A: not applicable.

Spatial presentation of the social vulnerability index (SVI) on compliance with World Health Organization advice on COVID-19 protective behaviors and patterns of changes in SVIs among (A) African countries at baseline (during Omicron), (B) African countries at follow-up (after Omicron), (C) Asia Pacific countries at baseline (during Omicron), and (D) Asia Pacific countries at follow-up (after Omicron). DR: Democratic Republic.

 SVI in APCs

At baseline, 1 factor accounting for 86.7% of the total variance was identified. This factor was defined by 6 indicators, including education, GDP per capita, HALE, nursing and midwifery personnel per 10,000 population, population using the internet, and population density. All indicators had positive correlations with the factor.

During the follow-up, our factor analysis identified 1 factor that explained 82.8% of the total variance. This factor was defined by the same set of indicators with the same direction of relationships as at baseline.

Figure 2 illustrates the factors and indicators used for constructing the SVI of compliance with WHO advice on COVID-19 protective behaviors. Factor loadings are shown in Figure S1 in Multimedia Appendix 1.

In Asia Pacific countries, the pattern of mean SVIs at baseline was similar to that in African countries in terms of range. Of 14 countries, 12 countries had mean SVIs ≤0.23, while only 2 countries had mean SVIs of 0.79 (South Korea) and 1.00 (Japan). The majority of Asia Pacific countries experienced positive changes in their SVI at follow-up, while the 2 most vulnerable countries (Cambodia and Lao People's Democratic Republic) experienced negative changes after Omicron’s peak in the region in 2022 (see Table 3 and Figure 3).

 Compliance With WHO Advice on COVID-19 Protective Health Behaviors

In African countries, when comparing the scores for compliance with COVID-19 protective health behaviors at baseline and follow-up, the score decreased in 5 countries (Cameroon, Democratic Republic of the Congo, Nigeria, South Africa, and Uganda). The score decreased most in Democratic Republic of the Congo, followed by Nigeria, Uganda, Cameroon, and South Africa. Positive changes were found in Kenya and Senegal.

In Asia Pacific countries, 3 countries had reduced compliance scores at the follow-up survey, with the greatest decrease in Mongolia, followed by Cambodia and Papua New Guinea. Positive changes in compliance scores were observed in Vietnam, Lao People's Democratic Republic, Japan, South Korea, Malaysia, and the Philippines (see Table 4).

Scores for compliance with World Health Organization advice on COVID-19 protective behaviors and changing patterns in the compliance scores in African and Asia Pacific countries at baseline and follow-up.

Country Baseline Follow-up Change, %
Survey respondents, n Compliance score, mean (SD)a Survey respondents, n Compliance score, mean (SD)a
Africa
Cameroon 1058 8.77 (4.04) 1008 8.56 (3.78) –2.39
Cote d’Ivoire 1129 10.71 (3.88) N/Ab N/A N/A
Democratic Republic of the Congo 1190 10.08 (3.57) 1021 5.74 (4.01) –43.06
Ghana 1094 8.71 (4.01) N/A N/A N/A
Kenya 1030 9.66 (3.46) 1078 11.22 (4.30) 16.15
Liberia 1011 9.87 (3.71) N/A N/A N/A
Mali 1152 6.45 (3.87) N/A N/A N/A
Niger 1032 13.36 (3.40) N/A N/A N/A
Nigeria 1024 12.69 (3.97) 1008 8.48 (3.25) –33.18
Senegal 1139 6.18 (3.99) 1001 7.75 (4.48) 25.40
Sierra Leone 1104 7.37 (4.08) N/A N/A N/A
South Africa 1171 11.65 (4.44) 982 11.62 (3.65) –0.26
South Sudan 1219 6.35 (2.92) N/A N/A N/A
Uganda 1022 11.83 (4.09) 1081 10.94 (4.03) –7.52
All African countries 15,375 9.47 (4.46) 7179 9.21 (4.43) N/A
Western Pacific
China 2000 0.77 (0.10) N/A N/A N/A
Cambodia 1000 0.70 (0.17) 1000 0.64 (0.19) –8.67
Vietnam 1044 0.81 (0.10) 1003 0.86 (0.12) 6.10
Lao People's Democratic Republic 1000 0.58 (0.21) 1000 0.60 (0.19) 4.10
Japan 1040 0.68 (0.22) 1066 0.76 (0.19) 11.48
South Korea 1133 0.68 (0.27) 1155 0.75 (0.19) 11.15
Malaysia 1000 0.70 (0.24) 1000 0.72 (0.21) 2.56
Philippines 1000 0.64 (0.17) 1000 0.67 (0.16) 4.52
Mongolia 1000 0.77 (0.13) 1000 0.69 (0.15) –10.39
Fiji 521 0.79 (0.20) N/A N/A N/A
Papua New Guinea 552 0.46 (0.19) 513 0.43 (0.18) –6.49
Solomon Islands 527 0.43 (0.16) N/A N/A N/A
Tonga 517 0.53 (0.11) N/A N/A N/A
Vanuatu 532 0.46 (0.16) N/A N/A N/A
All Western Pacific countries 12,866 0.67 (0.21) 8737 0.71 (0.19) N/A

aMean score for African countries and mean normalized score for Asia Pacific countries.

bN/A: not applicable.

 Associations Between SVI and Compliance Scores

Table 5 displays the factors that predicted compliance with WHO advice on COVID-19 protective behaviors in African countries at baseline and follow-up. SVIs in African countries strongly predicted compliance during both waves. At baseline, the mean score for compliance with COVID-19 protective behaviors statistically significantly increased by 2.68 with every unit of SVI and by 0.9 among participants with a high concern about COVID-19 infection. During the follow-up, the mean compliance score increased by 3.64 with every unit of SVI and by 0.49 among men compared with women.

Associations between the social vulnerability index (SVI) and compliance with World Health Organization advice on COVID-19 protective behaviors among participants from African countries at baseline and follow-up.

Indicator Coefficient (95% CI) P valuea
Baseline (N=15,375)b
SVI 2.68 (0.20 to 5.15) .04
Gender (reference: female)
Male 0.21 (–0.09 to 0.51) .16
Employment status (reference: unemployed)
Retired/student/stay-at-home parent (no own income) –0.23 (–1.00 to 0.50) .51
Working part time –0.76 (–1.60 to 0.09) .08
Working full time 0.24 (–0.44 to 0.92) .46
COVID-19 risk perception (threat; reference: no threat)
Very low threat –0.32 (–0.98 to 0.35) .32
Fairly low threat 0.19 (–0.82 to 1.21) .69
Moderate threat 0.04 (–0.84 to 0.92) .93
Fairly high threat 0.21 (–0.75 to 1.18) .64
Very high threat 0.61 (–0.37 to 1.60) .20
COVID-19 risk perception (concern about being infected; reference: no concern)
Low 0.13 (–0.53 to 0.79) .67
Moderate 0.32 (–0.34 to 0.99) .31
High 0.90 (0.39 to 1.41) .002
Follow-up (N=7179)c
SVI 3.64 (1.10 to 6.18) .01
Gender (reference: female)
Male 0.49 (0.06 to 0.92) .03
HALEd 0.46 (–0.40 to 1.32) .24
Employment status (reference: unemployed)
Retired/student/stay-at-home parent (No own income) 0.46 (–0.08 to 1.01) .08
Working part time –0.51 (–1.21 to 0.19) .13
Working full time –0.12 (–1.20 to 0.95) .79

aSignificance set at P<.05.

bCameroon, Cote d’Ivoire, Democratic Republic of the Congo, Ghana, Kenya, Liberia, Mali, Niger, Nigeria, Senegal, Sierra Leone, South Africa, South Sudan, and Uganda: R2=0.0463, F(13;15,361)=57.38, P<.001.

cCameroon, Democratic Republic of the Congo, Kenya, Nigeria, Senegal, South Africa, and Uganda: R2= 0.1137, F(6,7172)=169.84, P<.001.

dHALE: health-adjusted life expectancy.

Table 6 shows the predictors of compliance with WHO advice on COVID-19 protective behaviors in Asia Pacific countries at baseline and follow-up. SVIs in Asia Pacific countries predicted compliance scores only during follow-up, when the mean score increased by 1.71 with every unit of SVI. At baseline, the mean scores for compliance among participants aged 35 years to 44 years was higher than scores among those aged 18 years to 24 years (reference group), by 0.54. The mean score among men was lower than that of women, by 0.47. There were negative relationships between the scores and the belief that the threat from COVID-19 is exaggerated.

Associations between the social vulnerability index (SVI) and compliance with World Health Organization advice among participants from Asia Pacific countries at baseline and follow-up.

Indicator Coefficient (95% CI) P valuea
Baseline (N=12,866)b
SVI 0.97 (–1.22 to 3.16) .36
Age (years; reference: 18-24 years)
25-34 0.45 (–0.06 to 0.95) .08
35-44 0.54 (0.02 to 1.06) .04
45-54 0.70 (–0.11 to 1.51) .08
≥55 1.30 (–0.01 to 2.62) .052
Gender (reference: female)
Male –0.47 (–0.84 to –0.10) .02
Employment status (reference: unemployed)
Retired/student/stay-at-home parent (no own income) 0.51 (–0.12 to 1.14) .11
Working part time 0.33 (–0.88 to 1.55) .57
Working full time 1.54 (0.20 to 2.87) .03
Agree that the threat from COVID-19 is exaggerated (reference: strongly agree)
Agree –0.68 (–1.24 to –0.13) .02
Disagree –2.15 (–3.16 to –1.14) .001
Strongly disagree –1.89 (–3.05 to –0.72) .004
Follow-up (N=8737)c
SVI 1.71 (0.05 to 3.37) .04
Age (years; reference: 18-24 years)
25-34 –0.02 (–0.36 to 0.32) .90
35-44 0.19 (–0.30 to 0.69) .40
45-54 0.28 (–0.28 to 0.85) .28
≥55 0.76 (–0.07 to 1.59) .07
Gender (reference: female)
Male –0.39 (–0.65 to –0.14) .008
Employment status (reference: unemployed)
Retired/student/stay-at-home parent (no own income) 0.76 (–0.25 to 1.77) .12
Working part time 0.92 (–0.74 to 2.58) .24
Working full time 1.14 (–0.40 to 2.69) .13
Area of residence (reference: urban)
Rural –0.10 (–0.91 to 0.71) .78
Agree that the threat from COVID-19 is exaggerated (reference: strongly agree)
Agree –0.06 (–0.75 to 0.63) .84
Disagree 0.61 (–0.43 to 1.65) .22
Strongly disagree 0.52 (–0.31 to 1.36) .19

aSignificance set at P<.05.

bChina, Cambodia, Vietnam, Lao People's Democratic Republic, Japan, South Korea, Malaysia, Philippines, Mongolia, Fiji, Papua New Guinea, Solomon Islands, Tonga, and Vanuatu: R2=0.0800, F(12,13)=18.21, P<.001.

cCambodia, Vietnam, Lao People's Democratic Republic, Japan, South Korea, Malaysia, Philippines, Mongolia, and Papua New Guinea: R2=0.0687, F(14,8722)=48.71, P<.001.

 Discussion Principal Findings

The SVIs that we developed indicated that most countries in Africa and Asia Pacific were extremely socially vulnerable, with significant gaps in SVIs across countries. After the Omicron wave, these social vulnerability gaps became wider due to the deteriorating situations among the most vulnerable populations in both regions. Our results also suggest that compliance with the WHO advice was determined by a combination of multilevel socioeconomic and sociopsychological factors. SVIs in African countries were a strong predictor of scores for compliance with WHO advice both at baseline and during follow-up, while SVIs in Asia Pacific countries showed predictability only at the follow-up survey.

Our findings on social vulnerability, COVID-19, and inequity gaps align with those of previous studies. Individuals with income security and high-level occupations were more likely to adopt COVID-19 protective behaviors than those with lower status [51]. Financially vulnerable populations often face barriers such as limited access to adequate housing conditions, sanitation, personal protective equipment, education, information, job security, quality health care services, and COVID-19 vaccines [21,52,53]. Many studies have indicated that vulnerable groups and low-income countries have fewer capabilities to protect themselves from COVID-19 and recover from negative economic impacts, all of which widens the existing inequity gaps and increases their vulnerability to future public health emergency events [53-57]. This evidence highlights the vicious cycle of social vulnerability and COVID-19.

In African countries, social vulnerability was primarily explained by 3 factors including literacy and media use, trust, and country-level income and infrastructure. Among these factors, literacy and media use had the most significant impact, followed by trust and country-level income and infrastructure. In the aftermath of the Omicron wave, changes in these factors led to a reduction in vulnerability. To ensure equitable recovery and bolster future pandemic preparedness, vital actions encompass launching health literacy campaigns, advancing media literacy, nurturing transparent community engagement to foster trust, investing in health care infrastructure, and creating safety nets for socioeconomic stability.

In Asia Pacific countries, the SVI was intricately linked to critical socioeconomic factors including education, internet accessibility, GDP per capita, health care capacity, healthy life expectancy, and population density. Variations in SVI predominantly stemmed from fluctuations in education, GDP per capita, and population density. To effectively address these dynamics during pandemic recovery, policy priorities should include targeted educational programs to enhance literacy and digital skills, ensure affordable internet access, embrace sustainable urban planning strategies, promote equitable health care access, and introduce programs aiming to reduce income inequality.

The predictability of our SVIs depended on the comprehensiveness of the variables used and the phases of the COVID-19 outbreak. The stronger predictability of the SVIs in African countries can be attributed to the inclusion of a more comprehensive list of relevant variables in its construction. Findings from Asia Pacific countries demonstrate a variation in the predictability of SVIs at different phases of the COVID-19 outbreak. A relationship between Asia Pacific countries’ SVIs and WHO advice compliance scores was found only after Omicron, even though the influential indicators remained consistent both before and after Omicron.

There were changes in parameters that could contribute to the variation in predictability of SVIs in Asia Pacific countries. Upward trends of the SVI and scores for compliance to the WHO advice were found in most Asia Pacific countries, except among certain of the most vulnerable countries. Other studies also reported that COVID-19 protective behaviors increased with number of the COVID-19 cases [58] and social class [59]. Therefore, it is possible that experiencing Omicron triggered better compliance with the WHO advice; however, participants with low levels of social barriers improved their protective behaviors at a higher level than others who had higher social barriers. This resulted in significant differences between the 2 groups.

Our findings improve the understanding of how socioeconomic and various factors determine SVI, how SVI determines compliance with WHO recommendations, and how the SVI and compliance scores varied temporally as the pandemic evolves. This could lead to better policy response in identifying vulnerable populations, minimizing their social barriers, mobilizing external assistance, and monitoring progress of implementation. Regular assessment of SVIs across population groups contributes to specific and effective interventions.

 Limitations

This study also has limitations. First, certain important variables for the construction of the SVI were absent, especially in Asia Pacific countries where there are no parameters related to trust and media use. Second, the index used in this study represents social vulnerability at the individual and national levels; therefore, it is not practical for guiding community-based actions to minimize vulnerability. Third, we used a single factor analysis model for each data set to develop the index, so the changes in SVI values across different time periods should be interpreted with caution.

 Conclusions

The SVI, constructed from a comprehensive list of socioeconomic and sociopsychological variables, can predict compliance with WHO advice on protective behaviors against COVID-19. Substantial gaps in social vulnerability to comply with the WHO advice existed at baseline and were exacerbated after the Omicron wave in African and Asia Pacific countries. The SVI developed in this study, particularly for African countries, could be used to identify vulnerable populations and monitor the progress of policy responses to minimize social barriers to adopting protective behaviors against COVID-19. This study can contribute to the design of prevention measures for future public health emergencies.

Supplementary tables and figures.

 Abbreviations GDP

gross domestic product

 HALE

health-adjusted life expectancy

 SVI

social vulnerability index

 WHO

World Health Organization

This work was supported by AIR@InnoHK administered by Innovation and Technology Commission, Africa Centres for Disease Control and Prevention, and the John D. and Catherine T. MacArthur Foundation.

 Data Availability

The data sets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

SP and LL conceptualized the study. SP implemented the methodology, conducted the data analysis, and prepared the initial draft. VT and KL provided advice about the methodology. HJL, LL, VT, and KL contributed to revising and editing the manuscript. HJL and LL launched the project and provided supervision. All authors reviewed and approved the final draft.

None declared.

 Karan A The covid-19 pandemic will end with public health tools, not clinical ones BMJ 2022 06 24 377 o1561 10.1136/bmj.o1561 35750340 O'Mahoney LL Routen A Gillies C Ekezie W Welford A Zhang A Karamchandani U Simms-Williams N Cassambai S Ardavani A Wilkinson TJ Hawthorne G Curtis F Kingsnorth AP Almaqhawi A Ward T Ayoubkhani D Banerjee A Calvert M Shafran R Stephenson T Sterne J Ward H Evans RA Zaccardi F Wright S Khunti K The prevalence and long-term health effects of Long Covid among hospitalised and non-hospitalised populations: A systematic review and meta-analysis EClinicalMedicine 2023 01 55 101762 10.1016/j.eclinm.2022.101762 36474804 S2589-5370(22)00491-6 PMC9714474 Du Y Zhao W Huang S Huang C Li C Chen Y Huang Y Yang L Li C Zhang H Guo H Liu J Gray matter thickness and subcortical nuclear volume in men after SARS-CoV-2 Omicron infection JAMA Netw Open 2023 11 01 6 11 e2345626 10.1001/jamanetworkopen.2023.45626 38032639 2812387 PMC10690469 Kolahchi Z De Domenico M Uddin L Cauda V Grossmann I Lacasa L Grancini G Mahmoudi M Rezaei N COVID-19 and its global economic impact Adv Exp Med Biol 2021 1318 825 837 10.1007/978-3-030-63761-3_46 33973214 Murray CJL COVID-19 will continue but the end of the pandemic is near The Lancet 2022 01 399 10323 417 419 10.1016/s0140-6736(22)00100-3 Looi M What could the next pandemic be? BMJ 2023 06 05 381 909 10.1136/bmj.p909 37277142 Willett BJ Grove J MacLean OA Wilkie C De Lorenzo G Furnon W Cantoni D Scott S Logan N Ashraf S Manali M Szemiel A Cowton V Vink E Harvey WT Davis C Asamaphan P Smollett K Tong L Orton R Hughes J Holland P Silva V Pascall DJ Puxty K da Silva Filipe A Yebra G Shaaban S Holden Matthew T G Pinto Rute Maria Gunson Rory Templeton Kate Murcia Pablo R Patel Arvind H Klenerman Paul Dunachie Susanna PITCH Consortium COVID-19 Genomics UK (COG-UK) Consortium Haughney John Robertson David L Palmarini Massimo Ray Surajit Thomson Emma C SARS-CoV-2 Omicron is an immune escape variant with an altered cell entry pathway Nat Microbiol 2022 08 7 8 1161 1179 10.1038/s41564-022-01143-7 35798890 10.1038/s41564-022-01143-7 PMC9352574 Ferdinands JM Rao S Dixon BE Mitchell PK DeSilva MB Irving SA Lewis N Natarajan K Stenehjem E Grannis SJ Han J McEvoy C Ong TC Naleway AL Reese SE Embi PJ Dascomb K Klein NP Griggs EP Liao I Yang D Fadel WF Grisel N Goddard K Patel P Murthy K Birch R Valvi NR Arndorfer J Zerbo O Dickerson M Raiyani C Williams J Bozio CH Blanton L Link-Gelles R Barron MA Gaglani M Thompson MG Fireman B Waning of vaccine effectiveness against moderate and severe covid-19 among adults in the US from the VISION network: test negative, case-control study BMJ 2022 10 03 379 e072141 10.1136/bmj-2022-072141 36191948 PMC9527398 Advice for the public: Coronavirus disease (COVID-19) World Health Organization 2023 03 18 2024-07-16 https://www.who.int/emergencies/diseases/novel-coronavirus-2019/advice-for-public COVID-19 transmission World Health Organization Western Pacific 2024-07-16 https://www.who.int/westernpacific/emergencies/covid-19/information/transmission-protective-measures Lauring AS Tenforde MW Chappell JD Gaglani M Ginde AA McNeal T Ghamande S Douin DJ Talbot HK Casey JD Mohr NM Zepeski A Shapiro NI Gibbs KW Files DC Hager DN Shehu A Prekker ME Erickson HL Exline MC Gong MN Mohamed A Johnson NJ Srinivasan V Steingrub JS Peltan ID Brown SM Martin ET Monto AS Khan A Hough CL Busse LW Ten Lohuis CC Duggal A Wilson JG Gordon AJ Qadir N Chang SY Mallow C Rivas C Babcock HM Kwon JH Halasa N Grijalva CG Rice TW Stubblefield WB Baughman A Womack KN Rhoads JP Lindsell CJ Hart KW Zhu Y Adams K Schrag SJ Olson SM Kobayashi M Verani JR Patel MM Self WH InfluenzaOther Viruses in the Acutely Ill (IVY) Network Clinical severity of, and effectiveness of mRNA vaccines against, covid-19 from omicron, delta, and alpha SARS-CoV-2 variants in the United States: prospective observational study BMJ 2022 03 09 376 e069761 10.1136/bmj-2021-069761 35264324 PMC8905308 Lio CF Cheong HH Lei CI Lo IL Yao L Lam C Leong IH Effectiveness of personal protective health behaviour against COVID-19 BMC Public Health 2021 04 29 21 1 827 10.1186/s12889-021-10680-5 33926406 10.1186/s12889-021-10680-5 PMC8082215 The Lancet The COVID-19 pandemic in 2023: far from over The Lancet 2023 01 401 10371 79 10.1016/s0140-6736(23)00050-8 Burger R Christian C English R Maughan-Brown B Rossouw L Predictors of mask-wearing during the advent of the COVID-19 pandemic: Evidence from South Africa Transl Behav Med 2022 01 18 12 1 E 10.1093/tbm/ibab132 34865174 6448738 PMC8690224 Chen X Ran L Liu Q Hu Q Du X Tan X Hand hygiene, mask-wearing behaviors and its associated factors during the COVID-19 epidemic: a cross-sectional study among primary school students in Wuhan, China Int J Environ Res Public Health 2020 04 22 17 8 2893 10.3390/ijerph17082893 32331344 ijerph17082893 PMC7215913 Pertwee E Simas C Larson HJ An epidemic of uncertainty: rumors, conspiracy theories and vaccine hesitancy Nat Med 2022 03 10 28 3 456 459 10.1038/s41591-022-01728-z 35273403 10.1038/s41591-022-01728-z Haug N Geyrhofer L Londei A Dervic E Desvars-Larrive A Loreto V Pinior B Thurner S Klimek P Ranking the effectiveness of worldwide COVID-19 government interventions Nat Hum Behav 2020 12 16 4 12 1303 1312 10.1038/s41562-020-01009-0 33199859 10.1038/s41562-020-01009-0 Benfer EA Vlahov D Long MY Walker-Wells E Pottenger JL Gonsalves G Keene DE Eviction, health inequity, and the spread of COVID-19: housing policy as a primary pandemic mitigation strategy J Urban Health 2021 02 07 98 1 1 12 10.1007/s11524-020-00502-1 33415697 10.1007/s11524-020-00502-1 PMC7790520 Boro E Stoll B Barriers to COVID-19 health products in low-and middle-income countries during the COVID-19 pandemic: a rapid systematic review and evidence synthesis Front Public Health 2022 7 22 10 928065 10.3389/fpubh.2022.928065 35937225 PMC9354133 Jiwani SS Antiporta DA Inequalities in access to water and soap matter for the COVID-19 response in sub-Saharan Africa Int J Equity Health 2020 06 03 19 1 82 10.1186/s12939-020-01199-z 32493409 10.1186/s12939-020-01199-z PMC7267763 Bayati M Noroozi R Ghanbari-Jahromi M Jalali FS Inequality in the distribution of Covid-19 vaccine: a systematic review Int J Equity Health 2022 08 30 21 1 122 10.1186/s12939-022-01729-x 36042485 10.1186/s12939-022-01729-x PMC9425802 Tan EY Albarazi D Saw YE Buvanaswari P Doshi K Liu JC Confidence in government and rumors amongst migrant worker men involved in dormitory outbreaks of COVID-19: A cross-sectional survey J Migr Health 2021 4 100069 10.1016/j.jmh.2021.100069 34664038 S2666-6235(21)00036-2 PMC8514324 Lang R Benham JL Atabati O Hollis A Tombe T Shaffer B Burns KK MacKean G Léveillé T McCormack B Sheikh H Fullerton MM Tang T Boucher J Constantinescu C Mourali M Manns BJ Marshall DA Hu J Oxoby RJ Attitudes, behaviours and barriers to public health measures for COVID-19: a survey to inform public health messaging BMC Public Health 2021 04 21 21 1 765 10.1186/s12889-021-10790-0 33882896 10.1186/s12889-021-10790-0 PMC8058588 Mahdavian F Warren GW Evensen D Bouder FE The relationship between barriers and drivers of COVID-19 protective behaviors in Germany and the UK Int J Public Health 2022 9 8 67 1604970 10.3389/ijph.2022.1604970 36158783 1604970 PMC9492856 Crouzet L Scarlett H Colleville A Pourtau L Melchior M Ducarroz S Impact of the COVID-19 pandemic on vulnerable groups, including homeless persons and migrants, in France: A qualitative study Prev Med Rep 2022 04 26 101727 10.1016/j.pmedr.2022.101727 35155084 S2211-3355(22)00034-1 PMC8824256 Mah JC Andrew MK Social vulnerability indices: A pragmatic tool for COVID-19 policy and beyond Lancet Reg Health Eur 2022 03 14 100333 10.1016/j.lanepe.2022.100333 35194590 S2666-7762(22)00026-6 PMC8850161 Dow K Exploring differences in our common future(s): the meaning of vulnerability to global environmental change Geoforum 1992 1 23 3 417 436 10.1016/0016-7185(92)90052-6 Katic K Social Vulnerability Assessment Tools for Climate Change and DRR Programming United Nations Development Programme 2017 09 2024-07-16 https://adaptation-undp.org/sites/default/files/resources/social_vulnerability05102017_0.pdf Reckien D What is in an index? Construction method, data metric, and weighting scheme determine the outcome of composite social vulnerability indices in New York City Reg Environ Change 2018 1 18 18 5 1439 1451 10.1007/s10113-017-1273-7 31007595 1273 PMC6448355 The Africa COVID Community Vulnerability Index Surgo Ventures Precision for COVID 2024-07-16 https://precisionforcovid.org/africa Report: Vulnerable Communities and COVID-19 Surgo Ventures 2021 01 27 2024-07-16 https://surgoventures.org/resource-library/report-vulnerable-communities-and-covid-19 Macharia PM Joseph NK Okiro EA A vulnerability index for COVID-19: spatial analysis at the subnational level in Kenya BMJ Glob Health 2020 08 24 5 8 e003014 10.1136/bmjgh-2020-003014 32839197 bmjgh-2020-003014 PMC7447114 Welsh CE Sinclair DR Matthews FE Static socio-ecological COVID-19 vulnerability index and vaccine hesitancy index for England Lancet Reg Health Eur 2022 03 14 100296 10.1016/j.lanepe.2021.100296 34981041 S2666-7762(21)00282-9 PMC8717085 Al-Rasheed M Protective behavior against COVID-19 among the public in Kuwait: an examination of the protection motivation theory, trust in government, and sociodemographic factors Soc Work Public Health 2020 09 01 35 7 546 556 10.1080/19371918.2020.1806171 32970542 Vardavas C Odani S Nikitara K El Banhawi H Kyriakos C Taylor L Lown G Becuwe N Perceptions and practice of personal protective behaviors to prevent COVID-19 transmission in the G7 nations Population Medicine 2020 6 19 2 June 17 10.18332/popmed/123821 Pagliaro S Sacchi S Pacilli MG Brambilla M Lionetti F Bettache K Bianchi M Biella M Bonnot V Boza M Butera F Ceylan-Batur S Chong K Chopova T Crimston CR Álvarez B Cuadrado I Ellemers N Formanowicz M Graupmann V Gkinopoulos T Kyung Jeong EH Jasinskaja-Lahti I Jetten J Muhib Bin K Mao Y McCoy C Mehnaz F Minescu A Sirlopú David Simić A Travaglino G Uskul AK Zanetti C Zinn A Zubieta E Trust predicts COVID-19 prescribed and discretionary behavioral intentions in 23 countries PLoS One 2021 3 10 16 3 e0248334 10.1371/journal.pone.0248334 33690672 PONE-D-21-03505 PMC7946319 WHO COVID-19 dashboard World Health Organization Data 2020 11 3 2024-07-16 https://data.who.int/dashboards/covid19/vaccines Share of SARS-CoV-2 sequences that are the omicron variant Our World in Data 2023 2024-07-16 https://ourworldindata.org/grapher/covid-cases-omicron Viana R Moyo S Amoako D Tegally H Scheepers C Althaus C Anyaneji UJ Bester PA Boni MF Chand M Choga WT Colquhoun R Davids M Deforche K Doolabh Deelan du Plessis Louis Engelbrecht Susan Everatt Josie Giandhari Jennifer Giovanetti Marta Hardie Diana Hill Verity Hsiao Nei-Yuan Iranzadeh Arash Ismail Arshad Joseph Charity Joseph Rageema Koopile Legodile Kosakovsky Pond Sergei L Kraemer Moritz U G Kuate-Lere Lesego Laguda-Akingba Oluwakemi Lesetedi-Mafoko Onalethatha Lessells Richard J Lockman Shahin Lucaci Alexander G Maharaj Arisha Mahlangu Boitshoko Maponga Tongai Mahlakwane Kamela Makatini Zinhle Marais Gert Maruapula Dorcas Masupu Kereng Matshaba Mogomotsi Mayaphi Simnikiwe Mbhele Nokuzola Mbulawa Mpaphi B Mendes Adriano Mlisana Koleka Mnguni Anele Mohale Thabo Moir Monika Moruisi Kgomotso Mosepele Mosepele Motsatsi Gerald Motswaledi Modisa S Mphoyakgosi Thongbotho Msomi Nokukhanya Mwangi Peter N Naidoo Yeshnee Ntuli Noxolo Nyaga Martin Olubayo Lucier Pillay Sureshnee Radibe Botshelo Ramphal Yajna Ramphal Upasana San James E Scott Lesley Shapiro Roger Singh Lavanya Smith-Lawrence Pamela Stevens Wendy Strydom Amy Subramoney Kathleen Tebeila Naume Tshiabuila Derek Tsui Joseph van Wyk Stephanie Weaver Steven Wibmer Constantinos K Wilkinson Eduan Wolter Nicole Zarebski Alexander E Zuze Boitumelo Goedhals Dominique Preiser Wolfgang Treurnicht Florette Venter Marietje Williamson Carolyn Pybus Oliver G Bhiman Jinal Glass Allison Martin Darren P Rambaut Andrew Gaseitsiwe Simani von Gottberg Anne de Oliveira Tulio Rapid epidemic expansion of the SARS-CoV-2 Omicron variant in southern Africa Nature 2022 03 603 7902 679 686 10.1038/s41586-022-04411-y 35042229 10.1038/s41586-022-04411-y PMC8942855 Cutter SL Boruff BJ Shirley WL Social vulnerability to environmental hazards Social Science Quarterly 2003 05 19 84 2 242 261 10.1111/1540-6237.8402002 Deml MJ Githaiga JN Determinants of COVID-19 vaccine hesitancy and uptake in sub-Saharan Africa: a scoping review BMJ Open 2022 11 18 12 11 e066615 10.1136/bmjopen-2022-066615 36400736 bmjopen-2022-066615 PMC9676416 Ju I Ohs J Park T Hinsley A Interpersonal communication influence on health-protective behaviors amid the COVID-19 crisis Health Commun 2021 08 08 1 11 10.1080/10410236.2021.1956792 34365864 COVID-19 vaccine doses administered per 100 people, by income group Our World in Data 2023 2024-07-16 https://ourworldindata.org/grapher/cumulative-covid-vaccinations-income-group Scholz U Freund AM Determinants of protective behaviours during a nationwide lockdown in the wake of the COVID-19 pandemic Br J Health Psychol 2021 09 13 26 3 935 957 10.1111/bjhp.12513 33847029 PMC8250218 Ritchie H Mathieu E Roser M Ortiz-Ospina E Internet Our World in Data 2024-07-16 https://ourworldindata.org/internet Population density, 2022 Our World in Data 2024-07-16 https://ourworldindata.org/grapher/population-density Population with basic handwashing facilities at home (%) World Health Organization The Global Health Observatory 2024-07-16 https://www.who.int/data/gho/data/indicators/indicator-details/GHO/population-with-basic-handwashing-facilities-at-home-(-) Density of nursing and midwifery personnel (per 10 000 population) World Health Organization Data 2024-07-16 https://data.who.int/indicators/i/B54EB15/5C8435F Healthy life expectancy (HALE) at birth World Health Organization The Global Health Observatory 2024-07-16 https://www.who.int/data/gho/indicator-metadata-registry/imr-details/66 Hair JF Tatham R Anderson R Black WC Multivariate data analysis, Fifth edition 1998 London, England Prentice Hall Urbanin G Meira W Serpa A Costa DDS Baldaçara L da Silva AP Guatimosim R Lacerda AM Oliveira EA Braule A Romano-Silva MA da Silva AG Malloy-Diniz L Pappa G Miranda DM Social determinants in self-protective behavior related to COVID-19: association rule-mining study JMIR Public Health Surveill 2022 06 15 8 6 e34020 10.2196/34020 35704360 v8i6e34020 PMC9202654 Bergen N Kirkby K Fuertes CV Schlotheuber A Menning L Mac Feely S O'Brien K Hosseinpoor AR Global state of education-related inequality in COVID-19 vaccine coverage, structural barriers, vaccine hesitancy, and vaccine refusal: findings from the Global COVID-19 Trends and Impact Survey The Lancet Global Health 2023 02 11 2 e207 e217 10.1016/s2214-109x(22)00520-4 Patel J Nielsen F Badiani A Assi S Unadkat V Patel B Ravindrane R Wardle H Poverty, inequality and COVID-19: the forgotten vulnerable Public Health 2020 06 183 110 111 10.1016/j.puhe.2020.05.006 32502699 S0033-3506(20)30165-7 PMC7221360 Buffie EF Adam C Zanna L Kpodar K Loss-of-learning and the post-Covid recovery in low-income countries J Macroecon 2023 03 75 103492 10.1016/j.jmacro.2022.103492 36591598 S0164-0704(22)00085-4 PMC9788846 Building Forward Together: Towards an Inclusive and Resilient Asia and the Pacific Asian Development Bank 2022 03 2024-07-16 https://www.adb.org/publications/building-forward-together-inclusive-resilient-asia-pacific Mahler DG Yonzan N Lakner C The Impact of COVID-19 on Global Inequality and Poverty World Bank Group 2022 10 2024-07-16 https://documents1.worldbank.org/curated/en/099250510052241154/pdf/IDU01d94e70603dc804f990b6130751d75dccb52.pdf Jensen N Kelly AH Avendano M The COVID-19 pandemic underscores the need for an equity-focused global health agenda Humanit Soc Sci Commun 2021 01 18 8 1 1 10.1057/s41599-020-00700-x O'Connell CP Johnson KJ Kinzer H Olagoke A Weng O Kreuter MW When do people increase COVID-19 protective behaviors? A longitudinal analysis of the impact of infections among close contacts and in the community Prev Med Rep 2023 08 34 102251 10.1016/j.pmedr.2023.102251 37234564 S2211-3355(23)00142-0 PMC10195772 Lüdecke D von dem Knesebeck O Protective behavior in course of the COVID-19 outbreak-survey results from Germany Front Public Health 2020 9 24 8 572561 10.3389/fpubh.2020.572561 33072712 PMC7543680