Original article

 

Prevalence and factors associated with the intention to be vaccinated against COVID-19 in Peru

 

Percy Herrera-Añazco 1,2,3, Nephrologist
Ángela Uyen-Cateriano 4, Master’s degree in Health Business Administration and master’s degree in International Relations
Diego Urrunaga-Pastor 5, Physician
Guido Bendezu-Quispe 2,6, Master’s degree in Biomedical Informatics
Carlos J. Toro-Huamanchumo 7,8, Epidemiologist
Alfonso J. Rodríguez-Morales 9,10, Tropical medicine physician and senior researcher
Adrian V. Hernández 11,12, PhD in Clinical Epidemiology
Vicente A. Benites-Zapata 2,13, Master’s degree in Epidemiological Research

1 Universidad Privada San Juan Bautista. Lima, Perú.
2 Red Internacional en Salud Colectiva y Salud Intercultural. Ciudad de México, México.
3 Instituto de Evaluación de Tecnologías en Salud e Investigación, EsSalud. Lima, Perú.
4 Médicos Sin Fronteras. Política Sanitaria. Bruselas, Bélgica.
5 Universidad Científica del Sur. Lima, Perú.
6 Centro de Investigación Epidemiológica en Salud Global, Universidad Privada Norbert Wiener. Lima, Perú.
7 Facultad de Medicina Humana, Universidad de San Martín de Porres. Chiclayo, Perú.
8 Unidad de Investigación Multidisciplinaria, Clínica Avendaño. Lima, Perú.
9 Grupo de Investigación Biomedicina, Facultad de Medicina, Fundación Universitaria Autónoma de las Américas. Pereira, Colombia.
10 Asociación Colombiana de Infectología. Bogotá, Colombia.
11 University of Connecticut. Storrs, Connecticut, Estados Unidos.
12 Unidad de Revisiones Sistemáticas y Meta-análisis, Guías de Práctica Clínica y Evaluaciones Tecnológicas Sanitarias. Universidad San Ignacio de Loyola, Lima, Perú.
13 Unidad para la Generación y Síntesis de Evidencias en Salud, Universidad San Ignacio de Loyola. Lima, Perú.

 


ABSTRACT

Objectives: To estimate the prevalence and factors associated with the intention to be vaccinated (ITV) against COVID-19 in Peru.

Materials and methods: Analytical cross-sectional study using the survey conducted by the University of Maryland, USA, on Facebook. The dependent variable is the ITV. Crude and adjusted prevalence ratios (PR) were calculated, with their 95% confidence intervals (95% CI) using generalized linear models of the Poisson family, in order to evaluate the association of sociodemographic variables, compliance with community mitigation strategies, symptoms of COVID-19, mental health and acceptance of vaccination before the recommendation (AVR) by various actors and health authorities, with the ITV.

Results: Data from 17,162 adults were analyzed. The overall prevalence of the ITV was 74.9%. A lower prevalence of the ITV was associated with the female sex (PR=0.95; 95% CI: 0.94-0.97), living in a town (PR=0.95; 95% CI: 0.91-0.99) or village or other rural area (PR=0.90; 95% CI: 0.86-0.93) and the AVR of politicians (PR=0.89; 95% CI: 0.87-0.92). Conversely, having COVID-19 symptoms (PR=1.06; 95% CI: 1.03-1.09), economic insecurity (PR=1.04; 95% CI: 1.01-1.06), fears of becoming seriously ill or that a family member becomes seriously ill from COVID-19 (PR=1.49; 95% CI: 1.36-1.64) and the AVR of family and friends (PR=1.10; 95% CI:  1.08-1.12), healthcare workers (PR=1.29; 95% CI: 1.26-1.32), World Health Organization (PR=1.34; 95% CI: 1.29-1.40) and government officials (PR=1.18; 95% CI: 1.15-1.22) was associated with a higher prevalence of the ITV.

Conclusions: Three-quarters of the respondents had the ITV. There are potentially modifiable factors that could improve vaccine acceptance.

Keywords: COVID-19; SARS-Co-V2; COVID-19 Vaccines; Vaccination; Vaccination Refusal; Peru (source: MeSH NLM).

 


INTRODUCTION

On June 7, 2021, the World Health Organization (WHO) reported 173,005,553 confirmed cases of COVID-19, including 3,727,605 deaths (1). With no effective treatment and few therapies to modify the course of the disease, the global hope of controlling the disease rests on the effective and universal distribution of available vaccines (2).

Vaccination is key for succeeding in controlling the disease (3). Despite the growing number of safe and effective vaccines on the market, reluctance to vaccinate is a growing problem with global implications (4). This has become an important phenomenon due to outbreaks of preventable diseases that were previously controlled with vaccines (5). In the context of the COVID-19 pandemic, vaccination acceptance is a relevant discussion due to misinformation, mistrust and conspiracy theories that have hindered the adoption of community mitigation measures against the disease, such as vaccines (6).

There are several studies related to the acceptance of vaccination against COVID-19. Some countries such as China, United States, Ecuador, Malaysia, Indonesia, South Korea, Brazil, South Africa, Denmark and United Kingdom have a high acceptance rate, between 65% and 97% (7-10). In contrast, other countries have low acceptance rates, between 55% and 62%, such as Russia and France (11,12). Vaccine acceptance varies according to sociodemographic factors such as gender, belonging to ethnic minorities, rural population, economic income or sociological factors such as political tendencies, among others (6,10,13-16). We recently reported that countries from Latin America and the Caribbean intended to vaccinate 80% of their population by February 2020; and that fears of becoming seriously ill, having a family member fall ill from COVID-19 and having depressive symptoms were associated with a higher probability of having the intention to be vaccinated (17). In contrast, being female and non-binary was associated with a lower vaccination intention (17).

Peru has been one of the countries most affected by the COVID-19 pandemic. According to the situation room of the Peruvian Ministry of Health (MINSA), more than 1.9 million cases and more than 186,500 deaths due to COVID-19 were reported by June 7, 2021 (18). On February 7, 2021, the first batch of vaccines arrived in Peru, initiating the vaccination process against COVID-19. Although more than four million doses have been administered to date (18), the vaccine is not fully accepted in Peru, as in other parts of the world. According to a survey published by Ipsos in February 2021, if a free vaccine against COVID-19 were available, 35% of the country’s population would not get vaccinated, the first reason being fear of adverse effects (19). A previous study conducted by the authors on the vaccination intention in Latin America and the Caribbean did not include variations at the departmental level, in order to identify aspects that could individualize vaccination strategies in each of the departments of Peru (17). Therefore, the aim of this research was to determine the prevalence and factors associated with the intention to be vaccinated (ITV) against COVID-19 in Peru.

 

KEY MESSAGES

Motivation for the study: Despite the fact that Peru is one of the countries most affected worldwide by the COVID-19 pandemic, the prevalence of the intention to be vaccinated against this disease is uncertain.

Main findings: Three out of four respondents on Facebook intended to be vaccinated. There are modifiable and non-modifiable factors associated with the intention to be vaccinated against COVID-19 in Peru.

Implications: Communication strategies targeting population groups that influence vaccination intention may favor vaccination against COVID-19 in Peru.

 

MATERIALS AND METHODS

Study design and database

We conducted a secondary analysis of a database collected by the University of Maryland, USA, and the social network Facebook (Facebook, Inc.) by means of a survey aimed at assessing different characteristics of respondents in the context of the COVID-19 pandemic. The survey includes demographic information, self-report of COVID-19 symptomatology, assessment of food and economic security, mental health, and a module on attitudes toward vaccination. The survey was first conducted on April 23, 2020, and has since been administered daily in more than 200 countries or territories, translated into the primary language of each country (20). The selection of the participants was random, within the sampling frame of the total number of Facebook users according to geographic region and country. Likewise, each selected participant was weighted according to the region and country in which they responded to the survey. In case people declined the invitation or omitted to participate in the survey, Facebook invited another person within the same geographic area who had not responded to the survey within the last eight weeks.

Population and sample

The survey population included Facebook users aged 18 years and older. For this analysis, we included participants from Peru who had responded to the survey between January 15 and February 1 (n = 29,140 adults). We excluded participants who did not have data on the variables of interest for this study. Thus, we analyzed data from 17,162 adults in Peru.

Variables

The outcome of the study was the ITV. ITV was assessed by the following question, "If you were offered a vaccine today to prevent COVID-19, would you choose to be vaccinated?". This question had four possible answers: "Yes, definitely", "probably yes", "probably no", "definitely no". The variable was dichotomized by considering the last two alternatives as non-ITV against COVID-19 and the first two alternatives as ITV.

Independent variables

Sociodemographic characteristics

Gender (male, female, non-binary), age (18-24, 25-34, 35-44, 45-54, 55-64, 65-74, 75 and older) and the participant’s area of residence (city, town, village or other rural area) were included.

Compliance with community mitigation strategies and COVID-19 symptoms at the time of the survey.

The presence of suspected COVID-19 symptomatology at the time of the survey was defined as three or more of the following symptoms in the last 24 hours (21): fever, cough, respiratory distress, fatigue, coryza, muscle pain, sore throat, chest pain, nausea, loss of smell, eye pain, and headache.

Compliance and adherence to the three main community mitigation strategies to reduce coronavirus transmission were included: hand washing, use of masks, and physical distancing. Compliance with physical distancing was considered when the participant reported not having been in direct physical contact (including touching, shaking hands, hugging, kissing) for no more than one minute in the past 24 hours and not having been within two meters of any person with whom he or she does not currently live. Handwashing compliance was considered when participants reported having washed their hands at least once in the last 24 hours. In addition, compliance with facemask use was considered when participants reported having worn a facemask in public (at least sometime) during the last seven days. A variable was created considering compliance with the three community mitigation strategies previously mentioned.

Mental Health

Fear that the participant or a member of his or her family might become seriously ill with COVID-19 was assessed by the following question, "How worried are you that you or someone in your immediate family might become seriously ill with coronavirus (COVID-19)?". The question had the following possible responses: "Very worried", "somewhat worried", "not very worried", "not worried at all". A dichotomous variable was created considering the last alternative as the absence of fear that the participant or a family member would become seriously ill with COVID-19 and the remaining three as the presence of fear.

Food and economic insecurity

Food security was assessed using the following question, "Are you worried about having enough food for the next week?". This question had four possible answers: "Very worried", "somewhat worried", "not very worried", "not at all worried". The variable was dichotomized by considering the first three alternatives as food insecurity.

Economic security was assessed by the following question: "Are you worried about your household’s economy for the next month?". This question had four alternatives: "very worried", "somewhat worried", "not very worried", "not at all worried". The variable was dichotomized by considering the first three responses as economic insecurity.

Likelihood of vaccination acceptance on the recommendation of different actors

We assessed the influence that friends and family, physicians and other health professionals providing medical care, the World Health Organization (WHO), government health authorities, and politicians might have on the participant’s ITV. This was assessed by the following question, "Would you be more or less likely to be vaccinated against COVID-19 if it was recommended by each of the following...?". This question had three responses: "More likely", "about the same", "less likely". The last two alternatives were considered as the lack of influence on the acceptance of vaccination, and the first alternative as the presence of influence on the acceptance of vaccination.

Statistical analysis

We downloaded the database in Microsoft Excel® 2010 format and imported it into the statistical package STATA® v14.0 (StataCorp, TX, USA). We carried out the statistical analyses considering the complex sampling of the survey, using the svy command.

We described the qualitative variables using absolute frequencies and weighted proportions according to the complex sampling of the survey with their respective 95% confidence intervals (95% CI). We also carried out bivariate analysis between covariates of interest and outcome variables using Pearson’s Chi-square test with Rao-Scott correction, and generalized linear Poisson family models with log link function to assess factors associated with ITV. We calculated crude (CPR) and adjusted (APR) prevalence ratios with their respective 95% CIs. We employed a statistical criterion to choose the variables that we would include in the adjusted model (those with a p < 0.05 in the crude model) and to evaluate the possible collinearity between the covariates included in the final model. Statistical significance was set at p < 0.05.

Ethical Aspects

To carry out this study we used a database provided by the University of Maryland without personal identifiers; for that reason, the study did not require approval from an institutional ethics committee. Participants gave their consent before starting the survey; therefore, their privacy was not compromised.

RESULTS

Characteristics of the study sample

We analyzed a sample of 17,162 adults, from which 49.8% (n = 8512) were male, 47.1% (n = 9124) were younger than 35 years, and 81.2% (n = 14 229) lived in a city. From the total, 29.9% (n = 5264) had suspected COVID-19 symptomatology at the time of the survey, 82.0% (n = 14,026) reported having food insecurity while 90.2% (n = 15,502) reported economic insecurity. In addition, 44.6% (n = 7740) reported that they would have greater acceptance of vaccination if it were recommended by government health authorities, while only 8.8% (n = 1443) would have greater acceptance if the recommendation was made by politicians. 74.9% (n = 13,175) had ITV (Table 1).

 

Table 1. Descriptive analysis of the characteristics of the study sample (n = 17,162). 

Characteristics

Total

Absolute frequency of the included participants

Weighted proportion of each category

n

%

95% CI

Gender

 

 

 

Male

8512

49.8

47.3-2.4

Female

8505

49.2

46.7-1.8

Non-binary

145

1.0

0.8-1.1

Age (years)

 

 

 

18-24

4260

20.1

18.5-1.8

25-34

4864

27.0

26.0-8.0

35-44

3625

20.9

19.7-2.1

45-54

2494

17.1

16.4-7.8

55-64

1374

9.0

8.2-9.8

65-74

468

5.2

4.2-6.4

75 or more

77

0.8

0.4-1.4

Area of residence

 

 

 

City

14,229

81.2

72.0-7.9

Town

1756

10.8

64.8-7.4

Village or other rural area

1177

8.0

5.7-11.1

Suspicious symptomatology of COVID-19

 

 

 

No

11,898

70.1

66.0-3.9

Yes

5264

29.9

26.1-4.0

Compliance with community mitigation strategies

 

 

 

No

9120

54.1

51.2-6.9

Yes

8042

45.9

43.1-48.8

Food insecurity

 

 

 

No

3136

18.0

15.9-0.4

Yes

14,026

82.0

80.0-4.1

Economic insecurity

 

 

 

No

1660

9.8

9.0-10.6

Yes

15,502

90.2

89.4-1.0

Fear of a family member getting ill with COVID-19

 

 

 

No

705

4.8

3.9-6.0

Yes

16,457

95.2

94.0-6.1

Probability of vaccination acceptance on the recommendation of family and friends.

 

 

 

Lower acceptance/Indifferent

10,238

60.0

58.3-1.7

Greater acceptance

6924

40.0

38.3-1.7

Likelihood of vaccination acceptance on the recommendation of physicians and other health care professionals who provide medical care

 

 

 

Lower acceptance/Indifferent

8461

50.1

48.2-2.0

Greater acceptance

8701

49.9

48.0-1.8

Probability of vaccination acceptance based on WHO recommendation.

 

 

 

Lower acceptance/Indifferent

7834

46.3

44.7-7.9

Greater acceptance

9328

53.7

52.1-5.3

Probability of vaccination acceptance upon the recommendation of government health authorities.

 

 

 

Lower acceptance/Indifferent

9422

55.4

53.2-7.5

Greater acceptance

7740

44.6

42.5-6.8

Likelihood of vaccination acceptance upon recommendation by politicians.

 

 

 

Lower acceptance/Indifferent

15,719

91.2

90.6-1.7

Greater acceptance

1443

8.8

8.3-9.4

Intention to be vaccinated

 

 

 

No

3987

25.1

23.0-7.3

Yes

13,175

74.9

72.7-7.0

Fear of adverse effects of the vaccine

 

 

 

No

1542

9.5

8.8-10.2

Yes

15,620

90.5

89.8-1.2

95% CI: 95% confidence intervals.

 

Prevalence of the intention to be vaccinated by departments

The departments with the highest ITV prevalence were provincial Lima (81.4%), metropolitan Lima (77.7%), Junín (76.7%), Callao (75.7%), Huancavelica (75.7%) and Loreto (75.7%). On the other hand, those with the lowest ITV were Madre de Dios (53.9%), Ayacucho (66.1%), Puno (69.5%), Ucayali (69.9%) and Tacna (70.2%) (Figure 1).

 

Figure 1. Prevalence of vaccination intention according to Peruvian departments

 

Bivariate analysis according to the intention to be vaccinated.

Significant differences were found between ITV and the included covariates, with the exception of age groups (p = 0.213) and compliance with community mitigation strategies (p = 0.062) (Table 2).

 

Table 2. Descriptive and bivariate analysis of study characteristics according to the intention to be vaccinated in the study sample (n = 17,162).

Characteristics

Intention to be vaccinated

No

Yes

Absolute frequency of included participants

Proportion weighted according to each category

Absolute frequency of included participants

Proportion weighted according to each category

p-value

n

%

95% CI

n

%

95% CI

 

Gender

 

 

 

 

 

 

 

Male

1845

23.6

23.2-26.3

6667

76.4

73.7-78.8

0.010

Female

2091

26.3

24.5-28.3

6414

73.7

71.7-75.5

 

Non-binary

51

37.3

23.0-54.1

94

62.7

45.9-77.0

 

Age (years)

 

 

 

 

 

 

 

18-24

971

24.4

22.1-26.8

3289

75.6

73.2-77.9

0.213

25-34

1110

25.4

22.6-28.5

3754

74.5

71.5-77.4

 

35-44

869

25.6

23.3-28.1

2756

74.4

71.9-76.7

 

45-54

589

25.1

22.3-28.1

1905

74.9

71.9-77.7

 

55-64

323

25.1

22.1-28.3

1051

74.9

71.7-77.9

 

65-74

99

22.4

18.4-27.1

369

77.6

72.9-81.6

 

75 or more

26

35.6

25.6-46.9

51

64.4

53.1-74.4

 

Area of residence

 

 

 

 

 

 

 

City

3146

23.7

22.0-25.6

11,083

76.3

74.4-78.0

<0.001

Town

451

28.3

25.1-31.7

1305

71.7

68.3-74.9

 

Village or other rural area

390

34.6

31.2-38.2

787

65.4

61.8-68.8

 

Suspicious symptomatology of COVID-19

 

 

 

 

 

 

 

No

2861

26.6

23.4-29.9

9037

73.4

70.1-76.6

0.007

Yes

1126

21.6

20.2-23.1

4138

78.4

76.9-79.8

 

Compliance with community mitigation strategies

 

 

 

 

 

 

 

No

2034

24.4

22.3-26.7

7086

75.6

73.3-77.7

0.062

Yes

1953

25.9

23.7-28.2

6089

74.1

71.8-76.3

 

Food insecurity

 

 

 

 

 

 

 

No

800

28.9

25.3-32.7

2336

71.1

67.3-74.7

0.001

Yes

3187

24.3

22.2-26.4

10,839

75.7

73.6-77.8

 

Economic insecurity

 

 

 

 

 

 

 

No

463

31.0

27.3-35.1

1197

69.0

64.9-72.7

<0.001

Yes

3524

24.4

22.4-26.6

11,978

75.6

73.4-77.6

 

Fear of a family member getting ill with COVID-19

 

 

 

 

 

 

 

No

383

56.6

61.7-61.4

322

43.4

38.6-48.3

<0.001

Yes

3604

23.5

21.6-25.5

12,853

76.5

74.5-78.4

 

Probability of vaccination acceptance on the recommendation of family and friends.

 

 

 

 

 

 

 

Lower acceptance/Indifferent

3160

33.3

30.9-35.8

7078

66.7

64.2-69.1

<0.001

Greater acceptance

827

12.8

11.3-14.4

6097

87.2

85.6-88.7

 

Likelihood of vaccination acceptance on the recommendation of physicians and other health care professionals who provide medical care

 

 

 

 

 

 

 

Lower acceptance/Indifferent

3431

43.3

41.0-45.6

5030

56.7

54.4-59.0

<0.001

Greater acceptance

556

6.9

5.8-8.1

8145

93.1

91.9-94.2

 

Probability of vaccination acceptance based on WHO recommendation.

 

 

 

 

 

 

 

Lower acceptance/Indifferent

3249

44.4

41.7-47.2

4585

55.6

52.8-58.3

<0.001

Greater acceptance

738

8.4

7.3-9.7

8590

91.6

90.3-92.7

 

Probability of vaccination acceptance upon the recommendation of government health authorities.

 

 

 

 

 

 

 

Lower acceptance/Indifferent

3585

40.7

39.1-42.3

5837

59.3

57.7-60.9

<0.001

Greater acceptance

402

5.8

4.4-7.6

7338

94.2

92.4-95.6

 

Likelihood of vaccination acceptance upon recommendation by policymakers

 

 

 

 

 

 

 

Lower acceptance/Indifferent

3855

26.5

24.4-28.7

11,864

73.5

71.3-75.6

<0.001

Greater acceptance

132

10.5

8.0-13.6

1311

89.5

86.4-92.0

 

95% CI: 95% confidence intervals.

 

Factors associated with the intention to be vaccinated.

In the adjusted regression model, female gender (APR = 0.95; 95%CI: 0.95-0.97; p < 0.001), compared to male gender, was associated with a lower prevalence of having the ITV. Likewise, living in a town (APR = 0.95; 95%CI: 0.91-0.99; p = 0.034), village or other rural area (APR = 0.90; 95%CI: 0.86-0.93; p < 0.001), compared to a city, was associated with a lower likelihood of having the ITV. In addition, having COVID-19 symptomatology (APR = 1.06; 95%CI: 1.03- 1.09; p < 0.001), economic insecurity (APR = 1.04; 95%CI: 1.01-1.06; p = 0.006) and fear of getting sick or having a family member get sick with COVID-19 (APR = 1.49; 95%CI: 1.36- 1.64; p < 0.001) were associated with a higher prevalence of having the ITV. On the other hand, recommendations from WHO (APR = 1.34; 95%CI: 1.29-1.40; p < 0.001), physicians and other health professionals providing medical care (APR = 1.29; 95%CI: 1.26-1.32; p < 0.001), government health authorities (APR = 1.18; 95%CI: 1.15-1.22; p < 0.001), and family and friends (APR = 1.10; 95%CI: 1.08-1.12; p < 0.001), were associated with a higher prevalence of having the ITV. In contrast, recommendations by politicians (APR = 0.89; 95%CI: 0.87-0.92; p < 0.001) were associated with a lower probability of having the ITV (Table 3).

 

Table 3. Crude and adjusted regression models to assess the association between study characteristics and vaccination intention in the study sample. 

Characteristics

Intention to be vaccinated

Crude

Adjusted

CPR

95% CI

p-value

APR

95% CI

p-value

Gender

 

 

 

 

 

 

Male

Reference

-

-

Reference

-

-

Female

0.96

0.95-0.98

0.001

0.95

0.94-0.97

<0.001

Non-binary

0.82

0.65-1.04

0.104

0.86

0.73-1.02

0.089

Age (years)

 

 

 

 

 

 

18-24

Reference

-

-

 

 

 

25-34

0.99

0.95-1.02

0.377

 

 

 

35-44

0.98

0.95-1.02

0.345

 

 

 

45-54

0.99

0.96-1.03

0.578

 

Not included*

 

55-64

0.99

0.95-1.03

0.624

 

 

 

65-74

1.03

0.96-1.10

0.445

 

 

 

75 or more

0.85

0.73-0.99

0.039

 

 

 

Area of residence

 

 

 

 

 

 

City

Reference

-

-

Reference

-

-

Town

0.94

0.89-0.99

0.021

0.95

0.91-0.99

0.034

Village or other rural area

0.86

0.82-0.90

<0.001

0.90

0.86-0.93

<0.001

Suspicious symptomatology of COVID-19

 

 

 

 

 

 

No

Reference

-

-

Reference

-

-

Yes

1.07

1.02-1.12

0.011

1.06

1.03-1.09

<0.001

Compliance with community mitigation strategies

 

 

 

 

No

Reference

-

-

Not included*

Yes

0.98

0.96-1.00

0.062

Food insecurity

 

 

 

 

No

Reference

-

-

Not included**

Yes

1.06

1.02-1.11

0.002

Economic insecurity

 

 

 

 

 

 

No

Reference

-

-

Reference

-

-

Yes

1.10

1.05-1.14

<0.001

1.04

1.01-1.06

0.006

Fear of a family member getting ill with COVID-19

 

 

 

 

 

 

No

Reference

-

-

Reference

-

-

Yes

1.76

1.56-1.99

<0.001

1.49

1.36-1.64

<0.001

Probability of vaccination acceptance on the recommendation of family and friends.

 

 

 

 

 

 

Lower acceptance/Indifferent

Reference

-

-

Reference

-

-

Greater acceptance

1.31

1.27-1.35

<0.001

1.10

1.08-1.12

<0.001

Likelihood of vaccination acceptance on the recommendation of physicians and other health care professionals who provide medical care.

 

 

 

 

 

 

Lower acceptance/Indifferent

Reference

-

-

Reference

-

-

Greater acceptance

1.64

1.59-1.70

<0.001

1.29

1.26-1.32

<0.001

Probability of vaccination acceptance based on WHO recommendation.

 

 

 

 

 

 

Lower acceptance/Indifferent

Reference

-

-

Reference

-

-

Greater acceptance

1.65

1.58-1.72

<0.001

1.34

1.29-1.40

<0.001

Probability of vaccination acceptance upon the recommendation of government health authorities

 

 

 

 

 

 

Lower acceptance/Indifferent

Reference

-

-

Reference

-

-

Greater acceptance

1.59

1.56-1.62

<0.001

1.18

1.15-1.22

<0.001

Likelihood of vaccination acceptance upon recommendation by politicians.

 

 

 

 

 

 

Lower acceptance/Indifferent

Reference

-

-

Reference

-

-

Greater acceptance

1.22

1.19-1.25

<0.001

0.89

0.87-0.92

<0.001

95% CI: 95% confidence intervals; CPR: crude prevalence ratio; APR: adjusted prevalence ratio; *Not included because there was no statistically significant association in the crude model. **Not included because of collinearity with economic insecurity.

 

DISCUSSION

Our results show a high ITV against COVID-19. This is similar to what was found in a multinational study that included Brazil and found that 71.5% of its participants reported a very high or some likelihood of accepting the COVID-19 vaccine (11). In this study, acceptability varied by country, ranging from as high as 90% in China to as low as 55% in Russia (11). Other studies in different countries have also shown different acceptance rates (7,14,22). The variability among these results may depend on how the research question is posed, which limits comparability (14). For example, a multinational study conducted by the Imperial College of London in November 2020 asked whether there was a "definite intention" to get the COVID-19 vaccine, with responses ranging from 18 to 65% (22). Our study does not pose the answer in terms of a "definite intention", which would explain the lower percentages.

The variability in the results may also be due to the timing of the study (14). In the United States, the acceptance rate ranged from 72% in April to 48% in October 2020 (14). In Italy, vaccine acceptance increased after confinement (23). In Peru, the Ipsos survey showed that the percentage of Peruvians accepting vaccination decreased from 75% in August 2020 to 59% in February 2021 (19). The differences found between that survey and our study could be because confidence increased over time due to the advent of vaccines and more information available about vaccines (24). A previous study in countries in the Latin American and Caribbean region, which used the same source of information as this study, reported that the country with the highest ITV was Mexico (88.4%) and the lowest ITV was Haiti (43.2%) (17).

Likewise, it is possible that as the risk perception increased in the country, so did the ITV (14,23). In the weeks prior to the survey, due to the increase of the number of cases and deaths (18), the news of the lack of oxygen and available hospital beds may have increased the sense of vulnerability and, with it, the acceptance of the vaccine. A study in Turkey showed that 40% of those who initially had doubts about the vaccine considered it necessary as the pandemic progressed (25). Similarly, in France, more nurses shifted from rejection to hesitancy or acceptance of the vaccine (26).

Similar to our study, other countries show lower ITV in women (14,27). Although the reasons for these gender differences are not entirely clear, a European multinational study suggested that it might be due to women showing more concern regarding adverse effects and vaccine safety than men (27).

Although few studies have included rural populations (14), some aspects may explain our finding: living in a village or other rural area was associated with lower ITV. Rural residents are often reluctant to seek medical care or engage in preventive health behaviors compared to urban populations (28). Similarly, difficult access to the Internet limits telemedicine (29) and access to disease- and vaccine-related information, leading to centralization of information and its dissemination by less rigorous means that promote ineffective therapies to the detriment of the vaccine (30).

As with our study, other authors have suggested that the perceived risk of becoming infected, fear of the severity of the disease, a history of having been infected, or knowing an infected friend or relative were predictors of ITV (14). These findings are not limited to the COVID-19 vaccine, as similar results have been found for the acceptability of other vaccines (31). On the other hand, the prospect of not being able to work and the consequent economic insecurity and mental health problems could explain why people with depressive symptomatology and food insecurity are more likely to accept vaccination.

Most studies suggest that the influence of medical advice is the most important factor in accepting vaccination (14). In our study, although the advice of health workers was significant, WHO recommendations were more so. Despite the fact that the WHO disseminated discrepant information during the course of the pandemic (32), its status as the governing body on public health issues gives it credibility, so that reinforcing the importance of its messages would increase the ITV in our population. Similarly, as health workers are an important factor, the dissemination of standardized evidence-based messages should be considered, for example, during teleconsultation or with representative medical figures in regions with lower vaccine acceptance. In China, confidence in physicians as disseminators of vaccine-related information is 80% and in the United States it is 62%. On the other hand, in the United States, only 54% of the population trust the vaccine if it is approved by the Food and Drug Administration (FDA) (14). The fact that politicians’ recommendations are associated with a lower probability of ITV is worrisome, since they are frequently present in the media; however, it is understandable due to the lack of trust in them (33). In the United States, political factors and the influence of former President Donald Trump affected vaccine acceptance, which should also be considered in our country (14).

Our study has some limitations. First, it is based on the responses of users of a social network to which not everyone has access. Nevertheless, it is a social network used by 94% of Peruvians, according to a survey conducted by Ipsos in 2020 (34). Secondly, the variables included and their definition are subject to the pre-established definition of the matrix survey. Third, the data were obtained by self-reporting, so there may be an underreporting of information. Fourth, causality cannot be established between the variables evaluated due to the study design. Fifth, the measurements used to assess food and economic security have not been validated, but they provide relevant information for the study. Sixth, the findings could be biased due to the rejection rate of users in a Facebook survey, as well as due to the possibility of occurrence of voluntary response bias. Seventh, some of the associated factors found had a low measure of association, so their interpretation should be done cautiously, despite the statistical significance that the result may have. In spite of this, this is a study with a large sample size with national representativeness that can help to understand the research topic.

In conclusion, three out of four respondents reported having the ITV. Adequate communication strategies based on potentially modifiable factors could increase the possibility of acceptance of the COVID-19 vaccine in our country. Considering that the vaccination campaign will be a long-term task, continuous monitoring of vaccine acceptance is necessary to steer strategies in order to achieve the results proposed by the government.

Conflicts of interest: The authors declare that they have no conflicts of interest.

Funding: The study was self-funded.

Acknowledgments: The authors thank the University of Maryland for conducting the survey with which this study was carried out.

Author contributions: PHA, DUP and VABZ conceived the article. DUP and VABZ collected and analyzed the data. PHA, AUC, DUP, GBQ, CJTH, AJRM, AVH and VABZ drafted the manuscript and approved its final version.

 

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Cite as: Herrera-Añazco P, Uyen-Cateriano A, Urrunaga-Pastor D, Bendezú-Quispe G, Toro-Huamanchumo CJ, Rodríguez-Morales AJ, et al. Prevalence and Factors Associated with the Intention to be Vaccinated Against COVID-19 in Peru. Rev Peru Med Exp Salud Publica. 2020;38(3):381-90. doi: https://doi.org/10.17843/rpmesp.2021.383.7446

Correspondence: Vicente A. Benites-Zapata, vbeniteszapata@gmail.com

Recibido: 23/02/2021

Aprobado: 14/07/2021

En línea: 26/08/2021