Abstract
The current outbreak of SARS-Cov-2, a virus responsible for COVID-19, has infected millions and caused a soaring death toll worldwide. Vaccination represents a powerful tool in our fight against the transmission of SARS-CoV-2. Ecuador is one of the Latin American countries most impacted by COVID-19. Despite free COVID-19 vaccines, Ecuadorians still hesitate to get vaccinated. A multivariate binary logistic regression was used to analyze data from the Ecuadorian National Institute of Statistics and Censuses. This study investigated socio-demographics, economic, and individual reasons associated with a person having “no intention” to receive COVID-19 vaccine across the study period of October 2021 to March 2022. The survey revealed an increase of unvaccinated people having no intention of COVID-19 vaccination from 57.4% (October-December 2021) to 72.9% (January-March 2022). COVID-19 vaccine hesitancy was dependent on factors like sex, age and ethnicity. Socio-economic characteristics and education level were not found to be statistically significant in lack of vaccine intention, but most vaccination hesitancy was due to distrust in the COVID-19 vaccine. People who believed that the vaccine could be unsafe because of possible side effects represented half of the surveyed participants, a proportion that barely diminished during the progress of the vaccination campaign across October-December 2021 (57.04%) and January-March 2022 (49.59%) periods. People who did not believe that the vaccine was effective enough increased from 11.47 to 18.46%. Misbeliefs about effectiveness and safety of vaccines should be considered in the implementation of public health initiatives of communication, education and intervention to improve vaccination campaigns.
Supplementary Information
The online version contains supplementary material available at 10.1007/s10900-023-01188-7.
Keywords: COVID-19, sociodemographic factors, sociological factors, surveys and questionnaires, vaccination hesitancy
Introduction
The epidemic of COVID-19 caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has so far been responsible for more than 534 million new cases and caused more than 6.3 million deaths globally [1]. Vaccination represents a powerful tool in the fight against COVID-19 epidemic that reduces the transmission of SARS-CoV-2, saves lives, and minimizes economic and public health impacts. Despite their effectiveness in mitigating the spread of the epidemic, success largely depends on the vaccination acceptance by the population [2], which exhibits a large variability across countries ranging from 24% in Kuwait to 94.3% in Malaysia [2].
Vaccine hesitancy (as high as 60% in some countries) may represent a problem for the global efforts to control COVID-19 epidemic [3]. The lack of information and the mistrust in information promotes COVID-19 vaccine hesitancy in Latin American populations (e.g., Brazil, Colombia, and Guatemala), where fear of vaccine adverse events is an important factor influencing overall vaccine acceptance [4]. Refusal for vaccination also relies on additional factors like cultural, religious or personal beliefs, inaccessibility to health care services, costs, economic problems [4, 5], and socio-demographic factors such as the economic and education levels [5].
Ecuador is one of the countries in Latin America most affected by the COVID-19 epidemic. It reports the seventh-highest COVID-19 prevalence worldwide (June 2020) as well as a high death toll [6]. A database of the massive vaccination program reported 35,300,621 doses of COVID-19 vaccine administered in Ecuador, with vaccination rates of 87.16% and 37.47% for one and two doses respectively [7]. Despite the availability of free of charge COVID-19 vaccines, a significant part of the Ecuadorian population still hesitates to get vaccinated [7]. In consequence, this study was aimed to investigate the socio-demographics, economic and individual reasons associated with the “no intention” to receive COVID-19 vaccine in Ecuador.
Methods
Study Population and Dataset
Ecuador with a population of 17,510,643 inhabitants in 2020 [8] is a mix of ethnic identities: 71.9% mixed race, 7.4% non-Andean indigenous (coast), 7.2% black (Afro-Ecuadorian), 7.0% Andean indigenous (Andes ridge), 6.1% white, and 0.4% other ethnicity. This retrospective longitudinal observational study was based on datasets extracted from the National Survey on Employment, Unemployment, and Sub-employment (ENEMDU) website supported by the Ecuadorian National Institute of Statistics and Censuses (INEC; https://www.ecuadorencifras.gob.ec/estadisticas-laborales-abril-2022/).
Beginning in September 2021, the ENEMDU survey included new questions about COVID-19 infected and vaccinated population to measure, monitor, and characterize the epidemic during the COVID-19 vaccination campaign using a probabilistic sampling. This study encompassed two ENEMDU datasets: 91,169 individuals (October-December 2021) and 90,799 individuals (January-March 2022) in such a way that it was guaranteed the representativeness of national, urban, and rural areas as well as the five largest cities. Survey design and sampling calculation are described on the ENEMDU’s report [9] provided by the INEC. This study included all COVID-19 unvaccinated individuals at least 18 years of age.
Study Design
There were two dependent variables in the analysis, the intention to get vaccinated against COVID-19 and the reason for COVID-19 vaccine reluctance. The following question was posited to find out the intention to receive COVID-19 vaccine: “Are you interested in getting vaccinated against COVID-19?“. The response variable was binary in terms of “Yes” (positive intention) or “No” (no intention to get vaccinated). To explore the reason for the reluctance against COVID-19 vaccine, participants were asked the following: “What is the main reason for not being interested in getting vaccinated against COVID-19?” The response variable was categorical and included 6 unordered levels: (a) Believe that the vaccine can be unsafe because of possible side effects; (b) Do not believe that the vaccine is effective enough; (c) Already had COVID-19 and recovered; (d) Do not believe that the coronavirus is dangerous for one’s health; (e) Not in favor of vaccines in general and (f) Other reasons.
Independent variables included socio-demographics and economic characteristics of subjects associated with the no intent to get COVID-19 vaccinated like age, sex, area of residence, ethnicity, marital status, education level, employment, monthly household income per capita, and occupation type (International Standard Industrial Classification of All Economic Activities 4th Revision). ISIC variables were filtered (arbitrary number based on total ISIC data n > 40, based on total weighted data) to remove the remaining spurious data that would potentially affect model output, so that a total of 5 variables were left to model for this category.
Age groups were divided in 3 categories: young adults (18–39 years), middle-aged adults (40–64 years) and elders (≥ 65 years). Monthly household income per capita (MHIPC) was classified in 3 categories [10]: no poverty (≥ 85.60 USD), poverty (< 85.60 USD) and extreme poverty (< 48.24 USD). In addition, antecedents of COVID-19 disease were also analyzed.
Statistical Analysis
Analyses were run using STATA software (v17.0) for Windows. The survey (svy:) command was run to analyze the complex sample survey data. The svyset command was used for declaring data as survey data, whereas the variables needed for the declaration of the survey design were taken from the ENEMDU’s report [9]. Separated analyses were conducted for each of the two ENEMDU datasets. Subpopulation analyses of the survey datasets were carried out with an approach of variance estimation that took the full complex design of the sample into account [11]. To perform analyses with this approach, we used the subpop option available for all svy. commands in Stata [11].
Categorical variables were described using absolute frequencies (n) and weighted percentages (w%), while continuous variables were described as weighted means and standard deviation (± SD). Data weighting was conducted as follows: Weighted Average = (Sum of variables * Weight) / (Sum of all weights). Multivariate design-based binary logistic regression was used to compute adjusted odds ratios (aOR) with their 95% confidence intervals (CI) to assess socio-demographic and economic factors affecting COVID-19 vaccine intention (No vs. Yes). Besides the combined and non-stratified analysis of the general adult population, a stratified analysis was run by age groups. To assess the effects of sociodemographic and economic factors on the reason for the negative intention to get vaccinated against COVID-19, it was performed a multivariate design-based multinomial logistic regression due to the unordered categorical nature of the outcome variable. From the question “What is the main reason for not being interested in getting vaccinated against COVID-19?”, the category of “Other reasons” was chosen to be the baseline (reference) category for the outcome variable. All variables were chosen based on average variables with similar factor structures [12–14] to avoid spurious data variables on the model.
Results from the multinomial logistic regression were presented as adjusted relative risk ratios (aRRR) with 95% CI. The selection of variables entered in the multivariate binary and multinomial logistic regression analyses was based on the evidence of vaccine intention and data availability. Alpha-value was set at 0.05 for statistical significance.
Ethics
The evaluation of an ethics committee was not required given the retrospective nature of the study, which consisted of non-experimental research with secondary registries available in the public domain. Datasets did not contain any sensitive or confidential information that might violate the rights to the protection of personal data. Surveys were anonymous to ensure that no harm was caused, or confidentiality breached Helsinki declaration [15], international and national guidelines of good practice, we used this anonymous datasets ensuring that no harm was caused, or confidentiality breeched. We followed STROBE guidelines to report this study.
Results
Nature and Distribution of the Sample
The unvaccinated study population from October to December 2021 (Table 1 and Suppl. Table 1) included 5,463 individuals with a mean age of 39.8 ± 15.0 years and 2,791 females (51.51%). A total of 2,164 individuals (42.57%) had a positive COVID-19 vaccination intention, while the rest (3,299 individuals, 57.43%) refused it. In the period of Jan-Mar 2022 (Table 1 and Suppl. Table 2), the analysis included 3,401 individuals with an average age of 41.0 ± 15.6 years and 1,726 males (51.98%). The number of people without COVID-19 vaccine intention rose to 72.94% (n = 2,510) during the first quarter of 2022. Rates of COVID-19 vaccine intention between males and females were quite similar across these periods.
Table 1.
Socio-demographic and economic characteristics by COVID-19 vaccine intention groups during the October-December 2021 and January-March 2022 periods
| Variables | October-December 2021 (n = 5,463) | January-March 2022 (n = 3,401) | ||
|---|---|---|---|---|
| Yes (n = 2,164) | No (n = 3,299) | Yes (n = 891) | No (n = 2,510) | |
| n (w%) | n (w%) | n (w%) | n (w%) | |
| Age (Mean ± SD) | 37.2 ± 13.2 | 41.7 ± 16.1 | 38.5 ± 15.7 | 41.9 ± 15.5 |
| Age Group (years) | ||||
| Young adult (18–39) | 1,355 (47) | 1,565 (53) | 518 (30.9) | 1,167 (69.1) |
| Middle-aged (40–64) | 599 (38.9) | 1,210 (61.1) | 255 (22.2) | 970 (77.8) |
| Elderly (≥ 65) | 210 (30.4) | 524 (69.6) | 118 (24.9) | 373 (75.1) |
| Sex | ||||
| Male | 968 (39.1) | 1,704 (60.9) | 409 (24.8) | 1,317 (75.2) |
| Female | 1,196 (45.8) | 1,595 (54.2) | 482 (29.5) | 1,193 (70.5) |
| Area of residence | ||||
| Urban | 1,409 (42.2) | 2,053 (57.8) | 536 (25.2) | 1,561 (74.8) |
| Rural | 755 (43) | 1,246 (57) | 355 (29.3) | 949 (70.8) |
| Ethnicity | ||||
| Mixed race | 1,623 (44.5) | 2,280 (55.5) | 648 (28.9) | 1,741 (71.1) |
| White | 35 (22.4) | 73 (77.6) | 16 (33.1) | 48 (66.9) |
| Andean indigenous | 272 (43.7) | 591 (56.3) | 124 (24.3) | 476 (75.7) |
| Black | 109 (30.6) | 165 (69.4) | 53 (29.9) | 104 (70.1) |
| Non-Andean indig. | 80 (36.4) | 127 (63.7) | 34 (22.8) | 106 (77.2) |
| Other | 45 (31.4) | 63 (68.6) | 16 (21.3) | 35 (78.7) |
| Marital status | ||||
| Married | 486 (42.5) | 941 (57.5) | 209 (23.9) | 717 (76.1) |
| Common law | 666 (43.7) | 815 (56.3) | 250 (28.1) | 614 (71.9) |
| Single | 701 (45.1) | 966 (54.9) | 282 (29.7) | 720 (70.3) |
| Divorced/Separated | 206 (37.2) | 354 (62.8) | 92 (28.1) | 288 (71.9) |
| Widower | 105 (34.6) | 223 (65.4) | 58 (22.6) | 171 (77.5) |
| Education | ||||
| Tertiary | 296 (37.5) | 437 (62.5) | 92 (22) | 320 (78) |
| Secondary | 867 (44.7) | 1,143 (55.3) | 397 (26.4) | 1,098 (73.6) |
| Primary | 912 (43.1) | 1,492 (57) | 344 (29) | 936 (71) |
| None | 89 (33.2) | 227 (66.8) | 58 (23.2) | 156 (76.8) |
| Employment | ||||
| Yes | 1,377 (41.9) | 2,206 (58.1) | 536 (26.1) | 1,701 (73.9) |
| No | 77 (41) | 119 (59) | 22 (19.6) | 79 (80.4) |
| Unknown | 710 (44.2) | 974 (55.8) | 333 (29.8) | 730 (70.2) |
| MHIPC (Mean ± SD) | 129.7 ± 125.4 | 171 ± 303.3 | 146.4 ± 130.3 | 156.2 ± 155.5 |
| Poverty level | ||||
| No poverty | 1,508 (39.3) | 2,341 (60.7) | 628 (26.4) | 1,767 (73.7) |
| Poverty | 392 (42.5) | 586 (57.5) | 138 (22.4) | 414 (77.6) |
| Extreme poverty | 259 (52.9) | 353 (47.1) | 121 (33.8) | 296 (66.2) |
| Unknown | 5 (31.9) | 19 (68.1) | 4 (17.3) | 33 (82.7) |
| Already had COVID-19 | ||||
| Yes | 546 (46) | 711 (54.1) | 233 (23.6) | 643 (76.4) |
| No | 1,592 (41.6) | 2,542 (58.4) | 640 (28.3) | 1,810 (71.7) |
| Unknown | 26 (33.5) | 46 (66.5) | 18 (20.4) | 57 (79.6) |
| Occupation | ||||
| A. | 525 (44.3) | 868 (55.7) | 242 (29.1) | 686 (70.9) |
| B. | 12 (76.8) | 20 (23.2) | 5 (11.3) | 19 (88.7) |
| C. | 109 (29.6) | 201 (70.4) | 44 (27.5) | 156 (72.5) |
| D | 0 | 0 | 0 (0) | 1 (100) |
| E. | 6 (48.4) | 4 (51.6) | 1 (35.8) | 1 (64.2) |
| F. | 87 (39) | 164 (61) | 33 (20.4) | 119 (79.6) |
| G. | 305 (41.8) | 450 (58.2) | 96 (21.8) | 316 (78.2) |
| H. | 55 (36.1) | 96 (63.9) | 24 (26.1) | 77 (73.9) |
| I. | 95 (36) | 142 (64.1) | 32 (26.1) | 98 (73.9) |
| J. | 6 (36.1) | 16 (63.9) | 3 (20.1) | 10 (79.9) |
| K. | 2 (29.1) | 3 (70.9) | 0 (0) | 3 (100) |
| L. | 2 (17.6) | 5 (82.4) | 0 (0) | 6 (100) |
| M. | 20 (42.9) | 19 (57.1) | 8 (17.6) | 20 (82.4) |
| N. | 25 (27.6) | 31 (72.4) | 8 (10.6) | 27 (89.5) |
| O. | 15 (33.4) | 20 (66.6) | 3 (14.7) | 22 (85.3) |
| P. | 19 (24) | 49 (76.1) | 2 (3.5) | 29 (96.5) |
| Q. | 18 (40.8) | 22 (59.2) | 2 (1.9) | 24 (98.1) |
| R. | 14 (58.9) | 13 (41.1) | 1 (3.4) | 6 (96.6) |
| S. | 40 (60.6) | 57 (39.5) | 22 (31.5) | 59 (68.5) |
| T. | 22 (48.7) | 26 (51.3) | 10 (22) | 22 (78) |
| Unknown | 787 (44) | 1,093 (56) | 355 (29.1) | 809 (70.9) |
Abbreviations: n, absolute frequency; w%, weighted percentage; SD, standard deviation; y, years; MHIPC, monthly household income per capita in United States dollar; A: Agriculture, forestry and fishing; B: Mining and quarrying; C: Manufacturing; D: Electricity, gas, steam and air conditioning supply; E: Water supply, sewerage, waste management and remediation activities; F: Construction; G: Wholesale and retail trade, repair of motor vehicles and motorcycles; H: Transportation and storage; I: Accommodation and food service activities; J: Information and communication; K: Financial and insurance activities; L: Real estate activities; M: Professional, scientific and technical activities; N: Administrative and support service activities; O: Public administration and defense, compulsory social security; P: Education; Q: Human health and social work activities; R: Arts, entertainment and recreation; S: Other service activities; T: Activities of households as employers, undifferentiated goods-and services-producing activities of households for own use
The three most common reasons for responding “No” to the intent of COVID-19 vaccination were: (1) people who believed that the vaccine could be unsafe because of possible side effects (Oct-Dec 2021: 57.04%; Jan-Mar 2022: 49.59%); (2) people not being in favor of vaccines in general (Oct-Dec 2021: 15.12%; Jan-Mar 2022: 18.46%) and (3) people who did not believe that the vaccine was effective enough (Oct-Dec 2021: 11.47%; Jan-Mar 2022: 18.46%). Reasons like the SARS-CoV-2 virus was dangerous for one’s health, already had the disease and recovered and “other reasons” (see Suppl. Tables 3 and 4) accounted for less than 11%.
Factors Associated with no Intention to have COVID-19 Vaccination
Table 2 (Oct-Dec 2021) and Table 3 (Jan-Mar 2022) represent socio-demographic and economic characteristics associated with no COVID-19 vaccination intention. In terms of statistical relevance, age, sex, ethnicity, marital status, and professional occupation were the more interesting characteristics of the surveys run in both Oct-Dec 2021 and in Jan-Mar 2022. During these periods, area of residence, education level, poverty level and not having prior experience with COVID-19 had a scarce impact (inexistent in some cases) in COVID-19 vaccine intention.
Table 2.
Design-based binary logistic regression determining factors associated with responding “no” regarding intention to get vaccinated against COVID-19 during the October-December 2021 period
| Variables | Total | Young adults (18–39 years old) |
Middle-aged (40–64 years old) |
Elderly adults (≥ 65 years old) |
|---|---|---|---|---|
| aOR (95% CI) | aOR (95% CI) | aOR (95% CI) | aOR (95% CI) | |
| Age Group | ||||
| Young adult (18-39y) | Reference | a Reference | d Reference | g 65-74y: Reference |
| Middle-aged (40-64y) | 1.39 (1.04–1.85)* | b 0.91 (0.59–1.42) | e 1.34 (0.88–2.03) | h 0.75 (0.41–1.38) |
| Elderly (≥ 65y) | 1.94 (1.24–3.02)** | c 1.16 (0.72–1.86) | f 1.95 (1.07–3.55)* | i 1.81 (0.82–4.01) |
| Sex | ||||
| Male | Reference | Reference | Reference | Reference |
| Female | 0.72 (0.59–0.88)*** | 0.63 (0.5–0.8)*** | 0.89 (0.61–1.31) | 1.39 (0.81–2.41) |
| Area of residence | ||||
| Urban | Reference | Reference | Reference | Reference |
| Rural | 1.1 (0.77–1.57) | 1.02 (0.66–1.58) | 1.35 (0.77–2.36) | 0.75 (0.39–1.44) |
| Ethnicity | ||||
| Mixed race | Reference | Reference | Reference | Reference |
| White | 2.53 (1.25–5.12)** | 2.29 (0.86–6.08) | 2.68 (0.99–7.27) | 10.41 (1.85–58.54)** |
| Andean indigenous | 1.46 (0.96–2.21) | 1.49 (0.9–2.45) | 1.29 (0.72–2.31) | 1.82 (0.77–4.27) |
| Black | 2.03 (1.17–3.54)** | 2.1 (1.02–4.32)* | 2.42 (1.07–5.46)* | 1.16 (0.27-5) |
| Non-Andean indig. | 1.25 (0.68–2.3) | 1.23 (0.53–2.84) | 1.26 (0.53-3) | 1.54 (0.46–5.19) |
| Other | 1.92 (0.99–3.75) | 1.87 (0.86–4.08) | 2.1 (0.66–6.63) | 1.71 (0.33–8.76) |
| Marital status | ||||
| Married | Reference | Reference | Reference | Reference |
| Common law | 1.08 (0.71–1.66) | 0.99 (0.53–1.85) | 1.23 (0.59–2.56) | 0.36 (0.13–0.98)* |
| Single | 0.96 (0.67–1.38) | 0.92 (0.49–1.72) | 0.96 (0.5–1.87) | 0.42 (0.15–1.16) |
| Divorced/Separated | 1.3 (0.84–2.02) | 0.81 (0.41–1.62) | 2.16 (1.13–4.14)* | 0.91 (0.37–2.24) |
| Widower | 1.15 (0.62–2.12) | 0.33 (0.06–1.98) | 0.65 (0.29–1.49) | 0.79 (0.34–1.8) |
| Education | ||||
| Tertiary | Reference | Reference | Reference | Reference |
| Secondary | 0.82 (0.54–1.26) | 0.65 (0.41–1.02) | 1.73 (0.76–3.94) | 3.41 (0.57–20.45) |
| Primary | 0.74 (0.5–1.08) | 0.64 (0.4–1.04) | 0.97 (0.49–1.91) | 1.39 (0.36–5.38) |
| None | 0.86 (0.52–1.42) | 1.6 (0.62–4.12) | 1.7 (0.64–4.51) | 0.75 (0.17–3.36) |
| Employment | ||||
| Yes | Reference | Reference | Reference | Reference |
| No | 1.1 (0.43–2.84) | 2.13 (0.47–9.75) | 0.63 (0.13-3) | 0.02 (0-3.1) |
| Unknown | 0.89 (0.38–2.1) | 1.58 (0.37–6.77) | 0.25 (0.06–1.13) | 1.58 (0.08–32.54) |
| Poverty level | ||||
| No poverty | Reference | Reference | Reference | Reference |
| Poverty | 0.89 (0.6–1.32) | 0.92 (0.61–1.37) | 1.07 (0.53–2.16) | 1.02 (0.43–2.43) |
| Extreme poverty | 0.56 (0.38–0.83)** | 0.56 (0.32–1.01) | 0.66 (0.36–1.22) | 0.3 (0.08–1.08) |
| Unknown | 1.15 (0.3–4.4) | 0.26 (0.04–1.55) | Omitted | 0.29 (0.03–2.44) |
| Already had COVID-19 | ||||
| Yes | Reference | Reference | Reference | Reference |
| No | 1.26 (0.91–1.74) | 1.19 (0.8–1.77) | 1.17 (0.77–1.79) | 2.71 (1.27–5.79)** |
| Unknown | 1.67 (0.75–3.7) | 1.74 (0.57–5.27) | 2.56 (0.62–10.61) | 0.33 (0.04–2.84) |
| ISIC Rev. 4 | ||||
| A. | Reference | Reference | Reference | Reference |
| C. | 1.83 (1.03–3.26)* | 1.69 (0.8–3.56) | 2.22 (1.09–4.51)* | 1.48 (0.29–7.54) |
| F. | 1.06 (0.56–2.01) | 0.95 (0.46–1.93) | 2.48 (1.13–5.44)* | 0.05 (0-0.95)* |
| G. | 1.08 (0.7–1.67) | 1.02 (0.6–1.74) | 1.19 (0.63–2.25) | 1.34 (0.41–4.39) |
| H. | 1.17 (0.47–2.95) | 1 (0.29–3.44) | 1.35 (0.34–5.33) | 0.33 (0.03–3.75) |
| I. | 1.39 (0.75–2.55) | 1.54 (0.68–3.47) | 1.22 (0.49–3.06) | 0.49 (0.06–4.17) |
| S. | 0.46 (0.2–1.07) | 0.49 (0.19–1.31) | 0.36 (0.07–1.73) | 0.11 (0.01–1.29) |
Abbreviations: aOR, adjusted odds ratio; CI, confidence intervals; y, years; A: Agriculture, forestry and fishing; C: Manufacturing; F: Construction; G: Wholesale and retail trade, repair of motor vehicles and motorcycles; H: Transportation and storage; I: Accommodation and food service activities; S: Other service activities
*P < 0.05; **P ≤ 0.01; ***P ≤ 0.001
Table 3.
Design-based binary logistic regression determining factors associated with responding “no” regarding intention to get vaccinated against COVID-19 during the January-March 2022 period
| Variables | Total | Young adults (18–39 years old) |
Middle-aged adults (40–64 years old) | Elderly adults (≥ 65 years old) |
|---|---|---|---|---|
| aOR (95% CI) | aOR (95% CI) | aOR (95% CI) | aOR (95% CI) | |
| Age Group | ||||
| Young adult (18-39y) | Reference | a Reference | d Reference | g Reference |
| Middle-aged (40-64y) | 1.67 (1.16–2.4)** | b 0.65 (0.35–1.2) | e 0.98 (0.5–1.89) | h 0.58 (0.24–1.43) |
| Elderly (≥ 65y) | 1.46 (0.77–2.76) | c 1.37 (0.81–2.34) | f 0.93 (0.42–2.05) | i 0.77 (0.32–1.86) |
| Sex | ||||
| Male | Reference | Reference | Reference | Reference |
| Female | 0.73 (0.52–1.02) | 0.63 (0.41–0.96)* | 0.76 (0.41–1.41) | 1.72 (0.9–3.27) |
| Area of residence | ||||
| Urban | Reference | Reference | Reference | Reference |
| Rural | 0.77 (0.48–1.21) | 0.75 (0.43–1.29) | 0.93 (0.42–2.07) | 0.92 (0.41–2.07) |
| Ethnicity | ||||
| Mixed race | Reference | Reference | Reference | Reference |
| White | 0.8 (0.34–1.9) | 1.47 (0.43–5.05) | 0.91 (0.21–3.96) | 0.5 (0.09–2.84) |
| Andean-Indigenous | 2.24 (1.25–4.01)** | 2.21 (1.18–4.12)** | 1.98 (0.86–4.57) | 1.35 (0.4–4.61) |
| Black | 1.15 (0.58–2.28) | 1.73 (0.68–4.4) | 1.06 (0.4–2.82) | 0.34 (0.08–1.49) |
| Non-Andean indig. | 1.88 (0.8–4.43) | 1.53 (0.6–3.87) | 3.82 (1.13–12.93)* | 1.12 (0.37–3.39) |
| Other | 2.2 (0.44–11.15) | 2.46 (0.47–12.77) | 1.64 (0.23–11.63) | 0.18 (0.01–2.75) |
| Marital status | ||||
| Married | Reference | Reference | Reference | Reference |
| Common law | 0.95 (0.64–1.42) | 1.34 (0.72–2.49) | 0.66 (0.37–1.15) | 2.93 (0.79–10.93) |
| Single | 0.88 (0.55–1.41) | 1.37 (0.71–2.63) | 0.37 (0.18–0.74)** | 1.06 (0.34–3.27) |
| Divorced/Separated | 0.84 (0.54–1.32) | 0.86 (0.41–1.81) | 1.19 (0.58–2.47) | 0.51 (0.16–1.62) |
| Widower | 1.19 (0.66–2.13) | 0.69 (0.13–3.86) | 1.57 (0.36–6.79) | 0.92 (0.38–2.27) |
| Education | ||||
| Tertiary | Reference | Reference | Reference | Reference |
| Secondary | 0.87 (0.53–1.41) | 0.86 (0.45–1.65) | 1.43 (0.59–3.43) | 0.44 (0.08–2.45) |
| Primary | 0.73 (0.43–1.23) | 0.88 (0.41–1.9) | 0.71 (0.3–1.68) | 0.18 (0.04–0.96)* |
| None | 0.83 (0.38–1.82) | 0.52 (0.15–1.78) | 1.77 (0.43–7.25) | 0.19 (0.03–1.2) |
| Employment | ||||
| Yes | Reference | Reference | Reference | Reference |
| No | 2.26 (0.86–5.92) | 1.74 (0.57–5.36) | 5.19 (1.16–23.14)* | Omitted |
| Unknown | 1.19 (0.74–1.91) | 0.92 (0.47–1.79) | 1.68 (0.74–3.84) | 1.88 (0.64–5.56) |
| Poverty level | ||||
| No poverty | Reference | Reference | Reference | Reference |
| Poverty | 1.31 (0.83–2.07) | 1.27 (0.77–2.1) | 1.21 (0.56–2.6) | 3.36 (0.94–12.08) |
| Extreme poverty | 0.62 (0.35–1.12) | 0.61 (0.32–1.19) | 0.61 (0.27–1.39) | 0.62 (0.12–3.13) |
| Unknown | 1.47 (0.17–12.61) | 1.55 (0.15–16.2) | 0.7 (0.06–7.8) | Omitted |
| Already had COVID-19 | ||||
| Yes | Reference | Reference | Reference | Reference |
| No | 0.8 (0.52–1.23) | 0.72 (0.4–1.3) | 0.95 (0.53–1.71) | 1.38 (0.66–2.87) |
| Unknown | 1.26 (0.5–3.17) | 1.15 (0.3–4.45) | 0.66 (0.18–2.48) | Omitted |
| ISIC Rev. 4 | ||||
| A | Reference | Reference | Reference | Reference |
| C | 1.05 (0.52–2.12) | 0.68 (0.24–1.95) | 1.85 (0.7–4.93) | 0.62 (0.11–3.48) |
| F | 1.42 (0.74–2.73) | 1.08 (0.39–2.99) | 1.5 (0.53–4.21) | Omitted |
| G | 1.5 (0.75–3.01) | 1.26 (0.44–3.61) | 1.56 (0.59–4.14) | 3.76 (0.4-35.61) |
| H | 1.27 (0.56–2.88) | 0.87 (0.26–2.92) | 3.05 (0.89–10.48) | 5.16 (0.16-168.34) |
| I | 1.36 (0.53–3.48) | 0.92 (0.25–3.31) | 2.65 (0.76–9.17) | 0.5 (0.02–15.27) |
| S | 0.93 (0.38–2.29) | 0.76 (0.21–2.79) | 1.57 (0.3–8.18) | 0.06 (0–1) |
Abbreviations: aOR, adjusted odds ratio; CI, confidence intervals; y, years; A: Agriculture, forestry and fishing; C: Manufacturing; F: Construction; G: Wholesale and retail trade, repair of motor vehicles and motorcycles; H: Transportation and storage; I: Accommodation and food service activitiesS: Other service activities
*P < 0.05; **P ≤ 0.01; ***P ≤ 0.001
Individual Reasons for COVID-19 Vaccine Hesitancy
Multivariate multinomial logistic regression analysis of socio-demographic and economic factors associated with the reasons for COVID-19 vaccination reluctance are presented in Table 4 (Oct-Dec 2021) and Table 5 (Jan-Mar 2022). As to whether “the vaccine could be unsafe because of possible side effects” (Reason A), this was one of the reasons why white people (aRRR 5.33, 95% CI 1.29–22.08) and other ethnicities (aRRR 7.58, 95% CI 1.53–37.41) hesitated about COVID-19 vaccination compared to mixed race during Oct-Dec 2021. In the first quarter of 2022, elderly adults (aRRR 0.26, 95% CI 0.10–0.70) were less likely to believe that the vaccine could be unsafe because of possible side effects. Regarding to the “belief that vaccine may not be effective enough” (Reason B), during Oct-Dec 2021, people in rural areas (aRRR 2.40, 95% CI 1.03–5.58), white people (aRRR 4.60, 95% CI 1.08–19.7) and other ethnicities (aRRR 11.59, 95% CI 1.93–69.5) were more likely to doubt about vaccine effectiveness. Only middle-aged adults pointed to Reason B during Jan-Mar 2022 (aRRR 2.21, 95% CI 1.05–4.62).
Table 4.
Design-based multinomial logistic regression of the reasons for the negative intention to get vaccinated against COVID-19 during the October-December 2021 period
| Variables | A | B | C | D | E |
|---|---|---|---|---|---|
| aRRR (95% CI) | aRRR (95% CI) | aRRR (95% CI) | aRRR (95% CI) | aRRR (95% CI) | |
| Age Group | |||||
| Young adult (18-39y) | Reference | Reference | Reference | Reference | Reference |
| Middle-aged (40-64y) | 1.48 (0.75–2.95) | 1.77 (0.93–3.38) | 3.71 (1.31–10.5)** | 1.27 (0.64–2.51) | 2.07 (1.13–3.78)* |
| Elderly (≥ 65y) | 0.79 (0.39–1.57) | 0.58 (0.25–1.38) | 1.23 (0.24–6.3) | 0.73 (0.28–1.96) | 1.2 (0.54–2.65) |
| Sex | |||||
| Male | Reference | Reference | Reference | Reference | Reference |
| Female | 0.65 (0.46–0.92)* | 0.56 (0.39–0.81)** | 0.79 (0.35–1.8) | 0.69 (0.39–1.22) | 0.84 (0.59–1.2) |
| Area of residence | |||||
| Urban | Reference | Reference | Reference | Reference | Reference |
| Rural | 1.65 (0.72–3.76) | 2.4 (1.03–5.58)* | 1.82 (0.25–13.4) | 4.24 (1.34–13.4)** | 2.79 (1.08–7.19)* |
| Ethnicity | |||||
| Mixed race | Reference | Reference | Reference | Reference | Reference |
| White | 5.33 (1.29–22.08)* | 4.6 (1.08–19.7)* | 0 (0–0) | 12.89 (1.98–84.13)** | 4 (0.78–20.37) |
| Andean-indigenous | 0.5 (0.15–1.66) | 0.34 (0.09–1.26) | 0.3 (0.02–4.6) | 0.93 (0.15–5.71) | 0.36 (0.08–1.58) |
| Black | 0.29 (0.13–0.63)** | 0.76 (0.35–1.64) | 0.58 (0.1–3.19) | 0.26 (0.04–1.55) | 1.07 (0.43–2.64) |
| Non-Andean indig. | 0.17 (0.03–0.87)* | 0.17 (0.03-1) | 0.14 (0.01–2.45) | 0.35 (0.06–2.02) | 0.15 (0.03–0.9)* |
| Other | 7.58 (1.53–37.41)** | 11.59 (1.93–69.5)** | Omitted | Omitted | 8.23 (1.45–46.87)* |
| Marital status | |||||
| Married | Reference | Reference | Reference | Reference | Reference |
| Common law | 0.85 (0.41–1.77) | 0.64 (0.26–1.54) | 0.94 (0.17–5.27) | 0.37 (0.06–2.23) | 1.07 (0.39–2.94) |
| Single | 0.64 (0.31–1.31) | 0.45 (0.21–0.96)* | 0.36 (0.06–2.12) | 0.62 (0.16–2.46) | 0.92 (0.44–1.94) |
| Divorced/Separated | 0.72 (0.29–1.78) | 0.35 (0.12-1) | 1.21 (0.24–6.13) | 0.33 (0.08–1.46) | 0.52 (0.17–1.62) |
| Widower | 0.77 (0.32–1.84) | 1.06 (0.38–2.96) | 0.87 (0.08–9.46) | 0.81 (0.19–3.47) | 0.45 (0.16–1.26) |
| Education | |||||
| Tertiary | Reference | Reference | Reference | Reference | Reference |
| Secondary | 0.4 (0.19–0.85)* | 0.22 (0.09–0.52)*** | 0.24 (0.04–1.35) | 0.16 (0.04–0.72)* | 0.18 (0.07–0.48)*** |
| Primary | 0.54 (0.24–1.19) | 0.22 (0.09–0.54)*** | 0.3 (0.06–1.34) | 0.23 (0.05-1) | 0.25 (0.1–0.66)** |
| None | 1.02 (0.38–2.76) | 0.25 (0.07–0.88)* | 0.31 (0.01–7.12) | 0.16 (0.02–1.4) | 0.18 (0.05–0.62)** |
| Poverty level | |||||
| No poverty | Reference | Reference | Reference | Reference | Reference |
| Poverty | 1.21 (0.45–3.25) | 0.93 (0.33–2.63) | 0.41 (0.06–2.8) | 1.1 (0.23–5.18) | 0.71 (0.23–2.17) |
| Extreme poverty | 1 (0.55–1.82) | 0.74 (0.35–1.6) | 0.58 (0.05–6.32) | 2.69 (0.93–7.73) | 0.62 (0.26–1.49) |
| Unknown | 0.59 (0.06–6.21) | 0.14 (0.01–1.49) | 0.77 (0.03–21.54) | 2.13 (0.19–23.42) | 0.47 (0.04–5.37) |
| Already had COVID-19 | |||||
| Yes | Reference | Reference | Reference | Reference | Reference |
| No | 1 (0.52–1.94) | 1.34 (0.67–2.7) | Omitted | 1.09 (0.39–3.01) | 0.69 (0.3–1.58) |
| Unknown | 2.05 (0.47–8.89) | 1.35 (0.26–7.03) | Omitted | 0.95 (0.11–7.94) | 1.46 (0.17–12.33) |
Abbreviations: A, Belief that the vaccine can be unsafe because of possible side effects; B, Do not believe that the vaccine is effective enough; C, Already had COVID-19 and recovered; D, Do not believe that the coronavirus is dangerous for one’s health; E, Not in favor of vaccines in general; aRRR, adjusted relative risk ratio; CI, confidence intervals; y, years
*P < 0.05; **P ≤ 0.01; ***P ≤ 0.001
Table 5.
Design-based multinomial logistic regression of the reasons for the negative intention to get vaccinated against COVID-19 during the January-March 2022 period
| Variables | A | B | C | D | E |
|---|---|---|---|---|---|
| aRRR (95% CI) | aRRR (95% CI) | aRRR (95% CI) | aRRR (95% CI) | aRRR (95% CI) | |
| Age Group | |||||
| Young adult (18-39y) | Reference | Reference | Reference | Reference | Reference |
| Middle-aged (40-64y) | 1.63 (0.79–3.37) | 2.21 (1.05–4.62)* | 0.5 (0.12–2.11) | 1.54 (0.54–4.36) | 2.49 (1.18–5.26)* |
| Elderly (≥ 65y) | 0.26 (0.1–0.7)** | 0.27 (0.09–0.75)** | 0.01 (0-0.14)*** | 0.47 (0.14–1.51) | 0.39 (0.14–1.11) |
| Sex | |||||
| Male | Reference | Reference | Reference | Reference | Reference |
| Female | 0.67 (0.37–1.23) | 0.57 (0.31–1.07) | 1.31 (0.47–3.65) | 0.7 (0.34–1.43) | 0.65 (0.35–1.21) |
| Area of residence | |||||
| Urban | Reference | Reference | Reference | Reference | Reference |
| Rural | 1.25 (0.54–2.9) | 1.61 (0.66–3.93) | 0.31 (0.08–1.22) | 2.04 (0.67–6.26) | 1.38 (0.52–3.72) |
| Ethnicity | |||||
| Mixed race | Reference | Reference | Reference | Reference | Reference |
| White | 1.55 (0.25–9.65) | 2.56 (0.38–17.39) | 856.19 (4.72-155173.34)** | 2.8 (0.22–35.63) | 2.57 (0.38–17.42) |
| Andean indigenous | 1.98 (0.66–5.94) | 2.06 (0.7–6.05) | 9.97 (2.05–48.57)** | 2.69 (0.68–10.58) | 1.88 (0.49–7.24) |
| Black | 2.26 (0.6–8.49) | 2.98 (0.8-11.07) | Omitted | 2.59 (0.28–23.85) | 3.6 (1.05–12.38)* |
| Non-Andean indig. | 2.39 (0.55–10.36) | 0.8 (0.2–3.2) | Omitted | 0.52 (0.06–4.42) | 0.97 (0.2–4.77) |
| Other | 0.09 (0.02–0.54)** | 0.21 (0.02–1.93) | 0.27 (0.01–10.79) | Omitted | 0.08 (0.01–0.55)** |
| Marital status | |||||
| Married | Reference | Reference | Reference | Reference | Reference |
| Common law | 1.75 (0.62–4.93) | 0.93 (0.34–2.59) | 0.52 (0.07–4.14) | 3.96 (1.05–14.97)* | 1.77 (0.58–5.39) |
| Single | 1.16 (0.46–2.88) | 1 (0.4–2.46) | 0.5 (0.09–2.77) | 3.24 (0.98–10.67) | 1.19 (0.46–3.12) |
| Divorced/Separated | 0.87 (0.35–2.16) | 0.47 (0.19–1.2) | 2.85 (0.42–19.26) | 1.4 (0.37–5.26) | 0.66 (0.26–1.66) |
| Widower | 1.44 (0.33–6.23) | 0.84 (0.18–3.84) | 12.68 (0.8-200.92) | 1.8 (0.32–10.13) | 1.95 (0.43–8.82) |
| Education | |||||
| Tertiary | Reference | Reference | Reference | Reference | Reference |
| Secondary | 0.84 (0.34–2.07) | 0.55 (0.22–1.39) | 2.71 (0.46–16.06) | 1.28 (0.35–4.71) | 0.41 (0.17–1.02) |
| Primary | 0.7 (0.28–1.73) | 0.44 (0.18–1.05) | 1.44 (0.25–8.2) | 0.54 (0.13–2.19) | 0.25 (0.1–0.63)** |
| None | 1.12 (0.29–4.36) | 0.67 (0.19–2.4) | 2.27 (0.23–22.5) | 1.25 (0.2–7.6) | 0.5 (0.13–1.96) |
| Poverty level | |||||
| No poverty | Reference | Reference | Reference | Reference | Reference |
| Poverty | 0.72 (0.24–2.17) | 0.84 (0.28–2.49) | 0.01 (0-0.55)* | 0.72 (0.18–2.83) | 0.48 (0.16–1.47) |
| Extreme poverty | 0.59 (0.21–1.61) | 0.78 (0.21–2.97) | 0.1 (0.01–0.67)* | 0.19 (0.03–1.08) | 1.41 (0.45–4.36) |
| Unknown | 5.46 (0.63–47.56) | 6.18 (0.6-63.87) | Omitted | 16.02 (0.81-318.07) | 1.86 (0.15–23.71) |
| Already had COVID-19 | |||||
| Yes | Reference | Reference | Reference | Reference | Reference |
| No | 0.6 (0.28–1.26) | 0.42 (0.16–1.11) | Omitted | 0.31 (0.09–1.01) | 0.85 (0.34–2.16) |
| Unknown | 0.31 (0.07–1.51) | 1.79 (0.32–9.88) | Omitted | 1.52 (0.17–13.63) | 0.3 (0.05–1.95) |
Abbreviations: A, Belief that the vaccine can be unsafe because of possible side effects; B, Do not believe that the vaccine is effective enough; C, Already had COVID-19 and recovered; D, Do not believe that the coronavirus is dangerous for one’s health; E, Not in favor of vaccines in general; aRRR, adjusted relative risk ratio; CI, confidence intervals; y, years
*P < 0.05; **P ≤ 0.01; ***P ≤ 0.001
As to whether “the subject already had COVID-19 and recovered” (Reason C), that was an explanatory reason for vaccine hesitancy among middle-aged adults (aRRR 3.71, 95% CI 1.31–10.50) in the Oct-Dec 2021 survey and for white people (aRRR 856, 95% CI 4.72-155173) and Andean indigenous (aRRR 9.97, 95% CI 2.05–48.57) in the Jan-Mar 2022 survey. With regard to the “belief that the coronavirus was not dangerous for one’s health” (Reason D), the Oct-Dec 2021 survey suggested that coronavirus was perceived of as not dangerous by people from rural areas (aRRR 4.24, 95% CI 1.34–13.4) as well as by white people (aRRR 12.89, 95% CI 1.98–84.13). Reason D did not sufficiently explain vaccine hesitancy in Jan-Mar 2022, since only people with a common law marital status claimed this reason (aRRR 3.96, 95% CI 1.05–14.97). Finally, among those not being in favor of vaccines in general (Reason E), they were found middle-aged adults who were surveyed in Oct-Dec 2021 (aRRR 2.07, 95% CI 1.13–3.78) and Jan-Mar 2022 (aRRR 2.49, 95% CI 1.18–5.26) as well as people from rural areas who answered the survey during Oct-Dec 2021 (aRRR 2.79, 95% CI 1.08–7.19).
Discussion
The main finding of this observational retrospective analysis in Ecuador was that the rate of unvaccinated people having no intention to receive the COVID-19 vaccine increased from 57.4% in Oct-Dec 2021 to 72.9%, during Jan-Mar 2022. Vaccine hesitancy depended on factors like sex, age and ethnicity. The economic and education level, and prior COVID-19 disease did not have a significant impact on the intention to get vaccinated. Interestingly, hesitancy was largely due to doubts about COVID-19 vaccine safety and efficacy, as well as to distrust in vaccines in general.
Whereas some meta-analysis across different countries suggest that intention of getting vaccinated against COVID-19 increases with age [14, 16], other studies conducted in Chinese populations [13], as well as in populations from low and middle-income countries like Mozambique, Pakistan, and Rwanda, point to the younger ages as the most likely to accept COVID-19 vaccination [17]. Our study population showed that, from October 2021 to March 2022, only the middle-aged population was reluctant to get the COVID-19 vaccine. Acceptance in older populations increased with vaccine safety awareness [18]. A higher risk of suffering severe COVID-19 would explain the change of intention in the elderly age group during the first quarter of 2022. This suggests that the association was not as linear as initially thought. The association of COVID-19 vaccination intention with age actually depended on the knowledge about the vaccine (see Table 5).
Several studies have shown a low intention to get vaccinated against COVID-19 among women [14, 16, 19]. Large sex gaps in the intention to receive the vaccine have been found in the United States (17%) and Russia (16%), while that difference is reduced to 10% in low and middle-income countries [17]. In a developing country like Ecuador, with rising infections and increasing mortality, positive experiences of millions of people with COVID-19 vaccination as well as the very high initial uptake in high-risk groups [19] could have contributed to the higher vaccine acceptance rates among females compared to males found in this study. Our findings also showed that Ecuadorian females were less likely to have negative beliefs against COVID-19 vaccination. In agreement with previous studies [2, 20], the black and white populations in our study presented the highest odds of not having the intention to get vaccinated when the vaccines first became available in Ecuador in Oct-Dec 2021. Negative beliefs against COVID-19 vaccines may be the main reason for the hesitance in the white population.
Some evidence suggests that people living in rural areas across Colombian, Ecuadorian, and Venezuelan are more likely to believe that the vaccine is ineffective and that COVID-19 is not dangerous [7], have conspiracy beliefs and distrust in vaccines [20, 21]. Although the risk of misinformation and a lack of adequate coverage of health promotion activities would contribute to the resistance to immunization and community mitigation strategies in rural areas [21], we could not confirm such hypothesis. In the same vein of our study, a systematic review and meta-analysis of the COVID-19 vaccine acceptance did not find any significant differences between people living in urban and rural areas either [16].
Although it could be thought that the higher education and economic levels, the higher COVID-19 vaccine intention [14, 16, 20], research on the association of vaccine hesitancy with economic status and education levels are not yet convincing. Consistent with other studies around the world [13, 14], this study did not find any significant OR in either COVID-19 vaccine intention or in negative beliefs regarding vaccination. Even if people with higher household income are more likely to intend COVID-19 vaccination [14, 16], income level would not have a great influence in COVID-19 vaccination in a free vaccine scenario where COVID-19 vaccination willingness is higher in the low-income group [13]. The relative strength of influence of each factor in COVID-19 vaccine hesitancy is complex, context-specific, and varies across time, place, and vaccine type [22].
To the best of our knowledge, this is the first observational study made in Ecuador on the intention to get vaccinated against COVID-19 in a real scenario of free-of-charge vaccine availability, instead of a hypothetical setting as in 2020. One of the strengths of this study is the national representativeness of the survey data and the face-to-face or telephone interviews for data collection (most of the research has so far relied on online questionnaires). Yet the self-reported data gathering and the secondary datasets preclude causal inferences, the analysis of the moderating effects of socio-demographic, economic, and individual factors on COVID-19 vaccination intention warrants the development of new public health initiatives of communication, education and intervention to improve vaccination.
Conclusion
This study demonstrated that the negative COVID-19 vaccine intention depended not only on socio-demographic factors, but also on the misbeliefs about effectiveness and safety of vaccines, due to fear of side effects.
Electronic Supplementary Material
Below is the link to the electronic supplementary material.
Acknowledgements
This study was supported by Research Institute and Faculty of Health Sciences of the Universidad Técnica de Manabí, Portoviejo, Ecuador.
Authors’ contribution
Lapo-Talledo G. J: interpretation of data, analysis and writing of the manuscript. Talledo-Delgado J.A: interpretation of data, analysis and writing of the manuscript. Portalanza D: statistical analysis. Ballaz S: Writing and prepared the final draft of the manuscript. Siteneski A: supervision, writing and prepared the final draft of the manuscript.
Funding
Not applicable.
Data Availability Statement
The generated during and/or analysed during the current study are available in: https://www.ecuadorencifras.gob.ec/estadisticas-laborales-abril-2022/.
Code Availability
Not applicable.
Declarations
Conflict of interest/Competing interest
Not applicable.
Ethics Approval
Not applicable.
Consent to Participate
Not applicable.
Consent for publication
Not applicable.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The generated during and/or analysed during the current study are available in: https://www.ecuadorencifras.gob.ec/estadisticas-laborales-abril-2022/.
Not applicable.