Abstract
Background
Initial management of coronavirus disease 2019 (COVID-19) focused on preventing spread and providing supportive care. Authorities disseminated disease information and enforced preventive measures. This intricate task was affected by factors such as initial emotional distress or perceived risk, which influenced adherence to public health recommendations. This study examined the association between trust in local health authorities and national/regional leaders who have provided information about COVID-19 and adherence to COVID-19 preventive behaviors.
Methods
Data from the International COVID-19 Awareness and Responses Evaluation (iCARE) study was used for the analyses (surveys 5–15, n = 14,453) from 116 countries (September 2020-January 2022). The main predictor was participants' trust, which was categorized into four levels. The primary outcome was adherence to preventive behaviors: (1) handwashing, (2) mask wearing, (3) physical distancing, (4) avoiding gatherings, and (5) self-quarantining. Additional variables included demographics, socioeconomic status, health history and COVID-19 perceptions. We conducted multivariable logistic regressions to determine the association between the level of trust in local health authorities and national/regional leaders and adherence to COVID-19-related preventive health behaviors.
Results
14,453 respondents worldwide, 2852 (25.43%) reported no trust or no trust at all in political authorities, and 1847 (16.84%) reported no trust or no trust at all in healthcare authorities. The unadjusted regression models revealed that those without trust in political and health authorities were less likely to adhere to all the included preventive behaviors. Multivariable models indicated that those who did not trust at all in political leaders had lower odds of adopting self-quarantine (OR = 0.70, 95% CI: 0.49–0.99) and of avoiding gatherings (OR = 0.59, 95% CI: 0.42–0.84). Distrust in healthcare authorities also lowered the odds of adherence to protective behaviors, particularly avoiding gatherings (OR = 0.37, 95% CI: 0.25–0.53), and self-quarantine (OR = 0.49, 95% 0.33- 0.72).
Conclusion
Our findings revealed a positive association between trust in health and political authorities, and adherence to preventive health behaviors, independent of demographic, socioeconomic, or perceptual/belief factors. Therefore, prioritizing trust between public health institutions and people is crucial for effectively managing infectious disease outbreaks. Future steps must be tailored to each population, addressing privacy concerns, enhancing community communication, and encouraging public engagement in health policy.
Supplementary Information
The online version contains supplementary material available at https://doi.org/10.1186/s12889-026-28320-1.
Keywords: COVID-19, Global, Health behaviors, Level of trust
Background
Coronavirus disease 2019 (COVID-19) was a global health emergency that challenged healthcare and public health systems [1–3]. The lack of knowledge about the disease and its underlying pathophysiology made management extremely complex [1, 2]. The main obstacles to controlling the disease were the high transmission rate, heterogeneous symptomatology, the absence of a vaccine, and the lack of targeted therapy [2–4]. Therefore, before approved vaccines were available, situational management prioritized stopping the spread of the disease and providing supportive care [2–4]. During pandemics, local, national, and global authorities are responsible for disseminating accurate information and enforcing preventive measures to control the spread of the disease [5, 6]. Some of these behaviors included the use of face masks, stay-at-home orders, hand washing, travel restrictions, and physical distancing [7]. However, dissatisfaction with political authorities or healthcare services is a common barrier to implementing recommended protective behaviors [8].
In addition to dissatisfaction, initial emotional distress, the level of trust in the general population related to information sources and/or perceived risk could influence the unconditional willingness to adhere to public health recommendations [6, 9, 10]. A longitudinal study in Chile found that government trust indirectly influenced the acceptance of protective behaviors against COVID-19 in young adults [11]. Similarly, a longitudinal cohort study in Switzerland showed that young adults with low government trust had less compliance with public health measures [12]. Additionally, the level of trust among information sources varies across stakeholders [8]. While healthcare professionals are often among the most trusted sources of information, trust in public media is more heterogeneous and may vary depending on the nature, consistency, and ideological orientation of messaging [6, 13].
Moreover, various sociodemographic characteristics that evolve over time, significantly influence the adoption of preventive health behaviors. A meta-analysis of respiratory infectious disease epidemics and pandemics showed that women were 50% more likely to adopt non-pharmaceutical, preventive health behaviors than men were [14]. Prior to the COVID-19 pandemic, a literature review on the influenza pandemic concluded that elderly women who were well-educated and self-identified as non-white were associated with increased chances of adopting protective measures behaviors [15]. On the other hand, a more recent analysis of COVID-19 stated that adhering to face mask recommendations among women and men was less likely in the employed group or if they were living in a country with greater gender equity [16]. Therefore, understanding the numerous factors influencing adherence to preventive health behaviors is critical.
However, most studies are limited in their geographical scope or focus on specific populations [11]. Consequently, from a public policy standpoint, it is imperative to understand the factors that influence adherence to preventive measures globally [14–16]. This study aims to evaluate the association between the level of trust of the general population in local health authorities and national/regional leaders who have provided information about COVID-19 and adherence to COVID-19 preventive behaviors. We hypothesized that a higher level of trust in the information source would increase adherence to preventive health behaviors among the participants.
Methods
Study design
Data from the International COVID-19 Awareness and Responses Evaluation (iCARE) study (https://icare.mbmc-cmcm.ca/) led by the Montreal Behavioural Medicine Centre, Quebec, Canada was used for the analyses. The design and methods of this study have been comprehensively outlined in a prior publication [17]. This cross-sectional survey study was conducted across multiple countries and waves, and focused on public awareness, attitudes, and reactions to COVID-19 public health policies. The surveys included questions about sociodemographic characteristics, physical/mental health history, beliefs and behaviors related to COVID-19, vaccination, and testing [17, 18]. Data were collected from the iCARE global convenience and snowball sampling strategy, and adapted and modified for the country-specific context. The survey was administered online via LimeSurvey© (Montreal, Canada) and through a network of more than 200 international collaborators [17].
Participants
This study included data from Survey 5 to Survey 15 (n = 14,453), collected between September 15, 2020, and January 11, 2022, as the measure of trust in actors who provide COVID-19 information was introduced during those survey waves. The study included participants aged 17 years or older from 116 countries, all of whom provided informed consent.
Independent variable: trust in information sources
The study’s main predictor was participants’ level of trust in entities that have provided information about COVID-19. It was assessed with the question: “Please rate your level of trust in the following people and organizations who have provided information about COVID-19: a. My local health authorities, b. my national political leader and c. regional political leader”. National and regional political leaders were combined into a single variable called political leaders after initial analysis revealed similar trust levels across these groups, with only a very weak association between leadership level and trust (Cramér's V = 0.07). The level of trust variable was then categorized as follows: a. I trust to a great extent, b. I trust somewhat, c. I do not trust (distrust), d. I do not trust at all. Detailed information on the iCARE questionnaires for each period of data collection can be found online (https://osf.io/nswcm/).
Outcome measures
The main outcome was the adoption of preventive behaviors. It was assessed by the frequency with which participants adopted each behavior in the previous seven days. The following behaviors included (1) hand washing with soap and water, (2) wearing a face mask, (3) staying at least 1–2 m away from other people, (4) avoiding gatherings (working at home rather than going to work or school, avoiding going out to bars/pubs/restaurants, avoiding large social gatherings, avoiding small social gatherings, avoiding indoor social), and (5) self-quarantining if someone had the virus or believed they had the virus. The answers were categorized as binary variables, such as adherence (most of the time) or non-adherence (otherwise). We include those variables because they were proposed as evidence-based preventive measures and align with recommendations from global public health authorities (7).
Statistical analysis
This analysis was conceptually guided by constructs from the COM-B model and the Health Belief Model, which informed the selection of variables and interpretation of relationships. In this framework, adherence to preventive behaviors is influenced by capabilities, opportunities, and motivations, as well as perceptions [16]. Trust in the actors who provided information sources is conceptualized in this study as a key motivational and perceptual factor that may influence adherence behaviors.
Descriptive analysis was performed to summarize participant characteristics, stratified by level of trust in political and healthcare authorities. Observations were excluded from the analysis if data were missing or if participants indicated the response ‘I don’t know/I prefer not to answer’. A bivariate analysis was conducted to examine differences in characteristics as a function of trust levels in local health authorities and political governments. Bivariate analyses were conducted using Pearson’s chi-square tests to examine (1) differences in participant characteristics across levels of trust in local health authorities and political governments, and (2) the association between trust levels and adherence to preventive behaviors.
Afterward, multivariable logistic regression models were conducted. We included the following variables as part of the analysis country of residence categorized by Socio-Demographic Index (SDI), age, educational attainment, perception of home income, living area, chronic disease, mental disease, employment status, living alone or with more persons, living with kids, the perception of the importance of preventive measures, the perception of the authorities’ actions, and the perception of the risk of being infected. We assessed variance inflation factors to rule out multicollinearity. Odds ratios (ORs) with their corresponding 95% confidence intervals (CIs) were calculated and reported for each model. All statistical tests were two-tailed, and a p-value < 0.05 was considered statistically significant. Statistical analyses were conducted using SAS, version 9.4 (SAS Institute Inc., Cary, North Carolina, United States).
Results
Descriptive characteristics
A total of 14,453 participants were included in the analysis, with the main findings summarized in Table 1. Of these, 11,216 participants responded about their level of trust in political leaders, and 10,966 about their trust in health authorities. Most respondents identified their biological sex as female, accounting for 8219 (73.85%) of those responding about political leaders and 8029 (73.79%) of those responding about health authorities. Regarding age, the majority of respondents were between 25 and 64 years old, representing 7983 (71.19%) among the political leaders responders and 7816 (71.29%) among the health authority responders.
Table 1.
Characteristics of survey respondents by level of trust, political authorities (n = 11,216) and health authorities (n = 10,966)
| CHARACTERISTIC/TRUST | Total | Trust to a great extent | Trust somewhat | Do not trust | Do not trust at all | p-valueA | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Political Authorities, n (%) | 11,216 | (100.00) | 2958 | (26.37) | 5406 | (48.20) | 1580 | (14.09) | 1272 | (11.34) | |
| Age (y), mean ± SDB | 46.07 | 16.94 | 48.81 | 17.60 | 46.32 | 17.04 | 42.34 | 15.77 | 43.25 | 15.02 | |
| Age categories, n (%) | <.0001 | ||||||||||
| 17–24 | 1380 | (12.31) | 331 | (11.19) | 650 | (12.03) | 229 | (14.49) | 170 | (13.36) | |
| 25–64 | 7983 | (71.19) | 1944 | (65.74) | 3855 | (71.32) | 1192 | (75.44) | 992 | (77.99) | |
| 65y or more | 1851 | (16.51) | 682 | (23.06) | 900 | (16.65) | 159 | (10.06) | 110 | (8.65) | |
| Missing/Unknown | 2 | (0.02) | |||||||||
| Biological Sex, n (%) | <.0001 | ||||||||||
| Male | 2911 | (26.15) | 792 | (26.95) | 1296 | (24.13) | 437 | (27.92) | 386 | (30.73) | |
| Female | 8219 | (73.85) | 2147 | (73.05) | 4074 | (75.87) | 1128 | (72.08) | 870 | (69.27) | |
| Missing/Unknown | 86 | (0.77) | |||||||||
| SDIC Country, n (%) | <.0001 | ||||||||||
| Low-middle | 1287 | (11.68) | 234 | (8.01) | 483 | (9.13) | 315 | (20.28) | 255 | (20.33) | |
| High | 9735 | (88.32) | 2689 | (91.99) | 4809 | (90.87) | 1238 | (79.72) | 999 | (79.67) | |
| Missing/Unknown | 194 | (1.73) | |||||||||
| Area, n (%) | <.0001 | ||||||||||
| Rural | 1423 | (12.97) | 391 | (13.45) | 660 | (12.43) | 205 | (13.43) | 167 | (13.54) | |
| Suburban | 3082 | (28.08) | 896 | (30.81) | 1546 | (29.13) | 373 | (24.44) | 267 | (21.65) | |
| Urban | 6470 | (58.95) | 1621 | (55.74) | 3102 | (58.44) | 948 | (62.12) | 799 | (64.80) | |
| Missing/Unknown | 241 | (2.15) | |||||||||
| Education level, n (%) | <.001 | ||||||||||
| Low | 2308 | (20.98) | 682 | (23.41) | 1103 | (20.75) | 279 | (18.15) | 244 | (19.73) | |
| High | 8694 | (79.02) | 2231 | (76.59) | 4212 | (79.25) | 1258 | (81.85) | 993 | (80.27) | |
| Missing/Unknown | 214 | (1.91) | |||||||||
| Work status, n (%) | <.0001 | ||||||||||
| Unemployed | 4856 | (43.56) | 1424 | (48.29) | 2333 | (43.44) | 655 | (41.72) | 444 | (35.27) | |
| Employed | 6293 | (56.44) | 1525 | (51.71) | 3038 | (56.56) | 915 | (58.28) | 815 | (64.73) | |
| Missing/Unknown | 67 | (0.60) | |||||||||
| Annual perceived household income, n (%) | <.0001 | ||||||||||
| Bottom third | 1630 | (16.45) | 420 | (15.77) | 731 | (15.24) | 266 | (19.94) | 213 | (19.12) | |
| Middle third | 5501 | (55.53) | 1490 | (55.95) | 2665 | (55.57) | 734 | (55.02) | 612 | (54.94) | |
| Top third | 2776 | (28.02) | 753 | (28.28) | 1400 | (29.19) | 334 | (25.04) | 289 | (25.94) | |
| Missing/Unknown | 1309 | (11.67) | |||||||||
| Number of adults ≥ 18y in the household, n (%) | <.001 | ||||||||||
| 0 | 1740 | (15.61) | 431 | (14.66) | 860 | (15.99) | 239 | (15.20) | 210 | (16.71) | |
| 1 | 4787 | (42.95) | 1303 | (44.33) | 2362 | (43.93) | 603 | (38.36) | 519 | (41.29) | |
| 2 or more | 4618 | (41.44) | 1205 | (41.00) | 2155 | (40.08) | 730 | (46.44) | 528 | (42.00) | |
| Missing/Unknown | 71 | (0.63) | |||||||||
| Number of children ≤ 18y in the household, n (%) | <.0001 | ||||||||||
| 0 | 8008 | (72.50) | 2195 | (75.20) | 3918 | (73.37) | 1067 | (68.93) | 828 | (66.83) | |
| 1 | 1485 | (13.44) | 359 | (12.30) | 683 | (12.79) | 243 | (15.70) | 200 | (16.14) | |
| 2 or more | 1553 | (14.06) | 365 | (12.50) | 739 | (13.84) | 238 | (15.37) | 211 | (17.03) | |
| Missing/Unknown | 170 | (1.52) | |||||||||
| Chronic disease, n (%) | <.0001 | ||||||||||
| None | 7111 | (63.40) | 1776 | (60.04) | 3432 | (63.49) | 1063 | (67.28) | 840 | (66.04) | |
| At least one | 4105 | (36.60) | 1182 | (39.96) | 1974 | (36.51) | 517 | (32.72) | 432 | (33.96) | |
| Mental disease, n (%) | <.0001 | ||||||||||
| None | 9002 | (80.26) | 2439 | (82.45) | 4363 | (80.71) | 1228 | (77.72) | 972 | (76.42) | |
| At least one | 2214 | (19.74) | 519 | (17.55) | 1043 | (19.29) | 352 | (22.28) | 300 | (23.58) | |
| Believed to have the infection, n (%) | <.0001 | ||||||||||
| No | 9048 | (87.45) | 2531 | (90.52) | 4486 | (89.20) | 1162 | (82.53) | 869 | (78.08) | |
| Yes, but I have recovered | 1032 | (9.97) | 207 | (7.40) | 424 | (8.43) | 209 | (14.84) | 194 | (17.25) | |
| Yes, and I am still having symptoms | 223 | (2.16) | 48 | (1.72) | 105 | (2.09) | 32 | (2.27) | 38 | (3.41) | |
| Yes, but I am not having symptoms | 43 | (0.42) | 10 | (0.36) | 14 | (0.28) | 5 | (0.36) | 14 | (1.26) | |
| Missing/Unknown | 870 | (7.76) | |||||||||
| Do you think you are in high risk, n (%) | <.0001 | ||||||||||
| No | 6504 | (67.28) | 1783 | (68.76) | 3165 | (68.39) | 824 | (62.24) | 732 | (65.24) | |
| Yes | 3163 | (32.72) | 810 | (31.24) | 1463 | (31.61) | 500 | (37.76) | 390 | (34.76) | |
| Missing/Unknown | 1549 | (13.81) | |||||||||
| Protective measures importance, n (%) | <.0001 | ||||||||||
| Important | 10,364 | (92.92) | 2931 | (99.29) | 5270 | (97.74) | 1358 | (86.88) | 805 | (64.55) | |
| Non-important | 790 | (7.08) | 21 | (0.71) | 122 | (2.26) | 205 | (13.12) | 442 | (35.45) | |
| Missing/Unknown | 62 | (0.55) | |||||||||
| Recommendation beliefs, n (%) | <.0001 | ||||||||||
| Too strict | 1459 | (13.26) | 142 | (4.83) | 447 | (8.37) | 351 | (23.05) | 519 | (43.18) | |
| About right | 6111 | (55.54) | 2042 | (69.53) | 3272 | (61.27) | 562 | (36.84) | 236 | (19.63) | |
| Too lenient | 3432 | (31.19) | 753 | (25.64) | 1621 | (30.36) | 611 | (40.12) | 447 | (37.19) | |
| Missing/Unknown | 214 | (1.91) | |||||||||
| Health Authorities, n (%) | 10,966 | (100.00) | 3761 | (34.30) | 5358 | (48.86) | 1075 | (9.80) | 772 | (7.04) | |
| Age (y), mean ± SD | 45.82 | 17.07 | 46.48 | 17.58 | 46.29 | 16.96 | 43.48 | 15.54 | 43.31 | 14.24 | |
| Age categories, n (%) | <.0001 | ||||||||||
| 17–24 | 1382 | (12.60) | 525 | (143.96) | 636 | (11.87) | 138 | (12.84) | 83 | (10.75) | |
| 25–64 | 7816 | (71.29) | 2553 | (67.90) | 3814 | (71.20) | 825 | (76.74) | 624 | (80.83) | |
| 65y or more | 1766 | (16.11) | 682 | (18.14) | 907 | (16.93) | 112 | (10.42) | 65 | (8.42) | |
| Missing/Unknown | 2 | (0.02) | |||||||||
| Biological Sex, n (%) | <.001 | ||||||||||
| Male | 2852 | (26.21) | 1004 | (26.99) | 1303 | (24.43) | 305 | (28.53) | 240 | (31.66) | |
| Female | 8029 | (73.79) | 2716 | (73.01) | 4031 | (75.57) | 764 | (71.47) | 518 | (68.34) | |
| Missing/Unknown | 85 | (0.78) | |||||||||
| SDIC Country, n (%) | |||||||||||
| Low-middle | 1283 | (11.90) | 296 | (7.96) | 637 | (12.15) | 213 | (20.09) | 137 | (18.03) | |
| High | 9498 | (88.10) | 3421 | (92.04) | 4607 | (87.85) | 847 | (79.91) | 623 | (81.97) | |
| Missing/Unknown | 185 | (1.69) | |||||||||
| Area, n (%) | <.0001 | ||||||||||
| Rural | 1377 | (12.84) | 486 | (13.19) | 624 | (11.87) | 142 | (13.67) | 125 | (16.82) | |
| Suburban | 2948 | (27.49) | 1045 | (28.35) | 1477 | (28.10) | 274 | (26.37) | 152 | (20.46) | |
| Urban | 6400 | (59.67) | 2155 | (58.46) | 3156 | (60.03) | 623 | (59.96) | 466 | (62.72) | |
| Missing/Unknown | 241 | (2.20) | |||||||||
| Education level, n (%) | 0.853 | ||||||||||
| Low | 2266 | (21.06) | 798 | (21.54) | 1091 | (20.72) | 221 | (21.17) | 156 | (20.97) | |
| High | 8492 | (78.94) | 2907 | (78.46) | 4174 | (79.28) | 823 | (78.83) | 588 | (79.03) | |
| Missing/Unknown | 208 | (1.90) | |||||||||
| Work status, n (%) | <.0001 | ||||||||||
| Unemployed | 4739 | (43.49) | 1703 | (45.47) | 2374 | (44.58) | 408 | (38.45) | 254 | (33.16) | |
| Employed | 6158 | (56.51) | 2042 | (54.53) | 2951 | (55.42) | 653 | (61.55) | 512 | (66.84) | |
| Missing/Unknown | 69 | (0.63) | |||||||||
| Annual perceived household income, n (%) | <.001 | ||||||||||
| Bottom third | 1599 | (16.55) | 551 | (16.37) | 741 | (15.76) | 163 | (17.41) | 144 | (21.85) | |
| Middle third | 5382 | (55.70) | 1858 | (55.22) | 2614 | (55.58) | 539 | (57.59) | 371 | (56.30) | |
| Top third | 2682 | (27.76) | 956 | (28.41) | 1348 | (28.66) | 234 | (25.00) | 144 | (21.85) | |
| Missing/Unknown | 1303 | (11.88) | |||||||||
| Number of adults ≥ 18y in the household, n (%) | <.0001 | ||||||||||
| 0 | 1700 | (15.60) | 572 | (15.27) | 836 | (15.70) | 177 | (16.59) | 115 | (15.17) | |
| 1 | 4666 | (42.83) | 1658 | (44.27) | 2319 | (43.55) | 375 | (35.15) | 314 | (41.42) | |
| 2 or more | 4529 | (41.57) | 1515 | (40.45) | 2170 | (40.75) | 515 | (48.27) | 329 | (43.50) | |
| Missing/Unknown | 71 | (0.65) | |||||||||
| Number of children ≤ 18y in the household, n (%) | <.0001 | ||||||||||
| 0 | 7787 | (72.15) | 2757 | (74.17) | 3849 | (72.90) | 699 | (66.51) | 482 | (64.70) | |
| 1 | 1466 | (13.58) | 474 | (12.75) | 686 | (12.99) | 176 | (16.75) | 130 | (17.45) | |
| 2 or more | 1540 | (14.27) | 486 | (13.08) | 745 | (14.11) | 176 | (16.75) | 133 | (17.85) | |
| Missing/Unknown | 173 | (1.58) | |||||||||
| Chronic disease, n (%) | 0.284 | ||||||||||
| None | 6953 | (63.41) | 2343 | (62.30) | 3414 | (63.72) | 700 | (65.12) | 496 | (64.25) | |
| At least one | 4013 | (36.59) | 1418 | (37.70) | 1944 | (36.28) | 375 | (34.88) | 276 | (35.75) | |
| Mental disease, n (%) | <.001 | ||||||||||
| None | 8814 | (80.38) | 3059 | (81.33) | 4332 | (80.85) | 838 | (77.95) | 585 | (75.78) | |
| At least one | 2152 | (19.62) | 703 | (18.67) | 1026 | (19.15) | 237 | (22.05) | 187 | (24.22) | |
| Believed to have the infection, n (%) | <.0001 | ||||||||||
| No | 8842 | (87.44) | 3212 | (90.94) | 4344 | (87.90) | 792 | (82.93) | 494 | (72.33) | |
| Yes, but I have recovered | 1007 | (9.96) | 251 | (7.11) | 483 | (9.77) | 126 | (13.19) | 147 | (21.52) | |
| Yes, and I am still having symptoms | 222 | (2.20) | 61 | (1.73) | 100 | (2.02) | 30 | (3.14) | 31 | (4.54) | |
| Yes, but I am not having symptoms | 41 | (0.41) | 8 | (0.23) | 15 | (0.30) | 7 | (0.73) | 11 | (1.61) | |
| Missing/Unknown | 854 | (7.79) | |||||||||
| Do you think you are in high risk, n (%) | <.001 | ||||||||||
| No | 6303 | (66.80) | 2265 | (68.53) | 3035 | (66.75) | 552 | (61.13) | 451 | (66.32) | |
| Yes | 3132 | (33.20) | 1040 | (31.47) | 1512 | (33.25) | 351 | (38.87) | 229 | (33.68) | |
| Missing/Unknown | 1531 | (13.96) | |||||||||
| Protective measures importance, n (%) | <.0001 | ||||||||||
| Important | 10,131 | (92.90) | 3728 | (99.36) | 5143 | (96.40) | 873 | (82.13) | 388 | (51.32) | |
| Non-important | 774 | (7.10) | 24 | (0.64) | 192 | (3.60) | 190 | (17.87) | 368 | (48.68) | |
| Missing/Unknown | 61 | (0.56) | |||||||||
| Recommendation beliefs, n (%) | <.0001 | ||||||||||
| Too strict | 1430 | (13.30) | 169 | (4.54) | 581 | (11.06) | 285 | (27.40) | 395 | (53.52) | |
| About right | 5962 | (55.44) | 2493 | (66.94) | 3038 | (57.86) | 335 | (32.21) | 96 | (13.01) | |
| Too lenient | 3361 | (31.26) | 1062 | (28.52) | 1632 | (31.08) | 420 | (40.28) | 247 | (33.47) | |
| Missing/Unknown | 213 | (1.94) | |||||||||
AP-values were determined through Chi2 test
BSD: Standard Deviation
CSDI: Sociodemographic index, low-middle (0–0.7905) and high (0.7905–1)
Bold p-value < 0.05 was considered statistically significant
Most participants resided in countries with a high SDI (88.32% for political authority and 88.10% for health authority responders) and had attained a high level of education (79.02% and 78.94%, respectively). Approximately half of the respondents were employed (56.44% and 56.51%), lived in urban areas (58.95% and 59.67%), and had an annual income in the middle third (55.53% and 55.70%).
Factors associated with adoption of protective behaviours
Regarding the adoption of preventive behaviors (Table 2), for social distancing, 80.91% of respondents who trusted political authorities to a great extent adhered to this behavior, 76.76% who trusted somewhat, 67.22% who did not trust (distrust), and 56.83% who did not trust at all (p < 0.0001). Similar trends were observed for other protective behaviors such as wearing a face mask, hand washing, avoiding gatherings, and self-quarantining. Trust in health authorities showed comparable patterns, with a higher percentage of responders adhering to protective behaviors among those with greater trust. For instance, 95.45% of respondents who trusted health authorities greatly avoided gatherings, while only 68.52% of respondents who did not trust at all avoided gatherings (p < 0.0001).
Table 2.
Preventive behaviors by level of trust in information sources, political authorities and health authorities
| PROTECTIVE BEHAVIORS/LEVEL OF TRUST | Total | Trust to a great extent | Trust somewhat | Do not trust | Do not trust at all | p-valueA | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Political Authorities, n (%) | 11,216 | (100.00) | 2958 | (26.37) | 5406 | (48.20) | 1580 | (14.09) | 1272 | (11.34) | |
| Hand washing, n (%) | <.0001 | ||||||||||
| Non-Adherence | 1481 | (13.24) | 298 | (10.07) | 662 | (12.27) | 278 | (17.68) | 243 | (19.32) | |
| Adherence | 9701 | (86.76) | 2660 | (89.93) | 4732 | (87.73) | 1294 | (82.32) | 1015 | (80.68) | |
| Wearing a face mask, n (%) | <.0001 | ||||||||||
| Non-Adherence | 704 | (6.32) | 130 | (4.42) | 223 | (4.15) | 135 | (8.59) | 216 | (17.24) | |
| Adherence | 10,442 | (93.68) | 2814 | (95.58) | 5155 | (95.85) | 1436 | (91.41) | 1037 | (82.76) | |
| Staying at least 2 m away from other people, n (%) | <.0001 | ||||||||||
| Non-Adherence | 2877 | (25.73) | 564 | (19.09) | 1253 | (23.24) | 516 | (32.78) | 544 | (43.17) | |
| Adherence | 8303 | (74.27) | 2390 | (80.91) | 4139 | (76.76) | 1058 | (67.22) | 716 | (56.83) | |
| Avoiding gatherings, n (%) | <.0001 | ||||||||||
| Non-Adherence | 1005 | (8.96) | 143 | (4.83) | 372 | (6.88) | 200 | (12.66) | 290 | (22.80) | |
| Adherence | 10,211 | (91.04) | 2815 | (95.17) | 5034 | (93.12) | 1380 | (87.34) | 982 | (77.20) | |
| Self-quarantine, n (%) | <.0001 | ||||||||||
| Non-Adherence | 792 | (14.14) | 149 | (10.32) | 295 | (11.44) | 171 | (19.54) | 177 | (25.18) | |
| Adherence | 4808 | (85.86) | 1295 | (89.68) | 2283 | (88.56) | 704 | (80.46) | 526 | (74.82) | |
| Health Authorities, n (%) | 10,966 | (100.00) | 3761 | (34.30) | 5358 | (48.86) | 1075 | (9.80) | 772 | (7.04) | |
| Hand washing, n (%) | <.0001 | ||||||||||
| Non-Adherence | 1457 | (13.33) | 430 | (11.44) | 659 | (12.33) | 198 | (18.52) | 170 | (22.31) | |
| Adherence | 9476 | (86.67) | 3328 | (88.56) | 4685 | (87.67) | 871 | (81.48) | 592 | (77.69) | |
| Wearing a face mask, n (%) | <.0001 | ||||||||||
| Non-Adherence | 692 | (6.35) | 156 | (4.17) | 244 | (4.57) | 108 | (10.18) | 184 | (24.34) | |
| Adherence | 10,204 | (93.65) | 3585 | (95.83) | 5094 | (95.43) | 953 | (89.82) | 572 | (75.66) | |
| Staying at least 2 m away from other people, n (%) | <.0001 | ||||||||||
| Non-Adherence | 2823 | (25.82) | 725 | (19.33) | 1339 | (25.03) | 376 | (35.17) | 383 | (50.20) | |
| Adherence | 8109 | (74.18) | 3026 | (80.67) | 4010 | (74.97) | 693 | (64.83) | 380 | (49.80) | |
| Avoiding gatherings, n (%) | <.0001 | ||||||||||
| Non-Adherence | 968 | (8.83) | 171 | (4.55) | 399 | (7.45) | 155 | (14.42) | 243 | (31.48) | |
| Adherence | 9998 | (91.17) | 3590 | (95.45) | 4959 | (92.55) | 920 | (85.58) | 529 | (68.52) | |
| Self-quarantine, n (%) | <.0001 | ||||||||||
| Non-Adherence | 781 | (14.12) | 172 | (9.24) | 363 | (13.63) | 112 | (19.51) | 134 | (31.02) | |
| Adherence | 4750 | (85.88) | 1689 | (90.76) | 2301 | (86.37) | 462 | (80.49) | 298 | (68.98) | |
AP-values were determined through Chi2 test
Bold p-value < 0.05 was considered statistically significant
Multivariable models demonstrated that who do not trust at all and distrust in political leaders reduced the odds of avoiding gatherings (OR = 0.59, 95% CI: 0.42, 0.84) and (OR = 0.51, 95% CI: 0.37–0.69) respectively. Distrust in political leaders lowered the odds of adhering toself-quarantine (OR = 0.58, 95% CI: 0.42–0.79). Distrust at all in healthcare authorities lowered the odds of adopting protective behaviors, particularly avoiding gatherings (OR = 0.37, 95% CI: 0.25–0.53), self-quarantine (OR = 0.50, 95% CI:0.34–0.74) and staying at least 1–2 m away from other people (OR = 0.62, 95% CI: 0.50- 0.78). Indeed, those who did not trust at all had even lower odds of adhering to these protective behaviors (p < 0.05) (Table 3).
Table 3.
Multivariable logistic regression of level of trust and adherence to protective behaviors
| Protective behaviors/Level of trust | Hand washing | Wearing a face mask | Staying at least 2 m away from other people | Avoiding gatherings | Self-quarantine | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| OR (95% CI) | p-value | OR (95% CI) | p-value | OR (95% CI) | p-value | OR (95% CI) | p-value | OR (95% CI) | p-value | ||||||
| Political leaders | |||||||||||||||
| To a great extent (ref) | |||||||||||||||
| Somewhat | 0.77 | (0.64, 0.91) | .003 | 1.18 | (0.90, 1.55) | .242 | 0.87 | (0.75, 0.99) | .049 | 0.72 | (0.57, 0.93) | <.001 | 1.05 | (0.81, 1.37) | .705 |
| Do not trust | 0.61 | (0.48, 0.77) | <.0001 | 1.04 | (0.71, 1.50) | .857 | 0.83 | (0.68, 1.02) | .071 | 0.51 | (0.37, 0.69) | <.0001 | 0.58 | (0.42, 0.79) | <.001 |
| Do not trust at all | 0.8 | (0.60, 1.05) | .107 | 0.92 | (0.65, 1.32) | .664 | 0.82 | (0.65, 1.03) | .082 | 0.59 | (0.42, 0.84) | .003 | 0.71 | (0.50, 1.01) | .057 |
| Health authority | |||||||||||||||
| To a great extent (ref) | |||||||||||||||
| Somewhat | 0.84 | (0.71, 0.98) | .029 | 0.99 | (0.76, 1.31) | .993 | 0.72 | (0.63, 0.82) | <.0001 | 0.62 | (0.49, 0.78) | <.0001 | 0.63 | (0.49, 0.80) | <.001 |
| Do not trust | 0.64 | (0.50, 0.82) | <.001 | 0.79 | (0.53, 1.17) | .230 | 0.62 | (0.50, 0.78) | <.0001 | 0.39 | (0.28, 0.53) | <.0001 | 0.54 | (0.38, 0.77) | <.001 |
| Do not trust at all | 0.87 | (0.63, 1.20) | .391 | 0.62 | (0.42, 0.91) | .014 | 0.59 | (0.45, 0.77) | <.001 | 0.37 | (0.25, 0.53) | <.0001 | 0.50 | (0.34, 0.74) | <.001 |
Bold p-value< 0.05 was considered statistically significant
*OR Odds ratio, CI Confidence Interval
*Adjusted for: Socio-Demographic Index (SDI), age, educational attainment, perception of home income, living area, chronic disease, mental disease, employment status, living alone or with more persons, living with kids, the perception of the importance of the preventive measures, the perception of the authorities’ actions, and the perception of risk of being infected
In the model with trust in health authorities as the main predictor, female respondents were more likely to adhere to all protective behaviors, such as handwashing (OR = 2.23, 95% CI: 1.93–2.58) and self-quarantine (OR = 1.80, 95% CI: 1.45–2.23), when adjusted for all included variables. In contrast, individuals living in low- and middle-income countries and those with lower household incomes were less likely to practice protective measures, such as handwashing (OR = 0.75, 95% CI: 0.61–0.92; OR = 0.73, 95% CI: 0.59–0.91). However, the level of education, the presence of chronic or mental illness, employment status, the number of family members or the presence of children did not show a significant association with adherence behaviors. Regarding the political leaders trust as the main predictor model, responders who believed they had an infection and still exhibited symptoms were less likely to follow protective behaviors, such as handwashing (OR = 0.55, 95% CI: 0.36–0.84), wearing facemasks (OR = 0.28, 95% CI: 0.17–0.48), and avoiding gatherings (OR = 0.44, 95%CI: 0.25–0.78).
Finally, individuals who believed the recommendations were either too lenient or just right were more likely to adhere to all protective behaviors. In contrast, those who considered the measures unimportant were less likely to comply across all models. The factors associated with adherence to protective behaviors from the multivariable logistic model are presented in Additional file 1: Table S1–S2.
Discussion
This study examined the association between trust in actors who provided COVID-19 information, including local health authorities and political leaders, and adherence to COVID-19 preventive behaviors across a large, international sample. Overall, higher levels of trust were consistently associated with an increased likelihood of adherence to recommended preventive measures. This finding aligns is consistent with previous research conducted during the influenza A (H1N1) outbreaks in Hong Kong and Spain, which reported that trust in government-provided information was positively associated with compliance with hand-washing and social distancing measures [19, 20]. Although these studies focus specifically on information sources, the findings are broadly aligned with our results, suggesting that trust in information providers more generally plays an important role in shaping adherence to public health recommendations.
However, our findings highlight variations in behavioral responses across different contexts and demographic groups. For instance, females demonstrated a higher overall adherence than males, consistent with previous research indicating that women are more likely to engage in preventive behaviors, particularly in contexts of low trust [21]. Age-related differences were also observed. Younger individuals were less likely to practice social distancing, whereas mask-wearing did not significantly differ by age. These results are consistent with other studies that have shown that older adults are more likely to adhere to protective behaviors [21, 22].
Interestingly, this study did not find consistent associations between adherence and factors such as medical or psychological conditions, educational attainment, household composition, or employment status after adjusting for the level of trust in health authorities and political leaders. This contrasts with studies that reported that higher education or the presence of a chronic medical condition increased the likelihood of adopting preventive behaviors [23, 24].
In our study, the country’s SDI was strongly associated with adherence, suggesting that broader socio-economic conditions may be more influential than individual-level characteristics. Individuals in low- and middle-development status were generally less likely to follow protective measures. This may be influenced by broader cultural and political contexts [25, 26]. For instance, cultural orientations—such as power distance and uncertainty avoidance—mediate the effect of trust on behavioral intentions across countries, suggesting that structural and societal norms play a critical role in shaping public health responses [25]. This could also influence the different strategies adopted and endorsed across countries regarding preventive behavior [27].
Our study further revealed that trust in local health authorities had a stronger association with adherence to protective behaviors than trust in political leaders. While previous studies identified healthcare professionals and government communications as the most trusted sources of information [23, 28], our findings suggest that trust in health-specific institutions may be a more robust driver of behavioral compliance than general political trust. This distinction is critical for designing effective public health messaging and interventions.
These findings have important implications for public health policy. They underscore the need to ensure consistent messaging across sources and to adapt communication strategies for populations in diverse global contexts to support effective public health responses. In particular, involving local healthcare professionals can improve adherence to recommended measures. The lack of consistent associations between adherence and some individual-level characteristics after adjustment for trust suggests that interventions focusing solely on individual attributes may be insufficient. Instead, the strong association observed with country SDI highlights the importance of broader structural determinants in shaping adherence such as addressing contextual barriers that may limit the feasibility of preventive behaviors.
This study has several limitations. First, we conceptualize the association as primarily direct at the level of statistical modelling; however, we acknowledge that underlying behavioral pathways are likely complex and may involve unmeasured mediating mechanisms [10]. Furthermore, given the cross-sectional design, the observed association between trust levels and behavioral adherence should not be interpreted as causal. Second, response bias may be present, as participants who chose to respond could differ systematically from non-respondents [29]. Additionally, the sample mainly consisted of individuals from high-SDI countries with high education levels. These may limit its generalizability. Third, given the global scope and timeframe, we were unable to fully account for context-specific events or temporal dynamics that may have influenced trust in political and healthcare representatives or the adoption of preventive behaviors. Although we adjusted for a range of sociodemographic and perceptual variables, residual confounding due to unmeasured contextual and time-varying factors cannot be excluded.
Conclusions
Trust has an important role in fostering adherence to recommended health measures. The findings of our study reveal a positive association between trust in health and political authorities and adherence to preventive health behaviors, independent of demographics, socioeconomic factors, or perceptual factors. These results underscore the importance of promoting trust in public institutions among the general population, particularly in preparedness for future public health crises. While further research is needed, these findings have practical implications for improving the delivery of health measures before and during pandemics. Strategies should be tailored to each population's specific needs and characteristics. Key priorities include strengthening community-based communication efforts and actively involving the public in health decision-making and policy development.
Supplementary Information
Acknowledgements
The authors wish to express their gratitude to the iCARE study team for granting access to the iCARE data. They would particularly like to acknowledge the invaluable contributions of the iCARE participants. Additionally, the authors would like to thank the lead investigators and all collaborators involved in the iCARE study. Lead investigators: Kim L. Lavoie, PhD, University of Quebec at Montreal (UQAM) and CIUSSS-NIM, CANADA; Simon L. Bacon, PhD, Concordia University and CIUSSS-NIM, CANADA. Collaborators (in alphabetical order): ARGENTINA: Nora Granana, PhD, Hospital Durand; AUSTRALIA: Jacqueline Boyle, PhD, Monash University; Margie Danchin, PhD, Melbourne Medical School; Joanne Enticott, PhD, Monash University; Jessica Kaufman, PhD, Murdoch Children's Research Institute; AUSTRIA: Alexandra Kautzky-Willer, MD, Medizinische Universität Wien; BRAZIL: Eduardo Caputo, PhD, Universida de Federal de Pelotas; CANADA: Mohamad Baydoun, PhD, University of Regina; Andrea Gershon, PhD, Sunnybrook Research Institute; Ariane Bélanger-Gravel, PhD, Université Laval; Tavis Campbell, PhD, University of Calgary; Linda Carlson, PhD, University of Calgary; Kim Corace, PhD, University of Ottawa; Rubee Dev, PhD, University of British Colombia; Olivier Drouin, MD, CHU Sainte-Justine/Université de Montréal; Gary Garber, PhD, University of Ottawa/Public Health Ontario; Samir Gupta, MD, University of Toronto; Catherine Herba PhD, UQAM; Jack Jedwab, PhD, Canadian Institute for Identities and Migration and the Association for Canadian Studies; Joanne Liu, PhD, McGill University; Sandra Pelaez, PhD, Université de Montréal; Paul Poirier, MD, Université Laval; Justin Presseau, PhD, University of Ottawa; Eli Puterman, PhD, University of British Columbia; Joshua Rash, PhD, Memorial University; Johanne Saint-Charles, PhD, UQAM; Jovana Stojanovic, PhD, Concordia University; Michael Spivock, PhD, Shared Services Canada; Geneviève Szczepanik, PhD, MBMC; Michael Vallis, PhD, Dalhousie University; Vincent Gosselin Boucher, PhD, The University of British Columbia, Faculty of Eduction, School of Kinesiology; Claudia Trudel-Fitzgerald, PhD, Department of Psychology, Université du Québec à Trois-Rivières; Tamara Cohen, PhD, University of British Colombia; Alysha Deslippe, PhD, University of British Colombia; Nazeem Muhajarine, PhD, University of Saskatchewan; Rachel Burns, PhD, Carleton University; Tristan Renaud, BSc, Carleton University; Sean Locke, PhD, Brock University. COLOMBIA: Mariantonia Lemos-Hoyos, PhD, Universidad EAFIT; CYPRUS: Angelos Kassianos, PhD, University of Cyprus; Mauricio Hincapié Montoya, PhD/MD, Universidad EAFIT; Biviana Marcela Suárez Sierra, PhD, Universidad EAFIT. FRANCE: Gregory Ninot, PhD, Université de Montpellier; Mathieu Beraneck, PhD, Université Paris Cité, CNRS; GERMANY: Beate Ditzen, PhD, Heidelberg University; GREECE: Theodora Skoura, PhD, Aretaieio Hospital Athens University; INDIA: Delfin Lovelina Francis MDS (PhD) Saveetha Dental College and Hospitals, SIMATS. HUNGARY: Beatrix Oroszi, PhD, Semmelweis University; Annamária Ferenczi, MSc, Semmelweis University. IRELAND: Hannah Durand, PhD, University of Stirling; Oonagh Meade, PhD, University of Galway; Gerry Molloy, PhD, University of Galway; Chris Noone, PhD, University of Galway; ITALY: Stefania Paduano, PhD, University of Modena and Reggio Emilia; Valeria Raparelli, MD PhD, University of Ferrara; KENYA: Hildah Oburu, PhD, University of Nairobi. NEPAL: Niroj Bhandari, MBBS, Institute for Implementation Science and Health. SAUDI ARABIA: Abu Zeeshan Bari, PhD, Taibah University; SLOVAKIA: Iveta Nagyova, PhD, PJ Safarik University—UPJS; SWITZERLAND: Susanne Fischer, PhD, University of Zurich; TURKEY: Ceprail Şimşek, MD Health Science University; UK: Keven Joyal-Desmarais, PhD, University of Leeds; Joanne Hart, PhD, Manchester University; Lucie Byrne-Davis, PhD, University of Manchester; Nicola Paine, PhD, Loughborough University; Susan Michie, PhD, University College London; USA: Michele Okun, PhD, University of Colorado; Sherri Sheinfeld Gorin, PhD, University of Michigan; Johannes Thrul, PhD, John Hopkins University; Abebaw Yohannes, PhD, Azusa Pacific University. Students (in alphabetical order): AUSTRALIA: Shrinkhala Dawadi, MSc, Monash University; Kushan Ranakombu, PhD, Monash University; BRAZIL: Daisuke Hayashi Neto, Msc, Unicamp; CANADA: Frédérique Deslauriers, BA, UQAM and CIUSSS-NIM; Amandine Gagnon-Hébert, BA, UQAM and CIUSSS-NIM; Mahrukh Jamil, BA, Concordia University and CIUSSS-NIM; Camille Léger, BSc, UQAM and CIUSSS-NIM; Callum MacLeay, BA, UQAM and CIUSSS-NIM; Ariany Marques Vieira, MSc, Concordia University and CIUSSS-NIM; Sarah O’Connor, BA, Université Laval; Zackary van Allen, PhD, University of Ottawa; COLOMBIA: Susana Torres, MSc, Universidad EAFIT. Community Participants: CANADA: Sophie Duval, MSc; Johanne O’Malley; Katherine Séguin, BA; Kyle Warkentin; INDIA: Sarah Tanishka Nethan.
Abbreviations
- CI
Confidence interval
- COVID-19
Coronavirus disease 2019
- iCARE
International COVID-19 Awareness and Responses Evaluation
- OR
Odds ratio
- SDI
Socio-Demographic Index
Authors’ contributions
All authors contributed to the preparation of this manuscript (MBC, MD, RD) MBC and RB developed the research question and study design. MBC participated in drafting and revising the manuscript. MD provided detailed comments. RB provided substantial revisions and intellectual content to the manuscript. MD analyzed the data, and MB checked its integrity and accuracy. All authors (MBC, MD, RD) read and approved the final version of the manuscript.
Funding
iCARE is supported by the Canadian Institutes of Health Research (CIHR: MM1-174903; MS3-173099; SMC-151518), the Canada Research Chairs Program (950–232522, Chair holder: Dr. Kim L. Lavoie), the Fonds de recherche du Québec—santé (FRQ-S: 251618 and 34757), the Fonds de recherche du Québec – Société et culture (FRQSC: 2019-SE1-252541), and the Ministère de l'Économie et de l’Innovation du Québec (2020–2022-COVID-19-PSOv2a-51754). Study sponsors had no role in the design of the database and data collection. This work was funded by the iCARE Summer Student Scholarship 2024.
Data availability
The datasets are available from the iCARE team upon reasonable request.
Declarations
Ethics approval and consent to participate
The iCARE study received ethical approval from the Research Ethics Committee of the Comité d’éthique de recherche du CIUSSS-NIM (Centre Intégré Universitaire de Santé et de Services Sociaux du Nord-de-l’île-de-Montréal; approval number: 2020–2099/25–03-2020). All participants in the original study provided informed consent to participate prior to data collection. The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and applicable institutional and national research ethics guidelines. The present study uses de-identified data from the iCARE study for secondary analysis. As no identifiable information was used, and no direct contact with participants occurred, additional informed consent for this secondary analysis was not required. No participants under the age of 16 were included in the study; hence, consent to participate was not required.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
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 datasets are available from the iCARE team upon reasonable request.