Edited by: Michel Goldman, Université Libre de Bruxelles, Belgium
Reviewed by: Niharika Khanna, University of Maryland, United States
Titilola D. Kalejaiye, Duke University, United States
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Public trust in health experts has been decreasing leading to decreased adherence to expert recommendations.
To evaluate public perceptions of conflict and uncertainty among experts in healthcare recommendations and association with decreased trust in health entities for accurate health information.
Analysis of the US nationally representative Health Information National Trends Survey (HINTS 6–2022). Adults aged 18 and older were respondents to the survey (unweighted
There was high trust in doctors for health information (95%) versus 84% in scientists and 70% in government health agencies. Only 18% have high trust in the health information on social media. Respondents who felt expert recommendations change often were less likely to have high trust (65%) in government agencies compared to those who felt that the recommendations did not often change (82%) (
The public has low trust in government health agencies and perceptions of conflict among experts over recommendations is likely playing a role in the erosion of trust in health experts.
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Patient trust is critical to the provision of health care (
Trust in government health agencies like the Food and Drug Administration (FDA) and the Centers for Disease Control and Prevention (CDC) in the US has been falling in some patient groups (
One factor that may be playing a role in the erosion of trust in medical experts, and in particular, government health agencies is the changing nature of healthcare treatment and screening recommendations (
On the other hand, the uncertainty associated with some of the advice from experts can be contrasted with a variety of individuals and groups spreading “pseudoscience” on social media who speak with a high degree of certainty (
What is unclear is whether the public perception of conflict and uncertainty among experts in healthcare recommendations is associated with decreased trust in health entities as providers of accurate health information. This study is an examination of the relationship between perceived conflict and uncertainty in expert health recommendations and trust in different sources of health information in a nationally representative sample of US adults.
We conducted an analysis of the National Cancer Institute’s Health Information National Trends Survey (HINTS) data, a nationwide and population-based survey [Survey Instruments | HINTS (
Uncertainty among experts on health recommendations was measured with two different questions. One question focused on experts changing health recommendations and the other focused on conflicting health recommendations. The study assessed changing health recommendation by the question “How often do health recommendations from experts seem to change over time?” Experts providing conflicting health recommendations was measured by the question “How often do health recommendations from experts seem to conflict or contradict one another?” The responses for both questions were categorized into two categories of high or often (“often,” “very often”) and low or not often (“never,” “rarely”).
The study measured trust in medical experts from the question “In general, how much would you trust information about cancer from a doctor, government health agency, or scientists?” For each of the options, the respondents answered their amount of trust with each entity as “Not at all,” “a little,” “some” or “a lot.” For this analysis we recoded the responses to be high trust as “some” or “a lot” and low trust as “not at all” or “a little.” Attitude toward social media was measure by the question, “How much of the health information that you see on social media do you think is false or misleading?” Among those who reported using social media, the responses for that question were “None,” “a little,” “some” or “a lot.” We recoded the responses of “none” or “a little” high trust in the health information on social media while we considered the responses of “some” or “a lot” as low trust in social media information.
We conducted data analysis using the “survey” package in R studio, which accounts for the complex survey design including stratification, clustering, and weighting. Sampling weights based on the HINTS complex sample design were used in the statistical analysis. The survey package functions of “svytable” and “svychisq” were utilized to derive nationally representative estimates of non-institutionalized US population, and chi-squared tests to assess the association between trust in medical experts and uncertainty in health recommendations. We also examined the relationship between having trust in social media for health information and trust in government health entities. The statistical significance of the tests was defined as a
In an effort to assess the independent relationship between uncertainty and trust in the health care information from government health agencies we conducted a series of logistic regressions. The regressions included age, sex, race, total annual income, education and trust in health care information on social media along with the variables measuring uncertainty among experts (health recommendations from experts seem to change over time, health recommendations from experts conflict). Because of shared variance between the two perceived uncertainty questions we conducted three regressions. The first included both measures of perceived uncertainty, while the second only included perceived conflict among experts and the third only included the variable of perception of changes in recommendations.
The analysis was based on an unweighted sample of 5,842 representing 241,082,764 individuals. The population characteristics of adults aged 18 years or older are detailed in
Population estimates for demographic characteristics of adults aged ≥ 18 years, 2022 (unweighted
| Factors | Population proportion (%) |
|---|---|
| Unweighted sample size | 5,842 |
| Weighted population size | 241,082,764 |
|
|
|
| Male | 49.24 |
| Female | 50.76 |
|
|
|
| 18–39 years | 32.65 |
| 40–59 years | 36.53 |
| 60–79 years | 25.87 |
| 70 and above | 4.95 |
|
|
|
| Non-Hispanic White | 59.24 |
| Non-Hispanic Black | 10.64 |
| Hispanic | 16.26 |
| Other | 13.86 |
|
|
|
| Less than high school | 7.46 |
| High school and above | 92.54 |
|
|
|
| Less than $20,000 | 14.34 |
| $20,000–$90,999 | 31.41 |
| $100,000 and above | 54.25 |
Relationship between perceived uncertainty in experts health recommendations in different types of health information sources.
| Trust in information source | Recommendations change-Often (%) | Recommendations change-Not often (%) |
|
|---|---|---|---|
| Doctors | 94.87 | 94.22 | 0.64 |
| Scientists | 83.58 | 86.48 | 0.04 |
| Government | 67.56 | 75.95 | 0.01 |
Relationship between perceived uncertainty in trust in different types of health information sources.
| Trust in information source | Conflicting recommendations-Often (%) | Conflicting recommendations-Not often (%) |
|
|---|---|---|---|
| Doctors | 99.44 | 94.69 | 0.97 |
| Scientists | 82.65 | 86.9 | 0.02 |
| Government | 61.44 | 82.02 | 0.01 |
On the other hand, as shown in
Relationship between perceived uncertainty of social media in different types of health information sources.
| Trust in information source | Misleading social media-Often (%) | Misleading social media-Not often (%) |
|
|---|---|---|---|
| Doctors | 95.27 | 92.27 | 0.02 |
| Scientists | 85.46 | 82.96 | 0.07 |
| Government | 69.14 | 73.69 | 0.11 |
Logistic regression results of the association with high trust in government health agencies as a source of health information.
| Odds ratios (95% CI) | |||
|---|---|---|---|
| Model 1 | Model 2* | Model 3^ | |
| Perception that changes in expert recommendations are not often | 0.95 (0.62, 1.46) | – | 1.55 (1.08, 2.24) |
| Perception of conflict among experts in recommendations is not often | 2.86 (1.96, 4.15) | 2.81 (1.98, 3.98) | – |
Controls for age, sex, race, education, income, and trust in social media for health information.
Model 1—Includes both perception of changes in health care recommendation and perception of conflict in health care recommendations.
*Model 2—Includes only perception of conflict in health care recommendation.
^Model 3—Includes only perception of changes in health care recommendations.
This study investigated the relationship between the public perception of conflict and uncertainty among experts around health recommendations and trust in various sources of health information among a nationally representative sample of US adults. The findings underscore the high level of stable trust in doctors as sources of health information and the importance of consistency and clarity in health messaging, especially from authoritative sources such as government health agencies and scientists.
The high level of trust in doctors as sources of health information, compared to lower trust levels in scientists and government health agencies, suggests that personal relationships and direct interactions with healthcare providers play a crucial role in establishing trust. More importantly, this trust is not impacted by the perceived uncertainty. This is significant given the plethora of misinformation readily available through social media platforms, suggesting that trust in doctors may minimize the impact of personal narratives without a sound scientific basis (
The significant relationship between perceived uncertainty among health experts on their recommendations and reduced trust in scientists and in particular, government health agencies is noteworthy. As might be expected, if the public perceives that the experts do not seem to be confident in their knowledge, trust in experts is correspondingly reduced. Conflicting advice from experts can also create confusion and skepticism about the motivations behind health recommendations.
The implications of these findings are significant for public health communication strategies. To maintain and enhance public trust, it is crucial for health scientists and government health agencies to communicate health recommendations in a clear, consistent, and transparent manner. Efforts should be made to explain the rationale behind changes in health guidance and to address any perceived conflicts in recommendations directly. Given the intensity of partisan political divides that characterize modern American discourse and the movement away from “traditional” sources of information for a variety subject matters, it is unsurprising that trust in governmental health agencies ranked the lowest in our analysis (
Important differences and similarities between doctors, scientists, institutions, and social media should be noted. Doctors trust scientists to provide evidence-based conclusions that help inform their clinical practice. Government institutions also rely on scientists for their expertise in data analysis to develop guidelines and recommendations that will help to inform clinical practice. Among doctors, scientists, and institutions there are systems and policies in place to ensure information is accurate, such as peer review. The speed at which information can travel through social media makes it difficult to ensure that the information is valid through the same systems and policies relied upon by doctors, scientists, and institutions.
There are several limitations to our study. First, the question about trust in sources for accurate health information was asked in the context of information about cancer. Although there may be some specific attitudes related to cancer information that may not be reflected if the question were focused on another disease like diabetes it is important that the respondents framed their responses around a specific disease. This allowed them to think about information about a disease rather than a more general assessment of “health information” which would likely be too broad. Moreover, attitudes toward preventive services like vaccinations, which became particularly controversial during COVID-19 may be different. Second, the HINTS 6–2022 survey used two modes of data collection offering respondents a paper survey or an Internet option. This may have affected responses. Third, the cross-sectional design inhibits causal inference. Fourth, although decreased trust has been found in multiple countries including both Europe and Asia, our findings regarding government health care entities and their recommendations may not be generalizable outside of the US. Fifth, it is possible that some people may have higher or lower trust in the health care information based on the specific age/sex of the experts. Unfortunately, the HINTS survey did not include any questions that would indicate the age or sex of the experts.
In conclusion, this study highlights the critical role of perceived uncertainty among experts making recommendations and conflicting information in shaping trust in health information sources. Addressing this issue through consistent, clear, and transparent communication and emphasizing physician-patient relationship is essential for maintaining public trust in health experts and ensuring the effective dissemination of health recommendations.
Publicly available datasets were analyzed in this study. This data can be found at: Health Information National Trends Survey
AM: Conceptualization, Methodology, Supervision, Writing – original draft, Writing – review & editing. PS: Formal analysis, Methodology, Writing – review & editing. LY: Data curation, Formal analysis, Methodology, Writing – review & editing. TW: Conceptualization, Writing – review & editing. BJ: Conceptualization, Writing – review & editing. GH: Writing – review & editing.
The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.
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