
==== Front
PEC Innov
PEC Innov
PEC Innovation
2772-6282
Elsevier

S2772-6282(24)00081-5
10.1016/j.pecinn.2024.100333
100333
Articles from Special issue on Unraveling the web: Insights and strategies for addressing health misinformation; Edited by Skyler Johnson and Carma L. Bylund
Accurate and inaccurate beliefs about cancer risk factors among Spanish-preferring adults in the United States
King Andy J. andy.king@utah.edu
ab⁎
Lyons Benjamin A. a
Rivera Yonaira M. cd
Kogan Marina e
Hernandez Leandra H. a
Liao Yi a
Kaphingst Kimberly A. ab
a Department of Communication, University of Utah, Salt Lake City, UT, United States of America
b Cancer Control & Population Sciences, Huntsman Cancer Institute, Salt Lake City, UT, United States of America
c Department of Communication, Rutgers University, New Brunswick, NJ, United States of America
d Cancer Prevention & Control Program, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, United States of America
e Kahlert School of Computing, University of Utah, Salt Lake City, UT, United States of America
⁎ Corresponding author at: Huntsman Cancer Institute, University of Utah, 2000 Circle of Hope Drive, Salt Lake City, UT 84112, United States of America. andy.king@utah.edu
19 8 2024
15 12 2024
19 8 2024
5 10033329 2 2024
1 8 2024
18 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Objective

To characterize inaccurate and accurate beliefs about cancer risk factors held among Spanish-preferring adults in the United States.

Methods

From a national probability panel, we surveyed 196 Hispanic adults who prefer completing questionnaires in Spanish. We also used data from a representative sample of 1200 adults in the US to compare belief acceptance.

Results

Many less accepted accurate beliefs about cancer risk factors related to topics like fruit/vegetable consumption, weight loss, and alcohol use. Several inaccurate beliefs were widely held, with some being more accepted in the Spanish-preferring sample than the general US adult sample. Higher levels of self-reported media literacy and information scanning associated with more acceptance of both accurate and inaccurate beliefs. Access to the internet at home associated with discernment between accurate and inaccurate beliefs about cancer risk factors.

Conclusion

Acceptance of accurate beliefs and rejection of inaccurate beliefs varied across potential cancer risk factors. Future Spanish-language public health messaging should address these belief inconsistencies when providing up-to-date cancer-related recommendations or correcting inaccurate information in the public communication environment.

Innovation

Our study provides comprehensive information about cancer beliefs among Spanish-preferring adults in the United States, which was not previously available, and find that media literacy is a concept likely to be important to consider when putting together intervention tools to combat misinformation.

Highlights

• Many less accepted accurate beliefs about cancer risk factors related to topics like fruit/vegetable consumption, weight loss, and alcohol use.

• Several inaccurate beliefs were widely held, with some being more accepted in the Spanish-speaking sample than the general US adult sample.

• Higher levels of self-reported media literacy and information scanning associated with more acceptance of both accurate and inaccurate beliefs.

• Access to the internet at home associated with discernment between accurate and inaccurate beliefs about cancer risk factors.

• Our study provides novel information about cancer risk factor beliefs that may be more subject to misinformation, confusion, and uncertainty.

Keywords

Cancer risk
Spanish
Health misinformation
Cancer knowledge
Cancer misperceptions
==== Body
pmc1 Background

Concerns about inaccurate health information influencing health decision making have existed for years [1]. (Southwell et al., 2019). Health misinformation, as an area of study, has grown considerably as a subfield of health communication as a result of the influx of misinformation, and misinformation-related research, during the COVID-19 pandemic [2,3]. This influx of health misinformation research often focuses on politically charged health topics (e.g., COVID-19, vaccines), with fewer studies examining misinformation related to chronic conditions like diabetes, pain management, and cancer [[3], [4], [5]], though studies on topics like complementary and alternative medicine information quality have been common over the past few decades [6,7].

Inaccurate cancer information exists in the public communication environment for multiple reasons. There are longstanding myths and misconceptions about cancer in public discourse that are widely known by cancer advocates [8]. Many people also hold misperceptions about cancer risks and screening rates [9], likely due to confusion and uncertainty about interpreting epidemiological estimates. Further, researchers found connections between endorsement of inaccurate beliefs about cancer risk factors and other conspiratorial thinking [10], as well as harmful effects of misinformation generally [11]. Accurate beliefs about cancer risk factors are linked to objective cancer knowledge [12], and cancer knowledge is linked to positive behavioral outcomes [13]. Other recent research indicated that pre-existing cancer misperceptions were highly correlated with acceptance of other inaccurate cancer misinformation [14]. As such, misperceptions about cancer could complicate health decision making and contribute to the reinforcement or proliferation of health disparities [15].

Related to cancer misperceptions and misinformation, researchers have noted that health misinformation research has almost entirely focused on English language information, even though over 40 million people speak Spanish at home in the United States and Spanish-language health misinformation can be easily identified [16]. Decades ago, researchers found that cancer misconceptions were more common among Latino (compared to white) respondents [17], and other studies on other cancer-related topics found similar results [18,19]. While these studies offer insights into beliefs Latino/a/x and Hispanic individuals hold, most focus on respondents located in one region (e.g., Southern California) [18] or from a single country (e.g., El Salvador) [19]. No studies have looked at a national sample of Spanish-preferring adults about their cancer misperceptions, an important endeavor given the link between cancer misperceptions and endorsement of inaccurate cancer information.

The current study surveyed Spanish-preferring adults in the United States to characterize accurate and inaccurate beliefs about cancer risk factors. Identifying accurate information that is not believed, inaccurate information that is believed, and factors that associate with discernment of accurate and inaccurate information provides insights to future researchers interested in developing interventions to improve the discernment of accurate, useful information about cancer. Three research questions underlie our current study: (1) What inaccurate and accurate beliefs about cancer risk factors are accepted by Spanish-preferring adults in the United States? (2) How does the rate of acceptance of accurate/inaccurate beliefs differ in the Spanish-preferring population compared to a general population sample of adults in the United States? (3) What demographic, social determinants of health, health and media literacy, and communication factors associate with acceptance of accurate and inaccurate beliefs about cancer risk factors?

2 Method

For the current study, we use two different datasets. NORC/AmeriSpeak, specifically their Latino Panel, invited Spanish-preferring adult panelists from their large probability-based panel in the United States, who indicated a preference to take surveys in Spanish, to participate in the study. Our team provided the survey questionnaire to NORC/AmeriSpeak in English and they professionally translated the survey. Data collection occurred online, started the last week of July 2023, and ended the second week of September 2023. NORC/AmeriSpeak processed and checked collected data for quality (e.g., removing speeders and high refusal-rate respondents; n = 8) and provided us with a final data set, with population weights, for analysis (N = 196). Our second dataset comes from a survey (N = 1200) conducted by YouGov in November 2023. Respondents were selected by YouGov's matching and weighting algorithm to approximate the demographic and political attributes of the United States population. The institutional review board of the first author approved all study procedures.

2.1 Participants & procedures

NORC/AmeriSpeak panelists interested in the study were provided information about the research and their rights as participants. Participants then completed the survey. Median duration for survey completion was 21 min and participants were offered the cash equivalent of $2 for completion. The data presented in this paper represent a subset of data from the entire survey. YouGov participants similarly consented and provided responses to the cancer belief questions reported below, as well as several other questions prior to participating in an experiment [reference blinded for review]. All information about the two studies, as well as the relevant data sets, are available from the first author upon reasonable request.

2.2 Measures

We measured a variety of standard demographic items (age, sex, education, and cancer history) in the main study (NORC/AmeriSpeak) data set. We also measured variables related to social determinants of health (having health insurance, access to the internet at home, ability to speak English), literacy (health and media), communication (cancer information seeking and scanning), and cancer risk factor beliefs (accurate and inaccurate). For the YouGov data, we provide only weighted estimates of one variable (beliefs about cancer risk factors) and these weighted estimates represent data that are representative of the United States population as a whole. Further demographics information about the YouGov data are available upon request.

2.2.1 Social determinants of health

Single items assessed health insurance (“Do you currently have health insurance?”), access to the internet at home (“Do you have home internet access?), and ability to speak English (“How would you self-rate your English?”). Insurance and internet were coded as yes or no. Self-rated English scores ranged from low (1) to advanced (4).

2.2.2 Literacy

For health literacy, we used a three-item scale validated in past research [20]. Health literacy is coded where higher scores indicate lower health literacy levels. For media literacy, we used the validated 11-item critical consumption subscale of the new media literacy scale [21], where higher scores indicate higher media literacy levels. Examples of items included in the scale are, “I can distinguish different functions of media (communication, entertainment, etc.),” “I can compare news and information across different media environments,” “I can assess media in terms of credibility, reliability, objectivity, and currency,” and “It is easy for me to make decisions about the accuracy of media messages.”

2.2.3 Communication

We assessed two self-reported health communication behaviors—cancer information seeking and cancer information scanning—adapting measures from previous research [22]. The seeking item asked participants, “Thinking about the last 12 months, did you actively look for information about cancer from doctors, from other people, from online sources, or from the media?” The scanning item asked, “Thinking about the last 12 months, did you hear about or come across information about cancer from doctors, other people, online sources, or the media when you were NOT actively looking for it?” Participants could respond yes, no, or I don't recall, with I don't recall responses categorized as no for analysis.

2.2.4 Beliefs about cancer risk factors

We used two validated measurement tools to assess accurate beliefs [12] and inaccurate beliefs [23] about cancer risk factors, as they had been adapted in recent research [10]. The accurate beliefs scale featured 11 statements and the inaccurate beliefs scale featured 16 statements (12 from [10] and four added related to vaccines and 5G). Accurate and inaccurate statements were randomly presented to participants, who rated their agreement with each statement on a 1 (strongly disagree) to 5 (strongly agree), in response to the prompt, “How much do you agree that each of these can increase a person's chance of developing cancer?”

Because RQ1 focuses on proportions holding a given belief, for these results responses to the belief items were dichotomized as being either accepted or unaccepted by participants to allow ease of interpretation. If participants agreed or strongly agreed, they were coded accepting that particular cancer belief. For example, if a person marked agree to the statement “Being over 70 years old,” they were classified as accepting that accurate cancer belief. If a person marked agree to the statement “Using cleaning products,” they were classified as accepting that inaccurate cancer belief. For the YouGov comparison data, we only asked about a subset of these beliefs. For RQ2, we used tests of the equality of proportions to determine if there were statistically significant differences in the proportions of beliefs reported across the samples.

Because RQ3 focuses on explaining variation in these beliefs, we use the full five-point scale to prevent information loss in those analyses. We also calculated overall discernment of accurate versus inaccurate beliefs at the respondent level to determine if certain measured factors were associated with the ability to distinguish between accurate and inaccurate cancer risk factor beliefs. Discernment is important to consider in case people just believe everything (or nothing) causes cancer, for example, or if they are generally uncertain about what causes cancer. To answer RQ3, we ran a series of three, weighted ordinary least squares (OLS) regression models with different outcomes: (1) acceptance of inaccurate beliefs about cancer risk factors, (2) acceptance of accurate beliefs about cancer risk factors, and (3) discernment of accurate and inaccurate beliefs, using the predictors mentioned above. All RQ3 models (see Table 5) include survey weights. Each individual rating of a belief item is an observation. Thus, for example, with 196 people we have 3136 observations across the 16 inaccurate perceptions. Because each individual provided multiple ratings, we cluster the standard errors by respondent. Because each belief item that is rated might differ in baseline plausibility, we include fixed effects for each item. This analytic approach is superior to running many separate models or averaging across many different items [[24], [25], [26]]. Discernment is computed as the difference score of accurate – inaccurate beliefs and is modelled at the respondent level.

3 Results

The unweighted sample (N = 196) of the NORC/AmeriSpeak data included more women (n = 130, 66%) than men (n = 66, 34%), with an average age of 47 (SD = 13.4). Most participants had health insurance and internet access and did not have a history of cancer. Most also indicated some English language ability. Table 1 provides full information about the NORC/AmeriSpeak Latino Panel sample.Table 1 Descriptive NORC/AmeriSpeak participant information (unweighted).

Table 1Variable	n	%	M	SD	Min	Max	
Age			46.98	13.40	18	79	
Sex			0.66	0.47	0	1	
 Female	130	66					
 Male	66	34					
Education			2.63	1.21	1	5	
 Less than high school	39	20					
 High school graduate	54	28					
 Some college	63	32					
 College degree	20	10					
 Post-graduate	20	10					
Cancer History			0.09	0.29	0	1	
 Yes	18	9					
 No	178	91					
Health Insurance			0.71	0.45	0	1	
 Yes	139	71					
 No	56	29					
Internet Access			0.88	0.33	0	1	
 Yes	172	88					
 No	24	12					
English Language Ability			2.48	0.89	1	4	
Health Literacy			2.05	0.75	1	4	
Media Literacy			3.47	0.64	1	5	
Seeking			0.35	0.48	0	1	
 Yes	69	35					
 No	127	65					
Scanning			0.39	0.49	0	1	
 Yes	77	39					
 No	119	61					
NOTE: N = 196, except for Health Insurance (n = 195). Cronbach's alpha = 0.92 for the media literacy scale and 0.59 for the health literacy scale. Health literacy is coded so that higher scores = lower health literacy. All participants identified as Hispanic.

The unweighted sample (N = 1200) of the YouGov data included slightly more women (n = 638, 53%) than men (n = 543, 45%) or people identifying as non-binary/other (n = 19, 2%). Most participants identified as white (n = 853, 71%) with 9% (n = 106) identifying as Hispanic. About half of the sample had some college or less formal education (n = 576, 48%), with an average age of 52 (SD = 15.9) and few had a personal history of cancer (n = 141, 12%). RQ1 and RQ3 use data from the NORC/AmeriSpeak sample only, while RQ2 uses data from both sources.

3.1 RQ1: What inaccurate and accurate beliefs about cancer risk factors are accepted by Spanish-preferring adults in the United States?

To answer RQ1, we examined the proportion of accurate and inaccurate belief acceptance of the weighted sample. Fig. 1 visualizes the percentage of respondents who accepted various accurate and inaccurate beliefs about cancer risk factors. Related, we provide proportions of participants accepting inaccurate beliefs (Table 2) and accurate beliefs (Table 3) about cancer risk factors.Fig. 1 Proportion of participants' acceptance of accurate and inaccurate* cancer risk factor beliefs.

Fig. 1

Table 2 Inaccurate cancer risk factor belief acceptance.

Table 2Inaccurate Cancer Risk Factor Belief	Proportion Accepting Belief	
Eating food containing additives	0.597	
 Comer alimentos con aditivos	(0.035)	
Eating genetically modified food	0.536	
 Comer alimentos modificados genéticamente	(0.036)	
Eating food containing artificial sweeteners	0.526	
 Comer alimentos con azúcares artificiales	(0.036)	
Feeling stressed	0.413	
 Sentirse estresado/a	(0.035)	
Using aerosol containers	0.383	
 Utilizar aerosoles	(0.035)	
Using cleaning products	0.372	
 Utilizar productos de limpieza	(0.035)	
Exposure to non-ionizing electromagnetic frequencies (WiFi, TV, radio)	0.372	
 Exposición a frecuencias electromagnéticas no ionizantes (WiFi, TV, radio)	(0.035)	
Using microwave ovens	0.357	
 Utilizar horno microondas	(0.034)	
Living near power lines	0.327	
 Vivir cerca de líneas de alta tensión (electricidad)	(0.034)	
Exposure to 5G signals	0.321	
 Exposición a señales 5G	(0.033)	
Living near 5G towers	0.306	
 Vivir cerca de torres 5G	(0.033)	
Using mobile phones	0.281	
 Utilizar teléfonos celulares	(0.032)	
Drinking from plastic bottles	0.235	
 Beber de botellas de plástico	(0.030)	
Physical trauma, for example a punch or squeeze	0.219	
 Trauma físico, por ejemplo, un golpe o apretón	(0.03)	
Getting a COVID-19 vaccine	0.112	
 Vacunarse contra el COVID-19	(0.023)	
Getting vaccines every year for seasonal flu	0.092	
 Vacunarse anualmente contra la gripe	(0.021)	
NOTE: Beliefs listed in order from most to least accepted. Standard errors (SEs) are in parentheses in the Proportion Accepting Belief column. The translated belief statements, as presented to participants, are italicized under the corresponding belief statement in English. These proportions and SEs are weighted.

Table 3 Accurate cancer risk factor belief acceptance.

Table 3Accurate Cancer Risk Factor Belief	Proportion Accepting Belief	
Smoking any cigarettes at all	0.724	
 Fumar cualquier tipo de cigarrillos	(0.032)	
Exposure to another person's cigarette smoke	0.699	
 Exponerse al humo del cigarrillo de otra persona	(0.033)	
Having a close relative with cancer	0.556	
 Tener un familiar cercano con cáncer	(0.036)	
Being overweight or obese (BMI over 25)	0.480	
 Tener sobrepeso y obesidad (IMC superior a 25)	(0.036)	
Infection with HPV (human papillomavirus)	0.474	
 Infección con VPH (virus del papiloma humano)	(0.036)	
Eating red or processed meat once a day or more	0.403	
 Comer carne roja o procesada una o más veces por día	(0.035)	
Getting sunburnt more than once as a child	0.367	
 Haber sufrido quemaduras de sol más de una vez cuando era niño/a	(0.035)	
Drinking more than 1 serving of alcohol a day	0.316	
 Tomar más de 1 porción de alcohol por día	(0.033)	
Doing less than 30 min of physical activity 5 times a week	0.265	
 Realizar menos de 30 minutos de actividad física 5 veces a la semana	(0.032)	
Being over 70 years old	0.224	
 Tener más de 70 años	(0.03)	
Eating less than 5 portions of fruit and vegetables a day	0.168	
 Comer menos de 5 porciones de frutas y verduras por día	(0.027)	
NOTE: Beliefs listed in order from most to least accepted. Standard errors are in parentheses in the Proportion Accepting Belief column. The translated belief statements, as presented to participants, are italicized under the corresponding belief statement in English. These proportions and SEs are weighted.

For accurate beliefs about cancer risk factors, the following five were least accepted by participants: low fruit and vegetable consumption (17%), being over 70 years old (22%), low physical activity (27%), overconsumption of alcohol (32%), and getting sunburnt as a child (37%). For inaccurate beliefs about cancer risk factors, the following were the most accepted by participants: eating food containing additives (60%), eating genetically modified food (54%), eating food containing artificial sweeteners (53%), feeling stressed (41%), and using aerosol containers (38%). Table 2, Table 3 list inaccurate and accurate beliefs, respectively, from most to least accepted.

3.2 RQ2: How does the rate of acceptance of accurate/inaccurate beliefs differ in the Spanish-preferring population compared to a general population sample of adults in the United States?

To answer RQ2, we calculated belief acceptance proportions in the YouGov general US population data (N = 1200) for seven accurate and seven inaccurate beliefs about potential cancer risk factors. We also calculated the proportions among just Hispanic-identifying respondents (n = 106) from the YouGov general US population data as another point of comparison. The seven accurate belief statements focused on alcohol use, excessive weight, sunburn, age, family health history, human papillomavirus, and physical activity. The seven inaccurate belief statements focused on non-ionizing electromagnetic frequencies, stress, cleaning products, genetically modified food, COVID-19 vaccines, microwave ovens, and drinking from plastic bottles. The comparative proportions and standard errors are reported in Table 4.Table 4 Comparison of cancer risk factor belief acceptance across samples.

Table 4	Spanish-preferring Sample	General US Population Sample	General US Population (Hispanic)	
	
Inaccurate Cancer Risk Factor Beliefs	
Exposure to non-ionizing electromagnetic frequencies (WiFi, TV, radio)	0.372
(0.035)	0.158***
(0.011)	0.274
(0.043)	
Feeling stressed	0.413
(0.035)	0.360
(0.014)	0.349
(0.046)	
Using cleaning products	0.372
(0.035)	0.296*
(0.013)	0.292
(0.044)	
Eating genetically modified food	0.536
(0.036)	0.158***
(0.011)	0.279***
(0.043)	
Getting a COVID-19 vaccine	0.112
(0.023)	0.122
(0.009)	0.100
(0.030)	
Using microwave ovens	0.357
(0.034)	0.141***
(0.01)	0.217*
(0.040)	
Drinking from plastic bottles	0.235
(0.030)	0.182
(0.011)	0.283
(0.043)	


	
Accurate Cancer Risk Factor Beliefs	
Drinking more than 1 serving of alcohol a day	0.316
(0.033)	0.323
(0.013)	0.406
(0.048)	
Being overweight or obese (BMI over 25)	0.480
(0.036)	0.478
(0.014)	0.509
(0.049)	
Getting sunburnt more than once as a child	0.367
(0.035)	0.558***
(0.014)	0.472
(0.048)	
Being over 70 years old	0.224
(0.03)	0.425***
(0.014)	0.415***
(0.048)	
Having a close relative with cancer	0.556
(0.036)	0.726***
(0.013)	0.594
(0.048)	
Infection with HPV (human papillomavirus)	0.474
(0.036)	0.552*
(0.014)	0.510
(0.049)	
Doing less than 30 min of physical activity 5 times a week	0.265
(0.032)	0.293
(0.013)	0.292
(0.044)	
NOTE: We report the results of tests of the equality of proportions between the Spanish-preferring sample and the two comparison groups, *p < .05, **p < .01,***p < .001. The column for the Spanish-preferring sample duplicates some information in Table 2, Table 3. The middle column represents proportions for the general US population sample described (N = 1200). The rightmost column represents proportions among only the general US population sample who identified as Hispanic (n = 106). Standard errors (SEs) in parentheses. Proportions and SEs are weighted within their respective data sets. We also found statistically significant differences between the General US Population Sample and the Hispanic participants for that sample for four inaccurate beliefs (exposure to non-ionizing frequencies, p = .002; eating genetically modified food, p = .002; using microwave ovens, p = .034; drinking from plastic bottles, p = .011) and one accurate belief (having a close relative with cancer, p = .004).

Table 5 OLS regression model of correlates of cancer risk factor beliefs and belief discernment.

Table 5	(1)
Accurate Belief Acceptance	(2)
Inaccurate Belief Acceptance	(3)
Discernment	
Age	0.003	0.001	0.003	
	(0.002)	(0.002)	(0.004)	
Female	0.052	0.27***	−0.193	
	(0.061)	(0.05)	(0.123)	
Education	−0.065*	−0.039*	−0.034	
	(0.028)	(0.022)	(0.049)	
Cancer History	0.048	−0.099	0.152	
	(0.097)	(0.083)	(0.138)	
Health Insurance	−0.119*	0.024	−0.13	
	(0.069)	(0.054)	(0.119)	
Internet Access	0.161*	−0.032	0.235*	
	(0.081)	(0.057)	(0.119)	
English Language Ability	0.051	−0.047	0.114	
	(0.042)	(0.035)	(0.082)	
Health Literacy	−0.037	−0.01	−0.01	
	(0.043)	(0.034)	(0.085)	
Media Literacy	0.288***	0.149***	0.142	
	(0.058)	(0.045)	(0.086)	
Seeking	−0.001	−0.198***	0.168	
	(0.068)	(0.053)	(0.103)	
Scanning	0.118*	0.111*	0.02	
	(0.07)	(0.052)	(0.098)	
Constant	2.849***	2.648***	−0.734*	
	(0.283)	(0.207)	(0.357)	
Observations	2114	3022	191	
Respondent N	191	191	191	
R2	0.178	0.136	0.150	
NOTE: Coefficients reported are weighted, unstandardized, with standard errors in parentheses. Accurate and Inaccurate Belief Acceptance are modelled at the item level and models include item-fixed effects. Discernment is modelled at the respondent level. Health literacy is coded so that higher scores = lower health literacy. ***p < .001, **p < .01, *p < .05

There were several statistically significant differences between the Spanish-preferring sample and the general US sample on endorsement of inaccurate beliefs. We found these differences specifically for non-ionizing electromagnetic frequencies, eating genetically modified foods, and using microwave ovens (see Table 4). Similarly, we found differences between the Hispanic respondents and the full YouGov sample on perceptions related to non-ionizing electromagnetic frequencies, eating genetically modified foods, using microwaves, and drinking from plastic bottles (see note in Table 4). Those findings suggest similar misperceptions are held among Latina/o/x and Hispanic individuals regardless of language preference.

We generally found the inverse pattern of differences for accurate beliefs, where Spanish-preferring or Hispanic respondents reported lower endorsement of certain accurate beliefs. The general US sample reported statistically greater levels of beliefs related to sunburn, age, family health history, and human papillomavirus. We did not find the same pattern of results among Hispanic respondents in the YouGov data, except for the family health history perception (see note in Table 4).

3.3 RQ3: What demographic, social determinants of health, literacy, and communication factors associate with acceptance of accurate and inaccurate beliefs about cancer risk factors?

Higher education levels and having health insurance were associated with lower levels of accurate cancer risk factor belief acceptance. Conversely, having access to the internet at home, having a higher level of media literacy, and engaging in cancer information scanning in the last 12 months all associated with higher levels of accurate cancer risk factor beliefs. The model accounted for about 18% of the variance in accurate belief acceptance.

Identifying as female, having higher levels of media literacy, and reporting engaging in cancer information scanning in the last 12 months all associated with higher levels of acceptance of inaccurate cancer risk factor beliefs. Factors that associated with lower levels of acceptance of inaccurate cancer risk factor beliefs included higher education levels and reporting engaging in cancer information seeking in the last 12 months. The model accounted for about 14% of the variance in inaccurate belief acceptance.

With discernment as the outcome, only access to the internet associated with distinguishing between accurate and inaccurate beliefs about cancer risk factors. The model predicted about 15% of the variance in discernment.

4 Discussion

The complexity of cancer causes and risks make communication about prevention challenging. Understanding how populations differ in their beliefs, both accurate and inaccurate, about these causes and risks improves the likelihood that future communication efforts will be successful. As it relates to misinformation, understanding what myths and misconceptions about cancer causes are more accepted helps to identify potential pathways to correct misinformation, confusion, and uncertainty about a variety of topics. This is particularly important to understand because other research connects inaccurate beliefs with acceptance of misinformation [14] and acceptance of misinformation with online behavior to low-credibility sites [27].

In terms of accurate beliefs about cancer risk factors, several modifiable behaviors were not widely accepted by Spanish-preferring respondents. For example, less than 40% of respondents agreed that eating less than recommended levels of fruits and vegetables, failing to follow physical activity guidelines, consuming more than recommended alcohol amounts, and avoiding excessive sun exposure would increase cancer risk. These four behaviors are four key modifiable cancer prevention behaviors and not accepting these as cancer causes undoubtedly affects population level cancer prevention efforts.

In terms of inaccurate beliefs, more than 40% of Spanish-preferring respondents believed stress, artificial sweeteners, food additives, and genetically modified foods are causes of cancer. Most of these are related to diet and nutrition, but other inaccurate beliefs related to aerosols, cleaning products, and electromagnetic currents were held by about 1 in 3 participants. Given past research finding endorsement of these beliefs is related to acceptance of other conspiracy theorizing and inaccurate information, there is concern that—for reasons not discovered in the current study—Spanish-speaking/preferring populations in the United States might be particularly susceptible to certain types of misinformation. We also found evidence that English-speaking Hispanic respondents were less likely to hold accurate beliefs about some major cancer causes compared to a broader population of U.S. respondents, suggesting further complexities about the knowledge and perceptions of bilingual Latino/a/x and Hispanic individuals in the U.S.

Future research should clearly identify if these differences in acceptance of accurate and inaccurate beliefs about cancer risk factors relate to culture, communication, or other structural influences. Examining the differences in acceptance among the Spanish-preferring sample, the general United States population sample, and the Hispanic participants of that population sample, some patterns are worth discussion. Of the seven inaccurate beliefs available for comparison, four were statistically significantly higher in the Spanish-preferring sample. Of the seven accurate beliefs available for comparison, differences between the samples are smaller in size and less consistent, though four of the beliefs were statistically significantly lower in the Spanish-preferring population. Our data do not allow for any clear explanation of why the inaccurate beliefs are more accepted by the Spanish-preferring sample, but future work should examine if the differences are replicated in other samples, if the issues are somehow linked to translation of the survey items, or something else entirely.

Looking at the model testing associations between demographic, social determinants of health, literacy, communication factors, and the three beliefs outcomes (accurate acceptance, inaccurate acceptance, and discernment), an interesting pattern of results emerge. Higher media literacy levels and engaging in cancer information scanning associated with higher levels of acceptance of both accurate and inaccurate beliefs about cancer risk factors. One possible explanation of this is that scanning might be linked to higher levels of social media use, which can increase both knowledge and misperceptions [28]. Another explanation would be a systematic difference in the public and nonpublic communication environments of Spanish speakers, or the experienced health communication ecosystems specifically (e.g., sources reinforcing certain inaccurate beliefs, etc.), though that explanation is somewhat challenged by information seeking being negatively associated with acceptance of inaccurate beliefs. Internet access at home being associated with accurate belief acceptance, as well as the only significant correlate of discernment, suggests other non-measured social determinants of health (e.g., food insecurity) may factor into belief acceptance as well. Of course, again, the data provide challenges in interpretation because having insurance in these data was associated with lower levels of acceptance of accurate beliefs.

Considerably more research on Spanish-preferring adults experiences of, and exposures to, cancer misinformation and accurate cancer information is needed. While our study contributes to this burgeoning line of research, some limitations merit discussion. Identifying participants who preferred to take surveys in Spanish was more challenging than expected and even applying population weights there are cultural, identity, and structural (e.g., immigration status) factor differences that other Spanish-preferring populations—those not on survey panels—may be experiencing that would shift estimates of beliefs about cancer risk factors. Additionally, we only were able to provide data on a subset of beliefs for comparison between the wholly Spanish-preferring sample and the general United States population sample. There may be beliefs specific to Latina/o/x and Hispanic populations in the United States that were not captured by the scales used. Finally, given the limited variance explained by the factors measured, there are clearly other important factors future research should consider.

4.1 Innovation

Our study provides comprehensive information about cancer perceptions held by Spanish-preferring adults in the U.S., which was previously unavailable. We find several differences in accurate and inaccurate perceptions between Spanish-preferring participants, a general U.S. sample, and Hispanic-identifying participants from the larger U.S. sample. We also identified media literacy as an important concept to consider in future work on cancer misinformation, which offers a novel and interesting pathway for health media literacy interventions moving forward.

4.2 Conclusion

Spanish-preferring adults in the United States seem to differ in their acceptance of inaccurate beliefs of cancer risk factors compared to the general population of United States adults. There were smaller differences identified related to accurate beliefs. Future research should replicate these belief estimates and continue to explore what factors are associated with the discernment of accurate from inaccurate beliefs about cancer risk factors.

Financial disclosures

The authors have no financial disclosures for this submission.

CRediT authorship contribution statement

Andy J. King: Writing – review & editing, Writing – original draft, Validation, Supervision, Project administration, Methodology, Formal analysis, Data curation, Conceptualization. Benjamin A. Lyons: Writing – review & editing, Writing – original draft, Methodology, Formal analysis. Yonaira M. Rivera: Writing – review & editing. Marina Kogan: Writing – review & editing. Leandra H. Hernandez: Writing – review & editing. Yi Liao: Writing – review & editing, Writing – original draft. Kimberly A. Kaphingst: Writing – review & editing, Methodology, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors have no conflicts of interest for this submission.

Acknowledgments

This work was supported by the 10.13039/100000054 National Cancer Institute (NCI) of the 10.13039/100000002 National Institutes of Health (NIH) under award number P30CA040214 . Huntsman Cancer Foundation also supported this work. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or Huntsman Cancer Foundation.
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