Abstract
Background
Research indicates that about 40% of all cancer cases within the European Union (EU) are preventable. Public awareness of modifiable risk factors is essential for informed health-related decision-making. Systematic assessments of public awareness are crucial for identifying awareness gaps and guiding targeted public health interventions. This study aimed to examine awareness of cancer risk factors among the Swedish general public, and to examine the attitude towards lifestyle changes for cancer prevention.
Methods
This cross-sectional study used a pre-existing data set with a randomly selected sample of 1520 participants (18–84 years old) recruited from a Swedish online survey panel in April 2024. Statistical analyses utilized post-stratification weights to make the results representative for the general Swedish population. Pearson’s χ2-test and weighted adjusted logistic regression were used to test for associations between demographic characteristics, believing that changed lifestyle habits could reduce one’s cancer risk, and awareness of 20 established risk factors for cancer.
Results
A majority (63.6%) of the respondents believed that one’s cancer risk could be reduced through changed lifestyle habits. Most were aware of smoking (97.1%), sun exposure (92.4%), hereditary factors (91.0%), sunbeds (90.2%), and air pollution (90.2%), while fewer were aware of alcohol (64.9%), obesity (61.6%), overweight (58.1%), and processed meat (53.3%) as cancer risk factors. A minority of the responders were aware of low levels of physical activity (48.1%), red meat (38.9%), low intake of fruit and vegetables (32.9%), low intake of whole grains (23.7%) and not breast-feeding one’s child (9.3%) as risk factors. For most risk factors, the awareness was significantly higher among college/university educated respondents.
Conclusions
Beside significant awareness gaps among the Swedish general public regarding several established cancer risk factors, this study found an educational gradient, illuminating important differences in cancer prevention awareness. Achieving meaningful improvements in cancer prevention awareness requires coordinated system-level and policy-level actions to reduce the educational gradient and ensure equitable access to information. This could in turn increase people’s ability to make well-informed decisions regarding their lifestyle habits and preventive measures.
Keywords: Cancer awareness, Cancer risk factors, Cancer prevention, Joint Action Prevent NCD
Background
The cancer incidence is continuously increasing both globally [1, 2], and in Sweden [3], placing a significant burden on the people affected, healthcare organizations and societies. It has been estimated that approximately 40% of all cancer cases within the European Union (EU) could be prevented through healthier lifestyles, preventive measures (such as vaccination and screening) as well as through healthier environments and workplaces [4].
Since 1987, the European Code Against Cancer (ECAC) has aimed to raise awareness for modifiable cancer risk factors. The recently updated edition outlines 14 evidence-based recommendations for reducing cancer risk (IARC) [5]. Despite its longevity, awareness about the ECAC has been reported as low. A recent study showed that only 3.7% of the Swedish general public had heard of the ECAC [6].
Despite the general low ECAC awareness, research suggests that public awareness concerning specific cancer risk factors varies. The carcinogenic effects of tobacco have been evident for decades [7] and is commonly recognized by the general public [8–11]. In contrast, several other risk factors (despite being supported by robust epidemiological evidence), remain considerably less recognized. International research finds that people are least aware of the cancer risks concerning alcohol consumption, dietary habits and human papillomavirus (HPV) [8, 9, 11, 12], even if the awareness differs both between and within countries.
Awareness of the cancer risk factors is a significant prerequisite for making well-informed health related decisions and a key aspect of individual autonomy. Thus, it is concerning that previous research finds a discrepancy between the available evidence on cancer risk factors and public awareness. Additionally, previous studies indicate that cancer risk factor awareness is influenced by socioeconomic factors [11, 13].
The body of evidence regarding cancer risk factors and prevention is constantly evolving. In recent years for example, significant scientific progress has been made in understanding the role of both obesity [14–16] and physical inactivity [16]. Consequently, the systematic examination of public awareness of cancer risk factors is a critical component of effective public health strategies. Such research enables health authorities to identify awareness gaps, prioritize resources and design targeted interventions that address the specific needs of diverse population groups. As the field of cancer prevention continues to evolve, it is imperative that public awareness progresses in tandem with these scientific developments to ensure informed decision-making. To the authors’ knowledge, research on public awareness of cancer risk factors in Sweden is both scarce and out-dated.
Aim
The aim of the present study is to examine awareness of cancer risk factors among the Swedish general public and to examine the attitude towards lifestyle changes for cancer prevention.
Methods
Study design
The present cross-sectional study is part of a larger study on awareness of and attitudes towards the ECAC. A detailed description of the cohort and data collection process is presented in Hultstrand et al. [6].
Study population
Respondents consist of panellists from an online survey, recruited from Sverigepanelen (Sweden Panel), a survey panel operated by the data analysis company Novus™ [17] with approximately 50 000 panellists living in Sweden, aged 18–84 years.
Questionnaire
An online questionnaire, developed by the research team and described in more detail in a previous publication [6], was used. The questionnaire included 15 items on awareness of risk factors for cancer and attitudes and behaviours related to cancer prevention. The present study examines the results from items focusing on cancer risk factor awareness.
Data collection
The data were collected between the 2nd and the 11th of April, 2024. In total, 3099 panellists were consecutively recruited, until the intended goal of 1520 panellists had accepted to participate and completed the questionnaire (response rate 49.0%). The sample size was chosen to enable valid sub-analyses. Respondents could not alter registered responses. The questionnaire was programmed and administrated to the panel by Novus.
Study variables
Outcomes
The respondents’ beliefs that changed lifestyle habits could reduce risk and awareness of cancer risk factors were measured using the following two questionnaire items:
“I believe that people can reduce their risk of getting cancer by changing their lifestyle habits”
“Would you say that the following factors increase the risk of developing cancer?”
Item # 1 had Likert scale response options (1–5 points), with 1 point labelled as “Do not agree at all” and 5 points as “Totally agree”, in addition to the “Do not know” option. For these items, responses at 4–5 points were categorized as “Yes”, while all other answers were categorized as “No”.
Item # 2 presented risk factors with strong evidence, risk factors with limited evidence and myths. Risk factors were categorized based on the ECAC (5th ed.), as well as the Word Cancer Research Fund's recommendations [18] and assessment of current available evidence [19, 20]. Consequently, strong evidence is considered to be “evidence strong enough to support a judgment of a convincing or probable casual relationship and generally justify making recommendations” (p. 40) [20].
For the scope of this study, the following 20 risk factors are included in the analyses: smoking; second-hand smoking; heredity factors; sun exposure; sunbeds; air pollution; radon; alcohol; obesity; overweight; hormone therapy; processed meat; low level of physical activity; red meat; low intake of fruit; low fibre intake; viral and bacterial infections; sedentary; low intake of whole grains and not breastfeeding your child. The following response options were available: “Yes”, “No”, “Do not know”, which were dichotomized as “Yes” and “No” (with “Do not know” responses included in the “No” category).
Predictors
The demographic characteristics collected from the study sample included gender, age (years), national background, highest completed level of education, personal income measured as Swedish Krona (SEK; €1 ≈ 10 SEK) per month, marital status, and geographic area of residence. Further details are provided in a previous publication [6]. Gender had the following three response options: Male, Female, and Other. However, since no “Other” responses were provided, gender was dichotomized as Male/Female. National background was dichotomized as Swedish/Foreign. Income levels were categorized into the following three groups; “ < 20,000 SEK/month or Other”, “20,000–39,999 SEK/month”, and “ ≥ 40,000 SEK/month”, with those answering No income classified as < 20,000 SEK/month and those responding Don’t know or Does not want to disclose classified as Other. Education levels were measured using the three main categories of the Swedish education system; Primary school (Grundskola or equivalent), Secondary school (Gymnasium or equivalent), or College/university (Högskola/universitet). In addition, the respondents had the possibility to select the response option “None completed”, although none of the respondents selected this alternative. For the present study, education level was dichotomized as College/University education (Yes/No). Marital status had the five following response categories; Married, Cohabiting, Living alone, Partnership, Living with parents, and Other. For the present study, these five categories were dichotomized as Living alone (Yes/No), with those giving their marital status as “Living alone” classified as Yes and all the other classified as No. Finally, the respondents’ geographic areas of residence were classified into the following six Swedish healthcare regions (HCRs); Stockholm-Gotland, Mid-Sweden, Southeast, East, South, West, and North.
Statistical analyses
Unless otherwise specified, the statistical analyses utilized post-stratification weights to make the results representative for the general Swedish population. As described in our previously published study, the post-stratification (made in order to adjust for possible biases in the sample compared to the target population) was performed with regards to gender, age, education, and expressed political party orientation [6].
Categorical data are presented as frequencies and percentages, n (%), while continuous data are given as mean values with accompanying standard deviations (SDs). Tests of differences due to demographic characteristics were performed using Pearson’s χ2-test with Rao-Scott second-order corrections, with P-values calculated using a Satterthwaite approximation to the distribution and denominator degrees of freedom according to Thomas and Rao [21]. For this purpose, age was categorized as 18–34, 35–49, 50–64, and 65–84 years old.
The magnitudes of the associations between demographic characteristics (predictors) and the outcomes (believing that the risk of getting cancer may be reduced by changed lifestyle habits and awareness of the 20 cancer risk factors) were estimated using weighted adjusted logistic regression models, calculated using generalised linear models with a quasi-binomial family and a logit link function. Together with inverse-probability weighting and design-based standard errors, separately for the 20 risk factors. For all models, predictors were included simultaneously as independent variables, with age (years) as a continuous variable. Female as reference category for gender. Foreign/Other as reference category for national background. No as reference category for college/university education. “ < 20,000/Other” as reference category for income. No as reference category for living alone and Stockholm-Gotland as reference category for HCR. The results are reported as adjusted odds ratios (AORs) with 95% confidence intervals (CIs).
All statistical analyses were performed using R 4.3.1 or higher (R Foundation for Statistical Computing, Vienna, Austria) together with the R package survey [22] with two-sided P-values < 0.05 were considered statistically significant.
Results
Demographics
The demographic characteristics, with weighted and unweighted distributions among the 1520 participants, are presented in Table 1. We found a slight difference in gender, with 50.5% men and 49.5% women among the respondents. Younger persons (< 50 years old) were somewhat underrepresented in the sample, in particular those 18–34 years old, which constituted only 18.9% of the respondents and were thus up-weighted to 28.1%. Conversely, those aged ≥ 50 years were somewhat overrepresented and thus had to be down-weighted. This in particular affected those aged 65–84 years old, who were down-weighted from 30.5% to 23.4%.
Table 1.
Demographic characteristics with weighted and unweighted distributions among the 1520 participants in the present study
| Unweighted | Weighted | |||
|---|---|---|---|---|
| Variable | n | % | n | % |
| Gender | ||||
| Male | 767 | 50.5 | 768 | 50.5 |
| Female | 753 | 49.5 | 752 | 49.5 |
| Age (years)a | ||||
| 18–34 | 288 | 18.9 | 428 | 28.1 |
| 35–49 | 362 | 23.8 | 376 | 24.7 |
| 50–64 | 407 | 26.8 | 361 | 23.8 |
| 65–84 | 463 | 30.5 | 355 | 23.4 |
| National background | ||||
| Swedish | 1243 | 81.8 | 1216 | 80.0 |
| Foreign | 102 | 6.7 | 99 | 6.5 |
| Missing | 175 | 11.5 | 205 | 13.5 |
| Education level | ||||
| Primary school | 65 | 4.3 | 114 | 7.5 |
| Secondary school | 506 | 33.3 | 812 | 53.4 |
| College/University | 949 | 62.4 | 594 | 39.1 |
| Income (SEK/month) | ||||
| < 20,000 | 323 | 21.2 | 427 | 28.1 |
| 20,000–39,999 | 595 | 39.1 | 598 | 39.3 |
| ≥ 40,000 | 538 | 35.4 | 433 | 28.5 |
| Don’t know/Does not want to disclose | 64 | 4.2 | 62 | 4.1 |
| Living alone | ||||
| Yes | 366 | 24.1 | 378 | 24.8 |
| No | 1154 | 75.9 | 1142 | 75.2 |
| Swedish Health Care Region | ||||
| Stockholm-Gotland | 393 | 25.9 | 373.1 | 24.5 |
| Mid-Sweden | 312 | 20.5 | 314.6 | 20.7 |
| Southeast | 135 | 8.9 | 148.8 | 9.8 |
| South | 253 | 16.6 | 277.1 | 18.2 |
| West | 274 | 18.0 | 274.4 | 18.1 |
| North | 153 | 10.1 | 131.9 | 8.7 |
SD Standard deviation, SEK Swedish Krona. 10,000 SEK ≈ €885
aThe unweighted mean (SD) age was 53.3 (17.5) years, while the weighted mean (SD) age was 48.7 (18.5) years
In total, 81.8% of the participants reported having a Swedish background, a slight overrepresentation that resulted in a down-weight to 80.0%. Participants with a college/university education were heavily over-represented at 62.4%, resulting in a down-weight to 39.1%, while those with a secondary school education level were up-weighted from 33.3% to 53.4%. Income level was somewhat more representative among the respondents, with an up-weighting of those having an income of < 20,000 SEK/month from 21.2% to 28.1% and a down-weighting of those having an income of ≥ 40,000 SEK/month from 35.4% to 28.5%. Living alone was reported by 24.1% of the respondents, which was quite representative compared to the general population, and thus resulting in only a small up-weighting to 24.8%. Geographic area of residence was likewise quite representative, resulting in only minor up- and down-weightings.
Beliefs about cancer risk and changes in lifestyle habits
Results from the weighted adjusted logistic regression analyses of the associations between demographic characteristics and the belief that cancer risks may be reduced by changed lifestyle habits are provided in Table 2. Significant associations were found for gender, age, education level and HCR, with men being 1.91 times more likely to have this belief, compared to women (P < 0.001). Older respondents were somewhat less likely to have this belief (AOR 0.99 per each additional year of age; P = 0.022). College/university educated respondents were 1.98 times more likely to have this belief compared to those without such education (P < 0.001). Finally, those living in the Southeast HCR were 1.91 time more likely to believe that cancer risks may be reduced by changed lifestyle habits, compared to those living in the Stockholm-Gotland HCR (P = 0.015).
Table 2.
Results from weighted adjusted logistic regression analyses of the association between demographic characteristics and believing that the risk of getting cancer may be reduced by changed lifestyle habits
| Frequencies and percentages | Regression model | |||||
|---|---|---|---|---|---|---|
| Variable | n | % | P-valuea | AOR | 95% CI | P-value |
| Total | 966 | 63.6 | N/A | |||
| Gender | < 0.001 | |||||
| Male | 533 | 69.5 | 1.91 | 1.44–2.54 | < 0.001 | |
| Female | 433 | 57.5 | Ref | |||
| Age (years) | 0.006 | 0.99 | 0.98–1.00 | 0.022 | ||
| 18–34 | 308 | 72.2 | ||||
| 35–49 | 217 | 57.8 | ||||
| 50–64 | 220 | 61.0 | ||||
| 65–84 | 220 | 62.0 | ||||
| National background | 0.815 | |||||
| Swedish | 775 | 63.8 | 1.06 | 0.74–1.51 | 0.760 | |
| Foreign/Missing | 191 | 62.8 | Ref | |||
| College/University education | < 0.001 | |||||
| Yes | 418 | 70.4 | 1.98 | 1.51–2.60 | < 0.001 | |
| No | 548 | 59.2 | Ref | |||
| Income (SEK/month) | 0.571 | |||||
| < 20,000/Otherb | 299 | 61.2 | Ref | |||
| 20,000–39,999 | 386 | 64.6 | 1.04 | 0.74–1.46 | 0.822 | |
| ≥ 40,000 | 281 | 64.9 | 0.79 | 0.55–1.14 | 0.212 | |
| Living alone | 0.639 | |||||
| Yes | 245 | 64.8 | 1.12 | 0.82–1.53 | 0.463 | |
| No | 721 | 63.1 | Ref | |||
| Swedish Health Care Region | 0.308 | |||||
| Stockholm-Gotland | 231 | 61.9 | Ref | |||
| Mid-Sweden | 187 | 59.6 | 0.93 | 0.63–1.38 | 0.720 | |
| Southeast | 110 | 73.8 | 1.91 | 1.13–3.22 | 0.015 | |
| South | 178 | 64.2 | 1.11 | 0.73–1.70 | 0.623 | |
| West | 174 | 63.6 | 1.13 | 0.76–1.68 | 0.541 | |
| North | 85 | 64.8 | 1.11 | 0.68–1.81 | 0.675 | |
Participants were classified as agreeing on the statement I believe that people could reduce their risk of getting cancer by changing their lifestyle habits if they answered 4 or 5 on a scale of 1–5 points, where a value of 5 points was stated to mean Agree completely and a value of 1 point was stated to mean Disagree completely. Those answering 1–3 or Don’t know were classified as not agreeing. Frequencies and percentages are estimated based on post-stratification weights and then rounded, meaning that not all numbers may add up due to rounding errors. Significant P-values are given in bold
AOR Adjusted odds ratio, CI Confidence interval, Ref. Reference category, SEK Swedish Krona (10,000 SEK ≈ €885)
aCalculated using Pearson’s χ2-statistic with the Rao-Scott second-order correction
bIncluding Don’t know and Does not want to disclose
Awareness of risk factors for cancer for different demographic groups—univariate results
Awareness of the 20 cancer risk factors according to the weighted distribution of the demographic characteristics are provided in Tables 3, 4, 5 and 6. It should be noted that the awareness of smoking, second-hand smoking, hereditary factors, sun exposure, sunbeds, air pollution and radon was overall high (with the overall awareness being > 85%). Awareness was between 50 and 65%, for alcohol, obesity, overweight, and processed meat. In contrast, the awareness of low intake of whole grains and not breastfeeding was lower, with the overall awareness being < 25% for both risk factors.
Table 3.
Awareness of smoking, secondhand smoking, hereditary factors, sun exposure, and sunbeds as risk factors for cancer according to the weighted distribution of the demographic characteristics
| Smoking | Secondhand smoking | Hereditary factors | Sun exposure | Sunbeds | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Variable | n | % | P-value | n | % | P-value | n | % | P-value | n | % | P-value | n | % | P-value |
| Total | 1476 | 97.1 | N/A | 1306 | 85.9 | N/A | 1383 | 91.0 | N/A | 1404 | 92.4 | N/A | 1371 | 90.2 | N/A |
| Gender | 0.524 | 0.878 | 0.275 | 0.651 | 0.005 | ||||||||||
| Male | 743 | 96.8 | 658 | 85.7 | 690 | 89.8 | 713 | 92.8 | 670 | 87.3 | |||||
| Female | 733 | 97.5 | 647 | 86.1 | 693 | 92.2 | 691 | 91.9 | 701 | 93.2 | |||||
| Age (years) | 0.933 | 0.549 | 0.881 | 0.258 | 0.581 | ||||||||||
| 18–34 | 415 | 97.2 | 362 | 84.8 | 394 | 92.2 | 389 | 90.9 | 382 | 89.4 | |||||
| 35–49 | 366 | 97.4 | 330 | 87.6 | 342 | 91.0 | 344 | 91.4 | 337 | 89.6 | |||||
| 50–64 | 349 | 96.5 | 316 | 87.4 | 327 | 90.4 | 332 | 91.9 | 323 | 89.3 | |||||
| 65–84 | 346 | 97.4 | 298 | 83.9 | 320 | 90.1 | 339 | 95.6 | 329 | 92.8 | |||||
| National background | 0.424 | 0.184 | 0.784 | 0.675 | 0.024 | ||||||||||
| Swedish | 1179 | 96.9 | 1036 | 85.2 | 1105 | 90.8 | 1126 | 92.6 | 1085 | 89.2 | |||||
| Foreign/Missing | 298 | 98.0 | 270 | 88.7 | 279 | 91.7 | 278 | 91.5 | 286 | 94.2 | |||||
| College/University education | 0.012 | 0.012 | < 0.001 | 0.012 | 0.001 | ||||||||||
| Yes | 586 | 98.6 | 529 | 89.0 | 567 | 95.3 | 563 | 94.8 | 557 | 93.7 | |||||
| No | 891 | 96.2 | 777 | 83.9 | 817 | 88.2 | 841 | 90.8 | 815 | 88.0 | |||||
| Income (SEK/month) | 0.747 | 0.772 | 0.027 | 0.453 | 0.549 | ||||||||||
| < 20,000/Othera | 477 | 97.4 | 426 | 87.1 | 426 | 87.1 | 449 | 91.9 | 433 | 88.6 | |||||
| 20,000–39,999 | 577 | 96.6 | 510 | 85.4 | 553 | 92.5 | 546 | 91.5 | 545 | 91.3 | |||||
| ≥ 40,000 | 423 | 97.5 | 370 | 85.3 | 405 | 93.4 | 408 | 94.2 | 393 | 90.6 | |||||
| Living alone | 0.524 | 0.670 | 0.211 | 0.141 | 0.415 | ||||||||||
| Yes | 364 | 96.5 | 321 | 85.1 | 335 | 88.7 | 340 | 90.0 | 346 | 91.7 | |||||
| No | 1112 | 97.3 | 984 | 86.2 | 1048 | 91.8 | 1064 | 93.2 | 1025 | 89.7 | |||||
| Swedish Health Care Region | 0.817 | 0.746 | 0.376 | 0.190 | 0.310 | ||||||||||
| Stockholm-Gotland | 363 | 97.3 | 330 | 88.3 | 342 | 91.8 | 349 | 93.6 | 342 | 91.6 | |||||
| Mid-Sweden | 303 | 96.5 | 261 | 82.9 | 277 | 88.0 | 282 | 89.6 | 275 | 87.3 | |||||
| Southeast | 145 | 97.4 | 128 | 86.2 | 141 | 94.9 | 135 | 90.9 | 126 | 84.6 | |||||
| South | 267 | 96.3 | 236 | 85.1 | 245 | 88.4 | 254 | 91.5 | 255 | 92.2 | |||||
| West | 271 | 98.7 | 236 | 86.1 | 255 | 92.8 | 266 | 96.9 | 253 | 92.1 | |||||
| North | 127 | 96.5 | 115 | 87.1 | 123 | 93.1 | 118 | 89.6 | 121 | 91.5 | |||||
Frequencies and percentages are estimated based on post-stratification weights and then rounded, meaning that not all numbers may add up due to rounding errors. P-values are calculated using Pearson’s χ2-statistic with the Rao-Scott second-order correction. Significant P-values are given in bold
SEK Swedish Krona (10,000 SEK ≈ €885)
a Including Don’t know and Does not want to disclose
Table 4.
Awareness of air pollution, radon, alcohol, obesity, and overweight as risk factors for cancer according to the weighted distribution of the demographic characteristics
| Air pollution | Radon | Alcohol | Obesity | Overweight | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Variable | n | % | P-value | n | % | P-value | n | % | P-value | n | % | P-value | n | % | P-value |
| Total | 1371 | 90.2 | N/A | 1303 | 85.7 | N/A | 986 | 64.9 | N/A | 937 | 61.6 | N/A | 883 | 58.1 | N/A |
| Gender | 0.164 | 0.134 | 0.004 | 0.208 | 0.084 | ||||||||||
| – Male | 703 | 91.5 | 644 | 83.8 | 464 | 60.5 | 488 | 63.5 | 467 | 60.8 | |||||
| – Female | 668 | 88.8 | 659 | 87.6 | 522 | 69.4 | 449 | 59.7 | 416 | 55.3 | |||||
| Age (years) | 0.242 | 0.064 | 0.559 | < 0.001 | < 0.001 | ||||||||||
| – 18–34 | 384 | 89.8 | 349 | 81.7 | 288 | 67.5 | 311 | 72.7 | 290 | 67.8 | |||||
| – 35–49 | 329 | 87.5 | 316 | 84.1 | 237 | 62.9 | 227 | 60.5 | 220 | 58.5 | |||||
| – 50–64 | 327 | 90.6 | 325 | 89.9 | 226 | 62.5 | 200 | 55.3 | 189 | 52.3 | |||||
| – 65–84 | 331 | 93.1 | 312 | 87.9 | 236 | 66.4 | 198 | 55.9 | 184 | 51.7 | |||||
| National background | 0.253 | 0.837 | 0.905 | 0.202 | 0.207 | ||||||||||
| – Swedish | 1090 | 89.7 | 1041 | 85.6 | 788 | 64.8 | 737 | 60.6 | 693 | 57.0 | |||||
| – Foreign/Missing | 280 | 92.3 | 262 | 86.3 | 198 | 65.3 | 200 | 65.7 | 189 | 62.3 | |||||
| College/University education | 0.433 | < 0.001 | < 0.001 | 0.234 | 0.214 | ||||||||||
| – Yes | 541 | 91.0 | 541 | 91.0 | 419 | 70.6 | 378 | 63.7 | 358 | 60.3 | |||||
| – No | 830 | 89.6 | 762 | 82.3 | 567 | 61.3 | 558 | 60.3 | 524 | 56.6 | |||||
| Income (SEK/month) | 0.865 | 0.086 | 0.735 | 0.821 | 0.609 | ||||||||||
| – < 20,000/Othera | 437 | 89.4 | 402 | 82.3 | 320 | 65.4 | 297 | 60.7 | 280 | 57.3 | |||||
| – 20,000–39,999 | 541 | 90.5 | 514 | 85.9 | 393 | 65.8 | 367 | 61.4 | 340 | 56.9 | |||||
| – ≥ 40,000 | 393 | 90.6 | 387 | 89.3 | 273 | 63.0 | 273 | 63.1 | 262 | 60.5 | |||||
| Living alone | 0.759 | 0.623 | 0.467 | 0.617 | 0.976 | ||||||||||
| – Yes | 339 | 89.7 | 320 | 84.7 | 238 | 62.9 | 228 | 60.3 | 219 | 58.0 | |||||
| – No | 1032 | 90.4 | 983 | 86.0 | 749 | 65.5 | 709 | 62.1 | 664 | 58.1 | |||||
| Swedish Health Care Region | 0.032 | 0.130 | 0.026 | 0.231 | 0.572 | ||||||||||
| – Stockholm-Gotland | 336 | 90.2 | 321 | 86.0 | 244 | 65.3 | 216 | 58.0 | 215 | 57.7 | |||||
| – Mid-Sweden | 265 | 84.2 | 253 | 80.3 | 188 | 59.8 | 192 | 61.1 | 169 | 53.6 | |||||
| – Southeast | 140 | 94.4 | 137 | 92.0 | 116 | 77.9 | 101 | 68.0 | 92 | 61.9 | |||||
| – South | 257 | 92.7 | 235 | 84.8 | 164 | 59.2 | 184 | 66.3 | 170 | 61.3 | |||||
| – West | 252 | 91.9 | 237 | 86.4 | 184 | 66.9 | 157 | 57.2 | 155 | 56.3 | |||||
| – North | 120 | 91.0 | 120 | 91.2 | 91 | 69.0 | 86 | 65.4 | 82 | 62.3 | |||||
Frequencies and percentages are estimated based on post-stratification weights and then rounded, meaning that not all numbers may add up due to rounding errors. P-values are calculated using Pearson’s χ2-statistic with the Rao-Scott second-order correction. Significant P-values are given in bold
SEK Swedish Krona (10,000 SEK ≈ €885)
a Including Don’t know and Does not want to disclose
Table 5.
Awareness of hormone therapy, processed meat, low level of physical activity, red meat, and low intake of fruit and vegetables as risk factors for cancer according to the weighted distribution of the demographic characteristics
| Hormone therapy | Processed meat | Low level of physical activity | Red meat | Low intake of fruit and vegetables | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Variable | n | % | P-value | n | % | P-value | n | % | P-value | n | % | P-value | n | % | P-value |
| Total | 676 | 44.5 | N/A | 810 | 53.3 | N/A | 731 | 48.1 | N/A | 592 | 38.9 | N/A | 500 | 32.9 | N/A |
| Gender | 0.027 | 0.047 | 0.299 | 0.141 | 0.282 | ||||||||||
| Male | 315 | 41.0 | 384 | 50.1 | 382 | 49.8 | 282 | 36.7 | 240 | 31.3 | |||||
| Female | 361 | 48.0 | 425 | 56.6 | 349 | 46.4 | 310 | 41.2 | 260 | 34.5 | |||||
| Age (years) | 0.034 | 0.263 | 0.135 | 0.129 | 0.133 | ||||||||||
| 18–34 | 167 | 39.1 | 231 | 54.1 | 226 | 52.9 | 171 | 40.0 | 126 | 29.4 | |||||
| 35–49 | 183 | 48.6 | 217 | 57.7 | 188 | 49.9 | 164 | 43.6 | 125 | 33.4 | |||||
| 50–64 | 180 | 49.9 | 189 | 52.2 | 159 | 43.9 | 120 | 33.3 | 111 | 30.8 | |||||
| 65–84 | 146 | 41.2 | 173 | 48.6 | 159 | 44.7 | 136 | 38.4 | 138 | 38.8 | |||||
| National background | 0.349 | 0.239 | 0.854 | 0.607 | 0.516 | ||||||||||
| Swedish | 551 | 45.3 | 635 | 52.2 | 583 | 48.0 | 468 | 38.5 | 394 | 32.4 | |||||
| Foreign/Missing | 126 | 41.3 | 174 | 57.4 | 148 | 48.7 | 123 | 40.6 | 106 | 35.0 | |||||
| College/University education | < 0.001 | < 0.001 | < 0.001 | < 0.001 | < 0.001 | ||||||||||
| Yes | 306 | 51.5 | 386 | 65.0 | 328 | 55.1 | 307 | 51.6 | 234 | 39.4 | |||||
| No | 370 | 40.0 | 423 | 45.7 | 404 | 43.6 | 285 | 30.8 | 266 | 28.7 | |||||
| Income (SEK/month) | 0.002 | 0.039 | 0.186 | 0.064 | 0.309 | ||||||||||
| < 20,000/Othera | 180 | 36.9 | 236 | 48.2 | 214 | 43.8 | 171 | 34.9 | 145 | 29.6 | |||||
| 20,000–39,999 | 276 | 46.2 | 319 | 53.4 | 298 | 49.9 | 230 | 38.4 | 203 | 34.0 | |||||
| ≥ 40,000 | 220 | 50.7 | 255 | 58.8 | 219 | 50.5 | 191 | 44.2 | 152 | 35.1 | |||||
| Living alone | 0.425 | 0.934 | 0.186 | 0.554 | 0.820 | ||||||||||
| Yes | 160 | 42.3 | 202 | 53.5 | 168 | 44.4 | 153 | 40.5 | 126 | 33.5 | |||||
| No | 516 | 45.2 | 608 | 53.2 | 563 | 49.3 | 439 | 38.4 | 374 | 32.7 | |||||
| Swedish Health Care Region | 0.431 | 0.325 | 0.719 | 0.038 | 0.548 | ||||||||||
| Stockholm-Gotland | 164 | 43.8 | 220 | 58.9 | 183 | 49.0 | 173 | 46.3 | 121 | 32.5 | |||||
| Mid-Sweden | 130 | 41.5 | 152 | 48.5 | 142 | 45.2 | 104 | 33.2 | 93 | 29.6 | |||||
| Southeast | 80 | 53.5 | 78 | 52.2 | 80 | 53.6 | 51 | 34.5 | 52 | 34.9 | |||||
| South | 118 | 42.7 | 140 | 50.5 | 127 | 45.7 | 95 | 34.4 | 87 | 31.4 | |||||
| West | 120 | 43.6 | 152 | 55.4 | 131 | 47.7 | 113 | 41.2 | 105 | 38.4 | |||||
| North | 64 | 48.9 | 68 | 51.3 | 69 | 52.3 | 55 | 41.5 | 41 | 31.2 | |||||
Frequencies and percentages are estimated based on post-stratification weights and then rounded, meaning that not all numbers may add up due to rounding errors. P-values are calculated using Pearson’s χ2-statistic with the Rao-Scott second-order correction. Significant P-values are given in bold
SEK Swedish Krona (10,000 SEK ≈ €885)
aIncluding Don’t know and Does not want to disclose
Table 6.
Awareness of low fibre intake, viral and bacterial infections, sedentary, low intake of whole grains, and not breastfeeding one’s child as risk factors for cancer according to the weighted distribution of the demographic characteristics
| Low fibre intake | Viral and bacterial infections | Sedentary | Low intake of whole grains | Not breastfeeding one’s child | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Variable | n | % | P-value | n | % | P-value | n | % | P-value | n | % | P-value | n | % | P-value |
| Total | 486 | 32.0 | N/A | 489 | 32.2 | N/A | 649 | 42.7 | N/A | 360 | 23.7 | N/A | 141 | 9.3 | N/A |
| Gender | 0.042 | 0.069 | 0.803 | 0.006 | 0.721 | ||||||||||
| Male | 223 | 29.1 | 268 | 34.9 | 331 | 43.1 | 155 | 20.2 | 74 | 9.6 | |||||
| Female | 262 | 34.9 | 221 | 29.4 | 318 | 42.3 | 205 | 27.2 | 67 | 8.9 | |||||
| Age (years) | 0.001 | 0.379 | 0.603 | 0.023 | 0.430 | ||||||||||
| 18–34 | 103 | 24.2 | 152 | 35.6 | 182 | 42.6 | 75 | 17.5 | 40 | 9.3 | |||||
| 35–49 | 119 | 31.7 | 124 | 32.9 | 167 | 44.5 | 91 | 24.2 | 43 | 11.6 | |||||
| 50–64 | 119 | 32.9 | 104 | 28.8 | 142 | 39.2 | 98 | 27.2 | 30 | 8.4 | |||||
| 65–84 | 144 | 40.6 | 109 | 30.8 | 158 | 44.4 | 96 | 27.1 | 27 | 7.7 | |||||
| National background | 0.964 | 0.994 | 0.059 | 0.672 | 0.369 | ||||||||||
| Swedish | 389 | 32.0 | 392 | 32.2 | 499 | 41.1 | 292 | 24.0 | 108 | 8.8 | |||||
| Foreign/Missing | 97 | 31.8 | 98 | 32.2 | 149 | 49.1 | 68 | 22.5 | 33 | 11.0 | |||||
| College/University education | < 0.001 | < 0.001 | 0.001 | < 0.001 | 0.011 | ||||||||||
| Yes | 250 | 42.1 | 232 | 39.1 | 288 | 48.5 | 177 | 29.9 | 72 | 12.1 | |||||
| No | 236 | 25.5 | 257 | 27.8 | 360 | 38.9 | 183 | 19.7 | 69 | 7.5 | |||||
| Income (SEK/month) | 0.016 | 0.011 | 0.198 | 0.211 | 0.217 | ||||||||||
| < 20,000/Othera | 128 | 26.3 | 130 | 26.6 | 188 | 38.5 | 104 | 21.3 | 35 | 7.2 | |||||
| 20,000–39,999 | 198 | 33.1 | 193 | 32.3 | 266 | 44.5 | 138 | 23.1 | 57 | 9.5 | |||||
| ≥ 40,000 | 160 | 36.8 | 167 | 38.5 | 195 | 44.9 | 118 | 27.1 | 49 | 11.3 | |||||
| Living alone | 0.254 | 0.113 | 0.137 | 0.241 | 0.418 | ||||||||||
| Yes | 132 | 34.9 | 137 | 36.3 | 146 | 38.6 | 100 | 26.5 | 30 | 8.1 | |||||
| No | 354 | 31.0 | 352 | 30.8 | 503 | 44.0 | 260 | 22.8 | 111 | 9.7 | |||||
| Swedish Health Care Region | 0.286 | 0.680 | 0.678 | 0.109 | 0.451 | ||||||||||
| Stockholm-Gotland | 118 | 31.6 | 120 | 32.3 | 154 | 41.3 | 78 | 20.9 | 27 | 7.3 | |||||
| Mid-Sweden | 91 | 28.8 | 96 | 30.6 | 135 | 42.9 | 69 | 21.9 | 30 | 9.5 | |||||
| Southeast | 59 | 39.7 | 53 | 35.5 | 67 | 45.3 | 45 | 30.3 | 21 | 13.9 | |||||
| South | 85 | 30.6 | 78 | 28.0 | 106 | 38.1 | 61 | 22.1 | 21 | 7.7 | |||||
| West | 98 | 35.6 | 95 | 34.6 | 125 | 45.5 | 81 | 29.5 | 29 | 10.5 | |||||
| North | 36 | 27.0 | 47 | 35.8 | 62 | 46.9 | 26 | 19.4 | 13 | 9.7 | |||||
Frequencies and percentages are estimated based on post-stratification weights and then rounded, meaning that not all numbers may add up due to rounding errors. P-values are calculated using Pearson’s χ2-statistic with the Rao-Scott second-order correction. Significant P-values are given in bold.
SEK Swedish Krona (10,000 SEK ≈ €885)
a Including Don’t know and Does not want to disclose
Having a college/university education was the demographic characteristic most commonly associated with a significantly higher cancer risk factor awareness, with a significant association observed for 17 of the 20 risk factors in the univariate analyses. No significant association was found for air pollution, overweight, and obesity (Fig. 1).
Fig. 1.
Awareness of the 20 risk factors for cancer according to education level
Among the other characteristics (Tables 3, 4, 5 and 6), gender was significantly associated with awareness for six risk factors, in all cases with women having a higher awareness than men. Age was significantly associated with awareness for five risk factors. We found notably higher awareness regarding obesity and overweight among younger participants (aged < 50 years old), higher awareness of hormone therapy among middle-aged participants (aged 36–64 years old), as well as for low fibre intake among the oldest participants (aged 65–84 years old). We also found lower awareness regarding low intake of whole grains, among the youngest age group (18–34 years old).
Income level was significantly associated with awareness of hereditary factors (P = 0.027), hormone therapy (P = 0.002), processed meat (P = 0.039), low fibre intake (P = 0.016), and viral and bacterial infections (P = 0.011). Participants with higher reported income also showed higher levels of awareness. National background was only significantly associated with awareness of one cancer risk factor (sunbeds) (P = 0.024), with a lower awareness observed among those with a Swedish background (Tables 3, 5 and 6).
Finally, as shown in Tables 4 and 5, HCR was significantly associated with awareness of air pollution (P = 0.032), alcohol (P = 0.026) and red meat (P = 0.038). The highest awareness of air pollution and alcohol was observed among respondents from the Southeast HCR, while the Stockholm-Gotland HCR had the highest awareness of red meat. Notably, for alcohol, the difference in awareness between respondents in the Southeast HCR (77.9%) and the South HCR (59.2%) was 18.7 percentage points (Fig. 2).
Fig. 2.
Awareness of alcohol as a risk factor for cancer in the six Swedish health care regions
Associations between demographic characteristics and awareness of risk factors for cancer—regression results
Results from the weighted adjusted logistic regression analyses of the association between demographic characteristics and awareness of the 20 cancer risk factors are provided in Tables 7, 8, 9 and 10. Again, having a college/university education was significantly associated with higher awareness for 17 cancer risk factors (Fig. 3).
Table 7.
Results from weighted adjusted logistic regression analyses of the association between demographic characteristics and awareness of smoking, secondhand smoking, hereditary factors, sun exposure, and sunbeds as risk factors for cancer
| Smoking | Secondhand smoking | Hereditary factors | Sun exposure | Sunbeds | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Variable | AOR | 95% CI | P-value | AOR | 95% CI | P-value | AOR | 95% CI | P-value | AOR | 95% CI | P-value | AOR | 95% CI | P-value |
| Male gender | 0.92 | 0.40–2.16 | 0.855 | 1.11 | 0.75–1.64 | 0.596 | 0.73 | 0.42–1.25 | 0.251 | 1.23 | 0.73–2.07 | 0.435 | 0.50 | 0.30–0.84 | 0.008 |
| Age (years) | 1.00 | 0.98–1.02 | 0.938 | 1.00 | 0.99–1.01 | 0.795 | 1.00 | 0.98–1.01 | 0.576 | 1.01 | 1.00–1.03 | 0.069 | 1.01 | 0.99–1.02 | 0.262 |
| Swedish background | 0.65 | 0.22–1.91 | 0.434 | 0.73 | 0.45–1.19 | 0.209 | 0.87 | 0.42–1.82 | 0.710 | 1.12 | 0.58–2.18 | 0.732 | 0.51 | 0.27–0.95 | 0.035 |
| College/University education | 2.90 | 1.18–7.10 | 0.020 | 1.74 | 1.19–2.54 | 0.004 | 2.37 | 1.44–3.90 | 0.001 | 1.84 | 1.09–3.09 | 0.022 | 1.79 | 1.18–2.71 | 0.006 |
| Income (SEK/month) | |||||||||||||||
| < 20,000/Othera | Ref | Ref | Ref | Ref | Ref | ||||||||||
| 20,000–39,999 | 0.67 | 0.25–1.84 | 0.440 | 0.80 | 0.49–1.29 | 0.356 | 1.80 | 1.00–3.24 | 0.049 | 0.78 | 0.42–1.48 | 0.449 | 1.35 | 0.76–2.39 | 0.301 |
| ≥ 40,000 | 0.66 | 0.19–2.33 | 0.522 | 0.64 | 0.38–1.09 | 0.104 | 1.69 | 0.88–3.23 | 0.113 | 0.88 | 0.43–1.77 | 0.715 | 1.16 | 0.62–2.20 | 0.641 |
| Living alone | 0.75 | 0.28–1.97 | 0.556 | 0.88 | 0.58–1.32 | 0.532 | 0.75 | 0.44–1.29 | 0.300 | 0.64 | 0.37–1.11 | 0.112 | 1.26 | 0.70–2.28 | 0.436 |
| Swedish Health Care Region | |||||||||||||||
| Stockholm-Gotland | Ref | Ref | Ref | Ref | Ref | ||||||||||
| Mid-Sweden | 0.80 | 0.28–2.29 | 0.673 | 0.65 | 0.37–1.12 | 0.117 | 0.70 | 0.34–1.42 | 0.321 | 0.62 | 0.30–1.26 | 0.186 | 0.67 | 0.34–1.30 | 0.233 |
| Southeast | 1.12 | 0.23–5.46 | 0.889 | 0.85 | 0.39–1.87 | 0.692 | 1.97 | 0.66–5.91 | 0.224 | 0.73 | 0.29–1.83 | 0.500 | 0.58 | 0.25–1.36 | 0.213 |
| South | 0.72 | 0.23–2.23 | 0.567 | 0.74 | 0.42–1.29 | 0.283 | 0.76 | 0.36–1.63 | 0.483 | 0.77 | 0.32–1.82 | 0.551 | 1.18 | 0.55–2.54 | 0.664 |
| West | 2.06 | 0.50–8.49 | 0.319 | 0.80 | 0.46–1.39 | 0.430 | 1.24 | 0.57–2.72 | 0.588 | 2.10 | 0.82–5.38 | 0.122 | 1.12 | 0.49–2.57 | 0.783 |
| North | 0.77 | 0.16–3.65 | 0.739 | 0.88 | 0.42–1.82 | 0.728 | 1.31 | 0.44–3.88 | 0.628 | 0.67 | 0.26–1.69 | 0.391 | 1.06 | 0.42–2.66 | 0.907 |
The regression models included all variables in the table. Significant P-values are given in bold
AOR Adjusted odds ratio, CI Confidence interval, Ref. Reference category, SEK Swedish Krona (10,000 SEK ≈ €885)
a Including Don’t know and Does not want to disclose
Table 8.
Results from weighted adjusted logistic regression analyses of the association between demographic characteristics and awareness of air pollution, radon, alcohol, obesity, and overweight as risk factors for cancer
| Air pollution | Radon | Alcohol | Obesity | Overweight | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Variable | AOR | 95% CI | P-value | AOR | 95% CI | P-value | AOR | 95% CI | P-value | AOR | 95% CI | P-value | AOR | 95% CI | P-value |
| Male gender | 1.44 | 0.91–2.28 | 0.119 | 0.74 | 0.48–1.14 | 0.170 | 0.74 | 0.55–0.98 | 0.035 | 1.18 | 0.90–1.54 | 0.241 | 1.25 | 0.96–1.64 | 0.100 |
| Age (years) | 1.01 | 1.00–1.02 | 0.083 | 1.01 | 1.00–1.03 | 0.018 | 1.00 | 0.99–1.01 | 0.714 | 0.98 | 0.98–0.99 | < 0.001 | 0.99 | 0.98–0.99 | < 0.001 |
| Swedish background | 0.68 | 0.38–1.21 | 0.185 | 0.83 | 0.48–1.45 | 0.514 | 0.95 | 0.66–1.37 | 0.799 | 0.85 | 0.61–1.19 | 0.349 | 0.85 | 0.61–1.20 | 0.364 |
| College/University education | 1.24 | 0.79–1.93 | 0.344 | 1.93 | 1.29–2.88 | 0.001 | 1.60 | 1.23–2.08 | 0.001 | 1.21 | 0.94–1.57 | 0.144 | 1.20 | 0.93–1.55 | 0.160 |
| Income (SEK/month) | |||||||||||||||
| < 20,000/Othera | Ref | Ref | Ref | Ref | Ref | ||||||||||
| 20,000–39,999 | 1.04 | 0.59–1.81 | 0.900 | 1.19 | 0.73–1.94 | 0.484 | 1.01 | 0.71–1.43 | 0.974 | 1.05 | 0.76–1.46 | 0.755 | 1.01 | 0.73–1.40 | 0.971 |
| ≥ 40,000 | 0.92 | 0.50–1.69 | 0.784 | 1.42 | 0.79–2.57 | 0.241 | 0.81 | 0.56–1.17 | 0.254 | 1.07 | 0.75–1.52 | 0.719 | 1.07 | 0.75–1.52 | 0.702 |
| Living alone | 0.91 | 0.56–1.48 | 0.715 | 0.89 | 0.57–1.39 | 0.599 | 0.88 | 0.65–1.20 | 0.412 | 0.98 | 0.73–1.32 | 0.904 | 1.05 | 0.79–1.40 | 0.746 |
| Swedish Health Care Region | |||||||||||||||
| Stockholm-Gotland | Ref | Ref | Ref | Ref | Ref | ||||||||||
| Mid-Sweden | 0.59 | 0.33–1.07 | 0.084 | 0.72 | 0.41–1.27 | 0.252 | 0.80 | 0.54–1.18 | 0.258 | 1.15 | 0.78–1.69 | 0.471 | 0.86 | 0.58–1.25 | 0.423 |
| Southeast | 2.00 | 0.80–5.01 | 0.137 | 2.28 | 0.94–5.54 | 0.068 | 1.93 | 1.12–3.32 | 0.018 | 1.59 | 0.97–2.59 | 0.064 | 1.23 | 0.75–2.01 | 0.405 |
| South | 1.37 | 0.68–2.79 | 0.378 | 1.01 | 0.53–1.95 | 0.970 | 0.78 | 0.51–1.18 | 0.237 | 1.44 | 0.96–2.16 | 0.074 | 1.17 | 0.78–1.74 | 0.447 |
| West | 1.26 | 0.67–2.38 | 0.471 | 1.09 | 0.58–2.03 | 0.797 | 1.06 | 0.71–1.58 | 0.784 | 0.98 | 0.67–1.44 | 0.937 | 0.96 | 0.66–1.41 | 0.852 |
| North | 1.20 | 0.50–2.88 | 0.683 | 2.02 | 0.80–5.11 | 0.138 | 1.17 | 0.69–1.97 | 0.563 | 1.29 | 0.80–2.09 | 0.295 | 1.15 | 0.72–1.83 | 0.565 |
The regression models included all variables in the table. Significant P-values are given in bold
AOR Adjusted odds ratio, CI Confidence interval, Ref. Reference category, SEK Swedish Krona (10,000 SEK ≈ €885)
a Including Don’t know and Does not want to disclose
Table 9.
Results from weighted adjusted logistic regression analyses of the association between demographic characteristics and awareness of hormone therapy, processed meat, low level of physical activity, red meat, and low intake of fruit and vegetables as risk factors for cancer
| Hormone therapy | Processed meat | Low level of physical activity | Red meat | Low intake of fruit and vegetables | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Variable | AOR | 95% CI | P-value | AOR | 95% CI | P-value | AOR | 95% CI | P-value | AOR | 95% CI | P-value | AOR | 95% CI | P-value |
| Male gender | 0.71 | 0.54–0.92 | 0.010 | 0.79 | 0.60–1.04 | 0.096 | 1.20 | 0.92–1.58 | 0.186 | 0.88 | 0.67–1.16 | 0.372 | 0.91 | 0.68–1.20 | 0.491 |
| Age (years) | 1.00 | 0.99–1.01 | 0.677 | 0.99 | 0.99–1.00 | 0.159 | 0.99 | 0.99–1.00 | 0.039 | 1.00 | 0.99–1.00 | 0.270 | 1.01 | 1.00–1.01 | 0.157 |
| Swedish background | 1.13 | 0.81–1.58 | 0.476 | 0.82 | 0.58–1.18 | 0.285 | 0.99 | 0.70–1.40 | 0.951 | 0.93 | 0.66–1.31 | 0.682 | 0.85 | 0.60–1.21 | 0.364 |
| College/University education | 1.37 | 1.07–1.76 | 0.014 | 2.05 | 1.59–2.63 | < 0.001 | 1.66 | 1.29–2.13 | < 0.001 | 2.30 | 1.78–2.98 | < 0.001 | 1.57 | 1.21–2.03 | 0.001 |
| Income (SEK/month) | |||||||||||||||
| < 20,000/Othera | Ref | Ref | Ref | Ref | Ref | ||||||||||
| 20,000–39,999 | 1.47 | 1.06–2.04 | 0.020 | 1.18 | 0.85–1.66 | 0.326 | 1.20 | 0.86–1.68 | 0.273 | 1.06 | 0.76–1.50 | 0.718 | 1.16 | 0.82–1.64 | 0.413 |
| ≥ 40,000 | 1.72 | 1.22–2.44 | 0.002 | 1.23 | 0.86–1.76 | 0.250 | 1.02 | 0.72–1.45 | 0.906 | 1.06 | 0.74–1.52 | 0.744 | 1.09 | 0.76–1.57 | 0.631 |
| Living alone | 0.93 | 0.69–1.25 | 0.640 | 1.05 | 0.77–1.42 | 0.765 | 0.84 | 0.63–1.13 | 0.258 | 1.11 | 0.82–1.50 | 0.511 | 1.05 | 0.77–1.42 | 0.769 |
| Swedish Health Care Region | |||||||||||||||
| Stockholm-Gotland | Ref | Ref | Ref | Ref | Ref | ||||||||||
| Mid-Sweden | 0.96 | 0.65–1.40 | 0.828 | 0.69 | 0.47–1.02 | 0.065 | 0.88 | 0.60–1.27 | 0.487 | 0.61 | 0.42–0.89 | 0.010 | 0.92 | 0.64–1.32 | 0.638 |
| Southeast | 1.63 | 1.00–2.64 | 0.048 | 0.86 | 0.52–1.41 | 0.555 | 1.28 | 0.78–2.08 | 0.329 | 0.69 | 0.42–1.12 | 0.129 | 1.24 | 0.75–2.07 | 0.398 |
| South | 1.04 | 0.70–1.56 | 0.831 | 0.76 | 0.50–1.16 | 0.205 | 0.90 | 0.59–1.35 | 0.600 | 0.64 | 0.42–0.98 | 0.041 | 1.00 | 0.65–1.53 | 0.991 |
| West | 1.03 | 0.71–1.50 | 0.868 | 0.90 | 0.60–1.33 | 0.593 | 0.97 | 0.66–1.43 | 0.894 | 0.84 | 0.57–1.25 | 0.395 | 1.34 | 0.89–2.02 | 0.155 |
| North | 1.31 | 0.81–2.11 | 0.264 | 0.74 | 0.46–1.20 | 0.226 | 1.13 | 0.71–1.80 | 0.612 | 0.84 | 0.52–1.36 | 0.476 | 1.01 | 0.62–1.64 | 0.980 |
The regression models included all variables in the table. Significant P-values are given in bold
AOR Adjusted odds ratio, CI Confidence interval, Ref. Reference category, SEK Swedish Krona (10,000 SEK ≈ €885)
a Including Don’t know and Does not want to disclose
Table 10.
Results from weighted adjusted logistic regression analyses of the association between demographic characteristics and awareness of low fibre intake, viral and bacterial infections, sedentary, low intake of whole grains, and not breastfeeding one’s child as risk factors for cancer
| Low fibre intake | Viral and bacterial infections | Sedentary | Low intake of whole grains | Not breastfeeding one’s child | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Variable | AOR | 95% CI | P-value | AOR | 95% CI | P-value | AOR | 95% CI | P-value | AOR | 95% CI | P-value | AOR | 95% CI | P-value |
| Male gender | 0.80 | 0.61–1.05 | 0.101 | 1.30 | 0.98–1.72 | 0.071 | 1.08 | 0.82–1.41 | 0.583 | 0.69 | 0.52–0.93 | 0.015 | 1.11 | 0.73–1.67 | 0.634 |
| Age (years) | 1.02 | 1.01–1.03 | < 0.001 | 0.99 | 0.99–1.00 | 0.187 | 1.00 | 0.99–1.01 | 0.610 | 1.01 | 1.00–1.02 | 0.003 | 1.00 | 0.98–1.01 | 0.514 |
| Swedish background | 0.89 | 0.62–1.27 | 0.521 | 0.99 | 0.69–1.43 | 0.965 | 0.70 | 0.49–0.98 | 0.039 | 0.99 | 0.66–1.47 | 0.944 | 0.76 | 0.45–1.28 | 0.298 |
| College/University education | 1.99 | 1.52–2.60 | < 0.001 | 1.60 | 1.22–2.11 | 0.001 | 1.49 | 1.16–1.92 | 0.002 | 1.58 | 1.19–2.11 | 0.002 | 1.67 | 1.11–2.52 | 0.014 |
| Income (SEK/month) | |||||||||||||||
| < 20,000/Othera | Ref | Ref | Ref | Ref | Ref | ||||||||||
| 20,000–39,999 | 1.28 | 0.91–1.81 | 0.154 | 1.26 | 0.88–1.81 | 0.204 | 1.19 | 0.85–1.65 | 0.310 | 1.09 | 0.75–1.58 | 0.653 | 1.27 | 0.70–2.30 | 0.430 |
| ≥ 40,000 | 1.35 | 0.95–1.93 | 0.093 | 1.43 | 0.99–2.06 | 0.057 | 1.07 | 0.75–1.52 | 0.706 | 1.32 | 0.90–1.93 | 0.159 | 1.37 | 0.77–2.42 | 0.283 |
| Living alone | 1.21 | 0.89–1.67 | 0.229 | 1.38 | 1.00–1.89 | 0.049 | 0.82 | 0.61–1.09 | 0.172 | 1.25 | 0.89–1.77 | 0.203 | 0.88 | 0.54–1.43 | 0.606 |
| Swedish Health Care Region | |||||||||||||||
| Stockholm-Gotland | Ref | Ref | Ref | Ref | Ref | ||||||||||
| Mid-Sweden | 0.96 | 0.67–1.37 | 0.813 | 1.00 | 0.67–1.50 | 0.987 | 1.10 | 0.76–1.60 | 0.621 | 1.16 | 0.78–1.70 | 0.463 | 1.42 | 0.80–2.54 | 0.235 |
| Southeast | 1.76 | 1.07–2.91 | 0.026 | 1.33 | 0.79–2.26 | 0.285 | 1.30 | 0.80–2.11 | 0.289 | 1.91 | 1.14–3.20 | 0.014 | 2.36 | 1.13–4.91 | 0.022 |
| South | 1.05 | 0.68–1.61 | 0.827 | 0.88 | 0.56–1.39 | 0.581 | 0.89 | 0.59–1.35 | 0.592 | 1.16 | 0.71–1.89 | 0.545 | 1.14 | 0.58–2.22 | 0.706 |
| West | 1.27 | 0.85–1.89 | 0.238 | 1.21 | 0.80–1.84 | 0.364 | 1.21 | 0.82–1.78 | 0.333 | 1.65 | 1.09–2.51 | 0.019 | 1.58 | 0.82–3.03 | 0.170 |
| North | 0.92 | 0.56–1.53 | 0.755 | 1.23 | 0.76–1.99 | 0.407 | 1.32 | 0.84–2.07 | 0.232 | 1.02 | 0.61–1.70 | 0.935 | 1.44 | 0.69–3.00 | 0.337 |
The regression models included all variables in the table. Significant P-values are given in bold
AOR Adjusted odds ratio, CI Confidence interval, Ref. Reference category, SEK Swedish Krona (10,000 SEK ≈ €885)
a Including Don’t know and Does not want to disclose
Fig. 3.
Association between having a college/university education, compared to not having a college/university education, and awareness of the 20 risk factors for cancer
Men were less likely to be aware of the following cancer risk factors; sunbeds, alcohol, hormone therapy and low intake of whole grains (compared to women). Older participants were more likely to be aware of the following cancer risk factors; radon, obesity, overweight, physical activity, low fibre intake and low intake of whole grains (Tables 7, 8, 9 and 10).
Moreover, participants with a Swedish background were less likely to be aware of sunbeds and a sedentary lifestyle as cancer risk factors (compared with participants with a foreign background). Participants that were living alone were 1.38 as likely to be aware of viral and bacterial infections as a risk factor for cancer, after adjusting for the other demographic characteristics (P = 0.049), compared with participants not living alone (Tables 7, 9 and 10).
Finally, we found significant differences between the HCRs related to the awareness of alcohol, hormone therapy, red meat, low fibre intake, low intake of whole grains and not breastfeeding as cancer risk factors, after adjusting for the other demographic characteristics (Tables 8, 9 and 10).
Discussion
This cross-sectional study on cancer risk factors among the general public in Sweden found that awareness varies significantly between risk factors. Almost all respondents (97%) successfully identified smoking as a risk factor, whilst only 9% recognized that breastfeeding is a protective factor. Most respondents were aware that sun exposure, heredity factors, sunbeds and air pollution are risk factors. On the contrary, in descending order, low levels of awareness (< 50%) were found for physical inactivity (48%), hormone therapy (45%), red meat (39%), low intake of fruit and vegetables (33%), viral and bacterial infections (32%) and low intake of whole grains (24%).
Results in context
Our results are in line with findings reported by other scholars. A study from Sweden and Denmark reports modest levels of public awareness on cancer risk factors [10]. Lagerlund et al. reported that the lowest levels of awareness among the Swedish participants were found for HPV-infections, low intake of fruits and vegetables and alcohol consumption [10]. It is noteworthy that the results from the former and the current study are similar, despite being conducted a decade apart, indicating that the public awareness has not improved.
Another recent study found limited awareness on the causal link between alcohol and cancer among almost 20 000 participants from 14 European countries. Only 53% of the participants knew that alcohol could cause cancer and even fewer (15%) reported that they knew that alcohol could cause female breast cancer [23].
In the context of cancer prevention, our findings, together with results reported by others [10, 23, 24], challenge the common perception that “everyone already knows what healthy lifestyle habits are”. In fact, for nine of the 20 established risk factors included in this study, awareness was below 50%. The low awareness of the cancer risk associated with low intake of whole grains, low levels of fruit and vegetables and sedentary behaviour is, from a broader public health perspective, particularly concerning. Especially since low intake of whole grains is among the leading contributors to non-communicable morbidity within the EU [25]. Moreover, dietary habits and physical inactivity are indirectly causally linked to overweight and obesity, which in turn are associated with several cancer types as well as other common noncommunicable diseases [18, 25].
In Sweden, unhealthy lifestyle habits are prevalent across groups with different socioeconomic positions [26]. There is, however, a social gradient across the whole cancer continuum, from prevention to mortality [27]. Disparities due to education level are prevalent for almost all cancer diagnoses. Vaccarella [28] found that across Europe, people with a shorter education had higher mortality rates for almost all cancer types, compared with people with a longer education.
This study found a discernible statistically significant educational gradient across nearly all risk factors, even when adjusting for confounding factors, indicating that individuals with higher education have greater awareness. Only for overweight, obesity, and air pollutions did the associations fail to reach statistical significance. These findings are particularly concerning, illuminating inequities in cancer prevention awareness in Sweden.
Being informed is a prerequisite for making well-informed health related decisions. Therefore, the inequity in cancer prevention awareness identified in this study is concerning. This has for example been studied in the adjacent field of cancer screening participation, where an educational gradient has been consistently observed across Europe (including Sweden) indicating lower participation rate among individuals with shorter education [29, 30]. Improving overall awareness, and particularly addressing disparities in awareness, may positively influence cancer prevention behaviours.
Previous research highlights the need for public information to be tailored to best fit the diverse needs and preferences of the population, in order to achieve effective outcomes [31]. Further, communicated information needs to be easy to understand, minimize stigmatization and guilt as well as being transparent and evidence-based, in order to support individuals [32–34]. Our results portray the importance of tailoring cancer prevention information to heterogeneous groups, to decrease inequities in awareness. In line with this, Wu et al. describe how advances in artificial intelligence (AI) may offer a strategy for enhancing awareness of cancer risk factors by supporting targeted, person‑centered communication strategies tailored to individual needs [35]. Moreover, research also suggests that mobile applications may serve as effective tools for increasing public awareness, but further studies are needed to strengthen the evidence base [36].
Furthermore, while others have reported the opposite [37], the results from this study showed that men (70%) were significantly more likely than women (58%) to agree that by changing lifestyle habits, people could reduce their cancer risk. To interpret this finding, it is important to consider how respondents may have understood the statement. Some participants may have perceived it as an evaluation of whether lifestyle factors matter when it comes to cancer risk, whereas others may have interpreted it as a statement about people’s capacity to successfully change their lifestyle. These distinct interpretations could have influenced response patterns.
Although not directly assessed in this study, the observed gender difference may be interpreted through the framework of locus of control (LoC). LoC refers to the extent to which individuals attribute life outcomes to internal versus external factors [38]. An external LoC denotes the belief that outcomes are primarily determined by forces beyond personal control, while a strong internal LoC reflects the perception that outcomes largely depend on one’s own actions and behaviours. Prior research has shown that men, on average, tend to score higher on measures of internal LoC [39, 40], suggesting that men may be more inclined to believe in the efficacy of personal agency and behavioural modification. At the same time, evidence indicates that women report greater cancer-related worry than men [41, 42]. This juxtaposition highlights the complexity of gendered patterns in cancer risk perception.
Clinical implications and further research
The findings from this study provide actionable insights on how cancer prevention awareness may be strengthened. Even though effective prevention requires strategies that go well beyond awareness alone, our results underscore the continued importance of public health communication and awareness-raising initiatives. In particular, our findings highlight the critical need to prioritize efforts that promote equity. Addressing disparities in cancer prevention awareness requires targeted interventions reducing the educational gradient identified in this study. Such efforts are crucial for equitable access to cancer prevention information across diverse population groups.
To maximize the potential public health benefits of awareness initiatives, it is essential to prioritize those risk factors for which awareness levels are comparatively low. We see no justification for refraining from pursuing high levels of awareness across all cancer risk factors, despite their differing contributions to cancer incidence. Consequently, addressing current awareness gaps in cancer prevention among the Swedish general public requires system-level actions, with both public health and healthcare sectors playing central roles in supporting such efforts. However, promoting equity in cancer prevention awareness cannot be achieved through isolated efforts or by individual organizations. It requires long-term collaboration between different stakeholders and sectors of society.
Further research is needed to explore how cancer prevention information can be effectively tailored to meet the needs of diverse target audiences, particularly within an evolving communication landscape shaped by AI technologies, social media platforms and other emerging information sources.
Strengths and limitations
Few studies have previously examined awareness of cancer risk factors among the general public in Sweden. Thus, this study adds important information to the scientific field. Additional strengths are the large and representative sample, as well as the use of post-stratifications weights in the statistical analyses. However, there are some limitations that need to be considered. Firstly, our results only provide a snapshot of cancer prevention awareness; more research and repeated surveys are needed in order to follow trends over time. This is particularly relevant since at the time of data collection, a nationwide public campaign aiming at eliminating cervical cancer in Sweden through human papillomavirus (HPV) vaccination took place. It is therefore possible that public awareness of viral and bacterial infections, such as HPV, as cancer risk factors is higher than prior to the campaign. Secondly, as mentioned in Hultstrand et al. [6], our results may inherent some selection bias since only Swedish-speaking persons with digital access have completed the questionnaire.
Conclusions
Besides significant awareness gaps among the Swedish general public regarding several established cancer risk factors, this study found an educational gradient for most risk factors, illuminating important differences in cancer prevention awareness. Achieving meaningful improvements requires coordinated system-level and policy-level actions to reduce the educational gradient and ensure equitable access to information. This could, in turn, increase people’s abilities to make well-informed decisions about their lifestyle habits and preventive measures.
Acknowledgements
The authors would like to thank the participants for taking part in this survey. We also would like to express acknowledgments towards Helena Björck and Maria Klaréus at Novus, for administrating the data collection.
Abbreviations
- AOR
Adjusted odds ratio
- ECAC
European code against cancer
- EU
European Union
- HCR
Health care region
- HPV
Human papillomavirus
- LoC
Locus of control
- SD
Standard deviation
- OR
Odds ratio
Authors’ contributions
CH: Conceptualization (lead); Investigation (equal); Methodology (equal); Project administration (lead); Supervision (equal); Writing – original draft preparation (equal); Writing – review & editing (equal). EB: Investigation (equal); Methodology (equal); Writing – original draft preparation (supporting); Writing – review & editing (equal). AR: Conceptualization (supporting); Data curation (lead); Formal analysis (lead); Methodology (supporting); Software (lead); Visualization (lead); Writing – original draft preparation (equal); Writing – review & editing (equal). ALS: Writing – review & editing (equal) TBE: Writing – review & editing (equal) LS: Funding acquisition (lead); Supervision (equal); Writing – review & editing (equal).
Funding
Open access funding provided by Umea University. This study was funded by the Regional Cancer Centre Stockholm-Gotland, Sweden. This study is part of the Joint Action Prevent NCD (GA – 101128023) and is co-funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the EU or the European Health and Digital Executive Agency (HaDEA). Neither the EU nor HaDEA can be held responsible for them.
Data availability
The datasets used and analysed during the current study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
The study was conducted in compliance with the Helsinki Declaration and has undergone a review by the Swedish Ethical Review Authority, who concluded that the study was not covered by the Ethical Review Act (2003:460), since no sensitive personal data was included (Dnr 2024–00635-01). Participants gave informed consent to participate in the study by completing the questionnaire.
Participation in the study was voluntary and all data was handled confidentially in data systems with adequate security measures. The authors only had access to pseudonymized data.
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.
Data Availability Statement
The datasets used and analysed during the current study are available from the corresponding author upon reasonable request.