Abstract
Background
Cancer literacy is a crucial component of cancer prevention, and disparities in cancer burden are observed based on socioeconomic status (SES). Evidence on the relationship between SES and cancer literacy among Chinese population is limited. This study aims to evaluate the association between SES disparities and cancer literacy.
Methods
This multicenter cross-sectional survey included 12 sites in China, employing a multistage proportional stratified cluster random sampling approach. Cancer literacy and sociodemographic information were collected through face-to-face interviews with participants aged 15 to 69 years. Cancer literacy was assessed using a validated questionnaire, with total scores ranging from 0 to 68; a score of 55 or higher indicated adequate cancer literacy. SES was defined by integrating household income, education, and occupation, with levels (low, medium, and high) determined through latent class analysis. Multivariable logistic regression was conducted to examine the association between SES and cancer literacy.
Results
Among 4,784 participants, 865 (18.08%) were identified as having adequate cancer literacy. The percentage of literate participants differed across SES groups, with 9.54% in the low SES, 14.11% in the medium SES, and 39.27% in the high SES. Compared to participants in the low SES group, the odds ratio (OR) for adequate cancer literacy was 4.74 (95% CI: 3.66–6.14) in the high SES group, and 1.38 (95% CI: 1.11–1.72) in the medium SES group. Subgroup analyses revealed that younger individuals, females, and urban residents with high SES exhibited significantly higher cancer literacy. Similar results were observed across five specific dimensions of cancer literacy.
Conclusions
SES is a significant determinant of cancer literacy. Improving cancer literacy may enhance cancer prevention by promoting informed screening participation, earlier help-seeking, and healthier preventive behaviors, and should therefore be incorporated as a key component of cancer prevention strategies aimed at reducing SES-related disparities.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-026-26540-z.
Keywords: Cancer, Literacy, Inequality, Socioeconomic status, Prevention
Introduction
Cancer poses a substantial public health threat, with an estimated 20 million new cases and 9.7 million deaths worldwide, according to GLOBOCAN 2022 [1]. In China specifically, cancer exhibits significant regional disparities in incidence and mortality. Over the past two decades, the overall cancer incidence in China has increased by approximately 1.4% annually, contributing to an escalating disease burden [2]. Health literacy, defined as the ability to acquire, understand, and apply health information to make informed healthcare decisions, has become a critical factor in cancer prevention [3]. Previous studies have indicated that health literacy is associated with health outcomes, particularly those related to cancer [4, 5]. Extensive research has highlighted cancer literacy as a foundational tool, crucially shaping the development and implementation of cancer prevention and treatment strategies [6]. Greater knowledge of cancer is associated with a reduced cancer risk [7]. Understanding the characteristics of cancer literacy is essential, as it may aid in the development of effective preventive measures to improve compliance and treatment, ultimately promoting healthy aging and longevity.
Socioeconomic status (SES), which encompasses education level, income, and occupation, plays a crucial role in individuals’ health and well-being. Socioeconomic disparities are a key determinant of disease outcomes, including cancer [8–10]. Prior research has shown that lower SES is associated with higher mortality rates across most cancer types, particularly in Europe and the United States [11, 12]. However, these studies are primarily based in high-income countries, with limited evidence from other regions. Moreover, China has experienced a profound transformation in terms of SES in recent decades, although a significant portion of the population still resides in areas with notable disparities [13]. Consequently, examining the relationship between SES and cancer literacy within Chinese populations is imperative.
Considering the multiple challenges of cancer disparities in China, including differences in socioeconomic development and healthcare access, a large-scale population survey was carried out in eastern China to explore whether, and if so, to what extent, cancer literacy varies by SES across Zhejiang Province. This research will offer new insights into addressing health disparities related to SES in China.
Methods
Study population
This study employed a multistage stratified cluster random sampling method, gathering data from 12 monitoring districts across 11 cities in Zhejiang Province. The geographic distribution of the sampling sites is presented in Fig. 1. A representative sample of 4,800 participants was planned to be enrolled from 96 primary sampling units, evenly distributed between 48 urban and 48 rural areas, using a probability proportional to size sampling method that encompassed 50 households per primary sampling unit. One participant aged 15 to 69 years was selected from each household utilizing the Kish grid method [14].
Fig. 1.
The distribution map of 12 sampling sites across Zhejiang Province, China
The sample size was determined by the 2020 prevalence of cancer literacy in Zhejiang Province, estimated at 74.7%, aiming for 85% power and a 5% significance. The sample size calculation followed the formula: 
, where p represents the prevalence (74.7%), uα is set to u0.05 (1.96), and deff is 2, with a relative error of 15% (δ = 0.15×p). Taking into account stratified sampling by community and sex, and a 15% non-response rate, the final sample size was calculated to be 4,440.
Data collection
Data collection took place between August and November 2023 among 4,800 residents aged 15–69 who had resided in Zhejiang Province for over six months. The study received approval from the Ethics Committee of Zhejiang Cancer Hospital (IRB-2024-787), with informed consent obtained from all participants.
This project collected demographic and lifestyle information as covariates, administered by local trained staff. Covariates included age interval (15–44, 45–59, or 60–69), sex (male or female), marital status (married or others), family history of cancer (yes, no, or unknown), location (urban or village), and self-reported health status (good/relatively good or average/relatively poor/poor). Body mass index (BMI) was recorded according to Chinese criteria for obese (≥ 24 kg/m2) and normal (< 24 kg/m2) from self-reported height and weight [15, 16]. Smoking data encompassed participants’ cigarettes history; those who had smoked previously or were currently smoking were classified as smokers, while individuals who had never smoked were categorized as non-smokers.
Definition of cancer literacy
The survey participants’ cancer literacy was assessed using a cancer literacy questionnaire designed by the National Cancer Center of China and the Chinese Center for Health Education (See Supplementary File 1 for the questionnaire). Detailed information has been described previously [17]. In short, this scale consists of 52 items covering 5 dimensions of cancer prevention, namely “basic knowledge (12 items)”, “primary prevention (13 items)”, “early detection and treatment (13 items)”, “treatment (10 items)”, and “recovery of cancer (6 items)”. The scale consists of three question types: true or false, single-answer, and multiple-answer. Each correct response to a multiple-answer question earns 2 points, while a correct response to a single-answer or judgment question receives 1 point. The total score ranges from 0 to 68, with a score of 55 or above (80%) indicating adequate cancer literacy, while a score of 0–54 signifies limited cancer literacy. Across the five specific dimensions of cancer literacy score, we similarly used the 80% threshold to classify participants into two groups. Cancer literacy was defined as the primary outcome, and its five specific dimensions were examined as secondary outcomes.
Definition of socioeconomic status
Consistent with prior research and available data, SES was determined through household income, occupation, and education level using latent class analysis (LCA). Household income was self-reported and divided into thirds (low, medium, and high). Occupations were categorized into three groups—professional or managerial, skilled, and unskilled—based on prior relevant research [18]. Education levels were classified as follows: a) None or primary school, b) Secondary school, c) Higher school, or d) Associate degree and above. To identify the optimal model, we evaluated LCA models ranging from two to five clusters, employing the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to assess model fit. The final number of latent SES categories was determined using the BIC with the stability of the solution; lower BIC values indicated optimal models and thereby the ideal number of categories. Clinical interpretability of the proposed category characteristics was also considered before finalizing the model selection. Consequently, the three-cluster model, comprising low, medium, and high SES, was selected for this study. The LCA was conducted using the poLCA package in R [19]. Individuals categorized as having low SES generally possessed lower education, held unskilled occupations, and fell in the lowest third of household income (Table S2-S3; Figure S1).
Statistical analysis
All data were presented as numbers and percentages for categorical variables. To compare three SES clusters for baseline characteristics, we conducted a chi-squared (χ²) test. Logistic regression analyses were performed to estimate the odds ratios (OR) with a corresponding 95% confidence interval (CI), quantifying the association of SES and its indicators (education, occupation, household income) with cancer literacy.
The multivariable model was adjusted for age interval, sex, marital status, family history of cancer, smoking status, BMI group, location, and self-reported health status when SES and its indicators were considered as exposures. Trend tests were conducted to evaluate the presence of a dose-response association.
We additionally conducted subgroup analyses to evaluate the robustness of the results and to identify high-risk populations. We repeated analyses stratified by age interval (15–44, 45–59, or 60–69), sex (males or females), marital status (married or others), family history of cancer (yes, no, or unknown), smoking status (yes or no), BMI group (< 24 kg/m2 or ≥ 24 kg/m2), and location (urban or village) to test the potential variations. R software (version 4.3.1) was used for all statistical analyses. All tests were two-tailed with a significance level of 0.05.
Results
Baseline characteristics of study participants
Of the 4,800 participants initially enrolled, 4,784 were included in the final analysis after excluding 16 participants due to missing or incorrect baseline data, yielding a response rate of 99.67%. Table 1 and Table S1 presented the baseline characteristics of the participants, with 865 (18.08%) exhibiting adequate cancer literacy. Across the five distinct dimensions of cancer literacy, the proportions of participants exhibiting adequate cancer literacy were as follows: basic knowledge (1,727, 36.10%), cancer prevention (2,501, 42.87%), early detection and treatment (2,408, 50.33%), cancer treatment (2,857, 59.72%), and cancer recovery (2,367, 49.48%) (Table 3). Overall, 1,067 (22.30%), 1,998 (41.76%), and 1,719 (35.93%) of participants were categorized as having high, medium, and low SES, respectively. In general, compared to those with low SES, participants with high SES were more likely to have a normal BMI, reside in urban areas, report better self-reported health status, be non-smokers, be male, be younger, be other marital status, and be aware of their family history of cancer.
Table 1.
Baseline characteristics according to socioeconomic status
| Variable | Overall, N = 4,784 |
Low SES, N = 1,719 |
Medium SES, N = 1,998 |
High SES, N = 1,067 |
P-value |
|---|---|---|---|---|---|
| Age, yrs | < 0.001 | ||||
| 15–44 | 1,272 (26.59%) | 69 (4.01%) | 418 (20.92%) | 785 (73.57%) | |
| 45–59 | 1,937 (40.49%) | 625 (36.36%) | 1,079 (54.00%) | 233 (21.84%) | |
| 60–69 | 1,575 (32.92%) | 1,025 (59.63%) | 501 (25.08%) | 49 (4.59%) | |
| Sex | < 0.001 | ||||
| Male | 2,392 (50.00%) | 758 (44.10%) | 1,067 (53.40%) | 567 (53.14%) | |
| Female | 2,392 (50.00%) | 961 (55.90%) | 931 (46.60%) | 500 (46.86%) | |
| Marital status | < 0.001 | ||||
| Married | 3,921 (81.96%) | 1,423 (82.78%) | 1,758 (87.99%) | 740 (69.35%) | |
| Others | 863 (18.04%) | 296 (17.22%) | 240 (12.01%) | 327 (30.65%) | |
| Education | < 0.001 | ||||
| None or primary school | 1,504 (31.44%) | 1,303 (75.80%) | 201 (10.06%) | 0 (0.00%) | |
| Secondary school | 1,735 (36.27%) | 333 (19.37%) | 1,402 (70.17%) | 0 (0.00%) | |
| Higher school | 831 (17.37%) | 83 (4.83%) | 342 (17.12%) | 406 (38.05%) | |
| Associate degree and above | 714 (14.92%) | 0 (0.00%) | 53 (2.65%) | 661 (61.95%) | |
| Occupation | < 0.001 | ||||
| Unskilled | 2,910 (60.83%) | 1,698 (98.78%) | 1,163 (58.21%) | 49 (4.59%) | |
| Skilled | 583 (12.19%) | 21 (1.22%) | 411 (20.57%) | 151 (14.15%) | |
| Professional or managerial | 1,291 (26.99%) | 0 (0.00%) | 424 (21.22%) | 867 (81.26%) | |
| Family history of cancer | 0.001 | ||||
| No | 3,330 (69.61%) | 1,199 (69.75%) | 1,412 (70.67%) | 719 (67.39%) | |
| Yes | 1,266 (26.46%) | 430 (25.01%) | 522 (26.13%) | 314 (29.43%) | |
| Unknown | 188 (3.93%) | 90 (5.24%) | 64 (3.20%) | 34 (3.19%) | |
| Smoking status | < 0.001 | ||||
| No | 3,216 (67.22%) | 1,177 (68.47%) | 1,257 (62.91%) | 782 (73.29%) | |
| Yes | 1,568 (32.78%) | 542 (31.53%) | 741 (37.09%) | 285 (26.71%) | |
| BMI, kg/m2 | < 0.001 | ||||
| < 24 | 2,846 (59.49%) | 1,003 (58.35%) | 1,132 (56.66%) | 711 (66.64%) | |
| ≥ 24 | 1,938 (40.51%) | 716 (41.65%) | 866 (43.34%) | 356 (33.36%) | |
| Location | < 0.001 | ||||
| Urban | 734 (15.34%) | 101 (5.88%) | 239 (11.96%) | 394 (36.93%) | |
| Rural | 4,050 (84.66%) | 1,618 (94.12%) | 1,759 (88.04%) | 673 (63.07%) | |
| Self-reported health status | < 0.001 | ||||
| Good/relatively good | 3,334 (69.69%) | 1,065 (61.95%) | 1,441 (72.12%) | 828 (77.60%) | |
| Average/relatively poor/poor | 1,450 (30.31%) | 654 (38.05%) | 557 (27.88%) | 239 (22.40%) | |
| Household income per year | < 0.001 | ||||
| < 50,000 CNY | 1,247 (26.07%) | 1,002 (58.29%) | 107 (5.36%) | 138 (12.93%) | |
| 50,000-100,000 CNY | 2,288 (47.83%) | 546 (31.76%) | 1,382 (69.17%) | 360 (33.74%) | |
| > 100,000 CNY | 1,249 (26.11%) | 171 (9.95%) | 509 (25.48%) | 569 (53.33%) |
For categorical variables, data is expressed as n (%) of the total. P value was derived using the χ2 test for categorical variables. SES was generated through latent class analysis using information on education level, occupation, and household income. <¥50,000, ¥50,000–¥100,000, and > ¥100,000 of household income represented the low, medium, and high household income level, respectively
SES socioeconomic status, BMI body mass index
Table 3.
Socioeconomic status in terms of the dimension-specific rates of cancer literacy
| Dimensions | No. of participants | Adequate literacy (%) | Crude OR (95%CI) | Adjusted OR (95%CI) |
|---|---|---|---|---|
| Basic sense of cancer | ||||
| SES | 4,784 | 1,727 (36.10%) | ||
| Low | 1,719 | 431 (25.07%) | 1.00 (reference) | 1.00 (reference) |
| Medium | 1,998 | 679 (33.98%) | 1.54 (1.33–1.77, P < 0.001) | 1.39 (1.19–1.63, P < 0.001) |
| High | 1,067 | 617 (57.83%) | 4.10 (3.48–4.82, P < 0.001) | 3.53 (2.86–4.35, P < 0.001) |
| Cancer prevention | ||||
| SES | 4,784 | 2,051 (42.87%) | ||
| Low | 1,719 | 648 (37.70%) | 1.00 (reference) | 1.00 (reference) |
| Medium | 1,998 | 992 (46.65%) | 1.63 (1.43–1.86, P < 0.001) | 1.47 (1.28–1.70, P < 0.001) |
| High | 1,067 | 861 (80.69%) | 6.91 (5.77–8.28, P < 0.001) | 4.69 (3.77–5.84, P < 0.001) |
| Early detection | ||||
| SES | 4,784 | 2,408 (50.33%) | ||
| Low | 1,719 | 686 (39.91%) | 1.00 (reference) | 1.00 (reference) |
| Medium | 1,998 | 946 (47.35%) | 1.35 (1.19–1.54, P < 0.001) | 1.35 (1.17–1.55, P < 0.001) |
| High | 1,067 | 776 (72.73%) | 4.02 (3.40–4.74, P < 0.001) | 3.78 (3.06–4.66, P < 0.001) |
| Cancer treatment | ||||
| SES | 4,784 | 2,857 (59.72%) | ||
| Low | 1,719 | 714 (41.54%) | 1.00 (reference) | 1.00 (reference) |
| Medium | 1,998 | 1228 (61.46%) | 2.24 (1.97–2.56, P < 0.001) | 1.79 (1.55–2.07, P < 0.001) |
| High | 1,067 | 915 (85.75%) | 8.47 (6.96–10.31, P < 0.001) | 4.98 (3.93–6.31, P < 0.001) |
| Recovery | ||||
| SES | 4,784 | 2,367 (49.48%) | ||
| Low | 1,719 | 584 (33.97%) | 1.00 (reference) | 1.00 (reference) |
| Medium | 1,998 | 979 (49.00%) | 1.87 (1.64–2.13, P < 0.001) | 1.50 (1.30–1.73, P < 0.001) |
| High | 1,067 | 804 (75.35%) | 5.94 (5.01–7.05, P < 0.001) | 3.54 (2.87–4.37, P < 0.001) |
Multivariable logistic model, adjusted for age group, sex, marital status, family history of cancer, smoke status, BMI group, urban versus rural location and self-reported health status
Associations between socioeconomic status and cancer literacy
After adjusting for variables such as age interval, sex, marital status, family history of cancer, smoking status, BMI category, location, and self-reported health status, participants with high SES had an adjusted OR of 4.74 (95% CI: 3.66–6.14, P < 0.001) for adequate cancer literacy compared to those with low SES. Meanwhile, participants with medium SES also showed increased likelihood (adjusted OR: 1.38, 95% CI: 1.11–1.72). Similar results were obtained when SES was assessed using three common indicators (education, occupation, and household income), as shown in Table 2. The positive association between SES and the five specific dimensions of cancer literacy remained statistically significant (P < 0.05) (Fig. 2, Table 3).
Table 2.
Determination in socioeconomic-status and its indicators for cancer literacy
| Variable | No. of participants | Adequate literacy (%) | Crude OR (95%CI) | Adjusted OR (95%CI) |
|---|---|---|---|---|
| SES | ||||
| Low | 1,719 | 164 (9.54%) | 1.00 (reference) | 1.00 (reference) |
| Medium | 1,998 | 282 (14.11%) | 1.56 (1.27–1.91, P < 0.001) | 1.38 (1.11–1.72, P = 0.004) |
| High | 1,067 | 419 (39.27%) | 6.13 (5.01–7.51, P < 0.001) | 4.74 (3.66–6.14, P < 0.001) |
| P for trend test | P < 0.001 | P < 0.001 | ||
| Education | ||||
| None or primary school | 1,504 | 126 (8.38%) | 1.00 (reference) | 1.00 (reference) |
| Secondary school | 1,735 | 217 (12.51%) | 1.56 (1.24–1.97, P < 0.001) | 1.46 (1.15–1.87, P < 0.002) |
| Higher school | 831 | 175 (21.06%) | 2.92 (2.28–3.74, P < 0.001) | 2.77 (2.11–3.63, P < 0.001) |
| Associate degree and above | 714 | 347 (48.60%) | 10.34 (8.18–13.07, P < 0.001) | 9.21 (6.87–12.35, P < 0.001) |
| P for trend test | P < 0.001 | P < 0.001 | ||
| Occupation | ||||
| Unskilled | 2,910 | 378 (12.99%) | 1.00 (reference) | 1.00 (reference) |
| Skilled | 583 | 94 (16.12%) | 1.29 (1.01–1.65, P = 0.044) | 0.99 (0.76–1.28, P = 0.938) |
| Professional or managerial | 1,291 | 393 (30.44%) | 2.93 (2.50–3.44, P < 0.001) | 1.84 (1.53–2.23, P < 0.001) |
| P for trend test | P < 0.001 | P < 0.001 | ||
| Household income | ||||
| < 50,000 CNY | 1,247 | 154 (12.35%) | 1.00 (reference) | 1.00 (reference) |
| 50,000-100,000 CNY | 2,288 | 377 (16.48%) | 1.40 (1.14–1.71, P = 0.001) | 1.19 (0.96–1.47, P = 0.111) |
| > 100,000 CNY | 1,249 | 334 (26.74%) | 2.59 (2.10–3.20, P < 0.001) | 1.84 (1.47–2.30, P < 0.001) |
| P for trend test | P < 0.001 | P < 0.001 | ||
Multivariable logistic model, adjusted for age group, sex, marital status, family history of cancer, smoke status, BMI group, urban versus rural location and self-reported health status
Fig 2.
SES socioeconomic status and its indicators in terms of the dimension-specific rates of cancer literacy
Subgroup analysis and sensitivity analysis
We further examined the impact of socioeconomic status on cancer literacy across various subgroups defined by age, sex, marital status, family history of cancer, smoking status, BMI category, and location (Table S4). Our findings revealed that participants aged 15–44 years (OR = 7.50, 95% CI: 3.18–17.69), females (OR = 4.83, 95% CI: 3.38–6.92), and those residing in urban areas (OR = 6.21, 95% CI: 4.28–8.99) had a significantly higher likelihood of adequate cancer literacy compared to older participants, males, and those living in rural areas, respectively. No significant interactions between SES and cancer literacy for different subgroups were observed. In sensitivity analyses, the high SES group was alternatively used as the reference category to examine SES-related disparities from a public health equity perspective. As expected, the direction of the OR was reversed compared with the main analysis using low SES as the reference, while the statistical significance remained unchanged. The results were reported in Table S5.
Discussion
Cancer literacy is a multidimensional construct that mainly includes tertiary prevention of cancer, which will provide a comprehensive assessment of cancer literacy. Recent studies on cancer literacy have examined across the United States, Canada, Portugal, Switzerland, and China [20–23]. Low cancer literacy poses a significant barrier for cancer patients and is closely linked to increased cancer risk [24]. Insufficient cancer literacy has been associated with poorer self-management and unhealthy behaviors, resulting in worse health outcomes [25]. This multicenter cross-sectional survey demonstrated that only 18.08% of respondents possess adequate cancer literacy in Zhejiang province.
Consistent with literature reporting lower prevalence of cancer literacy, our study used latent class analysis to derive an integrated SES classification and demonstrated clear differences in cancer literacy across SES classes among residents of Zhejiang Province. This integrated SES approach facilitates the identification of population subgroups with distinct cancer literacy profiles and may inform more targeted cancer prevention and intervention strategies. One significant finding is that a relatively high SES of respondents serves as a positive predictor of adequate cancer literacy in Zhejiang Province. The lower prevalence of cancer literacy, identified in approximately one-fifth of respondents, has, to our knowledge, not been previously reported on this scale.
Evidence on the relationships between cancer literacy (as a component of primary prevention) and SES is still scarce in China. Consequently, this study aimed to address this gap in the literature by providing robust evidence of the positive associations between the prevalence of cancer literacy and SES, utilizing data from population-based research involving residents of Zhejiang. The findings underscore the necessity of targeting health strategies towards enhancing cancer literacy among Zhejiang residents, particularly those with lower SES. Participants with lower SES in our study consistently demonstrated limited cancer literacy. This observation aligns with a substantial body of existing evidence indicating that SES plays a critical role across the cancer continuum. Our findings indicate that participants with lower SES tend to have limited cancer literacy. An umbrella review has shown that SES is strongly associated with cancer incidence, prognosis, screening participation, and access to cancer treatment [26]. Epidemiological studies have further shown that greater socioeconomic deprivation was linked to higher cancer incidence [27], poorer survival outcomes [28, 29], and lower uptake of cancer screening services [30, 31]. The paramount importance of early cancer screening and treatment cannot be overstated. Nevertheless, it is deeply concerning that individuals with the lowest SES frequently encounter significant obstacles to accessing effective screening and treatment measures. Additionally, a qualitative study suggests that socioeconomic inequality hinders access to healthcare for individuals with advanced cancer, contributing to suboptimal cancer outcomes [32].
Previous studies have primarily concentrated on delineating the impact of individual or selected SES indicators. Socioeconomically disadvantaged populations consistently exhibit poorer cancer outcomes across different settings. Korean researchers found that income disparities in 5-year cancer survival rates revealed a significant overall improvement, with higher survival rates corresponding to increased income levels [33]. U.S. cancer survivors indicated that unemployment, a family income-to-poverty ratio below 2.4, and education less than high school were significantly linked to nearly a two-fold elevation in the risk of all-cause and cancer-specific mortality [34]. A national study including three cancer screening programs in Denmark revealed that women with low incomes, less than 11 years of education, and those outside the workforce had significantly higher odds of non-participation [35]. In contrast to this approach, the present study adopted a LCA framework to define SES by jointly integrating household income, occupation, and educational attainment, thereby capturing multidimensional socioeconomic profiles within the population.
Using this SES classification, we simultaneously evaluated the associations between SES and overall cancer literacy and its five distinct dimensions. Among the five dimensions of cancer literacy, the association between SES and cancer treatment literacy was the most pronounced (OR: 4.98, 95% CI: 3.93–6.31), while significant associations were also observed for cancer prevention, early detection and treatment, recovery, and basic knowledge. No significant interactions between SES and cancer literacy were observed across subgroups, while stronger positive correlations were evident among younger individuals, females, and urban residents. Greater exposure to health education, higher receptivity to new health information, and stronger health awareness in these groups may collectively contribute to higher levels of cancer knowledge and more proactive health behaviors [36, 37].
Existing evidence suggests that lower cancer literacy is associated with reduced participation in cancer screening, delayed diagnosis, poorer treatment adherence, and unhealthy lifestyle behaviors such as smoking, physical inactivity, and suboptimal diet, all of which contribute to higher cancer incidence and mortality [38, 39]. Importantly, emerging evidence suggests that cancer literacy is not only socially patterned but also modifiable. Targeted cancer prevention and intervention strategies tailored to different socioeconomic groups—such as SES-sensitive health education, culturally appropriate communication, and community-based outreach—have demonstrated effectiveness in improving cancer knowledge, risk awareness, and screening uptake, particularly among low-SES populations [40]. Taken together, these findings suggest that implementing differentiated, SES-specific cancer prevention and intervention strategies may represent a feasible and actionable approach for clinicians and public health professionals to reduce cancer disparities and improve population health outcomes.
Lower levels of literacy have been consistently associated with adverse cancer-related outcomes, including reduced participation in cancer screening, delayed help-seeking and diagnosis, poorer treatment adherence, and a higher prevalence of unhealthy lifestyle behaviors such as smoking and physical inactivity, which are known contributors to cancer incidence and mortality [41–47]. These studies suggest that cancer literacy may influence cancer outcomes indirectly through these intermediate behavioral and clinical pathways. Consequently, improving cancer literacy represents a potentially modifiable and promising target for cancer prevention and control, particularly among socioeconomically disadvantaged populations.
Our study highlighted the importance of the association between the SES and cancer literacy using a relatively large sample and strict quality control procedures. Additionally, both integrated SES and its three indicators, commonly employed in prior studies, were incorporated into the regression analysis. Yet we could not determine whether improvements in SES lead to improvements in healthy behaviors. Therefore, further studies need to elucidate whether SES can lead to elevated cancer literacy and behavior change. Nevertheless, several limitations should be acknowledged. First, due to the observational cross-sectional design, these results cannot establish a causal relationship between SES and cancer literacy. Second, SES and covariate data were primarily self-reported by participants, potentially introducing recall bias. Additionally, SES and its indicators may shift over time, and our classification of participants into SES clusters based on posterior item probabilities may carry some inaccuracy. Future research should consider examining SES transitions from early life stages and identifying more precise methods for classification.
Conclusion
The findings of this study indicate that when employing LCA to identify clusters of socioeconomic factors, lower SES is significantly associated with limited cancer literacy.
Priority should be given to low-SES populations through targeted health education and improved access to cancer information, with the aim of reducing future cancer incidence, mortality, and even socioeconomic inequalities. Future research should further examine how SES influences cancer-related outcomes across the life course and evaluate the effectiveness of targeted cancer literacy interventions among low-SES populations.
Supplementary Information
Supplementary Material 1. National survey on the rates of cancer literacy in China.
Supplementary Material 2. Table S1. Baseline characteristics according to cancer literacy. Table S2. Prevalence of latent classes, and item-response probabilities in models with the best three latent classes. Table S3. G2 statistics, AIC, and BIC in models with different numbers of latent socioeconomic status classes. Table S4. Association between socioeconomic status indicators and cancer literacy, stratified by various baseline characteristics. Table S5. Sensitivity analysis of the association between socioeconomic status and cancer literacy using the high SES as the reference. Figure S1. Item-response Probabilities in Models.
Supplementary Material 3. STROBE Statement—Checklist of items that should be included in reports of cross-sectional studies.
Acknowledgements
The authors thank all participants enrolled in the study. We are grateful to the support and dedicated work in data collection of all the investigators from 12 sites of Zhejiang province.
Abbreviations
- SES
Socioeconomic status
- LAC
Latent class analysis
- AIC
Akaike Information Criterion
- BIC
Bayesian Information Criterion
- BMI
Body mass index
Authors’ contributions
LW conceptualized the study, contributed to the methodology, formal analysis, investigation, and the manuscript writing - original draft and review & editing. HT conceptualized the study, contributed to the formal analysis, visualization, and the manuscript writing - original draft and review & editing. DY contributed to the investigation, methodology and writing - review & editing. YD contributed to writing - review & editing. WR contributed to data curation, and investigation. LD and YX contributed to the conceptualization, supervision, and writing - review & editing.
Funding
No.
Data availability
The data are not publicly accessible due to privacy constraints. For further information, please email with the corresponding author.
Declarations
Ethics approval and consent to participate
This study was approved by the Research Ethics Committee of Zhejiang Cancer Hospital, China (IRB-2024-787), and conducted in accordance with the guidelines of the Declaration of Helsinki. Written informed consent was obtained from all participants.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
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Le Wang, Huan-Qing Tao and Ding-Ming Yao contributed equally to this work.
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Yue Xu, Email: yxu@cdc.zj.cn.
Ling-Bin Du, Email: dulb@zjcc.org.cn.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary Material 1. National survey on the rates of cancer literacy in China.
Supplementary Material 2. Table S1. Baseline characteristics according to cancer literacy. Table S2. Prevalence of latent classes, and item-response probabilities in models with the best three latent classes. Table S3. G2 statistics, AIC, and BIC in models with different numbers of latent socioeconomic status classes. Table S4. Association between socioeconomic status indicators and cancer literacy, stratified by various baseline characteristics. Table S5. Sensitivity analysis of the association between socioeconomic status and cancer literacy using the high SES as the reference. Figure S1. Item-response Probabilities in Models.
Supplementary Material 3. STROBE Statement—Checklist of items that should be included in reports of cross-sectional studies.
Data Availability Statement
The data are not publicly accessible due to privacy constraints. For further information, please email with the corresponding author.