GRAPHICAL ABSTRACT

INTRODUCTION

The National Cancer Screening Program (NCSP), launched in Korea in 1999, has played a pivotal role in facilitating early cancer detection and reducing cancer-related mortality [1]. Despite these achievements, substantial socioeconomic disparities persist in screening participation, with marked differences observed across income groups [2,3]. Because most cancer screenings are available at minimal or no cost, these inequalities suggest that non-economic determinants contribute meaningfully to screening participation behaviors.

Income-related disparities in cancer screening are well established in both Korean and international contexts [2,4]. Health literacy (HL), defined as the ability to access, comprehend, appraise, and apply health information [5], has been identified as a potential mediator linking socioeconomic status to preventive health behaviors [6,7]. While systematic reviews have documented associations between HL and cancer screening participation [3], most empirical studies have focused primarily on direct income–screening relationships [8–10], leaving the specific mechanisms through which HL operates relatively underexplored.

The potential mediating role of HL can be conceptualized through multiple pathways. First, HL may enhance information-processing capacity, thereby enabling more accurate comprehension of screening guidelines and personal cancer risk [7]. Second, higher HL may facilitate medical decision-making by improving navigation of healthcare systems and communication with healthcare providers [5]. Third, HL may strengthen the formation of behavioral intentions through increased self-efficacy and perceived control over health outcomes [6]. Collectively, these pathways suggest that HL functions as a critical mechanism through which socioeconomic resources are translated into preventive health behaviors.

From a life course perspective, cumulative advantage/disadvantage theory posits that socioeconomic inequalities compound over time, resulting in widening health disparities during midlife and older age [11,12]. In Korea, age 40 marks the initiation of the NCSP for stomach cancer, the most prevalent cancer type, as well as for liver and breast cancers [1]. Stomach and liver cancer screenings apply to both men and women, establishing age 40 as a universal screening entry point across genders. This alignment renders age 40 a critical transition point at which income-related disparities may begin to translate into differential levels of HL and preventive health behaviors [13].

Drawing on these theoretical perspectives, this study employs a moderated mediation framework to examine how age shapes the pathway through which income influences cancer screening participation via HL. We hypothesize that the mediating role of HL strengthens with age for 2 primary reasons. First, socioeconomic disparities in information access and cognitive resources accumulate over the life course, making HL gaps between income groups more pronounced in older adults [14]. Second, the salience of HL for screening decisions increases with age as chronic disease burden rises and preventive care becomes more complex [15].

This age-dependent mediation pattern carries important policy implications. If the mediating role of HL strengthens in older adulthood, interventions targeting this pathway would be expected to yield greater efficiency when directed toward older age groups. However, identifying the specific age at which this strengthening begins requires empirical investigation. Previous studies have primarily examined mediation effects across the adult population as a whole [3,5], leaving unresolved the critical questions of when these effects emerge and how intervention efficiency varies by age.

Therefore, this study investigated how the mediating role of HL in the relationship between income and cancer screening participation varies by age among Korean adults aged ≥40 years and assessed the efficiency of potential age-specific interventions.

METHODS

Study Design and Participants

This study used data from the 2023 Korea National Health and Nutrition Examination Survey (KNHANES) [16]. The target population comprised adults aged ≥40 years. Of the 5234 individuals surveyed, 1063 were excluded due to missing data on HL (n=763) or other key variables (n=300). The final analytic sample comprised 4171 participants. The age threshold of 40 years was selected in accordance with national cancer screening guidelines, which designate this age as the common starting point for multiple cancer screening programs [1].

Measures

The primary independent variable was income level, operationalized as equivalized household income (monthly household income divided by the square root of household size) and categorized into quintiles (Q1: lowest 20% to Q5: highest 20%). The dependent variable, cancer screening participation, was defined as a binary indicator of whether an individual had undergone either national or private cancer screening within the previous 2 years (1=yes, 0=no).

HL, the mediating variable, was assessed using the Health Literacy Instrument for Community (HLIC), which was newly included in the 2023 KNHANES and developed by Yoon et al. [17]. The HLIC comprises 10 self-reported items covering 4 domains: disease prevention, health promotion, health management, and resource utilization. Each item is rated on a 4-point scale, yielding total scores ranging from 10 to 40, with higher scores indicating greater ability to comprehend health-related information. Age, treated as a continuous moderator, was mean-centered by subtracting the weighted sample mean (58.4 years) to reduce multicollinearity and improve interpretability.

Covariates included educational attainment, sex, residential area, marital status, employment status, smoking status, alcohol consumption frequency, physical activity, presence of chronic disease, and self-rated health.

Statistical Analysis

All analyses accounted for the complex survey design of KNHANES [16]. Descriptive statistics, chi-square tests, analysis of variance, and logistic regression analyses were conducted using SPSS Complex Samples version 29.0 (IBM Corp., Armonk, NY, USA).

To evaluate whether the mediating effect of HL on the relationship between income and cancer screening participation varied by age, we conducted moderated mediation analysis (Supplemental Material 1). This analysis represents statistical mediation rather than causal mediation; given the cross-sectional study design, the findings should be interpreted as statistical association patterns that are consistent with theoretically hypothesized pathways, rather than as definitive causal effects [18,19]. The analysis employed the lavaan.survey package in R version 4.3.2 [20] to properly account for the complex sampling design of KNHANES, incorporating stratification (192 strata), clustering (primary sampling units), and sampling weights (wt_itvex) [21]. Path coefficients were estimated using maximum likelihood with robust standard errors (MLR estimator), and indirect effects were tested using the Delta method [19].

The Johnson–Neyman technique [22] was used as an exploratory approach to identify the age threshold at which the conditional effect of income on HL became statistically significant.

For policy relevance, total and conditional indirect effects were transformed from log-odds to the number needed to benefit (NNB) using the formula NNB=1/[β×p×(1−p)], where β represents the effect coefficient and p denotes the baseline cancer screening rate (72.5%) [23]. The NNB reflects the number of individuals who would need to receive an intervention to generate 1 additional cancer screening participant, under the assumption that the observed associations approximate causal effects. Statistical significance was defined as p<0.05.

Ethics Statement

This study was approved with exemption by the Institutional Review Board of Kyungpook National University Hospital (IRB No. KNUH 2025-08-004).

RESULTS

Participant Characteristics

Table 1 presents the weighted socio-demographic characteristics of the 4171 participants according to cancer screening participation status. The mean age of participants was 58.4 years, and 57.6% were female. Overall, 72.5% reported undergoing cancer screening within the previous 2 years. Screening participation varied significantly across income levels, ranging from 59.9% in the lowest income quintile to 79.3% in the highest quintile, representing a 19.3-percentage-point difference (p<0.001).

Higher screening participation rates were observed among females, individuals with higher educational attainment, married individuals, and those in higher income groups (all p<0.001). Participants engaging in aerobic physical activity demonstrated significantly higher participation rates (p<0.001). In contrast, current smokers exhibited lower screening participation compared with never or former smokers (p<0.001). Mean HL scores were significantly higher among participants who underwent screening (29.8±0.1) than among those who did not (28.5±0.2; p<0.001).

Association Between Income and Cancer Screening

Table 2 summarizes the results of the complex-sample logistic regression analysis adjusted for covariates. Each 1-quintile increase in income was associated with higher odds of cancer screening participation (odds ratio [OR], 1.16; 95% confidence interval [CI], 1.09 to 1.24; p<0.001). Similarly, each 1-point increase in HL score was associated with an increased likelihood of screening participation (OR, 1.03; 95% CI, 1.01 to 1.04; p<0.001). Being married (OR, 1.49; 95% CI, 1.23 to 1.80; p<0.001), having a chronic disease (OR, 1.23; 95% CI, 1.04 to 1.45; p=0.015), and engaging in aerobic physical activity (OR, 1.24; 95% CI, 1.03 to 1.48; p=0.025) were also positively associated with screening participation. In contrast, current smoking was negatively associated with screening participation (OR, 0.52; 95% CI, 0.39 to 0.69; p<0.001). Age was not significantly associated with screening participation. Model fit was moderate (Nagelkerke R²=0.083).

Age-moderated Mediation Effects

Table 3 shows the results of the age-moderated mediation analysis. Higher income was significantly associated with greater HL (a₁ path: B=0.234, p=0.004), which, in turn, was positively related to cancer screening participation (b path: B=0.026, p=0.001). The direct effect of income on screening participation remained significant after adjustment for HL (c′ path: B=0.151; 95% CI, 0.089 to 0.214; p<0.001). Notably, the interaction term between income and age demonstrated a significant moderating effect on HL (a₃ path: B=0.017; 95% CI, 0.002 to 0.032; p=0.026). The index of moderated mediation confirmed the statistical significance of this conditional indirect process (index=0.000438; 95% CI, 0.000020 to 0.000896; p=0.048), indicating that the strength of the income–HL–screening pathway increases with age.

Age-specific Conditional Indirect Effects (Johnson–Neyman Analysis)

Table 4 presents the results of the Johnson–Neyman analysis, which identified age 54.2 years as the threshold at which the conditional effect of income on HL became statistically significant (p=0.050). The effect was not significant at age 50 (B=0.092; 95% CI, −0.096 to 0.281; p=0.338) but reached statistical significance at age 55 (B=0.177; 95% CI, 0.017 to 0.336; p=0.030). The magnitude of the effect continued to increase with advancing age, reaching B=0.429 (95% CI, 0.182 to 0.677; p<0.001) at age 70 and B=0.598 (95% CI, 0.223 to 0.973; p<0.001) at age 80. At age 40, the conditional effect was negative but not statistically significant (B=−0.076, p=0.616), suggesting that the income–HL pathway emerges gradually during the 50s.

Policy Efficiency Analysis

Table 5 quantifies the total and indirect effects of income on cancer screening participation and their corresponding efficiencies by age group. The proportion of the income effect mediated through HL increased from 0.1% among participants aged 40–49 years to 8.1% among those aged 70 years or older. Although the NNB for the total effect decreased modestly with age, from 33 in the 40–49 age group to 30 among those aged ≥70 years, the NNB specific to the HL pathway was 372 for participants aged ≥70 years, indicating relatively high intervention efficiency in older adults. In contrast, the estimated NNB for the 50–59 age group (1084) was not statistically significant. Overall, the results indicate a transitional period during the 50s, during which the indirect effect begins to emerge, followed by statistically significant mediation from the 60s onward, with intervention efficiency improving progressively with increasing age.

DISCUSSION

This study investigated how the mediating role of HL in the relationship between income and cancer screening participation varies with age among Korean adults aged 40 years and older. The findings can be summarized in 3 main points. First, income was a robust and independent predictor of cancer screening participation after adjustment for multiple confounding variables. Second, the mediating effect of HL emerged from the mid-50s onward (Johnson–Neyman threshold: 54.2 years). Third, the contribution of this HL pathway to the total income effect increased progressively with age, reaching its highest level (8.1%) among individuals aged 70 years or older, underscoring the importance of age-differentiated policy interventions. Each 1-quintile increase in income corresponded to a 16.0% higher likelihood of undergoing cancer screening, consistent with established income gradients in screening participation [2,4]. Such disparities persist despite the low cost of NCSP examinations [1], suggesting that structural factors, including time availability, access to information, and social support, play significant roles beyond direct economic considerations [8]. HL significantly mediated the income–screening association, corroborating prior research identifying HL as a key mechanism linking socioeconomic status to preventive health behaviors [3,14,24]. The modest model fit (Nagelkerke R²=0.083) is typical for behavioral outcomes [25,26], in which multiple unmeasured factors influence screening decisions.

The age-specific mediation analysis revealed that HL effects were non-significant below age 50 but became statistically significant from the mid-50s onward, suggesting that income exerts a stronger influence on HL during this transitional period, which subsequently affects screening participation. These findings are consistent with cumulative advantage theory [11,12], which posits that socioeconomic inequalities accumulate across the lifespan and widen health gaps in middle and later life. As multiple cancer screening programs are initiated during the 50s, disparities in accumulated socioeconomic resources may manifest as substantial differences in HL and screening behavior. Similar age-related patterns have been reported in previous studies [24,27,28]. The non-significant negative effect observed at age 40 warrants consideration. This pattern may reflect that HL in younger adults is more strongly shaped by recent educational experiences rather than accumulated socioeconomic resources, and that cancer screening is not yet routine for most cancer types at this age [24]. The transition to positive effects from the mid-50s aligns with the initiation of multiple cancer screening programs. As screening becomes more personally salient, accumulated socioeconomic resources increasingly shape HL, which in turn influences screening participation. This age-dependent pattern supports targeted intervention strategies for adults in their mid-50s and older, when HL disparities begin to translate into observable behavioral inequalities. However, the cross-sectional design precludes definitive separation of age effects from cohort effects [29,30]. The observed patterns may therefore partly reflect cohort-specific characteristics. Older cohorts experienced markedly different educational systems and access to digital information compared with younger cohorts, potentially contributing to the stronger income–HL association observed among older adults. Although disentangling these explanations lies beyond the scope of this study, the findings nonetheless indicate that current older adults face structural disadvantages in HL, underscoring the need for age-sensitive interventions.

The NNB estimates provide a population-level perspective on the potential impact of addressing income-related disparities in cancer screening. These estimates are derived from observed statistical associations and assume that such associations approximate causal effects. The estimated total NNB of 30–33 suggests that, if this assumption holds, addressing income disparities could meaningfully increase screening uptake. However, these values should be interpreted as approximate indicators of intervention efficiency rather than as precise predictions. For comparison, randomized trials have reported NNBs ranging from 143 to 250 [31,32], although direct comparison is limited because the present estimates are derived from observational data rather than experimental interventions. Applying the NNB estimates to the lowest income quintile suggests that addressing income disparities could yield meaningful increases in screening participation at the population level. This finding illustrates Rose [33]’s prevention paradox, whereby modest individual-level associations can translate into substantial population-level benefits. Nonetheless, these projections remain theoretical and may differ in practice due to unmeasured confounding or implementation challenges. Among adults aged 70 years or older, the HL pathway accounted for 8.1% of the total income effect, suggesting that integrated approaches combining income support with HL enhancement may be particularly effective in this age group. Experimental validation is required to determine whether HL-focused interventions can reproduce these naturally occurring mediating relationships. While cancer screening entails potential harms [34,35], Korea’s NCSP targets cancers for which screening benefits are well established [1], and the observed disparities primarily reflect underuse among disadvantaged groups. Future interventions should therefore emphasize informed decision-making rather than simple uptake promotion. These findings support age-differentiated strategies. For older adults, particularly those in their mid-50s and older who may experience limited information access due to cognitive decline, digital disparities, or social isolation, economic support alone may be insufficient. Choi et al. [15] highlighted the necessity of age-appropriate health education and the mediating role of self-efficacy between HL and health-promoting behaviors in rural older populations [36].

This study makes notable methodological contributions by applying moderated mediation analysis and the Johnson–Neyman technique to quantify age-specific thresholds, thereby advancing methodological rigor in health inequality research. The use of NNB analysis to estimate policy efficiency offers a practical framework for translating statistical associations into actionable policy insights. Nevertheless, several limitations warrant consideration. First, the cross-sectional design precludes definitive causal inference, particularly with respect to disentangling age effects from cohort effects. Second, unmeasured confounders, including cognitive function, social support, and health-related attitudes, may have influenced the observed associations, potentially biasing the estimated effects. Third, cancer screening participation was measured as a binary outcome (screened vs. not screened), which may not fully capture the nuanced and multifactorial nature of screening decisions. In addition, analyses were not stratified by cancer type, which may have obscured variation in screening initiation age and cancer-specific screening practices. Finally, both HL and cancer screening participation were self-reported, introducing the possibility of common method bias that could have inflated mediation estimates. Collectively, these limitations underscore the need for cautious interpretation of the findings.

Despite these constraints, this study demonstrates—using large-scale, nationally representative KNHANES data—how the mediating effect of HL on the income–cancer screening relationship varies by age and identifies critical timing for HL interventions. The Johnson–Neyman analysis identified the mid-50s as a key transition period (threshold: 54.2 years). However, this threshold reflects an exploratory, data-driven estimate that may vary across model specifications or populations and should therefore be interpreted as an approximate indicator rather than a precise cutoff. Independent validation is warranted before adopting this threshold as a definitive policy benchmark. Nevertheless, the broader finding that the mediating role of HL strengthens substantially from the mid-50s onward extends theoretical understanding of life-course mechanisms underlying health inequalities and provides empirical support for policy formulation using NNB-based efficiency analyses.

Future research should pursue several directions. First, age–period–cohort analyses using longitudinal data are needed to disentangle age effects from cohort influences. Second, age-dependent mediation patterns should be examined across other preventive health behaviors. Third, randomized controlled trials of HL interventions stratified by age group are essential to establish causality. Finally, international comparative studies should explore how cultural and institutional contexts shape the relationships among HL, socioeconomic status, and health behavior.

In conclusion, HL significantly mediated the association between income and cancer screening participation beginning at age 54.2 years (corresponding approximately to age 55 in practical terms), with the magnitude of mediation increasing to 8.1% among individuals aged ≥70 years. Intervention efficiency, as reflected by the NNB, was clearly demonstrated in older adults (NNB=372 for those aged ≥70), whereas efficiency in the 50–59 age group was not statistically established.

These findings suggest that age-differentiated strategies may be beneficial. For younger adults, prioritizing structural accessibility improvements may be more effective. For individuals in their mid-50s and older, particularly those aged ≥70 years, integrated interventions combining income support with HL enhancement may be warranted. If the observed associations reflect causal relationships, such strategies could meaningfully reduce income-related disparities in cancer screening participation.

Given the cross-sectional nature of the data, future experimental and longitudinal studies are required to confirm causal relationships and to validate the effectiveness of age-specific intervention strategies.

Supplemental Materials

Notes

Conflict of Interest

The authors have no conflicts of interest associated with the material presented in this paper.

Funding

None.

Acknowledgements

None.

Author Contributions

Conceptualization: Hong H, Kim JY. Data curation: Hong H, Goo HJ, Kim JY. Formal analysis: Hong H, Goo HJ, Kim JY. Funding acquisition: None. Methodology: Hong H, Kam S, Kim JY. Writing – original draft: Hong H, Goo HJ, Choi H, Kim JY. Writing – review & editing: Kam S, Kim JY.

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