# Does health literacy mediate the relationship between socioeconomic status and navigation in the healthcare system? The French national Health Literacy Survey (HLS19)

Rajae Touzani, Camelia Protopopescu, Alexandra Rouquette, Cécile Allaire,

Agnès Dumas, Jean-Benoît Hardouin, Julien Mancini

► To cite this version:

Rajae Touzani, Camelia Protopopescu, Alexandra Rouquette, Cécile Allaire, Agnès Dumas, et al. Does health literacy mediate the relationship between socioeconomic status and navigation in the healthcare system? The French national Health Literacy Survey (HLS19). Patient Education and Counseling, 2025, 138, pp.109204-109204. 10.1016/j.pec.2025.109204. hal-05237155

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Submitted on 23 Sep 2025

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This study underscores the pivotal role of general HL as a mediator in the relationships between socioeconomic factors and navigational HL. Interventions aimed at enhancing general HL could substantially improve the ability to navigate the healthcare system, leading to more efficient healthcare navigation and better health outcomes.

# 1. Introduction

Health literacy (HL), defined as the ability to access, understand, analyze and use the health information needed to make appropriate health decision, is crucial for achieving good health outcomes and effective engagement with healthcare systems [1]. Previous studies have showed links between HL and various health outcomes, such as better self-management of chronic diseases, higher rates of preventive care use and good general health [2–5]. Moreover, socioeconomic factors such as income, education level and age have been extensively associated with HL [3,6,7].

However, as healthcare systems become increasingly complex, there is growing recognition of the importance of specific HL dimensions, especially a navigational HL. This specific dimension refers to people's knowledge, motivation and skills to access, understand, appraise, and apply information and communication in various forms as necessary to navigate healthcare systems and services adequately, so as to get the most suitable healthcare for oneself or related persons [8]. This skillset is essential to ensure that users of the healthcare system can engage with it effectively and fairly, make informed decisions and achieve better health outcomes [9,10]. Although two validated instruments exist for measuring navigational HL - the dimension 7 of the HLQ scale [11], and more recently, a HLS19-NAV scale developed by the HLS19 Consortium of M-POHL (WHO Action Network on Measuring Population and Organizational Health Literacy) [8,12,13] - research specifically examining variables associated with navigational HL remains limited. Initial analyses of the HLS19 survey showed that financial deprivation and self-perceived social status were significant predictors of navigational HL [12] but also large overlaps between navigational and general HL using HLS19-NAV and HLS19-Q12 scales respectively, particularly in France. Despite a strong correlation more specific analyses confirmed that HLS19-NAV and HLS19-Q12 scales measure two distinct latent traits in France [13].

Other studies have shown that HL is an important predictor of the ability to navigate the healthcare system [14]. Indeed, people with a high level of general HL are more likely to have the skills needed to navigate this complex environment, resulting in regular follow-ups with doctors [15], appropriate use of preventive care services [16,17] and a reduction in hospital admissions [18]. This optimal use of healthcare services not only reduces costs to society, but also leads to better health outcomes. Moreover, enhancing the ability to navigate properly the healthcare system helps reduce disparities in healthcare access and outcomes, particularly among vulnerable populations [7]. For example, economic status, education level, and social support have been shown to impact general HL, which in turn may influence an individual's ability to navigate the healthcare system [19,20]. Ultimately, navigational and general HL are related and both determined by sociodemographic and socioeconomic factors [12]. However, the role of general HL as a mediating variable in the relationships between socioeconomic variables and navigational HL has not been thoroughly investigated. Understanding this mediating role is critical, to better understand if disadvantaged social and economic conditions contribute directly to difficulties in navigating the healthcare system as it can help identify which interventions might be most effective in improving navigational HL by, first, enhancing general HL.

Given its critical role, it is important to better understand the factors that influence navigational HL and how it is related to general HL. Our study aims to fill these gaps by examining the factors associated with navigational HL and assessing the mediating role of general HL for these relationships. By using a mediation analysis, we aimed to elucidate the

complex relationships between various socioeconomic variables and both general and navigational HL.

# 2. Methods

## 2.1. Data source

The World Health Organization has launched the Action Network on Measuring Population and Organizational Health Literacy, which elaborated the Health Literacy Survey 2019–2021 (HLS19) [21]. Carried out in 17 countries in the WHO European region, the aim of HLS19 was to measure HL in the general population, but also specific HL such as navigational HL, communicative HL, digital HL and vaccination HL, using newly developed instruments [22]. HLS19 was conducted in France over two waves (June 2020 and January 2021), with 2 003 respondents aged 18–75 years, representative of the French general population [23–25]. The survey was conducted online on the Ipsos iSay panel, using the quota sampling method according to gender, age, region and place of residence. The average response time for the questionnaire was 22 min [25].

The study methodology was reviewed and approved by the Ethics Evaluation Committee of the French National Health and Medical Research Institute (CEEI, IRB 00003888).

## 2.2. Measurements

Respondents' ability to navigate the healthcare system was measured using the HLS19-NAV scale [12,13]. This scale assesses individuals' ability to access, understand, evaluate and apply information while communicating in various forms necessary for navigating healthcare systems and services effectively, in order to ensure the acquisition of the most appropriate care for themselves or their loved ones [8].

General HL was measured using the HLS19-Q12 scale [13,26]. It has been defined as the knowledge, motivation and skills of people to access, understand, evaluate and apply information in order to form judgments and make decisions in terms of healthcare, disease prevention and health promotion so as to improve quality of life over the life course [26]. Both scales consist of 12 items on a four-point Likert scale: “3” very easy, “2” easy, “1” difficult and “0” very difficult. Scores were standardized on a scale from 0 to 100, with 100 representing the highest level of navigation and general HL [13].

Although both scales were developed based on the 12-cell conceptual matrix HL of Sørensen [1,27], they capture distinct latent traits [13]. The HLS19-Q12 scale provides a broad assessment of HL across the three domains of healthcare, disease prevention, and health promotion, while the HLS19-NAV scale specifically targets the ability to navigate the healthcare system, focusing on access to care, interaction with healthcare providers, and understanding of system organization.

The subjective social status (SSS) was declared using a visual analog scale according the question "on the following scale, with 1 corresponding to the lowest level in society and 10 to the highest level in society, can you tell us what level you think you are at?"[28].

Financial difficulties were measured using a score ranging from 0 to 100. This score was determined by standardizing the average of answers to three questions relating to participants' (i) ability to pay all monthly bills, (ii) ability to buy necessary medicines, and (iii) ability to cover medical examinations and treatments not covered by health insurance, with response categories: '0' very easy, '1' easy, '2' difficult, and '3' very difficult. A high score indicated a higher financial difficulties [13,23].

Chronic conditions were assessed by asking respondents if they had
any chronic or long-term illness or health problem, with responses dichotomized into ‘yes’, including those with one or more health problems, and ‘no’.

Proximity to the healthcare system was measured using a proxy linked to healthcare training. Respondents were asked to specify whether they had ever received health-related training (yes vs. no).

The survey wave (first vs. second, i.e. June 2020 vs. January 2021) was also taken into account in order to evaluate whether the period had an effect on the results, bearing in mind that the first wave was carried out at the end of the first lockdown of the COVID-19 health crisis in France. In addition, sociodemographic data, including age, gender, area of residence (urban vs. rural), level of education and professional status (active vs. inactive), were collected and used in this study.

## 2.3. Statistical analyses

After descriptive analyses, bivariate analyses were employed to investigate factors linked to both general (HLS19-Q12) and navigational (HLS19-NAV) HL. The relationships between categorical variables and HL scores were described using means, standard deviations (SD), and Student's t-test. The Pearson linear correlation coefficient was used for quantitative variables. The statistical significance level was set at 5%.

Based on Baron and Kenny [29], a mediation analysis was carried out to explore the mediating effects of general HL on the relationships between socioeconomic status, chronic conditions, and healthcare system proximity and navigational HL using a structural equation model (SEM) [30]. Indeed, according to existing studies, socioeconomic status and health status were correlated with general HL, and the latter was significantly correlated with navigational HL ($r = 0.70$) [12], therefore, socioeconomic status and chronic conditions could have an indirect effect on the ability to navigate the healthcare system through the mediating effects of general HL. To account for the differences in healthcare needs and usage patterns between men and women, the analysis was stratified by gender. The effects of covariates age and study wave were controlled for in the SEM analysis. The details of the mediation analysis are presented in Fig. 1. Direct, indirect and total effects were calculated, along with the ratio of indirect effect to total effect (RIT), to assess the magnitude of the indirect effect [31]. Complete mediation occurred when the effect of the independent variable on the dependent variable (i.e., navigational HL) was fully explained by the mediator (i.e., general HL), resulting in a non-significant direct effect. In contrast, partial mediation was observed when the independent variable still had a significant direct effect on the dependent variable, even after accounting

for the mediator. This indicates that the mediator only partially explains the relationship between the independent and dependent variables [29]. Overall model fit was assessed using the Comparative Fit Index (CFI), the Tucker-Lewis Index (TLI), the Root Mean Square Error of Approximation (RMSEA) and the Standardized Root Mean Square Residual (SRMSR). The model was considered acceptable when the CFI and TLI values were greater than 0.90 and the RMSEA and SRMSR were lower than 0.06 [32–34]. The estimation of the SEM was conducted using the maximum likelihood method. All weighted analyses were performed using Stata (version 18.0; StataCorp LLC). The medsem command was used for the mediation analysis based on a model estimated using “sem” command [31].

# 3. Results

## 3.1. Study sample

The description of the respondents' characteristics is presented in Table 1. The average age was 46 (SD=15.7) years, and 51.4 % of the sample were women. 61.4 % of the respondents had less than a high-school degree and 61.7 % were employed. Two out of ten respondents (19.9 %) reported having training in the health sector, and just over half (55.5 %) had no chronic illnesses or long-term health problems. The average financial difficulties score of the respondents was 34.0 out of 100 (SD= 21.5), and the average SSS was 5.6 on a scale from 1 to 10 (SD= 1.5).

Respondents had an average score of 77.5 for general HL and 50.4 for navigational HL. A significant difference was observed in navigational HL, with women scoring lower than men (47.7 vs. 53.2, p < 0.001). Similarly, women reported a lower SSS (p < 0.001) and more financial difficulties than men (p < 0.001).

## 3.2. Factors associated with HL levels

The bivariate analysis showed that, among respondents from the first wave of the study, individuals without chronic conditions and those who had received training in the health sector had significantly higher levels of both general and navigational HL (Table 2). Men ($P < 0.001$) and younger people ($P < 0.001$) had higher levels of navigational HL. These two variables were not significantly associated with general HL. Moreover, the education level and the professional status were not associated with either general or navigational HL. In terms of financial difficulties and SSS, higher levels of financial difficulties were significantly

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Conceptual model of general HL as a mediator of the association between different characteristics and navigational HL. * The model takes into account the moderating effect of respondents' gender. The model's covariates are age and wave of study (June 2020 or January 2021).</figcaption></figure>
<table><caption>**Table 1**

Characteristics of total sample and by gender.</caption><thead><tr><th rowspan="2"></th><th>Total (N = 2 003)</th><th colspan="2">Gender</th><th rowspan="2">P-values</th></tr><tr><th>N (%)</th><th>Men n = 986 (48.6 %)</th><th>Women n = 1 017 (51.4 %)</th></tr></thead><tbody><tr><td><strong>Navigational HL (HLS<sub>19</sub>-NAV)</strong></td><td>50.4 (34.1)*</td><td>53.2 (35.2)*</td><td>47.7 (32.9)*</td><td><strong>&lt; 0.001</strong></td></tr><tr><td><strong>General HL (HLS<sub>19</sub>-Q12)</strong></td><td>77.5 (22.9)*</td><td>77.2 (23.5)</td><td>77.7 (22.2)*</td><td>0.499</td></tr><tr><td><strong>Survey wave</strong></td><td></td><td></td><td></td><td>0.999</td></tr><tr><td>First (June 2020)</td><td>1 003 (50.1)</td><td>497 (48.6)</td><td>506 (51.4)</td><td></td></tr><tr><td>Second (January 2021)</td><td>1 000 (49.9)</td><td>489 (48.6)</td><td>511 (51.4)</td><td></td></tr><tr><td><strong>Age</strong></td><td>46.1 (15.7)*</td><td>47.1 (15.4)</td><td>45.2 (15.8)</td><td>0.016</td></tr><tr><td><strong>Area of residence</strong></td><td></td><td></td><td></td><td>0.007</td></tr><tr><td>Urban</td><td>1 580 (77.6)</td><td>801 (50.2)</td><td>779 (49.8)</td><td></td></tr><tr><td>Rural</td><td>423 (22.4)</td><td>185 (42.9)</td><td>238 (57.1)</td><td></td></tr><tr><td><strong>Level of education</strong></td><td></td><td></td><td></td><td>0.183</td></tr><tr><td>Less than high-school degree</td><td>1 229 (61.4)</td><td>590 (47.4)</td><td>639 (52.6)</td><td></td></tr><tr><td>High-school degree and higher education</td><td>774 (38.6)</td><td>396 (50.5)</td><td>378 (49.5)</td><td></td></tr><tr><td><strong>Professional status</strong></td><td></td><td></td><td></td><td><strong>&lt;0.001</strong></td></tr><tr><td>Active</td><td>1 235 (61.7)</td><td>644 (51.6)</td><td>591 (48.4)</td><td></td></tr><tr><td>Inactive</td><td>768 (38.3)</td><td>342 (43.7)</td><td>426 (56.2)</td><td></td></tr><tr><td><strong>Subjective social status [1–10]</strong></td><td>5.6 (1.5)*</td><td>5.8 (1.6)*</td><td>5.5 (1.5)*</td><td><strong>&lt; 0.001</strong></td></tr><tr><td><strong>Financial difficulties [0–100]</strong></td><td>34.0 (21.5)*</td><td>31.2 (22.1)*</td><td>36.7 (20.5)*</td><td><strong>&lt; 0.001</strong></td></tr><tr><td><strong>Chronic diseases or long-term health problems</strong></td><td></td><td></td><td></td><td>0.577</td></tr><tr><td>No</td><td>1 103 (55.5)</td><td>538 (48.0)</td><td>565 (52.0)</td><td></td></tr><tr><td>Yes</td><td>900 (44.5)</td><td>448 (49.3)</td><td>452 (50.7)</td><td></td></tr><tr><td><strong>Training in the health sector</strong></td><td></td><td></td><td></td><td>0.571</td></tr><tr><td>No</td><td>1 606 (80.1)</td><td>795 (48.9)</td><td>811 (51.1)</td><td></td></tr><tr><td>Yes</td><td>397 (19.9)</td><td>191 (47.3)</td><td>206 (52.7)</td><td></td></tr></tbody></table>

Frequency values are crude, while percentages and averages are weighted to be representative of the French population according to gender, age, region, and place of residence.

associated with lower general and navigational HL, while SSS was positively correlated with both (all *P* < 0.001).

### 3.3. Mediation by general HL

#### 3.3.1. For women

The SEM model results for women (n = 1 017) are presented in

**Table 3.** Model fit indices revealed a satisfactory fit except for TLI (CFI=0.976, TLI=0.845, RMSEA=0.008, and SRMSR=0.022). General HL was strongly associated with navigational HL (β=0.88, *P* < 0.001). Age was still negatively associated with navigational HL (β=-0.195 for one-year increase, *P* < 0.001) in the SEM model. All the other variables introduced into the model had no significant direct effect on women's level of navigational HL. However, by introducing general HL as a

<table><caption>**Table 2**

Factors associated with general (HLS<sub>19</sub>-Q12) and navigational (HLS<sub>19</sub>-NAV) health literacy: bivariate analysis.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Navigational HL (HLS<sub>19</sub>-NAV)</th><th colspan="2">General HL (HLS<sub>19</sub>-Q12)</th></tr><tr><th>M(SD)</th><th>P-values</th><th>M(SD)</th><th>P-values</th></tr></thead><tbody><tr><td><strong>Survey wave</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>First (June 2020)</td><td>52.0 (34.3)</td><td><strong>0.040</strong></td><td>79.0 (22.6)</td><td><strong>0.004</strong></td></tr><tr><td>Second (January 2021)</td><td>48.8 (33.9)</td><td></td><td>75.9 (23.0)</td><td></td></tr><tr><td><strong>Age</strong></td><td>-0.097*</td><td><strong>&lt; 0.001</strong></td><td>0.024*</td><td>0.280</td></tr><tr><td><strong>Area of residence</strong></td><td></td><td>0.470</td><td></td><td>0.650</td></tr><tr><td>Urban</td><td>51.4 (33.4)</td><td></td><td>77.1 (22.2)</td><td></td></tr><tr><td>Rural</td><td>50.1 (34.3)</td><td></td><td>77.6 (23.1)</td><td></td></tr><tr><td><strong>Gender</strong></td><td></td><td><strong>&lt; 0.001</strong></td><td></td><td>0.500</td></tr><tr><td>Men</td><td>53.2 (35.2)</td><td></td><td>77.2 (23.5)</td><td></td></tr><tr><td>Women</td><td>47.7 (32.9)</td><td></td><td>77.7 (22.2)</td><td></td></tr><tr><td><strong>Level of education</strong></td><td></td><td>0.350</td><td></td><td>0.107</td></tr><tr><td>Less than high-school degree</td><td>49.8 (34.1)</td><td></td><td>76.8 (23.6)</td><td></td></tr><tr><td>High-school degree and higher education</td><td>51.2 (34.2)</td><td></td><td>78.5 (21.7)</td><td></td></tr><tr><td><strong>Professional status</strong></td><td></td><td>0.580</td><td></td><td>0.589</td></tr><tr><td>Active</td><td>50.7 (34.6)</td><td></td><td>77.2 (23.1)</td><td></td></tr><tr><td>Inactive</td><td>49.8 (33.4)</td><td></td><td>77.8 (22.5)</td><td></td></tr><tr><td><strong>Financial difficulties [0–100]</strong></td><td>-0.191*</td><td><strong>&lt; 0.001</strong></td><td>-0.214*</td><td><strong>&lt; 0.001</strong></td></tr><tr><td><strong>Subjective social status [1–10]</strong></td><td>0.184*</td><td><strong>&lt; 0.001</strong></td><td>0.190*</td><td><strong>&lt; 0.001</strong></td></tr><tr><td><strong>Chronic diseases or long-term health problems</strong></td><td></td><td><strong>&lt; 0.001</strong></td><td></td><td><strong>0.003</strong></td></tr><tr><td>No</td><td>53.1 (34.3)</td><td></td><td>78.8 (22.3)</td><td></td></tr><tr><td>Yes</td><td>47.0 (33.6)</td><td></td><td>75.7 (23.5)</td><td></td></tr><tr><td><strong>Training in the health sector</strong></td><td></td><td><strong>&lt; 0.001</strong></td><td></td><td><strong>&lt; 0.001</strong></td></tr><tr><td>No</td><td>48.5 (34.0)</td><td></td><td>76.5 (23.4)</td><td></td></tr><tr><td>Yes</td><td>57.9 (33.5)</td><td></td><td>81.4 (20.0)</td><td></td></tr></tbody></table>

M: Mean; SD: Standard Deviation

Averages are weighted to be representative of the French population according to gender, age, region, and place of residence.

<footnote>* Mean (Standard Deviation)</footnote>

<footnote>* Pearson correlation coefficient</footnote>
<table><caption>**Table 3**
Path coefficients based on the mediation model applied to women (n = 1 017).</caption><thead><tr><th></th><th>Coefficients</th><th>P</th><th></th><th></th></tr></thead><tbody><tr><td colspan="5"><strong>Navigational Health Literacy (HLS<sub>19</sub>-NAV)</strong></td></tr><tr><td>General Health Literacy (HLS<sub>19</sub>-Q12)</td><td><strong>0.878</strong></td><td><strong>&lt; 0.001</strong></td><td></td><td></td></tr><tr><td>Financial difficulties</td><td>-0.079</td><td>0.100</td><td></td><td></td></tr><tr><td>Subjective social status (SSS)</td><td>0.693</td><td>0.286</td><td></td><td></td></tr><tr><td>Training in the health sector (ref. No)</td><td>2.962</td><td>0.161</td><td></td><td></td></tr><tr><td>Chronic diseases or long-term health problems (ref. No)</td><td>1.464</td><td>0.405</td><td></td><td></td></tr><tr><td>Age</td><td><strong>-0.195</strong></td><td><strong>&lt; 0.001</strong></td><td></td><td></td></tr><tr><td>Survey wave (ref. First)</td><td>-0.123</td><td>0.941</td><td></td><td></td></tr><tr><td colspan="5"><strong>General Health Literacy (HLS<sub>19</sub>-Q12)</strong></td></tr><tr><td>Financial difficulties</td><td><strong>-0.164</strong></td><td><strong>&lt; 0.001</strong></td><td>RIT*</td><td>Complete</td></tr><tr><td>Subjective social status (SSS)</td><td><strong>1.423</strong></td><td><strong>0.006</strong></td><td>64 %</td><td>Complete</td></tr><tr><td>Training in the health sector (ref. No)</td><td><strong>4.618</strong></td><td><strong>0.003</strong></td><td>58 %</td><td>Complete</td></tr><tr><td>Chronic diseases or long-term health problems (ref. No)</td><td>-0.347</td><td>0.803</td><td>No mediation</td><td></td></tr></tbody></table>

<table><caption>**Table 4**
Path coefficients based on the mediation model applied to men (n = 986).</caption><thead><tr><th></th><th>Coefficients</th><th>P</th><th></th><th></th></tr></thead><tbody><tr><td colspan="5"><strong>Navigational Health Literacy (HLS<sub>19</sub>-NAV)</strong></td></tr><tr><td>General Health Literacy (HLS<sub>19</sub>-Q12)</td><td><strong>0.948</strong></td><td><strong>&lt; 0.001</strong></td><td></td><td></td></tr><tr><td>Financial difficulties</td><td>-0.014</td><td>0.752</td><td></td><td></td></tr><tr><td>Subjective social status (SSS)</td><td><strong>1.249</strong></td><td><strong>0.042</strong></td><td></td><td></td></tr><tr><td>Training in the health sector (ref. No)</td><td><strong>5.612</strong></td><td><strong>0.003</strong></td><td></td><td></td></tr><tr><td>Chronic diseases or long-term health problems (ref. No)</td><td><strong>-3.446</strong></td><td><strong>0.045</strong></td><td></td><td></td></tr><tr><td>Age</td><td><strong>-0.299</strong></td><td><strong>&lt; 0.001</strong></td><td></td><td></td></tr><tr><td>Survey wave (ref. First)</td><td>-1.364</td><td>0.402</td><td></td><td></td></tr><tr><td colspan="5"><strong>General Health Literacy (HLS<sub>19</sub>-Q12)</strong></td></tr><tr><td>Financial difficulties</td><td><strong>-0.171</strong></td><td><strong>&lt; 0.001</strong></td><td>RIT*</td><td>Complete</td></tr><tr><td>Subjective social status (SSS)</td><td><strong>1.664</strong></td><td><strong>0.002</strong></td><td>92 %</td><td>Partial</td></tr><tr><td>Training in the health sector (ref. No)</td><td><strong>3.569</strong></td><td><strong>0.031</strong></td><td>56 %</td><td>Partial</td></tr><tr><td>Chronic diseases or long-term health problems (ref. No)</td><td><strong>-5.129</strong></td><td><strong>&lt; 0.001</strong></td><td>59 %</td><td>Partial</td></tr></tbody></table>

mediator, we observed significant indirect effects of financial difficulties (-0.164 × 0.878 = -0.144, *P* < 0.001) and SSS (1.423 × 0.878 = 1.249, *P* = 0.006) on navigational HL. Mediation by general HL was considered complete and 64 % of the total effect of financial difficulties and also 64 % of the total effect of SSS on navigation HL level was mediated by general HL. Health sector training also showed a significant indirect effect (4.618 × 0.878 = 4.055, *P* = 0.003) representing 58 % of its total effect on navigational HL. Chronic conditions did not show any significant effect (without any mediation).

### 3.3.2. For men

Table 4 details the SEM model results for men (n = 986). Model fit indices for men revealed an adequate fit (CFI=0.996, TLI=0.972, RMSEA=0.039, and SRMSR=0.013). Similar to women's, men's general HL level was strongly associated with navigational HL (β=0.948, *P* < 0.001), while age had a significant direct negative effect (β=-0.299 for one-year increase, *P* < 0.001). Financial difficulties had a significant indirect effect through general HL, with an indirect effect ratio of 92 %, and a SEM model indicating complete mediation. The other variables had both significant direct and indirect effects indicating partial mediation. Despite a large part of the association with SSS, training in the health sector and the lack of chronic condition were mediated by general HL (RIT of 56 %, 38 %, and 59 % respectively), there were still significant direct associations with navigational HL.

## 4. Discussion

Our study investigated the variables associated with navigational HL among men and women, specifically examining the mediating effect of

general HL using SEM to identify relationships. Our findings support the hypothesis that HL plays a predominant role in facilitating navigation in the healthcare system but highlighted differences between men and women.

As a mediator, general HL enhances individuals' abilities to utilize appropriately healthcare services, avoid medical errors, and reduce unnecessary hospitalizations [4]. Furthermore, our results indicate that general HL exerts a significant direct and positive effect on navigational HL for both women and men, which is in line with previous works [19, 20]. Indeed, the ability to access, understand, and use health information is fundamental to navigate effectively complex healthcare systems. Our findings are also in line with previous research indicating that better HL is linked to more effective communication with healthcare professionals and a greater ability to follow medical instructions [2,35]. This suggests that efforts to improve general HL could also improve individuals' ability to make optimal use of healthcare services.

Bivariate analyses showed no association between age and general HL. However, older age had a direct negative effect on the ability to navigate the healthcare system, regardless of gender. This negative effect might be explained by the cognitive decline that occurs with aging, and/or care pathways getting more complex with a higher tendency to accept medical paternalism and a lower involvement in decision making [36,37]. In addition, older people may be less familiar with digital technologies and the online platforms used to access healthcare information and services [38], which can result in difficulties to navigate the modern and complex healthcare system [39].

<footnote>* Ratio of indirect effect to total effect</footnote>

<footnote>* Ratio of indirect effect to total effect</footnote>
## 4.1. Effect of health status

The results of our study suggest a notable difference between men and women regarding the impact of chronic conditions on the ability to navigate the healthcare system. Unlike men, for whom the presence of chronic conditions had a negative effect on this ability, no association was found among women. Indeed, men and women tend to adopt different health behaviors, which influences how they navigate the healthcare system when facing with chronic conditions. This difference can be influenced by a combination of behavioral and social factors [40]. Generally, women are more proactive in managing their own health. They are more likely actively to seek health information, ask questions during medical consultations, and follow treatment recommendations [41]. This proactivity could compensate for the potential negative effects of chronic conditions on their ability to navigate the healthcare system, which may explain the absence of effect among women. Women are also often the primary health managers within households, not only for themselves but also for other family members. This familiarity with managing healthcare may make women more “trained” at navigating the healthcare system, regardless of their health status, compared to men. More generally, the literature has shown that men tend to have lower HL than women [35], even in the presence of chronic illnesses [35]. Furthermore, they may be less inclined to seek help or acknowledge health difficulties [42], which could explain the negative effect observed on their ability to navigate the healthcare system when chronic conditions are present.

## 4.2. Associations with subjective social status and proximity to the healthcare system

Concerning SSS and training in the healthcare sector, the results showed a complete mediation of general HL on the ability to navigate the healthcare system in women while, in men, this mediation was partial. These differences can be interpreted through the prism of social capital theory, which suggests that the resources and support available through social networks and interpersonal relationships (relational capital) influence health behavior [43]. This may explain why, for women, social capital seems to play an important role in increasing their level of general HL, which in turn improves their ability to navigate the healthcare system. Women tend to have more extensive social networks and to use these networks to obtain health information and emotional support, thereby increasing their HL [44]. For men, on the other hand, partial mediation could indicate that their social networks are often less focused on emotional support and more on pragmatic exchanges of information or services [45]. This finding can also be applied to the partial mediation found with healthcare training for men. While women probably use their training to strengthen their social capital and thus improve their level of general HL, men directly use the skills acquired during medical training to navigate the healthcare system.

## 4.3. Effect of economic status

Our results indicate that financial difficulties have a complete and significant effect on the level of navigational HL through the level of general HL, with an indirect effect accounting for 92 % of the total effect in men and 64 % of the total effect in women. This underlines the well-known association between economic resources and general HL [3,46], which in turn facilitates better navigation of the healthcare system. Studies have shown that individuals with low socioeconomic status tend to have lower HL, which limits their access to care and their appropriate use of health services [6,35]. Indeed, general HL has been shown to help to reduce inequalities in access to and use of healthcare, as it is strongly correlated with more efficient use of available resources and a reduction in medical errors and unnecessary hospitalizations [4].

## 5. Conclusions

This study is the first to explore the variables influencing navigational HL, while taking into account the mediating role of general HL on a large representative sample of the French population. This approach provides a better understanding of the complex mechanisms through which socioeconomic variables influence the ability to navigate the healthcare system and how enhancing general HL could improve navigational HL, ultimately leading to more effective healthcare utilization and improved health outcomes.

From these results we believe that interventions aimed at improving general HL can indirectly strengthen navigational HL, leading to a better use of health services and better health outcomes. This understanding of mediation mechanisms paves the way for more targeted and effective public health strategies.

To improve navigational HL, it is crucial to target general HL, particularly among socio-economically vulnerable populations. By strengthening general HL skills, these populations will be better equipped to understand and manage their health conditions, navigate complex healthcare systems, and use healthcare services effectively. It is also essential to recognize gender differences in general and navigational HL, as men and women may face distinct challenges due to differences in experiences of chronic disease and access to resources. Among men, several variables showed a direct association with the ability to navigate. The healthcare system ought to be improved to facilitate the navigation of men without social help when facing chronic conditions.

## Funding

This research was supported by the National Public Health agency (Santé Publique France, 21DPPA040-0) and by Ligue contre le cancer (LIGUE2019).

## CRediT authorship contribution statement

**Jean-Benoit Hardouin:** Writing – review & editing, Validation, Supervision, Methodology, Conceptualization. **Julien Mancini:** Writing – review & editing, Validation, Supervision, Methodology, Funding acquisition, Conceptualization. **Rajae Touzani:** Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Conceptualization. **Camelia Protopopescu:** Writing – review & editing, Validation, Methodology. **Alexandra Rouquette:** Writing – review & editing, Validation, Conceptualization. **Cécile Allaire:** Writing – review & editing, Funding acquisition, Conceptualization. **Agnès Dumas:** Writing – review & editing.

## Declaration of Competing Interest

I have nothing to declare.

## Acknowledgments

We thank Santé Publique France and the Réseau Francophone de la Littératie en Santé (REFLIS) network for their collaboration in the European Health Literacy Survey 2019 (HLS19). Finally, our thanks to Julia Tinland for the English revision and copyediting of the manuscript.

## Using the scales

The two scales HLS19-Q12 and HLS19-NAV are the property of the HLS19 Consortium. Use of the authorized French version of the scales requires a contractual agreement between a non-profit applicant and the HLS19 Consortium. Further information is available at https://m-pohl.net/HLS19Instruments
# Data availability

The data sets generated and/or analyzed during this study are available from the corresponding author upon reasonable request.

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