# Psychometric validation of the French version of two scales measuring general (HLS19-Q12) and navigational (HLS19-NAV) health literacy using the Rasch model

Rajae Touzani, Alexandra Rouquette, Emilien Schultz, Cécile Allaire, Patrizia

Carrieri, Julien Mancini, Jean-Benoit Hardouin

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Rajae Touzani, Alexandra Rouquette, Emilien Schultz, Cécile Allaire, Patrizia Carrieri, et al.. Psychometric validation of the French version of two scales measuring general (HLS19-Q12) and navigational (HLS19-NAV) health literacy using the Rasch model. BMC Public Health, 2024, 24 (1), pp.3079. 10.1186/s12889-024-20504-x. inserm-05031250

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# Psychometric validation of the French version of two scales measuring general (HLS₁₉-Q12) and navigational (HLS₁₉-NAV) health literacy using the Rasch model

Rajae Touzani<sup>1,2,7*</sup>, Alexandra Rouquette<sup>3,4</sup>, Emilien Schultz<sup>1</sup>, Cécile Allaire<sup>5</sup>, Patrizia Carrieri<sup>1</sup>, Julien Mancini<sup>1,6†</sup> and Jean-Benoît Hardouin<sup>7,8,9†</sup>

**Abstract**

**Background** To examine the psychometrics properties of the French version of two scales measuring general (HLS₁₉-Q12) and navigational (HLS₁₉-NAV) health literacy (HL) using validation methods based on modern psychometric test theories: a Rasch model analysis.

**Methods** The data on representative samples of the French adult population came from the Health Literacy Survey (N=2 003), conducted in France in two waves (2020 and 2021), and from the third wave of SLAVACO study (N=2 022), conducted in December 2021. A Rasch analysis was performed using a partial credit model with marginal maximum likelihood estimation adapted to polytomous data. Category probability curves were used to examine whether participants found it consistently difficult to distinguish between response options. Differential item functioning (DIF) was also examined for each item in the two scales as a function of age, gender, and economic status. A Person Separation Index (PSI) of 0.7 has conventionally been considered to be the minimum acceptable PSI level. Chi-square fit statistics, expressed as infit and outfit mean square statistics, were calculated for each item in the two scales. An acceptable fit corresponded to average values between 0.7 and 1.3. The dimensionality of the scales was assessed using Principal Components Analysis (PCA) on the residual correlation matrix to identify whether items are locally independent. Additionally, Smith's method was employed to detect potential multidimensionality.

**Results** The PSI values for both scales were greater than 0.90. No disordered categories were observed. No evidence of significant DIF was found when associated with gender and economic status. However, two items for both scales had a DIF which was dependent on age but did not have a significant impact on scale scores between younger and older people. Minor local dependence was noted on the HLS₁₉-NAV scale. However, a PCA and Smith's method demonstrated the unidimensionality of the French version of the scales measuring general and navigational HL.

Full list of author information is available at the end of the article

<footnote>†Julien Mancini and Jean-Benoit Hardouin contributed equally to this work.</footnote>

<footnote>*Correspondence:
Rajae Touzani
rajae.touzani@inserm.fr</footnote>
**Conclusions** Our results, based on a rigorous statistical analysis, verified the psychometric parameters of the French version of the HLS19-Q12 and HLS19-NAV scales. Despite strong correlation between both scales, each measured a different latent trait. Moreover, despite the fact that presence of DIF was weak, attention must nonetheless be paid when comparing scale response scores between young and older respondents.

**Keywords** Health literacy survey, SLAVACO, Health literacy, HLS19-Q12 scale, HLS19-NAV scale, Rasch model, PCM

## Background

Scott K. Simonds first introduced the notion of health literacy (HL) in the 1970s [1]. Initially defined by the American Medical Association as “the ability to read, write, understand written texts and compute in the context of health” [2] and mainly used in the USA and Canada, today HL is an internationally recognized healthcare and public health issue [3]. In a context of increasing health democratization, patient empowerment and persistent social inequalities, HL has been identified as a priority in public health policy [4].

In parallel with this evolution in its importance, the concept of HL has evolved from focusing on understanding care-related issues, to embracing a diversity of health related aspects including prevention and health promotion [5]. Accordingly, international definitions of HL have evolved to encompass the cognitive and social skills needed to obtain, understand and use information in ways that promote good health. In line with these developments, Sørensen et al. developed a definition of HL that encompasses all the knowledge, motivation and skills individuals have to access, understand, evaluate and apply health-related information. From this definition, the same authors created a conceptual model which aimed to understand the diversity of all that enables individuals to form judgments and make decisions on a daily basis regarding healthcare, disease prevention and health improvement, with the aim of maintaining and/or improving their quality of life [6].

Based on Sørensen's conceptual model, a general self-reported measure of HL was developed [7] as part of the European health literacy survey (HLS-EU) in eight European countries, with two versions: one with 47 items (HLS-EU-Q47) and a shorter version with 16 items (HLS-EU-Q16) [6–8]. As the 16-item scale had limitations in terms of validity [9] and to better represent the underlying model, a new 12 item scale (HLS19-Q12) was developed [10].

While it is possible to capture sub-dimensions of HL with the conceptual matrix, it is not possible to precisely evaluate all the situations requiring other HL skills. One important category in specific HL skills is the ability to navigate a healthcare system [11]. Difficulties in navigating this complex system can lead to delays in diagnosis and treatment, avoidance of care and, more generally, a lack of trust in healthcare staff and the healthcare system [12], resulting in an increase in the global disease burden

[13]. Therefore the 12-item HLS19-NAV scale was developed to measure navigational HL in the general population [10].

The present study aimed to evaluate the psychometric properties of these two new scales in the French context, using a large sample representative of the general population. Validation was conducted based on modern psychometric theories by conducting a Rasch analysis. The classical approach hinges on sample characteristics, with measurement validity contingent upon the inter-item correlation patterns within the scale. Conversely, the Rasch model overcomes these limitations by placing both respondents and items in a unified measurement scale for the underlying trait, a feature common to all item response theory models.

In summary, the application of the Rasch model in this validation of the two HL scales was chosen to provide useful information by obtaining robust instruments for accurately assessing general and navigational HL levels in the general French population.

## Methods

### Data source

The data used came from two cross-sectional studies conducted on representative quota samples of the French population, in which both the HLS19-Q12 and HLS19-NAV scales were used.

The first was the Health Literacy Survey 2019–2021 (HLS19), conducted to measure general and specific HL of the population in several countries in the WHO European region [14]. In France, the HLS19 study was conducted online in two waves (June 2020 and January 2021); it involved 2 003 respondents aged between 18 and 75 years [15, 16].

The second was the SLAVACO study, whose main objective was to track changes in the attitudes of the French population to the COVID-19 vaccine and associated socioeconomic determinants, as well as the effect of general and specific HL [17]. The SLAVACO study, which included HLS19-Q12 and HLS19-NAV scales, was conducted online in December 2021 among 2 022 French people over the age of 18 years. The use this study in the present analysis enabled us to include people over 75 years old, and therefore to perform psychometric validation of the two scales on all adult age groups in France.

Respondents to both studies were selected from two access panels (Ipsos iSay and Bilendi). The online studies
were approved by the Ethics Evaluation Committee of the French National Health and Medical Research Institute (CEEI, IRB 00003888 and #21e770).

## Measures of health literacy

### General health literacy

As described above, the HLS₁₉-Q12 scale was developed by the World Health Organization's Action Network on Measuring Population and Organizational Health Literacy [14]. This 12-item scale is based on items from previously validated instruments HLS-EU-Q47 and HLS-EU-Q16 [9, 18] and was inspired by previous work in Asia and in Norway, where shorter versions of the HLS-EU were tested on national populations [19, 20]. Each item corresponds to one of the 12 cells of Sørensen et al. conceptual matrix of HL, which reflects the skills needed to access, understand, evaluate and apply health-related information, in the fields of care, disease prevention and health promotion [6, 7]. The items were selected and validated by factor analysis and Rasch measurement theory using European data from the HLS-EU study [10].

### Navigational health literacy

To measure people's ability to navigate across the healthcare system, we used a new scale called HLS₁₉-NAV. The first version of this instrument with 12 items was developed in Germany (translated into German and English); it is based on the definitions, concepts and tools associated with navigating the healthcare system and its organization [21]. The HLS₁₉-NAV measures people's knowledge, motivation and skills in accessing (3 items), understanding (3 items), evaluating (3 items), and applying (3 items) information and communicating in a variety of forms in order to navigate the healthcare system and services appropriately, and to obtain the most suitable care for themselves or their loved ones [21].

### Translation

Two professional translators from Switzerland and Belgium separately translated both scales from English into French. For each scale, the two translations were merged by experts from Switzerland (4 participants), Belgium (1 participant), and France (2 participants) at a consensus meeting, in order to produce an agreed French language version of the two scales (see Appendix 1).

For both scales, responses were collected using a four-point Likert scale: [3] very easy [2], easy [1], difficult, and (0) very difficult. Non-standardized scores (0–36) for both scales were standardized on a 0-100 scale, with 100 representing the highest level of general or navigational HL.

## Other variables

To measure the association between HL and the socio-demographic characteristics of the French population, we also collected data on gender, age and level of education. In addition, a financial deprivation score was calculated using three further questions covering the following: people's ability to (i) pay all their bills at the end of the month, (ii) buy medications if needed, and (iii) pay for medical examinations and treatments not covered by health insurance. By averaging the responses to these three questions and transforming them into a 0 to 100 scale, a financial deprivation score was derived, the higher the score, the greater was the financial deprivation [14, 15].

## Statistical analyses

A Rasch analysis was performed, using a partial credit model (PCM) with marginal maximum likelihood estimation designed to handle polytomous data [22].

We observed the ordering of the response categories. Both our scales had four-point Likert (0 "very difficult" to 3 "very easy") polytomous responses, with ordered categories producing ordinal-level categorical data. This means that the items were characterized by distinct sequential threshold parameters, representing the points on the latent trait where neighboring response categories have an equal probability of being selected [23]. In contrast, disordering of thresholds can occur when respondents have difficulty consistently discriminating between response options [24].

First, we conducted a differential item functioning (DIF) analysis of the two scales to assess the consistency of item locations across distinct subgroups. DIF was evaluated for each scale item based on age (younger, ≤50 years vs. older, >50 years, according to the sample's median age), gender (women vs. men), and economic status (measured by the financial deprivation score). Economic status was dichotomized into two categories precarious and non-precarious using a median score of 33.3 as the split point. We investigated DIF by estimating item locations for each subgroup and comparing them using a global contrast. As a criterion, a significant difference was defined as a mean difference greater than 0.5 logit between the difficulty parameters of a given item across the two tested groups [25, 26]. When DIF was detected for certain items on both scales, the scores for each DIF group were graphically represented by plotting the expected scores as a function of the estimated latent trait for each group. If DIF is considered as meaningful, these graphical representations should show distinct patterns between groups, indicating significant differences in scores [27]. A difference was typically considered significant if it exceeds one point on the non-standardized
scores or shows a notable divergence in the expected score patterns.

Subsequently, a PCM was fitted, taking into account for meaningful DIF. The goodness of fit of the Rasch model was evaluated by analyzing both Infit and Outfit indices, with the expectation that the mean-square (MnSq) values for both would approach 1. An acceptable MnSq range falls between 0.70 and 1.30 [28, 29]. Values lower than 0.70 indicate excessive fit between the items and the model, while values above 1.30 suggest a lack of fit. Additionally, the dimensionality of the scales was evaluated through Principal Components Analysis (PCA) on the residuals [30]. If the first eigenvalue does not show a significant value, the residuals are not indicating a new structure suggestive of multidimensionality. The correlation matrix can be used to identify local dependencies if correlation are greater than 0.2. To further confirm unidimensionality, Smith's method was applied [31]. The parameters of the Rasch model were estimated both for the entire set of items and for the subset of items presenting local dependence (residual correlations > 0.2) and misfitting items (according to Infit/Outfit). Individuals' latent traits estimated from these sets of items were then compared using t-tests, and the proportion of significant tests (with a 95% confidence interval) was determined. A proportion below 5% is considered as evidence supporting sufficient unidimensionality.

The measurement reliability of both scales was evaluated using the person separation index (PSI), with a minimum acceptable PSI value of 0.70 [32]. Finally, a Wright map was used to visually inspect the range of HLS₁₉-Q12 and HLS₁₉-NAV item measures relative to the distribution of respondents' general and navigational health literacy levels. The map displayed the range of respondent

measures and item threshold parameters, with higher threshold values located on the right-hand side, indicating a higher level of health literacy (HL), and lower threshold values on the left-hand side, indicating a relatively lower level of HL.

Floor or ceiling effects were studied at the score level. Effects were considered present if more than 15% of respondents obtained the lowest or highest possible scale score [33, 34]. Linear regression models were used to identify sociodemographic factors associated with general and navigational HL scores. The statistical significance level was set at 5%. All analyses were performed using Stata (version 18.0; StataCorp LLC), applying the command "pcm" for the Rasch analysis according to PCM [35].

## Results

### Description of the sample and different items

Our pooled sample comprised 4 025 respondents. Half were women and half had tertiary education. The mean age was 49 years (SD=17.3) and the mean financial deprivation score was 38.9 (SD=22.9). A statistically significant difference in socio-demographic characteristics was observed between the two studies we analyzed (i.e., HLS₁₉ and SLAVACO) (Table 1). Furthermore, the average HLS₁₉-Q12 score was higher than the average HLS₁₉-NAV score (63.4 vs. 51.4). General HL was significantly higher in the HLS₁₉ study compared to SLAVACO (65.9 vs. 60.9), but navigational HL was lower (50.5 vs. 52.3) (Table 1).

As responses in both online studies were mandatory, there were no missing data for any of the items in either scale. No floor or ceiling effects were observed at the score level for either scale.

### Rasch analyses

With regard to the usefulness of the response options, the category probability curves showed an ordered threshold for each response category for both scales (Table 2).

Regarding the DIF analysis, no significant DIF was found in either scale based on gender or economic status. However, age-based DIF was detected for two items in HLS₁₉-Q12 and two items in HLS₁₉-NAV. Specifically, items 5 "...to find information on how to handle mental health problems?" (DIF=0.84; *p*<0.001) and 6 "...to understand information about recommended health screenings or examinations?" (DIF=0.60; *p*<0.001) in HLS₁₉-Q12 were easier for older respondents (>50 years), while items 7 "...to find information on the quality of a particular health service?" and 8 "...to judge if a particular health service will meet your expectations and wishes on health care?" in HLS₁₉-NAV were easier for younger respondents (DIF=0.51; *p*<0.001 for both items). Comparing the HLS₁₉-Q12 scale scores, a difference was

<table><caption>**Table 1** Respondents' characteristics and comparison by survey (N=4 025)</caption><thead><tr><th>Characteristics</th><th>N=4 025</th><th>HLS<sub>19</sub><br>survey<br>(n=2 003)</th><th>SLAVACO<br>survey<br>(n=2 022)</th></tr></thead><tbody><tr><td>Age<sup>*</sup>(years)</td><td>49.02 (17.3)</td><td>46.5 (15.6)</td><td>51.5 (18.5)</td></tr><tr><td>Gender</td><td></td><td></td><td></td></tr><tr><td>Women</td><td>2 106 (52.3)</td><td>1 017 (50.8)</td><td>1 089<br>(53.9)</td></tr><tr><td>Men</td><td>1 918 (47.7)</td><td>986 (49.2)</td><td>932 (46.1)</td></tr><tr><td>Education level</td><td></td><td></td><td></td></tr><tr><td>Less than or equal to high<br>school</td><td>2 006 (49.8)</td><td>794 (39.6)</td><td>1 212<br>(59.9)</td></tr><tr><td>Tertiary education</td><td>2 019 (50.2)</td><td>1 209 (60.4)</td><td>810 (40.1)</td></tr><tr><td>Financial deprivation<br>scoreM(SD)</td><td>38.9 (22.9)</td><td>33.9 (21.5)</td><td>43.9 (23.2)</td></tr><tr><td>HLS<sub>19</sub>-Q12 score<sup>*</sup></td><td>63.4 (16.5)</td><td>65.9 (15.5)</td><td>60.9 (17.1)</td></tr><tr><td>HLS<sub>19</sub>-NAV score<sup>*</sup></td><td>51.4 (19.8)</td><td>50.5 (20.4)</td><td>52.3 (19.3)</td></tr></tbody></table>

<footnote>* Represented by means and standard deviations in brackets. The rest of the variables are presented in frequencies and percentages</footnote>
<table><caption>**Table 2** Summary of scale diagnostics for HLS<sub>19</sub>-Q12 and HLS<sub>19</sub>-NAV</caption><thead><tr><th>HLS<sub>19</sub>-Q12</th><th>Category</th><th>Count (%)</th><th>Threshold</th><th>HLS<sub>19</sub>-NAV</th><th>Category</th><th>Count (%)</th><th>Threshold</th></tr></thead><tbody><tr><td rowspan="4">Item 1</td><td>0</td><td>98 (2.4)</td><td>-</td><td rowspan="4">Item 1</td><td>0</td><td>167 (4.1)</td><td>-</td></tr><tr><td>1</td><td>730 (18.1)</td><td>-4.07</td><td>1</td><td>1264 (31.4)</td><td>-4.52</td></tr><tr><td>2</td><td>2267 (56.3)</td><td>-1.91</td><td>2</td><td>2036 (50.6)</td><td>-1.05</td></tr><tr><td>3</td><td>930 (23.1)</td><td>1.57</td><td>3</td><td>558 (13.9)</td><td>2.84</td></tr><tr><td rowspan="4">Item 2</td><td>0</td><td>100 (2.5)</td><td>-</td><td rowspan="4">Item 2</td><td>0</td><td>110 (2.7)</td><td>-</td></tr><tr><td>1</td><td>1009 (25.1)</td><td>-4.24</td><td>1</td><td>868 (21.6)</td><td>-4.98</td></tr><tr><td>2</td><td>2287 (56.8)</td><td>-1.41</td><td>2</td><td>2341 (58.2)</td><td>-1.90</td></tr><tr><td>3</td><td>629 (15.6)</td><td>2.29</td><td>3</td><td>706 (17.5)</td><td>2.42</td></tr><tr><td rowspan="4">Item 3</td><td>0</td><td>279 (6.9)</td><td>-</td><td rowspan="4">Item 3</td><td>0</td><td>452 (11.2)</td><td>-</td></tr><tr><td>1</td><td>1661 (41.3)</td><td>-3.21</td><td>1</td><td>1584 (39.4)</td><td>-3.07</td></tr><tr><td>2</td><td>1660 (41.2)</td><td>-0.19</td><td>2</td><td>1536 (38.2)</td><td>-0.06</td></tr><tr><td>3</td><td>425 (10.6)</td><td>2.83</td><td>3</td><td>453 (11.2)</td><td>3.14</td></tr><tr><td rowspan="4">Item 4</td><td>0</td><td>37 (0.9)</td><td>-</td><td rowspan="4">Item 4</td><td>0</td><td>663 (16.5)</td><td>-</td></tr><tr><td>1</td><td>219 (5.4)</td><td>-4.60</td><td>1</td><td>1962 (48.8)</td><td>-2.51</td></tr><tr><td>2</td><td>2300 (57.1)</td><td>-3.57</td><td>2</td><td>1145 (28.4)</td><td>0.93</td></tr><tr><td>3</td><td>1469 (36.5)</td><td>0.68</td><td>3</td><td>255 (6.3)</td><td>4.17</td></tr><tr><td rowspan="4">Item 5</td><td>0</td><td>233 (5.8)</td><td>-</td><td rowspan="4">Item 5</td><td>0</td><td>313 (7.8)</td><td>-</td></tr><tr><td>1</td><td>1439 (35.7)</td><td>-3.36</td><td>1</td><td>1627 (40.4)</td><td>-3.67</td></tr><tr><td>2</td><td>1879 (46.7)</td><td>-0.58</td><td>2</td><td>1661 (41.3)</td><td>-0.20</td></tr><tr><td>3</td><td>474 (11.8)</td><td>2.70</td><td>3</td><td>424 (10.5)</td><td>3.30</td></tr><tr><td rowspan="4">Item 6</td><td>0</td><td>115 (2.9)</td><td>-</td><td rowspan="4">Item 6</td><td>0</td><td>327 (8.1)</td><td>-</td></tr><tr><td>1</td><td>794 (19.7)</td><td>-3.92</td><td>1</td><td>1555 (38.6)</td><td>-3.58</td></tr><tr><td>2</td><td>2284 (56.7)</td><td>-1.77</td><td>2</td><td>1698 (42.2)</td><td>-0.31</td></tr><tr><td>3</td><td>832 (20.7)</td><td>1.78</td><td>3</td><td>445 (11.1)</td><td>3.22</td></tr><tr><td rowspan="4">Item 7</td><td>0</td><td>116 (2.9)</td><td>-</td><td rowspan="4">Item 7</td><td>0</td><td>380 (9.4)</td><td>-</td></tr><tr><td>1</td><td>700 (17.4)</td><td>-3.82</td><td>1</td><td>1716 (42.6)</td><td>-3.38</td></tr><tr><td>2</td><td>2293 (56.9)</td><td>-1.95</td><td>2</td><td>1524 (37.9)</td><td>0.05</td></tr><tr><td>3</td><td>916 (22.8)</td><td>1.60</td><td>3</td><td>405 (10.1)</td><td>3.36</td></tr><tr><td rowspan="4">Item 8</td><td>0</td><td>256 (6.4)</td><td>-</td><td rowspan="4">Item 8</td><td>0</td><td>334 (8.3)</td><td>-</td></tr><tr><td>1</td><td>1298 (32.2)</td><td>-3.18</td><td>1</td><td>1975 (49.1)</td><td>-3.64</td></tr><tr><td>2</td><td>1979 (49.2)</td><td>-0.77</td><td>2</td><td>1391 (34.5)</td><td>0.42</td></tr><tr><td>3</td><td>492 (12.2)</td><td>2.66</td><td>3</td><td>325 (8.1)</td><td>3.76</td></tr><tr><td rowspan="4">Item 9</td><td>0</td><td>74 (1.8)</td><td>-</td><td rowspan="4">Item 9</td><td>0</td><td>113 (2.8)</td><td>-</td></tr><tr><td>1</td><td>502 (12.5)</td><td>-4.19</td><td>1</td><td>664 (16.5)</td><td>-4.81</td></tr><tr><td>2</td><td>2463 (61.2)</td><td>-2.50</td><td>2</td><td>2403 (59.7)</td><td>-2.36</td></tr><tr><td>3</td><td>986 (24.5)</td><td>1.50</td><td>3</td><td>845 (21.0)</td><td>2.07</td></tr><tr><td rowspan="4">Item 10</td><td>0</td><td>96 (2.4)</td><td>-</td><td rowspan="4">Item 10</td><td>0</td><td>342 (8.5)</td><td>-</td></tr><tr><td>1</td><td>622 (15.4)</td><td>-3.99</td><td>1</td><td>1869 (46.4)</td><td>-3.58</td></tr><tr><td>2</td><td>2514 (62.5)</td><td>-2.19</td><td>2</td><td>1474 (36.6)</td><td>0.25</td></tr><tr><td>3</td><td>793 (19.7)</td><td>1.91</td><td>3</td><td>340 (8.4)</td><td>3.69</td></tr><tr><td rowspan="4">Item 11</td><td>0</td><td>129 (3.2)</td><td>-</td><td rowspan="4">Item 11</td><td>0</td><td>334 (8.3)</td><td>-</td></tr><tr><td>1</td><td>940 (23.3)</td><td>-3.87</td><td>1</td><td>1710 (42.5)</td><td>-3.58</td></tr><tr><td>2</td><td>2212 (55.0)</td><td>-1.48</td><td>2</td><td>1600 (39.7)</td><td>-0.03</td></tr><tr><td>3</td><td>744 (18.5)</td><td>1.97</td><td>3</td><td>381 (9.5)</td><td>3.50</td></tr><tr><td rowspan="4">Item 12</td><td>0</td><td>112 (2.8)</td><td>-</td><td rowspan="4">Item 12</td><td>0</td><td>916 (22.8)</td><td>-</td></tr><tr><td>1</td><td>957 (23.8)</td><td>-4.07</td><td>1</td><td>1846 (45.9)</td><td>-1.90</td></tr><tr><td>2</td><td>2212 (55.0)</td><td>-1.47</td><td>2</td><td>985 (24.5)</td><td>1.17</td></tr><tr><td>3</td><td>744 (18.5)</td><td>1.97</td><td>3</td><td>278 (6.9)</td><td>3.93</td></tr></tbody></table>

observed between younger and older respondents at the lowest scale scores (standardized score<50 / non-standardized score<18), but no difference was found at the highest scale scores (Fig. 1A). Conversely, for the

HLS<sub>19</sub>-NAV scale, a difference in scores between younger and older respondents was observed at the highest scale scores (standardized score>41 / non-standardized score<15) (Fig. 1B). However, these differences between
<figure><img src="image_3.png" /><figcaption>**Fig. 1** Comparison of the overall scores for younger (<50 years) and older (>50 years) persons for items where a DIF was present. **A:** HLS19-Q12 scale. **B:** HLS19-NAV scale</figcaption></figure>

the two curves remained small (less than one point of non-standardized scores) (Fig. 1).

The difference between the estimated individual latent traits, whether considering DIF or not, was less than 0.07 for the HLS19-Q12 scale (with a minimal standard error of measurement equal to 0.45) and less than 0.15 for the HLS19-NAV scale (with a minimal standard error of measurement equal to 0.53). Thus, the impact of DIF is not considered meaningful and is therefore ignored for the remainder of the analysis.

Rasch analyses indicated that all HLS19-Q12 and HLS19-NAV items had acceptable Infit and Outfit values (Table 3), except for item 9 of the HLS19-NAV scale, which showed underfitting (Infit MnSq=1.41 and Outfit MnSq=1.36).

For the HLS19-Q12 scale, examination of the residual correlation matrix did not reveal any local dependencies (all positive correlations were less than 0.2), nor other dysfunctional items (all negative correlations were higher than -0.2). The PCA on residuals correlation matrix did not reveal an important first eigenvalue (1.56–13% of information). Additionally, Smith's method confirmed the unidimensionality of the scale, showing a low percentage of significant differences at 0.8% [95% CI: 0.6–1.2%], further supporting its unidimensionality.

However, for the HLS19-NAV scale, examination of the residual correlation matrix revealed local dependencies between items 1 and 2 (0.295), items 2 and 12 (-0.210), and items 7 and 8 (0.334). The PCA on residuals correlation matrix did not reveal an important first eigenvalue (1.88–15% of information). Unidimensionality was then confirmed through Smith's method, which showed a low percentage of significant differences at 1.0% [95% CI: 0.7–1.3%]. These values do not overlap the 5% threshold, indicating that the impact of local dependence is moderate, thus supporting the approximate unidimensionality of the scale.

In addition, the HLS19-Q12 and HLS19-NAV scales had relatively high PSI values of 0.91 and 0.94, respectively.

In terms of the distribution of HLS19-Q12 item thresholds (Fig. 2), we observed a majority concentration to the left of the map, which corresponded to the average suitability of the scale for respondents, since the distribution of scores showed that a high proportion of respondents had a high probability of obtaining high scores in general HL. This result shows that the instrument is too easy for respondents. The items the respondents perceived to be easiest were 4 (-2.50 logits) and 9 (-1.73 logits). In contrast, items 5 (-0.41 logits) and 3 (-0.19 logits) were perceived as the most difficult. The locations of items 11 and 12 as well as 6 and 7 were very close to each other, suggesting item redundancy (Fig. 2A).

The distribution of items on the HLS19-NAV scale tended to concentrate around zero. The items perceived as easiest were item 9 (-1.70 logits) and item 2 (-1.49 logits). In contrast, item 4 (0.86 logits) and item 12 (1.07 logits) were considered the most difficult. Furthermore, several items have similar location: items 5 and 6, as well as items 8 and 10 (Fig. 2B). However, they can still contribute to measurement accuracy and ensure coverage. Despite this, the HLS19-Q12 and HLS19-NAV scales showed an increase in mean latent traits with scores. The associated standard errors vary, indicating maximum precision of estimates for intermediate scores. The results highlight the relationship between observed scores and underlying latent traits, enabling accurate assessment of HL levels (Appendix 1).

## Factors associated with general and navigational health literacy

After multiple adjustment, the HLS19-Q12 score was not associated with age or education level. However, it was negatively associated with men's gender and financial deprivation (all *p*<0.001). The HLS19-NAV score was
<table><caption>**Table 3** Summary of Rasch analysis-based internal validity for HLS19-Q12 and HLS19-NAV</caption><thead><tr><th>HLS19-Q12<br>"On a scale from very easy to very difficult, how easy would you say it is:"</th><th>Infit<br>MnSq</th><th>Outfit<br>MnSq</th><th>HLS19-NAV<br>"On a scale from very easy to very difficult, how easy would you say it is:"</th><th>Infit<br>MnSq</th><th>Outfit<br>MnSq</th></tr></thead><tbody><tr><td>1...to find out where to get professional help (such as doctor, nurse, pharmacist, psychologist) when you are ill?</td><td>1.11</td><td>1.10</td><td>1...to understand information on how the health care system works? (e.g., which type of health services are available)</td><td>1.04</td><td>1.01</td></tr><tr><td>2...to understand information about what to do in a medical emergency?</td><td>0.94</td><td>0.92</td><td>2...to judge which type of health service you need in case of a health problem?</td><td>1.13</td><td>1.12</td></tr><tr><td>3...to judge the advantages and disadvantages of different treatment options?</td><td>1.04</td><td>1.03</td><td>3...to judge to what extent your health insurance covers a particular health service? (e.g., are there any co-payments)</td><td>1.05</td><td>1.03</td></tr><tr><td>4...to act on advice from your doctor or pharmacist?</td><td>1.07</td><td>1.03</td><td>4...to understand information on ongoing health care reforms that might affect your health care?</td><td>0.98</td><td>0.96</td></tr><tr><td>5...to find information on how to handle mental health problems? (e.g., stress, depression or anxiety)</td><td>1.01</td><td>1.01</td><td>5...to find out about your rights as a patient or user of the health care system?</td><td>0.91</td><td>0.88</td></tr><tr><td>6...to understand information about recommended health screenings or examinations? (e.g., colorectal cancer screening, blood sugar test)</td><td>0.94</td><td>0.90</td><td>6...to decide for a particular health service? (e.g., choose from different hospitals)</td><td>0.88</td><td>0.84</td></tr><tr><td>7...to judge if information on unhealthy habits, such as smoking, low physical activity or drinking too much alcohol, are reliable?</td><td>0.92</td><td>0.87</td><td>7...to find information on the quality of a particular health service?</td><td>0.92</td><td>0.89</td></tr><tr><td>8...to decide how you can protect yourself from illness using information from the mass media? (e.g., Newspapers, TV or Internet)</td><td>1.05</td><td>1.05</td><td>8...to judge if a particular health service will meet your expectations and wishes on health care?</td><td>0.85</td><td>0.83</td></tr><tr><td>9...to find information on healthy life styles such as physical exercise, healthy food or nutrition?</td><td>0.90</td><td>0.89</td><td>9...to understand how to get an appointment with a particular health service?</td><td>1.41</td><td>1.36</td></tr><tr><td>10...to understand advice concerning your health from family or friends?</td><td>1.11</td><td>1.10</td><td>10...to find out about support options that may help you to orientate yourself in the health care system?</td><td>0.86</td><td>0.83</td></tr><tr><td>11...to judge how your housing conditions may affect your health and well-being?</td><td>0.95</td><td>0.93</td><td>11...to locate the right contact person for your concern within a health care institution? (e.g., in a hospital)</td><td>0.91</td><td>0.89</td></tr><tr><td>12...to make decisions to improve your health and well-being?</td><td>0.99</td><td>0.97</td><td>12...to stand up for yourself if your health care does not meet your needs?</td><td>1.07</td><td>1.08</td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>**Fig. 2** Individual-item distribution (Map) of HLS19-Q12 (A) and HLS19-NAV (B)</figcaption></figure>

<footnote>*Infit / Outfit in bold italics: misfitting item</footnote>
negatively associated with age, financial deprivation score and education level (all *p*<0.001) (Table 4).

Discussion

We assessed the psychometric properties of both the HLS19-Q12 and HLS19-NAV scales to determine their suitability for measuring general and navigational HL in the French context. To do this, we used data from two studies to ensure a large representative sample of the French population that covered all age categories. To the best of our knowledge, this validation study of the French versions of these two scales is also the first to jointly investigate French scales measuring general and navigational HL. The psychometric properties of both scales were tested in two stages using Rasch analyses. This approach results in reliable and fair measures, while facilitating an understanding of the relationships between items, latent traits and respondent performance, thus improving, in this case, the quality of HL measures in diverse contexts and populations.

Our results showed that despite the strong correlation between general and navigational HL (0.77; 95% CI: 0.75 to 0.78), they still measure two different latent traits. This correlation was expected, given that navigational HL is a specific component of general HL and given that both instruments were developed using the four cognitive domains of the HL matrix based on Sørensen's conceptual model [6, 7]. In addition, internal consistency and reliability were high for both scales, which was confirmed by the Rasch analyses, since the PSI was well above 0.8. This result is comparable to reliability values in other countries participating in the HLS19 study [18, 36].

<table><caption>**Table 4** Socio-demographic factors associated with general (HLS19-Q12 score) and specific (HLS19-NAV) health literacy scores (multivariable linear regression models)</caption><thead><tr><th rowspan="2"></th><th colspan="2">HLS19-Q12 score</th><th colspan="2">HLS19-NAV</th></tr><tr><th>Coefficients</th><th>p-values</th><th>Coefficients</th><th>p-values</th></tr></thead><tbody><tr><td>Age(per one year increase)</td><td>-0.03</td><td>0.086</td><td>-0.07</td><td>&lt;0.001</td></tr><tr><td>Gender</td><td></td><td></td><td></td><td></td></tr><tr><td>Women</td><td>1</td><td></td><td>1</td><td></td></tr><tr><td>Men</td><td>-2.04</td><td>&lt;0.001</td><td>0.50</td><td>0.414</td></tr><tr><td>Education level</td><td></td><td></td><td></td><td></td></tr><tr><td>Less than or equal to high school</td><td>1</td><td></td><td>1</td><td></td></tr><tr><td>Tertiary education</td><td>0.15</td><td>0.771</td><td>-3.20</td><td>&lt;0.001</td></tr><tr><td>Financial deprivation score</td><td>-0.21</td><td>&lt;0.001</td><td>-0.22</td><td>&lt;0.001</td></tr></tbody></table>

The results of the Rasch model for the HLS19-Q12 and HLS19-NAV scales provide crucial information on the structure and reliability of these scales. Analysis of the residual correlation matrix showed no local dependency within the items of the HLS19-Q12 scale, and the unidimensionality of this scale was confirmed by Smith's method. As for the HLS19-NAV scale, the matrix of residual correlations revealed moderate local dependencies between a few items. The local dependence of the navigational HL scale can be explained by the fact that the scale was based on a framework with theoretical levels (system, organization and interaction). This result has also been found in other countries where the scale has been validated [36]. However, the unidimensionality of the scale was confirmed by Smith's method.

The Rasch analyses confirmed that the four response categories of the HLS19-Q12 and HLS19-NAV functioned correctly. In other words, the ordered response modalities were able to distinguish differentiated levels of each of the latent traits measured, and therefore were appropriate to ensure a fair measurement of general and navigational HL [37]. All HLS19-Q12 items showed a good fit with the Rasch model, meaning that the scale items were appropriate for measuring general HL. The same applied to the HLS19-NAV items, with the exception of item 9 which showed a misfit. This result has also been observed in other countries participating in HLS19 [36]. It is likely that this item not only measures the ability to understand how to make medical appointments, but also inadvertently measures the difficulty encountered in obtaining a medical appointment. This is all the more true given that our study was conducted at the time of the COVID-19 health crisis, which contributed to a change in the management of health services and access to appointments in France [38]. However, this item has a low level of misfit, which means it could be retained in the scale.

Moreover, mean latent traits increased with scores on both scales, in line with Rasch model theory where higher scores reflect higher levels of the latent trait being measured (Appendix 1). Standard errors varied, being generally lower for intermediate scores and increasing at score extremes. This indicates that the latent trait estimates of both scales are more accurate for intermediate scores.

In our current analysis, the difficulty of the items of the HLS19-NAV showed quite an even distribution (Fig. 2B). There was a good match between person-specific HL level and item difficulty. However, the HLS19-Q12 items had an asymmetrical distribution centered to the left of the Wright map (Fig. 2A). This suggests the absence of items which require a high level of general HL. Clearly, the difficulty indices do not cover the entire latent trait, since the HLS19-NAV items do not extend to the extremes of the construct and the HLS19-Q12 items do
not extend to the right. This finding is supported by the person-item map of the two scales (Fig. 2A and B), which indicates that the item blocks lie in the middle (HLS₁₉-NAV) and to the left (HLS₁₉-Q12) of the continuum of the two traits. Consequently, the two scales may provide more accurate and reliable information of French people with average and low levels of, respectively, navigational and general HL than those with higher levels.

In addition, the analysis revealed somewhat redundant items in both scales, specifically items 6 and 7, and items 11 and 12 of HLS₁₉-Q12. The first two items deal mainly with the understanding and appraisal of prevention information, while the last two measure the appraisal and application of information to promote health in general. Although each item dealt with different HL skills, respondents tended to answer similarly. For the latter pair, this effect can be explained by the fact that our study was conducted during the COVID-19 health crisis. Respondents were probably over-informed about the risks and measures to be taken to avoid contamination within the same household. Redundant items were also observed in the HLS₁₉-NAV scale, specifically items 5 and 6, as well as items 8 and 10. The first two items mainly concern access to and application of health rights, while the last two measure evaluation and access to appropriate care services. The two pairs therefore address quite similar and correlated aspects. Accordingly, it may be useful to revise or reworded these items to better capture the variation in individuals' skills in navigating the healthcare system. This is even truer, considering the age-dependent DIF (despite being small) observed for item 8 of the HLS₁₉-NAV scale, and the presence of local dependency between the latter and item 7 (>0.30).

The presence of DIF for items 5 and 6 of HLS₁₉-Q12 was much more significant, these items being easier for people over the age of 50. We believe that these items need to be reworded to include examples of screenings or psychological problems that are more common in younger people (e.g., HPV screening, unhappiness). In the event that these items are retained without modification, comparisons of scores between the two different age groups should be interpreted with caution. We found that the presence of DIF had only a very slight impact on scores when comparing scores on the two scales (Fig. 1A, B). Accordingly, any comparisons of scores below 50 (18 for the non-standardized score) for general HL and above 41 (15 for the non-standardized score) for navigation HL between younger and older people should be made with caution.

The analysis of characteristics associated with general HL showed that while age or education were not associated with it, gender and economic status were. On the other hand, the ability to navigate the healthcare system found an association with age, economic status and level

of education. Specifically, older people and persons living in great precariousness tended to have lower HLS₁₉-NAV scores. This reflects findings in previous studies [39]. A closer look at the correlation of age with HLS₁₉-NAV items reveals a negative and significant correlation with items 7 and 8. In other words, older people were less likely to access information on the quality of services and to determine whether these services met their expectations; instead, they had blind trust in the healthcare system and their representatives.

These results reinforce the validity of the HLS₁₉-Q12 and HLS₁₉-NAV scales for measuring, respectively, levels of general HL and ability to navigate the healthcare system, while highlighting potential adjustments to be made to the HLS₁₉-NAV scale to improve the instrument. One of the strengths of this study is the in-depth psychometric validation of two scales using a modern psychometric approach, namely Rasch analyses. In addition, the study's excellent power and the fact that it is representative of the French general population ensures the external validity of the results. Finally, the two measures used to assess general and navigational HL were constructed in accordance with a conceptual definition of HL, and are therefore closely related to theoretical concepts of HL, which strengthens their content validity.

In terms of limitations, it is important to recognize that this study was conducted in the specific context of the COVID health crisis, and was carried out online, which may have biased respondent profiles (i.e., more persons with a higher level of education, fewer persons living precariousness). Consequently, the results may not be directly applicable to other populations or contexts, which may limit the generalizability of the findings.

## Conclusions

This in-depth psychometric validation of the French versions of the general and navigational HL scales confirmed their robustness and underlined that despite their strong correlation, each measured a different latent trait. Accordingly, they can be considered validated tools for measuring general and specific HL in the general French population.

### Abbreviations

<table><tr><td>HLS<sub>19</sub>-Q12</td><td>12-item general health literacy scale</td></tr><tr><td>HLS<sub>19</sub>-NAV</td><td>12-item navigational health literacy scale</td></tr><tr><td>PCA</td><td>Principal components analysis</td></tr><tr><td>PCM</td><td>Partial credit model</td></tr><tr><td>PSI</td><td>Person separation index</td></tr><tr><td>DIF</td><td>Differential item functioning</td></tr><tr><td>HL</td><td>Health literacy</td></tr><tr><td>HLS-EU</td><td>European health literacy survey</td></tr><tr><td>HLS-EU-Q47</td><td>47-item European health literacy scale</td></tr><tr><td>HLS-EU-Q16</td><td>16-item European health literacy scale</td></tr><tr><td>HLS<sub>19</sub></td><td>Health literacy survey 2019–2021</td></tr><tr><td>MnSq</td><td>Mean-square</td></tr></table>
**Supplementary Information**

The online version contains supplementary material available at https://doi.org/10.1186/s12889-024-20504-x.

Supplementary Material 1

**Acknowledgements**

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 Jude Sweeney for the English revision and copyediting of the manuscript.

**Author contributions**

Conceptualization, R.T., A.R., E.S., J-B.H. and J.M.; Formal analysis, R.T. and J-B.H.; Writing—original draft, R.T.; Writing—review and editing, R.T., A.R., E.S., C.A. P.C., J-B.H. and J.M. J-B. H has developed the “pcm” command in Stata, which can be used to run the Rasch partial credit model. All authors read and gave their approval for this version of the manuscript to be published.

**Funding**

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

**Data availability**

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

**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.

**Declarations**

**Ethics approval and consent to participate**

The French HLS19 and SLAVACO studies were approved by the Ethics Evaluation Committee of the French National Health and Medical Research Institute (CEEI, IRB 00003888 and #21e770). All participants voluntarily gave their informed consent to participate in the study after being informed about the purpose of the study. The questionnaires were anonymous to ensure confidentiality and data reliability.

**Consent for publication**

Not applicable.

**Competing interests**

The authors declare no competing interests.

**Author details**

¹Aix Marseille Univ, INSERM, IRD, ISSPAM, SESSTIM, Sciences Economiques & Sociales de la Santé & Traitement de l'Information Médicale, Equipe Labellisée Ligue 2019, 232 Boulevard de Sainte-Marguerite, Marseille, France

²Institut Paoli-Calmettes, SESSTIM U1252, Marseille, France

³Public Health and Epidemiology Department, AP-HP, Bicêtre Hôpitaux Universitaires Paris Sud, Le Kremlin-Bicêtre, France

⁴Université Paris-Saclay, Univ. Paris-Sud, UVSQ, CESP, INSERM U1018, Villejuif, France

⁵Santé publique France, Saint-Maurice, France

⁶APHM, Hop Timone, BioSTIC, Biostatistique et Technologies de l'Information et de la Communication, Marseille, France

⁷INSERM, Université de Nantes – Université de Tours, UMR U1246 SPHERE, Methods in patient- centered outcomes and health research, Nantes, France

⁸University Hospital of Nantes, Public Health Department – Unit of Methodology and Biostatistics, Nantes, France

⁹INSERM UMR 1136 (Institut Pierre Louis d'Epidémiologie et Santé Publique) - Team of Social Epidemiology (ERES), Paris, France

Received: 9 November 2023 / Accepted: 24 October 2024

Published online: 07 November 2024

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