Impact of Health Literacy, Social Support, and Socioeconomic Position on Serum Uric Acid Level of Asymptomatic Hyperuricemia Patients in China: a structural equation model

doi:10.21203/rs.3.rs-3139282/v1

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Research Article

Impact of Health Literacy, Social Support, and Socioeconomic Position on Serum Uric Acid Level of Asymptomatic Hyperuricemia Patients in China: a structural equation model

https://doi.org/10.21203/rs.3.rs-3139282/v1

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Background

Hyperuricemia poses a significant public health challenge on a global scale, with a concerning trend of affecting younger populations, while the rates of awareness and control remain suboptimal. The role of health literacy in influencing health outcomes is of utmost importance, and enhancing health literacy is helpful for patients in managing risk factors. Additionally, social support and socioeconomic position (SEP) have been identified as potential factors influencing health. However, the precise relationship between these factors and hyperuricemia remains uncertain. The study aims to investigate the status of health literacy among patients with hyperuricemia and explore the relationship between health literacy, social support, SEP, and serum uric acid.

Methods

A cross-sectional study was conducted among 349 participants with asymptomatic hyperuricemia in Luzhou of China. The research instrument included the Sociodemographics characteristics questionnaire, Health Literacy Management Scale (HeLMS), and Social Support Scale (SSRS). The construction of the SEP index was achieved through the application of principal component analysis. Univariate and hierarchical regression analyses were used to measure the association of health literacy, social support, SEP, and serum uric acid. Furthermore, structural equation modeling (SEM) was utilized to examine this association.

Results

(1) Most patients exhibited low health literacy (90.18 ± 15.11), and only 44.4% possess basic health literacy. (2) SEP was positively correlated with SUA (β = 4.086, P< 0.001), health literacy was negatively related to SUA level (β = -0.399, P < 0.001). There was no significant relationship between social support and SUA (β = 0.051, P= 1.085). (3) Health literacy mediates the association between SEP and serum uric acid (β = -0.490, 95% CI: -0.620 to -0.382). The direct effect of SEP on SUA and health literacy was 0.723 and 0.696, respectively and the total effect of SEP on SUA was 0.233.

Conclusions

The findings indicate a low level of health literacy among patients with AHU and suggest that health literacy may play a mediating role in the relationship between socioeconomic position (SEP) and serum uric acid (SUA) levels. Consequently, it is recommended that future initiatives prioritize health literacy and devise appropriate intervention strategies to enhance the self-management capabilities of patients with AHU.

Hyperuricemia

healthy literacy

serum uric acid

social support

socioeconomic position

Asymptomatic hyperuricemia (AHU) is a chronic metabolic disorder resulting from an imbalance in purine metabolism, characterized by serum uric acid (SUA) levels exceeding 420 µmol/L (7.0 mg/dL) regardless of gender with a purine diet normally[1]. The prevalence of hyperuricemia (HUA)was 17.4% in mainland China[2], 17.66% in the United States[3], and 16.3% in Spain[4], which had become a global public health concern and is becoming younger. Due to the absence of apparent symptoms, patients with AHU often lack awareness and proper management of the condition, resulting in low rates of attention (35.29%) and control (8.0%)[5, 6]. Previous studies have proved that HUA was related to gout and uric acid nephropathy[7]. Recent studies have also demonstrated an association between elevated SUA levels and the risk of stroke, coronary heart disease[8], diabetes[9], hypertension[10], and chronic kidney disease[11]. Therefore, it is crucial to control SUA levels in patients with HUA to alleviate the burden of the disease.

The SUA level of HUA patients is not only related to disease related factors but also affected by other factors such as health literacy, social support, and socioeconomic position.

Health literacy is defined as the “ability of person to obtain, process, and comprehend the fundamental health information and services needed to make health decisions”[12]. Individuals who have adequate health literacy could help one’s obtain the knowledge, abilities, and confidence to improve their lifestyle and manage the disease. Evidence demonstrates that inadequate health literacy usually contribute to adverse health outcomes, such as more likely to be obese, have higher comorbidity prevalence, and is a risk factor for chronic kidney disease onset[13–15]. Previous studies supported that chronic conditions were associated with health literacy level, and improve health literacy could become a prospective lever to help patients better self-manage their health[16]. The existing literature has also shown that the main reason leading to the discontinuation of urate-lowering drugs among gout patients was poor health literacy[17], and suggested health professionals utilize online booklets to enhance gout health literacy to control SUA level[18]. However, health literacy has not been introduced into the research of HUA patients, particularly among AHU patients.

Social support is defined as the “social resources that persons perceive to be available or that are provided to them”. Studies have demonstrated that greater social support was associated with better self-rated health, and fewer health-impairing behaviors[19]. Previous studies found social support has a positive relationship with health literacy, and they played a chain mediating effects on education level and medication adherence[20], the self-management of type 1 diabetes[21]. For gout patients, studies have found that their self-management behavior is related to social support[22]. Evidence suggests that social support is independent protective factors for hyperuricemia (OR = 0.944)[23]. Unfortunately, the relationship of health literacy and social support among patients with AHU in China has not been explored.

Moreover, socioeconomic position (SEP) is another important factor influencing individual health outcomes. Some studies found that low family SEP related to obesity and overweight in children and adolescents[24]. Other studies corroborate that higher SEP persons had a lower risk of depressive disorder onset[25]. A randomized controlled trial demonstrated that providing education improvement programs to AHU patients could significantly reduce the SUA level[26]. In existing literature, low SEP has been identified as a risk factor for low-level health literacy[27]. Some studies showed that education could influence glycated hemoglobin (HbA1c) through health literacy[21].

Based on the above-mentioned existing literature, health literacy, social support, SEP, and health outcomes are related and have complicated relationships. However, it is still unknown how these variables and their combined effects may affect AHU. And there has been no study exploring the mediation effect of health literacy and SEP for AHU patients using structural equation modeling (SEM). Therefore, this study aims to investigate the status of health literacy, social support, and SUA level, explore the influencing factors of SUA level, validate the association between health literacy, SEP indicators (education, occupation, and income), and SUA level among AHU patients by constructing SEM.

All procedures were performed in accordance with the Declaration of Helsinki, and the study was approved by the Ethics Committee of the Affiliated Hospital of Southwest Medical University (KY2022-175). All research participants gave their informed consent and were kept anonymous.

2.1 Study design and population

We conducted a cross-sectional study from March 2022 to September 2022 among the AHU patients in Luzhou City of Sichuan Province, China. The survey used a convenience sampling method to recruit participants from the Affiliated Hospital of Southwest Medical University Health Management Center and two community health service centers.

The inclusion criteria were : (1) diagnosis of hyperuricemia by Chinese guidelines for diagnosis and treatment of gout and hyperuricemia 2019; (2) > 18 years of age; (3) having capacity or with the help of the investigator to understand the study and fill in the required research questionnaire; (4) conscious; (5) voluntary participation in this study and sign the inform consent.

The exclusion criteria were: (1) those who had a diagnosis of gout; (2) those who were occurred clinical symptoms related to gouts, such as gouty arthritis and tophus; (3) those having a psychotic disorder or disturbance of consciousness; (4) pregnant women; (5) those who combined with serious liver, kidneys and other important organ dysfunction, such as hepatic sclerosis and renal failure; (6) patients with subsequent hyperuricemia (e.g., an inborn error of metabolism, myeloproliferative disorders, malignant tumor).

The sample size was calculated according to the cross-sectional study formula, [28], with a 95% confidence interval, 80% power, and health literacy possession rates of diabetes patients. Taking 13.9% proportion of health literacy possession rates from previous studies and an expected non-response rate of 20%[29]. Finally, the sample size was set at > 220.

2.2 Study measures

The questionnaire includes sociodemographic characteristics, disease-related information, and two scales selected according to the research purpose. Sociodemographic characteristics include age, gender, place of residence, living type, marital status, and the ability to pay for medical payments. Disease-related information: the number of comorbidities and hyperuricemia duration.

Socioeconomic position (SEP) SEP was measured by education, occupation, and family per capita monthly income. Education level was categorized as follows: primary school or below; junior high school; high school or Special Secondary; junior college; bachelor’s degree or higher. According to the Family Socioeconomic Status Rating Scale compiled by Baoguo S and Jiliang S[30], occupation was classified into 5 levels: casual workers, the unemployed and agricultural laborers; manual workers and the self-employed; clerical workers; professional and technical staff; senior managers. Patients reported family per capita monthly income using 5 answer options (1 = less than RMB 2000; 2 = RMB 2001–3000; 3 = RMB 3001–4000; 4 = RMB 4001–5000; 5 = over RMB 5001). Principal component analysis was used to construct the SEP index. Bartlett’s test of sphericity was significant (P < 0.001) and the Kaiser-Meyer-Olkin (KMO) value is 0.688. The first component explained 77.383% of the total variation.

Health Literacy Management Scale (HeLMS) In this study, a health literacy scale for chronic patients was used. The HeLMS was developed by Jordan et al.[31] and translated by Sun[32]. It contains 24 items and each item has five options ranging from 1 (with great difficulty or very unwilling) to 5 (with no problem or very willing). The ability to procure information, communication and interaction ability, health improvement willingness, and economic support willingness were measured respectively. The final score ranged from 24 to 120 points with a higher score indicating a higher health literacy level, and respondents who correctly answered 80% or more of the survey content (≥ 96 points) were considered to possess basic health literacy. In this study, Cronbach's alpha of the scale is 0.930, and the KMO value is 0.915, which means relatively good reliability and validity.

Social Support Rating Scale (SSRS)[33] The SSRS includes 10 items, and has 3 dimensions: objective support, subjective support, and support utilization. The scale was developed and validated by Chinese scholar Shuiyuan Xiao and was used to evaluate the types and levels of social support received by the elderly. The total score ranged from 7–56, and the scores of < 20, 20–30, 30 and above represented low, medium, and high levels of social support, respectively. Higher scores mean higher levels of social support. Cronbach's alpha of the overall scale was 0.716.

2.3 Data collection

The one-for-one, face-to-face interviews were conducted by investigators who had received adequate training before the questionnaire. During the formal survey, the purpose, significance, and content of the study were explained to all patients and written informed consent was obtained. If patients couldn't fill in the questionnaire by themselves, they completed it with the help of investigators.

2.4 Statistical analysis

Data were entered twice independently using EpiData software (version 3.0, EpiData Association, Odense, Denmark). Data analysis was performed using SPSS 26.0 ( IBM SPSS Statistics, USA) and AMOS 24.0. The numerical variables were expressed as mean (SD) for normal distribution and as the median and interquartile range for non-normal distribution. The categorical variables are expressed as n (%) and compared by the chi-squared test. A principal component analysis (PCA) was used to construct the SEP comprehensive index. The association among study variables was analyzed using the Spearman rank correlation method. Hierarchical regression models was used to analyzed the effects of SEP, social support, health literacy on SUA level. The direct and indirect relationships among variables were estimated by structural equation modeling (SEM), fitting by the maximum likelihood estimation method. A P-value of less than 0.05 was considered significant.

2.5 Missing Data

Variables missing > 15% of data were excluded from analyses. A total of 381 questionnaires were sent out and 349 valid questionnaires were collected; the validity rate was 91.60%. For categorical variables, missing data was replaced with mode. For continuous variables, missing data of normally distributed data were replaced with mean, and median (for non-normally distributed data).

3.1 Characteristics of patients with asymptomatic hyperuricemia

Overall, 349 patients with AHU were enrolled in this study, of whom 292 (83.7%) were men and 57 (16.3%) were women. The mean age was 49.15 \(\pm\) 12.73 years with the youngest 20 years and the oldest 83 years. The median SUA was 526.00 µmol/L. Most respondents live in urban (251, 71.9%), had bachelor’s degree or higher (103, 29.5%), were professional and technical staff (153, 98.0%), the family per capita monthly income ≥ 5001 RMB (116, 33.2%). The characteristics of the participants are shown in Table 1.

Table 1

Characteristics of study participants (n = 349).
Variable	Classification	n(%)
Gender	Male	292(83.7)
	Female	57(16.3)
Age	> 45	124(35.5)
	45 ~ 60	166(47.6)
	> 60	59(16.9)
Residence	Rural	98(28.1)
	Urban	251(71.9)
Residence style	Living alone	14(4.0)
	Not living alone	335(96.0)
Marital status	Married	303(86.8)
	Unmarried, divorced and widow	46(13.2)
Education level	Primary school or lower	61(17.5)
	Junior high school	81(23.2)
	High school or Special Secondary	48(13.8)
	Junior college	56(16.0)
	Bachelor’s degree or higher	103(29.5)
Occupation	Casual workers, the unemployed and agricultural laborers	150(43.0)
	Manual workers and the self-employed	35(10.0)
	Clerical workers	4(1.1)
	Professional and technical staff	153(98.0)
	Senior managers	7(2.0)
Family per capita monthly income	≤ 2000 RMB	50(14.3)
	2001–3000 RMB	42(12.0)
	3001–4000 RMB	78(22.3)
	4001-5000RMB	63(18.1)
	≥ 5001RMB	116(33.2)
The ability to pay for family medical costs	Difficult to pay	24(6.9)
	Can barely pay	142(40.7)
	Able to pay	183(52.4)

In this study, the total scores of social support and health literacy and their domains in hyperuricemia were demonstrated in Table 2. The mean score of social support was 42.15 \(\pm\) 7.00, and there were 3 (0.9%) patients in the poor group, 197 (56.4%) patients were in a suitable group, and 149 (42.7%) patients were excellent level. The health literacy score of participants was 90.18 \(\pm\) 15.11, and 194 (55.6%) participants have adequate health literacy.

Table 2

Social support and health literacy score and their dimensions in participants (n = 349).
Variable	Classification	Mean \(\pm\)SD/ frequency(% )
Social support and its dimensions	Objective support (1–22)	9.53 \(\pm\) 2.68
	Subjective support (8–32)	25.50 \(\pm\) 5.13
	Support utilization (3–12)	7.09 \(\pm\) 2.07
	Total score of Social support (12–66)	42.15 \(\pm\) 7.00
Social support level	Poor (≤ 22)	3 (0.9%)
	Suitable (23–44)	197 (56.4%)
	Excellent (≥ 45)	149 (42.7%)
Health literacy and its dimensions	Information acquisition ability (9–45)	38.74 \(\pm\) 8.05
	Communication interaction ability (9–45)	27.10 \(\pm\) 7.32
	Health improvement willingness (4–20)	16.05 \(\pm\) 1.64
	Economic support willingness (2–10)	8.29\(\pm\) 1.97
	Total score of Health literacy (24–120)	90.18 \(\pm\) 15.11
Health literacy level	Possessing Health literacy	155 (44.4%)
	Not possessing Health literacy	194 (55.6%)

The results of the Chi-square test, independent t-tests, and ANOVA showed that young age (P < 0.001), higher education (P < 0.001), and higher famliy per capita monthly income (P < 0.001) were associated with high social support scores and the proportion of health literacy. Social support was significantly higher in the not living alone and married compared with the living alone and unmarried, divorced and widow group. The proportion of health literacy was significantly higher in the rural than urban patients (P < 0.001).

3.2 Correlations between study variables

Spearman's correlations for the SEP, SUA, social support, and health literacy are graphically displayed in Fig. 1. And the correlation coefficients and significance levels are shown in Table 3. SEP (r = 0.245, P < 0.05), and social support (r = 0.205, P < 0.01) were positively associated with SUA, while health literacy was negatively correlated with SUA (r = -0.190, P < 0.01). SEP was positively correlated with social support (r = 0.412, P < 0.01), and health literacy (r = 0.566, P < 0.01), and social support was positively correlated with health literacy (r = 0.230, P < 0.01).

Table 3

Spearman's correlation matrix of the study variables
Variable	1	2	3	4	5	6	7	8	9	10
1. SEP
2. SUA	0.245^*
3. Social support	0.412^**	0.205^**
4. Objective support	-0.048	-0.320^**	0.414^**
5. Subjective support	0.412^**	0.417^**	0.845^**	0.023
6. Support utilization	0.409^**	0.104	0.587^**	0.065	0.345^**
7. Health literacy	0.566^**	-0.190^**	0.230^**	0.142^**	0.107^*	0.317^**
8. Information acquisition ability	0.603^**	-0.159^**	0.178^**	0.055	0.098	0.272^**	0.814^**
9. Communication interaction ability	0.372^**	-0.155^**	0.224^**	0.140^**	0.123^*	0.278^**	0.851^**	0.462^**
10. Health improvement willingness	0.157^**	0.056	0.180^**	0.082	0.146^**	0.234^**	0.479^**	0.200^**	0.499^**
11. Economic support willingness	0.644^**	-0.104	0.176^**	0.021	0.128^*	0.245^**	0.711^**	0.774^**	0.395^**	0.136^*
Note: P < 0.05, *P < 0.01

3.3 Hierarchical Regression Analyses

Table 4 shows the result of the hierarchical regression model. The model was conducted to determine the association of SEP, social support and health literacy on SUA in AHU patients. In Model 1, age, gender and HUA duration were added as control variables. In Model 2, SEP was added, allowing the effects of SEP to be considered in the second step, after accounting for control variables. The results showed that the relationship between SEP and SUA level was not statistically significant (β = 0.017, P > 0.05). In Model 3, incorporate social support and health literacy into the third hierarchy to determine if it explained any further variance beyond that explained by SEP. The results showed that SEP was positively correlated with SUA (β = 4.086, P < 0.001), health literacy was negatively related to SUA level (β = -0.399, P < 0.001), but there was no significant relationship between social support and SUA (β = 0.051, P = 1.085). The variance explained by the total model was 38.2%, F = 35.203, P < 0.001.

Table 4

Results of hierarchical regression analysis.
Variable	Model 1			Model 2			Model 3
Variable	B	β	t	B	β	t	B	β	t
Constant	588.854	-	23.723***	586.346	-	22.409***	825.494	-	15.346***
Age	-1.714	-0.194	-4.111***	-1.645	-0.186	-3.478**	-1.510	-0.171	-3.428**
Gender	-14.479	-0.048	-1.004	-14.663	-0.048	-1.015	-14.819	-0.049	-1.103
HUA duration	15.920	0.481	10.457***	42.409	0.377	7.604***	12.928	0.391	8.370***
SEP				0.946	0.017	0.307	14.644	0.256	4.086***
Social support							0.823	0.051	1.085
Health literacy							-2.973	-0.399	-7.454***
R²	0.280			0.280			0.382
∆R²	0.280			0.000			0.101
F	44.754***			33.501***			35.203***
Note: P < 0.05, * P < 0.01, ***P < 0.001

3.4 Construction and testing of structural equation model

The final model fit values showed that the relative chi-square was 2.872, the goodness of fit index was 0.982, the adjusted goodness of fit index was 0.945, the comparative fit index was 0.984, the normed fit index was 0.976, Tucker-Lewis index was 0.965, the incremental fit index was 0.984 and the root-mean-squared error associated was 0.073, suggesting a relatively satisfactory model fit to the data. (Table 5).

Table 5

Model fit index.
Variable	χ²/df	GFI	AGFI	CFI	NFI	TLI	IFI	RMSEA
Fit index	2.872	0.982	0.945	0.984	0.976	0.965	0.984	0.073
Reference value	< 5.00	> 0.90	> 0.90	> 0.90	> 0.90	> 0.90	> 0.90	< 0.08
Note: GFI goodness of fit index, AGFI adjusted GFI, CFI comparative fit index, NFI normed fit index, TLI = Tucker-Lewis index, IFI incremental fit index, RMSEA root mean square error of approximation.

The model indicates the association between SEP and SUA level, and the effect of health literacy on this association. Figure 2 and Table 6 shows all of the estimated direct effect from path analyses. The results of significance testing of the mediating pathways shows that health literacy mediated the influence of SEP on SUA. The direct effect of SEP on SUA and health literacy was 0.723 (95% CI: 0.606 to 0.859) and 0.696, respectively. The indirect effect of SEP on SUA was − 0.490 (95% CI: -0.620 to -0.382) and the total effect of SEP on SUA was 0.233 (95% CI: 0.130 to 0.336).

Table 6

Path analysis results of serum uric acid among asymptomatic hyperuricemia.
Path	Estimate	Standardized Estimate	S.E.	C.R.	P
SEP→Health literacy	2.561	0.696	0.169	14.920	< 0.001
SEP→SUA	41.329	0.723	4.004	10.323	< 0.001
Health literacy→SUA	-11.133	-0.704	1.220	-9.124	< 0.001
Note: S. E. Standard Error in Estimate, C. R. Critical Ratio

This study is the first to investigate the status of health literacy in patients with AHU in China and the results show that 155 (44.4%) of the subjects possess basic health literacy. Besides, this is the first study that, to our knowledge, constructs SEM to examine the pathway between health literacy, SEP, and SUA. The findings show that health literacy plays a mediation role between SEP and SUA.

In this study, the mean health literacy score of AHU patients was 90.18 \(\pm\) 15.11, meaning the health literacy of the sample was inadequate. However, the health literacy level of AHU patients is higher than that of patients with polycystic ovary syndrome (87.20 \(\pm\) 17.20) and diabetes (88.83 \(\pm\) 12.57)[34, 35]. Considering that most respondents included in this study were urban residents (71.9%), and their education level was generally medium and above, which may explain the higher health literacy. Besides, the patients volunteered to participate, and we offered incentives in the survey, which could bias the results. Despite this, the majority of AHU patients lack health literacy, which may be due to the asymptomatic hyperuricemia without clinical manifestation and there is a lack of awareness and management about related disease knowledge, even primary healthcare physicians[36]. As we all know, the higher health literacy, the better self-management. Thus, it is important to further improve the health literacy level of AHU patients, strengthen the continuing education and reinforce the management of chronic diseases about AHU for primary healthcare physicians.

The results of our study revealed that health literacy was negatively related to SUA level. A Study found that enhancing health literacy has been considered a possible intervention target to improve patients' self-care behaviors[37]. A study demonstrated that health literacy significantly and positively correlated with physical exercise, diet, and sleep[38, 39]. Individuals with higher health literacy would be more likely to perform health behaviors by using health information, pay attention to health status, and have more confidence to manage their SUA level. Possibly, other factors also contribute to the disparities in SUA. For example, a healthy lifestyle is beneficial for decreasing the level of SUA. Some studies showed that drinking alcohol, high BMI, and central obesity are risk factors for HUA[40]. Even though other factors may play a role, our study still indicated that health literacy may serve as a key predictor of self-management of SUA level among patients with AHU. Health literacy includes four core elements: knowledge, attitude, skill, and behavior, education must be transformed into individuals' cognition and attitude, and then fed back on one's skills and behaviors. Health literacy is the result of understanding and utilizing health-related information. In the case of patients with AHU, health literacy presents the ability and channels to capture knowledge to some extent, which in return act on one's behavior, efforts, and management to SUA.

Interestingly, our study evidenced that health literacy plays a mediating role in the relationship between SEP and SUA. In addition, we found that high SEP was associated with high SUA levels, which is consistent with a study in the general Korean population[41]. This may be related to the individual's diet habits and lifestyle. Patients with higher SEP have easier access to processed foods and high-energy foods, which increases the risk of metabolic disorders [42]. Meanwhile, those with high SEP may favor processed food consumption more than their peers because of lacking knowledge about nutrition and more work-related entertainment. Therefore, HUA patients with high SEP are more likely to be discovered during routine medical examinations. SEP can improve health literacy among AHU patients, thereby reducing the level of SUA. And health literacy is more easily modified than socio-economic status, thus this research suggests an intervention that may improve the SUA level of patients.

The mean score of social support was 42.15, which showed that AHU patients received medium social support, and the proportion of individuals with high-level social support was 42.7%. The reason for this phenomenon may be that the patients of our study are middle-aged and elderly people, and caring for grandchildren influences their social participation. So, their social support is mainly from family members, lacking support from friends, organizations, and professional medical personnel. Prior research findings showed that increased social support was related to lower SUA levels[43]. And patients with low-level social support are more likely to develop cardiovascular disease and have a higher risk of high blood pressure[44, 45]. Thus, it deserves to pay attention to the insufficient social support available to AHU patients, especially elderly patients.

In our study, the zero-order correlations showed that social support was significantly positively correlated with SUA, but was not significant after controlling for numerous confounders. The results is consistent with a study's findings, which was investigated in Mexican-origin families[46]. Thus, this study did not include social support as a mediate variable in the SEM. But, the effectiveness of social support in improving health outcomes is increasingly obvious[47]. Additionally, that higher social support was linked to improved health literacy is consistent with a prior study[48]. Given the modifiable nature of social support, different interventions can be taken to enhance social support, such as education, physical exercise, group activities, and so on. Future research should focus on understanding how interventions might be applied to lessen health disparities.

Our study did have some limitations. Firstly, the sample of this study was selected mainly from the community using convenience sampling method, moreover, the sample is middle-aged and elderly people mostly which limits generalizability. Secondly, the SEP indicators (education, income, and occupation) were self-reported by the participants, which may be influenced by the subjective perception of patients, overestimating or underestimating the results. Thirdly, the HeLMS used in this study measured general chronic health literacy, rather than hyperuricemia-specific health literacy. Future research could use a larger random sample and specifically develop a health literacy scale for HUA patients. In addition, it will be important to explore the multiple mediating roles on health outcomes among patients with HUA, such as lifestyle factors.

In conclusion, our study indicated that the AHU patients had low health literacy and health literacy may serve as a pathway by which SEP affects SUA level. But social support was not significantly associated with SUA. Measures should be taken to improve health literacy to enhance patient self-management and medical staff continuing education. Next, implementing health literacy screening and tailored interventions for patients will ultimately contribute to improving health literacy and decreasing the SUA level of patients.

AHU asymptomatic hyperuricemia

HUA hyperuricemia

SUA serum uric acid

SEP socioeconomic position

HeLMS Health Literacy Management Scale

SSRS Social Support Rating Scale

PCA principal component analysis

SEM structural equation modelling

KMO Kaiser-Meyer-Olkin

Authors' contributions

LM and YFJ designed the work. LLM conducted data collection, statistical analysis, and manuscript writing. YFZ designed the questionnaire. XHL, JJ, and JL took part in the collection of the data. All authors read and approved the final manuscript.

Funding

Funding by Suining First People's Hospital-Southwest Medical University Science and Technology Strategic Cooperation Project (2021SNXNYD09).

Availability of data and materials

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

Ethics approval and consent to participate

The study was approved by the Ethics Committee of the Affiliated Hospital of Southwest Medical University (KY2022-175). All research participants gave their informed consent and were kept anonymous.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

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Impact of Health Literacy, Social Support, and Socioeconomic Position on Serum Uric Acid Level of Asymptomatic Hyperuricemia Patients in China: a structural equation model

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Abstract

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1. Introduction

2. Materials and methods

2.1 Study design and population

2.2 Study measures

2.3 Data collection

2.4 Statistical analysis

2.5 Missing Data

3. Results

3.1 Characteristics of patients with asymptomatic hyperuricemia

3.2 Correlations between study variables

3.3 Hierarchical Regression Analyses

3.4 Construction and testing of structural equation model

4. Discussion

5. Conclusion

Abbreviations

Declarations

References

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