Influence of socioeconomic determinants on health literacy and self- management practices among elderly residents in Thailand

doi:10.21203/rs.3.rs-4712385/v1

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Research Article

Influence of socioeconomic determinants on health literacy and self- management practices among elderly residents in Thailand

https://doi.org/10.21203/rs.3.rs-4712385/v1

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Background

Socioeconomic factors are known to influence health literacy and self- management among elderly populations. This study investigated the influence of socioeconomic determinants on health literacy and self- management among elderly people living in both rural and urban areas in Thailand.

Methods

This study was conducted among elderly people living in one rural area (Phon Ngam village) and one urban area (Tha Ka Suem village). Data were collected between 1 March and 31 July 2021 using multiple validated tools with appropriate data analyses.

Results

A total of 2870 participants were recruited; comprising of 1430 participants in the rural area and 1440 participants in the urban area with a mean age of 69.7 ± 7.77 years. There was a significantly higher proportion of elderly people with financial dependent status in the rural area compared to those in the urban area (91.0% vs 81.3%, respectively, p < 0.001). Participants in the rural area significantly received health information more than those living in the urban area (95.1% vs 80.9%, p < 0.001), particularly health information from community health workers (88.5% vs 33.1%, respectively p < 0.001). The proportion of poor health literacy in many perspectives were significantly higher in the rural group than their urban counterpart, nonetheless the elderly in the rural area were more likely to perform self-care practices based on provided health information compared to those living in the urban area (42.5% vs 35.7%, respectively, p < 0.001) Overall, participants were overweight (mean BMI = 23.2 ± 3.9 Kg/m²), but the rural group had healthier BMI than the urban group (BMI 22.8 ± 4.01 vs 23.5 ± 3.80 Kg/m² respectively, p < 0.001). Receiving health information was a significant predictor of appropriate self-management practices such as healthy weight management, healthy diet and being active.

Conclusions

Majority of the study population have received health information and had fair health literacy and self-management practices. Nonetheless, differences in health literacy and self-management existed between rural and urban areas. Future investigation of this public health issue is warranted to gain deep insights into the needs and barriers elderly people face in both rural and urban areas in Thailand. This will assist create health interventions and policies for this vulnerable population.

As populations age globally, understanding the factors that influence health outcomes in the elderly has become a critical public health priority [1]. Thailand's rapidly aging population underscores the importance of addressing these disparities. By 2040, it is projected that 20% of Thailand's population will be aged 65 or older, which poses significant challenges for the healthcare system [2]. In Thailand, a country undergoing significant demographic and socioeconomic transitions, the elderly population is rapidly increasing, presenting unique challenges and opportunities for health management. Socioeconomic status (SES) is a well-established determinant of health, impacting access to resources, health behaviours, and overall well-being. This demographic shift necessitates a thorough understanding of the social determinants of health among the elderly, particularly in terms of how SES influences health literacy and self-management practices [3].

Functional health literacy, a subset of health literacy, refers specifically to the basic reading and writing skills necessary to function effectively in a healthcare environment [4]. This includes the ability to read and comprehend prescription labels, appointment slips, and other essential health-related materials. Functional health literacy is crucial for effective self-management, particularly for chronic conditions prevalent in the elderly, such as diabetes, hypertension, and heart disease [5]. Adequate functional health literacy enables individuals to navigate the healthcare system, understand medical instructions, and engage in preventive health behaviours [6]. Research has consistently shown that low functional health literacy is associated with poorer health outcomes, including higher rates of hospitalization, less frequent use of preventive services, and increased mortality [7].

In the context of the elderly, who often face multiple chronic conditions, the ability to manage health effectively is particularly critical. Studies in various countries have demonstrated that low SES is strongly linked to lower levels of functional health literacy. For instance, a study in the United States found that lower SES groups had significantly lower functional health literacy, leading to poorer health management and outcomes [8]. Similar patterns have been observed in European countries, where lower SES is associated with reduced health literacy and increased health disparities [9].

In Thailand, disparities between rural and urban areas are pronounced. Rural regions often face limited access to healthcare services, lower educational attainment, and reduced economic opportunities compared to urban areas [2]. These disparities can exacerbate differences in functional health literacy and self-management capabilities among the elderly. For instance, elderly individuals in rural areas might have lower functional health literacy due to fewer educational opportunities and less exposure to health information [10]. The rural healthcare infrastructure is often under-resourced, leading to challenges in managing chronic diseases and accessing regular medical care [11]. Conversely, urban areas, while generally offering better access to healthcare and information, present their own challenges. High living costs, social isolation, and the fast-paced lifestyle in cities can negatively impact the elderly's ability to manage their health effectively [12]. Urban elderly populations might experience stress and anxiety related to urban living conditions, which can affect their health behaviours and outcomes. Moreover, the urban-rural health divide is not just about access to services but also about the quality and cultural appropriateness of the care received [13].

The intersection of SES and geographic location can lead to compounded disadvantages for certain groups of elderly people. For example, those with low SES in rural areas might experience the dual burden of economic hardship and limited healthcare access, further hindering their functional health literacy and self-management abilities [14]. Low SES is linked to poorer educational attainment, limited financial resources, and reduced social support networks, all of which are critical for maintaining functional health literacy and effective self-management [15].

Addressing functional health literacy and self-management in the elderly is vital for several reasons. Enhanced functional health literacy can lead to better health outcomes by empowering individuals to make informed health decisions, adhere to prescribed treatments, and engage in preventive health behaviours [16]. Improved self-management skills can reduce the burden on healthcare systems by decreasing hospital admissions and promoting more efficient use of healthcare resources [17]. This study aims to investigate the associations between socioeconomic determinants and level of health literacy and self-management practices among elderly people in Thailand. The independence predictors of common self-management practices (healthy weight, healthy diet, and being active) were also examined.

Study Design, Setting and Participants

This prospective cross-sectional study was conducted in the two villages in the Northeast region of Thailand. Phon Ngam is categorised as a rural area and Tha Ka Suem is categorised as an urban area, both areas are located in the Northeast region of Thailand. The eligible elderly participants living in the two study sites were recruited during 1 Mar 2021 to 31 July 2021. Participants were recruited into the study if they were:1) aged 60 years or over; 2) living in one of the two study sites during the study period; 3) able to understand and communicate in Thai language; and 4) able to provide consent. Participants were excluded from this study if they: 1) relocated from the two study sites during the study period; 2) were unable to provide consent form; 3) had been diagnosed with any type of mental health issue or any serious health condition; and 4) had impaired vision or unable to perform verbal communication.

Data Collection

The structured, self-report questionnaire were used to collect data in nine domains: 1) Demographic; 2) Medical history; 3) Stress assessment; 4) Quality of life (WHOQOL OLD-THAI); 5) Capability in perform daily routine; 6) Health awareness; 7) Attitude to self-management; 8) Health literacy; 9) health behaviours. The participants completed the questionnaire in-person and support by trained research assistants were available if required. To reflects the appropriateness of Body Mass Index (BMI) classifications for Asian populations [18], the Asia-Pacific BMI classifications [19] was used to categorised obesity status in this study: underweight (< 18.5 kg/m²); normal 18.5–22.90 kg/m²); overweight (23.0-24.90 kg/m2); and obese (≥ 25 kg/m2).

Statistical Analysis

The data was analysed using IBM SPSS statistic version 27, with a p < 0.05 level of significance and a 95% confidence interval (CI). The participants’ demographics, BMI, financial status, and ability in reading and writing were stratified by study areas: Phon Ngam (rural) and Tha Ka Surm (urban). Continuous data with normal distribution was presented as means and standard deviations (SD), and an independent t-test was used to determine the difference of means between the two groups. For skewed distribution, data was described as median and (25th percentile, 75th percentile) and Mann-Whiney U test was performed to examine the relationship between groups. Categorical variables were presented in frequencies and percentages, and a Chi-square was used to examine relationship between groups.

Three logistic regression models were used to examine potential predictors of appropriate self-management practices: healthy weight, healthy diet (take vegetable and fruits every day) and being active (perform physical activities at least 30 minutes/day, 3–5 days/week) and determine if area of living (rural/urban) was a significant independent predictor of those behaviours. Nine variables (gender, area of living, financial dependent status, read and writing ability, receiving health information by training, receiving health information from media, receiving health information from health professionals) were included in the multivariate models (Table 4–6).

In total, 2870 eligible participants were included in the study and were allocated into two groups based on their area of residence: Phon Ngam (rural, n = 1430, 49.8%) and Tha Ka Suem (urban, n = 1440, 50.2%). The mean age of this study population was 69.7 ± 7.77 years. The proportion of female participants were greater than that of males (57.0% vs 43.0%). The mean Body Mass Index (BMI,) was 23.2 ± 3.9 kg/m2 (overweight), and a half of all participants were overweight or obese (n = 1418, 49.4%). Overall, participants were covered by universal health insurance (n = 2571, 89.6%) and worked as farmers (n = 1788, 62.3%). Financial dependent status was reported at a high proportion of 86.1% and more than two-thirds of the study population experience financial difficulty (n = 2018, 70.3%). The majority of participants were living with family and only 5.2% of them were living alone. Primary school was most reported as the highest education level (89.9%), and slightly over a half of participants reported being fluent in reading (54.1%) and writing (51.0%).

Differences in socioeconomic status between rural and urban areas

Participants in the rural areas were significantly older than those living in the urban area (70.26 ± 7.6 vs 69.67 ± 7.9 years, p = 0.044), but there was no significant difference in the proportion of gender between the two groups. Those in the rural area had a significantly lower proportion of overweight or obese compared to those living in the urban area (45.3% vs 53.5%, p < 0.001). Financial dependent status was reported to be significantly higher in the rural area compared to theurban area (91.0% vs 81.3%, p < 0.001., Similarly the proportion of who reported experiencing financial difficulties were higher in the rural area compared to the urban area (71.9% vs 68.7%, p < 0.001). A significant higher proportion of the elderly people in the rural area lived alone compared to those elderly in the urban area (6.7% vs 3.8%, p < 0.001). There were differences in education levels between the two groups. Participants in the rural area who reported ‘no school’ were significantly lower than participants in the urban area (0.8% vs 1.9%, p < 0.001). Also their attendance of high school were twice less than the elderly people in the urban area (5.8% vs 11.9%, p < 0.001). Just under 95% of participants in both areas reported that they can read and write at a basic or fluent level. Rural participants had a higher proportion at basic level for reading and writing abilities, but they had a significant smaller proportion at fluent level compared to their urban counterparts (Table 1).

Associations of socioeconomic status and health literacy

Majority of participants receive health information from all resources, including training sessions, media, health professionals, and community healthcare workers, (n = 2525, 88.0%) (Fig. 1). Overall, elderly people in the rural area significantly received health information more than those living in the urban area (95.1% vs 80.9%, p < 0.001). In terms of resources of health information, participants in the rural area received information from media, health professionals and community workers compared to the urban participants. However, the rural participants were less likely to gain health information by attending training sessions compared to the participants in the urban area (Fig. 1).

Table 1

Summary of participants’ characteristics by area
Characteristics	All (n = 2870)	Rural Area (n = 1430)	Urban Area (n = 1440)	p
Age (years), Mean ± SD	69.7 ± 7.77	70.3 ± 7.62	69.7 ± 7.91	0.044*
Male, n (%)	1233 (43.0)	599 (41.9)	634 (44.9)	0.247
Body Mass Index (Kg/m²), Mean ± SD	23.2 ± 3.9	22.8 ± 4.01	23.5 ± 3.80	< 0.001*
Obesity status, n (%)				< 0.001**
Underweight (< 18.5 kg/m²)	264 (9.2)	156 (10.9)	108 (7.5)
Normal weight (18.5–22.90 kg/m²)	1188 (41.4)	627 (43.8)	561 (39.0)
Overweight (23.0-24.90 kg/m²)	618 (21.5)	304 (21.3)	314 (21.8)
Obese (≥ 25 kg/m²)	800 (27.9)	434 (24.0)	457 (31.7)
Health insurance, n (%)				< 0.001**
Universal health coverage	2571 (89.6)	1343 (93.9)	1228 (85.3)
Other health insurance	299 (10.4)	87 (6.1)	212 (14.7)
Occupation, n (%)				< 0.001**
Unemployed	831 (29.0)	491 (34.3)	340 (23.6)
Farmer	1788 (62.3)	823 (57.6)	965 (67.0)
Others	251 (8.7)	116 (8.1)	135 (9.4)
Financial dependent, n (%)	2471 (86.1)	1301 (91.0)	1170 (81.3)	< 0.001**
Financial Difficulty Status, n (%)				0.040**
Difficulty	490 (17.1)	232 (16.2)	258 (17.9)
Sometimes Difficulty	1528 (53.2)	797 (55.7)	731 (50.8)
Sufficient	736 (25.6)	341 (23.8)	395 (27.4)
Saving	116 (4.0)	60 (4.2)	56 (3.9)
Education Level, n (%)				< 0.001**
No School	39 (1.4)	12 (0.8)	27 (1.9)
Primary School	2576 (89.9)	1335 (93.4)	1241 (86.2)
High School or higher	255 (8.9)	83 (5.8)	172 (11.9)
Reading Ability, n (%)				< 0.001**
Cannot Read	152 (5.3)	76 (5.3)	76 (5.3)
Basic	1166 (40.6)	658 (46.0)	508 (35.3)
Fluent	1552 (54.1)	696 (48.7)	856 (59.4)
Writing Ability, n (%)				< 0.001**
Cannot Write	164 (5.7)	78 (5.2)	86 (6.0)
Basic	1241 (43.2)	687 (48.0)	554 (38.5)
Fluent	1465 (51.0)	665 (46.5)	800 (55.6)

a: Financial dependent: participants rely on family income or social support funds

* Significant difference between rural and urban areas p < 0.05 by t-test

** Significant difference between rural and urban areas p < 0.05 by Chi-square test

There were significant associations between socioeconomic status and level of health literacy among the study population (Table 2). Participants in the rural area had a significantly higher proportion of poor health literacy than those in the urban area (35.2% vs 22.6%, p < 0.001). No formal education and lack of literacy abilities were significantly related to poor health literacy (p < 0.001). Half of the elderly people who had no financial difficulty were reported to have poor health literacy followed by 35.5% of the elderly who experienced financial difficulties.

Proportion of poor health literacy were higher in the rural group compared to the urban group for most of the items (Table 3). By contrast, participants in the rural area who reported a good level of ‘Be able to perform self-care practice based on health in-formation provided by health professionals such as doctor, nurse and community healthcare worker’ were significantly higher than those in the urban area (42.5% vs 35.7%, p < 0.001). Just under a half of the rural group reported a poor level of ‘Be able to filter health information from social media before sharing the appropriate information to others’ compared to the urban area (49.0% vs 30.7%, p < 0.001). There was no significant difference in ‘Be able to exchange knowledge and practice about self-care with other people such as fall prevention, diets and nutrition, and physical activities’ between the two groups.

Table 2

Associations between socioeconomic status and level of health literacy
Socioeconomic status	Be able to find health information about physical activity, diet, smoking and alcohol consumption
Socioeconomic status	Poor (n = 829)	Fair (n = 1205)	Good (n = 836)	p
Gender (%)				0.384
Male	29.4	40.6	30.1
Female	28.5	43.1	28.4
Location area (%)				< 0.001
Rural	35.2	43.1	21.6
Urban	22.6	40.8	36.6
Financial Difficulty Status (%)				< 0.001
Difficulty	35.5	39.0	25.5
Sometimes Difficulty	26.9	41.2	31.9
Sufficient	25.3	47.1	27.6
Saving	50.0	31.9	18.1
Education Level (%)				<0.001
No School	30.8	35.9	33.3
Primary School	29.5	42.7	27.8
High School or higher	22.4	35.7	42.0
Reading Ability (%)				<0.001
Cannot Read	37.5	38.8	23.7
Basic	30.4	43.9	25.6
Fluent	26.9	40.9	32.3
Writing Ability (%)				0.018
Cannot Write	35.4	36.0	28.7
Basic	29.7	43.8	26.5
Fluent	27.4	41.2	31.4
* Significant p < 0.05 by Chi-square test

Table 3

Differences in health literacy between participants in rural and urban areas
Health literacy	Rural Area (n = 1430)	Urban Area (n = 1440)	p
Be able to find health information about physical activity, diet, smoking and alcohol consumption, (%)
Poor	35.2	22.6
Fair	43.1	40.8
Good	21.6	36.6
Be able to find information about healthcare settings for treatment, (%)			< 0.001*
Poor	33.3	21.1
Fair	44.4	41.9
Good	22.3	37.0
Be able to find health information to answer questions about health and health prevention, (%)			< 0.001*
Poor	24.8	23.5
Fair	56.0	47.8
Good	19.2	28.7
Be able to perform self-care practice based on health information provided by health professionals such as doctor, nurse and community healthcare worker, (%)			< 0.001*
Poor	12.7	23.5
Fair	44.8	40.8
Good	42.5	35.7
Be able to filter health information from social media before sharing the appropriate information to others, (%)			< 0.001*
Poor	49.0	30.7
Fair	36.2	39.6
Good	14.8	29.6
Be able to exchange knowledge and practice about self-care with other people such as fall prevention, diets and nutrition, and physical activities, (%)			0.063
Poor	24.6	24.1
Fair	49.3	46.0
Good	26.1	29.9
* Significant p < 0.05 by Chi-square test

Appropriate self-management practices and independent predictors

Healthy weight and independent predictors

The binary logistic regression analysis of healthy weight (BMI = 18.5–22.90 kg/m²) was performed and results are available in Table 4. Adjusting for socio-demographic and health literacy factors, area of living (rural vs urban) was not associated with self-manage healthy weight. The finding revealed that males were 1.2 times (95% CI: 1.02, 1.41, p = 0.025) more likely than females to self-manage their healthy weight within the recommended BMI (18.5–22.90 kg/m2). Elderly people receiving health information from health professionals were 1.28 times (95% CI: 1.06, 1.55, p = 0.009) more likely to maintain their healthy weight within the recommended BMI than those who did not receive health information from health professionals.

Table 4

Independent predictors of healthy weight*
Predictor	Odds ratio	95% CI	p
Male	1.20	1.02, 1.41	0.025
Receive health information from health professionals	1.28	1.06, 1.55	0.009
*Healthy weight refers to ‘Normal weight’, BMI = 18.5–22.90 kg/m²

Healthy diet and independent predictors

Healthy diet in this binary logistic regression analysis refers to consumption of fruits and vegetables every day. There were two factors which significantly contributed to the prediction of self-management practice for healthy diet (Table 5). Receiving health information from media increased the likelihood of maintaining a healthy diet by 1.75 times (95% CI: 1.41, 2.17, p < 0.001). Receiving health information from community health workers (CHW) was a second factor that positively influenced the odds of consuming a healthy diet by 1.26 times (95% CI: 1.01, 1.57, p = 0.041).

Table 5

Independent predictors of healthy diet*
Predictor	Odds ratio	95% CI	p
Receive health information from Media	1.75	1.41, 2.17	< 0.001
Receive health information from community health workers	1.26	1.01, 1.57	0.041
*Healthy diet refers to consume fruits and vegetables every day

Being active and independent predictors

Regular physical activity in this binary logistic regression analysis is defined as performing physical activities for at least 30 minutes per day, 3–5 days a week. Adjusting for socio-demographic and health literacy factors, living in the urban area increased the chance of being physically active by 1.95 times (95% CI: 1.58, 2.41, p < 0.001). Financial dependency, attending elderly health training sessions, and receiving health information from media sources, significantly contributed to the prediction of being physically active (p < 0.050). Elderly people with financial dependent status were 1.55 times more likely than those who were financially independent to engage in physical activity (95% CI: 1.23, 1.96, p < 0.001). Joining elderly health training sessions increased the likelihood of being physical active by 1.48 times (95% CI: 1.20, 1.84, p < 0.001). Receiving health information from media positively influenced the likelihood of being active regularly by 1.26 times (95% CI: 1.10, 1.67, p = 0.004) (Table 6).

Table 6

Independent predictors of being active
Predictor	Odds ratio	95% CI	p
Urban Area	1.95	1.58, 2.41	< 0.001
Financial dependent	1.55	1.23,1.96	< 0.001
Trained in elderly health	1.48	1.20, 1.84	< 0.001
Receive health information from Media	1.36	1.10, 1.67	0.004
*Being Active refers to physical activities 30 minutes/day, 3–5 days/week

The findings from this study revealed significant disparities in socioeconomic status and health literacy between elderly populations in rural and urban areas in Thailand, highlighting critical areas for intervention to improve health outcomes. This extensive discussion explores these disparities; examining their implications for health behaviours and outcomes and suggesting targeted interventions to address the identified gaps.

Socioeconomic Status

The findings demonstrate that elderly residents in the rural area are significantly older and face higher financial dependency plus financial difficulties compared to their urban counterparts. The higher financial dependency and difficulties observed in the rural area could be attributed to the predominant agricultural economy, which often yields lower and less stable incomes. These financial constraints likely contribute to the higher proportion of elderly individuals in rural areas living alone and experiencing greater financial difficulties [20]. This is consistent with the findings from various studies that have highlighted the economic vulnerability of rural populations, particularly among the elderly [21, 22]. The elderly in Thailand, like most Asian nations, have relied on their children for personal care and financial support; however declining fertility and internal migration of young people have led to increased loneliness, isolation and the loss of financial assistance for elderly parents [23]. Additionally, the study found significant differences in body mass index (BMI) and obesity status between rural and urban elderly residents. Rural areas had a lower proportion of overweight or obese individuals compared to urban areas, which could be linked to differences in dietary patterns and physical activity levels. Urban residents may have greater access to diverse food options, including processed and high-calorie foods, contributing to higher obesity rates. Conversely, rural residents, who often engage in physically demanding agricultural work, may maintain lower BMI levels [21, 22].

Education level also varied significantly between rural and urban areas. Elderly people in the rural area were less likely to have attended high school and reported lower levels of education overall. This educational disparity contributes to the broader socioeconomic inequalities observed between these populations, as education is closely linked to economic opportunities and health literacy. The lower educational attainment in rural areas may limit residents' ability to access and understand health information, further exacerbating health disparities [22, 24]. Another significant finding was the role of financial dependency in influencing health behaviours. Financially dependent individuals were more likely to engage in physical activity, possibly due to the support and encouragement they receive from family members or caregivers. This highlights the importance of social support networks in promoting healthy behaviours among the elderly. Interventions aimed at reducing financial dependency and enhancing social support can have a positive impact on the health and well-being of elderly populations[21, 22].

Health Literacy and self-management

Health literacy, a crucial determinant of health outcomes, shows marked differences between the rural and urban residents. Rural elderly participants exhibit significantly poorer health literacy levels, especially in the ability to find and use health information. This reflects an educational level of primary school in the rural area and a high school level for the urban group [25]. The lower educational attainment in rural areas, coupled with less access to high-quality health information, could explain these differences. Our findings align with existing literature that underscores the challenges faced by rural populations in accessing and comprehending health information, which can impede effective self-management practices and adherence to healthy behaviours [24, 26]. Kindig DA et al.[27] reported that health literacy is the interplay of individuals' capacities within health contexts, the healthcare system, the education system, and the overarching cultural and social factors prevalent at home, in the workplace, and within the community. Despite these challenges, rural participants reported receiving more health information from various sources, particularly from health professionals and community health workers, compared to urban participants. This paradox may indicate that while health information dissemination is more frequent in rural areas, the quality and comprehensibility of this information may not be sufficient to enhance health literacy. Additionally, rural residents were less likely to receive health information through training sessions, which could be a key area for targeted interventions to improve health literacy. Improving the quality of health information and providing it in more accessible formats could help bridge the health literacy gap between rural and urban populations [21, 22]. Rural residents with poor health literacy were significantly more likely to struggle with health-related self-management tasks, such as filtering health information from social media and finding reliable health information. However, they reported better self-care practices based on information from health professionals, suggesting that direct, personal interactions with healthcare providers are particularly beneficial in these settings [24, 26]. The study also found that socioeconomic status and health literacy significantly influenced health behaviours such as maintaining a healthy weight, diet, and physical activity levels. Receiving health information from healthcare professionals and media sources emerged as significant predictors of maintaining a healthy weight and diet, respectively [21]. Conspicuously, residing in urban areas, financial dependency, and participation in health training programs were associated with higher levels of physical activity. These findings highlight the importance of targeted health communication strategies and the need for accessible, reliable health information tailored to the specific needs of elderly populations in different settings[22]. Similarly, a study in Vietnam found age, occupation, taking care of children, social activities, participation in sports and physical exercise, social connections, and engaging in listening and reading activities to be associated health literacy [28]. In contrast, an Iranian study reported no significant associations between age, sex, marital status, socioeconomic position, or physical function and health literacy [29]. Health literacy efforts may need to be integrated into existing community structures such as schools and local gatherings. Several social determinants of health that are associated with health literacy and contribute to reduced educational attainment and increased poverty rates are more prevalent in rural communities [30]. Factors such as limited educational attainment, linguistic barriers, difficulties with reading, older age, and inadequate access to resources in rural areas might potentially lead to suboptimal levels of health literacy [31]. Individuals residing in rural areas perceive their health as a means of exercising independence and self-sufficiency, expressing concerns that a decline in health could potentially diminish their sense of independence and impose burdens on others which may promote self-management practices [32]. Therefore, improving health literacy would offer positive impact on health outcomes through quality of self-management practices. Additionally, gender emerged as a significant predictor of health behaviours, with males more likely to maintain a healthy weight. This gender difference in health behaviours has been documented in various studies and may be attributed to differences in lifestyle, dietary habits, and physical activity levels between men and women. Understanding these gender differences can help tailor health interventions to better address the specific needs of male and female elderly residents[22, 24].

Implications for Policy and Practice

These findings have important implications for health policy and practice. To address the disparities in health literacy and socioeconomic status, tailored interventions that focus on improving access to education, economic opportunities, and high-quality health information are essential. For rural areas, enhancing the comprehensibility and applicability of health information through community health workers and training sessions could significantly improve health literacy and outcomes. In urban areas, strategies to reduce financial dependency and enhance social support networks may help address the challenges faced by elderly residents [21]. Policy makers should consider implementing comprehensive education programs that not only focus on health literacy but also on general literacy and numeracy skills. Such programs can be integrated into community centres and local health clinics, making them more accessible to the elderly population. Furthermore, leveraging technology, such as mobile health applications and telehealth services, can help bridge the information gap, especially in rural areas where healthcare resources are limited [22].

Improving economic opportunities for the elderly, particularly in rural areas, can also have a significant impact on their health and well-being. Initiatives that promote sustainable agricultural practices, provide financial literacy training, and create employment opportunities for older adults can help reduce financial dependency and improve overall quality of life. Additionally, ensuring that elderly individuals have access to affordable and nutritious food options can help address the disparities in obesity and related health conditions [24, 26]. Finally, fostering strong community networks and social support systems is crucial for promoting healthy behaviours and improving health outcomes among the elderly [21]. Community-based programs that encourage physical activity, provide social engagement opportunities, and offer support for self-management of chronic conditions can help elderly individuals lead healthier and more fulfilling lives. Collaborating with local organizations, healthcare providers, and policymakers can create a supportive environment that promotes the well-being of elderly residents in both rural and urban areas [24].

This comprehensive approach requires collaboration between various stakeholders, including government agencies, healthcare providers, community organizations, and the elderly themselves. By working together, we can create a more equitable and supportive environment that ensures all elderly individuals have the resources and support they need to live healthy and fulfilling lives.

To conclude, majority of the study population have health information and a fair health literacy and self-management. However, differences existed between the elderly population in the rural and urban areas in terms of health literacy and self-management. To enhance understanding of the correlation between health literacy and self-management, adopting a broader perspective on health literacy encompassing both conceptual and behavioural dimensions is necessary. Further research emphasising qualitative studies is needed to understand better the needs and barriers that elderly people experience in rural and urban areas in Thailand to assist in creating population-targeted interventions and policies.

Data availability

The data of this study are not publicly available upon the ethical restrictions. However, some parts of Raw data are available from the corresponding authors on reasonable request.

Acknowledgments

The authors acknowledge and appreciate all participants involved in this study for their invaluable contributions. We are also most grateful for professional assistance and remarkable collaborations from all stakeholders who contribute throughout this project.

Funding

This study was financially support by Mahasarakham University, Thailand

Author Contributions

Conceptualization, N.T., N.K. and K.H.W.; methodology, N.T., and K.H.W; software, N.T..; validation, N.T. K.H.W., and P.A.D.; formal analysis, K.H.W., T.N., N.G.R. and R.R.; investigation, N.T. and K.H.W.; writing—original draft preparation, K.H.W., T.N., N.G.R. and R.R. ; writing—review and editing, N.T., P.A.D. and N.K.; supervision, K.H.W., and N.T.; project administration, N.T.; funding acquisition, N.T. All authors contributed to the manuscript revision and read and approved the submitted version.

Ethics declarations

Ethics approval and consent to participate

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board Kalasin Provincial Public Health Office Research Ethics Committee, Thailand (EC KLS.REC07/2564). Informed consent was obtained from all participants involved in the study. A privacy statement and participant information sheet were provided to individual participants in advance of participation.

Competing interests

The authors declare no competing interests.

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No competing interests reported.

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Influence of socioeconomic determinants on health literacy and self- management practices among elderly residents in Thailand

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Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Background

Methods

Study Design, Setting and Participants

Data Collection

Statistical Analysis

Results

Differences in socioeconomic status between rural and urban areas

Associations of socioeconomic status and health literacy

Appropriate self-management practices and independent predictors

Healthy weight and independent predictors

Healthy diet and independent predictors

Being active and independent predictors

Discussion

Socioeconomic Status

Health Literacy and self-management

Implications for Policy and Practice

Conclusions

Declarations

References

Additional Declarations

Status:

Version 1