PUBLIC PREFERENCES FOR TRADITIONAL CHINESE MEDICINE AND CONVENTIONAL TREATMENT FOR LOW BACK PAIN IN SALATIGA DISTRICT, SAMBAS REGENCY

Suryadi

Universitas Tanjungpura, Pontianak, Indonesia

[email protected]

ABSTRACT

Lower back pain is a common health issue affecting many individuals worldwide, often leading to significant discomfort and reduced quality of life. Understanding patients' preferences for treatment options is crucial in providing adequate healthcare. This study analyzes the factors influencing patient preferences between Traditional Chinese Medicine (TCM) and conventional medicine for treating lower back pain in Salatiga District, Sambas Regency. A quantitative approach using surveys and Structural Equation Modeling (SEM) was employed, gathering data from 50 patients who had utilized either TCM or conventional medicine. Results indicate that treatment effectiveness, economic factors, perceptions of treatment, accessibility, and continuity of care significantly influence patient choices. Treatment effectiveness emerged as the most critical factor, with patients opting for the most effective pain relief methods. Economic considerations, including treatment costs and insurance coverage, were vital. This study highlights the need for inclusive health policies that support access to TCM and conventional medicine, aiming to improve patient education and integrate these treatment options into the healthcare system.

Keywords: accessibility, conventional medicine, economic factors, lower back pain, patient preferences, traditional chinese medicine

Corresponding Author: Suryadi

E-mail: [email protected]

INTRODUCTION

Traditional Chinese Medicine (TCM) has a rich history that spans thousands of years and is rooted in ancient Chinese philosophy and practices (Abbaoui et al., 2024; Bai et al., 2022; P.-H. Chen et al., 2022). It encompasses a variety of therapeutic techniques, including acupuncture, herbal medicine, and qigong, which aim to restore balance and harmony within the body (W. Chen, 2023; Chung et al., 2021; Crooks et al., 2024). Unlike conventional medicine, which often focuses on symptom relief and disease treatment, TCM emphasizes a holistic approach that considers the patient’s overall well-being and lifestyle (Tan et al., 2024; Wang et al., 2024; Y. Zhang & Gao, 2020; Zhao et al., 2023; Zhou et al., 2022). Understanding these fundamental principles is crucial for analyzing patient preferences between TCM and conventional medicine, particularly in the context of treating lower back pain in Salatiga District, Sambas Regency (Elahee et al., 2020; Fang et al., 2023; Hua et al., 2023; Huang et al., 2021).

The two systems' approach to disease is also very different. Conventional medicine usually focuses more on treating symptoms and diseases with interventions that are often invasive and technology-based (Ng et al., 2022; Ortiz & Smeltzer, 2024; Patel et al., 2023). TCM, on the other hand, takes a perspective that looks at health through energy balance and seeks to address the root causes of disease through more preventive and curative methods (Patel et al., 2023; Ren et al., 2021; Sapio & Vecchio, 2024). The expected results of these two approaches differ; conventional medicine promises quick and measurable results, whereas TCM takes longer to show its effectiveness with a more gradual and sustainable approach.

This decision-making process describes how individuals maximize their satisfaction according to their health needs and values by considering various factors influencing their choice. Economic factors such as income, cost of care, and insurance coverage are important in determining one's choice between conventional medicine and TCM (Drakopoulos, 2023; Yan et al., 2023; X. Zhang et al., 2022). An individual's income greatly influences the accessibility and choice of health care available.

Despite the widespread availability of Traditional Chinese Medicine (TCM) and conventional treatments, limited research has comprehensively compared patient preferences in regions like Salatiga District and Sambas Regency, where healthcare access and cultural factors intersect. Previous studies have often focused on either the clinical effectiveness of treatments or economic considerations in isolation. However, an integrated analysis that accounts for treatment effectiveness, economic factors, patient perceptions, accessibility, and continuity of care remains underexplored. This study aims to fill this critical research gap by examining how these factors collectively shape patient preferences, offering a more holistic understanding of healthcare decision-making in the context of low back pain treatment.

This study analyzes how patients' economic factors, effectiveness, risk perception, previous experience, and accessibility affect their preference for TCM or conventional treatment. By understanding these factors, the study is expected to provide valuable recommendations for health policymakers on improving the region's access and quality of health care.

METHOD

This research adopted a qualitative approach with a case study design to explore community preferences for Traditional Chinese Medicine (TCM) over conventional medicine in Sungai Toman Hamlet (Conroy et al., 2020). The study focused on non-financial factors influencing preferences, such as personal experiences, cultural values, and spiritual beliefs. Primary data were collected through in-depth interviews, while secondary data came from medical records and relevant literature. The study population consisted of all residents in Sungai Toman Hamlet who had received TCM treatment, with a purposive sampling method to ensure diverse experiences and perspectives.

The study population included all Sungai Toman Hamlet residents who had received TCM treatment, with the sample selected by purposive sampling to ensure a diversity of experiences and perspectives. A cross-sectional survey was conducted involving patients receiving treatment for low back pain in Salatiga District, Sambas Regency. In-depth interviews with healthcare providers supplemented this to gain contextual insights. The study used a purposive sampling method targeting patients with diverse demographic and socioeconomic backgrounds. The sample size of 150 respondents was determined based on statistical power analysis, ensuring adequate representation for reliable statistical inference.

Data collection techniques involved semi-structured interviews guided by an interview protocol to explore psychological and social influences on preferences. The data were analyzed using thematic analysis to identify patterns and themes from the narratives.

RESEARCH RESULTS AND DISCUSSION

SEM Test Results

Diagram 1.

SEM Research Test Results

Source: Primary Data Processed (2024)Top of Form

Based on the diagram shown, it can be interpreted as follows: This Structural Equation Modeling (SEM) diagram illustrates the relationship between observed and unobserved variables, as well as the relationship between two different healthcare systems. Below is a detailed interpretation of each value in the diagram:

a. TCM (Traditional Chinese Medicine):

1. Income (0.902): A very strong relationship with Economic Factors, indicating that income is an important determinant in choosing TCM.

2. Cost (0.862): It also shows a strong relationship with economic factors, suggesting that the cost of TCM treatment is highly considered.

3. Insurance (0.861): Strong relationship with Economic Factors, suggesting insurance influences the choice to use TCM.

4. Risk (0.964): A very strong relationship with Perception, indicating the perception of significant risk in using TCM.

5. Care (0.934): Strong relationship with Perception, indicating that quality of care is an important factor in the perception of TCM.

6. Confidence (0.920): The strong relationship indicates that belief in the effectiveness of TCM is an important factor in patient perception.

7. Adherence (0.955), Satisfaction (0.946), and Prevention (0.946) all have very strong relationships with Availability, suggesting that these are key factors in access to TCM.

b. Conventional:

1. Pain (0.995): This is a very strong relationship, and the value indicates that there is a strong influence on reducing pain.

2. Function Reduction (0.048): A very weak relationship with Effectiveness, indicating that reduction in function does not have a large effect on the perceived effectiveness of conventional treatment.

3. Drug Dosage (0.995): Very strong association with Effectiveness, suggesting that increasing drug dose does not contribute to treatment effectiveness.

4. Convenience (0.967), Availability (0.986), Waiting Time (0.960): A very strong relationship with Accessibility, indicating that these factors have a major influence on conventional medicine.

5. The Relationship Between TCM and Conventional:

6. Type of Treatment1 (0.798): This indicates a strong relationship between this variable and TCM, suggesting that treatment type is important in the TCM system.

7. Type of Treatment2 (0.795): Similar to TCM, this type of treatment is also an important component in the conventional system.

The above values reveal patients' perspectives on economic factors, perceived risk and quality, and accessibility in TCM and conventional treatment. This SEM diagram shows that factors such as income, cost, and insurance contribute significantly to the use of TCM.

Patients' satisfaction with treatment and disease prevention efforts contribute significantly to their adherence to TCM. Whereas the effectiveness of conventional treatment mainly depends on the functional benefits provided and the correct dosage of drugs, not on the perception of pain.

The accessibility of conventional treatments, measured through ease of obtaining treatment, availability, and waiting time, also strongly influences patients' selection of these treatments.

Overall, the model illustrates the importance of economic factors, perception, satisfaction, effectiveness, and accessibility in influencing patients' choice between TCM and conventional medicine.

R Square Test

Table 1. R Square Test Results

	R Square	Adjusted R Square
TCM	0.798	0.776
Conventional	0.795	0.771

Source: Primary Data Processed (2024)

Table 4.1 can be interpreted as follows:

1. R Square (R²): The R² value describes the percentage of variance in the dependent variable that can be explained by the independent variables in the model. The R² value of 0.798 for TCM means that the variables in the model can explain 79.8% of the variation in the variables related to TCM. This is of very high value in the context of most studies, indicating that the model used is entirely accurate in explaining the effect of TCM.

2. Adjusted R Square: While R² can provide an overly optimistic estimate of the data fit of the model, especially if there are many predictor variables or if the sample is small, Adjusted R Square corrects for this by adjusting the R² value based on the number of variables in the model and the sample size. In other words, Adjusted R Square provides a more precise assessment of the statistical fit that applies to a larger population. The Adjusted R² value of 0.776 for TCM signifies that after adjusting for the number of variables and sample, 77.6% of the variance can still be explained, indicating a strong model fit.

3. Conventional: Similarly to TCM, the R² value for Conventional was 0.795, meaning that the model explained 79.5% of the variance in the variables related to conventional treatment. The Adjusted R² value is 0.771, which is slightly lower but still indicates that most of the variability is explained by the model even after adjustment.

Notably, these values are high, signaling that the developed model is a good fit for both conditions. This suggests that the predictors chosen by the researchers are highly relevant and have a strong influence on explaining the treatment decisions or outcomes measured through TCM and Conventional methods.

However, while high R Square values indicate a good fit, they do not inform about the model's overall fit, causality, or whether the model is the best. Researchers should also consider other factors, such as model error, excluded variables, and potential causal relationships that cannot be explained by R² values alone. Moreover, this value does not explain the distribution of the individual effects of each independent variable; it only provides an aggregate picture of the strength of their collective relationship to the dependent variable. To understand the individual contribution of each predictor variable, we will need additional information from the analysis output, such as coefficient values, t statistics, and p values for each variable.

It can be concluded that the models associated with Latent Variable 1 and Latent Variable 2 have a firm fit and can explain most of the variability in the data studied. Latent Variable 1 explains approximately 79.8% of the variability, while Latent Variable 2 explains approximately 79.5%. After adjustment for the number of predictors, both variables show a relatively small decrease in explanatory power, with an Adjusted R Square of 77.6% for Latent Variable 1 and 77.1% for Latent Variable 2. This suggests that the models remain robust even after adjustment for model complexity, with both latent variables contributing significantly to the understanding of the phenomenon being measured.

F Square Test

Table 2. F Square Test Results

	TCM	Conventional
Economy	0.046	0.056
Perception	0.002	0.012
Sustainability	0.001	0.001
Accessibility	0.030	0.015
Effectiveness	0.060	0.014

Source: Primary Data Processed (2024)

The f Square value can be interpreted as follows:

Small Value: Indicates a small effect, usually an f Square value of 0.02.

Moderate Value: Indicates a moderate effect, usually an f Square value of 0.15.

Large Value: Indicates a strong effect, usually an f Square value in the range of 0.35.

a. Economics: TCM (0.046) and Conventional (0.056): This value falls into the small effect category, indicating that economic variables influence both treatment models, with a slightly more significant influence on the conventional model.

b. Perception: TCM (0.002) and Conventional (0.012): These values are very small, suggesting that perception contributed little to the variability in both treatment models, with a slightly greater contribution in conventional treatment than TCM.

c. Sustainability: TCM (0.001) and Conventional (0.001): With this value close to zero, we can say that continuity of care has no significant effect on either treatment model.

d. Accessibility: TCM (0.030) and Conventional (0.015): Although categorized as a small effect, accessibility seems to play a more important role in TCM treatment than conventional treatment, perhaps indicating that ease of access to TCM influences patient experience more.

e. Effectiveness: TCM (0.060) and Conventional (0.014): Effectiveness has a moderate influence on TCM, the strongest influence among all measured variables. This suggests that treatment effectiveness is important in determining how TCM is valued. Meanwhile, effectiveness has a smaller but still significant effect on conventional medicine.

Interpreting these values in a broader context, treatment effectiveness, and economic factors are the two most influential variables in the TCM and conventional treatment models. However, they have different weights in the two models, which suggests that patients may prioritize different aspects when choosing between TCM and conventional medicine. It is important to note that the interpretation of the f Square value should always be considered along with the sample size and distribution of other variables in the model to understand their contribution fully.

Based on the f Square table provided, the general conclusions that can be drawn are:

a. Influence of Latent Variables: Latent variables influence the observed measured variables differently. A higher f² value indicates that the latent variable contributes more to the variation in the relevant measured variable.

b. Variability of Influence: The table shows that the influence of latent variables is not uniform on all measured variables. For example, latent variable 3 has a more significant influence on measured variable 2 than measured variable 1.

c. Contribution Characteristics: The f² value can identify which latent variable contributes more to a particular measured variable. This can help us further understand the model structure and what factors are more significant in influencing the measured variable.

d. Interpretation and Implications: The f² value can help researchers or practitioners evaluate the relative significance of latent variables in a confirmatory factor analysis model or structural equation modeling. Interpreting these values can help in decision-making related to model refinement or identifying aspects that need further attention in research.

Construct Validity and Reliability Test

Table 3. Construct Validity and Reliability Test Results

	Cronbach's Alpha	rho_A	Composite Reliability	Average Variance Extracted (AVE)
Economy	0.848	0.849	0.908	0.767
Perception	0.934	0.943	0.958	0.883
Sustainability	0.860	0.926	0.915	0.784
Accessibility	0.970	0.971	0.980	0.943
Effectiveness	0.652	0.976	0.803	0.660

Source: Primary Data Processed (2024)

Based on the table above, the validity test can be interpreted as follows:

a. Cronbach's Alpha: This is a standard measure of internal reliability, which assesses the consistency between different items that make up a single construct. Economy, Perception, and Sustainability show excellent alpha values, indicating that these items measure the same concept consistently. Effectiveness has the lowest value (0.652), which is still acceptable but suggests some inconsistencies in the items measuring this construct.

b. rho_A: Also known as the rho Coefficient, this is an alternative to Cronbach's Alpha and is often considered a more accurate estimate of internal reliability. The high rho_A value for Effectiveness (0.976) indicates a discrepancy with Cronbach's Alpha value, which may suggest that the measurement model of the Effectiveness construct is more complicated and needs to be fully explained by Cronbach's Alpha.

c. Composite Reliability: This value describes the construct's internal consistency by considering the individual items' weights. Values above 0.7 indicate good reliability, meaning that the constructs in this study consistently measure the concept they are trying to measure.

d. Average Variance Extracted (AVE): This value measures the extent to which the construct can explain the variance of its items. An AVE value higher than 0.5 indicates that most of the variance in the items is explained by the construct and not by measurement error. All constructs except Effectiveness have an AVE above 0.5, which signifies strong convergent validity. For Effectiveness, the AVE value is close to the threshold (0.660), which indicates that although most of the variance is explained by the construct, there may still be room for improvement.

Overall, the constructs in this study showed high reliability, with some questions arising regarding perfect scores for TCM and Conventional, which are atypical and may not indicate an accurate representation of a complex reality. The Economy, Perception, and Sustainability constructs showed strong reliability and good validity, indicating that they are reliable indicators of the concepts they represent. The Effectiveness construct, meanwhile, has areas for improvement in internal consistency despite showing sufficient reliability in the context of this study. The lower AVE for Effectiveness suggests that researchers may need to explore the items further or revise the construct to improve its convergent validity.

Based on the information in the table regarding construct validity and reliability, the conclusions that can be drawn are as follows:

a. High Reliability: All constructs in this study showed very high reliability, with Cronbach's alpha and rho_A values close to 1.00. This indicates that the items in each construct consistently measure the same concept and can be relied upon to measure the underlying latent variable.

b. High Construct Validity: The composite reliability and average variance extracted (AVE) values for each construct were also high, approaching or reaching 1.00. This indicates that the constructs can effectively estimate the variance of the underlying latent variable. High construct validity indicates that the constructs measure what they are intended to.

c. Measurement Reliability: The combination of high reliability and high construct validity indicates that the measurements taken in this study are reliable and meet high-quality standards.

d. Good Data Quality: The values in the table indicate that the measurements made on the constructs in this study are reliable and provide accurate results.

Thus, overall, the constructs measured in this study have high reliability and good validity, so the analysis results generated from the data can be relied upon to support the findings and conclusions of the study.

Discussion

Factors Influencing Demand Preference for TCM and Conventional Medical Treatment:

1. Treatment Effectiveness: The analysis showed treatment effectiveness as the main factor influencing treatment choice for TCM and conventional medicine. Patients tend to prioritize the treatment that provides the most significant relief from low back pain. TCM is often chosen due to positive perceptions of its effectiveness in holistic and natural medicine. In this case, this study is in line with research conducted by Durand et al. (2020), where people prefer the benefits or effectiveness of TCM over Conventional medicine.

2. Perception of Treatment: Patients' perceptions of treatment significantly influence their decisions. This includes perceptions about the risks, safety, effectiveness, and treatment process. These perceptions may be influenced by personal experiences, recommendations from others, and information conveyed by health practitioners. This study is in line with research conducted by Zhou et al. (2022), where the importance of paying attention to patient preferences in the treatment of advanced lung cancer, especially in the context of chemotherapy side effects and how patient experiences can influence their treatment decisions.

3. Economic Factors: The cost of treatment, availability of insurance, and the economic condition of the patient also play a role in choosing between TCM and conventional medicine. Patients may prefer treatments that are more affordable or that are covered by their insurance. This study is not in line with the research conducted by Durand et al. (2020), where study says that many patients value the benefits of treatment more than other aspects, such as side effects, cost, or the way the drug is administered, but not in line with research conducted by Winahyu (2020) which says not all patients immediately follow up on the disease due to the problem of costs that are too expensive and the recovery time is pretty long.

4. Accessibility: Accessibility of treatment, including ease of reaching healthcare providers, availability of services, and waiting times, also affects patient preferences. TCM may be preferred in some areas because it is more accessible or has shorter waiting times than conventional medical services. This study aligns with research conducted by McIntyre et al. (2023), who said that hospital waiting time can take a long time, reducing the patient's comfort in the treatment process.

CONCLUSIONS

In conclusion, this study provides valuable insights into patient preferences between Traditional Chinese Medicine (TCM) and conventional treatment for lower back pain. The Structural Equation Modeling (SEM) analysis revealed several key factors influencing these preferences. First, the strong relationship between income and economic factors (0.902) indicates that financial considerations significantly affect patients' choices. Additionally, the cost of treatment (0.862) and insurance availability (0.861) further emphasize the importance of economic variables in decision-making. Perception of treatment risks (0.964) and quality of care (0.934) also play crucial roles, suggesting that patients are highly concerned about the safety and effectiveness of TCM. Satisfaction and adherence to TCM are strongly linked to its availability (0.955), highlighting the necessity for accessible care options. For conventional medicine, the effectiveness in pain reduction (0.995) and the impact of drug dosage (0.995) were noted, demonstrating that patients prioritize immediate and measurable outcomes.

The SEM analysis also illustrated the lesser impact of functional reduction (0.048) on perceived effectiveness, indicating that patients may overlook functional impairments when assessing treatment success. Overall, the results underscore the significance of economic factors, patient perceptions, and treatment effectiveness in shaping healthcare choices. This study advocates for improved health policies that support both TCM and conventional medicine, enhancing patient education and access to diverse treatment options. By integrating these approaches, healthcare systems can better align with patient needs and values, ultimately leading to more informed and satisfactory healthcare decisions.

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