Exploring the roots of chronic pain: Socioeconomic and environmental insights

 Corresponding author: Thae Yadanar Oo Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0. Exploring the roots of chronic pain: Socioeconomic and environmental insights Thae Yadanar Oo * University at Albany. World Journal of Advanced Research and Reviews, 2025, 27(03), 1824-1836 Publication history: Received on 26 July 2025; revised on 07 September 2025; accepted on 09 September 2025 Article DOI: https://doi.org/10.30574/wjarr.2025.27.3.3174 Abstract Chronic pain is increasing as one of the greater global health disorders while social and environmental determinants remain underestimated. This study investigates how the national economic situation, civil strife, education, age, and gender determine the prevalence of chronic pain in various populations. Using a confidential cross-national health dataset, we applied multivariate regression analyses to determine the associations of chronic pain to macroand micro-level determinants. The results show an inverse relation between national GDP and chronic pain prevalence, whereas civil conflict increases reported pain significantly. Among individual-level factors, increasing age and female gender are strong predictors of chronic pain; education does the contrary. Interaction analyses also show that education mitigates the effects of low income and conflict on pain outcomes, thus highlighting the social determinants' aspect. This underpins the need for multipronged strategies that, besides clinical intervention, address structural inequality, enhancement of the education system, and health with an eye to the conflict context. Together, socioeconomic and environmental factors enhance our understanding of the etiopathogenesis of chronic pain and directly go toward possible working interventions that can be managed in global health policy and practice. Keywords: Chronic pain; Socioeconomic factors; Environmental determinants; Civil conflict; Education; Public health 1. Introduction Chronic pain is a pervasive health issue affecting millions of people worldwide, significantly impairing individuals' quality of life and productivity. Traditionally, research has focused on biological factors such as genetics, injury, and disease as primary causes of chronic pain. However, emerging evidence suggests that environmental and socioeconomic factors also play a critical role in determining the frequency and severity of chronic pain. Factors beyond medical conditions, such as income disparity, education levels, access to healthcare, urbanization, and civil unrest, influence how individuals experience and manage chronic pain. Understanding these broader determinants is crucial, as chronic pain does not only impact the well-being of individuals but also poses substantial social and economic challenges to public health systems. People suffering from chronic pain often experience diminished quality of life, reduced productivity, and increased dependency on healthcare services, thereby straining public health resources. Moreover, socio-economic and environmental disparities can worsen the burden of chronic pain, particularly for disadvantaged populations. World Journal of Advanced Research and Reviews, 2025, 27(03), 1824-1836 1825 1.1. This research addresses the question How do socio-economic and environmental factors at both individual and country levels impact the occurrence of chronic pain? By investigating the interplay of these factors, the study aims to provide valuable insights to policymakers and public health programs, particularly in regions affected by conflict or inequality. Addressing chronic pain from a holistic perspective will help develop targeted interventions and equitable public health strategies to enhance the wellbeing of communities, ultimately mitigating the negative effects of chronic pain on both individuals and society. 2. Literature review The existing research on chronic pain has traditionally centered around biological factors such as injury, genetics, and disease. However, recent studies emphasize the significant role of socio-economic, environmental, and psychosocial factors. Age is a key determinant, with older individuals being more likely to experience chronic pain due to aging processes, especially musculoskeletal issues (Miller et al., 2020). Gender also influences pain perception; women report higher levels of chronic pain, possibly due to hormonal differences and social factors (Smith et al., 2021). Education impacts pain management outcomes, as individuals with higher education levels tend to access better healthcare and pain management strategies (Johnson et al., 2022). Economic disparities, such as poverty and income inequality, also elevate the risk of chronic pain, primarily due to restricted healthcare access in low-income areas (Huang and Lei, 2021). Environmental factors, such as exposure to civil conflict, exacerbate mental and physical health issues, increasing chronic pain prevalence (Lambe et al., 2020). Socio-economic status further affects pain management resources—higher SES groups benefit from better access to treatment, while lower SES groups face greater challenges (Johnson et al., 2022). Chronic pain is also linked to health conditions like arthritis and back pain, which are often worsened by environmental and socio-economic stressors, underscoring the importance of accessible healthcare and lifestyle interventions (Liu et al., 2021). 3. Hypotheses Statement and Implications 3.1. GDP per Capita (H1) 3.1.1. Alternative Hypothesis (H1) Higher GDP per capita has a significant negative relationship with the occurrence of chronic pain, suggesting that individuals from countries with higher GDP per capita are less likely to experience chronic pain due to better healthcare infrastructure and resources. Implication If this hypothesis is supported, it implies that economic development is crucial for reducing chronic pain through improved healthcare infrastructure. It suggests that countries with lower GDP should prioritize policies aimed at enhancing healthcare systems and making pain management resources more accessible. 3.2. Civil Conflict (H2) 3.2.1. Alternative Hypothesis (H2) Living in countries experiencing civil conflict has a significant positive relationship with chronic pain. Individuals in these areas are expected to have higher average body pain scores due to increased stress, lack of access to healthcare, and exposure to traumatic events. Implication Supporting this hypothesis would imply that exposure to conflict substantially affects physical well-being, increasing chronic pain occurrence. This highlights the importance of integrating mental health and pain management services into humanitarian aid and post-conflict recovery programs. World Journal of Advanced Research and Reviews, 2025, 27(03), 1824-1836 1826 3.3. Education Level (H3) 3.3.1. Alternative Hypothesis (H3) Higher education levels have a significant negative relationship with average body pain scores. Individuals with more education are expected to have better access to healthcare resources, pain management knowledge, and healthier lifestyles, resulting in reduced occurrences of chronic pain. Implication If this hypothesis is confirmed, it indicates that education plays a critical role in mitigating chronic pain. This suggests that policies promoting education can have secondary benefits for health, and education campaigns could be used as tools for improving public health outcomes related to pain. 3.4. Age (H4) 3.4.1. Alternative Hypothesis (H4): Age has a significant positive relationship with chronic pain. As individuals grow older, the average body pain score tends to increase, suggesting that aging is associated with a greater prevalence of pain-related issues. Implication If supported, this hypothesis suggests that chronic pain should be treated as a key issue in elderly populations. Healthcare providers may need to focus on early intervention and ongoing management of chronic pain in older adults to improve their quality of life. 3.5. Gender (Female) (H5) 3.5.1. Alternative Hypothesis (H5) Females are significantly associated with higher levels of chronic pain compared to males. Chronic pain is expected to be higher for females due to biological differences, hormonal factors, and variations in pain sensitivity and reporting behavior. Implication If this hypothesis is supported, it highlights the importance of gender-specific approaches to managing chronic pain. Tailored support and targeted healthcare interventions may help address the unique pain management needs of females, reducing disparities in chronic pain prevalence and treatment outcomes. 3.6. Data 3.6.1. Data and Variables The data for this study comes from the “Confidential cross-national health dataset” which includes both individual-level and country-level information to examine factors influencing chronic pain. The following are the detailed descriptions for the key variables used in the analysis: • Log GDP per Capita (gdp_cap): Average income per person, log-transformed to reduce skewness. Used to assess economic influence on chronic pain. • Civil Conflict (e_civil_war): Dummy variable (0 = no conflict, 1 = conflict) indicating if the individual lives in a country with civil conflict. • Age: Participant age (20-60 years) used to assess how chronic pain varies by age. • Education Level (education): Highest level of education categorized into three groups: No/Primary, High School/College, and Bachelor/Postgraduate. Used to explore the impact of education on chronic pain. • Gender (female): Dummy variable (0 = male, 1 = female) representing the participant's gender. • Body Pain (body_pain): Body pain score from 1 (low) to 5 (severe). Used as the dependent variable for assessing chronic pain. World Journal of Advanced Research and Reviews, 2025, 27(03), 1824-1836 1827 Table 1 Summary Statistics of Chronic Pain by Socio-Economic Factors Statistic (log)GDP e_civil_war age Education Gender Body Pain Count 54,019 54,019 54,019 54,019 54,019 54,019 Mean 8.44 0.0729 38.94 2.98 0.624 2.30 Standard Deviation 0.92 0.260 11.08 1.65 0.484 1.10 Minimum 6.65 0.000 20.00 1.00 0.000 1.00 Median 8.43 0.000 38.00 3.00 1.000 2.00 Maximum 11.08 1.000 60.00 7.00 1.000 5.00 The table 1 provides an insightful overview of the summary statistics of the variables in the dataset, highlighting key trends and distributions. The average log-transformed GDP per capita is 8.44, with a range from 6.65 to 11.08. Using a logarithmic transformation helps reduce skewness, ensuring a more normalized distribution that captures the economic disparities across countries without the overwhelming impact of extreme values. This transformation is particularly important for subsequent analyses that seek to understand the relationship between economic conditions and health outcomes, such as chronic pain. The variable for civil conflict (e_civil_war) shows that only 7.29% of the sample population lives in countries with ongoing civil conflicts, based on an average value of 0.0729. With 54,019 valid observations, most values are zeros, indicating that most of the data comes from regions without civil conflict. This variable is represented as a dummy (0 or 1), with "1" representing an ongoing civil conflict, which allows for a clear understanding of the presence or absence of conflict in each observation. The age variable has 54,019 valid observations, with a mean age of approximately 38.94 years. This suggests that the dataset primarily includes a middle-aged population, ranging from a minimum age of 20 to a maximum age of 60 years. The filter applied to include only individuals between these ages ensures that my analysis focuses on a specific workingage population, which is particularly relevant to examining chronic pain trends across different age groups. Education is another important factor in this analysis. The average level of education is 2.98, which roughly translates to a level corresponding to high school or some college education. Education levels range from 1 to 7, representing a wide spectrum of educational attainment, from no formal education to postgraduate degrees. It’s worth noting that many individuals in the dataset have lower educational attainment, which may have implications for their health outcomes and access to healthcare services. Regarding gender, the mean value of 0.624 indicates that 62.4% of the individuals are female, and this dataset seems to have a higher representation of females. The variable is binary, where 1 represents females and 0 represents males. The significant representation of females may help in analyzing gender-specific patterns, especially in health conditions such as chronic pain. Lastly, for body pain, the mean score is 2.30 on a scale from 1 to 5, indicating that most individuals report moderate levels of body pain. The range spans from 1 (low pain) to 5 (severe pain), with a median value of 2, suggesting that most participants experience mild to moderate pain. These descriptive insights are crucial for forming a clearer understanding of the patterns of chronic pain and for setting a foundation for subsequent hypotheses. 4. Methodology OLS regression was chosen for Hypothesis 1 to examine the relationship between GDP per capita and chronic pain, with chronic pain measured using the body pain score and log-transformed GDP per capita as the independent variable. This approach controlled for age, education, gender, and the presence of civil conflict while assessing how economic conditions impact chronic pain levels. Logistic regression was selected for Hypothesis 2 to investigate whether living in a country with civil conflict increases the likelihood of experiencing chronic pain. Chronic pain was treated as a binary dependent variable, with civil conflict as the independent variable. By including controls for age, gender, education, and GDP per capita, this method determined whether conflict environments are associated with higher rates of chronic pain. World Journal of Advanced Research and Reviews, 2025, 27(03), 1824-1836 1828 OLS regression was utilized for Hypothesis 3 to explore the relationship between education level and chronic pain. Chronic pain served as the dependent variable, and education level was the independent variable. By controlling for age, gender, civil conflict, and GDP per capita, the analysis aimed to assess whether individuals with higher education levels report lower levels of chronic pain. OLS regression was applied for Hypothesis 4 to examine how age influences chronic pain. Chronic pain was the dependent variable, and age was the independent variable. Controls for gender, education level, civil conflict, and GDP per capita were included to evaluate whether chronic pain increases with age while accounting for other factors. A simple regression model was used for Hypothesis 5 to examine whether gender influences chronic pain levels. In this model, chronic pain (measured by body pain scores) was the dependent variable, while gender (female: 1, male: 0) was the independent variable. This approach assessed the direct relationship between gender and chronic pain levels without controlling for other variables, focusing solely on the impact of gender differences. 4.1. Preliminary descriptive analysis Figure 1 Correlation Heatmap The figure1 illustrates the relationships between the main variables in this study. Notably, there is a moderate positive correlation between log-transformed GDP per capita and education (0.54), suggesting that individuals from wealthier countries are more likely to attain higher education. Age also shows a positive correlation with body pain (0.15), indicating that body pain tends to increase with age. Additionally, education is negatively correlated with body pain (- 0.13), suggesting that higher education levels may reduce the experience of pain. These relationships provide preliminary insights into how socio-economic factors might influence chronic pain, guiding further analysis. World Journal of Advanced Research and Reviews, 2025, 27(03), 1824-1836 1829 Figure 2 Average Body Pain by GDP per Capita The figure2 shows that average body pain remains relatively consistent across GDP categories, with minor variations. Interestingly, countries with higher GDP per capita (>50k) tend to report slightly lower average body pain levels compared to lower GDP categories (<5k, 5k-10k). This may suggest that greater economic resources could contribute to better healthcare and pain management, resulting in lower body pain scores. Figure 3 Average Body Pain by Presence of Civil Conflict This figure 3 illustrates the average body pain scores among individuals living in areas with and without civil conflict. The data shows that individuals in regions with ongoing civil conflict tend to report higher body pain scores compared to those living in regions without conflict. This suggests that the stress and adverse conditions associated with civil conflict may contribute to increased levels of chronic pain. World Journal of Advanced Research and Reviews, 2025, 27(03), 1824-1836 1830 Figure 4 Average Body Pain Score by Education Level The figure 4 shows the average body pain score by education level. Individuals with no education or only primary education report the highest average body pain scores, while those with higher education, such as "High School or College" and "Bachelor and Postgraduate," report lower scores. This suggests that education may play a role in reducing body pain, potentially through better access to healthcare, healthier lifestyle choices, and more effective pain management strategies. Figure 5 Average Body Pain Score by Gender The figure 5, titled "Average Body Pain Score by Gender," shows the average level of body pain reported by males and females. From the chart, it is clear that females report a higher average body pain score compared to males. This suggests that women tend to experience or report more body pain on average than men. This difference could be influenced by various biological, psychological, or social factors, but further research would be needed to fully understand why this pattern exists. World Journal of Advanced Research and Reviews, 2025, 27(03), 1824-1836 1831 Figure 6 Body Pain Score by Age Group This figure 6 shows the body pain scores for different age groups, segmented into categories of 20-30, 30-40, 40-50, and 50-60 years. It highlights the variation in pain levels across age groups, showing that older age groups, specifically 4050 and 50-60, tend to have more variability and slightly higher average pain scores compared to younger groups. The presence of outliers is also noted, particularly in the younger age groups, indicating some individuals reporting unusually high levels of pain. 5. Results and discussion Table 2 Results of Hypothesis Testing GDP per Capita and Chronic Pain Variable Dependent Variable: Chronic Pain Observations Coefficient Std. Error t/z/F Value P Value Conclusion GDP per capita (log-transformed) Body Pain 54,019 -0.0282 0.002 -12.13 0.0 Significant Age Body Pain 54,019 0.0168 0.0 135.84 0.0 Significant Education Body Pain 54,019 -0.0644 0.001 -43.48 0.0 Significant Gender Body Pain 54,019 0.2242 0.004 56.462 0.0 Significant Living in Civil Conflict zone Body Pain 54,019 0.2415 0.009 28.19 0.0 Significant The table 2 show that the coefficient for ‘GDP per capita’ is -0.0282, which means there's a significant negative relationship between GDP per capita and chronic pain (p-value < 0.001). In simpler terms, countries with a higher GDP per capita tend to have less chronic pain. This might be because these countries can provide better healthcare and support, making it easier for people to get the help they need to manage pain. World Journal of Advanced Research and Reviews, 2025, 27(03), 1824-1836 1832 Table 3 Results of Hypothesis Testing: Civil Conflict and Chronic Pain Variable Dependent Variable: Chronic Pain Observations Coefficient Std. Error t/z/F Value P Value Conclusion Living in Civil Conflict zone Body Pain 54,019 0.4143 0.019 21.480 0.000 Significant Age Body Pain 54,019 0.0322 0.000 109.66 0.000 Significant education Body Pain 54,019 -0.1280 0.004 -35.53 0.000 Significant Gender Body Pain 54,019 0.4449 0.010 45.570 0.000 Significant GDP per capita (logtransformed) Body Pain 54,019 -0.0372 0.006 -6.554 0.000 Significant The table 3 is showing the coefficient for ‘Living in Civil Conflict Zone’ is 0.4143, a significant positive link with the likelihood of having chronic pain (p-value < 0.001). This means people living in countries with ongoing civil conflict are more likely to report chronic pain. It makes sense since conflict can create stressful environments and make it harder to get proper healthcare, leading to more people suffering from pain. Table 4 Results of Hypothesis Testing: Education Level and Chronic Pain Variable Dependent Variable: Chronic Pain Observations Coefficient Std. Error t/z/F Value P Value Conclusion Education Body Pain 54,019 -0.0644 0.001 -43.48 0.000 Significant age Body Pain 54,019 0.0168 0.000 135.84 0.000 Significant Gender Body Pain 54,019 0.2242 0.004 56.462 0.000 Significant Living in Civil Conflict zone Body Pain 54,019 0.2415 0.009 28.190 0.000 Significant GDP per capita (logtransformed) Body Pain 54,019 -0.0282 0.002 -12.12 0.000 Significant Table 4 illustrates the significant negative relationship between education and chronic pain, as indicated by a coefficient of -0.0644 and a p-value of <0.001. This demonstrates that individuals with higher education levels are less likely to experience chronic pain. This outcome can be attributed to their better understanding of health management, greater health literacy, and increased access to healthcare resources. Additionally, the control variables, such as age (coef = 0.0168, p < 0.001), female gender (coef = 0.2242, p < 0.001), civil conflict (e_civil_war, coef = 0.2415, p < 0.001), and GDP per capita (log_gdp_cap, coef = -0.0282, p < 0.001), also play significant roles in influencing chronic pain, supporting the idea that socio-economic and demographic factors collectively shape health outcomes. Table 5 Results of Hypothesis Testing: Age and Chronic Pain Variable Dependent Variable: Chronic Pain Observations Coefficient Std. Error t/z/F Value P Value Conclusion Age Body Pain 54,019 0.0168 0.000 135.84 0.000 Significant Education Body Pain 54,019 -0.0644 0.001 -43.84 0.000 Significant Gender Body Pain 54,019 0.2242 0.004 56.462 0.000 Significant Living in Civil Conflict zone Body Pain 54,019 0.2415 0.009 28.190 0.000 Significant