POS0435 MEASUREMENT OF SYNDEMICS OF RHEUMATIC AND MUSCULOSKELETAL DISEASES IN MAYAN-YUCATECAN INDIGENOUS COMMUNITIES: A NETWORK ANALYSIS

Background: Syndemic theory explores how disease interactions are exacerbated by social, economic, and political disadvantages, leading to poor health outcomes. Rheumatic and musculoskeletal diseases (RMDs), particularly in Indigenous Mayan communities in Yucatán, Mexico, are often associated with comorbidities like diabetes and hypertension. These co-occurrences, combined with poor healthcare access, poverty, and structural discrimination, create syndemics that worsen health and quality of life. Objectives: This study aimed to quantify the syndemic burden of RMDs using a syndemic index, and analyze the interplay between biological, social, and economic factors through network analysis. Methods: A cross-sectional study was conducted among four Indigenous communities using the Community Oriented Program for the Control of Rheumatic Diseases (COPCORD). Identifying and treating RMD is part of the program through household surveys, clinical assessments, diagnostic and therapeutic evaluations. Sociodemographic, clinical, and socioeconomic data were collected. A syndemic index was constructed using logistic regression to identify negative factors associated with RMDs and comorbidities. Network analysis and clustering techniques were applied to reveal patterns of disease aggregation and contextual vulnerabilities. The network was analyzed using Gephi software. Results: The study included 508 participants. 69.29% were women, with differences across communities in language proficiency, education, income, and comorbidities (Table 1). The identified variables in the regression included in the syndemic index were history of pain, education years, family history of RMDs, hypercholesterolemia, depression, pain self-report, disability measured with HAQ and comorbidities. Patients with RMDs had a higher syndemic index (mean 0.346, SD 0.127) than those without RMDs (mean 0.121, SD 0.133, p < 0.005), indicating more negative health effects and contextual factors in these patients. Network analysis (Figure 1) identified 22 clusters, with RMD cases dispersed across distinct groups. Typical combinations like low educational level, family history of RMD and historical pain were found in the network (Figure 1). This suggests that the impact of RMDs varies across the population, influenced by vulnerabilities and disease interactions. Figure 1 Visualization of clustered data or network components. Each colored cluster represents interconnected patients or nodes, grouped by variable similarity in the syndemic index. Larger, denser clusters indicate higher similarity among patients. Cluster positions reflect their degree of similarity—closer clusters are more similar. Node size indicates disease status, with larger nodes representing patients with rheumatic and musculoskeletal diseases. a) Cluster membership, showing overall network structure. b) Family history of rheumatic disease (green: "Yes," pink: "No"). c) Historical pain reports (green: "Yes," pink: "No"). d) Report of pain in the EuroQoL index (green: "Yes," pink: "No"). Various patterns are visible across combinations. Conclusion: The syndemic framework and network analysis revealed complex disease interactions driving health disparities in Mayan Indigenous communities. The variation in the distribution and impact of RMDs underscores the need for tailored interventions. Strategies should focus on the vulnerabilities of each cluster, addressing cultural, social, and economic factors to mitigate the syndemic burden of RMDs and improve quality of life. REFERENCES: [1] Singer M, Bulled N, Ostrach B, et al. Syndemics and the biosocial conception of health. The Lancet 2017; 389(10072):941-950. https://doi.org/10.1016/S0140-6736(17)30003-X. Table 1Descriptive Statistics: Sociodemographic, Clinical, Functional, and Economic Characteristics.Xcopteil n=179Xkalakdzonot n=212Yaxunah n=117p value~Socio-demographicGender (female) n (%)131 (73.18%)142 (66.98%)79 (67.52%)0.371Age (years) Me (SD)49.72 (16.69)49.58 (17.45)50.55 (16.58)0.856Years of education Me (SD)5.69 (4.53)5.73 (4.44)6.44 (3.79)0.021*Spanish speakers n (%)140 (78.21%)173 (81.6%)106 (90.6%)0.021*Mayan speakers n (%)172 (96.09%)206 (97.17%)116 (99.15%)0.290ClinicWeight (kg) Me (SD)59.86 (15.78)64.73 (12.79)65.31 (15.47)<0.005*Height (m) Me (SD)148.51 (91.83)148.94 (8)128.18 (50.79)<0.005*Glucose (mg/dl) Me (SD)155.49 (82.77)154.11 (204.48)163.47 (112.15)0.684Pain medication usage n (%)55 (30.73%)94 (44.34%)52 (44.44%)0.010*Family history of Rheumatic Disease n (%)27 (15.08%)24 (11.32%)28 (23.93%)0.010*Historic pain n (%)60 (33.52%)86 (40.57%)40 (34.19%)0.292Acute pain n (%)33 (18.44%)59 (27.83%)42 (35.9%)<0.005*Diabetes n (%)45(25.14%)41(19.34%)16(13.68%)0.051Hypertension n (%)39(21.79%)55(25.94%)18(15.38%)0.086Anxiety n (%)6(3.35%)19(8.96%)12(10.26%)0.038*FunctionalityFunctional capacity (HAQ) – With impairment n (%)15 (8.38%)18 (8.49%)16 (13.68%)0.242Self-reported state of health (EQ5D) - Bad health state n (%)35(19.55%)61(28.77%)52(44.44%)<0.005*EconomicHours of work per day8.89 (3.72)7.62 (2.98)8.78 (3.99)<0.005*Weekly Income (USD) Me (SD)23.49 (37.70)24.90 (35.59)32.58 (39.76)0.047*Transportation Cost (USD) Me (SD)1.86 (8.08)2.47 (10.37)5.03 (13.23)<0.005*~ Statistical tests: categorical variables (X²), continuous variables (Kruskal-Wallis).* Statistically significant difference ( p value < 0.05 )Abbreviations: Me: mean, SD: standard deviation, HAQ: Health Assessment Questionnaire, EQ5D: Euro-Qol 5D-3L questionnaire. USD: US dollars of 2023. Acknowledgements: Partial funding from the Marista University of Merida. CONAHCYT (CVU 671018 and CVU1145201). Disclosure of Interests: None declared . © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.

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