OP0357-HPR A Syndemic Care Model for the Management of Rheumatic and Musculoskeletal Diseases in Indigenous Maya-Yucatec Populations: A Mixed-Methods Study
Bibliographic record
Abstract
Background: Rheumatic and Musculoskeletal Diseases (RMSDs) in the Maya-Yucatec population generate a syndemic when interacting with the most common Non-Communicable Chronic Diseases (NCDs), such as diabetes, hypertension, and obesity, leading to disability and a decrease in quality of life. A syndemic refers to the negative synergy between two or more diseases within an unfavorable context, resulting in worst health outcomes. The Syndemic Care Model (SCM) is an intervention designed to address the syndemia between RMSDs and NCDs, considering the structural factors that hinder adequate healthcare for marginalized populations. Using a Community-Based Participatory Research (CBPR) strategy, partnerships are established between researchers, communities, and authorities to address specific health needs. Objectives: To co-design and Syndemic Care Model in collaboration with the community to address health needs related to the syndemic produced by the interaction between RMSDs and NCDs. Methods: A parallel-convergent mixed-methods study following the phases of CBPR in three Maya-Yucatec communities. The quantitative component was a cross-sectional study, and the qualitative component was an ethnographic study. Phase 1: Census using the COPCORD methodology to identify RMSDs/NCDs. The following questionnaires were administered: HAQ-Di, EuroQoL 5D-3L, and service utilization, all validated in Maya and Spanish. Semi-structured interviews with patients and healthcare professionals and ethnographic records/field notes were made. Phase 2: Identifying and prioritizing health needs with community leaders and patients. Quantitative analysis: descriptive analysis of continuous and categorical variables using RStudio 4.4.2. Qualitative analysis: inductive coding using Atlas.ti and thematic analysis. The project was approved by the Ethics and Research Committee of the General Hospital of Mexico (DI/23/404-B/05/22). Results: A total of 508 people participated in the census, 69% were women, with an average age of 49 years; 53% reported being homemakers. 42% of participants were from Xkalakdzonot, 33% from Xcopteil, and the rest from Yaxunah. The prevalence of RMSDs was 8%. Eleven patients with RMSDs and six healthcare professionals were interviewed. Two focus groups, multiple community assemblies, and meetings with health and municipal authorities were conducted to strengthen collaborations. Barriers and facilitators to accessing healthcare services were identified, and the health needs expressed by the communities were prioritized (see Table 1). Based on the findings, the elements of the SCM were established, including, 1) building connections between local and regional healthcare systems to address NCDs, 2) collaborating with the municipality for the provision of medications and transportation, 3) caring for RMSDs through rheumatology and rehabilitation consultations in each community, and 4) educating to address and prevent NCDs complications (see Figure 1). Conclusion: We co-designed a culturally sensitive SCM through the implementation of a mixed methods research study that allowed us to identify and prioritize the health needs of the participant Mayan Communities. Therefore, we anticipate that the future implementation of this model will result in improved health outcomes for those living with RMSDs/NCDs. REFERENCES: [1] Singer M, Bulled N, Ostrach B, Mendenhall E. Syndemics and the biosocial conception of health. The Lancet. 2017;389(10072):941-950. [2] Ramírez-Flores MF, Cadena-Trejo C, Motte-García E, Juárez-Cruz ID, Fernández-García MF, Gastelum-Strozzi A, et al. A Mixed-Methods Systematic Review on Syndemics in Rheumatology. J Clin Rheumatol. 2023 Apr 1;29(3):113-1. 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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".