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AB1137 The role of social determinants on the prevalence of rheumatic diseases in latin america. a multilevel copcord study
  1. J Moreno-Montoya1,
  2. C Pacheco-Tena2,
  3. Y Granados3,
  4. J Londoño4,
  5. S Guevara-Pacheco5,
  6. B Pons-Estel6,
  7. O Vega-Hinojosa7,
  8. M Garza-Elizondo8,
  9. MV Goycochea-Robles9,
  10. R Quintana10,
  11. F Julian-Santiago11,
  12. J Alvarez-Nemegyei12,
  13. AM Santos-Granados13,
  14. R Burgos-Vargas14,
  15. I Pelaez-Ballestas14,
  16. on behalf of GEEEMA/GLADERPO
  1. 1Epidemiology, Universidad del Bosque, Bogota, Colombia
  2. 2Faculty of Medicine, Universidad Autonoma de Chihuahua, Chihuahua, Mexico
  3. 3Rheumatology Unit, Hospital Dr. Manuel Nuñez Tovar, Maturin, Venezuela, Bolivarian Republic Of
  4. 4Faculty of Medicine, Universidad de la Sabana, Bogota, Colombia
  5. 5Faculty of Medicine, Universidad de Cuenca, Cuenca, Ecuador
  6. 6Rheumalogy Unit, Hospital Provincial, Rosario, Argentina
  7. 7Research Unit, Clinica Reumacenter, Juliaca, Peru
  8. 8Rheumatology Dpt., Hospital Universitario, Monterrey
  9. 9Research Unit, IMSS, Mexico City, Mexico
  10. 10Rheumatology Dpt., Hospital Provicial, Rosario, Argentina
  11. 11UNAM, Mexico
  12. 12Research Unit, HARE, Merida, Mexico
  13. 13Universidad de la Sabana, Bogota, Colombia
  14. 14Rheumatology Unit, Hospital General de Mexico, Mexico, Mexico


Objectives To determine the impact of individual and regional variables on the geographic distribution of RD across six Latin-American countries

Methods This is a secondary multilevel analysis of cross-sectional data of COPCORD studies that investigated the prevalence of RD in Argentina, Colombia, Ecuador, México, Peru, and Venezuela. Individual factors were sex, age, comorbidities, job status, and Health Assessment Questionnaire (HAQ) score. Contextual level variables were country and subject's identification as indigenous. RD predictors, including individual and regional variables, particularly indigenous status were identified with logistic regression models. The effect of contextual variables was estimated with median odds ratio's (OR) estimation.

Results Most individuals included in this analysis came from urban areas (82.40%); their mean age was 43.12 years (95% CI 43.01–43.35); and 56.0% were women. Nearly all of them reported >1 comorbidity (94.70%) and 72.19% were economically active. The prevalence of any RD varied from 1.55% in Peru to 26.09% in Argentina. The mean prevalence of Rheumatoid Arthritis (RA) was 1.58 (range 0.64 to 2.47) (table 1). Aside comorbidities, individual level variables associated to any RD were sex (OR: 1.35; 95% CI 1.28–1.43), age (OR: 1.02; 95% CI 1.01 -1.03), and HAQ score (OR: 3.71; 95% CI 3.22–4.28). Crude comparisons showed significant variations among countries (p<0.01) and indigenous groups (OR: 1.69; 95% CI 1.58–1.81). These findings were confirmed by adjusted analysis (Median OR 1.26; 95% CI 1.14–1.38) (table 2).

Table 1.

General prevalences and sample sizes across countries

Table 2.

Individual and contextual factors associated to any RD

Conclusions There common factors associated to the prevalence of RD in the region, however, the estimation of its impact varies in significant way across countries and related to the fact of belong to an indigenous group indicating an increase in the estimated ORs.

Acknowledgements National Council for Science and Technology (CONACYT);Colegio Mexicano de Reumatologia (Mexico). EsSalud (Perú). Universidad de Cuenca (Ecuador).ASOREUMA (Colombia). Federico Wilhelm Agricola Foundation (Argentina). PDVSA East, SUELOPETROL and Bristol-Myers Laboratory (Venezuela)

Disclosure of Interest None declared

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