Abstract
Introduction
Although low back pain (LBP) is a high-impact health condition, its burden has not been examined from the syndemic perspective.
Objective
To compare and assess clinical, socioeconomic, and geographic factors associated with LBP prevalence in low-income and upper-middle-income countries using syndemic and syndemogenesis frameworks based on network and cluster analyses.
Methods
Analyses were performed by adopting network and cluster design, whereby interrelations among the individual and social variables and their combinations were established. The required data was sourced from the databases pertaining to the six Latin-American countries.
Results
Database searches yielded a sample of 55,724 individuals (mean age 43.38 years, SD = 17.93), 24.12% of whom were indigenous, and 60.61% were women. The diagnosed with LBP comprised 6.59% of the total population. Network analysis showed higher relationship individuals’ variables such as comorbidities, unhealthy habits, low educational level, living in rural areas, and indigenous status were found to be significantly associated with LBP. Cluster analysis showed significant association between LBP prevalence and social variables (e.g. Gender inequality Index, Human Development Index, Income Inequality).
Conclusions
LBP is a highly prevalent condition in Latin-American populations with a high impact on the quality of life of young adults. It is particularly debilitating for women, indigenous individuals, and those with low educational level, and is further exacerbated by the presence of comorbidities, especially those in the mental health domain. Thus, the study findings demonstrate that syndemic and syndemogenesis have the potential to widen the health inequities stemming from LBP in vulnerable populations.
Key points • Syndemic and syndemogenesis evidence health disparities in Latin-American populations, documenting the complexity of suffering from a disease such as low back pain that is associated with comorbidities, unhealthy habits, and the social and regional context where they live. • The use of network and cluster analyses are useful tools for documenting the complexity and the multifaceted impact in health in large populations as well as the differences between countries. • The variability and impact of socioeconomic indicators (e.g., Gini index) related to low back pain and comorbidities could be felt through the use of cluster analysis, which generates evidence of regional inequality in Latin America. • Populations can be studied from different models (network and cluster analysis) and grouping, presenting new interpretations beyond geographical groupings, such as syndemic and inequity in health. |
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References
Hurwitz EL, Randhawa K, Yu H, Côté P, Haldeman S (2018) The global spine care initiative: a summary of the global burden of low back and neck pain studies. Eur Spine J 27(Suppl 6):796–801. https://doi.org/10.1007/s00586-017-5432-9
Hartvigne J, Hancock MJ, Kongsted A, Louw Q, Ferreira ML, Genevay S et al (2018) What low back pain is and why we need to pay attention. Lancet 391:2356–2367. https://doi.org/10.1016/S0140-6736(18)30480-X
Blyth FM, Briggs AM, Schneider CH, Hoy DG, March LM (2019) The global burden of musculoskeletal pain—where to from here? Am J Public Health 109(1):35–40. https://doi.org/10.2105/AJPH.2018.304747
GBD 2017 (2018) Disease and Injury Incidence and Prevalence Collaborators—global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 392:1789–1858. https://doi.org/10.1016/S0140-6736(18)32279-7
Hoy D, March L, Brooks P, Blyth F, Woolf A, Bain C et al (2014) The global burden of low back pain: estimates from the Global Burden of Disease 2010 study. Ann Rheum Dis 73:968–974. https://doi.org/10.1136/annrheumdis-2013-204428
Singer M, Bulled N, Ostrach B, Mendenhall E (2017) Syndemics and the biosocial conception of health. Lancet 389(10072):941–950. https://doi.org/10.1016/S0140-6736(17)30003-X
Lerman S (2018) The syndemogenesis of depression. Med Anthr Theory 5:56–85. https://doi.org/10.17157/mat.5.4.549
Peláez-Ballestas I, Granados Y, Quintana R, Loyola-Sánchez A, Julián-Santiago F, Rosillo C et al (2018) Epidemiology and socioeconomic impact of the rheumatic diseases on indigenous people: an invisible syndemic public health problem. Ann Rheum Dis 77(10):1397–1404. https://doi.org/10.1136/annrheumdis-2018-213625
Chopra A (2013) The COPCORD world of musculoskeletal pain and arthritis. Rheumatology (Oxford) 52:1925–1928. https://doi.org/10.1093/rheumatology/ket222
Pelaéz-Ballestas I, Flores-Camacho R, Rodriguez-Amado J, Sanin LH, Valerio JE, Navarro-Zarza E et al (2011) Prevalence of back pain in the community. A COPCORD based study in the Mexican population. J Rheum Suppl 86:26–30. https://doi.org/10.3899/jrheum.101035
Goycochea-Robles MV, Sanin LH, Moreno-Montoya J, Alvarez-Nemegyei J, Burgos-Vargas R, Garza-Elizondo M, Rodríguez-Amado J, Madariaga MA, Zamudio JA, Cuervo GE, Cardiel-Ríos MH, Peláez-Ballestas I, Grupo de Estudio Epidemiológico de Enfermedades Músculo Articulares (GEEMA) (2011) Validity of the COPCORD core questionnaire a classification tool for rheumatic diseases. J Rheumatol Suppl 86:31–35. https://doi.org/10.3899/jrheum.100955
Peláez-Ballestas I, Sanin LH, Moreno-Montoya J, Alvarez-Nemegyei J, Burgos-Vargas R, Garza-Elizondo M et al (2011) Epidemiology of the rheumatic diseases in Mexico. A study of 5 regions based on the COPCORD methodology. J Rheumatol Suppl 86:3–8. https://doi.org/10.3899/jrheum.100951
Londoño L, Peláez Ballestas I, Cuervo F, Angarita I, Giraldo R, Rueda JC et al (2018) Prevalence of rheumatic disease in Colombia according to the Colombian Rheumatology Association (COPCORD) strategy. Prevalence study of rheumatic disease in Colombian population older than 18 years. Rev Colomb Reumatol 25(4):245–256. https://doi.org/10.1016/j.rcreu.2018.08.003
Guevara-Pacheco S, Feicán-Alvarado A, Sanín LH, Vintimilla-Ugalde J, Vintimilla-Moscoso F, Delgado-Pauta J et al (2016) Prevalence of musculoskeletal disorders and rheumatic diseases in Cuenca, Ecuador: a WHO-ILAR COPCORD study. Rheumatol Int 36(9):1195–1204. https://doi.org/10.1007/s00296-016-3446-y
Vega-Hinojosa O, Cardiel MH, Ochoa-Miranda P (2018) Prevalence of musculoskeletal manifestations and related disabilities in a Peruvian urban population living at high altitude. COPCORD Study. Stage I. Reumatol Clin 14(5):278–284. https://doi.org/10.1016/j.reuma.2017.01.011
Granados Y, Cedeño L, Rosillo C, Berbin S, Azocar M, Molina ME et al (2015) Prevalence of musculoskeletal disorders and rheumatic diseases in an urban community in Monagas State, Venezuela: a COPCORD study. Clin Rheumatol 34(5):871–877. https://doi.org/10.1007/s10067-014-2689-9
Quintana R, Silvestre AM, Goñi M, García V, Mathern N, Jorfen M et al (2016) Prevalence of musculoskeletal disorders and rheumatic diseases in the indigenous Qom population of Rosario, Argentina. Clin Rheumatol 35(Suppl 1):5–14. https://doi.org/10.1007/s10067-016-3192-2
Guevara SV, Feicán EA, Peláez I, Valdiviezo WA, Montaleza MA, Molina GM et al (2019) Prevalence of rheumatic diseases and quality of life in the Saraguro indigenous people, Ecuador: a cross-sectional community-based study. J Clin Rheumatol. https://doi.org/10.1097/RHU.0000000000001131
Julián-Santiago F, García-García C, García-Olivera I, Goycochea-Robles MV, Pelaez-Ballestas I (2016) Epidemiology of rheumatic diseases in Mixtec and Chontal indigenous communities in Mexico: a cross-sectional community-based study. Clin Rheumatol 35(Suppl 1):35–42. https://doi.org/10.1007/s10067-015-3148-y
Del Río Nájera D, Santana N, Peláez-Ballestas I, González-Chávez SA, Quiñonez-Flores CM et al (2016) Prevalence of rheumatic diseases in Raramuri people in Chihuahua, Mexico: a community-based study. Clin Rheumatol 35(Suppl 1):43–52. https://doi.org/10.1007/s10067-016-3225-x
Peláez-Ballestas I, Alvarez-Nemegyei J, Loyola-Sánchez A, Escudero ML (2016) Prevalence and factors associated with musculoskeletal disorders and rheumatic diseases in indigenous Maya-Yucateco people: a cross-sectional community-based study. Clin Rheumatol 35(Suppl 1):15–23. https://doi.org/10.1007/s10067-015-3085-9
Granados Y, Rosillo C, Cedeño L, Martínez Y, Sánchez G, López G et al (2016) Prevalence of musculoskeletal disorders and rheumatic disease in the Warao, Kari’ña, and Chaima indigenous populations of Monagas State, Venezuela. Clin Rheumatol 35(Suppl 1):53–61. https://doi.org/10.1007/s10067-016-3194-0
Human Development Indices and Indicators: 2018 Statistical update (2018) New York. http://hdr.undp.org/sites/default/files/2018_human_development_statistical_update.pdf. Accessed 20 June 2019
Core Team R (2017) R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.121.9220 Accessed 12 June 2019
Schratz P (2017) R package “oddsratio”: odds ratio calculation for GAM(M)s & GLM(M)s. https://doi.org/10.5281/zenodo.1095472. Accessed 12 June 2019
Bastian M, Heymann S, Jacomy M (2009) Gephi: an open source software for exploring and manipulating networks. Third International AAAI Conference on Weblogs and Social Media. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.172.7704. Accessed 22 July 2019
Jacomy M, Venturini T, Heymann S, Bastian M (2014) ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software. PLoS One 9(6). https://doi.org/10.1371/journal.pone.0098679
Ester M, Kriegel HP, Sander J, Xu X (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. CiteSeer* http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.121.9220. Accessed 26 July 2019
Hahsler M, Piekenbrock M, Arya S, Mount D (2019) R package “dbscan.” https://cran.r-project.org/web/packages/dbscan/dbscan.pdf. Accessed 12 Sept 2019
GBD 2016 Brazil Collaborators (2018) Burden of disease in Brazil, 1990–2016: a systematic subnational analysis for the Global Burden of Disease Study 2016. Lancet 392(10149):760–775. https://doi.org/10.1016/S0140-6736(18)31221-2
Gouveia N, Rodrigues A, Eusébio M, Ramiro S, Machado P, Canhão H et al (2016) Prevalence and social burden of active chronic low back pain in the adult Portuguese population: results from a national survey. Rheumatol Int 36(2):183–197. https://doi.org/10.1007/s00296-015-3398-7
Fliesser M, De Witt Huberts J, Wippert PM (2017) The choice that matters: the relative influence of socioeconomic status indicators on chronic back pain—a longitudinal study. BMC Health Serv Res 17(1):800. https://doi.org/10.1186/s12913-017-2735-9
Garcia JB, Hernandez-Castro JJ, Nunez RG, Pazos MA, Aguirre JO, Jreige A (2014) Prevalence of low back pain in Latin America: a systematic literature review. Pain Phys 17(5):379–391
Clark P, Denova-Gutiérrez E, Razo C, Rios-Blancas MJ, Lozano R (2018) The burden of musculoskeletal disorders in Mexico at national and state level, 1990−2016: estimates from the global burden of disease study 2016. Osteoporos Int 29(12):2745–2760. https://doi.org/10.1007/s00198-018-4698-z
Dutmer AL, Schiphorts Preuper HR, Soer R, Brouwer S, Bultmann U, Dijkstra PU et al (2019) Personal and societal impact of low back pain: the Groningen Spine Cohort. Spine. https://doi.org/10.1097/BRS.0000000000003174
Bento TPF, Genebra CVDS, Maciel NM, Cornelio GP, Simeão SFAP, Vitta A (2019) Low back pain and some associated factors: is there any difference between genders? Braz J Phys Ther S1413-3555(18):31012–31018. https://doi.org/10.1097/BRS.0000000000003174. https://doi.org/10.1016/j.bjpt.2019.01.012
Global Alliance for Musculoskeletal Health of the Bone and Joint Decade (G-MUSC) (2019) www.ailchi.mp/3777cef81f60/g-musc-march-2019-newsletter-2447689?e=0997e821d9. Accessed 22 Sept 2019
World Spine Care (2019) Providing evidence-based care in low and middle income countries. www.worldspinecare.org. Accessed 22 Sept 2019
Diex-Roux AV (1988) Bringing context back into epidemiology: variables and fallacies in multilevel analysis. Am J Public Health 88:216–222. https://doi.org/10.2105/ajph.88.2.216
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As the present investigation involves data collected as a part of prior studies, no specific study protocol approval was needed, as all Institutional and Ethics Committees of each participating institution had already approved pertinent studies. Moreover, all participants in the original studies were informed of the study procedures and voluntarily signed informed consent forms prior to taking part in data collection. All data was blinded for the secondary analysis. The datasets from the different countries contain variables that are related to patient personal information that was deleted for the leader researcher before the computer scientist manages the data. The algorithm developed to create the ultimate database contains a filter to remove those columns from all the calculations; each user is identified by a randomly generated identification (ID) key that can be used by clinicians to find the specific person if needed.
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Strozzi, A.G., Peláez-Ballestas, I., Granados, Y. et al. Syndemic and syndemogenesis of low back pain in Latin-American population: a network and cluster analysis. Clin Rheumatol 39, 2715–2726 (2020). https://doi.org/10.1007/s10067-020-05047-x
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DOI: https://doi.org/10.1007/s10067-020-05047-x