Identification of population multimorbidity patterns in 3.9 million patients from Bogota in 2018

Multimorbidity has emerged as a challenge for health systems due to its association with adverse clinical outcomes. Given the limited information available on multimorbidity, particularly in low- and middle-income countries, this study characterizes multimorbidity patterns in the population of Bogot...

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Autores:
Saavedra-Moreno, Carolina
Hurtado, Rafael
Velasco, Nubia
Ramírez, Andrea
Tipo de recurso:
Article of investigation
Fecha de publicación:
2024
Institución:
Universidad de Ibagué
Repositorio:
Repositorio Universidad de Ibagué
Idioma:
eng
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oai:repositorio.unibague.edu.co:20.500.12313/5905
Acceso en línea:
https://hdl.handle.net/20.500.12313/5905
https://sciencedirect.unibague.elogim.com/science/article/pii/S2590113324000373
Palabra clave:
Multimobiliario
Análisis de redes
Expedientes médicos
Sistemas de salud
Colombia
Healthcare systems
Medical records
Multimorbidity
Network analysis
Rights
openAccess
License
. © 2024
id UNIBAGUE2_85c8ef08d22470fb1e06182446b68d48
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network_acronym_str UNIBAGUE2
network_name_str Repositorio Universidad de Ibagué
repository_id_str
dc.title.eng.fl_str_mv Identification of population multimorbidity patterns in 3.9 million patients from Bogota in 2018
title Identification of population multimorbidity patterns in 3.9 million patients from Bogota in 2018
spellingShingle Identification of population multimorbidity patterns in 3.9 million patients from Bogota in 2018
Multimobiliario
Análisis de redes
Expedientes médicos
Sistemas de salud
Colombia
Healthcare systems
Medical records
Multimorbidity
Network analysis
title_short Identification of population multimorbidity patterns in 3.9 million patients from Bogota in 2018
title_full Identification of population multimorbidity patterns in 3.9 million patients from Bogota in 2018
title_fullStr Identification of population multimorbidity patterns in 3.9 million patients from Bogota in 2018
title_full_unstemmed Identification of population multimorbidity patterns in 3.9 million patients from Bogota in 2018
title_sort Identification of population multimorbidity patterns in 3.9 million patients from Bogota in 2018
dc.creator.fl_str_mv Saavedra-Moreno, Carolina
Hurtado, Rafael
Velasco, Nubia
Ramírez, Andrea
dc.contributor.author.none.fl_str_mv Saavedra-Moreno, Carolina
Hurtado, Rafael
Velasco, Nubia
Ramírez, Andrea
dc.subject.armarc.none.fl_str_mv Multimobiliario
Análisis de redes
Expedientes médicos
Sistemas de salud
topic Multimobiliario
Análisis de redes
Expedientes médicos
Sistemas de salud
Colombia
Healthcare systems
Medical records
Multimorbidity
Network analysis
dc.subject.proposal.eng.fl_str_mv Colombia
Healthcare systems
Medical records
Multimorbidity
Network analysis
description Multimorbidity has emerged as a challenge for health systems due to its association with adverse clinical outcomes. Given the limited information available on multimorbidity, particularly in low- and middle-income countries, this study characterizes multimorbidity patterns in the population of Bogotá, Colombia in 2018. Methods: In a cross-sectional study, we analyzed 16 million medical consultation records from Bogotá reported in the National Service Delivery Records in 2018. Using network analysis, we quantified the prevalence of multimorbidity in the population and identified the most common associations between diagnoses, with data stratified by age, sex, and socioeconomic status. Results: The study found that the prevalence of multimorbidity in the population was 44.2 %, increased with age, and was higher in women and in people affiliated to the contributory health scheme. Allergies and vasomotor rhinitis with asthma were common in young people. In women aged 19–39 years, obesity with hypothyroidism was common, while men in the same age group had obesity with dyslipidemia. In people aged 60 years and older, essential hypertension with dyslipidemia was the most common. In addition, some associations between diagnoses showed a higher association in people affiliated to the subsidized health scheme, with notable associations with trauma, especially in men. Conclusion: Overall, the results provide valuable insights into multimorbidity in the population and highlight inequalities based on sociodemographic factors. Future research should investigate whether the lower prevalence of multimorbidity in vulnerable groups is related to biases in data collection or to underlying inequalities in healthcare access.
publishDate 2024
dc.date.issued.none.fl_str_mv 2024-12
dc.date.accessioned.none.fl_str_mv 2025-11-06T16:49:35Z
dc.date.available.none.fl_str_mv 2025-11-06T16:49:35Z
dc.type.none.fl_str_mv Artículo de revista
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dc.identifier.citation.none.fl_str_mv Saavedra-Moreno, C., Hurtado, R., Velasco, N. y Ramírez, A. (2024). Identification of population multimorbidity patterns in 3.9 million patients from Bogota in 2018. Global Epidemiology, 8. DOI: 10.1016/j.gloepi.2024.100171
dc.identifier.doi.none.fl_str_mv 10.1016/j.gloepi.2024.100171
dc.identifier.issn.none.fl_str_mv 25901133
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12313/5905
dc.identifier.url.none.fl_str_mv https://sciencedirect.unibague.elogim.com/science/article/pii/S2590113324000373
identifier_str_mv Saavedra-Moreno, C., Hurtado, R., Velasco, N. y Ramírez, A. (2024). Identification of population multimorbidity patterns in 3.9 million patients from Bogota in 2018. Global Epidemiology, 8. DOI: 10.1016/j.gloepi.2024.100171
10.1016/j.gloepi.2024.100171
25901133
url https://hdl.handle.net/20.500.12313/5905
https://sciencedirect.unibague.elogim.com/science/article/pii/S2590113324000373
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.citationvolume.none.fl_str_mv 8
dc.relation.ispartofjournal.none.fl_str_mv Global Epidemiology
dc.relation.references.none.fl_str_mv Ryan A, Wallace E, O’Hara P, Smith SM. Multimorbidity and functional decline in community-dwelling adults: a systematic review. Health Qual Life Outcomes 2015: 13. https://doi.org/10.1186/s12955-015-0355-9
Multimorbidity - Understanding the challenge. Richmond Group of Charities; 2018.
Nunes BP, Thum´e E, Facchini LA. Multimorbidity in older adults: magnitude and challenges for the Brazilian health system chronic disease epidemiology. BMC Public Health 2015;15:1–11. https://doi.org/10.1186/s12889-015-2505-8
Palladino R, Pennino F, Finbarr M, Millett C, Triassi M. Multimorbidity and health outcomes in older adults in ten European health systems, 2006–15. Health Aff 2019;38:613–23. https://doi.org/10.1377/hlthaff.2018.05273.
Bahler ¨ C, Huber CA, Brüngger B, Reich O. Multimorbidity, health care utilization and costs in an elderly community-dwelling population: a claims data based observational study. BMC Health Serv Res 2015;15:1–12. https://doi.org/10.1186/ s12913-015-0698-2
Payne RA, Abel GA, Guthrier B, Mercer SW. The effect of physical multimorbidity, mental health conditions and socioeconomic deprivation on unplanned admissions to hospital: a retrospective cohort study Rupert. CMAJ 2013;185:1–8. https://doi. org/10.1503/cmaj.121349/-/D
Huaqui´ a-Diaz ´ AM, Chal´ an-Davila ´ TS, Carrillo-Larco RM, Bernabe-Ortiz A. Multimorbidity in Latin America and the Caribbean: a systematic review and metaanalysis. BMJ Open 2021;11:1–8. https://doi.org/10.1136/bmjopen-2021- 050409
Oni T, Unwin N. Why the communicable/non-communicable disease dichotomy is problematic for public health control strategies: implications of multimorbidity for health systems in an era of health transition. Int Health 2015;7:390–9. https://doi. org/10.1093/inthealth/ih
Basto-Abreu A, Barrientos-Gutierrez T, Wade AN, Oliveira de Melo D, Semeao ˜ de Souza AS, Nunes BP, et al. Multimorbidity matters in low and middle-income countries. J Multimorbid Comorbidi 2022;12. https://doi.org/10.1177/ 26335565221106074. 263355652211060
Vinjerui KH, Bjerkeset O, Bjorngaard JH, Krokstad S, Douglas KA, Sund ER. Socioeconomic inequalities in the prevalence of complex multimorbidity in a Norwegian population: findings from the cross-sectional HUNT study. BMJ Open 2020;10:1–9. https://doi.org/10.1136/bmjopen-2020-036851.
Valderas JM, Starfi B, Sibbald B, Salisbury S, Roland M. Defining comorbidity: implications for understanding health and health services. Ann Fam Med 2009;7: 357–63. https://doi.org/10.1370/afm.983.Martin.
WHO. Multimorbidityvol. 47. Geneva: World Health Organization; 2017. https:// doi.org/10.1097/01.NURSE.0000524761.58624.1f
Skou ST, Mair FS, Fortin M, Guthrie B, Nunes BP, Miranda JJ, et al. Multimorbidity. Nat Rev Dis Primers 2022;8:48. https://doi.org/10.1038/s41572- 022-00376-4
Nicholson K, Makovski TT, Griffith LE, Raina P, Stranges S, van den Akker M. Multimorbidity and comorbidity revisited: refining the concepts for international health research. J Clin Epidemiol 2019;105:142–6. https://doi.org/10.1016/j. jclinepi.2018.09.008
Van Den Akker M, Buntinx F, Knottnerus JA. Comorbidity or multimorbidity: what’s in a name? A review of literature. Eur J Gen Pract 1996;2:65–70. https:// doi.org/10.3109/13814789609162146
Gaspar A, Miranda JJ. Burden of treatment as a measure of healthcare quality: an innovative approach to addressing global inequities in multimorbidity. PLOS Glob Public Health 2022;2:e0000484. https://doi.org/10.1371/journal.pgph.0000484.
Huntley AL, Johnson R, Purdy S, Valderas JM, Salisbury C. Measures of multimorbidity and morbidity burden for use in primary care and community settings: a systematic review and guide. Ann Fam Med 2012;10:134–41. https:// doi.org/10.1370/afm.1363
Abebe F, Schneider M, Asrat B, Ambaw F. Multimorbidity of chronic noncommunicable diseases in low- and middle-income countries: a scoping review. J Comorbid 2020;10. https://doi.org/10.1177/2235042x20961919. 2235042X2096191.
Alfonso-Sierra E, Arcila A, Bonilla J, Latorre M, Porras A, Urquijo L. Situacion ´ de multimorbilidad en Colombia 2012–2016. Bogota ´ - Colombia: Ministerio de Salud y Proteccion ´ Social - Banco Mundial; 2016
Kalgotra P, Sharda R, Croff JM. Examining health disparities by gender: a multimorbidity network analysis of electronic medical record. Int J Med Inform 2017;108:22–8. https://doi.org/10.1016/j.ijmedinf.2017.09.014
Hern´ andez B, Reilly RB, Kenny RA. Investigation of multimorbidity and prevalent disease combinations in older Irish adults using network analysis and association rules. Sci Rep 2019;9:1–12. https://doi.org/10.1038/s41598-019-51135-7
Glicksberg BS, Li L, Badgeley MA, Shameer K, Kosoy R, Beckmann ND, et al. Comparative analyses of population-scale phenomic data in electronic medical records reveal race-specific disease networks. Bioinformatics 2016;32:101–10. https://doi.org/10.1093/bioinformatics/btw282
Kalgotra P, Sharda R, Croff JM. Examining multimorbidity differences across racial groups: a network analysis of electronic medical records. Sci Rep 2020;10:1–9. https://doi.org/10.1038/s41598-020-70470-8
Liu J, Ma J, Wang J, Zeng DD, Song H, Wang L, et al. Comorbidity analysis according to sex and age in hypertension patients in China. Int J Med Sci 2016;13: 99–107. https://doi.org/10.7150/ijms.13456.
DANE. Proyecciones de poblacion. ´ https://www.dane.gov.co/index.php/estadis ticas-por-tema/demografia-y-poblacion/proyecciones-de-poblacion; 2020.
Hilarion-Gait ´ an ´ L, Díaz-Jim´enez D, Cotes-Cantillo K, Castaneda-Orjuela ˜ C. Desigualdades en salud según r´egimen de afiliacion ´ y eventos notificados al Sistema de Vigilancia (Sivigila) en Colombia, 2015. Biom´edica 2019;39:737–47. https://doi.org/10.7705/biomedica.4453
Urdinola P, Bejarano Salcedo V, Espinosa O, Silva PLDN. Estudio de caracterizacion ´ socioeconomica ´ y demogr´ afica de los afiliados al r´egimen subsidiado de salud, Colombia 2019-2020. Ens Econ 2023;33:85–116. https://doi.org/10.15446/ede. v33n63.104918.
Ministerio de Salud y Proteccion ´ Social. ?Qu´e es SISPRO? SISPRO- Sistema Integrado de Informacion ´ de La Proteccion ´ Social. https://www.sispro.gov.co/ Pages/Home.asp
Marengoni A, Angleman S, Melis R, Mangialasche F, Karp A, Garmen A, et al. Aging with multimorbidity: a systematic review of the literature. Ageing Res Rev 2011; 10:430–9. https://doi.org/10.1016/j.arr.2011.03.003
Murtagh KN, Hubert HB. Gender differences in physical disability among an elderly cohort. Am J Public Health 2004;94:1406–11. https://doi.org/10.2105/ AJPH.94.8.1406
Bertakis KD, Azari R, Helms LJ, Callahan EJ, Robbins JA. Gender differences in the utilization of health care services. J Fam Pract 2000;49:147–5
Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. The Lancet 2012;380:37–43. https://doi.org/10.1016/ S0140-6736(12)60240-2.
St. John P, Tyas S, Menec V, Tate R. Multimorbidity, disability, and mortality in community-dwelling older adults Multi-morbidit´e, incapacit´e et mortalit´e chez les personnes ˆ ag´ees vivant dans la communaut´e. Research Web Exclusive Can Fam Physician 2014;60:272–80
Mora-Moreo L, Estrada-Orozco K, Espinosa O, Mesa-Melgarejo L. Characterization of the population affiliated to the subsidized health insurance scheme in Colombia. A systematic review and meta analysis. Int J Equity Health 2023;22:1–23. https:// doi.org/10.1186
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spelling Saavedra-Moreno, Carolinaa52bb00c-83b1-4bfc-aabb-b98a854483cc-1Hurtado, Rafael7ecdc11b-433e-47e8-9f08-673a1c44b7fd-1Velasco, Nubia3f191349-2514-4801-9708-4a2a0ef8d998-1Ramírez, Andrea02da8164-9a56-48b5-8502-dc33fb99d902-12025-11-06T16:49:35Z2025-11-06T16:49:35Z2024-12Multimorbidity has emerged as a challenge for health systems due to its association with adverse clinical outcomes. Given the limited information available on multimorbidity, particularly in low- and middle-income countries, this study characterizes multimorbidity patterns in the population of Bogotá, Colombia in 2018. Methods: In a cross-sectional study, we analyzed 16 million medical consultation records from Bogotá reported in the National Service Delivery Records in 2018. Using network analysis, we quantified the prevalence of multimorbidity in the population and identified the most common associations between diagnoses, with data stratified by age, sex, and socioeconomic status. Results: The study found that the prevalence of multimorbidity in the population was 44.2 %, increased with age, and was higher in women and in people affiliated to the contributory health scheme. Allergies and vasomotor rhinitis with asthma were common in young people. In women aged 19–39 years, obesity with hypothyroidism was common, while men in the same age group had obesity with dyslipidemia. In people aged 60 years and older, essential hypertension with dyslipidemia was the most common. In addition, some associations between diagnoses showed a higher association in people affiliated to the subsidized health scheme, with notable associations with trauma, especially in men. Conclusion: Overall, the results provide valuable insights into multimorbidity in the population and highlight inequalities based on sociodemographic factors. Future research should investigate whether the lower prevalence of multimorbidity in vulnerable groups is related to biases in data collection or to underlying inequalities in healthcare access.application/pdfSaavedra-Moreno, C., Hurtado, R., Velasco, N. y Ramírez, A. (2024). Identification of population multimorbidity patterns in 3.9 million patients from Bogota in 2018. Global Epidemiology, 8. DOI: 10.1016/j.gloepi.2024.10017110.1016/j.gloepi.2024.10017125901133https://hdl.handle.net/20.500.12313/5905https://sciencedirect.unibague.elogim.com/science/article/pii/S2590113324000373engElsevier Inc.Elsevier Inc.8Global EpidemiologyRyan A, Wallace E, O’Hara P, Smith SM. Multimorbidity and functional decline in community-dwelling adults: a systematic review. Health Qual Life Outcomes 2015: 13. https://doi.org/10.1186/s12955-015-0355-9Multimorbidity - Understanding the challenge. Richmond Group of Charities; 2018.Nunes BP, Thum´e E, Facchini LA. Multimorbidity in older adults: magnitude and challenges for the Brazilian health system chronic disease epidemiology. BMC Public Health 2015;15:1–11. https://doi.org/10.1186/s12889-015-2505-8Palladino R, Pennino F, Finbarr M, Millett C, Triassi M. Multimorbidity and health outcomes in older adults in ten European health systems, 2006–15. Health Aff 2019;38:613–23. https://doi.org/10.1377/hlthaff.2018.05273.Bahler ¨ C, Huber CA, Brüngger B, Reich O. Multimorbidity, health care utilization and costs in an elderly community-dwelling population: a claims data based observational study. BMC Health Serv Res 2015;15:1–12. https://doi.org/10.1186/ s12913-015-0698-2Payne RA, Abel GA, Guthrier B, Mercer SW. The effect of physical multimorbidity, mental health conditions and socioeconomic deprivation on unplanned admissions to hospital: a retrospective cohort study Rupert. CMAJ 2013;185:1–8. https://doi. org/10.1503/cmaj.121349/-/DHuaqui´ a-Diaz ´ AM, Chal´ an-Davila ´ TS, Carrillo-Larco RM, Bernabe-Ortiz A. Multimorbidity in Latin America and the Caribbean: a systematic review and metaanalysis. BMJ Open 2021;11:1–8. https://doi.org/10.1136/bmjopen-2021- 050409Oni T, Unwin N. Why the communicable/non-communicable disease dichotomy is problematic for public health control strategies: implications of multimorbidity for health systems in an era of health transition. Int Health 2015;7:390–9. https://doi. org/10.1093/inthealth/ihBasto-Abreu A, Barrientos-Gutierrez T, Wade AN, Oliveira de Melo D, Semeao ˜ de Souza AS, Nunes BP, et al. Multimorbidity matters in low and middle-income countries. J Multimorbid Comorbidi 2022;12. https://doi.org/10.1177/ 26335565221106074. 263355652211060Vinjerui KH, Bjerkeset O, Bjorngaard JH, Krokstad S, Douglas KA, Sund ER. Socioeconomic inequalities in the prevalence of complex multimorbidity in a Norwegian population: findings from the cross-sectional HUNT study. BMJ Open 2020;10:1–9. https://doi.org/10.1136/bmjopen-2020-036851.Valderas JM, Starfi B, Sibbald B, Salisbury S, Roland M. Defining comorbidity: implications for understanding health and health services. Ann Fam Med 2009;7: 357–63. https://doi.org/10.1370/afm.983.Martin.WHO. Multimorbidityvol. 47. Geneva: World Health Organization; 2017. https:// doi.org/10.1097/01.NURSE.0000524761.58624.1fSkou ST, Mair FS, Fortin M, Guthrie B, Nunes BP, Miranda JJ, et al. Multimorbidity. Nat Rev Dis Primers 2022;8:48. https://doi.org/10.1038/s41572- 022-00376-4Nicholson K, Makovski TT, Griffith LE, Raina P, Stranges S, van den Akker M. Multimorbidity and comorbidity revisited: refining the concepts for international health research. J Clin Epidemiol 2019;105:142–6. https://doi.org/10.1016/j. jclinepi.2018.09.008Van Den Akker M, Buntinx F, Knottnerus JA. Comorbidity or multimorbidity: what’s in a name? A review of literature. Eur J Gen Pract 1996;2:65–70. https:// doi.org/10.3109/13814789609162146Gaspar A, Miranda JJ. Burden of treatment as a measure of healthcare quality: an innovative approach to addressing global inequities in multimorbidity. PLOS Glob Public Health 2022;2:e0000484. https://doi.org/10.1371/journal.pgph.0000484.Huntley AL, Johnson R, Purdy S, Valderas JM, Salisbury C. Measures of multimorbidity and morbidity burden for use in primary care and community settings: a systematic review and guide. Ann Fam Med 2012;10:134–41. https:// doi.org/10.1370/afm.1363Abebe F, Schneider M, Asrat B, Ambaw F. Multimorbidity of chronic noncommunicable diseases in low- and middle-income countries: a scoping review. J Comorbid 2020;10. https://doi.org/10.1177/2235042x20961919. 2235042X2096191.Alfonso-Sierra E, Arcila A, Bonilla J, Latorre M, Porras A, Urquijo L. Situacion ´ de multimorbilidad en Colombia 2012–2016. Bogota ´ - Colombia: Ministerio de Salud y Proteccion ´ Social - Banco Mundial; 2016Kalgotra P, Sharda R, Croff JM. Examining health disparities by gender: a multimorbidity network analysis of electronic medical record. Int J Med Inform 2017;108:22–8. https://doi.org/10.1016/j.ijmedinf.2017.09.014Hern´ andez B, Reilly RB, Kenny RA. Investigation of multimorbidity and prevalent disease combinations in older Irish adults using network analysis and association rules. Sci Rep 2019;9:1–12. https://doi.org/10.1038/s41598-019-51135-7Glicksberg BS, Li L, Badgeley MA, Shameer K, Kosoy R, Beckmann ND, et al. Comparative analyses of population-scale phenomic data in electronic medical records reveal race-specific disease networks. Bioinformatics 2016;32:101–10. https://doi.org/10.1093/bioinformatics/btw282Kalgotra P, Sharda R, Croff JM. Examining multimorbidity differences across racial groups: a network analysis of electronic medical records. Sci Rep 2020;10:1–9. https://doi.org/10.1038/s41598-020-70470-8Liu J, Ma J, Wang J, Zeng DD, Song H, Wang L, et al. Comorbidity analysis according to sex and age in hypertension patients in China. Int J Med Sci 2016;13: 99–107. https://doi.org/10.7150/ijms.13456.DANE. Proyecciones de poblacion. ´ https://www.dane.gov.co/index.php/estadis ticas-por-tema/demografia-y-poblacion/proyecciones-de-poblacion; 2020.Hilarion-Gait ´ an ´ L, Díaz-Jim´enez D, Cotes-Cantillo K, Castaneda-Orjuela ˜ C. Desigualdades en salud según r´egimen de afiliacion ´ y eventos notificados al Sistema de Vigilancia (Sivigila) en Colombia, 2015. Biom´edica 2019;39:737–47. https://doi.org/10.7705/biomedica.4453Urdinola P, Bejarano Salcedo V, Espinosa O, Silva PLDN. Estudio de caracterizacion ´ socioeconomica ´ y demogr´ afica de los afiliados al r´egimen subsidiado de salud, Colombia 2019-2020. Ens Econ 2023;33:85–116. https://doi.org/10.15446/ede. v33n63.104918.Ministerio de Salud y Proteccion ´ Social. ?Qu´e es SISPRO? SISPRO- Sistema Integrado de Informacion ´ de La Proteccion ´ Social. https://www.sispro.gov.co/ Pages/Home.aspMarengoni A, Angleman S, Melis R, Mangialasche F, Karp A, Garmen A, et al. Aging with multimorbidity: a systematic review of the literature. Ageing Res Rev 2011; 10:430–9. https://doi.org/10.1016/j.arr.2011.03.003Murtagh KN, Hubert HB. Gender differences in physical disability among an elderly cohort. Am J Public Health 2004;94:1406–11. https://doi.org/10.2105/ AJPH.94.8.1406Bertakis KD, Azari R, Helms LJ, Callahan EJ, Robbins JA. Gender differences in the utilization of health care services. J Fam Pract 2000;49:147–5Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. 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Int J Equity Health 2023;22:1–23. https:// doi.org/10.1186. © 2024info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/MultimobiliarioAnálisis de redesExpedientes médicosSistemas de saludColombiaHealthcare systemsMedical recordsMultimorbidityNetwork analysisIdentification of population multimorbidity patterns in 3.9 million patients from Bogota in 2018Artículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Textinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPublicationTEXTArtículo.pdf.txtArtículo.pdf.txtExtracted texttext/plain4597https://repositorio.unibague.edu.co/bitstreams/d46ec094-b548-4dc4-be23-40829d8f1e44/downloadda404aeacb949dc718b88b829f3d6ef5MD53THUMBNAILArtículo.pdf.jpgArtículo.pdf.jpgIM Thumbnailimage/jpeg27403https://repositorio.unibague.edu.co/bitstreams/efc0519a-bf5c-42d9-b173-49c72e13cc18/download13b4ea8a767d8b8f3a3506e16466f2b1MD54LICENSElicense.txtlicense.txttext/plain; charset=utf-8134https://repositorio.unibague.edu.co/bitstreams/cfce0032-4c91-4fd4-9a41-834d5ee6705c/download2fa3e590786b9c0f3ceba1b9656b7ac3MD51ORIGINALArtículo.pdfArtículo.pdfapplication/pdf250580https://repositorio.unibague.edu.co/bitstreams/40e7530b-f265-4b7f-8da2-8f6d5151d2da/download503d0b547215952507f705f4f77fb666MD5220.500.12313/5905oai:repositorio.unibague.edu.co:20.500.12313/59052025-11-07 03:01:00.31https://creativecommons.org/licenses/by-nc-nd/4.0/. © 2024https://repositorio.unibague.edu.coRepositorio Institucional Universidad de Ibaguébdigital@metabiblioteca.comQ3JlYXRpdmUgQ29tbW9ucyBBdHRyaWJ1dGlvbi1Ob25Db21tZXJjaWFsLU5vRGVyaXZhdGl2ZXMgNC4wIEludGVybmF0aW9uYWwgTGljZW5zZQ0KaHR0cHM6Ly9jcmVhdGl2ZWNvbW1vbnMub3JnL2xpY2Vuc2VzL2J5LW5jLW5kLzQuMC8=

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