Associação entre a taxa moderadora e a frequência na consulta aos serviços de urgência em pacientes adultos com asma, usuários do regime contributivo na Colômbia
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Jun 30, 2021
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Objetivo. Determinar a relação entre o nível da taxa moderadora e a frequência de consulta aos serviços de urgência em pacientes com asma que pertencem ao regime contributivo na Colômbia. Métodos. Estudo observacional analítico, de coorte retrospectiva, no qual se incluíram as pessoas de idade superior aos 18 anos, diagnosticadas com asma, e que pagam uma mensalidade dentro do regime contributivo, segundo seu registo na base de dados para o cálculo da Unidade por Capitação entre os anos 2012 e 2014. A seleção dos pacientes aconteceu durante o ano 2013 e a coorte de estudo foi monitorada durante um ano desde a data de ingresso. A variável de saída empregada foi a frequência de consulta ao serviço de urgências. Foi avaliada a influência das principais variáveis de confusão e utilizado um modelo de regressão binomial negativa para a análise dos dados. Resultados. Se incluíram 54 516 pacientes asmáticos com suas comorbidades, dos quais um 13,69% consultaram o serviço de urgências. Depois de controlar pela idade deste tipo de consulta e o índice de Charlson, a probabilidade de consulta do serviço de urgências foi 1,1 vezes mais frequente no nível 3 de taxa moderadora, se comparado com o nível 1. Conclusões. As taxas moderadoras poderiam se comportar como um obstáculo ao acesso aos serviços de saúde em pacientes asmáticos. Recomenda-se a realização de estudos que avaliem de modo mais preciso esta hipótese.
Keywords
Asma, servicio de urgencias, cuota moderadora, régimen contributivo, ColombiaAsma, serviço de urgências, taxa moderadora, regime contributivo, ColômbiaAsthma, emergency services, moderating fee, contributive regimen, Colombia
References
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24. de Groot V, Beckerman H, Lankhorst GJ, Bouter LM. How to measure comorbidity. A critical review of available methods. J Clin Epidemiol. 2003;56(3):221-229. http://www.ncbi.nlm.nih.gov/pubmed/12725876
25. Rui P, Kang K. National Hospital Ambulatory Medical Care Survey: 2015 Emergency Department Summary Tables. Available from: http://www.cdc.gov/nchs/data/ahcd/nhamcs_emergency/2015_ed_web_tables.pdf
26. DANE. Indicadores demográficos según departamento 1985-2020. Conciliación Censal 1985-2005 y Proyecciones de Población 2005-2020. www.dane.gov.co
27. Cadarette SM, Wong L. An introduction to health care administrative data. Can J Hosp Pharm. 2015;68(3):232-237. https://doi.org/10.4212/cjhp.v68i3.1457
28. Harbaugh CM, Cooper JN. Administrative databases. Semin Pediatr Surg. 2018;27(6):353-360. https://doi.org/10.1053/j.sempedsurg.2018.10.001
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31. Sinha S, Peach G, Poloniecki JD, Thompson MM, Holt PJ. Studies using english administrative data (hospital episode statistics) to assess health-care outcomes-systematic review and recommendations for reporting. Eur J Public Health. 2013;23(1):86-92. https://doi.org/10.1093/eurpub/cks046
32. Gini R, Schuemie M, Brown J, Ryan P, Vacchi E, Coppola M, et al. Data extraction and management in networks of observational health care databases for scientific research: A comparison among EU-ADR, OMOP, Mini-Sentinel And MATRICE Strategies. eGEMs (Generating Evid Methods to Improv patient outcomes). 2016;4(1):2. http://dx.doi.org/10.13063/2327-9214.1189
33. Zhan C, Miller M. Administrative data based patient safety research: A critical review BMJ. BMJ Qual Saf. 2003;12(ii):58-63. https://doi.org/10.1136/qhc.12.suppl_2.ii58
34. Mazzali C, Paganoni AM, Ieva F, Masella C, Maistrello M, Agostoni O, et al. Methodological issues on the use of administrative data in healthcare research: The case of heart failure hospitalizations in Lombardy region, 2000 to 2012. BMC Health Serv Res. 2016;16(1):1-10. http://dx.doi.org/10.1186/s12913-016-1489-0
35. Jones N, Schneider G, Kachroo S, Rotella P, Avetisyan R, Reynolds MW. A systematic review of validated methods for identifying acute respiratory failure using administrative and claims data. Pharmacoepidemiol Drug Saf. 2012;21(S1):261-264. https://doi.org/10.1002/pds.2326
36. Sharifi M, Krishanswami S, McPheeters ML. A systematic review of validated methods to capture acute bronchospasm using administrative or claims data. Vaccine. 2013;31(S10):K12-K20. http://dx.doi.org/10.1016/j.vaccine.2013.06.091
37. Maringe C, Fowler H, Rachet B, Luque-Fernandez MA. Reproducibility, reliability and validity of population-based administrative health data for the assessment of cancer non-related comorbidities. PLoS One. 2017;12(3):1-14. https://doi.org/10.1371/journal.pone.0172814
38. McPheeters ML, Sathe NA, Jerome RN, Carnahan RM. Methods for systematic reviews of administrative database studies capturing health outcomes of interest. Vaccine. 2013;31(S10):K2-K6. http://dx.doi.org/10.1016/j.vaccine.2013.06.048
2. Arrow K. Uncertainty and the welfare economics of medical care. Am Econ Rev. 1963;53(5):941-973. http://www.jstor.org/stable/1812044
3. Rothschild M, Stiglitz J. Equilibrium in competitive insurance markets: An essay on the economics of imperfect information. Q J Econ. 1976;(90):629-649. https://doi.org/10.2307/1885326
4. Acuerdo 260 de 2004. Consejo Nacional de Seguridad Social en Salud. Por el cual se define el régimen de pagos compartidos y cuotas moderadoras dentro del Sistema General de Seguridad Social en Salud.
5. Eaddy M, Cook C, O’Day K, Burch S, Cantrell C. How patient cost-sharing trends affect adherence and outcomes: A literature review. P T. 2012;37(1):45-55. https://pubmed.ncbi.nlm.nih.gov/22346336/
6. Cutler DM, Zeckhauser RJ. The anatomy of health insurance. In: Culyer AJ, Newhouse JP (eds). Handbook of Health Economics; 2000. p.629-631.
7. Goldman DP, Joyce GF, Zheng Y. Prescription drug cost sharing: Associations with medication and medical utilization and spending and health. JAMA.2007;298(1):61-69. https://doi.org/10.1001/jama.298.1.61
8. Dormuth C, Glynn R, Neumann P, Maclure M, Brookhart A, Schneeweiss S. Impact of two sequential drug cost-sharing policies on the use of inhaled medications in older patients with chronic obstructive pulmonary disease or asthma. Clinical Therapeutics. 2006;28(6):964-978. https://doi.org/10.1016/j.clinthera.2006.06.007
9. Karaca Mandic P, Anupam J, Geoffrey F, Goldman D. Out-of-pocket medication costs, medication utilization, and use of healthcare services among children with asthma. JAMA. 2012;307(12):1284-1291. https://doi.org/10.1001/jama.2012.340
10. Mann B, Barnieh L, Tang K, Campbell D, Clement F. Association between drug insurance cost sharing strategies and outcomes in patients with chronic diseases: A systematic review. PLoS One. 2014;9(3):e89168. https://doi.org/10.1371/journal.pone.0089168
11. Chandra A, Gruber J, McKnigth R. The impact of patient cost-sharing on low-income populations: Evidence from Massachusetts. J Health Econ. 2014;33:57-66. https://doi.org/10.1016/j.jhealeco.2013.10.008
12. Chernew M, Gibson T, Yu-Isenberg K, Sokol M, Rosen A, Fendrick M. Effects of increased patient cost sharing on socioeconomic disparities in health care. J Gen Intern Med. 2008;23(8):1131-1136. https://doi.org/10.1007/s11606-008-0614-0
13. Gourzoulidis G, Kourlaba G, Stafylas P, Giamouzis G, Parissis J, Maniadakis N. Association between copayment, medication adherence and outcomes in the management of patients with diabetes and heart failure. Health Policy. 2017;121(4):363-377. http://dx.doi.org/10.1016/j.healthpol.2017.02.008
14. Landsem MM, Magnussen J. The effect of copayments on the utilization of the GP service in Norway. Soc Sci Med. 2018;205(March):99-106. https://doi.org/10.1016/j.socscimed.2018.03.034
15. Jakobsson N, Svensson M. Copayments and physicians visits: A panel data study of Swedish regions 2003-2012. Health Policy (New York). 2016;120(9):1095-1099. http://dx.doi.org/10.1016/j.healthpol.2016.07.010
16. Goldman D, Joyce G, Zheng Y. Prescription drug cost sharing. Associations with medication and medical utilization and spending and health. JAMA. 2007;298(1):61-69. https://doi.org/10.1001/jama.298.1.61
17. Global Strategy for Asthma Management and Prevention, 2016. http://www.ginasthma.org
18. Harver A, Kotses H. Asthma, Health and Society A Public Health Perspective. Charlotte, North Carolina. Springer; 2010.
19. Ungar W, Kozyrskyl A, Paterson M, Ahmad F. Effect of cost- sharing on use of asthma medication in children. Arch Pediatr Adolesc Med. 2008;162(2):104-110. https://doi.org/10.1001/archpediatrics.2007.21
20. Campbell J, Allen-Ramey F, Sajjan S, Maiese E, Sullivan S. Increasing Pharmaceutical copayments: Impact on asthma medication utilization and outcomes. Am J Manag Care. 2011;17(10):703-710. https://pubmed.ncbi.nlm.nih.gov/22106463/
21. Sundararajan V, Henderson T, Perry C, Muggivan A, Quan H, Ghali WA. New ICD-10 version of the Charlson comorbidity index predicted in-hospital mortality. J Clin Epidemiol. 2004;57(12):1288-1294. https://doi.org/10.1016/j.jclinepi.2004.03.012
22. Stagg V. Charlson: Stata module to calculate Charlson index of comorbidity, Statistical Software Components S456719, Boston College Department of Economics, 2006, revised 13 Sep 2017.
23. Yurkovich M, Avina-Zubieta JA, Thomas J, Gorenchtein M, Lacaille D. A systematic review identifies valid comorbidity indices derived from administrative health data. J Clin Epidemiol. 2015;68(1):3-14. http://dx.doi.org/10.1016/j.jclinepi.2014.09.010
24. de Groot V, Beckerman H, Lankhorst GJ, Bouter LM. How to measure comorbidity. A critical review of available methods. J Clin Epidemiol. 2003;56(3):221-229. http://www.ncbi.nlm.nih.gov/pubmed/12725876
25. Rui P, Kang K. National Hospital Ambulatory Medical Care Survey: 2015 Emergency Department Summary Tables. Available from: http://www.cdc.gov/nchs/data/ahcd/nhamcs_emergency/2015_ed_web_tables.pdf
26. DANE. Indicadores demográficos según departamento 1985-2020. Conciliación Censal 1985-2005 y Proyecciones de Población 2005-2020. www.dane.gov.co
27. Cadarette SM, Wong L. An introduction to health care administrative data. Can J Hosp Pharm. 2015;68(3):232-237. https://doi.org/10.4212/cjhp.v68i3.1457
28. Harbaugh CM, Cooper JN. Administrative databases. Semin Pediatr Surg. 2018;27(6):353-360. https://doi.org/10.1053/j.sempedsurg.2018.10.001
29. Harron K, Dibben C, Boyd J, Hjern A, Azimaee M, Barreto ML, et al. Challenges in administrative data linkage for research. Big Data Soc. 2017;4(2). https://doi.org/10.1177/2053951717745678
30. Saczynski JS, Andrade SE, Harrold LR, Tjia J, Cutrona SL, Dodd KS, et al. Mini-Sentinel systematic evaluation of health outcome of interest definitions for studies using administrative and claims data: Heart failure. Pharmacoepidemiol Drug Saf. 2013;21(1):1-22. http://minisentinel.org/foundational_activities/related_projects/
31. Sinha S, Peach G, Poloniecki JD, Thompson MM, Holt PJ. Studies using english administrative data (hospital episode statistics) to assess health-care outcomes-systematic review and recommendations for reporting. Eur J Public Health. 2013;23(1):86-92. https://doi.org/10.1093/eurpub/cks046
32. Gini R, Schuemie M, Brown J, Ryan P, Vacchi E, Coppola M, et al. Data extraction and management in networks of observational health care databases for scientific research: A comparison among EU-ADR, OMOP, Mini-Sentinel And MATRICE Strategies. eGEMs (Generating Evid Methods to Improv patient outcomes). 2016;4(1):2. http://dx.doi.org/10.13063/2327-9214.1189
33. Zhan C, Miller M. Administrative data based patient safety research: A critical review BMJ. BMJ Qual Saf. 2003;12(ii):58-63. https://doi.org/10.1136/qhc.12.suppl_2.ii58
34. Mazzali C, Paganoni AM, Ieva F, Masella C, Maistrello M, Agostoni O, et al. Methodological issues on the use of administrative data in healthcare research: The case of heart failure hospitalizations in Lombardy region, 2000 to 2012. BMC Health Serv Res. 2016;16(1):1-10. http://dx.doi.org/10.1186/s12913-016-1489-0
35. Jones N, Schneider G, Kachroo S, Rotella P, Avetisyan R, Reynolds MW. A systematic review of validated methods for identifying acute respiratory failure using administrative and claims data. Pharmacoepidemiol Drug Saf. 2012;21(S1):261-264. https://doi.org/10.1002/pds.2326
36. Sharifi M, Krishanswami S, McPheeters ML. A systematic review of validated methods to capture acute bronchospasm using administrative or claims data. Vaccine. 2013;31(S10):K12-K20. http://dx.doi.org/10.1016/j.vaccine.2013.06.091
37. Maringe C, Fowler H, Rachet B, Luque-Fernandez MA. Reproducibility, reliability and validity of population-based administrative health data for the assessment of cancer non-related comorbidities. PLoS One. 2017;12(3):1-14. https://doi.org/10.1371/journal.pone.0172814
38. McPheeters ML, Sathe NA, Jerome RN, Carnahan RM. Methods for systematic reviews of administrative database studies capturing health outcomes of interest. Vaccine. 2013;31(S10):K2-K6. http://dx.doi.org/10.1016/j.vaccine.2013.06.048
Como Citar
Aranguren Bello, H. C., Buitrago Gutiérrez, G. ., & Ruiz Morales, Álvaro . (2021). Associação entre a taxa moderadora e a frequência na consulta aos serviços de urgência em pacientes adultos com asma, usuários do regime contributivo na Colômbia. Gerencia Y Políticas De Salud, 20. https://doi.org/10.11144/Javeriana.rgps20.acmf
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