RESUMEN
Introducción: La artritis reumatoide es una enfermedad autoinmune, progresiva que sin tratamiento deriva en discapacidad significativa y deterioro en la calidad de vida. Objetivo: Determinar la incidencia de morbilidad laboral con incapacidad temporal en pacientes con diagnóstico de artritis reumatoide evaluados en el Centro de Reumatología. Métodos: Se realizó un estudio observacional, descriptivo prospectivo de corte longitudinal en 86 personas con artritis reumatoide durante octubre del 2018 a octubre del 2019. Los pacientes fueron evaluados a través del DAS 28, el HAQ-CU y la pérdida de días laborables (costos indirectos). Resultados: La edad promedio fue de 53,1 ± 6,6 años; predominó el sexo femenino (80,2 por ciento), con menos de un año de evolución (48,8 por ciento) y los pacientes universitarios (39,5 por ciento). El 69,8 por ciento presentó una actividad clínica moderada con discapacidad moderada (58,1 por ciento). Se presentó una media de 49,6 días perdidos por paciente, con un coste indirecto promedio de 883,9 cup. Se evidenció una relación débil positiva entre el índice de discapacidad y la duración de los certificados (coeficiente=0,407; p=0,000). Además, relación moderada entre la actividad clínica con el total de días perdidos (coeficiente=0,511; p=0,000); y relación débil positiva entre la duración del certificado (coeficiente=0,381; p=0,000) y la cantidad de certificados (coeficiente=0,342; p=0,001). Conclusión: Los pacientes con artritis reumatoide presentan importante morbilidad laboral con incapacidad temporal debido a la limitación funcional y discapacidad que genera la enfermedad(AU)
Introduction: Rheumatoid arthritis is a progressive autoimmune Disease that without treatment leads to significant disability and deterioration in quality of life. Methods: An observational, descriptive, prospective, longitudinal - cut study was conducted in 86 people with Rheumatoid Arthritis from October 2018 to October 2019. The patients were evaluated through DAS 28, HAQ CU and lost working days (indirect costs). Results: The average age was 53,1±6,6 years, the female sex predominated (80.2 percent), with less than 1 year of evolution (48.8 percent), university education level (39.5 percent). Most of the patients are in clinical activity and moderate disability. There was an average of 49.6 days lost per patient, with an average indirect cost of 8983.9 cup. A weak positive relationship was evidenced between the disability index and the duration of the certificates (coefficient ꞊0,407 p=0,000). In addition, moderate relationship between Clinical Activity and total days lost (coefficient = 0,511 p=0,000). With a weak positive relationship between the duration of the certificate (coefficient = 0,381 p=0,000) and the number of certificates (coefficient = 0,342 p=0,001). Conclusions: Patients with rheumatoid arthritis present significant work morbidity with temporary disability due to functional limitation and disability generated by the disease(AU)
Asunto(s)
Humanos , Epidemiología Descriptiva , Estudios ProspectivosRESUMEN
The main objective of this paper is to present a systematic analysis and review of the state of the art regarding the prediction of absenteeism and temporary incapacity using machine learning techniques. Moreover, the main contribution of this research is to reveal the most successful prediction models available in the literature. A systematic review of research papers published from 2010 to the present, related to the prediction of temporary disability and absenteeism in available in different research databases, is presented in this paper. The review focuses primarily on scientific databases such as Google Scholar, Science Direct, IEEE Xplore, Web of Science, and ResearchGate. A total of 58 articles were obtained from which, after removing duplicates and applying the search criteria, 18 have been included in the review. In total, 44% of the articles were published in 2019, representing a significant growth in scientific work regarding these indicators. This study also evidenced the interest of several countries. In addition, 56% of the articles were found to base their study on regression methods, 33% in classification, and 11% in grouping. After this systematic review, the efficiency and usefulness of artificial neural networks in predicting absenteeism and temporary incapacity are demonstrated. The studies regarding absenteeism and temporary disability at work are mainly conducted in Brazil and India, which are responsible for 44% of the analyzed papers followed by Saudi Arabia, and Australia which represented 22%. ANNs are the most used method in both classification and regression models representing 83% and 80% of the analyzed works, respectively. Only 10% of the literature use SVM, which is the less used method in regression models. Moreover, Naïve Bayes is the less used method in classification models representing 17%.