19 research outputs found

    Health-related Quality of Life in Type 1 Diabetes Mellitus Pediatric Patients and Their Caregivers in Spain: An Observational Cross-Sectional Study

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    Objectives: This study assessed the health-related quality of life (HRQOL) of pediatric patients with type 1 diabetes mellitus (T1DM) and their caregivers.Methods: CHRYSTAL was an observational cross-sectional study conducted in Spain in 2014 on 275 patients under 18 years old diagnosed with T1DM. Patient/caregiver pairs were stratified by patients' HbA1c level (?7.5% versus <7.5%) and by presence or absence of T1DM complications and/or comorbidities. EQ-5D and PedsQL questionnaires were administered to patients and caregivers.Results: On the EQ-5D, according to caregivers' perception, 17.7% of children experienced moderate pain or discomfort, 9.7% suffered problems performing usual activities, and 13.2% demonstrated moderate anxiety or depression. Mean EQ-5D index score was 0.95 and mean visual analog scale (VAS) score was 86.1. By HbA1c level (?7.5% versus <7.5%), mean index scores were 0.94 and 0.95, and mean VAS scores were 82.8 and 89.2, respectively. Mean index scores were 0.91 for children with complications and/or comorbidities and 0.96 for children without. Mean VAS scores were 83.7 and 87.2, respectively. HRQOL per the PedsQL tool ranged from 68.1 (ages 2-4) to 73.1 (ages 13-18). EQ-5D index and VAS scores were significantly correlated (rho = 0.29-0.43) with several age groups of the PedsQL. EQ-5D scales showed significant moderate correlation between EQ-5D-Y and EQ-5D-3L proxy VAS score (rho = 0.45; p < .001).Conclusions: Patients with few complications and controlled HbA1c reported a relatively high HRQOL. The results suggest that parent-proxy EQ-5D ratings are valid for use as part of an overall health outcomes assessment in clinical studies of T1DM in pediatric patients

    Pharmaceutical cost and multimorbidity with type 2 diabetes mellitus using electronic health record data

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    © 2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.[EN] Background: The objective of the study is to estimate the frequency of multimorbidity in type 2 diabetes patients classified by health statuses in a European region and to determine the impact on pharmaceutical expenditure. Methods: Cross-sectional study of the inhabitants of a southeastern European region with a population of 5,150,054, using data extracted from Electronic Health Records for 2012. 491,854 diabetic individuals were identified and selected through clinical codes, Clinical Risk Groups and diabetes treatment and/or blood glucose reagent strips. Patients with type 1 diabetes and gestational diabetes were excluded. All measurements were obtained at individual level. The prevalence of common chronic diseases and co-occurrence of diseases was established using factorial analysis. Results: The estimated prevalence of diabetes was 9.6 %, with nearly 70 % of diabetic patients suffering from more than two comorbidities. The most frequent of these was hypertension, which for the groups of patients in Clinical Risk Groups (CRG) 6 and 7 was 84.3 % and 97.1 % respectively. Regarding age, elderly patients have more probability of suffering complications than younger people. Moreover, women suffer complications more frequently than men, except for retinopathy, which is more common in males. The highest use of insulins, oral antidiabetics (OAD) and combinations was found in diabetic patients who also suffered cardiovascular disease and neoplasms. The average cost for insulin was 153€ and that of OADs 306€. Regarding total pharmaceutical cost, the greatest consumers were patients with comorbidities of respiratory illness and neoplasms, with respective average costs of 2,034.2€ and 1,886.9€. Conclusions: Diabetes is characterized by the co-occurrence of other diseases, which has implications for disease management and leads to a considerable increase in consumption of medicines for this pathology and, as such, pharmaceutical expenditure.This study was financed by a grant from the Fondo de Investigaciones de la Seguridad Social Instituto de Salud Carlos III, the Spanish Ministry of Health (FIS PI12/0037).Sancho Mestre, C.; Vivas Consuelo, DJJ.; Alvis, L.; Romero, M.; Usó Talamantes, R.; Caballer Tarazona, V. (2016). Pharmaceutical cost and multimorbidity with type 2 diabetes mellitus using electronic health record data. BMC Health Services Research. 16(394):1-8. https://doi.org/10.1186/s12913-016-1649-2S1816394Whiting DR, Guariguata L, Weil C, Shaw J. IDF Diabetes Atlas: Global estimates of the prevalence of diabetes for 2011 and 2030. Diabetes Res Clin Pract. 2011;94:311–21. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22079683Soriguer F, Goday A, Bosch-Comas A, Bordiu E, Calle-Pascual A, Carmena R, et al. Prevalence of diabetes mellitus and impaired glucose regulation in Spain: the [email protected] Study. Diabetologia. 2012;55:88–93. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21987347WHO | Rio Political Declaration on Social Determinants of Health. WHO. World Health Organization; 2011. Available from: http://www.who.int/sdhconference/declaration/Rio_political_declaration.pdf?ua=1Fortin M, Soubhi H, Hudon C, Bayliss EA, van den Akker M. Multimorbidity’s many challenges. BMJ. 2007;334:1016–7. BMJ Group [cited 2016 Aug 4]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/17510108Valderas JM, Starfield B, Sibbald B, Salisbury C, Roland M. Defining comorbidity: implications for understanding health and health services. Ann Fam Med. 2009;7:357–63. [cited 2016 Aug 4]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/19597174Sundararajan 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;57:1288–94. Available from: http://www.ncbi.nlm.nih.gov/pubmed/15617955Glynn LG, Valderas JM, Healy P, Burke E, Newell J, Gillespie P, et al. The prevalence of multimorbidity in primary care and its effect on health care utilization and cost. Fam Pract. 2011;28:516–23. [Internet]. 2011 [cited 2016 Aug 4]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21436204Barnett 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. Lancet. 2012;380:37–43. [cited 2014 Nov 5] Available from: http://www.ncbi.nlm.nih.gov/pubmed/22579043Holden L, Scuffham PA, Hilton MF, Muspratt A, Ng SK, Whiteford HA. Patterns of multimorbidity in working Australians. Popul Heal Metr. 2011;9:15. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21635787Starfield B. Threads and yarns: weaving the tapestry of comorbidity. Ann Fam Med. 2006;4:101–3. [cited 2016 Aug 4]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/16569711Abdul-Rahim HF, Holmboe-Ottesen G, Stene LCM, Husseini A, Giacaman R, Jervell J, et al. Obesity in a rural and an urban Palestinian West Bank population. Int J Obes. 2003;27:140–6. Available from: http://dx.doi.org/10.1038/sj.ijo.0802160Boutayeb A, Boutayeb S, Boutayeb W. Multi-morbidity of non communicable diseases and equity in WHO Eastern Mediterranean countries. Int J Equity Heal. 2013;12:60. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23961989Teljeur C, Smith SM, Paul G, Kelly A, O’Dowd T. Multimorbidity in a cohort of patients with type 2 diabetes. Eur J Gen Pract. 2013;19:17–22. Available from: http://informahealthcare.com/doi/abs/10.3109/13814788.2012.714768 , http://www.ncbi.nlm.nih.gov/pubmed/23432037Hughes JS, Averill RF, Eisenhandler J, Goldfield NI, Muldoon J, Neff JM, et al. Clinical Risk Groups (CRGs): a classification system for risk-adjusted capitation-based payment and health care management. Med Care. 2004;42:81–90. [cited 2016 Feb 29]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/14713742Vivas-Consuelo D, Alvis-Estrada L, Uso-Talamantes R, Caballer-Tarazona V, Buigues-Pastor L, Sancho-Mestre C. Multimorbidity Pharmaceutical Cost of Diabetes Mellitus. Value in Health. 2014;17:A341–2. Elsevier [cited 2016 Apr 21]. Available from: http://www.sciencedirect.com/science/article/pii/S1098301514026102Inoriza JM, Pérez M, Cols M, Sánchez I, Carreras M. Análisis de la población diabética de una comarca : perfil de morbilidad, utilización de recursos, complicaciones y control metabólico. Aten Primaria. 2016;45. Available from: http://www.sciencedirect.com/science/article/pii/S0212656713001340Vivas-Consuelo D, Usó-Talamantes R, Trillo-Mata JL, Caballer-Tarazona M, Barrachina-Martínez I, Buigues-Pastor L. Predictability of pharmaceutical spending in primary health services using Clinical Risk Groups. Health Policy. 2014;116:188–95. Available from: http://www.sciencedirect.com/science/article/pii/S0168851014000256Kho AN, Hayes MG, Rasmussen-Torvik L, Pacheco JA, Thompson WK, Armstrong LL, et al. Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study. J Am Med Inform Assoc. 2016;19:212–8. [cited 2016 Feb 18]. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3277617&tool=pmcentrez&rendertype=abstractPrados-Torres A, Poblador-Plou B, Calderón-Larrañaga A, Gimeno-Feliu LA, González-Rubio F, Poncel-Falcó A, et al. Multimorbidity patterns in primary care: interactions among chronic diseases using factor analysis. PLoS One. 2012;7:e32190. Public Library of Science [cited 2016 Apr 21]. Available from: http://dx.doi.org/10.1371/journal.pone.0032190Islam MM, Valderas JM, Yen L, Dawda P, Jowsey T, McRae IS. Multimorbidity and comorbidity of chronic diseases among the senior Australians: prevalence and patterns. PLoS One. 2014;9:e83783. Public Library of Science [cited 2016 Mar 25]. Available from: http://dx.doi.org/10.1371/journal.pone.0083783Fortin M, Bravo G, Hudon C, Lapointe L, Dubois MF, Almirall J. Psychological distress and multimorbidity in primary care. Ann Fam Med. 2006;4:417–22. Available from: http://www.ncbi.nlm.nih.gov/pubmed/17003141Nuttall M, van der Meulen J, Emberton M. Charlson scores based on ICD-10 administrative data were valid in assessing comorbidity in patients undergoing urological cancer surgery. J Clin Epidemiol. 2006;59:265–73. Available from: http://www.ncbi.nlm.nih.gov/pubmed/16488357Klompas M, Eggleston E, McVetta J, Lazarus R, Li L, Platt R. Automated detection and classification of type 1 versus type 2 diabetes using electronic health record data. Diabetes Care. 2013;36:914–21. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23193215Alonso-Moran E, Orueta JF, Fraile Esteban JI, Arteagoitia Axpe JM, Luz Marques Gonzalez M, Toro Polanco N, et al. The prevalence of diabetes-related complications and multimorbidity in the population with type 2 diabetes mellitus in the Basque Country. BMC Public Health. 2014;14. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4197247/Pantalone KM, Hobbs TM, Wells BJ, Kong SX, Kattan MW, Bouchard J, et al. Clinical characteristics, complications, comorbidities and treatment patterns among patients with type 2 diabetes mellitus in a large integrated health system. BMJ open diabetes Res Care. 2015;3:e000093. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4513350&tool=pmcentrez&rendertype=abstractAlonso-Moran E, Satylganova A, Orueta JF, Nuno-Solinis R. Prevalence of depression in adults with type 2 diabetes in the Basque Country: relationship with glycaemic control and health care costs. BMC Public Health. 2014;14. Available from: http://bmcpublichealth.biomedcentral.com/articles/10.1186/1471-2458-14-769Kilzieh N, Rastam S, Maziak W, Ward KD. Comorbidity of depression with chronic diseases: a population-based study in Aleppo, Syria. Int J Psychiatry Med. 2008;38:169–84. Available from: http://www.ncbi.nlm.nih.gov/pubmed/18724568Almawi W, Tamim H, Al-Sayed N, Arekat MR, Al-Khateeb GM, Baqer A, et al. Association of comorbid depression, anxiety, and stress disorders with Type 2 diabetes in Bahrain, a country with a very high prevalence of Type 2 diabetes. J Endocrinol Invest. 2008;31:1020–4. Available from: http://www.ncbi.nlm.nih.gov/pubmed/19169060Giralt Muiña P, Gutiérrez Ávila G, Ballester Herrera MJ, Botella Romero F, Angulo Donado JJ. Prevalencia de diabetes y diabetes oculta en adultos de Castilla-La Mancha. TITLEREVISTA. 2011;137:484–90. Available from: http://zl.elsevier.es/es/revista/medicina-clinica-2/prevalencia-diabetes-diabetes-oculta-adultos-castilla-la-mancha-90028329-originales-2011Mata-Cases M, Roura-Olmeda P, Berengué-Iglesias M, Birulés-Pons M, Mundet-Tuduri X, Franch-Nadal J, et al. Fifteen years of continuous improvement of quality care of type 2 diabetes mellitus in primary care in Catalonia, Spain. Int J Clin Pract. 2012;66:289–98. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3584513&tool=pmcentrez&rendertype=abstractEgede LE, Gebregziabher M, Zhao Y, Dismuke CE, Walker RJ, Hunt KJ, et al. Differential Impact of Mental Health. 2015;21:535–44. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26295353Huber CA, Diem P, Schwenkglenks M, Rapold R, Reich O. Estimating the prevalence of comorbid conditions and their effect on health care costs in patients with diabetes mellitus in Switzerland. Diabetes Metab Syndr Obes. 2014;7:455–65. Dove Press [cited 2016 Aug 4]. Available from: https://www.dovepress.com/estimating-the-prevalence-of-comorbid-conditions-and-their-effect-on-h-peer-reviewed-article-DMS

    Social economic costs of type 1 diabetes mellitus in pediatric patients in Spain: CHRYSTAL observational study

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    AIMS: To estimate the social-economic costs of Type 1 Diabetes Mellitus (T1DM) in patients aged 0-17 years in Spain from a social perspective. METHODS: We conducted a cross-sectional observational study in 2014 of 275 T1DM pediatric outpatients distributed across 12 public health centers in Spain. Data on demographic and clinical characteristics, healthcare utilization and informal care were collected from medical records and questionnaires completed by clinicians and patients' caregivers. RESULTS: A valid sample of 249 individuals was analyzed. The average annual cost for a T1DM patient was €27,274. Direct healthcare costs were €4070 and direct non-healthcare cost were €23,204. Informal (familial) care represented 83% of total cost, followed by medical material (8%), outpatient and primary care visits (3.1%) and insulin (2.1%). Direct healthcare cost per patient statistically differed by glycated haemoglobin (HbA1c) level [mean cost €4704 in HbA1c ?7.5% (?58mmol/mol) group vs. €3616 in HbA1c<7.5% (<58mmol/mol) group)]; and by the presence or absence of complications and comorbidities (mean cost €5713 in group with complications or comorbidities vs. €3636 in group without complications or comorbidities). CONCLUSIONS: T1DM amongst pediatric patients incurs in considerable societal costs. Informal care represents the largest cost category

    Generación automática de resúmenes de vídeos obtenidos desde cámaras móviles utilizando vectores de movimiento generados por un codificador H.264/AVC

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    El estudio de vídeos de tráfico para detectar infracciones o conductas antirreglamentarias así como la investigación de las causas de los accidentes de tráfico puede ser de una gran utilidad para la sociedad. El acceso a un contenido particular en un fichero de vídeo puede ser complejo debido a dos factores principales: la propia naturaleza de los archivos de video y el gran número de contenidos que existen. Con los resúmenes de vídeo, se pretende que el usuario acceda a alguna representación de éste que le permita conocer dicho contenido de la manera más rápida posible. También se trata que la navegación a través del vídeo sea dirigida, pudiendo acceder a las secciones concretas que sean de interés para cada usuario en particular. La generación de estos resúmenes es muy costosa desde el punto de vista de eficiencia computacional y temporal, debido principalmente al tamaño de los datos que hay que procesar. En esta Tesis se proponen una serie de técnicas que van a permitir la generación automática y eficiente de este tipo de resúmenes. Para conseguir dicha eficiencia, se trabaja de manera directa con la información almacenada en el video comprimido. Con esta decisión, se ahorrará todo el tiempo que se necesita para descomprimir el vídeo y además a diferencia de una gran mayoría de algoritmos que utilizan como entrada la señal del vídeo comprimido, en este trabajo sólo se trabajará con la información que obtienen el codificador H.264/AVC de la estimación y codificación del movimiento, es decir, los vectores de movimiento. Con respecto al ámbito o entorno de aplicación, ha de indicarse que una gran mayoría de técnicas que utilizan la información del movimiento para procesar vídeo, lo hacen en entornos controlados en los que suele existir información a priori del escenario del contenido del propio vídeo, además, en la mayoría de los casos los vídeos son capturados desde cámaras ubicadas en un punto fijo. En contraste con lo anterior, las técnicas y algoritmos propuestos en esta Tesis se han diseñado para poder ser utilizados en vídeos grabados desde cámaras móviles. En definitiva, el propósito general de esta Tesis consiste en obtener resúmenes de vídeo de tráfico, capturados desde cámaras on-board, analizando de manera exclusiva la información de la estimación y compensación de movimiento de H.264 para así minimizar la información a procesar y poder obtener estos resúmenes de la manera más eficiente posible

    Action-research and health. A pedagogical adventure (II)

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    El siguiente artículo describe una experiencia educativa basada en el modelo de investigación-acción llevada a cabo por un grupo de profesionales sanitarios. Mediante este método se pretendió aumentar la formación pedagógica y mejorar la práctica laboral de los profesionales sanitarios. Los resultados mostraron un elevado grado de satisfacción del personal sanitario con su tarea profesional y una mejora general de todas las variables estudiadas relacionadas con el estado de salud integral de sus pacientes pediátricos.This paper describes an educational experience, based on the action-research model, carried out by a health professional group. The aim of this method was improving the health workers pedagogic formation and professional practice. The results showed a high level of satisfaction with their professional skill and a general improvement of all the studied variables related to their pediatric patients health state

    Psychoeducational intervention for children and adolescents with type 1 diabetes

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    Objetivo: Evaluar los efectos de un programa de intervención psicoeducativa dirigido a niños y adolescentes con diabetes para promover el cuidado autónomo de la enfermedad y la mejora de su estado de salud. Métodos: Se utilizó un diseño cuasi experimental, longitudinal, de medidas repetidas, o diseño intrasujeto. Participaron 24 pacientes con diabetes mellitus tipo 1 de 8-15 años de edad. Los sujetos acudieron a seis sesiones de 90 minutos. Se hicieron dos grupos según la edad de los sujetos. A cada grupo se le aplicó un método distinto: juego didáctico para los niños, y entrenamiento en el afrontamiento de tareas con los adolescentes. Se realizaron tres mediciones: pretest, postest y seguimiento. Resultados: La intervención obtuvo resultados significativos en el grupo de los niños en algunas de las variables estudiadas: calidad de vida (p= 0,021), conocimientos sobre la diabetes (p= 0,003) y control metabólico (registro de la hemoglobina glucosilada) (p= 0,036). En el resto de las variables (responsabilidad y autocuidado) se dieron mejoras que no llegaron a ser estadísticamente significativas. En el grupo de los adolescentes las mejoras no fueron signifi cativas en ninguno de los casos. Conclusiones: Las estrategias metodológicas adaptadas a las características e intereses de los pacientes, como es el caso del juego educativo en los niños, ayudan a conseguir mejoras en los resultados clínicos y psicosociales.Objective: To evaluate the effects of a psychoeducational intervention for children and adolescents with type 1 diabetes to promote the autonomy in diabetes self-care and to improve their health status. Methods: A longitudinal near-experimental design of repeated measures was used. Twenty-four patients with diabetes type 1, between 8 and 15 years of age, participated in this research. They attended six sessions of 90 minutes. Two groups were made considering their age. A different method was applied to each group: didactic games for the children, and coping skills training for adolescents. Three measurements were made: pretest, postest and follow-up. Results: Signifi cant results were found in some variables in the children’s group: quality of life (p= 0,021), knowledge about diabetes (p= 0,033) and metabolic control (HbA1c) (p= 0,036). Regarding responsibility and self-care, we did not found signifi cant improvements. Our intervention with adolescents did not show any signifi cant improvement in the main dependent variables. Conclusions: Methodologic strategies adapted to the patients’ interests and characteristics, such as didactic games for children, help to obtain improvements in clinical and psycho-social results
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