76 research outputs found

    Analysis of the Adherence and Safety of Second Oral Glucose-Lowering Therapy in Routine Practice From the Mediterranean Area : A Retrospective Cohort Study

    Get PDF
    Altres ajuts: AstraZeneca/ESR-16-12628Altres ajuts: Applied Research Collaboration East Midlands (ARC EM)Altres ajuts: National Institute for Health Research (NIHR)Altres ajuts: Imperial Biomedical Research Centre (NIHR)The aims of our study was compare adherence measured by the medical possession ratio (MPR), time until discontinuation and describe adverse events after adding a DPP-4i, SGLT-2i, or sulfonylureas (SU) to metformin in a primary care population with insufficient glycemic control. We used routinely-collected health data from the SIDIAP database. The included subjects were matched by propensity score. The follow-up period was up to 24 months or premature discontinuation. The primary outcomes were the percentage of subjects with good adherence, treatment discontinuation and adverse events among treatment groups. The proportion of patients with good adherence (MPR> 0.8) after the addition of DPP-4i, SGLT-2i or SU was 53.6%, 68.7%, and 43.0%, respectively. SGLT-2i users were 1.7 times more likely to achieve good adherence compared with DPP-4i users (odds ratio [OR]:1.72, 98% confidence interval [CI]:1.51, 1.96), and 2.8 times more likely compared with SU users (OR: 0.35, 98% CI: 0.07, 0.29). The discontinuation hazard ratios were 1.43 (98%CI: 1.26; 1.62) and 1.60 (98%CI: 1.42; 1.81) times higher among SGLT-2i and SU users than DPP-4i users during the follow-up period. No differences were observed for adverse events among the treatment groups. In conclusion, in our real-world setting, the combination of SGLT-2i with metformin was associated with better adherence. The mean time until discontinuation was longer in the SGLT-2i group in comparison with the DPP-4i or SU groups

    ¿Adelantan el diagnóstico de la diabetes tipo 2 los nuevos criterios de la Asociación Americana de Diabetes?

    Get PDF
    ObjetivoAnalizar el intervalo temporal entre la primera hiperglucemia basal ocasional (HBO) y el diagnóstico de diabetes mellitus tipo 2 (DM2) al aplicar los criterios de la OMS y de la Asociación Americana de Diabetes (ADA).DiseñoEstudio observacional, retrospectivo. Ámbito del estudio. Centro de atención primaria urbano.SujetosUn total de 104 pacientes con DM2, diagnosticados entre 1991 y 1995, con antecedentes de HBO.Mediciones o intervencionesEdad, género y otros factores de riesgo, fechas de la primera HBO (glucemia basal 3 110 mg/dl), del diagnóstico según criterios OMS (2 glucemias basales 3 140 mg/dl o 3 200 mg/dl a las 2 horas de la sobrecarga oral de glucosa [SOG]) y aplicando criterios ADA (2 glucemias basales 3 126 mg/dl) y los intervalos en meses entre ellas.ResultadosDe los 222 pacientes diagnosticados, 104 (47%) presentaban antecedentes de HBO. La edad en el momento del diagnóstico fue 60,8 años (DE, 10,1), siendo un 53% mujeres. En 51 casos (49%) se realizó SOG. La mediana (rango) del intervalo entre la primera HBO y el diagnóstico fue de 16 meses (0–101) en los que se realizó la SOG y de 45 (1–104) en los que no se practicó (p = 0,003). En estos últimos, los criterios ADA lo redujeron a 31 meses (0–97) (p < 0,001) y en 27 de ellos que no cumplían ambos criterios a la vez el intervalo fue de sólo 10 meses (0–93) (p < 0,001). Conclusiones. La no realización de la SOG comporta un retraso en el diagnóstico que puede ser contrarrestado con la aplicación de los criterios de la ADA.ObjectiveTo analyze the period of time between the first occasional fasting hyperglycaemia (OFH) and the diagnosis of type 2 diabetes mellitus (DM2), using the World Health Organization (WHO) criteria or the American Diabetes Association (ADA) criteria.DesignRetrospective, observational study.SettingUrban primary care centre.Subjects104 patients with DM2 diagnosed between 1991 and 1995 who had a previous OFH.MeasurementsAge, gender and other risk factors, dates of the first OFH (fasting plasma glucose 3 110 mg/dl), the diagnosis according to WHO criteria (2 fasting plasma glucose 3 140 mg/dl or 3 200 mg/dl two hours after the oral glucose test tolerance (OGTT)) or with the ADA criteria (2 fasting plasma glucose 3 126 mg/dl), and the intervals in months between them.ResultsOf the 222 diagnosed patients, 104 (47%) had previous OFH. Age at diagnosis was 60.8 (SD 10.1) and 53% were women. OGTT was performed in 51 cases (49%). The median (range) of the interval between the first OFH and diagnosis was 16 months (0–101) for those who were undertaken an OGTT, and 45 months (1–104) for those who were not (p = 0.003). In these last ones, ADA criteria reduced the interval to 31 months (0–97) (p < 0.001). In 27 of these patients who did not satisfy both criteria at the same time, ADA criteria reduced the interval to 10 months (0–93) (p < 0.001).ConclusionsNot performing the OGTT means a delay in diagnosis which can be countered by applying the ADA criteria

    Trends in HbA1c thresholds for initiation of hypoglycemic agents:Impact of changed recommendations for older and frail patients

    Get PDF
    Aims: Less strict glycated hemoglobin (HbA1c) thresholds have been recommended in older and/or frail type 2 diabetes (T2D) patients than in younger and less frail patients for initiating hypoglycemic agents since 2011. We aimed to assess trends in HbA1c thresholds at initiation of a first hypoglycemic agent(s) in T2D patients and the influence of age and frailty on these trends. Materials and methods: The groningen initiative to analyze type 2 diabetes treatment (GIANTT) database was used, which includes primary care T2D patients from the north of the Netherlands. Patients initiating a first non-insulin hypoglycemic agent(s) between 2008 and 2014 with an HbA1c measurement within 120 days before initiation were included. The influence of calendar year, age, or frailty and the interaction between calendar year and age or frailty were assessed using multilevel regression analyses adjusted for confounders. Results: We included 4588 patients. The mean HbA1c threshold at treatment initiation was 7.4% up to 2010, decreasing to 7.1% in 2011 and increasing to 7.4% in 2014. This quadratic change over the years was significant (P 0.05). Conclusions: HbA1c thresholds at initiation of a first hypoglycemic agent(s) changed significantly over time, showing a decrease after 2010 and an increase after 2012. The HbA1c threshold at initiation was not influenced by age or frailty, which is in contrast with recommendations for more personalized treatment

    Burden of disease, healthcare pathways and costs of cardiovascular high-risk patients with type 2 diabetes: a real world analysis:

    Get PDF
    Objective: To estimate the burden of disease and to describe healthcare pathways and costs of type-2diabetes (DMT2) patients at high cardiovascular risk (HRCV). Methods: A real-world analysis was performed by using a subset of the AR-CO database, containing administrative health data of >4.3 million of inhabitants. A cohort of adult patients with DMT2 and HRCV was selected in 2013, and followed for 1 year. Through this period, information on antidiabetic and cardiovascular therapies, other co-treatments, hospitalisations, and outpatient services, was collected and analysed. The costs associated with each variable were assessed to estimate the integrated health care expenditure. Results: Overall, 7,167 patients with DMT2 and HRCV were identified, corresponding to 3.1% of all diabetic patients and 0.2% of adult population. During the 1-year follow-up, 90.1% of the cohort received at least a prescription of an antidiabetic drug, 98.0% of a cardiovascular medication and 95.9% used at least an outpatient service. 44.5% had an admission during the follow-up period, especially for cardiovascular events. The integrated cost analysis showed that the overall average cost for each subject was € 13,567. Hospitalisations generated 86.8% of this expenditure, followed by drugs (7.7%) and by outpatient services (5.5%). Conclusions: Although patients with DMT2 and HRCV represent a small percentage of the overall population with diabetes, they generate very high costs for National Healthcare System. These costs are mainly due to the hospitalisations, especially for cardiovascular events. New therapeutic strategies involving these patients should allow reduction of hospital admission, resulting in savings for National Healthcare System

    Subjects With Diabetes Mellitus Are at Increased Risk for Developing Tuberculosis : A Cohort Study in an Inner-City District of Barcelona (Spain)

    Get PDF
    Altres ajuts: Spanish Ministry of Economy and the Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (Catalan Health Institute, PREDOC_ECO-19/2).Background: Tuberculosis is the leading cause of mortality from lung infectious disease worldwide in recent years, and its incidence has re-emerged in large cities in low-incidence countries due to migration and socioeconomic deprivation causes. Diabetes mellitus and tuberculosis are syndemic diseases, with diabetes being considered a risk factor for developing tuberculosis. Objective: To investigate whether diabetic patients were at increased risk of tuberculosis living in an inner-district of a large city of northeastern Spain. Methods: Observational matched retrospective cohort study based on clinical records from the population of the lowest socioeconomic status in Barcelona (Ciutat Vella district). A cohort including patients with type 1 and type 2 diabetes mellitus in 2007 and new cases until 2016 (8004 subjects), matched 1:1 by sex and age with a non-diabetic cohort. Follow-up period was until December 31st 2018. We evaluated the risk of developing tuberculosis in diabetic patients compared to non-diabetic patients during the follow up period. We used time-to-event analysis to estimate the incidence of tuberculosis, and competing risks regression by clusters and conditional Cox regression models to calculate the hazard ratio (HR) and its 95% confidence intervals (CI). Results: Among the 16,008 included subjects, the median follow-up was 8.7 years. The mean age was 57.7 years; 61.2% men and 38.8% women in both groups. The incidence of tuberculosis was 69.9 per 100,000 person-years in diabetic patients, and 40.9 per 100,000 person-years in non-diabetic patients (HR = 1.90; CI: 1.18-3.07). After adjustment for the country of origin, chronic kidney disease, number of medical appointments, BMI, alcoholism and smoking, the risk remained higher in diabetic patients (1.66: CI 0.99-2.77). Additionally, subjects from Hindustan or with a history of alcohol abuse also showed a higher risk of developing tuberculosis (HR = 3.51; CI:1.87-6.57, and HR = 2.73; CI:1.22-6.12 respectively). Conclusion: People with diabetes mellitus were at higher risk of developing tuberculosis in a large cohort recruited in an inner-city district with a high incidence for this outcome, and low socioeconomic conditions and high proportion of migrants. This risk was higher among Hindustan born and alcohol abusers

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

    Get PDF
    © 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
    corecore