Análisis de la población diabética de una comarca: perfil de morbilidad, utilización de recursos, complicaciones y control metabólico

Inoriza, José María; Perez, M Jimenez; Cols, Montserrat; Sánchez, Inma; Carreras, Marc; Coderch, Jordi

doi:10.1016/j.aprim.2013.04.007

Cited by 12 publications

(10 citation statements)

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“…In Spain, CRG is used by various autonomous regions and organisations: the Basque country, the Valencian Community (VC) [32,33] and the Baix Empordà (Girona) [11,[34][35][36][37]. In these cases, the regression models used are adjusted by OLS either in their original form or through the logarithmic transformation of the dependent variable.…”

Section: Introductionmentioning

confidence: 99%

Predicting healthcare expenditure by multimorbidity groups

2019

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This article has two main purposes. Firstly, to model the integrated healthcare expenditure for the entire population of a health district in Spain, according to multimorbidity, using Clinical Risk Groups (CRG). Secondly, to show how the predictive model is applied to the allocation of health budgets. MethodsThe database used contains the information of 156,811 inhabitants in a Valencian Community health district in 2013. The variables were: age, sex, CRG's main health statuses, severity level, and healthcare expenditure. The two-part models were used or predicting healthcare expenditure. From the coefficients of the selected model, the relative weights of each group were calculated to set a case-mix in each health district. ResultsModels based on multimorbidity-related variables better explained integrated healthcare expenditure.In the first part of the two-part models, a logit model was used, while the positive costs were modelled with a log-linear OLS regression. An adjusted R 2 of 46-49% between actual and predicted values was obtained. With the weights obtained by CRG, the differences found with the case-mix of each health district proved most useful for budgetary purposes. ConclusionsThe expenditure models allowed improved budget allocations between health districts by taking into account morbidity, as opposed to budgeting based solely on population size.

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Section: Introductionmentioning

confidence: 99%

Predicting healthcare expenditure by multimorbidity groups

2019

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“…No obstante, a pesar de la mejoría, más de la mitad de los pacientes con DM tipo 2 no tenían una medición de HbA1c en el último año o si la tenían no estaba bien controlada. Estos mismos hallazgos que muestran mejoría pero que también evidencian que aún existe una importante oportunidad de mejora han sido descritos en estudios similares en otros entornos20, 21, 22, 23, 24, 25.…”

Section: Discussionunclassified

Tendencias sobre los parámetros del grado de control de los pacientes con diabetes tipo 2 desde el año 2010 al año 2015

et al. 2018

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The percentages of patients with each parameter measured in 2010, 2013 and 2015 were: HbA1c: 36.4%, 37.0%, 62.0% (P<.001); BP: 33.2%, 43.3%, 65.0% (P<.001); LDL: 32.9%, 33.2%, 43.5% (P<.001). The percentages of patients with each parameter measured and controlled in 2010, 2013, and 2015 were: HbA1c: 59.6%, 59.1%, 79.6% (P<.001); BP: 74.9%, 67.4%, 79.2% (P<.001); LDL: 41.8%, 58.3%, 58.8% (P<.001) CONCLUSION: In the 2010-2015 period, a sustained but insufficient trend of better control of HbA1c, BP and LDL was observed in patients with diabetes. The frequency of the measurements of these parameters improved more than the control of them. It seems that efforts to improve care for the patient with diabetes pay off, but they still have to be maintained.

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“…Todos los seleccionados fueron DM tipo 2. Como controles, se seleccionaron aleatoriamente 84 personas, emparejadas por edad, sexo, tipo de insulina, grado de control de la DM y estado de salud según Clinical Risk Groups (CRG)18, 19 (fig. 1).…”

Section: Materials Y Métodosunclassified

“…Otras variables dependientes fueron los datos acumulados a los 12 meses de número de visitas a atención primaria (AP), urgencias de AP, visitas a consultas externas, urgencias hospitalarias, hospitalizaciones (altas y días de estancia), consumo de medicamentos, consumo de tiras reactivas y cumplimiento de la remisión de datos al programa. Igualmente se registraron presión arterial, IMC, perímetro de cintura, datos analíticos (colesterol total, colesterol LDL) y riesgo cardiovascular según REGICOR18, 25.…”

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Efectividad e impacto económico de un programa de atención integrada con soporte de telemedicina a pacientes diabéticos tipo 2 tratados con insulina (estudio GITDIABE)

et al. 2017

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Análisis de la población diabética de una comarca: perfil de morbilidad, utilización de recursos, complicaciones y control metabólico

Abstract: Comorbidity is decisive in the consumption of resources. Just a few part of this consumption is specifically related to the management of diabetes. Results obtained provide a whole population approach to the main existing studies in our national and regional context.

Cited by 12 publications

References 19 publications

Predicting healthcare expenditure by multimorbidity groups

Predicting healthcare expenditure by multimorbidity groups

Tendencias sobre los parámetros del grado de control de los pacientes con diabetes tipo 2 desde el año 2010 al año 2015

Efectividad e impacto económico de un programa de atención integrada con soporte de telemedicina a pacientes diabéticos tipo 2 tratados con insulina (estudio GITDIABE)

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