viernes, 30 de septiembre de 2016


Jaume Puig-Junoy, Santiago Rodríguez-Feijóo, Beatriz González López-Valcárcel, Vanessa Gómez-Navarro

Rev Esp Salud Pública. Vol. 90: 29 de abril de 2016: e1-e14.

Fundamentos: El objetivo fue conocer si la reforma del copago farmacéutico español en 2012 ha afectado el consumo de los medicamentos para enfermedades crónicas tales como antidiabéticos, antitrombóticos y agentes contra padecimientos obstructivos de las vías respiratorias.
Método: Estudio observacional longitudinal retrospectivo. Se utilizaron modelos de regresión lineal segmentada general para series de tiempo interrumpido. Las variables analizadas fueron el número de dosis diarias definidas (DDDs) y el importe de la facturación de las dispensaciones financiadas y no financiadas por el Sistema Nacional de Salud (SNS) desde septiembre de 2010 hasta agosto de 2015 (T=60).
Resultados: La tasa de variación estimada de las DDDs fue negativa pero decreciente para la mayoría de los 3 subgrupos terapéuticos a los 6, 12, 24 y 38 meses de la intervención: -0,1% para antidiabéticos a los 6 meses  y 0,3% a los 38 meses; -3,7% para antitrombóticos a los 6 meses y -4,6% a los 38 meses; -2,7% a los 6 meses para anti-asma y EPOC y -1,3% a los 38 meses. Se estimó una reducción mantenida y significativa del gasto únicamente en el subgrupo para asma y EPOC: -5,2% a los 6 meses, -7,0% a los 12 meses y a los 24 meses y -6,2% a los 38 meses.
Conclusiones: La reforma del copago farmacéutico de 2012 ha ocasionado una reducción inmediata y significativa en el número de DDDs de los tres grupos terapéuticos seleccionados en este estudio. Este efecto nivel no ha sido permanente ya que ha ido acompañado de un cambio en la tendencia de crecimiento en los meses post-intervención que, en parte, ha compensado el efecto sobre el nivel.

jueves, 15 de septiembre de 2016

How efficient are referral hospitals in Uganda? A data envelopment analysis and tobit regression approach

Paschal N Mujasi  Jaume Puig-Junoy  Anthony Mbonye 

Mujasi et al. BMC Health Services Research (2016) 16:230

Hospitals represent a significant proportion of health expenditures in Uganda, accounting for about 26 % of total health expenditure. Improving the technical efficiency of hospitals in Uganda can result in large savings which can be devoted to expand access to services and improve quality of care. This paper explores the technical efficiency of referral hospitals in Uganda during the 2012/2013 financial year.


This was a cross sectional study using secondary data. Input and output data were obtained from the Uganda Ministry of Health annual health sector performance report for the period July 1, 2012 to June 30, 2013 for the 14 public sector regional referral and 4 large private not for profit hospitals. We assumed an output-oriented model with Variable Returns to Scale to estimate the efficiency score for each hospital using Data Envelopment Analysis (DEA) with STATA13. Using a Tobit model DEA, efficiency scores were regressed against selected institutional and contextual/environmental factors to estimate their impacts on efficiency.


The average variable returns to scale (Pure) technical efficiency score was 91.4 % and the average scale efficiency score was 87.1 % while the average constant returns to scale technical efficiency score was 79.4 %. Technically inefficient hospitals could have become more efficient by increasing the outpatient department visits by 45,943; and inpatient days by 31,425 without changing the total number of inputs. Alternatively, they would achieve efficiency by for example transferring the excess 216 medical staff and 454 beds to other levels of the health system without changing the total number of outputs. Tobit regression indicates that significant factors in explaining hospital efficiency are: hospital size (p < 0.01); bed occupancy rate (p < 0.01) and outpatient visits as a proportion of inpatient days (p < 0.05).


Hospitals identified at the high and low extremes of efficiency should be investigated further to determine how and why production processes are operating differently at these hospitals. As policy makers gain insight into mechanisms promoting hospital services utilization in hospitals with high efficiency they can develop context-appropriate strategies for supporting hospitals with low efficiency to improve their service and thereby better address unmet needs for hospital services in Uganda.

jueves, 1 de septiembre de 2016

Cost and Budget Impact Analysis of an Accurate Intraoperative Sentinel Lymph Node Diagnosis for Breast Cancer Metastasis

Yuko Saruta & Jaume Puig-Junoy

Volume 14, Issue 3, pp 323-335


Conventional intraoperative sentinel lymph node biopsy (SLNB) in breast cancer (BC) has limitations in establishing a definitive diagnosis of metastasis intraoperatively, leading to an unnecessary second operation. The one-step nucleic amplification assay (OSNA) provides accurate intraoperative diagnosis and avoids further testing. Only five articles have researched the cost and cost effectiveness of this diagnostic tool, although many hospitals have adopted it, and economic evaluation is needed for budget holders.


We aimed to measure the budget impact in Japanese BC patients after the introduction of OSNA, and assess the certainty of the results.


Budget impact analysis of OSNA on Japanese healthcare expenditure from 2015 to 2020. Local governments, society-managed health insurers, and Japan health insurance associations were the budget holders. In order to assess the cost gap between the gold standard (GS) and OSNA in intraoperative SLNB, a two-scenario comparative model that was structured using the clinical pathway of a BC patient group who received SLNB was applied. Clinical practice guidelines for BC were cited for cost estimation.


The total estimated cost of all BC patients diagnosed by GS was US$1,023,313,850. The budget impact of OSNA in total health expenditure was -US$24,413,153 (-US$346 per patient). Two-way sensitivity analysis between survival rate (SR) of the GS and OSNA was performed by illustrating a cost-saving threshold: y ≅ 1.14x - 0.16 in positive patients, and y ≅ 0.96x + 0.029 in negative patients (x = SR-GS, y = SR-OSNA). Base inputs of the variables in these formulas demonstrated a cost saving.


OSNA reduces healthcare costs, as confirmed by sensitivity analysis.