Literature DB >> 16954513

Selecting indicators for the quality of diabetes care at the health systems level in OECD countries.

Antonio Nicolucci1, Sheldon Greenfield, Soeren Mattke.   

Abstract

PURPOSE: In the context of the Organization for Economic Cooperation and Development (OECD) Quality Indicators Project, a set of quality indicators for diabetes care was developed, to be used for benchmarking the performance of health care systems.
BACKGROUND: Diabetes complications markedly reduce quality and length of life and are also responsible for enormous health care costs. A large body of evidence has shown that several effective treatments and practices may substantially reduce this burden. However, a marked variability has been documented in preventive and therapeutic approaches, thus suggesting that the level of diabetes care currently delivered may not produce the possible health-related gains.
METHODS: Existing quality indicators have been reviewed, with particular attention to the work done by the National Diabetes Quality Improvement Alliance (NDQIA) in the US. All the measures identified were evaluated for their importance, scientific soundness, and feasibility. In addition, the panel members selected new distal outcome measures. These measures are currently not used in provider comparisons, but they could reveal valuable insight into the differential performance of health systems.
RESULTS: /b>. Four process and two proximal outcome measures were selected among those endorsed by the NDQIA. In addition, three new long-term outcome measures have been proposed to gain insight into whether and to what degree differences in the processes and intermediate outcomes that are captured by the established measures translate into better outcomes for patients.
CONCLUSIONS: The measures selected can contribute to policymakers' and researchers' understanding of differences in the quality of diabetes care between health systems. Further work is required to assess the availability of reliable and comparable data across OECD countries.

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Year:  2006        PMID: 16954513     DOI: 10.1093/intqhc/mzl023

Source DB:  PubMed          Journal:  Int J Qual Health Care        ISSN: 1353-4505            Impact factor:   2.038


  42 in total

Review 1.  Review of electronic decision-support tools for diabetes care: a viable option for low- and middle-income countries?

Authors:  Mohammed K Ali; Seema Shah; Nikhil Tandon
Journal:  J Diabetes Sci Technol       Date:  2011-05-01

2.  Quality of Diabetic Care among Patients in a Tertiary Care Hospital in Bangalore, South India: A Cross-sectional Study.

Authors:  Carolin Elizabeth George; Sapna Mathew; Gift Norman; Devashri Mukherjee
Journal:  J Clin Diagn Res       Date:  2015-07-01

3.  Development of a core set of quality indicators for paediatric primary care practices in Europe, COSI-PPC-EU.

Authors:  Dominik A Ewald; Gottfried Huss; Silke Auras; Juan Ruiz-Canela Caceres; Adamos Hadjipanayis; Max Geraedts
Journal:  Eur J Pediatr       Date:  2018-04-14       Impact factor: 3.183

4.  Factors Affecting the Quality of Diabetic Care in Primary Care Settings in Oman: A qualitative study on patients' perspectives.

Authors:  Mohammed Al-Azri; Hilal Al-Azri; Fatma Al-Hashmi; Samira Al-Rasbi; Kawther El-Shafie; Abdullah Al-Maniri
Journal:  Sultan Qaboos Univ Med J       Date:  2011-05-15

5.  Diabetes care in the dispersed population of Greenland. A new model based on continued monitoring, analysis and adjustment of initiatives taken.

Authors:  Michael Lynge Pedersen
Journal:  Int J Circumpolar Health       Date:  2019       Impact factor: 1.228

6.  Patient characteristics correlated with quality indicator outcomes in diabetes care.

Authors:  Michal Shani; Sasson Nakar; Alex Lustman; Tuvia Baievsky; Reena Rosenberg; Shlomo Vinker
Journal:  Br J Gen Pract       Date:  2010-09       Impact factor: 5.386

7.  Validation of Malaysian Versions of Perceived Diabetes Self-Management Scale (PDSMS), Medication Understanding and Use Self-Efficacy Scale (MUSE) and 8-Morisky Medication Adherence Scale (MMAS-8) Using Partial Credit Rasch Model.

Authors:  Safaa Ahmed Al Abboud; Sohail Ahmad; Mohamed Badrulnizam Long Bidin; Nahlah Elkudssiah Ismail
Journal:  J Clin Diagn Res       Date:  2016-11-01

Review 8.  Comparison of diabetes management in five countries for general and indigenous populations: an internet-based review.

Authors:  Damin Si; Ross Bailie; Zhiqiang Wang; Tarun Weeramanthri
Journal:  BMC Health Serv Res       Date:  2010-06-17       Impact factor: 2.655

9.  Longitudinal approaches to evaluate health care quality and outcomes: the Veterans Health Administration diabetes epidemiology cohorts.

Authors:  Donald R Miller; Leonard Pogach
Journal:  J Diabetes Sci Technol       Date:  2008-01

10.  Performance Analysis of Conventional Machine Learning Algorithms for Diabetic Sensorimotor Polyneuropathy Severity Classification.

Authors:  Fahmida Haque; Mamun Bin Ibne Reaz; Muhammad Enamul Hoque Chowdhury; Geetika Srivastava; Sawal Hamid Md Ali; Ahmad Ashrif A Bakar; Mohammad Arif Sobhan Bhuiyan
Journal:  Diagnostics (Basel)       Date:  2021-04-28
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