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Full-Text Articles in Medicine and Health Sciences

Effectiveness Of Quality Incentive Payments In General Practice (Equip-Gp): A Study Protocol For A Cluster-Randomised Trial Of An Outcomes-Based Funding Model In Australian General Practice To Improve Patient Care, Gregory Peterson, Grant Russell, Jan Radford, Nicholas Arnold Zwar, Danielle Mazza, Simon Eckermann, Judy Mullan, Marijka Batterham, Athena Hammond, Andrew D. Bonney Jan 2019

Effectiveness Of Quality Incentive Payments In General Practice (Equip-Gp): A Study Protocol For A Cluster-Randomised Trial Of An Outcomes-Based Funding Model In Australian General Practice To Improve Patient Care, Gregory Peterson, Grant Russell, Jan Radford, Nicholas Arnold Zwar, Danielle Mazza, Simon Eckermann, Judy Mullan, Marijka Batterham, Athena Hammond, Andrew D. Bonney

Illawarra Health and Medical Research Institute

Background There is international interest in whether improved primary care, in particular for patients with chronic or complex conditions, can lead to decreased use of health resources and whether financial incentives help achieve this goal. This trial (EQuIP-GP) will investigate whether a funding model based upon targeted, continuous quality incentive payments for Australian general practices increases relational continuity of care, and lessens health-service utilisation, for high-risk patients and children. Methods We will use a mixed methods approach incorporating a two-arm pragmatic cluster randomised control trial with nested qualitative case studies. We aim to recruit 36 general practices from Practice-Based Research …


Geographic Variation In Cardiometabolic Risk Distribution: A Cross-Sectional Study Of 256,525 Adult Residents In The Illawarra-Shoalhaven Region Of The Nsw, Australia, Renin Toms, Darren J. Mayne, Xiaoqi Feng, Andrew D. Bonney Jan 2019

Geographic Variation In Cardiometabolic Risk Distribution: A Cross-Sectional Study Of 256,525 Adult Residents In The Illawarra-Shoalhaven Region Of The Nsw, Australia, Renin Toms, Darren J. Mayne, Xiaoqi Feng, Andrew D. Bonney

Illawarra Health and Medical Research Institute

Introduction Metabolic risk factors for cardiovascular disease (CVD) warrant significant public health concern globally. This study aims to utilise the regional database of a major laboratory network to describe the geographic distribution pattern of eight different cardiometabolic risk factors (CMRFs), which in turn can potentially generate hypotheses for future research into locality specific preventive approaches. Method A cross-sectional design utilising de-identified laboratory data on eight CMRFs including fasting blood sugar level (FBSL); glycated haemoglobin (HbA1c); total cholesterol (TC); high density lipoprotein (HDL); albumin creatinine ratio (ACR); estimated glomerular filtration rate (eGFR); body mass index (BMI); and diabetes mellitus (DM) status …


Cross-Sectional Study Of Area-Level Disadvantage And Glycaemic-Related Risk In Community Health Service Users In The Southern.Iml Research (Simlr) Cohort, Roger Cross, Andrew D. Bonney, Darren J. Mayne, Kathryn M. Weston Jan 2019

Cross-Sectional Study Of Area-Level Disadvantage And Glycaemic-Related Risk In Community Health Service Users In The Southern.Iml Research (Simlr) Cohort, Roger Cross, Andrew D. Bonney, Darren J. Mayne, Kathryn M. Weston

Faculty of Science, Medicine and Health - Papers: part A

Objectives. The aim of the present study was to determine the association between area-level socioeconomic disadvantage and glycaemic-related risk in health service users in the Illawarra-Shoalhaven region of New South Wales, Australia. Methods. HbA1c values recorded between 2010 and 2012 for non-pregnant individuals aged 18 years were extracted from the Southern.IML Research (SIMLR) database. Individuals were assigned quintiles of the Socioeconomic Indices for Australia (SEIFA) Index of Relative Socioeconomic Disadvantage (IRSD) according to their Statistical Area 1 of residence. Glycaemic risk categories were defined as HbA1c 5.0-5.99% (lowest risk), 6.0-7.49% (intermediate risk) and 7.5% (highest risk). Logistic regression models were …