BACKGROUND: Diabetes Meillitus (DM) and hypertension (HT) are important causes of end-stage renal disease (ESRD) and renal replacement therapy (RRT) is the standard active treatment. Financially, incentivized quality initiatives for primary care include pay-for-performance (P4P) in DM and HT. Our aim was to examine any effect of disease prevalence and P4P on RRT incidence and regional variation. METHODS: The incidence of RRT, sex and ethnicity data and P4P disease register and achievement data were obtained for each NHS locality. We calculated correlation coefficients for P4P indicators since 2004/05 and socio-demographic data for these 152 localities. We then developed a regression model and regression coefficient (R(2)) to assess to what extent these variables might predict RRT incidence. RESULTS: Many of the P4P indicators were weakly but highly significantly correlated with RRT incidence. The strongest correlation was 2004/05 for DM prevalence and 2006/07 for HT quality. DM prevalence and the percentage with blood pressure control in HT target (HT quality) were the most predictive in our regression model R(2) = 0.096 and R(2) = 0.085, respectively (P < 0.001). Combined they predicted a fifth of RRT incidence (R(2) = 0.2, P < 0.001) while ethnicity and deprivation a quarter (R(2) = 0.25, P < 0.001). Our final model contained proportion of population >75 years, DM prevalence, HT quality, ethnicity and deprivation index and predicted 40% of variation (R(2) = 0.4, P < 0.001). CONCLUSION: Our findings add prevalence of DM and quality of HT management to the known predictors of variation in RRT, ethnicity and deprivation. They raise the possibility that interventions in primary care might influence later events in specialist care.
BACKGROUND:Diabetes Meillitus (DM) and hypertension (HT) are important causes of end-stage renal disease (ESRD) and renal replacement therapy (RRT) is the standard active treatment. Financially, incentivized quality initiatives for primary care include pay-for-performance (P4P) in DM and HT. Our aim was to examine any effect of disease prevalence and P4P on RRT incidence and regional variation. METHODS: The incidence of RRT, sex and ethnicity data and P4P disease register and achievement data were obtained for each NHS locality. We calculated correlation coefficients for P4P indicators since 2004/05 and socio-demographic data for these 152 localities. We then developed a regression model and regression coefficient (R(2)) to assess to what extent these variables might predict RRT incidence. RESULTS: Many of the P4P indicators were weakly but highly significantly correlated with RRT incidence. The strongest correlation was 2004/05 for DM prevalence and 2006/07 for HT quality. DM prevalence and the percentage with blood pressure control in HT target (HT quality) were the most predictive in our regression model R(2) = 0.096 and R(2) = 0.085, respectively (P < 0.001). Combined they predicted a fifth of RRT incidence (R(2) = 0.2, P < 0.001) while ethnicity and deprivation a quarter (R(2) = 0.25, P < 0.001). Our final model contained proportion of population >75 years, DM prevalence, HT quality, ethnicity and deprivation index and predicted 40% of variation (R(2) = 0.4, P < 0.001). CONCLUSION: Our findings add prevalence of DM and quality of HT management to the known predictors of variation in RRT, ethnicity and deprivation. They raise the possibility that interventions in primary care might influence later events in specialist care.
Authors: Vincent A Van Gelder; Nynke D Scherpbier-De Haan; Wim J C De Grauw; Gerald M M Vervoort; Chris Van Weel; Marion C J Biermans; Jozé C C Braspenning; Jack F M Wetzels Journal: Scand J Prim Health Care Date: 2016-02-06 Impact factor: 2.581
Authors: Simon de Lusignan; Simon de Lusignana; Hugh Gallagher; Simon Jones; Tom Chan; Jeremy van Vlymen; Aumran Tahir; Nicola Thomas; Neerja Jain; Olga Dmitrieva; Imran Rafi; Andrew McGovern; Kevin Harris Journal: Kidney Int Date: 2013-03-27 Impact factor: 10.612
Authors: Mohammad Tahir; Simon Hassan; Simon de Lusignan; Lazza Shaheen; Tom Chan; Olga Dmitrieva Journal: BMC Nephrol Date: 2014-05-07 Impact factor: 2.388