0 1.00 Case 1.447945 <0.001 [1.4242 - 1.4720] Diabetes 0 1.00 Case 1.414926 <0.001 [1.3883 - 1.4420] Atrial Fibrillation 0 1.00 Case 5.220019 <0.001 [5.0962 - 5.3467] External validation – Bland Altman plots We externally validated the model-estimated prevalence by carrying out a disagreement analysis between model-estimated and QOF prevalence (%) of HF in practices. We estimated three principal components of disagreement (discordance as measured by Kendall's tau-a, bias as measured by median difference, and calibration as measured by the Theil-Sen median slope). Kendall's tau-a between mean prevalence and prevalence difference of heart failure at practice level is 0.254 (95% CIs (0.239 – 0.270, p<0.001), showing that prevalence means and model-QOF differences are 25.4 percent more likely to be concordant than to be discordant. Table 15 shows the percentile differences between practice-level model-estimated and QOF prevalence of heart failure. Table : Percentile differences between model-estimated and QOF prevalence of heart failure Percent Percentile (95% CI) 0 -2.3 (-2.3, -2.3) 25 0.4 (0.4, 0.4) 50 0.6 (0.6, 0.6) 75 0.9 (0.9, 0.9) 100 15.9 (5.9, 5.9) The best way to display the data is to plot the difference between the measurements by the two methods for each subject against their mean, using Bland-Altman plots. Figure 3 shows the plot for the practice-level QOF and estimated prevalence for HF with higher modelled prevalence compared with QOF registered prevalence. Figure 4 is a scatter plot of practice-level model-estimated and QOF prevalence of diagnosed HF. In both cases, GP-diagnosed prevalence is lower than CPRD prevalence as defined above. Figure : Bland-Altman plot for practice-level model-estimated and QOF prevalence of heart failure Figure : Scatter plot of practice -level model-estimated and QOF prevalence of diagnosed heart failure
0 1.00 Case 1.447945 <0.001 [1.4242 - 1.4720] Diabetes 0 1.00 Case 1.414926 <0.001 [1.3883 - 1.4420] Atrial Fibrillation 0 1.00 Case 5.220019 <0.001 [5.0962 - 5.3467] External validation – Bland Altman plots We externally validated the model-estimated prevalence by carrying out a disagreement analysis between model-estimated and QOF prevalence (%) of HF in practices. We estimated three principal components of disagreement (discordance as measured by Kendall's tau-a, bias as measured by median difference, and calibration as measured by the Theil-Sen median slope). Kendall's tau-a between mean prevalence and prevalence difference of heart failure at practice level is 0.254 (95% CIs (0.239 – 0.270, p<0.001), showing that prevalence means and model-QOF differences are 25.4 percent more likely to be concordant than to be discordant. Table 15 shows the percentile differences between practice-level model-estimated and QOF prevalence of heart failure. Table : Percentile differences between model-estimated and QOF prevalence of heart failure Percent Percentile (95% CI) 0 -2.3 (-2.3, -2.3) 25 0.4 (0.4, 0.4) 50 0.6 (0.6, 0.6) 75 0.9 (0.9, 0.9) 100 15.9 (5.9, 5.9) The best way to display the data is to plot the difference between the measurements by the two methods for each subject against their mean, using Bland-Altman plots. Figure 3 shows the plot for the practice-level QOF and estimated prevalence for HF with higher modelled prevalence compared with QOF registered prevalence. Figure 4 is a scatter plot of practice-level model-estimated and QOF prevalence of diagnosed HF. In both cases, GP-diagnosed prevalence is lower than CPRD prevalence as defined above. Figure : Bland-Altman plot for practice-level model-estimated and QOF prevalence of heart failure Figure : Scatter plot of practice -level model-estimated and QOF prevalence of diagnosed heart failure