We have developed new models to give accurate predictions of CKD. Increasing age, female gender, white ethnicity and cardiovascular disease were all associated with an increased prevalence of CKD. In addition, we have also shown that there is a complex association with age which in turn interacts with cardiovascular disease. The effects of these diseases were greater amongst younger than older adults. The pattern of this interaction was very similar for all the cardiovascular diseases.
The results of our study support those previously published by confirming the important roles of age, gender
[5–8, 29, 30] and CVD
[31–33] in predicting cases of CKD. We also found statistically significant associations with deprivation and ethnicity, but these were not clinically significant; this may explain why there is weak or mixed evidence on their importance in predicting the prevalence of CKD
There are four studies that look at multivariable models for predicting the prevalence of CKD
[5–8]. ORs for female varied between 1.19 and 1.49. All four studies considered the effect of hypertension and diabetes; for the ORs ranged from 1.4 to 1.72, whilst for the latter ORs ranged from 0.9 to 2.68. None of the studies considered interactions between the cardiovascular diseases and age.
Both Bang et al.
 and Whaley-Connell et al.
 considered ethnicity. Bang et al.
 found that, compared to non-Whites, Whites had a statistically significant univariable odds ratio (2.1, p = 0.03) of having CKD, but that ethnicity was not a significant predictor in the multivariable model. Whaley-Connell et al.
 considered two different cohorts; in one White ethnicity was associated with a statistically significant odds ratio of 1.23 (p < 0.001), in the other it had a non-significant odds ratio of 0.91 (p = 0.2).
All four studies confirm the important effect of age; Chadban et al.
 compared subjects aged under 65 to those aged over 65 and reported an odds ratio of 102 (p < 0.001). The other three studies categorised age, and reported significant odds ratios for all categories. Our study further shows that age has a complex association with CKD.
The results of this model are also consistent with cohort studies of CKD in showing that there are interactions between CKD, age and cardiovascular disease
. Other interactions between age and cardiovascular disease have also been reported in the literature
This is the first study that we are aware of that provides multivariable models for predicting the prevalence of CKD in England. Our results are similar to those based in other countries in identifying important variables, but the magnitude of the associations often vary. For example, we found an odds ratio for female gender of about two for all three models; a larger value than that reported in the other studies.
We have identified important interactions between age and cardiovascular disease in predicting the prevalence of CKD. These interaction have not been included in any of the existing models for predicting the prevalence of CKD (or in models for predicting the incidence of CKD
[29, 30]) despite evidence of its importance in the literature. Service planning based on existing models, which fail to capture these interactions, may result in a mismatch between supply and demand for renal services in primary and secondary care. More accurate predictions of CKD prevalence may allow more accurate targeting of resources toward areas of unmet need. A particular strength of our study is the large sample size available. This resulted in increased power to estimate coefficients, especially for interactions. The large sample size, along with the consistency of findings when employing sample-splitting, suggest that the interactions identified in this study will generalise to the rest of the England CKD population.
The QICKD study includes patients whose CKD has not been diagnosed in general practice, and so these estimates may be compared with the P4P CKD indicator to determine areas with high levels of un-met need. At a national level, the P4P indicator in England gives a prevalence of CKD of 4.3%
, we reported a prevalence of 6.76%, suggesting that over a third of people with CKD are not known to their GP. This confirms findings in the recent Health Survey for England, which also included cases of CKD not diagnosed in general and reported a prevalence of 6%
. Analysis of patients with unidentified CKD suggests that their risk profile may be different to patients with identified CKD, this is an area that requires further research.
Using cross-sectional data is a limitation, as it is known that rates of progression vary by patient characteristics
. The results of this analysis may be used to identify areas with a high prevalence of CKD, where early identification will be beneficial in reducing both progression to renal failure and morbidity from cardiovascular disease. However, when targeting resources for CKD, consideration should also be given to variations in rates of progression across populations. We also made no distinction between varying levels of kidney disease. The available literature suggests that the risk profile for CKD may vary as kidney disease progresses; for example it has been shown that the proportion of males with CKD increases with worsening stage
, and a recent study found that non-white ethnicity was a significant predictor of renal replacement therapy
. As renal failure can be devastating for the patient and very expensive
, more research is required into rates of progression.
We have assumed that people without a serum creatinine measurement did not have CKD. Whilst this is consistent with previous approaches
, there was no measurement recorded for 56% of the sample. Hence the prevalence of CKD reported here is likely to be an under-estimate.
The choice to use clinical significance instead of statistical significance posed some problems. In particular the choice of whether or not to include the multi-categorical variables ethnicity and smoking status was slightly arbitrary. The importance of all the omitted variables warrants further research. For the continuous variables the value of the odds ratio (and hence their clinical significance) depends on the units reported. We used the odds ratio per 10-year increase in age, which is commonly employed in the literature
[5, 8, 29, 30, 34]. For deprivation we used a 10-point increase (deprivation values range between 0.75 and 77.37). Using the results from the full main-effects model, we would need to use a 45-point increase in deprivation for it to become clinically significant, and a 5-year increase in age for it to become not clinically significant.
We did not anticipate a priori the nature of the observed interactions between age and the cardiovascular diseases and this feature needs to be independently confirmed. In addition there is scope to improve the modelling of this interaction; noticeably the choice of at what age to start modelling the interaction warrants further research.