Socio-economic status influences chronic kidney disease prevalence in primary care: a community-based cross-sectional analysis

Background: Primary care chronic kidney disease (CKD) registers report widely varying prevalence within the UK. We examined the effects of laboratory ascertainment and adjusting for practice-level variables on the variation in CKD prevalence. We carried out an Ayrshire-wide laboratory database analysis of primary care practices (PCPs). Methods: We analysed 54 PCPs with 313 639 registered patients aged ≥18. All patients with a low estimated glomerular filtration rate (<60 mL/min/1.73 m2) had their serum creatinine values extracted from 1st January 2009 to 31st March 2012. Individuals with CKD stage 3–5 were identified with an algorithm that confirmed chronicity. These data were linked to PCP attributes from Information Services Division, Scotland. Using laboratory-ascertained CKD prevalence, we examined whether adjusting for practice-level factors [socio-economic status (SES), rurality and patients to general practitioner ratio (PGR)] and patient-level factors (age, gender) explained some of the observed variation among PCPs. Individual and combined hierarchical multilinear regression models were used. Results: Eighteen thousand two hundred and eighty-five (5.8%) had CKD stage 3–5 on 31 March 2011. SES, rurality and PGR predicted 39% (F(3,50) = 12.37, P < 0.001) of the variation in prevalence with SES exerting the most influence (25%). With the stepwise addition of explanatory variables, variation between practices fell from 3.9-fold using PCP register prevalence to laboratory ascertained (3.1-fold variation), with age and gender adjustment (further fall to 2.1-fold), and lastly to 1.8-fold variation with adjustment for SES. Funnel plots using these adjustments reduced the number of outliers outside of 3 SD from 15 to 7 to 6, and outliers between 2 and 3 SD by 16 to 13 to 5. Conclusions: Laboratory ascertainment is practicable, reduces variation and facilitates benchmarking. PCP attributes other than age and gender impact on prevalence. Over a third of variation in CKD prevalence among PCPs can be explained by rurality, PGR and especially SES even after age and gender stratification.