Socioeconomic status, comorbidity and mortality in patients with type 2 diabetes mellitus in Scotland 2004-2011: a cohort study.

BACKGROUND: Mortality in people with and without diabetes often exhibits marked social patterning, risk of death being greater in deprived groups. This may reflect deprivation-related differences in comorbid disease (conditions additional to diabetes itself). This study sought to determine whether the social patterning of mortality in a population with type 2 diabetes mellitus (T2DM) is explained by differential comorbidity. METHODS: Hospital records for 70 197 men and 56 451 women diagnosed with T2DM at 25 years of age and above in Scotland during the period 2004-2011 were used to construct comorbidity histories. Sex-specific logistic models were fitted to predict mortality at 1 year after diagnosis with T2DM, predicted initially by age and socioeconomic status (SES) then extended to incorporate in turn 5 representations of comorbidity (including the Charlson Index). The capacity of comorbidity to explain social mortality gradients was assessed by observing the change in regression coefficients for SES following the addition of comorbidity. RESULTS: After adjustment for age and Charlson Index, the OR for the contrast between the least deprived and most deprived quintiles of SES for men was 0.79 (95% CI 0.67 to 0.94). For women, the OR was 0.81 (0.67 to 0.97). Similar results were obtained for the 4 other comorbidity measures used. CONCLUSIONS: The social patterning of mortality in people with T2DM is not fully explained by differing levels of comorbid disease additional to T2DM itself. Other dimensions of deprivation are implicated in the elevated death rates observed in deprived groups of people with T2DM.

Differences in progression to ESRD between black and white patients receiving predialysis care in a universal health care system.

BACKGROUND AND OBJECTIVES: Studies performed in the United States showed that blacks progress from CKD to ESRD faster than do whites. Possible explanations are differences in health care system factors. This study investigated whether progression is also faster in a universal health care system, where all patients receive comparable care. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Data from the PREdialysis PAtient REcord study, a multicenter follow-up study of patients with CKD who started predialysis care in The Netherlands (1999-2011), were analyzed. Time-dependent Cox proportional hazards models were used to estimate the hazard ratio (HR) for starting renal replacement therapy (RRT), and linear mixed models were used to compare renal function decline (RFD) between blacks and whites. To explore possible mechanisms, analyses were adjusted for patient characteristics. RESULTS: At initiation of predialysis care, blacks (n=49) were younger and had more diabetes mellitus, higher proteinuria levels, and a higher estimated GFR than whites (n=946). Median follow-up time in months was similar (blacks: 13.9 [boundaries of interquartile range (IQR), 5.3 to 19.5]; whites: 13.1 [IQR, 5.1 to 24.0]). For blacks compared with whites, the crude HR for starting RRT within the first 15 months was 0.86 (95% confidence interval [CI], 0.55 to 1.34) and from 15 months onward, 1.93 (95% CI, 1.02 to 3.68), which increased after adjustment. RFD was faster by 0.18 (95% CI, 0.05 to 0.32) ml/min per 1.73 m(2) per month in blacks compared with whites. CONCLUSION: Blacks receiving predialysis care in a universal health care system have faster disease progression than whites, suggesting that health care system factors have a less influential role than had been thought in explaining black-white differences.

Development and validation of a general population renal risk score.

BACKGROUND AND OBJECTIVES: There is a need for prediction scores that identify individuals at increased risk for developing progressive chronic kidney disease (CKD). Therefore, this study was performed to develop and validate a "renal risk score" for the general population. Design, setting, participants, & measurements For this study we used data from the PREVEND (Prevention of Renal and Vascular ENdstage Disease) study, a prospective population-based cohort study with a median follow-up of 6.4 years. Participants with two or three consecutive estimated GFR (eGFR) measurements during follow-up were included. Participants within the group who had the most renal function decline (top 20% of the total population) and had an eGFR value <60 ml/min per 1.73 m² during follow-up were defined as having progressive CKD. Possible predictors for progressive CKD were selected on the basis of univariable logistic regression analyses. RESULTS: A final prediction model was built using backward logistic regression analysis. Besides baseline eGFR, the model contained age, urinary albumin excretion, systolic BP, C-reactive protein, and known hypertension. The area under the receiver operating characteristic (ROC) curve was 0.84. We performed internal validation by using a bootstrapping procedure. As expected, after the regression coefficients were corrected for optimism, the area under the ROC curve was still 0.84. For clinical use we divided all predictors in meaningful clinical categories to develop a score chart. The area under the ROC curve was 0.83, indicating the high discriminative value of this model. CONCLUSIONS: Given the high internal validity of this renal risk score, this score can be helpful to identify individuals at increased risk for progressive CKD.