OBJECTIVETo compare whether depressive symptoms are more strongly related to subsequent or prior glycemic control in type 2 diabetes and to test whether patient characteristics modify these longitudinal associations.RESEARCH DESIGN AND METHODSOn two occasions separated by 6 months, depressive symptoms and glycemic control were assessed in 253 adults with type 2 diabetes. Regression analyses examined depressive symptoms as both a predictor and outcome of glycemic control and tested whether medication regimen (e.g., insulin versus oral drugs) was an effect modifier before and after adjusting for baseline levels of the outcome being predicted.RESULTSDepressive symptom severity predicted poor glycemic control 6 months later (P = 0.018) but not after baseline glycemic control was taken into account (P = 0.361). Although baseline glycemic control did not generally predict depressive symptoms 6 months later (P = 0.558), it significantly interacted with regimen (P = 0.008). Specifically, glycemic control predicted depressive symptoms among patients prescribed insulin (β = 0.31, P = 0.002) but not among those prescribed oral medication alone (β = −0.10, P = 0.210). Classifying depression dichotomously produced similar but weaker findings.CONCLUSIONSDepressive symptoms do not necessarily lead to worsened glycemic control. In contrast, insulin-treated patients in poor glycemic control are at moderate risk for worsening of depressive symptoms. These patients should be carefully monitored to determine whether depression treatment should be initiated or intensified.

The generally weak association between depression and glycaemic control is concentrated among patients who are prescribed insulin. Similarly, the association between depression and illness quality of life is strongest in patients prescribed insulin. Because this is not attributable to depression-related adherence problems, psychophysiological mechanisms unique to this group ought to be carefully investigated. Clinicians might be especially vigilant for depression in Type 2 DM patients who use insulin and consider its potential impact upon their illness course.

BackgroundAccurate estimates of hypertension prevalence are critical for assessment of population health and for planning and implementing prevention and health care programs. While self-reported data is often more economically feasible and readily available compared to clinically measured HBP, these reports may underestimate clinical prevalence to varying degrees. Understanding the accuracy of self-reported data and developing prediction models that correct for underreporting of hypertension in self-reported data can be critical tools in the development of more accurate population level estimates, and in planning population-based interventions to reduce the risk of, or more effectively treat, hypertension. This study examines the accuracy of self-reported survey data in describing prevalence of clinically measured hypertension in two racially and ethnically diverse urban samples, and evaluates a mechanism to correct self-reported data in order to more accurately reflect clinical hypertension prevalence.MethodsWe analyze data from the Detroit Healthy Environments Partnership (HEP) Survey conducted in 2002 and the National Health and Nutrition Examination (NHANES) 2001–2002 restricted to urban areas and participants 25 years and older. We re-calibrate measures of agreement within the HEP sample drawing upon parameter estimates derived from the NHANES urban sample, and assess the quality of the adjustment proposed within the HEP sample.ResultsBoth self-reported and clinically assessed prevalence of hypertension were higher in the HEP sample (29.7 and 40.1, respectively) compared to the NHANES urban sample (25.7 and 33.8, respectively). In both urban samples, self-reported and clinically assessed prevalence is higher than that reported in the full NHANES sample in the same year (22.9 and 30.4, respectively). Sensitivity, specificity and accuracy between clinical and self-reported hypertension prevalence were ‘moderate to good’ within the HEP sample and ‘good to excellent’ within the NHANES sample. Agreement between clinical and self-reported hypertension prevalence was ‘moderate to good’ within the HEP sample (kappa =0.65; 95% CI = 0.63-0.67), and ‘good to excellent’ within the NHANES sample (kappa = 0.75; 95%CI = 0.73-0.80). Application of a ‘correction’ rule based on prediction models for clinical hypertension using the national sample (NHANES) allowed us to re-calibrate sensitivity and specificity estimates for the HEP sample. The adjusted estimates of hypertension in the HEP sample based on two different correction models, 38.1% and 40.5%, were much closer to the observed hypertension prevalence of 40.1%.ConclusionsApplication of a simple prediction model derived from national NHANES data to self-reported data from the HEP (Detroit based) sample resulted in estimates that more closely approximated clinically measured hypertension prevalence in this urban community. Similar correction models may be useful in obtaining more accurate estimates of hypertension prevalence in other studies that rely on self-reported h...

The PRCE is worthy of additional study and could prove valuable to other organizations.