Risk prediction of major complications in individuals with diabetes: the Atherosclerosis Risk in Communities Study.

AIMS: To develop a prediction equation for 10-year risk of a combined endpoint (incident coronary heart disease, stroke, heart failure, chronic kidney disease, lower extremity hospitalizations) in people with diabetes, using demographic and clinical information, and a panel of traditional and non-traditional biomarkers. METHODS: We included in the study 654 participants in the Atherosclerosis Risk in Communities (ARIC) study, a prospective cohort study, with diagnosed diabetes (visit 2; 1990-1992). Models included self-reported variables (Model 1), clinical measurements (Model 2), and glycated haemoglobin (Model 3). Model 4 tested the addition of 12 blood-based biomarkers. We compared models using prediction and discrimination statistics. RESULTS: Successive stages of model development improved risk prediction. The C-statistics (95% confidence intervals) of models 1, 2, and 3 were 0.667 (0.64, 0.70), 0.683 (0.65, 0.71), and 0.694 (0.66, 0.72), respectively (p < 0.05 for differences). The addition of three traditional and non-traditional biomarkers [ß-2 microglobulin, creatinine-based estimated glomerular filtration rate (eGFR), and cystatin C-based eGFR] to Model 3 significantly improved discrimination (C-statistic = 0.716; p = 0.003) and accuracy of 10-year risk prediction for major complications in people with diabetes (midpoint percentiles of lowest and highest deciles of predicted risk changed from 18-68% to 12-87%). CONCLUSIONS: These biomarkers, particularly those of kidney filtration, may help distinguish between people at low versus high risk of long-term major complications.

Seasonality of tuberculosis in New York City, 1990-2007.

SETTING: Several non-US-based studies have found seasonal fluctuations in the incidence of tuberculosis (TB). OBJECTIVE: The current study examined patterns of TB seasonality for New York City verified TB cases from January 1990 to December 2007. DESIGN: Autocorrelation functions and Fourier analysis were used to detect a cyclical pattern in monthly incidence rates. Analysis of variance was used to compare seasonal mean case proportions. RESULTS: A cyclical pattern was detected every 12 months. Of the 34,004 TB cases included, 21.9% were in the fall (September-November), 24.7% in winter (December-February), 27.3% in spring (March-May), and 26.1% in the summer (June-August). The proportion of cases was lowest in fall (P < 0.0001) and highest in the spring (P < 0.0002). CONCLUSION: Possible explanations for seasonal variations in TB incidence include lower vitamin D levels in winter, leading to immune suppression and subsequent reactivation of latent TB; indoor winter crowding, increasing the likelihood of TB transmission; and providers attributing TB symptoms to other respiratory illnesses in winter, resulting in a delay in TB diagnosis until spring. Understanding TB seasonality may help TB programs better plan and allocate resources for TB control activities.