Burden and Socio-Behavioral Correlates of Uncontrolled Abnormal Glucose Metabolism in an Urban Population of India.

BACKGROUND: Progressive burden of diabetes mellitus is a major concern in India. Data on the predictors of poor glycemic control among diabetics are scanty. A population-based cross-sectional study nested in an urban cohort was thus conducted in West Bengal, India to determine the burden and correlates of total and uncontrolled abnormalities in glucose metabolism (AGM) in a representative population. METHODS: From 9046 adult cohort-members, 269 randomly selected consenting subjects (non-response = 7.24%) were interviewed, examined [blood pressure (BP), anthropometry], tested for fasting plasma glucose (FPG) and glycosylated hemoglobin (HbA1C). Those having pre-diagnosed diabetes or FPG ≥126 or HbA1c≥6.5 were defined as diabetic. Among non-diabetics, subjects with FPG (mg/dl) = 100-125 or HbA1C(%) = 5.7-6.4 were defined as pre-diabetic. Pre-diagnosed cases with current FPG ≥126 were defined as uncontrolled AGM. Descriptive and regression analyses were conducted using SAS-9.3.2. RESULTS: Among participants, 28.62% [95% Confidence Interval (95%CI) = 23.19-34.06)] were overweight [body mass index(BMI) = (25-29.99)kg/meter2], 7.81% (4.58-11.03) were obese(BMI≥30kg/meter2), 20.82% (15.93-25.70) were current smokers, 12.64% (8.64-16.64) were current alcohol-drinkers and 46.32% of responders (39.16-53.47) had family history of diabetes. 17.84% (13.24-22.45) had stage-I [140≤average systolic BP (AvSBP in mm of mercury)<160 or 90≤average diastolic BP (AvDBP)<100] and 12.64% (8.64-16.64) had stage-II (AvSBP≥160 or AvDBP≥160) hypertension. Based on FPG and HbA1c, 10.41% (6.74-14.08) were diabetic and 27.88% (22.49-33.27) were pre-diabetic. Overall prevalence of diabetes was 15.61% (11.25-19.98). Among pre-diagnosed cases, 46.43% (26.74-66.12) had uncontrolled AGM. With one year increase in age [Odds Ratio(OR) = 1.05(1.03-1.07)], retired subjects [OR = 9.14(1.72-48.66)], overweight[OR = 2.78(1.37-5.64)], ex-drinkers [OR = 4.66(1.35-16.12)] and hypertensives [ORStage I = 3.75(1.42-9.94); ORStage II = 4.69(1.67-13.17)] had higher odds of diabetes. Relatively older subjects [OR = 1.06(1.02-1.10)], unemployed [OR = 19.68(18.64-20.78)], business-owners [OR = 25.53(24.91-16.18)], retired [OR = 46.53(45.38-47.72)], ex-smokers [OR = 4.75(1.09-20.78)], ex-drinkers [OR = 22.43(4.62-108.81)] and hypertensives [ORStage II = 13.17(1.29-134.03)] were more likely to have uncontrolled AGM. CONCLUSIONS: Burden of uncontrolled AGM was high among participants. Efforts to curb the diabetes epidemic in urban India should include interventions targeting appropriate diabetic control among relatively older persons, unemployed, business-owners, retired, ex-smokers, ex-drinkers and hypertensives.

Comparing risk-prediction methods using administrative or clinical data in assessing excess in-hospital mortality in patients with acute myocardial infarction.

OBJECTIVES: To compare results of statistical process-control analyses of in-hospital deaths of patients with acute myocardial infarction by using either administrative or clinical data sources and prediction models, and to assess variation in results according to selected patient characteristics. DESIGN: Retrospective, cross-sectional study comparing variable life-adjusted display (VLAD) curves derived by using administrative or clinical prediction models applied to a single patient sample. PARTICIPANTS AND SETTING: Data from 467 consecutive patients admitted to a tertiary hospital in Queensland, between 1 July 2003 and 31 March 2006, with a coded discharge diagnosis of acute myocardial infarction. MAIN OUTCOME MEASURE: Statistical estimates of cumulative lives gained or lost in excess of those predicted at the end of the study period. RESULTS: The two prediction models, when applied to all patients, generated almost identical VLAD curves, showing a steadily increasing excess mortality over the study period, culminating in an estimated 11 excess deaths. Risk estimates for individual patients from each model were significantly correlated (r = 0.46, P < 0.001). After exclusion of misclassified cases, out-of-hospital cardiac arrests and deaths within 30 minutes of presentation, replotting the curves reversed the mortality trend and yielded, depending on the model, a net gain of three or seven lives. After further exclusion of transfers in from other hospitals and patients whose care had a palliative or conservative intent, the net gain increased to seven or 10 lives. CONCLUSION: Appropriate patient selection is more important than choice of dataset or risk-prediction model when statistical process-control methods are used to flag unfavourable mortality trends suggestive of suboptimal hospital care.