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Infiltration of pancreatic islets with different leukocyte subtypes likely contributes to deterioration of glycemia in diabetes mellitus. Different subsets of leukocytes have been previously associated with type 1 or type 2 diabetes. This study aimed at examining these subsets at different stages of diabetes progression and possible relationships with metabolic parameters. A total of 206 patients, 76 with type 1 and 130 with type 2 diabetes, were studied within the first year of diabetes diagnosis. In addition, 31 patients with type 1 and 73 with type 2 diabetes were examined at 5 years after diagnosis. Whole body insulin sensitivity was assessed by hyperinsulinemic–euglycemic clamps; insulin secretion by glucagon stimulation tests and white blood cells were analyzed by flow cytometry. The percentage of peripheral CD8+ cells was 15% lower in patients with type 1 diabetes at 5 years than in patients at diabetes onset and correlated positively with fasting glycemia, total cholesterol and high-sensitive C-reactive protein (hsCRP) (all r > 0.37, p

The number needed to treat (NNT) is a popular measure to describe the absolute effect of a new treatment compared with a standard treatment or placebo in clinical trials with binary outcome. For use of NNT measures in epidemiology to compare exposed and unexposed subjects, the terms 'number needed to be exposed' (NNE) and 'exposure impact number' (EIN) have been proposed. Additionally, in the framework of logistic regression a method was derived to perform point and interval estimation of NNT measures with adjustment for confounding by using the adjusted odds ratio (OR approach). In this paper, a new method is proposed which is based upon the average risk difference over the observed confounder values (ARD approach). A decision has to be made, whether the effect of allocating an exposure to unexposed persons or the effect of removing an exposure from exposed persons should be described. We use the term NNE for the first and the term EIN for the second situation. NNE is the average number of unexposed persons needed to be exposed to observe one extra case; EIN is the average number of exposed persons among one case can be attributed to the exposure. By means of simulations it is shown that the ARD approach is better than the OR approach in terms of bias and coverage probability, especially if the confounder distribution is wide. The proposed method is illustrated by application to data of a cohort study investigating the effect of smoking on coronary heart disease.

Objective The estimation of the number needed to be exposed (NNE) with adjustment for covariates can be performed by inverting the corresponding adjusted risk difference. The latter can be estimated by several approaches based on binomial and Poisson regression with or without constraints. A novel proposal is given by logistic regression with average risk difference (LR-ARD) estimation. Finally, the use of ordinary linear regression and unadjusted estimation can be considered. Study Design and Setting LR-ARD is compared with alternative approaches regarding bias, precision, and coverage probability by means of an extensive simulation study. Results LR-ARD was found to be superior compared with the other approaches. In the case of balanced covariates and large sample sizes, unadjusted estimation and ordinary linear regression can also be used. In general, however, LR-ARD seems to be the most appropriate approach to estimate adjusted risk differences and NNEs. Conclusions To estimate risk differences and NNEs with adjustment for covariates, the LR-ARD approach should be used.

Background Standard effect measures such as risk difference and attributable risk are frequently used in epidemiological studies and public health research to describe the effect of exposures. Recently, so-called impact numbers have been proposed, which express the population impact of exposures in form of specific person or case numbers. To describe estimation uncertainty, it is necessary to calculate confidence intervals for these new effect measures. In this paper, we present methods to calculate confidence intervals for the new impact numbers in the situation of cohort studies. Methods Beside the exposure impact number (EIN), which is equivalent to the well-known number needed to treat (NNT), two other impact numbers are considered: the case impact number (CIN) and the exposed cases impact number (ECIN), which describe the number of cases (CIN) and the number of exposed cases (ECIN) with an outcome among whom one case is attributable to the exposure. The CIN and ECIN represent reciprocals of the population attributable risk (PAR) and the attributable fraction among the exposed (AFe), respectively. Thus, confidence intervals for these impact numbers can be calculated by inverting and exchanging the confidence limits of the PAR and AFe. Examples We considered a British and a Japanese cohort study that investigated the association between smoking and death from coronary heart disease (CHD) and between smoking and stroke, respectively. We used the reported death and disease rates and calculated impact numbers with corresponding 95% confidence intervals. In the British study, the CIN was 6.46, i.e. on average, of any 6 to 7 persons who died of CHD, one case was attributable to smoking with corresponding 95% confidence interval of [3.84, 20.36]. For the exposed cases, the results of ECIN = 2.64 with 95% confidence interval [1.76, 5.29] were obtained. In the Japanese study, the CIN was 6.67, i.e. on average, of the 6 to 7 persons who had a stroke, one case was attributable to smoking with corresponding 95% confidence interval of [3.80, 27.27]. For the exposed cases, the results of ECIN = 4.89 with 95% confidence interval of [2.86, 16.67] were obtained. Conclusion The consideration of impact numbers in epidemiological analyses provides additional information and helps the interpretation of study results, e.g. in public health research. In practical applications, it is necessary to describe estimation uncertainty. We have shown that the calculation of confidence intervals for the new impact numbers is possible by means of known methods for attributable risk measures. Therefore, estimated impact numbers should always be complemented by appropriate confidence intervals.