Age-specific incidence rates for <i>t</i> = 1996.5 based on the direct estimation and <i>B</i> = 2000 bootstraps. True (blue line) and estimated incidence (black lines): median (solid line) and 95% confidence bounds (dashed).

<p>Age-specific incidence rates for <i>t</i> = 1996.5 based on the direct estimation and <i>B</i> = 2000 bootstraps. True (blue line) and estimated incidence (black lines): median (solid line) and 95% confidence bounds (dashed).</p

Absolute relative errors of the incidence estimates (per 10⁵).

<p>Median and maximum refer to all age-specific estimates for the corresponding point in time (<i>t</i>^⋆).</p

Age-specific incidence rate for <i>t</i> = 1995.5: true (dashed line) and estimated incidence (solid).

<p>Age-specific incidence rate for <i>t</i> = 1995.5: true (dashed line) and estimated incidence (solid).</p

Age-specific prevalences for the years 1995, 1998, 2001, and 2004.

<p>Age-specific prevalences for the years 1995, 1998, 2001, and 2004.</p

Surveillance of the Incidence of Non-Communicable Diseases (NCDs) with Sparse Resources: A Simulation Study Using Data from a National Diabetes Registry, Denmark, 1995–2004

<div><p>We propose two new methods to estimate secular trends in the incidence of a chronic disease from a series of prevalence studies and mortality data. One method is a direct inversion formula, the second method is a least squares estimation. Both methods are validated in a simulation study based on data from a diabetes register. The results of the validation show that the proposed methods may be useful in epidemiological settings with sparse resources, where running a register or a series of follow-up studies is difficult or impossible.</p></div

Chronic disease model with three states and the corresponding transition rates.

<p>People in the state <i>Healthy (H)</i> are healthy with respect to the disease under consideration. After onset of the disease, they transition to the state <i>Ill (I)</i>.</p

Two age courses of the prevalence surveyed at <i>t</i>₁ and <i>t</i>₂ are necessary to estimate the age-specific incidence at some point in time <i>t</i>^⋆.

<p>Two age courses of the prevalence surveyed at <i>t</i>₁ and <i>t</i>₂ are necessary to estimate the age-specific incidence at some point in time <i>t</i>^⋆.</p

Resampling algorithm.

<p>The algorithm simulates the effects of the increased active travel scenario compared to business as usual scenario.</p><p>Resampling algorithm.</p

Projected Effect of Increased Active Travel in German Urban Regions on the Risk of Type 2 Diabetes

<div><p>Background</p><p>Future transportation policy is likely to reduce emissions in the cities and urban regions by strengthening active travel. Increased walking and cycling are known to have positive effects on health outcomes. This work estimates effects of increased active travel on type 2 diabetes in Germany, where 64% of the population live in urban regions.</p><p>Methods</p><p>Based on the effect size of an increased active travel scenario reported from a recent meta-analysis, we project the change in the life time risk, the proportion of prevented cases and the change in diabetes free life time in a German birth cohort (born 1985) compared to business as usual.</p><p>Results</p><p>The absolute risk reduction of developing type 2 diabetes before the age of 80 is 6.4% [95% confidence interval: 3.7-9.7%] for men and 4.7% [2.2-7.7%] for women, respectively. Compared to business as usual, the increased active travel scenario prevents 14.0% [8.1-21.2%] of the future cases of diabetes in men and 15.8% [9.3-23.1%] in women. Diabetes free survival increases by 1.7 [1.0-2.7] years in men and 1.4 [0.6-2.3] in women.</p><p>Conclusions</p><p>Our projection predicts a substantial impact of increased active travel on the future burden of type 2 diabetes. The most striking effect may be seen in the number of prevented cases. In all urban regions with an increased active travel transport policy, about one out of seven male and one out of six female cases can be prevented.</p></div

Illness-death model.

<p>Each individual in the population is in one of the three states. The transition rates <i>i</i>, <i>m</i>₀, and <i>m</i>₁ between the states may depend on calender time <i>t</i>, and age <i>a</i>.</p