Long working hours, socioeconomic status, and the risk of incident type 2 diabetes : a meta-analysis of published and unpublished data from 222 120 individuals

Background Working long hours might have adverse health effects, but whether this is true for all socioeconomic status groups is unclear. In this meta-analysis stratified by socioeconomic status, we investigated the role of long working hours as a risk factor for type 2 diabetes. Methods We identified four published studies through a systematic literature search of PubMed and Embase up to April 30, 2014. Study inclusion criteria were English-language publication; prospective design (cohort study); investigation of the effect of working hours or overtime work; incident diabetes as an outcome; and relative risks, odds ratios, or hazard ratios (HRs) with 95% CIs, or sufficient information to calculate these estimates. Additionally, we used unpublished individual-level data from 19 cohort studies from the Individual-Participant-Data Meta-analysis in Working-Populations Consortium and international open-access data archives. Effect estimates from published and unpublished data from 222 120 men and women from the USA, Europe, Japan, and Australia were pooled with random-effects meta-analysis. Findings During 1.7 million person-years at risk, 4963 individuals developed diabetes (incidence 29 per 10 000 person-years). The minimally adjusted summary risk ratio for long (>= 55 h per week) compared with standard working hours (35-40 h) was 1.07 (95% CI 0.89-1.27, difference in incidence three cases per 10 000 person-years) with significant heterogeneity in study-specific estimates (I-2 = 53%, p = 0.0016). In an analysis stratified by socioeconomic status, the association between long working hours and diabetes was evident in the low socioeconomic status group (risk ratio 1.29, 95% CI 1.06-1.57, difference in incidence 13 per 10 000 person-years, I-2 = 0%, p = 0.4662), but was null in the high socioeconomic status group (1. 00, 95% CI 0.80-1.25, incidence diff erence zero per 10 000 person-years, I-2 = 15%, p = 0.2464). The association in the low socioeconomic status group was robust to adjustment for age, sex, obesity, and physical activity, and remained after exclusion of shift workers. Interpretation In this meta-analysis, the link between longer working hours and type 2 diabetes was apparent only in individuals in the low socioeconomic status groups. Copyright (C) Kivimaki et al. Open Access article distributed under the terms of CC BY.Peer reviewe

Fujita and ogawa revisited:An agent-based modeling approach

This paper builds on and extends a classic paper (hereafter referred to as F – O) published by Masahisa Fujita and Hideaki Ogawa in 1982. Their paper models the emergence of urban centers brought about by household and firm location decisions in the context of spatially differentiated labor and land market interactions. Their approach is an analytical one that seeks to characterize the equilibrium values of the system. In contrast, we employ an agent-based modeling (ABM) approach that seeks to replicate the individual household and firm behaviors that lead to equilibrium or nonequilibrium outcomes. The F – O model has little to say about what happens outside of equilibrium, while the ABM approach is preoccupied with this question and is particularly well suited to address questions of path dependency and bounded rationality that lie well beyond the scope of the F – O original. We demonstrate that the urban outcomes that emerge depend critically upon the bidding behavior of agents and the institutional context within which their decisions are made

Adjusting spatial-entropy measures for scale and resolution effects

In this paper we revisit the concept of entropy as it manifests itself in spatial terms. We focus specifically on the question of how entropy measures applied to different urban contexts can be adjusted to allow for meaningful comparisons between cities with differing geographic dimensions. It is well known that entropy is affected by the number of geographic units over which it is computed. As a result, the size and number of census tracts in an urban area constitute an intervening factor in making direct comparisons. Some authors advocate addressing this problem by normalizing entropy to its maximum value to derive a ‘relative entropy’ measure. We prove that this conventional normalization procedure does not suffice, and we show further that Theil’s decomposition method does provide the proper solution. We then demonstrate how to apply this technique through the use of census data for US cities in 2000, with the empirical results clearly underlying the importance of making these adjustments.