Ambient air pollution is a long-standing and significant public health issue. The aim of this review is to systematically examine the peer-reviewed evidence on social inequalities and ambient air pollution in the World Health Organization European Region. Articles published between 2010 and 2017 were analyzed in the review. In total 31 articles were included in the review. There is good evidence from ecological studies that higher deprivation indices and low economic position are usually linked with higher levels of pollutants such as particulate matter (particulate matter under 2.5 and 10 microns in diameter, PM_2.5, PM₁₀) and oxides of nitrogen (e.g., NO₂, and NO_x). There is also evidence that ethnic minorities experience a mixed exposure in comparison to the majority population being sometimes higher and sometimes lower depending on the ethnic minority under consideration. The studies using data at the individual level in this review are mainly focused on pregnant women or new mothers, in these studies deprivation and ethnicity are more likely to be linked to higher exposures of poor air quality. Therefore, there is evidence in this review that the burden of higher pollutants falls disproportionally on different social groups.

Background: Housing conditions and environmental quality of residential areas are differentially distributed in the population. Less affluent population groups are more often affected by inadequate housing conditions and higher environmental burden in their residential neighbourhoods. A synthesis of the dispersed evidence on health-related housing characteristics and social status is needed to provide support for housing policies addressing social inequities. Methods: The literature on social inequities and environmental risks related to housing and residential location was searched in health, environmental and geographical databases and reviewed to summarize the evidence. Household-level socio-economic status and income were considered as indicators of social status. The review was limited to European evidence. Results: Adequate studies were only available for few countries. Most studies identified the less affluent population groups as most exposed to environmental risks in the place of residence. Inequities were reported for risks experienced within the dwelling (such as exposure to dampness, chemical contamination, noise, temperature problems and poor sanitation) and related to residential location (neighbourhood quality, traffic-related pollution, proximity to pollution sites). Increased exposure to environmental risks within more affluent population groups was rarely identified. Conclusions: The review indicates that social status and especially low income are strongly associated with increased exposure to environmental risks in the private home or related to residential location. However, due to the methodological variety of the available studies and the lack of data for many countries, it is not possible to provide a general assessment of the magnitude of inequity in Europe at the present time.

Deprivationsindizes stellen wertvolle Instrumente zur Analyse regionaler Effekte auf die Gesundheit der Bevölkerung dar. Bei fehlenden sozioökonomischen Individualdaten können diese Indizes auch als Surrogat verwendet werden. Im Vereinigten Königreich sind derartige regionale Deprivationsindizes fester Bestandteil der Public Health – Diskussion, in Deutschland dagegen steht diese Entwicklung erst am Anfang. Unser Ziel war es, auf Grundlage einer etablierten britischen Methode einen kleinräumigen und mehrdimensionalen Index Multipler Deprivation (IMD) für Deutschland zu entwickeln. Als Modellregion wählten wir das Bundesland Bayern mit seinen n=2 056 Gemeinden. Unter Verwendung soziodemografischer, sozioökonomischer und umweltrelevanter Daten der amtlichen Statistik entwickelten wir hierfür einen Bayerischen Index Multipler Deprivation (BIMD). Um die Eignung dieses Index für epidemiologische Analysen zu prüfen, führten wir anschließend sowohl eine Korrelationsanalyse (Korrelationskoeffizient nach Spearman) als auch eine Poisson-Regression unter Verwendung von Daten zur vorzeitigen Mortalität (Altersgruppe < 65 Jahre) sowie zur Gesamtmortalität (alle Altersgruppen) durch. Die Korrelationsanalyse zeigte einen positiven signifikanten Zusammenhang zwischen regionaler Deprivation und Mortalität. Die Poisson-Regression ergab einen klaren Gradienten, d. h. das Mortalitätsrisiko stieg mit zunehmender regionaler Deprivation stufenweise an. Verglichen mit den Gemeinden im Quintil mit der niedrigsten Deprivation war das relative Risiko bei den Gemeinden mit der höchsten Deprivation deutlich größer, sowohl bei der vorzeitigen Mortalität [RR 1,49 (95% KI: 1,42 − 1,57)] als auch der Gesamtmortalität [RR 1,21 (95% KI: 1,18 − 1,25)]. Unter Verwendung des neu gebildeten Index konnte am Beispiel Bayerns gezeigt werden, dass höhere regionale Deprivation mit höherer Mortalität assoziiert ist. Dieser Index stellt somit ein neues und potentiell sehr nützliches Werkzeug für gesundheitswissenschaftliche Studien in Deutschland dar. Deprivation indices are valuable instruments for the analysis of regional effects on health. They may also be used as a surrogate when individual socioeconomic data are not available. These regional deprivation indices are integral parts of the public health discussion in the United Kingdom. In Germany, however, the discussion on this topic has just begun. Our aim was to develop a small-area based, multidimensional Index of Multiple Deprivation (IMD) for Germany, based on an established British method. We chose the German state of Bavaria with its n=2 056 communities as a model region. We used official sociodemographic, socioeconomic and environmental data and created a Bavarian Index of Multiple Deprivation (BIMD). In order to test the applicability of the BIMD in epidemiological analyses we then performed a correlation analysis (Spearman’s correlation coefficient) as well as a Poisson regression using data on premature mortality (< 65 years) and on total mortality (all age groups). The correlation analysis showed a positive and significant association between regional deprivation and mortality. The Poisson regression showed a clear gradient, i. e., we found a stepwise increase of mortality risk with increasing regional deprivation. Compared with communities in the lowest deprivation quintile, communities in the highest deprivation quintile showed a clearly higher mortality risk, both for premature mortality [RR 1.49 (95% CI: 1.42 − 1.57)] and for total mortality [RR 1.21 (95% CI: 1.18 − 1.25)]. Using this new index, we could demonstrate for communities in Bavaria that higher regional deprivation is associated with higher mortality. This Index of Multiple Deprivation is a new and potentially useful tool for epidemiological and public health related studies in Germany.

The local impacts of industrial pollution can take many forms and�whilst uncertain in their scale, severity and distribution�are widely recognised. The question of who in society potentially experiences these impacts through living near to emission sources has been little explored, at least in the UK. This paper reports on a study carried out for the Environment Agency, which examined the distribution of sites coming within the Industrial Pollution Control (IPC) regime against patterns of deprivation. Our analysis provides evidence of a socially unequal distribution of IPC sites in England, with sites disproportionately located and clustered together in deprived areas and near to deprived populations. In discussing these results we emphasise the methodological limitations of this form of environmental justice analysis and the crucial differences between proximity, risk and impact. We also consider the distinction between inequality and injustice and the difficult policy questions which arise when evaluating evidence of environmental inequality, including potential grounds for policy intervention

We evaluate the effect of innovation support programs on output innovation by small and medium enterprises in traditional manufacturing industry. This focus is motivated by a definition of traditional manufacturing industry that includes capacity for innovation, and by evidence of its continued importance in European Union employment. We conducted a survey in seven European Union regions to generate the data needed to estimate pre-published switching models by means of the copula approach, from which we derived treatment effects on a wide range of innovation outputs. We find that for participants the estimated effects of innovation support programs are positive, typically increasing the probability of innovation and of its commercial success by around 15%. Yet, we also find that a greater return on public investment could have been secured by supporting firms chosen at random from the population of innovating traditional sector small and medium enterprises. These findings indicate the effectiveness of innovation support programs while suggesting reform of their selection procedures.