Evaluation of data availability on population health indicators at the regional level across the European Union.

BACKGROUND: The ability to measure regional health inequalities across Europe and to build adequate population health indices depends significantly on the availability of reliable and comparable data at the regional level. Within the scope of the EU-funded project EURO-HEALTHY, a Population Health Index (PHI) was built. This model aggregates 39 indicators considered relevant by experts and stakeholders to evaluate and monitor population health on the regional level within the European Union (269 regions). The aim of this research was to assess the data availability for those indicators. As a subsequent aim, an adequate protocol to overcome issues arising from missing data will be presented, as well as key messages for both national and European statistical authorities meant to improve data collection on population health. METHODS: The methodology for the study includes three consecutive phases: (i) assessing the data availability for the respective indicators at the regional level for the last year available (ii) applying a protocol for missing data and completing the database and (iii) developing a scoring system ranging from 0 (no data available; worst) to 1 (all data available; best) to evaluate the availability of data by indicator and EU region. RESULTS: Although the missing data on the set of the PHI indicators was significant, the mean availability score for the EURO-HEALTHY PHI indicators is 0.8 and the regional availability score is 0.7, which reveal the strength of the indicators as well as the data completeness protocol for missing data. CONCLUSIONS: This study provides a comprehensive data availability assessment for population health indicators from multiple areas of concern, at the EU regional level. The results highlight that the data completeness protocol and availability scores are suitable tools to apply on any indicator's data source mapping. It also raises awareness to the urgent need for sub-national data in several domains and for closing the data gaps between and within countries. This will require policies clearly focused on improving equity between regions and a coordinated effort from the producers of data (the EU28 national statistics offices and EUROSTAT) and the stakeholders who design policies at EU, regional and local level.

Spatial variability in air pollution exposure in relation to socioeconomic indicators in nine European metropolitan areas: A study on environmental inequality.

A limited number of studies have addressed environmental inequality, using various study designs and methodologies and often reaching contradictory results. Following a standardized multi-city data collection process within the European project EURO-HEALTHY, we conducted an ecological study to investigate the spatial association between nitrogen dioxide (NO2), as a surrogate for traffic related air pollution, and ten socioeconomic indicators at local administrative unit level in nine European Metropolitan Areas. We applied mixed models for the associations under investigation with random intercepts per Metropolitan Area, also accounting for the spatial correlation. The stronger associations were observed between NO2 levels and population density, population born outside the European Union (EU28), total crimes per 100,000 inhabitants and unemployment rate that displayed a highly statistically significant trend of increasing concentrations with increasing levels of the indicators. Specifically, the highest vs the lowest quartile of each indicator above was associated with 48.7% (95% confidence interval (CI): 42.9%, 54.8%), 30.9% (95%CI: 22.1%, 40.2%), 19.8% (95%CI: 13.4%, 26.6%) and 15.8% (95%CI: 9.9%, 22.1%) increase in NO2 respectively. The association with population density most probably reflects the higher volume in vehicular traffic, which is the main source of NO2 in urban areas. Higher pollution levels in areas with higher percentages of people born outside EU28, crime or unemployment rates indicate that worse air quality is typically encountered in deprived European urban areas. Policy makers should consider spatial environmental inequalities to better inform actions aiming to lower urban air pollution levels that will subsequently lead to improved quality of life, public health and health equity across the population.