Agata Żółtaszek (p), Alicja Olejnik

Classic DEA (Data Envelopment Analysis) assesses relative technical efficiency of a number of objects with various outputs and inputs. Moreover, the results point out fully efficient objects (100% efficiency) that exemplarily utilize their resources and those that underperform (efficiency lower than 100%) with additional information on the technical sources of their inefficiency. The efficiency analysis allows to assess the productivity of transformation of inputs into outputs without any prior knowledge on the technological aspects of literal or metaphorical production. Therefore, this approach is applicable in regional studies, as any legal, historical, social, and cultural peculiarities may be simply omitted with the regions being compared.
The main objective of this paper is to introduce a new approach to measuring efficiency in regional studies through SDEA (Spatial Data Envelopment Analysis). Our approach expands the classical DEA method by the explicit incorporation of spatial interactions (through the W matrix) into the model. The set of variables can be expanded by the spatially autocorrelated inputs and outputs. As a result, the efficiency analysis takes into consideration that inputs in one region affect outputs in neighbouring regions, as well as outputs are allowed to act as inputs/outputs for bordering locations. This makes the efficiency analysis more flexible as it accounts for interregional cooperation and competition.
An example study concerning healthcare efficiency of EU regions will be presented to illustrate SDEA. In this paper, the inputs are: number of medical doctors and financial recourse, while the outputs are measured by mortality of chosen Western diseases. These SDEA results divide regions into two groups with effective healthcare and with ineffective healthcare. The former should be considered as benchmarks for policy makers. Applying SDEA approach shows that while healthcare systems are mostly homogenies within country borders, the efficiency varies across provinces and neighbouring states.

Philip McBride (p), Karyn Morrissey, Ben Wheeler, Stefan Reis

Understanding persistent and increasing spatial inequalities in health is an important field of academic enquiry for epidemiologists. The aim of this research project is to explore the impact of air pollution on asthma rates in childhood, controlling for area level deprivation across the English regions. The co-location of air pollution and socio-economic deprivation is increasingly well documented. The socio-economic patterning of residential opportunities means that individuals that are constrained financially face limited choices of where to live, and are more likely to reside near major sources of pollution.
The relationship between socio-economic status (SES), air pollution and poor health outcomes has been termed triple jeopardy. An example of this is poor health feeding back to impact on potential employment opportunities, income, mobility and access to power. This then impedes the person’s ability to remove themselves from, or to try to mitigate hazards within their community. Evidence suggests that deprivation could worsen the effects of environmental exposure for some people, and the social causation theory must also be considered.
Currently there is no single dataset containing all the relevant data required for this research so this project will make use of spatially disaggregated data on air pollution and area level deprivation, available from the Centre for Ecology and Hydrology via EMEP4UK and the Index of Multiple Deprivation respectively, as well as longitudinal, spatially referenced microdata containing demographic, socio-economic and health outcomes available from the Millennium Cohort Study. The data shall be linked at the LSOA level to create a small area population and pollution dataset for the UK over time. The small scale used in this research is important as it will show the spatial variability of air pollution and how this impacts on asthma rates of children over very small distances.
This project will use a time series multilevel modelling approach to estimate the impact of air pollution on asthma rates in childhood, controlling for individual and area level socio-economic profile. Initial analysis indicates that SES and asthma rates among children are related. Receiving free school meals resulted in a significant increase in asthma rates whilst having employed parents resulted in a significant decrease in asthma. Further results shall presented and available for discussion. Understanding the distinction and contributing magnitude of individual versus area level deprivation on childhood asthma outcomes is important as it will allow policy makers to target the required mix of individual and community based health strategies.

Boris A. Portnov (p)

Breast cancer incidence rates in Connecticut are among the highest in the United States, and are unevenly distributed within the state. Using BC records obtained from the Connecticut Tumor Registry, we applied the double kernel density (DKD) estimator to produce a continuous relative risk surface of a disease throughout the State. A multi-variate analysis compared DKD and census track estimates with population density, fertility rate, percent of non-white population, population below poverty level, and ALAN levels. The analysis identified a “halo” geographic pattern of BC incidence, with the highest rates of the disease observed at distances 5-15 km from the state’s major cities. The “halo” was of high-income communities, located in the suburban fringes of the state’s main cities.