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The present work programme (2007-2009) encompasses the following projects
which use advanced computing, GIS, statistical methods and specialised
procedures to enhance exposure assessment:
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Multiple deprivation and health
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Growing policy concerns about health inequalities highlight the importance
of examining associations between deprivation and health outcome more
comprehensively than has been done to date. We have therefore extended
the work done on the Multiple Deprivation study, aiming at quantifying
associations between socio-economic factors, such as income, employment
and education, on the one hand, and major causes of mortality and morbidity,
on the other. We are considering all cause mortality as well as mortality
from specific causes and incidence of specific cancers. The purpose
is to quantify the variation in disease rates at small area scale and
determine the degree to which that variation can be explained by socio-economic
factors.
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Environmental Health Atlas
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SAHSU collects, integrates and analyses an increasingly large volume
and varied range of information on environmental health in England and
Wales. Many of the data have direct relevance to policy as indicators
of the state of environmental health. As part of the SAHSU work plan,
therefore, we will analyse and present these data in the form of an
environmental health atlas of England and Wales. The atlas will comprise
two sections: one describing patterns of exposure to environmental hazards
across England and Wales (primarily chemicals, via various pathways,
but also some physical risk factors, such as radon), and the second
describing selected health outcomes on a geographic scale. Each will
comprise a series of small-area scale maps, together with an interpretive
commentary. Histograms and bar charts will be used to show statistical
distributions across the population and trends over time (where suitable
temporal data are available). To aid interpretation, contextual information
– including population distribution, socio-economic status, urban/rural
areas, selected geophysical characteristics (e.g. climate) – will
be provided. Indicators to be included in the atlas are not yet finalised,
but selection criteria will comprise policy relevance, data availability
and quality, and interpretability (i.e. known or suspected links between
environment and health). Examples of exposure indicators might include
air pollution (e.g. NO2, SO2, PM10,
ozone and benzene), contaminants in drinking water (e.g. THMs), noise
from airports and roads, and modelled exposures to RF emissions from
mobile phone base stations. Examples of health outcomes might include
mortality (due to all causes, cardiovascular disease, respiratory disease
and selected cancers), cancer incidence (e.g. leukaemia, lung cancer,
prostate cancer, breast cancer) and hospital admissions due to cardiovascular
and respiratory disease. Computation of these indicators will be carried
out using state-of-the-art GIS and statistical techniques. Where appropriate,
programs will be developed to automate this process, so that maps can
be updated as required. Manual and automatic checking will be carried
out during analysis, both to ensure the validity of the indicators and
also to provide a means of checking the input data (an added advantage
of these analyses will be that they provide a means of routinely mapping
and validating SAHSU data).
Childhood onset (type-1) insulin-dependant diabetes mellitus (IDDM)
shows spatial and temporal trends in incidence. Incidence varies widely
across Europe and the world, and there has been a fairly consistent
increase in incidence over time throughout most of Europe and in the
UK. In addition, seasonal trends (especially in older children), spatial
clustering, and time-space clustering have been observed. There is currently
no national register of IDDM; however, records of type-1 diabetes in
children admitted to hospital for this condition (estimated to be ~2500
per year) are likely to be nearly complete. SAHSU holds national hospital
admissions data for the period 1991-2005, which could potentially be
used to assess spatial and temporal trends in incidence of childhood
diabetes across England and Wales. Diabetes is a chronic condition requiring
regular treatment and is associated with many complications; as such
it is likely that someone diagnosed with diabetes might be admitted
to hospital on more than one occasion. The hospital admissions data
would need to be cleaned to remove duplicate records, so that the remaining
data represented the first admissions of each child for diabetes. To
enable a validation of the cleaned hospital admissions dataset, childhood
diabetes registrations by gender, age group and year have been obtained
from the Yorkshire Register of Diabetes in Children and Young People
for the period 1992-2000. This register covers the former Yorkshire
region of West Yorkshire, North Yorkshire and the former county of Humberside
(based on 1991 administrative boundaries). Records pertaining to cases
of childhood diabetes in this same area of ‘Yorkshire’ were
identified from the cleaned hospital admissions dataset to allow direct
comparison. If the hospital admissions data prove to be an acceptable
source of data on childhood diabetes, work can be carried out to:
- Check whether incidence rates fit with other data from England and
Wales;
- Investigate time trends across England and Wales;
- Assess seasonal trends (by age group);
- Map childhood diabetes at an appropriate resolution across England and Wales;
- Assess space-time clustering as part of SAHSU's space-time clustering
methods project.
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Health effects of large airports - the London Heathrow example
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Heathrow is the world’s busiest international airport, operates
the country’s busiest bus / coach station and manages rail connections
into London. Continuous urban development has resulted in Heathrow today
being an integral part of West London, thereby affecting local air quality
along with other sources of pollution such as road traffic. The local
population is therefore exposed to a considerable amount of pollution
coming from noise and aircraft exhaust emissions and from airport associated
road traffic.
Past Health Impact Assessments (HIAs) have been carried out historically
for all submitted planning applications for airport expansion or construction.
They have identified several health impacts possibly related to exposure
to airport noise and air pollution and a number of air pollutants have
been found to have an effect on cardiovascular as well as respiratory
health. Noise exposure has also been linked with cardiovascular disease.
Several studies have investigated the relationship between noise exposure
and blood pressure, including the EU-funded HYENA study (2002-2006),
which mainly assessed the impact of exposure to noise on blood pressure.
See the HYENA website www.hyena.eu.com
for recently-published papers reporting the main findings.
We will now study health effects in the vicinity of Heathrow airport associated with air pollution and noise using SAHSU data on mortality, cancer incidence and hospital admissions.
Comprehensive SAHSU Data Documentation has been produced.
This includes an introduction to the dataset, the tables and logical views,
field definitions, annual record counts and column frequencies.
The aim of the SAHSU postcode dataset is to capture the temporal
and geographic elements of a postcode in a single value, the SAHSU
geo-reference or SGR. This can then be used to perform analysis. To achieve
this, a history of all postcodes, including changes, is being created and
validated, and includes grid references and accuracy.
Postcodes that refer to PO boxes, Northern Ireland, the
Channel Islands and the Isle of Man can also be identified. Postcodes that
have changed location can be identified together with the pattern of change
and the year(s) (and month) of re-introduction.
The first comprehensive SAHSU Data Documentation has been
produced. This includes an introduction to the dataset, the tables and
logical views, field definitions, annual record counts and column
frequencies.
Work has been carried out to clean postcodes, ICD9 and ICD10
codes; develop improved data verification and extraction tools and to
introduce the use of version control software.
All new data sets are now put through the new postcode
checking process. This will be reviewed later to improve the documentation.
The postcode sets have been cleaned to produce better
matching, thereby improving the ED91 to postcode link. The cleaning involves
conversion to the formal Post Office format with the correct number of spaces
in the middle and the flagging of invalid characters. Feedback on postcode
quality was provided to the National Down’s Syndrome Cytogenetics Register (NDSCR).
As an additional quality check, in future, all datasets will
be analysed by region and year for a number of (to be defined) disease rates
to ascertain that they have been loaded correctly and to better understand
and document any limitations in the dataset. These will be compared to
published rates.
Issues with the data extraction and processing carried out
for some studies have shown the need for clear coding guidelines and standard
processes. These are currently being drawn up. The aim is to ensure that data
extracts are reproducible, and fully documented.
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