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International Journal of Epidemiology
Journal Prestige (SJR): 3.969
Citation Impact (citeScore): 5
Number of Followers: 289  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0300-5771 - ISSN (Online) 1464-3685
Published by Oxford University Press Homepage  [412 journals]
  • Software application profile: the Rapid Inquiry Facility 4.0: an open
           access tool for environmental public health tracking
    • Authors: Piel F; Parkes B, Hambly P, et al.
      Abstract: AbstractThe Rapid Inquiry Facility 4.0 (RIF) is a new user-friendly and open-access tool, developed by the UK Small Area Health Statistics Unit (SAHSU), to facilitate environment public health tracking (EPHT) or surveillance (EPHS). The RIF is designed to help public health professionals and academics to rapidly perform exploratory investigations of health and environmental data at the small-area level (e.g. postcode or detailed census areas) in order to identify unusual signals, such as disease clusters and potential environmental hazards, whether localized (e.g. industrial site) or widespread (e.g. air and noise pollution). The RIF allows the use of advanced disease mapping methods, including Bayesian small-area smoothing and complex risk analysis functionalities, while accounting for confounders. The RIF could be particularly useful to monitor spatio-temporal trends in mortality and morbidity associated with cardiovascular diseases, cancers, diabetes and chronic lung diseases, or to conduct local or national studies on air pollution, flooding, low-magnetic fields or nuclear power plants.
      PubDate: Wed, 15 Apr 2020 00:00:00 GMT
      DOI: 10.1093/ije/dyz094
      Issue No: Vol. 49, No. Supplement_1 (2020)
  • Using large and complex datasets for small-area environment-health
           studies: from theory to practice
    • Authors: Piel F; Cockings S.
      Abstract: Humans are exposed to a wide range of pollutants throughout their lifetime, many of which pose a potential risk to their health. Such hazards include features of the natural, human-modified, social and economic environments. In this supplement, we are primarily concerned with risks to human health resulting from hazards of the human-modified environment, although many of the concepts, methods and tools are equally applicable to investigations of the health impacts of other types of environmental hazards. Amongst human-modified environmental hazards, air pollution has been identified as the world’s largest killer, being responsible for an estimated 6.4 million deaths per year (1 in 9 deaths).1 According to the World Health Organization, two billion children live in areas where outdoor air pollution exceeds recommended international limits and 300 million children live in areas where outdoor air pollution exceeds six times those international limits. Other hazards of the human-modified environment include water pollutants, such as chemicals and microplastics; radiation from mobile phones, powerlines or nearby nuclear installations; and soil contaminants such as heavy metals.
      PubDate: Wed, 15 Apr 2020 00:00:00 GMT
      DOI: 10.1093/ije/dyaa018
      Issue No: Vol. 49, No. Supplement_1 (2020)
  • Advances in mapping population and demographic characteristics at
           small-area levels
    • Authors: Fecht D; Cockings S, Hodgson S, et al.
      Abstract: AbstractTemporally and spatially highly resolved information on population characteristics, including demographic profile (e.g. age and sex), ethnicity and socio-economic status (e.g. income, occupation, education), are essential for observational health studies at the small-area level. Time-relevant population data are critical as denominators for health statistics, analytics and epidemiology, to calculate rates or risks of disease. Demographic and socio-economic characteristics are key determinants of health and important confounders in the relationship between environmental contaminants and health.In many countries, census data have long been the source of small-area population denominators and confounder information. A strength of the traditional census model has been its careful design and high level of population coverage, allowing high-quality detailed data to be released for small areas periodically, e.g. every 10 years. The timeliness of data, however, becomes a challenge when temporally and spatially highly accurate annual (or even more frequent) data at high spatial resolution are needed, for example, for health surveillance and epidemiological studies. Additionally, the approach to collecting demographic population information is changing in the era of open and big data and may eventually evolve to using combinations of administrative and other data, supplemented by surveys.We discuss different approaches to address these challenges including (i) the US American Community Survey, a rolling sample of the US population census, (ii) the use of spatial analysis techniques to compile temporally and spatially high-resolution demographic data and (iii) the use of administrative and big data sources as proxies for demographic characteristics.
      PubDate: Wed, 15 Apr 2020 00:00:00 GMT
      DOI: 10.1093/ije/dyz179
      Issue No: Vol. 49, No. Supplement_1 (2020)
  • Advances in spatiotemporal models for non-communicable disease
    • Authors: Blangiardo M; Boulieri A, Diggle P, et al.
      Abstract: AbstractSurveillance systems are commonly used to provide early warning detection or to assess an impact of an intervention/policy. Traditionally, the methodological and conceptual frameworks for surveillance have been designed for infectious diseases, but the rising burden of non-communicable diseases (NCDs) worldwide suggests a pressing need for surveillance strategies to detect unusual patterns in the data and to help unveil important risk factors in this setting. Surveillance methods need to be able to detect meaningful departures from expectation and exploit dependencies within such data to produce unbiased estimates of risk as well as future forecasts. This has led to the increasing development of a range of space-time methods specifically designed for NCD surveillance. We present an overview of recent advances in spatiotemporal disease surveillance for NCDs, using hierarchically specified models. This provides a coherent framework for modelling complex data structures, dealing with data sparsity, exploiting dependencies between data sources and propagating the inherent uncertainties present in both the data and the modelling process. We then focus on three commonly used models within the Bayesian Hierarchical Model (BHM) framework and, through a simulation study, we compare their performance. We also discuss some challenges faced by researchers when dealing with NCD surveillance, including how to account for false detection and the modifiable areal unit problem. Finally, we consider how to use and interpret the complex models, how model selection may vary depending on the intended user group and how best to communicate results to stakeholders and the general public.
      PubDate: Wed, 15 Apr 2020 00:00:00 GMT
      DOI: 10.1093/ije/dyz181
      Issue No: Vol. 49, No. Supplement_1 (2020)
  • Availability, access, analysis and dissemination of small-area data
    • Authors: Hodgson S; Fecht D, Gulliver J, et al.
      Abstract: AbstractIn this era of ‘big data’, there is growing recognition of the value of environmental, health, social and demographic data for research. Open government data initiatives are growing in number and in terms of content. Remote sensing data are finding widespread use in environmental research, including in low- and middle-income settings. While our ability to study environment and health associations across countries and continents grows, data protection rules and greater patient control over the use of their data present new challenges to using health data in research. Innovative tools that circumvent the need for the physical sharing of data by supporting non-disclosive sharing of information, or that permit spatial analysis without researchers needing access to underlying patient data can be used to support analyses while protecting data confidentiality. User-friendly visualizations, allowing small-area data to be seen and understood by non-expert audiences, are revolutionizing public and researcher interactions with data. The UK Small Area Health Statistics Unit’s Environment and Health Atlas for England and Wales, and the US National Environmental Public Health Tracking Network offer good examples. Open data facilitates user-generated outputs, and ‘mash-ups’, and user-generated inputs from social media, mobile devices and wearable tech are new data streams that will find utility in future studies, and bring novel dimensions with respect to ethical use of small-area data.
      PubDate: Wed, 15 Apr 2020 00:00:00 GMT
      DOI: 10.1093/ije/dyz051
      Issue No: Vol. 49, No. Supplement_1 (2020)
  • Automation of cleaning and reconstructing residential address histories to
           assign environmental exposures in longitudinal studies
    • Authors: Fecht D; Garwood K, Butters O, et al.
      Abstract: AbstractBackgroundWe have developed an open-source ALgorithm for Generating Address Exposures (ALGAE) that cleans residential address records to construct address histories and assign spatially-determined exposures to cohort participants. The first application of this algorithm was to construct prenatal and early life air pollution exposure for individuals of the Avon Longitudinal Study of Parents and Children (ALSPAC) in the South West of England, using previously estimated particulate matter ≤10  µm (PM10) concentrations.MethodsALSPAC recruited 14 541 pregnant women between 1991 and 1992. We assigned trimester-specific estimated PM10 exposures for 12 752 pregnancies, and first year of life exposures for 12 525 births, based on maternal residence and residential mobility.ResultsAverage PM10 exposure was 32.6  µg/m3 [standard deviation (S.D.) 3.0  µg/m3] during pregnancy and 31.4 µg/m3 (S.D. 2.6  µg/m3) during the first year of life; 6.7% of women changed address during pregnancy, and 18.0% moved during first year of life of their infant. Exposure differences ranged from -5.3  µg/m3 to 12.4  µg/m3 (up to 26% difference) during pregnancy and -7.22  µg/m3 to 7.64  µg/m3 (up to 27% difference) in the first year of life, when comparing estimated exposure using the address at birth and that assessed using the complete cleaned address history. For the majority of individuals exposure changed by <5%, but some relatively large changes were seen both in pregnancy and in infancy.ConclusionsALGAE provides a generic and adaptable, open-source solution to clean addresses stored in a cohort contact database and assign life stage-specific exposure estimates with the potential to reduce exposure misclassification.
      PubDate: Wed, 15 Apr 2020 00:00:00 GMT
      DOI: 10.1093/ije/dyz180
      Issue No: Vol. 49, No. Supplement_1 (2020)
  • Electric field and air ion exposures near high voltage overhead power
           lines and adult cancers: a case control study across England and Wales
    • Authors: Toledano M; Shaddick G, de Hoogh K, et al.
      Abstract: AbstractBackgroundVarious mechanisms have been postulated to explain how electric fields emitted by high voltage overhead power lines, and the charged ions they produce, might be associated with possible adult cancer risk, but this has not previously been systematically explored in large scale epidemiological research.MethodsWe investigated risks of adult cancers in relation to modelled air ion density (per cm3) within 600 m (focusing analysis on mouth, lung, respiratory), and calculated electric field within 25 m (focusing analysis on non-melanoma skin), of high voltage overhead power lines in England and Wales, 1974–2008.ResultsWith adjustment for age, sex, deprivation and rurality, odds ratios (OR) in the highest fifth of net air ion density (0.504–1) compared with the lowest (0–0.1879) ranged from 0.94 [95% confidence interval (CI) 0.82–1.08] for mouth cancers to 1.03 (95% CI 0.97–1.09) for respiratory system cancers, with no trends in risk. The pattern of cancer risk was similar using corona ion estimates from an alternative model proposed by others. For keratinocyte carcinoma, adjusted OR in the highest (1.06–4.11 kV/m) compared with the lowest (<0.70 kV/m) thirds of electric field strength was 1.23 (95% CI 0.65–2.34), with no trend in risk.ConclusionsOur results do not provide evidence to support hypotheses that air ion density or electric fields in the vicinity of power lines are associated with cancer risk in adults.
      PubDate: Wed, 15 Apr 2020 00:00:00 GMT
      DOI: 10.1093/ije/dyz275
      Issue No: Vol. 49, No. Supplement_1 (2020)
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Heriot-Watt University
Edinburgh, EH14 4AS, UK
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