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- Fahey, Michael T., et al.
(författare)
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Identifying dietary patterns using a normal mixture model: application to the EPIC study
- 2012
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Ingår i: Journal of Epidemiology and Community Health. - : BMJ. - 1470-2738 .- 0143-005X. ; 66:1, s. 89-94
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Tidskriftsartikel (refereegranskat)abstract
- Background Finite mixture models posit the existence of a latent categorical variable and can be used for probabilistic classification. The authors illustrate the use of mixture models for dietary pattern analysis. An advantage of this approach is taking classification uncertainty into account. Methods Participants were a random sample of women from the European Prospective Investigation into Cancer. Food consumption was measured using dietary questionnaires. Mixture models identified latent classes in food consumption data, which were interpreted as dietary patterns. Results Among various assumptions examined, models allowing the variance of foods to vary within and between classes fit better than alternatives assuming constant variance (the K-means method of cluster analysis also makes the latter assumption). An eight-class model was best fitting and five patterns validated well in a second random sample. Patterns with lower classification uncertainty tended to be better validated. One pattern showed low consumption of foods despite being associated with moderate body mass index. Conclusion Mixture modelling for dietary pattern analysis has advantages over both factor and cluster analysis. In contrast to these other methods, it is easy to estimate pattern prevalence, to describe patterns and to use patterns to predict disease taking classification uncertainty into account. Owing to substantial error in food consumptions, any analysis will usually find some patterns that cannot be well validated. While knowledge of classification uncertainty may aid pattern evaluation, any method will better identify patterns from food consumptions measured with less error. Mixture models may be useful to identify individuals who under-report food consumption.
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| 2. |
- Perez, Laura, et al.
(författare)
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Chronic burden of near-roadway traffic pollution in 10 European cities (APHEKOM network)
- 2013
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Ingår i: European Respiratory Journal. - Sheffield, England : European Respiratory Society. - 0903-1936 .- 1399-3003. ; 42:3, s. 594-605
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Tidskriftsartikel (refereegranskat)abstract
- Recent epidemiological research suggests that near road traffic-related pollution may cause chronic disease, as well as exacerbate related pathologies, implying that the entire "chronic disease progression" should be attributed to air pollution, no matter what the proximate cause was. We estimated the burden of childhood asthma attributable to air pollution in 10 European cities by calculating the number of cases of 1) asthma caused by near road traffic-related pollution, and 2) acute asthma events related to urban air pollution levels. We then expanded our approach to include coronary heart diseases in adults.Derivation of attributable cases required combining concentration-response function (CRF) between exposures and the respective health outcome of interest (obtained from published literature), an estimate of the distribution of selected exposures in the target population, and information about the frequency of the assessed morbidities.Exposure to roads with high vehicle traffic, a proxy for near road traffic-related pollution, accounted for 14% of all asthma cases. When a causal relationship between near road traffic-related pollution and asthma is assumed, 15% of all episodes of asthma symptoms were attributable to air pollution. Without this assumption, only 2% of asthma symptoms were attributable to air pollution. Similar patterns were found for coronary heart diseases in older adults.Pollutants along busy roads are responsible for a large and preventable share of chronic disease and related acute exacerbation in European urban areas.
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