Estimation of Population Exposure in Ecological Studies

Plummer, Martyn; Clayton, David

doi:10.1111/j.2517-6161.1996.tb02070.x

Cited by 33 publications

(27 citation statements)

References 20 publications

(16 reference statements)

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“…With estimation model M 0 ,β 0 may be a biased estimate of β. Plummer and Clayton (1996) noted that the estimation of β 0 could be a good estimation of β when β is "weak", or when the within-area variances are small, or constant over the domain or not correlated with the exposure means. However, if the within-area distribution is Gaussian and the individual relationship is exponential, the true ecological model is the model M 1 as defined in Eq.…”

Section: Estimation Methodsmentioning

confidence: 99%

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Misspecification of within-area exposure distribution in ecological Poisson models

Fortunato¹,

Guihenneuc‐Jouyaux²,

Tirmarche³

et al. 2007

Environ Ecol Stat

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Section: Estimation Methodsmentioning

confidence: 99%

“…7. As a result, the individual relation is nearly linear and thus the ecological relation is also nearly linear with the same parameters (Plummer and Clayton 1996).…”

Section: Applicationmentioning

confidence: 99%

Misspecification of within-area exposure distribution in ecological Poisson models

Fortunato¹,

Guihenneuc‐Jouyaux²,

Tirmarche³

et al. 2007

Environ Ecol Stat

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“…Within-unit variability in these factors could lead to bias in risk estimates (Elliott and Wakefield 2000). Recently, interest has focused on semiecologic designs that combine data on the general population with individual-level survey data (Plummer and Clayton 1996). For example, the INTERSALT study, a cross-sectional study of over 10,000 people in 32 countries, assessed both individual and group effects.…”

Section: Challengesmentioning

confidence: 99%

Spatial Epidemiology: Current Approaches and Future Challenges

Elliott

Wartenberg

2004

Environ Health Perspect

610

436

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Spatial epidemiology is the description and analysis of geographic variations in disease with respect to demographic, environmental, behavioral, socioeconomic, genetic, and infectious risk factors. We focus on small-area analyses, encompassing disease mapping, geographic correlation studies, disease clusters, and clustering. Advances in geographic information systems, statistical methodology, and availability of high-resolution, geographically referenced health and environmental quality data have created unprecedented new opportunities to investigate environmental and other factors in explaining local geographic variations in disease. They also present new challenges. Problems include the large random component that may predominate disease rates across small areas. Though this can be dealt with appropriately using Bayesian statistics to provide smooth estimates of disease risks, sensitivity to detect areas at high risk is limited when expected numbers of cases are small. Potential biases and confounding, particularly due to socioeconomic factors, and a detailed understanding of data quality are important. Data errors can result in large apparent disease excess in a locality. Disease cluster reports often arise nonsystematically because of media, physician, or public concern. One ready means of investigating such concerns is the replication of analyses in different areas based on routine data, as is done in the United Kingdom through the Small Area Health Statistics Unit (and increasingly in other European countries, e.g., through the European Health and Environment Information System collaboration). In the future, developments in exposure modeling and mapping, enhanced study designs, and new methods of surveillance of large health databases promise to improve our ability to understand the complex relationships of environment to health.

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“…There is a vast literature describing sources of ecological bias [48, 41, 21, 19, 22, 42, 57, 34, 39, 47, 64, 66, 68, 60]. The fundamental problem with ecological inference is that the process of aggregation reduces information, and this information loss usually prevents identification of parameters of interest in the underlying individual-level model.…”

Section: 3 Ecological Biasmentioning

confidence: 99%