scholarly journals Use of deprivation indices in small area studies.

1996 ◽  
Vol 50 (6) ◽  
pp. 689-690 ◽  
Author(s):  
A Lawson
Epidemiology ◽  
2009 ◽  
Vol 20 (3) ◽  
pp. 411-418 ◽  
Author(s):  
Ricardo Ocaña-Riola ◽  
Alberto Fernández-Ajuria ◽  
José María Mayoral-Cortés ◽  
Silvia Toro-Cárdenas ◽  
Carmen Sánchez-Cantalejo

2019 ◽  
Vol 74 (1) ◽  
pp. 20-25 ◽  
Author(s):  
Mirjam Allik ◽  
Alastair Leyland ◽  
Maria Yury Travassos Ichihara ◽  
Ruth Dundas

Biostatistics ◽  
2017 ◽  
Vol 20 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Yingbo Wang ◽  
Monica Pirani ◽  
Anna L Hansell ◽  
Sylvia Richardson ◽  
Marta Blangiardo

2013 ◽  
Vol 23 (suppl_1) ◽  
Author(s):  
G Zannoupas ◽  
D Lamnisos ◽  
O Kolokotroni ◽  
P Yiallouros ◽  
N Middleton

2020 ◽  
Vol 49 (2) ◽  
pp. 686-699 ◽  
Author(s):  
Frédéric B Piel ◽  
Daniela Fecht ◽  
Susan Hodgson ◽  
Marta Blangiardo ◽  
M Toledano ◽  
...  

Abstract Small-area studies offer a powerful epidemiological approach to study disease patterns at the population level and assess health risks posed by environmental pollutants. They involve a public health investigation on a geographical scale (e.g. neighbourhood) with overlay of health, environmental, demographic and potential confounder data. Recent methodological advances, including Bayesian approaches, combined with fast-growing computational capabilities, permit more informative analyses than previously possible, including the incorporation of data at different scales, from satellites to individual-level survey information. Better data availability has widened the scope and utility of small-area studies, but has also led to greater complexity, including choice of optimal study area size and extent, duration of study periods, range of covariates and confounders to be considered and dealing with uncertainty. The availability of data from large, well-phenotyped cohorts such as UK Biobank enables the use of mixed-level study designs and the triangulation of evidence on environmental risks from small-area and individual-level studies, therefore improving causal inference, including use of linked biomarker and -omics data. As a result, there are now improved opportunities to investigate the impacts of environmental risk factors on human health, particularly for the surveillance and prevention of non-communicable diseases.


Sign in / Sign up

Export Citation Format

Share Document