scholarly journals Using ecological propensity score to adjust for missing confounders in small area studies

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

Uncontrolled Migrations as a Cause of Inequality in Health and Mortality in Small-area Studies

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
Keyword(s):  
Small Area ◽  
Area Studies ◽  
Inequality In Health ◽  
Health And Mortality

Applications of Small Area Estimation to Generalization With Subclassification by Propensity Scores

2017 ◽  
Vol 43 (2) ◽  
pp. 182-224
Author(s):  
Wendy Chan
Keyword(s):  
Propensity Score ◽  
Small Area ◽  
Propensity Scores ◽  
Small Area Estimation ◽  
Auxiliary Information ◽  
Population Data ◽  
Small Sample ◽  
Estimation Methods ◽  
Sample Sizes ◽  
Area Estimation

Policymakers have grown increasingly interested in how experimental results may generalize to a larger population. However, recently developed propensity score–based methods are limited by small sample sizes, where the experimental study is generalized to a population that is at least 20 times larger. This is particularly problematic for methods such as subclassification by propensity score, where limited sample sizes lead to sparse strata. This article explores the potential of small area estimation methods to improve the precision of estimators in sparse strata using population data as a source of auxiliary information to borrow strength. Results from simulation studies identify the conditions under which small area estimators outperform conventional estimators and the limitations of this application to causal generalization studies.

scholarly journals Small-area methods for investigation of environment and health

2020 ◽  
Vol 49 (2) ◽  
pp. 686-699 ◽  
Author(s):  
Frédéric B Piel ◽  
Daniela Fecht ◽  
Susan Hodgson ◽  
Marta Blangiardo ◽  
M Toledano ◽  
...  
Keyword(s):  
Small Area ◽  
Environmental Pollutants ◽  
Population Level ◽  
Data Availability ◽  
Area Studies ◽  
Individual Level ◽  
Environment And Health ◽  
Disease Patterns ◽  
Geographical Scale ◽  
Study Designs

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.

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