Project Background

This is a methodological project to help enhance the use of administrative data such as hospital admissions, cancer registrations and mortality in health research.  Assessing the impact of a risk factor/exposure X on a health outcome Y in observational (epidemiological) studies is invariably subject to confounding issues. Cohort (individual-level) studies are an ideal source of information as they typically contain a rich set of individual level variables. Nevertheless a study based only on a cohort may suffer from problems of selection bias and lack of population representativeness. Cohort studies may also lack statistical power to assess rare outcomes, and geographical or other group-level variations which limits the extent to which contextual factors such as area level social deprivation can be investigated.

Study Aims

Routinely collected administrative data are a good alternative in terms of representativeness; however, these data sources typically have a limited number of variables for a large population, and might miss important predictors/confounders leading to potentially biased estimation of the risks.

We propose a general framework that integrates these two sources of data and build a propensity score like index to summarise the values of the confounders from the cohorts/surveys so we will need to impute only one variable when missing; through a flexible model the index will be included in the epidemiological analysis to provide a direct estimate of the link between X and Y.

Period and geography

1994 - 2001
2006 - 2011
1999-2003

Data

NHS Digital HES admitted patient care

CVD/Asthma

Respiratory conditions

ONS Cancer registrations - lung cancer

Health Survey for England Millennium Cohort study

Data from Health Survey for England for 2002 onwards will also be requested, UK Biobank 2006-2010

Contact

Professor Marta Blangiardo

Benefits to Public:

This project will identify the need for integration of data sources in order to account for residual confounding, thus reducing/eliminating the bias in the estimates of epidemiological risk. Output will consists of scientific papers and computer code, the project is estimated to be completed by 2018.

Publications

Wang Y, Pirani M, Hansell AL, Richardson S, Blangiardo M. Using ecological propensity score to adjust for missing confounders in small area studies. Biostatistics. 2017 Nov 9. https://academic.oup.com/biostatistics/article/20/1/1/4607906

MRC Centre for Environment and Health