Developing integrated analyses of the England and Wales, Scottish and Northern Ireland Census LSs

Emily Grundy and Harriet Young, London School of Hygiene & Tropical Medicine, Paul Boyle, University of St Andrews and Dermot O'Reilly, Queen's University Belfast

(Project no. 30086)

This project aims to investigate the feasibility of undertaking a combined analysis using the three longitudinal studies in the UK (ONS-LS, SLS and NILS). We will demonstrate how parallel and if possible combined analysis may be undertaken to provide UK-wide results. To do this, we use an exemplar project focusing on the relationship between self-reported morbidity and mortality. We aim to negociate with Census offices and support units a set of procedures facilitating UK-wide analysis and we will produce a guide for other users hoping to do the same.

The aims of the study are both developmental and substantive. We aim to demonstrate how parallel, and if possible, combined analyses of the three longitudinal studies may be undertaken to provide UK-wide results, and to provide guidance for others hoping to do the same. Our substantive aim is to investigate whether there are differences between England and Wales, Scotland and Northern Ireland in the association between reported limiting long term illness and poor self rated health in 2001 and with mortality 2001-2005. We aim to analyse data in 3 ways: carrying out separate analyses on the ONS LS, SLS and NILS firstly using individual level data and secondly, aggregated data and finally, we hope to analyse a combined dataset.

This application is for use of the ONS LS to analyse socio-economic and socio-demographic variations in reporting long limiting long-term illness and self rated health in 2001 within England & Wales, and to analyse associations between reporting of health status in 2001 and subsequent mortality (taking account of socio-economic and socio-demographic factors). First, analysis will be carried out on individual level records at the ONS safe setting. Analysis will then be carried out on the aggregated LS data at LSHTM, and we will compare results between the individaul and aggregated data.