BACKGROUND: Income is predictive of many health outcomes and is therefore an important potential confounder to control for in studies. However it is often missing or poorly measured in epidemiological studies because of its complexity and sensitivity. This paper presents and validates an alternative approach to the survey collection of reported income through the estimation of a synthetic wage measure based on occupation.METHODS: A synthetic measure of weekly wage was calculated using a multilevel random effects model of wage predicted by a Standard Occupational Classification (SOC) fitted in data from the UK Labour Force Survey (years 2001-2010)a. The estimates were validated and tested by comparing them to reported income and then contrasting estimated and reported income's association with measures of health in the Scottish Health Survey (SHS) 2003 and wave one (2009) of the UK Household Longitudinal Study (UKHLS).RESULTS: The synthetic estimates provided independent and additional explanatory power within models containing other traditional proxies for socio-economic position such as social class and small area based measures of socio-economic position. The estimates behaved very similarly to 'real', reported measures of both household and individual income when modelling a measure of 'general health'.CONCLUSIONS: The findings suggest that occupation based synthetic estimates of wage are as effective in capturing the underlying relationship between income and health as survey reported income. The paper argues that the direct survey measurement of income in every study may not actually be necessary or indeed optimal.
Algorithms Biomedical Research