Abstract
AbstractResearch consistently shows that those in poor health are less likely to migrate over long distances, but analyses rarely consider what constitutes a long distance in this context. Additionally, the migration literature often fails to account for place of residence effects on migration behaviour. This paper addresses these issues through analysis of the distance of residential moves by working age adults in the year preceding the 2011 Census. Multilevel logistic regression models predict the odds of having moved long‐distance relative to short distance, for different definitions of long distance: ≥10 km, ≥20 km and ≥50 km. We test whether those reporting a limiting long‐term illness (LLTI) are less likely to move long distance in all models, controlling for local authority at the time of the 2011 Census. We find no evidence for health selection in long‐distance migration in the 10 and 20 km models, but uncover a significant effect in the 50 km model. By age, the odds of having moved long distance do not vary for middle‐working age adults (25–54) by LLTI, whereas those with an LLTI in the pre‐retirement age group (55–64) are less likely to move long distance in all models. We uncover clusters of local authorities where those with an LLTI are more likely to have moved long distance in the 10 and 20 km models, but in the 50 km model, only two of these areas remain significantly positive. We conclude that health selection in distances moved occurs above a cut‐off somewhere between 20 and 50 km.
Highlights
A large body of research is dedicated to establishing whether variations in health behaviours and outcomes are the result of “places” affecting health or a reflection of varying population characteristics across areas (Kearns & Moon, 2002; Smyth, 2008)
We model the log odds of having moved long distance (p = 1|X) relative to the odds of having moved short distance (p = 0|X) for migrant i living in Local Authorities (LAs) j as follows: logðoddsÞij 1⁄4 β0 þ βnXn þ LLTIi þ μ0j þ μ1j þ ei where β0 is a fixed constant, βnXn is the matrix of fixed covariates defined in Table 2, LLTIi is the fixed coefficient for individuals with an limiting long‐ term illness (LLTI), μ0j is the random intercept associated with LA j, μ1j is the random slope for individuals with an LLTI in LA j, an additional effect for the population with an LLTI, and ei an error term for individual i
Our measure of health (LLTI) is a self‐reported measure, whereas the healthy migrant theory is mainly drawn from research on mortality (Abraído‐ Lanza et al, 1999), which finds that individuals who move have lower future mortality rates than those who do not move
Summary
A large body of research is dedicated to establishing whether variations in health behaviours and outcomes are the result of “places” affecting health or a reflection of varying population characteristics across areas (Kearns & Moon, 2002; Smyth, 2008). In the United Kingdom (UK), healthy people tend to move to less deprived areas, whereas those in poor health tend to move to more deprived areas; these migration patterns widen regional health inequalities as some areas of the UK have a positive net migration for unhealthy migrants, whereas others have a negative net migration (Boyle, Norman, & Popham, 2009; Brimblecombe, Dorling, & Shaw, 1999; Norman & Boyle, 2014) The size of this effect is small, as the majority of migrants move between areas with similar mortality patterns (Green, Subramanian, Vickers, & Dorling, 2015), but migration patterns do have a significant effect on geographies of health. This phenomenon is not particular to the UK, as similar patterns have been found for rates of smoking in New Zealand (Pearce & Dorling, 2010) and poor self‐rated health in the Netherlands (Dijkstra, Kibele, Verweij, Van Der Lucht, & Janssen, 2015).
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