County drift: A type of geographic mobility of chronic psychiatric patients

Breslow, Richard E.; Klinger, Burton I.; Erickson, Bonnie J.

doi:10.1016/s0163-8343(97)00117-5

Cited by 20 publications

(15 citation statements)

References 6 publications

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“…Gender was associated with mobility in only one of the regression models, with significantly higher odds of moving for males than for females. While some other studies have reported higher mobility for males than for females (e.g., Breslow et al, 1998;Buehler et al, 1995), the opposite has also been observed. Findley (1988), for example, found that women with serious chronic illnesses were more likely to move than men.…”

Section: Discussionmentioning

confidence: 73%

“…Mobility may be related to access to health care; individuals may move in order to live in close proximity to specialized psychiatric treatment services (Breslow et al, 1998). Mobility may also arise because housing needs are not satisfied.…”

Section: Discussionmentioning

confidence: 99%

“…Other determinants of residential mobility may be explored, including the presence of comorbid conditions. Lamont et al (2000), for example, included a subsidiary diagnosis of alcohol or drug abuse, while Breslow et al (1998) considered a secondary diagnosis of substance abuse or a personality disorder in their analyses of the determinants of residential mobility. Individuals with more complex illnesses may be more mobile than those with less complex conditions.…”

Section: Discussionmentioning

confidence: 99%

“…Inter-region migration, including rural-to-urban migration, may account for geographic concentrations of individuals who require specialized health care services. Previous research has found, for example, that individuals with mental illness tend to move into areas where they have previously obtained health care services (Breslow, Klinger, & Erickson, 1998;Lamont et al, 2000). Health service planners can use data on mobility in order to ensure a good match between population needs and the provision of services.…”

mentioning

confidence: 99%

“…These comparative data are critical for understanding how the mobility of individuals with SMI may differ from that of other groups. Finally, previous research has often defined residential mobility only on the basis of movement between counties or other large geographic areas (Breslow et al, 1998;Dembling et al, 2002;Youseff, Scully, Kinsella, & Waddington, 1999); these measures may not be sufficiently sensitive to detect actual differences in an individual's change of residence.…”

mentioning

confidence: 99%

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Residential Mobility and Severe Mental Illness: A Population-based Analysis

Lix

Hinds

DeVerteuil

et al. 2006

Adm Policy Ment Health

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This research uses population-based administrative data linking health service use to longitudinal postal code information to describe the residential mobility of individuals with a severe mental illness (SMI), schizophrenia. This group is compared to two cohorts, one with no mental illness, and one with a severe physical illness of inflammatory bowel disease. The percentage of individuals with one or more changes in postal code in a 3-year period is examined, along with measures of rural-to-rural regional migration and rural-to-urban migration. Demographic, socioeconomic, and health service use characteristics are examined as determinants of mobility. The odds of moving were twice as high for the SMI cohort as for either of the other two cohorts. There were no statistically significant differences in rural-to-rural or rural-to-urban migration among the cohorts. Marital status, income quintile, and use of physicians are consistent determinants of mobility. The results are discussed from the perspectives of health services planning and access to housing.

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Section: Discussionmentioning

confidence: 73%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Residential Mobility and Severe Mental Illness: A Population-based Analysis

Lix

Hinds

DeVerteuil

et al. 2006

Adm Policy Ment Health

View full text Add to dashboard Cite

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How far is a long distance? An assessment of the issue of scale in the relationship between limiting long‐term illness and long‐distance migration in England and Wales

Wilding

Martin

Moon

2017

Population Space and Place

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Research 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 longterm 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.

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