Individual and community determinants of residential mobility among individuals with mental illness in Manitoba
Bibliographic record
Abstract
The purpose ofthis research was to examine the individual and community characteristics that are associated with residential mobility among individuals with several types of diagnosed mental illness.Physician billing claims and hospital separations in the Manitoba centre for Health Policy (McHp) population Health Research Repository were used to identiS' individuals with diagnosed schizophrenia, anxiety disorders, substance abuse disorders, and personality disorders in the two-year period from April 1, 199g to March 31, 2000.Postal codes from the population registry from June 199g to June 2004were used to construct a residential history, Individualand community-level predictors were developed from the population registry, physician billing claims, hospital separations, Statistics Canada Census, and physician resource data.The degree, frequency, and direction ofresidential mobility were modeled using hierarchical logistic regression.Separate models were developed for winnipeg Regional Health Authority (WRHA) residents and rural RHA residents.The geographic distribution of location of residence varied by type of mental disorder.overcll, 16.20/o and 32.3o/o ofthe cohort moved in an 18-month and four-year period, respectively.The majority ofmovers only moved once, but the degree, frequency, and direction ofresidential mobility varied by diagnostic group.After controlling for the individual and community-level characteristics, the schizophrenia (degree of mobility for wRHA residents only), anxiety, and substance abuse disorders groups were less likely to move and move often compared to a group with co-occurring disorders.Age, marital status, income quintile, prior residential mobility, and use ofhealth services were associated with the degree and frequency of moving.The schizophrenia group was less likely to move from the inner co¡e to the suburbs, while the substance abuse and anxiety disorders groups were less likely to move from the suburbs to the inner core compared to the co-occurring disorders group.IndividualJevel characteristics were more important determinants ofresidential mobility than the community-level characteristics.The results of this research can be used to identiff individuals who are at high risk for moving, and to ensure that these individuals have access to resources to reduce their need to move and prevent discontinuities in the receipt ofhealth and social services.2002).Defining mobility as a move over a large geographic area may not be sensitive enough to detect differences in mobility between individuals with different types ofhealth conditions.There is little research examining residential mobitity among individuals with different mental illnesses (e.g., schizophrenia and anxiety) and among individuals with different levels ofseverity ofillness (e.g., individuals with a single mental illness versus individuals with multiple mental illnesses or individuals with a mental illness and one or more physical illnesses).The degree, frequency, and direction ofmobility likely vary by type and severity ofdiagnosis.This study uses population-based administrative data from Manitoba Health that is housed at the Manitoba centre for Health Policy (MCHP).The MCHp population Health Research Data Repository contains anonl'rnized administrative health records for all Manitoba residents eligible to receive health services, such that virtually all physician visits and hospitalizations are captured and databases are linked via an encrypted personal health identification number (PHIN) to create a history of health service use.Thus, all residents in the province of Manitoba with physician-diagnosed mental disorders within a specified period of time can be easily identified.This data source also contains longitudinal information on location ofresidence, allowing for a residential history within the province to be constructed.Location of residence is available at various geographic scales by using the six-digit postal code as the basic building block to construct different measures of mobility.The benefits of administrative data specific to this study are: 1) the ability to construct a representative cohort ofindividuals with different diagnosed mental disorders, and 2) the ability to examine residential mobility across different geographic scales over time.This research is important from a policy perspective.In order to provide the most equitable distribution ofhealth and social sewices, it is important to know how need for services is distributed (i.e., where people live) as well as the likelihood that the distribution ofneed changes over time due to residential mobility.Knowing the level and direction ofresidential mobility over time will help policy makers and service p¡oviders monitor whether the placement of (new) sewices unintentionally induces residential mobility þarticularly into stigrnatized and disadvantaged neighbourhoods) and will allow them to assess whether the mental health reform goal ofproviding service ,as close to a person's home as possible'has been achieved.If this mental health goal is achieved, few people will be moving to access services.Moving can be stressful.It can disrupt social support networks and create an increased sense ofsocial isolation and lack ofsuppof.The stress associated with moving, on already wlnerable individuals, may worsen their sl.rnptoms,affect their ability to function, and contribute to a relapse.Thus, unwanted and unnecessary residential mobility should be kept to a minimum for this population.studies of mobility can inform policy makers and service providers about the magnitude of the problem and be used as evidence for the need for funding for initiatives to reduce residential mobility (e.g., money management training, housing advocates, affordable housing options).Frequent residential mobility has the potential to create discontinuities in the receipt ofhealth care.In Manitoba, health care records do not accompany the patient {ìom one health service provider to another.This study may be useful in promoting use of the electronic health record, a lifetime electronic record ofan individual's health information available to authorized personnel.An electronic health record might be one way to reduce discontinuities that may arise because ofresidential mobility.Chapter 2: Revierv of Literature This chapter begins by describing the geographic diskibution of mental illness and the two main theories to explain this geographic variation.Research on the methodological issues associated with defining location ofresidence and residential mobility, and defining mental disorders from administrative health data are discussed next.Theories about why people move, from the larger residential mobility literature, are discussed next.The following section focuses on ¡esidential mobility among individuals with mental illness.Three aspects ofresidential mobility are discusseddegree, direction, and frequencyas well as the determinants of mobility.The next two sections summarize the literature on residential mobility among individuals with other health conditions and residential mobility ofother m arginalized and disadvantaged populations.The summary of the literature finishes with a discussion of the effects of neighbourhoods on health and health-related behaviors.Background and Theoretical Framework Beginning with the pioneering work ofFaris and Dunham (1967) first published in 1939, research has repeatedly demonstrated spatial variation in location ofresidence among individuals with mental illness (
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".