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Estimating health-selective migration in patients with systemic lupus
erythematosus or Sjogren’s from administrative data
Jeremy Labrecque Department of Epidemiology, Biostatistics and Occupational Health
McGill University October 2012
A thesis submitted to McGill University in partial fulfillment of the requirements of the
degree of Master of Science
© Jeremy Labrecque 2012
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Table of Contents Table of Contents..............................................................................................................................1 List of figures and tables ................................................................................................................3 Abstract ...............................................................................................................................................5 Résumé ................................................................................................................................................7 Acknowledgements .........................................................................................................................9 1 Introduction................................................................................................................................. 11 1.1 Description of systemic lupus erythematosus and Sjogren’s.............................................14 1.2 Rationale..............................................................................................................................................15 1.3 Specific Objectives ............................................................................................................................17 1.4 Thesis structure.................................................................................................................................18
2 Literature review....................................................................................................................... 19 2.1 Search strategy ..................................................................................................................................20 2.2 Historical research (1871-1970).................................................................................................20 2.3 Modern research (1970-2011).....................................................................................................21
2.3.1 Migration as the outcome..................................................................................................22 2.3.2 Comparative health of migrants and non-migrants .................................................29 2.3.3 Surveys on reasons for migration ..................................................................................34
2.3.4 How selective migration can affect estimates ............................................................37 2.4 Summary of health selective migration ....................................................................................38 2.5 Administrative databases ..............................................................................................................39 2.6 SLE, Sjogren’s and health-selective migration ........................................................................40
3 Methodology................................................................................................................................ 43 3.1 Overview ..............................................................................................................................................43 3.2 Data sources........................................................................................................................................44 3.2.1 Administrative databases .................................................................................................44 3.2.2 Systemic autoimmune rheumatic disease cohort .....................................................46 3.2.3 Montreal CMA comparison cohort .................................................................................47 3.2.4 Outcomes ................................................................................................................................49 3.2.5 Potential confounders ........................................................................................................50 3.3 Statistical methods ...........................................................................................................................51 3.3.1 Descriptive and bivariate analyses................................................................................52 3.3.2 General population comparison .....................................................................................53 3.3.3 Pre-diagnosis comparison ................................................................................................55 3.3.4 Regional migration comparison .....................................................................................56 3.3.5 A note on comparisons.......................................................................................................56
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4 Results ........................................................................................................................................... 59 4.1 Descriptive and bivariate analyses.............................................................................................59 4.2 Health-selective migration comparisons..................................................................................67 4.2.1 General population comparison .....................................................................................69
4.2.2 Pre-diagnosis comparison ................................................................................................72 4.2.3 Regional migration comparison .....................................................................................75
5 Discussion .................................................................................................................................... 79 5.1 How the results compare to the literature...............................................................................80 5.2 Strengths ..............................................................................................................................................82 5.3 Limitations ..........................................................................................................................................83 5.4 Overall conclusions ..........................................................................................................................86 5.5 Future research .................................................................................................................................87
Bibliography ................................................................................................................................... 89 Appendix A – Forward sortation areas and ICD-9 codes used in this thesis ............. 99 Appendix B – Models considered in analyses ....................................................................101 Appendix C – Sample WinBUGS code ....................................................................................103 Appendix D – Coefficients from all models .........................................................................107
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List of figures and tables Figures Figure 1 Regional migration rates among SLE and Sjogren’s patients compared to provincial estimates ......................................................................................................................... 63 Figure 2 Predicted probabilities of migration in women diagnosed at 30 and 50 years in the general population comparison with 95% CrI error bars .................................................. 72 Figure 3 Predicted probabilities of migration in people diagnosed at 30 and 50 years in the pre-diagnosis analysis with 95% CrI error bars.......................................................................... 74 Figure 4 Predicted probabilities of migration of people diagnosed at 30 and 50 years in the regional migration analysis with 95% CrI error bars. ............................................................... 77
Tables Table 1 Summary of the three comparisons performed ........................................................... 51 Table 2 Sample sizes for each analysis ........................................................................................ 59 Table 3 Number of between-FSA moves and crude between-FSA migrations rates (in moves per year with 95% CI in parentheses) in the frequency-matched general population sample, SLE and Sjogren’s patients by disease, disease duration and age in the general population comparison ...............................................................................................................................................................61 Table 4 Number of moves and crude migrations rates (in moves per year with 95% CI in parentheses) by disease, disease duration and age in A) the pre-diagnosis comparison for between-FSA moves and in B) the regional comparison for regional moves .......................... 62 Table 5 Comparison between populations in the full Montreal CMA comparison cohort to the 2001 Canadian Census. Proportion of the population found in each age group is found in parentheses to allow comparison of age distribution by sex ................................................ 64 Table 6 Descriptive statistics of variable in the general population and pre-diagnosis data sets....................................................................................................................................................... 65 Table 7 Coefficients (and 95% CrI) from bivariate hierarchical logistic regression analyses in the healthy control, pre-diagnosis and regional migration analyses .................................. 67 Table 8 Odds ratios (95% CrI) of migration relative to controls by age and disease duration strata ................................................................................................................................. 68 Table 9 The progression of odds ratios (95% CrI) relative to controls for people diagnosed at 30 and 50 years old for all three comparisons ........................................................................ 70
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Abstract
Canadian public health agencies have a mandate to monitor the prevalence,
incidence and patterns of chronic disease. These agencies are increasingly using
administrative health data for these purposes. However, valid use of administrative data
for chronic disease surveillance requires an understanding of some inherent limitations.
Health-‐selective migration, which occurs when people migrate differentially by health
status, is a limitation that has not been estimated in administrative data sources. To
investigate this issue, we estimated health-‐selective migration in a cohort of systemic
lupus erythematosus (SLE) and Sjogren’s patients, identified from physician and hospital
claims databases in Quebec and compared them to rates in an age and sex frequency-‐
matched sample from Montreal, Quebec using hierarchical logistic regression. The
association between disease and migration was modified by both age and disease
duration. Both SLE and Sjogren’s patients migrated less than controls when young. For
example, 30-‐year-‐old SLE (OR 0.54, 95% CrI 0.45-‐0.64) and Sjogren’s (OR 0.41, 95% CrI
0.28-‐0.56) patients with two years of disease duration had lower odds of moving than
frequency-‐matched controls. Above age 50, the odds of migration in SLE and Sjogren’s
patients was comparable or slightly higher than in controls. Patients at age 70 with two
years of disease duration had an OR of moving of 1.29 (95% CrI 1.04-‐1.58) in SLE and
1.09 (95% CrI 0.81-‐1.42) in Sjogren’s. The associations between migration and disease
duration in SLE and Sjogren’s were qualitatively different. One year of SLE duration was
associated with an OR of 0.96 (95% CrI 0.93-‐0.98) and one year of Sjogren’s duration
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was associated with an OR of 1.05 (95% CrI 1.00-‐1.10). Results were similar when using
SLE and Sjogren’s patients pre-‐diagnosis as the control and when looking at migration on
a regional scale. Overall, SLE and Sjogren’s have an impact on migration rates which
varies by age, disease and disease duration.
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Résumé
Les organismes canadiens de santé publique ont le mandat de surveiller la
prévalence, incidence, et les tendances des maladies chroniques dans notre pays. De plus
en plus, ces agences utilisent des bases de données administratives sur la santé à ces fins.
Cependant, l’utilisation valable de ces sources de données pour la surveillance des
maladies chroniques exige une compréhension de certaines limites inhérentes, en
particulier la migration sélective par l’état de santé, ce qui se produit lorsque les gens
migrent de façon différente du à leur état de santé. Nous avons effectué une évaluation
des déménagements chez des patients ayant le lupus érythémateux disséminé ou le
syndrome de Sjogren’s tel qu’identifiés dans les données des réclamations des médecins
et des hôpitaux du Québec. La régression logistique hiérarchique a été utilisée pour
comparer les taux de migration chez des patients atteints de lupus ou de Sjogren’s aux
taux de déménagement dans un échantillon de concordance des fréquences par âge et
par sexe de la population de Montréal.
Dans notre échantillon, l’association entre les maladies et la migration a démontré
des modifications par l’âge du patient et la durée de sa maladie. Les patients lupiques ou
ayant le Sjogren’s déménagent moins quand ils sont plus jeunes. Par exemple, à 30 ans et
atteint de l’une de ces maladies depuis deux ans les patients lupiques (RC: 0.54,
intervalle crédible (ICr) 95% 0.45-‐0.6) et ceux atteints de Sjogren’s (RC: 0.41, ICr 95%
0.28-‐0.56) ont des cotes de déménagement moins élevés que ceux du groupe témoin.
Par contre, à 50 ans et plus, les cotes de migration des patients lupiques et ayant le
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Sjogren’s sont comparables et même légèrement supérieures à celles du groupe témoin.
Les patients âgées de 70 ans étant malades depuis 2 ans ont un rapport de cotes de
déplacement de 1,29 (ICr 95%: 1,04-‐1,58) pour ceux atteints du lupus et de 1,09 (ICr
95%, 0,81-‐1,42) pour ceux ayant le Sjogren’s. Aussi, l’association entre la migration et la
durée de la maladie était qualitativement différente entre les patients lupiques et les
patients atteints de Sjogren’s. Par exemple, les patients ayant le lupus depuis un an ont
montrés un rapport de cotes de 0,96 (ICr 95%, 0,93-‐098) et les patients atteint de
Sjogren’s ont montrés un rapport de cotes de 1,05 (ICr 95% 1,00-‐1,10) par an de durée
de maladie.
Les résultats étaient similaires lorsque le groupe témoin était composé de
patients qui n’avaient pas encore été diagnostiqués ou lorsqu’on regarde les migrations à
l’échelle régionale. Ce mémoire supporte l’idée que les maladies chroniques peuvent
affecter les taux de migration et que ceux-‐ci peuvent varier en fonction de l’âge du
patient, de la maladie et de la durée de cette maladie.
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Acknowledgements
First and foremost I would like to thank my co-‐supervisors: Dr. Sasha Bernatsky
and Dr. Lawrence Joseph, each of which has made invaluable contributions to my
progress as a student of epidemiology and to my research. Dr. Bernatsky allowed me the
independence to explore my own ideas while always being a source of inspiration, new
ideas and criticism. Dr. Joseph shared his immense wealth of knowledge of statistics and
methodology as well as invaluable guidance through the writing of my thesis.
Thank you to my committee members, Dr. David Buckeridge and Dr. Nancy Ross
who were available when I wanted to tap their expertise. A special thank you to Dr.
Buckeridge and his student Aman Verma for giving me access to data and taking the time
to teach me the skills needed to use it.
Thank you to Jennifer Lee for all her help, administrative and otherwise, from the
beginning to the end of my thesis.
A special thanks to my family and friends for all of their support and
encouragement through the ups and downs of thesis writing. Their support makes this
type of work possible.
Lastly, this work would not have been possible without the Graduate Masters
Training Award I received from the Canadian Arthritis Society.
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1 Introduction
Health-‐related mobility lies at the crossroads of epidemiology and health
geography, aiming to describe how health can affect movement between geographic or
socioeconomic strata. Health-‐related mobility occurs when people with different health
statuses move differentially between socioeconomic or geographic strata (Fox et al.,
1982). When the variable in question is geography, this phenomenon can be referred to
more specifically as health-‐selective migration. Health-‐selective migration encompasses
both overall health measures, for example over-‐all quality of life, and more specific
health states, such as a given disability or diagnosis.
Health-‐selective migration is important to epidemiology, especially when
estimating associations between disease and spatial characteristics, such as investigating
possible variations in disease prevalence over different regions (Bentham, 1988; Lewis,
2003; O'Reilly & Stevenson, 2003; Ocaña-Riola et al., 2009; Patrick, 1980; Polissar, 1980;
Tiefelsdorf, 2007; Veugelers & Guernsey, 1999). Whether health-‐selected or not,
migration can cause misclassification or measurement error of residential exposure over
time (Polissar, 1980; Rogerson & Han, 2002; Tousignant et al., 1994), possibly creating
biases in estimated associations.
Research on health-‐selective migration has included diverse populations such as
the elderly (Flynn, 1980; Meyer & Speare, 1985), cancer survivors (Haenszel & Dawson,
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1965; Kliewer, 1992; Polissar, 1980), people with long-‐term disabilities (Longino et al.,
1991; Speare et al., 1991), chronic illnesses (Larson et al., 2004; Yiannakoulias et al., 2007)
and poor self-‐rated health (Halliday & Kimmitt, 2008). Despite the quantity of research,
the relationship between health and migration often remains unclear in terms of both
magnitude and direction. There are number of factors that contribute to this.
The first and likely most important factor is that different specific health
conditions can be associated with either increased or decreased migration (Barsby &
Cox, 1975; Nelson & Winter, 1975), and the net effect across a given population can be
either positive or negative (Patrick, 1980). Therefore, lessons learned from research on
one set of health conditions may not necessarily be applicable to other conditions. Even
within one condition, estimates from one region may not apply to other regions.
Second, the relationship between health and migration may be non-‐linear when
health is measured on an ordered scale, such as self-‐rated health, or when using a
continuous measure such as number of disabilities (Findley, 1988; Silverstein &
Zablotsky, 1996). The health-‐migration association can also be modified by factors such
as age (Kolcić & Polasek, 2009; Norman et al., 2005), sex (Bentham, 1988; Halliday &
Kimmitt, 2008) and disease duration. In addition, it may be important to consider
migration in terms of distance migrated (Bentham, 1988; Boyle et al., 2001). Effect
modification by age is particularly important given that a large portion of the literature
on health-‐selective migration focuses on the elderly making it difficult or impossible to
extrapolate to younger cohorts. If non-‐linearity or effect modification is present, any
estimate ignoring these phenomena will be inaccurate.
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Third, the potential for a large number of confounders depends strongly on the
health measure in question. Many variables are related to migration, for example age,
sex, education, income, marital status and employment (Patrick, 1980). In turn, each of
these may or may not also be related to different measures of health, and therefore may
or may not be confounders. For instance, income might be related to self-‐rated health
(Kennedy et al., 1998) and therefore is likely a confounder in that context, but might not
be related to a genetic susceptibility to a disease, and therefore not a confounder in that
context.
Fourth, diseases with long latency periods from initial symptoms to clinical
manifestation are associated with increased misclassification because the disease may be
attributed as incident in a location different form where it was in fact incident (Polissar,
1980; Rogerson & Han, 2002). This is particularly important when assessing regionally
varying environmental exposures and their relationship with disease; that is, invalid
estimates of association could arise if persons affected by that disease move out of the
exposed region because of subclinical symptoms. For example, persons living in regions
where air quality is poor may develop respiratory symptoms, and move to a region of
better air quality where their ailment is then clinically diagnosed.
Further complicating the association between health and migration is the
possibility that migration itself may cause a decline in health (Ferraro, 1983; Findley,
1988). Some health-‐selective migration research that has been done on health-‐selective
migration uses data that cannot establish the temporal order of health and migration
events, and so is only able to estimate associations. In order to properly establish that a
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change in health has either precipitated or prevented a migration event, data must be
able to resolve whether migration happened before or after a decline in health.
1.1 Description of Systemic Lupus Erythematosus and Sjogren’s
This thesis will look specifically at health-‐selective migration in systemic lupus
erythematosus (SLE; ICD9 710.0, ICD10 M32) and Sjogren’s syndrome (ICD9 710.2,
ICD10 M35) patients, both of which fall under the rubric of systemic autoimmune
rheumatic diseases. Health selective migration within these chronic disease states is of
considerable interest, as will be discussed below.
SLE is a multi-‐systemic autoimmune disorder of unknown etiology. Its prevalence
in the province of Quebec is estimated to be about 45 cases per 100,000 and is most
common among women of childbearing age (Bernatsky et al., 2007). SLE is associated
with many physical manifestations, including arthritis, as well as constitutional
symptoms and psychosocial problems such as fatigue or depression (Tench et al., 2002).
In a sample of SLE patients in the Netherlands, two-‐thirds reported “that their disease
had either a periodic or permanent effect on their ability to perform everyday activities
at present” (Boomsma et al., 2002). Of 829 SLE patients sampled from clinical and
community-‐based sources in California, 28% reported that their disease affected their
ability to perform basic self-‐care, 42.5% reported it affected their ability to walk to get
around and 72.9% said it affected their ability to sleep. SLE disease duration is also
associated with work disability (Bertoli et al., 2006; Mau et al., 2005; Yelin et al., 2007).
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Overall, the relationship between disease duration and physical and emotional health is
unclear (Mcelhone et al., 2006).
Sjogren’s syndrome is another autoimmune rheumatic disease but tends to affect
older individuals, again, predominantly women, and have less severe involvement. The
hallmark characteristic of Sjogren’s is inflammation of the lacrimal and salivary glands,
with resultant decrease in tears and saliva, leading to very marked dry eyes and mouth
(Gran, 2002). In a small proportion of Sjogren’s patients, more severe involvement (e.g. of
kidneys or the central nervous system) can occur. Estimates of Sjogren’s prevalence are
less consistent because they depend on the definition and criteria used as well as the
target age group and range from 4-‐2,700 per 100,000 (Avina-Zubieta et al., 2011; Hansen,
1991; Kabasakal et al., 2006; Pillemer et al., 2001). Female Sjogren’s patients have been
found to have increased functional disability (Strömbeck et al., 2003) and lower perceived
health-‐related quality of life (Meijer et al., 2009).
1.2 Rationale
There are two important reasons to study health-‐selective migration in SLE and
Sjogren’s patients. The first is that geographical patterns such as clustering or differences
in urban/rural prevalence, incidence or mortality have been used to make inferences
about possible etiological factors or patterns of health care use in rheumatic diseases
(Alamanos et al., 2003; Andrianakos et al., 2003; Barnabe et al., 2012; Gómez-Rubio &
López-Quílez, 2010; Hart et al., 2009; Kurahara et al., 2007; Labrecque, Joseph, et al., 2010;
Labrecque, Smargiassi, et al., 2010; Walsh & Gilchrist, 2006; Ward, 2010). If rheumatic
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diseases, such as SLE or Sjogren’s, affect migration rates or destinations, the inferences
made from clustering or urban/rural differences may be limited. For these reasons, an
assessment of whether and how health-‐selective migration may occur is very important
in these conditions. Of interest as well, is whether health-‐selective migration could also
occur in patients with rheumatic diseases pre-‐diagnosis due to the presence of pre-‐
clinical symptoms. If, for instance, we observe similar estimates of health-‐selective
migration relative to the general population and also relative to SLE and Sjogren’s
patients before their diagnosis, it would provide evidence for weak or no pre-‐diagnosis
health-‐selective migration. Secondly, decisions about moving often consider opposing
reasons to stay or move (Hull, 1979; Patrick, 1980). Therefore, from a public health
perspective, an increase in migration among those with these diseases may indicate an
aspect of their disease is increasing their necessity to move. This may occur because
healthcare resources are either absent, inaccessible or more distant (Borders et al., 2000;
Buchanan et al., 2006) and may alert public health planners of areas where additional
resources are required. Canadian studies have found that the elderly in rural areas tend
to have less access to medical specialists (Allan & Cloutier-Fisher, 2006; McDonald &
Conde, 2010). One study in Iowa, however, suggested this may not be the case among
arthritis sufferers (Saag et al., 1998). From the same perspective, a decrease in migration
may indicate that some aspect of SLE and Sjogren’s is decreasing their ability or desire to
move rendering migrations that would have happened for other reasons (e.g. work,
retirement) difficult or impossible.
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There is also evidence that health-‐selective migration may depend on the
geographical distance migrated. For this reason, it is important to estimate health-‐
selective migration on more than one scale.
1.3 Specific objectives
The overall objective of this thesis is to estimate health-‐selective migration in SLE
and Sjogren’s patients using Quebec administrative databases. However, because health-‐
selective migration is a broad and complex topic the overall objective will be broken
down into three more specific objectives:
1) To estimate health-‐selective migration in SLE and Sjogren’s patients in Montreal,
Quebec relative to the general population using Quebec administrative databases.
2) To estimate health-‐selective migration in SLE and Sjogren’s patients in the
province of Quebec relative to their own pre-‐diagnosis migration rates.
3) To estimate health-‐selective migration on a regional scale in SLE and Sjogren’s
patients in the province of Quebec relative to their pre-‐diagnosis migration rates.
These objectives will be realized using two different comparison groups and
estimating migration on two geographic scales. Together, they will address the questions
of whether health-‐selective migration exists among SLE and Sjogren’s patients and
whether migration differs by comparison group and geographic scale.
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1.4 Thesis structure
This thesis is divided into five chapters. This introductory chapter has briefly
discussed health-‐selective migration, SLE and Sjogren’s disease, and set out the
objectives of this thesis. Chapter 2 provides a literature review of previous related
research, focusing on health-‐selective migration. A description of the data and methods
used to meet the three main objectives enumerated above follows in Chapter 3. The
fourth chapter will report the main results from the analyses carried out in this thesis.
The fifth and final chapter will discuss the results and provide overall conclusions.
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2 Literature review
This chapter presents a critical review of the literature pertaining to health-‐
selective migration. It will begin with a description of the search strategy used to identify
the literature on health-‐selective migration. The review itself will be divided into
historical research, modern research and a section on theoretical papers on how
selective-‐migration can affect estimates of association. The section on modern research
will be further subdivided into three subsections according to how the research was
performed and will by followed by a short summary.
Although much work has been done regarding health-‐selected international
immigration (Borràs et al., 1995; Gushulak & MacPherson, 2006; Harding & Balarajan,
1996; Newbold, 2005), given the objectives of this thesis, this literature review will focus
on internal migration, i.e. migration within national, provincial or state borders. There is
also a sizable literature on how health-‐selective mobility (whether social or geographic)
can reinforce inequitable distributions of SES or deprivation (Boyle et al., 2009; Curtis et
al., 2009; O'Reilly, 1994; Richardson et al., 2009). This topic will also not be covered
except where it directly pertains to the topic at hand.
The chapter concludes with a short discussion of the limitations of administrative
databases, and why health-‐selective migration is of particular interest within SLE and
Sjogren’s patients.
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2.1 Search strategy
Searches were conducted in MEDLINE and EMBASE, covering the years from
1946 to 2011. Search terms used were: migration, residential mobility or immigration
and emigration AND health and health status. Selected articles were hand-‐searched for
additional references. Only English-‐language papers on health-‐selective migration from
North American and Europe were included. After screening titles and abstracts and
hand-‐searching articles, 50 references were selected for the literature review spanning
from 1871 to 2011.
2.2 Historical research (1871-1970)
The earliest mention of health–selective migration, though not by that name, was
by Thomas A. Welton (1871) who remarked “that the mortality happening in London is
diminished, ‘because domestic servants, shopwomen and milliners,’ who have come from
the country, retire when health fails them ‘to their native air'." He found his efforts to
analyze patterns of mortality in England and Wales frustrating, because “[i]t is vain, it
would seem to look (at least in this country) for any quiet self contained place, where
population remains undisturbed by migrations, and where statistics of mortality can
therefore be obtained, requiring no rectification.” He observed, more generally, that sick
people migrate away from hazards and toward care.
In response to a comment from a founding father of epidemiology Dr. William
Farr, “that migration appeared to go on without any definite law,” Ernest George
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Ravenstein published two seminal papers entitled “The Laws of Migration” (1885; 1889)
wherein he laid out, for the first time, rules governing the flow of migrants. Though
Ravenstein’s work focused on differential migration with respect to sex and urbanicity, it
was the basis for much of the work on health-‐selective migration in the first half of the
20th century, leading to research on how physical fitness, mental illness and intelligence
might influence migration patterns (Thomas, 1938). After the second world war, the U.S.
census started taking an interest in what caused people to move (Bureau of the U.S.
Census, 1947), but research on health-‐selective migration was sparse until after 1970
when patterns of migration in the elderly became of interest. Up to that point, health-‐
selective migration was often seen as unimportant. In fact, Lee (1966) even failed to
mention health as an obstacle or motive for migration in his own work despite the fact
that Haenszel and Dawson had published the year before (Haenszel & Dawson, 1965)
that estimates of the prevalence of difference health conditions within a region differed,
depending on whether one considered all or only long-‐term residents of a given region.
2.3 Modern research (1970-2011)
Research into health-‐selective migration regained importance after 1970 when
researchers interested in patterns of elderly migration began understanding health as a
determinant. Many different methods have been employed to investigate the
relationship between health and migration. Often, the nature of a data set required
researchers to use one method or another, with each study having different strengths
and weaknesses. The research can be grouped into three main categories. The first uses
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migration as the outcome and measures of health as a predictor. The second compares
health in migrants and non-‐migrants using crude comparisons, standardization or
multivariate regression modeling. The last uses surveys to directly elicit reasons for
migration from migrants. Each of these areas will now be summarized in turn.
2.3.1 Migration as the outcome
This section will group studies that have migration as the outcome. This is the
method used in this thesis and will therefore be most pertinent. Most such studies rely
on survey data although health administrative data (e.g. physician billing and
hospitalization) and census data have also been used. Analytical methods include crude
comparisons, matching and logistic or multinomial regression.
The most widely used dataset on health-‐selective migration is the US Longitudinal
Study on Aging. It is composed of data on Americans 70 years and older from the 1984
Supplement on Aging to the National Health Interview Survey and a follow-‐up surveys in
1986, 1988 and 1990. It is worth elaborating on these articles because they use slightly
different measures and methods for the same dataset, allowing us to investigate how
robust results are to these differences. The 1984 survey contained questions about
health, disability, functional ability, family relationships, living arrangements and social
support (Speare et al., 1991). The follow-‐up surveys repeated questions on health,
disability and living arrangements as well as whether any moves had occurred in the
past two years. The measure of health used in these papers is the number of activities of
daily living (ADLs; activities considered essential to daily living such as self-‐feeding,
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personal hygiene, bowel and bladder control, etc.) or instrumental activities of daily
living (IADLs; activities that aren’t considered essential but allow for independence such
as housework, shopping, using the telephone, etc.) with which a person has difficulty.
One important caveat of this data is that disability at baseline can be said with certainty
to have occurred before migration events reported in follow-‐up survey. However, the
same cannot be said for change in health or disability after baseline, because
respondents did not report the order in which migration events and changes in
disabilities occurred. Also, each paper uses slightly different inclusion criteria for
subjects and different model selection methods.
Speare et al. (1991) looked at how the sum of the number of ADLs and IADLs with
which a person had difficulty is related to three outcomes: migration to an institution,
any migration, and a change in living arrangement that involves living with an adult
other than a spouse. They found that baseline disability is related to a move to an
institution (OR 1.20, 95% CI: 1.14-‐1.26), and change in living arrangement (OR 1.12,
95% CI: 1.06-‐1.19) but was not necessarily associated with general mobility (OR 0.97,
95% CI:0.91-‐1.03). Longino et al. (1991) look specifically at IADLs and general mobility
finding an OR of 1.08 (95% CI: 0.98-‐1.19). Jackson et al. (1991) found similar results to
Longino et al. when controlling for a few more potential confounders and including a
change in living arrangements with migration as part of the outcome (OR 1.09, 95% CI
1.01-‐1.17). Worobey and Angel (1990) looked at increases in dependence (living with an
adult or moving to an institution) with polychotomous ordered response regression and
found that ADLs were associated with a small increase in dependence with an OR 1.06
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for an increase in dependence (the result was statistically significant but no standard
error was reported). De Jong et al. (1995) found baseline ADLs (OR=0.97, 95% CI 0.92-‐
1.03) may be related to a decrease in migration using all waves of the survey from 1986-‐
1990. Silverstein and Zablotsky (1996) also used the 1984-‐1990 waves of the LSOA and
multinomial regression with different migration destinations as the outcomes
(retirement community service-‐rich, retirement community service poor, other general
community, institution). They found that migration to retirement communities, whether
service-‐rich or poor, peaked at moderate levels of disability (OR=2.83, reported as a
statistically significant result but no standard error given) and were near null for those
with little or no disability as well as among those with high levels of disability. Synthesis
of all these results is difficult to fully quantify due to the lack of reported standard errors
or confidence intervals though most point to an increase in migration in elderly with
disabilities. However, interpretation of all studies using this dataset should consider the
possible non-‐linearity between disabilities and migration.
Two studies were done during different time periods using the Panel Study of
Income Dynamics. The first only used data on people aged 60 and over from 1969-‐1980
(Henretta, 1986). The association between the head of the household having a health
limitation and migration using logistic regression was inconclusive due to wide
confidence intervals. A second study used data on all ages from 1984-‐1993 and also
made sure that the self-‐reported health was measured before the migration event for
each observation (Halliday & Kimmitt, 2008). Using age-‐stratified probit regression, men
older than 60 self-‐reporting either very good or excellent health and those reporting fair
25
or poor health were associated with increased migration relative to those in good health.
This non-‐linearity is opposite to that found in Silverstein and Zablotsky, where migration
peaks at the ends of the health distribution, not in the middle, though different measures
of health are being used. This relationship, however, was not found among women over
60 years. Among men younger than 60, poorer health is associated with a 32-‐40%
decrease in migration (a range representing possible values depending on covariates and
model specifications). A smaller, yet not statistically significant decrease of 12% to 18%
was found among women.
The remaining studies use limiting long-‐term illness (LLTI) or chronic diseases as
a measure of health though some supplement this with other measures. A study of
people 50 years and older using the British Household Panel Study looked at migration
rates stratified by ten-‐year age groups and by no/non-‐limiting/limiting long-‐term illness
found that men and women with a LLTI tended to have higher migration rates
particularly among those older than 80 years (Evandrou et al., 2010). The relationship
between changes in self-‐rated health and migration was less clear. A change in self-‐
reported health of two points on the Likert scale, whether an improvement or worsening
of health, appeared to be associated with increased migration except among those aged
70-‐79 where a worsening of health was associated with a decrease in migration and an
improvement in health was associated with an increase in migration. This non-‐linearity
is similar to Halliday and Kimmit (2008) where migration is lowest among those in the
middle of the health distribution.
A study in three cities in Quebec of elderly subjects over age 65 used discriminant
26
analysis to determine whether a list of health problems, including number of chronic
diseases and self-‐rated health, economic and social variables predicted either filing an
application to move to an institution or desire to remain at home (Béland, 1984). Their
resulting model found that the number of chronic diseases was positively associated
with filing an application to move to an institution. Self-‐rated health and reduced activity
due to health were not related to either filing an application or a desire to stay home
although no estimates were reported, only that they were selected out of the model.
Findley (1988) did an exploratory analysis using data from the 1979 and 1980
waves of the National Health Interview Survey in the United States finding that, in all age
groups but particularly among people older than 45 years, participants were more likely
to experience a chronic illness after a migration than vice versa. However, the survey
contained no direct data on onset of chronic illness and the last date a doctor was seen
for a chronic condition was used as a proxy. The result is that the date of onset was
necessarily always moved forward in time increasing the likelihood that any migration
events between the actual onset and the interview would occur before and not after the
proxy onset. This measurement error could easily create a situation where a change in
health status precipitated a migration look like the migration precipitated the change in
health status. The crude likelihood of migrating, stratified by age, was roughly the same
among those who had not experience a health event in the past two years as among
those who had. The unknown magnitude of the measurement error, however, makes it
difficult to draw any reliable inference from this paper.
27
A comparison of urban and rural movers and stayers in and around Eindhoven in
the Netherlands using a longitudinal survey also suffers from methodological flaws
rendering reliable inferences difficult (Verheij et al., 1998). Two logistic regression
analyses comparing movers to stayers and urban migrants to rural migrants both suffer
from mis-‐specification because they consider age interactions without including main
effects for age. In bivariate comparisons, movers had better perceived health and
reported less chronic conditions than stayers.
An Australian longitudinal survey investigated how health characteristics such as
self-‐reported health, number of chronic conditions and visits to a specialist could
subsequently lead to within postal code, between postal code and rural to urban
migration among women aged 45-‐50 (Larson et al., 2004). Only p-‐value inequalities
(p<0.05, p<0.01) were presented making it difficult to correctly interpret the importance
of the results. Also, the likely strong collinearity between health measures included in
the model make each individual coefficient difficult to interpret. Having two or more
chronic conditions was associated with an increased likelihood of moving within and
between postal codes as well as rural to urban moves. Poor mental health and smoking
were associated with increased migration within and between postal codes. Three or
more visits to a specialist in the previous year was also associated with rural to urban
moves.
Another Australian study compared directly standardized migration rates in healthy
people of all ages and those with a serious disease where migration was a move between
metropolitan, rural and remote regions (Moorin et al., 2006). They found that people
28
with a serious disease were less likely to migrate in any direction (more or less urban).
People with connective tissue and rheumatoid disorders, a category including lupus and
Sjogren’s, were less likely to move out of the city than healthy controls (incidence rate
ratio: 0.46, 95% CI 0.27-‐0.75). Unfortunately, results that were not statistically
significant were not presented but could have been of interest if the confidence intervals
included values of interest. Though temporality could not be established in this case, it is
more likely that diseases like lupus and Sjogren’s precipitated a migration event than
vice-‐versa. Breslow et al. (1998) looked at a similar phenomenon, which they termed
“county drift”, in the city of Albany, New York, the location of a psychiatric hospital
serving the entire county of Albany. They found that patients of the hospital were more
likely to move to Albany than they were to move away.
Of particular interest, Yiannakoulias et al. (2007) has many characteristics in
common with this thesis. It used administrative data to estimate health-‐selective
migration, takes place in Canada and considers specific diseases. Parkinson’s disease
(PS) and multiple sclerosis (MS) patients identified in fee-‐for-‐service and hospitalization
databases were matched to members of the general population base on sex, age (within
five years), SES (based on health insurance premium subsidy level) and municipality.
They found that MS patients were slightly more likely to migrate at least once when
compared to controls (45.9% in MS patients vs. 43.6% of controls) and PS patients
where much more likely to migrate at least once than controls (35.7% among PS patients
vs. 26.2% of controls). One implicit assumption this analysis makes is that the effect of
being diagnosed with MS or PS is homogeneous across both age and disease duration. If
29
one or both of these assumptions is not met, and there is literature that suggests this
could be the case (Boyle et al., 2002; Halliday & Kimmitt, 2008), the estimates presented
become a summary estimate of heterogeneous effects by age and disease duration
influenced by the effect size in different strata and the structure of the population and no
longer retains any meaningful interpretation. Working with the knowledge that MS
affects people in mid-‐life and PS people of older age, we could expect these estimates to
be influenced most by rate ratios in those ages groups.
2.3.2 Comparative health of migrants and non-migrants
Many researchers have chosen to investigate health-‐selective migration by
comparing measures of health in migrants and non-‐migrants using logistic regression,
standardization or crude comparisons. In these studies, researchers were more likely to
stratify by age but often, due to the nature of the data sources, could not resolve the
temporal order of health and migration events. The primary health outcomes
investigated are LLTI, mortality and specific health problems. Mortality, in this context,
must be seen a proxy for poor health because it clearly cannot occur before a migration
event.
Bentham (1988), using the British census, analyzed the proportion of
‘permanently’ or ‘temporarily’ sick among non-‐migrants and local and regional migrants.
The author found that young local migrants were less likely to be permanently sick when
compared to the district average but that older migrants were more likely to be
permanently sick. A similar pattern was found among the temporarily sick although the
30
effect was not as pronounced. For regional migrants, the proportion permanently or
temporarily sick was, for the most part, less than the average of the district. No measures
of uncertainty were reported which limits the interpretability of inferences from this
study.
Brimblecombe, Dorling and Shaw (2000) used data from the first six waves of the
British Household Panel Survey (1991-‐1996) and compared migrants to non-‐migrants
stratified living in a high or low-‐mortality district. Men and women moving from high to
low mortality districts self-‐reported being in better health and that health was less likely
to limit their activities than those remaining in high mortality districts. Men moving from
low to high mortality districts were less likely to report that health limits their work and
more likely to score poorly on a questionnaire rating mental health.
Three papers considered specific health problems as the outcome. Among men
40-‐59 years old in the UK, internal migrants were 23% (95% CI: 6-‐37%) less likely to
experience a major ischemic heart disease event than non-‐migrants, and had lower
systolic (3.0 mmHg, 95% CI 2.0-‐4.0) and diastolic (1.2 mmHg, 95% CI 0.6-‐1.8) blood
pressure (Elford et al., 1990; 1989). In response, Martyn et al. (Martyn et al., 1993) claim
that this pattern may be explained, in part at least, by selective migration by birth
weight. Using hospital records, they found that men that migrated away had, on average,
higher birth weight than those who stayed. Among women the difference was
inconclusive. A cross-‐sectional study in Croatia, where health outcomes were measured
by nurses, found that migrants (excluding employment-‐related migrants) in all age
groups tended to have lower prevalence of hypertension, overweight and obesity than
31
non-‐migrants (Kolcić & Polasek, 2009). Exceptions were that hypertension and obesity
in those aged 18-‐34 was higher in migrants and overweight in those aged 50-‐64 was high
in migrants.
LLTIs, particularly in the UK, are a popular measure of health. A study using the
1991 British Census, found long distance migrants (>50km) in England and Wales 18-‐64
years old had lower odds (OR 0.86, 95% CI:0.78-‐0.95) of reporting a LLTI than non and
short-‐distance migrants (Boyle et al., 2001). This study was repeated in Scotland using
the same data source but a different cut point for short and long distance migrants.
Short-‐distance migrants (<10km) were more likely to have a LLTI than non-‐migrants and
long-‐distance migrants less likely to migrate than non-‐migrant between 30-‐65 years old
(Boyle et al., 2002). After retirement, long-‐distance migrants had similar levels of LLTI as
non-‐migrants, whereas short-‐distance migrants had higher levels. This study
demonstrates some heterogeneity by age but includes no confidence intervals nor does it
include the coefficients from the model.
In the Netherlands, a national survey was used to study the relationship between
three measures of health (self-‐reported health, long-‐standing health problems, and
disabilities) and migration stratified by deprived and non-‐deprived areas (Jongeneel-‐
Grimen et al., 2011). They found that people moving to non-‐deprived areas had worse
self-‐reported health and more disabilities than the people already living there. It was
also found that people moving to deprived areas had better health than the people
already there. This may suggest that the area a person moves to might be an effect
measure modifier. Wiggins et al. (2002) found no association between LLTI and internal
32
migration in English and Welsh women aged 35-‐65 using the Office for National
Statistics’ Longitudinal Survey (ONS-‐LS) though the estimate and confidence intervals
were not reported because the result was not statistically significant. Also, migration was
only considered between 1971-‐1981 and presence of an LLTI was assessed in 1991. The
ten-‐year gap between exposure and outcome assessment likely weakens the association.
Another study used the same data source as Wiggins et al. but over a different time
period (1981-‐2001) and with mortality as the outcome found different results (Riva et
al., 2011). Using logistic regression stratified by age (<65, 65+), time period (1981-‐2001,
1991-‐2001) and migrants type (urban outmigrant, rural outmigrant, long-‐term rural
resident, long-‐term urban resident), they found, among people 65 years and over,
migrating out of an urban area between 1981-‐2001 was associated with lower odds of
death between 2001-‐2005 (OR 0.86, 95% CI: 0.77-‐0.94). Among people younger than 65
years, conclusively lower odds of death were found when limiting the migration period
to 1991-‐2001 though the confidence intervals included many low OR values when
migration was measured between 1981-‐2001. Heterogeneity in ORs by age is suggested
in this study but the confidence intervals overlap slightly.
Two studies were found comparing residents to outmigrants of an area in order
to assess the impact of health-‐selective migration on their research questions.
Tousignant et al. (1994) found that people who migrated out of an area before a cross-‐
sectional study on the health effects of point source pollution were less likely to have
heart disease (RR: 0.17, 95% CI 0.10-‐0.29), hypertension (RR: 0.25, 95% CI 0.17-‐0.36)
and migraines (RR: 0.59, 95% CI 0.43-‐0.81). Only 1.3% of migrants, when asked,
33
reported moving away for health reasons. Matthews et al. (2004) found that people 65
and over who moved away between sample selection and baseline interview were 1.8
times (1.3-‐2.4) more likely to die within two years. Those who moved away between
successive waves of a survey were more likely to be smokers, demented or depressed.
(Larson et al., 2004).
A number of studies have used standardized mortality ratios to compare health
with migration in ecological studies. Indirect standardization may lead to spurious
results in this context because it is unlikely that the assumption of similar population
composition (Tsai & Wen, 1986) is met between migrant and non-‐migrant populations.
Therefore, all studies using indirect standardization should be interpreted with caution.
Fox et al. (1982) used SMRs to compare migrants to the entire population of England and
Wales. They found that within-‐county migrants had an SMR of 1.10 (95% CI: 1.07-‐1.13)
and between-‐county and between-‐region migrations had SMRs of 0.93 (95% CI: 0.86-‐
1.00) and 0.91 (95% CI: 0.84-‐0.98) respectively. Therefore, short distance migrants were
less healthy than long distance migrants. One study Norman et al. (2005) was the only
other paper to use SMRs of general mortality. Migrants and non-‐migrants had similar
mortality except in the 60-‐79 age group where migrants had lower mortality. They found
that among people between 40-‐59 years old, migrants had less LLTI than non-‐migrants
but the relationship was less clear for older age groups.
A number of studies looked at mortality due to a specific cause. Using U.K. census
data linked to the death registry, Strachan et al. (1995) compared SMRs adjusted for sex,
age, calendar period, housing tenure, and car ownership among non-‐migrants and
34
migrants using Poisson regression. They found that migrants had lower death rates than
non-‐migrants for both ischemic heart disease (RR: 0.90, 95% CI: 0.87-‐0.94) and stroke
(RR: 0.94, 95% CI: 0.88-‐0.99). The average age is not reported but the information given
allows the calculation that participants were at least 32 and the distribution of person-‐
years is likely mostly above 65 years of age.
Two American studies found that the SMRs of migrants differed both in
magnitude and direction depending on the state. Kliewer (1992) compared colon and
stomach cancer mortality rates among migrants and non-‐migrants in 11 western U.S.
states using SMRs and found that they differed from state to state. Lanska and Peterson
(1995) found similar result in stroke mortality looking at all the coterminous U.S. states.
2.3.3 Surveys on reasons for migration
Six surveys where health related migration was investigated were found. Four of
these sampled exclusively elderly people and two looked at people from a variety of age
groups.
A prospective cohort of Rhode Islanders who had moved since the inception of a
cohort 12 years earlier were asked the reason for their move (Meyer & Speare, 1985).
The proportion of migrants, who moved for health reasons, either closer to friends or
relatives for assistance reasons or entry into a nursing home, remained roughly constant
around 7% between 27-‐59 years old but increased to 18.5% and 34.5% among 60-‐69
and 70+ year olds respectively indicating that health-‐selective migration exists among
younger people but is particularly important among the elderly. Meyer and Speare,
35
however, only report interviews with migrants and therefore could not address the
question of whether some potential migrants were prevented from migrating by poor
health. If an equal number of people were prevented from moving because of their
health, health-‐selective would be difficult to observe. The representativeness among this
cohort is also called into question because, though the cohort was validated as being
representative of the Rhode Island population at its inception, 28% of those alive at the
end of the cohort were lost to follow up. Those lost to follow up were less likely to be
married, less likely to be employed, in poorer health and more likely to be renters
meaning that proportions of health-‐related migrants are likely underestimates. Analysis
of variance was performed on demographic characteristics of different types of movers
(including non-‐movers). Some interesting patterns are observed such as important
differences between local and out of state health-‐related migrants in terms of age,
education and income but no variances estimates are given making it impossible to
evaluate the importance of the differences between groups of movers. Of 31 Alaskans
(ages ranged from 21 to 64) who had moved from a rural to urban area that participated
in a qualitative survey, over half gave a medical reason for the move (Driscoll et al.,
2010).
The remaining surveys only looked at retired individuals, finding mixed results.
Twenty four percent of 92 recent migrants to retirement community in Arizona reported
moving there for health reasons (Gober & Zonn, 1983). In a city in upstate New York,
60% of senior citizens surveyed reported considering a move. Interestingly, health
condition and the presence of health problems were not associated with considering a
36
move but an inability to perform certain activities of daily living was negatively
associated with considering a move. This is evidence, though tenuous, against the
hypothesis that disabilities cause moves through requiring special care and contradicts
research from previous sections (Nelson & Winter, 1975).
Silverstein and Angelello (Silverstein & Angelelli, 1998) used the Asset and Health
Dynamics of the Oldest Old, a representative national sample of Americans over 70, to
investigate characteristics that make the elderly consider migration. People were
considered at risk of a move if they reported a great than 20% chance of moving in the
next five years. Self-‐rated health was positively related to considering a move, while
difficulty with IADLs and cognitive impairment were inversely related with considering a
move. Therefore, poorer health led to lower odds of considering a move. Among those
with children, self-‐rated health was not related to the expectation of moving closer to or
in with one of their children unless they lived alone. In this case, it was strongly
positively related, suggesting living alone is an effect measure modifier of the health-‐
migration relationship.
Sergeant and Ekerdt (2008) performed a qualitative survey of 30 movers in a
Mid-‐western US state aged 60-‐87. Many cited health problems as a reason to move but
saw health problems as related to a diagnosis as distinct from functional limitation,
which they saw as related to effect of aging.
37
2.3.4 How selective migration can affect estimates
This section will present simulation studies of health-‐selective migration and how
it affects estimates of prevalence or associations with exposures.
A number of studies have looked at how health-‐selective migration might affect
estimates of the relationship between place and health with the methods employed
increasing in complexity. One of the earliest studies estimated how non-‐differential
migration with regards to health could influence relative excess risk of cancer incidence
using U.S. data on continuous residence and cancer incidence rates (1980). Depending on
different assumptions about scale of analysis (place, county, state) and latency period,
migration could reduce the attributable risk of an environmental exposure anywhere
from 8% to 73%. Cancers with increased latency periods and analyses using small-‐scale
unites were more prone to bias.
In Sydney, Nova Scotia, Veugelers and Guernsey (1999) performed a sensitivity
analysis to investigate how health-‐selective migration could influence life expectancy in
men. They assumed outmigrants were either slightly healthier (had a life expectancy
equal to the average Canadian male) or much healthier (had a life expectancy equal to
the average Canadian female) than the Sydney population and found that health-‐
selective outmigration from Sydney might be responsible for a decrease in life
expectancy of up to three years from 1950-‐1995.
Over twenty years later, a similar approach to the Polissar model found similar
results (Rogerson & Han, 2002). Migration probabilities were based on disease
presence/absence. Using data on migration between counties in New York State and
38
looking at lung cancer as the target disease, they found that a county with high mobility
would retain about half of its excess risk while a county with relatively low mobility
retained nearly all of it (95%).
More recently, Tiefelsdorf (2007) incorporated an explicit spatial model of
prostate cancer rates by State Economic Area (SEA) to correct for migration (which is
assumed to be non-‐differential with respect to health). He finds that an unadjusted
model trying to predict prostate cancer rates by SEA finds radon levels to be a strong
explanatory variable but this effect disappears when the data are corrected for
migration.
2.4 Summary of health-selective migration
Overall, though much of the evidence has weaknesses, it appears that young
migrants are more likely to be healthy while the evidence for health-‐selective migration
in older people is mixed. Three studies showed evidence of a non-‐linear relationship
between health and migration with two finding that migration peaks at poor and
excellent health and another finding it peaks at intermediate health. There is also
evidence that the distance migrated may be qualitatively related to health. Short-‐
distance migrants may be in poorer health than non-‐migrants and long-‐distance
migrants may be in better health. These conclusions must remain tentative due to the
numerous problems with these studies including model selection, model
misspecification, use of indirect standardization, lack of variance estimates and an
inability to properly order the health and migration events.
39
The majority of the research has taken place in Europe and the United States. Only
three studies of Canadian health-‐selective migration have been carried out to date
(Béland, 1984; Tousignant et al., 1994; Yiannakoulias et al., 2007).
2.5 Administrative databases
The use of administrative databases in epidemiological research is increasing
(Leal & Laupland, 2009; Schneeweiss & Avorn, 2005; Tricco et al., 2008), in part because
they provide access to large amounts of data without the costs associated with primary
data collection. However, because administrative data are collected for purposes other
than research they can be prone to misclassification, and prevalence and other estimates
can substantially depend on the methods used (Ladouceur et al., 2006).
Administrative databases have been widely used in rheumatic diseases. A 2012
systematic review (Widdifield et al., 2011) identified 23 studies that validated the use of
administrative databases to identify rheumatic diseases. Sensitivities ranged from 20-‐
100% and specificities ranged from 74-‐97.1% depending on the algorithm and database
used. Only two studies reported either the sensitivity or specificity of algorithms to
identify SLE or Sjogren’s cases in administrative databases. No other study validated
Sjogren’s diagnosis using administrative data. Katz et al. (1997) compared 34 SLE
patients identified by chart review to SLE cases in Medicare physician claims data
identified as anyone who had received at least one SLE diagnosis from a rheumatologist.
They found a sensitivity of 85% (95% CI: 73-‐0.97%). Bernatsky et al. (2011) used the
same algorithm as in this thesis in the Canadian province of Nova Scotia. Validation was
40
done using chart review of patients at an arthritis centre in Halifax. A sensitivity of
98.2% (95% CI 95.5-‐99.3%) and a specificity of 72.5% (95% CI 68.7-‐75.9%) were found.
The low specificity may have due to the control group that consisted of patients with
similar rheumatic diseases at the same arthritis centre. Bernatsky et al. also used the
same methodology for Sjogren’s patients and found a sensitivity of 95.5% (95% CI 88.8-‐
98.2) and a specificity of 95.8% (95% CI 94.1-‐97.0). However, even a specificity near
96% could mean that the majority of subjects identified as having the condition will not
in fact have it. For example, if a condition occurs in 1% of the population, even if all such
cases are found, a specificity of 95% means that there will be five times as many non-‐
cases as true cases in the supposedly case population. This obviously casts a large
shadow of doubt over any research using administrative databases that has either not
adjusted for this misclassification or verified that sensitivity and specificity are high
enough to avoid large misclassification errors.
This literature demonstrates that for SLE and Sjogren’s, administrative data can
be prone to misclassification. Therefore, it must be kept in mind that any study
identifying SLE or Sjogren’s patients using administrative data, including this one, will
incur some level of misclassification.
2.6 SLE, Sjogren’s and health-selective migration
Health-‐selective migration has never been investigated specifically in either SLE
or Sjogren’s. Only one study, to our knowledge, has looked at health-‐selective migration
in patients with rheumatic diseases, finding that these patients were less likely to move
41
out of the city than healthy controls. Some of the previously mentioned studies that
looked at disabilities that could be applied to SLE and Sjogren’s, given that these diseases
are associated with increases in disabilities (P. Katz et al., 2008; Strömbeck et al., 2003).
Given that many studies of prevalence, incidence or mortality in rheumatic diseases rely
on geographical patterns abstracted from administrative databases (Alamanos et al.,
2003; Andrianakos et al., 2003; Barnabe et al., 2012; Gómez-‐Rubio & López-‐Quílez, 2010;
Hart et al., 2009; Kurahara et al., 2007; Labrecque, Joseph, et al., 2010; Labrecque,
Smargiassi, et al., 2010; Walsh & Gilchrist, 2006; Ward, 2010) or from sources prone to
health-‐selective migration (Alamanos et al., 2003; Andrianakos et al., 2003; Gómez-‐Rubio
& López-‐Quílez, 2010; Hart et al., 2009; Walsh & Gilchrist, 2006), it is important to have
an understanding of how this process operates in patients with rheumatic diseases. For
instance, Walsh and Gilchrist (2006) investigate clustering at the county level of SLE
mortality in the lower 48 United States finding that high SLE mortality clusters are
related to poverty, Hispanic ethnicity and exposure to ultraviolet B radiation. Articles in
this literature review provide evidence for the possibility of health-‐selective migration,
particularly among the elderly, explaining at least part of this clustering (Gober & Zonn,
1983; Litwak & Longino, 1987; Sergeant & Ekerdt, 2008). Similarly, any urban/rural
differences in rates of rheumatic diseases may be explained by increased migration
towards cities or a decreased tendency to move out of them (Moorin et al., 2006).
Health-‐selective migration in either SLE or Sjogren’s patients may also be
indicative of gaps in access to health care. Increased migration may be expected in these
diseases given Canadian data documenting reduced access to healthcare specialists
42
among the rural elderly (Allan & Cloutier-‐Fisher, 2006; McDonald & Conde, 2010).
Conversely, reduced migration among SLE or Sjogren’s patients may suggest an inability
to migrate when it would be required or desired for other reasons. However, more
detailed data would be required to tease apart conflicting reasons for migrating or
staying in the same location.
43
3 Methodology
This chapter is divided into the three sections. The first section is a brief overview
of the methods and how they relate to the specific objectives. The second section
describes the data sources used. The third section describes the three analyses that were
conducted and the statistical methods used. The McGill University Ethics Review Board
approved this study.
3.1 Overview
The three specific objectives, as described in the introduction to this thesis, will
be achieved by comparing between-‐forward sortation area (FSA; the first three digits of
the postal code) or regional migration rates among SLE and Sjogren’s patients to rates in
two controls groups. The estimates will be derived from physician billing and
hospitalization databases.
The first objective will be met by comparing between-‐FSA migration rates in post-‐
diagnosis SLE and Sjogren’s patients to an age and sex matched sample from the general
population of Montreal, Quebec. This will estimate health-‐selective migration in SLE and
Sjogren’s patients living in Montreal relative to the general population. The second
objective will compare pre-‐ and post-‐diagnosis between-‐FSA migration rates in SLE and
Sjogren’s patients from the entire province of Quebec. This analysis will estimate health-‐
selective migration as relative to pre-‐SLE and pre-‐Sjogren’s populations. The third
44
objective will be met using the same comparison as the second objective but substituting
regional migration rates for between-‐FSA migration rates. This last analysis will estimate
health-‐selective migration in SLE and Sjogren’s patients at regional scales and will allow
comparison with between-‐FSA health-‐selective migration.
3.2 Data sources
The data in this thesis come from a variety of sources. Administrative databases
are used to measure migration events, to determine SLE and Sjogren’s diagnosis and
diagnosis date, and demographic data. Other sources were used for possible
confounders, as described below.
3.2.1 Administrative databases
Quebec is a province of 7.5 million people where healthcare is universally
available to residents, though a 3 month waiting period is required before newcomers
can enrol. Administrative databases are kept in the province recording all medical acts.
This study used two of these Quebec administrative data sources: the Régie d’assurance
maladie du Québec (RAMQ) and the Ministry of Health’s Maintenance et Exploitation des
Données pour l’Étude de la Clientèle Hospitalière (MEDECHO).
The RAMQ is a physician-‐billing database containing information on all
provincially reimbursed physician services including the primary diagnosis assigned by
the physician (or his/her staff) for the patient (identified by the unique provincial
beneficiary number) and the place of treatment. Each physician has a unique identifier,
45
and the RAMQ physician database contains information about the physician, including
his or her specialty. A diagnostic code is required for the physician to receive payment,
but the code is not validated and only one code is allowed per visit. During the years in
question for this thesis, the RAMQ used diagnostic codes according to the ninth version
of the International Classification of Diseases (ICD-‐9). Each claim in the billing database
contains information on the ICD-‐9 code associated with the visit, the date of the
encounter, the FSA of residence of the beneficiary and codes allowing the claims
database to be linked to the RAMQ physician database and a beneficiaries demographics
database. It is important to note that FSA of residence of a beneficiary (i.e. patient) is
only available in years where a physician bills RAMQ for an act performed on that
beneficiary. Another important note is that the FSA of residence associated with each
visit is the FSA of residence on July 1st the year of the visit and not the FSA of residence
the date the visit was made. Therefore, only one FSA is recorded per individual per year.
The beneficiaries’ demographics database contains information about all Quebec
beneficiaries of the provincial health plan including age, sex and date of death. These
databases can further be linked to the provincial hospitalization database, described next
(as well as, for those residents who qualify for provincial drug insurance, to a database of
all drugs dispensed by pharmacies).
The province-‐wide hospital database, MEDECHO, contains admission date,
diagnosis date, a primary diagnosis and up to 15 secondary diagnoses. However, the
diagnoses in MEDECHO are abstracted from a patient’s chart by medical clerks and
therefore not necessarily identical to the diagnoses recorded by the RAMQ.
46
Data were obtained from two different cohorts, one composed of residents for
whom a physician or hospital submitted a claim or a systemic autoimmune rheumatic
disease and a second (serving as a proxy for general-‐population that uses the healthcare
system) was formed of all Quebec residents for whom a physician or hospital submitted
a claim for any flu-‐like illness or symptom (described in detail shortly) in the census
metropolitan area of Montreal, Quebec. A true random sample of RAMQ beneficiaries
was not available, therefore the rationale for using this as a proxy for the general-‐
population was that, aside from chronic diseases, this is one of the most common
reasons, in the general population, for seeking medical care, and would presumably form
a cohort that resembled the general population that accesses medical services in terms of
age, sex, and other unmeasured attributes.
3.2.2 Systemic autoimmune rheumatic diseases cohort
The systemic autoimmune rheumatic diseases cohort that was studied included
all individuals who registered at least one International Classification of Diseases code of
710 between 1989 and 2003 in either the RAMQ physician billing or MEDECHO
hospitalization databases. These databases were linked (using the unique provincial
beneficiary number) to each other and also to the physician and provincial beneficiaries
RAMQ databases allowing all patient, physician and diagnosis information associated
with a claim to be combined. For all patients who had at least one physician or hospital
visit for an ICD-‐9 code of 710, information was available for all medical contact
47
(physician visits and hospitalizations) from January 1, 1989 to the earliest date between
the date of death, emigration out of the province or December 31, 2003.
Individuals in the systemic autoimmune rheumatic diseases cohort were
considered SLE or Sjogren’s patients if they fulfilled at least one of the following three
criteria:
1) Two or more billing diagnoses by any physician at least eight weeks apart but
within two years
2) At least one billing diagnosis by a rheumatologist
3) At least one hospital discharge diagnosis
The diagnoses in question for SLE and Sjogren’s were ICD-‐9 710.0 and 710.2
respectively. The diagnosis date was considered to be the first date at least one of these
three criteria was satisfied.
Regional migration rates by five year age groups in this cohort will be compared
to estimates of regional migration from the Quebec government (Institut de la statistique
Québec, 2007) to check the validity of using administrative data to detect migration
events.
3.2.3 Montreal CMA comparison cohort
To compare migration in SLE and Sjogren’s patients to the general population, we
drew from a general population cohort that contained approximately 86% of the
population of the census metropolitan area (CMA) of Montreal. Inclusion criteria in this
cohort were:
48
1) Having lived at least one year in the census metropolitan area (CMA) of Montreal
(a list of FSAs considered part of the CMA of Montreal can be found in Appendix
A) between 1999 and 2003, inclusively
2) Having had medical contact (physician billing or hospitalization) for at least one
flu-‐like illness or symptom in RAMQ or MEDECHO during the same time period.
Among the conditions considered under flu-‐like illness or symptom are: the flu, the
common cold, pneumonia, bronchitis, sinusitis, laryngitis, pharyngitis and codes for
specific symptoms such as fever, chest pain, sore throat and cough (a full list of ICD-‐9
codes included in this cohort for both RAMQ and MEDECHO databases can also be found
in the Appendix A). All RAMQ and MEDECHO records during the entire time period were
obtained for individuals who satisfied both criteria. The age and sex distribution in all
people living in the CMA of Montreal in 2001 in this cohort will be compared to the 2001
Canadian Census data population estimates for the CMA of Montreal to determine how
representative the sample is (Statistics Canada, 2001a).
The two datasets (Sjogren’s/SLE cohort and the general-‐population cohort) were
obtained from the province in two separate data requests, with the original unique
provincial identifier removed. We could, therefore, not distinguish to what extent some
patients appeared in both. However, given the relatively small number SLE and Sjogren’s
patients in Montreal and the large number of people in the Montreal FSA comparison
cohort, it is unlikely that more than 70 SLE or Sjogren’s patients appear in the 50,000
person sample.
49
3.2.4 Outcomes
Two outcomes will be used in the following analysis: between-‐FSA migration and
regional migration. A between-‐FSA migration will be considered a year when the FSA of
residence has changed relative to the FSA of residence of the previous year. For example,
if the FSA recorded for an individual in 1990 differed from the FSA recorded in 1989, this
was considered a migration event. If the two FSAs were the same, the individual was
considered not to have migrated. Therefore, within-‐FSA migration, for the purposes of
this thesis, will not be considered a migration event. Also, migration can only be
measured from year to year and not on shorter intervals. An individual migrating out of
their FSA and back within one year cannot be measured. Another limitation this poses is
that no specific date can be attributed to the move other than to say it occurred at some
point between July 1st of the previous and current year. A consequence of this is that it is
not possible to tell whether a migration event occurring the same year as an SLE or
Sjogren’s diagnosis happened before or after the diagnosis. Therefore observations
occurring the year of diagnosis were excluded.
The number of migration events in 1992 in the systemic autoimmune rheumatic
diseases cohort was anomalously low in comparison to other years (less than 2% the
number of migration events in other years). The primary analyses were therefore done
excluding this year the analysis. Sensitivity analyses were also done using only data from
1993 to 2003, and the results were unchanged, so only the results from the primary
analyses are presented.
50
In the third part of the analysis, regional migration will be used as an outcome.
Regional migration will be defined similarly to between-‐FSA migration but considering a
change in administrative region instead of a change in FSA.
3.2.5 Potential confounders
Socioeconomic status variables were obtained at the FSA level from the Canadian
Census, which is performed every five years. Data were available from the 2001
Canadian census on percent of households that are renting, employment rate, average
income and a composite education variable (calculated as the proportion of the
population with a university degree, college degree or diploma minus the proportion
who have not graduated high school) (Statistics Canada, 2001b). These variables are
ecological (at the FSA level) and therefore can be both seen as a proxy for the true value
for an individual and as a measure of the neighbourhood average. The SES covariates
associated with an observation are those from the FSA from the previous year in order to
measure the association between the previous location and residential mobility, not the
destination. For example, if a move was recorded in 1990 (i.e. the FSA in 1990 of a
beneficiary differed from the FSA in 1989), SES covariates associated with that move
would be those from the FSA in 1989 and not 1990.
An FSA was considered in a CMA if over 50% of the postal codes within the FSA
fell within one of the six CMAs in Quebec (Montreal, Quebec City, Sherbrooke,
Ottawa/Gatineau, Trois Rivières and the Saguenay). This was done using postal code
conversion files available through the University of Toronto (Statistics Canada, 2002).
51
Data on which FSAs had rheumatologists were obtained from a database of
rheumatologists according to the Quebec Rheumatologist Association (Association des
médecins rhumatologues du Québec, 2010).
3.3 Statistical methods
The statistical methods sections will include a description of the descriptive and
bivariate analyses performed followed by a detailed description of the three main
comparisons.
Table 1 describes the three main comparisons carried out. Each differs in terms of
control group, years included, geographic extent and geographic scale of migration in
order to address specifically each of the three objectives of this thesis. In all three
comparisons, one observation will be considered one person-‐year where the FSA of
residence of the current year and of the previous year are known. Therefore, each
individual can contribute multiple observations: one for each year a migration could
have been measured.
Table 1-Summary of the three comparisons performed
Comparison Patients Controls Years Outcome
1) General population comparison
Subset that meet geographic criteria for Montreal CMA comparison cohort
Age and sex frequency-matched from Montreal CMA comparison cohort
1999-2003 Between FSA migration
2) Pre-diagnosis comparison From entire province
People with SLE or Sjogren's before their diagnosis
1990-2003 (except 1992)
Between FSA migration
3) Regional migration comparison From entire province
People with SLE or Sjogren's before their diagnosis
1990-2003 (except 1992)
Interregional migration
52
3.3.1 Descriptive and bivariate analyses
The number of moves and migration rate (with 95% CI) are calculated for
controls, SLE and Sjogren’s patients within strata of disease duration and ten-‐year age
group to ensure positivity in combinations of age and disease duration.
Regional migration rates by age in SLE and Sjogren’s patients pre-‐ and post-‐
diagnosis are compared to regional migration rates in the Quebec population (Institut de
la statistique Québec, 2007) allowing the assessment of validity of using the RAMQ
billing database to estimate migration rates.
In order to assess the validity of using the Montreal CMA comparison cohort as a
proxy for the general population, the overall population and age distribution of all people
from the Montreal CMA comparison cohort in 2001 is compared to the 2001 Canadian
census estimates for the CMA of Montreal (Statistics Canada, 2001a).
Finally, descriptive statistics (mean and standard deviation) are then calculated
for each variable in the general population comparison and pre-‐diagnosis comparison
datasets. Descriptive statistics for the regional migration analysis are not necessary
because it uses the same data set as the pre-‐diagnosis comparison. Bivariate analyses
were also conducted using hierarchical logistic regression for the same set of variables
described previously in section 3.2. Hierarchical logistic regression was used to account
for multiple observations within individuals. In these analyses, the crude relationship
between disease duration and migration was estimated with the disease main effect in
the model. Also, the relationship between age and migration was estimated with a
quadratic age term.
53
3.3.2 General population comparison
The first part of the health-‐selective migration analysis, which will henceforth be
referred to as the ‘general population comparison’, will compare migration rates in all
Sjogren’s and SLE patients that lived in the CMA of Montreal from 1999-‐2003 to 50,000
age (by five year age groups) and sex frequency-‐matched subjects from the general
population cohort (35,000 matched to SLE patients and 15,000 matched to Sjogren’s
patients) using hierarchical logistic regression. All observations in people younger than
25 years were excluded to avoid measuring migration associated with parental
migration. The date of diagnosis of SLE and Sjogren’s patients used to calculate disease
duration will be calculated using the full range of data from the systemic autoimmune
rheumatic diseases cohort (1989-‐2003) but only observations after or including 1999
are included in the regression. Therefore, up to five observations, from 1999 to 2003,
could be included for each individual in this comparison.
Independent variables included indicator variables for SLE and for Sjogren’s, as
well as disease duration for each, age centered at 50 years, and sex. The FSA-‐level
variables included were the presence of a rheumatologist in the FSA, whether or not the
FSA was in a metropolitan area, and four census variables: employment level (%),
average income (in thousands of dollars), average educational attainment (%) and
proportion of households that are renters (%). A random intercept by individual was
required to account for within-‐individual correlations between observations. Three
models were considered, as detailed in appendix B. The simplest was a model that
54
included a random intercept, Sjogren’s and SLE as indicator variables, sex, age and a
quadratic age term. The quadratic age term was added because both provincial estimates
of migration by age group are non-‐linear. The second model considered included all
terms in model 1 as well as an SLE by age interaction, Sjogren’s by age interaction, SLE
duration and Sjogren’s duration. The last model included all terms in model 2 and all the
potential confounders listed in section 3.2: whether a person lived in an FSA with a
rheumatologist, lived in a census metropolitan area, and FSA-‐level average income,
percent of households that are renters, a composite education variable, and employment
rate.
The presence of age by disease interaction and disease duration was assessed by
whether its inclusion caused important differences in ORs of migration by strata of age
and disease duration. An important difference was considered to be 0.10. Assessment of
confounding was done by examining correlations between possible confounders and
SLE, Sjogren’s and the outcome as well as using a 10% change of estimate criteria
(Mickey & Greenland, 1989) in the OR. Odds ratios (OR) and predicted probabilities, with
95% credible intervals (CrI) were calculated from the selected models. Most migration
rates in these analyses are below ten percent so the OR reasonably approximates the risk
ratio (Sinclair & Bracken, 1994). Because the model is hierarchical, predicted
probabilities were calculated using the mean and standard deviation of the overall mean
intercept.
55
For patients in the systemic autoimmune rheumatic diseases cohort that did not
have medical contact in a specific year, the FSA was unknown for that year. These
observations made up less than 4% of observations in all analyses and were excluded.
All analyses were carried out using Bayesian inference. Bayesian analyses
calculate a joint posterior probability distribution across all parameters of interest by
updating a prior joint density with the likelihood function via Bayes’ Theorem. The prior
probabilities used in this analysis were all diffuse, meaning that they do not contribute
meaningfully to the posterior probabilities of the parameters. Ninety-‐five percent
credible intervals for the parameters are calculated by taking the 2.5% and 97.5%
percentiles of the posterior probability distribution for each parameter. Credible
intervals can be interpreted as an interval with a 95% probability of containing the
parameter of interest as opposed to confidence intervals that rely on long run
probabilities (i.e. the method used to calculated a confidence interval will contain the
parameter of interest 95% of the time, through application to different problems). All
analyses were carried out using WinBUGS 1.43 (Lunn et al. 2000; see appendix C for
exact WinBUGS programs and the parameters of the priors used.).
This comparison gives insight on health-‐selective migration in SLE and Sjogren’s
patients relative to the general population.
3.3.3 Pre-diagnosis comparison
The second comparison, which will be referred to as the ‘pre-‐diagnosis
comparison’, compares between-‐FSA migration in SLE and Sjogren’s patients province-‐
56
wide with migration rates in the same population of patients before their diagnosis using
hierarchical logistic regression. Observations were included from the entire range of the
dataset from 1990 to 2003 (migration events could not be recorded for 1989 because
there was no previous FSA for comparison). Observations of people less than 25 years
old were excluded.
The hierarchical logistic regression and model selection was carried out in the
same manner as in the general population comparison.
This second comparison will be less prone to individual level unmeasured
confounders because it uses migration rates in SLE and Sjogren’s patients before their
diagnosis. However, the diagnosis date is a somewhat artificial distinction between
healthy and SLE or Sjogren’s person-‐time. Patients experience symptoms before their
diagnosis, therefore, the control group in this comparison is likely less healthy than in
the ‘general population comparison’. This analysis will therefore measure how migration
rates can change following diagnosis.
3.3.4 Regional migration comparison
The final analysis, the ‘regional migration comparison’ uses the same data and
criteria as the pre-‐diagnosis comparison except the outcome is defined as a move
between Quebec’s administrative regions (of which there are 17), as opposed to between
FSAs (which are generally much smaller than the administrative regions). The
hierarchical logistic regression and model selection was carried out in the same manner
as the other two comparisons.
57
This comparison will investigate health-‐selective migration over larger distances.
3.3.5 A note on comparisons
It is important to recall that these control groups differ in key ways other than the
descriptors in their names. Firstly, patients from general population cohort are likely
healthier than the SLE or Sjogren’s patients even previous to their diagnosis when they
may suffer from pre-‐clinical symptoms. That is, the majority of those seeking medical
care for a flu-‐like illness would presumably be those without major chronic medical
disease (the distribution would likely match the population that uses medical care).
Secondly, the general population analysis only includes people who lived in the
metropolitan area of Montreal between 1999 and 2003 whereas the pre-‐diagnosis
control analysis includes Sjogren’s and SLE patients from the entire province. Lastly, the
general population control analysis only includes person-‐time from 1999-‐2003 whereas
the pre-‐diagnosis control analysis includes person-‐time from 1989-‐2003 (excluding
1992).
58
59
4 Results
This chapter begins with a section describing the descriptive and bivariate
analyses including a comparison of regional migration rates in SLE and Sjogren’s patients
to provincial estimates of regional migration and a comparison of the Montreal CMA
comparison cohort to the general population of the CMA of Montreal. The following three
sections will describe the results from each of the three comparisons.
4.1 Descriptive and bivariate analyses The sample sizes in both datasets used in this thesis can be found in Table 2.
People without at least one observation either because the FSA was missing or they did
not have two consecutive years where their FSA was known are considered to have
incomplete data and were excluded. The general population comparison included 2,713
people with SLE (85% female with an average age 42.7 years at SLE diagnosis), 1,114
people with Sjogren’s (90% female with an average age 52.2 years at Sjogren’s
diagnosis), and 48,608 (86% female) age and sex frequency matched general population
Table 2—Sample sizes for each analysis
Pre-diagnosis comparison*
General population comparison**
SLE Sjogren's SLE Sjogren's Control Met criteria 5182 2605 3184 1306 50000 Incomplete data 102 60 471 192 1392 In analysis 5080 2545 2713 1114 48608 * total number of people 7010 because of overlap, ** total number of people 52286 because of overlap
60
controls. The pre-‐diagnosis analysis included 5,080 with SLE (85% female with an
average age of 42.6 years at SLE diagnosis) and 2,545 with Sjogren’s (89% female with
an average age of 55.7 years at Sjogren’s diagnosis) people for a total of 7,010 (615
people met the criteria for both SLE and Sjogren’s). Table 3 displays the distribution of
person-‐time and moves in the general population comparison by ten-‐year age groups
and five-‐year periods of disease duration. In Sjogren’s and SLE patients as well as the
general population, moves are concentrated below the age of 85 and in disease duration
from 0-‐9 years.
Table 4A displays the distribution of person-‐time, regional moves and between-‐
FSA moves in the pre-‐diagnosis comparison, by ten-‐year age groups and five-‐year
periods of disease duration. As with the general population comparison, moves are
concentrated below the age of 85 and between disease durations of 0-‐9 years. Regional
moves are particularly sparse at disease durations greater than ten years (Table 4B).
In order to assess the validity of using administrative data to detect migration
events, regional migration rates pre-‐ and post-‐Sjogren’s and SLE diagnosis were
compared to provincial estimates (Figure 1). Pre-‐diagnosis migration rates closely
matched rates observed provincially among people less than 45 years old. After 45, the
migration rates among pre-‐diagnosis individuals are lower than the provincial estimates,
tended to be about 5 to 7 moves per 1,000 people lower. The crude regional migration
rates in Sjogren’s and SLE patients differ slightly from from provincial migration
patterns. The difference is largest in the 25-‐34 year age group where migration is about
50% lower than provincial estimates. This difference decreases in 35-‐44 year olds
61
62
63
and increases again among 45-‐54 year olds. In people over 60, the difference in regional
migration is relatively small.
In order to get an idea of the representativeness of the entire Montreal CMA
comparison cohort (not just the 50,000 in the frequency matched sample), the age and
sex distribution is compared to population estimates from the 2001 Canadian Census of
the CMA of Montreal. Overall, the population in the Montreal CMA comparison cohort is
86% the total population of Montreal (Table 5). By sex, the Montreal CMA comparison
Figure 1—Regional migration rates among SLE and Sjogren’s patients compared to provincial estimates
30 40 50 60 70 80
010
2030
4050
60
Age (years)
Reg
iona
l mig
ratio
n ra
te (m
oves
/100
0 pe
ople
)
Provincial estimatePre-diagnosisSLESjogren's
64
cohort captures between 86% and 94% of women in the Canadian Census and between
77% and 99% of men in every twenty-‐year age year age group. The Montreal CMA
comparison cohort is 53.1% female whereas the Canadian Census is 51.6% female. The
age distribution among women in the Montreal CMA comparison cohort is nearly
identical to the age distribution in the 2001 Canadian Census. The age distribution
among men in the Montreal CMA comparison cohort differs particularly in the age
groups below 40.
Results from the descriptive analysis of the independent variables can be found in
Table 6. In both analyses, Sjogren’s patients were roughly ten years older than SLE
patients as would be expected, since Sjogren’s tends to occur in older people than SLE
!
Table&5&–&Comparison&between&populations&in&the&full&Montreal&CMA&comparison&cohort&to&the&2001&Canadian&Census.&Proportion&of&the&population&found&in&each&age&group&is&found&in&parentheses&to&allow&comparison&of&age&distribution&by&sex&
Sex Age
Population in Montreal
comparison cohort
Population in Canadian Census
Proportion of the population
captured
0-19 380373 (0.24) 404230 (0.23) 0.94 20-39 449929 (0.29) 511105 (0.29) 0.88 40-59 439614 (0.28) 510155 (0.29) 0.86 60-79 239272 (0.15) 273510 (0.15) 0.87 80+ 64155 (0.04) 69715 (0.04) 0.92
Female
Total 1573343 1768715 0.89
0-19 383531 (0.28) 419850 (0.25) 0.91 20-39 390783 (0.28) 505930 (0.31) 0.77 40-59 389281 (0.28) 483000 (0.29) 0.81 60-79 194410 (0.14) 218520 (0.13) 0.89 80+ 30160 (0.02) 30335 (0.02) 0.99
Male
Total 1388165 1657635 0.84 Entire population 2961508 3426350 0.86
!
65
66
(Gaubitz, 2006). Of interest, the proportion of females in the pre-‐diagnosis analysis was
lower in the controls than in either SLE or Sjogren’s patients. This may be unexpected
since they are composed entirely of pre-‐SLE or pre-‐Sjogren’s patients. This result comes
about because the proportion of females among patients with no migration data after
diagnosis is approximately 10% lower than the proportion of females among patients
with migration data before diagnosis. Percent renters, education level, employment rate
and income are similar between all groups in each analysis. People in the general
population comparison were more likely to live in a metropolitan area because this was
part of the entry criteria. These values were below 100% because people were only
required to live in Montreal for one year during this period. Controls in the general
population comparison were less likely to live in an FSA with a rheumatologist.
Bivariate logistic regression analyses (Table 7) suggest that SLE and Sjogren’s
both have lower migration rates than the comparator, in all three comparisons. Disease
duration in SLE patients is associated with reduced migration but the relationship in
Sjogren’s patients is unclear (the 95% credible intervals include a wide range of values
on each side of the null). Females were less likely to migrate in the general population
comparison but more likely to migrate in the pre-‐diagnosis comparison. Living in a CMA
was associated with decreased odds of migration in the general population and regional
migration comparisons and was associated with increased odds of migration in the pre-‐
diagnosis comparison. This association in the general population comparison may be in
part an artifact because of living in the CMA of Montreal was an entry criterion for the
comparator cohort. Living in an FSA with a rheumatologist was associated with
67
increased odds of migration in the general population comparison, inconclusive in the
pre-‐diagnosis comparison and associated with decreased odds of migration in the
regional migration comparison. The pattern in the FSA-‐level SES variables was different
in all three comparisons except for income, which was associated with decreased odds of
migration.
4.2 Health-selective migration comparisons
Model 2, with SLE, Sjogren’s, disease duration, age, sex, and age by disease
interactions, was retained in each comparison. Disease by age interactions and disease
Table 7—Coefficients (and 95% CrI) from bivariate hierarchical logistic regression analyses in the healthy control, pre-diagnosis and regional migration analyses
Variable General population
comparison Pre-diagnosis comparison
Regional migration comparison
SLE -0.407 (-0.508,-0.309) -0.285 (-0.361,-0.207) -0.400 (-0.535,-0.265)
SJO -0.475 (-0.638,-0.319) -0.385 (-0.494,-0.273) -0.589 (-0.790,-0.386) disease duration in SLE* -0.202 (-0.332,-0.077) -0.171 (-0.265,-0.082) -0.124 (-0.276,0.025)
disease duration in Sjogren's* 0.162 (-0.084,0.402) -0.010 (-0.162,0.142) 0.082 (-0.211,0.369)
female -0.052 (-0.108,-0.002) 0.087 (-0.020,0.203) 0.018 (-0.179,0.229) Census metropolitan area -0.268 (-0.315,-0.220) 0.433 (0.346,0.523) -0.321 (-0.467,-0.179)
Rheumatologist in FSA 0.107 (0.062,0.150) 0.042 (-0.054,0.137) -0.176 (-0.339,-0.013)
Household that are renters (%)** 0.046 (0.038,0.054) 0.071 (0.052,0.089) -0.085 (-0.114,-0.055)
Education (%)** 0.051 (0.034,0.071) -0.088 (-0.124,-0.055) 0.116 (0.053,0.176) Employment rate (%)** -0.071 (-0.093,-0.048) 0.093 (0.043,0.135) 0.178 (0.099,0.259)
Average income (000’s)** -0.061 (-0.071,-0.051) -0.020 (-0.041,0.001) -0.070 (-0.112,-0.024)
age*** -0.044 (-0.044,-0.042) -0.030 (-0.032,-0.027) -0.033 (-0.038,-0.028)
age2*** 1.02E-03 (9.44E-4,1.10E-3) 8.28E-04 (6.82E-4,9.86-4) 6.06E-4 (3.32E-4,8.75E-4) * estimated with disease main effect, coefficient per five year increase ** coefficient per ten unit increase *** estimated in the same model
68
duration both changed the OR point estimates associated with SLE and Sjogren’s in
different age groups and disease durations by greater than 0.10. Model 3 was not
retained because none of the potential confounders was strongly related to disease
status and their addition to model 2 did not change the estimates of OR point estimate
for the effect of SLE or Sjogren’s appreciably (maximum change in ORs of 2.6%). The
presence of interactions between disease and age indicates that measures of association
are heterogeneous across strata of age and disease duration. Therefore, ORs are
presented for each stratum.
In these comparisons, a complex picture of health-‐selective migration emerges
which involves the interplay between disease, disease duration and age. Odds ratios for
migration in SLE and Sjogren’s, relative to general population controls, by strata of age
and disease duration, are presented in Table 8.
Table 8—Odds ratios (95% CrI) of migration relative to controls by age and disease duration strata
Disease Age Disease duration
General population control analysis
Pre-diagnosis comparison
Regional migration comparison
30 2 0.41 (0.28-0.56) 0.55 (0.43-0.69) 0.50 (0.32-0.74) 50 2 0.66 (0.51-0.83) 0.74 (0.63-0.86) 0.62 (0.46-0.82) 70 2 1.09 (0.81-1.42) 1.01 (0.84-1.20) 0.79 (0.55-1.10)
30 10 0.59 (0.40-0.83) 0.61 (0.46-0.79) 0.64 (0.37-1.01) 50 10 0.95 (0.73-1.22) 0.82 (0.67-1.00) 0.79 (0.54-1.11)
Sjogren's
70 10 1.56 (1.18-2.02) 1.11 (0.90-1.37) 0.99 (0.65-1.44) 30 2 0.54 (0.45-0.64) 0.68 (0.60-0.77) 0.58 (0.47-0.72) 50 2 0.83 (0.71-0.96) 0.87 (0.79-0.96) 0.76 (0.64-0.90) 70 2 1.29 (1.04-1.58) 1.12 (0.95-1.31) 1.01 (0.74-1.34)
30 10 0.38 (0.32-0.44) 0.53 (0.46-0.61) 0.50 (0.39-0.63) 50 10 0.59 (0.51-0.67) 0.68 (0.61-0.76) 0.65 (0.53-0.79)
SLE
70 10 0.91 (0.74-1.11) 0.88 (0.73-1.03) 0.86 (0.62-1.15)
69
4.2.1 General population comparison
In the general population comparison, both SLE and Sjogren’s are associated with
reduced migration in young people, regardless of disease duration (Table 8, column 1).
For example, in 30 year olds with two years of disease duration the OR of migration is
0.54 (95% CrI: 0.45-‐0.64) in SLE patients and 0.41 (95% CrI: 0.28-‐0.56) in Sjogren’s
patients. Within strata of disease duration, increasing age increases the OR towards the
null, and in some cases crosses the null. For example, higher odds of migration are
suggested in 70-‐year-‐old SLE patients with two years of disease duration (OR: 1.29, 95%
CrI: 1.04-‐1.58) and 70-‐year-‐old Sjogren’s patients with ten years of disease duration (OR:
1.56, 95% CrI: 1.18-‐2.02).
Disease duration among Sjogren’s patients is associated with an increased
probability of moving (OR for a five year increase in disease duration=1.26, 95% CrI:
0.99-‐1.61). The association between disease duration and migration is qualitatively
different among SLE patients, where the OR for a five year increase in disease duration is
0.80 (95% CrI: 0.71-‐0.92). For this reason, the lowest ORs (for the effect of disease on
migration) are found among younger SLE patients with longer disease duration and
younger Sjogren’s patients with shorter disease duration. Similarly, the highest ORs (for
the effect of disease on migration) are found in older SLE patients with shorter disease
duration and in older Sjogren’s patients with longer disease duration. Only the general
population comparison found females had different odds of migration compared to
males (OR:0.83 95% CrI: 0.80-‐0.88).
In individuals with either SLE or Sjogren’s, age and disease duration necessarily
70
increase together. Therefore it is of interest to examine how migration changes as both
disease duration and age change together. Table 9 presents a progression of ORs for
people diagnosed with SLE or Sjogren’s at 30 and at 50 years old (column 1). Older ages
are not presented because the few events in these categories means that error bars are
wide and thus the results are uninformative.
Migration generally decreases with age in all groups (with the exception of ages
greater than around 70 where it increases slightly again), therefore the effect of Sjogren’s
duration tends to counteract the effect of age. However, the effect of SLE duration works
in concert with it. This can be seen in the first column of table 8. Sjogren’s is associated
with a decrease in migration early after disease diagnosis but, for both people diagnosed
at 30 and 50, the ORs trend back toward the null, with increasing disease duration.
Therefore, the credible interval of the Sjogren’s/migration association among older
Table 9—The progression of odds ratios (95% CrI) relative to controls for people diagnosed at 30 and 50 years old for all three comparisons
Disease Age Disease Duration
General population comparison
Pre-diagnosis comparison
Regional migration
comparison
32 2 0.43 (0.30-0.58) 0.57 (0.45-0.70) 0.51 (0.33-0.75) 35 5 0.52 (0.40-0.68) 0.61 (0.50-0.73) 0.57 (0.40-0.79)
40 10 0.75 (0.55-1.00) 0.70 (0.55-0.88) 0.71 (0.45-1.04)
52 2 0.70 (0.54-0.87) 0.77 (0.66-0.88) 0.64 (0.47-0.83)
55 5 0.85 (0.73-1.00) 0.83 (0.74-0.93) 0.72 (0.58-0.87)
Sjogren's
60 10 1.22 (0.94-1.54) 0.95 (0.78-1.16) 0.88 (0.60-1.23)
32 2 0.56 (0.47-0.67) 0.70 (0.62-0.78) 0.60 (0.49-0.73)
35 5 0.52 (0.46-0.59) 0.66 (0.6-0.72) 0.59 (0.50-0.69)
40 10 0.47 (0.41-0.54) 0.60 (0.54-0.68) 0.57 (0.46-0.69)
52 2 0.87 (0.74-1.01) 0.89 (0.81-0.99) 0.78 (0.65-0.94)
55 5 0.81 (0.72-0.91) 0.85 (0.77-0.92) 0.77 (0.65-0.90)
SLE
60 10 0.73 (0.62-0.86) 0.77 (0.67-0.88) 0.75 (0.58-0.94)
71
Sjogren’s patients is consistent with anywhere from a slightly negative to a strongly
positive association with migration. For people diagnosed with Sjogren’s at a younger
age the credible interval is consistent with anywhere from a strongly negative
association between Sjogren’s and migration to a null association. In SLE patients,
disease duration further decreases the ORs to low levels among 60 year olds diagnosed
at 50 (OR 0.72, 95% CrI: 0.61-‐0.84) and to very low levels among 40 year olds diagnosed
at 30 (OR 0.48, 95% CrI: 0.42-‐0.55).
In order to illustrate absolute migration rates, Figure 2 graphs the predicted
probabilities of migration in women diagnosed with SLE or Sjogren’s at 30 and 50 years.
These results are presented specifically in women because calculating predicted
probabilities from logistic regression requires specification of all variables in the model.
Predicted probabilities in men were 2-‐7% higher depending on the age but the overall
ORs and conclusions were similar. Migration rates in Sjogren’s patients diagnosed at 30
tend to remain stable up to age 50 (with wide credible intervals) at around 7%. In this
same age range, the migration rate among general population controls goes from 17% to
7%. SLE patients diagnosed at 30 see their migration rate decrease from 9% to 3%.
Among people diagnosed at 50, predicted probability of migration remains lower than
controls in SLE. In Sjogren’s, the predicted probability of migration increases with age
whereas it decreases in controls. After 15 years of disease duration, Sjogren’s patients
diagnosed at age 50 have a higher predicted probability of migration than general
population controls.
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4.2.2 Pre-diagnosis comparison
Using pre-‐diagnosis SLE and Sjogren’s patients as a comparator yields results that
are similar to the general population comparison, except the point estimates are slightly
closer to the null (though the CrIs all overlap). SLE and Sjogren’s are uniformly
associated with lower odds of migration at younger ages (30-‐50 years) regardless of
Figure 2 – Predicted probabilities of migration in women diagnosed at 30 and 50 years in the general population comparison with 95% CrI error bars
30 40 50 60
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diagnosed at 30 diagnosed at 50
73
disease duration (Table 8, column 2). The lowest and highest ORs among Sjogren’s
patients are also found in younger patients with short disease duration (OR 0.55, 95%
CrI: 0.43-‐0.69) and in older patients with long disease duration (OR 1.11, 95% CrI: 0.90-‐
1.37), respectively. The lowest and highest ORs among SLE patients are found in younger
patients with long disease duration (OR 0.53, 95% CrI: 0.46-‐0.61) and in older patients
with short disease duration (OR 1.12, 95% CrI: 0.95-‐1.31), respectively.
Five years of disease duration among Sjogren’s patients was associated with an
OR of 1.06 (95% CrI: 0.91-‐1.25), which is inconclusive because the CrI is wide and
includes important values above and below the null. In SLE patients, five years of disease
duration was associated with an OR of 0.89 (95% CrI: 0.79-‐0.94).
The progression of ORs with age and disease duration among SLE and Sjogren’s
patients diagnosed at 30 and 50 (table 8, column 2) follows a similar pattern to the
general population comparison. However, in Sjogren’s patients, the ORs tend to change
less quickly in the pre-‐diagnosis comparison than in the general population comparison.
For example when diagnosed at 50, the ORs in Sjogren’s patients increases from 0.77 to
0.95 in the pre-‐diagnosis comparison whereas it increases from 0.70 to 1.22 in the
general population comparison. In SLE patients, the progression of ORs with age and
disease duration is roughly the same in the general population and pre-‐diagnosis
comparisons though the estimates tend to be slightly lower in the former.
Predicted probabilities of migration (Figure 3) follow a similar pattern to that of
the general population analysis. Migration rates in SLE patients diagnosed at 30 and at
50 remain lower than migration rates of pre-‐diagnosis patients. Sjogren’s patients
74
diagnosed at 30 migrate at lower rates, although at 15 years of disease duration the
credible intervals are wide and include the null values. In Sjogren’s patients diagnosed at
50, there is a suggestion that migration rates may be higher than controls at a disease
Figure 3 – Predicted probabilities of migration in people diagnosed at 30 and 50 years in the pre-diagnosis analysis with 95% CrI error bars.
30 40 50 60
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Sjogren'sSLEPre-diagnosis
diagnosed at 30 diagnosed at 50
75
duration of 15 years, though, again, the credible interval are very wide.
4.2.3 Regional migration comparison
In the ‘regional migration comparison’ interregional migration is used as the
outcome. The OR point estimates are almost all lower than the pre-‐diagnosis comparison
(Table 8, column 3). The ORs estimating the effects of SLE and Sjogren’s on migration, do
tend to follow the same patterns (for age groups and disease durations) as was found in
the previous two comparison, but the credible intervals are wider because there were
fewer inter-‐regional moves than between-‐FSA moves.
Regional migration in Sjogren’s patients is lowest relative to pre-‐diagnosis
patients at younger ages and short disease duration (OR: 0.50, 95% CrI: 0.32-‐0.74 among
30 year old patients with two years of disease duration) and highest at older ages and
longer disease duration (OR: 0.99, 95% CrI: 0.65-‐1.44 among 70 year old patients with
ten years of disease duration). In SLE patients, migration relative to pre-‐diagnosis
patients is lower at younger ages with long disease duration (OR: 0.50, 95% CrI: 0.39-‐
0.63 among 30 year old patients with ten years of disease duration) and highest among
older patients with short disease duration (OR: 1.01, 95% CrI: 0.74-‐1.34 among 70 year
old patients with two years of disease duration).
Five years of disease duration is associated with an OR of 1.15 (95% CrI: 0.85-‐
1.54) in Sjogren’s patients and an OR of 0.90 (95% CrI: 0.78-‐1.05) in SLE patients. As
disease duration and age increase (Table 9, column 3) SLE patients migrate between
76
regions at about the same rate relative to controls. In Sjogren’s, health-‐selective
migration decreases (becomes closer to the null) with age and disease duration.
In absolute terms, wide confidence intervals make it difficult to draw conclusions
but for SLE, as in other analyses, patients migrate at lower rates than controls both for
patients diagnosed at age 30 and age 50 (Figure 4). Migration rates for Sjogren’s patients
approach the migration rates of controls, as disease duration increases.
77
Figure 4 – Predicted probabilities of migration of people diagnosed at 30 and 50 years in the regional migration analysis with 95% CrI error bars
30 40 50 60
0.000
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79
5 Discussion
Overall, SLE and Sjogren’s patients in Quebec appear to migrate less often than
either the general population or pre-‐diagnosis SLE and Sjogren’s patients. However, age
and disease duration are important effect measure modifiers, and, for some
combinations of age and disease duration, SLE and particularly Sjogren’s were associated
with increased odds of the migration. Depending on age and disease duration, SLE can be
associated with approximately 62% lower odds of migrating and 29% higher odds of
migration relative to general population controls, and 47% lower odds of migration and
possibly slightly increased odds of migrating relative to pre-‐diagnosis controls. Similarly,
Sjogren’s is associated with between 59% lower odds and 56% higher odds of migration
relative to general population controls, and 45% lower odds and slightly higher odds of
migration relative to pre-‐diagnosis controls. ORs estimates for regional migration are
similar to estimates in pre-‐diagnosis controls.
As expected, estimates of health-‐selective migration in the general population
comparison are further from the null than the pre-‐diagnosis controls, though the
credible intervals overlap. Also, the estimates for regional moves among pre-‐diagnosis
controls are further from the null than between-‐FSA migration although, again, the
credible intervals overlap. Disease duration appears to have a different effect among SLE
and Sjogren’s patients, decreasing the odds of migration in the former and increasing the
odds in the latter. This may indicate that Sjogren’s patients (who on average will have
80
less severe manifestations than SLE patients) are better able to adapt to their disease
while disease progression in SLE makes it increasingly difficult to migrate. It was not
expected that Sjogren’s patients would have lower odds of migration shortly after
disease onset than SLE patients, though this is reversed when looking among patients
with ten years of disease duration.
Comparisons of provincial migration estimates to regional migration rates
estimated from administrative data demonstrate that it is feasible to estimate migration
using administrative data.
5.1 How the results compare to the literature
Findley (1988) described the concept of ‘health-‐migration relations with dynamic
effects’, and hypothesized that at disease onset, a relatively minor condition (e.g. like
Sjogren’s) could experience a stronger decrease in migration when compared to the
serious condition (i.e. SLE) because the lag between health change and migration would
be shorter in the former. As disease duration increase, this trend would reverse with
disease progression. This pattern, though based entirely on theory and not on empirical
evidence, precisely what is seen in this thesis. No other study looked specifically at
disease duration.
The finding that age is an effect measure modifier for the health-‐migration
relationship is found in a number of other studies (Bentham, 1988; Boyle et al., 2002;
Halliday & Kimmitt, 2008; Riva et al., 2011). The often observed pattern of young
migrants being healthier than non-‐migrants and older migrants being as or less healthy
81
than non-‐migrants found in the literature can found in this thesis as well. This is
particularly evident when looking at the literature on the relationship between disability
and migration. The qualitative differences found in short versus regional-‐scale migration
are not observed in this thesis (Boyle et al., 2002) but this may be because between FSA
migration is not sufficiently small scale to observe this difference. Methodologically,
Yiannakoulias et al. (2007) is the most comparable study to the results presented in my
thesis because it also used administrative data and looked at specific illnesses. However,
Yiannakoulias et al. used matching and waere thus unable to account for age interactions.
Also, Yiannakoulias et al. considered migration between six digit postal codes, which
would include many moves on a shorter scale (as compared to the analyses in this thesis,
which was unable to examine moves with greater detail than between FSAs).
The finding that older SLE and Sjogren’s patients may migrate more than controls
is consistent with many of the findings using the US Longitudinal Study on Aging which
found that both activities of daily living and instrumental activities of daily living were
associated with increased migration (Longino et al., 1991; Speare et al., 1991). SLE and
Sjogren’s are associated with an increase in disability making these references
particularly relevant.
Silverstein and Zablotsky (1996) found that intermediate disability levels are
associated with increased migration among people 70 years and older. Sjogren’s incurs
less disability than SLE but more disability than a person in good health, therefore that
finding is also consistent with the results presented in this thesis. However, two other
studies found the reverse relationship where migration peaked at poor and excellent
82
health rather than at intermediate health (Evandrou et al., 2010; Halliday & Kimmitt,
2008).
5.2 Strengths
Verheij et al. (1998) state that, “to investigate [health-‐]selection effects,
longitudinal data are needed on large numbers of people, including data on mobility and
(past) illnesses and health risk factors.” Administrative data provide this type of data for
mobility and illnesses by allowing observation of the same individual both before and
after diagnosis, in this case, with SLE or Sjogren’s. This type of data also allows the
identification of the temporal order of health and migration events. When comparing
migration pre-‐ and post-‐diagnosis, this design can help reduce confounding to a certain
extent for some variables, such as race and SES, with the notable exception of age, which
can be controlled for.
Administrative data have some important advantages over surveys, which many
studies have used to study health-‐selective migration. Survey data can be biased due to
non-‐response whereas administrative data, particularly in Quebec, includes all residents,
at least in theory. Surveys also rely on both migration and health self-‐report, which can
be prone to bias due to not understanding questions, difficulty recalling the information
asked, and social desirability bias (Johnson & Fendrich, 2005; Verheij et al., 1998). Also,
the inclusion of two control groups with different strengths and weaknesses allows the
findings in this thesis to be checked for robustness. The general population comparison
provides a comparison to the general health-‐care using population but may be more
prone to unmeasured confounding because it was not possible to match on variables
83
such as SES and race. The pre-‐diagnosis and regional analyses use the same population
pre-‐ and post-‐diagnosis making confounding less likely. Despite the fact that the control
groups have different health profiles, the similarity of the results and consistency of the
effect measure modifiers points to the robustness of the findings.
5.3 Limitations
The results of this thesis are must be considered in the context of some limitations.
The most important limitation of this thesis is the accuracy of administrative databases
with regard to SLE and Sjogren’s diagnosis. A systematic review of validation studies of
using administrative databases to identify rheumatic diseases reported sensitivities
ranging from 20% to 100% and specificities ranging from 74% to 97.1% (Widdifield et
al., 2011). Bernatsky et al. (2011) used the same criteria as this thesis to identify cases in
administrative data from Nova Scotia and found a sensitivity of 98.2% (95% CI: 95.5-‐
99.3) and specificity of 72.5% (95% CI: 68.7-‐75.9) for SLE and a sensitivity of 95.5%
(95% CI: 88.8-‐98.2) and specificity of 95.8% (95% CI: 94.1-‐97.0) for Sjogren’s. The
controls used were patients with other rheumatic diseases, therefore, the specificity of
SLE diagnosis is likely much higher in the general population. Administrative databases
are also prone to the same problems as morbidity measures as measured by physicians,
which is a function not only of ill-‐health but also of people’s propensity to visit the doctor
and of the doctor’s diagnostic and recording habits (McAlister, 2004).
Larson et al. (2004) address other limitations that are pertinent to this thesis such
as movers are more likely to be single and live alone. Data on civil status were not
84
available. However, additional analyses restricted to women and using the ICD code for
childbirth as a proxy for civil status produced values consistent with the findings of this
thesis. Larson et al. also point out that the size and shape of zones against which
migration is measured may confound results. This may be relevant to the analyses in this
thesis because of the diversity of shapes and sizes of FSAs both on the island of Montreal
and province wide. The area of FSAs varies from 0.33 km2 for an FSA in Montreal to
nearly 500,000 km2 for an FSA that covers a large portion of the north of the province
(Statistics Canada, 2006). Further complications arise because FSAs in smaller cities and
towns can be entirely contained inside a rural FSA. Both these characteristics of FSAs
mean that a long distance move in rural areas are less likely to be captured in this
analysis whereas shorter urban moves are more likely to be captured. It is difficult to
speculate on how this limitation might bias this analysis.
Another limitation of the data was the reason for extremely low levels of
migration in 1992 in the systemic autoimmune rheumatic diseases cohort was not clear.
The comparison of regional migration rates in the systemic autoimmune rheumatic
diseases cohort to provincial estimates reassures, to a certain degree, that the data on
migration for the remaining years is valid. Also, the number of migration events in the
years before and after 1992 are consistent and close to what would be expected.
Disease duration was measured with error among cases that were incident prior
to 1989. This measurement error would be concentrated among older patients who were
more likely to be diagnosed prior to 1989. Also, disease duration is taken as starting
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from the date of diagnosis while pre-‐clinical symptoms of SLE and Sjogren’s may
influence migration in the pre-‐diagnosis and regional analyses.
Controlling for SES using variables at the FSA level is not ideal and implies
measurement error. SES has been found to be related to the incidence, disease severity
and manifestation of SLE (Demas & Costenbader, 2009; Studenski et al., 1987; Sutcliffe et
al., 1999) though the relative causal role played by genetics and social variables is in
question (Sule & Petri, 2006). SES, as measured by education (Dahl, 2002), income (Islam
& Choudhury, 1991; Yankow, 1999) and employment (Hacker, 2000), is also related to
migration making SES a potential confounder. However, a number of studies looking at
income have not found evidence that it is either a confounder (Larson et al., 2004; Riva et
al., 2011) or mediator (Zimmerman et al., 1993). Nonetheless, it is conceivable that the
lack of individual level SES measures induces bias into the analysis. The direction and
magnitude of this bias would depend on the direction and magnitude of the health-‐SES
and migration-‐SES relationships. Many SES variables are likely simultaneously mediators
and confounders. If this is the case, the only appropriate way to control for them is using
time-‐series data using appropriate models such as marginal structural models.
Lastly, ethnicity may be a possible confounder because it is a risk factor for SLE
(Danchenko et al., 2006; McCarty et al., 1995; Peschken & Esdaile, 2000; Serdula &
Rhoads, 1979) and Sjogren’s (Schein et al., 1999). Race is also related to migration (Frey
et al., 2005; Robinson, 1993). As with civil status, data on race were not available and
was, therefore, unable to be controlled for.
86
5.4 Overall Conclusions
This thesis provides evidence that chronic diseases can impact migration rates
and that this impact can vary by age, disease and disease duration. It is one of the first
uses of administrative data to measure health-‐selective migration.
These results may indicate that when people are diagnosed with either SLE or
Sjogren’s at a young age, their illness prevents moves that they would have made had
they been healthy. The reason for this may be to remain near specialty care, family or
friends, or because the disabilities associated with these illnesses may make it too
difficult to migrate. Older SLE and particularly Sjogren’s patients demonstrate a
tendency toward increased migration relative to the general population possibly
indicating that migration may be necessary to be closer to care or because they are
unable to function sufficiently in their own home. Both of these possibilities may indicate
shortcomings in care received by these patients.
This thesis also suggests that patterns in SLE prevalence could potentially be
influenced by health-‐selective migration. If the migrations SLE patients are making or
not making are of a specific kind, for example, if they were more inclined to move into an
urban area or less inclined to move out of an urban area because of the presence of
specialized care, this could explain, in part at least, higher prevalence of SLE in urban
areas. If pre-‐clinical symptoms also cause this type of health-‐selective migration, it could
also explain patterns in incidence. In older age groups, patterns of Sjogren’s prevalence
could be influenced directly by patients moving to specific areas for specific reasons.
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5.5 Future Research
There are a number of avenues for future research in health-‐selective migration in
rheumatic diseases. Firstly, health-‐selective migration should be estimated in different
rheumatic diseases as different disabilities and symptoms may affect migration in
different ways. Secondly, health-‐selective migration post-‐diagnosis may affect
prevalence of a condition but health-‐selective migration may also operate pre-‐diagnosis
due to pre-‐clinical symptoms. If health-‐selective migration can occur pre-‐diagnosis,
regional estimates of incidence as well as prevalence would be affected. Thirdly, research
should be done on whether this health-‐selective migration has any specific geographic
destinations such as toward urban areas or away from areas with poor health services.
Lastly, very little work has been done validating addresses or postal codes from
administrative databases. Validation studies of addresses from administrative databases
could make estimates of health-‐selective migration using these databases more reliable.
88
89
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Appendix A—Forward sortation areas and ICD-9 codes used in this thesis
FSAs considered part of the CMA of Montreal: H1A, H1B, H1C, H1E, H1G, H1H, H1J, H1K, H1L, H1M, H1N, H1P, H1R, H1S, H1T, H1V, H1W, H1X, H1Y, H1Z, H2A, H2B, H2C, H2E, H2G, H2H, H2J, H2K, H2L, H2M, H2N, H2P, H2R, H2S, H2T, H2V, H2W, H2X, H2Y, H2Z, H3A, H3B, H3C, H3E, H3G, H3H, H3J, H3K, H3L, H3M, H3N, H3P, H3R, H3S, H3T, H3V, H3W, H3X, H3Y, H3Z, H4A, H4B, H4C, H4E, H4G, H4H, H4J, H4K, H4L, H4M, H4N, H4P, H4R, H4S, H4T, H4V, H4W, H4X, H4Y, H4Z, H5A, H5B, H7A, H7B, H7C, H7E, H7G, H7H, H7J, H7K, H7L, H7M, H7N, H7P, H7R, H7S, H7T, H7V, H7W, H7X, H7Y, H8N, H8P, H8R, H8S, H8T, H8Y, H8Z, H9A, H9B, H9C, H9E, H9G, H9H, H9J, H9K, H9P, H9R, H9S, H9W, H9X, J0K, J0L, J0N, J0P, J0R, J0S, J0V, J3E, J3G, J3H, J3L, J3N, J3V, J3X, J3Y, J3Z, J4B, J4G, J4H, J4J, J4K, J4L, J4M, J4N, J4P, J4R, J4S, J4T, J4V, J4W, J4X, J4Y, J4Z, J5A, J5B, J5C, J5K, J5L, J5M, J5R, J5T, J5W, J5X, J5Y, J5Z, J6A, J6J, J6K, J6N, J6R, J6V, J6W, J6X, J6Y, J6Z, J7A, J7B, J7C, J7E, J7G, J7H, J7J, J7K, J7L, J7M, J7N, J7P, J7R, J7T, J7V, J7W, J7X, J7Y, J7Z, J8H, J8L RAMQ ICD-9 codes for entry into the influenza-like illness cohort: 010, 0100, 0101, 0108, 0109, 011, 0110, 0111, 0112, 0113, 0114, 0115, 0116, 0117, 0118, 0119, 020, 0203, 0204, 0205, 0219, 022, 0221, 024, 0249, 025, 0259, 032, 0320, 0321, 0322, 0323, 0329, 033, 0330, 0331, 0338, 0339, 034, 0340, 0529, 0551, 073, 0739, 0741, 0790, 0793, 0798, 0799, 0830, 1124, 1149, 115, 1150, 1151, 1159, 1309, 1363, 382, 3820, 3824, 3829, 460, 4609, 461, 4618, 4619, 462, 4629, 463, 4639, 464, 4640, 4641, 4642, 4643, 4644, 465, 4650, 4658, 4659, 466, 4660, 4661, 4789, 480, 4800, 4801, 4802, 4808, 4809, 481, 4819, 482, 4820, 4821, 4822, 4823, 4824, 4828, 4829, 483, 4839, 484, 4841, 4843, 4845, 4846, 4847, 4848, 485, 4859, 486, 4869, 487, 4870, 4871, 4878, 490, 4909, 491, 4910, 4911, 4918, 4919, 507, 5070, 5071, 5078, 511, 5110, 5111, 5118, 5119, 513, 5130, 5131, 518, 5180, 5184, 5188, 5192, 7806, 7841, 7860, 7861, 7862, 7865, 7953, V018
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MEDECHO ICD-9 codes for entry into the influenza-like illness cohort: 0100, 0101, 0108, 0109, 0110, 0111, 0112, 0113, 0114, 0115, 0116, 0117, 0118, 0119, 0203, 0204, 0205, 0219, 0221, 0249, 0259, 0320, 0321, 0322, 0323, 0329, 0330, 0331, 0338, 0339, 0340, 0529, 0551, 0739, 0741, 0790, 0793, 0798, 0799, 0830, 1124, 1149, 1150, 1151, 1159, 1309, 1363, 3820, 3824, 3829, 4609, 4618, 4619, 4629, 4639, 4640, 4641, 4642, 4643, 4644, 4650, 4658, 4659, 4660, 4661, 4789, 4800, 4801, 4802, 4808, 4809, 4819, 4820, 4821, 4822, 4823, 4824, 4828, 4829, 4839, 4841, 4843, 4845, 4846, 4847, 4848, 4859, 4869, 4870, 4871, 4878, 4909, 4910, 4911, 4918, 4919, 5070, 5071, 5078, 5110, 5111, 5118, 5119, 5130, 5131, 5180, 5184, 5188, 5192, 7806, 7841, 7860, 7861, 7862, 7865, 7953, V018, 01000, 01001, 01002, 01003, 01004, 01005, 01009, 01010, 01011, 01012, 01013, 01014, 01015, 01019, 01080, 01081, 01082, 01083, 01084, 01085, 01089, 01090, 01091, 01092, 01093, 01094, 01095, 01099, 01100, 01101, 01102, 01103, 01104, 01105, 01109, 01110, 01111, 01112, 01113, 01114, 01115, 01119, 01120, 01121, 01122, 01123, 01124, 01125, 01129, 01130, 01131, 01132, 01133, 01134, 01135, 01139, 01140, 01141, 01142, 01143, 01144, 01145, 01149, 01150, 01151, 01152, 01153, 01154, 01155, 01159, 01160, 01161, 01162, 01163, 01164, 01165, 01169, 01170, 01171, 01172, 01173, 01174, 01175, 01179, 01180, 01181, 01182, 01183, 01184, 01185, 01189, 01190, 01191, 01192, 01193, 01194, 01195, 01199, 05290, 05291, 05297, 05298, 05299, 51881, 51882, 51889, 78650, 78651, 78652, 78659
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APPENDIX B—Models considered in analyses Each model uses the notation of observation i within individual j: Model 1
€
logit(moveij ) = α j + β1 * SLEij + +β2 * Sjogren'sij + β3 * ageij + β4 * age2ij + β5 * femaleij
Model 2
€
logit(moveij ) = α j + β1 * SLEij + +β2 * Sjogren'sij + β3 * ageij + β4 * age2ij + β5 * femaleij +
β6 * SLEij * ageij + β7 * Sjogren'sij * ageij + β8 * SLE diseasedurationij +
β9 * Sjogren'sdiseasedurationij
Model 3
€
logit(moveij ) = α j + β1 * SLEij + +β2 * Sjogren'sij + β3 * ageij + β4 * age2ij + β5 * femaleij +
β6 * SLEij * ageij + β7 * Sjogren'sij * ageij + β8 * SLE diseasedurationij +
β9 * Sjogren'sdiseasedurationij + β10 *educationij + β11 * incomeij + β12 * rentij + β13 *employmentij +
β14 *CMAij + β15 * rheumato logist in FSAij
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APPENDIX C – Sample WinBUGS code Example of WinBUGS code for model 2: model { for (j in 1:(number of individuals)) # loop 1: over every individual { for (i in index[j]:index2[j]) # loop 2: over every observation within each individual { logit(p[i]) <- alpha[j] + b.SLE*SLE[i] + b.SJO*SJO[i] + b.female*female[i] + b.agec*agec[i] + b.age2*age2[i] + b.SLE.age*SLE.age[i] + b.SJO.age*SJO.age[i] b.dd.SLE*dd.SLE[i] + b.dd.SJO*dd.SJO[i] move[i] ~ dbern(p[i]) # distribution of the outcome } # end loop 1 alpha[j] ~ dnorm(mu,tau) # random intercept } # end loop 2 #priors mu ~ dnorm(0,0.01) tau <- 1/(sigma*sigma) sigma ~ dunif(0,10) b.SLE ~ dnorm(0,0.01) b.SJO ~ dnorm(0,0.01) b.female ~ dnorm(0,0.01) b.agec ~ dnorm(0,0.01) b.age2 ~ dnorm(0,0.01) b.SLE.age ~ dnorm(0,0.01) b.SJO.age ~ dnorm(0,0.01) b.dd.SLE ~ dnorm(0,0.01) b.dd.SJO ~ dnorm(0,0.01) #-----------------------------# #-- Predicted probabilities --# #-----------------------------# # No disease--------------------------------- pp.30.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*0 + b.female*1 + b.agec*-20 + b.age2*400 + b.SLE.age*0*-20 + b.SJO.age*0*-20 + b.dd.SLE*0 + b.dd.SJO*0))) pp.32.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*0 + b.female*1 + b.agec*-18 + b.age2*324 + b.SLE.age*0*-20 + b.SJO.age*0*-20 + b.dd.SLE*0 + b.dd.SJO*0))) pp.35.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*0 + b.female*1 + b.agec*-15 + b.age2*225 + b.SLE.age*0*-15 + b.SJO.age*0*-20 + b.dd.SLE*0 + b.dd.SJO*0))) pp.40.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*0 + b.female*1 + b.agec*-10 + b.age2*100 + b.SLE.age*0*-10 + b.SJO.age*0*-20 + b.dd.SLE*0 + b.dd.SJO*0))) pp.45.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*0 + b.female*1 + b.agec*-5 + b.age2*25 + b.SLE.age*0*-5 + b.SJO.age*0*-20 + b.dd.SLE*0 + b.dd.SJO*0))) pp.50.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*0 + b.female*1 + b.agec*0 + b.age2*0 + b.SLE.age*0*0 + b.SJO.age*0*0 + b.dd.SLE*0 + b.dd.SJO*0))) pp.52.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*0 + b.female*1 + b.agec*2 + b.age2*4 + b.SLE.age*0*0 + b.SJO.age*0*0 + b.dd.SLE*0 + b.dd.SJO*0))) pp.55.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*0 + b.female*1 + b.agec*5 + b.age2*25 + b.SLE.age*0*-15 + b.SJO.age*0*-20 + b.dd.SLE*0 + b.dd.SJO*0))) pp.60.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*0 + b.female*1 + b.agec*10 + b.age2*100 + b.SLE.age*0*-10 + b.SJO.age*0*-20 + b.dd.SLE*0 + b.dd.SJO*0))) pp.65.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*0 + b.female*1 + b.agec*15 + b.age2*225 + b.SLE.age*0*-5 + b.SJO.age*0*-20 + b.dd.SLE*0 + b.dd.SJO*0)))
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pp.70.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*0 + b.female*1 + b.agec*20 + b.age2*400 + b.SLE.age*0*20 + b.SJO.age*0*20 + b.dd.SLE*0 + b.dd.SJO*0))) pp.72.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*0 + b.female*1 + b.agec*22 + b.age2*484 + b.SLE.age*0*20 + b.SJO.age*0*20 + b.dd.SLE*0 + b.dd.SJO*0))) pp.75.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*0 + b.female*1 + b.agec*25 + b.age2*625 + b.SLE.age*0*20 + b.SJO.age*0*20 + b.dd.SLE*0 + b.dd.SJO*0))) pp.80.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*0 + b.female*1 + b.agec*30 + b.age2*900 + b.SLE.age*0*20 + b.SJO.age*0*20 + b.dd.SLE*0 + b.dd.SJO*0))) pp.85.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*0 + b.female*1 + b.agec*35 + b.age2*1225+ b.SLE.age*0*20 + b.SJO.age*0*20 + b.dd.SLE*0 + b.dd.SJO*0))) pp.30.M <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*0 + b.female*0 + b.agec*-20 + b.age2*400 + b.SLE.age*0*-20 + b.SJO.age*0*-20 + b.dd.SLE*0 + b.dd.SJO*0))) pp.50.M <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*0 + b.female*0 + b.agec*0 + b.age2*0 + b.SLE.age*0*0 + b.SJO.age*0*0 + b.dd.SLE*0 + b.dd.SJO*0))) pp.70.M <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*0 + b.female*0 + b.agec*20 + b.age2*400 + b.SLE.age*0*20 + b.SJO.age*0*20 + b.dd.SLE*0 + b.dd.SJO*0))) # SLE------------------------------------- pp.SLE.30.dd2.F <- 1/(1+exp(-1*(mu + b.SLE*1 + b.SJO*0 + b.female*1 + b.agec*-20 + b.age2*400 + b.SLE.age*1*-20 + b.SJO.age*0*-20 + b.dd.SLE*2 + b.dd.SJO*0))) pp.SLE.50.dd2.F <- 1/(1+exp(-1*(mu + b.SLE*1 + b.SJO*0 + b.female*1 + b.agec*0 + b.age2*0 + b.SLE.age*1*0 + b.SJO.age*0*0 + b.dd.SLE*2 + b.dd.SJO*0))) pp.SLE.70.dd2.F <- 1/(1+exp(-1*(mu + b.SLE*1 + b.SJO*0 + b.female*1 + b.agec*20 + b.age2*400 + b.SLE.age*1*20 + b.SJO.age*0*20 + b.dd.SLE*2 + b.dd.SJO*0))) pp.SLE.30.dd2.M <- 1/(1+exp(-1*(mu + b.SLE*1 + b.SJO*0 + b.female*0 + b.agec*-20 + b.age2*400 + b.SLE.age*1*-20 + b.SJO.age*0*-20 + b.dd.SLE*2 + b.dd.SJO*0))) pp.SLE.50.dd2.M <- 1/(1+exp(-1*(mu + b.SLE*1 + b.SJO*0 + b.female*0 + b.agec*0 + b.age2*0 + b.SLE.age*1*0 + b.SJO.age*0*0 + b.dd.SLE*2 + b.dd.SJO*0))) pp.SLE.70.dd2.M <- 1/(1+exp(-1*(mu + b.SLE*1 + b.SJO*0 + b.female*0 + b.agec*20 + b.age2*400 + b.SLE.age*1*20 + b.SJO.age*0*20 + b.dd.SLE*2 + b.dd.SJO*0))) pp.SLE.30.dd10.F <- 1/(1+exp(-1*(mu + b.SLE*1 + b.SJO*0 + b.female*1 + b.agec*-20 + b.age2*400 + b.SLE.age*1*-20 + b.SJO.age*0*-20 + b.dd.SLE*10 + b.dd.SJO*0))) pp.SLE.50.dd10.F <- 1/(1+exp(-1*(mu + b.SLE*1 + b.SJO*0 + b.female*1 + b.agec*0 + b.age2*0 + b.SLE.age*1*0 + b.SJO.age*0*0 + b.dd.SLE*10 + b.dd.SJO*0))) pp.SLE.70.dd10.F <- 1/(1+exp(-1*(mu + b.SLE*1 + b.SJO*0 + b.female*1 + b.agec*20 + b.age2*400 + b.SLE.age*1*20 + b.SJO.age*0*20 + b.dd.SLE*10 + b.dd.SJO*0))) pp.SLE.30.dd10.M <- 1/(1+exp(-1*(mu + b.SLE*1 + b.SJO*0 + b.female*0 + b.agec*-20 + b.age2*400 + b.SLE.age*1*-20 + b.SJO.age*0*-20 + b.dd.SLE*10 + b.dd.SJO*0))) pp.SLE.50.dd10.M <- 1/(1+exp(-1*(mu + b.SLE*1 + b.SJO*0 + b.female*0 + b.agec*0 + b.age2*0 + b.SLE.age*1*0 + b.SJO.age*0*0 + b.dd.SLE*10 + b.dd.SJO*0))) pp.SLE.70.dd10.M <- 1/(1+exp(-1*(mu + b.SLE*1 + b.SJO*0 + b.female*0 + b.agec*20 + b.age2*400 + b.SLE.age*1*20 + b.SJO.age*0*20 + b.dd.SLE*10 + b.dd.SJO*0))) pp.SLE.32.dd2.F <- 1/(1+exp(-1*(mu + b.SLE*1 + b.SJO*0 + b.female*1 + b.agec*-18 + b.age2*324 + b.SLE.age*1*-18 + b.dd.SLE*2 ))) pp.SLE.35.dd5.F <- 1/(1+exp(-1*(mu + b.SLE*1 + b.SJO*0 + b.female*1 + b.agec*-15 + b.age2*225 + b.SLE.age*1*-15 + b.dd.SLE*5 ))) pp.SLE.40.dd10.F <- 1/(1+exp(-1*(mu + b.SLE*1 + b.SJO*0 + b.female*1 + b.agec*-10 + b.age2*100 + b.SLE.age*1*-10 + b.dd.SLE*10))) pp.SLE.45.dd15.F <- 1/(1+exp(-1*(mu + b.SLE*1 + b.SJO*0 + b.female*1 + b.agec*-5 + b.age2*25 + b.SLE.age*1*-5 + b.dd.SLE*15))) pp.SLE.52.dd2.F <- 1/(1+exp(-1*(mu + b.SLE*1 + b.SJO*0 + b.female*1 + b.agec*2 + b.age2*4 + b.SLE.age*1*2 + b.dd.SLE*2 ))) pp.SLE.55.dd5.F <- 1/(1+exp(-1*(mu + b.SLE*1 + b.SJO*0 + b.female*1 + b.agec*5 + b.age2*25 + b.SLE.age*1*5 + b.dd.SLE*5 ))) pp.SLE.60.dd10.F <- 1/(1+exp(-1*(mu + b.SLE*1 + b.SJO*0 + b.female*1 + b.agec*10 + b.age2*100 + b.SLE.age*1*10 + b.dd.SLE*10))) pp.SLE.65.dd15.F <- 1/(1+exp(-1*(mu + b.SLE*1 + b.SJO*0 + b.female*1 + b.agec*15 + b.age2*225 + b.SLE.age*1*15 + b.dd.SLE*15)))
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pp.SLE.72.dd2.F <- 1/(1+exp(-1*(mu + b.SLE*1 + b.SJO*0 + b.female*1 + b.agec*22 + b.age2*484 + b.SLE.age*1*22 + b.dd.SLE*2 ))) pp.SLE.75.dd5.F <- 1/(1+exp(-1*(mu + b.SLE*1 + b.SJO*0 + b.female*1 + b.agec*25 + b.age2*625 + b.SLE.age*1*25 + b.dd.SLE*5 ))) pp.SLE.80.dd10.F <- 1/(1+exp(-1*(mu + b.SLE*1 + b.SJO*0 + b.female*1 + b.agec*30 + b.age2*900 + b.SLE.age*1*30 + b.dd.SLE*10))) pp.SLE.85.dd15.F <- 1/(1+exp(-1*(mu + b.SLE*1 + b.SJO*0 + b.female*1 + b.agec*35 + b.age2*1225 + b.SLE.age*1*35 + b.dd.SLE*15))) # SJO -------------------------------------------- pp.SJO.30.dd2.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*1 + b.female*1 + b.agec*-20 + b.age2*400 + b.SLE.age*0*-20 + b.SJO.age*1*-20 + b.dd.SLE*0 + b.dd.SJO*2))) pp.SJO.50.dd2.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*1 + b.female*1 + b.agec*0 + b.age2*0 + b.SLE.age*0*0 + b.SJO.age*1*0 + b.dd.SLE*0 + b.dd.SJO*2))) pp.SJO.70.dd2.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*1 + b.female*1 + b.agec*20 + b.age2*400 + b.SLE.age*0*20 + b.SJO.age*1*20 + b.dd.SLE*0 + b.dd.SJO*2))) pp.SJO.30.dd2.M <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*1 + b.female*0 + b.agec*-20 + b.age2*400 + b.SLE.age*0*-20 + b.SJO.age*1*-20 + b.dd.SLE*0 + b.dd.SJO*2))) pp.SJO.50.dd2.M <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*1 + b.female*0 + b.agec*0 + b.age2*0 + b.SLE.age*0*0 + b.SJO.age*1*0 + b.dd.SLE*0 + b.dd.SJO*2)) pp.SJO.70.dd2.M <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*1 + b.female*0 + b.agec*20 + b.age2*400 + b.SLE.age*0*20 + b.SJO.age*1*20 + b.dd.SLE*0 + b.dd.SJO*2))) pp.SJO.30.dd10.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*1 + b.female*1 + b.agec*-20 + b.age2*400 + b.SLE.age*0*-20 + b.SJO.age*1*-20 + b.dd.SLE*0 + b.dd.SJO*10))) pp.SJO.50.dd10.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*1 + b.female*1 + b.agec*0 + b.age2*0 + b.SLE.age*0*0 + b.SJO.age*1*0 + b.dd.SLE*0 + b.dd.SJO*10))) pp.SJO.70.dd10.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*1 + b.female*1 + b.agec*20 + b.age2*400 + b.SLE.age*0*20 + b.SJO.age*1*20 + b.dd.SLE*0 + b.dd.SJO*10))) pp.SJO.30.dd10.M <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*1 + b.female*0 + b.agec*-20 + b.age2*400 + b.SLE.age*0*-20 + b.SJO.age*1*-20 + b.dd.SLE*0 + b.dd.SJO*10))) pp.SJO.50.dd10.M <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*1 + b.female*0 + b.agec*0 + b.age2*0 + b.SLE.age*0*0 + b.SJO.age*1*0 + b.dd.SLE*0 + b.dd.SJO*10))) pp.SJO.70.dd10.M <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*1 + b.female*0 + b.agec*20 + b.age2*400 + b.SLE.age*0*20 + b.SJO.age*1*20 + b.dd.SLE*0 + b.dd.SJO*10))) pp.SJO.32.dd2.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*1 + b.female*1 + b.agec*-18 + b.age2*324 + b.SJO.age*1*-18 + b.dd.SJO*2 ))) pp.SJO.35.dd5.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*1 + b.female*1 + b.agec*-15 + b.age2*225 + b.SJO.age*1*-15 + b.dd.SJO*5 ))) pp.SJO.40.dd10.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*1 + b.female*1 + b.agec*-10 + b.age2*100 + b.SJO.age*1*-10 + b.dd.SJO*10))) pp.SJO.45.dd15.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*1 + b.female*1 + b.agec*-5 + b.age2*25 + b.SJO.age*1*-5 + b.dd.SJO*15))) pp.SJO.52.dd2.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*1 + b.female*1 + b.agec*2 + b.age2*4 + b.SJO.age*1*2 + b.dd.SJO*2 ))) pp.SJO.55.dd5.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*1 + b.female*1 + b.agec*5 + b.age2*25 + b.SJO.age*1*5 + b.dd.SJO*5 ))) pp.SJO.60.dd10.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*1 + b.female*1 + b.agec*10 + b.age2*100 + b.SJO.age*1*10 + b.dd.SJO*10))) pp.SJO.65.dd15.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*1 + b.female*1 + b.agec*15 + b.age2*225 + b.SJO.age*1*15 + b.dd.SJO*15))) pp.SJO.72.dd2.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*1 + b.female*1 + b.agec*22 + b.age2*484 + b.SJO.age*1*22 + b.dd.SJO*2 ))) pp.SJO.75.dd5.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*1 + b.female*1 + b.agec*25 + b.age2*625 + b.SJO.age*1*25 + b.dd.SJO*5 ))) pp.SJO.80.dd10.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*1 + b.female*1 + b.agec*30 + b.age2*900 + b.SJO.age*1*30 + b.dd.SJO*10))) pp.SJO.85.dd15.F <- 1/(1+exp(-1*(mu + b.SLE*0 + b.SJO*1 + b.female*1 + b.agec*35 + b.age2*1225 + b.SJO.age*1*35 + b.dd.SJO*15)))
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#---------------------------------------------------------# # -- ORs -------------------------------------------------# #---------------------------------------------------------# #SLE------------------------------------------------------- or.SLE.30.dd2 <- exp(b.SLE + -20*b.SLE.age + 2*b.dd.SLE) or.SLE.50.dd2 <- exp(b.SLE + 0*b.SLE.age + 2*b.dd.SLE) or.SLE.70.dd2 <- exp(b.SLE + 20*b.SLE.age + 2*b.dd.SLE) or.SLE.30.dd10 <- exp(b.SLE + -20*b.SLE.age + 10*b.dd.SLE) or.SLE.50.dd10 <- exp(b.SLE + 0*b.SLE.age + 10*b.dd.SLE) or.SLE.70.dd10 <- exp(b.SLE + 20*b.SLE.age + 10*b.dd.SLE) or.SLE.32.dd2 <- exp(b.SLE + -18*b.SLE.age + 2*b.dd.SLE) or.SLE.35.dd5 <- exp(b.SLE + -15*b.SLE.age + 5*b.dd.SLE) or.SLE.40.dd10 <- exp(b.SLE + -10*b.SLE.age + 10*b.dd.SLE) or.SLE.45.dd15 <- exp(b.SLE + -5*b.SLE.age + 15*b.dd.SLE) or.SLE.52.dd2 <- exp(b.SLE + 2*b.SLE.age + 2*b.dd.SLE) or.SLE.55.dd5 <- exp(b.SLE + 5*b.SLE.age + 5*b.dd.SLE) or.SLE.60.dd10 <- exp(b.SLE + 10*b.SLE.age + 10*b.dd.SLE) or.SLE.65.dd15 <- exp(b.SLE + 15*b.SLE.age + 15*b.dd.SLE) or.SLE.72.dd2 <- exp(b.SLE + 22*b.SLE.age + 2*b.dd.SLE) or.SLE.75.dd5 <- exp(b.SLE + 25*b.SLE.age + 5*b.dd.SLE) or.SLE.80.dd10 <- exp(b.SLE + 30*b.SLE.age + 10*b.dd.SLE) or.SLE.85.dd15 <- exp(b.SLE + 35*b.SLE.age + 15*b.dd.SLE) #SJO-------------------------------------------------------- or.SJO.30.dd2 <- exp(b.SJO + -20*b.SJO.age + 2*b.dd.SJO) or.SJO.50.dd2 <- exp(b.SJO + 0*b.SJO.age + 2*b.dd.SJO) or.SJO.70.dd2 <- exp(b.SJO + 20*b.SJO.age + 2*b.dd.SJO) or.SJO.30.dd10 <- exp(b.SJO + -20*b.SJO.age + 10*b.dd.SJO) or.SJO.50.dd10 <- exp(b.SJO + 0*b.SJO.age + 10*b.dd.SJO) or.SJO.70.dd10 <- exp(b.SJO + 20*b.SJO.age + 10*b.dd.SJO) or.SJO.32.dd2 <- exp(b.SJO + -18*b.SJO.age + 2*b.dd.SJO) or.SJO.35.dd5 <- exp(b.SJO + -15*b.SJO.age + 5*b.dd.SJO) or.SJO.40.dd10 <- exp(b.SJO + -10*b.SJO.age + 10*b.dd.SJO) or.SJO.45.dd15 <- exp(b.SJO + -5*b.SJO.age + 15*b.dd.SJO) or.SJO.52.dd2 <- exp(b.SJO + 2*b.SJO.age + 2*b.dd.SJO) or.SJO.55.dd5 <- exp(b.SJO + 5*b.SJO.age + 5*b.dd.SJO) or.SJO.60.dd10 <- exp(b.SJO + 10*b.SJO.age + 10*b.dd.SJO) or.SJO.65.dd15 <- exp(b.SJO + 15*b.SJO.age + 15*b.dd.SJO) or.SJO.72.dd2 <- exp(b.SJO + 22*b.SJO.age + 2*b.dd.SJO) or.SJO.75.dd5 <- exp(b.SJO + 25*b.SJO.age + 5*b.dd.SJO) or.SJO.80.dd10 <- exp(b.SJO + 30*b.SJO.age + 10*b.dd.SJO) or.SJO.85.dd15 <- exp(b.SJO + 35*b.SJO.age + 15*b.dd.SJO) } # end model
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APPENDIX D – Coefficients from all models
Coefficients (95% CrI) from model 1:
Variable General population
comparison Pre-diagnosis comparison
Regional comparison analysis
Intercept -3.522 (-3.916,-3.128) -4.690 (-5.162,-4.218) -4.829 (-5.075,-4.581)
Sjogren's -0.510 (-0.830,-0.190) -0.336 (-0.530,-0.144) -0.366 (-0.570,-0.169)
SLE -0.117 (-0.303,0.071) -0.086 (-0.215,0.038) -0.451 (-0.589,-0.313
age -0.045 (-0.046,-0.043) -0.034 (-0.037,-0.031) -0.032 (-0.037,-0.027)
age2 9.38E-4 (8.64E-4,1.02E-3) 6.61E-4 (5.00E-4,8.22E-4) 5.63E-4 (2.94E-4,8.26E-4)
female -0.129 (-0.182,-0.077) -0.016 (-0.126,0.102) 0.003 (-0.207,0.210)
Coefficients (95% CrI) from model 2 (presented in body of thesis):
Variable General population
comparison Pre-diagnosis comparison
Regional comparison analysis
Intercept -2.764 (-2.814,-2.709) -3,059 (-3.204,-2.941) -4.801 (-5.028,-4.565)
Sjogren's -0.511 (-0.835,-0.199) -0.324 (-0.526,-0.131) -0.538 (-0.913,-0.170)
Sjogren's x age 0.025 (0.015,0.035) 0.015 (0.008,0.023) 0.011 (-0.002,0.024)
SLE -0.104 (-0.294,0.090) -0.079 (-0.200,0.043) -0.235 (-0.455,-0.018)
SLE x age 0.022 (0.016,0.028) 0.012 (0.007,0.018) 0.014 (0.004,0.023)
age2 1.01E-3 (9.29E-4,1.08E-3) 7.11E-4 (5.51E-4,8.68E-4) 5.05E-4 (2.33E-4,7.78E-4)
age 0.045 (-0.046,-0.044) -0.034 (-0.037,-0.031) -0.037 (-0.043,-0.031) Sjogren's disease duration 0.045 (-0.001,0.093) 0.012 (-0.019,0.045) 0.028 (-0.033,0.086) SLE disease duration -0.043 (-0.069,-0.018) -0.030 (-0.048,-0.013) 0.020 (-0.051,0.011)
female -0.130 (-0.182,-0.078) -0.002 (-0.116,0.117) -0.048 (-0.240,0.147)
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Coefficients (95% CrI) from model 3:
Variable General population
comparison Pre-diagnosis comparison
Regional comparison analysis
Intercept -3.522 (-3.916,-3.128) -4.690 (-5.162,-4.218) -4.914 (-6.268,-2.884)
Sjogren's -0.510 (-0.830,-0.190) -0.336 (-0.530,-0.144) -0.533 (-0.890,-0.178)
Sjogren's x age 0.023 (0.013,0.033) 0.016 (0.009,0.023) 0.011 (-0.003,0.025)
SLE -0.117 (-0.303,0.071) -0.086 (-0.215,0.038) -0.239 (-0.467,-0.021)
SLE x age 0.021 (0.015,0.027) 0.013 (0.007,0.018) 0.016 (0.006,0.025)
age2 9.38E-4 (8.64E-4,1.02E-3) 6.61E-4 (5.00E-4,8.22E-4) 4.86E-4 (1.99E-4,7.66E-4)
age -0.045 (-0.046,-0.043) -0.034 (-0.037,-0.031) -0.036 (-0.042,0.031) Sjogren's disease duration
0.042 (-0.005,0.090) 0.013 (-0.019,0.043) 0.028 (-0.031,0.085)
SLE disease duration
-0.042 (-0.067,-0.017) -0.031 (-0.049,-0.014) -0.023 (-0.054,0.009)
Education -0.002 (-0.008,0.002) -0.004 (-0.016,0.003) 0.002 (-0.016,0.015)
Employment 0.011 (0.003,0.016) 0.021 (0.006,0.031) 0.019 (0.001,0.033)
female -0.129 (-0.182,-0.077) -0.016 (-0.126,0.102) -0.018 (-0.218,0.0178) FSA with a rheumatologist
0.092 (0.048,0.136) -0.064 (-0.158,0.028) -0.022 (-0.199,0.152)
Income -0.002 (-0.004,0.000) -0.007 (-0.011,-0.003) -0.013 (-0.023,-0.05)
rent 0.009 (0.007,0.011) 0.009 (0.005,0.012) -0.004 (-0.010,0.001)
CMA -0.559 (-0.623,-0.494) 0.188 (0.075,0.305) -0.140 (-0.326,0.037)
