Exploring Sleep and the Hispanic Paradox in Mexico-Born U.S

EXPLORING SLEEP AND THE HISPANIC PARADOX

IN MEXICO-BORN U.S. ADULT IMMIGRANTS

SINZIANA SEICEAN MD, MPH

Submitted in partial fulfillment of the requirements

For the degree of Doctor of Philosophy

Dissertation Adviser: Dr. DUNCAN NEUHAUSER

Department of Epidemiology and Biostatistics

CASE WESTERN RESERVE UNIVERSITY

August, 2010

CASE WESTERN RESERVE UNIVERSITY

SCHOOL OF GRADUATE STUDIES

We hereby approve the thesis/dissertation of

SINZIANA SEICEAN MD, MPH ______

PhD candidate for the ______degree *.

DUNCAN NEUHAUSER PhD (signed)______(chair of the committee)

______

SUSAN REDLINE MD, MPH ______

SIRAN KOROUKIAN-HAJINAZARIAN PhD ______

______

JULY 1, 2010 (date) ______

*We also certify that written approval has been obtained for any proprietary material contained therein. 2

DEDICATION:

To all women in my family, beloved role models and sources of inspiration for my moral values and academic career aspirations: my grandmothers Aurora Popescu-

Silisteni and Maria Pârlog, my mother Olimpia Pârlog, my aunt Hortensia Pârlog, and my daughter Andreea Ana-Maria Seicean. To my grandfather Gheorghe Pârlog, my uncle

George Manole, and my son Dan Nicholas Seicean, always loving and supporting me.

To all U.S. immigrants of all times, whose courage and determination for a better life for their families and future generations have served to build America.

TABLE OF CONTENTS:

Committee Signature……………………………………………………………………...2

Dedication………………………………………………………………………...... 3

Table of Contents………………………………………………………………...... 4

List of Tables……………………………………………………………………...... 7

List of Figures……………………………………………………………………………10

Acknowledgements……………………………………………………………………....13

Abstract…………………………………………………………………………………..15

CHAPTER 1……………………………………………………………………………..17

Introduction

1.1 Immigrant Health………………………………………………………………….…18

1.2 Former Theories/Models Used in Immigrant Health Research……………………...19

1.3 Critical Realism Theory in Immigrant Health Research……………………………..21

1.4 WHO? Mexico-born immigrants in the US………………………………………….23

1.5 WHAT? Sleep Health……………………………………………………………….25

1.6 WHY? Sleep in Mexico-born Immigrants………………………………………...... 26

1.7 Summary………………………………………………………………………...... 27

CHAPTER 2……………………………………………………………………...……...29

Conceptual Framework

2.1 Background on Immigrant Health…………………………………………………...30

2.2 Acculturation and Acculturation Theories…………………………………………...31

2.3 The Hispanic Paradox and Explanatory Theories…………………………………....37

2.4 Critical Realism Theory and the Hispanic Paradox………………………………….43

2.5 The Open System Conceptual Model of Volunteer (No –asylum, Non-refugee)

Immigrant Health ………………………………………………………………………..56

CHAPTER 3…………………………………………………………………………..…74

An Exploration of Differences in Sleep Characteristics between Mexico-born

U.S. Immigrants and Other Americans to Address the Hispanic Paradox

3.1 Reviewing the conceptual framework applicability for comparisons between immigrant and non-immigrant populations in U.S………………………………………74

3.2 Introduction…………………………………………………………………………..79

3.3 Methods……………………………………………………………………………....82

3.4 Results………………………………………………………………………………..89

3.5 Discussion…………………………………………………………………………..117

CHAPTER 4………………………………………………………………………...….123

Proportion of Lifetime in Immigration (PLI) and Cohort Analysis in

Immigrant Populations: Longitudinal Perspective and Non-linear

Relationship with Unsatisfactory Health Status in Mexico-born U.S. Immigrants

4.1. Cohort Analysis and PLI in Immigrant Health Research ………………………...124

4.2 Conceptual Framework and Multi-Theory Approach applicability in

Predicting Non-Linear association between self-reported unsatisfactory

health status (UHS) and proportion lifetime in U.S. immigration (PLI)

in Mexico-born individuals (MI)……………………………………………………....127

4.3 Quantitative Research……………………………………………………………....130

4.3.1 Abstract ………………………………………………………………………..…130

4.3.2 Introduction……………………………………………………………………….131

4.3.3 Methods…………………………………………………………………………...132

4.4 Results………………………………………………………………………………138

4.5 Discussion ……………….…………………………………………………………148

CHAPTER 5…………………………………………………………………………....151

Mexico-Born Ethnicity and Insomnia in U.S. Immigrants Adjusting for the Proportion of

Lifetime Spent in Immigration in the U.S.

5.1 Abstract……………………………………………………………………………..153

5.2 Introduction………………………………………………………………………....154

5.3 Methods……………………………………………………………………………..156

5.4 Results………………………………………………………………………….…...163

5.5 Discussion…………………………………………………………………………..180

CHAPTER 6...... 185

Conclusion

Bibliography…………………………………………………………………………....195

Bibliography Ch 1…………………………………………………………….………...195

Bibliography Ch 2……………………………………………………………….……...199

Bibliography Ch 3………………………………………………………………….…...210

Bibliography Ch 4………………………………………………………………….…...217

Bibliography Ch 5…………………………………………………………………...... 222

Bibliography Ch 6…………………………………………………………………...... 230

LIST OF TABLES

Table 2.1 Theories Incorporated into “The Open System of Conceptual Model

of Immigrant Health”…………………………………………... ……………………….66

Table 3.1a General Characteristics: Mexico-born immigrants,

Mexican–Americans and All U.S. individuals, By Gender………………...……………91

Table 3.1b Sleep Characteristics Mexico-born immigrants,

Mexican–Americans and All U.S. individuals, By Gender………………..……………95

Table 3.2 Age adjusted population prevalence of Poor Sleep Outcomes in U.S.

Adults 20 years and Older in Mexico-born U.S. Immigrants, U.S.-born Mexican

Americans and the General U.S. Population Sample, By Gender…………………...….96

Table 3.3 Multivariate Odds Ratios (OR) with 95% Confidence Intervals

(95% CI) of Poor Sleep Outcomes Associated with Mexico-Born status

in the Overall U.S. Analytic Sample and By Gender……………………………..……102

Table 3.4 Univariate Odds Ratios (OR) with 95% Confidence Intervals

(95% CI) of Poor Sleep Outcomes, By Gender in Mexican-Americans ...... ….…..104

Table 3.5 Multivariate Odds Ratios (OR) with 95% Confidence Intervals

(95% CI) of Poor Sleep Outcomes, By Gender in Mexican-Americans

Without* and With**Adjustments for Language Preference at home…………………106

Table 3.6 Step 2 referred as “correlations” in fig 3.2 (significant associations):

Univariate Odds Ratio OR (95% CI) between Mexico-born Immigrant Status (X)

and Covariates (Y) as potential mediators (Proc survey analyses) ……………………113

Table 3.7 Referred as “correlations” in fig 3.2 (significant associations):

Regression coefficients, standard error and p value between Mexico-born

Immigrant status (X) and continuous Covariates (Y) as potential mediators

(Proc survey analyses) ……………………………………………...... 113

Table 3.8 Percent of total effect (%)*of the Mexico-born immigrant

status (x) on sleep outcomes (Y on columns) mediated by covariates

and ratios of the indirect to the direct effect** (Bivariate analyses to test

partial mediation just for qualifying covariates based on step 2 and 3)……………...... 114

Table 3.9 Insomnia with short sleeping hours: Example of Assessing

Multiple Mediation in Mexican American Men………………………………….……115

Table 4.1 Individual Predictions of the Theories Incorporated into

“The Open System of Conceptual Model of Immigrant Health”……………………... 129

Table 4.2 General Characteristics of the Mexico-born Immigrant cohort……..………139

Table 4.3 Correlations between Individual and Immigration related Participant

Characteristics of Mexico-Born immigrants……………………………………………141

Table 4.4 Univariate Odds Ratios (OR) with 95% Confidence Intervals

(95% CI) of Unsatisfactory Health Status (UHS) in Mexico-Born U.S.

Immigrants (MI)……………………………………………………………………..…146

Table 4.5 Multivariate Odds Ratios (OR) with 95% Confidence Intervals

(95% CI) of Unsatisfactory Health Status (UHS) in Mexico-born U.S.

Immigrants (MI)…………………………………………………………………...……147

Table 5.1a General Characteristics Mexico-born U.S. Immigrants,

Other Latino U.S. Immigrants and Non-Latino U.S. Immigrants, By Gender...………166

Table 5.1b Insomnia and other Sleep Characteristics Mexico-born

U.S. Immigrants, Other Latino U.S. Immigrants and Non-Latino U.S.

Immigrants, By Gender…………………………………………………………………167

Table 5.2 Age adjusted population prevalence of Poor Sleep Outcomes

in U.S. Adults 20 years and Older in Mexico-born U.S. Immigrants,

Other Latino U.S. Immigrants and Non-Latino U.S. Immigrants, By Gender……...…173

Table 5.3 Univariate and Multivariate Odds Ratios (OR) with 95% Confidence

Intervals (95% CI) of Severe Insomnia in U.S. immigrants…………………………....178

LIST OF FIGURES

Figure 2.1 Conceptual Model……………………………………………………………64

Figure 2.2 Independent variables used in chapters 3-5……………………………….....67

Figure 2.3 Dependent variables used in chapters 3-5………………………………...…68

Figure 2.4 Covariates (2) used in this research at least in sensitivity analyses…………69

Figure 2.5 Covariates used in this research……………………………………………...70

Figure 2.6 Other covariates (1)………………………………………………………….71

Figure 2.7 Other covariates (2)……………………………………………………….…72

Fig 3.1 Mediation steps used to verify mediation (Baron and Kenny, 1986) ………..….77

Fig 3.2 Applying theories of probability, statistics, and error to the portion of

the Open System conceptual model used to assess the relationship between

Mexico-born immigrant ethnicity and sleep outcomes in Mexican

American population, as to test theories of Hispanic paradox………………….……...... 78

Fig 3.3 Descriptive in Men cohort, NHANES 2005-2006………………………………93

Fig 3.4 Descriptive in Women cohort, NHANES 2005-2006…………………………...94

Fig 3.5 Adjusted prevalence of Poor Sleep in Men cohort,

NHANES 2005-2006………………………………………………………………….....97

Fig 3.6 Adjusted prevalence of Poor Sleep in Women cohort

, NHANES 2005-2006………………………………………………………………...…98

Fig 3.7 Multivariate Odds Ratios (OR) with 95% Confidence Intervals

(95% CI) of Insomnia with Short habitual Sleep Time in

Mexican-American Men not included cultural changes…………………………….....109

Fig 3.8 Predicted Probabilities of Insomnia with Short habitual

Sleep Time in Mexican-American Men not including cultural changes……….………110 10

Fig 3.9 Multivariate Odds Ratios (OR) with 95% Confidence Intervals

(95%CI) of Insomnia with Short habitual Sleep Time in

Mexican-American Men including cultural changes……………………………...……111

Fig 3.10 Predicted Probabilities of Insomnia with Short habitual Sleep Time in Mexican-American Men including cultural changes……….….112

Fig 4.1 Significant Unadjusted Quadratic Contribution of

Proportion of Lifetime (PLI) in Immigration in U.S. to the

Prediction of Unsatisfactory Health status (UHS) in U.S.

Mexico-born Immigrants (MI): Smoothing component for UHS

with 95% Bayesian Confidence Limits…………………………………………...……144

Fig 4.2 Significant Adjusted Quadratic Contribution of Proportion of

Lifetime in Immigration (PLI) in U.S. to the Prediction of Unsatisfactory

Health status (UHS) in U.S. Mexico-born Immigrants (MI):

Smoothing component for UHS with 95% Bayesian Confidence Limits……………....145

Fig 5.1 General sample characteristics ………………………………………………...168

Fig 5.2 Age-Adjusted Population Prevalence of Poor Sleep Outcomes

in U.S. Immigrants……………………………..……………………………………....172

Fig 5.3 Significant Quadratic Contribution of Proportion of Lifetime

Spent in Immigration in U.S. to the Prediction of Severe Insomnia

in the U.S. Immigrant Sample: Non-parametric Generalized Logistic

Additive model adjusted for age ……………………………………………………..175

Fig 5.4 Significant Quadratic Contribution of Proportion of

Lifetime Spent in Immigration in U.S. to the Prediction of

Severe Insomnia in the U.S. Immigrant Sample:

Semiparametric Generalized Logistic Additive Model adjusted for

other risk factors of severe insomnia except depression………………………….……176

Fig 5.5 Quadratic Contribution of Proportion of Lifetime Spent

in Immigration in U.S. to the Prediction of Severe Insomnia

in the U.S. Immigrant Sample Semiparametric Generalized

Logistic Additive Model fully adjusted for other risk factors of

Severe Insomnia including Depression………...... ….177

Fig 5.6 Multivariate Odds Ratios (95% CI) of Severe Insomnia in the Latino U.S. Immigrant Sample……………………………………………..……183

Fig 5.7 Multivariate Odds Ratios (95% CI) of Severe Insomnia

in the Latino U.S. Immigrant Sample…………………………………..……………...184

ACKNOWLEDGMENTS:

I express my profound gratitude to my Mentors, Dr. Susan Redline, and Dr.

Kingman Strohl, for their constant support, patience, shared expertise, and guidance.

My warmest thanks and sincere appreciation go to the Chair of my committee, Dr.

Duncan Neuhauser and to Dr. Siran Koroukian, inspiring, encouraging, advising, and supporting me during my PhD training, and in writing this dissertation.

Special thanks go to Dr. Jeffrey Longhofer for introducing me to Critical Realism thinking, and for guiding me in redefining my personal philosophical ontology that sustained my current medical research.

Many Professors at CWRU deserve my heartfelt thanks: Dr. Mendel Singer, Dr.

Denise Babineau, Dr. Scott Frank, Dr. Alfred Rimm, Dr. Kathleen Smyth, Dr. Robert

Binstock, Dr. Ralph O'Brien, Dr Ashwini Sehgal, Dr. Joseph Sudano, Dr.Sana Loue, Dr.

Myreya Diaz, are just few others exceptional educators I had the chance to interact with during my MPH and my Ph.D journey at CWRU.

The contribution of the generous academic opportunities I had in my country of origin, Romania, should not be forgotten. My humble thanks and recognition also go to all my Rumanian teachers and scholars, for inspiring my deep love and respect for education and human rights.

My daughter Andreea and my aunt Hortensia were always a reliable source of

encouragement and intellectual support.

My friend Michael Sering provided great psychological support, by showing me

the “caring face of America”, and by reinforcing my personal beliefs that homelessness

and social vulnerability can be solved with compassion and respect.

Afro-American parents and caregivers of children with sickle cell anemia I had the chance to interact with, during a research study overlapping with my PhD training years deserve also great appreciation and humble thanks for their example of humanity and inspiration.

Behind theories and conceptual frameworks, statistics and health policy regulations, real men and women struggling with poverty, social inequalities and disease somehow manage to withstand extreme conditions and inspire hope for a better tomorrow.

My deepest gratitude and love go to my mother Olimpia Pirlog, and to my children Andreea and Nicholas Seicean, for their patience, understanding, and continuous support during my many years of academic and professional training.

This work was supported in part by the National Institutes of Health grant T32HL079

Exploring Sleep and the Hispanic Paradox in Mexico-born U.S. Adult Immigrants

Abstract

SINZIANA SEICEAN

This dissertation explores the application of Critical Realism Theory

(CRT) to immigrant health research, by proposing “The Open System Conceptual

Model of Immigrant Health”, as a tool for conducting health research in volunteer

(non-refugees, non-asylum seekers) immigrants.

Quantitative epidemiological health research related to The Hispanic

Paradox in Mexico-born U.S. adult immigrants provides examples of the

applicability of the proposed multi-theory conceptual framework.

Two studies demonstrate the Hispanic Paradox on sleep health outcomes:

lower risk of short habitual sleep time, insomnia, and sleep related daily

functional impairments in Mexico-born U.S. immigrants, as compared to the

general U.S. population, to their U.S. born Mexican-American counterparts,

and/or Latino U.S. immigrants counterparts.

One study explores “Proportion of Lifetime in Immigration” (PLI) as a

measure of the “experience in immigration” effect, in addition to age effects, in

cohort analysis. The theoretical grounding of “The Open System Conceptual

Model of Immigrant Health” predicts PLI as being significantly correlated with

the age at immigration, the decade at the time of arrival to the U.S., but not with

the participant age.

The significance of the conceptual framework and the results of these

15 studies are presented and further epidemiological and health policy implications are proposed.

CHAPTER 1

Introduction

The process of immigration is highly complex. With globalization and economic

disparity, there has occurred a rapid increase in immigration worldwide and a growing

epidemiological interest in health differences between immigrant and native-born populations. This thesis utilizes a Critical Realism Theory (CRT) platform, a newly designed conceptual model capturing many components that impact on individual health.

Our CRT-based model called “Open System Conceptual Model of Immigrant Health

Outcomes” addresses a need for a theory-based analytic framework and can be applied to any non-refugee, non-asylum immigrant population regardless of the country of origin and host country. Furthermore it can be used to focus in on any subtopic in health. For the purpose of this thesis, we have chosen Mexico-born immigrants in the U.S. as our population of interest, and sleep health as our health subtopic of interest. This choice further allows us to explore The Hispanic Paradox, which includes a long term unexpected observation on better health status in Mexico-born U.S. immigrant population.

The aim of this chapter is to provide the necessary background information to: justify the need for the development of a new conceptual model for looking at immigrant health, and the importance of assessing sleep health in Mexico-born immigrants in the

U.S.A.

This aim is achieved though the following sections:

1. Introduces the topic of immigrant health.

2. Reviews former theories/models that have been used in immigrant health

research

3. Introduces Critical Realism Theory and explains how we have applied it to

immigrant health research.

4. Provides background information about our research population of interest:

Mexico-born immigrants in the U.S.A.

5. Provides background about our health subtopic of interest: sleep health.

6. Explains why we have chosen to focus on sleep health in Mexico-born

immigrants to the U.S.A.

7. Iteration of the research question addressed by this thesis.

1.1 Immigrant Health

Nationally conducted epidemiological studies have shown immigrants in the

U.S.A. as having overall better health, being less likely to suffer from most chronic pathologies and having lower mortality rates compared to the U.S.-born population, despite having more unfavorable socio-demographic characteristics and limited access to health care (Argeseanu 2008, Dey AN 2006). These results are inconsistent with an increasing number of international epidemiological studies (Wändel 2007, Steiner 2007,

Syed 2006, Asakura 2006, Schweitzer 2006, Saraiva 2005, Wiking 2004, Bayard-

Burfield 2001) reporting higher prevalence of chronic conditions in immigrant populations compared to the host population.

Recent studies in the U.S., Canada, the U.K., and Australia reveal a poorly understood phenomenon that surfaced after analyzing different characteristics of health in

18 relation to time spent in the host country. Immigrants appear to lose over time some important health-related advantages. After having resided in the host country for 10 years or more, there are no significant health differences found between the initially healthier immigrant population and the native born population (Antecol 2006, Jasso 2004 and

Singh 2002, in the US; Deri 2005, Ng. 2005, Wu 2005, McDonald 2004, Newbold 2003).

There is neither conceptual framework nor philosophical methodology to explain these findings.

The next section is intended to provide an overview of theories formerly used in immigrant health, and will be followed by a section introducing our application of

Critical Realism theory to generate a new approach to immigrant health research.

1.2 Former Theories/Models Used in Immigrant Health Research

Former immigrant health research studies have adopted various methodological approaches, but few are grounded in a well-defined theoretical framework. Frequently studies completely fail to present any sort of clearly defined conceptual model. These studies that do have a theoretical background use mono-theories, meaning that these studies choose to bring scientific evidence pro or against one single theory, so the analyses will address one single possible explanatory model in attempting to describe and/or explain health outcomes. However, health related outcomes are recognized as being affected by and in turn affecting many variables all at the same time and their etiology is multi-factorial. Some researchers have noted that it is overly simplistic and problematic to analyze multi-factorial processes using mono-explanatory models (Kothari

2002, Diez-Roux 1998, McKinaly 1993, Link 1995, Susser 1985). The current view is

not an exclusive use of any mono-theory approach as it may wrongly focus on a specific health related risk factor at an individual level, ignoring the interrelatedness between

physical, social, historical, and economic environment and their subsequent effect on

morbidity and mortality.

Nevertheless, it is important to review prior mono-theories used in immigrant health studies. These studies can be broken down into two major categories: using either causal theories, or social and ethical theories:

Causal theories are generally used in epidemiology and try to measure causation between two factors, in this case health and immigration status, using the theory of probability, statistics, error measures, behavioral and biological theories (Week 2001).

The most frequently used causal (category of) theory is called “acculturation”. An in- depth explanation of acculturation theory can be found later in Chapter 2, section II. As a brief and oversimplified example, acculturation states that, in time, immigrants become more knowledgeable of the environment of their host country and more like the population of the host country in language, beliefs, and behaviors. Because of these factors, acculturation predicts a change in health and wellbeing in a linear fashion, using only based on the time spent in the host country by the immigrant, as a causal factor.

Though used much less frequently than causal theories, the second major category

of theories used in immigrant health research, are the social and ethical theories. These

focus on society and social processes, ethics, history, law, economics, and politics (Weed

, 2001). The major problem with social theories is that often there is a focus on one

single aspect, one single belief that it praises as the explanation for a complex multi-

dimensional interaction. For example, in the context of immigrant health, the “Salmon

bias effect theory” proposes that Mexico-born U.S. immigrants are healthier than the

native- born Americans because they return home when they get sick. This theory, if

true, is probably contributing to the outcome, but certainly cannot be the sole explanation

for the complex relationships in immigrant health status.

No philosophical methodology has formerly been proposed as a means to

integrate the theories of causation with the theories of social aspects and ethics. Such a

synthesis would entail a multi-factorial approach to conceptualizing the complex

interactions of health with all aspects of personal life, including immigration. We will

address exactly this limitation by applying Critical Realism Theory (CRT) briefly

described next.

1.3 Critical Realism Theory in Immigrant Health Research

The very foundation of modern Critical Realism Theory (CRT) is built upon the

notion of interconnectedness and integration. CRT believes that all real world

interactions happen in an “open system” where individual, familial, community, social, and world factors all add together, subtract from each other, influence each other, and build upon each other. This is no less true for health outcomes in immigrants and non- immigrants alike. Furthermore, the CRT doctrine incorporates the idea that even when

we can’t see or measure certain things directly, these can nevertheless influence what we

do see. To simplify this notion, I use an example pertaining to immigrant health as

perceived discrimination and its effect on sleep. If an individual feels that he is being

discriminated against, due to his social status of “immigrant" and the fact that s/he may

speak with an accent, even though we cannot observe the discrimination and quantify it,

the individual may have problems sleeping because of his anxiety, with further health

implications. The fact that others cannot perceive, logically explain or even “measure”

the individual level of intensity of perceived discrimination, therefore, does not mean that

it does not exist, and that is not causing health effects that we can see. By now it should

be easy to understand that application of CRT to immigrant health could allow one to

combine the causal, as well as the social and ethical theories formerly used as individual

mono-theories, into one model. This approach then could allow one to probe for

differences in health between immigrants and native-born populations, and may also

enable one to measure what risk “living as an immigrant” in itself poses on health.

The benefits of CRT comes from an integration of gender, age, social determinants of health vulnerability, health related cultural attitudes/beliefs, health related behavioral risk factors, unknown factors including perceived discrimination (in the form of a latent variable), and living “life as an immigrant” permitting an analysis of bidirectional effects on health. We can assess the risk for poor health attributed to “life as an immigrant” both directly and through its impact on the other factors that influence health, for example looking at how “life as an immigrant” involves “health related

behavioral risks.” We then use the proportion of lifetime in immigration (PLI) as a measure of the risk that an individual runs as concerns his/her health , due to living as an immigrant in the host country, as well as due to prior experience . In this manner, PLI is represented by dividing the number of years that the immigrant has spent in the host country by the sum obtained by adding the age of the immigrant at the time of arrival in the host country and the number of years that the immigrant has spent in the host country.

This conceptual model is generally useful in providing evidence-based data related to how “life as an immigrant” in any country of interest is related to health changes (improvement or decline). The model can be applied to any immigrant population regardless of origin or host country, and to any health subtopic, due to an integration of various formerly proposed immigrant health concepts into one comprehensible “open system” approach. I have chosen to apply it to Mexico-born

(volunteer) immigrants in the U.S. and to focus on sleep health. Some reasons for this choice include the fact that Mexico-born immigrants are the largest homogeneous subgroup of immigrants in the U.S. I have chosen to address sleep health because problems related to sleep are among the most common complaints in the general U.S. population.

There is also emerging evidence about the role of sleep health in the causal pathways for general health status and increased risk of specific chronic pathology. The next three sections are intended to provide a background on Mexico-born immigrants in the U.S, then on sleep health, and finally on sleep health in Mexico-born immigrants.

1.4 WHO? Mexico-born immigrants in the U.S.

Immigration from Mexico to the U.S. has witnessed a rapid acceleration since the

1970’s. In 2004, the U.S. Census Bureau (U.S. Census Bureau, Foreign-Born Profiles) estimated that over 12 million individuals emigrated from Central America/ Mexico and are living in the U.S.

Troubled by multiple national problems including crime, corruption, drugs, and limited economic opportunities, one in three Mexicans nationals report that they would emigrate, and more than one half believe that life is better in the U.S. (2009 Pew Global

Attitudes Survey in Mexico). Thirty nine percent of Mexicans report to have friends or

relatives in the U.S., with up to one in five reporting receiving money from relatives who

have immigrated (2009 Pew Global Attitudes Survey in Mexico).

Mexico-born individuals have continuously contributed to the growth of the U.S.

population and the workforce, given their younger age and higher fertility rates compared

to the general U.S. population (Fix M., 2001). Mexico-born immigrants and their

descendents born in the U.S. are frequently referred to as Mexican-Americans. They

represent the largest and the youngest ethnic subpopulation in the U.S. with up to 31

million individuals of which 36 percent are individuals less than 18 years old (2008 U.S.

Census Bureau).

As part of the efforts to decrease/eliminate health care disparities in the U.S., one

of the goals of Healthy People 2010 revolves around exploring factors that explain

significant differences in health indicators between U.S. minorities groups. This includes

a better understanding of the role of Mexican ethnicity in relation to low specific

morbidity and mortality risks found in Mexican-Americans, independent from their poor

socio-economic status and reduced access to care, compared to other racial and ethnic groups living in the U.S.

By choosing to focus this thesis on Mexico-born immigrants to the U.S., my research is consistent with this goal, and is significant for bringing together epistemological, conceptual, methodological and epidemiological findings. Further, by choosing this population of interest, we are able to contribute to a better understanding of

“The Hispanic Paradox”, previously described by intra and inter-ethnic mortality and specific morbidity risk differences favorable to Mexico-born U.S. immigrants when

24 compared to any of the following groups: the general U.S. population, their U.S.-born ethnic counterparts (Mexican-Americans born in U.S.), and other Latino U.S. immigrants.

The next section will provide a general background on sleep, and is meant to facilitate a basic understanding as to the importance of this health subtopic, thereby justifying our decision to focus on it.

1.5 WHAT? Sleep Health

The importance of sleep on general health has been demonstrated extensively.

Short sleep duration of less than 7 hours per night is linked to increased risk of mortality

(Heslop 2002, Kripke 2002, Tamakoshi 2004, Patel 2004), obesity (Sekine 2002, Hasler

2004, Taheri 2004, Gangwisch 2005), impaired glucose metabolism including incident diabetes (Ayas 2003), hypertension (Gangwisch 2006), coronary heart disease (Ayas

2003), as well as to other impairments at different levels of the neuroendocrine systems

(Spiegel 1999, 2004, 2004, , Mullington 2003).

Sleep quality is also strongly related to psychiatric conditions, including depression and anxiety (Vaughn 2000, Pearson 2006). Epidemiologic research conducted in western societies has shown that stress-related conditions and socioeconomic variables

(Stamatakis 2007) may influence both sleep duration and quality in the studied populations. Therefore, it would be logical to suspect that because immigrants may be exposed to different stress-related conditions and socioeconomic variables then the native born population, they may also have different sleep health.

The next section provides a brief background on sleep health in immigrant

populations, and also includes the reasons why we have choosen to look at sleep health in

Mexico-born immigrants.

1.6 WHY? Sleep in Mexico-born Immigrants

The field of sleep remains largely unexamined in the U.S. immigrant population,

despite extensive recent research findings documenting the relationship of sleep to one’s

overall health and well being. In fact, no systematic research has been conducted in any

U.S. immigrant population looking at sleep and/or possible health implications due to

stress and socioeconomic conditions.

There is limited international research about sleep in immigrant populations.

Those studies targeting certain ethnic groups of immigrants consistently showed evidence

of poor sleep quality, proposing this to be associated with acculturation and stressful

lifestyle, including existential uncertainty and coping behaviors mechanisms and poor

sleep quality, including insomnia- a common complaint in immigrant populations (Lin

1992, Rosmond 2000, Jablensky 2001, Knipscheer 2001, Voss 2008 ). A study looking

at the 1996 Swedish National Board of Health and Welfare Immigrant Survey and the

Swedish Annual Level-of-Living Survey found significantly more self-reported sleep

problems among the Turkish-born immigrant subgroup (Steiner 2007). A recent German study (Voss 2008) of 112 female immigrants of Portuguese and Moroccan origin reported a significantly greater number of sleep complaints of these women compared with their

German counterparts. Two studies involving convenient small samples of U.S. elderly immigrants of Asian origin (Sok 2008, Hsu 2001) likewise found poor quality sleep in the study population. Poor sleep quality has been proposed as a potential pathway

through which perceived racism may affect health (Steffen 2006). Identity vulnerability,

self-perception of discrimination, and other undisclosed social phenomena related to

immigrant life may be related to the decrease in quality of sleep found in immigrants

worldwide.

Despite the increasing number of epidemiological studies targeting Mexico-born

immigrants to the U.S.A., no study has focused on sleep health in this population. Our

research fills this important gap, utilizing a CRT based conceptual model called the

“Open System Conceptual Model of Immigrant Health Outcomes”. In doing so, we are able to re-analyze the Hispanic paradox as it pertains to the domain of sleep.

1.7 Summary

Immigration health is emerging as an important feature of public health. It is

challenging to define and infer causality given the bidirectional nature of acculturation,

residence time, health beliefs, nutrition, access to care etc. and health status. Sleep

epidemiology in ethnic subpopulation in the U.S. remains largely unexplored, but

potentially insightful in regard to overall health status and outcome.

The limited knowledge currently available about habitual short sleep time, sleep

quality, and sleep-related functional impairments in Mexico-born U.S. immigrants,

Mexican-Americans, and/or other Latino U.S. immigrants and increasing evidence about

the role of sleep on general health and subjective wellbeing highlights the importance of

the principal research question: “Is there any evidence for The Hispanic Paradox, which

proposes a lower risk of short habitual sleep time, insomnia, and sleep related daily

functional impairments in Mexico-born U.S. immigrants compared to the general U.S.

population and other subpopulations in the U.S.”?

To answer this question, we have designed a new CRT based conceptual model,

the “Open System Conceptual Model of Immigrant Health Outcomes” applicable to all

immigrant populations, regardless of their country of origin or host country, designed to

explore multiple effects and interactions between individual health and the external

world, and incorporating the notion that living one’s “life as an immigrant” may in itself

impact health both directly and indirectly, with manifest and latent proprieties. One

example exploiting this novel model will be the analyses of the dimensions of the

Hispanic Paradox as it relates to sleep epidemiology in the Mexico-born U.S. immigrant population.

To conclude this chapter, a summary the content of this thesis follows, broken down by chapter. The aims of each chapter are listed first, some of which are followed by hypotheses, after which we have included a breakdown of the sections of each chapter.

CHAPTER 2

Conceptual Framework

"A single action or event is interesting, not because it is

explainable, but because it is true." (Johann Wolfgang von Goethe)

Aim(s)

1. Briefly introduce the topic of immigrant health research theories.

2. Provide a background on “acculturation” and why previous

“acculturation” theories should be used but not be the centerpiece of the

conceptual framework in immigrant health research.

3. Define the Hispanic Paradox, and describe the explanatory theories that

have been proposed to explain it.

4. Propose the application of Critical Realism theory (CRT) to immigrant

health research, by:

a. Noting non-critical realism theories that have shaped CRT

b. Detailing the philosophical concepts of CRT

c. Reviewing the strengths of the CRT and providing examples of its

application to other fields and topics.

d. Explaining how CRT can be applied to immigrant epidemiological

health research.

5. Describe our newly created conceptual model, “The Open System

Conceptual Model of Immigrant Health”, meant to conduct research of

volunteers (non-refugees, non-asylum seekers) immigrants and to apply

CRT to epidemiological immigrant health research.

2.1. Background on Immigrant Health

Many studies have been conducted in the U.S. attempting to show how the U.S.

immigration experience may affect individual health outcomes of immigrants. Various

methodological approaches have been adopted; however, frequently, studies fail to

present any clearly defined conceptual model. Of the ones that do, most use acculturation

theories. The conceptual models of these epidemiological studies are nearly all mono-

theory, meaning that they use a single explanatory model in attempting to describe and/or

explain health outcomes, which are recognized as being multi-factorial.

The purpose of this chapter is to review the various theories formerly used in immigrant health-related research and to propose for use Critical Realism, not formerly applied in this field. For this thesis, will apply this approach to Mexico-born immigrants, and choose to focus on sleep health as a health variable of interest. Both the immigrant sub-population and the health area of focus can be varied without changing the theoretical conceptual framework for research For the purpose of simplification, many of the theoretical application examples provided are in relationship to the population and the health variables of interest.

Due to the extreme popularity of acculturation theories in U.S. immigrant epidemiological research, the next section describes the origins of these theories, quantitative measurements used to capture “acculturation” and their epidemiological applicability, as well as the risks stemming from focusing immigrant population health research on acculturation and acculturation theories. A short critical review of some criticisms reflecting on each theory and the means used for its applicability are also presented.

2.2 Acculturation and Acculturation Theories

“Acculturation” was first used in the English language by J.W. Powell (1880), who defines it later (Powell, 1883) as “psychological changes induced by cross cultural imitation” (Rudmin, 2003). However, the most accepted and used definition of acculturation was given by Redfield in 1936: "Acculturation comprehends those phenomena which result when groups of individuals having different cultures come into continuous first-hand contact, with subsequent changes in the original culture patterns of either or both groups" (Redfield, 1936).

The epistemology1 of acculturation continues to confuse the world of social and applied science researchers, as its use and meanings are constantly recreated. The interpretation of the meanings of the acculturation is dependent on many variables: the focus of the field (ex: sociology, anthropology, psychology, political science, linguistics, economics, health science, and education) and the primary motivation of the research.

This approach is often based on ideology and political orientation as well as the historical context of when and where the research takes place.

Immigrant health research disclosing acculturation theories shows strong sociology, political science, and psychology influences on the variability of the interpretation of what acculturation is and how it affects population health. Other terms borrowed from the same noted fields also show similar ambiguity when used as predictors of health in immigrant epidemiology. These include: assimilation, westernization, ethnic identity, traditionalism, modernization, and urbanization.

The risk of using the term “acculturation” in epidemiology, public health, and health policy research stems from underestimation of the powerful ideological, historical,

and political context underlying this term. For better understanding of how acculturation

interpretation may be heavily contaminated by ideology and political orientations,

examples from the history of acculturation are given below.

The concept of acculturation as encompassing “alteration or changes in culture”

has roots in the ancient Greek world, and is documented in Plato’s Dialogs. The Greek

society’s historical perspectives on foreigners were captured in these texts through

reference to “xenelasia” and “cultural contamination” (Plato, 348BC). Plato attempted to

explain to his peers why “cultural isolation” may be impossible: “the refusal of states to

receive others, and for their own citizens never to go to other places, is an utter impossibility, and to the rest of the world is likely to appear ruthless and uncivilized”

(Plato, 348 BC).

Plato’s Dialogs is consistent with the ideological beliefs shared by his society at

the time when they were written. These included “minimize(ation) (of) the cultural

contamination” of the Greek culture from “bad foreign ways” with avoiding “xenelasia or

banishment of strangers.” Plato’s Dialogs recommend spatial restrictions of the

“foreigners” (as to be allowed only outside of cities and mainly in the port areas), and

also age restrictions (over 40) for Greek citizens, when traveling to foreign lands.

The history of theorizing acculturation skips to the 19th century, with

DeTocqueville’s (1835) “De la Démocratie en Amerique”, a text which is considered to

be one of the first manuscripts describing the term “cultural assimilation”. A French

historian and thinker, DeTocqueville’s opinions on the topic are reflective of his political

perspective against the French model of colonization.

Boas (1888), ‘the father” of American anthropology, introduced the concept of

cultural traits in the context of historical events. He wrote: "It is not too much to say that

there are no people whose customs have developed uninfluenced by foreign culture that

has not borrowed arts and ideas which it has developed in its own way" (Boas, 1888).

His concepts of cultural plurality and cultural environment which are affected by

historical non-cultural factors reflect an “anthropology perspective” on acculturation. In this perspective there are “moral consequences” of any scientific construct, and researchers should be primarily “citizen scientists” (Boas, 1938).

As pioneers in the area of the sociology of migration, Thomas and Znaniecki studied the migration of Polish to the USA and argue that culture is comprised of shared apperceptive3 processes (Thomas, 1920). Their work introduced the concept of

“culturalism” as a different perspective on how to deal with the effects of culture.

Znaniecki’s “culturalism” is, in fact, arguing against empirical work and towards the idea that social reality should be studied entirely subjectively.

Berkson (1920) was the first to introduce the notion of “Americanization” and

“melting pot” as assimilation in acculturation theories, which likely shaped Gordon’s perspective of culturalism (Gordon, 1961, 1964). Widely praised in epidemiological immigrant research, Gordon’s theory of assimilation proposes seven stages.

Acculturation is the first step, characterized by the efforts of immigrants to adopt language, dress, and daily customs of the host society. This theory remains one of the most frequently used mono-theories in immigrant health research, especially when looking at longitudinal health changes in immigrant populations in the U.S. The health research application of Gordon’s theory of assimilation proposes the improvement of immigrants’ ability to navigate the health care system and gain greater access to health

services with assimilation, thereby suggesting that immigrants are able to maintain or

even improve their general health with increased time spent in immigration (Salant,

2003). Criticisms of the use of assimilation theory in immigrant health revolve around its

unidirectional focus, rather than showing a complex multidirectional process, and around

the fact that it ignores the possibility of bicultural identity (Rogler, 1991, Palinkas, 1995).

Berry's bi-dimensional acculturative stress model (Berry, 1980, 1987, 1989,

1997) is among the most used in immigrant health-related research, especially in the

domain of mental health. Berry’s theory sustains diminished physical and mental health

status peri-immigration, but also predicts mental health improvements with

“acculturation.” He introduces the notions of biculturalism and pluralism as possible

causes of segregation.

As previously described, acculturation and acculturation theories are at risk of

using the same construct (acculturation) for totally different purposes and meanings. Due

to this, and the increasing popularity of immigrant research, acculturation theories and

proposed measures of acculturation have been flourishing in the past two decades.

Rudmin (2003) found over 1500 published studies of acculturation just between 1991-

2000, focused on adjustment of accommodation, assimilation, marginalization,

segregation, integration, multiculturalism, exclusion, country orientation, cultural

alienation, withdrawal, pluralism border-crossing, differentialism, and other acculturation

constructs.

The following paragraphs describe why measures of acculturation borrowed from

social science have resulted in non-reproducibility of findings across immigrant epidemiological research studies:

Through use of various psychometric scales of acculturation ranging from simple to complex, immigrant epidemiology research in the U.S. aims at some consensus and consistency, related to recommended methods, for capturing health changes due to immigration. Because acculturation, as a predictor of health, may also be understood as a substitute for "immigration” risks on health, a variety of measurements are used in immigrant epidemiology literature: ranging from individual measures of cultural participation, social contacts, personal and traditional beliefs and values, to constructs incorporating subjective perceptions, such as loneliness or social discrimination. As expected, no consensus has been reached for immigrant U.S. populations, due to non- reproducibility across the field. Studies are reporting conclusions ranging from elements of acculturation having no effect, having protective effects, or even having negative effects on health.

In conclusion, acculturation-based methodology used to assess immigrant health outcomes varies widely and there is a non-reproducibility of findings. Often the design and analyses using acculturation “construct” places the onus of “culture” on the individual, rather than bringing in perspectives related to other social processes, including, but not restricted to, the ascribed status in the racial hierarchy of the new country. This approach can result in increasing cultural focused individual-centered interventions, rather than stimulating health policy changes addressing social and economic inequities (Viruell-Fuentes, 2007).

The next two paragraphs describe simple measures previously used for capturing

“acculturation” in different U.S. epidemiological studies; they are used in our study to measure the separate domains of health risk factors in immigrants. This clarification

departs from the chaos of definitions and subsequent ideologies brought about through

the use of terms such as acculturation, accommodation, assimilation, marginalization,

segregation, integration, multiculturalism, exclusion, country orientation, cultural

alienation, withdrawal, pluralism border-crossing, differentialism, and other acculturation

constructs.

Measures used to capture “acculturation” include birthplace, language used by

immigrants at home or elsewhere, generation, age at the time of immigration, and length

of time in immigration. These are appealing to researchers as they are relatively simple to

collect. In fact, the main criticism for using these measures in drawing health-related

conclusions was that these variables have been used solely in conceptual frameworks as

acculturation related measurements (Salant, 2003).

The proportion of lifetime spent in the country of immigration (PLI) was more

recently used by a few studies as another dimension in acculturation (Maxwell, 1998,

2000, Juon, 2000). PLI was calculated by dividing the number of years that the

immigrant has spent in his/her host country X by the sum of the age of the individual

when s/he arrived in the host country and the number of years spent in that country, the

result being multiplied by 100. This measurement is appreciated for taking into account

differences mediated by both the total number of years in immigration and the age at the

time of emigration, but also critiqued because it intends to be used solely as a measurement of acculturation (Salant, 2003). Additional limitations we believe are an undisclosed conceptual framework and the linear assumptions previously used in statistical analyses to model the relationship between PLI and acculturation. While in recent theories of acculturation it is acknowledged that acculturation is a non linear

process, cross-sectional study designs often assumed linearity of those processes over

lifetime in immigration.

Because Mexicans are currently the largest group of immigrants in the U.S., the

thesis will focus on this group. The Hispanic paradox will be introduced in the next

section, together with both acculturation and non-acculturation theories as means that have been utilized in trying to understand it. Criticisms raised by each theory are also presented.

2.3 The Hispanic Paradox and Explanatory Theories

Mexico-born immigrants face increased risks of social determinants for health vulnerability, including lower socioeconomic status, restricted access to healthcare, language barriers, cultural barriers, and immigration-related discrimination (Fix, 2001,

Kaiser, 2004, Dey, 2006, Pitkin, Derose, 2009). Despite such known risk factors, evidence-based epidemiological research reports that Mexico-born U.S. immigrants have significantly lower morbidity and mortality, compared to the general U.S. population and to their U.S.-Born Mexican counterparts. This trend was first observed by Karno in

1969, when studying perceived need and usage for mental health services in Mexican immigrants to the U.S., and was confirmed by a variety of other studies focusing on various health issues (Markides, 1986, Sorlie, 1993, Arias, 2002, Hunt, 2002, Dey,2006).

Such individuals were remarked as generally healthier, compared to other groups in the

U.S., called “The Hispanic Paradox”, is a term often used as referring to Mexican immigrants, who have a low prevalence of hypertension, cardiovascular disease (CVD), and symptoms of serious psychological distress. The etiology of this phenomenon is

poorly defined.

Contributions to better understand “The Hispanic Paradox” are important, in part

due to evidence highlighting that, except for the most recent arrivals, immigrants

worldwide experience worse health status across most dimensions compared to the country of immigration’s native-born population and/or to the country of origin (Asakura,

2006, Syed, 2006, Fagerli, 2007). Similar trends have been described by some U.S. epidemiological studies (Singh, 2002, 2004, Jasso, 2004, Antecol, 2006). However,

Mexico-born immigrants seem to be protected against the rapid health decline present in immigrants from other countries of origin. Previous studies have reported lower infant and adult mortality in Mexico-born U.S. immigrants, compared to other Hispanic immigrants (Ronwaike, 1987, Shai, 1987, Markides, 1997).

Mono-theories have attempted to explain influences identified as “traditional” health behaviors, social networks, and family/friend relationships of Mexico-born immigrants, such as being married or living with a partner, as exerting a protective effect on their health.

“The Social Buffering Theory” was first described by the epidemiologists

Nucholls, Callell and Kaplin in 1972. When looking at stress in pregnant women, they found that those that had had high levels of stress and no social support were significantly more likely to suffer from complications than those with high stress and high social support. Applied to immigrants, the social buffering effect theory proposes that social networks and strong social support, including better family ties seen especially in recently arrived immigrants, contribute to better health outcomes in Mexico-born immigrants, compared to other groups, including their U.S. born counterparts (Scribner,

1996, Palloni, 2001, Arias, 2002).

Cultural buffering Theory suggest that compared to the U.S. culture, other

cultures have better norms and values which may restrain risky behaviors , including

smoking, alcohol, or illegal drugs use, and also better nutrition (Hummer, 1999, 2000).

As immigrants are more acculturated with longer duration in the United States, they are proposed to lose these advantages.

Adepts of acculturation theories placed the social buffering theory at the

“acculturation” level and proposed that, as Mexico-born immigrants “acculturate”, these ties are progressively lost, which may justify why Mexican-Americans born in the U.S. may not benefit from these advantages. Acculturation theories attempting to explain the

Hispanic Paradox are mainly criticized for focusing on the individual level, instead of drawing attention to more important sources of dysfunction related to the societal context of migration (Viruell-Fuentes, 2007).

Other theories have also been proposed in trying to understand the Hispanic

Paradox. These can be classified as either proposing that the differences observed are a result of selection bias, or proposing that Mexico-born U.S. immigrants have real physical and mental health advantages.

The selection bias theories include “The Healthy Immigrant Effect” theory, “The

Salmon Bias Effect” theory, and “The Misreporting /Misclassification” theory, the last one related to the direct participation of Mexico-born individuals in epidemiological studies. These theories attempt to describe the paradox, as resulting from selection

(and/or self-selection) due to migration processes, or due to biased methodology used by previous epidemiological research.

“The Healthy Immigrant Effect” theory explains that people who have poor health do not tend to emigrate to another country (Marmot, 1984, Young, 1990, Kliewer, 1992).

Furthermore, the country of immigration requires immigrants to undergo medical screening, allowing only healthy individuals to immigrate. Also, the selection of immigrants by the host country for employability is an indication of good health (Chen,

1996). To apply this to Mexican immigrants, the healthy immigrant effect theory states that choosing to emigrate and the process of emigration itself, including being granted permission to emigrate to the U.S., selects the healthier, younger, and most resourceful individuals. However, the healthy immigrant effect theory fails to account for the difference between Mexico-born immigrants and immigrants from all other countries, since, in general, the same U.S. immigration health screening and means for being granted permission to immigrate are applied to all immigrants, regardless of their country of origin.

Dr. Pablo-Mendez, a Mexican-born U.S. physician, epidemiologist, and leader in

Global Health, proposed “The Salmon Bias Effect” theory in a 1994 letter to the editor of

JAMA, following the publication of a study showing low morbidity and mortality in

Hispanics, compared to the general US population (Sorlie, 1993). This theory makes note of the multiple temporary and/or permanent returns to Mexico of individuals from the Mexico-born U.S. immigrant subpopulation. Returning to Mexico was associated with personal and/or family health crises occurring during stay in the U.S. (Palloni,

2004). The hypothesis is therefore that the U.S. immigrant population retains the healthiest segment of Mexico-born immigrant population at any given time, and raises concerns about conclusions being biases on loss to follow-up related to the exposure

(living in the U.S. as an immigrant) and outcome (health decline) reported in cohort

studies design.

Misreporting/informational bias is also proposed in the context of language

barriers and health related illiteracy, as well as misclassification with regard to “self-

selection” of those who usually agree to participate in research studies. These theories

sustain that misreporting and misrepresentation are biasing results, independent of the

epidemiological methodology (Palloni, 2003, 2004, Carter-Pokras, 2008).

Non-acculturative theories proposing real health advantages of Mexico-born

U.S. immigrants over other U.S. subpopulation categories derive mainly from the fields of sociology and psychoanalysis.

Freud’s psychoanalytical theory (Freud, 1929), expanded upon by Jung and

Erikson, and translated by contemporaneous psychoanalysts is the basis of the “Cultural

Mourning theory” (Ainslie, 1994, 1998, 2002). It places strong emphasis on the

workings of the human unconscious mind and proposes that immigrants are “perpetual

mourners.” The Cultural Mourning theory defines the immigrant experience as a

complex case of mourning, which revolves not only around the loss of loved individuals

and relationships, but also loss of spaces by geographical dislocation (Ainslie, 1995,

1998). However, the proximity of Mexico to the U.S. and the “relative fluidity of their

borders”, along with increasingly more affordable access of modern communication

technologies “alter the character of cultural mourning” in Mexico-born immigrants

(Ainslie, 1998) As Ainslie remarks, Mexico-born “immigrant’s ties to the native country are maintained and continually replenished,” so “mourning is comparatively less necessary…Accessibility allows immigrants to remain intimately connected to the world

they have left, thereby actively sustaining their intrapsychic representations and fantasy

of return. In this context the emotional ties of Mexico-born immigrants remain rich and

potent while immigrants from more distant countries have been forced (by time, distance,

and cultural tensions) to dilute or relinquish them altogether… Simply put, they have

relatively less to mourn” (Ainslie, 1998).

Recently, research applying evidence from the field of sociology and psychoanalysis has attempted to capture effects related to self-perception of discrimination on health outcomes (Pascoe, 2009, Williams, 2003). It has been proposed that chronic exposure to discrimination including “othering” a process that ‘‘serves to mark and name those thought to be different from oneself’’ (Weis, 1995), may be a critical psychological process eroding overall health in immigrants and their descendents

(Grove, 2006, Viruell-Fuentes, 2007). The focus of this theory shifts the attention from the acculturative processes to unconscious mental mechanisms of protection against self-

discrimination, and perceptions related to stigmatization. This theory applied to the

Hispanic paradox in that it proposes that Mexico-born immigrants’ motivation for

migration, including robust feelings of hope and optimism with regards to individual and

family future opportunities in the U.S., may counteract the negative effects of

discrimination on perception on health outcomes (Viruell-Fuentes, 2007). These

particularities are postulated as self-protective factors, and are proposed as modulating individual perceptions, reactions, and ability to cope with different aspects of social stress, including better ability to internalize self-negotiation of U.S.-based ethnic and racial identities (Grove, 2006, Viruell-Fuentes, 2007).

In conclusion, the several mono-theories proposed to explain the Hispanic

paradox don’t necessarily overlap or exclude each other. The prediction power of each is

limited when using a mono-theory model.

The next section is dedicated to introducing a multidimensional approach and

theory not previously employed in any U.S. immigrant research, that of Critical Realism

Theory (CRT). In order to attempt a simplification of the complex concepts presented,

examples pertinent to immigrant health issues are provided. The section is also broken

up into smaller sub-sections, a. through d., to allow the reader for easier accessibility to the content. Section a. is intended to describe non-critical realism theories that have shaped CRT. Section b. contains the philosophical concepts of CRT. Section c. then includes the strong points of the CRT and gives examples of how CRT has been applied to other fields and topics. Section d concludes by explaining how CRT can be applied to immigrant epidemiological health research.

2.4 Critical Realism Theory and the Hispanic Paradox

a. Non-Critical Realism Theories

Prior to the 1960’s, the dominating theory applied to virtually all humanitarian sciences was that of positivism. According to Comte (1830), positivist belief is that there are universal law-like explanations for all phenomena. Because of this, the goal of research is to quantify what can be observed, which can then be used to assess relationships and draw conclusions about interactions. Epidemiological studies have traditionally adopted the positivist approach, focusing on data collected about individuals and/or frequencies and trying to correlate these to outcomes of interest (Byrne, 2004).

Criticisms against positivism are based on the notion that it is a reductionist or overly

43 simplistic way of looking at real world phenomenon. This is because positivism focuses on quantifying that which can be observed. The argument against positivisms is that it is unreasonable to ignore the impact of everything that is not readily observable and/or may be unquantifiable (Bhaskar, 1975). The theories that emerged as a contrast to positivism are relativism, idealism, and critical realism.

Relativism argues that knowledge is developed through social processes and relative to the perception of the individual (Crick, 1962, Kuhn, 1962, Feyerabend, 1987), and that Western science claims to truths are dogmatic (Parker, 2001). The problem with relativism though is that there is no way to resolve competing claims to knowledge itself

(Clark, 2008). Idealism, dating back to ancient Greece (Antiphon, 411 BC), was applied in attempting to take a middle ground between positivism and relativism (Bhaskar, 1998).

The notion of idealism is that objects are not objective in themselves, they are subjective interpretations by individuals and thus they are impossible to measure or interpret through a scientific method. However, the strict application of this theory would also argue against the utility of any research.

b. Critical Realism Theory: Philosophical Concepts

The basis for the ideas that shaped into what would later be called Critical

Realism was first introduced by Renee Descartes (1641) through his writings employing methodological skepticism. Using this method, Descartes argues about the reality and truth of what science, religion, and even the use of our senses can provide. John Locke expanded on this view, again suggesting that, though the world is real, it is impossible to assess its reality in a quantitative manner through the use of one’s senses, as these

transduce and modify the information away from the truth (Locke, 1690). Modern

critical realism is attributed to Roy Bhaskar, who developed the theories of transcendental realism and critical naturalism, later to be merged into the Critical Realism

theory (CRT) (Bhaskar, 1975, 1993, 1998).

CRT views “physical and social entities as having an independent existence from

human knowledge” (Clark, 2008). Therefore even if individuals do not see or

recognize the existence of a structure, this does not mean that it is not in existence.

To make this idea easier to grasp, take the example of ethnic discrimination at work.

Discrimination may exist independently of whether the management or individual

workers (other than the individual perceiving that discrimination) are aware of it, or want

or are able to recognize its existence (Clark, 2008).

Along this same view, CRT indicates that individual beliefs are equally

important, and perhaps even more so, their quantitative measurements. For

example, it has been consistently found that self-reported health, a measure that positivist

theory would dismiss for being highly “subjective” in nature, especially if the individual

has poor access/contact with health services, is a robust and reliable predictor of mortality

and morbidity. This measure was previously proposed as associated not just with

physician diagnosed chronic diseases, laboratory testing, functional disabilities and

impairments, but also with behavioral risk factors (Appels, 1996, Idler, 1997, Doorslaer,

2003). This finding supports the CRT idea about the importance of intrinsic subjective

value. When applying this notion to our area of health interest, we conclude that asking

an individual for his/her subjective report of general health status may be a good indicator

of health quality in that particular individual. Another example pertinent to our empiric

research is the question concerning sleep quality and self-reported symptoms of sleep disturbances, presently used as criterion for insomnia (National Center of Sleep Disorders

Research, 1998).

According to Bhaskar (1998), CRT divides “reality” into three domains: the

“actual” (events and actions that are more likely to be observed), the “real” (underlying powers, tendencies, and structures, whether exercised or not, that cause events in the actual domain), and the “empirical” (fallible human experiences and perceptions)”

(Clark, 2008). “Transfactuality” is a major concept in CRT, and refers to the potential for misalignment between the actual, real, and empirical domains. Empirical domains are not a complete or accurate measure of the real or actual domains (Bhaskar, 1998). For example, the empirical domains of variables collected through a physical exam are not a complete or accurate measurement of a patients’ well-being. Also, the “phenomenon in the real domain may not be visible or exercise influence on the actual domain at any one point in time”, but at other times it can become active and influential (Bhaskar, 1998).

For example, negative cultural/family/personal beliefs about sleep as a “weakness” may not affect as person’s sleep pattern, if that patient has a relaxed schedule, like during a vacation; however when there is a pressure of time constraints, having the cultural belief that sleep is important and necessary vs. a waste of time would play a significant role in whether that individual would decrease the number of hours slept per night or not.

Further, the physician interpretation of a report about the number of hours of sleep per night is depending of many factors. To summarize this example, cultural belief is not an entity that is visibly perceived or readily measurable. This may be operative even if the data collection of sleep-wake behaviors is “objectively” conducted over days, or with

portable monitoring in the patient bedroom. Undisclosed factors (ex: performance during

vacation or time constraint period), response to monitoring, etc., may or may not impact

the interpretation of the data for any given subject. In short the visibility and influence of

the real domain on reality itself cannot be denied.

CRT supports an emergent ontology, meaning that “a relationship between two

features or aspects as one arises out of the other …. (but) remains causally and taxonomically irreducible to it” (Lawson, 2003). What this means is that events and interactions are complexly related to one another causally, so, although one may lead to another, it is wrong to think that, by merely quantifying the 2nd event, it is possible to also

capture the 1st. For example, it was pointed out (Domino, 1986, Taub, 1971) that cultural beliefs (factor/event 1) may impact on the amount of time that an individual spends sleeping each night (factor/event 2). However, it would be wrong to assume that

knowing how many hours an individual sleeps at night (factor/event 2) means that you

were able to capture the cultural belief of the individual with regard of sleep need

perception (factor/event 2). Thus, according to CRT, causation is not linear or

secessionist in nature. Adding the concept of “transfactuality” would further indicate that

“What causes something to happen has nothing to do with the number of times we have

observed it happening” (Lawson, 2003). Instead, Lawson proposes that explanations

should be based on identifying the causal mechanisms and discovering the complex

process that leads to their activation, trying to determine under what conditions activation

takes place. Therefore, when trying to understand why phenomenon occurs, researchers

focus beyond the observable events (the actual) and explore possibilities of the

underlying powers, tendencies, and structures (the real), that in an interconnected and

dependent manner cause the observable events (Clark, 2008). This train of thought has in

fact already been applied by some of the explanatory theories to the Hispanic Paradox,

when providing a hypothesis for why the actual observable health differences between

Mexico-born, other immigrants, and native-born populations are present. The great weakness of all of these already existent theories is hit upon by the latest major idea

emerging from CRT, that of “open systems”, which will be discussed in what follows.

CRT maintains that all social phenomena occur in “open systems”, rather than the

artificially created closed systems used in laboratory experimental design (Sayer, 2000).

Many different “real” factors combine, adding to each other and/or taking away from

each other’s impact, and causing any single “actual” event to occur. In an “open system”

model, these interactions are also bi-directional. For example, social determinants of

health, health related beliefs, and health related behavioral risks, all influence each other

mutually, thus modifying each other. Furthermore, they all combine influencing one’s

physical and mental health, which itself then influence all of these factors, potentially

modifying them. CRT itself is supported by and supports the existence of “paradoxes.”

By definition, the term “paradox” is indicative of contradicting notions that have not yet

been ramified. CRT would argue that the reason for this is due to researcher failure to

combine the domains of the actual, the real, and the empirical into the complex multi-

directional system that is representative of “reality”. Thus, based on the CRT doctrine, it

is impossible for one single explanatory theory to represent “reality”, and this is the

reason why the mono-theories used so far in trying to explain the Hispanic paradox have

failed.

The next sub-section is meant to review some key strengths of CRT and to

48 provide a short background for the prior use of CRT in various fields, thereby helping to set the stage for our application of CRT to immigrant health research.

c. Critical Realism Theory: Strengths and Former Applications

CRT combines the strengths and ramifies the weaknesses of positivism, relativism, and idealism, through a complex approach that allows for both scientific and human perspective to be merged. The CRT “open system” notion and recognition of complexity and multi-leveled nature of interactions and perceptions allow for the combination and integration of many theories on any given topic into one model. CRT offers the advantages of a theory of ontology, entailing epistemological implications, while still sustaining neo-positivist methodological approaches. This includes supporting validated quantitative methods in establishing association relationships. This approach provides common philosophical ground for the unification and transfer of results and conclusions obtained from congruent fields, including medical care, epidemiology, anthropology, social work, and health services research.

CRT has shown a potential for application, and some examples from various fields where CRT has been applied are noted. In the field of nursing, critical realism- driven research has been applied in several studies about evaluation of interventions

(Tolson, 2006, Wilson, 2006). Application has resulted in effectiveness being looked at in a context acknowledging manner, and focus being placed on complex causations

(Pawson, 1997). Lawson was the first of several economists to apply CRT in that field, refocusing the field from the traditional causal mathematical explanations on the complex interactions within the “real world” (Lawson, 1997, 2003). Sociologist Jackson applied

CRT to poverty, arguing that poverty is in fact a state of being and exploring the

multilevel nature of the diverse issues that it entails and affects (Jackson 1992). CRT has

also been introduced into educational research (Scott, 2000, Willmott, 2002). These

examples are far from being all-inclusive, but are only intended as a sample

demonstrating the broad range of fields and topic areas to which CRS has already been

applied. The next section summarizes our application of CRT to immigrant health, our

selected population of interest and health sub-topic being Mexico-born immigrants and

sleep health.

d. Critical Realism Theory: Immigrant Health Research & the Hispanic

Paradox

This section is intended to describe how the basic principles of CRT can be used

when looking at immigrant health. As noted in Chapter 1 of this thesis, we have chosen

to focus on Mexico-born immigrants and sleep health, though the critical model we have designed on the basis of CRT can be used in any volunteer immigrant sub-population and any health issue.

CRT is built up on the notion that all social phenomena occur in “open systems”, as described in Section 2.4. c. We may begin by saying that individual health is best analyzed in an open system model, as this is believed to be consistent with the “reality” of the world. There are many causal factors that impact individual health; and these factors act together, modifying each other, and combining to affect health. Furthermore, many of the relationships among causal factors and between causal factors and health are bi-directional in nature, so not only do causal factors modify each other, but health also

modifies the causal factors that modify it.

Immigrant status is a social construct and is not in itself a condition that can be

said to act as a causal factor impacting health. Therefore, this could not explain “per se” why we may find significant health differences when looking at the health of an immigrant population and a native-born population. However, as previously referenced, epidemiological studies have found health differences between immigrant populations and native born populations, and there are also significant differences between those results worldwide and those found in the U.S. Putting all of this together, we can say that if an immigrant population is found to have different health (better than found in the U.S. or worse than found elsewhere) compared to the native born population, there must be something (or rather “multiple” things) about the “reality” of life experience in the country of immigration that impacts health.

To validate this statement for the purpose of our study, we give quantitative research examples applying this theory to differences in sleep health between the foreign- born and the non-immigrant (native-born) population. As a first step, we seek to decrease differences in biological factors that may lead to a difference in health outcomes between the immigrant and non-immigrant group, by adjusting for age and gender, based on the

Healthy immigrant effect theory and previous knowledge about immigrant population being younger and with higher male proportions. For better control of any racial genetic predisposition, we shall further restrict the comparison of non-immigrant population to individuals with ancestry from the same country (Mexico in our case). If we find that there is a significant relationship between immigration status and sleep outcome, consistent with the CRT claiming (see section 2.4.c), we shall be able to anticipate that

there may be “something about being an immigrant” that affects health. Next, we can adjust the model for all known risk factors attributed to poor health outcomes, including social determinants of health vulnerability, cultural attitudes/beliefs possible related to health outcome of interest, and health related behavioral risks. If the significance of the relationship between immigrant status and health continues to exist, then that may be attributed to the “latent variable” – the “real”, but not readily perceived or directly measurable factors still influencing the relationship between immigration status and health. Although the “immigrant status” per se is not a risk, but just an artificial (social) construct, we can still conclude that immigration status is a risk factor to health outcomes, and although not explained or understood at the point of the present research, it is still a reality and may need to be further addressed by health policy regulations.

The next application refers strictly to immigrant populations. In this example, we

will show how we can attempt to quantify the general question “is life experience in

country of immigration is a health risk factor?” The risk factor is how much of his/her

life an immigrant has spent in the host country of interest (U.S.), and is this significantly

associated to health outcomes (of interest)? So the proportion of life spent in

immigration (PLI) is no longer “temporally” related, although it has temporal metrics.

PLI can be computed by dividing the number of years that the immigrant has spent in

host country X by the age of the immigrant at the time of data collection, all multiplied

by 100. Another way of stating this is by saying that PLI can be calculated as: the

number of years that the immigrant has spent in the host country X divided by the sum of

the age of the individual at the time of immigration and the number of years spent in the

USA, all multiplied by 100. This latter statement is logical, as adding the age of the

52 individual at the time of immigration to the number of years spent in the USA would obviously produce the age of the individual at the time of data collection. As you may recall from section 2.2, this formula now incorporates several attributes of interest: age at the time of immigration, number of years in immigration, and time, but it really represents a proportion of a risk. Therefore PLI is not a temporal measurement in our application, but a risk factor used to answer the question “Is life experience in the country of immigration (U.S.) a risk factor for poor health?” Acculturation theorists have previously used PLI, but as a non-scale temporal measure of “acculturation” (Maxwell,

1998, 2000, Juon, 2000). Instead, we transform this measure into a continuous measure of the risk of “life as immigrant”.

Moreover, by being able to use the prediction of multiple previous proposed theories, now we can think “multidisciplinary” of how this relation (PLI- health outcome) may look like. Analyzing our Mexico-born US immigrant population example, by using the multi-theory approach and previously described theories of the Hispanic effect prediction, we may attempt to “build” what would be the approximate trajectory of this relationship. Based on Healthy immigrant effect, predicts lower prevalence of health reported problems. The social buffering theory, the theory of “othering” and cultural mourning predict increase of prevalence of poor health outcomes with PLI. However,

“The Salmon effect” suggests that the longer the PLI, the higher chance it would be to

“lose” the sick, so the decline of risk is expected for the longer the PLI. The more theories one adds, the higher chance to predict an accurate nonlinear relationship between

PLI and the health outcome. So in conclusion, nonlinearity is expected to be seen without and with adjusting for age and gender.

Thus, the next question would be: how can we know better what part of the immigrant life is really mediating the relationship between PLI and the health outcome.

In a way similar to the immigrant status, our construct PLI is artificial, and also too general. In answering this question, we can begin by referring back to Bhaskar’s writing the “reality” of anything is made up of the “actual”, the “empirical”, and the “real”. As noted in section 2.4c, the “actual” are the observed events/relationships that are perceived, the “real” are the factors that may or may not be directly perceived and are not readily quantifiable, and the “empirical” are factors that are perceived and quantified.

We can apply this principle to understanding in what way is it that the “reality” of “life experience in the country of immigration” poses a risk factor for health, by breaking down “reality” of this risk into its three components. Bhaskar’s “actual” is the relationship observed between PLI and health. The “empirical” are the recognized health risk factors for which there are quantifiable data and includes “social determinants of health vulnerability”, “cultural attitudes/beliefs which may affect health”, and “health related behavioral risks.” The “real” are the unknown factor or factors that we are not able to measure, that nevertheless affect health. Being labeled the “latent variable,” for simplicity, we can suggest that this can include many “leftovers”, as individual perception of the “immigrant reality of life”.

According to CRT, the “reality” life experience in the country of immigration and its health effects is made up of the “actual” observed relationship between PLI and the health results, a combination of “empirical” and “real” factors. Therefore, though it may not be possible to know what all the components of the “real” are and how much they affect the individual’s health, it is possible to deduce that any relationship between PLI

and health which is not accounted by all the “empirical” factors, is caused by the “real”

factors, our “latent variable.”

In applying this to our model, to show the nonlinear significant

relationship between PLI and health, we can adjust it, to the best of our knowledge and

ability, for all of the “empirical” risk factors. If an association still remains, this can be

attributed to the “latent variable”.

To conclude our summary application of CRT to immigrant health, it is

important to emphasize that CRT allows us to combine elements from all formerly

proposed explanations as to why health deteriorates in immigrant populations. It

does this by allowing us to adjust the relationship between PLI and health for all formerly

proposed quantifiable risk factors for poor health- the “empirical” ones. These include,

age, gender, social determinants of health vulnerability, health/sleep related cultural

attitudes/beliefs, health related behavioral risks etc. It also allows for the possibility of

theories from different fields that propose concepts not readily open to direct perception

or measurement to be applied to the model, as contributing to the “latent variable”. Some

examples of what the “latent variable” may encompass are: identity vulnerability,

“perpetual mourning”, and “othering”. Those theories may come from contradictory

branches of philosophy and could not be used simultaneously in our predictions, without

the CRT “umbrella”. In conclusion , CRT rejects mono-theory models of immigrant health as being an over simplistic view of the world, and instead has facilitates an all encompassing “open system” approach to understanding the relationship between “life per se” as an immigrant, and health.

The next section is provides a written explanation of the conceptual model figure

55 presented as Figure 1.

2.5 The Open System Conceptual Model of Volunteer (No –asylum, Non-refugee)

Immigrant Health

The proposed conceptual model, The Open System Conceptual Model of

Immigrant Health Outcomes, is labeled as Figure 1. The goal of the model is show the

“reality” of how life impacts health/sleep health outcomes in immigrants and non- immigrants. It should be clear, after reading Section 2.4b, that the “reality” incorporates all of the “actual”, “real” and “empirical” factors that impact the health of an individual.

The model separates immigrants from non-immigrants, because we believe that “life experience as an immigrant is different from life experience as an individual born and lived his/her life in his/her country of birth and leads to differences in health /sleep health between immigrants and non-immigrants.

Components and relationships within the independent entities used in the conceptual model are described in more detail in the appendix, Figures 2B, 3B, 4B. The variables used in this research are detailed in Fig 2A, 3A, 4A. For the sake of easier referencing between the text of this paragraph and the conceptual model, the terms that are used in the conceptual model will be placed between quotation marks within the text of this section. Examples are also provided as a means for the reader to more readily grasp key concepts, much in the same manner as has been done in previous sections. A final and very important point of clarification is that, within the conceptual model, we used the term “life experience as (immigrant/non-immigrant)” to signify the “reality of life as (immigrant or non-immigrant).”

Consistent with the “open system” doctrine of CRT, our model shows the

interconnectivity between the outcome of health and sleep health, for which we have data

collected at the “individual” level, to his/her “family”, “community”, “society”, and the

“world”. Dashed lines are utilized in order to illustrate the openness of the system,

because cause and effects flow in both directions. For example, the poor health of an

“individual” can affect the “family”, as poor health places a financial and emotional

burden, the “community”, if the individual cannot participate in ways that he used to be

able to, the “society”, because, if many individuals are sick at the same time (like with an

epidemic such as the flu), then there is decreased social functioning with people unable to

go to work, and even the “world”, as has been happening with the HIV/AIDS pandemic

affecting the global economy etc. Of course these relationships also work in reverse,

with “world” events impacting down to the “individual” level, affecting health.

The model also shows individual progression through life stages, so read from left to right, it shows progression from birth (on the far left) to death (on the far right). It is logical to consider these as unidirectional relationships: between the entities of “birth”,

“age-ing”, “health and sleep”, and “death”.

The model is dichotomized into two separate pathways based on the place of birth. This allows us to separate the immigrants from the native born population. For the purpose of this study, the two possibilities are born “elsewhere”, (thereby “yes”, an immigrant), and born in the “U.S.A”,( thereby “no”, not an immigrant).

Next, each of the two pathways incorporates “life experience in the country of birth”, with this term being equivalent to the “reality” of “life as a non-immigrant”. This

is followed by “peri-immigration experience”, which is defined as the “reality” of “life

during the actual process of immigration”, and by “life experience in the country of

immigration”, which is equivalent to “reality” of “life as an immigrant.” With economic globalization, the possibility of repetitive cycles of migration is present for all individuals. Thus, both pathways allow for “peri-immigration experience” and “life experience in country of immigration” to be multiplied by “N”, representing the number of different migrations. The possibility of returning to the country of origin (“the Salmon experience”) is also allowed for in the model, by incorporating “life experience in the country of birth upon return” and “life experience in the U.S.A upon return”.

The difference in the representation between the two pathways based on “country of birth” is due to the number of components in the mathematical representation of migration cycles. By virtue of being born “elsewhere”, an immigrant in the U.S. must have undergone at least 1 migration experience, which is listed in this track, and then also has the potential of having undergone other migration experiences as well. However, it is

possible that a “U.S.” born” individual may have undergone 0 cycles of migration. The

simplest scenario for comparison is between a U.S. born individual who never migrated

outside of the U.S.A and a “born-elsewhere” U.S. immigrant, migrating straight from the

country of birth, and dying in the U.S. without any further migration experiences.

“Health” and “sleep health” are our outcomes of interest, and they affect each

other bi-directionally. Both “health” and “sleep health” are shown as uni-directionally

affected by “age” and “gender”. This is logical, as health and sleep are each affected by

the gender and the age of the individual. So, for example, a young woman would not be

expected to have the same health or sleep as an old man.

“Health” and “sleep health” are bi-directionally affected by “social determinants

of health vulnerability”, health/sleep related cultural attitudes and beliefs”, and “health

related behavioral risks”, consistent with previously proposed health behavioral models

(Redding, 2000). This means that health and sleep are affected by, but also affect each of

these three categories. For example, low income, a “social determinate of health

vulnerability”, can negatively affect health. However, poor health can also lead to

unemployment, and therefore low income, making the association bidirectional.

The “social determinants of health vulnerability”, “health/sleep related cultural

attitudes and beliefs”, and “health related behavioral risks” not only affect “health” and

“sleep health” bi-directionally, but also each of these three former factors affects the

latter two bi-directionally. This means that “social determinants of health vulnerability”

affect both “health/sleep related cultural attitudes and beliefs” and “health related

behavioral risks” bi-directionally, and that “health/sleep related cultural attitudes and

beliefs” also affect “health related behavioral risks” bi-directionally. In order to make these notions easier to understand, we have provided some examples to put in context. A

“social determinant of health vulnerability” like Hispanic race can impact on “sleep related cultural attitude” through a perception of sleep as “good”, which can then impact on “health related behavioral risk factors”, such as taking a siesta (afternoon nap).

Another example can be “health related behavioral risks” like drug use, which can impact on a “social determinant of health vulnerability”, like for example addiction problems, triggering high spending to acquire the drug, and decreased productivity, and result in lower income. However, the low income is also a risk factor for drug use, testifying to the bidirectional nature of the association. To expand on this example, different cultures have different perceptions of drug use, with some being more accepting of the practice

than others. Thus, the cultural attitude about drugs is also a risk factor for drug use, while

the historical rate of drug use in turn affects cultural attitudes about it.

We have already stated that “age” and “gender affect “sleep” and “health” uni-

directionally. “Gender” is also uni-directionally associated with the “place of birth”, because men are more likely to emigrate than women. “Age/Aging” is bi-directionally associated with “place of birth”, because middle aged individuals are more likely to emigrate than the elderly, and, in reverse, all people age. In addition, both “age” and

“gender” also affects “behavioral related risk factors” and “social determinants of health vulnerability” uni-directionally. An example of how “age” affects a “health related behavioral risk” is that teenagers are more likely to get into car accidents than middle aged adults. An example of how “age” affects “social determinants of health vulnerability” is that the elderly experience a higher rate of poverty than middle aged adults. An example of how “gender” affects a “health related behavioral risk” is that men are more likely to consume alcohol than women. An example of how “gender” affects

“social determinants of health vulnerability” is that women are more likely to be paid less than men, when working the same job, and thereby are at a higher risk for low income.

All of the variables described thus far impact on “health” and “sleep health” in both immigrants and non-immigrants. The difference between immigrants and non- immigrants is that immigrants have some “life experience in the country of immigration”, which can be measured using “PLI”. As detailed in section 2.4d, “PLI” is a measure of the risk to health/sleep health that is attributed to the reality of life as an immigrant (life experience in the country of immigration).

To review, PLI is the number of years that the immigrant has spent in the host

60 country X divided by the sum of the age of the individual at the time of immigration and the number of years spent in the USA, all multiplied by 100. The conceptual model shows that “PLI” bi-directionally affects “social determinants of health vulnerability” and

“health/sleep related cultural attitudes and beliefs”. “PLI” uni-directionally affects

“health related behavioral risks”. Again, in order to clarify the logic to these statements, we provide examples.

An example of how “PLI” and “social determinants of health vulnerability” affect each other bi-directionally is the following: coming here at a younger age and being educated through the American system would increase one’s English proficiency, with poor English skills being a “social determinant of health vulnerability”. In reverse,

“social determinants of health vulnerability”, such as poor English proficiency or low income, can lead to one’s return to the country of birth, thereby affecting the “PLI”.

An example of how “PLI” can affect “health related behavioral risks” uni- directionally is that coming to the U.S.A. at a younger age can increase one’s risk for becoming a smoker, because teenagers are more likely to engage in substance use; smoking may be more common in the U.S. than in the country of origin, and cigarettes may be more readily available here. However, it is not logical to say that engaging in health related risk behaviors would directly cause an individual to return to his/her country of birth, making this relationship unidirectional.

“PLI” is also shown to have a bidirectional association with the “latent variable,” which is then bi-directionally associated with “health/sleep health”. The definition and significance of the “latent variable” has been extensively described in section 2.4d, but as a short summary, the “latent variable” is representative of all of the “real” factors that are

or are not open to direct perception and not readily quantifiable, but do affect the

“reality” of how life as an immigrant affects health. Differences found between the

health/sleep health of immigrant vs. non-immigrant population that are otherwise unexplained, are likely being caused by the “latent variable.” We have already made some suggestions of some of what the “latent variable” may be capturing by combining different social-health theories such as a) identity vulnerability at the ontological level, b)

“perpetual mourning” around the loss of individuals, relationships, and spaces in the country of origin; and c) self-perception of discrimination and “othering” processes related to social stigmatization. To provide an example of the bi-directionality of “PLI”

and the “latent variable”, and the “latent variable” and “health/sleep health”, we use the

notion of “perpetual mourning”:

“Perpetual mourning” is not open to direct perception nor is it readily measurable.

For example, an individual who emigrated to the U.S. at the age of 3 with his parents and

has been living in the U.S. for the past 20 years would likely have a different sense of

consecutiveness to his/her native land than a 60 yr old individual, who has spent all of his

life up to that point in his native country, so the “PLI” is affecting the “latent variable.”

The relationship is bidirectional, for example the 60 yr old individual, who is a recent

immigrant and has spent all of his life previously is his native country, may experience

such a sense of loss of place, of disconnectedness from the land of his ancestors, that he

chooses to return to his native country, affecting the PLI. The “latent variable” can bi-

directionally affect “health/sleep health” as follows: the 60 yr old man is having a hard

time falling asleep at night because he keeps thinking about going back to his native

country, doubting his decision to leave. In reverse, becoming ill can make the man

62 increasingly more mournful of his loss of connectedness with his home land.

The conceptual model shows that “health/sleep health” affects “PLI” uni- directionally. An example can be provided by the “Salmon Bias Theory”, which suggests that immigrants are returning to their native country if they become ill. Finally, health/sleep health are shown as uni-directionally associated with death, either in the

U.S.A. or not (home land or another place of migration). This follows the natural progression of life from left to right in the model.

In conclusion, the proposed model focuses on health outcomes collected at an individual level, while incorporating social dynamics and historical context through an open-system representation. In doing this, it provides a robust theoretical background, potentially useful in future epidemiological immigrant research worldwide. Furthermore, it allows for interventions to be designed to address the specific areas that are found to contribute to poor health in immigrants.

Figure 2.1 Conceptual Model

The orange arrows show the relationships of interest explored in this thesis: Q1 (Chapter 3): Is there a difference in sleep health between Mexico-born immigrants to the U.S., the general U.S. population, and U.S.-born Mexican Americans? Q2 (Chapter 4): Is PLI associated with health decline in Mexico-born immigrants?

Q3 (Chapter 4): Is PLI associated with sleep health decline in Mexico-born immigrants?

The + and – symbols signify that the events that are contained within the parenthesis may of + may not occur. _

An individual can immigrate several times, so we allow for the possibility of Life this by multiplied the life experiences associated with immigration by N, with experience in country of X N N being representative of the number of immigrations. immigration experience Periimigration

The “latent variable” is a new introduction to conceptual framework based on the Critical Realism Theory, and represents the residual effect of living “life as an Latent Variable immigrant” on sleep/sleep health after controlling for all described risk factors for poor health/sleep health.

The major constructs are further described in separate fig(s)., which also exemplify what variables are used in the quantitative research done in Chapters 3-5.

Conceptual Model KEY: The diagram is consistent with an “open system” philosophy in showing this individual is imbedded in the community, which is encompassed by the society, and then by the greater world. The dashed lines are representative of the fluidity of interactions and influences between the individual, family, etc.

The conceptual model should be read from left to right (from birth to death), consistent with the linear progression of human life. Life progression components of the model are shown as blue figures.

The rhomboid box is indicative of a starting/ending point in life: so birth and death are represented by rhomboid shapes.

Other life periods /experiences are represented by blue rounded rectangles and ovals: life experience in country of birth, etc. These periods are not developed in this research thesis.

The quantitative entities focused on in the thesis are shown as yellow figures.

Table 2.1 Theories Incorporated into “The Open System of Conceptual Model of Immigrant

Health”

Previously used in Scientific field theory first Theory Founder & Date immigrant introduced health studies Acculturation Boas 1888 Anthropology Yes

Assimilation Thomas & Znaniecki, 1920 Sociology Yes

Bi-dimensional acculturative Berry 1980 Mental health Yes

Hispanic Paradox Karno, Edgerton 1969 Epidemiology - Mental Yes health Cultural buffering Hummer 1999-2000 Social science Yes

Social buffering Effect Nucholls, Callell,Kaplin, 1972 Epidemiology – Pregnancy Yes

The Healthy Immigrant Effect Marmot 1984 Epidemiology Yes

The Salmon Bias Effect Pablos-Mendez 1994 Public Health Epidemiology Yes

Psychoanalytical theory Freud 1929 Psychiatry No

Cultural mourning theory Ainslie 1994 Psychoanalysis No

Othering Weis 1995 Feminism Yes /qualitative

Early Critical Realism Ideas Descartes 1641 Philosophy Yes

Figure 2.2 Independent variables used in chapters 3-5

PREDICTOR VARIABLES (X):

Chapter 3, 5:

IMMIGRATION FROM MEXICO

Chapter 4, 5:

PROPORTION LIFETIME IN IMMIGRATION IN U.S.

Figure 2.3 Dependent variables used in chapters 3-5

HEALTH-SLEEP ENTITY SPECIFYING

OUTCOMES USED BY THE STUDY (Y)

PHYSICAL HEALTH

Self-reported overall

DEPENDENT VARIABLE chapter 4

………………… ………………

SLEEP-poor

Insomnia+- short time sleeping

DEPENDENT VARIABLE chapter 3, 5

………………… ………………

MENTAL HEALTH

Used as covariate only (1): Depression

Figure 2.4 Covariates (2)

SOCIAL DETERMINANTS OF HEALTH

VULNERANILITY

SOCIO-ECONOMICS: Education Poverty Leaving with partner/married ………………… ……………… ACCES TO CARE: Health insurance status ………………… ……………… SOCIAL SUPPORT ………………… ………………

ENGLISH LITERACY as Ability to navigate systems and not cultural attributes (ex language chosen in questionnaire completion)

Figure 2.5 Covariates used in this research

HEALTH RELATED BEHAVIORAL RISK FACTORS

COVARIATES (3)

SMOKING ALCOHOL ILICIT DRUGS CAFFEINE SHORT SLEEP TIME <7h, <6 h (ALSO SLEEP OUTCOME) SEDENTARY LIFESTYLE (TV AND COMPUTER USE FOR RECREATIONAL PURPOSES) (FOOD / ACTIVITY BALANCE REFLECTED BY) OVERWEIGHT (OBESITY)

Figure 2.6 Other covariates:

Gender (those are used for all analyses)

Health/sleep related cultural attributes, beliefs

Language of communication at home

Figure 2.7 Other covariates (2)

SOCIAL DETERMINANTSECONOMIC: OF HEALTH EDUCATION (public/private, infrastructure, safety) INCOME

EMPLOYMENT HOUSING (public/private, infrastructure, safety) ENVIROMENT ……………… ………………………………………. POLITICAL NATIONAL / INTERNATIONAL HUMAN RIGHTS GOVERMENTAL PUBLIC HEALTH ………………. …………………………………….. ACCES TO CARE: HEALTH INSURANCE STATUS HEALTH SERVICES TRANSPORT (public/private, infrastructure, safety) CAREGIVER(S) ENGLISH LITERACY as Ability to navigate systems and not cultural attributes (ex language chosen in questionnaire completion) ……………… …………………………………….. PHYCHOSOCIAL PROTECTIVE LEGISLATION AND SERVICES (government, lawsuits, jails) VIOLENCE (community (gang activity)/family exposure, police protection/repression) SOCIAL SUPPORT POLITICAL ORGANIZATIONS FAITH BASED ORGANIZATIONS VOLUNTEER ORGANIZATIONS UNION ORGANIZATIONS “OTHERING”

NOTES:

1epistemology (noun, Greek etymology); branch of philosophy concerned by the nature and scope of knowledge: it refers in this case to questions like “what is what we know?, “how do we know?” , “ what other people know?” ,“how do we really know what we know?”

2taxonomy (noun, French etymology) refers to the study of general principles of scientific classifications

3 apperceptive (noun, French etymology) refers to introspective, self- consciousness, mental process of understanding, based on previous experience

CHAPTER 3

An Exploration of Differences in Sleep Characteristics between Mexico-born U.S.

Immigrants and Other Americans to Address the Hispanic Paradox

Aims:

1. Provide evidence of the applicability of the proposed “Multi-theories” conceptual framework described in the previous chapter in quantitative epidemiological research used for comparisons between immigrant and non-immigrant populations.

2. Demonstrate the Hispanic Paradox extended to sleep outcomes: lower risk of short habitual sleep time, insomnia, and sleep related daily functional impairments in

Mexico-born U.S. immigrants, compared to general U.S. population, and also to their

U.S. born Mexican-American counterparts.

3. Testing previous etiological theories related to the mediator effect of: cultural related changes, social support, and health-related behavioral risk factors on the relationships between habitual short sleep time and Mexico-born immigrant status in

Mexican Americans.

3.1 Reviewing the conceptual framework applicability for comparisons between immigrant and non-immigrant populations in U.S.

This chapter is an example of a quantitative cross-sectional study based on the proposed Open- System Conceptual model and Critical Realism Theory. The model is used to compare Mexico-born immigrants with U.S.-born non-immigrant individuals.

A brief review related to the “contextual approach” (Kothari A., 2002) of this model is explained in two figures: Figure 3.1 describes the statistical translation of the

Conceptual Model segment addressing the Q1 (Chapter 2, Fig 2.1). This is the

relationship between the Mexico-born Immigrant status which is the entity:” Where? /

Place of Birth”, and the entity: “Health: Sleep” in the Conceptual Model (Fig 2.1) (Q 1 in

Mexican-American population). Further, the effect of the entity “Health/sleep related cultural attitudes/beliefs (ethnic culture) on this relationship is tested, based on the Baron and Kenny steps proposed for verifying mediation (Baron and Kenny, 1986). These steps are explained in detail on Fig 3.1. For simplicity, we change “Country of Birth” with

“Mexican Immigrant status” (MI), show it as the predictor variable (X); the sleep outcome(s) is the dependent variable (y) in this simple mediation approach. This figure uses as an example the cultural changes reflected by language use at home as a potential mediator (M), based on previously discussed theories of acculturation. However, even if necessary to assess mediation at the individual level, this is a mono-theory approach, not encouraged by the conceptual framework presented, unless as a preliminary step to determine if a variable may be qualifying as a mediator based on the theory of probability, statistics, and error.

The ultimate goal of the research is to simultaneously test proposed theories related to the Hispanic Paradox. This approach is exemplified in Fig 3.2. Mediators of the relationship of interest (proposed by different existent theories) are organized by groups. “Mexico-born immigrant status” is in this example mainly a “social construct”, so less likely to incorporate latent genetic and/or biological dimensions.

In what follows, I offer examples of predictions given by using simultaneous different proposed theories applied to sleep outcomes, and tested simultaneously by using the Open System Conceptual Model.

The Healthy Immigrant Effect Theory and the Salmon Bias Effect Theory

may be translated on sleep outcomes as follow:

1. Mexico-born U.S. immigrants have lower age-adjusted prevalence rates of

insomnia, including insomnia with short sleeping hours, compared to the general U.S.

population, and to their U.S –born Mexican-American counterparts.

Cultural related theories referring to sleep propose cultural differences in attitudes related

to sleep (Domino, 1986). Hispanic culture may reinforce sleep as necessary and positive

in contrast to cultures that perceive sleep as a factor that negatively influences work

productivity. Based on this theory it is predicted that:

2. There are increased odds of habitual short sleeping time in Mexican Americans

with increased use of English language at home, as a measure of adaptation to American culture.

3. Cultural changes may mediate the relationship between short habitual sleep time and Mexico-born status in Mexican Americans.

The Social Buffering Effect Theory predicts that

4. Absence of social support is a significant health risk factor in Mexican culture so it should be associated with increased odds of poor sleep outcomes in Mexican-

Americans.

Acculturation theory related to health behaviors predicts that:

5. Health behaviors may be acting as mediator of the relationship between

Mexico-born ethnicity and sleep. All these theories are integrated by the

approach shown in Fig 3.2, and will be further exemplified under the next

aims.

Fig 3.1 Mediation steps used to verify mediation (Baron and Kenny, 1986)

Language of preference

(LPH) at home (M)

Step 2 Step 3

Step 1 MI status (x) SLEEP outcomes (y)

Step 1: MI status is “correlated” with sleep outcomes (Univariate regression equation establishing that there is an effect between MI status and the sleep outcomes that may be mediated): Step 2: MI status is ‘correlated” with LPH (or other covariates tested as mediators) (Univariate regression equation establishing that MI status is significantly predicting acculturation /other covariates of interest): Step 3: LPH (or other covariates tested as mediators) affects sleep outcomes (Univariate regression equation establishing that Acculturation (or other covariates tested as mediators) is significantly affecting sleep outcomes): Step 4: LPH (or other covariates tested as mediators) mediates MI status (x) - SLEEP outcomes (y) relationship(s) (Bivariate regression equation establishing the effect of MI status on SLEEP outcomes controlling for LPH) The effect should be zero if complete mediation, decreased if partial mediation, or increased if inconsistent mediation or suppressor effect. To determine mediation the percent of total effect (%)*of the Mexico-born immigrant status on sleep outcomes and ratios of the indirect to the direct effect calculated

Fig 3.2 Applying theories of probability, statistics, and error to the portion of the

Open System conceptual model used to assess the relationship between Mexico-born immigrant ethnicity and sleep outcomes in Mexican American population, as to test theories of Hispanic paradox

Aims 2+3: Demonstrate the Hispanic Paradox extends on sleep outcomes and determine if changes from the Mexican culture to the American culture measured as language spoken at home are mediating the relationships between habitual short sleep time and insomnia with habitual short sleep time and Mexico-born immigrant status, in Mexican

Americans.

3.2 Introduction

Sleep duration and quality are consistently reported as associated with general health and subjective well-being. Short sleep duration has been linked to increased mortality risk (Tamakoshi, 2004, Patel, 2004, Heslop,2002, Kripke, 2002), obesity

(Gangwisch, 2005, Taheri , 2004, Sekine, 2002), impaired glucose metabolism, including incident diabetes (Ayas, 2003), hypertension (Gangwisch, 2006), coronary heart disease

(Ayas, 2003), and altered metabolism and neuroendocrine profile (Spiegel,2005,

Mullington, 2003). Insomnia symptoms in individuals reporting short sleep duration also have been associated with an increased risk of hypertension (Vgontzas, 2009). Sleep quality is strongly related to mood and emotions in healthy adults and to psychiatric conditions, including depression, anxiety (Kahn-Greene, 2006, Pearson, 2006, Vaughn,

2000), and suicide (NIH, 2005). Insufficient / inadequate sleep physiologically impairs attention, alertness, reaction time, and overall social functioning (Killgore, 2009, Doran

2001). How this body of work relates to health status in immigrant population is largely unexplored.

Mexico-born immigrants (MI) represent the largest segment of the U.S. foreign- born population, rising from over 9 million non-institutionalized individuals in 2000

(U.S. Census Bureau, 2000) to 11.5 million in 2008 (Pew Hispanic Center, 2008).

Despite social and economic disadvantages, including limited access to healthcare, this population has been reported as having significantly lower mortality and morbidity, including a lower prevalence of hypertension, cardiovascular disease (CVD), and symptoms of serious psychological distress when compared to the U.S.-born population or with to U.S.-born ethnic counterparts (Dey, 2006, Markides,1986). The phenomenon is still poorly understood and often referred to as "The Hispanic Paradox". Several theories have been proposed to explain these observations, including a) The “Healthy

Immigrant Effect”, or self-selection of healthier individuals emigrating from Mexico to the U.S.; b) the “Salmon Effect”, referring to Mexican immigrants’ returning to their homeland when older or when their health declines; and c) the “Social Buffering Effect,” referring to the positive health influences of strong social support and family ties among

Mexico-born U.S. immigrants (Arias, 2002, Palloni, 2001). It is also plausible that reported differences in health status among ethnic and immigrant groups could reflect d) selection biases if Mexico-born individuals participating in epidemiological research studies represent a subgroup of healthier individuals, or because of e) reporting biases leading to misclassification (Carter-Pokras, 2008, Thomas, 1996). Overall, “The

Hispanic Paradox" conflicts with findings of international immigrant health research.

According to studies conducted in countries other than the U.S., except for the most recent arrivals, immigrants often experience worse health status across most dimensions when compared to the country of immigration native–born population (Wändell, 2007,

Syed, 2006, Asakura, 2006.)

A recent brief communication (Hale, 2009) proposed that compared to the MI,

U.S.-born Mexican-Americans may be at an increased risk of short habitual sleep time.

The relationship was reported to be attenuated by the addition of the following health

behavior variables: smoking status, body mass index, and self-reported stress level.

However, no other sleep related outcomes and/or acculturation measures were available

to this study, and the authors did not further explore the individual effect of the reported

attenuating covariates on the relationship between Mexican-Immigrant status and short

sleep time.

The present study, attempts to further clarify the potential influences of socio- cultural factors on sleep outcomes in MI and assess if the notion of “Hispanic Paradox" may apply to sleep outcomes.

We propose that Mexican-immigrant status is associated with better sleep quantity and quality, and that such associations are positively influenced by Mexican culture. Our analyses are guided by previous studies of cross-cultural psychology (Domino, 1986,

Taub, 1971) describing differences in sleep habits between adults living in Mexico and the U.S. and/or England. Also, Mexicans have been reported to have a tendency to sleep longer at night and during the day (siesta) (Domino, 1986). Cultural differences in attitudes related to sleep have been proposed as possible causative factors (Domino,

1986). Hispanic culture may reinforce sleep as necessary and positive in contrast to cultures that perceive sleep as a factor that negatively influences work productivity

(Domino, 1986).

Furthermore, I hypothesize that transitions and assimilation of the Anglo-oriented

U.S. culture shape health beliefs and behaviors, adversely influencing sleep quality and quantity in MA born in the U.S. Thus, we propose that a measure of cultural change should be significantly associated with poor sleep outcomes in all MA.

The following specific hypotheses are proposed:

1. Compared to a sample of nationally representative U.S. individuals, including MA born in the U.S., MI have lower age-adjusted population prevalence and lower odds of reporting short habitual sleep time (SHST) and/or insomnia. These differences will persist after adjusting for social determinants of health vulnerability, self-reported general health characteristics and behavioral health risk factors.

2. Cultural change exhibited by MA from Mexican culture to American culture, measured by this study through language use at home (English vs. Spanish) is associated with an increased risk of poor sleep outcomes in MA individuals.

3.3 Methods

3.3.1 Data Source

Data were derived from the 2005-2006 National Health and Nutrition

Examination Surveys NHANES (CDC 2006) which is a two year cycle of cross-sectional studies conducted by The National Center for Health Statistics, Center for Disease

Control (NCHS/CDC) on civilian non-institutionalized individuals. Questionnaires that included assessment of sleep habits and sleep-related problems were administered by interviewers in the homes of participants’ aged 16 years and older using Computer-

Assisted Personal Interview (CAPI) technology, with help from trained bilingual

interviewers. A physical examination generally occurred within 1 to 2 weeks after the in-

home interview in a set of specially-designed and equipped Mobile Examination Centers

(MEC), which travel to survey locations throughout the country. The survey team consisted of a physician, medical and health technicians, and dietary and health interviewers. Complete details on recruitment, design, and content of the used surveys are described on the NHANES website http://www.cdc.gov/nchs/nhanes.htm (CDC

2006).

Sleep variables were obtained by merging the sleep-related data collected with the

questionnaire with other files, including the demographic, health insurance, acculturation,

social support, current health status, medical conditions, depression screener

questionnaire files, body measurements, physical activity, serum cotinine, alcohol use,

food frequency questionnaire and drug use, by respondent sequence number. The

NHANES study received approval from a human subjects committee and the proposed

analysis, as de-identified data, were exempted from receiving approval by the University

Hospitals Case Medical Center of Cleveland Institutional Review Board (IRB).

3.3.2 Subjects

Sleep questionnaire data were available for a total of 6139 individuals’ ≥16 years

old. Exclusion criteria for these analyses were: age < 18 years old (576 participants, due

to our interest in adult population), pregnancy status (364 participants, due to associated

changes in sleep physiology), and incomplete data on immigration/naturalization status

(39 participants), resulting in an analytic sample of 5160 individuals.

3.3.3 Sleep Characteristics

Sleep duration was identified based on response to the question: “How much

sleep do you usually get at night on weekdays or workdays?” Short habitual sleep time

was dichotomized (yes/no) on a cutoff of <7hrs/weeknight.

Insomnia symptoms were based on the following questions: “trouble falling

asleep”, “waking up during the night and having trouble to get back to sleep”, “waking

up too early in the morning and being unable to get back to sleep”, “feeling unrested

during the day, no matter how many hours of sleep one had.” Responses to each sleep

question were collapsed as follows: occurring 2-4 times per month or less (considered

negligible symptom), 5-15 times per month (“some level of insomnia” or “mild/moderate

insomnia”), and >15 times/month (“severe insomnia”). Insomnia (yes/no) was defined

by using the National Heart, Lung, and Blood Institute (NHLBI) Working Group

definition (National Center on Sleep Disorders Research 1998) as one of four sleep

complaints plus at least one self-reported daytime functional impairment due to lack of

sleep. Insomnia with short sleep duration (yes/no) was defined as any one of the insomnia symptoms reported as occurring 5-15 times per month with daytime functional impairments reported as moderate or extreme and sleep duration <7 hrs/weeknight.

Additional dichotomized outcomes (yes/no) include self-reported perception of insufficient sleep during the past month, and “trouble sleeping” ever reported to a physician or other health professional. The study also collected data on functional impairments related to sleepiness, including difficulties carrying out specific regular daily activities in the last month in the following areas: “concentrating on the things”,

“remembering things”, “getting things done because too sleepy or tired to drive or take

public transportation?”, “performing employed or volunteer work or attending school”,

“working on a hobby, for example, sewing, collecting, gardening”, and “taking care of

financial affairs and doing paperwork (for example, paying bills or keeping financial

records)”.

3.3.4 Mexico-born status and Mexican-American ethnicity variables

MI status (dichotomized yes/no) was assigned based on the answer “born in

Mexico” to the question “in what country were you born?”

MA (dichotomized yes/no) was considered based on the response to the self-

reported ethnicity (i.e., Mexican American). Both were collected with the demographic

questionnaire.

3.3.5 Covariates

Covariates included self-reported demographics, health related variables, and

substance use. Age was reported in years at the time of NHANES screening. Education

was dichotomized with a cutoff set at high school graduation. Race was coded as

Mexican American (MA), other Hispanic, Non-Hispanic White, Non-Hispanic Black, and other race including multiracial. Financial strain was measured as a continuous variable by poverty income ratio (PIR), a variable obtained by dividing the family income by the poverty threshold. Marital status was defined based on living with partner/married or other. Insurance status was dichotomized as covered by any type of health insurance vs. not insured. Social support was available for individuals 40 years old and older. For

these analyses, we coded this as “insufficient,” if the participant reported the absence of

someone to either: a)” help…paying any bills, housing costs, hospital visits, or providing with food or clothes; or b) provide emotional support “such as talking over problems or helping make a difficult decision”, or “have you used more emotional support than received in the last year?”. Spanish language preference was used as a proxy measure of cultural change of minority individuals (MA) to the majority culture (American) (Taub,

1971, Marin, 1987). Language preference was determined based on the language the participant reported to use at home with 5 levels: “all Spanish”, primarily Spanish (“more

Spanish than English”), about equal use of Spanish and English (“both equally“),

primarily English (“more English than Spanish”), and nearly all English (“only English”)

(CDC, 2006).

Self-reported “overall general health” was also dichotomized by collapsing “fair”

and “poor health” groups into the newly created variable “unsatisfactory health” vs.

“satisfactory health”. Depression was assessed by using the nine- item Patient Health

Questionnaire (PHQ), and applying a diagnostic algorithm by using the PHQ score of

greater than 10 as indication of major depression (Kroenke, 2001). Weight and height

were collected by trained health technicians and body mass index (BMI) (National

Institute of Health report, 1998) was computed as the ratio of weight to height squared.

The daily average total number of hours of TV, video, computer usage and computer

games for entertainment was calculated by summing the number of hours per day that

the participant reported engaging in such activities over the past 30 days, and used as

continuous and categorical variable proxy for sedentary leisure time.

Substance use ascertained by the NHANES examinations which may influence

sleep quality included nicotine use, caffeine intake, alcohol, and recreational drugs use.

Current smoking status was assessed using study collected serum cotinine, a biomarker of

environmental tobacco smoke exposure. Participants with cotinine levels ≤10 ng/mL

were considered nonsmokers (Vartiainen, 2002, Emmons, 1994). Caffeine consumption

was defined as positive if respondents reported having at least one cup/day of a

caffeinated drink. Self-reported use of any alcohol was available for participants 20 years

old and older and was defined as at least one alcohol drink per month. Illegal narcotics or

stimulant drug use was coded positive if past use of marijuana, hashish, cocaine, heroin

or methamphetamine was reported.

3.3.6 Statistical Analysis

General and sleep characteristics of the U.S. population were analyzed according

to Mexican origin, immigrant status, and gender, by using sample weights analyses in

SAS 9.2 (Proc Survey, SAS Institute, Inc., Cary, NC) and the Taylor Series Linearization

approach (Rust 1985). Age-standardization to the U.S. Census 2000 population

estimates was performed by the direct method, to generate age-adjusted prevalence rates and standard errors, based on the CDC - NCHS recommendations (CDC, 2006). Group

differences for age-adjusted prevalence ratios included differences between Mexico-born

immigrants vs. Non-Hispanic white immigrants, and also between U.S. born Mexican –

Americans vs. U.S. –born Non-Hispanic-whites were also tested by using unpaired t-

tests, knowing that whites in the U.S. were found with the lower risk of SHST (Hale,

2007). Models included adjustments for gender, age, education, PIR, marital status,

caffeine, alcohol, smoking, recreational drug use, sedentary leisure time, self-reported

general health, depression. In addition, in the MA cohort, models also included

adjustment for language preference at home. Univariate and multivariate nested

hierarchical logistical regression modeling for the Mexican-Americans analytic sample

was performed after the weights were normalized (standardized) to the size of the

subsamples (Delgado, 1990)

All analyses were stratified by gender due to known sleep-related gender

differences and potential interactions between gender and other variables of interest.

To asses to what extend immigration from Mexico status effect on sleep outcomes

was mediated by covariates, single-mediator models were built, based on Baron and

Kenny four step approach (fig3.2).(Baron, 1986, MacKinnon, 2007). Due to the fact that

tested mediation included one or more binary variables, rather than all continuous

variables, the MacKinnon and Dwyer method of standardization (MacKinnon, 1993) was

used first as to bring logistic regression coefficients into the same scale. Once the

coefficients were standardized, computing the estimate of mediation and the Sobel test

was done. This method is described in detail elsewhere (Jasti, 2008). Change equal or

greater than 15% of the β coefficient values was considered as evidence of mediation

(Hosmer, 1999).

Sensitivity analyses were conducted within age-specific groups and by using

alternative definitions for exposures, including alcohol and substance use. The

consistency of the presented weighted results was tested in unweighted analyses (Patel,

2004). In additional sensitivity analyses, BMI was included as a covariate. However,

since inclusion of BMI did not alter the relationships of interest, and because obesity may

be a consequence of poor sleep (Gangwisch, 2005, Taheri, 2004, Sekine, 2002) the final

models did not present the BMI-adjusted results. Heath insurance status was found to be

significantly co-linear with social support and cultural changes in the MA cohort, so this

variable was excluded from the final models that included these variables.

Two-tailed p-values of < 0.05 were considered significant.

3.4 Results

The general characteristics of MI, MA born in the U.S., and the overall U.S.

samples are displayed below in Table 3.1a, and in fig 3.3 and 3.4, stratified by gender.

Compared to their MA counterparts, MI males were on average significantly poorer, less

educated, more likely to be married or living with a partner, not covered by any health

insurance including Medicaid and Medicare, and reported absence of social support.

Compared to the MA born in the U.S., they were significantly more likely to speak

Spanish at home, reported less time spent watching TV or using a computer for

entertainment, and had a lower prevalence of smoking, and using illegal drugs.

Compared to their MA counterparts born in the U.S., MI men also showed a lower

prevalence of depression and a lower mean BMI.

Compared to the general sample of U.S. males, MI males were significantly younger and reported higher rates of poverty, lower rates of high school completion, lower rates of health insurance coverage, and higher rates of living with a partner. MI males also had lower prevalence of caffeine use and reported less time spent watching

TV or using the computer for entertainment. A significantly higher proportion of MI males reported fair and poor health, compared to the overall U.S. male cohort.

Among females, the distributions of demographics, health status, and health behaviors, among MI, U.S.-born MA and the general U.S. sample were generally similar to that reported for males (above).

Table 3.1a: General Characteristics: Mexico-born immigrants, Mexican–Americans and All U.S. individuals‡‡, By Gender MALES FEMALES

U.S.-born U.S.-born Mexico-born Mexican- Mexico-born Mexican- male American All U.S. female American All U.S. immigrants males males‡‡ immigrants females females‡‡ (N=352) (N=191) (N=2654) (N=267) (N=232 ) (N=2506)

Mean Age (at screening, in years) 36.5 (1.0) ** 37.6 (0.8) 44.7 (0.8) 40.1 (1.2)** 39.8 (1.5) 47.1 (0.8)

Mean Poverty income 1.6 (0.1) * 2.9 (0.1) 3.2 (0.1) 1.5 (0.1) * 2.6 (0.1) 3.0 (0.1) ratio (family ** ** income/poverty threshold) Education 71.5 (3.0) * 26.6 (3.9) 17.8 (1.6) 67.3 (3.2) * 19.8 (2.8) 16.0 (1.1) (% individuals reporting no High ** ** School graduation) Insurance Status 67.4 (4.7) * 31.7 (3.9) 21.2 (2.0 ) 65.3(4.3) * 19.4 (5.4) 15.8 (1.8) (% individuals reporting not covered ** ** by any type of health insurance) Marital Status 74.6 (3.1) * 51.3 (5.0) 66.9 (1.7) 75.4 (2.5) * 51.8 (2.8) 59.4 (1.6) (% individuals reporting married or ** ** leaving with partner ) No Social Support*** 23.9 (2.6)* 14.5 (2.3) 23.3 (1.3) 29.7 (3.5) * 16.9 (2.8) 22.1 (1.3) ( % individuals reporting getting no ** social support) Language use at home Only Spanish (0) 70.0 (4.4) * 0.8 (0.5) N/A 70.8 (4.5) * 1.5 (0.7) N/A Spanish > English (1) 15.5 (3.1) * 5.1 (1.8) 18.1 (4.0) * 4.5 (1.9) Spanish = English (2) 9.2 (1.8) * 17.1 (2.0) 7.4 (2.0) * 27.2 (4.2) Spanish < English 3.5 (1.4) * 36.0 (3.3) 2.8 (0.3) * 22.7 (4.3) (3)Only English (4) 1.8 (0.8) * 41.5 (3.8) 0.9 (0.6) * 44.1 (3.9)

(% individuals )

Mean Sedentary 2.3 (0.1) * 3.4 (0.3) 3.3 (0.1) 2.4 (0.1) * 3.1 (0.1) 3.2 (0.1) leisure time (number hours per day ** ** of TV watching and computer use past month for entertaining) Current smokers 18.1 (2.5) * 30.5 (3.5) 31.4 (1.3) 6.8 (1.6) * 12.0 (3.0) 20.4 (1.1) (% individuals with Serum Cotinine levels ** ** >10 ng/mL) Caffeine use 23.2 (3.0) ** 26.2 (4.3) 29.1 (1.6) 15.5 (2.7) * 24.3 (3.7) 25.6 (1.8) (% individuals reporting drinking

≥ 1 cup caffeinated ** drink /day) Alcohol use**** 72.9 (4.6) 69 (5.1) 72.7 (3.6) 29.7 (2.8) * 53.3 (2.6) 55.7 (2.0) (% individuals reporting drinking ** alcohol ≥ 1 time/month) Illegal drugs use 19 (2.8)* 30.8 (2.5) 21.1 (1.8) 0.8 (0.6) * 16.8 (3.0) 12.6 (1.9) (% individuals reporting ever use of ** marihuana, hashish cocaine, heroin, methamphetamine) Unsatisfactory health 29.9 (2.5) ** 24.9 (3.6) 14.1 (0.7) 30.4 (2.3) * 19.4 (2.7) 15.0 (1.0) (% individuals reporting fair /poor ** health) Mean BMI 27.3 (0.3) * 29.9 (0.7) 28.5 (0.2) 29.3 (0.4) 29.2 (0.6) 28.5 (0.3) (at screening, kg/m²) Depression 4.1 (1.3)* 7.2 (2.2) 4.8 (0.3) 7.4 (2.6) 7.9 (2.3 ) 7.1 (0.7) (% individuals with PHQ-9 score ≥ 10)

‡ Analyses conducted using survey analyses: Mean or Percentage (%) (standard deviation) ‡‡ All U.S (male and female) cohorts include both Mexico-born immigrants and Mexican-Americans born in the U.S. * P-values <0.05, comparing Mexico-born immigrants with U.S.-born Mexican-Americans (calculated separately for each gender) ** P-values <0.05, comparing Mexico-born immigrants with the All U.S. cohort (calculated separately for the male and female groups) N/A: Not calculated as no correlations with Spanish language at home *** Data on social support was available only for those ≥ 40 years and older. The proportion was calculated including three categories: yes, no, missing data **** Data on alcohol use was available only for those ≥ 20 years old and older. The proportion was calculated including three categories: yes, no, missing data

Fig 3.3 Descriptive in Men cohort, NHANES 2005-2006

*p values< 0.05, comparing Mexico-born immigrants with U.S.-born Mexican Americans **p values <0.05, comparing Mexico-born immigrants with the All U.S. cohort

Fig 3.4 Descriptive in Women cohort, NHANES 2005-2006

*p values< 0.05, comparing Mexico-born immigrants with U.S.-born Mexican Americans **p values <0.05, comparing Mexico-born immigrants with the All U.S. cohort

Table 3.1b Sleep Characteristics‡ Mexico-born immigrants, Mexican–Americans and All

U.S. individuals, By Gender

MALES FEMALES

Mexico-born U.S.-born All U.S. Mexico-born U.S.-born All U.S. male Mexican- males‡ female Mexican- females‡‡ immigrants American (N=2654) immigrants American (N=2506) (N=352) males (N=267) females (N=191) (N=232 ) SHST< 7 hrs/weeknight 28 (1.3)* 48.7 (5.7) 37.6 (0.9) 36.5 (2.9) 42.5 (2.7) 34.1 (1.8) (% individuals reporting Short Habitual Sleep ** Time < 7 h/night) Sleep Deprivation Self- 14.0 (2.6)*** 27.0 (5.6) 23.6 (1.2) 20.2 (3.3) 27.8 (4.6) 27.2 (1.0) Perception (% individuals reporting insufficient sleep in the past month) Mild/Moderate Insomnia 9.3 (2.5)* 20.6 (3.9) 22.4(0.9) 20.5 (3.1) )* 27.9 (3.3) 29.7 (1.5) (% individuals with insomnia symptoms >5-15 ** ** times /month) Severe Insomnia 2.3 (0.8)*** 9.2 (2.1) 6.0 (0.4) 5.5 (2.1) 9.1 (2.3) 8.2 (0.9) (% individuals with insomnia symptoms >15 times /month) Insomnia with SHST< 7 4.1 (1.2)* 14.5 (3.0) 12.6 (0.6) 11.3 (2.6) 15.7 (3.0) 14.6 (1.0) hrs/weeknight (% individuals ** with insomnia symptoms >5-15 times/month also reporting Short Habitual Sleep Time < 7 h/night) Poor Sleep Quality 4.4 (1.4)* 22.0 (3.2) 18.1 (0.9) 10.3 (2.3)* 22.0 (3.0) 27.8 (1.3) reported to a physician (% individuals reporting ** ** talking to the physician about having poor sleep quality ) Difficulty concentrating 2.1 (0.8)* 4.2 (1.8) 4.4 (0.4) 1.9 (0.9)* 8.4 (1.6) 4.7 (0.5) due to sleepiness (% individuals reporting ** ** it) Memory impairment due 1.3 (0.4)* 8.4 (2.7) 3.0 (0.4) 2.0 (1.0) 3.9 (1.4) 4.1 (0.5) to sleepiness (% individuals reporting ** it) ‡ Analyses conducted using survey analyses: Mean or Percentage (%), (standard deviation) ‡‡ All U.S (male and female) cohorts include both Mexico-born immigrants and Mexican-Americans born in the U.S. * P-values <0.05, comparing Mexico-born immigrants with U.S.-born Mexican-Americans (calculated separately for each gender) ** P-value <0.05, comparing Mexico-born immigrants with the All U.S. cohort (calculated separately for each gender)

Table 3.2 Age adjusted population prevalence of Poor Sleep Outcomes in U.S. Adults 20 years and Older in Mexico-born U.S. Immigrants, U.S.-born Mexican Americans and the General U.S. Population Sample, By Gender‡

MALES FEMALES U.S. –born U.S. –born Sleep related Mexico-born Mexican- All U.S. Mexico- Mexican- condition male American males born female American All U.S. immigrants males (N=2654) immigrants females females (N=352) (N=191) (N=267) (N=232 ) (N=2506)

SHST< 7 28.3 (2.2) * 51.4 (4.0 ) 38.9 (1.2) * 36.2 (3.0) 45.1 (3.3 ) 33.7 (2.0 ) hrs/weeknight ** (Short Habitual Sleep *** Time < 7 h/night) Sleep Deprivation 15.8 (2.4) ** 26.3 (5.0) 24.8 (1.3) 21.0 (3.0)** 29.2 (5.6) 28.6 (1.3) Self-Perception *** (past month)

Poor Sleep Quality 5.1 (1.5) * 23.6 (3.7) 20.2 (1.0) * 12.3 (2.3)** 24.5 (2.9) 29.6 (1.6) reported to a ** *** physician ***

Mild/Moderate 11.8 (2.2) * 21.8 (4.5) 24.3 (0.8) * 21.3 (3.4)** 28.5 (4.3) 30.8 (1.7) Insomnia ** (insomnia symptoms *** >5-15 times /month) Severe Insomnia 4.2 (1.4) 8.0 (1.7) 6.5 (0.5) 6.8 (2.5) 10.0 (2.3) 8.5 (1.0) (insomnia symptoms >15 times /month) Insomnia with 5.9 (1.6)* 17.6 (4.1) 13.8 (0.7) 12.2 (2.8) 17.7 (3.4) 15.1 (1.2) SHST< 7 ** hrs/weeknight *** (insomnia symptoms >5-15 times/month also reporting Short Habitual Sleep Time < 7 hrs/weeknight) Daytime sleepiness 11.0 (2.3) ** 15.7 (3.6) 16.2(1.0)* 12.9 (3.0) ** 21.0 (3.0) 21.2 (1.3)

Difficulty 2.7 (1.3) 4.6(2.1) * 3.9 (0.4) 2.7 (1.2) *** 8.6 (1.5) 4.8 (0.5) concentrating due to sleepiness

Memory impairment 2.9 (1.2) *** 8.3 (2.6) 2.9 (0.4 ) 1.9 (1.0)** 4.4 (1.6) 4.3 (0.6) due to sleepiness

‡ Age standardized to the 2000 Census population by the direct method proposed by CDC Prevalence showed as Percentage % (St Deviation) * P-value <0.05 between Males and Females (calculated separately for each cohort: Mexico-born Immigrants, U.S.-born Mexican-Americans, and All other U.S. born population) ** P-value <0.05 between Mexico-born Immigrants and All other U.S. born population (calculated separately for each gender) ***P-value <0.05 between Mexico-born Immigrants and U.S.-born Mexican-Americans (calculated separately for each gender)

Fig 3.5 Adjusted prevalence of Poor Sleep in Men cohort, NHANES 2005-2006

Fig 3.6 Adjusted prevalence of Poor Sleep in Women cohort, NHANES 2005-2006

Notable differences in the patterns of sample characteristics in the male and

female cohorts were for alcohol use which was lower among MI females compared to

their MA counterparts and the general U.S. female sample (with no such differences seen

by Mexican ethnicity observed in males), and for BMI and depression which were

comparable among all female subgroups (although differing among the male subgroups).

Sleep characteristics of the groups of interest are displayed in Table 3.1b, stratified by gender.

Compared either to their U.S.-born counterparts or the overall sample of U.S. males, MI male immigrants had a lower unadjusted prevalence of SHST, insomnia

(including insomnia with short sleep time, mild/moderate insomnia and severe insomnia), self-perceived sleep deprivation, poor sleep quality reported to a physician, and daily sleep-related functional impairments. Although MI females also tended to report a lower frequency of sleep symptoms than the comparison groups, the only significant differences in unadjusted prevalence ratios were for reports of mild/moderate insomnia, poor sleep quality reported to a physician, and difficulty concentrating due to sleepiness.

Further estimates of standardized (2000 Census data) population age-adjusted prevalence of sleep characteristics and sleep-related impairments are shown in Table 3.2, and fig.3.5 and 3.6.

Compared to the general U.S. male cohort, MI men tended to have lower standardized age-adjusted prevalence population estimates for all sleep outcomes, and the differences attained significance for: SHST < 7 hours/ weeknight, self-perceived sleep- deprivation, poor sleep quality reported to a physician, mild/moderate insomnia with/without short sleeping hours, and daytime sleepiness. Compared to their MA

99 counterparts, MI men had lower age-adjusted prevalence rates for SHST < 7 hrs/weeknight, self-perceived sleep deprivation, mild/moderate insomnia with and without short sleep, and daily sleepiness.

Compared to the general U.S. female sample, MI women also tended to have lower age-adjusted prevalence rates of sleep problems or short sleep, with significant differences found for self-perceived sleep deprivation, poor sleep quality reported to a physician, some level of insomnia, daytime sleepiness and memory impairments due to sleepiness.

Compared to their U.S. born MA counterparts, MI women had lower age-adjusted population prevalence rates for poor sleep quality reported to a physician and difficulty concentrating due to sleepiness.

Table 3.2 also shows the overall differences in sleep symptoms by gender within each sample. A lower proportion of MI men than MI women reported SHST < 7 hrs/weeknight, poor sleep reported to a physician, and mild/moderate insomnia with or without short sleep (all p’s<0.05). These gender differences generally paralleled what was observed in the general U.S. sample with the exception of SHST, which was more common in men compared to women in the general sample.

Table 3.3 shows the results of multivariate logistic regression models, stratified by gender, and adjusted for socio-demographic characteristics (age, race, marital status, income, education, health insurance), substance use (caffeine, alcohol, smoking, recreational drug use), sedentary leisure time (TV and computer use for recreational purposes), and health characteristics (self-reported general health and depression). MI

100 status was significantly associated with lower odds of sleep-related problems in the combined male and female sample. In both men and women, the odds of insomnia and poor sleep quality reported to a physician were over 40% lower in MI compared to all other U.S. individuals. Significantly lower odds of SHST <7 hrs/weeknight were found with MI status in men but not in women.

Sleep, Mexico-born Status, Cultural Change, and other Risk Factors for Poor Sleep in Mexican-Americans

Additional statistical modeling was performed to further explore the role of language/culture change (using language preference as a proxy for changes from

Mexican to American culture), and other potential predictors of poor sleep characteristics in the MA sample.

In MA men, predominant or exclusive Spanish speaking at home was associated with most sleep outcomes in univariate models. In addition, significant univariate predictors of SHST < 6 hrs/weeknight in MA men were: age, family income higher than poverty level, and living single (table 3.4).

Sedentary leisure time was also significantly associated with SHST <7 hrs/weeknight. No social support, higher family income, higher education, and sedentary leisure time were significantly associated with sleep deprivation self-perception.

Mild/moderate insomnia with and without SHST <7 hrs/weeknight in MA men were also associated with no social support, daily use of caffeinated drinks, self-reported fair/poor health, and depression.

101

Table 3.3 Multivariate* Odds Ratios (OR) with 95% Confidence Intervals (95% CI) of Poor

Sleep Outcomes Associated with Mexico-Born status in the Overall U.S. Analytic Sample and By Gender

Entire US Sample Males Females

Sleep related condition Adjusted OR ‡ Adjusted OR ‡‡ Adjusted OR ‡‡

(95% CI) (95% CI) (95% CI)

(N=5160) (N=2654) (N=2506)

SHST< 7 hrs/weeknight 0.7 (0.6-0.9) 0.6 (0.5-0.8) 1.0 (0.8-1.3) (Short Habitual Sleep Time < 7 h/night)

Mild/Moderate Insomnia 0.4 (0.2-0.5) 0.3 (0.1-0.5) 0.5 (0.3-0.9) (insomnia symptoms >5-15 times /month)

Severe Insomnia 0.3 (0.2-0.5) 0.2 (0.1-0.5) 0.3 (0.1-0.9) (insomnia symptoms >15 times /month)

Insomnia with SHST< 7 hrs/weeknight 0.3 (0.2-0.5) 0.2 (0.1-0.4) 0.5 (0.3-0.9) (insomnia symptoms >5-15 times/month with Short Habitual Sleep Time < 7 h/night)

Poor Sleep Quality reported to a 0.3 (0.2-0.5) 0.2 (0.1-0.5) 0.3 (0.1-0.5) physician Difficulty concentrating due to sleepiness 0.4 (0.2-0.8) 0.5 (0.2-1.6) 0.3 (0.1-0.8)

Memory impairment due to sleepiness 0.3 (0.1-0.5) 0.2 (0.1-0.5) 0.3** (0.1-1.0)

*Analyses conducted using Proc Survey ** P-value <0.05 ‡ Adjusted for gender , age, race, education, marital status, poverty income ratio (a ratio of family income to poverty threshold), insurance status, caffeine, alcohol, smoking, recreational drug usage, number of hours used on TV and computer in the past month, self reported general health, and depression ‡‡ Adjusted for age, race, education, marital status, poverty income ratio (a ratio of family income to poverty threshold), insurance status, caffeine, alcohol, smoking, recreational drug usage, number of hours used on TV and computer in the past month, self reported general health, and depression

102

In MA women, MI status and language use at home were not significant univariate predictors of SHST or self-perceived sleep deprivation. All sleep outcomes other then SHST <7 hrs/weeknight were associated with self-reported fair/poor overall health and depression. In addition, no social support was associated with severe insomnia, family income over poverty level and daily caffeine use was associated with mild/moderate insomnia with/without SHST <7 hrs/weeknight, and current smoking was associated with sleep deprivation self-perception.

To further evaluate the potential independent correlates of poor sleep in MA men and women, and also to assess potential mediators in the relationships between sleep outcomes and MI status in MA men, multivariate models without and with inclusion of language use at home were performed (Table 3.5).

103

Table 3.4 Univariate Odds Ratios (OR) with 95% Confidence Intervals (95% CI) of Poor Sleep Outcomes, By Gender in Mexican-Americans*

Sleep Outcomes

SHST< 7 Sleep Mild/Moderate Severe Insomnia with hrs/weeknight Deprivation Insomnia Insomnia SHST< 7 (Short Self- (insomnia (insomnia hrs/weeknight Habitual Sleep Perception symptoms >5-15 symptoms >15 (insomnia Risk Factors Time < 7 (past month) times /month) times /month) symptoms >5-15 Unadjusted hrs/night) times/month OR with Short Habitual Sleep (95% CI) Time < 7h/night)

MEN (N=543)

Immigrant 0.4 (0.3-0.6) 0.4 (0.2-0.8) 0.4 (0.2-0.7) 0.2 (0.1-0.5) 0.3 (0.1-0.5) (Mexico-born ) Language use at home Only Spanish (0) 1.0(reference) 1.0(reference) 1.0 (reference) 1.0(reference) 1.0 (reference) Spanish > English (1) 2.2 (1.4-3.5) 1.3 (0.6-3.1) 1.9 (0.4-8.6) 1.0 (0.3-3.7) 2.5 (0.5-11.6) Spanish = English (2) 1.7 (0.5-5.5) 2.5 (1.1-5.9) 1.8 (0.9-3.4) 2.5 (1.2-5.3) 2.9 (1.1-8.1) Spanish < English (3) 4.6 (3.1-6.8) 3.7 (1.7-7.7) 1.9 (0.9-4.0) 1.8 (0.9-3.5) 4.3 (1.3-14.4) Only English (4) 2.5 (1.2-5.1) 2.6 (1.3-5.4) 3.2 (1.6-6.2) 1.6 (0.8-3.2) 5.6 (2.2-13.9)

No social support 0.8 (0.6-1.1) 2.6 (1.3-5.0) 5.0 (2.5-9.8) 3.8 (1.018.7)** 2.5 (1.3-4.7)

Age 1.0 (1.0-1.1) 1.0 (1.0-1.1) 1.1 (1.0-1.2)** 1.1 (0.9-1.3) 1.2 (1.1-1.3) (5 years increments)

Family Income 1.6 (0.8-3.1) 2.6 (1.2-5.7) 3.1 (1.3-7.4) 2.1 (0.7-6.2) 2.3 ( 1.0-5.3)** (twice or more over poverty level) No High School 0.5 (0.3-1.0) 0.4 (0.2-0.7) 0.8 (0.4-1.6) 0.6 (0.3-1.4) 0.9 (0.5-1.6) diploma

Single 1.0 (0.8-1.2) 1.6 (0.9-2.7) 1.3 (0.8-2.1) 1.1 (0.5-2.2) 1.3 (0.8-2.3) Sedentary leisure 3.4 (1.5-7.5) 2.1 (1.1-3.9) 3.3 (1.3-8.5) 2.9 (0.7-11.5) 3.6 (2.1-6.1) time (TV& computer use ≥ 3h/day) Current smoker 0.9 (0.5-1.8) 0.7 (0.3-1.5) 0.9 (0.5-1.6) 1.1 (0.3-3.8) 1.2 (0.5-3.0)

Drinking 1.2 (0.7-1.9) 1.4 (0.8-2.3) 1.9 (1.3-2.9) 2.1 (1.1-4.0) 2.5 (1.6-3.9) caffeinated drink (≥ 1 cup/day) Drinking alcohol 0.7 (0.5-1.0) 0.7 (0.3-1.3) 0.8 (0.3-2.1) 0.6 (0.2-1.8) 0.6 (0.2-1.5) (≥ 1 time/month) Ever use of illegal 1.1 (0.7-1.6) 1.1 (0.6-2.2) 1.3 (0.8-2.1) 0.4 (0.1-1.9) 1.4 (0.7-2.6) drugs Self-reported fair or 0.9 (0.7-1.2) 1.5 (0.9-2.3) 2.1 (1.2-3.9) 1.7 (0.6-5.3) 2.1 (1.0-4.5 )** poor overall general health Depression 1.5 (0.5-4.8) 1.4 (0.6-3.0) 4.3 (1.4-13.2) 4.8 (1.7-13.8) 6.3 (2.3-17.3) (PHQ-9 score ≥ 10)

104

WOMEN (N=499)

Immigrant 0.8 (0.5-1.2) 0.7 (0.3-1.5) 0.7 (0.4-1.0) 0.6 (0.2-1.4) 0.7 (0.4-1.2) (Mexico-born ) Language use at home Only Spanish (0) 1.0(reference) 1.0 (reference) 1.0 (reference) 1.0 (reference) 1.0(reference) Spanish > English (1) 1.0 (0.6-1.5) 1.5 (0.6-3.5) 0.8 (0.5-1.5) 1.0 (0.2-4.2) 0.6 (0.1-2.7) Spanish = English (2) 1.3 (0.9-1.8) 1.5 (0.7-2.9) 1.5 (1.1-2.3) 2.8 (1.2-6.6) 1.1 (0.5-2.2) Spanish < English (3) 1.3 (0.8-2.0) 1.0 (0.3-3.1) 1.3 (0.7-2.4) 0.8 (0.2-3.6) 1.1 (0.5-2.7) Only English (4) 1.3 (0.9-1.8) 1.5 (0.7-3.1) 1.7 (1.1-2.6) 1.4 (0.5-3.8) 1.3 (0.7-2.7)

No social support 1.0 (1.0-1.1) 1.0 (0.9-1.1) 1.0 (1.0-1.1) 1.1 (1.0-1.2)** 1.1 (1.0-1.2)

Age 1.0 (0.6-1.6) 0.8 (0.4-1.7) 0.5 (0.2-1.0) 0.8 (0.2-2.7) 0.5 (0.2-1.4) (5 years increments) Family Income 1.5 (1.0-2.1) 1.2 (0.6-2.3) 1.7 (1.2- 2.5) 1.6 (0.6-4.5) 2.4 (1.1-5.1) (twice or more over poverty level) No High School 0.8 (0.5-1.4) 0.8 (0.5-1.4) 0.7 (0.5-0.9) 0.8 (0.4-1.4) 0.8 (0.4-1.3) diploma Single 1.1 (0.7-1.5) 1.0 (0.6-1.6) 1.3 (0.9-2.1) 1.3 (0.6-2.8) 1.3 (0.8-2.2)

Sedentary leisure 1.4 (0.7-2.9) 1.2 (0.5-2.9) 1.0 (0.4-2.4 ) 0.7 (0.1-3.8) 1.2 (0.5-2.8) time (TV &computer use ≥ 3h/day) Current smoker 1.7 (0.9-3.1) 1.9 (1.1-3.3) 1.5 (0.9-2.6) 2.4 (0.8-7.1) 0.8 (0.3-2.4)

Drinking 1.4 (0.6-3.3) 1.1 (0.6-1.8) 1.7 (1.2-2.5) 1.5 (0.5-4.3) 2.0 (1.1-3.7) caffeinated drink (≥ 1 cup/day) Drinking alcohol 1.5 (0.9-2.5) 1.2 (0.6-2.4) 1.3 (0.6-2.5) 0.8 (0.5-1.5) 1.3 (0.8-2.2) (≥ 1 time/month) Ever use of illegal 2.4 (0.8-7.5) 0.6 (0.2-1.9) 0.9 (0.4-1.9) 0.4 (0.1-2.1) 1.5 (0.7-3.3) drugs Self-reported fair 1.7 (1.1-2.5) 1.9 (1.3-3.0) 2.1 (1.3-3.5) 4.6 (2.5-8.5) 3.3 (2.0-5.5) or poor overall general health Depression 2.5 (0.9-6.6) 2.2 (1.2-4.3) 5.8 (2.8-12.3) 4.3 (1.9-9.5) 7.2 (3.6-14.3) (PHQ-9 score ≥ 10) *Analyses conducted using Proc Survey ** P-value <0.05

105

Table 3.5 Multivariate Odds Ratios (OR) with 95% Confidence Intervals (95% CI) of

Poor Sleep Outcomes, By Gender in Mexican-Americans Without* and With**

Adjustments for Language Preference at home

Sleep Outcomes

SHST< 7 Sleep Mild/Moderate Severe Insomnia Insomnia with hrs/weeknight Deprivation Insomnia (insomnia SHST< 7 (Short Habitual Self-Perception (insomnia symptoms > 15 hrs/weeknight times /month) Sleep Time < 7 (past month) symptoms (insomnia hrs/weeknight) > 5-15 times symptoms >15 /month) times/month Risk Factors Sleep Time < Adjusted OR 7hrs/weeknight) (95% CI)

MEN (N=543)

Immigrant 0.4 (0.3-0.6) 0.4 (0.2-0.8) 0.4 (0.2-0.7) 0.2 (0.1-0.5) 0.3 (0.1-0.5) Univariate: (for reference) Immigrant 0.5 (0.3-0.9)* 0.6 (0.3-1.2)* 0.4 (0.2-0.8)* 0.2 (0.1-0.4)* 0.2 (0.1-0.5)* (Mexico-born) 0.8 (0.4-1.6)** 1.0 (0.4-2.4)** 0.4 (0.1-1.2)** 0.04(0.002-1.4)** 0.4 (0.1-1.5)**

Language use at home** Only Spanish (0) 1.0 (reference) 1.0 (reference) 1.0 (reference) 1.0 (reference) 1.0 (reference) Spanish English (1) 2.1 (1.2-3.7) 1.2 (0.5-2.8) 1.7 (0.4-7.8) 0.3 (0.0-3.4) 2.6 (0.4-15.2) 1.3 (0.5-3.8) 2.5 (0.8-7.7) 1.2 (0.3-3.9) 0.2 (0.0-30.1) 2.1 (0.4-10.9) Spanish= English (2) 3.7 (1.7-7.8) 3.4 (1.0-11.6) 0.9 (0.3-2.7) 0.3 (0.0-15.4) 3.4 (0.5-22.2) Spanish< English (3) 1.9 (0.6-5.8) 2.2 (0.6-8.3) 1.3 (0.3-5.5) 0.1 (0.0-9.2) 3.5 (0.4-27.7) Only English (4)

No social support 0.9 (0.6-1.4)* 4.2 (1.9-9.6) 8.2 (2.4-28.3)* 6.0 (0.6-61.7)* 3.8 (1 -14.0)*‡ 0.9 (0.5-1.4)** 4.2 (1.8-9.8)** 8.1 (2.2-20.3)** 6.6 (0.8-50.1)** 3.8 (0.9-15.5)**

Age 1.0 (0.9-1.1)* 1.0 (0.8-1.1)* 1.1 (0.9-1.3)* 0.9 (0.6-1.4)* 1.1 (0.9-1.4)* (5 years increments) 1.0 (0.9-1.1)** 0.9 (0.8-1.1)** 1.1 (0.9-1.3)** 1.0 (0.6-1.4)** 1.1 (0.8-1.4)**

Family Income 1.0 (0.7-1.5)* 1.1 (0.8-1.6)* 1.5 (0.9-2.4)* 0.9 (0.4-2.0)* 1.6 (0.9-2.8)* (twice or more over 0.9 (0.6-1.4)** 1.0 (0.7-1.4)** 1.5 (0.9-2.4)** 0.8 (0.3-2.2)** 1.4 (0.8-2.5)** poverty level) No High School 0.8 (0.3-2.0)* 0.4 (0.2-1.0)* 1.2 (0.4-3.1)* 0.8 (0.2-3.2)* 1.9 (1.0-3.5)* diploma 0.9 (0.3-2.5)** 0.5 (0.2-1.3)** 1.2 (0.4-3.1)** 0.6 (0.1-3.1)** 2.3 (1.2-4.6)**

Single 0.8 (0.6-1.1)* 1.2 (0.5-2.9)* 0.8 (0.4-1.6)* 0.7 (0.1-3.4)* 0.9 (0.4-1.9)* 0.9 (0.6-1.3)** 1.3 (0.6-2.6)** 0.8 (0.5-1.5)** 0.7 (0.1-2.8)** 1.0 (0.4-2.0)**

Sedentary leisure 2.6 (1.2-6.0)* 1.4 (0.6-3.4)* 2.9 (0.9-1.2)* 1.9 (0.4-10.1)* 2.6 (1.2-5.8)* time 2.7 (1.1-6.6)** 1.3 (0.5-3.2)** 2.8 (0.9-8.8)** 2.2 (0.4-12.3)** 2.5 (1.1-5.8)** (T.V. & computer use ≥ 3h/day) Current smoker 0.9 (0.5-1.6)* 0.6 (0.3-1.4)* 0.6 (0.3-1.0)* 0.8 (0.2-3.1)* 0.7 (0.3-1.7)* 0.9 (0.5-1.7)** 0.6 (0.3-1.4)** 0.6 (0.3-1.0)** 0.8 (0.2-2.7)** 0.7 (0.3-1.5)**

Drinking 1.1 (0.7-1.8)* 1.5 (1.0-2.5)* 2.0 (1.3-2.9)* 1.8 (0.7-4.6)* 2.4 (1.4-4.3)* caffeinated drink 1.1 (0.7-1.8)** 1.5 (0.9-2.6)** 1.9 (1.2-2.9)** 1.9 (0.7-4.8)** 2.3 (1.3-4.1)** (≥ 1 cup/day)

106

Drinking alcohol 0.7 (0.5-1.0)* 0.6 (0.3-1.4)* 0.7 (0.3-2.2)* 0.8 (0.3-2.3)* 0.6 (0.2-1.9)* (≥ 1 time/month) 0.7 (0.5-1.1)** 0.6 (0.3-1.5)** 0.7 (0.3-2.0)** 0.8 (0.2-2.9)** 0.6 (0.2-1.8)**

Ever use of illegal 0.9 (0.6-1.3)* 1.0 (0.4-2.4)* 1.4 (0.7-2.7)* 0.3 (0.1-1.0)* 1.3 (0.5-3.3)* drugs 0.8 (0.5-1.2)** 1.0 (0.4-2.3)** 1.3 (0.7-2.7)** 0.3 (0.1-1.1)** 1.2 (0.4-3.4)**

Self-reported fair or 0.9 (0.6-1.4)* 1.9 (1.0-3.4)* ‡ 2.1 (1.0-4.2)*‡ 1.7 (0.4-7.3)* 1.7 (0.6-5.5)* poor overall general 1.0 (0.7-1.5)** 2.0 (1.2-3.5)** ‡ 2.0 (1.0-4.4)** ‡ 1.7 (0.4-7.2)** 1.8 (0.6-5.8)** health Depression 1.3 (0.4-4.9)* 0.8 (0.4-1.6)* 3.0 (1.0-9.1)* 2.3 (0.7-7.3)* 4.4 (1.3-14.7)* (PHQ-9 score ≥ 10) 1.5 (0.4-5.8)** 0.8 (0.4-1.7)** 3.0 (1.1-8.8)** 2.6 (1.0-7.0)** 4.8(1.4-16.8)** WOMEN (N=499) Immigrant Univariate: 0.8 (0.5-1.2) 0.7 (0.3-1.5) 0.7 (0.4-1.0) 0.6 (0.2-1.4) 0.7 (0.4-1.2) (for reference) Immigrant 0.9 (0.5-1.8)* 0.5 (0.2-1.3)* 0.7 (0.4-1.2)* 0.5 (0.2-1.5)* 0.7 (0.3-1.5)* (Mexico-born) 0.9 (0.4-2.1)** 0.4 (0.1-1.5)** 1.0 (0.5-2.1)** 0.8 (0.2-3.3)** 0.7 (0.3-1.5)**

Language use at home** Only Spanish (0) 1.0 (reference) 1.0 (reference) 1.0 (reference) 1.0 (reference) 1.0 (reference) Spanish > English (1) 0.9 (0.5-1.5) 1.2 (0.4-3.4) 2.8 (0.4-1.8) 1.0 (0.3-3.9) 0.7 (0.2-2.9) Spanish = English (2) 1.1 (0.6-1.2) 0.8 (0.2-3.1) 1.6 (0.8-3.5) 3.1 (0.8-11.3) 0.7 (0.2-2.9) Spanish < English (3) 0.9 (0.3-2.4) 0.5 (0.1-2.7) 1.3 (0.7-2.5) 0.7 (0.2-2.2) 0.7 (0.2-1.8) Only English (4) 0.9 (0.3-2.4) 0.7 (0.1-3.5) 2.4 (1.0-6.1) 1.6 (0.6-4.3) 1.3 (0.4-4.3)

No social support 1.6 (1.1-2.2)* 1.2 (0.7-2.3)* 2.0 (1.4-2.8)* 1.5 (0.6-4.1)* 2.7 (1.3-5.4)* 1.5 (1.0-2.3)** 1.3 (0.7-2.5)** 2.0 (1.4-2.7)** 1.4 (0.5-4.3)** 2.8 (1.4-5.7)**

Age 1.0 (0.9-1.1)* 0.9 (0.7-1.0)* 1.0 (0.8-1.1)* 1.0 (0.8-1.2)* 1.0 (0.8-1.2)* (5 years increments) 1.0 (0.9-1.1)** 0.9 (0.7-1.0)** 1.0 (0.8-1.1)** 1.0 (0.8-1.3)** 1.0 (0.8-1.2)**

Family Income 1.2 (0.8-1.7)* 0.8 (0.6-1.2)* 0.7 (0.5-1.0)* 0.8 (0.4-1.4)* 0.7 (0.4-1.1)* (twice or more over 1.2 (0.8-1.8)** 0.9 (0.5-1.4)** 0.6 (0.5-0.9)** 0.8 (0.5-1.5)** 0.7 (0.4-1.1)** poverty level) No High School 0.9 (0.4-1.7)* 0.7 (0.4-1.5)* 0.4 (0.2-0.8)* 0.5 (0.2-1.0)* 0.4 (0.2-0.8)* diploma 0.9 (0.4-1.8)** 0.7 (0.4-1.3)** 0.4 (0.2-0.9)** 0.6 (0.3-1.0)** 0.4 (0.2-0.8)**

Single 1.0 (0.6-1.8)* 1.0 (0.6-1.5)* 1.2 (0.7-2.0)* 1.2 (0.4-3.4)* 1.4 (0.7-2.8)* 1.0 (0.6-1.7)** 0.9 (0.6-1.4)** 1.1 (0.7-1.8)** 1.1 (0.4-3.1)** 1.3 (0.7-2.5)** Sedentary leisure time 1.3 (0.5-3.3)* 1.1 (0.3-3.8)* 0.9 (0.3-2.5)* 0.6 (0.2-2.2)* 1.4 (0.4-5.2)* (TV & computer use ≥ 1.4 (0.6-3.2)** 1.2 (0.4-3.7)** 0.9 (0.3-2.4)** 0.9 (0.2-3.4)** 1.4 (0.4-5.0)** 3h/day) Current smoker 1.5 (0.9-2.6)* 1.9 (1.1-3.4)* 1.1 (0.5-2.6)* 2.5 (0.6-10.0)* 0.5 (0.1-2.3)* 1.5 (0.8-2.8)** 1.8 (0.9-3.7)** 1.0 (0.5-2.3)** 2.7 (0.7-9.8)** 0.4 (0.1-2.3)** Drinking caffeinated drink 1.3 (0.5-3.6)* 0.9 (0.6-1.4)* 1.3 (0.9-2.0)* 1.2 (0.4-3.5)* 1.6 (0.7-3.2)* (≥1 cup/day) 1.3 (0.5-3.7)** 0.9 (0.6-1.4)** 1.3 (0.9-1.9)** 1.2 (0.4-3.3)** 1.6 (0.7-3.4)**

Drinking alcohol 1.3 (0.7-2.6)* 1.4 (0.7-2.8)* 1.4 (0.6-3.1)* 0.9 (0.5-1.8)* 1.4 (0.8-2.5)* (≥1 time/month) 1.4 (0.7-2.7)** 1.4 (0.7-2.9)** 1.4 (0.6-3.2)** 1.0 (0.5-1.9)** 1.4 (0.7-2.8)**

Ever use of illegal 1.5 (0.4-5.4 )* 0.3 (0.1-1.0)* 0.4 (0.1-1.5)* 0.2 (0.0-1.1)* 0.7 (0.1-4.2)* drugs 1.6 (0.4-5.8)** 0.3 (0.1-1.0)** 0.4 (0.1-1.4)** 0.3 (0.1-1.3)** 0.7 (0.1-3.3)**

Self- 1.8 (1.2-2.7)* 2.1 (1.3-3.5)* 2.1 (1.2-3.8)* 4.6 (2.1-9.7)* 3.0 (1.8-5.0)* reported fair or poor 1.8 (1.2-2.8)** 2.1 (1.3-3.6)** 2.3 (1.3-4.1)** 4.6 (2.1-10.1)** 3.3 (2.0-5.4)** overall general health Depression 2.1 (0.6-6.7)* 1.6 (0.7-3.8)* 4.8 (1.9-12.4)* 2.8 (1.3-5.9)* 4.8 (1.8-12.4)* (PHQ-9 score ≥ 10) 2.1 (0.6-7.0)** 1.8 (0.9-3.6)** 5.5 (2.1-14.2)** 3.4 (1.9-6.1)** 5.2 (2.1-13.2)** *Partially adjusted models for age, education, income ratio twice or higher then poverty income ration, marital status, social support, three or more hours daily use of TV and computer for recreational purposes in the past month , caffeine, alcohol, smoking, recreational drug usage, self reported general health, and depression (Analyses conducted using Proc Survey) ** Fully adjusted models adding language use at home to the all other covariates (analyses conducted using Proc Survey) ‡ P-value <0.05

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In MA men, adjusted analyses showed both less consistent associations between

MI status and all sleep outcomes, and also between language preference and all sleep outcomes. SHST <7 hrs/weeknight was associated with sedentary leisure time. No social support, and self-reported poor general health status were significantly associated with sleep deprivation self-perception. Except for language use at home, MI status, and depression, all univariate significant predictors of mild/moderate insomnia remain significant in the final model in MA men. Significant adjusted predictors of some insomnia with SHST in men included a low education level attainment, daily use of caffeinated drinks and depression. Fig 3.7, Fig 3.8, Fig 3.9 and Fig 3.10 show the graphs of this outcome in Models without and with adjustment for language preference at home.

Depression was the only significant predictor of severe insomnia in adjusted models in

MA men.

To further explore the full extent to which the MI status effect on sleep outcomes in MA men could be mediated by covariates, we developed single and multiple mediators’ models (MacKinnon 1993). Fig 3.1 shows the steps taken for single mediator models; table 3.4 includes Step 1 and Step 3 in verifying mediation, and the following tables show in detail all remain steps used in assessing potential mediators, based on previous described Multiple Theories.

Fig 3.2 shows the steps taken for three mediators’ model used (MacKinnon

2008), and Table 3.9 shows an example of multiple mediation approach for the outcome

Insomnia with short sleeping hours in Mexican American Men, based on the conceptual model proposed in Chapter 2.

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Fig 3.7 Multivariate Odds Ratios (OR) with 95% Confidence Intervals (95% CI) of

Insomnia with Short habitual Sleep Time in Mexican-American Men not including cultural changes

Partially adjusted models for age, education, income ratio twice or higher than poverty income ration, marital status, social support, three or more hours daily use of TV and computer for recreational purposes in the past month , caffeine, alcohol, smoking, recreational drug usage, self reported general health, and depression (Analyses conducted using Proc Survey)

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Fig 3.8 Predicted Probabilities of Insomnia with Short habitual Sleep Time in

Mexican-American Men not including cultural changes

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Fig 3.9 Multivariate Odds Ratios (OR) with 95% Confidence Intervals (95%CI) of Insomnia with Short habitual Sleep Time in Mexican-American Men including cultural changes

Fully adjusted models adding language use at home to age, education, income ratio twice or higher then poverty income ration, marital status, social support, three or more hours daily use of TV and computer for recreational purposes in the past month , caffeine, alcohol, smoking, recreational drug usage, self reported general health, and depression (analyses conducted using Proc Survey)

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Fig 3.10 Predicted Probabilities of Insomnia with Short habitual Sleep Time in

Mexican-American Men including cultural changes

Fully adjusted models adding language use at home to age, education, income ratio twice or higher than poverty income ration, marital status, social support, three or more hours daily use of TV and computer for recreational purposes in the past month , caffeine, alcohol, smoking, recreational drug usage, self reported general health, and depression (analyses conducted using Proc Survey)

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Table 3.6 Step 2 referred as “correlations” in fig 3.2 (significant associations): Univariate

Odds Ratio OR (95% CI) between Mexico-born Immigrant Status (X) and Covariates (Y)

as Potential Mediators (Proc survey analyses)

Covariates No social No High Single Current Drinking Drinking Ever use Self- Depression support School smoker at least alcohol of illegal reported diploma one cup or at least drugs fair or more of one time poor caffeinated per overall drink per month general day health

Males: 1.2 6.3 0.4 0..5 0.9 0.8 0.5 1.2* 0.6

(0.7-2.0) (4.7-8.4) (0.2-0.5) (0.3-0.8) (0.4-1.7) (0.5-1.2) (0.3-0.9) (1.0-1.8) (0.3-0.9)

Females: 1.2 7.2 0.4 0.5 0.6 0.6 0.1 2.1 0.9

(0.7-1.9) (4.2-12.4) (0.3-0.5) (0.2-1.4) (0.4-0.8) (0.4-0.8) (0.0-0.2) (1.3-3.4) (0.5-1.7)

Table 3.7 Referred as “Correlations” in Fig 3.2 (significant associations): Regression

Coefficients, Standard Error and p Value between Mexico-born Immigrant Status (X) and

Continuous Covariates (Y) as Potential Mediators (proc survey analyses)

Poverty income ratio : Sedentary lifestyle Age (increments of family income Number of hours per day of TV watching Covariates twice higher than / poverty and computer use threshold )

Males: 0.23 (0.32) -0.61 (0.07) -0.52 (0.13) P=0.47 P<0.0001 P=0.0012

Females: 0.04 (0.39) -0.54 (0.06) -0.37 (0.09) P=0.9 P<0.0001 P=0.0013

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Table 3.8 Percent of total effect (%)*of the Mexico-born immigrant status (x) on sleep outcomes (Y on columns) mediated by covariates and ratios of the indirect to the direct effect** (Bivariate analyses to test partial mediation just for qualifying covariates based on step 2 and 3)

Qualifying covariates for mediation based on step 2:

Habitual sleep Sleep deprivation Some level of Some level of duration self-perception insomnia insomnia with sleep < 7 h /per night (yes/no) (5-15 times duration <7 h /per /month) night Cultural changes 15.6.%* 30.5%* 23.4%* 47.9%* (English language used 0.15** 0.9** 0.7** 2.1** during NHANES interview)

Poverty income ratio 16.4%* 16.3%* n/a 10.5%* ( increments of family 0.2** 0.2** 0.1** income twice higher than / poverty threshold )

No High School n/a 9.7%* n/a n/a diploma 0.1** Single n/a n/a n/a n/a

Sedentary lifestyle Number of hours per n/a n/a 10.1 %* 7.7%* day of TV watching and computer use 0.1** 0.1** (past month) (two hours increments) Self-reported fair or n/a n/a (1.2%)* (1.0)* poor overall general -0.1** -0.01** health Depression n/a n/a 5.8%** 7.0%* (PHQ-9 score >=10) 0.6 0.6** n/a = not qualified as mediator by step 3: mediator not significant associated with sleep outcome; ‡ For the inconsistent mediation (suppression) effect not required significant relationship between Mexico-born immigrant status and outcome (MacKinnon 2007, 2002, McFatter, 1979) ‡‡ direct mediation so possible not qualified as mediator due to the step 1: X (Mexico- born immigrant status) is not significant associated with sleep outcome (Baron and Kenny 1981). However, newer approaches (Kenny 1998) extensively used by analysts do not require step 1

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Table 3.9 Insomnia with short sleeping hours: Example of Assessing Multiple Mediation in Mexican American Men

OR OR Proportion of the MODELS Adjustment for ( 95% CI) ( 95% CI) absolute total DESCRIPTION: confounding* Without With effects that is (Nested Hierarchically Models mediator(s) mediator(s) mediated built by serially adding (ratios of the indirect to confounders in clusters) the direct effect) A. MEN MEDIATOR 1‡ (M1): Cultural Changes as language use at home

MODEL 1.A. Include Immigrant (Mexico- born) ± Cultural changes Immigrant (Mexico-born ) 0.3 (0.1-0.5) 0.4 (0.1-1.4) 55% Cultural changes as (1.2) Language use at home Only Spanish (0) 1.0 (reference) Spanish > English (1) 2.5 (0.5-11.6) Spanish = English (2) 2.9 (1.1-8.1) Spanish < English (3) 4.3 (1.3-14.4) Only English (4) 5.6 (2.2-13.9)

MODEL 2.A Immigrant (Mexico-born ) 0.2 (0.1-0.4) 0.3 (0.1-1.1) Model 1+ social determinants No social support 3.8 (1.5-9.7) 3.8 (1.4-10.2) 55% of health vulnerability Age (every 5 years) 1.1 (0.9-1.3) 1.1 (0.9-1.3) (1.2) covariates** No High School diploma 1.6 (1.00-2.5) 1.9 (1.1-3.2) Single 1.0 (0.5-2.0) 1.0 (0.5-2.0)

MODEL 3.A Immigrant (Mexico-born ) 0.2 (0.1-0.4) 0.3 (0.1-1.2) Model 2+ health related Current smoker 0.8 (0.4-1.6) 0.7 (0.4-1.5) 52.7% behavioral risk factors Drinking ≥ cup or more of 2.6 (1.5-4.4) 2.6 (1.5-4.4) (1.1) covariates** not qualified as caffeinated drink per day mediators Drinking alcohol (≥1 time 0.7 (0.3-2.0) 0.7 (0.3-1.9) month) 1.4 (0.5-3.5) 1.2 (0.4-3.7) Ever use of illegal drugs MODEL 4. Immigrant (Mexico-born ) 0.2 (0.1-0.4) 0.4 (0.1-1.5) 63.3% Model 3+ health status (1.7) covariates** Self-reported fair or poor 1.6 (0.5-5.1) 1.7 (0.6-4.9) overall general health

Depression(PHQ-9 score ≥ 3.8 (1.2-12.8) 4.0 (1.3-12.7) 10)

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MEDIATOR 2‡ (M2) : FAMILY INCOME (≥ 2 x Poverty Income) MODEL 5. Immigrant status +All Immigrant 0.2 (0.1-0.4) 0.2 (0.1-0.4) 38% covariates (Mexico-born ) (0.62) + M2 (INCOME as ≥ 2 x Income N/A 2.0 (0.7-5.3) M2=C2 Poverty income) ( but not ( ≥ 2 x Poverty income) Cultural changes)

MEDIATOR 3‡ (M2) :Sedentary lifestyle (≥ 3h hours per day of TV watching and computer use in past month) (M3) MODEL 6 Immigrant 0.2 0.2 29% Immigrant status +All (Mexico-born ) (0.1-0.4) (0.1-0.5) (0.41) covariates + M3=C3 M3 (sedentary lifestyle) Sedentary lifestyle n/a 2.6 (1.2-5.6)

MODEL 7 (final model) Immigrant 0.2 0.4 Cultural changes Immigrant status +All (Mexico-born ) (0.1-0.4) (0.1-1.9) (M2=0, M3=0) covariates +M1+C2+C3 62 % (1.6) Income (M1=0, M3=0) 50 % (1.0) Sedentary lifestyle (M1=0, M2=0) 59 % (1.4) In conclusion, language use at home, and a family income of at least twice over poverty levels reduced the parameter estimates for MI status by over 15 % for SHST<7 hrs/weeknight in the fully adjusted model. Sedentary leisure time and daily use of caffeinated drinks were associated with some sleep outcomes in men but did not mediate the relationship between the MI status and sleep outcomes.

In adjusted models in MA women, language preference was not significantly associated with sleep outcomes nor operated as a mediator. In fully adjusted models, self-reported fair/poor overall general health was significantly associated with all poor sleep outcomes in MA women. No social support was associated with SHST <7 hrs/weeknight and some insomnia with/without SHST in MA women. Depression was also a significant predictor in MA women for all insomnia outcomes in adjusted models.

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3.5 Discussion

To my knowledge, this is the first study showing a significant overall lower risk

of poor sleep and sleep-related outcomes in adult MI compared to the general U.S.

population, and/or to U.S.- born MA. Several striking differences in self-reported short

sleep duration, insomnia, and sleep-related daily impairments were observed when

comparing the MI population both to the U.S. population and to their U.S.-born MA

counterparts. In age-adjusted analyses, MI men were significantly less likely to report

SHST < 7 hrs/weeknight, insomnia, sleep deprivation, and to report poor sleep to a

physician as compared both to the U.S.-born MA and the general sample of U.S. men.

Fewer differences were seen in women in age-adjusted analyses. Sleep duration did not

differ between the MI women and other groups. However, compared to the general U.S.

female sample, MI women had a lower prevalence of perceived sleep deprivation,

daytime sleepiness and memory impairment due to sleepiness and were less likely to

report sleep problems to a physician. Compared to their U.S.-born MA counterparts, MI

women had a lower prevalence of concentrating due to sleepiness and were less likely to

report sleep problems to a physician. Focusing on the comparison between MI and the

general U.S. sample in multivariable adjusted models, MI men were significantly less

likely to report SHST < 7 hrs/weeknight, insomnia, and sleep-related daily neuropsychological impairments. Similar trends were observed among MI women when compared to the general sample of U.S. women, although findings were not significant for differences in sleep duration.

Although poorer access to health care may partially explain the lower odds ratios

for the outcome “poor sleep reported to a physician”, differences in access to care are

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unlikely to explain differences in the other sleep outcomes and suggest that sleep

behaviors and sleep disorders differ in MI compared to the general U.S. population.

Better sleep-related outcomes despite significant socio-economic disadvantages including

limited access to health care in this segment of the U.S. population are consistent with

other previously reported health advantages in Mexico-born U.S. immigrants (often

referred to as “The Hispanic Paradox”).

In these analyses gender differences occur in sleep outcomes among MI that only partially paralleled what was observed in the general U.S. population. A significantly lower prevalence of SHST <7 hrs/weeknight in MI men compared to MI women was unexpected, since men have been reported to sleep less than women in other minority groups in the U.S. (Hale, 2007). This observation underscores the potential importance of cultural factors in influencing sleep behaviors.

In men, differences found in short sleep duration between MI and MA born in the

U.S. may reflect differences related to a shift in the cultural beliefs, value –orientations and attitudes regarding preferred life pace (Cervantes, 1985, Castro, 1985) and/or

importance of sleep (Domino, 1986) during MA men transition from Mexican culture to

American culture. The MI status was significantly associated with SHST < 7 hrs/weeknight and all insomnia outcomes after adjusting for all other health and demographic variables, except for language used at home. In a behavioral study comparing MA with Anglo American students, relaxation as an adaptive daily coping strategy was described as a positive coping mechanism reflecting the influence of

Mexican cultural values (Castro, 1985). Further research is needed to better assess the

role of cultural factors in shaping sleep behaviors and subsequent health, including the

118 use of more precise measurements related to cultural changes than available in the

NHANES dataset.

The lower frequency of short sleep among MI men compared to MI women contrasted with the findings observed in the general U.S. sample. Cultural gender-based norms and behaviors related to the division of the household labor may favorably influence the sleep of MI. Further research should determine if the increasing roles and responsibilities related to household maintenance and child rearing in MA men with acculturation (Brabeck, 2009) may contribute to increased prevalence of short sleep duration in U.S.-born MA men, and if there is a potential gender based shift in sleep time availability in this population with the length of time in immigration in the U.S. in MI.

In MA women, all poor sleep outcomes, including SHST were significantly associated with self-reported fair/poor health status, a finding consistent with the influence of health status on sleep observed generally (Briones, 1996). Also, as widely recognized, depression was associated with all insomnia outcomes in MA men and women.

Analyses did not replicate the mediator effect of smoking on the relationship between MI ethnicity and SHST in MA reported in a recent brief communication (Hale,

2009). Alcohol and substance use were also not significantly associated with sleep outcomes. This may be due to misclassification of exposures using self-report data.

However, current smoking status was carefully measured by this study by using study collected serum cotinine, a biomarker of environmental tobacco smoke exposure. The potential mediating effect of cultural changes, as measured by language preference at

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home, was associated with sleep outcomes in MI men, but did not consistently attenuate

the observed associations between MI status and all sleep outcomes.

The potential importance of the differences in sleep outcomes in this analysis is

supported by a large body of literature that has consistently demonstrated that short sleep

and insomnia are associated with increased risk of hypertension, obesity, coronary artery

disease and death (Gangwisch, 2005, 2006, Spiegel, 2005, Patel, 2004, Taheri, 2004,

Tamakoshi, 2004, Ayas, 2003, Heslop, 2002, Kripke, 2002, Mullington, 2003, Sekine,

2002). Further research is needed to determine whether favorable sleep patterns among

MI men contribute to their relatively favorable hypertension and CVD risk profiles and

whether changes in sleep patterns in the descendants of MA contribute to their increasing risk of obesity, diabetes, CVD, and all-cause mortality.

A strength of the study was the use of data from a nationally representative and

large sample (NHANES), collected by highly structured protocols. NHANES database

offers the advantage of community-based cohort recruitment, with balanced gender representation and geographic diversity, and of a conscious effort to avoid referral biases that may occur from studies of sleep clinic-based samples. Multiple sensitivity analyses

were done to assess the consistency of the reported results.

Study limitations include the reliance on predominantly self-reported data. The insomnia definition used is modeled after established criteria. However, since insomnia

may represent an element of sleep duration misperception, it is possible that those

categorized as with insomnia and short sleep duration overlapped with the

“mild/moderate insomnia” group. Differences between MI, U.S.-born MA, and U.S. general population may be partially attributed to differences in risk factors associated

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with poor sleep not completely accounted for by the measurements. Finally, no data were

available on psychological characteristics other than depression.

Systematic biases in self-reporting sleep related conditions among the groups cannot be excluded. Language barriers, including differences in meaning between the

English and Spanish versions of the available questionnaires may also affect our conclusions.

Further behavioral research should also clarify if social patterns of underreporting emotional problems in MA, (associated with a “weakness of character” (Newton, 1978)

by the Mexican traditional culture) may contribute to the relatively lower prevalence of

poor sleep outcomes in MI men. Alternatively, further research is needed to clarify

whether better sleep in MI men compared to their U.S.-born counterparts may be causally

associated to differences related to social perceptions and beliefs, such as hope due to

economical gain associated with immigration to the U.S. or plans to return to their

homeland (Viruell-Fuentes, 2007). Such factors have been speculated to reduce

vulnerability to discrimination perception (Viruell-Fuentes, 2007), which could have a

potential beneficial effect on sleep. Our observations also may be related to a “healthy

immigrant effect” whereby relatively healthy segments of a population emigrate.

In summary, our findings demonstrate that MI have more favorable sleep-related outcomes than the general U.S. population. For MI men, sleep patterns also were favorable when compared to their U.S. - born MA counterparts. Differences were not

accounted for by measurable differences in gender, age, education, marital status, poverty

income ratio, self reported general health, insurance status, caffeine, alcohol, smoking,

recreational drug usage and depression, or because of differences in BMI. Further

121 research is needed to clarify the role of ethnic influences and culture as well as westernization on sleep quality and also to specifically address whether relatively favorable sleep patterns in Mexican immigrants may explain their relatively good health profile (i.e.,. the “Hispanic paradox” )

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CHAPTER 4

Proportion of Lifetime in Immigration (PLI) and Cohort Analysis in

Immigrant Populations: Longitudinal Perspective and Non-linear Relationship with

Unsatisfactory Health Status in Mexico-born U.S. Immigrants

Aims:

1. Explain the longitudinal perspective and cohort analysis in epidemiological

immigrant research, and the use of proportion lifetime spent in immigration (PLI)

in the U.S. as a measure of the “experience in immigration” effect, in addition to

age effects in cohort analysis (4.1)

1. Relate PLI to the conceptual framework by showing the superiority of a multi-

theory approach when predicting associations between PLI and health outcomes

(self-reported unsatisfactory health status: UHS) in Mexico-born U.S. immigrants

(MI) (4.2).

2. Provide an example of quantitative cross-sectional epidemiological study

analyzing the relationship between PLI and UHS in MI and testing the following

hypotheses:

1. PLI is significantly correlated with: age at immigration, decade at the time of arrival to

the U.S, and English health-related illiteracy, but not with participant age.

2. PLI is associated with self-reported unsatisfactory health in a non-linear but rather

quadratic relationship.

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4.1. Cohort Analysis and PLI in Immigrant Health Research

Age standardization (adjustment) is largely used in epidemiology when

comparing two or more populations at one point in time, or one population at two or

more points in time. The importance of age effects, including the moderator role of this

variable on many health outcomes, is largely demonstrated in cohort analysis. This study

proposes that the field of immigrant health research would further benefit from PLI

standardization (adjustment) when comparing two or more immigrant populations at one

point in time or one immigrant population at two or more points in time. The proposed

method is expected to adjust for differences in immigration experience effects for

immigrant populations within the same host country and be useful especially in cohort

analysis including for subpopulations of immigrants known as having high mobility

abroad, for example Mexico-born U.S. immigrants (MI) (see 4.1.1).

The next section focuses on the mobility abroad of MI in order to illustrate key

differences between immigrant and non-immigrant cohorts as related to the openness of these systems.

4.1.1 Patterns of abroad mobility in MI

Mexico-born legal U.S. immigrants (MI) account for 32% of immigrants residing

in U.S., and over 65% of the U.S. Hispanic immigrant population (Passel J, 2009). The

bidirectional migratory flow of this group may be an important source of informational

bias in epidemiological U.S. immigrant studies. One in 10 Mexicans were reported as

legally residing in the U.S. at least at some point in their life (Terrazas, 2010). Over

430,000 legal MI are reported as returning to Mexico every year (Passel, 2009). These

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findings are likely more complicated when taking into accounting the 10.8 million

unauthorized Mexico-born migrants estimated as living in the U.S. in Jan 2009. Together

with the 500,000 U.S.-born individuals reported as presently residing in Mexico

(Terrazas, 2010), these legal and unauthorized Mexico-born immigrants form a “dynamic

open-system” characterized by a continuous bidirectional flow of individuals between

Mexico and the U.S.

The bidirectional migrant flow between the U.S. and Mexico is expected to remain high, though with periodic fluctuations based on historical, political and/or economic context, including immigration and health policy related legislation changes in either country (ex: new health reform law in the U.S.) With Mexican-born US immigrants constituting the single largest ethnic group of immigrants in the U.S., the high level of mobility of this group is also of interest due to possible biasing effects on prevalence population estimates (Gushulak, 2006) and general inferences/conclusions related to health risk factors and overall morbidity findings in both countries (Ocana-

Riola, 2009). For example, the bidirectional migrant flow of Mexico-born individuals

and their U.S-born Mexican-American counterparts between the U.S. and Mexico may

underestimate the U.S. Hispanic population risk of co-morbidities. This is because

previous studies have looked at MI cohorts as “closed” populations, which is inaccurate.

In doing so co-morbidities may have been underestimated due to retention of the

healthiest MI in the US, with the sick ones returning to Mexico (Salmon-bias effect).

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4.1.2 Age effects and PLI effects and their role in cohort analysis in MI

Cohorts’ analyses and population estimates of health indicators are

methodologically challenging when the target population includes, or it is limited to,

immigrant individuals due to the “open system” nature of these populations.

The classic epidemiological definition of a cohort is that of a “closed population”

using the time period between2 established calendar dates as the time point of entry and

exit shared by all members of the group (Susser, 2001). Any time period defined by

calendar dates reflects three dimensions in non-immigrant populations. These are: cohort

or generational effect as historical experience of generations up to that time, period or

environmental effect as a measure of experience related to the environment, and age

effect as a measure of biological changes/maturation. For immigrant populations, the

time frames may also include a new dimension related to the date of arrival into the host

country. For simplicity, I will name this an “immigration effect”. This new temporal

dimension is defined by two characteristics: the time spent by the immigrant in the host

country and the individual’s age at the time of immigration, knowing that it has been largely demonstrated that the experience encountered by immigrant cohorts of the same

age grossly differ, based on the individual’s age at the time of immigration. In

accordance with Critical Realism Theory, although each of these time related variables is

distinct and quantifiable (as time periods defined by exact calendar dates), isolating the

individual contribution of any one of these may be unrealistic and also unnecessarily,

because all are operating simultaneously. Based on this rationality, PLI may be the best

measure of the immigration effect because it combines both the age at time of

immigration as well as the number of years in immigration.

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PLI is proposed as highly correlated with cohorts or generation effects related to

the year of immigration and to the age at the time of immigration, but not to the

participant’s age at the time of data collection. As previously mentioned in the

introductory chapters, PLI will no longer be used to control for cultural (“acculturation”)

or linguistic-related changes in immigrant populations. Covariates measuring those

domains will be added into the final models based on the conceptual model described in

chapter 2. PLI is expected to be significantly and non-linearly associated with different

health outcomes, after adjusting for known risk factors. If this is further demonstrated,

the potential confounder effect of PLI should be addressed by further immigrant research.

Age and PLI standardization (adjustment) may be a helpful method used for

comparisons of health outcomes in two or more immigrant groups residing in the same

host country at one point in time, or in one immigrant population at two or more points in

time.

4.2 Conceptual Framework and Multi-Theory Approach applicability in predicting non-linear association between self-reported unsatisfactory health status

(UHS) and proportion lifetime in U.S. immigration (PLI) in Mexico-born individuals (MI)

This subchapter is meant to provide a liaison between the proposed conceptual framework (chapter 2) and the quantitative study using statistical methodology in verifying the existence of a non-linear association between self-reported unsatisfactory health status (UHS) in Mexico-born U.S. immigrants and PLI.

Table 4.1 reiterates some of the individual mono-theories on immigrant health

127 presented in chapter 2, and contains additional interpretations and predictions related to how we expect PLI to be associated with UHS. As already discussed extensively in

Chapter 2, many mono-theories can hold true simultaneously. This is conceptualized by using the Critical Realism Theory, which states that mono-theories are not necessarily competing with each other or excluding each other, but in fact are rather complementary in their predictions. By combining the predictions of mono-theories addressing the

Hispanic Paradox in MI, the relationship between PLI and UHS is expected to be non- linear. As explained in Table 4.1, the combination of the predictions of these theories produces an inverse U-shape function. . Therefore, the quantitative study will test the non-linear quadratic relationship between PLI and UHS.

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Table 4.1 Individual Predictions of the Theories Incorporated into “The Open System of Conceptual Model of Immigrant Health”

Theory Theory interpretation related to UHS* Predictions translated to the Odds of UHS with PLI** Acculturation Immigrants are able to maintain or even (Gordon’s) improve their general health with increased UHS decline with PLI (linear) time spent in immigration Bi-dimensional Immigrants diminish physical and mental Initial increase in the odds of acculturative (Berry) health status peri-immigration, but health reporting UHS with PLI, then UHS improvements with “acculturation. progressively declines with PLI. (non-linear - inverse V shape ) Social buffering Self-reported health status is influenced by effects culture; norms and values which may UHS progressively declines with (Nucholls ) and restrain risky behaviors, better nutrition, PLI. Social support and behaviors Cultural buffering and better social support. As immigrants mediate the relationships between effects are more acculturated with longer duration PLI and UHS (linear). (Hummer ) of stay in the United States, they lose these advantages. The Healthy “Lower proportions of Mexico- Immigrant Effect “Selection” of healthy individuals at the born U.S. immigrants reporting (Marmot) entry level in the U.S. UHS in the first quintile of PLI (0- 20%)” (linear). The Salmon Bias “Reverse selection” with generally ill Decline of the proportions of Effect individuals leaving U.S. and returning to individuals reporting UHS in the (Pablos-Mendez) Mexico with increased PLI. last quintiles of PLI (ex: 80-100%) (the longer the stay in the host country, the healthier the immigrant population becomes as a whole because the sicker individuals progressively return to their native country) (linear). Cultural mourning Subjective health status is expected to theory (Ainslie) decline with PLI in the immigrant No significant changes in UHS population as a whole, due to “perpetual should be seen in Mexico-born mourning” and somatization of immigrants. (linear) physiological grief. However, for Mexico- born immigrants this phenomenon may be less obvious than in other immigrant groups, due to “less mourning” resulting from the immediate proximity of Mexico to the U.S. Othering Social marginality: Subjective perception of health (Weis) status is expected to progressive decline with PLI (linear). Critical Realism Previously described theories are not A multi-theory approach predicts a Theory and Open necessarily competing against/excluding non-linear relationship between System Conceptual each other, but are rather unsatisfactory health status and Model: (Bhaskar) complementary. PLI, with lower odds of unsatisfactory health status at the tails (ex: intervals 0-20% and 80- 100%). (inverse U-shape function) * UHS= self-reporting unsatisfactory health status ** PLI= proportion of lifetime spent as an U.S. immigrant (PLI is divided in increments of 20 %, for easier representation of theories predictions).

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4.3 Quantitative Research 4.3.1 Abstract

Background: Unsatisfactory (fair/poor) self-reported health status (UHS) is widely used as a measure of poor health and a strong predictor of increased mortality risk. Previous research in Mexico-born U.S. immigrants (MI) reported no significant relationship between the proportion of lifetime in immigration (PLI) and UHS.

Hypotheses: PLI is highly correlated with the age at immigration and decade at the time of arrival in the U.S, but not with the participant age. A significant non-linear (quadratic)

association between PLI and UHS is predicted based on previously proposed theories

related to the Hispanic Paradox.

Methods: A cross-sectional analysis of a sample of 340 MI ≥ 40 years of age,

participating in 2005-2008 National Health and Nutrition Examination Survey, is carried

out with complete data related to immigration information and social support. Statistical

analyses included preliminary non-parametric regression modeling, by using generalized

additive models to test the significance and shape of the relationship between PLI and

UHS, and survey analyses, including logistic survey modeling a quadratic association

between PLI and UHS, independently of other respondent attributes.

Results: PLI is significantly and highly correlated with: age at time of immigration

(-0.88), decade at the time of arrival in the U.S. (-0.87), and English illiteracy (-.38), and is not correlated with the participant’s age (0.07). A significant quadratic association between PLI and UHS [OR for 1% of PLI = 1.1; CI 95% (1.0-1.1), p<0.001] persisted after adjusting for confounders.

Conclusion: The significant non-linear association between UHS and PLI should be interpreted in the context of multifactorial theories previously proposed in the field of

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Latino health. Further adjustments for PLI are indicated when comparing differences in

UHS between MI and other U.S. immigrant groups.

4.3.2 Introduction

MI represent the largest and most mobile group of U.S. immigrants (U.S. Census

Bureau, Foreign-Born Profiles), with 11.5 million reported living in the U.S. in early

2009 (Passel, 2009) and over 430,000 MI reported as returning back to Mexico every

year (Passel, 2009).

The self-reported health status is a subjective measure of health previously well

validated as predictor of mortality (Sudano, 2006, McGee, 1999, Idler, 1997, Angel,

1989), and significantly correlated with healthy risk behaviors (Manderbacka, 1999).

Mexico-born immigrants are previously described to report higher prevalence of UHS

(Franzini, 2004, Shetterly, 1996), despite multiple health advantages (Williams, 2001), and lower odds of poor health related behaviors risk factors (Morales, 2002). These discrepancies were extensively analyzed in the past due to largely inconsistent results and conclusions (Viruell-Fuentes, 2009).

Mono-theories associated with UHS are: the social and cultural differences in the self-perceptions of health (Bzostek, 2007, Kandula, 2007), somatization tendencies among recently arrived Hispanic immigrants with emotional distress (Bzostek, 2007,

Arcia, 2001, Angel 1998), and, more recently, an artifact due to differences of meaning between the Spanish and the English language versions of the SRH questions (Phillips,

2005, Franzini, 2004). UHS was not previously found to be significantly associated with

PLI in MI (Bzostek 2007).

The present study proposes a significant non-linear association of PLI with UHS

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in MI 40 years old and older, independently of social factors of health vulnerability and

cultural and linguistic related changes in MI. Based on Critical Realism Theory and the

Open -System conceptual model, a multi-factorial causality is proposed to influence the reporting of UHS in MI.

4.3.3 Methods

4.3.3.1 Study protocol and subjects

Cross-sectional data collected on a nationally representative civilian, non-

institutionalized, U.S. population, through the National Health and Nutrition Examination

Survey (NHANES), (CDC), between 2005-2008 are used by this study. The study population consisted of Mexico-born U.S. immigrants’ ≥40 years of age, which completed the surveys on Social Support between 2005- 2006, and 2007-2008. Complete

details on recruitment and design of the NHANES are described elsewhere (CDC,

NHANES webpage). Participants used Computer-Assisted Personal Interview (CAPI) technology to complete in-home bilingual (English - Spanish) interviewer administered questionnaires.

The dataset used for these analyses was assembled via inclusion of all individuals participating in the Social Support Questionnaire section during two NHANES cycles:

2005-2006 and 2007-2008. All data used are self-reported. Variables of interest were selected based on the conceptual model (fig 2.1) by merging the data found in the

Demographic, Social Support, Hospital Utilization and Access to Care, Health Insurance and Acculturation files, by utilizing the respondent sequence number. The NHANES study received approval from a human subjects committee (Protocols #2005-06 and

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#2007-08). The proposed analyses use de-identified data and were exempt from requiring

approval by the University Hospitals Case Medical Center of Cleveland Institutional

Review Board (IRB).

340 Mexico-born U.S. immigrants with complete data on immigration were

analyzed in a cohort of 1,299 (312 from the 2005-2006 and 987 from the 2007-2008

cohorts respectively) civilian, non-institutionalized U.S. immigrants ≥40 years of age, participating through completion of the social support questionnaire.

4.3.3.2 Proportion lifetime in Immigration (PLI) (Independent variable)

The number of years in U.S. immigration was approximated by using the midpoint of the ranges of the “Length of the time in U.S.” variable posted by NHANES in increment intervals. The intervals they used were: 'Less than 1 year', '1 yr less than 5 yrs', '5 yrs less than 10 yrs', '10 yrs less than 15 yrs', '15 yrs less than 20 yrs', '20 yrs less than 30 yrs', '30 yrs less than 40 yrs', '40 yrs less than 50 yrs', '50 years or more'. Thus for

'less than 1 year', the number of years in U.S. immigration was approximated as 0.5 years, for '1 yr less than 5 yrs' the number of years in U.S. immigration was approximated as 3 years, etc. (CDC, m 2005-2008). PLI is calculated as the total number of years in immigration divided by the sum of the individual’s age at the time of immigration to the

U.S. plus the total length of time spent in the U.S. after immigration. The PLI variable has the benefit of capturing a time dimension and of being derived from two time related variables: the length of time in immigration and the age at which the individual immigrated.

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4.3.3.3 Self-reported unsatisfactory health (UHS) (Dependent variable)

The variable “overall general health” was collected through the question “Would you say your health in general is: excellent, very good, good, fair, or poor?” Data was further dichotomized by collapsing the groups “fair” and “poor” into “unsatisfactory health” and the groups “excellent”, “very good”, and “good” into “satisfactory health” (Phillips 2005,

Ren 1996, Shetterly 1996). Sensitivity analyses were also conducted by using only “poor health” as the outcome of interest.

4.3.3.4 Covariates

Covariates were selected as indicated by the conceptual model (fig 2.1). These consist of: age, gender, social determinants of health vulnerability (fig 2.4), and health- related attributes/beliefs related to ethnic culture, including shifts from Mexican to

American culture. “Age” was reported in years at the time of NHANES screening.

Gender was reported as male/female.

4.3.3.4.1 Social determinants of health vulnerability covariates

Social determinants of health vulnerability covariates (fig 2.4) used by this study are income, education, living with partner/married, social support, and access to health care. Previous studies conducted in the Hispanic American population reported family income and educational attainment as strongly associated with UHS (Bzostek, 2007,

Ostrove, 1996).

Family income was used as a dichotomized variable, derived from the poverty income ratio (PIR) variable (see NHANES webpage for full details). Participants were classified as “poor,” if the PIR value was at or below 1.00 (the official poverty threshold)

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(dichotomized yes/no).

“Education” was dichotomized with the cutoff set at high school graduation /

receiving a high school diploma.

“Marital status” was used as a dichotomized variable: “alone” vs. “living with a

partner” or “married”.

“Lack of Social support” was dichotomized as “present” if the participant responded “yes” to any of the following questions: “Have you used more emotional support than received in the last year?”, “ Can you count on no one to help (by paying any bills, housing costs, hospital visits, or providing with food or clothes?),” or “Is there no one to provide with emotional support such as talking over problems or helping make a difficult decision.” Sensitivity analyses were done by testing every component of social support separately.

Other variables used included health insurance status (insured yes/no) and language used for interview (Spanish vs. English) during the NHANES data collection.

“Insurance status” was dichotomized as covered under any type of health insurance vs. not insured.

The English language chosen by participants at the time of NHANES/interviews

(dichotomized yes/no) was used as a measure of linguistic competence, reflecting the

MIs’ ability to navigate U.S. health systems and to understand professional health advice

(Johnson 2010). However, adjusting for this variable has been previously proposed as also controlling for potential differences in the language meaning of the questions when questionnaires are completed in the Spanish vs. the English language (Bzostek, 2007,

Phillips, 2005, Franzini, 2004). This variable was used in both final models and in

135 sensitivity analyses looking at the association between PLI and UHS as stratified by language of interview.

4.3.3.4.2 Health/sleep-related cultural attributes/beliefs covariate

Spanish language preference at home was used as a proxy measure of cultural practices and beliefs changes from Mexican to American culture (Bzostek, 2007, Alba

2003, Portes, 2001, Marin, 1987). Self-reported language used at home was collected by using the Acculturation Questionnaire, was dichotomized as Spanish vs. American cultural influences, with a cutoff at the level of “primary English” and “only English”, as indicative of a shift toward the American culture. Sensitivity analyses were done by using all five levels of this variable: (“all Spanish”, “primarily Spanish”, “about equal use of

Spanish and English”, “primarily English”, “only English”).

“U.S. citizenship” status (dichotomized: yes/no) was collected by the demographic questionnaire through use of the question: “Are you a citizen of the United

States?”

4.3.3.6 Statistical Analysis

General characteristics include mean, standard deviation (SD), median, interquartile range, and frequencies for continuous and categorical data analysis of the

Mexico-born U.S. immigrant sample were calculated by using sample weights analyses in SAS 9.2 (Proc Survey, SAS Institute, Inc., Cary, NC) and the Taylor Series

Linearization approach to adjust for the sampling design and non-response (Rust, 1985).

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Unweighted analyses were initiated prior to weighted analyses, and included nonparametric regression modeling using generalized additive models (proc gam). This approach allowed for the exploration of the shape and significance of the relationship between PLI and UHS. UHS was plotted against PLI, before and after fully adjusting for covariates. As the general trends in the smoothing component plots for PLI suggested quadratic dependence between PLI and UHS, a quadratic transformation of PLI was further used in all unweighted and weighted parametric models. Next, the Spearman correlation coefficient was used to assess correlations between the control and predictor variables. Unweighted logistic regression models were built after verifying possible multi-collinearity between covariates of interest using a variance inflation factor cutoff of

1.5. All of those models were verified for robustness by using the Hosmer and

Lemeshow Goodness--Fit Test. No interactions or collinear relationships were identified in the final models. Weighted logistical regression modeling was then performed to fit the quadratic polynomial of PLI as the main predictor for UHS after the weights were normalized (standardized) to the size of the Mexico-born analytic sample (Delgado

1990).

Sensitivity analyses were conducted for multiple purposes: The shape of the relationship between PLI and UHS was also verified by using the restricted cubic polynomial spline (Harrell 1988). For clarity purposes, the full description of the restricted cubic polynomial spline and graph that were obtained by using this method are presented within the appendix of chapter 4. Other sensitivity analyses done included weighted logistic regression analyses using increments of 20% PLI (PLI transformed

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from continuous into a 5 level variable), and also by dichotomizing at a cutoff of 50%.

All models were tested for age and gender interactions.

Lastly, the relationship between PLI and UHS was verified by stratifying using

the English language chosen by participants at the time of NHANES/ interviews. Two-

tailed p-values of < 0.05 were considered significant.

4.4 Results

Descriptive statistics of the sample are presented in Table 4.1. The distribution of

demographic, immigration characteristics, and self-reported health status of the analyzed

cohort appears similar to previously reported population-based data on U.S. MI (Pew

Hispanic Center, 2008, and U.S. Census Bureau, Foreign-Born Profiles). The analyzed

sample has equal gender distribution. The mean age is 52 years and the mean PLI is

47%. Less than one in three participants’ reported having completed high school.. 44% have their family income at or under the poverty level in the U.S. Two out of three participants reported lack of social support. One in 4 MI reported living alone/single, and one in two participants reported not being covered by any type of health insurance at the time of data collection.

A high proportion of the participants (80%) used the NHANES Spanish version of the questionnaires and 63% reported speaking only Spanish at home. Nearly one in two participants reported unsatisfactory health status.

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Table 4.2 General Characteristics‡ of the Mexico-born Immigrant Cohort

Participant Characteristics N=340

1. Proportion of Lifetime in Immigration: (PLI) (X) 0.47 (0.02)*

0.52 (0.03)**

2. Age (years) 52.1 (1.3)*

49.2 (1.9)**

3. Male 52 (2.4)***

General social determinants of health vulnerability:

4. Education (High School diploma or more) 29.1 (3.2)***

5. Poverty (Percentage of individuals with income at or below the poverty level) 43.9 (4.6)***

6. Single/Living alone 29.0 (1.9)**

7. Lack of support 67.3 (1.9)***

8. No health insurance 55 (3.3)***

Immigration related social determinants of health vulnerability:

9. U.S. Citizen 35.7 (3.3)***

10. Age at time of immigration 27.6 (0.9)*

25.7 (1.5)**

11. Decade at time of immigration 1970*

1970**

12. Spanish language preference for NHANES interview 79.5 (4.7)***

13. Use English language at home 9.1 (1.5)***

14. Unsatisfactory overall general health status (UHS) (Y) 45.9 (4.4)***

‡ Analyses conducted using survey analyses *Mean (St Deviation) **Median (Interquartile Range for continuous variables) *** Percent and (Std error of percent)

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Correlations between PLI, UHS, and all covariates of interest described in the

methods section are shown in Table 4.2. Based on Cohen’s criteria (Cohen, 1988), PLI

was found to be significantly and highly correlated with participant age at the time of

immigration to the U.S. (r = -0.88, p<.0001), the decade at time of the immigration (r = -

0.87, p<.0001), and U.S. citizenship (r = -0.40, p<.0001). Moderate correlations were

found between PLI and language-use preference variables: language used at NHANES interview(r = -0.38, p<.0001), and language used at home (r = -0.46, p<.0001).

Additionally, PLI was found to be moderately correlated with not being covered by any

type of health insurance (r = 0.35, p<.0001). No correlations were found between PLI

and participant age.

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Table 4.3 Correlations between Individual and Immigration related Participant

Characteristics of Mexico-Born immigrants

Variable name: 1 2 3 4 5 6 7 8 9 10 11 12 13

1.Proportion lifetime in immigration (PLI)

2. Age .03 _

3. Males .03 .08

4. Education .06 .20**** .07 (High school or more)

5. Poverty .20*** .13** .06 .16**

- 6. Single 0.02 .0.21**** .21*** .09 .03

7. Lack of support .07 .10 02 .07 .01 .8

8. No health .40**** .37**** .13* .01 .02 .14** .02 insurance

9. U.S. .40 **** .21**** .13* .21*** ..16** .06 .08 .34**** Citizen

10. Age at immigration .88 **** .31**** .06 .18 *** .19*** .04 .01 .16** .27****

11. Decade at - immigration .87 **** .35**** .02 .02 .10 .13* .09 .47**** .46**** .64 **** 12. English Illiteracy 38 **** .11* .0 .3 **** .17** .06 .07 .20**** .34**** .35 .29**** related to **** navigating health systems - 13. Language .46**** .13** .01 .35**** .14** .03 .01 .21**** .23**** .44*** .34**** .6**** use at home

14.Unsatisfac .01 .11* .08 .36**** . 1 .05 .03 .05 .02 ..08 .06 .25**** .24 tory overall generalhealth *** status (UHS)

P-value <0.05*, <0.01**, <0.001***, <0.0001****

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The non-linear assumption of changes in health with PLI was assessed next by

using generalized additive models. Fig 4.1 shows a significant quadratic (inverse U-

shape) contribution of the PLI to the prediction of UHS in the MI sample. The

significance of this relationship was maintained in semi-parametric generalized logistic

additive model, adjusted for age, gender, income, education, marital status, social

support, health insurance, citizenship status, language use at home, and Spanish version

of the questionnaires (Fig 4.2). Since PLI is non-linearly associated with UHS, PLI was

further modeled in weighted logistic regression analyses as a continuous variable in a

quadratic transformation.

Table 4.3 shows univariate odds ratios (OR) with 95% confidence intervals of

UHS in Mexico-Born U.S. immigrants in weighted logistic analyses. Significant

univariate predictors of UHS non- related to immigration were: older than 65 years of

age, education, poverty, and living alone/single. Preference for Spanish at interview was significantly associated with increased odds of UHS, and the changes from Spanish to

American culture were found to be protective in relation to UHS.

The results of the final multivariate survey logistic regression model fully

adjusted for age over 65, gender, education, marital status, income, health insurance, lack

of social support, U.S. citizenship, language preference at the time of the interview, and

language preference at home are described in Table 4.4. The model didn’t include age at

the time of immigration nor decade at the time of immigration due to high co-linearity

between these variables and PLI (VIF >17).

The significant quadratic relationship of PLI with UHS remains unchanged after

adjustment for the other covariates. 1% of PLI in MI shows modest, but significant

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changes in the odds of UHS [OR 1.1, 95% CI (1.0-1.1), p<0.01]. The best predictors of

UHS are not graduating high school, which is associated with greater than 5 times increased odds for UHS, and Spanish language use for NHANES interview, associated with a 3.8 times increase in the odds of reporting UHS. No other covariates are significantly associated with UHS in MI 40 years of age and older in the final model.

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Fig 4.1 Significant Unadjusted Quadratic Contribution of Proportion of Lifetime

(PLI) in Immigration in U.S. to the Prediction of Unsatisfactory Health status (UHS) in

U.S. Mexico-born Immigrants (MI): Smoothing component for UHS with 95% Bayesian

Confidence Limits

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Fig 4.2 Significant Adjusted Quadratic Contribution of Proportion of Lifetime in

Immigration (PLI) in U.S. to the Prediction of Unsatisfactory Health status (UHS) in U.S.

Mexico-born Immigrants (MI): Smoothing component for UHS with 95% Bayesian

Confidence Limits

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Table 4.4 Univariate‡ Odds Ratios (OR) with 95% Confidence Intervals (95% CI) of

Unsatisfactory Health Status (UHS) in Mexico-Born U.S. immigrants (MI)

Risk Factors Unadjusted OR (95% CI) and significance

1.Proportion lifetime in immigration:

PLI (1%)(modeled by quadratic transformation) 1.1 (1.04-1.1)** PLI*PLI 1.0 (.99-.99)**

2.Age (≥65 years) 1.9 (1.3-2.9)

3. Male 0.9 (0.6-1.4)

4. Education ≥ high school diploma 0.1 (0.1-0.3)

5. At or below poverty level 1.5 (1.1-2.1)

6. Single/Living alone 1.4 (1.0-1.8)*

7. Composed lack of support 1.1 (0.8-1.6) 8. No health insurance 1.0 (0.4-2.4) 9. U.S. Citizen 1.2 (0.7-2.3)

10. a. Age at time of immigration 1.02 (1.0-1.03)*

10.b. Adult at time of immigration 1.9 (1.0-3.6)*

11. Decade at time of immigration 1.0 (1.0-1.004)

12. Spanish language preference for NHANES interview 4.7 (2.4-9.0)

13. English language used at home 0.2 (0.1-0.4)

‡ Analyses conducted using survey analyses P-value <0.05* P-value <0.0001**

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Table 4.5 Multivariate Odds Ratios‡ (OR) with 95% Confidence Intervals (95% CI) of

Unsatisfactory Health Status (UHS) in Mexico-born U.S. Immigrants (MI)

Risk Factors OR (Fully adjusted model) (95% CI)

Proportion Lifetime in Immigration (PLI): (modeled by quadratic transformation) PLI (1%) 1.1 (1.0-1.1)* (PLI x PLI) 0.99* (0.99-1.00)

Age (≥65 years) 1.0 (0.5-2.1)

Male 1.1 (0.7-1.9)

Education : not completing the High School education 5.3 (2.8-10.1)

Poor 1.2 (0.8-1.9)

Single 1.2 (0.9-1.7)

Composed Lack of support 0.9 (0.6-1.5)

No health insurance 1.0 (0.3-2.7)

U.S. Citizen 1.7 (0.8-3.7)

Spanish language preference at interview 3.8 (1.5-9.2)

English Language used at Home 1.1 (0.3-4.2) ‡ Analyses conducted using survey analyses *P-value <0.05

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4.5. Discussion

We believe this to be the first study proposing a significant quadratic relationship

between UHS and PLI in U.S MI 40 years old and older, independently of other known

social, immigration-related, and cultural determinants of health vulnerability risk factors

including age, gender, income, education, living with partner, social support, access to

health care, U.S. citizenship, perception of self-reported health status attributed to shifts

in cultural practices/beliefs towards the American culture, and language (English vs.

Spanish) used at the time of data collection.

Self-reported health status in MI is a variable of great interest due to the fact that

conceptually, it represents a composite measure of awareness and self-perception of mental and physical well being. This study has proposed PLI as a composed measure of

“life experience” in immigration, and not as a measure of cultural related changes in immigrant populations. The significant quadratic association found between PLI and

UHS may reflect fluctuations of social distress during the experience of life as a MI, based on the somatization hypothesis in individuals of Mexican ethnicity.

Previous studies examining UHS in Hispanics including MI suggested that the number of years residing in the host country is not an accurate measure of, but rather a

“stereotype” in relation to “acculturation” (Johnson, 2010, Hunt, 2004). Another study in

Latinos (Bzostek, 2007) also proposed age at the time of immigration and duration of residence in the U.S. as measures of acculturation, complementary with language use at home, and showed no significant relationships between these measures and self-reported status. The present study has used PLI not as a composed score of the variables indented to measure cultural changes, but as a measure of “life experience” in immigration.. It is

148 also important to avoid false assumptions as to the linearity of relationships, as these would trigger false conclusions such as there being no association between UHS and PLI.

After controlling for age and PLI, the study found that UHS in MI 40 years old and older only remains significantly associated with lower education. This finding is consistent with previous studies (Kandula, 2007, Contoyannis, 2005, Cashman, 2004,

Isaacs, 2004, Lahelma, 2004, Hayes, 1998). It has been proposed that that higher education attainment in MI may not only impact health behaviors and access to health care, but may also enhance a sense of control” in this population (Johnson, 2010).

The Spanish language used for the NHANES interview increased the odds of reporting UHS up to 4 times in MI 40 years old and older, but English household language use was not found significantly associated with UHS. This finding is consistent with another study of self-reported health status done on U.S. Hispanics (Bzostek, 2007).

Differences in reporting UHS, due to the Spanish translation of the questionnaire, including linguistic artifacts and/or cultural discrepancies in how the respondents may interpret the Spanish vs. the English version of the same questions, may explain this relationship.

This study did not find a correlation between the role of social support and self- reported UHS in MI ≥40 years of age.

The strength of the study includes the use of a robust conceptual framework and of a multi-theory approach. The shape of the relationship between the PLI and UHS was consistent with the prediction described at the beginning of the chapter, thereby showing potential benefits of use of the Open System Conceptual model and the multi-theory approach in quantitative immigrant health research. PLI showed a high correlation with

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the age at time of immigration and the decade at time of immigration, but not with the

age of the participant at the time of interview. Additional strengths of this study stem

from use of data from the NHANES study, which employs highly structured protocols

and offers the advantage of community-based cohort recruitment, with balanced gender

representation and geographic diversity. Multiple sensitivity analyses were done to

assess the consistency of the reported results.

Study limitations include the use of cross-sectional self-reported data, and lack of

adjustments for objective health conditions and behavioral risk factors. PLI was

calculated as a continuous variable by using the midpoint of each of the categories for the

“length of the time in the U.S.” variable posted by NHANES in increment intervals,

rather than computing this from the number of years residing in the U.S. or the age at the

time of immigration. The data was collected during two NHANES cycles and the cohort

was restricted to ≥40 years of age. However, due to NHANES policy of maintaining

systematic data collection over the cycles, minimum variations are expected to be

introduced. U.S. population inferences cannot be made, regardless of the fact that the study population was derived from a nationally representative U.S. population sample of non-institutionalized U.S. civilians.

In conclusion, this study has shown a significant non-linear association between

PLI and UHS in MI ≥ 40 years of age, independently of social determinants of health

vulnerability and cultural related factors in immigration. Further adjustment for PLI as a

measure of “life experience” in immigration may be useful, especially in cohort analyses

comparing health outcomes in subpopulation of immigrants with different

patterns/degrees of mobility abroad, such as Mexico-born U.S. immigrants.

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CHAPTER 5

Mexico-Born Ethnicity and Insomnia in U.S. Immigrants

Adjusting for the Proportion of Lifetime Spent in Immigration in the U.S.

Aim:

Explore the role of Mexico-born ethnicity on insomnia in U.S. immigrants after adjusting for risk factors including Proportion of Lifetime spent in Immigration in the U.S. as

“exposure of the experience of life as an immigrant in the U.S. per se”, and knowing that

Mexico-born U.S. immigrant population has increased patterns of abroad mobility.

Hypotheses:

1. Compared to other Hispanic U.S. immigrants (Latino immigrants) (from

Central and South American Spanish-speaking countries other than Mexico),

Mexico-born U.S. immigrants (MI) have both lower age adjusted prevalence of

insomnia and short habitual sleep time of less than 6 hours (SHST).

2. The Proportion of Lifetime in Immigration (PLI) is associated with severe

insomnia in a non-linear relationship.

3. Mexico-born ethnicity is protective against severe insomnia in U.S. immigrants

independently of age, gender, socio-economic determinants of health

vulnerability, behavioral health-related risk factors, cultural related changes

towards the American culture, self-reported general health characteristics,

overweight/obesity, depression, and PLI.

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Overall purpose of the chapter

This chapter provides a final example of a cross-sectional study in U.S. immigrants based on the Open-System Conceptual Model and the multi-theory approach and using Proportion of Lifetime in Immigration in the U.S. (PLI).

Previous theories (detailed in chapter 2) propose that Mexico-born immigrants

(MI) have high patterns of abroad mobility between their home country and the U.S. than other immigrant groups. This finding may be an important contribution to previously reported differences found in health outcomes between MI and other U.S. immigrants, including other non-Mexico-born Latino immigrants (LI).

PLI is used in this cohort analysis as a covariate to capture “life experience in immigration” effects (see chapter 4), overlapping the age effects. Addressing the hypothesis that Mexican ethnicity is associated with better sleep in U.S. immigrants should imply appropriate adjustment for potential differences in PLI between Mexico-

born immigrants and other immigrant groups.

Consistently with the previous chapters of this dissertation, the prediction of

significant non-linear association of PLI with insomnia in the U.S. immigrant population

would be tested first.

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5.1 Abstract

Background: Although sleep disorders vary by ethnicity and are influenced by cultural and environmental factors, little is known about the sleep characteristics of immigrants who are exposed to a change in their ecologic and social environment.

Study Objectives: To calculate age standardized population prevalence estimates of poor

sleep outcomes in U.S. Immigrants stratified by gender and Mexico-born ethnicity as:

Mexico-born U.S. Immigrants (MI), non-Mexican Latino U.S. immigrants (LI), and U.S.

non-Latino immigrants (OI). To test the hypothesis that Mexico-born ethnicity is

associated with significantly lower odds of severe insomnia symptoms in the U.S.

immigrant population, independent of the “life in immigration experience”, measured as

Proportion Lifetime in Immigration in the U.S., and other known risk factors for

insomnia.

Research Design and Methods: Cross-sectional analysis of adult U.S. immigrants (over

18 years old) enrolled in the National Health and Nutrition Examination Survey

(NHANES) between 2005 and 2008.

Setting: Bilingual interviews in the participants’ home using a sleep questionnaire.

Participants: 2509 U.S. immigrants: 1230 MI, 576 LI , 703 OI.

Results: Compared to LI and OI, MI showed significantly lower age standardized

population prevalence of short habitual sleep time < 6 hours/weeknight (SHST) and

severe insomnia with SHST. Among all U.S. immigrants, fully adjusted logistic

regression models showed that severe insomnia was associated with: depression (OR=6.8

CI: 3.1-15.2, p<0.0001), unsatisfactory health status (OR=2.7, CI: 1.5-4.9, p<0. 01), and

female gender (OR=1.8, CI: 1.1-3.0, p<0.05). Mexican ethnicity was associated with

153 decreased odds of severe insomnia (OR=0.4; CI: 0.2-0.6, p<0.0001).

Conclusion: Differences in prevalence of insomnia observed between different immigrant and native populations support the importance of socio-cultural and environmental factors in influencing sleep outcomes.

5.2 Introduction

There is increasing public health interest in gaining a better understanding of insomnia epidemiology due to mounting evidence highlighting the importance of good sleep on general health, safety, and overall quality of life of individuals.

Insomnia is well established as being highly co-morbid with, and a known risk factor for, psychiatric conditions, including anxiety, depression, and suicide (Basta, 2007,

NIH, 2005). Insomnia with short sleeping time (ISST) has been recently associated with increased risk of hypertension (Vgontas, 2009a) and Type 2 diabetes (Vgontas, 2009b).

Further, it has been proposed that insomnia is causally related to these medical conditions via hormonal changes activating the hypothalamic-pituitary-adrenal (HPA) axis

(Vgontas, 1998, 2001, Rodenbeck, 2003).

Susceptibility for development of insomnia is still unclear. Somatic and psychiatric health (NIH, 2005, Basta, 2007), female gender, and advances age have all previously been reported as associated with increased odds of insomnia (Pallesen, 2001).

Previous studies have reported inconsistent results related to the predictive value of demographics, such as low annual family income, unemployment, poor education, living alone, and/or ethnicity, on the odds of insomnia (Ohayon, 1997, 2002, Ishigooka, 1999,

Allaert, 2004, Paine, 2004, Phillips, 2005, Roth, 2006, Xiang, 2008).

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Conducting sleep research in an immigrant population provides a unique opportunity for testing relationships between insomnia, ethnicity, and the joint effects of socio-economic and cultural characteristics related to westernization. Several international epidemiological studies targeting immigrant subjects of different ethnicities have proposed that processes of social integration, from more traditional cultures to westernized industrialized cultures, may have a negative role on the sleep quality of immigrants (Lin, 1992, Steiner, 2007, Hsu, 2001, Jablensky, 2001, Knipscheer, 2001,

Sok, 2008, Voss, 2008). However, it still remains unclear whether ethnicity may be predictive of risk for insomnia, after controlling for other differences between subpopulations of immigrants, including social determinants of risk vulnerability, cultural changes toward American assimilation, behavioral-related risk factors such as alcohol and tobacco use, health related differences including general health , overweight/obesity, and depression, and the Proportion of Lifetime spent in Immigration (PLI), as a measure of “life experience in immigration” effect .

No previous studies have been proposed differences between the age adjusted population prevalence of insomnia symptoms in Mexico-born immigrants (MI) compared to U.S. immigrants from other Latino countries (LI). Limited information is also available on predictors for insomnia in the overall U.S. immigrant population and the subgroup of U.S. Hispanic immigrants.

Consistent with the predictions of multiple theories previously described in chapter 4, the aims of this study are to demonstrate that:

1. Mexico-born ethnicity is protective against insomnia in the general U.S. immigrant populations, independent of other risk factors.

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2. A significant non-linear association between PLI and severe insomnia is predicted in

U.S. immigrants.

5.3.2 Methods

5.3.1 Study protocol

This study uses cross-sectional data collected by the National Health and

Nutrition Examination Survey (NHANES) (CDC) between 2005-2008 (two cycles, 2005-

2006 and 2007-2008). Sleep data were available for 2509 civilian non-institutionalized

U.S. immigrants 18 years of age and older, including 1806 U.S. Latino Immigrants.

Participants used Computer-Assisted Personal Interview (CAPI) technology to complete in-home bilingual (English-Spanish) interviewer administered questionnaires. This was followed within 1 to 2 weeks by a physical examination and laboratory procedures which were conducted by a physician, medical and health technicians, and dietary and health interviewers. Complete details on recruitment, design, and content of the used surveys are described on the NHANES webpage (CDC).

The data collected with the “Questionnaire on Sleep Habits and Sleep-Related

Problems” was merged by respondent sequence number with other data corresponding to the proposed conceptual model found in the “Demographic”, “Acculturation”, “Hospital utilization and access to care”, “Health insurance”, “Depression Screener Questionnaire

Files”, “Body measurements”, “Serum cotinine” and “Alcohol use” files.

The NHANES study received approval from human subjects committees

(Protocol #2005-06, #2007-08), and the proposed analyses using de-identified data were exempt from receiving approval by the University Hospitals Case Medical Center of

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Cleveland Institutional Review Board (IRB).

5.3.2 Subjects

11288 nationally-representative civilian non-institutionalized U.S. individuals 18

years of age and older took the in-home computer assisted personal interview (CAPI) on sleep habits and disorders between 2005 and 2008. Of these, 2752 were U.S. immigrants

(1200 immigrants in 2005-2006, and 1552 immigrants in 2007-2008). 125 immigrant participants were excluded due to missing immigration related data, and another 132 female participants were excluded due to pregnancy status, because there are known changes in sleep physiology during pregnancy.

The final analytical sample consists of 2509 U.S. immigrants: 1230 MI, 576

Latino U.S. Immigrants (LI) and 703 Other Non-Hispanics U.S. immigrants (OI), 18 years of age and older. Cohorts characteristics were analyzed using data available from all NHANES participants (11288 individuals taking the survey between 2005-2008), in order to avoid errors related to wrong survey analyses assumption of “missing data” at random when calculating U.S. Immigrant subpopulations prevalence estimates and/or making inferences at a national level.

5.3.3 Proportion of lifetime in immigration (PLI) (Independent variable)

The number of years in U.S. immigration was approximated by using the midpoint of the ranges for the “Length of the time in U.S.” variable posted by NHANES in increment intervals. The intervals they used were: 'Less than 1 year', '1 yr less than 5 yrs', '5 yrs less than 10 yrs', '10 yrs less than 15 yrs', '15 yrs less than 20 yrs', '20 yrs less

157 than 30 yrs', '30 yrs less than 40 yrs', '40 yrs less than 50 yrs', and '50 years or more'.

Thus, for 'less than 1 year' the number of years in U.S. immigration was approximated as

0.5 years for '1 yr less than 5 yrs', the number of years in U.S. immigration was approximated as 3 years, etc. The proportion lifetime in immigration was calculated by dividing the approximate number of years in U.S. immigration by the age of the participant at the time of the data collection. It was used initially as a continuous variable, then in increments of 20% (nominal).

5.3.4 Mexico-born ethnicity (independent variable)

MI status (dichotomized yes/no) was assigned based on the answer “born in

Mexico” to the question “in what country were you born?” This variable was collected with the demographic questionnaire.

5.3.5 Sleep Characteristics (outcome variables)

Insomnia was defined by using the National Heart, Lung, and Blood Institute

(NHLBI) Working Group definition of Insomnia (National Center on Sleep Disorders

Research, 1998 #531), as one of four sleep complaints (quantified as “at least 5-14 times/month” for “some level of insomnia” and “> 15/month” as “severe insomnia”) plus at least one self-reported daytime functional impairment due to lack of sleep (with cutoff including “moderate” and “extreme difficulty”). These data were collected using questions that concerned: “trouble falling asleep,” “waking up during the night and having trouble to get back to sleep”, waking up too early in the morning and being unable to get back to sleep”, and “feeling un-rested during the day, no matter how many hours of

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sleep one had.” Functional impairments collected with regard to sleepiness included

difficulty carrying out specific regular daily activities in the last month in the following

areas: “concentrating on the things”, “remembering things”, “getting things done because

too sleepy or tired to drive or take public transportation”, “performing employed or

volunteer work or attending school”, “working on a hobby, for example, sewing,

collecting, gardening”, and “taking care of financial affairs and doing paperwork (for

example, paying bills or keeping financial records).”

Severe insomnia with short sleep duration was defined as any one insomnia

symptom reported as occurring 15 times or more per month, with daytime functional

impairments reported as “moderate” or “extreme difficulty”, and with daily sleep

duration of less than 6 hours/night (based on response to: “How much sleep {do you

/does SP} usually get at night on weekdays or workdays?”).

5.3.6 Covariates

Based on the conceptual model (fig 1), covariates assessed for are: age, gender, social determinants of health vulnerability (race, income, education, living with partner/married, health insurance status), behavioral risk factors (smoking, alcohol), and overall health characteristics (self-reported overall general health, overweight/obesity, and depression).

Age was reported in years at the time of NHANES screening and used as continuous variable with odds ratios reported in increments of 5 years. Male gender is

dichotomized as yes/no.

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5.3.6.1 Social determinants of health vulnerability

Race is used as a nominal variable with 4 levels: Whites, Hispanics, Blacks and

Other. Sensitivity analyses were done by using this variable as bi-level: Whites vs. Other

Financial strain was dichotomized (poor: yes/no) based on the participants’ family

poverty income ratio (PIR), a NHANES variable calculated by dividing the family

income by the U.S. poverty threshold. A family income equal to or less than the poverty

threshold was used to characterize financial vulnerability in this sample (poverty).

Education was also dichotomized by using graduation from high school as the

cutoff for educational vulnerability.

Marital status was used as a dichotomized variable: alone vs. leaving with

partner/married.

Insurance status was dichotomized as insured (any coverage) vs. not insured.

This variable was used to assess the burden of lack of access to health care utilization on

sleep.

5.3.6.2 Shift in Cultural beliefs/practices towards the American Culture in

the Latino Immigrant cohort

Additional cultural-related adjustments were used but only in the models for

Latino immigrant cohort analyses, because this data was only available for Spanish-

speaking immigrants). Shifts in beliefs and practices towards the American culture were

measured by Spanish language preference at home (Taub 1971, Marin 1987). This was dichotomized by collapsing the 5 levels “all Spanish”, “more Spanish than English”,

“both equally”, “more English than Spanish”, and “only English” collected by NHANES

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using the acculturation scale (CDC 2006) into two levels: “American culture” (when participant reported that language at home was “more English than Spanish” or “only

English”) vs. “other” (comprising the other 3 categories, where the amount of Spanish

that was spoken was at least equal to the amount of English).

5.3.6.3 Health-related behavioral risk factors

Nicotine (tobacco) and alcohol use variables were used to adjust for behavioral

risk factors previously reported as potentially associated with insomnia. Current smoking

status and/or exposure to smoking were measured by serum cotinine levels, a biomarker

of environmental tobacco smoke exposure collected by NHANES. Participants with

cotinine levels ≤10 ng/mL were considered nonsmokers (Emmons 1994, Vartiainen

2002).

Self-reporting any alcohol use was considered positive, if participants reported

consuming at least one alcoholic drink per month. (Sensitivity analysis was done looking

at daily alcohol use, and no differences were found, likely due to the high proportion of

missing data on the participant’s alcohol use).

5.3.6.4 Overall health characteristics

Self-reported “overall general health” was dichotomized by collapsing “fair” and

“poor health” groups into the newly created variable “unsatisfactory health” vs.

“satisfactory health.” Anthropometry data include: weight, height, and waist

circumference and were collected by the NHANES (trained health technicians). Body

Mass Index (BMI) was calculated as weight divided by the square of the height (kg/m²)

161 and overweight/obesity was defined according to the National Institute of Health Clinical guidelines for adults as a BMI ≥ 25 kg/m² ( NIH Clinical guidelines, 1998).

Depression was assessed by using the 9- item Patient Health Questionnaire

(PHQ), and applying a diagnostic algorithm using PHQ score of greater than 10 as an indicator of major depression (Kroenke 2001).

5.3.7 Statistical Analyses

Descriptive statistics including mean, standard deviation (SD), median, interquartile range, and frequencies for continuous and categorical data and analyses to determine age adjusted prevalence estimates of insomnia in U.S. immigrants were done stratified by gender, due to known gender differences in sleep outcomes and/or to verify interactions between gender and other variables of interest.

Sample weights analyses were done in SAS v 9.2 (Proc Survey, SAS Institute,

Inc., Cary, NC), for the entire analytical cohort taking the sleep questionnaire. These analyses used the Taylor Series Linearization approach and the assumption of missing data not at random (Rust 1985).

Age-standardization to the U.S. Census 2000 population estimates was performed by the direct method to generate age-adjusted prevalence rates and standard errors, based on the CDC - NCHS recommendations (CDC 2006).

Non-parametric regression modeling using generalized additive models to test the significance and shape of the relationship between PLI and severe insomnia was done before building survey logistic regression models. As the general trends in the smoothing component plots for PLI suggested a quadratic dependence between PLI and insomnia,

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PLI was further used in all unweighted and weighted parametric models as a nominal

variable (5 levels of 20% increments). Sensitivity analyses were also done by employing

a quadratic transformation of PLI when used as a continuous covariate. Spearman

correlation coefficient was used next, to assess correlations between the control and

predictor variables. Unweighted logistic regression models were built after verifying

possible multi-colinearity between covariates of interest by using a variance inflation factor cutoff of 1.5. All of these models were verified for robustness by using Hosmer and Lemeshow Goodness-Fit Test. No interactions or collinear relationships were

identified in the final models. Weighted logistical regression modeling was performed to

verify the relationship between Mexico-born ethnicity and severe insomnia, after

adjusting for confounders including PLI.

Sensitivity analyses were also conducted separately for the 2005-2006 and 2007-

2008 NHANES cohorts, using other definitions of insomnia by severity. Two-tailed p-

values of < 0.05 were considered significant.

5.4 Results

5.4.1 Sample Characteristics

Fig 5.1 shows differences in sample characteristics in all U.S. immigrants by

Mexico-born ethnicity, and Table 5.1 shows these descriptive stratified by gender and

ethnicity. The sample population consists of 2509 U.S. immigrants: 1230 Mexico-born immigrants (680 males and 550 females), 576 Other Latino U.S immigrants (273 males and 303 females), and 703 Non-Latino U.S. immigrants (370 males and 333 females).

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Compared to their Non-Mexican Latino counterparts (LI), MI males were on average, significantly younger and spending significantly less Proportion of their

Lifetime in Immigration in the U.S. They were also significantly poorer, less educated,

and not covered by any health insurance including Medicaid and Medicare. Compared to

Non-Mexican Latino men, Mexico-born men are also significantly less likely to speak

English at home, and showed higher prevalence of self-reported unsatisfactory health status, but lower prevalence of overweight/obesity, and central obesity. There are no significant differences found in living with a partner, self-reported social support, alcohol and tobacco use, nor in prevalence of depression between MI and LI men.

Compared to other U.S. non-Latino Immigrants males (OI), MI males were also significantly younger, and reported to spend significantly less Proportion of their

Lifetime in Immigration in the U.S. MI men had significantly higher rates of poverty, lower rates of high school completion, lower rates of health insurance coverage, and reported significantly higher rates of lack of social support. MI males had higher prevalence of alcohol use, and a significantly higher proportion of MI males reported fair and poor health compared to the OI male cohort.

The only significant differences in the patterns of sample characteristics when comparing MI and LI female cohorts was seen for education, insurance status, and language use at home with MI females being significantly less likely to: have completed

high school, have health insurance, and more likely to speak Spanish at home. Compared

to the other immigrants (OI) female sample, MI females are significantly different in

terms of income, education, insurance status social support, and alcohol use, all of which

were lower among MI. MI females had higher prevalence of unsatisfactory health,

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overweight/obesity and central obesity, and higher rates of depression compared to OI

females.

Sleep characteristics of the groups of interest are displayed in Table 5.1b,

stratified by gender. Compared either to both their LI and OI male counterparts, MI

males had a significantly lower unadjusted prevalence of SHST, mild/moderate insomnia,

mild/moderate insomnia with short sleep time less than 6 hours/night, and self-perceived sleep deprivation. No differences in the unadjusted prevalence of severe insomnia were found between MI and LI or OI. Lower unadjusted prevalence of poor sleep quality reported to a physician and daytime excessive sleepiness were reported in MI only when compared to OI men.

MI females reported significantly lower frequency of: mild/moderate insomnia with/without short sleep time less than 6 hours/night, poor sleep quality reported to a physician, daytime excessive sleepiness, and self-perceived sleep deprivation compared to LI females and/or the OI females. MI females also reported significantly lower odds of severe insomnia compared to their LI female counterparts. No significant differences were found between subgroups of U.S. females immigrants in relation to SHST of less than 6 hours.

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Table 5.1a General Characteristics: Mexico-born U.S. Immigrants, Other Latino U.S.

Immigrants and Non-Latino U.S. Immigrants, By Gender

MALES (N=1323) FEMALES (N=1186)

Mexico- Other Mexico- Other Non-Latino born U.S. Latino U.S. Non-Latino born U.S. Latino U.S. U.S. Male Male U.S. Male Female Female Female Immigrants Immigrants Immigrants Immigrants Immigrants Immigrants (N=680) (N=273) (N=370) (N=550) (N=303) (N=333) 37.1* 42.6 45.4 40.5 43.7 47.4 Mean Age (0.8)** (1.1) (0.5) (1.1) (1.6) (0.5) (at screening, in years) Mean proportion 39 %* 46 % 45% 42 % 45% 44 % lifetime in (1%) ** (3%) (3%) (2 %) (25) (2%) Immigration (PLI) Poor 39.8 (2.9) 28.0 (4.0) 18.7 (3.3) 42.4 (3.0) 31.5(5.2) 17.3 (2.7) (% individuals *** ** reporting family income ≤poverty level) Education 70.7 (2.6) 48.1 (4.0) 13.1 (3.4) 64.5 (2.6) 44.6 (4.9) 15.8 (2.4) (% individuals *** *** reporting no High School diploma) Insurance Status 29.7 (2.7) 62.2 (5.6) 78.7 (3.4) 38.3 (3.4) 67.0 (4.3) 82.2(3.4) (% individuals *** *** reporting covered by any type of health insurance) Marital Status 72.2 (2.5) 69.9 (3.6) 75.0 (2.8) 69.8 (2.8) 63.7 (4.1) 64.6 (3.0) (% individuals reporting married or leaving with partner) American Culture 4.2 (1.0)* 21.4 (3.8) n/a 7.4 (1.7)* 15.8 (4.0) n/a (% individuals Mainly English or Only English use at home) Current smokers 18.3 (1.5) 19.9 (3.7) 23. 5 (3.1) 7.0 (1.3) 8.0 (2.1) 8.6 (1.8) (% individuals with Serum Cotinine levels >10 ng/mL) Alcohol use**** 85.9 84.1 (3.1) 69.7 (4.3) 44.0 49.0(4.2) 55.1 (3.5) (% individuals (1.9)** (2.7)** reporting drinking alcohol ≥ 1 time/month) Unsatisfactory health 35.1 23.8 (3.8) 13.6 (2.0) 38.5 31.5 (4.1) 17.9 (2.1) (% individuals (2.2)*** (2.2)** reporting fair /poor health) Overweight/Obese 67.1 73.3 (3.1) 54.5 (3.5) 69.2 65.3 (4.2) 41.6 (3.6) (% individuals with (2.3)*** (2.3)**

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BMI≥25) Central Obesity 23.7 (2.6)* 38.1 (4.0) 22.1 (2.9) 62.3(2.0)** 54.6 (3.3) 37.9 (3.0) (% individuals with waist circumference ≥102cm) Depression 4.5 (1.0) 6.9 (1.3) 4.1 (1.3) 10.7 13.5 (1.5) 3.1 (1.0) (% individuals with (1.4)** PHQ-9 score ≥ 10)

‡ Analyses conducted using survey analyses: Mean or Percentage (%) (Standard deviation) * P-values <0.05, comparing Mexico-born immigrants with Other Latino U.S. Immigrants (calculated separately for each gender) ** P-values <0.05, comparing Mexico-born immigrants with Non-Latino U.S. Immigrants (calculated separately for the male and female groups) N/A: Not calculated as Spanish at home not representative *** Data on social support was available only for ≥ 40 years and older. The proportion was calculated including three categories: yes, no, missing data **** Data on alcohol use was available only for ≥ 20 years and older. The proportion was calculated including three categories: yes, no, missing data

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Fig 5.1 General sample characteristics

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Table 5.1b Insomnia and other Sleep Characteristics‡ Mexico-born U.S. Immigrants, Other Latino U.S. Immigrants and Non-Latino U.S. Immigrants, By Gender

MALES (N=1323) FEMALES (N=1186)

Mexico-born Other Latino Non-Latino Mexico-born Other Latino Non-Latino U.S. Male U.S. Male U.S. Male U.S. Female U.S. Female U.S. Female Immigrants Immigrants Immigrants Immigrants Immigrants Immigrants (N=680) (N=273) (N=370) (N=550) (N=303) (N=333)

Mild/Moderate Insomnia 8.5 (1.4)* 15.7 (2.4) 24.2 (3.1) 18.0 (2.0)* 27.2 (2.3) 24.7 (3.0) (% individuals ** ** with insomnia symptoms >5-15 times /month) Severe Insomnia 2.2 (0.5) 5.5 (1.6) 3.9 (1.0) 5.8 (1.2)* 14.5 (2.7) 8.4 (1.9) (% individuals with insomnia symptoms >15 times /month) Insomnia with 4.2 (0.9)* 11.3 (1.6) 11.2 (1.7) 9.0 (1.6)* 15.9 (2.3) 12.5 (2.4) SHST< 6 ** hrs/weeknight ** (% individuals with insomnia symptoms >5-15 times/month also reporting Short Habitual Sleep Time < 6 h/night) SHST< 6 9.1 (1.1)* 16.8 (2.8) 15.1 (2.3) 11.4 (1.6) 14.9 (3.7) 15.9 (2.7) hrs/weeknight ** (% individuals reporting Short Habitual Sleep Time < 6 h/night) Sleep Deprivation 11.9 (1.6)* 20.0 (1.6) 25.9 (2.2) 17.7 (2.0)* 25.3 (3.2) 31.4 (3.2) Self-Perception ** ** (% individuals reporting insufficient sleep in the past month)

Poor Sleep 5.1 (1.3) 9.8 (2.3) 13.0 (2.6) 9.8 (1.4)* 18.4 (3.7) 23.0 (2.1) Quality reported ** ** to a physician (% individuals reporting talking to the physician about having poor sleep quality ) Daytime excessive 7.1 (1.4) 7.4 (1.8) 16.4 (2.5) 12.4 (1.6)* 18.8 (2.5) 19.1 (2.8) sleepiness ** ** (% individuals reporting it)

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‡ Analyses conducted using survey analyses: Mean or Percentage (%), (standard deviation) ‡‡ All U.S (male and female) cohorts include both Mexico-born immigrants and Mexican-Americans born in the U.S. * P-values <0.05, comparing Mexico-born immigrants with U.S.-born Mexican-Americans (calculated separately for each gender) ** P-value <0.05, comparing Mexico-born immigrants with the All U.S. cohort (calculated separately for each gender)

5.3.2 Age Standardized prevalence of Sleep outcomes:

Further estimates of standardized (2000 Census data) population age-adjusted

prevalence of severe insomnia, SHST less than 6 hours and severe insomnia with SHST

<6 hours are shown in Fig 5.2 for all immigrants, and stratified by Mexico-born ethnicity.

Overall, MI showed significantly lower age standardized population prevalence of SHST

<6 hours and severe insomnia with SHST (<6 hours), compared to other LI and/or to OI.

Table 5.2 shows in detail gender stratified estimates of age-adjusted sleep outcomes in MI, LI, and OI cohorts.

Compared to their LI counterparts, MI men have lower age-adjusted prevalence rates for SHST <6 hrs/weeknight, self-perceived sleep deprivation, and mild/moderate insomnia with short sleep. Compared to the OI male cohort, MI men have significantly lower standardized age-adjusted prevalence population estimates for mild/moderate insomnia with/without short sleeping hours, self-perceived sleep-deprivation, poor sleep quality reported to a physician, and daytime sleepiness.

Except for lower age-adjusted prevalence of sleep deprivation self-perception and

poor sleep quality reported to a physician, no significant differences were found in the

age adjusted prevalence rates when comparing MI females with OI. However, compared

to the LI female sample, MI women tended to have significantly lower age-adjusted

170 prevalence rates of all types of insomnia, poor sleep quality reported to a physician, and all daytime sleepiness. Table 5.2 shows that a lower proportion of men compared to women reported severe insomnia, and poor sleep quality reported to a physician across all immigrant groups (all p’s<0.05).

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Fig 5.2 Age-Adjusted Population Prevalence of Poor Sleep Outcomes in U.S. Immigrants

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Table 5.2 Age adjusted population prevalence of Poor Sleep Outcomes in U.S. Adults 20 years and Older in Mexico-born U.S. Immigrants, Other Latino U.S. Immigrants and

Non-Latino U.S. Immigrants, By Gender

MALES (N=1323) FEMALES (N=1186) Mexico-born Other N M O No Sleep related U.S. Male Latino U.S. on-Latino exico-born ther Latino n-Latino condition Immigrants Male U.S. Male U.S. Female U.S. U.S. Female (N=680) Immigrants Immigrants Immigrants Female Immigrants (N=273) (N=370) (N=550) Immigrants (N=333) (N=303) Mild/Moderate 10.7 (1.4)* 15.5 (2.2)* 23.6 (2.9) 19.4 (2.1)** 27.1 (2.1) 24.4 (3.0) Insomnia *** (insomnia symptoms >5-15 times /month) Severe Insomnia 3.7 (0.8)* 5.8 (1.5)* 4.0(0.8)* 6.9 (1.4)** 14.0 (2.5) 8.2 (1.7) (insomnia symptoms >15 times /month) Insomnia with 6.0 (1.2)** 11.4 (1.6) 11.3 (1.7) 9.9 (1.6) ** 15.9 (2.2) 12.5 (2.4) SHST< 6 *** hrs/weeknight SHST< 6 10.8 (1.4)** 17.9 (2.8) 15.4 (2.4) 12.4 (1.5) 15.9 (3.6) 16.2 (2.7) hrs/weeknight (Short Habitual Sleep Time < 6 h/night) Sleep 13.7 (1.3)* 20.9 (2.8) 25.3 (2.2) 18.8 (2.0) 25.2 (2.7) 31.5 (3.3) Deprivation Self- ** *** Perception *** (past month) Poor Sleep 7.7 (1.7)* 10.7(2.4)* 13.5 (2.5)* 11.8 (1.2)** 19.2 (3.3) 22.4 (2.0) Quality reported *** *** to a physician Daytime 8.9 (1.3)* 7.4 (1.7)* 16.0 (2.3) 13.4(1.7)** 18.6 (2.3) 18.8 (2.6) sleepiness ***

‡ Age standardized to the 2000 Census population by the direct method proposed by CDC Prevalence showed as Percentage % (St Deviation) * P-value <0.05 between Males and Females (calculated separately for each cohort: Mexico-born Immigrants, other non Mexico-born Latino U.S. Immigrants, and Non-Latino U.S. Immigrants) ** P-value <0.05 between Mexico-born Immigrants and other non Mexico-born Latino U.S. Immigrants (calculated separately for each gender) ***P-value <0.05 between Mexico-born Immigrants and Non-Latino U.S. Immigrants (calculated separately for each gender)

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The following results focus only on severe insomnia as the outcome of interest

in the general U.S. immigrant population.

5.3.3. Non-Linear association between PLI and severe insomnia

The assumption of a non-linear relationship between PLI and severe insomnia in the overall U.S. immigrant sample was assessed next, by using generalized additive models. Fig 5.3 and Fig 5.4 show a significant quadratic (U-shape) contribution of the

PLI to the prediction of severe insomnia in both age-adjusted nonparametric models and

in semi-parametric generalized logistic additive model, which were adjusted for age,

White race, Mexico-born ethnicity, gender, income, education, marital status, health

insurance status, overweight/obesity, and self reported health status .

Fig 5.5 shows that addition of depression as a covariate in the previously

described semi-parametric generalized logistic additive model causes the model to lose

its significance (p=0.06).

Since PLI was shown to be non-linearly associated with severe insomnia,

weighted logistic regression analyses assessing the relationship between Mexico-born

ethnicity and Severe Insomnia will further adjust for this covariate by transforming the

continuous variable into a nominal variable (5 levels, 20% increments).

5.3.4. Mexico-born ethnicity and severe insomnia in U.S. Immigrants

Table 5.3 shows univariate and multivariate odds ratios (OR) with 95%

confidence intervals for severe insomnia in U.S. immigrants in weighted logistic

analyses. No interaction was found between these variables, and gender stratification

was not necessary when assessing for severe insomnia in U.S. immigrants.

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Fig 5.3 Significant Quadratic Contribution of Proportion of Lifetime Spent in Immigration in the U.S. to the Prediction of Severe Insomnia in the U.S. Immigrant

Sample: Non-parametric generalized logistic additive model adjusted for age

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Fig 5.4 Significant Quadratic Contribution of Proportion of Lifetime Spent in Immigration in the U.S. to the Prediction of Severe Insomnia in the U.S. Immigrant

Sample: Semiparametric generalized logistic additive model adjusted for other risk factors of severe insomnia except depression

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Fig 5.5 Quadratic Contribution of Proportion of Lifetime Spent in Immigration in U.S. to the Prediction of Severe Insomnia in the U.S. Immigrant Sample: Semiparametric generalized logistic additive model fully adjusted for other risk factors of severe insomnia including depression

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Table 5.3 Univariate‡ and Multivariate Odds Ratios (OR) with 95% Confidence Intervals

(95% CI) of Severe Insomnia in U.S. immigrants

Risk Factors Unadjusted OR Adjusted OR (95% CI) (95% CI) Mexico-born Immigrant 0.5 (0.3-0./7) 0.4 (0.2-0.6) Proportion Lifetime in Immigration < 20% (1) 1.0 (reference) 1.0 (reference) ≥ 20% and < 40% (2) 1.2 (0.6-2.4) 1.3 (0.6-2.9) ≥ 40% and < 60% (3) 1.1 (0.6-2.4) 1.3 (0.6-2.8) ≥ 60% and < 80% (4) 1.4 (0.7 -2.7) 1.0 (0.5 -1.8) ≥ 80% and < 100% (5) 1.0 (0.5-2.1) 0.9 (0.3-2.3) Age (5 year increments) 1.0 (1.0-1.03)* 1.0 (1.0-1.02) Female Gender 2.5 (1.6-4.0) 1.8 (1.1-3.0) Race: White (1) 1.0 (reference) 1.0 (reference) Hispanic (2) 0.9 (0.4-1.7 ) 1.6 (0.6-4.6 ) Black (3) 0.8 (0.3-2.2) 1.5 (0.4-5.8) Others (4) 0.8 (0.4-1.6) 1.9 (0.6-6.1) Poverty(PIR <=1) 1.7 (1.1-2.6) 1.4 (0.7-2.8) No High School diploma 1.8 (1.1-3.0) 1.4 (0.8-2.5) Living with partner 0.8 (0.5-1.1) 1.0 (0.6-1.6) No Health insurance 0.8 (0.5-1.2) 0.7 (0.4-1.3) Current smoker 1.6 (0.7-3.5) 1.7 (0.7-3.9) Drinking alcohol 0.8 (0.5-1.5) 1.0 (0.6-1.9) Overweight /Obesity 1.4 (0.8-1.7) 1.4 (0.7-2.8) Self-reported fair or poor 4.2 (2.8-6.1) 2.7 (1.5-4.9) overall general health Depression 5.7 (2.1-15.2) 6.8 (3.1-15.2) (PHQ-9 score ≥ 10) ‡ Analyses conducted using survey analyses P-value <0.05*

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The final multivariate model shows that the Mexico-born ethnicity has a significant protective effect against severe insomnia (OR=0.4, CI: 0.2-0.6, p<0.0001)

even after adjusting for age, gender, social determinants of health vulnerability (race,

marital status, income, education, health insurance), substance use (alcohol, smoking),

health characteristics (overweight/obesity, self-reported general health, and depression),

and differences in “life in immigration experience” exposure (PLI). Significant risk

factors for severe insomnia in the U.S. immigrant population were female gender

(OR=1.8, CI: 1.1-3.0, p<0.05), self-reported unsatisfactory (fair or poor) health status

(OR=2.7, CI: 1.5-4.9, p<0.001), and depression (OR=6.8, CI: 3.1-15.2, p<0.0001).

Poverty (OR=1.7, CI: 1.1-2.6, p<0.05), and low attained education (OR=1.8, CI: 1.1-3.0,

p<0.05) were also significantly associated with severe depression in univariate models,

but these didn’t attain significance in the final adjusted model.

The same directionality of the results was found when running extensive

sensitivity analyses using alternative definitions for depression (severe depression and

depression with short sleeping hours), as well as each of the 2005-2006 and 2007-2008

NHANES cohorts analyzed separately.

Analyses were also restricted to the Latino U.S. immigrant population, and

measures of cultural change towards the American culture replaced race in the final

models. Fig 5.5 and Fig 5.6 show that culturally-related changes were not significantly associated with SHST<6 hours and/or severe insomnia in the Latino immigrant U.S. population. All the other results from these models were consistent with the findings reported in the general U.S. immigrant population.

There was no significant interaction found between PLI, Mexican ethnicity, or

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gender in any of these models.

5.5 Discussion

To my knowledge, this is the first study showing a significant overall lower risk

of poor sleep in adult U.S. Mexican Immigrants, compared to other Latino U.S.

immigrant and to Non-Latino U.S. Immigrants.

Population age standardized prevalence of severe insomnia in MI (fig 5.2) was found to be significantly lower compared to OI, despite multiple disadvantages being identified in MI. This includes that, compared to both LI and OI, MI are significantly more likely to have income at or below the poverty level, not to have completed high school education, and to report their health as fair or poor. Previous studies (Ohayon,

2002, 1997, 1996, Allaert, 2004, Roth, 2006, Xiang, 2008) have found these factors to be predictors of significantly increased odds of insomnia.

These results can be explained by theories proposing sleep quality as being influenced by factors related to psychological characteristics and coping mechanisms

(Greene, 2006). The close proximity and access to Mexico (Ainslie 1998) together with other reported engaging psychological protective mechanisms, such as hoping and/or planning to return to their homeland after reaching some level of economic advantage, may also further protect MI against exposure to psychological process such as xenophobia and “othering”, previously reported as eroding overall health (Viruell-

Fuentes 2007) and sleep (chapter 3) in Latino immigrants.

The role of significantly lower age standardized prevalence of SHST<6 hours, and severe insomnia with SHST <6 hours in MI when compared to LI and/or OI, should be

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further investigate in relation to the Hispanic Paradox (Markides 1986 Scribner 1996,

Dey 2006).

MI men were found to have significantly lower age standardized population

prevalence of SHST <6 hours compared to their Latino U.S. immigrants counterparts.

However, standardized population prevalence of SHST <6 hours in women did not differ

between the MI women and other LI or OI counterparts. Cultural gender-based norms and behaviors related to the division of the household labor, overlapping with new responsibilities related to employment, may negatively influence the sleep duration of MI women.

A study of the prevalence of hypertension in older Mexicans who have migrated to U.S. and then returned to Mexico showed significant higher prevalence of hypertension in females compared to males, after adjusting for demographics, obesity, diabetes, alcohol use, and smoking (Salinas, 2008). The role of SHST <6 hours in relation to sex differences reported in hypertension risk for older Mexicans and Mexican

Americans (Salinas, 2008) suggest another great further avenue to explore in further research.

Consistent with other studies, depression, self-reported general health, and female gender were found to be the best predictors of severe insomnia in all immigrants.

Smoking was previously reported as a risk factors for insomnia (Kaneita, 2007, 2006,

Jefferson, 2005), but did not appear as a significant predictor of severe insomnia in U.S. immigrants.

PLI was found nonlinearly associated with severe insomnia for all U.S. immigrants in a quadratic U –shape. The non-linear shape appears consistent with

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theories related to non-linear changes in the perception of discrimination and “guarded

optimism” with acculturation (Ainslie, 1994, 1998, 2002, Viruell-Fuentes 2007).

Strengths and limitation of this study were already described in chapters 3 and 4.

PLI computed by this study should have a limited value to adjust for differences related to the abroad mobility between different immigrant subpopulations, knowing that the variable used to compute it was an approximate estimate of the total numbers of years in the U.S., and also knowing that no data was available to calculate the time spent abroad after the immigration in the U.S..

In summary, this is the first study showing overall better outcomes of sleep in MI compared to LI and/or OI, and also a non-linear relationship between PLI and severe insomnia in the U.S. immigrant population. Longitudinal studies assessing trends in sleep quality in immigrants, including insomnia incidence over the PLI, should further clarify the mechanisms and role of Mexican ethnicity and culture on sleep quality in U.S. immigrants.

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Fig 5.6 Multivariate Odds Ratios (95% CI) of Short Habitual Sleepless than 6 hours in the Latino U.S. Immigrant Sample

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Fig 5.7 Multivariate Odds Ratios (95% CI) of Severe Insomnia in the Latino U.S.

Immigrant Sample

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CHAPTER 6

Conclusion

Immigration is a highly complex and multifaceted process having extensive

impact on individual health and well-being. With immigration increasing extensively in the past 100 years worldwide, immigrant health has become a topic of increasing interest from a medical, social, public health, economic, ethical and political perspective. This has been accompanied by a growing number of studies conducted worldwide in several different countries and immigrant sub-populations. The results of these have been greatly

variable and conflicting in nature. Even when looking at whether immigrants have better

or worst health then the host country’s native born population, there has been great

discrepancy in findings: Immigrants in the U.S. have been reported as having overall

better health, being less likely to suffer from most chronic pathologies and having lower

mortality rates compared to the U.S.-born population, despite having more unfavorable

socio-demographic characteristics and limited access to health care (Argeseanu 2008,

Dey AN 2006). These results are inconsistent with an increasing number of international

epidemiological studies (Wändel 2007, Steiner 2007, Syed 2006, Asakura 2006,

Schweitzer 2006, Saraiva 2005) reporting higher prevalence of chronic conditions in

immigrant populations compared to the host population. Such discrepancy has made it

very difficult to come to any clear conclusions about how immigration affects individual

health, which could then be used in establishing policy and intervention.

This disparity in findings is likely to be triggered by disparity in research, with no conceptual model(s) applicable to the field of immigrant health research currently in existence. A number of theories have been formerly been proposed, each of these are

185 mono-theories seeking a unidirectional rational for understanding health changes related to the highly complex process of immigration. Failure to understand and control for important factors that impact individual health related to immigration may be responsible for the lack of agreement in the field.

This dissertation proposes “The Open System Conceptual Model of Immigrant

Health” grounded in Critical Realism theory, as an all encompassing model for studying immigrant health. We propose this model to have widespread applicability to any immigrant population residing in any host country, and to any health related outcome of interest.

Mexico-born immigrants (MI) comprise the greatest ethnic subgroup of U.S. immigrants. Furthermore, previous research in this subpopulation has found controversial results of better health compared to the U.S. native born population despite higher risk factors for poor access to medical care (The Hispanic Paradox). Knowledge at to the importance of sleep and its widespread health impacts has likewise been growing in research years. There is very limited knowledge about sleep health in MI. These factors triggered our decision to apply our “The Open System Conceptual Model of Immigrant

Health” to MI in the U.S., and to explore both general and sleep health in this population.

In doing so, we are able to make significant contributions to the both the fields of Health

Service Research and Epidemiology. By using actual date, we tested the predictions of our theoretical model and have proven the validity and utility of “The Open System

Conceptual Model of Immigrant Health” as a practical tool in immigrant health service research. In choosing to focus on MI health, we have added to the growing epidemiological knowledge as to the health of MI in the U.S., and have been the first to

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explore sleep changes related to immigration in this growing population.

Chapter 2 of this dissertation provides the theoretical work that went into

creating the “The Open System Conceptual Model of Immigrant Health”, with Chapter

3-5 each focusing on exploring health and sleep health in MI and comparing them to

other population subgroups. The key concepts and findings of each individual analysis

are highlighted as a summary by chapter.

Chapter 3 In order to validate the multi-theory “The Open System Conceptual

Model of Immigrant Health”, it was first necessary to explore the epidemiological

differences in self-reported general and sleep health between Mexico-born U.S.

Immigrants (MI), U.S.-born Mexican Americans (MA), and the general U.S. population

(Chapter 3). Our conceptual model dictates that there should in fact be quantifiable

differences in health related to immigration status, after adjusting for all know risk

factors. Furthermore, our proposed multi-theory model suggests that the observed

differences in health between immigrant and non-immigrant groups cannot be explained by any sole theory formerly proposed. Confirming that this is in fact the case serves as additional evidence supporting our proposed multi-theory based conceptual model for immigrant health.

We found that despite significant socio-economic disadvantages, including limited access to health care, MIs have more favorable sleep-related outcomes than the general U.S. population. In MI men, sleep patterns also were favorable when compared to their U.S.-born MA counterparts. Differences were not accounted for by measurable

differences in gender, age, education, marital status, poverty income ratio, self reported

general health, insurance status, caffeine, alcohol, smoking, recreational drug usage and

187 depression, or because of differences in BMI. These results are consistent with the

Hispanic paradox, which has been a previously reported observation across other health measures.

To our knowledge, this is the first study to demonstrate the Hispanic paradox holds true in the area of sleep health. These is no single theory can explain the observed differences by immigration status, as well as the gender differences between MI men and women. This reassures us that it is in fact the integrity of several of the existing theories captured through the application of Critical Realism theory in construction of our conceptual model that contributes to these observed differences. Some of the theories believed to contribute to these observations are shift in the cultural beliefs, value- orientations and attitudes regarding preferred life pace (Cervantes 1985, Castro 1985) and/or importance of sleep (Domino 1986), use of relaxation as an adaptive daily coping strategy (Castro 1985), gender-based norms and behaviors related to the division of the household labor, language barriers, hope due to economical gain associated with immigration to the U.S. or plans to return to their homeland (Viruell-Fuentes 2007), self- perception of vulnerability to discrimination; all of which are captured in the “The Open

System Conceptual Model of Immigrant Health.”

Thus, the epidemiological contributions of Chapter 3 steam from it showing the presence of the Hispanic Paradox in MI sleep health; while from a health service research perspective it furthermore useful in showing validity of key idea presented in the “The

Open System Conceptual Model of Immigrant Health.”

Chapter 4 The notion that not all factors impacting health can be individually or readily measured is another important contribution of the application of Critical Realism

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theory to immigrant health. Chapter 4 serves the vital role of introducing the use of

“Proportion of Lifetime in Immigration” (PLI) as a measure of the “experience in

immigration” effect, in addition to age effects, in cohort analysis. The theoretical

grounding of our “The Open System Conceptual Model of Immigrant Health” predicts

PLI as being significantly correlated with age at immigration, decade at the time of

arrival to the U.S, and English health-related illiteracy, but not with participant age.

Furthermore, to support the validity of the theoretical conceptual model, PLI would have

to be associated with self-reported unsatisfactory health in a non-linear but rather

quadratic relationship. We chose to test these predictions by running a cross-sectional

analysis of 2005-2008 National Health and Nutrition Examination Survey data gathered

from MI.

PLI was found to be significantly and highly correlated with: age at time of

immigration (-0.88), decade at the time of arrival in the U.S. (-0.87), and English illiteracy (-.38). PLI was not correlated with the participant’s age (0.07). We identified a significant quadratic association between PLI and UHS [OR for 1% of PLI = 1.1; CI 95%

(1.0-1.1), p<0.001], which persisted after adjusting for knows confounders including age, gender, income, education, living with partner, social support, access to health care, U.S. citizenship, perception of self-reported health status attributed to shifts in cultural practices/beliefs towards the American culture, and language (English vs. Spanish) used

at the time of data collection.

The study presented in chapter 4 is the first to show a significant quadratic

relationship between UHS and PLI in U.S. MI 40 years and older, independently of other

known social, immigration-related, and cultural determinants of health vulnerability risk

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factors. These finding is consistent with theoretical predictions based on “The Open

System Conceptual Model of Immigrant Health”, and validation of these prediction

serves as further evidence to the strength of our model. If the relationship between PLI and self-reported health status is validated through further research studies, this would partially explain the inconsistencies in results reported by previous studies with regards to how health status changes post immigration. There has been former research that has explored this relationship. However, these studies all assumed linearity between PLI and

UHS, which we have refuted both theoretically as well as epidemiologically within our sample population. If the quadratic relationship between PLI and UHS is validated, then future studies should use PLI when controlling for the “experience in immigration” exposure in models adjusted for age. This approach may be especially useful in cohort analyses comparing health outcomes in subpopulations with different patterns/degrees of mobility.

The finding that PLI is associated with UHS in a quadratic relationship would significant contribution to advancing health service research in the field of immigrant research, and quite possibly impact policy decisions as future research may begin to show consistency in results with PLI being taken into appropriate consideration in each study.

Chapter 5 serves as an integration of the findings from chapters 3 and 4, and

furthermore serves as a final example to the validity of the theoretical conceptualization

of the dissertation. Since a great strength of our proposed conceptual model is its

widespread applicability to any immigrant population in any host country, as well as to

any specific health-related outcome, we choose to focus in on insomnia in MI. The

conceptual model together with the work of Chapter 3 implies that MI should have less

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insomnia then non-Mexican immigrants, consistently with the predictions of the Hispanic paradox. The conceptual model and the findings in Chapter 4 would further predict that the data should show PLI, a measure of the risk to health attributed to immigration per se, as associated with insomnia (our chosen example of a health outcome of interest) in a nonlinear fashion. Critical Realism theory and our conceptual model also support the notion that there are immeasurable factors contributing to immigrant health that cannot be adjusted for, but which do nevertheless impact the outcomes observed.

Within our sample, MI were found to have income at or below the poverty level, not to have completed high school, and to report their health as fair or poor, all of this have been reported by previous studies as predictors for insomnia (Ohayon 2002, 1997,

1996, Allaert 2004, Roth 2006, Xiang 2008). In spite of this, the Hispanic Paradox held true for insomnia as well, with MI have significantly less prevalence compared to

Hispanic Non-Mexican Immigrants, and then all of the immigrants. As predicted by our model and also confirmed in chapter 4, PLI was found to be nonlinearly associated severe insomnia for all U.S. immigrants in a quadratic U –shape. MI men were found to have significantly lower age standardized population prevalence of SHST <6 hours compared to their Latino U.S. immigrants counterparts. However, standardized population prevalence of SHST <6 hours in women did not differ between the MI women and other

LI or OI counterparts. These findings are consistent with the “The Open System

Conceptual Model of Immigrant Health”, which note the existence of factors not open to direct measurement, but affecting outcomes. For example, cultural gender-based norms and behaviors related to the division of the household labor overlapping with new responsibilities related to employment perhaps having a negatively influence the sleep

191 duration of MI women.

To the best of our knowledge, we are the first to show a significant difference in insomnia prevalence between MI, Hispanic Non-Mexican immigrants, and other immigrants. This epidemiological finding is important in pursuing intervention programs aimed at improving health, and sleep health in immigrant populations. Since our study shows that MI are less likely to suffer from insomnia then all other immigrants, it may suggest that from a cost effective standpoint, that it would be worthwhile to invest in screening for insomnia in Non-Mexican immigrant population. These findings also may indicate that the reason for why former studies have not observed significant sleep disturbance in U.S. immigrants is that MI comprising such a large part of the total U.S. immigrant population, and their low rated of insomnia is balancing off the higher insomnia prevalence observed in other immigrant subgroups. This would have an important health policy implication, as we may be missing opportunities for intervening to improve sleep health in other immigrant subgroups because of the misinterpretation that they are suffering from lower prevalence.

Final Remarks

This dissertation has introduced and entirely new conceptual model for studying health changes in immigrant populations and tested the validity of the theoretical conceptualization trough application to MI in the U.S. using nationally representative data collected through NHANES. Application of Critical Realism theory has allowed for the integrations of the complex processes participating in individual health.

Previously described measures of “acculturation” related to individual abilities,

192 including English language proficiency, were intentionally avoided in this research because of the risks of “putting the onus on the individual” thereby “lending support to victim-blaming explanations for health outcomes” (Viruell-Fuentes 2007). Focusing on individual inability to speak the language of the host country is consistent with the

“rational choice perspective.” This is a dangerous and morally wrong ideology from a public health perspective, that promotes a narrow view focusing on victim blaming for inequities and disparities observed in health. Such an approach is proposed to feed into growing public cynicism towards U.S. immigrants, and to promote decreasing government involvement in reducing health inequities, including reducing the already limited health policy legislation addressing immigrants (Viruell-Fuentes 2007). Our model and research is intended to provide an all encompassing and accurate way to determine health-related changes with immigration, with the goal of increasing consistency of research findings.

This work provides additional evidence towards nullifying the concept of formerly proposed inevitability of complete assimilation of Mexican immigrants in the

U.S. The results refute the poststructuralist empirical work of Foucault (1972) and

Gordon (1964), who suggested that all individuals are initially resisting the dominant social practice, but are expected to absorb, implement, and change it. Instead, this dissertation provides a Critical Realism based multi-theory approach conceptual model capturing the entire experience of life an am immigrant with a focus on health related changes. This model has application in the field of immigrant health research, with proposed validity for any immigrant population residing in any host country.

Standardization of approach, capturing not just a few researcher-selected, but health

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related processes contributing to immigrant health is a notable contribution to the field of

health service research, and serves as the basis for this dissertation. In addition, this

Conceptual Model has the power to serve as a tool for designing policy changes, risk reduction based programs, and social service support avenues that can target the specific needs of the immigrant population, thus allowing for cost efficiency in interventions.

The contributions of this research from an epidemiological perspective are also noteworthy, with very limited work having formerly been done related to exploring sleep health of MI in the U.S.. With MI comprising the vast majority of immigrants to the

U.S., and thus a significant sector of the workforce, the importance of understanding health related changes in this population cannot be negated.

In order to advocate for change we need to know what it is that we are seeking to

change. The great inconsistency seen in immigrant health research is a key reason for the

lack of legislation and social programs targeting disparity reduction in this population.

Yet, immigrants have been consistently shows to have increased prevalence of

established risk factors for poor health, including lower levels of: income, education,

language proficiency, insurance etc. then the host population. Application of “The Open

System Conceptual Model of Immigrant Health” to immigrant health service research

worldwide can provide a nonbiased, nonjudgmental, and all encompassing model for

understanding health related changes in immigrants. Based on the findings, the model

can further be used to target intervention efforts towards the factors are shown to have the

greatest impact on immigrant health.

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232