exploring cardiovascular health differences in...

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EXPLORING CARDIOVASCULAR HEALTH DIFFERENCES IN LATINO/AS OF DIFFERENT COUNTRIES OF ORIGIN

By

FELIX E. LORENZO

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2016

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© 2016 Felix E. Lorenzo

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To my parents who have sacrificed everything for me, and all of my family and friends who have encouraged me along the way

A mis padres, quienes han sacrificado todo por mí, y a mis familiares y amigos que me

han apoyado a lo largo del camino

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ACKNOWLEDGMENTS

I thank my parents and family, for all they have sacrificed so that I can chase my

dreams. I also want to thank my committee chair and advisor, Dr. Tracey Barnett, and

the rest of my committee, Dr. Barbara Curbow, Dr. Efrain Barradas, and Dr. Giselle

Carnaby for their guidance and support. Additionally, I want to thank the McKnight

Doctoral Fellowship for allowing me to pursue my PhD training. Lastly, it is impossible to

identify every individual who has had a positive impact or helping hand during my

journey. Thank you to those who have, I will be forever grateful!

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TABLE OF CONTENTS page

ACKNOWLEDGMENTS .................................................................................................. 4

LIST OF TABLES ............................................................................................................ 7

ABSTRACT ..................................................................................................................... 9

CHAPTER

1 INTRODUCTION .................................................................................................... 11

Painting a Portrait of U.S. Latino/as ........................................................................ 11 Latino/a Health Paradox ......................................................................................... 14

Acculturation ........................................................................................................... 18

Acculturation and Cardiovascular Risk Factors in Latino/a Populations ................. 20 Research Outline .................................................................................................... 23

2 ACCULTURATION AND CARDIOVASCULAR RISK FACTORS IN A NATIONAL SAMPLE OF LATINO/AS ..................................................................... 27

Background ............................................................................................................. 27

Methods .................................................................................................................. 30 Data Source ..................................................................................................... 30

Sample Design ................................................................................................. 30 Respondents and Inclusion Criteria .................................................................. 31 Variables/Measures .......................................................................................... 31

Statistical Analysis ............................................................................................ 33 Accuracy and Missing Data .............................................................................. 33

Results .................................................................................................................... 34 Sample Characteristics..................................................................................... 34 Associations between Acculturation and CVD .................................................. 35

Discussion .............................................................................................................. 36

3 COUNTRY OF ORIGIN: IMPACT ON ACCULTURATION AND CARDIOVASCULAR RISK FACTORS IN A NATIONAL SAMPLE OF LATINO/AS ............................................................................................................. 46

Background ............................................................................................................. 46 Methods .................................................................................................................. 48

Data Source ..................................................................................................... 48 Sample Design ................................................................................................. 49 Respondents and Inclusion Criteria .................................................................. 50

Variables/Measures .......................................................................................... 50 Statistical Analysis ............................................................................................ 52

Accuracy and Missing Data .............................................................................. 52

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Results .................................................................................................................... 53 Sample Characteristics..................................................................................... 53 CVD Associations ............................................................................................. 54

Discussion .............................................................................................................. 57

4 ASSOCIATION BETWEEN COUNTRY OF ORIGIN, ACCULTURATION AND CARDIOVASCULAR RISK FACTORS IN A NATIONAL SAMPLE OF LATINO/AS ............................................................................................................. 70

Background ............................................................................................................. 70

Methods .................................................................................................................. 73 Data Source ..................................................................................................... 73 Sample Design ................................................................................................. 73

Respondents and Inclusion Criteria .................................................................. 74 Variables/Measures .......................................................................................... 75 Statistical Analysis ............................................................................................ 77

Accuracy and Missing Data .............................................................................. 78 Results .................................................................................................................... 78

Sample Characteristics..................................................................................... 78 Group Differences among Latino/a Countries of Origin .................................... 80 Acculturation and Associations of CVD Risk Factors ....................................... 81

Discussion .............................................................................................................. 85

5 CONCLUSION ...................................................................................................... 111

LIST OF REFERENCES ............................................................................................. 124

BIOGRAPHICAL SKETCH .......................................................................................... 134

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LIST OF TABLES

Table page 2-1 Participant demographics ................................................................................... 42

2-2 Participant CVD clinical risk factors .................................................................... 43

2-3 Binary logistic models of Latino/a CVD risk factors susceptibility (N=3430) ...... 44

3-1 Participant demographics ................................................................................... 64

3-2 Participant CVD clinical risk factors per Country of Origin .................................. 65

3-3 Binary logistic models of CVD risk factors susceptibility for Puerto Ricans compared to non-Puerto Rican Latino/as (N=3430) ........................................... 66

3-4 Binary logistic models of CVD risk factors susceptibility for Mexicans compared to non-Mexican Latino/as (N=3430) ................................................... 67

3-5 Binary logistic models of CVD risk factors susceptibility for Cubans compared to non-Cuban Latino/as (N=3430) ...................................................................... 68

3-6 Binary logistic models of CVD risk factors susceptibility for Dominicans compared to non-Dominican Latino/as (N=3430) ............................................... 69

4-1 Participant demographics ................................................................................... 92

4-2 Participant clinical and behavioral CVD risk factors per Country of Origin ......... 93

4-3 Binary logistic models of Latino/a CVD risk factors susceptibility (N=3430) ....... 94

4-4 Binary logistic models of CVD risk factors and smoking susceptibility for Puerto Ricans compared to non-Puerto Rican Latino/as (N=3430) .................... 95

4-5 Binary logistic models of CVD risk factors and physical activity susceptibility for Puerto Ricans compared to non-Puerto Rican Latino/as (N=3430) ............... 97

4-6 Binary logistic models of CVD risk factors and smoking susceptibility for Mexicans compared to non-Mexican Latino/as (N=3430) ................................... 99

4-7 Binary logistic models of CVD risk factors and physical activity susceptibility for Mexicans compared to non-Mexican Latino/as (N=3430) ........................... 101

4-8 Binary logistic models of CVD risk factors and smoking susceptibility for Cubans compared to non-Cuban Latino/as (N=3430) ...................................... 103

4-9 Binary logistic models of CVD risk factors and physical activity susceptibility for Cubans compared to non-Cuban Latino/as (N=3430) ................................. 105

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4-10 Binary logistic models of CVD risk factors and smoking susceptibility for Dominicans compared to non-Dominican Latino/as (N=3430) ......................... 107

4-11 Binary logistic models of CVD risk factors and physical activity susceptibility for Dominicans compared to non-Dominicans (N=3430) .................................. 109

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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

EXPLORING CARDIOVASCULAR HEALTH DIFFERENCES IN LATINO/AS OF

DIFFERENT COUNTRIES OF ORIGIN

By

Felix E. Lorenzo

August 2016

Chair: Tracey Barnett Major: Public Health

Latino/as are at a higher risk for cardiovascular disease (CVD) than non-Latino/a

whites. Additionally, Latino/as are disproportionately affected by low income, limited

access to health care, language barriers, and lack of health insurance, which further

increase their risk for CVD. In contrast to non-Latino/a whites, research shows that

Latino/as smoke less, consume a healthier diet, and exhibit higher levels of physical

activity at their time of arrival to the United States. However, as Latino/as acquire

attitudes and behaviors consistent with acculturation, their positive health behaviors

decrease. Although some research has explored the role that acculturation plays on

CVD risk factors, few have assessed how risk factors could be modified by country of

origin and specifically influenced by smoking or physical activity. This dissertation

assessed the role of acculturation in association with CVD-related risk factors in a

heterogeneous sample of Latino/as. Additionally, it evaluated the effect that distinct

Latino/a subgroups have on clinical (hypertension, high cholesterol, and heart

conditions) and behavioral (smoking, and physical activity) CVD risk factors. Secondary

data analyses conducted on the 2014 National Health Interview Survey (NHIS) found

significant country of origin differences in acculturation and the impact it has on

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hypertension, high cholesterol, heart conditions, smoking, and physical activity in a

sample of Puerto Ricans, Mexicans, Cubans, and Dominicans. These findings provide

insight into the potential impact that country of origin, and acculturation have on health.

The findings support implications for clinical and policy level interventions and suggest

that further research is needed to better understand the relationships contributing to

CVD among Latino/as.

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CHAPTER 1 INTRODUCTION

Painting a Portrait of U.S. Latino/as

During the 1960s, the Latino/a population in the United States (U.S.) accounted

for under 5% of the total population (Gallo, Penedo, Espinosa de los Monteros, &

Arguelles, 2009). At the turn of the decade, that number expanded to over 15% as

Latino/as attributed to more than half of the population growth in the nation (Van

Wieren, Roberts, Arellano, Feller, & Diaz, 2011). Currently, there are over 55.3 million

(17.3%) Latino/as in the U.S., a number projected to surpass 120 million (~30%) in the

next 40 years (Stepler & Brown, 2014). These Pew Research Center projections from

the U.S. Census Bureau thus estimate that by 2060, nearly 1 in 3 individuals in the U.S.

will be a Latino/a. Despite the overall growth, it is important to note that the increasing

Latino/a population originates from over 20 different countries.

Though the three largest countries of origin subgroups remain Mexico, Puerto

Rico, and Cuba respectively, they are no longer the fastest growing subgroups (Lopez &

Dockterman, 2011). According to 2010 Decennial Census data, Latino/as from

Guatemala (180.3%), El Salvador (151.7%), Colombia (93.1%), and the Dominican

Republic (84.9%) grew by more than double the average of other countries. The current

Latino/a subgroup estimates include nearly 32 million Mexicans (60%), 4.6 million

Puerto Ricans (9.2%), almost 2 million Cubans (3.5%), over 1.5 million Salvadorans

(3.3%) and 1.4 million Dominicans (2.8%). Regional differences are also prevalent. For

example, despite accounting for the majority of Latino/as, Mexicans are not the largest

subgroup in all of the nation’s metropolitan areas (Lopez & Dockterman, 2011). In New

York and New Jersey, the majority of the Latino/a population is Puerto Rican (30%) and

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Dominican (20%). In Miami, Cubans account for over half (51%) and in Washington

D.C., Salvadorans make up one third (33.7%) of the Latino/a population in the area.

Traditionally, applying the network theory of migration, Latino/as have populated

specific regions or communities through the value of direct relationships. This refers to

friends and family members that could assist by providing information on job

possibilities or even direct assistance such as housing, food, and transportation (Boyd,

1989). This pattern was mostly limited to five states in three separate regions of the

country: South, Southwest, and Northeast. Over the last twenty years, however, there

has been a steady decline in the intention to live in the usual five destination states of

California, New York, Texas, Florida, and New Jersey (Benjamin-Alvarado, DeSipio, &

Montoya, 2008). During the middle of the 1980s, the rate of intention to reside in these

five states was well over 65 percent, a number that has now dipped and remained

under 60 percent for the past couple of years (Zúñiga & Hernández-León, 2005).

Analysts cite the nation’s recent labor market shifts, the decentralization of cities, and

the restructuring of the nation’s economy as a motivator to explore many of the new

destinations that have engendered places with little history of Latino/as such as Atlanta,

GA or Charlotte, NC (Singer, Hardwick, & Brettell, 2008). Overall, more than 30 cities in

the South and Midwest that prior to the turn of the century had very minimal contact with

Latino/a populations now exceed 5,000 individuals of Latino/a origin (Benjamin-

Alvarado et al., 2008).

Overall, the U.S. Latino/a population is made up of complex individuals that are

identified through many labels (Oboler 1995; Davila, 2001; Gonzalez, 2011). The

significance of their colonization, liberation, involvement with the U.S. Government,

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education, and way of life of their respective countries undoubtedly shapes the

differences they come to portray in the U.S. (Gonzalez, 2011). Despite their differences,

Latino/a populations also share many similarities. Their legacy of Spanish colonialism

and language, adherence to Catholic theology, and a strong sense of family valuation,

have been explored (Blair, Blair, & Madamba, 1999; Gallo et al., 2009; Gonzalez, 2011).

In general, the cultures have a high regard for family, with non-familial dependence

seen only as a last resource, while the patriarchal views dominate family life.

Unfortunately, many also have shared unfavorable situations such as economic

instability and political oppression in their home nations which have pushed the

individuals to the U.S. in search of higher wages, religious tolerance, and political refuge

(Marquardt, Steigenga, Williams, & Vásquez, 2013). Moreover, as a group, the Latino/a

population has achieved less education than all other demographic groups (Gallo et al.,

2009). Thus, many Latino/as reside in poor housing communities with high

unemployment rates, which put them at increased risk for health problems such as

diabetes, obesity, and cervical cancer (Pérez-Stable, Marín, & Marín, 1994; Gallo et al.,

2009; Waldstein, 2010; CDC, 2016). Furthermore, they are three times less likely to

have health insurance than non-Latino/a whites, and are disproportionately exposed to

discrimination and occupational harassment (Friedman-Jimenez & Ortiz, 1994; Pérez-

Smith, Spirito, & Boergers, 2002; Gallo et al., 2009; Waldstein, 2010). Despite these

and many other barriers, Latino/a populations share an intricate layer of experiences

which provide them with protective health factors that account for favorable outcomes

when compared to non-Latino/a populations (Franzini, Ribble, & Keddie, 2001; Morales,

Lara, Kington, Valdez, & Escarce, 2002; Abraído-Lanza, Chao, & Florez, 2005; Lara,

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Gamboa, Kahramanian, Morales, & Bautista, 2005; Markides & Esbach, 2005;

Crimmins, Kim, Alley, Karlamangla, & Seeman, 2007; Gallo et al., 2009; Arias, 2010;

Waldstein, 2010; Van Wieren et al., 2011; Schachter, Kimbro, & Gorman, 2012).

Latino/a Health Paradox

An individuals’ health is influenced by a range of factors including environmental,

social, economic, and personal variables. As such, low socioeconomic status which is

often tied to low standard of living and quality of life has been associated with poor life

expectancy and increased mortality rates (Franzini et al., 2001; Waldstein, 2010). Given

the nature of many Latino/a communities in the U.S. (high unemployment, substandard

housing, and limited access to care) and the barriers facing Latino/as (educationally

disadvantaged, low salary positions, etc.) it is not surprising that they are at higher risk

for diabetes, obesity, and cervical cancer (Markides & Coreil, 1986, Friedman-Jimenez

& Ortiz, 1994; Franzini et al., 2001; Pérez-Stable et al., 2001, Waldstein, 2010; CDC,

2016). However, over the last three decades, data have shown that Latino/as fare better

than non-Latino/a whites in many health related measures and outcomes – a

phenomenon widely known as the Health paradox, also referred to as Hispanic health

paradox, Hispanic epidemiological paradox, Latino/a health paradox, and Latino/a

epidemiological paradox (Markides & Coreil, 1986; Franzini et al., 2001; Markides &

Esbach, 2005; Waldstein, 2010; Ruiz, Steffen, & Smith, 2013; Valles, 2016).

The Latino/a health paradox contradicts the current public health understanding

of social determinants of health. While factors such as limited access to care, difficult

working conditions, and economic hardships negatively impact an individual or

populations’ health so as to shorten their lifespan, this pattern is not observed among

Latino/as (Valles, 2016). All-cause mortality rates were reported to be 17.5% lower

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among Latino/as compared to non-Latino/a whites (Ruiz et al., 2013) despite higher risk

factors associated with Latino/as. This survival discordance (Goldman, 2016), has also

been observed for coronary and cardiovascular-related deaths (Overton, Phillips,

Moore, Campbell-Furtick, Gandhi, & Shafi, 2015). In order to fully understand the

Latino/a health paradox and address existing health disparities, the associations

between risk factors for cardiovascular diseases (CVD) and other variables such as

acculturation in the Latino/a population will be explored in later sections of this

dissertation.

Making sense of the Latino/a health paradox is challenging, and not all research

supports its existence (Morales et al., 2002). One counterargument that has been

proposed against the health paradox debate is known as the healthy migrant hypothesis

(Morales et al., 2002; Waldstein, 2010; Valles, 2016). This theory posits that only

individuals who are healthy make their way to the U.S., thus explaining greater longevity

or other positive health outcomes over the comparison group. However, data have

shown that Latino/as born in the U.S. also have lower mortality rates than non-Latino/a

whites (Abraído-Lanza, Dohrenwend, Ng-Mak, & Turner, 1999). This suggests that

something else is responsible for the paradox. Another argument, known as return

migration/salmon-bias hypothesis (Abraído-Lanza et al., 1999; Morales et al., 2002;

Waldstein, 2010; Valles, 2016), postulates that Latino/as retire to their country of origin

and thus are misrepresented in mortality statistics. Alternatively, a study that combined

data from the National Health and Nutrition Examination Survey and the Mexican Health

and Aging Survey reported similar levels of adult conditions among immigrants and

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Mexicans in America, thus providing no support for the return migration/salmon-bias

hypothesis theory (Crimmins, Soldo, Kim, & Alley, 2005).

While ethnographic literature has been scarcely used to describe the Latino/a

health paradox (Waldstein, 2010), some arguments against this phenomenon are

grounded in this field. The ethnic enclave advantage – also known as the barrio

advantage – is a theory that explores the cultural backgrounds and beliefs of the

Latino/a population (Valles, 2016). The researchers who developed this model argue

that the close-knit neighborhoods in which Latino/as thrive provide social support

structures that buffer the expected health outcomes associated with low income and

education (Eschbach, Ostir, Patel, Markides, & Goodwin, 2004). However, the study

only uses Mexican American participants which limits the generalizability of the findings

to other Latino/a populations. Lastly, the systematic data error hypothesis (Smith &

Bradshaw, 2005; Arias, Eschbach, Schauman, Backlund, & Sorlie, 2010) associates the

Latino/a health paradox simply to data biases and incorrect data reporting. Conversely,

while all studies are inherently subject to biases and errors, there are 30 years of

literature that support that these biases are insignificant (Abraído-Lanza et al., 1999;

Ruiz et al., 2013) and thus discredit such a hypothesis.

Despite the critiques and/or support, researchers agree that the concept of the

Latino/a health paradox has taken many forms in the past, and some have argued that

this is one of the reasons the theories have been so difficult to analyze (Morales et al.,

2002; Valles, 2016). One argument posed by Morales et al., (2002) is that the use of

self-reported data comprises most of the evidence for the paradox. Morales et al.,

(2002) posits that culture and knowledge significantly impact how we report information.

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Another argument involves mortality statistics and their reliability, or lack thereof (Ruiz

et al., 2013). Moreover, Palloni and Morenoff (2001) and Valles (2016) reported that

discrepancies in the operationalization of the concept itself has engendered the

opportunity for various researchers to analyze a changing dynamic. Additionally, Valles

(2016) argues that changes in the definition to include infant and child mortality, adult

mortality, birthweight, and adult health status, along with inconsistencies with respect to

the language used to compare metrics have unintentionally created multiple variations

of the paradox, each based on a different measure or population.

The contradictory manifestation of expected relationships between determinants

of health and reported health outcomes have perplexed public health experts and left

questions which continue to affect many facets of Latino/a health. In order to positively

influence change at a community and/or societal level and create Latino/a health equity,

it is imperative that the broad dimensions of health discussed over the last three

decades be explored further to include ethnic differences, country of origin, biological

measures, and individual behaviors. Particularly, the heterogeneity of the Latino/a

population must be addressed by researchers to accurately portray the population. Only

then can the public health and medical fields promote programs and interventions and

inform policy that will reduce or eliminate existing health disparities affecting this

population, as well as capitalize on protective factors. It is not the focus of this

dissertation to assess the credibility of each paradox-related argument but rather

attempt to elucidate some of the questions left unanswered after 30 years of literature,

as well as establish more in-depth perspectives to the intricacies that country of origin

and acculturation engender in this phenomenon.

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Acculturation

Health researchers interested in the Latino/a health paradox have singled out

acculturation as one of the most important explanatory variables to date (Schachter, et

al., 2012). While researchers have failed to agree on the best way to describe or

measure acculturation, most definitions propose that it is a process influenced by

temporal factors in which individuals come to accept and adopt behaviors and beliefs of

the host nation through peer-to-peer interactions (Morales et al., 2002; Halgunseth,

Ispa, & Rudy, 2006; Gallo, et al., 2009; Van Wieren et al., 2011; Schachter et al., 2012).

The foundation for the term acculturation comes from the early 1920s discussion of

assimilation from Park and Burgess (1969) and their mentee Gordon (1964), who

provided information on European immigrant groups. To Park, assimilation was an

irreversible cycle in which individuals or groups shared their experiences and history,

leading to attitude, memory, and knowledge acquisitions (Lara et al., 2005). For Gordon,

acculturation was a way to describe the adoption of cultural patterns, which he believed

extended beyond language acquisition (Alba & Nee, 1997).

Anchored in the literature by Gordon (1964) and Parks and Burgess (1969),

researchers have applied several acculturation scales and/or proxies of acculturation to

varying degrees of success. Scales measuring acculturation such as the Short

Acculturation Scale for Hispanics (SASH) focus heavily on language (Marin, Sabogal,

Marin, Otero-Sabogal, & Pérez-Stable, 1987). In a recent study, the authors observed a

positive relationship between years in the U.S. and prevalence of obesity among

Latino/as yet reported no association between acculturation and obesity when using the

scale (Isasi et al., 2015). Not only is measuring acculturation complex, it can be

challenging to include in health-related surveys (Lara et al., 2005). Additionally, those

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that have been inclusive of acculturation have focused predominantly on Mexican and

Mexican-American populations (Mitchell et al., 1990; Sundquist & Winkleby, 1999;

Crimmins et al., 2007; Gallo et al., 2009; Van Wieren et al., 2011; Daviglus et al., 2012).

The data from the National Health Interview Survey (NHIS) which is used in this

dissertation provides what researchers have recommended with regards to increased

sampling of Latino/as, including Latino/a subgroup identifiers, and various proxies of

acculturation (Morales et al., 2002).

One of the most used proxies for acculturation has been language, including

language of preference at home, language of choice for the interview/survey, and

overall language proficiency. Researchers, however, are undecided on this construct

(Lara et al., 2005). Those in support of language use report that items measuring

language are not only quick to administer and understand, but they largely explain the

variance of their perspective models (Marin, 1992). In contrast, detractors have

questioned if language can accurately gauge the complexity of biculturalism – in which

the host culture and the culture of origin are equally retained – arguing that in many

Latino/a communities, regardless of place of birth, it is common to find just one or both

languages being spoken (Lara et al., 2005). Additional proxies of acculturation include

citizenship status and years in the U.S., both of which will be further explored and

discussed in this dissertation.

Among Latino/as, acculturation has been associated with positive and negative

health-related behaviors (Morales at al., 2002). More specifically, some studies identify

a relationship in which as acculturation increases, there is a higher risk for negative

health outcomes (Gallo et al., 2009; Allen & Cummings, 2016). So for more acculturated

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individuals, self-rated physical health and self-rated mental health are lower (Schachter

et al., 2012). However, inconsistent measures, lack of heterogeneous study samples,

and other methodological limitations have engendered inconsistent findings with respect

to acculturation (Lara et al., 2005; Gallo et al., 2009). More research is needed to

identify pathways which explore how acculturation may drive health or how certain

factors influence this relationship.

Therefore, this dissertation focuses on exploring the process of acculturation in

various U.S. regions and across multiple Latino/a subgroups. Specifically, exploring the

hypothesis that as acculturation increases, Latino/as CVD related behaviors change in a

manner that mirrors that of the receiving nation, thus decreasing their cardiovascular

health status. This paradoxical idea, coupled with the fact that Latino/as are less likely

than non-Latino whites to be screened for CVD (Lee, Sobralske, & Fackenthall, 2015),

makes this a rising public health concern. Research in this area is integral in breaking

down complex and sometimes conflicting findings reported in the literature (Lara et al.,

2005) as well as for the development of public health best-practices, health awareness,

and health policy. Understanding the association of acculturation and specific CVD

health-related behaviors will require the exploration of risk factors such as physical

activity or smoking among Latino/a populations. This will then provide an efficient

pathway to mitigate the health disparities gap that exists for those with CVD.

Acculturation and Cardiovascular Risk Factors in Latino/a Populations

Cardiovascular diseases are responsible for over 17 million annual deaths

worldwide (Mozaffarian et al., 2015). While cardiovascular disease is the leading cause

of mortality regardless of race or ethnicity (Lee et al., 2015), Latino/as are more likely to

experience CVD related conditions such as hypertension, diabetes, and high cholesterol

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(Lee et al., 2015). Among the risk factors associated with incidence of CVD, tobacco

consumption, unhealthy diet, and physical inactivity have the deepest impact (Anderson

et al., 2009) among all populations, including Latino/as. Acculturation, as previously

discussed presents a unique and important role in the incidence, control, and prevention

of CVD as well. In this study, CVD related concerns including hypertension, high

cholesterol, heart conditions, tobacco consumption, and physical inactivity will be

examined in relation to acculturation across different countries of origin.

Disparities in levels of physical activity among Latino/a populations compared to

other populations have become a growing concern. While daily physical activity is

recommended for a myriad of overall health benefits among all ethnicities (Whitt-Glover

et al., 2009), studies show that Latino/as are the most sedentary ethnic group in the

U.S. (Crespo, Smit, Andersen, Carter-Pokras, & Ainsworth, 2000). Daily physical activity

is of particular importance for preventing CVD, thus exploring the nature of this

relationship remains a high public health concern. Some researchers have reported

conflicting findings on the effect of acculturation on level of physical activities. Although

some reports indicate that physical activity increases with acculturation (Abraído-Lanza

et al., 2005; Slattery et al., 2006), others report that higher acculturation leads to a

decrease in this behavior (Lara et al., 2005). Researchers have explored this complex

scenario by separating leisure time physical activity and occupational-related physical

activity (MMWR, 2007). Ham, et al. (2007), reported that prevalence of an active level of

usual daily activity among U.S. Latino/as is unrelated to acculturation, indicating that a

decrease in their rate of participation in occupational and transportation-related activities

is compensated for by an increase in their rate of participation in planned physical

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exercise, and other home or leisure related behaviors. Inconsistent findings in physical

activity support the need for more research in this area. Differences in physical activity

by country of origin may help explain the differences discussed in previous studies

which focused on Latino/as as a larger population.

The health risks associated with cigarette smoking are also widely understood.

The prevalence of cigarette smoking among Latino/as has been examined in several

studies. In 2014, 11.2% of Latino/a adults smoked compared to 17.5% of non-Latino/a

blacks and 18.2% of non-Latino/a whites (MMWR, 2015). The majority of the data

reported indicates that Latino/a adults are less likely to smoke cigarettes than non-

Latino/a white adults (MMWR, 2015). This trend has been observed over the past two

decades, with data reflecting an average of 10 less cigarettes smoked per day among

Latino/as than non-Latino/a whites (Perez-Stable et al., 2001).

Smoking and acculturation research also shows that the risk of being a current

smoker increases as length of time in the U.S. increases, particularly among women

(Abraído-Lanza et al., 2005). In contrast to those findings, several researchers using

predominantly Mexican and Central American populations have reported that only

women who were more acculturated were likely to show an increase in smoking, while

more acculturated men were less likely to smoke than their non-acculturated

counterparts (Marin et al., 1989; Pérez-Stable et al., 2001). These discrepancies, the

well-established link between smoking and CVD, and the questions engendered by

differences across different countries of origin highlight the need for further assessment

in this area of research. It is likely complex interactions between demographic factors

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plus migration that play an important role in the risk of cigarette smoking initiation and

persistence in this population.

Research Outline

The main objective of this dissertation is to assess the effect of acculturation on

the cardiovascular health of Latino/as of different countries of origin. The work

presented in this dissertation is divided into three distinct yet related studies. This

dissertation, (1) assesses the effect that acculturation has on Latino/a subgroups’

cardiovascular health status, (2) explores how country of origin impacts that

relationship, and (3) investigates the associations between two important behavioral

associations (smoking and physical activity) on reported risk factors (hypertension,

cholesterol, and heart conditions) among persons from different countries of origin.

While some studies have identified a relationship between CVD and acculturation

(Bethel & Schenker, 2005; Diez-Roux et al., 2005; Gallo et al., 2009; Morales et al.,

2011; Van Wieren et al., 2011), the majority of studies involving Latino/as have sampled

predominantly Mexican-Americans (Mitchell et al., 1990; Sundquist & Winkleby, 1999;

Crimmins et al., 2007; Gallo et al., 2009; Van Wieren et al., 2011; Daviglus et al., 2012).

Despite the pressing need to better understand the effects of acculturation on health,

and the call for novel perspectives on Latino/a identity (Evenson et al., 2004; Gallo et

al., 2009; Van Wieren et al., 2011; Taylor et al., 2012; Castañeda et al., 2016),

specifically subgroup differences, this area has not received much attention.

In Chapter 2, the relationships between acculturation (operationalized as U.S.

citizenship status among those who identify as Latino/a in the survey data) and the CVD

risk factors (hypertension/high blood pressure, high cholesterol, and heart conditions)

are examined for a composite sample of Latino/as from Puerto Rico, Mexico, Cuba, and

24

the Dominican Republic. The use of citizenship status as a proxy for acculturation has

been widely supported in the literature (Liang 1994; Yang 1994; Lopez-Gonzalez et al.,

2004; Aqtash 2007). U.S. naturalization laws require continuous within-country

residence for a minimum of three years for spouses of citizens or five years for non-

spousal naturalizations (INS 2000; Lopez-Gonzalez et al., 2004). Thus, citizenship

status correlates to time and exposure to the host nation’s culture and its members.

Additionally, Liang (1994) reported that indicative of acculturation, compared to non-

citizens, U.S. citizens were more likely to have increased residential and occupational

exposure to non-Latino/a whites. This study explores issues identified in Latino/a health

paradox literature (Franzini et al., 2001; Morales et al., 2002; Abraído-Lanza et al.,

2005; Lara et al., 2005; Markides & Esbach, 2005; Crimmins et al., 2007; Gallo et al.,

2009; Arias 2010; Waldstein 2010; Van Wieren et al., 2011) assessing whether as

acculturation increases, Latino/as CVD-related outcomes change in a manner that

mirrors that of the persons in the host nation, and thus their cardiovascular health status

decreases. This study demonstrates that for citizens compared to non-citizens

hypertension increases, and the same is found for high cholesterol, and heart conditions

in individuals over the age of 40.

Chapter 3 further documents the relationships between acculturation and the

CVD risk factors while investigating whether the respondents’ country of origin has an

impact on this relationship. The models analyzed will compare one subgroup of the

Latino/as in the survey to all other Latino/a subgroups (Puerto Ricans versus non-

Puerto Ricans, Mexicans versus non-Mexicans, Cubans versus non-Cubans, and

Dominicans versus non-Dominicans). As a result of varied political, socioeconomic and

25

cultural factors, country of origin has been shown to influence cardiovascular behaviors

of Latino/as in the U.S. (Pérez-Stable et al., 2001; Ayala et al., 2008; Neighbors et al.,

2008; Colon-Ramos et al., 2009; Van Wieren et al., 2011; Daviglus et al., 2012). These

studies have focused solely on behaviors such as physical activity, smoking, and

nutrition or clinical risk factors including hypertension and high cholesterol and have

grouped multiple countries together into broad Latino/a categories such as Central

Americans and South Americans. This study, however, not only accounts for four

unique countries of origin, but also integrates clinical risk factors of CVD including

hypertension, high cholesterol, and heart conditions while control for age, sex, social

economic status, multiple acculturation proxies, and education. This study shows that

there are relevant differences in hypertension, high cholesterol and heart conditions

among the Latino/a subgroups. Additionally, it indicates that the associations between

hypertension, high cholesterol, and heart conditions and acculturation vary across

Latino/a subgroups.

Chapter 4 examines the associations between two highly influential CVD

behavioral risk factors among the four Latino/a subgroups. Using regression analyses,

this part of the study examines the relationships between cigarette smoking and level of

physical activity on the reported measures of hypertension/high blood pressure, high

cholesterol, and heart conditions of a large representative sample (n=3,430) of Latino/as

in the U.S analyzed by country of origin. This study assesses mechanisms by which

cardiovascular health is influenced in the context of Latino/a populations. This study

also documents differences among Latino/a subgroups by country of origin and

establishes support for researchers to go beyond pan-ethnic generalizations (use of the

26

word Hispanics). Moreover, it is the first study to explore these relationships across

Latino/a subgroups from Puerto Rico, Mexico, Cuba, and the Dominican Republic. This

study shows that acculturation influences smoking and physical activity rates among

Latino/as. Furthermore, it demonstrates changes in the association between smoking

and physical activity and hypertension, high cholesterol and heart conditions across the

Latino/a subgroups.

Lastly, Chapter 5 summarizes the findings outlined in Chapters 2 – 4. The

implications of the findings including recommendations for future research, proposed

additions or changes to current public health policy and legislation, and potential

interventions to address this chronic disease disproportionately affecting Latino/as are

synthesized. The overall work presented in this dissertation can assist public health

professionals and clinicians to further understand disease control and prevention

particular to Latino/a populations from different countries of origin. While previous

studies have explored smoking rates and/or physical activity in Latino/a populations, this

dissertation will provide heterogeneous health profiles for multiple countries to inform

context to an area of Latino/a health previously marred by conflicting evidence and

paucity of research.

27

CHAPTER 2 ACCULTURATION AND CARDIOVASCULAR RISK FACTORS IN A NATIONAL

SAMPLE OF LATINO/AS

Background

The impact of cardiovascular diseases (CVD) on all populations are widely

understood and researched. CVD is the global leading cause of mortality (Lee et al.,

2016), with an estimated 17 million annual deaths (Mozaffarian et al., 2015). Despite the

wide-reaching effects of CVD, Latino/as are more likely to experience CVD-related

conditions such as hypertension and high cholesterol than any other group (Lee et al.,

2016). Currently, there are over 55 million Latino/as in the U.S, making them the largest

minority group in the country (Stepler & Brown, 2014). Moreover, many have begun to

establish in cities that previously had very minimal contact with Latino/as (Benjamin-

Alvarado et al., 2008). With the U.S. Census Bureau predicting a steady increase in this

group’s overall growth, the public health importance for this population has never been

higher.

Traditionally, the Latino/a population has experienced poor housing, low

employment opportunities, and diminished access to care which has put them at

increased risk for other health issues including diabetes and obesity (Pérez-Stable et

al., 1994; Gallo et al., 2009; Waldstein, 2010). Conversely, despite these and other

barriers, Latino/as have enjoyed favorable and protective health factors accounting for

lower mortality compared to non-Latino/a populations (Franzini et al., 2001; Morales et

al., 2002; Abraído-Lanza et al., 2005; Schachter et al, 2012; Goldman, 2016). This

phenomenon, commonly referred to as the Latino/a health paradox, has been the

subject of conflicting analyses over the past 30 years. While Latino/as have generally

demonstrated higher health outcomes compared to non-Latino/a whites (Markides &

28

Coreil, 1986; Gallo et al., 2009), researchers have struggled to agree on specific health

outcomes, including CVD. Recently, health professionals have assessed acculturation

as one possible explanatory variable for the paradox (Schachter et al., 2012).

While acculturation has been an area of focus for researchers interested in the

Latino/a health paradox, there has been little consensus in describing or measuring

acculturation. Despite most definitions centered around individuals accepting and

adopting new behaviors and beliefs (Morales et al., 2002; Halgunseth et al., 2006;

Gallo, et al., 2009; Van Wieren et al., 2011; Schachter et al., 2012), establishing

operational definitions into health-related surveys has been challenging (Lara et al.,

2005). Studies have previously operationalized acculturation in terms of preferred

language, cultural knowledge, and even food consumption (Lara et al., 2005).

Researchers have also explored acculturation in terms of proxies such as language

proficiency, years in the U.S., and citizenship status (Lopez-Gonzalez et al., 2005;

Aqtash, 2007). However, unlike citizenship status, it has been argued that language and

years in the U.S. do not accurately reflect the complexity of biculturalism, and thus do

not account for instances in which language or time are irrelevant in terms of influencing

behavior in Latino/a communities (Lara et al., 2005).

Yang (1994) reasoned that social identity theory played a major role in

connecting acculturation and citizenship status. Social identity theory refers to a sense

of group belongingness and in health has been associated with positive and negative

health outcomes (Haslam, Jetten, Postmes, & Haslam, 2009). Yang (1994), postulated

that a change in citizenship extended beyond a simple decision making process of cost-

benefit analysis, but rather an internal feeling about one’s identity. This was supported

29

by work by Haslam et al., (2009), which reported social identity as a determinant of

health, specifically for individuals engaging in health-related norms and behaviors.

Citizenship status as a proxy for acculturation has also been applied through the

framework of social capital. Liang (1994) argued that the higher an individual’s number

of social ties to people who were citizens, the greater the probability of them becoming

a citizen. These arguments thus view naturalization and therefore citizenship status as

the outcome of successfully integrating oneself into the receiving nation, hence

connecting citizenship to acculturation. Similarly, Lopez-Gonzalez et al., (2005),

suggested that citizenship status inherently measures an individual’s level of exposure,

presumably increased, to cultural and behavioral norms throughout the person’s time in

the U.S.

While health paradox research has reported a link between acculturation and

cardiovascular disease, few studies have analyzed this relationship outside of

predominantly Mexican and Mexican-American populations or isolated communities ().

Additionally, conflicting findings as they relate to the direction of the association

between acculturation and CVD-related measures continue to establish barriers towards

full understanding of this phenomenon (Abraído-Lanza et al., 2005; Lara et al., 2005).

The purpose of this study was to explore overlooked issues identified in Latino/a health

paradox literature (Franzini et al., 2001; Morales et al., 2002; Abraído-Lanza et al.,

2005; Lara et al., 2005; Markides & Esbach, 2005; Crimmins et al., 2007; Gallo et al.,

2009; Arias 2010; Waldstein 2010; Van Wieren et al., 2011). More specifically, to

assess the role that citizenship status as a proxy for acculturation plays in association

with CVD-related clinical risk factors in a heterogeneous sample of Latino/as. We

30

hypothesized that naturalization/citizenship, indicative of increased acculturation, would

be associated with increased prevalence of poor cardiovascular outcomes for the

sample of Latino/as.

Methods

Data Source

Data from the 2014 National Health Interview Survey (NHIS) was used, and was

considered exempt status by the University of Florida Institutional Review Board. As

part of the National Center for Health Statistics (NCHS) under the Centers for Disease

Control and Prevention (CDC), the NHIS is the primary data collection program of

noninstitutionalized civilians in the U.S. The purpose and scope of the NHIS is to collect

data on a broad range of health issues in order to monitor the overall health of the U.S.

population. Seven questionnaires – (1) Household, (2) Family, (3) Family Disability

Questions, (4) Adult, (5) Child, (6) Cover, and (7) Functioning and Disability – comprise

the 2014 NHIS; three of which will be used in this study: Household, Family, and Adult.

Sample Design

Data for the NHIS, a cross-sectional interview survey, were collected by U.S.

Census Bureau trained and employed personnel during annual household interviews.

The NHIS follows a multistage area probability design that allows for the representative

sampling of households and group quarters. Sampling takes place in over 400 primary

sampling units (PSU), selected from 1,900 geographic areas encompassing all 50

states and the District of Columbia. According to the CDC, metropolitan areas, counties,

and a group of bordering counties can all be considered PSU’s. Moreover, each PSU

can provide between four and sixteen addresses from which to sample from.

31

The sample design used in this version of the NHIS uses two oversampling

procedures to capture minority individuals and thus the Latino/as included in the study

will be weighted for data analysis. The first oversampling procedure screens for

households with one or more African-American, Asian-American, or Latino/a during the

Household questionnaire. This survey component records important demographic

measures. Households that meet these criteria are subject to the other six

questionnaires. The second oversampling method uses 2000 Census data to sample

areas with larger African-American, Asian-American, or Latino/a concentrations at a

higher rate. One randomly chosen adult and child is selected from each identified family

for further questioning regarding health status, health care services, and health

behaviors. Participation in the survey was completely voluntary and confidential.

Respondents and Inclusion Criteria

The NHIS collected data from over 50,000 homes and over 135,000 individuals

of varying demographics. The data included in this study represents respondents that

identified as Latino/a during the Household questionnaire of the NHIS. The survey does

not differentiate between pan-ethnic labels such as “Hispanic/Spanish.” Additionally, to

maximize statistical validity, only data from the four largest Latino/a subgroups were

selected and analyzed. This includes Mexico, Puerto Rico, Cuba, and the Dominican

Republic (n > 200 cases for each).

Variables/Measures

The NHIS dataset includes a set of questions relating to a respondents’

cardiovascular health. Participants were asked to answer yes (coded as 1) or no (coded

as 0) with the following qualifier: “Have you EVER been told by a doctor or other health

professional that you have or had –.” For this study, the following three items under that

32

qualifier were selected: (1) “Hypertension also called high blood pressure,” (2) “high

cholesterol,” and (3) “any kind of heart condition.” Demographic measures included

education, sex, citizenship status, and age. To measure education, interviewers asked

“What is the highest level of school completed or the highest degree received?”

Answers were coded continuously from (0) “never attended/kindergarten only” to (12)

“12th grade, no diploma.” The remainder answer choices were reported as follows: (13)

“GED or equivalent” (14) “High School Graduate” (15) “Some college, no degree” (16)

“Associate degree: occupational, technical, or vocational program” (17) “Associate

degree: academic program” (18) “Bachelor’s degree (Example: BA, AB, BS, BBA)” (19)

“Master’s degree (Example: MA, MS, MEng, Med, MBA)” (20) “Professional School

degree (Example: MD, DDS, DVM, JD)” and (21) “Doctoral degree (Example: PhD,

EdD).” For this study, education was recoded into a dichotomous variable including (0)

No high school completed and (1) High School completed.

The interviewers recorded sex as “are you male or female?” For this study we

coded sex as (0) Female, and (1) Male. To measure citizenship/naturalization status the

interviewers asked “is person a citizen of the United States?” Respondents could select

between: (1) “Yes, citizen of the United States” and (2) “No, not a citizen of the United

States” These selections were recoded as (0) No, and (1) Yes. Age, collected as year of

birth was coded as (0) for those “under 1 year,” continuously (1-84) for those between

the ages of 1 and 84, and (85) for anyone “85+ years”. Using only the adult sample, we

coded age continuously starting at age 18. Additionally, age was recoded into a new

dichotomous variable (Age > 40) to differentiate between those under (0) and over (1)

the age of 40, respectively.

33

Statistical Analysis

This study used the Statistical Package for the Social Sciences (SPSS), to

assess the responses from the CVD-related items and the acculturation proxy –

citizenship status. Response characteristics for all variables/measures were

summarized using descriptive and frequency statistics. Measures of skewness and

kurtosis along with means and standard deviations (SD) were explored for all items.

During final analysis, unengaged responses and missing data were excluded and the

models were created using a sample of 3,430 respondents. Additional models were

created without the inclusion of persons identified as Puerto Rican (n = 569) as they are

legally naturalized citizens upon birth. Correlations and binary logistic regression

models were tested on the variables/measures described above. Using females, non-

citizens, no high school completed, and under the age of 40 as reference groups (0),

odds ratios (ORs) with corresponding confidence intervals (CIs) were reported for a

sample of 2,861. ORs were calculated for the following items: (1) “Hypertension also

called high blood pressure,” (2) “high cholesterol,” and (3) “any kind of heart condition.”

Familywise error post hoc tests were conducted to account for the possibility of

cumulative Type I error, and Hosmer-Lemeshow tests assessed goodness of fit for each

logistic regression model.

Accuracy and Missing Data

Data were cleaned and coded to ensure completeness and accuracy of the

dataset prior to analyses. The NHIS categorizes nonresponse in a survey in three

different levels. The first, unit or household-level nonresponse, is defined as an event in

which no information is recorded for any of the members of the selected NHIS

household. The second level, item nonresponse, refers to missing information over a

34

specific item in the questionnaire. The last level of nonresponse occurs when

information for an entire section of the questionnaire goes unrecorded. Typically,

missing records are left missing. Data missingness was assessed through Little’s

MCAR test to explore if data were missing completely at random (MCAR). Little’s values

were > 0.05 which suggests that the data may be assumed to be MCAR. To ensure

accurate representation of the data in this study, all instances of household-level

nonresponse and section-level nonresponse were excluded from further analysis,

resulting in 27 case deletions from an original sample of 3,457 persons.

Results

Sample Characteristics

Participant demographics are displayed in Table 2-1. The NHIS 2014 sample

respondents analyzed for this study ranged from 18 to 85 years of age with a mean age

of 43.5 (SD 16) years. The majority of persons were over the age of 40 (53.8%) and

over one-third (37%) were between the ages of 40 and 60. For the four Latino/a

countries of origin selected, Puerto Rico (16.6%), Mexico (68.1%), Cuba (9.2%), and

Dominican Republic (6.1%), just over half (55.6%) of respondents were female. The

majority (58%) had completed high school, while very few (9.2%) pursued higher

education and had a Bachelor’s degree or higher. Over one-third (39%) were not U.S.

citizens.

The incidence of CVD clinical risk factors (hypertension, high cholesterol, and

heart conditions) for the aggregate sample of Latino/as from Puerto Rico, Mexico, Cuba,

and Dominican Republic are displayed on Table 2-2. Among all Latino/a respondents,

nearly one-fourth (24.5%) had been told they had hypertension while 23% were told

35

they had high cholesterol. Lastly, very few (3.5%) respondents reported being told that

they had a heart condition.

Associations between Acculturation and CVD

The results of the associations between CVD clinical risk factors including

hypertension, high cholesterol and heart conditions and acculturation (citizenship

status) and other covariates are summarized in Table 2-3.Citizenship status was

significantly associated with hypertension in a simple binary logistic model (data not

shown). After adjusting for demographic confounders including sex, high school

education, and age as a continuous variable, there was a significant and positive

association between acculturation (citizenship status) and hypertension (OR = 1.254,

CI: 1.01-1.60). Similarly, in a separate model where age was applied as a dichotomous

variable (over/under age 40), citizenship status also had a significant and positive

association with hypertension (OR = 1.70, CI: 1.40-2.10). Additionally, results indicated

that the prevalence of hypertension increased as age increased (OR = 1.07, CI: 1.06-

1.08) and for those over the age of 40 (OR = 6.64, CI: 5.27-8.40). Moreover, for those

over the age of 40, high school education was protective against hypertension (OR =

0.80, CI: 0.66-0.98). No statistically significant associations were found between males

or females.

In an unadjusted model (data not shown), analyses indicated that acculturation

as measured by citizenship status was positively and significantly associated with high

cholesterol. However, after adjusting for sex, high school education, and age as a

continuous variable, that association only approached significance (p = 0.051) (OR =

1.23, CI: 0.99-1.52). Conversely, for Latino/as over the age of 40, the association

between acculturation and high cholesterol was positive and significant (OR = 1.48, CI:

36

1.21-1.82). Furthermore, similar to the models with hypertension, age was positively

associated with high cholesterol. Additionally, for those over the age of 40, the

prevalence of high cholesterol was significantly higher (OR = 4.35, 95% CI: 3.52-5.37).

No statistically significant associations were found between high cholesterol and high

school education, or between high cholesterol and sex.

The results of the association between acculturation and heart condition were

significant and positive in a binary logistic model (data not shown). However, that

association was attenuated after adjusting for sex, high school education, and age as a

continuous variable (OR = 1.47, CI: 0.88-2.47). Nonetheless, similar to the associations

reported in the high cholesterol models, the association between acculturation and heart

conditions was positive and significant for those over the age of 40 (OR = 1.82, CI: 1.10-

3.02). In contrast to previous models measuring hypertension and high cholesterol, no

associations were found between high school education and heart conditions.

Additionally, there were no statistically significant findings to report between heart

conditions and sex (male/female).

Discussion

This study found differences in prevalence for various CVD risk factors including

hypertension, high cholesterol, and heart conditions in association with acculturation in

an aggregate sample of Latino/as from the 2014 NHIS. These findings provide insight

into the Latino/a health paradox. As the Latino/a population in the nation continues to

grow, it will become increasingly important to fully understand this construct. While the

mechanism underlying the association of acculturation and CVD-related clinical risk

factors remains discordant, the findings in this study add valuable evidence contributing

to our understanding of disease control and prevention in minority populations while

37

maintaining adequate quality of life. First, the study shows that Latino/as with different

citizenship status exhibited varying levels of hypertension, high blood pressure, and

heart conditions. These results are consistent with and supported by prior studies

examining acculturation and other CVD related measures (Pérez-Stable et al., 2001;

Abraído-Lanza et al., 2005; Lara et al., 2005; Van Wieren et al., 2011; Daviglus et al.,

2012). While the measures used and the population sampled are unique to this study,

the study addresses some of the limitations engendered by prior research and builds on

a collection of literature which suggests a harmful association between acculturation

and CVD (Van Wieren et al., 2011; Daviglus et al., 2012).

The results suggested a statistically significant increase in hypertension with

increased acculturation after adjusting for age. This finding is supported by Daviglus et

al., (2012) which reported higher prevalence of systolic blood pressure in a population

cohort from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL)

investigation. Additionally, our study demonstrated a statistically significant protective

factor against hypertension for those over the age of 40 who had completed a high

school education. The link between positive health outcomes and education is widely

understood. While the impact of education on health varies by age, increased schooling

has been linked to longevity and higher self-reported health status (Cutler & Lleras-

Muney, 2007). Moreover, our results are consistent with previous studies that have

shown that CVD risks increase for those over the age of 40 (Cutler & Lleras-Muney,

2007). Importantly, for those over the age of 40, citizenship status exhibited a harmful

effect on high cholesterol which was not present in the younger population. Additionally,

when adjusting for age, sex, and citizenship status, the protective factor displayed by

38

high school education in association with hypertension was attenuated for high

cholesterol. This may be attributable to genetic composition or behavioral differences

(Rodriguez, Hicks, & Lopez, 2012).

Eamranond et al., (2009), Pabon-Nau et al., (2010), and Daviglus et al., (2012),

reported no differences in hypertension across sex in age adjusted analyses. Likewise,

our findings did not indicate any association between sex and hypertension.

Additionally, we found no association between sex and high cholesterol or heart

conditions in our analyses. Contrastingly, Daviglus et al., (2012) reported high

cholesterol to be significantly associated with women only. Additional work on CVD and

sex has found differences in obesity, and diabetes among men and women (Castañeda

et al., 2016).This underscores the need to examine within-group sex differences that

may exist for specific subgroups.

Furthermore, the findings showed a harmful effect for acculturation and heart

conditions for those over the age of 40. While the term “heart conditions” has not been

used in prior studies, it supports related studies exploring this association as it pertains

to coronary heart disease in minority populations in the U.S. (Mooteri, Petersen,

Dagubati, & Pai, 2004; Van Wieren et al., 2011; Daviglus et al., 2012). Older adults are

also more likely to have an extended duration of residence in the country, given that the

median age of all Latino/as living in the U.S is 29 (Krogstad & Lopez, 2015). This

increases the likelihood that certain behavior changes including dietary habits, physical

activity, alcohol consumption, and smoking impact their cardiovascular health (Mooteri

et al., 2004; Abraído-Lanza et al., 2005; Van Wieren et al., 2011; Anderson et al., 2009;

Daviglus et al., 2012). These behaviors should be explored in the context of within-

39

groups and heterogeneous samples in order to determine the paradoxical associations

that remain.

This study has several limitations, most of which are inherent when analyzing

data from large datasets. First, the NHIS data are cross sectional and thus we cannot

track participants over time. Second, not all of the adults in the NHIS are asked about

hypertension, high cholesterol, and heart conditions. Even though the sample is

randomly selected by the NHIS researchers, differences may exist between those who

were asked and those that were not. Another important limitation is our use of self-

reported data such as hypertension instead of a biometric measure of blood pressure.

Not only is this retrospective method of data collection subject to recall and social

desirability bias, but studies have shown that conditions such as hypertension have

been underreported by Latino/as in the past (Yi et al., 2014). Despite concerns over

self-reported data (Smith & Bradshaw, 2005; Arias et al., 2010; Yi, Elfassy, Gupta,

Myers, & Kerker, 2014), some of our findings have been similar to studies using

biometric measures (Daviglus et al., 2012).

Despite its breadth as an overall sample of Latino/as, the sample sizes in certain

subgroups, particularly those from less represented Central American and South

American countries in the U.S., were relatively small and were omitted from analyses.

The reported sample for this study of Latino/as only included Mexicans, Cubans, and

Dominicans; thus, these findings should not be generalized to all Latino/a subgroups.

Additionally, the sample population and the subsequent models reported do not

differentiate between countries of origin. Moreover, despite the call for an inclusion of bi-

dimensional models of acculturation in public health and Latino/a research, these

40

measures are rarely, if ever, included in large data collection efforts such as this (Van

Wieren et al., 2011; Allen & Cummings, 2016). Since the NHIS only collects proxy

measures of acculturation, this study was limited to using citizenship status. While the

use of citizenship status as a proxy for acculturation has been strongly supported (Liang

1994; Yang 1994; Lopez-Gonzalez et al., 2005; Aqtash 2007), due to a high rate of

section-level nonresponse in the Family questionnaire, years in the U.S. could not be

used to complement citizenship status.

Limitations notwithstanding, the overall findings in this study have implications for

clinical and policy level interventions as well future research. Public health professionals

should take adequate steps to increase screenings, specifically for acculturated

Latino/as over the age of 40 who may be at-risk for hypertension, high cholesterol, and

heart conditions. Regulations that push for measures of acculturation, or incorporate

this information as part of patient medical records, may assist physicians and other

health care providers in delivering a more targeted health care experience. Additionally,

research focusing on Latino/a differences based on country of origin may facilitate the

understanding of such information and allow for population specific interventions. In the

future, researchers should increase their attention on data collection strategies that not

only integrate Latino/a subgroups, but also explore standardized acculturation

measures. The use of bi-dimensional measures that collectively isolate potential

confounders and group differences will enhance our understanding of the mechanism

underlying acculturation and health. Ultimately, this study allows potential contributions

resulting from an explanation of acculturation and health, along with the identification of

41

cultural differences in the cardiovascular health status and cardiovascular related

behaviors of Latino/as of different subgroups.

42

Table 2-1. Participant demographics

Characteristic % (N)

Age, mean (SD) 43.5 (16)

Age

< 40 46.2 (1583)

40+ 53.8 (1847)

Sex

Male 44.4 (1524)

Female 55.6 (1906)

Education

No high school completed 42 (1426)

High school completed 58 (1973)

U.S. Citizenship

No 39 (1336)

Yes 61 (2085) Note: Age was assessed continuously and as a dichotomous variable (over/under 40).

43

Table 2-2. Participant CVD clinical risk factors

Characteristic % (N)

Hypertension

No 75.5 (2590)

Yes 24.5 (840)

High cholesterol

No 77 (2641)

Yes 23 (789)

Heart condition

No 96.5 (3310)

Yes 3.5 (120) Note: Risk factor responses are based on the self-report of the 3,430 participants in our sample.

44

Table 2-3. Binary logistic models of Latino/a CVD risk factors susceptibility (N=3430)

Characteristic B O.R. 95% C.I. Wald p Model Fit

Model 1: Hypertension (n=840) 0.66

Sex -0.151 0.860 (0.704-1.051) 2.18 0.140

Citizenship 0.226 1.254 (1.010-1.562) 4.07 0.044

High school -0.057 0.944 (0.763-1.169) 0.03 0.598

Age 0.071 1.073 (1.066-1.081) 409.68 <.001


Sex -0.132 0.876 (0.724-1.059) 1.86 0.172

Citizenship 0.529 1.696 (1.379-2.087) 25.03 <.001

High school -0.219 0.804 (0.657-0.983) 4.51 0.034

Age > 40 1.893 6.640 (5.270-8.365) 257.98 <.001

Model 3: High Cholesterol (n=789) <.001

Sex -0.087 0.917 (0.757-1.109) 0.80 0.371

Citizenship 0.209 1.232 (0.999-1.520) 3.81 0.051

High school -0.042 0.959 (0.783-1.175) 0.16 0.687

Age 0.049 1.050 (1.044-1.057) 247.01 <.001

Model 4: High Cholesterol (n=789) 0.15

Sex -0.088 0.916 (0.759-1.104) 0.85 0.357

Citizenship 0.394 1.483 (1.210-1.817) 14.43 <.001

High school -0.148 0.863 (0.707-1.053) 2.12 0.146

Age > 40 1.469 4.346 (3.515-5.374) 183.96 <.001

Model 5: Heart Condition (n=120) 0.43

Sex -0.043 0.958 (0.616-1.489) 0.04 0.849

Citizenship 0.387 1.472 (0.878-2.468) 2.15 0.142

High school 0.870 1.332 (0.826-2.148) 1.38 0.240

Age 0.036 1.037 (1.024-1.051) 30.19 <.001

45

Table 2-3. Continued



Sex -0.040 0.961 (0.619-1.490) 0.03 0.857

Citizenship 0.599 1.821 (1.099-3.018) 5.41 0.020

High school 0.152 1.165 (0.723-1.875) 0.39 0.531

Age > 40 0.815 2.260 (1.385-3.688) 10.65 0.001 Note: N reflects the total number of respondents. n reflects the events for each dependent variable for that model. Age is measured continuously for ages 18-85. Age > 40 is binary (values less than 40 as reference). p is the Wald test significance (values < .0465 are significant – given Familywise error corrections). Model Fit values refer to Hosmer & Lemeshow goodness-of-fit (values >.05 are significant

46

CHAPTER 3 COUNTRY OF ORIGIN: IMPACT ON ACCULTURATION AND CARDIOVASCULAR

RISK FACTORS IN A NATIONAL SAMPLE OF LATINO/AS

Background

The Latino/a population in the U.S. is now over 55 million (Stepler & Brown,

2014). While nearly 60% of the population is Mexican, other subgroups such as

Dominicans are growing 85% more rapidly (Lopez & Dockterman, 2011), and

establishing in more diverse cities (Benjamin-Alvarado et al., 2008); contributing to the

continued growth of this population. Despite the extreme diversity that exists in terms of

economics, culture, and history (Gonzalez, 2001; Van Wieren et al., 2011), much of the

literature has traditionally clustered over 20 different countries interchangeably through

pan-ethnic labels such as Latino or Hispanic. Latino/as are currently described as

having lower socioeconomic status (SES) proxies such as poor housing conditions,

having higher unemployment rates, and less education compared to non-Latino/a

whites, which puts them at increased risk for cardiovascular disease (CVD) and other

health problems (Pérez-Stable, Marín, & Marín, 1994; Gallo et al., 2009; Waldstein,

2010). Despite these barriers data have shown that Latino/as fare better than non-

Latino/a whites in many health related measures and outcomes, known as the Latino/a

health paradox (Markides & Coreil, 1986; Franzini et al., 2001; Markides & Esbach,

2005; Waldstein, 2010; Ruiz, Steffen, & Smith, 2013; Valles, 2016).However, few

studies have examined this relationship across multiple Latino/a subgroups, and

variations related to country of origin are thought to exist (Morales et al., 2002;

Eamranond et al., 2009; Daviglus et al., 2012).

CVD is the leading cause of mortality across all Latino/a subgroups (Roger et al.,

2012), and their risk of CVD-related complications increases as they age (Daviglus et

47

al., 2012). Additionally, increased acculturation has been thought to increase their risk

of CVD (Vaeth & Willett, 2005; Daviglus et al., 2012). In Latino/as, acculturation has

been described as the adoption of U.S. values and customs (Morales et al., 2002;

Halgunseth, Ispa, & Rudy, 2006; Gallo et al., 2009; Van Wieren et al., 2011; Schachter

et al., 2012). CVD-related acculturation research among U.S Latino/as has been

predominantly homogeneous in their sample (Mitchell et al., 1990; Sundquist &

Winkleby, 1999; Crimmins et al., 2007; Gallo et al., 2009). In the limited studies where

country of origin was assessed, few subgroups and small sample sizes have raised

more questions than answers with regards to CVD-related outcomes and acculturation

(Moran et al., 2007; Derby et al., 2010; Van Wieren et al., 2011; Daviglus et al., 2012).

Moran et al., (2007) reported that lower English language proficiency was associated

with lower prevalence of hypertension especially for Mexicans. Similarly, Daviglus et al.,

(2012), reported that greater acculturation was associated with greater prevalence of

CVD. Additionally, Eamranond et al., (2009), reported that Spanish-speaking Latino/as

had higher systolic blood pressure. Lastly, Pabon-Nau et al., (2010), reported that

Puerto Ricans and Dominicans but not Mexicans experienced higher hypertension

prevalence with increased acculturation.

Health researchers interested in the Latino/a health paradox have singled out


al., 2012). In the past, acculturation in health has been measured using proxies such as

language (Marin et al., 1987; Marin, 1992), and citizenship status (Liang 1994; Yang

1994; Lopez-Gonzalez et al., 2005; Aqtash 2007). The relationship between

acculturation and CVD has been explored in the past using predominantly Mexican and

48

Mexican-American populations (Mitchell et al., 1990; Sundquist & Winkleby, 1999;

Crimmins et al., 2007; Gallo et al., 2009; Morales et al., 2011). Few studies have

analyzed this relationship in the context of country of origin (Pabon-Nau et al., 2010;

Daviglus et al., 2012; Rodriguez et al., 2012; Castañeda et al., 2016) but have focused

mostly on hypertension and diabetes and have reported conflicting findings. The aim of

this study was to further investigate the relationship between acculturation and CVD risk

factors while exploring whether respondents’ country of origin has an impact on this

relationship. More specifically, this study focused on the effect that distinct Latino/a

subgroups (Puerto Rican, Mexican, Cuban, and Dominican) had over three CVD clinical

risk factors (hypertension, high cholesterol, and heart conditions) across a spectrum of

acculturation (language proficiency and citizenship status), SES (income, education and

concern over health costs) and other confounders. Given the countries differences with

respect to their colonization, liberation, involvement with the U.S. government,

education, and diversity of customs (Gonzalez, 2001), we hypothesized that the

associations between risk factors and acculturation would vary by country of origin.

Methods

Data Source

Data from the 2014 National Health Interview Survey (NHIS) was used, and was

considered exempt status by the University of Florida Institutional Review Board. As

part of the National Center for Health Statistics (NCHS) under the Centers for Disease

Control and Prevention (CDC), the NHIS is the primary data collection program of

noninstitutionalized civilians in the U.S. The purpose and scope of the NHIS is to collect

data on a broad range of health issues in order to monitor the overall health of the U.S.

population. Seven questionnaires – (1) Household, (2) Family, (3) Family Disability

49

Questions, (4) Adult, (5) Child, (6) Cover, and (7) Functioning and Disability – comprise

the 2014 NHIS; three of which will be used in this study; Household, Family, and Adult.

Sample Design




















50









Variables/Measures

The NHIS dataset includes set of questions relating to a respondents’















51







States.” These selections were recoded as (0) No, and (1) Yes. Age, collected as year

of birth was coded as (0) for those “under 1 year,” continuously (1-84) for those between


coded age continuously starting at age 18. English language proficiency or “how well

English is spoken” was recorded as an ordinal scale ranging from one to four. The

responses were reversed coded into an English Language scale including: (1) “Not at

all,” (2) “Not well,” (3) “Well,” and (4) “Very Well.”

NHIS researchers also assessed participant concern over healthcare costs in

relation to access to care. Using a scale from one to four, they questioned “how worried

are you right now about not being able to pay medical costs for normal healthcare?”

where (1) “not worried at all,” (2) “not too worried,” (3) “moderately worried,” and (4)

“very worried,” were coded respectively. Moreover, researchers recorded Latino/a

subgroup details to account for country of origin: (1) Puerto Rico, (2) Mexico, (3) Cuba,

and (4) Dominican Republic. These were recoded into four distinct dichotomous

variables in which each specific country was coded (1), and the other three countries

were coded (0). This resulted in the following variables: (a) Puerto Ricans v non-Puerto

52

Rican Latino/as, (b) Mexicans v non-Mexican Latino/as, (c) Cubans v non-Cuban

Latino/as and (d) Dominicans v non-Dominican Latino/as.







During final analysis, unengaged responses and missing data were excluded and the

models were created using a sample of 3,430 respondents. Although persons identified

as Puerto Rican (n = 569) are recognized as legally naturalized citizens upon birth, this

was adjusted for through the inclusion of the English language proficiency measure.

Correlations and binary logistic regression models were tested on the

variables/measures described above. Using females, non-citizens, and no high school

completed as reference groups (0), odds ratios (ORs) with corresponding confidence

intervals (CIs) were reported for each country of origin (all other countries as the

reference). ORs were calculated for the following items: (1) “Hypertension also called

high blood pressure,” (2) “high cholesterol,” and (3) “any kind of heart condition.”

Familywise error post hoc tests were conducted to account for the possibility of

cumulative Type I error, and Hosmer-Lemeshow tests assessed goodness of fit for each

logistic regression model.




53

different levels. The first, unit or household-level nonresponse is defined as an event in











Results


Participant demographics are summarized in Table 3-1. The Latino/a subgroups

selected were represented as follows: Puerto Rican (16.6%), Mexican (68.1%), Cuban

(9.2%), and Dominican (6.1%). The selected Latino/a sample from the NHIS 2014

respondents analyzed ranged from 18 to 85 years of age with a mean age of 43.5 (SD ±

16) years. For country of origin, the Mexican subgroup was the youngest with a mean

age of 41.83 (SD 16.5) years while the Cuban subgroup was the oldest with a mean

age of 50.99 (SD 18.6) years. Slightly more than half (55.6%) of the selected Latino/a

respondents were female; with Cubans having the smallest female to male difference

across groups (51.6 and 48.4 respectively). Across groups, the majority (58%) had

completed high school, although very few (9.2%) pursued higher education and had a

Bachelor’s degree or higher. Additionally, Mexicans had the lowest percentage of

54

individuals who completed high school (52.4) while Cubans had the highest completion

percentage (79.4).

Participant’s English language proficiency ranged from not very well (11.8%) to

very well (52.7%). Among the four subgroups, Puerto Ricans demonstrated the highest

English language proficiency (75.6%) while Dominicans experienced the lowest

(17.8%). Over one-third (39%) of the overall selected sample were not U.S. citizens.

Puerto Rican participants not withstanding (naturalized U.S. citizens upon birth),

Cubans portrayed the highest U.S. citizenship percentage across groups (71.5) while

Mexicans experienced the lowest (50.9). Furthermore, participant’s worry over their

ability to pay for health care costs varied between not worried at all (26.7%) to very

worried (26.4%), with the largest difference across subgroups in the Puerto Rican

sample (41.3% not worried at all). The Mexican subgroup was the most worried about

paying for health care costs (28.9%). The incidence of CVD clinical risk factors

(hypertension, high cholesterol, and heart conditions) for the aggregate sample of

Latino/as from Puerto Rico, Mexico, Cuba, and Dominican Republic are displayed on

Table 3-2.

CVD Associations

The results summarized in Table 3-3, display the associations between CVD

clinical risk factors including hypertension, high cholesterol and heart conditions and

acculturation (citizenship status) and other covariates in logistic models comparing non-

Puerto Ricans to Puerto Ricans. With non-Puerto Ricans as the reference group,

citizenship status was significantly associated (OR = 1.328, CI: 1.04-1.22) with

hypertension after adjusting for demographic confounders including sex, high school

education, age, English language proficiency, and concern over health care costs.

55

Additionally, prevalence of hypertension increased as age increased (OR = 1.074, CI:

1.06-1.08), as worry over paying health care costs increased (OR = 1.128, CI: 1.04-

1.22), and for Puerto Ricans (OR = 1.324, CI: 1.03-1.69). Conversely, only increase in

age (OR = 1.049, CI: 1.04-1.06) and increase in worry over paying health care costs

(OR = 1.134, CI: 1.05-1.27) were significantly associated with prevalence of high

cholesterol. When assessing heart conditions prevalence, age (OR = 1.042, CI: 1.03-

1.05), worry over health care costs (OR = 1.202, CI: 1.02-1.42), and Puerto Ricans (OR

= 1.639, CI: 1.05-2.57) were associated. There were no significant findings for sex, high

school education, or English language proficiency.

Table 3-4 summarizes the associations between hypertension, high cholesterol

and heart conditions and acculturation and other covariates in logistic models

comparing non-Mexicans to Mexicans. With non-Mexicans as the reference group,

citizenship status (OR = 1.292, CI: 1.01-1.65) was significantly associated with


education, age, English language proficiency, and concern over health care costs.

Additionally, prevalence of hypertension increased as age increased (OR = 1.072, CI:

1.06-1.08), and as worry over paying health care costs increased (OR = 1.131, CI: 1.04-

1.23). Furthermore, being Mexican as opposed to any other non-Mexican Latino/a was

a protective factor (OR = 0.744, CI: 0.61-0.91) in likelihood of having hypertension.

Conversely, with regards to high cholesterol, there was no association between

countries of origin when comparing Mexican to non-Mexican Latino/a respondents.

However, age (OR = 1.049, CI: 1.04-1.06), citizenship (OR = 1.287, CI: 1.02-1.62), and

increase in worry over paying health care costs (OR = 1.128, CI: 1.05-1.22) were

56

associated with prevalence of high cholesterol. When assessing heart conditions

prevalence, age (OR = 1.040, CI: 1.03-1.05), worry over health care costs (OR = 1.207,

CI: 1.02-1.43), and Mexican (OR = 0.619, CI: 0.42-0.93) were statistically significant.

There were no statistically significant findings for sex, high school education, or English

language proficiency.

The associations between hypertension, high cholesterol and heart conditions

and acculturation and other covariates in logistic models comparing non-Cubans to

Cubans are summarized in Table 3-5. With non-Cubans as the reference group,

citizenship status was significantly associated (OR = 1.424, CI: 1.13-1.80) with


education, age, English language proficiency, and concern over health care costs. The

prevalence of hypertension also increased as age increased (OR = 1.073, CI: 1.06-

1.08), and as worry over paying health care costs increased (OR = 1.119, CI: 1.03-

1.21). Conversely, being Cuban was a protective factor for high cholesterol prevalence

(OR = 0.624, CI: 0.46-0.85). Additionally, findings indicated that an increase in age (OR

= 1.050, CI: 1.04-1.06), citizenship (OR = 1.280, CI: 1.02-1.60), and increase in worry

over paying health care costs (OR = 1.129, CI: 1.05-1.22) were significantly associated

with prevalence of high cholesterol. When assessing heart conditions prevalence, only

age (OR = 1.042, CI: 1.03-1.06), was associated. There were no significant findings for

sex, high school education, or English language proficiency.

Table 3-6 summarizes the associations between CVD clinical risk factors

including hypertension, high cholesterol and heart conditions and acculturation and

other covariates in binary logistic models comparing non-Dominicans to Dominicans.

57

With non-Dominicans as the reference group, citizenship status was significantly

associated (OR = 1.406, CI: 1.11-1.78) with hypertension after adjusting for

demographic confounders including sex, high school education, age, English language

proficiency, and concern over health care costs. Additionally, prevalence of

hypertension increased as age increased (OR = 1.073, CI: 1.06-1.08), and as worry

over paying health care costs increased (OR = 1.121, CI: 1.03-1.22). Conversely, only

increase in age (OR = 1.049, CI: 1.04-1.06) and increase in worry over paying health

care costs (OR = 1.131, CI: 1.05-1.22) were significantly associated with prevalence of

high cholesterol. When assessing heart conditions prevalence, only age (OR = 1.041,

CI: 1.03-1.05), was statistically significant. Moreover, being Dominican was associated

with higher prevalence of heart conditions (OR = 1.919, CI: 1.10-3.50). No significant

findings for sex, high school education, or English language proficiency were found.

Discussion

This study found differences for various CVD risk factors including hypertension,

high cholesterol, and heart conditions in association with acculturation in a Latino/a

sample. More specifically, findings indicated relevant differences across Latino/a

countries of origin which have been understudied in the past (Mitchell et al., 1990;

Sundquist & Winkleby, 1999; Crimmins et al., 2007; Gallo et al., 2009; Van Wieren et

al., 2011; Daviglus et al., 2012). These findings provide insight into the potential role

that country of origin and acculturation have in the health outcomes of Latino/a

populations. These cross-subgroup differences are especially important in

understanding the mechanism underlying the association of acculturation and CVD-

related risk factors in this growing population. Furthermore, the results provide valuable

58

evidence in support of the Latino/a health paradox and engender new research

questions.

The study showed that compared to the other subgroups in this sample, Puerto

Ricans are at greater risk for hypertension, a finding that is consistent with studies

which assessed hypertension in Puerto Rican subgroups (Daviglus et al., 2012; Yi et al.,

(2014). Data from the World Health Statistics 2015, might explain this finding, given that

Puerto Rico has the highest hypertension prevalence rate of the sample countries (Yi et

al., 2014). This association between hypertension and country of origin was not found

among the Dominican subgroup. Using a similar sample, Pabon-Nau et al., (2010),

reported significantly higher odds of hypertension for Puerto Ricans, and Dominicans

compared to Mexican-Americans after controlling for demographic and acculturation

differences. These differences may be explained by the variance in socio-economic and

acculturation measures among the subgroups. In contrast to the higher prevalence of

hypertension among Puerto Ricans, Mexican participants exhibited a protective factor

on hypertension prevalence. This finding is supported by Moran et al., (2007) whom

compared a Mexican and a Caribbean subgroup and found that Mexicans reported

lower hypertension.

The results suggested an increase in hypertension, high cholesterol, and heart

conditions across some of the Latino/a subgroups related to age and worry over paying

for health care costs. While previous studies have explored the relationship among CVD

and age, there are less studies examining the relationship between CVD and

acculturation (Castañeda et al., 2016). Latino/as, medical costs have been reported to

predict multiple access to care indicators including regular physician visits, regular

59

source of care, and even CVD-related screening (Morales et al., 2002). Studies show

that Latino/as with depressive disorders may experience poor disease management; the

findings reported here may be explained by links between stress and obesity and

smoking (Castañeda et al., 2016). It would be beneficial to assess these measures with

regards to subgroup differences and acculturation in order to further understand this

potential explanatory mechanism.

This study also demonstrated a protective factor for heart conditions among

Mexicans. Compared to non-Mexican Latino/as, Mexicans displayed lower risk of heart

conditions among our sample after adjusting for age, sex, and education. In contrast,

the results indicated that Dominicans were at increased risk for heart conditions

compared to Puerto Ricans, Mexicans, and Cubans. While this is the first study to

include this measure, other studies have indicated increased risks of diabetes and

hypertension for Dominicans and overall better CVD outcomes for Mexicans (Pabon-

Nau et al., 2010). The standardization of measures and inclusion of subgroups in future

studies would be necessary in understanding subgroup differences.

In contrast to research associating language-based acculturation measures and

hypertension (Moran et al., 2007; Eamranond et al., 2009; Derby et al., 2010), this study

found no association between English language proficiency and prevalence of CVD-

related risk factors among any of the subgroups. This finding is supported by Yi et al.,

(2014), whose sample was predominantly Puerto Rican and Dominican and thus more

heterogeneous than previous language-based studies which were predominantly

targeting Mexicans. However, citizenship status as an acculturation proxy was able to

predict for hypertension and high cholesterol across groups. In other words, we found

60

that acculturation was indeed associated with hypertension and high cholesterol rates

for the different countries of origin. This finding is supported by Rodriguez et al., (2012)

which reported hypertension rates in Mexican and non-Mexican subgroups using

citizenship status as a predictor. This study is the first to report a protective factor for

high cholesterol among Cubans. In the past, Daviglus et al., (2012), reported higher

rates of cholesterol in Cuban females of the HCHS/SOL population cohort. While our

study found no significant differences by sex, the contrasting findings may be a result of

the age difference in the samples. The women in our study were older (51.6 years to

43.5 years respectively) and more likely to experience the population-wide economic

crisis of the “special period” which saw a decline in diabetes and heart disease in

Cubans (Franco et al., 2013).

This study is not without limitations, some of which are inevitable when analyzing

large datasets. First, the NHIS data are cross sectional and thus we cannot track

participants over time. Second, not all of the adults in the NHIS were asked about

hypertension, high cholesterol, and heart conditions. Even though the sample is

randomly selected by the NHIS researchers, differences may exist between the

participants who were asked and those who were not. Another important limitation is our

use of self-reported data such as hypertension instead of a biometric measure of blood

pressure. Not only is this retrospective method of data collection subject to recall and

social desirability bias, but studies have shown that conditions such as hypertension

have been underreported by Latino/as in the past (Yi et al., 2014). However, some of

our findings are similar to Daviglus et al., (2012), which used biometric measures. This

lends validation to the data collection process and reduces concern over the use of self-

61

reported data Smith & Bradshaw, 2005; Arias et al., 2010; Yi, Elfassy, Gupta, Myers, &

Kerker, 2014).

Additionally, while the NHIS takes vast measures to ensure that their dataset is

representative of the U.S. population, we are limited by the questions asked and data

collected by the researchers. This limitation reduces our ability to analyze all possible

confounders and the effect these have. In spite of the oversampling of Latino/as

employed by NHIS researchers during data collection, the sample sizes in certain

subgroups, especially those from countries in Central America and South America are

small (n < 200 cases). Thus, for this study, the reported sample of Latino/as only

included Puerto Ricans, Mexicans, Cubans, and Dominicans; meaning that these

findings should not be generalized across other Latino/a subgroups. Furthermore, given

the limited sample size of certain subgroups and moderate cell counts for some of the

measures analyzed, interactions were not assessed. The dichotomous nature of the

measures in a sample that was not equally large for all groups would have resulted in

wide confidence intervals for the interaction term coefficients and would have presented

inconclusive results. Moreover, despite the need for bi-dimensional models of

acculturation to fully understand the Latino health paradox, these measures are rarely, if

ever, included in data collection efforts of this size (Van Wieren et al., 2011; Allen &

Cummings, 2016). Lastly, this study was limited to using citizenship status and English

language proficiency since the NHIS only collects proxy measures of acculturation.

While the use of these proxies has been widely supported (Liang 1994; Yang 1994;

Lopez-Gonzalez et al., 2005; Aqtash 2007), years in the U.S. could not be used to

62

complement them due to a high rate of section-level nonresponse in the Family

questionnaire.

In conclusion, our findings suggested that CVD risk factors including prevalence

of hypertension, high cholesterol, and heart conditions is impacted by Latino/a

subgroup/country of origin. Findings suggest possible clinical implications in

misrepresenting Latino/as based on studies that relied on a homogenous subset. It is

possible that health practitioners have underestimated the burden of CVD-related risk

factors across diverse Latino/a subgroups. Future research should continue to explore

these differences among a more diverse sample in order to inform prospective

interventions.

The period of acculturation and the experiences to which individuals are exposed

to are not uniform for all Latino/as. While previous research has shown that prolonged

time in the U.S. is significantly associated with negative health outcomes (Pabon-Nau et

al., 2010), these findings suggest that country of origin plays an important role in this

association and should be considered a ubiquitous factor in future explorations. It would

be of interest to explore social norms and cultural values pertinent to the country of

origin, as well as individual level factors including dietary and physical exercise habits

along with other risky behaviors that may explain disease prevalence. Currently, few

epidemiologic assessments are conducted in Latin American countries. Policies

promoting scientific collaboration across countries, or regulations that facilitate such

research, would provide health professionals the opportunity to assess some of these

factors first-hand. Understanding the baseline rates of disease for specific health

63

conditions in the sending nation can provide invaluable insight into disease prevention

in the host nation.

While our finding that higher acculturation was associated with a higher

prevalence of hypertension is consistent with other studies (Moran et al., 2007), others

such as Eamranond et al., (2009), have reported that higher English language

proficiency and longer time of residence was associated with improved cardiovascular

health. This conflicting finding is probably explained by the age differences among the

two studies. The mean age of the participants in that study was 20 years over the mean

of this study, which could indicate not only changing cultural and behavioral norms

associated with the country of origin, but also historical and contextual changes in that

particular country. This highlights the need for more comprehensive generational

implications among diverse Latino/a subgroups (Fang, Ayala, & Loustalot, 2012). In the

future, generational patterns among Latino/a subgroups should be further explored as

they may account for the age driven differences found across the literature. To further

explore these interactions it will be important to assess all measures accurately and

ensure large inclusion of all subgroups.

64


Characteristic P.R. % (N) Mexico % (N) Cuba % (N) D.R. % (N) p

Age, mean (SD) 44.8 (16.5) 41.83 (14.8) 50.99 (18.6) 47.83 (17.5) <.001

Sex 0.249

Male 41.7 (237) 44.7 (1045) 48.4 (153) 42.8 (89)

Female 58.3 (332) 55.3 (1292) 51.6 (163) 57.2 (119)

English Language Proficiency <.001

Not very Well 4.4 (25) 12.6 (295) 15.2 (48) 17.8 (37)

Not Well 8.3 (47) 23.4 (546) 22.2 (70) 21.2 (44)

Well 11.8 (67) 15.6 (365) 14.6 (46) 15.4 (32)

Very Well 75.6 (430) 48.4 (1131) 48.1 (152) 45.7 (95)

Health Cost Concern <.001

Not Worried at all 41.3 (229) 22.2 (508) 32.7 (101) 27.6 (56)

Not too Worried 21.3 (118) 22.5 (514) 15.5 (48) 26.6 (54)

Moderately Worried 17.3 (96) 26.3 (602) 29.8 (92) 24.1 (49)

Very Worried 20 (111) 28.9 (661) 22 (68) 21.7 (44)

Education <.001

No high school completed 31.7 (180) 47.6 (1100) 20.6 (65) 39.5 (81)

High school completed 68.3 (387) 52.4 (1211) 79.4 (251) 60.5 (124)

U.S. Citizenship <.001

No 1.1 (6) 50.9 (1185) 28.5 (90) 26.6 (55)

Yes 98.9 (563) 49.1 (1144) 71.5 (226) 73.4 (152)

Note: Participant demographics for a sample of 3,430 Latino/as of different countries of origin are displayed.

p values reflect difference in country of origin group means from ANOVAs.

65

Table 3-2. Participant CVD clinical risk factors per Country of Origin

Characteristic Country of Origin

% (N) p

Hypertension Puerto Rico Mexico Cuba Dominican Republic <.001

No 69.1 (393) 79.5 (1857) 65.5 (207) 63.9 (133)

Yes 30.9 (176) 20.5 (480) 34.5 (109) 36.1 (75)

High cholesterol 0.016

No 73.5 (418) 78.3 (1830) 77.5 (245) 71.2 (148)

Yes 26.5 (151) 21.7 (507) 22.5 (71) 28.8 (60)

Heart condition <.001

No 94 (535) 97.4 (2277) 96.2 (304) 93.3 (194)

Yes 6 (34) 2.6 (60) 3.8 (12) 6.7 (14) Note: Risk factor responses are based on the self-report of the 3,430 participants in our sample.


66

Table 3-3. Binary logistic models of CVD risk factors susceptibility for Puerto Ricans compared to non-Puerto Rican Latino/as (N=3430)



Sex -0.111 0.895 (0.745-1.074) 1.42 0.233

Age 0.071 1.074 (1.067-1.080) 469.72 <.001

High school -0.110 0.896 (0.728-1.102) 1.08 0.299

Language Proficiency 0.019 1.019 (0.917-1.133) 0.13 0.723

Citizenship 0.284 1.328 (1.041-1.694) 5.23 0.022

Worried Health Cost 0.121 1.128 (1.040-1.224) 8.40 0.004

Puerto Rican 0.280 1.324 (1.037-1.690) 5.07 0.024

Model 2: High Cholesterol (n=789) <.001

Sex -0.079 0.924 (0.776-1.099) 0.80 0.370

Age 0.048 1.049 (1.043-1.055) 266.67 <.001

High school -0.140 0.869 (0.714-1.058) 1.95 0.163


Citizenship 0.198 1.219 (0.968-1.536) 2.84 0.092


Puerto Rican 0.120 1.127 (0.891-1.426) 0.99 0.319


Sex -0.182 0.834 (0.568-1.224) 0.86 0.354

Age 0.041 1.042 (1.030-1.054) 46.38 <.001

High school 0.239 1.270 (0.816-1.974) 1.12 0.289


Citizenship 0.341 1.407 (0.813-2.433) 1.49 0.222


Puerto Rican 0.494 1.639 (1.047-2.566) 4.67 0.031

Note: N reflects the total number of respondents. n reflects the events for each dependent variable for that model. Age is measured continuously for ages 18-85. p is the Wald test significance (values < .0431 are significant – given Familywise error corrections). Model Fit values refer to Hosmer & Lemeshow goodness-of-fit (values >.05 are significant).

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Table 3-4. Binary logistic models of CVD risk factors susceptibility for Mexicans compared to non-Mexican Latino/as (N=3430)



Sex -0.114 0.892 (0.743-1.071) 1.49 0.222

Age 0.070 1.072 (1.066-1.079) 458.63 <.001

High school -0.157 0.854 (0.694-1.052) 2.20 0.138


Citizenship 0.257 1.292 (1.012-1.650) 4.24 0.040


Mexican -0.296 0.744 (0.609-0.909) 8.38 0.004


Sex -0.082 0.921 (0.774-1.096) 0.85 0.356

Age 0.048 1.049 (1.043-1.055) 265.51 <.001

High school -0.137 0.872 (0.715-1.063) 1.84 0.175


Citizenship 0.252 1.287 (1.021-1.623) 4.55 0.033


Mexican 0.073 1.076 (0.885-1.307) 0.54 0.464


Sex -0.191 0.826 (0.563-1.213) 0.95 0.330

Age 0.040 1.040 (1.028-1.053) 43.10 <.001

High school 0.158 1.171 (0.754-1.819) 0.49 0.482


Citizenship 0.302 1.352 (0.782-2.338) 1.17 0.280


Mexican -0.479 0.619 (0.415-0.925) 5.49 0.019


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Table 3-5. Binary logistic models of CVD risk factors susceptibility for Cubans compared to non-Cuban Latino/as (N=3430)



Sex -0.117 0.890 (0.741-1.068) 1.58 0.209

Age 0.071 1.073 (1.066-1.080) 459.77 <.001

High school -0.125 0.882 (0.715-1.089) 1.35 0.245


Citizenship 0.354 1.424 (1.127-1.801) 8.74 0.003


Cuban 0.028 1.028 (0.760-1.390) 0.03 0.858


Sex -0.074 0.929 (0.780-1.105) 0.69 0.405

Age 0.049 1.050 (1.044-1.056) 272.33 <.001

High school -0.089 0.915 (0.748-1.118) 0.76 0.384


Citizenship 0.247 1.280 (1.023-1.601) 4.67 0.031


Cuban -0.471 0.624 (0.458-0.852) 8.83 0.003


Sex -0.184 0.832 (0.567-1.221) 0.88 0.347

Age 0.041 1.042 (1.030-1.055) 46.14 <.001

High school 0.252 1.287 (0.821-2.019) 1.21 0.272


Citizenship 0.490 1.632 (0.960-2.774) 3.27 0.071


Cuban -0.305 0.737 (0.389-1.397) 0.87 0.350


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Table 3-6. Binary logistic models of CVD risk factors susceptibility for Dominicans compared to non-Dominican Latino/as (N=3430)



Sex -0.114 0.893 (0.744-1.072) 1.49 0.223

Age 0.070 1.073 (1.066-1.080) 465.69 <.001

High school -0.129 0.879 (0.715-1.081) 1.50 0.221


Citizenship 0.340 1.406 (1.111-1.778) 8.05 0.005


Dominican 0.301 1.351 (0.950-1.922) 2.81 0.094


Sex -0.080 0.923 (0.776-1.099) 0.81 0.368

Age 0.048 1.049 (1.043-1.055) 265.01 <.001

High school -0.148 0.862 (0.708-1.050) 2.17 0.140


Citizenship 0.221 1.247 (0.997-1.559) 3.75 0.053


Dominican 0.143 1.153 (0.820-1.623) 0.67 0.413


Sex -0.185 0.831 (0.566-1.220) 0.89 0.345

Age 0.040 1.041 (1.029-1.053) 44.76 <.001

High school 0.196 1.216 (0.781-1.894) 0.75 0.386


Citizenship 0.429 1.535 (0.904-2.606) 2.52 0.112


Dominican 0.652 1.919 (1.057-3.486) 4.58 0.032


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CHAPTER 4 ASSOCIATION BETWEEN COUNTRY OF ORIGIN, ACCULTURATION AND

CARDIOVASCULAR RISK FACTORS IN A NATIONAL SAMPLE OF LATINO/AS

Background

The number of Latino/as in the United States (U.S.) is projected to rise past 120

million (~30% of the total population) in the next 40 years from the 55.3 million (17.3%)

currently residing in the U.S. today (Stepler & Brown, 2014). The majority of this

population is estimated to include nearly 32 million Mexicans (60%), 4.6 million Puerto

Ricans (9.2%), almost 2 million Cubans (3.5%), over 1.5 million Salvadorans (3.3%) and

1.4 million Dominicans (2.8%) (Lopez & Dockterman, 2011). Additionally, though the

three largest countries of origin subgroups remain Mexico, Puerto Rico, and Cuba

respectively, they are no longer the fastest growing subgroups, with countries like

Dominican Republic growing by more than double their average growth (Lopez &

Dockterman, 2011). Furthermore, unlike previous decades where Latino/as populated

specific regions or communities mostly limited to five states (California, New York,

Texas, Florida and New Jersey) in three separate regions of the country: South,

Southwest, and Northeast (Benjamin-Alvarado, DeSipio, & Montoya, 2008), new cities

such as Atlanta, GA or Charlotte, NC (Singer, Hardwick, & Brettell, 2008) are

experiencing a rise in their Latino/a populations (Benjamin-Alvarado et al., 2008).

Despite the use of pan-ethnic labels such as Hispanic and Latino, variations

related to country of origin are thought to exist among U.S. Latino/as (Daviglus et al.,

2012). However, the majority of Latino/a research conducted has used predominantly

Mexican and Mexican-American samples (Morales et al., 2002; Eamranond et al., 2009;

Van Wieren et al., 2011). This has engendered gaps in our understanding of several

key public health concerns in this growing population. One example is in our

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understanding of the Latino/a health paradox. Health is influenced by a range of factors

including environmental, social, economic, and personal variables. As such, low

socioeconomic status which is often tied to low standard of living and quality of life has

been associated with poor life expectancy and increased mortality rates (Franzini et al.,

2001; Waldstein, 2010). Given the nature of many Latino/a communities in the U.S.

(high unemployment, substandard housing, and limited access to care) and the barriers

facing Latino/as (educationally disadvantaged, low salary positions, etc.) it is not

surprising that they are at higher risk for diabetes, obesity, and cervical cancer

(Markides & Coreil, 1986, Friedman-Jimenez & Ortiz, 1994; Franzini et al., 2001; Pérez-

Stable et al., 2001, Waldstein, 2010; CDC, 2016). However, over the last three

decades, data have shown that Latino/as fare better than non-Latino/a whites in many

health related measures and outcomes, thus the health paradox (Markides & Coreil,

1986; Franzini et al., 2001; Markides & Esbach, 2005; Waldstein, 2010; Ruiz, Steffen, &

Smith, 2013; Valles, 2016).

Health researchers interested in the Latino/a health paradox have identified


al., 2012). Acculturation is the process influenced by temporal factors in which

individuals come to accept and adopt behaviors and beliefs of the host nation through

peer-to-peer interactions (Morales et al., 2002; Halgunseth, Ispa, & Rudy, 2006; Gallo,

et al., 2009). In the past, acculturation in health has been measured using proxies such

as language (Marin et al., 1987; Marin, 1992), and citizenship status (Liang 1994; Yang

1994; Lopez-Gonzalez et al., 2005; Aqtash 2007). Moreover, given that cardiovascular

disease (CVD) is the leading cause of mortality across all ethnicities (Lee et al., 2015),

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some researchers have attempted to explore the Latino/a paradox (Goldman, 2016), as

it relates to CVD (Overton et al., 2015). However, in order to fully understand the

Latino/a health paradox and address existing health disparities, the associations

between risk factors for CVD and other variables such as acculturation and

socioeconomic status (SES) in the Latino/a population must be further explored. SES

has been previously assessed by income, education, or occupation (Adler, & Newman,

2002). Researchers have postulated that perhaps these determinants of health do not

directly affect health but rather serve as proxies to other factors such as affordability

and accessibility of care (Angell, 1993; Wood et al., 1999; Dunlop, Coyte, & McIsaac,

2000).

Among the risk factors associated with incidence of CVD, tobacco consumption,

unhealthy diet, and physical inactivity have the deepest impact (Anderson et al., 2009)

among all populations, including Latino/as. Acculturation, presents a unique and

important role in the incidence, control, and prevention of CVD as well. In this study,

CVD related concerns including hypertension, high cholesterol, heart conditions,

smoking and physical activity will be examined in relation to acculturation across

different countries of origin. Some reports indicate that physical activity increases with

acculturation (Abraído-Lanza et al., 2005; Slattery et al., 2006), while others report that

higher acculturation leads to a decrease in this behavior (Lara et al., 2005). These

discrepancies in the literature are also reported on smoking and acculturation (Marin et

al., 1989; Pérez-Stable et al., 2001; Abraído-Lanza et al., 2005; Parrinello et al., 2015).

Furthermore, studies focused on other risk factors including hypertension have also

reported conflicting findings (Pabon-Nau et al., 2010; Daviglus et al., 2012; Rodriguez et

73

al., 2012; Castañeda et al., 2016). The aim of this study is to further investigate the

relationship between acculturation and CVD risk factors in the context of different

countries of origin. More specifically, this study will assess the effect smoking and

physical activity have with hypertension, high cholesterol, and heart conditions across a

spectrum of acculturation for Latino/a subgroups from Puerto Rico, Mexico, Cuba and

Dominican Republic.

Methods

Data Source

Data used in this study comes from the 2014 National Health Interview Survey

(NHIS), and was considered exempt status by the University of Florida Institutional

Review Board. Under the Centers for Disease Control and Prevention (CDC), as part of

the National Center for Health Statistics (NCHS), the NHIS is the primary data collection

program of noninstitutionalized civilians in the U.S. The purpose and scope of the NHIS

is to collect data on a broad range of health issues in order to monitor the overall health

of the U.S. population. Seven questionnaires – (1) Household, (2) Family, (3) Family

Disability Questions, (4) Adult, (5) Child, (6) Cover, and (7) Functioning and Disability –

comprise the 2014 NHIS; three of which will be used in this study; Household, Family,

and Adult.

Sample Design






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75

Variables/Measures

The NHIS dataset includes set of questions relating to a respondents’





















States.” These selections were recoded as (0) No, and (1) Yes. Age, collected as year

of birth was coded as (0) for those “under 1 year,” continuously (1-84) for those between

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coded age continuously starting at age 18. English language proficiency or “how well

English is spoken” was recorded as an ordinal scale ranging from one to four. The

responses were reversed coded into an English Language scale including: (1) “Not at

all,” (2) “Not well,” (3) “Well,” and (4) “Very Well.” Moreover, participant concern over

healthcare costs in relation to access to care was documented. Using a scale from one

to four, they questioned “how worried are you right now about not being able to pay

medical costs for normal healthcare?” where (1) “not worried at all,” (2) “not too

worried,” (3) “moderately worried,” and (4) “very worried,” were coded respectively.

NHIS researchers also assessed Latino/a subgroup details to account for country

of origin: (1) Puerto Rico, (2) Mexico, (3) Cuba, and (4) Dominican Republic. These

were recoded into four distinct dichotomous variables in which each specific country

was coded (1), and the other three countries were coded (0). This resulted in the

following variables: (a) Puerto Ricans v non-Puerto Rican Latino/as, (b) Mexicans v

non-Mexican Latino/as, (c) Cubans v non-Cuban Latino/as and (d) Dominicans v non-

Dominican Latino/as. Additionally, smoking and physical activity were recorded. All

adults were asked if they had smoked at least 100 cigarettes in their entire life. Those

who answered “yes” were asked “on the average, how many cigarettes do you smoke a

day?” and if they “NOW smoked cigarettes every day, some days, or not at all.” Current

smokers were defined as persons who have ever smoked 100 cigarettes and who

currently smoke every day or some days. Non-smokers were coded as (0) and smokers

were coded as (1). To record physical activity, participants reported whether or not they

normally engaged in “vigorous leisure-time physical activities” or “light or moderate

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leisure-time physical activities” for “at least ten minutes that cause heavy sweating or

large increases in breathing or heart rate” and “for at least ten minutes that cause only

light sweating or a slight to moderate increase in breathing or heart rate” respectively.

Those who answered yes to either question were coded as yes (1) and those who

answered no to both were coded as (0).







ANOVAs were conducted to compare means across countries of origin and significant

differences were explored using post hoc Bonferroni’s methods. During final analysis,

unengaged responses and missing data were excluded and the models were created

using a sample of 3,430 respondents. Although persons identified as Puerto Rican (n =

569) are recognized as legally naturalized citizens upon birth, this was adjusted for

through the inclusion of the English language proficiency measure.

Correlations and binary logistic regression models were tested on the

variables/measures described above. Using females, non-citizens, no high school

completed, non-smokers and no physical activity as reference groups (0), odds ratios

(ORs) with corresponding confidence intervals (CIs) were reported for each country of

origin (all other countries as the reference). ORs were calculated for the following items:

(1) “Hypertension also called high blood pressure,” (2) “high cholesterol,” and (3) “any

kind of heart condition.” Additionally, to assess the association between acculturation

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and smoking and physical activity, ORs were calculated after adjusting for sex, age,

education and health cost concern. Familywise error post hoc tests were conducted to

account for the possibility of cumulative Type I error, and Hosmer-Lemeshow tests

assessed goodness of fit for each logistic regression model.




different levels. The first, unit or household-level nonresponse, is defined as an event in











Results


The Latino/a subgroups selected were represented as follows: Puerto Rican

(16.6%), Mexican (68.1%), Cuban (9.2%), and Dominican (6.1%). Table 4-1

summarizes the participant demographics. The selected Latino/a sample from the NHIS

2014 respondents analyzed ranged from 18 to 85 years of age with a mean age of 43.5

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(SD±16) years. In the selected Latino/a sample, the Mexican subgroup was the

youngest with a mean age of 41.83 (SD±16.5) years while the Cuban subgroup was the

oldest with a mean age of 50.99 (SD±18.6) years. Slightly more than half (55.6%) of the

respondents were female; with Cubans having the smallest female to male difference

across groups (51.6 and 48.4, respectively). Across groups, the majority (58%) had

completed high school, although very few (9.2%) pursued higher education and had a

Bachelor’s degree or higher. Additionally, Mexicans had the lowest percentage of

individuals who completed high school (52.4%) while Cubans had the highest

completion percentage (79.4%).

Participant’s self-reported English language proficiency ranged from not very well

(11.8%) to very well (52.7%). Among the four subgroups, Puerto Ricans demonstrated

the highest English language proficiency (75.6%) while Dominicans expressed the

lowest (17.8%). Over one-third (39%) of the overall selected sample were not U.S.

citizens. Puerto Rican participants not withstanding (naturalized U.S. citizens upon

birth), Cubans reported the highest U.S. citizenship percentage across groups (71.5%)

while Mexicans reported the lowest (50.9%). Furthermore, participant’s worry over their

ability to pay for health care costs varied between not worried at all (26.7%) to very

worried (26.4%), with the largest difference across subgroups in the Puerto Rican

sample (41.3% not worried at all). The Mexican subgroup was the most worried about

paying for health care costs (28.9%).

The incidence of CVD clinical and behavioral risk factors (hypertension, high

cholesterol, heart conditions, smoking and physical activity) for the sample of Latino/as

from Puerto Rico, Mexico, Cuba, and Dominican Republic are displayed in Table 4-2.

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Among all Latino/a respondents, nearly one-fourth (24.5%) had been told they had

hypertension while 23% were told they had high cholesterol. Among these, Mexicans

were the subgroup with the least cases of prevalence (20.5% hypertension and 21.7%

high cholesterol). Additionally, they were also the subgroup with the lowest prevalence

of heart conditions. In terms of smoking, one-fourth (25.6%) of the selected sample

were smokers, with the highest prevalence reported in the Puerto Rican subgroup

(38.4%). Over one-half (55.4%) of all respondents engaged in physical activity, among

these, the Mexican subgroup exhibited the highest physical activity (59.5%).

Dominicans were the subgroup with the lowest prevalence of smokers (19.4%) and had

the lowest prevalence of respondents who engaged in physical activity (43.7%).

Group Differences among Latino/a Countries of Origin

To assess the extent of the differences between countries of origin subgroups,

ANOVAs were calculated for age, sex, English language proficiency, health cost

concern, high school education, citizenship, hypertension, high cholesterol, heart

condition, smoking and physical activity. The calculated p values were reported in Table

4-1 and Table 4-2. ANOVAs were significant for age (p <.001), English language

proficiency (p <.001), health cost concern (p <.001), high school education (p <.001),

citizenship (p <.001), hypertension (p <.001), high cholesterol (p =.001), heart condition

(p <.001), smoking (p <.001), and physical activity (p <.001). Analysis for age indicated

that Puerto Ricans differed from Mexicans and Cubans but not from Dominicans.

Mexicans differed from all other subgroups whereas Cubans only differed from Puerto

Ricans and Mexicans. For English language proficiency and smoking, post hoc tests

showed that only Puerto Ricans differed from every other subgroup. Likewise, for

hypertension, only Mexicans exhibited a difference compared to the other three

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subgroups. Post hoc tests revealed that Mexicans differed in health cost concern from

all others, while Puerto Ricans also differed from Cubans. Education wise, Cubans

differed from all subgroups, while Puerto Ricans also differed from Mexicans. Bonferroni

analysis also indicated that Puerto Ricans and Mexicans differed from all other

subgroups in terms of U.S. citizenship. For heart conditions, subgroup differences were

noted for Puerto Ricans and Mexicans and for Dominicans and Mexicans. In terms of

physical activity, Puerto Ricans and Mexicans differed from Cubans and Dominicans.

Acculturation and Associations of CVD Risk Factors

The results summarized in Table 4-3, display the associations between

acculturation (English language proficiency and citizenship) and smoking and physical

activity. Results indicated that after adjusting for sex, age, high school education and

health cost concern, the proxies of acculturation are associated with smoking. Both

language proficiency (O.R. = 1.337, CI: 1.21-1.48) and citizenship (O.R. = 1.273, CI:

1.03-1.57) are significantly associated with smoking in a positive direction. For physical

activity, language proficiency was also positively associated. No significant association

was found between citizenship status and physical activity. Moreover, in both models,

age was associated; positively for smoking (O.R. = 1.019, CI: 1.01-1.02) and negatively

for physical activity (O.R. = 0.982, CI: 0.98-0.99).


and smoking and other covariates comparing non-Puerto Rican Latino/as to Puerto

Ricans are summarized in Table 4-4. With non-Puerto Ricans as the reference group,

increasing age (O.R. = 1.073, CI: 1.06-1.08), citizenship status (O.R. = 1.326, CI: 1.04-

1.70), worried about health costs (O.R. = 1.128, CI: 1.04-1.22) and smoking (O.R. =

1.364, CI: 1.11-1.67) were associated with hypertension. Additionally, age (O.R. =

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1.048, CI: 1.04-1.05), worried about health costs (O.R. = 1.132, CI: 1.05-1.22), and

smoking (O.R. = 1.373, CI: 1.13-1.67) were associated with high cholesterol.

Furthermore, age (O.R. = 1.041, CI: 1.03-1.05), and worried about health costs (O.R. =

1.198, CI: 1.01-1.42) were associated with heart conditions.


and heart conditions and physical activity and other covariates among non-Puerto Rican

Latino/as and Puerto Ricans. With non-Puerto Ricans as the reference group,

increasing age (O.R. = 1.073, CI: 1.06-1.08), and worried about health costs (O.R. =

1.118, CI: 1.03-1.22) were associated with hypertension. While significance values for

citizenship (O.R. = 1.279, CI: 1.00-1.64), and physical activity (O.R. = 0.828, CI: 0.69-

0.99) were <.05, they were not statistically significant given the Familywise error

corrections. Additionally, age (O.R. = 1.049, CI: 1.04-1.06), and worried about health

costs (O.R. = 1.140, CI: 1.05-1.23) were associated with high cholesterol. Moreover,

age (O.R. = 1.041, CI: 1.03-1.05), and Puerto Ricans (O.R. = 1.737, CI: 1.1-2.73) were

associated with heart conditions.


hypertension, high cholesterol and heart conditions and smoking and other covariates in

logistic models comparing non-Mexican Latino/as to Mexicans. With non-Mexicans as

the reference group, age (O.R. = 1.072, CI: 1.06-1.08), worried about health costs (O.R.

= 1.132, CI: 1.04-1.23), smoking (O.R. = 1.376, CI: 1.12-1.69), and Mexicans (O.R. =

0.761, CI: 0.62-0.93) were associated with hypertension. Additionally, age (O.R. =

1.048, CI: 1.04-1.05), citizenship (1.280, CI: 1.01-1.62), worried about health costs

(O.R. = 1.126, CI: 1.04-1.22), and smoking (O.R. = 1.389, CI: 1.14-1.69) were

83

associated with high cholesterol. Furthermore, age (O.R. = 1.040, CI: 1.03-1.05),

worried about health costs (O.R. = 1.204, CI: 1.02-1.42), and Mexican (O.R. = 0.632,

CI: 0.42-0.94) were associated with heart conditions.


and physical activity and other covariates comparing non-Mexican Latino/as to

Mexicans are summarized in Table 4-7. With non-Mexicans as the reference group, age

(O.R. = 1.072, CI: 1.06-1.08), worried about health costs (O.R. = 1.120, CI: 1.03-1.22),

and Mexican (O.R. = 0.777, CI: 0.63-0.95) were associated with hypertension.

Additionally, age (O.R. = 1.049, CI: 1.04-1.06), citizenship (O.R. = 1.289, CI: 1.02-1.63),

and worried about health costs (O.R. = 1.134, CI: 1.05-1.23) were associated with high

cholesterol. Moreover, sex (O.R. = 1.04, CI: 1.03-1.05), and Mexican (O.R. = 0.630, CI:

0.42-0.95) were associated with heart conditions.


and heart conditions and smoking and other covariates among non-Cuban Latino/as

and Cubans. With non-Cubans as the reference group, age (O.R. = 1.073, CI: 1.06-

1.08), citizenship (O.R. = 1.403, CI: 1.11-1.78), worried about health costs, (O.R. =

1.121, CI: 1.03-1.22), and smoking (O.R. = 1.398, CI: 1.41-1.71) were associated with

hypertension. Additionally, age (O.R. = 1.05, CI: 1.04-1.06), worried about health costs

(O.R. = 1.128, CI: 1.04-1.22), smoking (O.R. = 1.367, CI: 1.2-1.66), and Cuban (O.R. =

0.634, CI: 0.46-0.87) were associated with high cholesterol. Furthermore, an increase in

age (O.R. = 1.041, CI: 1.03-1.05) was associated with heart conditions.


hypertension, high cholesterol and heart conditions and physical activity and other

84

covariates in logistic models comparing non-Cuban Latino/as to Cubans. With non-

Cubans as the reference group, age (O.R. = 1.07, CI: 1.06-1.08), citizenship (O.R. =

1.354, CI: 1.07-1.72), and worried about health costs (O.R. = 1.110, CI: 1.02-1.21) were

associated with hypertension. Additionally, age (O.R. = 1.051, CI: 1.04-1.6), citizenship

(O.R. = 1.274, CI: 1.02-1.60), worried about health costs (O.R. = 1.136, CI: 1.01-1.23),

and Cuban (O.R. = 0.643, CI: 0.47-0.88) were associated with high cholesterol.

Moreover, age (O.R. = 1.041, CI: 1.03-1.05), was associated with heart conditions.


and smoking and other covariates comparing non-Dominican Latino/as to Dominicans

are summarized in Table 4-10. With non-Dominicans as the reference group, age (O.R.

= 1.073, CI: 1.06-1.08), citizenship (O.R. = 1.382, CI: 1.09-1.75), worried about health

costs (O.R. = 1.123, CI: 1.04-1.22), and smoking (O.R. = 1.409, CI: 1.15-1.73) were

associated with hypertension. Additionally, age (O.R. = 1.048, CI: 1.04-1.05), worried

about health costs (O.R. = 1.131, CI: 1.05-1.22), and smoking (O.R. = 1.387, CI: 1.14-

1.68) were associated with high cholesterol. Furthermore, sex (O.R. = 1.040, CI: 1.03-

1.05), smoking (O.R. = 1.552, CI: 1.04-2.33), and Dominican (O.R. = 1.972, CI: 1.08-

3.59) were associated with heart conditions.


and heart conditions and physical activity and other covariates among non-Dominican

Latino/as and Dominicans. With non-Dominicans as the reference group, age (O.R. =

1.073, CI: 1.06-1.08), citizenship (O.R. = 1.341, CI: 1.06-1.70), and worried about health

costs, (O.R. = 1.112, CI: 1.02-1.21) were associated with hypertension. Additionally,

age (O.R. = 1.049, CI: 1.04-1.06), and worried about health costs (O.R. = 1.138, CI:

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1.05-1.23), were associated with high cholesterol. Moreover, age (O.R. = 1.040, CI:

1.03-1.05), was associated with heart conditions. Associations between sex and

hypertension, high cholesterol, or heart conditions were not found for this or any other

countries of origin subgroup.

Discussion

This study found acculturation to be associated with smoking and physical

activity among Latino/as. Additionally, results demonstrated differences in smoking and

physical activity among Latino/as by age and education. Moreover, findings indicated

relevant differences across Latino/a countries of origin for hypertension, high

cholesterol, heart conditions, smoking, and physical activity. These findings provide

insight into the potential impact that country of origin, acculturation, and clinical and

behavioral risk factors have on health. These subgroup differences are especially

important in understanding the mechanism underlying the association of acculturation

and CVD-related risk factors in this growing population. The results provide valuable

evidence in support of the Latino/a health paradox and engender new research

questions.

First, data indicated that increased acculturation was associated with smoking

among all Latino/as, a finding that is supported by previous research (Pérez-Stable et

al., 2001; Abraído-Lanza et al., 2005; Lara et al., 2005; Van Wieren et al., 2011). In

other words, for those who were citizens and for those with higher English language

proficiency, smoking increased. Similarly, we found that males had considerably higher

smoking rates than females, regardless of acculturation and this is congruent with other

findings (Morales et al., 2002), but in opposition of Marin et al., (1989) who reported

higher rates only among females. Additionally, smoking was found to be associated with

86

higher rates of hypertension and high cholesterol among all subgroups after adjusting

for age, sex, education, acculturation, SES and country of origin. Puerto Ricans

particularly reported the highest smoking rates among the countries analyzed, which is

supported by research by Pabon-Nau et al., (2010). This is not surprising given the

overall high rates of smoking in Puerto Rico are higher (38.4%) than those of the other

three countries (21.8%).

Second, our findings indicated that increased acculturation (as measured by

reported increased English language proficiency) was associated with increased

leisure-time physical activity among this sample of Latino/as. This finding was in line

with our hypothesis and similar to Van Wieren et al., (2011) who reported a small

increase in physical activity among Latino/as with increased acculturation. Across

countries of origin, physical activity was protective for hypertension among Puerto

Ricans and Cubans. However, after adjusting for age, sex, education, acculturation,

SES, smoking, and country of origin, only the Mexican subgroup demonstrated a

protective factor for hypertension. This implies that physical activity (of which Mexicans

had the highest rate) might explain some intricacies of the Latino/a health paradox. If

Mexicans are the only subgroup benefitting from a protective factor for hypertension, it

is important to then explore variations related to country of origin. Additionally, it begs

the question whether outcomes previously believed to be improved for all Latino/as

given that previous studies focused mostly on Mexican samples, have been

exaggerated. If true, the Latino health paradox which has been difficult enough to

explain, would require that country of origin be analyzed for each individual health

outcome.

87

In the case of hypertension, this finding for Mexicans compared to non-Mexicans

can be in part due to their increased physical activity. More research is needed to

distinguish between types of exercises and even factors such as occupational prestige

that may influence this finding. Similarly, only the Cuban subgroup exhibited a protective

factor for high cholesterol. Once again engendering the need for more research

focusing on Latino/a country of origin differences. In the case of the Cuban subgroup,

this difference may be explained given their unique economic and nutritional history in

the context of the other countries. Research has shown that Cubans who experienced

the economic downfall and nutrition scarcity in the island known as the “special period,”

were more likely to have healthy cholesterol levels (Whiteford & Branch, 2008). In all, it

seems that the Latino/a health paradox prediction of protective barriers for health

outcomes is heavily associated with country of origin and behavioral components and

not as ubiquitous for all Latino/as.

Aside from the lower cholesterol difference noted for Cuban versus non-Cuban

subgroups, other differences can be noted for incidence of high cholesterol. Compared

to non-smokers, smokers experienced a rise in self-reported cholesterol. While

associations between smoking and cholesterol have been drawn in the past (Okusaga

et al., 2012), these findings were consistent even after adjusting for sex, age,

socioeconomic status (education and concern over health costs), acculturation

(language proficiency and citizenship), and country of origin. On the other hand,

physical activity was not associated with cholesterol for any of the Latino/a subgroups.

While this is consistent with findings reported in a systematic review by Dobbins,

Husson, Decorby, & LaRocca, (2013), health professionals have typically encouraged

88

physical activity in relation to cholesterol level management. This may suggest why

model fit statistics across logistic regression models were not adequate in multiple

cholesterol models. This finding suggests that the effect of physical activity on

cholesterol may be closely tied to factors such as genetics, nutrition, and environmental.

More research is needed to tease out the effects of this relationship.

While further evidence is needed to understand dissimilarities for physical activity

and smoking in self-reported cholesterol, more country of origin differences were found

for heart conditions. Similar to the protection shared by Mexicans when compared to

non-Mexicans in terms of hypertension, this was noted for heart conditions regardless of

smoking or physical activity. These findings suggest that despite two major behavioral

risk factors, Mexicans may have underlying contributing factors that improve their

cardiovascular health. While this may be expected in this Mexican subgroup since they

smoked less than Puerto Ricans, and were subject to more physical activity than all

other subgroups, the results are the same after adjusting for all other variables. Future

research should explore psychological and physiological differences that may explain

differences for Mexicans that are not present for other Latino/a subgroups. These were

not able to be included in this study due to limitations involving data collection and

application of the NHIS. Given that questions are limited, we were also unable to assess

the participants smoking status and level of physical activity prior to their arrival to the

U.S.

This study had several other limitations, some inherent of secondary data

analysis, specifically concerning large datasets. First, the NHIS data are cross sectional

and thus we cannot track participants over time. Second, not all of the adults in the

89

NHIS were asked about hypertension, high cholesterol, heart conditions, smoking and

physical activity. While the sample is randomly selected by the NHIS researchers, it is

possible that differences exist between the participants who were asked and non-

participants. Another limitation to consider is our use of self-reported data including

hypertension instead of a biometric measure of blood pressure. Not only is this

retrospective method of data collection subject to recall and social desirability bias, but

studies have shown that hypertension and similar conditions are commonly

underreported by Latino/as (Yi et al., 2014). However, some of our findings are similar

to Daviglus et al., (2012), which used biometric measures. This lends validation to the

data collection process and reduces concern over the use of self-reported data Smith &

Bradshaw, 2005; Arias et al., 2010; Yi, Elfassy, Gupta, Myers, & Kerker, 2014).

In addition, while the NHIS takes vast measures to ensure that their dataset is

representative of the U.S. population, we were limited by the questions asked and data

collected by the researchers. This limitation reduced our ability to analyze all possible

confounders and their effects. Despite the oversampling of Latino/as employed by NHIS

researchers during data collection, the sample sizes in certain subgroups, especially

those from countries in Central America and South America are small (n < 200 cases).

Thus, for this study, the reported sample of Latino/as only included Puerto Ricans,

Mexicans, Cubans, and Dominicans; meaning that these findings should not be

generalized across other Latino/a subgroups. Furthermore, given the limited sample

size of certain subgroups and moderate cell counts for some of the measures analyzed,

interactions were not assessed. The dichotomous nature of the measures in a sample

that was not equally large for all groups would have resulted in wide confidence

90

intervals for the interaction term coefficients and would have presented inconclusive

results. Moreover, even though bi-dimensional models of acculturation might better

inform the Latino health paradox, these measures are rarely, if ever, included in large

data collection studies (Van Wieren et al., 2011; Allen & Cummings, 2016). Lastly, this

study was limited to using citizenship status and English language proficiency since the

NHIS only collects proxy measures of acculturation. While the use of these proxies has

been widely supported (Liang 1994; Yang 1994; Lopez-Gonzalez et al., 2005; Aqtash

2007), years in the U.S. could not be used to complement them due to a high rate of

section-level nonresponse in the Family questionnaire. Similarly, occupational prestige

could not be assessed in relation to leisure-time physical activity.

In conclusion, our findings suggested that CVD risk factors including

hypertension, high cholesterol, heart conditions, smoking and physical activity are

impacted by Latino/a subgroup/country of origin. Findings suggest possible clinical

implications in misrepresenting Latino/as based on studies that relied on a homogenous

subset. It is possible that health practitioners have underestimated the burden of CVD-

related risk factors across diverse Latino/a subgroups. Future research should continue

to explore these differences among a more diverse sample in order to inform

prospective interventions. Moreover, future studies should explore differences between

leisure-time physical activity and occupational related physical activity. Additionally, the

impact of psychological distress and other cognitive factors that could be confounding

our understanding of these relationships should be explored. The period of acculturation

and the experiences to which individuals are exposed to are not uniform for all

Latino/as. While previous research has shown that prolonged time in the U.S. is

91

significantly associated with negative health outcomes (Pabon-Nau et al., 2010), these

findings suggest that country of origin plays an important role in this association and

should be considered a ubiquitous factor in future explorations. To further explore these

interactions it will be important to assess all measures accurately and ensure large

inclusion of all subgroups.

92


Characteristic P.R. % (N) Mexico % (N) Cuba % (N) D.R. % (N) p

Age, mean (SD) 44.8 (16.5) 41.83 (14.8) 50.99 (18.6) 47.83 (17.5) <.001

Sex 0.249

Male 41.7 (237) 44.7 (1045) 48.4 (153) 42.8 (89)

Female 58.3 (332) 55.3 (1292) 51.6 (163) 57.2 (119)

English Language Proficiency <.001

Not very Well 4.4 (25) 12.6 (295) 15.2 (48) 17.8 (37)

Not Well 8.3 (47) 23.4 (546) 22.2 (70) 21.2 (44)

Well 11.8 (67) 15.6 (365) 14.6 (46) 15.4 (32)

Very Well 75.6 (430) 48.4 (1131) 48.1 (152) 45.7 (95)

Health Cost Concern <.001

Not Worried at all 41.3 (229) 22.2 (508) 32.7 (101) 27.6 (56)

Not too Worried 21.3 (118) 22.5 (514) 15.5 (48) 26.6 (54)

Moderately Worried 17.3 (96) 26.3 (602) 29.8 (92) 24.1 (49)

Very Worried 20 (111) 28.9 (661) 22 (68) 21.7 (44)

Education <.001

No high school completed 31.7 (180) 47.6 (1100) 20.6 (65) 39.5 (81)

High school completed 68.3 (387) 52.4 (1211) 79.4 (251) 60.5 (124)

U.S. Citizenship <.001

No 1.1 (6) 50.9 (1185) 28.5 (90) 26.6 (55)

Yes 98.9 (563) 49.1 (1144) 71.5 (226) 73.4 (152)

Note: Participant demographics for a sample of 3,430 Latino/as of different countries of origin are displayed.


93

Table 4-2. Participant clinical and behavioral CVD risk factors per Country of Origin

Characteristic Country of Origin

% (N) p

Hypertension Puerto Rico Mexico Cuba Dominican Republic <.001

No 69.1 (393) 79.5 (1857) 65.5 (207) 63.9 (133)

Yes 30.9 (176) 20.5 (480) 34.5 (109) 36.1 (75)

High cholesterol 0.016

No 73.5 (418) 78.3 (1830) 77.5 (245) 71.2 (148)

Yes 26.5 (151) 21.7 (507) 22.5 (71) 28.8 (60)

Heart condition <.001

No 94 (535) 97.4 (2277) 96.2 (304) 93.3 (194)

Yes 6 (34) 2.6 (60) 3.8 (12) 6.7 (14)

Smoker <.001

No 61.6 (348) 76.4 (1780) 77.5 (244) 80.6 (166)

Yes 38.4 (217) 23.6 (549) 22.5 (71) 19.4 (40)

Physical Activity <.001

No 41.4 (222) 40.5 (925) 55 (172) 56.3 (112)

Yes 58.6 (314) 59.5 (1358) 45 (141) 43.7 (87)

Note: Risk factor responses are based on the self-report of the 3,430 participants in our sample.


94

Table 4-3. Binary logistic models of Latino/a CVD risk factors susceptibility (N=3430)


Model 1: Smoking (n=877) 0.050

Sex 0.952 2.590 (2.19-3.05) 129.23 <.001

Age 0.018 1.019 (1.013-1.024) 47.45 <.001

Language Proficiency 0.290 1.337 (1.209-1.477) 32.27 <.001

Citizenship 0.242 1.273 (1.032-1.571) 5.08 0.024

High school -0.510 0.600 (0.498-0.723) 28.99 <.001


Model 2: Physical Activity (n=1900) 0.129

Sex 0.067 0.924 (0.776-1.099) 0.84 0.361

Age -0.018 1.049 (1.043-1.055) 55.96 <.001

Language Proficiency 0.162 0.869 (0.714-1.058) 14.21 <.001

Citizenship -0.167 1.090 (0.986-1.206) 3.16 0.076

High school 0.273 1.219 (0.968-1.536) 10.79 0.001

Worried Health Cost -0.050 1.134 (1.049-1.226) 2.31 0.129


95

Table 4-4. Binary logistic models of CVD risk factors and smoking susceptibility for Puerto Ricans compared to non-Puerto Rican Latino/as (N=3430)



Sex -0.017 0.845 (0.700-1.020) 3.08 0.079

Age 0.071 1.073 (1.066-1.080) 459.27 <.001

High school -0.085 0.919 (0.746-1.132) 0.63 0.426


Citizenship 0.282 1.326 (1.039-1.693) 5.16 0.023


Smoker 0.311 1.364 (1.112-1.674) 8.86 0.003

Puerto Rican 0.229 1.257 (0.982-1.609) 3.30 0.069


Sex -0.142 0.867 (0.725-1.037) 2.43 0.119

Age 0.047 1.048 (1.042-1.054) 255.07 <.001

High school -0.106 0.899 (0.737-1.097) 1.10 0.294


Citizenship 0.199 1.220 (0.968-1.537) 2.84 0.092


Smoker 0.317 1.373 (1.130-1.669) 10.14 0.001

Puerto Rican 0.056 1.057 (0.833-1.342) 0.21 0.646

96




Sex -0.253 0.776 (0.524-1.150) 1.59 0.207

Age 0.041 1.041 (1.029-1.054) 44.13 <.001

High school 0.283 1.327 (0.851-2.068) 1.56 0.211


Citizenship 0.340 1.405 (0.812-2.431) 1.48 0.224


Smoker 0.373 1.452 (0.966-2.182) 3.22 0.073

Puerto Rican 0.446 1.562 (0.994-2.455) 3.74 0.053 Note: N reflects the total number of respondents. n reflects the events for each dependent variable for that model. Age is measured continuously for ages 18-85. p is the Wald test significance (values < .0421 are significant – given Familywise error corrections). Model Fit values refer to Hosmer & Lemeshow goodness-of-fit (values >.05 are significant).

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Table 4-5. Binary logistic models of CVD risk factors and physical activity susceptibility for Puerto Ricans compared to non-Puerto Rican Latino/as (N=3430)



Sex -0.105 0.900 (0.748-1.085) 1.22 0.269

Age 0.070 1.073 (1.066-1.080) 443.82 <.001

High school -0.116 0.891 (0.721-1.101) 1.15 0.284


Citizenship 0.246 1.279 (1.000-1.637) 3.84 0.050


Physical Activity -0.188 0.828 (0.688-0.998) 3.94 0.047

Puerto Rican 0.241 1.273 (0.988-1.639) 3.49 0.062


Sex -0.088 0.915 (0.767-1.092) 0.96 0.327

Age 0.048 1.049 (1.043-1.055) 256.80 <.001

High school -0.161 0.851 (0.696-1.041) 2.46 0.117


Citizenship 0.203 1.226 (0.970-1.548) 2.91 0.088


Physical Activity 0.102 1.108 (0.926-1.325) 1.25 0.264

Puerto Rican 0.086 1.090 (0.855-1.389) 0.49 0.486

98




Sex -0.124 0.883 (0.599-1.302) 0.39 0.531

Age 0.040 1.041 (1.028-1.053) 41.47 <.001

High school 0.201 1.222 (0..783-1.909) 0.77 0.378


Citizenship 0.379 1.461 (0.837-2.550) 1.77 0.183



Puerto Rican 0.552 1.737 (1.105-2.728) 5.73 0.017 Note: N reflects the total number of respondents. n reflects the events for each dependent variable for that model. Age is measured continuously for ages 18-85. p is the Wald test significance (values < .0421 are significant – given Familywise error corrections). Model Fit values refer to Hosmer & Lemeshow goodness-of-fit (values >.05 are significant).

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Table 4-6. Binary logistic models of CVD risk factors and smoking susceptibility for Mexicans compared to non-Mexican Latino/as (N=3430)



Sex -0.171 0.842 (0.698-1.017) 3.20 0.074

Age 0.070 1.072 (1.065-1.079) 449.69 <.001

High school -0.126 0.882 (0.715-1.087) 1.38 0.239


Citizenship 0.249 1.283 (1.004-1.639) 3.96 0.047


Smoker 0.319 1.376 (1.123-1.687) 9.48 0.002

Mexican -0.273 0.761 (0.623-0.931) 7.01 0.008


Sex -0.147 0.864 (0.722-1.033) 2.58 0.108

Age 0.047 1.048 (1.042-1.054) 255.27 <.001

High school -0.096 0.908 (0.744-1.109) 0.90 0.344


Citizenship 0.247 1.280 (1.014-1.615) 4.33 0.038


Smoker 0.329 1.389 (1.144-1.687) 11.00 0.001

Mexican 0.104 1.109 (0.912-1.350) 1.07 0.301

100




Sex -0.265 0.767 (0.518-1.137) 1.75 0.187

Age 0.039 1.040 (1.027-1.052) 41.16 <.001

High school 0.212 1.237 (0.794-1.926) 0.88 0.347


Citizenship 0.296 1.344 (0.777-2.325) 1.12 0.291


Smoker 0.399 1.490 (0.994-2.233) 3.72 0.054

Mexican -0.459 0.632 (0.423-0.943) 5.04 0.025 Note: N reflects the total number of respondents. n reflects the events for each dependent variable for that model. Age is measured continuously for ages 18-85. p is the Wald test significance (values < .0421 are significant – given Familywise error corrections). Model Fit values refer to Hosmer & Lemeshow goodness-of-fit (values >.05 are significant).

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Table 4-7. Binary logistic models of CVD risk factors and physical activity susceptibility for Mexicans compared to non-Mexican Latino/as (N=3430)



Sex -0.108 0.898 (0.745-1.081) 1.29 0.256

Age 0.070 1.072 (1.065-1.079) 434.48 <.001

High school -0.158 0.853 (0.690-1.056) 2.12 0.145


Citizenship 0.224 1.251 (0.976-1.602) 3.13 0.077



Mexican -0.253 0.777 (0.632-0.954) 5.81 0.016


Sex -0.090 0.914 (0.766-1.090) 1.00 0.317

Age 0.048 1.049 (1.043-1.056) 256.44 <.001

High school -0.153 0.858 (0.700-1.051) 2.19 0.139


Citizenship 0.254 1.289 (1.019-1.630) 4.47 0.035



Mexican 0.093 1.097 (0.898-1.340) 0.82 0.365

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Sex -0.136 0.873 (0.592-1.286) 0.47 0.492

Age 0.038 1.039 (1.026-1.051) 38.32 <.001

High school 0.115 1.122 (0.718-1.752) 0.26 0.613


Citizenship 0.363 1.437 (0.824-2.507) 1.64 0.201



Mexican -0.463 0.630 (0.419-0.947) 4.93 0.026 Note: N reflects the total number of respondents. n reflects the events for each dependent variable for that model. Age is measured continuously for ages 18-85. p is the Wald test significance (values < .0421 are significant – given Familywise error corrections). Model Fit values refer to Hosmer & Lemeshow goodness-of-fit (values >.05 are significant).

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Table 4-8. Binary logistic models of CVD risk factors and smoking susceptibility for Cubans compared to non-Cuban Latino/as (N=3430)



Sex -0.179 0.836 (0.693-1.009) 3.47 0.062

Age 0.070 1.073 (1.066-1.080) 448.93 <.001

High school -0.097 0.907 (0.734-1.121) 0.81 0.368


Citizenship 0.338 1.403 (1.108-1.775) 7.93 0.005


Smoker 0.335 1.398 (1.141-1.713) 10.47 0.001

Cuban 0.040 1.040 (0.768-1.409) 0.07 0.798


Sex -0.135 0.873 (0.730-1.045) 2.20 0.138

Age 0.048 1.050 (1.043-1.056) 260.93 <.001

High school -0.055 0.947 (0.774-1.159) 0.28 0.595


Citizenship 0.231 1.260 (1.007-1.577) 4.07 0.044


Smoker 0.313 1.367 (1.126-1.660) 9.95 0.002

Cuban -0.456 0.634 (0.464-0.865) 8.27 0.004

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Sex -0.266 0.767 (0.517-1.136) 1.75 0.185

Age 0.040 1.041 (1.029-1.054) 43.52 <.001

High school 0.298 1.347 (0.858-2.115) 1.67 0.196


Citizenship 0.477 1.611 (0.947-2.740) 3.10 0.078


Smoker 0.412 1.510 (1.008-2.263) 3.99 0.046

Cuban -0.282 0.754 (0.398-1.430) 0.75 0.388 Note: N reflects the total number of respondents. n reflects the events for each dependent variable for that model. Age is measured continuously for ages 18-85. p is the Wald test significance (values < .0421 are significant – given Familywise error corrections). Model Fit values refer to Hosmer & Lemeshow goodness-of-fit (values >.05 are significant).

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Table 4-9. Binary logistic models of CVD risk factors and physical activity susceptibility for Cubans compared to non-Cuban Latino/as (N=3430)



Sex -0.110 0.896 (0.743-1.079) 1.35 0.245

Age 0.070 1.073 (1.066-1.080) 435.12 <.001

High school -0.130 0.878 (0.708-1.090) 1.39 0.239


Citizenship 0.303 1.354 (1.067-1.718) 6.21 0.013



Cuban 0.035 1.036 (0.764-1.405) 0.05 0.821


Sex -0.082 0.921 (0.771-1.099) 0.83 0.361

Age 0.049 1.051 (1.044-1.057) 261.96 <.001

High school -0.109 0.897 (0.730-1.101) 1.09 0.298


Citizenship 0.242 1.274 (1.015-1.600) 4.37 0.037



Cuban -0.442 0.643 (0.471-0.879) 7.67 0.006

106




Sex -0.131 0.877 (0.595-1.292) 0.44 0.506

Age 0.040 1.041 (1.028-1.053) 41.61 <.001

High school 0.224 1.251 (0.794-1.973) 0.93 0.334


Citizenship 0.543 1.722 (1.003-2.955) 3.89 0.049



Cuban -0.359 0.698 (0.367-1.329) 1.20 0.274 Note: N reflects the total number of respondents. n reflects the events for each dependent variable for that model. Age is measured continuously for ages 18-85. p is the Wald test significance (values < .0421 are significant – given Familywise error corrections). Model Fit values refer to Hosmer & Lemeshow goodness-of-fit (values >.05 are significant).

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Table 4-10. Binary logistic models of CVD risk factors and smoking susceptibility for Dominicans compared to non-Dominican Latino/as (N=3430)



Sex -0.176 0.839 (0.695-1.012) 3.37 0.066

Age 0.070 1.073 (1.066-1.080) 455.07 <.001

High school -0.099 0.905 (0.735-1.115) 0.87 0.350


Citizenship 0.324 1.382 (1.092-1.751) 7.23 0.007


Smoker 0.343 1.409 (1.150-1.726) 10.92 0.001

Dominican 0.326 1.385 (0.972-1.973) 3.26 0.071


Sex -0.143 0.867 (0.725-1.036) 2.47 0.116

Age 0.047 1.048 (1.042-1.054) 253.61 <.001

High school -0.111 0.895 (0.734-1.091) 1.20 0.273


Citizenship 0.204 1.226 (0.980-1.534) 3.18 0.075


Smoker 0.327 1.387 (1.142-1.683) 10.91 0.001

Dominican 0.166 1.181 (0.838-1.663) 0.90 0.342

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Sex -0.269 0.764 (0.516-1.132) 1.80 0.180

Age 0.039 1.040 (1.028-1.053) 42.06 <.001

High school 0.248 1.282 (0.822-1.999) 1.20 0.274


Citizenship 0.418 1.519 (0.895-2.578) 2.40 0.122


Smoker 0.439 1.552 (1.035-2.326) 4.52 0.034

Dominican 0.679 1.972 (1.084-3.587) 4.95 0.026 Note: N reflects the total number of respondents. n reflects the events for each dependent variable for that model. Age is measured continuously for ages 18-85. p is the Wald test significance (values < .0421 are significant – given Familywise error corrections). Model Fit values refer to Hosmer & Lemeshow goodness-of-fit (values >.05 are significant).

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Table 4-11. Binary logistic models of CVD risk factors and physical activity susceptibility for Dominicans compared to non-Dominicans (N=3430)



Sex -0.108 0.898 (0.745-1.081) 1.29 0.256

Age 0.070 1.073 (1.066-1.080) 440.65 <.001

High school -0.131 0.877 (0.710-1.084) 1.47 0.225


Citizenship 0.293 1.341 (1.056-1.703) 5.80 0.016



Dominican 0.244 1.276 (0.889-1.834) 1.75 0.187


Sex -0.089 0.915 (0.767-1.092) 0.97 0.325

Age 0.048 1.049 (1.043-1.055) 255.61 <.001

High school -0.167 0.846 (0.692-1.035) 2.65 0.104


Citizenship 0.217 1.243 (0.991-1.560) 3.53 0.060



Dominican 0.134 1.143 (0.805-1.622) 0.56 0.454

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Sex -0.135 0.874 (0.593-1.287) 0.47 0.495

Age 0.039 1.040 (1.027-1.052) 40.10 <.001

High school 0.161 1.175 (0.751-1.837) 0.50 0.480


Citizenship 0.488 1.629 (0.951-2.791) 3.16 0.075



Dominican 0.555 1.742 (0.939-3.230) 3.10 0.078 Note: N reflects the total number of respondents. n reflects the events for each dependent variable for that model. Age is measured continuously for ages 18-85. p is the Wald test significance (values < .0421 are significant – given Familywise error corrections). Model Fit values refer to Hosmer & Lemeshow goodness-of-fit (values >.05 are significant).

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CHAPTER 5 CONCLUSION

The impact of cardiovascular diseases (CVD) on all populations are widely

understood and researched. CVD is the global leading cause of mortality (Lee et al.,

2016), with an estimated 17 million annual deaths (Mozaffarian et al., 2015). Despite the

wide-reaching effects of CVD, Latino/as are more likely to experience CVD-related

conditions such as hypertension and high cholesterol than any other group (Lee et al.,

2016). Currently, there are over 55 million Latino/as in the U.S, making them the largest

minority group in the country (Stepler & Brown, 2014). Not only is CVD the leading

cause of death for Latino/as in the United States (Mozaffarian et al., 2015), the

American Heart Association estimates that over 33% of Latino/as over the age of 20

suffered from CVD in 2014. Additionally, Latino/as are disproportionately affected by low

income, limited access to health care, language barriers, and lack of health insurance,

which further increase their risk for CVD. Although some research (Van Wieren et al.,

2011) has explored the role that acculturation plays on CVD risk factors, few have

assessed this relationship outside of Mexican samples. Of the studies using

heterogeneous samples, even less have assessed how risk factors could be modified

by country of origin and specifically influenced by smoking (Perez-Stable, et al., 2001)

or physical activity (Neighbors, et al., 2008). Despite these shortcomings, most of the

research to date is labeled under pan-ethnic terms such as Latino/a and Hispanic

(which are incorrectly used as interchangeable).

While studies have explored smoking, dietary intake, and physical activity in

Latino/a populations, the majority of the studies have focused primarily on participants

that identify as Mexican or Mexican-American (Van Wieren et al., 2011). Even though

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Mexican-Americans account for about 60% of the Latino/a population, pan-ethnic

generalizations made on such research could be hazardous to the other 40% of

Latino/as that originate elsewhere. In an attempt to address this challenge, this

dissertation assessed overlooked issues identified in Latino/a health paradox literature

(Franzini et al., 2001; Morales et al., 2002; Abraído-Lanza et al., 2005; Lara et al., 2005;

Markides & Esbach, 2005; Crimmins et al., 2007; Gallo et al., 2009; Arias 2010;

Waldstein 2010; Van Wieren et al., 2011). More specifically, this dissertation assessed

the role of citizenship status as a proxy for acculturation in association with CVD-related

clinical risk factors in a heterogeneous sample of Latino/as. Additionally, it evaluated the

effect that distinct Latino/a subgroups (Puerto Rican, Mexican, Cuban, and Dominican)

had over three CVD clinical risk factors (hypertension, high cholesterol, and heart

conditions) across a spectrum of acculturation (language proficiency and citizenship

status), SES (income, education and concern over health costs) and other confounders.

Lastly, it assessed the effect smoking and physical activity have on hypertension, high

cholesterol, and heart conditions across a spectrum of acculturation for Latino/a

subgroups from Puerto Rico, Mexico, Cuba and Dominican Republic.

Understanding the Latino/a health paradox and acculturation has presented

many challenges for public health researchers. While acculturation has been an area of

focus for researchers interested in the Latino/a health paradox, there has been little

consensus in describing or measuring acculturation. Despite most definitions centered

around individuals accepting and adopting new behaviors and beliefs (Morales et al.,

2002; Halgunseth et al., 2006; Gallo, et al., 2009; Van Wieren et al., 2011; Schachter et

al., 2012), establishing operational definitions into health-related surveys has been

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challenging (Lara et al., 2005). Studies have previously operationalized acculturation in

terms of preferred language, cultural knowledge, and even food consumption (Lara et

al., 2005). Researchers have also explored acculturation in terms of proxies such as

language proficiency, years in the U.S., and citizenship status (Lopez-Gonzalez et al.,

2005; Aqtash, 2007). In Chapter 2, we found differences in prevalence for various CVD

risk factors including hypertension, high cholesterol, and heart conditions in association

with acculturation (as measured by citizenship) in an aggregate sample of Latino/as

from the 2014 NHIS. The study showed that Latino/as with different citizenship status

exhibited varying levels of hypertension, high blood pressure, and heart conditions.

These results were consistent with and supported by prior studies examining

acculturation and other CVD related measures (Pérez-Stable et al., 2001; Abraído-

Lanza et al., 2005; Lara et al., 2005; Van Wieren et al., 2011; Daviglus et al., 2012).

More specifically, the findings indicated a significant increase in self-reported

hypertension for citizens over non-citizens of all ages, and a significant increase in

cholesterol, and heart conditions in citizens over non-citizens aged 40 or older.

Another challenge in understanding the Latino/a health paradox and

acculturation has been in addressing differences related to country of origin as opposed

to pan-ethnic labels such as Hispanic and Latino/a. Despite their similarities, the U.S.

Latino/a population is made up of complex individuals that are identified through many

labels (Oboler 1995; Davila, 2001; Gonzalez, 2011). The significance of their

colonization, liberation, involvement with the U.S. Government, education, and way of

life of their respective countries undoubtedly creates differences (Gonzalez, 2011).

While the findings from Chapter 2 indicated an association between acculturation and

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CVD in Latino/as, Chapter 3 sought to explore these differences among subgroups from

Puerto Rico, Mexico, Cuba and Dominican Republic. Given that Puerto Ricans are all

United States citizens, acculturation was also assessed through the use of an English

language proficiency proxy. Furthermore, measures of socioeconomic status were

extended beyond education to include participant concern over healthcare costs in

relation to access to care.

The findings presented in Chapter 3 demonstrated that relevant differences

across Latino/a countries of origin in association with acculturation and various CVD risk

factors including hypertension, high cholesterol, and heart conditions exist. Higher

hypertension, high cholesterol, and heart conditions odds ratios were reported for

citizens compared non-citizens. More specifically, the study showed that compared to

the other subgroups in this sample, Puerto Ricans were at greater risk for hypertension

and heart conditions. Additionally, the findings suggested that as acculturation

increased, the odds of having hypertension and high cholesterol were higher for non-

Mexican Latino/as. This study also demonstrated a protective factor for heart conditions

among Mexicans when compared to the other subgroups. Compared to non-Mexican

Latino/as, Mexicans displayed lower risk of heart conditions among our sample after

adjusting for age, sex, and education. Furthermore, compared to non-Cuban Latino/as,

findings indicated that risk of high cholesterol was lower for Cubans. Moreover, the

results indicated that Dominicans were at increased risk for heart conditions compared

to Puerto Ricans, Mexicans, and Cubans.

In addition to acculturation and country of origin which present challenging

barriers to overcome in our understanding of the Latino/a health paradox and CVD,

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other risk factors such as tobacco consumption and physical inactivity can deeply

impact this relationship. Discrepancies in the literature have been reported on smoking

and acculturation (Marin et al., 1989; Pérez-Stable et al., 2001; Abraído-Lanza et al.,

2005; Parrinello et al., 2015), as well as on physical activity and acculturation (Abraído-

Lanza et al., 2005; Lara et al., 2005; Slattery et al., 2006). Findings from Chapter 4

assessed the effect smoking and physical activity have on hypertension, high

cholesterol, and heart conditions across a spectrum of acculturation (English language

proficiency and citizenship) for Latino/a subgroups measured by country of origin (from

Puerto Rico, Mexico, Cuba and Dominican Republic). Building on the previous chapters,

Chapter 4 showed that acculturation was associated with varying levels of smoking and

physical activity among Latino/as. Specifically, that increased acculturation was

associated with increased smoking and increased physical activity.

In effect, the findings in Chapter 4 presented differences in smoking and physical

activity among Latino/as by age and education. Additionally, findings indicated relevant

differences across Latino/a countries of origin for hypertension, heart conditions,

smoking, and physical activity. Despite poor model fit statistics for analyses of

cholesterol, the tests’ dependence on grouped cutoff points and the models’ ability to

still discriminate between groups (Stoltzfus, 2011) should be considered before

dismissing the validity of those models. Analyses also indicated age differences among

Puerto Ricans and Mexicans (youngest) and Cubans (oldest) but not from Dominicans.

Post hoc tests showed that Puerto Ricans had higher English language proficiency and

prevalence of smoking compared to every other subgroup. Likewise, for hypertension,

only Mexicans exhibited a difference (lower) compared to the other three subgroups.

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Post hoc tests revealed that Mexicans differed in health cost concern from all others,

while Puerto Ricans also differed from Cubans. In terms of completing a high school

education, Cubans (highest) differed from all subgroups, while Puerto Ricans also

differed from Mexicans (lowest). Bonferroni analysis also indicated that Puerto Ricans

(highest) and Mexicans (lowest) differed from all other subgroups in terms of U.S.

citizenship. For heart conditions, subgroup differences were noted for Puerto Ricans

(higher) and Mexicans (lower) and for Dominicans (higher) and Mexicans (lower). In

terms of physical activity, Puerto Ricans (high) and Mexicans (high) differed from

Cubans (low) and Dominicans (low). These numerous differences highlight the need for

future research to focus on adequately sampling heterogeneous groups in order to

understand findings and develop treatments and interventions that are specific to small

groups of people rather than the large group identified as Latino/a. Predictive modeling

using the algorithms presented by the logistic regression equations in the models

reported in this dissertation should be assessed in the future to model other outcomes

and populations as they may be able to indicate risk.

Similarly, while our finding that higher acculturation was associated with a higher

prevalence of hypertension is consistent with other studies (Moran et al., 2007), others

such as Eamranond et al., (2009), have reported that higher English language

proficiency and longer time of residence was associated with improved cardiovascular

health. This conflicting finding is probably explained by the age differences among the

two studies. The mean age of the participants in the study by Eamranond et al., (2009),

was 20 years higher than the mean of this study, which could indicate not only changing

cultural and behavioral norms associated with the country of origin, but also historical

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and contextual changes in that particular country. This highlights the need for more

comprehensive generational implications among diverse Latino/a subgroups (Fang,

Ayala, & Loustalot, 2012). In the future, generational patterns among Latino/a

subgroups should be further explored as they may account for the age driven

differences found across the literature.

One possible solution would be to add acculturation measures or proxies into

existing longitudinal data collection efforts such as the National Health and Nutrition

Examination Survey (NHANES) as opposed having them in cross sectional. This would

allow researchers to compare not only differences over time, but also differences over

time inside the same family units (between generations). This would also provide a

much better depiction of the underlying mechanisms in acculturation. For example,

Cuba’s increased physical activity and decreased incidence of cardiovascular disease

may be explained by following individuals that lived during the country’s “Special

Period.” The “Special Period” is the name given to a time period in Cuba that saw the

economy collapse following the dissolution of the Soviet Union. During this period,

Cubans experienced dietary restrictions which reduced their average daily protein

intake to 15 - 20 g (CMAJ, 2008). In the future monitoring the changes throughout these

periods of time can likewise, help researchers to examine trends in these countries that

might help illustrate the health base of each Latino/a subgroup as explained by the

Latino Health Paradox.

The findings from this dissertation provide insight into the Latino/a health

paradox. As the Latino/a population in the nation continues to grow, it will become

increasingly important to fully understand this construct. While the mechanism

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underlying the association of acculturation and CVD-related clinical risk factors remains

discordant, the findings in this study add valuable evidence contributing to our

understanding of disease control and prevention in minority populations. The overall

findings in this study have implications for clinical and policy level interventions as well

future research. Public health researchers will need to collaborate with other

professionals including those in government and international sectors to really address

this problem.

Findings indicated that overall, higher acculturation was associated with

increased smoking and increased physical activity. Public health professionals should

take adequate steps to increase CVD screenings, specifically for acculturated Latino/as

over the age of 40 who may be at-risk for hypertension, high cholesterol, and heart

conditions. Findings also suggested that due to differences across countries of origin,

screenings may be more valuable for certain subgroups. Since Latino/as are less likely

to be screened that non-Latino whites, health professionals need to be aware of the

differences that exist among groups in order to advocate for those most at risk. For

example, Mexicans are less likely to report hypertension than Puerto Ricans. Since

Puerto Ricans also happen to have the highest smoking rates, if smoking increases with

increased acculturation, Puerto Ricans are subject to more risk as levels of

acculturation increase. In the future, researchers should also increase their attention on

data collection strategies that not only integrate Latino/a subgroups, but also explore

standardized acculturation measures. Furthermore, there should be a push for

regulations that encourage the incorporation of measures of acculturation as part of

patient medical records. This may assist physicians and other health care providers in

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delivering a more targeted health care experience. This would require continued

research focusing on Latino/a differences based on country of origin that may facilitate

the understanding of such information and allow for more population specific

interventions.

The use of bi-dimensional measures that collectively isolate potential

confounders and group differences could enhance our understanding of the

mechanisms underlying acculturation and health. One possible solution is to integrate

the use of social determinants of health to explore differences at the individual,

interpersonal, and societal level which may impact disease prevalence. Factors such as

education, occupational prestige, and stress have all been closely linked to health.

While education is included in the NHIS, occupational prestige and stress are not.

Inclusion of these and other items would allow researchers to better generalize their

findings without the need for proxies and assumptions. For example, occupational

prestige is a relative measure of job worthiness which has been used to describe social

economic status. This rating of worthiness could serve as a link to concepts including

personal and social identity (Berg, 2015) and in-group stability and legitimacy which

have been theorized to explain how perceived social identities are associated with

outcome expectancies and health outcomes (Haslam et al., 2009). This would allow

researchers to merge epidemiology and social behavioral components for a better

understanding.

Sullivan (1984), found differences in the occupational prestige of women from

Cuban and Mexican families. The findings suggested that citizenship was closely linked

to occupational prestige and reasonably postulated that naturalization was associated

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with structural assimilation, and a proxy for language training and education which in

turn improved occupational prestige. Moreover, the Sullivan study reported that unlike

the equal occupational prestige pattern across genders in the U.S., for both Mexican

and Cuban women, occupational prestige was lower than those of their male

counterparts. Having standardized measures would have increased the likelihood of

inclusion of these items in national surveys and would have allowed us to test this in our

sample population and include accurate comparisons. The findings reported in this

dissertation also suggest the need for standardizing the terms Latino/a, Hispanic, and

the need to incorporate country of origin instead. Currently, even Latino/as use the

terms Hispanic and Latino/a in a general sense to identify themselves and others.

However, the majority (51%) in a recent poll indicated that they most often identify

themselves and their kin through their country of origin, and only 24% responded

favorably towards the use of pan-ethnic labels (Taylor, et al., 2012).

The findings in this dissertation showed that country of origin differences in

acculturation and the experiences to which individuals are exposed are not uniform for

all Latino/as. While previous research has shown that prolonged time in the U.S. is

significantly associated with negative health outcomes (Pabon-Nau et al., 2010), these

findings suggest that country of origin plays an important role in this association and

should be considered a ubiquitous factor in future explorations. It would be of interest to

explore social norms and cultural values pertinent to the country of origin. Currently, few

epidemiologic assessments are conducted in Latin American countries. Policies

promoting scientific collaboration across countries, or regulations that facilitate such

research, would provide health professionals the opportunity to assess some of these

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factors first-hand. Understanding the baseline rates of disease for specific health

conditions in the sending nation can provide invaluable insight into disease prevention

in the host nation.

While further collaboration is needed to understand the relationship in health

across countries, more research is needed in the growing Latino/a subgroups in the

U.S. Overall, this dissertation looked at only four out of over 20 Latino/a subgroups

found in the country. As new cities become prevalent destinations for Latino/a

populations, research must keep up with the growing demand. One of the major flaws in

the public health research to date has been the use of predominantly Mexican samples

in the studies used to generalize and inform the Latino/a health paradox. With

Guatemalan, Salvadoran, and Colombian subgroups growing at double the rate of the

more established subgroups (Mexican, Puerto Rican, Cuban and Dominican), it is

possible that the differences reported in this dissertation will increase. Additionally, the

barrio advantage hypothesis discussed in Chapter 1 might become more prevalent. Will

acculturation be faster in new areas where less Latino/a influences are present than in

established areas such as New York and New Jersey where there are established

enclaves and the possibility of transnationalism?

In Chapter 2 we opted for citizenship status over language proficiency as a proxy

for acculturation in part for this growing concern over transnationalism and biculturalism

in which the host culture and the culture of origin are equally retained (Lara et al., 2005).

Transnationalism refers to a process where individuals can forge and sustain “multi-

stranded” social relations in which they linked their societies of origin with their new

place of settlement (Schiller, Basch, & Blanc, 1992). For example, in Miami, where the

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majority of the population is Latino/a, language is less of a factor in daily interactions.

Thus, moving forward, it will be important how we standardize measures and

operationalize research variables to account for all these differences while maintaining

congruency. Future research will also need to account for potential immigration reform

laws that are expected to largely influence the Latino/a population in the U.S. As more

questions arise, more research is needed before we can normalize concepts of

acculturation, and fully integrate them into our best practices. Future research will need

to distinguish between country of origin subgroups that are subject to political refuge

laws, and assess the impact that this has on their assimilation of cultural norms.

Ultimately, as the number of mechanisms that need to be accounted for to completely

understand acculturation and the Latino/a health paradox, researchers will need to

engender succinct yet inclusive acculturation scales.

To date, inclusion of many of these variables and measures has been limited in

large data collection studies. Ideally, future research would isolate acculturation and

health related behaviors to its own independent endeavor rather than as part of a larger

study. This would allow us to create targeted questions to focus on specific concerns.

As the push for personalized healthcare continues, and the emphasis on predictive

analytics and data managing increases, the public health field will see a rise in the

demand for more informed answers for specific subgroups and for specific health

outcomes. While the use of predictive algorithms to model health through machine

learning remains limited in our field (Cerrito, 2008), these methods can be used to

positively impact patient care and quality of life. As public health researchers, we must

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embrace an active collaboration with multiple professions and begin to tackle these

issues moving forward to improve health among the expanding population.

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BIOGRAPHICAL SKETCH

Felix Lorenzo was born in Havana, Cuba in 1989. A lifetime Gator, Felix earned

his Bachelor of Science degree, his Master of Public Health (MPH) degree with a

concentration in management and policy, and his doctoral degree in Public Health from

the University of Florida. During his PhD training, he worked on various research

projects relating to cancer and tobacco. Felix is a recipient of the McKnight Doctoral

Fellowship.

exploring cardiovascular health differences in...

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