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Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease logoLink to Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
. 2016 Feb 18;5(2):e002385. doi: 10.1161/JAHA.115.002385

Sex Differences in Cardiovascular Disease Risk of Ghanaian‐ and Nigerian‐Born West African Immigrants in the United States: The Afro‐Cardiac Study

Yvonne Commodore‐Mensah 1,✉, Martha Hill 2, Jerilyn Allen 2, Lisa A Cooper 3, Roger Blumenthal 3, Charles Agyemang 4, Cheryl Dennison Himmelfarb 2
PMCID: PMC4802474  PMID: 26896477

Abstract

Background

The number of African immigrants in the United States grew 40‐fold between 1960 and 2007, from 35 355 to 1.4 million, with a large majority from West Africa. This study sought to examine the prevalence of cardiovascular disease (CVD) risk factors and global CVD risk and to identify independent predictors of increased CVD risk among West African immigrants in the United States.

Methods and Results

This cross‐sectional study assessed West African (Ghanaian and Nigerian) immigrants aged 35–74 years in the Baltimore–Washington metropolitan area. The mean age of participants was 49.5±9.2 years, and 58% were female. The majority (95%) had ≥1 of the 6 CVD risk factors. Smoking was least prevalent, and overweight or obesity was most prevalent, with 88% having a body mass index (in  kg/m2) ≥25; 16% had a prior diagnosis of diabetes or had fasting blood glucose levels ≥126 mg/dL. In addition, 44% were physically inactive. Among women, employment and health insurance were associated with odds of 0.09 (95% CI 0.033–0.29) and 0.25 (95% CI 0.09–0.67), respectively, of having a Pooled Cohort Equations estimate ≥7.5% in the multivariable logistic regression analysis. Among men, higher social support was associated with 0.90 (95% CI 0.83–0.98) lower odds of having ≥3 CVD risk factors but not with having a Pooled Cohort Equations estimate ≥7.5%.

Conclusions

The prevalence of CVD risk factors among West African immigrants was particularly high. Being employed and having health insurance were associated with lower CVD risk in women, but only higher social support was associated with lower CVD risk in men.

Keywords: African immigrants, cardiovascular disease, migrant health

Subject Categories: Race and Ethnicity, Epidemiology, Risk Factors, Primary Prevention, Cardiovascular Disease

Introduction

Cardiovascular disease (CVD) remains the leading cause of death in the United States. Considering that 1 in 3 deaths is attributable to CVD and that the prevalence of CVD risk factors remains high, CVD represents a major public health challenge.1 The Framingham Heart Study and other landmark studies2, 3, 4 have demonstrated that CVD risk factors—smoking, obesity, hypertension, hyperlipidemia, physical inactivity, and diabetes mellitus—synergistically increase CVD risk and death. Likewise, in sub‐Saharan Africa, CVD is becoming a leading cause of morbidity and mortality because of the increasing prevalence of CVD risk factors attributed to the “epidemiological transition,” characterized by shifts in disease and mortality patterns from infectious diseases to noncommunicable diseases as major causes of morbidity and mortality.5, 6

The influx of African immigrants from sub‐Saharan Africa to the United States in the past 2 decades has been unprecedented. The size of this population grew 40‐fold between 1960 and 2007, from 35 555 to 1.4 million persons, with 36% originating from West Africa.7 Together, Ghanaian and Nigerian immigrants make up >30% of African immigrants in the United States. According to the US Census Bureau's 2005 American Community Survey, 114 000 African immigrants in the Washington metropolitan area accounted for about 11% of the area's total immigrant population.8 Although this immigrant population continues to burgeon, little is known about the CVD risk profile. This research gap stems from the fact that African immigrants in the United States are often lumped into the racial category of black or African American, along with African American and Afro‐Caribbean persons.9

The “healthy immigrant effect,”10, 11 which suggests that new immigrants are healthier than their host counterparts due to self‐selection and immigration policies, is a well‐accepted phenomenon; however, through acculturation, the health of immigrants declines or improves with increasing years of residence in high‐income countries through the loss of culture‐specific health‐protective practices or adoption of health behaviors of the host society.11, 12, 13 Changes in socioeconomic conditions, food supply, health systems and policies, and cultural traditions14, 15, 16, 17 experienced by immigrants have been posited as reasons for deteriorating or improving health.

The purpose of the Afro‐Cardiac Study was to examine the prevalence of CVD risk factors; global CVD risk (measured with the Pooled Cohort Equations [PCE] score); and independent socioeconomic, demographic, and behavioral factors associated with increased CVD risk in West African immigrants (WAI).

Methods

Conceptual Framework

A modification of the PRECEDE–PROCEED model,18 illustrated in Figure 1, was used as the conceptual framework for the study. It integrates health assessment, health education, social action, and behavioral change and maintenance principles. According to this model, the precise social, behavioral, environmental, genetic, and ecological determinants of health must be assessed to facilitate effective program design and implementation; therefore, we assessed the social, behavioral, economic, and cultural factors thought to be predisposing, reinforcing, or enabling determinants of elevated CVD risk as the first phase in this program of research. The model has also been applied to diverse populations including adult hypertensive periurban South African patients,19 elderly hypertensive Korean immigrants,20 diabetic Chinese older adults, and low‐income Hispanic immigrants.21

Figure 1.

Figure 1

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Conceptual framework: modified PRECEDE‐PROCEED model. CV indicates cardiovascular; CVD, cardiovascular disease.

Design and Setting

The Afro‐Cardiac Study was a community‐based, cross‐sectional study among first‐generation WAI aged 35 to 74 years who were born in Ghana or Nigeria and residing in the Baltimore–Washington metropolitan area.22 This study targeted Ghanaian and Nigerian WAI, 2 of the largest African immigrant populations in the United States, with an estimated total population of 25 000 in the Washington metropolitan area.8 In the absence of a population frame of WAI in the United States, convenience sampling was used. Participants were recruited between January 2013 and May 2014.

Participants

We recruited participants from 7 different churches attended by African immigrants. Participants were eligible based on the following criteria: (1) adults aged between 35 and 74 years at the time of enrollment, (2) self‐identify as WAI born in Ghana and Nigeria, (3) reside in the Baltimore–Washington metropolitan area, and (4) able to read and write English and provide informed consent. Study participants were excluded from the study if they were pregnant or born in the United States. Participants with diagnosed CVD were also excluded because the PCE estimate is derived from participants free of diagnosed CVD. A flow diagram of recruitment and enrollment is provided in Figure 2.

Figure 2.

Figure 2

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Flow diagram of recruitment and enrollment. CVD indicates cardiovascular disease; DC, District of Columbia.

Ethics

This study was approved by the institutional review board of Johns Hopkins Medicine.

Measurements

Research assistants performed physical measurements with validated devices according to standardized operational procedures. Study procedures were performed in private rooms provided by the leaders of the churches. Physical examinations consisted of assessment of anthropometrics (weight, height, waist circumference and hip circumference, and systolic and diastolic blood pressure) (Table 1). For each participant, a full fasting lipid profile (total cholesterol [TC], triglycerides [TG], and high‐density lipoprotein cholesterol [HDL‐C]) and glucose concentrations were obtained with a finger stick and measured using the point‐of‐care testing instrument Cholestech LDX analyzer (Cholestech Corporation). Accuracy and precision of the Cholestech LDX analyzer has been established previously.26

Table 1.

Variables Measures and Instruments in the Afro‐Cardiac Study

Themes Variable Questionnaire Instrument/Measures
Demographics Age, age at migration, sex, marital status, insurance status Self‐report in modified WHO STEPS23
Medical history HTN and diabetes history Self‐report in modified WHO STEPS23
Predisposing factors Socioeconomic status (employment and educational status) Self‐report in modified WHO STEPS23
Heart Disease Knowledge Heart Disease Fact Questionnaire24
Reinforcing factors Social support ESSI25
Enabling factors Health insurance status Self‐report in modified WHO STEPS23
Immigration status Self‐report in modified WHO STEPS23
Health behaviors Smoking status, physical activity, dietary intake Self‐report WHO STEPS23
Blood pressure Systolic and diastolic blood pressure, measured 3 times in a sitting position after at least 5 minutes of rest LifeSource UA‐767 Plus blood pressure monitor
Anthropometrics Weight SECA® Robusta 813
Height SECA® 213 Stadiometer
Waist circumference Measuring tape
Hip circumference Measuring tape
Diabetes Self‐report, fasting blood glucose, Cholestech LDX® analyzer
Lipid profile Total cholesterol, triglycerides, and high‐density lipoprotein cholesterol Cholestech LDX® analyzer
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ESSI indicates Enhancing Recovery in Coronary Heart Disease (ENRICHD) Social Support Inventory; HTN, hypertension; STEPS, STEPwise Approach to Surveillance; WHO, World Health Organization.

Variable Definitions

A description of variables, measures, and instruments used in the study is provided in Table 1. Hypertension was defined as self‐reported hypertension or history of taking antihypertensives per the Seventh Joint National Committee criteria for management of high blood pressure in adults.27

Overweight/obesity was defined as body mass index (BMI; in kg/m2) ≥25. Waist circumference and waist‐to‐hip ratio were measured in addition to BMI because the presence of central adiposity is more highly correlated with CVD risk factors than elevated BMI.28 Waist circumference >35 and 40 in for women and men, respectively, and a waist‐to‐hip ratio >0.85 and 0.90, respectively, were considered CVD risk factors.23

Hyperlipidemia was defined as self‐reported history of taking cholesterol‐lowering medications or TC ≥200 mg/dL. Diabetes was defined as self‐reported provider‐diagnosed diabetes or fasting blood glucose levels >126 mg/dL.29 Sociodemographic variables and health history data were obtained with a modified version of the World Health Organization (WHO) STEPwise Approach to Surveillance (STEPS) survey.23 The WHO STEPS survey is a simple standardized method for collecting, analyzing, and disseminating data on chronic disease risk factors in WHO member countries. We tailored some questions in the survey to improve relevance to WAI in the United States.

Social support, a reinforcing factor, was operationalized as scores on the Enhancing Recovery in Coronary Heart Disease (ENRICHD) Social Support Inventory (ESSI).25 The ESSI is a 7‐item self‐administered survey that measures 3 defining attributes of social support including emotional, instrumental, and structural social support. The individual items on the ESSI are summed for a total score ranging from 8 to 34, with higher scores representing greater social support.

CVD knowledge was assessed with the Heart Disease Fact Questionnaire,24 a 25‐item questionnaire containing true or false questions on knowledge of CVD risk in diabetes. Items in the questionnaire include “A person always knows when they have heart disease,” “Smoking is a risk factor for heart disease,” and “People with diabetes rarely have high cholesterol.” The scores range from 0 to 25, with higher scores corresponding to higher levels of knowledge of CVD risk.

Measures of Global CVD Risk

PCE Score

We calculated sex‐specific PCE scores using guidelines by Goff et al30 to estimate the risk of atherosclerotic CVD. This risk score has been shown to predict 10‐year risk for developing atherosclerotic CVD, defined as coronary death or nonfatal myocardial infarction or fatal or nonfatal stroke. Variables in the PCE score included sex, age, HDL‐C, TC, diabetes status, systolic blood pressure, treatment for hypertension, smoking status, and race. Participants were considered to be at “elevated” risk if the predicted PCE score was ≥7.5%, based on prior work by Goff et al.30

Summative measure of CVD risk factors

Because having ≥3 CVD risk factors is associated with a 10‐fold increase in CVD risk,31, 32 we created a summative measure of the number of CVD risk factors because of this “multiplier effect” of CVD risk factors in both sexes. We dichotomized this variable into <3 and ≥3 CVD risk factors.

Statistical Methods

We used independent t tests and chi‐square tests to determine differences in the sociodemographic, demographic, and behavioral factors and CVD risk by sex. Categorical data were summarized using percentages. Continuous data were reported using mean±SD. To determine whether the variables in our conceptual framework, derived from the PRECEDE‐PROCEED model,18 independently predicted having ≥3 CVD risk factors and PCE estimates ≥7.5%, we performed unadjusted and adjusted logistic regression analyses. For both outcomes, we fitted separate logistic regression models for men and women due to the variation in prevalence of CVD risk factors by sex. For the outcome of ≥3 CVD risk factors, age was included as a covariate in the adjusted model, along with the other predisposing, reinforcing, and enabling factors. For the outcome of PCE estimates ≥7.5%, age was not included because age is a component of the outcome (PCE score ≥7.5%), and chronological age is the dominant risk factor in the PCE score.33 We considered a 2‐tailed α with P<0.05 to be statistically significant for all analyses and used Stata13 (StataCorp) to perform all statistical analyses.

Results

Sample Characteristics

We recruited 256 WAI, although 3 participants were excluded from the analysis because of missing data. Participants were recruited from 7 churches in the Baltimore–Washington metropolitan area. The demographics of the sample are presented in Table 2. The mean age of participants was 49.5±9.2 years, 58% were female, and 60% had at least a college education; however, a high level of education did not translate into higher income because only 36% reported household income >$50 000. Only 52% had health insurance, and 77% reported being green card holders or US citizens. Together, green card holders and US citizens were more likely to be insured than those on visas or those who declined to provide that information (61% versus 20%, P<0.001). A total of 152 participants (60%) were born in Ghana, and the rest were born in Nigeria.

Table 2.

Demographics of Sample

Characteristic Total (N=253) Male (n=106) Female (n=147) P Value for Comparison of Male and Female
Age, mean±SD 49.5±9.2 49.7±9.2 49.3±9.2 0.7196
Educational status
Less than high school 61 (25) 19 (18) 42 (29) 0.055
High school 38 (15) 14 (13) 24 (17)
College or higher 150 (60) 72 (69) 77 (54)
Employed 181 (79) 81 (90) 100 (72) 0.001a
Household Income
<$25 000 44 (18) 16 (15) 28 (20) 0.007a
$25 000 to $50 000 113 (46) 39 (38) 74 (52)
>$50 000 88 (36) 49 (47) 39 (28)
Health insurance, yes 127 (52) 56 (55) 71 (49) 0.387
≥10 years of US residence (%) 170 (67) 81 (76) 89 (61) 0.008a
Green card/citizen 194 (77) 84 (80) 110 (75) 0.385
Country of birth
Ghana 152 (60) 60 (57) 92 (63) 0.338
Nigeria 101 (40) 46 (43) 55 (37)
ESSI score, mean±SD 28.7±5.5 28.5±6.0 28.9±5.1 0.6006
HDFQ score, mean±SD 20.6±2.8 20.4±2.9 20.7±2.7 0.3731
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Data are shown as n (%) except as indicated. ESSI indicates Enhancing Recovery in Coronary Heart Disease (ENRICHD) Social Support Inventory; HDFQ, Heart Disease Fact Questionnaire.

a

P<0.05.

CVD Risk

CVD risk factors are summarized in Table 2, and the distribution of CVD risk factors is illustrated in Figure 3. As shown in Figure 4, the majority of participants (95%) had at least 1 and 54% of participants had ≥3 of the 6 CVD risk factors. Women were more likely than men to have ≥3 CVD risk factors (63% versus 42%, P=0.002), whereas men were more likely than women to have a PCE estimate ≥7.5% (35% versus 23%, P=0.047). Figure 5 illustrates that the distribution of PCE estimates in this study is similar to that of the general US population. PCE scores estimated the 10‐year atherosclerotic CVD risk as very low (<2.5%) for 34% and very high (≥20%) for 13% of participants.

Figure 3.

Figure 3

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Prevalence of cardiovascular disease risk factors by sex.

Figure 4.

Figure 4

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Number of cardiovascular disease risk factors in the sample.

Figure 5.

Figure 5

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Comparison of Pooled Cohort Equation scores in the Afro‐Cardiac Study to the US population.

Hypertension

Mean systolic and diastolic blood pressures were 128±19 and 80±11 mm Hg, respectively (Table 3). Hypertension (prior diagnosis of hypertension or on antihypertensive medications) was present among 40% of the participants. Among nonhypertensive participants, 16% had elevated blood pressure, defined as mean systolic blood pressure ≥140 mm Hg or mean diastolic blood pressure ≥90 mmHg. In addition, 53% of those who had hypertension were on antihypertensive treatment, with women more likely than men to report taking antihypertensive medication (64% versus 36%, P=0.003). Although women were significantly more likely to be treated for hypertension, men (71%) were more likely than women (42%) to have their blood pressure controlled (P=0.045). There was a strong association (P=0.004) between high waist circumference and hypertension in women but not in men (P=0.359).

Table 3.

Cardiovascular Disease Risk of Sample

Characteristic Total (N=253) Male (n=10 642) Female (n=14 758) P Value for Comparison of Male and Female
SBP, mm Hg 128±19 130±20 127±19 0.223
DBP, mm Hg 80.3±10.9 79.9±11.6 80.7±10.4 0.594
Elevated blood pressurea 25 (16) 11 (17) 14 (15) 0.784
HTN diagnosisb 98 (40) 40 (39) 58 (41) 0.785
HTN treatmentc 63 (53) 17 (36) 46 (64) 0.003d
HTN control (on antihypertensives)e 30 (50) 12 (71) 18 (42) 0.045d
HTN control (no antihypertensives)f 12 (38) 8 (40) 4 (33) 0.706
Diabetes classificationg 40 (16) 18 (17) 22 (15) 0.594
Diabetes diagnosisf 30 (13) 14 (15) 16 (12) 0.449
On insulin/oral glycemic agents 19 (65) 7 (50) 12 (80) 0.089
LDL‐C 106.0±37.3 109.6±30.3 103.4±41.5 0.202
LDL‐C ≥130 (%) 84 (33) 38 (36) 46 (31) 0.448
HDL‐C 53.9±17.9 48.8±14.6 57.6±19.2 <0.001d
HDL‐C <40 for men/<50 women 74 (29) 25 (24) 49 (33) 0.093
TC 180.9±33.9 178.1±29.7 183.7±37.3 0.242
TC ≥200 69 (27) 28 (26) 41 (28) 0.795
TG 107.5±86.7 113.3±83.9 103.5±88.6 0.375
TG ≥200 23 (9) 11 (10) 12 (8) 0.546
Body mass index, kg/m2 29.8±4.8 28.4±3.9 30.8±5.1 <0.001d
Normal (18.5–24.9) 30 (12) 20 (19) 10 (7) 0.002d
Overweight (25–29.9) 112 (45) 51 (49) 61 (43)
Obese (≥30) 105 (43) 33 (32) 72 (50)
Waist circumference >35 for women/>40 for men 127 (53) 24 (23) 103 (75) <0.001d
Waist‐to‐hip ratio >0.90 for men/>0.85 for women 151 (60) 50 (47) 101 (69) 0.001d
Current tobacco smoker 1 (0.4) 1 (1) 0 (0) 0.236
Physical inactivity 135 (56) 58 (57) 77 (55) 0.754
PCE 6.1±6.8 7.7±6.4 5.0±6.9 0.002d
PCE ≥7.5 66 (28) 33 (35) 33 (23) 0.047d
≥3 CVD risk factors 137 (54) 45 (42) 92 (63) 0.002d
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CVD indicates cardiovascular disease; DBP, diastolic blood pressure; HDL‐C, high‐density lipoprotein cholesterol; HTN, hypertension; LDL‐C, low‐density lipoprotein cholesterol; PCE, Pooled Cohort Equations; SBP, systolic blood pressure; TC, total cholesterol; TG, triglycerides.

a

Defined as proportion of total sample with mean SBP ≥140 mm Hg or mean DBP ≥90 mm Hg.

b

Defined as proportion of total sample who self‐reported HTN diagnosis or history of taking antihypertensives per Seventh Joint National Committee criteria.

c

Defined as proportion of those diagnosed with HTN who self‐reported a history of taking antihypertensives in the past 2 weeks.

d

P<0.05.

e

Defined as proportion of those diagnosed with HTN and treated with antihypertensives who had mean SBP <140 mm Hg and mean DBP <90 mm Hg.

f

Defined as proportion of those diagnosed with HTN who were not treated with antihypertensives but had mean SBP <140 mm Hg and mean DBP <90 mm Hg.

g

Defined as self‐reported provider diagnosed diabetes or fasting blood glucose levels >126 mg/dL.

Defined provider diagnosed diabetes.

Overweight/Obesity and Central Adiposity

Mean BMI was 29.8±4.8, with women having significantly higher BMI than men (31±5.1 versus 28±3.9, P<0.00001) (Table 4). Similarly, 93% of women were considered overweight/obese in contrast to 81% of men (P=0.002). With regard to central adiposity, 23% of men had higher prevalence than women (75%) (P<0.0001). Similar results were obtained with waist‐to‐hip ratio at 69% of women versus 47% of men (P=0.001).

Table 4.

Multivariable Logistic Regression Models for Determinants of ≥3 CVD Risk Factors

Variables Male (n=106) Female (n=147)
Unadjusted Adjusteda Unadjusted Adjusteda
OR (95% CI) P Value OR (95% CI) P Value OR (95% CI) P Value OR (95% CI) P Value
Predisposing factors
CVD knowledge 1.02 (0.89–1.16) 0.766 1.06 (0.92–1.22) 0.386 1.09 (0.97–1.23) 0.159 1.09 (0.96–1.23) 0.174
Employment,
ref (unemployed) 		0.59 (0.20–1.79) 0.361 0.76 (0.22–2.55) 0.657 0.54 (0.25–1.20) 0.132 0.55 (0.24–1.27) 0.160
Reinforcing factor
Social support 0.92 (0.85–0.98) 0.01b2 0.92 (0.85–0.99) 0.024b 0.94 (0.88–1.01) 0.102 0.94 (0.87–1.01) 0.099
Enabling factor
Health insurance,
ref (uninsured) 		0.97 (0.44–2.09) 0.938 1.11 (0.48–2.58) 0.807 0.65 (0.33–1.27) 0.210 0.76 (0.38–1.53) 0.439
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CVD indicates cardiovascular disease; OR, odds ratio.

a

The adjusted models were adjusted for predisposing factors (CVD knowledge and employment), reinforcing factors (social support), enabling factors (health insurance), and age.

b

P<0.05.

Diabetes

Overall, 16% of the participants had a prior diagnosis of diabetes or had fasting blood glucose levels ≥126 mg/dL (Table 2). Women were significantly more likely to take medications to control their diabetes than their male counterparts (80% versus 43%, P=0.039). Of the diagnosed diabetic participants, there was no significant difference in diabetes control by sex. We identified 15 participants (6%) who had no prior diagnosis of diabetes but who had elevated fasting blood glucose (≥126 mg/dL).

Hyperlipidemia

Mean TC was 181±34 mg/dL, and 32% of participants had TC ≥200 mg/dL. Only 2 (15%) of the 14 participants who had hypercholesterolemia reported taking cholesterol‐lowering medications. Mean LDL‐C was 106±37 mg/dL, and 62% of participants had LDL‐C ≥100 mg/dL. A third of participants had low HDL‐C (<40 mg/dL in men, <50 mg/dL in women). Mean TG was 108±87 mg/dL, and only 9% of the sample had elevated TG (≥200 mg/dL) (Table 2).

Physical Inactivity

Almost half (44%) of participants reported inadequate moderate (<150 min/week) or vigorous (<75 min/week) work‐related or leisure physical activity. Of those participants, 29% reported participating in no work‐related or leisure physical activity. Based on WHO recommended levels of physical activity, only 56% met the recommended weekly physical activity guidelines, with no significant sex differences (Table 3).

Determinants of Elevated CVD Risk (≥3 CVD Risk Factors or PCE Score ≥7.5%)

The predisposing, enabling, and reinforcing factors associated with having ≥3 CVD risk factors or PCE score ≥7.5% are reported in Tables 4 and 5, respectively. Analyses were stratified because of sex‐based differences in CVD risk factor prevalence. CVD knowledge (mean score 20.5±2.8 on a 25‐point scale), a predisposing factor, was high in this sample but did not independently predict having ≥3 CVD risk factors or a PCE estimate ≥7.5%. Employment status, another predisposing factor, did not independently predict either outcome in men. In women, however, employment was associated with 91% lower odds of having a PCE estimate ≥7.5%. In that model, the interaction term between sex and employment was significant (P=0.012) The mean ESSI score was 28.7±5.5, with no significant differences by sex. In men, a higher ESSI score was significantly associated with 10% lower odds of having ≥3 CVD risk factors but was not associated with having a PCE estimate ≥7.5%. Notably, the interaction term between sex and social support was not significant (P=0.477) for the outcome of ≥3 CVD risk factors. We examined health insurance as an enabling factor and determined that in men, having health insurance was not significantly associated with having ≥3 CVD risk factors or a high PCE estimate; however, in women, having health insurance was associated with 75% lower odds of having a high PCE estimate. In that model, the interaction term between sex and health insurance was significant (P=0.004).

Table 5.

Multivariable Logistic Regression Models for Determinants of PCE Estimate ≥7.5%

Variables Male (n=106) Female (n=147)
Unadjusted Adjusteda Unadjusted Adjusteda
OR (95% CI) P Value OR (95% CI) P Value OR (95% CI) P Value OR (95% CI) P Value
Predisposing factors
CVD knowledge 1.04 (0.90–1.18) 0.612 1.08 (0.91–1.28) 0.402 0.93 (0.81–1.06) 0.278 0.89 (0.76–1.03) 0.139
Employment,
ref (unemployed) 		0.50 (0.17–1.50) 0.214 0.43 (0.12–1.58) 0.204 0.15 (0.07–0.35) <0.0001b 0.09 (0.033–0.29) <0.0001b
Reinforcing factor
Social support 0.95 (0.89–1.01) 0.110 0.93 (0.86–1.00) 0.070 0.98 (0.91–1.05) 0.519 0.89 (0.76–1.03) 0.66
Enabling factor
Health insurance,
ref (uninsured) 		1.17 (0.53–2.57) 0.703 1.33 (0.50–3.50) 0.566 0.28 (0.12–0.64) 0.003b 0.25 (0.09–0.67) 0.006b
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CVD indicates cardiovascular disease; PCE, Pooled Cohort Equations; OR, odds ratio.

a

The adjusted models were adjusted for predisposing factors (CVD knowledge and employment), reinforcing factors (social support), enabling factors (health insurance), and length of US residence (in years). Age was not adjusted for in this model because age is included in the calculation of the PCE score.

b

P<0.05.

Discussion

We examined the CVD risk profile of WAI and identified theoretically selected factors associated with increased CVD risk. For every 10 participants, 8 had at least 2 CVD risk factors; this result calls for immediate public health intervention. This high burden of CVD risk is troubling, given the relatively young age of the participants; nearly 30% were aged <45 years, and 94% were aged <65 years. The majority of CVD deaths in sub‐Saharan Africa occur among persons aged between 30 and 69 years, a range that is 10 years younger than the equivalent age group in Europe and the United States.34, 35 Consequently, WAI in the United States may be at high risk for CVD events at a younger age; however, there are currently no data on CVD events in WAI in the United States to support our assertion.

Using the PRECEDE‐PROCEED model,18 we conceptualized that predisposing (CVD knowledge, employment), reinforcing (social support), and enabling (health insurance) factors would each be associated with having elevated CVD risk. We found a significant negative association between social support and elevated CVD risk in men and a negative relationship between employment and health insurance and elevated CVD risk in women. Epidemiological evidence suggests that low social support is associated with increased incidence of CVD and poor CVD outcomes.36, 37 Immigration is a significant life transition through which previous social networks and social support may be disrupted.

Socioeconomic status is a powerful determinant of health and is inversely associated with CVD in high‐income countries38; however, this relationship is often paradoxical or weak among ethnic minorities.39, 40 We found that unemployed women were at higher risk for CVD than employed women. With regard to health insurance status, US immigrants have some of the highest uninsured rates: 33.5% of immigrants were uninsured compared with 12.9% of US‐born residents.41 In this study, almost half of the participants reported being uninsured. This finding is troubling because having of health insurance facilitates the utilization of preventive services and improves health outcomes42 in acute and chronic diseases.43 Of note, data collection occurred during the implementation of the Patient Protection and Affordable Care Act; therefore, it is possible that current insurance rates in this population may be higher.

Overweight and obesity are well‐established CVD risk factors2, 44 and were the most prevalent CVD risk factors in this study. The 88% prevalence (81% of men, 93% of women) of overweight/obesity in our sample is substantially higher than the reported 68% prevalence (73% of men, 64% of women) in US adults1 and the 76% prevalence (69% of men, 82% of women) in African American adults.45 Similar findings were observed by Agyemang et al46 in Dutch Ghanaian immigrants: 90% of participants were overweight/obese. Cultural perceptions may contribute to the high prevalence of overweight/obesity because in West African societies, obesity represents “good living” and is associated with wealth, feminine beauty, and freedom from HIV/AIDS.47, 48

Central adiposity is also linked to metabolic abnormalities and hypertension.49, 50 In this study, women were more likely to have central adiposity than men. In African immigrants, central adiposity may be more predictive of cardiometabolic disease because, at a lower BMI and waist circumference than African Americans, African immigrants may have more visceral adipose tissue and a higher rate of diabetes and prediabetes than African Americans.51 In our study, although 88% of participants were considered overweight/obese, only 53% had a high waist circumference. These findings suggest that BMI may not be the most reliable indicator of cardiometabolic health in WAI.

Hypertension is a major public health problem, and in our study, prevalence was 40%. Although hypertension treatment substantially lowers CVD risk, we observed that only half of hypertensive participants were on antihypertensive medications. Hypertension control was achieved in only 50% of those treated. Although not assessed in this study, compliance may be a barrier to hypertension treatment and control in WAI. In qualitative studies by Beune et al, Dutch Ghanaian immigrants stated that they altered their medications for fear of addiction and inability to afford their medications.52 We observed that men were significantly less likely than women to be taking antihypertensives. Similar results were observed in Dutch Ghanaian men who altered their antihypertensive medications for fear of negative effects on their sexual performance.52 The high prevalence of hypertension and overweight/obesity in this sample could be attributed to low physical activity reported for Ghanaian and Nigerian participants53, 54, 55 or high consumption of dietary sodium.56 Concurrently addressing hypertension and overweight/obesity in WAI is critical for preventing target organ damage and CVD, which is more prevalent in persons of African descent.57

The high prevalence of diabetes (16%) in this study reflects trends in sub‐Saharan Africa, where urban residence is associated with a 2‐ to 5‐fold higher risk of impaired glucose tolerance.58, 59 Oza‐Frank and Narayan60 reported that, compared with other immigrants in the United States, African men ranked second (7.8%) in prevalence of diabetes, and African women ranked third (4.6%). Because African‐origin populations are said to be 3 to 5 times more likely to have higher morbidity and mortality from diabetes than European populations,61 primary prevention strategies are needed. We identified a level of undiagnosed diabetes in participants that suggested secondary prevention efforts including screening and treatment must be improved. O'Connor et al51 observed that African immigrant men were more likely than African American men to have previously undiagnosed prediabetes (35% versus 22%, P<0.01) and diabetes (8% versus 0%, P<0.01). The high prevalence of overweight/obesity and central adiposity implies that if adequate and culturally appropriate prevention efforts are not implemented, the prevalence of diabetes in WAI may continue to rise.

Evaluating lipid profile is an integral aspect of assessing CVD risk. In our study, the lipid profile of the participants was favorable compared with the US population. A third of participants had high LDL‐C, high TC, and low HDL‐C, whereas 1 in 10 had high TG levels. Although persons of African descent typically exhibit a favorable lipid profile characterized by high HDL‐C levels,62, 63 it is unlikely that this atheroprotective trait will persist in WAI with the acquisition of other CVD risk factors and increased years of US residence.64 Elevated TG levels were relatively absent in this group, despite the high prevalence of central adiposity; therefore, the traditional definition of metabolic syndrome, which relies on 5 metabolic risk factors—central adiposity, high TG, low HDL‐C, high blood pressure, and high fasting blood glucose—may underestimate the CVD risk of WAI.

Physical inactivity increases the risk of overweight/obesity, CVD, stroke, and metabolic diseases.65 As in many low‐income regions, epidemiological data on physical activity in West Africa is scarce. The WHO estimated that 7.9% of men and 15.1% of women were physically inactive in Ghana66 and 41% in Nigeria were considered physically inactive.67 The low levels of physical activity in Africa may be explained in part by environmental and infrastructural barriers such as lack of recreational or sporting facilities.68 Consequently, prior to migration, West African persons may not engage in recreational physical activity; this poor health behavior may persist after migration. Indeed, in our study, 44% of participants did not meet WHO physical activity recommendations. Increasing physical activity levels in WAI is an important public health challenge and should be addressed with culturally appropriate strategies.

The prevalence of smoking in our study was very low, with only 1 smoker. We believe that our findings corroborate other studies that have found low prevalence of smoking in West African participants. Although reliable estimates of smoking prevalence in sub‐Saharan Africa are scant, the prevalence of smoking is 11% among Ghanaian men69 and 2.6% among Ghanaian women70; in Nigeria, 10% of men71 and 3% of women72 smoke.

This study has limitations. This study was cross‐sectional, so no causal relationships can be established, and we were unable to determine whether the PCE estimate had adequate discrimination. Because participants were recruited from churches, they may not be representative of all WAI in the United States. Participants may have underreported smoking behavior due to social desirability, and health behaviors of church attendees may differ from nonattendees; these aspects may limit the generalizability of our results. Ghanaian‐ and Nigerian‐born WAI residing in the Baltimore–Washington metropolitan area represent a subset of the WAI population in the United States; therefore, these results may not be generalizable to the entire WAI community. There is also the possibility of residual confounding because other pertinent variables including stress, dietary changes, and health care utilization were not measured.

This study also has strengths. To our knowledge, this study is the first community‐based epidemiological study of CVD risk in African immigrants in the United States and thus addresses a research gap in an ethnic minority population with scarce data; however, faith‐based settings provide access to ethnic minorities and a familiar and reassuring environment for targeting “hard‐to‐reach” groups and have provided successful recruitment of other immigrants.73 We also assessed the global atherosclerotic CVD risk using the new PCE estimate recommended by the American Heart Association and the American College of Cardiology30 to replace the Framingham CVD risk score in black participants.74 Furthermore, we used a point‐of‐care testing system that meets all relevant National Cholesterol Education Program guidelines75 and allowed for the immediate provision of individualized counseling. The use of the WHO STEPS questionnaire enhances the comparability of our results to those obtained in West Africa.

Conclusion

The Afro‐Cardiac Study complements the existing literature on CVD epidemiology in immigrants and provides valuable insights in a growing yet understudied population of WAI in the United States. The healthy immigrant effect—described as African immigrants with less obesity and better cardiometabolic health than African Americans76—may no longer hold for current WAI. The prevalence of CVD risk factors among relatively young WAI is particularly high. Primary prevention strategies including promotion of healthy lifestyle behaviors, early detection, and adequate control of traditional risk factors are necessary. Early intervention with culturally appropriate medical management and lifestyle changes may represent an opportunity to prevent the health of WAI from deteriorating on migration to the United States. Larger epidemiological studies are needed to confirm our findings.

Sources of Funding

This study was funded by the Center for Excellence in Cardiovascular Health National Institute of Nursing Research pilot grant P30NR011409.

Disclosures

None.

Acknowledgments

We would like to acknowledge all of the research assistants who assisted with data collection and made this study possible.

(J Am Heart Assoc. 2016;5:e002385 doi: 10.1161/JAHA.115.002385)

References

  • 1. Go AS, Mozaffarian D, Roger VL, Benjamin EJ, Berry JD, Blaha MJ, Dai S, Ford ES, Fox CS, Franco S, Fullerton HJ, Gillespie C, Hailpern SM, Heit JA, Howard VJ, Huffman MD, Judd SE, Kissela BM, Kittner SJ, Lackland DT, Lichtman JH, Lisabeth LD, Mackey RH, Magid DJ, Marcus GM, Marelli A, Matchar DB, McGuire DK, Mohler ER III, Moy CS, Mussolino ME, Neumar RW, Nichol G, Pandey DK, Paynter NP, Reeves MJ, Sorlie PD, Stein J, Towfighi A, Turan TN, Virani SS, Wong ND, Woo D, Turner MB; American Heart Association Statistics Committee and Stroke Statistics Subcommittee . Heart disease and stroke statistics–2014 update: a report from the American Heart Association. Circulation. 2014;129:e28–e292. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2. Yusuf S, Hawken S, Ounpuu S, Dans T, Avezum A, Lanas F, McQueen M, Budaj A, Pais P, Varigos J, Lisheng L; INTERHEART Study Investigators . Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case‐control study. Lancet. 2004;364:937–952. [DOI] [PubMed] [Google Scholar]
  • 3. Dawber TR, Moore FE, Mann GV. Coronary heart disease in the Framingham study. Am J Public Health Nations Health. 1957;47:4–24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4. Wilson PW, D'Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation. 1998;97:1837–1847. [DOI] [PubMed] [Google Scholar]
  • 5. Omran AR. The epidemiologic transition. A theory of the epidemiology of population change. Milbank Mem Fund Q. 1971;49:509–538. [PubMed] [Google Scholar]
  • 6. Commodore‐Mensah Y, Samuel LJ, Dennison‐Himmelfarb CR, Agyemang C. Hypertension and overweight/obesity in Ghanaians and Nigerians living in West Africa and industrialized countries: a systematic review. J Hypertens. 2014;32:464–472. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Terrazas A. African immigrants in the United States. Migration Information Source. 2009. Available at http://www.migrationpolicy.org/article/african-immigrants-united-states-0. Accessed September 20, 2014.
  • 8. Kent M. Immigration and America's black population. Popul Bull. 2007;62: 4. [Google Scholar]
  • 9. Commodore‐Mensah Y, Dennison Himmelfarb CR, Agyemang C, Sumner AE. Cardiometabolic health in African immigrants to the United States: a call to re‐examine research on African‐descent populations. Ethn Dis. 2015;25:373–380. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Choi SH. Testing healthy immigrant effects among late life immigrants in the United States: using multiple indicators. J Aging Health. 2012;24:475–506. [DOI] [PubMed] [Google Scholar]
  • 11. Kennedy S, McDonald JT, Biddle N. The healthy immigrant effect and immigrant selection: evidence from four countries. Soc Econ Dimens Aging Popul. 2006;164:1–50. [Google Scholar]
  • 12. Fuller‐Thomson E, Noack AM, George U. Health decline among recent immigrants to Canada: findings from a nationally‐representative longitudinal survey. Can J Public Health. 2011;102:273–280. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Uretsky MC, Mathiesen SG. The effects of years lived in the United States on the general health status of California's foreign‐born populations. J Immigr Minor Health. 2007;9:125–136. [DOI] [PubMed] [Google Scholar]
  • 14. Borrell LN, Crawford ND, Barrington DS, Maglo KN. Black/white disparity in self‐reported hypertension: the role of nativity status. J Health Care Poor Underserved. 2008;19:1148–1162. [DOI] [PubMed] [Google Scholar]
  • 15. Gordon‐Larsen P, Harris KM, Ward DS, Popkin BM; National Longitudinal Study of Adolescent Health . Acculturation and overweight‐related behaviors among Hispanic immigrants to the US: the National Longitudinal Study of Adolescent Health. Soc Sci Med. 2003;57:2023–2034. [DOI] [PubMed] [Google Scholar]
  • 16. Lauderdale DS, Rathouz PJ. Body mass index in a US national sample of Asian Americans: effects of nativity, years since immigration and socioeconomic status. Int J Obes Relat Metab Disord. 2000;24:1188–1194. [DOI] [PubMed] [Google Scholar]
  • 17. Steffen PR, Smith TB, Larson M, Butler L. Acculturation to Western society as a risk factor for high blood pressure: a meta‐analytic review. Psychosom Med. 2006;68:386–397. [DOI] [PubMed] [Google Scholar]
  • 18. Green LW, Kreuter MW, eds. Health Promotion Planning: An Educational and Ecological Approach. 4th ed New York: McGraw‐Hill; 2005. [Google Scholar]
  • 19. Dennison CR, Peer N, Steyn K, Levitt NS, Hill MN. Determinants of hypertension care and control among peri‐urban Black South Africans: the HiHi study. Ethn Dis. 2007;17:484–491. [PubMed] [Google Scholar]
  • 20. Kang JH, Han HR, Kim KB, Kim MT. Barriers to care and control of high blood pressure in Korean‐American elderly. Ethn Dis. 2006;16:145–151. [PubMed] [Google Scholar]
  • 21. Hu J, Wallace DC, Tesh AS. Physical activity, obesity, nutritional health and quality of life in low‐income Hispanic adults with diabetes. J Community Health Nurs. 2010;27:70–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Commodore‐Mensah Y, Sampah M, Berko C, Cudjoe J, Abu‐Bonsrah N, Obisesan O, Agyemang C, Adeyemo A, Himmelfarb CD. The Afro‐Cardiac Study: cardiovascular disease risk and acculturation in West African immigrants in the United States: rationale and study design. J Immigr Minor Health. 2015: doi: 10.1007/s10903‐015‐0291‐0. [Epub ahead of print] [DOI] [PubMed] [Google Scholar]
  • 23. World Health Organization . STEPwise Approach to Surveillance (STEPS). Geneva, Switzerland: WHO; 2008. [Google Scholar]
  • 24. Wagner J, Lacey K, Chyun D, Abbott G. Development of a questionnaire to measure heart disease risk knowledge in people with diabetes: the Heart Disease Fact Questionnaire. Patient Educ Couns. 2005;58:82–87. [DOI] [PubMed] [Google Scholar]
  • 25. Mitchell PH, Powell L, Blumenthal J, Norten J, Ironson G, Pitula CR, Froelicher ES, Czajkowski S, Youngblood M, Huber M, Berkman LF. A short social support measure for patients recovering from myocardial infarction: the ENRICHD Social Support Inventory. J Cardiopulm Rehabil. 2003;23:398–403. [DOI] [PubMed] [Google Scholar]
  • 26. Panz VR, Raal FJ, Paiker J, Immelman R, Miles H. Performance of the CardioChek PA and Cholestech LDX point‐of‐care analysers compared to clinical diagnostic laboratory methods for the measurement of lipids. Cardiovasc J S Afr. 2005;16:112–117. [PubMed] [Google Scholar]
  • 27. Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL Jr, Jones DW, Materson BJ, Oparil S, Wright JT Jr, Roccella EJ; National Heart, Lung, and Blood Institute Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure, National High Blood Pressure Education Program Coordinating Committee . The seventh report of the Joint National Committee on prevention, detection, evaluation, and treatment of high blood pressure: the JNC 7 report. JAMA. 2003;289:2560–2572. [DOI] [PubMed] [Google Scholar]
  • 28. Expert panel on detection, evaluation, and treatment of high blood cholesterol in adults. Executive summary of the third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III). JAMA. 2001;285:2486–2497. [DOI] [PubMed] [Google Scholar]
  • 29. American Diabetes Association . Standards of medical care in diabetes–2011. Diabetes Care. 2011;34(suppl 1):S11–S61. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Goff DC Jr, Lloyd‐Jones DM, Bennett G, Coady S, D'Agostino RB Sr, Gibbons R, Greenland P, Lackland DT, Levy D, O'Donnell CJ, Robinson J, Schwartz JS, Shero ST, Smith SC Jr, Sorlie P, Stone NJ, Wilson PW. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association task force on practice guidelines. Circulation. 2013;129(25 Suppl 2):S49–73. [DOI] [PubMed] [Google Scholar]
  • 31. National Heart, Lung, and Blood Institute . Heart disease risk factor multiplier effect in midlife women. 2014. Available at: http://www.nhlbi.nih.gov/health/educational/hearttruth/downloads/html/infographic-multiplier/infographic-multiplier.htm. Accessed September 20, 2014.
  • 32. National Heart, Lung, and Blood Institute . Who is at risk for heart disease?—NHLBI, NIH. 2014. Available at: http://www.nhlbi.nih.gov/health/health-topics/topics/hdw/atrisk.html. Accessed September 20, 2014.
  • 33. Blaha MJ, Blumenthal RS. Risk factors: new risk‐assessment guidelines‐more or less personalized? Nat Rev Cardiol. 2014;11:136–137. [DOI] [PubMed] [Google Scholar]
  • 34. Sliwa K, Wilkinson D, Hansen C, Ntyintyane L, Tibazarwa K, Becker A, Stewart S. Spectrum of heart disease and risk factors in a black urban population in South Africa (the Heart of Soweto Study): a cohort study. Lancet. 2008;371:915–922. [DOI] [PubMed] [Google Scholar]
  • 35. Baingana FK, Bos ER. Changing patterns of disease and mortality in Sub‐Saharan Africa: an overview In: Jamison DT, Feachem RG, Makgoba MW, Bos ER, Baingana FK, Hofman KJ, Rogo KO, eds. Disease and Mortality in Sub‐Saharan Africa. 2nd ed Washington, DC: The International Bank for Reconstruction and Development/The World Bank; 2006:1–6. [PubMed] [Google Scholar]
  • 36. Mookadam F, Arthur HM. Social support and its relationship to morbidity and mortality after acute myocardial infarction: systematic overview. Arch Intern Med. 2004;164:1514–1518. [DOI] [PubMed] [Google Scholar]
  • 37. Orth‐Gomer K, Rosengren A, Wilhelmsen L. Lack of social support and incidence of coronary heart disease in middle‐aged Swedish men. Psychosom Med. 1993;55:37–43. [DOI] [PubMed] [Google Scholar]
  • 38. Fiscella K, Tancredi D, Franks P. Adding socioeconomic status to Framingham scoring to reduce disparities in coronary risk assessment. Am Heart J. 2009;157:988–994. [DOI] [PubMed] [Google Scholar]
  • 39. Diez Roux AV, Detrano R, Jackson S, Jacobs DR Jr, Schreiner PJ, Shea S, Szklo M. Acculturation and socioeconomic position as predictors of coronary calcification in a multiethnic sample. Circulation. 2005;112:1557–1565. [DOI] [PubMed] [Google Scholar]
  • 40. Kaufman BD, Rodriquez‐Trias H. Participant and community issues in the recruitment and retention of women in clinical studies In: Recruitment and Retention of Women in Clinical Studies. (Office of Research on Women's Health), NIH Publ. No. 95‐3756, 1995;17–23, Washington, DC. [Google Scholar]
  • 41. Thamer M, Richard C, Casebeer AW, Ray NF. Health insurance coverage among foreign‐born US residents: the impact of race, ethnicity, and length of residence. Am J Public Health. 1997;87:96–102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42. Wilper AP, Woolhandler S, Lasser KE, McCormick D, Bor DH, Himmelstein DU. Health insurance and mortality in US adults. Am J Public Health. 2009;99:2289–2295. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43. McWilliams JM. Health consequences of uninsurance among adults in the United States: recent evidence and implications. Milbank Q. 2009;87:443–494. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44. Poirier P, Giles TD, Bray GA, Hong Y, Stern JS, Pi‐Sunyer FX, Eckel RH; American Heart Association, Obesity Committee of the Council on Nutrition, Physical Activity, and Metabolism . Obesity and cardiovascular disease: pathophysiology, evaluation, and effect of weight loss: an update of the 1997 American Heart Association Scientific Statement on Obesity and Heart Disease from the Obesity Committee of the Council on Nutrition, Physical Activity, and Metabolism. Circulation. 2006;113:898–918. [DOI] [PubMed] [Google Scholar]
  • 45. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011–2012. JAMA. 2014;311:806–814. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46. Agyemang C, Nicolaou M, Boateng L, Dijkshoorn H, van de Born BJ, Stronks K. Prevalence, awareness, treatment, and control of hypertension among Ghanaian population in Amsterdam, the Netherlands: the GHAIA study. Eur J Prev Cardiol. 2012;20:938–946. [DOI] [PubMed] [Google Scholar]
  • 47. Duda RB, Jumah NA, Hill AG, Seffah J, Biritwum R. Assessment of the ideal body image of women in Accra, Ghana. Trop Doct. 2007;37:241–244. [DOI] [PubMed] [Google Scholar]
  • 48. Amoah AG. Sociodemographic variations in obesity among Ghanaian adults. Public Health Nutr. 2003;6:751–757. [DOI] [PubMed] [Google Scholar]
  • 49. Folsom AR, Prineas RJ, Kaye SA, Munger RG. Incidence of hypertension and stroke in relation to body fat distribution and other risk factors in older women. Stroke. 1990;21:701–706. [DOI] [PubMed] [Google Scholar]
  • 50. Despres JP, Moorjani S, Lupien PJ, Tremblay A, Nadeau A, Bouchard C. Regional distribution of body fat, plasma lipoproteins, and cardiovascular disease. Arteriosclerosis. 1990;10:497–511. [DOI] [PubMed] [Google Scholar]
  • 51. O'Connor MY, Thoreson CK, Ricks M, Courville AB, Thomas F, Yao J, Katzmarzyk PT, Sumner AE. Worse cardiometabolic health in African immigrant men than African American men: reconsideration of the healthy immigrant effect. Metab Syndr Relat Disord. 2014;12:347–353. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52. Beune EJ, Haafkens JA, Agyemang C, Schuster JS, Willems DL. How Ghanaian, African‐Surinamese and Dutch patients perceive and manage antihypertensive drug treatment: a qualitative study. J Hypertens. 2008;26:648–656. [DOI] [PubMed] [Google Scholar]
  • 53. Biritwum R, Gyapong J, Mensah G. The epidemiology of obesity in Ghana. Ghana Med J. 2005;39:82–85. [PMC free article] [PubMed] [Google Scholar]
  • 54. Oladapo OO, Salako L, Sodiq O, Shoyinka K, Adedapo K, Falase AO. A prevalence of cardiometabolic risk factors among a rural Yoruba south‐western Nigerian population: a population‐based survey. Cardiovasc J Afr. 2010;21:26–31. [PMC free article] [PubMed] [Google Scholar]
  • 55. Ike SO, Aniebue PN, Aniebue UU. Knowledge, perceptions and practices of lifestyle‐modification measures among adult hypertensives in Nigeria. Trans R Soc Trop Med Hyg. 2010;104:55–60. [DOI] [PubMed] [Google Scholar]
  • 56. Kunutsor S, Powles J. Descriptive epidemiology of blood pressure in a rural adult population in Northern Ghana. Rural Remote Health. 2009;9:1095. [PubMed] [Google Scholar]
  • 57. Chaturvedi N, Bulpitt CJ, Leggetter S, Schiff R, Nihoyannopoulos P, Strain WD, Shore AC, Rajkumar C. Ethnic differences in vascular stiffness and relations to hypertensive target organ damage. J Hypertens. 2004;22:1731–1737. [DOI] [PubMed] [Google Scholar]
  • 58. Mbanya JC, Cruickshank JK, Forrester T, Balkau B, Ngogang JY, Riste L, Forhan A, Anderson NM, Bennett F, Wilks R. Standardized comparison of glucose intolerance in west African‐origin populations of rural and urban Cameroon, Jamaica, and Caribbean migrants to Britain. Diabetes Care. 1999;22:434–440. [DOI] [PubMed] [Google Scholar]
  • 59. Balde NM, Diallo I, Balde MD, Barry IS, Kaba L, Diallo MM, Kake A, Camara A, Bah D, Barry MM, Sangare‐Bah M, Maugendre D. Diabetes and impaired fasting glucose in rural and urban populations in Futa Jallon (Guinea): prevalence and associated risk factors. Diabetes Metab. 2007;33:114–120. [DOI] [PubMed] [Google Scholar]
  • 60. Oza‐Frank R, Narayan KM. Overweight and diabetes prevalence among US immigrants. Am J Public Health. 2010;100:661–668. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61. Oldroyd J, Banerjee M, Heald A, Cruickshank K. Diabetes and ethnic minorities. Postgrad Med J. 2005;81:486–490. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62. Sliwa K, Lyons JG, Carrington MJ, Lecour S, Marais AD, Raal FJ, Stewart S. Different lipid profiles according to ethnicity in the Heart of Soweto study cohort of de novo presentations of heart disease. Cardiovasc J Afr. 2012;23:389–395. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63. Sumner AE, Cowie CC. Ethnic differences in the ability of triglyceride levels to identify insulin resistance. Atherosclerosis. 2008;196:696–703. [DOI] [PubMed] [Google Scholar]
  • 64. Sumner AE, Zhou J, Doumatey A, Imoisili OE, Amoah A, Acheampong J, Oli J, Johnson T, Adebamowo C, Rotimi CN. Low HDL‐cholesterol with normal triglyceride levels is the most common lipid pattern in West Africans and African Americans with metabolic syndrome: implications for cardiovascular disease prevention. CVD Prev Control. 2010;5:75–80. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65. Bauman AE. Updating the evidence that physical activity is good for health: an epidemiological review 2000–2003. J Sci Med Sport. 2004;7:6–19. [DOI] [PubMed] [Google Scholar]
  • 66. Guthold R, Ono T, Strong KL, Chatterji S, Morabia A. Worldwide variability in physical inactivity a 51‐country survey. Am J Prev Med. 2008;34:486–494. [DOI] [PubMed] [Google Scholar]
  • 67. Adegoke BO, Oyeyemi AL. Physical inactivity in Nigerian young adults: prevalence and socio‐demographic correlates. J Phys Act Health. 2011;8:1135–1142. [PubMed] [Google Scholar]
  • 68. Amoah AG. Obesity in adult residents of Accra, Ghana. Ethn Dis. 2003;13:S97–S101. [PubMed] [Google Scholar]
  • 69. Smoking prevalence—males (% of adults) in Ghana. 2014. Available at: http://www.tradingeconomics.com/ghana/smoking-prevalence-males-percent-of-adults-wb-data.html. Accessed 9/8/2014.
  • 70. Smoking prevalence—females (% of adults) in Ghana. 2014. Available at: http://www.tradingeconomics.com/ghana/smoking-prevalence-females-percent-of-adults-wb-data.html. Accessed 9/8/2014.
  • 71. Smoking prevalence—males (% of adults) in Nigeria. 2014. Available at: http://www.tradingeconomics.com/nigeria/smoking-prevalence-males-percent-of-adults-wb-data.html. Accessed 9/8/2014.
  • 72. Smoking prevalence—females (% of adults) in Nigeria. 2014. Available at: http://www.tradingeconomics.com/nigeria/smoking-prevalence-females-percent-of-adults-wb-data.html. Accessed 9/8/2014.
  • 73. Jo AM, Maxwell AE, Yang B, Bastani R. Conducting health research in Korean American churches: perspectives from church leaders. J Community Health. 2010;35:156–164. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74. D'Agostino RB Sr, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, Kannel WB. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117:743–753. [DOI] [PubMed] [Google Scholar]
  • 75. Cholestech Cooperation . Accuracy and reproducibility of point‐of‐care lipid test methods are certified by the cholesterol reference method laboratory network. Available at: http://www.cholestech.com/docs/ldx_accuracy/MKT13415_A%20CRMLN%20Technical%20Brief.pdf. Accessed March 31, 2012.
  • 76. Singh GK, Hiatt RA. Trends and disparities in socioeconomic and behavioural characteristics, life expectancy, and cause‐specific mortality of native‐born and foreign‐born populations in the United States, 1979–2003. Int J Epidemiol. 2006;35:903–919. [DOI] [PubMed] [Google Scholar]

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