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Sociodemographics and Lifestyle Cancer Risk Factors among a Population of Caribbean Immigrants

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26 September 2024

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30 September 2024

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Abstract
Aim: The aim in this study was to conduct a prospective survey regarding the socio-demographics and lifestyle risk factors for cancer of a population of Caribbean immigrants living in the U.S. Materials and Methods: The data were processed and analysed using SPSS software 27 and Excel. Crosstabulations were performed. The chi-square test was used to evaluate differ-ent hypotheses. Statistical significance was defined as p < 0.05. Results: Statistically significant differences in the country of birth of the Caribbean immigrants were found for sex (p = 0.038) and the cleanness of their neighbourhoods (p = 0.045). We found differences in occupations between men and women (p = 0.001). Men were less unemployed than women (p = 0.011). Sex also showed statistically significant differences in how easily the immigrants balanced their work and per-sonal life (p = 0.044). Age groups depicted differences in the physical health of the immigrants (p = 0.001). The use of alcohol and tobacco was not an important risk factor among participants (p = 0.529). Conclusions: These findings suggest that the sociodemographics risk factors for cancer among a population of Caribbean immigrants were significantly different between the sexes. However, the use of tobacco and alcohol showed insignificant differences among the immigrants.
Keywords: 
Caribbean
;  
West Indians
;  
hypothesis
;  
biostatistics
;  
immigrants
;  
cancer risk factors
;  
Datasets
Subject: 
Public Health and Healthcare  -   Other
Siegel et al. (2012) reported that, in 2009, cancer surpassed heart disease as a leading cause of death. In 2010, the American Cancer Society (ACS) updated the previous report on cancer statistics of the 50.5 million Latinos living in the USA using data from the Centers for Disease Control and Prevention (CDC), the National Cancer Institute (NCI), and the North American Association of Central Cancer Registries (NAACCR), and using mortality data from National Center for Health Statistics (NCHS) [3].
Islami et al. (2018) estimated the proportion and number of invasive cancer cases and deaths for 26 cancer types in adults aged 30 years and older in the United States in 2014. They found that these cancers were attributable to modifiable risk factors such as cigarette smoking and alcohol intake [4].
In this study, we aimed to prospectively survey the sociodemographics and lifestyle cancer risk factors among a population of Caribbean immigrants living in the U.S.

2. Materials and Methods

2.1. Hypotheses

Hypotheses 1. 
Differences exist in country of origin between men and women.
Hypotheses 2. 
Overall, sex influences the cleanliness of the neighbourhoods of the participants.
Hypotheses 3. 
Sex influences the ease with which work and the personal life of the immigrants are balanced.
Hypotheses 4. 
Differences exist in occupation between men and women.
Hypotheses 5. 
Men are less unemployed than women.
Hypotheses 6. 
Difference exists in physical activity between men and women.
Hypotheses 7. 
Age group is associated with participants’ physical health.
Hypotheses 8. 
The use of alcohol and tobacco is an important risk factor for cancer.

2.2. Ethical Considerations

The Campus Ethical Committee of the University of the West Indies, St. Augustine, Trinidad and Tobago, approved this study.

2.3. Location and Time Frame of the Study

The location was in the USA region. The project started in August 2021 and was completed in August 2022. The study was conducted online, and a SurveyMonkey link was sent to the participants through a social media network by SurveyMonkey Co., Ltd. (San Mateo, CA, USA).

2.4. Inclusion/Exclusion Criteria

2.4.1. Inclusion Criteria

Immigrants with no personal history of cancer living in the USA and older than 18 years old were included in the study.

2.4.2. Exclusion Criteria

The exclusion criteria included Caribbean subjects who had cancer and were not immigrants as well as those under the age of 18 years.

2.5. Justification of Sample Size

We used simple random sampling. In this way, each questionnaire had an equal chance of being included in the sample, which would allow for an adequate representation. So, the final sample size was calculated by using the formula:
N = Zα//22 x (1-p)/d2
where N is the required sample size, Z α/2 is the standard normal deviation corresponding to the specified total population at the 95% confidence level of 1.96, p is the prevalence of cancer = 0.487 (1-p = 0.513), and d is the desired degree of accuracy = 0.05.
A total of 388 participants with no history of cancer, who were 18 years of age or older, and were living in the U.S. were enrolled.

2.6. Recruitment of Subjects

Participants were recruited through the distribution of an online questionnaire link, included in a database of SurveyMonkey Co., Ltd., along with a letter explaining the importance of this study via social media by SurveyMonkey Co., Ltd.

2.7. Statistical Methods of Data Analysis

The data were processed and analysed using SPSS 27 software. Crosstabulations were conducted. The chi-square test was used to evaluate hypotheses related to the difference in frequencies between groups. Statistical significance was defined as p < 0.05.
Hypotheses were developed to test whether the observed frequency was significantly different from what was expected via crosstabulation.
The null hypothesis was that sex and age group significantly differed in terms of country of birth of the participants, the cleanliness of their neighbourhoods, current occupation, balancing of work and personal life, employment status (employed, unemployed, or student), physical health, drinking and smoking habits, and engagement in physical activities.
These hypotheses were tested via crosstabulations that generated the Pearson chi-square.

2.8. Confidentiality

2.8.1. Methods for Storing and Securing Study/Biological Data

All collected data were stored in a password-protected SurveyMonkey account. Data will be stored securely for two years in an SPSS file, after which they will be discarded.

2.8.2. Methods for Protecting Participants’ Confidentiality

A disclaimer was placed at the beginning of the survey to inform the participants how their information would be handled. Respondents provided their consent to participate. Participants were not asked to disclose their names, addresses, or contact information during or after the online survey. Therefore, the study was anonymous.

2.9. Risk/Benefit

2.9.1. Level of Risk Associated with This Study

This level of risk of this study is minimal risk. Minimal risk to subjects means that the probability and magnitude of harm or discomfort anticipated in the study are not greater than those ordinarily encountered in daily life or during the performance of routine physical and psychological examinations or tests and that confidentiality is adequately protected.

2.9.2. Risk, Discomfort (Physical/Psychological), Inconvenience, Side Effects, and Financial Costs to Participants (Including Measures to Mitigate These Risks/Discomforts)

A mild level of psychological risk was associated with this study. No physical or invasive procedure was performed. We asked about the family history of cancer and a few screening procedures, among other questions, which may have been challenging for some participants. However, they had the benefit of not answering or withdrawing from the survey. In this survey, no people withdrew, but some did not answer some questions.

2.9.3. Direct Benefits to Participants

From participants, we obtained information about the importance of mitigating the use of tobacco and alcohol to prevent malignancies. The participants were informed of the importance of physical activity.

2.10. Compensation, Rewards, or Other Incentives for Participants

Participants were thanked for their participation in the questionnaire.

2.11. Process for Informed Consent

Because this study involved an online survey, a consent form was provided at the beginning of the online questionnaire to ratify the true willingness of the respondent to participate in the online study.

2.12. Survey Standardisation

SurveyMonkey Co. provided the questions to carry out this study, except question one (Caribbean countries where the participants were born).

3. Results and Discussion

3.1. Association between Country of Birth and Sex

We found statistically significant differences between men and women in their country of origin (p = 0.038), as shown in Table 1. Men were mainly from the U.S. Virgin Islands (32.1%), Puerto Rico (21.4%), Haiti (6.9%), and Cuba and the Dominican Republic (6.3%). Women were mainly from the U.S. Virgin Islands (48.0%), Puerto Rico (11.4%), Cuba (7.0%) and the Dominican Republic (6.6%), as shown in Table 2. Conversely, men were less frequently from Barbados, Antigua, and Barbuda (0.3%); women were less frequently from Anguilla and Antigua and Barbuda (0.3%).

3.2. Association between Cleanliness of Neighbourhood and Sex

We found a statistically significant difference between men and women in the cleanliness of their neighbourhood (p = 0.045), as shown in Table 3. A total of 20.1% of women lived in extremely clean, 44.9% lived in very clean, 31.9% in somewhat clean, and 3.1% not so clean neighbourhoods. A total of 22.7% of men lived in extremely clean, 44.7% in very clean, 23.8% in somewhat clean, 6.9% in not so clean, and 1.9% in not at all clean (1.9%) neighbourhoods. Women tended to live in cleaner neighbourhoods, as shown in Table 4.

3.3. Association between Current Occupation and Sex

We found a statistically significant difference between women and men in terms of their current occupation (p < 0.001), as shown in Table 5. More women were employed in the life, physical, and social fields (3.9%); legal (2.2%); education, training, and library (11.0%); healthcare practitioners and technical occupations (9.2%); healthcare support (7.8%); personal care and service (7.4%); office and administrative support (5.2%); and food preparation and serving-related occupations (6.1%). These percentages were higher for women than for men. Men surpassed women in occupations such as transportation and materials-moving (9.4%), management (5.7%), and production and installation, maintenance, and repair (4.4%) as depicted in Table 6.

3.4. Association between Work and Personal Life Balance and Sex

We noted a statistically significant difference between men and women in balancing their work and personal life (p = 0.044), as shown in Table 7. Women reported that finding this balance was “extremely easy” (18.8%), “very easy” (24.0%), “somewhat easy” (40.2%), “not so easy” (15.3%), and “not at all easy” (1.7%). Conversely, for men, these values were 11.3%, 32.1%, 43.4%, 9.4%, and 3.8%, respectively, as shown in Table 8.

3.5. Association between Current Occupation Status and Sex

We found a statistically significant difference between men and women in terms of their current occupation status (p = 0.011), as shown in Table 9. For women, 63.7% were employed. 26.4% were unemployed, and 9.9% were students. For men, these values were 70.8%, 13.9%, and 15.3%, respectively. The rate of unemployment was far higher in women, as shown in Table 10.

3.6. Association between Engagement in Physical Activity and Sex

Table 11 shows that we found a statistically significant difference between men and women in terms of engagement in physical activity (p = 0.025). As shown in Table 12, the percentage of women that engaged in physical activity every day was 27.6%, a few times a week was 33.3%, approximately once per week was 15.8%, a few times a month was 13.2%, once per month was 4.4%, and less than once per month was 5.7%. For men, these percentages were 30.8%, 44.0%, 14.5%, 5.0%, 1.3%, and 4.4%, respectively.

3.7. Association between Alcohol and Tobacco Use and Sex

Table 13 shows that we found no statistically significant difference between alcohol and tobacco use between the sexes (p = 0.529). As shown in Table 13, the percentages of women who engaged in drinking and smoking almost every day were 6.1% and 2.6%, respectively. The percentages of women who engaged in drinking and smoking more than once a week were 11.0% and 4.8%, respectively; the percentages of women that neither drank nor smoked was 36.4%. Conversely, the percentages of men that engaged in drinking and smoking almost every day were 9.4% and 4.4%, respectively. The percentages of men that engaged in drinking and smoking more than once a week were 9.4% and 3.8%, respectively; and the percentage of men that neither drank nor smoked was 28.3%, as shown in Table 14.

3.8. Association between Physical Health and Age Group

We found a statistically significant difference between the participant’s age group and their physical health (p = 0.005), as shown in Table 15. Approximately 2.8% of the participants aged 18 to 24 years were “extremely healthy”, 10.6% were “very healthy”, 9.0% were “somewhat healthy”, and 1.0% were “not so healthy”. A total of 6.4% of the immigrants aged 25 to 34 years were extremely healthy, 7.2% were very healthy, 10.8% were somewhat healthy, 1.0% were not so healthy and 0.5% were “not at all healthy”. Approximately 2.1% of the participants aged 35 to 44 years were extremely healthy, 6.4% were very healthy, 6.7% were somewhat healthy, and 0.8% were not so healthy. A total of 1.5% of the immigrants aged 45 to 54 years were extremely healthy, 5.7% were very healthy, 10.3% were somewhat healthy, and 1.0% were not so healthy. Approximately 0.8% of the participants aged 55 to 64 years were extremely healthy, 3.1% were very healthy, 6.4% were somewhat healthy, and 1.3% were not so healthy, as shown in Table 16.
Tao et al. examined the differences between foreign-born Hispanics settling in lower-status neighbourhoods and USA-born Hispanics [5]. Foreign-born Hispanics showed a health advantage in terms of survival after a diagnosis of breast, prostate, and lung cancer compared with those U.S.-born [6,7,8].
In 2010, 30.7% of Hispanics were uninsured and 26.6% lived in poverty, compared to 11.7% and 9.9% of non-Hispanic Whites (NHW), respectively. Heterogeneity was found within the Hispanic/Latino population. For instance, the socioeconomic profile of Cuban Americans was more similar to that of NHW than to Dominican Americans and Haitian Americans. Hispanic had a lower rate for the most common cancers (breast, lung, prostate, and colorectal) and higher rates of cancer of the liver, uterine cervix, and stomach than NHW, which may be due to the poor access to screening programmes in the immigrant population and low social status. In 2012, an estimated 113,000 new cases of cancer and 33,000 deaths among Hispanics/Latinos were predicted. Strategies to attenuate the cancer explosion among this leading minority in the USA have been effective interventions to decrease alcohol consumption, tobacco use, and obesity [3].
The Latino population in the U.S. will triple in size by 2050. It will account for half of the nation’s population growth if current migration trends continue, including the Caribbean Hispanic population [9]. The same authors studied preimmigration family cohesion. Family cohesion is a buffer against alcohol abuse and a protective factor against psychological distress among U.S. Latinos from Cuba and the Dominican Republic. In this study some respondents answered that they drank once per week (16.5%), more than once per week (10.3%) and almost every day (7.5%). In addition, in this study, regarding tobacco use, 17 of 388 smoked sometimes (4.4%), 13 (3.4%) often smoked, 18 (4.6%) smoked every day, and 42 (10.8%) did not smoke but drank alcohol. However, the chi-square result was p = 0.529, which is not significant [9].
Taylor et al. (1997) conducted a survey sampling 165 Haitian-born, 354 Caribbean-born, and 402 U.S.-born Blacks settled in New York City in 1992. Haitian-born and Caribbean-born respondents were more likely to smoke preferentially than their female counterparts. As well, both sexes that were USA-born were more likely to smoke than those who were Haitian-born and Caribbean-born. Alcohol consumption was combined with the act of smoking across the groups. Community education is essential in tackling this problem because participants believed that smoking was not related to cancer [10].
Vega et al. (1993) demonstrated that Cuban-American adolescents who were foreign-born were less likely to have ever smoked or consumed alcohol than Cuban Americans who were USA-born. The latter were more likely to undergo an acculturation process [11]. Lucas et al. (2005) found that over 87% of the foreign-born Black community in the USA believed that their health was excellent or very good, which was significantly higher than foreign-born white individuals and the same USA-born individuals. The foreign Black population had lower smoking rates, especially among women [12].
Nelson et al. (2016) examined the screening impact on breast cancer survival. Inequalities remain in breast cancer screening realisation related to socioeconomic deprivation, even with universal screening programmes in many European countries [13,14,15].
Household air pollution (HAP) arises from domestic activities such as heating, cooking, and lighting, and is usually measured indoors. It is a socioeconomic factor that causes respiratory cancers, especially in low- and middle-income countries; it is associated with poor neighbourhoods and could be found elsewhere. Three billion people worldwide are exposed to toxic amounts of HAP every day. Indoor air pollution deaths per million population is 0 to 10 in the U.S., Canada, and Australia. HAP is considered to be a modifiable exposure. Reducing HAP can improve human health with interventions such as the use of cookstoves, heaters, and improved fuels [16,17,18,19].
Plants have the capacity to absorb and catabolize various environmental toxic substances by a process called phytoremediation. In countries such as Indonesia, plants have been implemented in this capacity. However, plants are still not optimally utilized as a medium for room air purification. Different plants have been used including English ivy (Hedera helix), bamboo palm (Chamaedorea seifrizii), Aloe vera (Aloe vera), and banana (Musa oriana) [20,21,22,23].
“In 2018, 1,735,350 new cancer cases and 609,640 cancer deaths are projected to occur in the United States. An estimated one in three Americans will be diagnosed with an invasive cancer over their lifetimes’‘ [3]. Lifestyle changes provide an opportunity for cancer prevention [24,25,26,27]. They include abstinence from alcohol and tobacco [28,29,30,31], consumption of various serves of fruits and vegetables daily [32,33,34,35,36,37,38,39], prevention of viral infections such as HIV/AIDS and hepatitis viruses B and C by using adequate protection and safe sexual practices [40,41,42], and avoidance of obesity [43,44]. Physical activity is a protective factor against several cancers including colorectal cancer, bladder, breast, endometrial, and oesophageal adenocarcinoma. However, sedentary behaviour, independent of physical activity, predisposes one to the risk of endometrial, colon, and lung cancers owing to the effect on endogenous sex steroids and insulin sensitivity, metabolic hormones, and chronic inflammation [45,46].

3.9. Summary

Table 17 summarises the statistics study. The use of alcohol and tobacco is an important known risk factor for cancer; the participants in this survey drank and smoked but not in any significant way, as judged by the result of the chi-square test, which showed no significant associations between alcohol and tobacco use with sex (p > 0.05). We found a significant association between sex and overall cleanliness of the neighbourhoods of the immigrants, supported by the results of the chi-square test (p = 0.045). We found a significant association between sex and how easy it was to balance the work and personal life of the immigrants, as judged by the result of the chi-square test (p = 0.044). We noted differences in occupations between men and women. This was supported by the results of the Chi-square test, which showed significant results (p < 0.001). We noted significant associations between the country of birth and sex, as supported by the results of the chi-square test (p = 0.038). We identified an association between sex and current occupation status: men were less unemployed than women. The result was supported by the chi-square test, which showed p = 0.011. An association was found between age group and participants’ physical health, as judged by the result of the chi-square test, which showed significant association (p = 0.005).

4. Conclusions

The biostatistics were clear regarding accepting the association of socioeconomics/ lifestyle cancer risk factors with sex or age group among 388 immigrants living in the U.S. Several modifiable risk factors are attributed to the onset of many cancers, not limited to those discussed in this study. An agreement was found in the data, whereby distinct factors such as smoking, and alcohol consumption were not implicated in developing many cancer types. However, a low risk was found. The limitations of this study are the low participation of the elderly aged 75 years or older, which could have been due to low proficiency with social networks and internet management in general and lower accessibility to different device types. Cross-sectional studies have limitations regarding the lack of follow-up work; the inability to draw causal inferences; presence of prevalence-incidence bias, also called Neyman bias; and the different results that may be obtained if another time frame is chosen [47].

Author Contributions

The manuscript was written through the contributions of A.A.J-V and D.G. All authors have read and agreed to the published version of the manuscript.

Funding

This study did not receive any external funding.

Institutional Review Board Statement

Approved by the Ethical Committee of the University of the West Indies.

Data Availability Statement

It can be directly collected from the authors.

Conflicts of Interest

The authors declare no conflicts of interest.

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Table 1. Chi-square test results of the association between country of origin and sex.
Table 1. Chi-square test results of the association between country of origin and sex.
Value df Asymptotic Significance (2-Sided)
Pearson Chi-Square 24.622 a 14 0.038
Likelihood Ratio 25.679 14 0.028
Linear-by-Linear Association 5.142 1 0.023
N of Valid Cases 388
a. 13 cells (43.3%) had an expected count less than 5. The minimum expected count was 0.41.
Table 2. Chi-square test of percentages of men and women from Caribbean countries.
Table 2. Chi-square test of percentages of men and women from Caribbean countries.
Which Caribbean country are you from originally? Cuba Count 16 10 26
% of Total 7.0% 6.3% 6.7%
Haiti Count 10 11 21
% of Total 4.4% 6.9% 5.7%
Dominican Republic Count 15 10 25
% of Total 6.6% 6.3% 6.4%
Puerto Rico Count 26 34 60
% of Total 11.4% 21.4% 16.3%
Jamaica Count 14 12 26
% of Total 6.1% 7.5% 6.8%
Trinidad and Tobago Count 6 2 8
% of Total 2.6% 1.3% 2.0%
Bahamas Count 15 7 22
% of Total 6.6% 4.4% 5.4%
Belize Count 3 7 10
% of Total 1.3% 4.4% 2.9%
Barbados Count 2 1 3
% of Total 0.9% 0.6% 0.8%
Saint Lucia Count 0 2 2
% of Total 0.0% 1.3% 0.7%
United States Virgin Islands (USA) Count 110 51 161
% of Total 48.0% 32.1% 40.0%
Grenada Count 3 6 9
% of Total 1.3% 3.8% 2.6%
Antigua and Barbuda Count 1 1 2
% of Total 0.3% 0.6% 0.5%
Caribbean Netherlands (the Netherlands) Count 7 5 12
% of Total 3.1% 3.1% 3.0%
Anguilla (U.K.) Count 1 0 1
% of Total 0.4% 0.0% 0.2%
Total Count 229 159 388
% of Total 100.0% 100.0% 100.0%
Table 3. Chi-square test examining association between cleanliness of neighbourhood and sex.
Table 3. Chi-square test examining association between cleanliness of neighbourhood and sex.
Value df Asymptotic Significance (2-Sided)
Pearson Chi-Square 9.717 a 4 0.045
Likelihood Ratio 10.725 4 0.030
Linear-by-Linear Association 0.106 1 0.744
N of Valid Cases 388
a. Two cells (20.0%) had expected count less than five. The minimum expected count was 1.23.
Table 4. Percentages of participants by neighbourhood cleanliness and sex.
Table 4. Percentages of participants by neighbourhood cleanliness and sex.
Sex Total
Female Male
Overall, how clean is your neighbourhood? Extremely clean Count 46 36 82
% of Total 20.1% 22.7% 21.4%
Very clean Count 103 71 174
% of Total 44.9% 44.7% 44.8%
Somewhat clean Count 73 38 111
% of Total 31.9% 23.8% 27.9%
Not so clean Count 7 11 18
% of Total 3.1% 6.9% 5.0%
Not at all clean Count 0 3 3
% of Total 0.0% 1.9% 0.9%
Total Count 229 159 388
% of Total 100.0% 100.0% 100.0%
Table 5. Chi-square test results of the association between current occupation and sex.
Table 5. Chi-square test results of the association between current occupation and sex.
Value df Asymptotic Significance (2-Sided)
Pearson Chi-Square 69.338 a 22 <0.001
Likelihood Ratio 77.413 22 0.000
Linear-by-Linear Association 4.123 1 0.042
N of Valid Cases 388
a. Note that 12 cells (26.1%) had an expected count less than 5. The minimum expected count was 1.64.
Table 6. Percentages of the participants by occupations and sex.
Table 6. Percentages of the participants by occupations and sex.
Sex Total
Female Male
Which of the following best describes your current occupation? Other (please specify) Count 31 8 39
% of Total 13.5% 5.0% 8.4%
Management Occupations Count 5 9 14
% of Total 2.2% 5.7% 4.0%
Business and Financial Operations Occupations Count 10 12 22
% of Total 4.4% 7.5% 6.0%
Computer and Mathematical Occupations Count 10 14 24
% of Total 4.4% 8.8% 6.9%
Architecture and Engineering Occupations Count 8 9 17
% of Total 3.5% 5.7% 4.6%
Life, Physical, and Social Science Occupations Count 9 1 10
% of Total 3.9% 0.6% 2.3%
Community and Social Service Occupations Count 4 5 9
% of Total 1.7% 3.1% 2.4%
Legal Occupations Count 5 0 5
% of Total 2.2% 0.0% 1.1%
Education, Training, and Library Occupations Count 25 10 35
% of Total 11.0% 6.2% 7.6%
Arts, Design, Entertainment, Sports, and Media Occupations Count 7 8 15
% of Total 3.1% 5.3% 4.2%
Healthcare Practitioners and Technical Occupations Count 21 2 23
% of Total 9.2% 1.3% 5.6%
Healthcare Support Occupations Count 18 11 29
% of Total 7.8% 6.9% 7.4%
Protective Service Occupations Count 0 5 5
% of Total 0.0% 3.1% 1.6%
Food Preparation and Serving Related Occupations Count 14 9 23
% of Total 6.1% 5.6% 5.9%
Building and Grounds Cleaning and Maintenance Occupations Count 7 6 13
% of Total 3.1% 3.8% 3.5%
Personal Care and Service Occupations Count 17 3 20
% of Total 7.4% 1.9% 5.0%
Sales and Related Occupations Count 8 5 13
% of Total 3.5% 3.1% 3.3%
Office and Administrative Support Occupations Count 12 6 18
% of Total 5.2% 3.8% 4.5%
Farming, Fishing, and Forestry Occupations Count 2 2 4
% of Total 0.9% 1.3% 1.1%
Construction and Extraction Occupations Count 6 5 11
% of Total 2.6% 3.1% 2.9%
Installation, Maintenance, and Repair Occupations Count 3 7 10
% of Total 1.3% 4.4% 2.9%
Production Occupations Count 1 7 8
% of Total 0.4% 4.4% 4.8%
Transportation and Materials Moving Occupations Count 6 15 21
% of Total 2.6% 9.4% 5.0%
Total Count 229 159 388
% of Total 100.0% 100.0% 100.0%
Table 7. Chi-square test results of the association between participants’ work and personal life balance and sex.
Table 7. Chi-square test results of the association between participants’ work and personal life balance and sex.
Value df Asymptotic Significance (2-Sided)
Pearson Chi-Square 9.772 a 4 0.044
Likelihood Ratio 9.935 4 0.042
Linear-by-Linear Association 0.247 1 0.619
N of Valid Cases 388
a. 1 cell (10.0%) had an expected count less than 5. The minimum expected count was 4.10.
Table 8. Percentages of participants regarding balancing their work and personal life by sex.
Table 8. Percentages of participants regarding balancing their work and personal life by sex.
Sex Total
Female Male
How easy is it to balance your work life and personal life? Extremely easy Count 43 18 61
% of Total 18.8% 11.3% 15.0%
Very easy Count 55 51 106
% of Total 24.0% 32.1% 28.0%
Somewhat easy Count 92 69 161
% of Total 40.2% 43.4% 41.8%
Not so easy Count 35 15 50
% of Total 15.3% 9.4% 12.4%
Not at all easy Count 4 6 10
% of Total 1.7% 3.8% 2.8%
Total Count 229 159 388
% of Total 100.0% 100.0% 100.0%
Table 9. Chi-square test results of the association between participants’ current occupation status and sex.
Table 9. Chi-square test results of the association between participants’ current occupation status and sex.
Value df Asymptotic Significance (2-Sided)
Pearson Chi-Square 9.011 a 2 0.011
Likelihood Ratio 9.320 2 0.009
Linear-by-Linear Association .055 1 0.814
N of Valid Cases 356
a. No cells (0.0%) had an expected count of less than 5. The minimum expected count was 17.39.
Table 10. Percentages of participants by current occupation status and sex.
Table 10. Percentages of participants by current occupation status and sex.
Sex Total
Female Male
Which of the following best describes your current occupation? Employed Count 135 102 237
% of Total 63.7% 70.8% 67.3%
Unemployed Count 56 20 76
% of Total 26.4% 13.9% 20.1%
Student Count 21 22 43
% of Total 9.9% 15.3 12.6%
Total Count 212 144 356
% of Total 100.0% 100.0% 100.0%
Table 11. Chi-square test results of engagement in physical activity and sex.
Table 11. Chi-square test results of engagement in physical activity and sex.
Value df Asymptotic Significance (2-Sided)
Pearson Chi-Square 12.837 a 5 0.025
Likelihood Ratio 13.737 5 0.017
Linear-by-Linear Association 6.443 1 0.011
N of Valid Cases 387
a. Note that 1 cell (8.3%) had an expected count less than 5. The minimum expected count was 4.93.
Table 12. Percentages of participants’ engagement in physical activity and sex.
Table 12. Percentages of participants’ engagement in physical activity and sex.
Sex Total
Female Male
How often do you engage in physical activity? Every day Count 63 49 112
% of Total 27.6% 30.8% 29.2%
A few times a week Count 76 70 146
% of Total 33.3% 44.0% 38.7%
About once a week Count 36 23 59
% of Total 15.8% 14.5% 15.2%
A few times a month Count 30 8 38
% of Total 13.2% 5.0% 9.0%
Once a month Count 10 2 12
% of Total 4.4% 1.3% 2.9%
Less than once a month Count 13 7 20
% of Total 5.7% 4.4% 5.0%
Total Count 228 159 387
% of Total 100.0% 100.0% 100.0%
Table 13. Chi-square test results of the association between drinking and smoking and sex.
Table 13. Chi-square test results of the association between drinking and smoking and sex.
Chi-Square Tests
Value Df Asymptotic Significance (2-Sided)
Pearson Chi-Square 9.033 a 10 0.529
Likelihood Ratio 9.417 10 0.493
Linear-by-Linear Association 1.528 1 0.216
N of Valid Cases 387
a. Note that 4 cells (18.2%) had an expected count less than 5. The minimum expected count was 0.41.
Table 14. Percentages of participants who drank and smoked by sex.
Table 14. Percentages of participants who drank and smoked by sex.
Sex Total
Female Male
Do you drink alcohol or smoke cigarettes? Tick all that apply. I drink alcohol once a week Count 36 28 64
% of Total 15.8% 17.6% 16.7%
I drink alcohol more than once per week Count 25 15 40
% of Total 11.0% 9.4% 10.2%
I drink almost every day Count 14 15 29
% of Total 6.1% 9.4% 7.8%
I do not drink alcohol Count 15 14 29
% of Total 6.6% 8.8% 7.8%
I do not smoke Count 22 20 42
% of Total 9.7% 12.6% 11.1%
I smoke sometimes Count 11 6 17
% of Total 4.8% 3.8% 4.3%
I often smoke Count 6 7 13
% of Total 2.6% 4.4% 3.5%
I smoke every day Count 13 5 18
% of Total 5.7% 3.2% 4.4%
I do not drink nor smoke Count 83 45 128
% of Total 36.4% 28.3% 32.3%
I do not smoke marijuana Count 0 1 1
% of Total 0% 0.6% 0.3%
I do not drink, nor do I smoke cigarettes or marijuana Count 3 3 6
% of Total 1.3% 1.9% 1.6%
Total Count 228 159 387
% of Total 100.0% 100.0% 100.0%
Table 15. Chi-square test results of the association between the physical health and age group.
Table 15. Chi-square test results of the association between the physical health and age group.
Chi-Square Tests
Value df Asymptotic Significance (2-Sided)
Pearson Chi-Square 45.693 a 24 0.005
Likelihood Ratio 40.134 24 0.021
Linear-by-Linear Association 9.180 1 0.002
N of Valid Cases 388
a. Note that 16 cells (45.7%) had an expected count less than 5. The minimum expected count was 0.01.
Table 16. Percentages of participants by physical health and age group.
Table 16. Percentages of participants by physical health and age group.
How Physically Healthy Are You? Total
Extremely Very Somewhat Not So Not at All
What is your age? (years)
18 to 24 Count 11 41 35 4 0 91
% of Total 2.8% 10.6% 9.0% 1.0% 0.0% 23.5%
25 to 34 Count 25 28 42 4 2 101
% of Total 6.4% 7.2% 10.8% 1.0% 0.5% 26.0%
35 to 44 Count 8 25 26 3 0 62
% of Total 2.1% 6.4% 6.7% 0.8% 0.0% 16.0%
45 to 54 Count 6 22 40 4 0 72
% of Total 1.5% 5.7% 10.3% 1.0% 0.0% 18.6%
55 to 64 Count 3 12 25 5 0 45
% of Total 0.8% 3.1% 6.4% 1.3% 0.0% 11.6%
65 to 74 Count 2 4 5 4 0 15
% of Total 0.5% 1.0% 1.3% 1.0% 0.0% 3.9%
75 or older Count 0 2 0 0 0 2
% of Total 0.0% 0.5% 0.0% 0.0% 0.0% 0.5%
Total Count 55 134 173 24 2 388
% of Total 14.2% 34.5% 44.6% 6.2% 0.5% 100.0%
Table 17. Hypotheses demonstrated by crosstabulation.
Table 17. Hypotheses demonstrated by crosstabulation.
Hypotheses Statistical Test Supported/ Not Supported Statistical Significance
The use of alcohol and tobacco is an important risk factor for cancer Chi-square test Not supported p = 0.529
Sex influences overall cleanliness of neighbourhoods of the immigrants Chi-square test Supported p = 0.045
Sex influences how easy is to balance the work and personal life Chi-square test Supported p = 0.044
Differences exist in occupation between men and women Chi-square test Supported p < 0.001
Association exists between the country of born and sex Chi-square test Supported p = 0.038
Men are less unemployed than women Chi-square test Supported p = 0.011
Age group is associated with participants’ physical health Chi-square test Supported p = 0.005
Association exists between participants’ engagement in physical activity and sex Chi-square test Supported p = 0.025
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