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A Prospective Survey on Socio-Demographics and Lifestyle Factors among a Population of Caribbean Immigrants Living in the US

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27 June 2023

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28 June 2023

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Abstract
Aim: This study aimed to carrying out a prospective survey on socio-demographics and lifestyle factors among a population of Caribbean immigrants living in the US. Materials and Methods: The data were processed and analysed using the SPSS software and Excel. Crosstabulations were done. The Chi-square test was use to evaluate different hypotheses in this study. Statistical significance was defined as p<0.05.Results: Gender was found statistically significant difference with the country of birth of the Caribbean immigrants (p=0.038), and in the cleanness of their neighbourhoods (p=0.045). There were differences in occupations between males and females (p=0.001). Males were less unemployed than females (p=0.011). Gender also showed statistically significant difference in how easy the immigrants balanced their work and personal 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 facts suggest that socio-demographics among a population of Caribbean immigrants were significantly different among genders. However the use of tobacco and alcohol showed not significant differences among the immigrants.
Keywords: 
Subject: Public Health and Healthcare  -   Other

1. Introduction

“Cancer is the second leading cause of death in the Caribbean and has created tremendous challenges for healthcare services and expenditures throughout the region. According to the World Health Organization (WHO), cancer in the Caribbean will increase by 58%, from 84,703 cases in 2015 to 133,937 cases in 2035. Cancer mortality will increase by 67% during this period, from 52,282 to 87,430 deaths.” “Nationals of African ancestry exhibited the highest rates of the cancer incidence rate of 243 per 100,000 per year and mortality rate of 156 per 100,000 per year compared to their counterparts of Indian ancestry (incidence rate of 125 per 100,000 per year; and mortality rate of 66 per 100,000 per year) or mixed ancestry (incidence of 119 per 100,000 per year; and mortality: 66 per 100,000 per year) [1].”
Pinheiro et al. (2016) analysed 185,113 cancer deaths from 2008 to 2012 in the USA, of which 20,312 occurred in black populations. The authors computed cancer mortality rates of US- and Caribbean-born residents of Florida, specifically focusing on black populations compared them using age-adjusted mortality ratios obtained from Poisson regression models. The overall risk of death from cancer was 2.1 (95% CI: 1.97–2.17) and 1.6 (95% CI: 1.55–1.71) times higher for US-born blacks than black Caribbean men and women, respectively (p<0.001) [2].
Siegel et al. (2012) reported that in 2009 cancer surpassed heart disease as a leading cause of death. The American Cancer Society (ACS) 2010 updated the previous report on cancer statistics of the 50.5 million Latinos living in the US using data from the Center for Disease Control and Prevention (CDC), the National Cancer Institute (NCI), and the North American Association of Central Cancer Registries (NAACCR), and in addition 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. In this study was found that these cancers were attributable to modifiable risk factors such as cigarette smoking, and alcohol intake.
This study aimed to carry out a prospective survey on socio-demographics and lifestyle factors among a population of Caribbean immigrants living in the US.

2. Materials and Methods

2.1. Hypotheses

Hypotheses
  • There are differences in country of origin between males and females
  • Gender influences overall how clean the neighbourhoods of the participants are
  • Gender influences how easy is to balance the work and personal life of the immigrants.
  • There are differences in occupation between males and females
  • Males are less unemployed than females
  • There are differences in physical activity between males and females
  • Age groups are associated with participants’ physical health
  • 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 US region. The project started in August 2021 and was completed in August 2022. The study was carried out online, and a SurveyMonkey link was sent to the participants through a social media network by SurveyMonkey Co.

2.4. Inclusion/exclusion criteria

2.4.1. Inclusion Criteria

Immigrants with no personal history of cancer living in the US and older than 18 year-old.

2.4.2. Exclusion Criteria

Caribbean subjects who have cancer and are not immigrant.
Participants under the age of 18 years.

2.5. Research Related Justification for 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 that the final sample size is calculated by using the formula:
N = Z α / 2 2 x   p ( 1 − p ) d 2 , 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
  • d is the desired degree of accuracy = 0.05
A total of 388 participants with no history of cancer, 18 year-old or older, living in the US were enrolled.

2.6. Recruitment of Subjects

The recruitment of participants involved the distribution of an online questionnaire link to all participants, included in a database of SurveyMonkey Co, along with a letter explaining the importance of this study via social media by SurveyMonkey Co.

2.7. Statistical Methods of Data Analysis {\displaystyle i}

The data were processed and analysed using the SPSS 27 software. Crosstabulations were done. 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 (to test whether the observed frequency are significantly different from what was expected by crosstabulation).
Null hypothesis: Gender, and age groups are significantly different in country of birth of the participants, in how clean are their neighbourhoods, in current occupation, in balancing of the work life and personal life, in employment status (whether employed, unemployed or student), in physical health, in drinking and smoking habits, and in engagement in physical activities.
These hypotheses were tested by 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 it 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. Participants provided their consent. 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. Indicate what the level of risk associated with this research is?

More significant than minimal risk. Minimal risk to subjects means that the probability and magnitude of harm or discomfort anticipated in the research 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. Describe risk, discomfort (physical/psychological), inconvenience, side effects, and financial costs to participants (include measures to mitigate these risks/discomforts)

A mild level of psychological risk was associated with this study. No physical or invasive procedure was performed. It was asked about the family history of cancer and a few screening procedures, among other questions that may have been sensitive to some participants. However, they had the benefit of not answering or the surveying withdrawal. In this survey there was not people withdrawal, but some did not answer to some questions.

2.9.3. Indicate direct benefits to participants.

Participants 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. Describe the process for informed consent.

Since it is an online survey, a consent form statement was disclosed at the beginning of the online questionnaire to ratify the true willingness of the participant 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 born and gender

There were statistically significant differences between males and females and the country they were from originally (p=0.038) as shown in Table 1. The countries that have greatest percentages of males were US Virgin Islands (32.1%), Puerto Rico (21.4%), and Haiti (6.9%) and followed by Cuba and Dominican Republic with 6.3%. The countries that have greatest percentages of females were US Virgin Islands (48.0%), Puerto Rico (11.4%), Cuba (7.0%) and Dominican Republic (6.6%) as shown in Table 2. Conversely, the countries with fewer percentages of males were Barbados, and Antigua and Barbuda with 0.3%; and countries with fewer population of woman were Anguilla and Antigua and Barbuda with 0.3%.

3.2. Association between cleanliness of neighbourhood and gender

There was statistically significant difference between males and females in how clean their neighbourhood is (p=0.045) as shown in Table 3. The percentages of women that live in extremely clean neighbourhoods was 20.1%, in very clean (44.9%), in somewhat clean (31.9%) and not so clean neighbourhoods (3.1%). The percentages of men that live in extremely clean neighbourhoods was 22.7%, in very clean (44.7%), in somewhat clean (23.8%), in not so clean neighbourhoods (6.9%) and not at all clean (1.9%). Females tended to live in cleaner neighbourhoods as shown in Table 4.

3.3. Association between current occupation and gender

There was statistically significant difference between females and males in current occupations (p<0.001) as shown in Table 5. More females had occupations such as life, physical and social (3.9%); legal (2.2%); education, training and library (11.0%); healthcare practitioners and technical occupations (9.2%); health care support (7.8%); personal care and service (7.4%); office and administrative support occupations (5.2%); and food preparation and serving related occupation (6.1%). These percentages in current occupations of females are greater than the percentages of the male’s counterpart. Males surpassed women in occupation such as transportation and materials moving occupations (9.4%), management occupations (5.7%), and both production occupations, and installation, maintenance, and repair occupations with 4.4% as depicted in Table 6.

3.4. Association between participants’ work and personal life balance and gender

There was statistically significant difference between males and females in balancing their work and personal life (p=0.044) as shown in Table 7. Females in the categories of “extremely easy” showed 18.8%, in “very easy” showed 24.0%, in “somewhat easy” depicted 40.2%, in “not so easy” and “not at all easy” represented 15.3% and 1.7%, respectively. Conversely, males in the categories of “extremely easy” showed 11.3%, in “very easy” showed 32.1%, in “somewhat easy” depicted 43.4%, in “not so easy” and “not at all easy” represented 9.4% and 3.8%, respectively as shown in Table 8.

3.5. The association between current occupation status and gender

There was statistically significant difference between males and females in describing their best current occupation status (p=0.011) as shown in Table 9. Females displayed in the category of “employed” 63.7%, showed in the category of “unemployed” 26.4% and 9.9% as “student”. Males displayed in the category of “employed” 70.8%, showed in the category of “unemployed” 13.9% and “student” 15.3%. Males did greater than females in this crosstabulation. The rate of unemployment was far greater in female as shown in Table 10.

3.6. Association between engagement in physical activity and gender

Table 11 shows that there was statistically significant difference between males and females in engaging in physical activity (p=0.025). As shown in Table 12, the percentage of females that engage in physical activity “every day” was 27.6%, “a few times a week” was 33.3%, “about once a week” was 15.8%, “a few times a month” was 13.2%, “once a month” was 4.4% and “less than once a month” was 5.7%. The percentage of males that engage in physical activity “every day” was 30.8%, “a few times a week” was 44.0%, “about once a week” was 14.5%, “a few times a month” was 5.0%, “once a month” was 1.3% and “less than once a month” was 4.4%.

3.7. Association between alcohol and tobacco use and gender

Table 13 shows that there was not statistically significant difference between alcohol and tobacco use and gender (p=0.529). As shown in Table 13 the percentages of females that engaged in drinking and smoking almost every day were 6.1% and 2.6%, respectively. The percentages of females, which engaged in drinking and smoking more than once a week, were 11.0% and 4.8%, respectively; and the percentage of females that neither drank, nor smoked was 36.4%. Conversely, the percentages of males that engaged in drinking and smoking almost every day were 9.4% and 4.4%, respectively. The percentages of males that engaged in drinking and smoking more than once a week were 9.4% and 3.8%, respectively; and the percentage of males that neither drank nor smoked was 28.3% as shown in Table 14.

3.8. The association between the physical health and age groups

There was statistically significant difference between the participant’s age groups and their physical health (p=0.005) as shown in Table 15. About 2.8% of the participants aged 18 to 24 were in the category “extremely healthy”, 10.6% were in the category “very healthy”, 9.0% were in the category “somewhat healthy”, and 1.0% were in the category “not so healthy”. A 6.4% of the immigrants aged 25 to 34 were extremely healthy, 7.2% were very healthy, 10.8% were somewhat healthy, and 1.0% were not so healthy and 0.5% were in the category of “not at all healthy”. About 2.1% of the participants aged 35 to 44 were extremely healthy, 6.4% were very healthy, 6.7% were somewhat healthy, and 0.8% were not so healthy. A 1.5% of the immigrants aged 45 to 54 were extremely healthy, 5.7% were very healthy, 10.3% were somewhat healthy, and 1.0% were not so healthy. About 0.8% of the participants aged 55 to 64 were extremely healthy, 3.1% were very healthy, 6.4% were somewhat healthy, and 1.3% were not so healthy and so on as shown in Table 16.
Tao et al., 2014 examined the relationship between foreign-born Hispanic settling in lower-status neighbourhoods and USA-born Hispanics [5]. Foreign-born Hispanic also showed a health advantage with survival after diagnoses of breast, prostate, and lung cancer compared to US-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). There was an abysmal heterogeneity within the Hispanic/Latino population. For instance, the socioeconomic profile of Cuban Americans was more similar to NHW than to Dominican Americans and Haitian Americans. Hispanic had a lower rate for the most common cancers (breast, lung, prostate, and colorectal) and high rates for 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 background. In 2012, an estimated 113,000 new cases of cancer and 33,000 death among Hispanics/Latinos were predicted. Strategies to attenuate the cancer explosion among this leading minority in the US were effective interventions to decrease alcohol consumption, tobacco use, and obesity [3].
The Latino population in the US will triple in size by 2050. It will become 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 US Latinos from Cuba and the Dominican Republic. In this study some respondents answered that they drink once a week (16.5%), more than once a week (10.3%) and almost every day (7.5%). In addition, In this study, some interesting data about tobacco use were available. About 17 immigrants smoke sometimes (4.4%) from 388 participants, 13 respondents (3.4%) often smoke, 18 immigrants (4.6%) smoke every day, and 42 individuals (10.8%) do not smoke but drink alcohol. However, Chi-square test shows p=0.529, which is not significant [9].
Taylor et al. (1997) conducted a survey where it was sampled 165 Haitian-born, 354 Caribbean-born, and 402 US-born blacks settled in New York City in 1992. Haitian-born and Caribbean-born were more likely to smoke preferentially than their female counterpart. As well, both gender US-born were more likely to smoke compared to Haitian-born and Caribbean-born. Alcohol consumption was combined with the act of smoking across the groups. Community education would have been essential to tackle this problem because participants believed that smoking was not related to cancer [10].
Vega et al. (1993) demonstrated that Cuban-American adolescents, foreign-born were less likely to have ever smoked or consumed alcohol compared to Cuban American US-born. The latter were more likely to go through an acculturation process [11]. Lucas et al. (2005) suggested that over 87% of the foreign-born black community in the US believed that their health was excellent or very good, and significantly higher than foreign-born white individuals and the same US-born. The foreign black population had lower smoking rates, especially among women [12].
Nelson et al. (2016) positively examined the screening impact on breast cancer survival. Several authors in the literature refer that there are still inequalities 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 of heating, cooking, and lighting, and is usually measured indoors. It is a socioeconomic factor that causes respiratory cancers, specially in low- and middle income countries, but it is associated to poor neighbourhood and could be found elsewhere. Three 3 billion people worldwide are exposed to toxic amounts of HAP every day. Indoor air pollution deaths per-million population is 0-10 million in US, Canada, and Australia. HAP is considered now to be a modifiable exposure. 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 like Indonesia have been implemented. However, plants are still not optimally utilized as a medium for room’s 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 provides an opportunity for cancer prevention [24,25,26,27]. It includes 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 as HIV/AIDS and hepatitis viruses B and C by using adequate protection and safe sexual practices [40,41,42], and avoidance of the obesity [43,44]. Physical activity is a protecting factor against several cancers including colorectal cancer, bladder, breast, endometrial, and esophageal adenocarcinoma. However, sedentary behavour, independent of physical activity predisposes to the risk of endometrial, colon, and lung cancers; owing to the effect of effect on endogenous sex steroids and insulin sensitivity, metabolic hormones, and chronic inflammation [45,46].

3.9. Summary

Table 17 summarises the statistical 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 shows no significant associations between alcohol and tobacco use with gender (p>0.05). There was association between gender and overall how clean was the neighbourhoods of the immigrants, it was supported by the analysis done by the Chi-square test, which shows significant results (p=0.045). There was association between gender and how easy is to balance the work and personal life of the immigrants, as judged by the result of the Chi-square test, which shows significant association (p=0.044). There were differences in occupation between males and females. It was supported by the analysis done by the Chi-square test, which shows significant results (p<0.001). There were associations between the country of born and gender. It was supported by the analysis done by the Chi-square test, which shows significant results (p=0.038). There were association between gender and current occupation status. It shows that males were less unemployed than females. The analysis was supported by the Chi-square test, which showed p=0.011. There was an association between age groups and participants’ physical health, as judged by the result of the Chi-square test, which shows significant association (p=0.005).

3. Conclusion

The biostatistics was very clear in accepting or not the association of socioeconomics/ lifestyle factors with gender or age groups among 388 immigrants living in the US. Several modifiable risk factors are attributed to the onset of many cancers, not limited to those discussed in this study. An overlap can be seen in the data, whereby distinct factors such as smoking, alcohol consumption, and physical activity can be implicated in developing many of cancer types. But a low risk was found. The limitations of this study are: the low participation of the elderly 75 or older in this study could be due to low proficiency in social networks and internet management in general and lower accessibility to different device types. Cross-sectional studies have the limitation that there is no follow-up work; difficult to make causal inferences; presence of prevalence-incidence bias, also called Neyman bias; it may provide differing results if another timeframe had been chosen [47].

Author Contributions

The manuscript was written through the contributions of all authors, which 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.

Informed Consent Statement

Participants agreed to participate in the survey.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Warner, Wayne A., Tammy Y. Lee, Kimberly Badal, Tanisha M. Williams, Smriti Bajracharya, Vasavi Sundaram, Nigel A. Bascombe, et al. (2018). “Cancer Incidence and Mortality Rates and Trends in Trinidad and Tobago.” BMC Cancer 18 (1): 712.
  2. Pinheiro, P. S., Callahan, K. E., Ragin, C., Hage, R. W., Hylton, T., & Kobetz, E. N. (2016). Black Heterogeneity in Cancer Mortality: US-Blacks, Haitians, and Jamaicans. Cancer control: journal of the Moffitt Cancer Center, 23(4), 347–358. [CrossRef]
  3. Siegel, R., Naishadham, D., & Jemal, A. (2012). Cancer statistics for Hispanics/Latinos, 2012. CA: a cancer journal for clinicians, 62(5), 283–298. [CrossRef]
  4. Islami, F., Goding Sauer, A., Miller, K. D., Siegel, R. L., Fedewa, S. A., Jacobs, E. J., McCullough, M. L., Patel, A. V., Ma, J., Soerjomataram, I., Flanders, W. D., Brawley, O. W., Gapstur, S. M., & Jemal, A. (2018). Proportion and number of cancer cases and deaths attributable to potentially modifiable risk factors in the United States. CA: a cancer journal for clinicians, 68(1), 31–54. [CrossRef]
  5. Tao, L., Ladabaum, U., Gomez, S. L., & Cheng, I. (2014). Colorectal cancer mortality among Hispanics in California: differences by neighborhood socioeconomic status and nativity. Cancer, 120(22), 3510–3518. [CrossRef]
  6. Keegan, T. H., John, E. M., Fish, K. M., Alfaro-Velcamp, T., Clarke, C. A., & Gomez, S. L. (2010). Breast cancer incidence patterns among California Hispanic women: differences by nativity and residence in an enclave. Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 19(5), 1208–1218. [CrossRef]
  7. Patel, M. I., Schupp, C. W., Gomez, S. L., Chang, E. T., & Wakelee, H. A. (2013). How do social factors explain outcomes in non-small-cell lung cancer among Hispanics in California? Explaining the Hispanic paradox. Journal of clinical oncology: official journal of the American Society of Clinical Oncology, 31(28), 3572–3578. [CrossRef]
  8. Singh, G. K., & Siahpush, M. (2001). All-cause and cause-specific mortality of immigrants and native born in the United States. American journal of public health, 91(3), 392–399. [CrossRef]
  9. Dillon, F. R., De La Rosa, M., Sanchez, M., & Schwartz, S. J. (2012). Preimmigration Family Cohesion and Drug/Alcohol Abuse Among Recent Latino Immigrants. Family journal (Alexandria, Va.), 20(3), 10.1177/1066480712448860. [CrossRef]
  10. Taylor, K. L., Kerner, J. F., Gold, K. F., & Mandelblatt, J. S. (1997). Ever vs never smoking among an urban, multiethnic sample of Haitian-, Caribbean-, and U.S.-born blacks. Preventive medicine, 26(6), 855–865. [CrossRef]
  11. Vega, W. A., Gil, A. G., & Zimmerman, R. S. (1993). Patterns of drug use among Cuban-American, African-American, and white non-Hispanic boys. American journal of public health, 83(2), 257–259. [CrossRef]
  12. Lucas, J. W., Barr-Anderson, D. J., & Kington, R. S. (2005). Health status of non-Hispanic U.S.-born and foreign-born black and white persons: United States, 1992-95. Vital and health statistics. Series 10, Data from the National Health Survey, (226), 1–20.
  13. Nelson, H. D., Fu, R., Cantor, A., Pappas, M., Daeges, M., & Humphrey, L. (2016). Effectiveness of Breast Cancer Screening: Systematic Review and Meta-analysis to Update the 2009 U.S. Preventive Services Task Force Recommendation. Annals of internal medicine, 164(4), 244–255. [CrossRef]
  14. Peek, M. E., & Han, J. H. (2004). Disparities in screening mammography. Current status, interventions and implications. Journal of general internal medicine, 19(2), 184–194. [CrossRef]
  15. Zackrisson, S., Janzon, L., Manjer, J., & Andersson, I. (2007). Improved survival rate for women with interval breast cancer - results from the breast cancer screening programme in Malmö, Sweden 1976-1999. Journal of medical screening, 14(3), 138–143. [CrossRef]
  16. Gordon, S. B., Bruce, N. G., Grigg, J., Hibberd, P. L., Kurmi, O. P., Lam, K. B., Mortimer, K., Asante, K. P., Balakrishnan, K., Balmes, J., Bar-Zeev, N., Bates, M. N., Breysse, P. N., Buist, S., Chen, Z., Havens, D., Jack, D., Jindal, S., Kan, H., Mehta, S., … Martin, W. J., 2nd (2014). Respiratory risks from household air pollution in low and middle income countries. The Lancet. Respiratory medicine, 2(10), 823–860. [CrossRef]
  17. Schluger N. (2014). Household air quality in high-income countries: forgotten but not gone. The Lancet. Respiratory medicine, 2(10), 781–783. [CrossRef]
  18. Lee, A., Adobamen, P. R., Agboghoroma, O., Ahmed, F. O., Aigbokhaode, A., Amusa, G. A., Avokpaho, E., Awokola, B., Ibeh, J., Isiguzo, G., Kagima, J., Kuti, B. P., Lawin, H., Lufesi, N., Mokogwu, N., Ngadaya, E., Nganda, M. M., Nwankwo, O. N., Obiajunwa, P., Oghuvwu, S., … Ozoh, O. (2015). Household air pollution: a call to action. The Lancet. Respiratory medicine, 3(1), e1–e2. [CrossRef]
  19. Bagcchi S. (2014). One in three people worldwide is at risk of ill health from household air pollution. BMJ (Clinical research ed.), 349, g6102. [CrossRef]
  20. Susanto, A. D., Winardi, W., Hidayat, M., & Wirawan, A. (2020). The use of indoor plant as an alternative strategy to improve indoor air quality in Indonesia. Reviews on environmental health, 36(1), 95–99. [CrossRef]
  21. Brilli, F., Fares, S., Ghirardo, A., de Visser, P., Calatayud, V., Muñoz, A., Annesi-Maesano, I., Sebastiani, F., Alivernini, A., Varriale, V., & Menghini, F. (2018). Plants for Sustainable Improvement of Indoor Air Quality. Trends in plant science, 23(6), 507–512. [CrossRef]
  22. Khalifa, A. A., Khan, E., & Akhtar, M. S. (2023). Phytoremediation of indoor formaldehyde by plants and plant material. International journal of phytoremediation, 25(4), 493–504. [CrossRef]
  23. Han, Y., Lee, J., Haiping, G., Kim, K. H., Wanxi, P., Bhardwaj, N., Oh, J. M., & Brown, R. J. C. (2022). Plant-based remediation of air pollution: A review. Journal of environmental management, 301, 113860. [CrossRef]
  24. Hashemi, S. H., Karimi, S., & Mahboobi, H. (2014). Lifestyle changes for prevention of breast cancer. Electronic physician, 6(3), 894–905. [CrossRef]
  25. Albab, K., Salgado, B. C., Wilson, M., & Moreno, A. (2023). A Brief Report: Effectiveness of a One Time Health Promotion Intervention in Sustaining Knowledge About Cardiovascular Diseases Among Latino/Hispanic Women. Journal of immigrant and minority health, 25(2), 489–495. [CrossRef]
  26. Bhargava, A., Wartak, S. A., Friderici, J., & Rothberg, M. B. (2014). The Impact of Hispanic Ethnicity on Knowledge and Behavior Among Patients With Diabetes. The Diabetes educator, 40(3), 336–343. [CrossRef]
  27. Beavis, A. L., Smith, A. J., & Fader, A. N. (2016). Lifestyle changes and the risk of developing endometrial and ovarian cancers: opportunities for prevention and management. International journal of women's health, 8, 151–167. [CrossRef]
  28. Morrow, L., & Greenwald, B. (2022). Healthy Food Choices, Physical Activity, and Screening Reduce the Risk of Colorectal Cancer. Gastroenterology nursing : the official journal of the Society of Gastroenterology Nurses and Associates, 45(2), 113–119. [CrossRef]
  29. Iruzubieta, P., Crespo, J., & Fábrega, E. (2013). Long-term survival after liver transplantation for alcoholic liver disease. World journal of gastroenterology, 19(48), 9198–9208. [CrossRef]
  30. Taghizadeh, N., Vonk, J. M., & Boezen, H. M. (2016). Lifetime Smoking History and Cause-Specific Mortality in a Cohort Study with 43 Years of Follow-Up. PloS one, 11(4), e0153310. [CrossRef]
  31. Rigotti, N. A., Kruse, G. R., Livingstone-Banks, J., & Hartmann-Boyce, J. (2022). Treatment of Tobacco Smoking: A Review. JAMA, 327(6), 566–577. [CrossRef]
  32. Aune, D., Giovannucci, E., Boffetta, P., Fadnes, L. T., Keum, N., Norat, T., Greenwood, D. C., Riboli, E., Vatten, L. J., & Tonstad, S. (2017). Fruit and vegetable intake and the risk of cardiovascular disease, total cancer and all-cause mortality-a systematic review and dose-response meta-analysis of prospective studies. International journal of epidemiology, 46(3), 1029–1056. [CrossRef]
  33. Liu, W., Hu, B., Dehghan, M., Mente, A., Wang, C., Yan, R., Rangarajan, S., Tse, L. A., Yusuf, S., Liu, X., Wang, Y., Qiang, D., Hu, L., Han, A., Tang, X., Liu, L., Li, W., & PURE-China Investigators (2021). Fruit, vegetable, and legume intake and the risk of all-cause, cardiovascular, and cancer mortality: A prospective study. Clinical nutrition (Edinburgh, Scotland), 40(6), 4316–4323. [CrossRef]
  34. Yip, C. S. C., Chan, W., & Fielding, R. (2019). The Associations of Fruit and Vegetable Intakes with Burden of Diseases: A Systematic Review of Meta-Analyses. Journal of the Academy of Nutrition and Dietetics, 119(3), 464–481. [CrossRef]
  35. Vieira, A. R., Abar, L., Vingeliene, S., Chan, D. S., Aune, D., Navarro-Rosenblatt, D., Stevens, C., Greenwood, D., & Norat, T. (2016). Fruits, vegetables and lung cancer risk: a systematic review and meta-analysis. Annals of oncology : official journal of the European Society for Medical Oncology, 27(1), 81–96. [CrossRef]
  36. Yeh, M. C., Ickes, S. B., Lowenstein, L. M., Shuval, K., Ammerman, A. S., Farris, R., & Katz, D. L. (2008). Understanding barriers and facilitators of fruit and vegetable consumption among a diverse multi-ethnic population in the USA. Health promotion international, 23(1), 42–51. [CrossRef]
  37. Raynaud-Simon, A., & Aussel, C. (2012). Fruit and vegetable intake in older hospitalized patients. Current opinion in clinical nutrition and metabolic care, 15(1), 42–46. [CrossRef]
  38. Sarich, P. E., Ding, D., Sitas, F., & Weber, M. F. (2015). Co-occurrence of chronic disease lifestyle risk factors in middle-aged and older immigrants: A cross-sectional analysis of 264,102 Australians. Preventive medicine, 81, 209–215. [CrossRef]
  39. Fernandes Custodio, D., Ortiz-Barreda, G., & Rodríguez-Artalejo, F. (2014). Alimentación, actividad física y otros factores de riesgo cardiometabólico en la población inmigrante en España: revisión bibliográfica [Diet, physical activity and other cardiometabolic risk factors in the immigrant population in Spain: a review]. Revista espanola de salud publica, 88(6), 745–754. [CrossRef]
  40. Kenya, S., Carrasquillo, O., Fatil, M., Jones, J., Jean, C., Huff, I., & Kobetz, E. (2015). Human Papilloma Virus and Cervical Cancer Education Needs among HIV-Positive Haitian Women in Miami. Women's health issues : official publication of the Jacobs Institute of Women's Health, 25(3), 262–266. [CrossRef]
  41. Elmore, C. E., Laughon, K., & Mitchell, E. M. (2020). Self-collection of samples for HPV testing to increase participation in cervical cancer screening by immigrant women: An integrative review. Public health nursing (Boston, Mass.), 37(5), 677–695. [CrossRef]
  42. Ziv, E., John, E. M., Choudhry, S., Kho, J., Lorizio, W., Perez-Stable, E. J., & Burchard, E. G. (2006). Genetic ancestry and risk factors for breast cancer among Latinas in the San Francisco Bay Area. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 15(10), 1878–1885. [CrossRef]
  43. Avgerinos, K. I., Spyrou, N., Mantzoros, C. S., & Dalamaga, M. (2019). Obesity and cancer risk: Emerging biological mechanisms and perspectives. Metabolism: clinical and experimental, 92, 121–135. [CrossRef]
  44. Cofie, L. E., Hirth, J. M., & Wong, R. (2018). Chronic comorbidities and cervical cancer screening and adherence among US-born and foreign-born women. Cancer causes & control : CCC, 29(11), 1105–1113. [CrossRef]
  45. McTiernan, A., Friedenreich, C. M., Katzmarzyk, P. T., Powell, K. E., Macko, R., Buchner, D., Pescatello, L. S., Bloodgood, B., Tennant, B., Vaux-Bjerke, A., George, S. M., Troiano, R. P., Piercy, K. L., & 2018 PHYSICAL ACTIVITY GUIDELINES ADVISORY COMMITTEE* (2019). Physical Activity in Cancer Prevention and Survival: A Systematic Review. Medicine and science in sports and exercise, 51(6), 1252–1261. [CrossRef]
  46. 46. Friedenreich, Christine M., Charlotte Ryder-Burbidge, and Jessica McNeil. (2021). “Physical Activity, Obesity and Sedentary Behavior in Cancer Etiology: Epidemiologic Evidence and Biologic Mechanisms.” Molecular Oncology 15 (3): 790–800.
  47. Levin K. A. (2006). Study design III: Cross-sectional studies. Evidence-based dentistry, 7(1), 24–25. [CrossRef]
Table 1. Chi-square test examining the association between country of origin and gender.
Table 1. Chi-square test examining the association between country of origin and gender.
Value df Asymptotic Significance 2-sided)
Pearson Chi-Square 24.622a 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%) have expected count less than 5. The minimum expected count is .41.
Table 2. Chi-square test examining percentages of males and females in Caribbean countries.
Table 2. Chi-square test examining percentages of males and females in 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 (US) 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 (Netherlands) Count 7 5 12
% of Total 3.1% 3.1% 3.0%
Anguilla (UK) 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 gender.
Table 3. Chi-square test examining association between cleanliness of neighbourhood and gender.
Value Df Asymptotic Significance 2-sided)
Pearson Chi-Square 9.717a 4 0.045
Likelihood Ratio 10.725 4 0.030
Linear-by-Linear Association .106 1 0.744
N of Valid Cases 388
a. 2 cells (20.0%) have expected count less than 5. The minimum expected count is 1.23.
Table 4. Percentages of participants by neighbourhood cleanness and gender.
Table 4. Percentages of participants by neighbourhood cleanness and gender.
What is your gender? Total
Female Male
Overall, how clean is your neighborhood? 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 examining the association between current occupation and gender.
Table 5. Chi-square test examining the association between current occupation and gender.
Value Df Asymptotic Significance (2-sided)
Pearson Chi-Square 69.338a 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. 12 cells (26.1%) have expected count less than 5. The minimum expected count is 1.64.
Table 6. Percentages of the participants by occupations and gender.
Table 6. Percentages of the participants by occupations and gender.
What is your gender? 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 examining the association between participants’ work and personal life balance and gender.
Table 7. Chi-square test examining the association between participants’ work and personal life balance and gender.
Value Df Asymptotic Significance (2-sided)
Pearson Chi-Square 9.772a 4 0.044
Likelihood Ratio 9.935 4 0.042
Linear-by-Linear Association .247 1 0.619
N of Valid Cases 388
a. 1 cells (10.0%) have expected count less than 5. The minimum expected count is 4.10.
Table 8. Percentages of participants by balancing their work and personal life and gender.
Table 8. Percentages of participants by balancing their work and personal life and gender.
What is your gender? 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 examining the association between participants’ current occupation status and gender.
Table 9. Chi-square test examining the association between participants’ current occupation status and gender.
Value Df Asymptotic Significance (2-sided)
Pearson Chi-Square 9.011a 2 0.011
Likelihood Ratio 9.320 2 0.009
Linear-by-Linear Association .055 1 0.814
N of Valid Cases 356
a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 17.39.
Table 10. Percentages of participants by current occupation status and gender.
Table 10. Percentages of participants by current occupation status and gender.
What is your gender? 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 examining engagement in physical activity and gender Chi-Square Tests.
Table 11. Chi-square test examining engagement in physical activity and gender Chi-Square Tests.
Value Df Asymptotic Significance (2-sided)
Pearson Chi-Square 12.837a 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. 1 cells (8.3%) have expected count less than 5. The minimum expected count is 4.93.
Table 12. Percentages of participants’ engagement in physical activity and gender.
Table 12. Percentages of participants’ engagement in physical activity and gender.
What is your gender? 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 examining the association between drinking and smoking, and gender.
Table 13. Chi-square test examining the association between drinking and smoking, and gender.
Chi-Square Tests
Value df Asymptotic Significance (2-sided)
Pearson Chi-Square 9.033a 10 .529
Likelihood Ratio 9.417 10 .493
Linear-by-Linear Association 1.528 1 .216
N of Valid Cases 387
a. 4 cells (18.2%) have expected count less than 5. The minimum expected count is .41.
Table 14. Percentages of participants by drinking and smoking and gender.
Table 14. Percentages of participants by drinking and smoking and gender.
What is your gender? 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 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 marihuana 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 tests examining the association between the physical health and age groups.
Table 15. Chi-Square tests examining the association between the physical health and age groups.
Chi-Square Tests
Value Df Asymptotic Significance (2-sided)
Pearson Chi-Square 45.693a 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. 16 cells (45.7%) have expected count less than 5. The minimum expected count is .01.
Table 16. Percentages of participants by physical health and age groups.
Table 16. Percentages of participants by physical health and age groups.
How physically healthy are you? Total
Extremely healthy Very healthy Somewhat healthy Not so healthy Not at all healthy
What is your age? 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 SupportedNot supported Statistical significance
The use of alcohol and tobacco is an important risk factor for cancer Chi-square test Not supported by the analysis p=0.529
Gender influences overall how clean the neighbourhoods of the immigrants are Chi-square test Supported by the analysis p=0.045
Gender influences how easy is to balance the work and personal life of the immigrants Chi-square test Supported by the analysis p=0.044
There are differences in occupation between males and females Chi-square test Supported by the analysis p<0.001
There is association between the country of born and gender Chi-square test Supported by the analysis p=0.038
Males are less unemployed than females Chi-square test Supported by the analysis p=0.011
Age groups are associated with participants’ physical health Chi-square test Supported by the analysis p=0.005
There was association between participants’ engagement in physical activity and gender Chi-square test Supported by the analysis p=0.025
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