A Structural Equation Modeling of Prostate Cancer Screening Intention

Ji Hye Jeong; Nam Hee Park

doi:10.12799/jkachn.2019.30.4.471

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Jeong and Park: A Structural Equation Modeling of Prostate Cancer Screening Intention

Original Article

J Korean Acad Community Health Nurs 2019;30(4):471-483.

Published online: 12 December 2019

DOI: https://doi.org/10.12799/jkachn.2019.30.4.471

A Structural Equation Modeling of Prostate Cancer Screening Intention

Ji Hye Jeong¹, Nam Hee Park²

¹Visiting Professor, Department of Nursing, College of Koje, Geoje, Korea

²Associate Professor, Department of Nursing, Inje University, Busan, Korea

Corresponding author: Park, Nam Hee Department of Nursing, Inje University, 75 Bokji-ro, Busanjin-gu, Busan 47392, Korea. Tel: +82-51-890-6832, Fax: +82-51-896-9840, E-mail: parknh@inje.ac.kr

Received 8 July 2019 Revised 29 October 2019 Accepted 7 November 2019

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Purpose

The purpose of this study was to identify factors associated with the intention of the prostate cancer screening (PCS). To achieve this purpose, a structural equation model was established based on the health belief model and the theory of planned behavior.

Methods

The subjects of this study were 260 male participants who were between 40 and 74 years old and had not taken the PCS. Data were collected using a structured self-report questionnaire (i.e., perceived benefits, perceived barriers, attitude, subjective norms, perceived behavior control, and intention of the PCS). Descriptive statistics, reliability analysis, correlation analysis, confirmatory factor analysis, and fitness test were used to test hypotheses.

Results

The intention of the PCS was directly affected by the perceived behavior control and indirectly influenced by the perceived benefits. The structural equation model also showed that the perceived behavior control explained 78% of the intention.

Conclusion

To raise the intention to take the PCS, it is necessary to increase the confidence of a subject that may control its difficulties and inform the perceived benefits of the PCS to a subject.

Keywords: Prostatic neoplasms, Early detection of cancer, Intention, Attitude

References

1. Korea Central Cancer Registry. National Cancer Center. Annual report of cancer statistics in Korea in 2016. Annual Report. Goyang: Ministry of Health and Welfare;2018. December. Report No.: 11-1352000-000145-10.

2. Han JH, Yoon SJ, Lee YG. Background and validity of the promotion of prostate-specific antigen based prostate cancer screening to national cancer screening program. The Korean Journal of Urological Oncology. 2010; 8(1):1–9.

3. Oliver JS, Grindel CG, DeCoster J, Ford CD, Martin MY. Benefits, barriers, sources of influence, and prostate cancer screening among rural men. Public Health Nursing. 2011; 28(6):512–522. https://doi.org/10.1111/j.1525-1446.2011.00956.x.

4. Lee EN, Park JH, Kang SG, Yun S, Sin KM, Kim KM. Factors affecting prostate cancer screening behavior. Asian Oncology Nursing,. 2015; 15(4):256–263. https://doi.org/10.5388/aon.2015.15.4.256.

5. Berglund G, Nilsson S, Nordin K. Intention to test for prostate cancer. European Journal of Cancer. 2005; 41(7):990–997. https://doi.org/10.1016/j.ejca.2005.01.011.

6. Hevey D, Pertl M, Thomas K, Maher L, Chuinneagain SN, Craig A. The relationship between prostate cancer knowledge and beliefs and intentions to attend PSA screening among at-risk men. Patient Education and Counselling. 2009; 74(2):244–249. https://doi.org/10.1016/j.pec.2008.08.013.

7. Odedina FT, Campbell ES, LaRose-Pierre M, Scrivens J, Hill A. Personal factors affecting African-American men's prostate cancer screening behavior. Journal of the National Medical Association. 2008; 100(6):724–733. https://doi.org/10.1016/s0027-9684(15)31350-x.

8. Barati M, Bashirian S, Amirzarghar MA, Mousali A, Saatiasr MH. Prostate cancer screening behaviors and its related cognitive psychological factors among men over 50 years of age using the health belief model. Journal of Hayat. 2018; 23(4):345–357.

9. Becker MH. The health belief model and personal health behavior. Health Education Monograph. 1974; 2:324–508.

10. Ajzen I. The theory of planned behavior. Organizational Behavioral and Human Decision Processes. 1991; 50(2):179–211. https://doi.org/10.1016/0749-5978(91)90020-T.

11. Glanz K, Rimer BK, Lewis FM. Health behavior and health education. 3th ed. Yoo TK. Seoul: Gunza;2012. p. 612.

12. Yang ZJ. Predicting young adult's intentions to get the H1N1 vaccine: An integrated model. Journal of Health Communication. 2015; 20(1):69–79. https://doi.org/10.1080/10810730.2014.904023.

13. Gerend MA, Shephred JE. Predicting human papillomavirus vaccine uptake in young adult women: comparing the health belief model and theory of planned behavior. Annal of Behavioral Medicine. 2012; 44(2):171–180. https://doi.org/10.1007/s12160-012-9366-5.

14. Lee BK, Sohn YK, Lee SL, Yoon MY, Kim MH, Kim CR. An efficacy of social cognitive theory to predict health behavior: A meta-analysis on the health belief model studies in Korea. Journal of Public Relations. 2014; 18(2):163–206.

15. Ajzen I. Attitudes, personality and behavior. 2nd ed. England Milton-Keynes: McGraw-Hill;2005.

16. Ju HJ, Lee JH. Structural relationship of variables regarding nurse's preventive action against needle stick injury. Journal of Korean Academic Society of Nursing Education. 2015; 21(2):168–181. https://doi.org/10.5977/jkasne.2015.21.2.168.

17. Lee SJ, Park OK, Park MY. A structural equation model of pressure ulcer prevention action in clinical nurses. Journal of Korean Academy of Nursing. 2016; 46(6):572–582. https://doi.org/10.4040/jkan.2016.46.4.572.

18. Carter HB, Albertsen PC, Barry MJ, Etzioni R, Freedland SJ, Greene KL, et al. Early detection of prostate cancer: AUA guideline. The Journal of urology. 2013; 190(2):419–426. https://doi.org/10.1016/j.juro.2013.04.119.

19. The Korean Journal of Urological Oncology. Prostate cancer guideline [Internet]. Seoul: The Korean Journal of Urological Oncology;2015. [cited 2018 December 01]. Available from:. http://www.kuos.or.kr/data/sub05.html#.

20. Yu JP. The concept and understanding of structural equation modeling. Seoul: Hannare Publishing Co;2016. p. 567.

21. Çapik C, Gözüm S. Development and validation of health beliefs model scale for prostate cancer screenings (HBM-PCS): Evidence from exploratory and confirmatory factor analyses. European Journal of Oncology Nursing. 2011; 15(5):478–485. https://doi.org/10.1016/j.ejon.2010.12.003.

22. Ajzen I. Constructing a TPB questionnaire: Conceptual and methodological considerations. United States: Icek Ajzen;2010. [cited 2018 December 01]. Available from:. https://people.umass.edu/aizen/tpb.html.

23. Hahm MI, Choi KS, Kye SY, Kwak MS, Park EC. Factors influencing the intention to have stomach cancer screening. Journal of Preventive Medicine and Public Health. 2007; 40(3):205–212. https://doi.org/10.3961/jpmph.2007.40.3.205.

24. Kim RB, Park KS, Hong DY, Lee CH, Kim JP. Factors associated with cancer screening intention in eligible persons for national cancer screening program. Journal of Preventive Medicine and Public Health. 2010; 43(1):62–72. https://doi.org/10.3961/jpmph.2010.43.1.62.

25. Kim WP. The Structural equation modeling analysis. Seoul: Wise in Company Co;2017. p. 478.

26. Kang MJ, Lee MS. Integrating the precaution adoption process model and the health belief model to assess cancer screening readiness among Korean adults. Korean Journal of Health Education and Promotion. 2011; 28(3):83–98.

27. Fishbein M, Yzer MC. Using theory to design effective health behavior interventions. Communication Theory. 2003; 13(2):164–183. https://doi.org/10.1111/j.1468-2885.2003.tb00287.x.

28. Lee YJ. A study of effect of health belief on health information processing and health behavior intention [dissertation]. [Seoul]: Hanyang University;2013. p. 198.

29. Bae PW. Suh SR. Predictive factors on breast self-examination intention and behavior in middle aged women: Based on the theory of planned behavior. Journal of the Korea Academia-Industrial cooperation Society. 2013; 14(5):2349–2359. https://doi.org/10.5762/KAIS.2013.14.5.2349.

30. Sanghera S, Coast J, Martin RM, Donovan JL, Mohiuddin S. Cost-effectiveness of prostate cancer screening: A systematic review of decision-analytical models. BioMed Central Cancer. 2018; 18(1):84. https://doi.org/10.1186/s12885-017-3974-1.

Figure 1.

Path diagram for the modified model.

Table 1.

General Characteristics of the Subjects (N=260)

Characteristics	Categories	n (%) or M±SD
Age (year)		54.58±9.47
	40~49	93 (35.8)
	50~59	74 (28.5)
	60~69	74 (28.5)
	≥70	19 (7.3)
Marital state	Single	19 (7.3)
	Married	229 (88.1)
	Others	12 (4.6)
Living arrangement	Alone	34 (13.1)
	Living with family	226 (86.9)
Education level	≤Middle school	30 (11.5)
	High school	164 (63.1)
	≥College	66 (25.4)
Monthly income	≤199	56 (21.5)
(10,000 won)	≥200	204 (78.5)
Have a close friend recommended you the test?	No	229 (88.1)
	Yes	31 (11.9)
Have your family doctor recommended you the test?	No	243 (93.5)
	Yes	16 (6.5)
Prostate cancer history of	No	252 (96.9)
family	Yes	8 (3.1)
Health education for prostate cancer	No	242 (93.1)
	Yes	18 (6.9)
Perceived health status	Poor	13 (5.0)
	Moderate	176 (67.7)
	Good	71 (27.3)

Table 2.

Descriptive Statistics of the Main Variables (N=260)

Variables	M±SD	Range	Skewness	Kurtosis
Perceived benefit	3.94±0.54	1~5	-0.62	2.92
Benefit 1	4.13±0.77	1~5	-1.01	2.19
Benefit 2	3.48±1.03	1~5	-0.54	-0.59
Benefit 3	4.12±0.77	1~5	-1.03	1.96
Benefit 4	4.05±0.70	1~5	-0.94	2.56
Benefit 5	3.80±0.82	1~5	-0.74	0.70
Benefit 6	4.08±0.63	1~5	-0.53	1.83
Perceived barrier	3.11±0.52	1~5	-0.20	0.66
Barrier 1	3.36±0.97	1~5	-0.34	-0.41
Barrier 2	3.36±1.02	1~5	-0.75	-0.35
Barrier 3	3.15±0.84	1~5	-0.13	0.22
Barrier 4	3.38±0.92	1~5	-0.62	-0.30
Barrier 5	3.41±0.91	1~5	-0.69	0.28
Barrier 6	3.71±0.87	1~5	-1.26	1.75
Barrier 7	3.18±1.07	1~5	-0.42	-0.88
Barrier 8	2.94±0.98	1~5	0.00	-0.84
Barrier 9	2.45±0.98	1~5	0.79	0.06
Barrier 10	3.26±1.02	1~5	-0.40	-0.46
Barrier 11	2.98±0.99	1~5	-0.05	-0.69
Barrier 12	2.85±0.88	1~5	0.09	0.06
Barrier 13	2.46±0.88	1~5	0.29	-0.17
Barrier 14	3.02±0.96	1~5	-0.03	-0.23
Attitude	3.77±0.72	1~5	-0.27	-0.15
Attitude 1	3.79±0.96	1~5	-0.71	0.53
Attitude 2	3.78±0.89	1~5	-0.28	-0.38
Attitude 3	3.79±0.89	1~5	-0.20	-0.78
Attitude 4	3.76±1.05	1~5	-0.58	-0.20
Attitude 5	3.86±0.89	1~5	-0.43	-0.19
Attitude 6	3.63±1.02	1~5	-0.36	-0.47
Subjective norm	3.30±0.91	1~5	-0.37	-0.28
Subjective norm 1	3.29±1.08	1~5	-0.25	-0.68
Subjective norm 2	3.15±1.02	1~5	-0.23	-0.74
Subjective norm 3	3.45±0.94	1~5	-0.73	0.18
Perceived behavioral control	3.47±0.76	1~5	-0.62	0.89
Perceived behavioral control 1	3.49±0.86	1~5	-0.72	0.38
Perceived behavioral control 2	3.65±0.80	1~5	-0.94	1.36
Perceived behavioral control 3	3.27±0.91	1~5	-0.27	-0.18
Intention	3.15±0.86	1~5	-0.27	-0.20
Intention 1	3.15±0.90	1~5	-0.30	-0.39
Intention 2	3.20±0.89	1~5	-0.24	-0.31
Intention 3	3.12±0.91	1~5	-0.18	-0.26

Table 3.

Confirmatory Factor Analysis and Correlation Matrix (between measure variables) (N=260)

Latent factors	1	2	3	4	5	6	Factor loading	SE	CR	AVE	CR
Latent factors	r (p)	r (p)	r (p)	r (p)	r (p)	r (p)	Factor loading	SE	CR	AVE	CR
1. Perceived benefit of PCS	1						.54~.79	0.11~0.13	7.25~9.70	.62	.89
2. Perceived barrier of PCS	.05	1					.43~.86	0.14~0.21	5.57~8.32	.41	.82
	(.656)
3. Attitude of PCS	.38	.12	1				.57~.82	0.12~0.14	7.88~10.36	.55	.86
	(.005)	(.135)
4. Subjective norm of PCS	.32	.26	.41	1			.80~.91	0.05	18.45~15.46	.73	.89
	(.006)	(.007)	(.007)
5. Perceived behavioral control of PCS	.43	.16	.38	.74	1		.75~.87	0.06	13.92~17.28	.75	.90
	(.004)	(.116)	(.007)	(.004)
6. Intention of PCS	.26	.21	.36	.68	.87	1	.90~.94	0.04	25.10~28.07	.88	.96
	(.008)	(.024)	(.011)	(.005)	(.004)

SE=standards error; CR=critical ratio; AVE=average variance extracted; CR=compositive construct reliability; PCS=prostate cancer screening.

Table 4.

Effects of Predictor Variables on Endogenous Variables for Modified Model (N=260)

Endogenous variables	Exogenous variable	Standardized estimate (β)	CR	p	Direct effect	Indirect effect	Total effect	SMC
Endogenous variables	Exogenous variable	Standardized estimate (β)	CR	p	β (p)	β (p)	β (p)	SMC
Intention of PCS	Attitude	.04	0.89	.372	.04 (.541)		.04 (.541)	.78
	Subjective norm	.06	0.75	.453	.06 (.593)		.06 (.593)
	Perceived behavioral control	.83	9.94	＜.001	.83 (.004)		.83 (.004)
	Perceived benefit					.36 (.004)	.36 (.004)
	Perceived barrier					.16 (.068)	.16 (.068)
Attitude of PCS	Perceived benefit	.40	4.87	＜.001	.40 (.005)		.40 (.005)	.17
	Perceived barrier	.12	1.70	.089	.12 (.111)		.12 (.111)
Subjective norm of	Perceived benefit	.33	4.54	＜.001	.33 (.006)		.33 (.006)	.17
PCS	Perceived barrier	.25	3.53	＜.001	.25 (.005)		.25 (.005)
Perceived behavioral	Perceived benefit	.39	5.25	＜.001	.39 (.005)		.39 (.005)	.18
control of PCS	Perceived barrier	.17	2.48	.013	.17 (.076)		.17 (.076)

CR=critical ratio; SMC=squared multiple correlation; PCS=prostate cancer screening.

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