Association Between Sociodemographic Factors and Health Beliefs Related to Breast Cancer Screening Behavior Among Northern Thai Women: A Hospital-Based Study

doi:10.21203/rs.3.rs-3441239/v1

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Association Between Sociodemographic Factors and Health Beliefs Related to Breast Cancer Screening Behavior Among Northern Thai Women: A Hospital-Based Study

https://doi.org/10.21203/rs.3.rs-3441239/v1

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published 31 Mar, 2024

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Background

Early diagnosis of breast cancer is crucial for reducing mortality rates. The purpose of this study is to determine the impact of demographics/social determinants of health on beliefs about the practice of self-breast examination, using mammogram and ultrasound in the context of breast cancer screening among Thai women in a hospital-based setting for implying program planning and future research.

Methods

A cross-sectional study was conducted in two health centers in Chiang Mai Province from August 2021 to December 2021, involving 130 Thai women ages 40 to 70 years. Data were collected by a survey using a questionnaire to gather sociodemographic information, and health beliefs about breast cancer and screening behavior utilizing the modified Thai version of Champion's Health Belief Model Scale (MT-CHBMS). Descriptive statistics, t-tests, ANOVA, and linear regression models were employed for examining association between sociodemographic factors and health beliefs about the practice of self-breast examination (BSE), using mammogram (MG) and ultrasound (UTS).

Results

Health insurance schemes were associated with Benefit-MG, Barrier-BSE, Barrier-MG and Barrier-UTS subscales. Additionally, monthly income was associated with Barrier-MG and Barrier-UTS subscales. The most common barriers reported were “embarrassment”, “worry”, and “takes too much time”.

Conclusion

To enhance breast cancer screening in Thailand, program planning and future research should focus on health insurance schemes, especially women with social security schemes, as they may be the most appropriate target group for intervention.

Health sciences/Health care/Health services

Health sciences/Health care/Public health

breast cancer

screening

health beliefs

perception

Female breast cancer is the most commonly diagnosed cancer, with approximately 2.3 million new cases and 685,000 deaths reported in 2020 ¹. It is the leading or second leading cause of female cancer-related deaths in 95% of countries worldwide ². In 2022, breast cancer in Thailand accounted for 38,559 cases ³, making it the most prevalent female cancer, and accounting for 32.64% of the top five cancers in the northern region of Thailand ⁴. This region has been predicted to have the highest age-standardized incidence rate (ASR) and proportion of female cancer cases by 2025 ⁵. However, early diagnosis and treatment can significantly reduce breast cancer mortality rates and improve women's overall health ⁶.

Globally, high-income countries have adopted mammography as the standard screening method for early diagnosis of breast cancer, which helps reduce rates of advanced and fatal breast cancer ⁷. In contrast, low to moderate-income countries, including Thailand ⁸, often rely on breast self-examination (BSE) due to its insufficient mammography resources, although it is considered less reliable ⁹. Therefore, it is recommended that women regularly and accurately perform BSE and consult with their physicians, who might recommend mammography and/or ultrasound if a lump is found ¹⁰. It's important to note that BSE alone is not an effective method for reducing breast cancer mortality ¹¹. However, a recent population-based study of 1,906,697 women participating in a breast cancer awareness program in Thailand reported that women who regularly practiced BSE had better survival rates compared to non-practicing women. Additionally, a significantly higher proportion of smaller tumor sizes and earlier stages of breast cancer were observed in the group that regularly performed BSE. This positive outcome was attributed to the strong collaboration between village health volunteers and the use of BSE record booklets. Village health volunteers played a vital role in reminding women to perform BSE consistently, while the BSE record booklets helped women accurately follow the instructions and document their BSE practices ¹². Based on this evidence, initial BSE is deemed appropriate for Thailand as a low to moderate-income country. The practice of BSE among women is influenced by their knowledge and beliefs about breast cancer and screening methods.

In real-world practice, BSE is not widely adopted among most Thai women. Only 18.4% of women practice monthly BSE ¹³, indicating a low level of knowledge and awareness about breast cancer and the importance of BSE, mammography, and ultrasound screening that are the steps for increasing diagnosis of breast cancer. Before planning effective interventions to motivate the use of these screening methods, it is important to understand Thai women's knowledge and beliefs about breast cancer screening. Previous studies have shown that the Health Belief Model is a reliable and valid tool for measuring individuals' knowledge and beliefs about breast cancer and screening methods ¹⁴. This model predicts the behaviors of people who take action to prevent, screen for, or control illness conditions based on their personal beliefs or perceptions about a disease ¹⁵. Champion's Health Belief Model Scale (CHBMS) is the first and most widely used tool in the literature across continents, countries, cultures, and ethnicities to measure women's beliefs about breast cancer screening ^16–22.

The CHBMS comprises six main constructs: susceptibility, seriousness, benefits, barriers, health motivation, and confidence (self-efficacy). This scale has also been developed to assess perceived benefits and barriers of BSE and mammogram screening ^{18–20,22−24}. Recently, a modified Thai version of Champion's Health Belief Model Scale (MT-CHBMS) ²⁵ incorporated ultrasound items for breast cancer screening. The primary reason for this addition is that ultrasound can effectively detect small and dense tissue tumors, particularly in younger Asian women who tend to have denser breast tissue compared to Western women ²⁶. In terms of advanced technology, techniques such as artificial intelligence (e.g., deep-learning-enabled clinical decision support systems) and classification of ultrasound images have demonstrated superior accuracy in detecting breast cancers compared to various screening tools currently available ^27,28. The MT-CHBMS has been found to be valid and reliable among Thai women ²⁵. This scale can be comparing perceived benefits and barriers of BSE, mammogram and ultrasound screening from associate predictors of sociodemographic factors. These predictors could be implying the program design for increasing breast cancer screening.

Numerous studies have demonstrated the significant impact of sociodemographic factors on women's breast cancer screening behaviors, with results varying across cultures and values. For instance, research conducted in Middle Eastern countries revealed notable associations between age, title, giving birth, BC screening in the last 6 months, BSE training, chronic disease, mental illness, and BSE practice ²⁹. Conversely, a study in a similar cultural context showed that BSE and mammography practices among women were influenced by the only level of their knowledge about breast cancer ³⁰. In an African country, a study found significant associations between income status, marital status, age of first childbirth in the family, and perceived susceptibility, health motivation, convenience, perceived benefits, and self-efficacy for BSE ³¹.

Despite these findings, there is currently a lack of information regarding the health perception of Thai women, the scope of their health beliefs, and how demographics/social determinants impact these beliefs. Therefore, the objective of this study is to determine the impact of demographics/social determinants of health on beliefs about the practice of self-breast examination, using mammogram and ultrasound in the context of breast cancer screening among Thai women in a hospital-based setting for implying program planning and future research.

Study Design and Participants

A cross-sectional study was conducted in Chiang Mai province, Kingdom of Thailand, from August 2021 to December 2021. The study included a total of 130 participants, consisting of women from two health centers: Maharaj Nakorn Chiang Mai Hospital, located in an urban area, and San Pa Tong Hospital, situated in a rural area. A comprehensive description of the development of the MT-CHBMS has been previously published ²⁵.

Inclusion and Exclusion criteria

The inclusion criteria for the study were as follows: individuals between the ages of 40 and 70 years (the recommended age for mammograms), no prior history of breast cancer or any other types of cancer, and not currently pregnant or breastfeeding. The exclusion criteria included individuals who were unable to communicate effectively due to language barriers and those who expressed unwillingness to complete the questionnaires.

The Data Collection Tools

To collect data at the outpatient clinic, the researchers used questionnaires to gather socio-economic information. The questionnaires included items such as age, religion, marital status, education level, healthcare insurance schemes (including the three main public health insurance schemes: government or state enterprise officer, social security scheme, and universal coverage scheme), income, and residential area. Prior to completing the questionnaires, all participants provided written informed consent.

The questionnaire addressing on beliefs was the MT-CHBMS, a reliable and valid tool for measuring the Health Belief Model related to the practice of breast self-examination (BSE), as well as investigating attitudes towards mammograms and ultrasounds ²⁵. The questionnaire consisted of 64 items distributed among 10 subscales: susceptibility (5 items), seriousness (7 items), benefits of BSE (6 items), barriers to BSE (6 items), benefits of mammogram (6 items), barriers to mammogram (5 items), benefits of ultrasound (6 items), barriers to ultrasound (5 items), confidence (11 items), and health motivation (7 items). All items were formatted using an ordinal scale with a 5-point Likert scale response: 1 = "Strongly disagree," 2 = "Disagree," 3 = "Neutral," 4 = "Agree," and 5 = "Strongly agree" for positive statements.

Statistical analysis

The data were analysed using Stata version 15.0. Descriptive statistics, including mean, standard deviation (SD), frequency, and percentages, were used to describe the data. Internal consistency of the items within the health belief subscales was assessed using Cronbach's alpha. The association and comparison of items within the health belief subscales and across other variables were analysed using t-tests, analysis of variance (ANOVA), and linear regression models.

The sociodemographic characteristics of the 130 participants are presented in Table 1. The average age of the participants was 52.33 years (SD = 7.28). The majority of participants were single (61.54%). About 37.69% of the participants had attained a college-level education, while 51.54% had a monthly income exceeding 10,000 Baht (270 US dollars). Additionally, 41.54% of the participants had health insurance schemes through government or state enterprise officers.

Table 1

Sociodemographic Variables of Participants (N = 30)
Characteristic	Category	Frequency	%
Age	40–54	72	55.38
	55–70	58	44.62
Marital status	Married	50	38.46
	Single	80	61.54
Education	Primary or lower	42	32.31
	Secondary	39	30.00
	College	49	37.69
Monthly income	< 10,000 Baht*	63	48.46
	≥ 10,000 Baht	67	51.54
Health insurance schemes	Government or state enterprise officer	54	41.54
	Social security scheme	30	23.08
	Universal coverage scheme	46	35.38
*1 Thai Baht (THB) equals 0.027 US dollars (USD)

Table 2 presents the mean ranged from 2.46 to 4.35 and SD ranged from 3.56 to 8.00. The overall Cronbach's alphas for the health belief model subscales were found to be within an acceptable range (0.70 or higher), indicating good internal consistency ³².

Table 2

Means (M), Standard Deviations (SD), and Cronbach’s Alphas (α) for the Health Belief Model Subscales
Scale	Min-Max	M (diving)	SD	α	Number of items
10. Motivation	21–35	30.45 (4.35)	3.56	0.85	7
4. Benefit-MG	14–30	25.36 (4.23)	3.90	0.94	6
7. Benefit-UTS	8–30	24.62 (4.10)	4.12	0.91	6
3. Benefit-BSE	8–30	24.32 (4.05)	3.86	0.88	6
5. Barrier-BSE	12–30	23.63 (3.94)	4.77	0.86	6
6. Barrier-MG	8–25	19.07 (3.81)	3.71	0.75	5
8. Barrier-UTS	10–25	19.04 (3.81)	3.77	0.80	5
9. Confidence	13–55	38.35 (3.49)	8.00	0.91	11
2. Seriousness	7–33	22.04 (3.15)	5.65	0.85	7
1. Susceptibility	5–23	12.29 (2.46)	4.89	0.93	5
Abbreviations: BSE = breast self-examination; MG = mammogram; UTS = ultrasound

Table 3 presents the results of the statistical analyses conducted on various sociodemographic factors and their associations with the Health Belief Model subscales.

Table 3

Comparison of Sociodemographic Factors with Health Belief Model Subscales
Parameter	Category		1. Susceptibility		2. Seriousness		3. Benefit-BSE			4. Benefit-MG		5. Barrier-BSE
Age	40–54		12.65 ± 4.71		22.03 ± 5.53		23.78 ± 4.06			25.03 ± 4.01		23.60 ± 4.40
	55–70		11.84 ± 5.11		22.05 ± 5.84		25.00 ± 3.52			25.78 ± 3.76		23.67 ± 5.22
	t		0.94		-0.02		-1.81			-1.09		-0.09
	p		0.351		0.981		0.073			0.279		0.929
Marital Status	Married		12.62 ± 4.87		21.76 ± 5.47		24.42 ± 3.64			25.40 ± 3.68		23.70 ± 4.58
	Single		12.09 ± 4.92		22.21 ± 5.78		24.26 ± 4.02			25.34 ± 4.06		23.59 ± 4.91
	t		0.60		-0.44		0.23			0.09		0.13
	p		0.548		0.658		0.822			0.93		0.897
Education	Primary or lower		12.69 ± 5.25		23.38 ± 5.66		24.93 ± 3.99			25.07 ± 4.46		22.88 ± 5.15
	Secondary		12.31 ± 4.33		22.59 ± 4.67		23.59 ± 3.95			25.31 ± 4.16		22.33 ± 4.65
	College		11.94 ± 5.06		20.45 ± 6.05		24.39 ± 3.67			25.65 ± 3.17		25.31 ± 4.07
	F		0.26		3.44		1.23			0.25		5.32
	p		0.768		0.035*		0.296			0.776		0.006*
Monthly	< 10,000 Baht		12.29 ± 5.08		23.25 ± 5.37		24.35 ± 4.06			25.08 ± 4.13		22.49 ± 4.97
income	≥ 10,000 Baht		12.30 ± 4.74		20.90 ± 5.70		24.30 ± 3.70			25.63 ± 3.68		24.70 ± 4.34
	t		-0.02		2.43		0.08			-0.80		-2.71
	p		0.988		0.017*		0.941			0.426		0.008*
Health insurance schemes	Government or state enterprise officer		12.17 ± 5.10		21.35 ± 6.10		24.67 ± 3.77			26.19 ± 3.37		25.43 ± 4.34
	Social security scheme		11.23 ± 4.82		22.40 ± 4.87		23.60 ± 3.15			24.40 ± 4.28		21.37 ± 3.93
	Universal coverage scheme		13.13 ± 4.63		22.61 ± 5.59		24.39 ± 4.38			25.02 ± 4.11		22.96 ± 5.08
	F		1.41		0.69		0.74			2.34		8.50
	p		0.249		0.502		0.478			0.100		0.001*
Parameter		Category		6. Barrier-MG		7. Benefit-UTS		8. Barrier-UTS	9. Confidence		10. Motivation
Age		40–54		18.86 ± 3.71		24.29 ± 3.97		19.17 ± 3.75	38.42 ± 7.90		30.18 ± 3.64
		55–70		19.33 ± 3.73		25.02 ± 4.29		18.88 ± 3.82	38.26 ± 8.18		30.78 ± 3.47
		t		-0.71		-0.99		0.43	0.11		-0.95
		p		0.478		0.320		0.668	0.911		0.346
Marital Status		Married		18.96 ± 3.64		24.56 ± 4.54		19.02 ± 3.51	38.02 ± 8.22		30.64 ± 3.67
		Single		19.14 ± 3.78		24.65 ± 3.86		19.05 ± 3.95	38.55 ± 7.91		30.33 ± 3.51
		t		-0.26		-0.12		-0.04	-0.37		0.49
		p		0.792		0.904		0.965	0.715		0.626
Education		Primary or lower		19.05 ± 3.57		24.60 ± 4.91		18.43 ± 3.79	39.10 ± 6.81		30.52 ± 3.16
		Secondary		18.69 ± 4.18		24.64 ± 3.95		18.72 ± 3.97	39.44 ± 7.45		30.00 ± 3.98
		College		19.39 ± 3.48		24.61 ± 3.54		19.82 ± 3.53	36.84 ± 9.20		30.73 ± 3.58
		F		0.38		0		1.75	1.43		0.47
		p		0.686		0.999		0.178	0.244		0.625
Monthly		< 10,000 Baht		18.59 ± 3.63		24.35 ± 4.50		18.16 ± 3.81	39.16 ± 7.20		30.37 ± 3.40
income		≥ 10,000 Baht		19.52 ± 3.76		24.87 ± 3.73		19.87 ± 3.57	37.58 ± 8.67		30.52 ± 3.74
		t		-1.44		-0.71		-2.64	1.12		-0.25
		p		0.152		0.477		0.009*	0.263		0.803
Health insurance schemes		Government or state enterprise officer		19.83 ± 3.44		25.06 ± 3.84		20.28 ± 3.53	37.52 ± 9.20		30.72 ± 3.63
		Social security scheme		17.10 ± 3.98		24.57 ± 3.78		17.23 ± 3.58	38.63 ± 6.45		30.27 ± 3.85
		Universal coverage scheme		19.41 ± 3.51		24.13 ± 4.64		18.76 ± 3.71	39.13 ± 7.45		30.24 ± 3.35
		F		5.94		0.63		6.85	0.53		0.27
		p		0.003*		0.536		0.002*	0.593		0.760

Participants with education less than secondary school exhibited higher scores in the Seriousness subscale compared to other education level groups (F = 3.44, p = 0.035). Participants with a college educational level had higher scores in the Barrier-BSE subscale compared to other education level groups (F = 5.32, p = 0.006).

In terms of monthly income, participants in the lower 10,000 Baht income group demonstrated higher scores in the Seriousness subscale compared to the more than 10,000 Baht income group (t = 2.43, p = 0.017). Conversely, the more than 10,000 Baht income group had higher scores in the Barrier-BSE and Barrier-UTS subscales compared to the lower 10,000 Baht income group (t = -2.71, p = 0.008 and t = -2.64, p = 0.009).

Participants with health insurance schemes through government or state enterprise officer schemes exhibited higher scores in the Barrier-BSE and Barrier-UTS subscales compared to other groups (F = 8.50, p = 0.001 and F = 6.85, p = 0.002). Additionally, participants with health insurance schemes through government or state enterprise officer schemes and those covered under the universal coverage scheme had higher scores in the Barrier-MG subscale compared to the social security scheme group (F = 5.94, p = 0.003).

Table 4 presents the results of multiple linear regression analysis. None of the factors were found to be significant predictors of Seriousness subscale. However, health insurance schemes was found to be a significant predictor of the Benefit-MG and Barrier-BSE (β_m = -2.48, P = 0.023 and β_m = -3.38, P = 0.008, respectively). Both monthly income and health insurance schemes were significant predictors of the Barrier-MG and Barrier-UTS (β_m = 2.65, P = 0.008, β_h = -3.11, P = 0.002 and β_m = 2.49, P = 0.013, β_h = -3.40, P = 0.001, respectively).

Table 4

Multiple Linear Regression Model of Health Belief Model Subscales
		2. Seriousness			4. Benefit-MG			5. Barrier-BSE			6. Barrier-MG			8. Barrier-UTS
Parameter	Category	B	SE	p-value	B	SE	p-value	B	SE	p-value	B	SE	p-value	B	SE	p-value
Education	Primary or lower	ref			ref			ref			ref			ref
	Secondary	-0.25	1.37	0.855	0.43	0.96	0.655	-0.54	1.11	0.628	-0.57	0.87	0.517	-0.03	0.87	0.970
	College	-2.58	1.87	0.168	-0.96	1.30	0.464	-0.04	1.51	0.976	-1.97	1.18	0.098	-1.50	1.18	0.207
Monthly	< 10,000	ref			ref			ref			ref			ref
income	>= 10,000	-1.84	1.56	0.240	0.41	1.09	0.708	2.10	1.26	0.099	2.65	0.99	0.008*	2.49	0.99	0.013*
Health insurance schemes	Government or state enterprise officer	ref			Ref			ref			Ref			Ref
	Social security scheme	-0.65	1.54	0.673	-2.48	1.08	0.023*	-3.38	1.25	0.008*	-3.11	0.97	0.002*	-3.40	0.98	0.001*
	Universal coverage scheme	-1.95	1.65	0.234	-1.73	1.15	0.133	-0.74	1.33	0.579	0.20	1.04	0.851	-0.85	1.04	0.416
	constant	24.88	1.51		26.56	1.06		23.72	1.22		19.25	0.96		19.41	0.96
B = unstandardized coefficient, SE = standard error, MG = mammogram, BSE = breast self-examination, UTS = ultrasound

BM = item from benefit to mammogram, BARB = item from barrier to breast self-examination, BARM = item from barrier to mammogram, BAU = item from barrier of ultrasound.

To delve deeper into the specifics, each subscale item, including those related to the benefits and barriers of mammograms, breast self-examination, and ultrasound, was compared among different monthly income groups and health insurance schemes using t-tests and ANOVA analyses (Table 5). For the Barrier-BSE subscale, the group with an income of 10,000 Baht or more demonstrated higher scores in Barrier-BSE compared to the less than 10,000 Baht income group across the BARB1 (funny), BARB3 (embarrassing), and BARB5 (unpleasant) items. Additionally, participants with health insurance schemes through government or state enterprise officer schemes exhibited higher scores in Barrier-BSE compared to other groups across all BARB (1–6) items.

Table 5

Comparison of Monthly Income and Health insurance schemes with the significant Subscales of MT-CHBMS
	Monthly income (Baht)			Health insurance schemes
	< 10,000	≥ 10,000	p-value	Government or State Enterprise Officer	Social Security Scheme	Universal Coverage Scheme	p-value
4. Benefit-MG
BM1	4.16 ± 0.65	4.34 ± 0.59	0.093	4.39 ± 0.56	4.13 ± 0.68	4.17 ± 0.64	0.112
BM2	4.21 ± 0.74	4.30 ± 0.65	0.453	4.41 ± 0.60	4.13 ± 0.78	4.15 ± 0.73	0.105
BM3	4.21 ± 0.74	4.37 ± 0.71	0.195	4.44 ± 0.69	4.10 ± 0.71	4.24 ± 0.77	0.097
BM4	4.24 ± 0.82	4.22 ± 0.78	0.919	4.33 ± 0.75	4.03 ± 0.81	4.24 ± 0.82	0.252
BM5	4.11 ± 0.84	4.13 ± 0.81	0.874	4.26 ± 0.76	3.93 ± 0.87	4.09 ± 0.86	0.209
BM6	4.16 ± 0.77	4.25 ± 0.79	0.486	4.35 ± 0.76	4.07 ± 0.78	4.13 ± 0.78	0.191
Sum	25.08 ± 4.13	25.63 ± 3.68	0.426	26.19 ± 3.37	24.40 ± 4.27	25.02 ± 4.11	0.100
5. Barrier-BSE
BARB1	3.94 ± 1.13	4.36 ± 0.79	0.015*	4.48 ± 0.75	3.87 ± 0.94	3.96 ± 1.17	0.006*
BARB2	3.33 ± 1.20	3.64 ± 0.93	0.104	3.81 ± 1.03	3.20 ± 0.81	3.30 ± 1.21	0.014*
BARB3	3.98 ± 1.07	4.40 ± 0.78	0.012*	4.46 ± 0.88	3.93 ± 0.87	4.07 ± 1.02	0.024*
BARB4	3.87 ± 1.08	4.10 ± 1.03	0.215	4.26 ± 0.94	3.47 ± 1.11	4.02 ± 1.06	0.004*
BARB5	3.52 ± 1.12	4.03 ± 0.95	0.006*	4.06 ± 1.04	3.40 ± 0.93	3.72 ± 1.11	0.021*
BARB6	3.84 ± 1.05	4.16 ± 1.02	0.078	4.35 ± 0.93	3.50 ± 0.97	3.93 ± 1.08	0.001*
sum	22.49 ± 4.97	24.7 ± 4.34	0.008*	25.43 ± 4.34	21.37 ± 3.93	22.96 ± 5.08	0.001*
6. Barrier-MG
BARM1	3.60 ± 1.13	3.90 ± 1.00	0.121	4.00 ± 0.97	3.33 ± 1.12	3.74 ± 1.08	0.023*
BARM2	4.11 ± 0.95	4.37 ± 0.81	0.093	4.52 ± 0.67	3.77 ± 1.17	4.24 ± 0.79	0.001*
BARM3	3.84 ± 1.08	4.03 ± 0.92	0.285	4.13 ± 0.89	3.57 ± 1.10	3.96 ± 1.01	0.046*
BARM4	3.71 ± 1.04	3.66 ± 1.14	0.764	3.61 ± 1.20	3.40 ± 1.00	3.96 ± 0.94	0.074
BARM5	3.32 ± 1.25	3.57 ± 1.13	0.235	3.57 ± 1.18	3.03 ± 1.19	3.57 ± 1.19	0.097
sum	18.59 ± 3.63	19.52 ± 3.76	0.141	19.83 ± 3.44	17.1 ± 3.98	19.41 ± 3.51	0.003*

	Monthly income (Baht)			Health insurance schemes
	< 10,000	≥ 10,000	p-value	Government or State Enterprise Officer	Social Security Scheme	Universal Coverage Scheme	p-value
8. Barrier-UTS
BAU1	3.35 ± 1.23	3.81 ± 0.93	0.018*	3.80 ± 1.05	3.37 ± 0.96	3.48 ± 1.22	0.168
BAU2	3.92 ± 1.04	4.27 ± 0.73	0.028*	4.43 ± 0.69	3.50 ± 1.01	4.11 ± 0.88	0.001*
BAU3	3.83 ± 1.01	4.13 ± 0.81	0.056	4.22 ± 0.84	3.70 ± 0.92	3.89 ± 0.97	0.031*
BAU4	3.87 ± 1.02	3.99 ± 0.88	0.504	4.11 ± 0.88	3.53 ± 0.97	3.98 ± 0.95	0.025*
BAU5	3.19 ± 1.20	3.67 ± 1.11	0.019*	3.72 ± 1.17	3.13 ± 1.11	3.30 ± 1.17	0.054
sum	18.16 ± 3.81	19.87 ± 3.57	0.009*	20.28 ± 3.53	17.23 ± 3.58	18.76 ± 3.75	0.002*
Abbreviations: BM = item from benefit to mammogram, BARB = item from barrier to breast self-examination, BARM = item from barrier to mammogram, BAU = item from barrier of ultrasound.

Regarding the Barrier-MG subscale, participants with health insurance schemes through government or state enterprise officer schemes had higher scores in Barrier-MG compared to other groups across the BARM1 (worry), BARM2 (embarrassing), and BARM3 (take too much time) items.

In terms of the Barrier-UTS subscale, the group with an income of 10,000 Baht or more demonstrated higher scores in barrier-UTS compared to the less than 10,000 Baht income group across the BAU1 (worry), BAU2 (embarrassing), and BAU5 (cost too much money) items. Additionally, participants with health insurance schemes through government or state enterprise officer schemes had higher scores in Barrier-UTS compared to other groups across the BAU2 (embarrassing), BAU3 (take too much time), and BAU4 (painful) items.

The objective of the study was to investigate differences in beliefs related to breast examination among various sociodemographic variables in Thai women, and the results have confirmed their presence.

Using multiple linear regression analysis with the MT-CHBMS, the results indicated several findings. Health insurance schemes were associated with Benefit-MG, Barrier-BSE, Barrier-MG and Barrier-UTS subscales. Additionally, monthly income showed associations with the Barrier-MG and Barrier-UTS subscales. The most common barriers reported by participants were feeling “embarrassed”, “worry”, and feeling that it “takes too much time”.

In this hospital-based study, age, marital status, and education were not found to be associated with these health beliefs. This differs from population-based studies where lower screening rates were observed among individuals with older age (aged 55 and over), those who were single or widowed, and those with low educational levels ¹³.

Interestingly, our study observed that distinct income groups were associated with different results in the Barriers-MG and Barriers-UTS subscales. The current study found that individuals with higher income tended to encounter more barriers in performing BSE and UTS compared to those with lower income. It's important to note that there is limited existing literature available for direct comparison. However, our findings align somewhat with a study by Kirag and Kizilkaya ²⁹, which suggested that individuals with income exceeding expenses often placed greater value on the benefits of BSE, whereas those with income below expenses tended to prioritize the benefits of mammograms. In alignment with the findings of Altunkurek and Hassan Mohamed ³¹, it was observed that women with incomes below their expenses were more likely to perceive barriers to BSE. The connection between lower income and barriers to BSE is not easily explained. It is possible that there are intermediary variables requiring further investigation.

According to the Health Belief Model, perceived barriers have consistently been identified as the most influential predictor in various studies for practicing BSE and mammography ³³. Recent studies have also shown that perceiving more benefits, having higher confidence, and experiencing fewer barriers are positively associated with BSE practice ^34–36. Similarly, perceiving more benefits and fewer barriers is positively associated with mammography³⁴. In this study, it was found that the social security scheme associated with Barrier-BSE, Barrier-MG and Barrier-UTS. In addition, the social security scheme had lower scores than the government or state enterprise officer and universal coverage scheme in the barrier to BSE, barrier to mammogram, and barrier to ultrasound subscales. It is to note that the government or state enterprise officer scheme beneficiaries benefit from a higher level of healthcare coverage compared to the other two schemes. It offers a high level of coverage and includes access to government hospitals and medical facilities. This scheme beneficiaries typically have access to a comprehensive range of medical services, often with little or no out-of-pocket expenses. The scheme provides coverage for both routine healthcare and specialized treatments, including access to government-run healthcare facilities. The social security scheme members often enjoy relatively comprehensive healthcare benefits, and the quality of care is generally good. However, it is limited to formal sector employees and their dependents, which means that informal sector workers and those not covered by formal employment arrangements are not eligible. The universal coverage scheme aims to provide equitable access to healthcare for all, emphasizing the principle of social justice. The scheme may have limitations on specialized or high-cost medical treatments, and there may be variations in the quality of care among different facilities.

The impact of the healthcare scheme type on barriers to BSE, MG, or UTS may be influenced by numerous factors. Nevertheless, the results suggests that women who have health coverage through the social security scheme may benefit from targeted interventions to improve detection. Evidence for program planning should be implement in health insurance schemes groups such as health education, skill training and confidence in performing for BSE, reminders to perform BSE, regular use of BSE record booklets ^12,37.

One of the general barriers observed in this study is the lack of knowledge and awareness of breast cancer among the participants, as evidenced by their low scores in the Susceptibility, Seriousness, and Confidence scales. Knowledge is identified as the most influential barrier affecting the engagement of participants in BSE, particularly in low to middle-income countries and rural areas where resources are limited ³⁸. Participants in this study perceived their ability to perform the BSE technique as low, indicating a lack of knowledge or a lack of regular practice. Susceptibility refers to participants' perception of their chances of being at risk for a disease. In this study, participants perceived their chances of having a risk or disease as low, indicating a potential lack of knowledge regarding the risk factors of breast cancer, such as young age, no family history of cancer, and the absence of breast lumps. Seriousness pertains to participants' perception of the severity of the consequences associated with the disease. In this study, participants may perceive breast cancer as not causing pain, exhibiting no symptoms or signs, and not posing a significant threat. This suggests a lack of knowledge or the use of defence mechanisms such as denial or rationalization, similar to behaviours observed in smokers and alcohol drinkers ^39,40. Consistent with many Thai studies, interventions focusing on health education and skill training for BSE are recommended to address these knowledge gaps ^13,37,41.

One of the most common barriers to early screening detection identified in this study is the feeling of “embarrassment” and “worry”. Similar to Amin MN et al. ⁴², this study conducted a hospital survey. The feeling of embarrassment can be considered a cultural barrier, where women may feel too embarrassed to have their breasts examined by a male doctor. This cultural aspect can hinder their willingness to seek medical attention for abnormalities. Worry, on the other hand, is associated with feelings of anxiety. Women may experience worry related to breast lumps, the potential consequences of breast cancer, and concerns about health professionals and healthcare facilities. Additionally, the perception that screening “takes too much time” can be a deterrent. Women may feel that they are too busy, have limited time, or believe that they lack sufficient time to perform BSE and undergo screening procedures ³⁸. Interventions should focus on problem-solving approaches and aim to improve healthcare services in order to overcome barriers faced by the participants. By addressing these barriers and concerns, healthcare providers can create a more supportive and comfortable environment for women to engage in early screening and detection practices. Apart from the issue of “embarrassment”, “worry”, and “takes too much time”, which should be considered as one of the barriers to BSE, mammograms, and ultrasounds, there could be other contributing factors. Future research should incorporate qualitative studies to explore additional causal factors influencing the practice or non-practice of BSE, as well as the utilization or non-utilization of mammograms and ultrasounds. Additionally, it is recommended to compare interventions using a before-and-after study design involving the three main public health insurance schemes: government or state enterprise officer, social security scheme, and universal coverage scheme. This examination is necessary to identify effective interventions for women within each health insurance scheme who may face different barriers.

Participants in this study are to be more empowering their health. They have the highest score of Health Motivation and comparing Benefit-MG and Benefit-UTS more than Benefit-BSE. Conversely, Barrier-BSE when comparing Barrier-MG and Barrier-UTS is inverse. This is show that they would like to take investigate accuracy screening tools more than their manual. As health practitioners’ perspective of Thai study would like to drive a policy of national cancer act to enable women’s rights for accessing standardized screening tools ⁸.

Evidence for planning and Future research

There is associated between a monthly income and perceived Barriers-MG and Barriers-UTS. This predictor may be sensitive and difficult to approach regarding their monthly income when implementing intervention strategies targeting MG and UTS promotion. However, there is health insurance schemes which associated with Benefit-MG, Barrier-BSE, Barrier-MG and Barrier-UTS subscale. Also, health insurance schemes in the social security scheme is the predictor of perceived Barrier-BSE, Barrier-MG and Barrier-UTS. Specifically, the perceived barriers subscale can help identify the problems of implementation. Furthermore, attitudes toward BSE, mammograms, and ultrasounds can be compared in terms of their benefits and barriers. Such comparisons can yield valuable insights for the development of targeted interventions and approaches aimed at increasing breast cancer screening among Northern Thai women in a hospital-based setting. The design of programs and future research should take this evidence into account during implementation. Future research could employ a before-and-after study design, integrating health education and skill training for BSE, and incorporating qualitative studies to explore the additional causal factors influencing the practice or non-practice of BSE, using or non-using mammogram/ultrasound. Moreover, investigating how to improve healthcare services to ensure women's satisfaction would be beneficial.

Strength and Limitations

This study is the first research project known to utilize the MT-CHBMS to study the association between sociodemographic factors and health beliefs of breast cancer and screening behaviors. Additionally, the inclusion of new items related to ultrasound in the MT-CHBMS holds promise for the assessment of breast cancer beliefs among Thai women with dense breast masses and the potential integration of advanced technologies such as artificial intelligence in the future.

However, it is important to acknowledge the limitations of this study. Firstly, the cross-sectional design employed cannot establish causal relationships between beliefs and screening practices. Secondly, the results may not be generalizable to the entire population due to the selection of participants from a single geographic area and hospital setting in Northern Thailand. Lastly, certain factors such as family history of breast cancer and other breast masses were not specifically excluded from the study, which could potentially influence participants' beliefs regarding breast cancer and their practices related to screening methods.

This study marked the first use of the MT-CHBMS to investigate the association between sociodemographic factors and health beliefs related to breast cancer screening. The findings provide evidence for program design and future research aimed at increasing breast cancer screening among women in Northern Thailand in a hospital-based setting. By successfully implementing the interventions, the social security scheme represents the most targeted interventions can serve as role models for other health insurance schemes and contribute to enhancing the effectiveness of screening among women.

Funding

This research was self-funded.

Author Contributions

All authors contributed to this study’s conceptualization and methodology. Validation, S.J., K.P.,V.L.C., N.W. and T.W.; Formal analysis,S.J.,K.P.,T.W.,N.W.and C.A.; investigation, all.; data curation, S.J., C.A.;The original draft was written by Surin Jiraniramai and reviewed and edited by all authors. Resources, S.J.,K.P.,C.A.,W.J.,and T.W.; supervision, T.W. and N.W.

Ethical approval and consent to participate

This study was conducted under the review and approval of the Institutional Research Ethics Committee of the Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand (No. FAM 2564-08138) and Sanpatong Hospital Ethics Committee (No. SPT/REC 012/2564). All procedures were conducted following the relevant institutional guidelines and regulations.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

Profs. Wongpakaran are the editorial board members of Scientific Reports, and all the rest of the authors declare that they have no competing interests.

Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. May 2021;71(3):209-249. doi:10.3322/caac.21660
Global breast cancer initiative implementation framework: assessing, strengthening and scaling up of serviecs for the early detection and management of breast cancer: executive summary eGeneva (World Health Organizaion) (2023).
Department of Health MoPH. The most Thai female cancer is breast cancer, suggested to breast self-examination every month. Accessed 26 May 2023. https://multimedia.anamai.moph.go.th/news/140366/
Department of Medical Service. Report of the Ministry of Public Health, Budget year 2023. Reducing illness, reducing death and health security, Cancer branch, Chiang Mai Province, Round 1/2023. Accessed 1 July, 2023. http://www.chiangmaihealth.go.th/document/230305167799917158.pdf
Virani S, Bilheem S, Chansaard W, et al. National and Subnational Population-Based Incidence of Cancer in Thailand: Assessing Cancers with the Highest Burdens. Cancers. 2017;9(8):108.
Duffy S, Vulkan D, Cuckle H, et al. Annual mammographic screening to reduce breast cancer mortality in women from age 40 years: long-term follow-up of the UK Age RCT. Health Technol Assess. Oct 2020;24(55):1-24. doi:10.3310/hta24550
Tabár L, Dean PB. Recommendations for breast cancer screening. The Lancet Oncology. 2020;21(11):e511. doi:10.1016/S1470-2045(20)30495-2
Pongthavornkamol k WN, Khuhaprema T. . Breast Cancer Prevention and Screening System in Thailand in Health Practitioners' Perspectives. Thai Cancer Journal. 2019;39(3):77-92.
Birhane N, Mamo A, Girma E, Asfaw S. Predictors of breast self - examination among female teachers in Ethiopia using health belief model. Arch Public Health. 2015;73(1):39. doi:10.1186/s13690-015-0087-7
Erbil N, Bölükbaş N. Beliefs, attitudes, and behavior of Turkish women about breast cancer and breast self-examination according to a Turkish version of the Champion Health Belief Model Scale. Asian Pac J Cancer Prev. 2012;13(11):5823-8.
Hackshaw AK, Paul EA. Breast self-examination and death from breast cancer: a meta-analysis. Br J Cancer. Apr 7 2003;88(7):1047-53. doi:10.1038/sj.bjc.6600847
Thaineua V, Ansusinha T, Auamkul N, et al. Impact of regular Breast Self-Examination on breast cancer size, stage, and mortality in Thailand. Breast J. Apr 2020;26(4):822-824. doi:10.1111/tbj.13611
Mukem S, Sriplung H, McNeil E, Tangcharoensathien V. Breast cancer screening among women in Thailand: analyses of population-based household surveys. J Med Assoc Thai. Nov 2014;97(11):1106-18.
Liu N, Wang J, Chen DD, Sun WJ, Zhang W. Tools for the assessment of breast cancer screening beliefs in women: a literature review. J Comp Eff Res. Jul 2019;8(9):645-655. doi:10.2217/cer-2018-0142
Rosenstock IM. Why people use health services. Milbank Mem Fund Q. Jul 1966;44(3):Suppl:94-127.
Champion VL. Instrument development for health belief model constructs. ANS Adv Nurs Sci. Apr 1984;6(3):73-85. doi:10.1097/00012272-198404000-00011
Dewi TK. Validation of the Indonesian version of Champion's Health Belief Model Scale for breast self-examination. Psychol Res Behav Manag. 2018;11:433-438. doi:10.2147/prbm.S177124
Lee EH, Kim JS, Song MS. Translation and validation of champion's health belief model scale with Korean women. Cancer Nurs. Oct 2002;25(5):391-5. doi:10.1097/00002820-200210000-00010
Marmarà D, Marmarà V, Hubbard G. Maltese Translation and Adaptation of Champion's Health Belief Model Scale and the Revised Illness Perception Questionnaire for Breast Screening Among Maltese Women. J Nurs Meas. Dec 1 2017;25(3):486-503. doi:10.1891/1061-3749.25.3.486
Medina-Shepherd R, Kleier JA. Spanish translation and adaptation of Victoria Champion's Health Belief Model Scales for breast cancer screening--mammography. Cancer Nurs. Mar-Apr 2010;33(2):93-101. doi:10.1097/NCC.0b013e3181c75d7b
Noman S, Shahar HK, Rahman HA, Ismail S, Aljaberi MA, Abdulrahman MN. Factor structure and internal reliability of breast cancer screening Champion's Health Belief Model Scale in Yemeni women in Malaysia: a cross-sectional study. BMC Womens Health. Dec 29 2021;21(1):437. doi:10.1186/s12905-021-01543-7
Secginli S, Nahcivan NO. Reliability and validity of the breast cancer screening belief scale among Turkish women. Cancer Nurs. Jul-Aug 2004;27(4):287-94. doi:10.1097/00002820-200407000-00005
Champion VL. Revised susceptibility, benefits, and barriers scale for mammography screening. Res Nurs Health. Aug 1999;22(4):341-8. doi:10.1002/(sici)1098-240x(199908)22:4<341::aid-nur8>3.0.co;2-p
Parsa P, Kandiah M, Mohd Nasir MT, Hejar AR, Nor Afiah MZ. Reliability and validity of Champion's Health Belief Model Scale for breast cancer screening among Malaysian women. Singapore Med J. Nov 2008;49(11):897-903.
Suriyong P, Jiraniramai S, Wongpakaran N, et al. Translation, Adaptation, and Validation of the Modified Thai Version of Champion's Health Belief Model Scale (MT-CHBMS). Healthcare (Basel). Dec 31 2022;11(1)doi:10.3390/healthcare11010128
Shen S, Zhou Y, Xu Y, et al. A multi-centre randomised trial comparing ultrasound vs mammography for screening breast cancer in high-risk Chinese women. Br J Cancer. Mar 17 2015;112(6):998-1004. doi:10.1038/bjc.2015.33
Ragab M, Albukhari A, Alyami J, Mansour RF. Ensemble Deep-Learning-Enabled Clinical Decision Support System for Breast Cancer Diagnosis and Classification on Ultrasound Images. Biology (Basel). Mar 14 2022;11(3)doi:10.3390/biology11030439
The Lancet O. Can artificial intelligence improve cancer care? The Lancet Oncology. 2023;24(6):577. doi:10.1016/s1470-2045(23)00240-1
Kirag N, Kızılkaya M. Application of the Champion Health Belief Model to determine beliefs and behaviors of Turkish women academicians regarding breast cancer screening: A cross sectional descriptive study. BMC Womens Health. Nov 6 2019;19(1):132. doi:10.1186/s12905-019-0828-9
Dündar PE, Ozmen D, Oztürk B, et al. The knowledge and attitudes of breast self-examination and mammography in a group of women in a rural area in western Turkey. BMC Cancer. Feb 24 2006;6:43. doi:10.1186/1471-2407-6-43
Altunkurek Ş Z, Hassan Mohamed S. Determine knowledge and belief of Somalian young women about breast cancer and breast self-examination with champion health belief model: a cross-sectional study. BMC Med Inform Decis Mak. Dec 8 2022;22(1):326. doi:10.1186/s12911-022-02065-4
Danial WW CC. BIOSTATISTICS A Foundation for Analysis in the Health Sciences. . Tenth ed. NJ, Wiley.; 2013.
Victoria L. Champion, Skinner. CS. The Health Belief Model. In: Karen Glanz BKR, K. Viswanath., ed. Health behavior and health education theory, research,and practice. 4th ed. Jossey-Bass; 2008.
Darvishpour A, Vajari SM, Noroozi S. Can Health Belief Model Predict Breast Cancer Screening Behaviors? Open Access Maced J Med Sci. May 20 2018;6(5):949-953. doi:10.3889/oamjms.2018.183
Dewi TK, Massar K, Ruiter RAC, Leonardi T. Determinants of breast self-examination practice among women in Surabaya, Indonesia: an application of the health belief model. BMC Public Health. Nov 27 2019;19(1):1581. doi:10.1186/s12889-019-7951-2
Febriyanti NMA, Lubis DS, Wirawan DN, Suariyani NLP, Karmaya M. The determinants of early breast cancer detection via breast self-examination (BSE) in Denpasar, Bali. Public Health and Preventive Medicine Archive. 2018;
Satitvipawee P SS, Pitiphat W, Kalampakorn S, Parkin DM. . Factors Associated with Breast Self-Examination among Thai Women Living in Rural Areas in Northeastern Thailand. . J Med Assoc Thai 2009;92:29.
Francks L, Murray A, Wilson E. Barriers and facilitators to breast self-examination in women under 50 in an international context: A qualitative systematic review. International Journal of Health Promotion and Education. 03/02 2023:1-18. doi:10.1080/14635240.2023.2185804
Jiraniramai S, Jiraporncharoen W, Pinyopornpanish K, Jakkaew N, Wongpakaran T, Angkurawaranon C. Functional beliefs and risk minimizing beliefs among Thai healthcare workers in Maharaj Nakorn Chiang Mai hospital: its association with intention to quit tobacco and alcohol. Subst Abuse Treat Prev Policy. Jul 12 2017;12(1):34. doi:10.1186/s13011-017-0118-1
Jiraniramai S, Likhitsathian S, Jiraporncharoen W, Thaikla K, Aramrattana A, Angkurawaranon C. Risk-Minimizing Belief: Its Association with Smoking and Risk of Harm From Smoking in Northern Thailand. Journal of Ethnicity in Substance Abuse. 2015/10/02 2015;14(4):364-378. doi:10.1080/15332640.2014.991468
Pengpid S, Peltzer K. Knowledge, attitude and practice of breast self-examination among female university students from 24 low, middle income and emerging economy countries. Asian Pac J Cancer Prev. 2014;15(20):8637-40. doi:10.7314/apjcp.2014.15.20.8637
Amin MN, Uddin MG, Uddin MN, et al. A hospital based survey to evaluate knowledge, awareness and perceived barriers regarding breast cancer screening among females in Bangladesh. Heliyon. 2020/04/01/ 2020;6(4):e03753. doi:https://doi.org/10.1016/j.heliyon.2020.e03753

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Association Between Sociodemographic Factors and Health Beliefs Related to Breast Cancer Screening Behavior Among Northern Thai Women: A Hospital-Based Study

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