The Association Between Fatalism and Mammography Use in Korean American Immigrant Women

Abstract

Fatalism is reported as a salient cultural belief that influences cancer screening disparities in racial and ethnic minority groups. Previous studies provide a range of measures and descriptions of cancer fatalism, but no studies to our knowledge have analyzed how fatalistic views cluster together within subgroups to form distinct profiles, and how these profiles can be predicted. This study identified subgroups of Korean American immigrants with similar fatalistic beliefs toward cancer and examined the influence of fatalism, health belief variables, and health literacy on mammography use. A cross-sectional survey design was used to obtain a convenience sample of 240 Korean American immigrant women in Los Angeles, California. Latent class analysis was used to identify unobserved subgroups of fatalism. Hierarchical logistic regression models were used to identify predisposing, enabling, and need factors associated with recent mammography use. The latent class analysis model identified three cancer fatalism subgroups: high fatalism (17.8%), moderate fatalism (36.7%), and low fatalism (45.5%). Women in the high fatalism subgroup were more likely to have had a mammogram within the past 2 years than women in the low fatalism subgroup. Regression analysis revealed three facilitators of recent mammogram use: level of fatalism, perceived barriers to mammogram, and family history of cancer. Although cultural beliefs can have a powerful influence on health-seeking behavior, it is important to weigh individual and contextual factors that may weaken or mediate the relationship between fatalism and engaging in preventive care such as having a mammogram.

Keywords

Asian cancer cancer prevention and screening health disparities health equity latent class analysis population groups women’s health

Breast cancer is the most common and second most fatal form of cancer among Asian women in the United States (American Cancer Society, 2016). Furthermore, the incidence of breast cancer has increased annually in all Asian women in the United States. (Torre et al., 2016). A recent study reported that Korean American women had the highest breast cancer incidence rate increase (2.55%/year) between 1990 and 2014 when compared with Chinese, Filipina, Japanese, South Asian, and Vietnamese women (Tuan et al., 2020). Despite the annual increases in incident rates and the risks associated with late-stage breast cancer diagnosis, Korean American women’s screening rates are consistently reported to be lower than those of other groups of women. For instance, approximately 35% to 65% of Korean American women had mammograms within the past 2 years (Oh et al., 2017). Another study using 2015–2016 California Health Survey data reported that 69% of Korean women had a mammogram in the past 2 years while 80% of non-Korean women were screened (S. Y. Lee et al., 2018). The disparity in mammography use between Korean and non-Korean women is noteworthy and necessitates further investigation.

A systematic review of breast cancer screening in Korean Americans reported factors associated with screening participation, including sociodemographic characteristics (e.g., age, education, and income), acculturation (e.g., length of time living in the United States and English proficiency), accessibility to health care services (e.g., insurance and regular checkups), knowledge about screening guidelines, and health beliefs (e.g., susceptibility to breast cancer, benefits of mammogram, and barriers to screening; Oh et al., 2017). A review of the literature also revealed that culturally embedded factors such as family support, embarrassment, and preventive health orientation are key factors that are linked to screening behaviors (S. Y. Lee, 2015).

Culture plays a critical role in influencing a person’s beliefs about health and illness, as well as affecting their health-seeking behaviors. For instance, fatalism has been reported as a salient cultural belief that helps explain cancer screening disparities in racial and ethnic minority groups, particularly in African American and Latinx groups (Brittain et al., 2016; Crosby & Collins, 2017; Marván et al., 2016; Moreno et al., 2019; Russell et al., 2006). However, few studies have examined fatalistic beliefs in Korean American immigrants (M. H. Lee et al., 2019), even though a fatalistic view of cancer is a notable cultural barrier for Korean American women (S. Y. Lee, 2015). Korean culture teaches that illness is predetermined by fate, division of the soul and body, and past sins (Kim et al., 2010). According to this view, it is believed that if a woman is diagnosed with cancer, it is her fate to die in this manner. This fatalistic belief contributes to the low rate of Korean women’s participation in breast cancer screening due to the perception that breast cancer is predetermined and, therefore, its development cannot be prevented by early screening. As noted earlier, breast cancer–related health beliefs are linked to Korean American women’s screening behaviors (H. Y. Lee, Stange, & Ahluwalia, 2015; Oh et al., 2017). Although previous investigations have provided a range of measures and descriptions of cancer fatalism (e.g., predetermination, luck, and pessimism), no studies to our knowledge have analyzed how fatalistic views cluster together with health beliefs to form distinct profiles, and how these profiles can be predicted.

Research also has reported a potential association between inadequate health literacy and lower cancer screening rates (Mazor et al., 2016; Oldach & Katz, 2014). Health literacy, defined as individuals’ ability to obtain, process, and understand health information and services to make health-related decisions (Selden et al., 2000), could be an essential element to make informed decisions about cancer screening. Low health literacy may contribute to a lack of awareness and knowledge about the importance of cancer screening (Davis et al., 2002; Oldach & Katz, 2014). For example, a study conducted by Pagán et al. (2012) reported that women with adequate health literacy tended to have had a mammogram in their lifetime or within the past 2 years, compared with those with an inadequate or marginal level of health literacy. Although studies have reported a low level of health literacy among Korean immigrant women (H. Y. Lee, Choi, & Lee, 2015), no research to our knowledge has examined the correlation between health literacy and breast cancer screening in this population. Health literacy may be a factor that contributes to lower breast screening participation in this group.

Theoretical Models

The Andersen behavioral model (Andersen & Newman, 1973) along with the health belief model (HBM; Champion & Skinner, 2008) guided this study’s design and analysis. These two models have been widely used in studies on breast cancer screening behavior, either together (Choi et al., 2017) or independently (Ghaffari et al., 2019; M. H. Lee et al., 2019; Y. S. Lee et al., 2020). According to Andersen behavioral model, a person’s use of health services is determined by an interaction between predisposing, enabling, and need characteristics. Predisposing characteristics include exogenous factors like demographic characteristics and health beliefs; enabling characteristics reflect available health resources to individuals, such as accessibility and use of information sources; need characteristics contain perceived needs and illnesses to motivate individuals to promote behavioral changes in order to maintain their health (Ghaffari et al., 2019).

The HBM includes several key components (e.g., perceived susceptibility, perceived seriousness, perceived benefits and barriers to a behavior, cues to action, and self-efficacy) that predict what makes people use health services to prevent, screen for, and/or control illness conditions. For instance, women would be more likely to get a mammogram if they feel vulnerable to breast cancer, believe breast cancer is a serious disease, perceive benefits of screening as higher than barriers, feel confident getting a mammography, and receive a cue to action (Champion & Skinner, 2008). For the purpose of this investigation, we included age (E. E. Lee et al., 2016), years in the United States (H. Y. Lee, Stange, & Ahluwalia, 2015), education (Oh et al., 2017), income (Oh et al., 2017), and fatalism (S. Y. Lee, 2015; M. H. Lee et al., 2019) as predisposing factors. Enabling factors consisted of health literacy (Oldach & Katz, 2014; Pagán et al., 2012), perceived barriers to mammography (E. E. Lee et al., 2016; H. Y. Lee, Stange, & Ahluwalia, 2015), and self-efficacy of breast cancer screening (Jerome-D’Emilia & Suplee, 2015; E. E. Lee et al., 2016). A family history of cancer (M. H. Lee et al., 2019) was chosen as the need factor. Figure 1 shows the conceptual model of our study. Our aims in this study were to (1) identify subgroups of Korean American immigrant women with similar fatalistic beliefs toward cancer and (2) determine the association between fatalism, health belief variables, and health literacy and Korean American immigrant women’s mammography use. The study findings will be used to pinpoint personal and social motivators associated with mammogram use and inform the development of tailored and culturally appropriate breast cancer screening interventions in Korean American immigrants.

Figure 1.

Theoretical framework.

Method

Study Design and Sampling

This study utilized a cross-sectional survey design with a nonprobability sample. We recruited 240 Korean American immigrant women, aged 40 to 79 years, at various community-based sites (e.g., churches and social service agencies) between July and September 2016 in Los Angeles County, California. Participant recruitment was conducted using a study flyer and snowball sampling. The first author provided presentations about the study purpose, eligibility criteria, and the voluntary nature of participation at community sites. Detailed information concerning the recruitment process is described elsewhere (M. H. Lee et al., 2019). Among 240 surveys, 28 were excluded in data analysis due to incomplete information. A total of 212 surveys were analyzed.

Data Collection

Data were collected using a structured questionnaire. The questionnaire was developed in English and translated into Korean using a back-translation method (Bracken & Barona, 1991). Two pilot tests were conducted to assess the clarity and cultural sensitivity of the Korean language instrument. First, five Korean American immigrant women completed a paper version of the questionnaire and participated in a 1-hour focus group to discuss its format and content. Based on their feedback, some words in the questionnaire were changed. In the second pilot test, four Korean American immigrant women completed the revised questionnaire using iPads. No concerns were reported by these participants.

The survey was either self-administered (n = 188) or completed face-to-face with the first author (n = 24) using a paper survey or iPad. Study participants completed the survey in Korean at the recruitment sites or their preferred locations. The typical survey completion time was between 30 and 50 minutes. Written informed consent was obtained from all the participants prior to their participation. Each participant received a $20 gift card on completion of the survey. The study was approved by the institutional review board at the University of Minnesota.

Measurements

Dependent Variable

The dependent variable measured respondents’ self-reported mammogram uptake. Study participants were asked questions about whether they had ever had a mammogram in their life (1 = yes, 0 = no) and when they had their most recent mammogram. The receipt of mammography within the past 2 years (i.e., “recent mammogram use” for the purpose of this study) was used as the dependent variable.

Independent Variables

Predisposing factors

Five factors were identified and entered into our model: age, time in the United States, education level, income, and fatalism. Age was assessed using the participant’s birth year and month. We calculated a participant’s age by subtracting their birth year and month from the survey date. To measure time in the United States, participants were asked to report the number of years they have lived in the United States. Level of education was measured by identifying the participant’s highest level of education (1 = elementary school, 2 = middle school, 3 = high school or equivalent, 4 = college or university, 5 = graduate school). A participant’s income was ordinally coded as follows: 1 = less than $25,000, 2 = $25,000–$49,999, 3 = $50,000–$74,999, 4 = $75,000–$94,999, 5 = $95,000–$104,999, 6 = $105,000–$124,999, and 7 = $125,000 or more. A composite score of nine fatalism items (Liang et al., 2008) was used: (1) If I am meant to get cancer, I will get it; (2) If we get cancer, the best way to deal with it is to accept it, just like the old saying “listen to heaven and follow fate”; (3) Health or illness is a matter of fate. Some people are always healthy and others get sick very often; (4) I cannot control my destiny; (5) Avoiding cancer is a matter of personal luck; (6) No matter what I do, if I am going to get cancer, I will get it; (7) It is hard to prevent cancer; (8) Getting cancer is like being sentenced to death; and (9) It is best not to think about cancer. If we think about it too much, we probably will get cancer. All items were scored on a 4-point scale from 1 (strongly disagree) to 4 (strongly agree), with higher item scores indicating a higher level of fatalistic belief. Internal consistency for this scale was α = .79 in this study. The 4-point scale was subsequently converted into a dichotomous variable, 0 and 1. Zero indicated either disagreement or strong disagreement with fatalistic beliefs, and 1 indicated either agreement or strong agreement with fatalistic beliefs.

Enabling factors

Three variables were used: health literacy, perceived barriers to mammography use, and self-efficacy of breast cancer screening. To assess health literacy, three items measured on a 5-point scale were selected from the Centers for Disease Control and Prevention Behavioral Risk Factor Surveillance System Data study of 2016: (1) How difficult is it for you to get advice or information about health or medical topics if you need it? (1 = I do not look for health information, 2 = very difficult, 3 = somewhat difficult, 4 = somewhat easy, 5 = very easy); (2) How difficult is it for you to understand information that doctors, nurses, and other health professionals give you? (1 = I do not understand it at all to 5 = very easy); and (3) You can find written information about health on the internet, in newspapers and magazines, and in brochures in the doctor’s office and clinic. In general, how difficult is it for you to understand written health information? (1 = I don’t pay attention to written health information to 5 = very easy). The three items were summed into a single composite score, with higher scores corresponding to higher health literacy. Internal consistency for this measure was α = .69 in the current study.

Perceived barriers to mammography were assessed using 14 items measured on a 4-point scale ranging from 1 (strongly disagree) to 4 (strongly agree), with higher item total scores indicating a higher level of barriers to get a mammogram. Eleven items were developed by Champion (1999; e.g., “I don’t know how to go about getting a mammogram,” “Having a mammogram is too embarrassing”; α = .88), and three items measured barriers to immigrant women specifically (H. Lee et al., 2009; E. E. Lee et al., 2016; i.e., difficulty communicating with a physician in English, concern about the cost to get a mammogram, and transportation issues). Internal consistency for this scale was α = .83 in this study.

Self-efficacy of breast cancer screening was assessed using a 10-item scale developed by Champion et al. (2005; e.g., “I can arrange transportation to get a mammogram,” “I can talk to people at the mammogram center about my concerns”; α = .87). All items were measured on a 4-point scale ranging from 1 (unconfident) to 4 (confident), with higher total scores indicating a higher level of confidence in getting a mammogram. The word “You” in the original instruments was changed to “I” in this study. Internal consistency for this scale was α = .83 in the current study.

Need factor

Family history of cancer was the sole need factor used in our model. It was measured using a single item (i.e., “Has one of your family members, including a parent, grandparent, sibling, or close relative ever been diagnosed with cancer?”), and participants were asked to indicate “Yes,” “No,” or “I don’t know/not sure.”

Data Analysis

Latent class analysis (LCA) was performed to identify unobserved subgroups of fatalism in the sample. It started with extracting one-, two-, three-, and four-class approaches. Log-likelihood value, Akaike information criterion (AIC; Akaike, 1987), Bayesian information criterion (BIC; Schwarz, 1978), adjusted BIC (ABIC; Sclove, 1987), entropy, and the bootstrap likelihood ratio test (BLRT; Lo et al., 2001) were interchangeably used to identify the number of latent classes. These criteria were compared across one through four classes and the lowest value on each index indicating the best fitting model. For a given entropy estimate, its value approaching .80 or more than .80 is considered as the best fitting model as well.

Hierarchical logistic regression models were used to identify to what extent fatalism is associated with recent mammogram use. We developed three different hierarchical logistic regression models based on the Andersen behavioral model and HBM. Model 1 included predisposing factors—that is, demographic information and subgroups of fatalism. In the next step, Model 2, we added selected covariates as enabling factors. In the final step, the need factor of family history of cancer was added. All the analysis was conducted using Mplus Version 7 and SPSS 26.

Results

Sociodemographic Characteristics

As seen in Table 1, the mean age of the study participants was 59 years (SD = 11.3) and they lived in the United States for an average of 23 years (SD = 10.9). Two thirds (67.3%) had a college or graduate degree. About 58% reported that their income was less than $50,000. More than one third (38.8%) of the participants had a family history of cancer.

Table 1.

Sociodemographic Characteristics of the Study Participants (N = 212).

Variable	M (SD)	n (%)
Age (years)	58.6 (11.3)
Time in the United States (years)	23.1 (10.9)
Age at the time of immigration to the United States	35.4 (10.9)
Marital status
Single (never married)		8 (3.8)
Divorced		29 (13.7)
Widowed		31 (14.6)
Married or partnered		144 (68)
Education (N = 211)
Elementary school		3 (1.4)
Middle school		10 (4.7)
High school		56 (26.5)
College or university		123 (58.3)
Graduate school		19 (9)
Employment status (N = 211)
Full-time		68 (32.2)
Self-employed		32 (15.2)
Part-time		14 (6.6)
Unemployed		74 (35.1)
Retired		3 (1.4)
Other		20 (9.5)
Annual house income ($, N = 203)
Less than 25,000		70 (34.5)
25,000–49,999		48 (23.6)
50,000–74,999		36 (17.7)
75,000–94,999		13 (6.4)
95,000–104,999		13 (6.4)
105,000–124,999		11 (5.4)
125,000 or more		12 (5.9)
Health insurance
Yes		190 (89.6)
No		22 (10.4)
Regular checkup
Yes		165 (77.8)
No		47 (22.2)
Current health status
Poor		19 (9)
Fair		100 (47.2)
Good		76 (35.8)
Very good		13 (6.1)
Excellent		4 (1.9)
Family cancer history (N = 206)
Yes		79 (38.3)
No		115 (55.8)
Don’t know/not sure		12 (5.8)

Latent Class Analysis

The LCA model yielded three cancer fatalism subgroups: high, moderate, and low fatalism. The results from the AIC, BIC, ABIC, entropy, and BLRT indicated that three classes were better than two classes among the Korean American immigrants who had a recent mammogram. With similar fatalistic beliefs toward cancer, the bootstrapped parametric likelihood ratio test, having a p value of .03, indicated that the three-class solution was better than a two-class solution.

As shown in Figure 2, 17.8% of the participants belonged to the first class (high fatalism subgroup), 36.7% belonged to the second class (moderate fatalism subgroup), and 45.59% belonged to the third class (low fatalism subgroup). The high fatalism subgroup strongly agreed with three items (estimated probabilities were close to 1): If we get cancer, the best way to deal with it is to accept it, just like the old saying “listen to heaven and follow fate (Q2); Health or illness is a matter of fate (Q3); and Some people are always healthy, and others get sick very often (Q4).” The moderate fatalism subgroup agreed with three items (estimated probabilities were between 0.55 and 0.59): If I am meant to get cancer, I will get it (Q1); If we get cancer, the best way to deal with it is to accept it, just like the old saying “listen to heaven and follow fate (Q2); and No matter what I do, if I am going to get cancer, I will get it (Q6).” Interestingly, the moderate fatalism subgroup presented higher estimated probabilities than the higher fatalism subgroup for one question (0.6, 0.45, respectively): It is best not to think about cancer. If we think about it too much, we probably will get cancer (Q9). The low fatalism subgroup agreed with two items: If we get cancer, the best way to deal with it is to accept it, just like the old saying “listen to heaven and follow fate (Q2); and It is best not to think about cancer. If we think about it too much, we probably will get cancer (Q9; estimated probabilities were 0.29, 0.4, respectively). The low fatalism subgroup members neither agreed nor disagreed with several items such as No matter what I do, if I am going to get cancer, I will get it (Q6). Figure 2 also illustrates the estimated probabilities of fatalism items by three different classes with 89% accuracy in the prediction of the latent classes.

Figure 2.

Fatalism in three classes (1-high, 2-moderate, 3-low), sample proportions, and estimated probabilities.

Hierarchical Logistic Regression

Table 2 summarizes the results of a hierarchical regression for recent mammogram use. Model 1 indicates that fatalism significantly predicted recent mammogram use. Interestingly, the high fatalism subgroup (Fatalism 1) was more likely to have a mammogram within the past 2 years than the low fatalism subgroup (reference group; odds ratio [OR] = 2.94, 95% confidence interval [CI: 1.21, 7.18]; see Table 3). In Model 2, the high fatalism subgroup remained significant (OR = 3.32, [1.23, 8.51]). Model 2 further shows that one enabling factor (perceived barriers to mammogram) significantly predicted recent mammogram use (OR = 0.92, [0.86, 0.98]). In the final model, two factors from the previous model—high level of fatalism (OR = 3.54, [1.31, 9.59]) and perceived barriers to mammogram (OR = 0.91, [0.85, 0.97])—remained significantly associated with recent mammogram use. In addition, one need factor (family cancer history significantly) predicted recent mammogram use (OR = 1.45, [1.01, 2.08]).

Table 2.

Latent Class Analysis Model Fit Index Summary.

	Percentage of classes
Model fit statistics	1	2	3	4
Number of free parameters	9	19	29	39
Log-likelihood	−1126.00	−1009.89	−996.58	−987.15
AIC	2270.00	2057.78	2051.16	2052.30
BIC	2300.21	2121.55	2148.50	2183.21
ABIC	2271.69	2061.35	2056.60	2059.63
Entropy		0.79	0.73	0.78
Parametric Bootstrapped Likelihood Ratio Test		−1126.00**	−1009.89*	−996.58

Note. AIC = Akaike information criterion; BIC = Bayesian information criterion; ABIC = adjusted Bayesian information criterion

p < .05. ^**p < .01.

Table 3.

Results of Logistic Regression Models: Recent Mammography.

Model 1					Model 2					Model 3
	Β	SE	Sig.	Exp(Β)		B	SE	Sig.	Exp(B)		B	SE	Sig.	Exp(B)
Predisposing					Predisposing					Predisposing
Fatalism			0.05		Fatalism			0.04		Fatalism			0.04
Fatalism (1)	1.08	0.46	0.02	2.94	Fatalism (1)	1.20	0.48	0.01	3.32	Fatalism (1)	1.26	0.51	0.01	3.54
Fatalism (2)	0.49	0.34	0.14	1.64	Fatalism (2)	0.48	0.35	0.17	1.62	Fatalism (2)	0.52	0.36	0.15	1.69
Age	0.01	0.02	0.58	1.01	Age	0.01	0.02	0.79	1.01	Age	0.00	0.02	0.91	1.00
Education	0.27	0.23	0.24	1.31	Education	0.26	0.25	0.29	1.30	Education	0.19	0.25	0.46	1.21
Income	0.04	0.09	0.64	1.05	Income	−0.01	0.10	0.94	0.99	Income	−0.01	0.11	0.95	0.99
Time in United States	0.02	0.02	0.36	1.02	Time in United States	0.01	0.02	0.45	1.01	Time in United States	0.02	0.02	0.41	1.02
Constant	−1.85	1.56	0.24	0.16	Enabling					Enabling
					Get health advice or information	0.28	0.19	0.14	1.32	Get health advice or information	0.26	0.19	0.18	1.30
					Understand health information	−0.39	0.23	0.09	0.68	Understand health information	−0.37	0.23	0.11	0.69
					Find written health information	−0.06	0.18	0.76	0.95	Find written health information	−0.05	0.19	0.79	0.95
					Barriers	−0.09	0.03	0.01	0.92	Barriers	−0.09	0.03	0.01	0.91
					Self-efficacy	0.05	0.03	0.18	1.05	Self-efficacy	0.03	0.04	0.46	1.03
					Constant	0.21	2.38	0.93	1.23	Need
					Constant	0.21	2.38	0.93	1.23	Family cancer history	0.37	0.18	0.04	1.45
										Constant	1.22	2.47	0.62	3.39
Model summary
	−2 Log likelihood			256.15^a		−2 Log likelihood			238.68^a		−2 Log likelihood			227.53^a
	Cox & Snell R²			0.05		Cox & Snell R²			0.12		Cox & Snell R²			0.14
	Nagelkerke R²			0.06		Nagelkerke R²			0.17		Nagelkerke R²			0.19

Estimation terminated at Iteration Number 4 because parameter estimates changed by less than .001.

Discussion

Our study examined factors associated with Korean American immigrant women’s mammogram use within the past 2 years based on Andersen behavioral model and HBM. We also identified three classifications of fatalistic beliefs about cancer in the sample using latent class analysis—that is, approximately 18% of the participants belonged to the high fatalism subgroup, 37% belonged to the moderate fatalism subgroup, and 46% belonged to low fatalism subgroup.

Based on Andersen’s framework, the regression analysis revealed three facilitators that were significantly associated with recent mammogram use: level of fatalism (a predisposing factor), perceived barriers to mammogram (an enabling factor), and family history of cancer (a need factor). Interestingly, women with a high level of fatalism were more likely to have a mammogram within the past 2 years than those with a low level of fatalism. Previous studies have shown that cancer fatalism is related to low cancer screening rates, delays in cancer treatment after diagnosis, and reluctance to engage in healthy lifestyle practices in order to reduce cancer risks (Nelson et al., 2002). One possible explanation for participants in the high fatalism subgroup to seek mammography may be their ability to balance strong cultural beliefs with a need to exercise a sense of agency over their bodies and lives. Although these women espoused traditional cultural beliefs associated with fatalism and health, living in a region of the United States where diverse media outlets encourage women to be screened for breast cancer may have created a sense of cognitive dissonance between their cultural beliefs and the messages they receive about the primacy and benefits of mammography. The anomalous association between a high level of fatalism and mammogram uptake underscores the need for additional research to understand the personal and social factors that motivate immigrant women with strong fatalistic beliefs to engage in screening behavior more than women who are less inclined to believe that their health is beyond their control.

In addition, study participants who reported a high level of perceived barriers to mammography (e.g., cost, scheduling a mammogram, and communication with physician) were less likely to have a recent mammogram. This finding corroborates the outcomes of studies that examined the association between access to health care services and cancer screening behaviors (E. E. Lee et al., 2016; Oh et al., 2017). Efforts to reduce these barriers through community-based initiatives sponsored by public health departments and health care providers are needed to improve mammography uptake in the Korean American immigrant community. These efforts include strategies to increase health care accessibility, such as free or low-cost screening via Korean community events, bilingual cancer screening services, assistance with scheduling a mammogram, and screening schedule reminder services. Family history of cancer was significantly associated with a recent breast screening experience. This finding implies that study participants’ cancer risk awareness may motivate them to engage in early detection and other health behaviors in order to reduce their risk of getting cancer (Haber et al., 2012; Karvinen et al., 2019; Paalosalo-Harris & Skirton, 2017). The participants may have heard about or observed the consequences of early versus late-stage cancer diagnosis and cancer treatments, and this experience could have affected them emotionally, possibly changing their views on preventive health behavior. Women with a family history of cancer who have had a mammogram could serve as peer educators to help Korean American women engage in breast cancer screening. A recent study found a correlation between hearing about a mammogram experience from family members, friends, or neighbors and mammogram adherence (M. H. Lee et al., 2019). Sharing their screening experience along with cancer patients’ stories could serve as powerful messages to motivate Korean American women to get screened.

The regression analysis also yielded several predisposing factors (i.e., age, education, income, and time in the United States) and enabling factors (i.e., health literacy and self-efficacy) that were not significantly associated with recent mammogram use. Some studies have reported a lack of association between sociodemographic factors and mammogram use (M. H. Lee et al., 2019; Oh et al., 2017). However, the lack of association between factors such as self-efficacy or health literacy and mammogram use was not expected. Additional research is needed to identify potential intrinsic or extrinsic factors that may influence the strength and direction of these specific associations, especially in immigrant populations.

Limitations

The study findings need to be interpreted carefully in light of limitations associated with the sampling approach, sample size, cross-sectional design, and use of self-report data. Study participants were recruited in Los Angeles, California, using a convenience sampling method, which limits the generalizability of the findings. Korean Americans from this geographic area may not be representative of those residing in other regions of the United States where Korean communities may be smaller and have less access to cultural resources (e.g., native foods, events in Korean). Although the sample size of this study was smaller than the rule of thumb cutoff for LCA (i.e., 250), this sample size issue was addressed by using high-quality indicators that help compensate for marginal sample size–related issue (Collins & Wugalter, 1992; Marsh et al., 1998). The fatalism items in this study were considered such high-quality indicators. Due to the cross-sectional design of this study, we cannot determine temporal relationships between variables. Last, survey responses were self-reported, which may influence the results with regard to over- or underreporting of screening behavior. We note that a previous research investigation supports the validity of self-reported data for mammography by comparing self-reporting and medical record data for mammogram use in Korean American women (Nandy et al., 2016).

Conclusion

To promote breast cancer screening in the Korean American immigrant community, a multiprong public health effort is needed to assist women who may encounter barriers to mammography or have few or no personal connections to people with cancer. This study underscores the need to thoroughly examine the association between Korean American immigrant women’s fatalistic beliefs and their breast cancer screening behaviors. While cultural beliefs can have a powerful influence on health-seeking behavior, it is important to weigh individual and contextual factors that may weaken or mediate the relationship between fatalism and engaging in preventive care such as having a mammogram. Community-based efforts (e.g., peer support, education, advocacy) and financial resources are needed to support Korean American immigrant women’s breast health and overall well-being. To achieve this goal and to reduce the number of immigrant women who are diagnosed with late-stage breast cancer, we need to identify the range of personal and social motivators that will increase the uptake of breast cancer screening, rather than assume that addressing a culturally anchored belief such as fatalism is an inefficient use of scarce public health resources.

Footnotes

Acknowledgements

We want to express our appreciation for the Korean American community in Los Angeles, California, for their support in this study.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by an American Cancer Society Doctoral Training Grant in Oncology Social Work (DSW16-069-01-SW), which provided funding for part of the first author’s doctoral dissertation research.

ORCID iD

Mi Hwa Lee

References

Akaike

(1987). Factor analysis and AIC. In Parzen

Tanabe

Kitagawa

(Eds.), Selected papers of Hirotugu Akaike: Springer series in statistics (Perspectives in statistics; pp. 371–386). Springer. https://doi.org/10.1007/978-1-4612-1694-0_29

American Cancer Society. (2016). Cancer facts & figures: Special section: Cancer in Asian Americans, Native Hawaiians, and Pacific Islanders. https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/cancer-facts-figures-2016.html

Andersen

Newman

J. F.

(1973). Societal and individual determinants of medical care utilization in the United States. Milbank Memorial Fund Quarterly. Health and Society, 51(1), 95–124. https://doi.org/10.2307/3349613

Bracken

B. A.

Barona

(1991). State of the art procedures for translating, validating and using psychoeducational tests in cross-cultural assessment. School Psychology International, 12(1–2), 119–132. https://doi.org/10.1177/0143034391121010

Brittain

Christy

S. M.

Rawl

S. M.

(2016). African American patients’ intent to screen for colorectal cancer: Do cultural factors, health literacy, knowledge, age and gender matter? Journal of Health Care for the Poor and Underserved, 27(1), 51–67. https://doi.org/10.1353/hpu.2016.0022

Centers for Disease Control and Prevention. (2016). Behavioral Risk Factor Surveillance System (BRFSS) survey data and documentation. https://www.cdc.gov/brfss/annual_data/annual_2016.html

Champion

V. L.

(1999). Revised susceptibility, benefits, and barriers scale for mammography screening. Research in Nursing & Health, 22(4), 341–348. https://doi.org/10.1002/(SICI)1098-240X(199908)22:4%3C341::AID-NUR8%3E3.0.CO;2-P

Champion

V. L.

Skinner

C. S.

(2008). The health belief model. In Glanz

Rimer

B. K.

Viswanath

(Eds.), Health behavior and health education: Theory, research, and practice (4th ed., pp. 45–65). Jossey-Bass.

Champion

V. L.

Skinner

C. S.

Menon

(2005). Development of a self-efficacy scale for mammography. Research in Nursing & Health, 28(4), 329–336. https://doi.org/10.1002/nur.20088

10.

Choi

G. Y.

Koh

Choi

Cho

J. Y.

(2017). Understanding breast cancer screening behaviors of Korean American women in sociocultural contexts. Social Work in Health Care, 56(1), 45–63. https://doi.org/10.1080/00981389.2016.1244138

11.

Collins

L. M.

Wugalter

S. E.

(1992). Latent class models for stage-sequential dynamic latent variables. Multivariate Behavioral Research, 27(1), 131–157. https://doi.org/10.1207/s15327906mbr2701_8

12.

Crosby

R. A.

Collins

(2017). Correlates of community-based colorectal cancer screening in a rural population: The role of fatalism. Journal of Rural Health, 33(4), 402–405. https://doi.org/10.1111/jrh.12257

13.

Davis

T. C.

Williams

M. V.

Marin

Parker

R. M.

Glass

(2002). Health literacy and cancer communication. CA: A Cancer Journal for Clinicians, 52(3), 134–149. https://doi.org/10.3322/canjclin.52.3.134

14.

Ghaffari

Esfahani

S. N.

Rakhshanderou

Koukamari

P. H.

(2019). Evaluation of health belief model-based intervention on breast cancer screening behaviors among health volunteers. Journal of Cancer Education, 34(5), 904–912. https://doi.org/10.1007/s13187-018-1394-9

15.

Haber

Ahmed

N. U.

Pekovic

(2012). Family history of cancer and its association with breast cancer risk perception and repeat mammography. American Journal of Public Health, 102(12), 2322–2329. https://doi.org/10.2105/AJPH.2012.300786

16.

Jerome-D’Emilia

Suplee

P. D.

(2015). Mammogram use and self-efficacy in an urban minority population. Public Health Nursing, 32(4), 287–297. https://doi.org/10.1111/phn.12162

17.

Karvinen

K. H.

Brunet

Carr

L. J.

(2019). Family history of cancer as a cue to action for physical activity behaviour and beliefs. Psychology & Health, 35(8), 933–945. https://doi.org/10.1080/08870446.2019.1693570

18.

Kim

J. H.

Menon

Wang

Szalacha

(2010). Assess the effects of culturally relevant intervention on breast cancer knowledge, beliefs, and mammography use among Korean American women. Journal of Immigrant and Minority Health, 12(4), 586–597. https://doi.org/10.1007/s10903-009-9246-7

19.

Lee

E. E.

Nandy

Szalacha

Park

K. M.

Lee

Menon

(2016). Korean American women and mammogram uptake. Journal of Immigrant and Minority Health, 18(1), 179–186. https://doi.org/10.1007/s10903-015-0164-6

20.

Lee

Kim

Han

H.-R.

(2009). Do cultural factors predict mammography behavior among Korean immigrants? Journal of Advanced Nursing, 65(12), 2574–2584. https://doi.org/10.1111/j.1365-2648.2009.05155.x

21.

Lee

H. Y.

Choi

J. K.

Lee

M. H.

(2015). Health literacy in an underserved immigrant population: New implications toward achieving health equity. Asian American Journal of Psychology, 6(1), 97–105. https://doi.org/10.1037/a0037425

22.

Lee

H. Y.

Stange

M. J.

Ahluwalia

J. S.

(2015). Breast cancer screening behaviors among Korean American immigrant women: Findings from the health belief model. Journal of Transcultural Nursing, 26(5), 450–457. https://doi.org/10.1177/1043659614526457

23.

Lee

M. H.

Merighi

J. R.

Lee

H. Y.

(2019). Factors associated with mammogram use in Korean American immigrant women. American Journal of Health Behavior, 43(6), 1075–1085. https://doi.org/10.5993/AJHB.43.6.6

24.

Lee

S. Y.

(2015). Cultural factors associated with breast and cervical cancer screening in Korean American women in the US: An integrative literature review. Asian Nursing Research, 9(2), 81–90. https://doi.org/10.1016/j.anr.2015.05.003

25.

Lee

S. Y.

Lee

Natipagon-Shah

Toyama

(2018). Factors associated with breast cancer screening among Korean American women in California: Results from the California Health Interview Survey 2015–2016. Asian Pacific Journal of Cancer Prevention, 19(11), 3271–3277. https://doi.org/10.31557/APJCP.2018.19.11.3271

26.

Lee

Y. S.

Roh

Moon

Lee

K. H.

McKinley

LaPlante

(2020). Andersen’s behavioral model to identify correlates of breast cancer screening behaviors among Indigenous women. Journal of Evidence-Based Social Work, 17(1), 117–135. https://doi.org/10.1080/26408066.2019.1650316

27.

Liang

Wang

J. H.

Chen

M. Y.

Feng

Lee

Schwartz

M. D.

Pasick

R. J.

Mandelblatt

J. S.

(2008). Developing and validating a measure of Chinese cultural views of health and cancer. Health Education & Behavior, 35(3), 361–375. https://doi.org/10.1177/1090198106294893

28.

Mendell

N. R.

Rubin

D. B.

(2001). Testing the number of components in a normal mixture. Biometrika, 88(3), 767–778. https://doi.org/10.1093/biomet/88.3.767

29.

Marsh

Hau

Balla

Grayson

(1998). Is more ever too much? The number of indicators per factor in confirmatory factory analysis. Multivariate Behavioral Research, 33(2), 181–220. https://doi.org/10.1207/s15327906mbr3302_1

30.

Marván

M. L.

Ehrenzweig

Catillo-López

R. L.

(2016). Fatalistic beliefs and cervical cancer screening among Mexican women. Health Care for Women International, 37(1), 140–154. https://doi.org/10.1080/07399332.2014.959169

31.

Mazor

K. M.

Rubin

D. L.

Roblin

D. W.

Williams

A. E.

Han

P. K.

Gaglio

Cutrona

S. L.

Costanza

M. E.

Wagner

J. L.

(2016). Health literacy–listening skill and patient questions following cancer prevention and screening discussions. Health Expectations, 19(4), 920–934. https://doi.org/10.1111/hex.12387

32.

Moreno

P. I.

Yanez

Schuetz

S. J.

Wortman

Gallo

L. C.

Benedict

Brintz

C. E.

Cai

Castaneda

S. F.

Perreirah

K. M.

Gonzalez

II Isasi

C. R.

Penedo

F. J.

(2019). Cancer fatalism and adherence to national cancer screening guidelines: Results from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Cancer Epidemiology, 60(June), 39–45. https://doi.org/10.1016/j.canep.2019.03.003

33.

Nandy

Menon

Szalacha

L. A.

Park

Lee

E. E.

(2016). Self-report versus medical record for mammography screening among minority women. Western Journal of Nursing Research, 38(12), 1627–1638. https://doi.org/10.1177/0193945916647059

34.

Nelson

Geiger

A. M.

Mangione

C. M.

(2002). Effect of health beliefs on delays in care for abnormal cervical cytology in a multi-ethnic population. Journal of General Internal Medicine, 17(9), 709–716. https://doi.org/10.1046/j.1525-1497.2002.11231.x

35.

K. M.

Taylor

K. L.

Jacobsen

K. H.

(2017). Breast cancer screening among Korean Americans: A systematic review. Journal of Community Health, 42(2), 324–332. https://doi.org/10.1007/s10900-016-0258-7

36.

Oldach

B. R.

Katz

M. L.

(2014). Health literacy and cancer screening: A systematic review. Patient Education and Counseling, 94(2), 149–157. https://doi.org/10.1016/j.pec.2013.10.001

37.

Paalosalo-Harris

Skirton

(2017). Mixed method systematic review: The relationship between breast cancer risk perception and health-protective behaviour in women with family history of breast cancer. Journal of Advanced Nursing, 73(4), 760–774. https://doi.org/10.1111/jan.13158

38.

Pagán

J. A.

Brown

C. J.

Asch

D. A.

Armstrong

Bastida

Guerra

(2012). Health literacy and breast cancer screening among Mexican American women in South Texas. Journal of Cancer Education, 27(1), 132–137. https://doi.org/10.1007/s13187-011-0239-6

39.

Russell

K. M.

Perkins

S. M.

Zollinger

T. W.

Champion

V. L.

(2006). Sociocultural context of mammography screening use. Oncology Nursing Forum, 33(1), 105–112. https://doi.org/10.1188/06.ONF.105-112

40.

Schwarz

(1978). A Bayesian analysis of the minimum AIC procedure. Annals of the Institute of Statistical Mathematics, 30(1), 9–14. https://doi.org/10.1007/BF02480194

41.

Sclove

S. L.

(1987). Application of model-selection criteria to some problems in multivariate analysis. Psychometrika, 52(3), 333–343. https://doi.org/10.1007/BF02294360

42.

Selden

C. R.

Zorn

Ratzan

S. C.

Parker

R. M.

(2000). Current bibliographies in medicine 2000-1: Health literacy. National Institutes of Health. https://www.nlm.nih.gov/archive/20061214/pubs/cbm/hliteracy.html

43.

Torre

L. A.

Sauer

A. M. G.

Chen

M. S.

Jr. Kagawa-Singer

Jemal

Siegel

R. L.

(2016). Cancer statistics for Asian Americans, Native Hawaiians, and Pacific Islanders, 2016: Converging incidence in males and females. CA: A Cancer Journal for Clinicians, 66(3), 182–202. https://doi.org/10.3322/caac.21335

44.

Tuan

A. W.

Davis Lynn

B. C.

Chernyavskiy

Gomez

S. L.

Gierach

G. L.

Rosenberg

P. S.

(2020). Breast cancer incidence trends by estrogen receptor status among Asian American ethnic groups, 1990–2014. JNCI Cancer Spectrum, 4(2), pkaa005. https://doi.org/10.1093/jncics/pkaa005