Acculturating to Multiculturalism: A New Dimension of Dietary Acculturation among Asian American, Native Hawaiian, and Pacific Islander Women in the San Francisco Bay Area, USA

doi:10.21203/rs.3.rs-2753536/v1

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Acculturating to Multiculturalism: A New Dimension of Dietary Acculturation among Asian American, Native Hawaiian, and Pacific Islander Women in the San Francisco Bay Area, USA

https://doi.org/10.21203/rs.3.rs-2753536/v1

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Dietary acculturation is the process by which diet and dietary practises prevalent in a new environment are adopted and/or those from the environment of origin are retained or changed. Knowledge gaps exist on chracterising dietary acculturation among Asian American, Native Hawaiian, and Pacific Islander communities (AANHPI), despite rapid population growth the USA. This study characterise dietary patterns in a sample representative of AANHPI on key demographic characteristics. Data were from a 2013–2014 population-based case-control study in the San Francisco Bay Area, U.S. Survey items were adapted from dietary acculturation scales developed for Chinese, Filipinx, Vietnamese, Japanese, Korean, and Asian Indian and Pakistani populations. Validated measures assessed social networks, social standing, discrimination and immigration experiences. A principle components factor analysis characterise dietary patterns of acculturation. Three dietary patterns were identified: “Asian,” “Western,” and a distinct “Multicultural” factor. Respondents reporting a high-Asian diet tended to also report smaller social networks, higher levels of stress, and, among those born outside of the U.S., an educational standing that was better before immigration. Respondents who ate a high-Western diet tended to also report the highest level of discrimination. Those reporting a high-Multicultural diet tended to report higher neighbourhood collective efficacy. The finding of a distinct “Multicultural” factor beyond the typical “Asian” and “Western” factors may reflect the multidirectional nature of dietary acculturation, in which origin and destination cultures interact in complex ways and where foods from multiple ethnicities intermix.

Acculturation in the U.S. refers to the process by which a subgroup of a population, often an immigrant group, adopts characteristics of the “mainstream” U.S. culture and retains or relinquishes traits of their "traditional" culture [1–3]. Dietary acculturation is a dimension of acculturation pertaining to the process by which the diet and dietary practises prevalent in a new environment are adopted [3, 4]. Factors that influence dietary acculturation include individual characteristics (e.g., beliefs and values surrounding food, behaviours, and taste preferences), the social environment (e.g., ethnic enclaves), built environment (e.g., food availability), and socioeconomic status [3].

As dietary acculturation influences dietary behaviours, it has the potential to influence chronic disease patterns in the U.S. [5]. Immigration to the U.S. corresponds to a higher prevalence of chronic disease and chronic disease risk factors [6–9], with variation in this phenomenon across sociodemographic groups [10]. Studies comparing Black, Hispanic, Japanese, Chinese, Filipinx, and Korean populations who have immigrated to the U.S. with those residing in the country of origin demonstrated a higher prevalence of type 2 diabetes among those in the U.S., with changes in diabetes prevalence varying among ethnic groups [6]. With respect to Asian American, Native Hawaiian, and Pacific Islander populations (AANHPI), those born in the U.S. are more likely to be overweight or obese than those born outside of the U.S. [8], with second- and third-generation AANHPI adults more likely to be obese compared to first-generation AANHPI adults [9]. Japanese American women born in the U.S. have higher percentage body fat than those who have immigrated [11]. Filipinx women in Hawaii are more likely to be obese than those living in the Philippines [7]. A “Western” diet may be associated with a greater intake of fat, anthropometric risk factors (e.g., greater waist and hip circumference, body fat and body mass index), levels of c-reactive protein (a heart disease risk factor), and type 2 diabetes [11–15].

Despite making up approximately 7.7% of the U.S. population in 2020 [16], AANHPI represent an understudied group in the U.S. [17]. Existing research on dietary acculturation among AANHPI communities has applied varied measures of dietary acculturation, suggesting a lack of consensus on how to most effectively study dietary acculturation among AANHPI populations [18]. The application of consistent dietary acculturation measures is complicated by the diversity of AANHPI groups with respect to culture, language, and traditional foods [17, 19, 20].

Dietary acculturation among AANHPI populations may be assessed with two scales: one that measures the maintenance of a “traditional” diet from the country or culture of origin and one that measures adoption of a “Western” diet from the country of destination [3, 13, 14]. The “Western” dietary scale is limited in that it consists of relatively unhealthy foods (e.g., red meat, “fast food”). As a result, people with low scores on both the “traditional” and “Western” scales—such as those who adopt relatively healthy dietary patterns in the destination culture—are not well characterise [21]. For example, a 2018 study of dietary acculturation among ethnically Chinese people living in the U.S. found that approximately 20% of respondents had low scores on both the “Western” and “Chinese” dietary acculturation subscales, leading the authors to categorize this group as “other” [21]. There is a need to adequately describe the shift of AANHPI groups towards acculturation patterns that are neither “traditional” nor “Western” [22].

Geographical contexts are critically important to undersnding what determines the selective adoption and retention of cultures [22]. Understanding dietary acculturation that does not align with “Western” measures may be especially important in areas where a wider range of healthy foods may be available [23, 24]. The San Francisco Bay Area is an urban and peri-urban landscape in which nearly two thirds of residents are people of colour and/or of mixed race and ethnicity [16]. AANHPI people make up approximately 31% of the 7.8 million Bay Area residents [16], with more than half of them born outside of the U.S. [25]. The multicultural nature of the Bay Area, along with the range of dietary options it offers as a densely populated region, makes it a prime location to understand how dietary acculturation may occur among AANHPI populations in diverse settings.

Examining dietary acculturation in specific AANHPI cultures may be illuminating given cultural differences in the significance of food and diet. Food and diet are fundamental to Chinese medicine, which uses various foods for healing and maintaining balance. Among Filipinx migrant families, preparing traditional foods at home is an important way by which parents instill Filipinx identity in children [26]. Similarly, food procurement, meal planning, and meal preparation are traditionally relegated to the role of women. As such, dietary acculturation may be a particularly salient process to examine among women. In the present study, we describe dietary acculturation for a group of AANHPI women and specifically for Chinese and Filipinx Americans residing in a region of the U.S. with a long history of AANHPI immigration, the San Francisco Bay Area, California, USA.

As part of the Asian American Community Health Initiative, we conducted a case-control study of breast cancer in the San Francisco-Oakland-San Jose Bay Area. The control group (i.e., women without breast cancer) from that study comprises the study sample for the present analysis. Women were frequency-matched to cases on age- (five-year intervals) and ethnicity (Chinese, Filipinx, and other Asian American, Native Hawaiian, or Pacific Islander). The sample was recruited through targeted communications at online, community-based and address-based sampling methods. The control sample was found to be representative of AANHPI women in the San Francisco Bay Area on key demographic characteristics, including education and marital status [27]. Thus, it is a representative sample of the source population when compared against data from the California Health Interview Survey [27, 28]. This research was approved by the Institutional Review Board of the Cancer Prevention Institute of California.

Materials and measures

Data were collected via a one-hour telephone interview, and then participants were mailed a self-administered survey that took 20–30 minutes to complete. Interviews were conducted by bilingual, bicultural interviewers in Mandarin, Cantonese, Tagalog and English and all materials were available in these languages. Data collected included sample demographic characteristics such as age, nativity (U.S. or foreign-born), generational status (first, second, or third generation American if U.S.-born; age at immigration if foreign-born), English proficiency (Not good/poor, Okay/well, Very well, Only English), and income-to-poverty ratio (total annual household income divided by federal poverty thresholds based on household size).

Dietary acculturation

To assess dietary acculturation, we used items adapted from the scales developed by Satia and colleagues [13] for Chinese American women and augmented with data for Filipinx [19], Vietnamese [29], Japanese [30], Korean [31–33], and Asian Indian and Pakistani [34] populations living in the U.S. For the 27 specific food and beverage items, participants were asked how often they usually consumed that item or group of items in the past 12 months with response categories recorded as never/rarely, 1–3 times per month, 1–3 times per week, 4–6 times per week, once a day, or more than once a day. Items about dietary practises included frequency of consuming homemade foods, packaged or prepared foods, eating at fast-food restaurants and restaurants serving typically non-Asian foods, eating Asian-style breakfasts and dinners, eating traditionally preserved foods, balancing yin and yang or hot and cold properties of foods (if this was part of their cultural heritage), and shopping at Asian and American supermarkets. A summary item asked respondents to characterise the “culture” of the food in their overall diet, with responses ranging from mostly Asian foods to mostly non-Asian foods. All items were included in the telephone survey except the consumption of alcohol which was included in the self-administered survey. Of the 474 women who completed the diet section, we excluded 33 participants who did not respond to questions about alcohol consumption and 1 who did not complete all items on dietary acculturation, leaving 440 (92.8%) women in the present analysis.

Social network and status

We assessed home ownership (yes/no), number of people within social network (responses categorised into \(\le\)2, 3–4, or ≥5) and neighbourhood collective efficacy using five items (e.g., “How many neighbors do you know by name?”) rated on a 1–4 Likert-type scale (1, none, 4, a lot; Cronbach’s alpha=0.83).

Overall stress and experiences of discrimination

To assess overall stress in the past year, we used the 10-item version of the Perceived Stress Scale rated on a Likert-type scale (1, never, 5, very often)[35]. To assess experiences with discrimination, we included eight items from the Experiences of Discrimination on how often respondents experienced discrimination across various situations in their lifetime, if at all (e.g., “How often have you been treated unfairly at work”).[36] One item was adapted to be more contextually relevant to immigration experience (e.g., “How often have you been treated unfairly when seeking legal services related to immigration”). The number of lifetime discriminatory situations ever experienced was summarised into a single score (ranging from 0, indicating no discriminatory experiences in the lifetime to 8, indicating having experienced all eight of the discriminatory situations described). The Perceived Stress Scale (Cronbach’s alpha = 0.85) and the adapted experiences of discrimination scale (Cronbach’s alpha = 0.79) demonstrated strong reliability.

Immigration experiences

For those born outside of the U.S., we collected data pertaining to experiences related to immigration. Respondents were asked to select the importance of any of the following reason(s) or purpose(s) for immigration: to improve life, get better education, to join family, or to find a job. To assess immigration-related stress, we used a 14-item measure of how often respondents feel that living in the U.S. is stressful due to various reasons (e.g., “because you are treated as an outsider by other Americans”) rated on a 1–4 Likert-type scale (1, never, 4, very often; Cronbach’s alpha = 0.82). We used the Subjective Social Status scale to assess perceived social standing regarding finances, education, job or occupation whether it was perceived to be better before, the same as, or better since immigrating [37].

Statistical analysis

Assessing factorability of the original matrix of dietary acculturation items

Bartlett’s test of sphericity and the Kaiser-Meyer-Olkin measure of sampling adequacy (overall and individual) were used to determine if the correlation matrix of the dietary acculturation items was factorable. [For review only: Appendix A reports the results of the statistical procedures for matrix factorability.] Results from the Bartlett’s test of sphericity allowed us to reject the null hypothesis that the correlation matrix is an identity matrix (p < 0.001). The Kaiser-Meyer-Olkin statistic was 0.78, indicating that our sample size was adequate relative to the number of food categories. The individual measures of sampling adequacy were generally very high, with 31 categories attaining measures > 0.60. Overall, the correlations were strong enough to suggest that the matrix was amenable to factorisation. Thus, we proceeded to conduct an exploratory principle components factor analysis (PCFA) to derive dietary patterns, or factors, for our sample.

PCFA, also known as exploratory factor analysis (Bryant & Yarnold, 1995), is a data reduction method used to define patterns (i.e., principal components) that included items that were correlated with each other but not with the other components. We conducted an exploratory PCFA on the correlation matrix of 39 dietary acculturation items for respondents who completed all dietary and alcohol consumption questions (n = 440). All food and beverage items and some dietary practises were standardised to frequency per week, while dietary practises (e.g., shopping at Asian supermarkets) reported as never/rarely, sometimes, and frequently, were coded as ordinal. The summary diet item was split into two variables, Asian food in diet and Non-Asian food in diet and coded as ordinal (e.g., the three categories of “Asian food in diet” were (1) equal, more non-Asian, mostly non-Asian, (2) more Asian, and (3) mostly Asian). We selected the number of dietary factors retained in the PCFA using the following criteria: eigenvalues \(\ge\) 1; scree plot construction and examination of curve break points between factors; and interpretability of the factors [38]. We used an orthogonal varimax rotation procedure to obtain simpler loading patterns. Loading weights of each of the food and beverage items and dietary practises were used to label the retained dietary factors. Each participant had a score calculated for each dietary factor, with a higher value representing higher consumption of that dietary pattern.

Descriptive statistics were computed for all variables. We examined differences and similarities in dietary acculturation by sociodemographic, general acculturation, immigration, psychosocial, and behavioural variables for the retained dietary factors. For each dietary factor, participants were grouped into tertiles for low, medium, and high consumption. We conducted cross-tabulations on each variable against tertiles of the three dietary factors and used two-tailed Chi-square tests to assess differences among tertiles by each dietary factor. Data were analysed using Stata 15 in 2019.

Table I shows the sample characteristics for 440 participants. Over half were foreign-born (65.7%), married or living with a partner (66.1%), with a bachelor’s degree or higher (62.0%), 50 years or older (53.4%), and of Chinese descent (53.2%). Among those who were born outside of the U.S., 88.6% were 10 years or older at time of immigration. Among those who were U.S.-born, 47.6% were second- or third-generation.

PCFA identified three major dietary patterns (Table II). Based on item loadings, Factor 1 and Factor 2 were designated “Asian” and “Western,” respectively. Factor 3 emerged as a robust factor with eight unique loadings and one item loading onto both Western and Factor 3. Factor 3 items included fruit, vegetables, tofu, dried apricots or dates, homemade food, edamame, cereal, soy nuts, and pizza or western style pasta, prompting a designation of Factor 3 as the “Multicultural” factor.

Table III shows the distribution of dietary patterns and consumption level by demographic characteristics. The distribution of Asian diet by consumption varied across all demographic characteristics explored. Compared to respondents who consumed a low-Asian diet, those who ate a high-Asian diet tended to be 60 years or older; ethnically Chinese or Filipinx; less educated; less likely to be employed full time; least wealthy; married/living with a partner or formerly married; foreign born and immigrated after the age of 10; and less proficient in English.

The distribution of Western diet by consumption varied across age, ethnicity, education, nativity, immigration/generational status, and English proficiency. Compared to respondents who consumed a low- Western diet, respondents who ate a high-Western diet tended to be younger than 50 years of age; ethnically Filipinx or other AA; college graduates; U.S.-born; and English-only speakers.

The distribution of the Multicultural diet by consumption varied across age, ethnicity, English proficiency, and only marginally across marital status and immigration/generational status. Compared to respondents who consumed a low- Multicultural diet, respondents who ate a high-Multicultural diet tended to be older than 60 years of age; ethnically Chinese; married/living with partner; foreign born and immigrated after the age of 10; and poor English speakers.

Table IV shows the distribution of dietary patterns and consumption level by home ownership, social network, neighbourhood collective efficacy, experiences with discrimination, perceived stress; and among foreign-born respondents only, reasons for immigrating; immigration-related stress; and social standing vis-à-vis money, education, job before and after immigrating. The distribution of Asian diet by consumption varied across homeownership, size of social network, perceived stress; and among respondents born outside of the U.S., reason of immigrating and perceived social standing regarding education. Compared to respondents who consumed a low-Asian diet, those who ate a high-Asian diet tended to report not owning a home, smaller social networks (i.e., 0–2 people), higher levels of stress; and among respondents born outside of the U.S., joining family as a reason for immigrating and an educational standing that was better before immigrating. Respondents born outside of the U.S. who ate a high-Asian diet also tended to report higher levels of immigration-related stress compared to respondents born outside of the U.S. who ate a low-Asian diet.

By contrast, the distribution of Western diet by consumption varied only across experiences of discrimination. Compared to respondents who ate low-Western diet, those who ate a high-Western diet reported more lifetime experiences with discriminatory situations.

The distribution of Multicultural diet by consumption varied only across neighbourhood collective efficacy, and, among those born outside of the U.S., marginally by immigration-related stress. Compared to respondents who consumed a low-Multicultural diet, respondents who ate a high-Multicultural diet reported higher neighbourhood collective efficacy; and among those born outside of the U.S., marginally lower levels of immigration-related stress.

This is a cross-sectional study of dietary acculturation with 440 AANHPI women from the San Francisco Bay Area that builds upon the limited literature of dietary acculturation among AANHPI populations. A PCFA yielded three factors of dietary patterns: Asian, Western, and “Multicultural.” The multicultural dietary pattern is not captured by existing dietary acculturation measures which are often two dimensional (“Asian” and “Western”). The new “multicultural” dietary factor observed in this study appears to draw upon multiple cultural influences, including dried fruit and nuts (common in Mediterranean diets), edamame (common in East and Southeast Asia), and homemade food (a “traditional” component of all cultures).

The finding of a third factor beyond the typical Western and Asian factors may reflect the multidirectional nature of dietary acculturation, in which origin and destination cultures interact in complex and dynamic ways [1, 3, 5]. The third factor may also reflect what prior researchers have identified as a gap in dietary acculturation measures. For example, the focus on unhealthy food choices of the Western dietary acculturation dimension is limited for potentially excluding those who adopt healthy eating patterns in the U.S. [21]. Given the diverse environment of the nine-county San Francisco Bay Area—in which approximately 31% of residents are immigrants [25] and nearly two thirds of residents are people of colour and/or people of mixed race/ethnicity [16]—it is plausible that new, multicultural dietary patterns among AANHPI women emerge.

Our finding that respondents in the top tertile of Western diet were more likely to be younger than 50 years of age, college educated, and monolingual English speaking is consistent with prior research in which Asian immigrants more adapted to a Western lifestyle were more likely to score highly on “Western” dietary acculturation scales, and those less adapted to a Western lifestyle were more likely to score highly on “Asian” dietary acculturation scales [12, 13, 17]. With respect to “Multicultural” dietary acculturation, a pattern emerged whereby respondents in the top tertile reported higher neighbourhood collective efficacy compared to those in the bottom tertile. While existing literature on neighbourhood collective efficacy and dietary acculturation is lacking, prior research has discussed links between the neighbourhood social environment and diet-related outcomes like obesity [39], with neighbourhood collective efficacy found to be positively associated with the intake of healthy foods in some studies of other large, racially and ethically diverse urban areas of the U.S. [40, 41]. As some studies suggest that neighbourhood collective efficacy may play a role in moderating perceived discrimination, stress, and mental health conditions among immigrants in the U.S. and elsewhere [42, 43], further exploration of a potential relationship between collective efficacy and multicultural dietary patterns is warranted.

Limitations

There are no comparable data on dietary practises in the U.S. or Asian countries against which to evaluate actual change in groups (or sequential data for assessing change within individuals). While some efforts were made to disaggregate AANPHI subgroups when looking at the distribution of dietary patterns across respondents, most of the data remained aggregated, potentially blurring specific subgroup associations. Aggregated data on AANHPI populations potentially blur specific subgroup associations and there is a need to disaggregate further with larger datasets. Data in this study were collected in 2013–2014; given the rapid demographic change in the San Francisco Bay Area, as well as impactful societal events (including the COVID-19 pandemic and parallel rise in attention to hate crimes against AANPHI people) that have taken place since, these data may not fully reflect the current context in which many AANPHI people live. PCFA is a relatively agnostic approach looking for similarities within the data. Whereas a priori can be easily replicated across studies, PCFA and latent class analyses are data-based and may well be population-specific, which is a strength in the present study to be able to demonstrate multiculturalism in the San Francisco Bay Area, a place that affords us looking at multiculturalism in terms of AANHPI racial/ethnic composition and immigration history. Finally, we report the distribution of participants with respect to tertiles of each diet profiles; however, we did not analyse the distribution of participants by their overall diet profile, which may have included being in the top tertile of multiple diets (e.g., top tertile in Multicultural and Asian). Further research could explore how the demographic and social variables studied vary across diet profiles.

The study findings call for further exploration of dimensions of dietary acculturation beyond the traditional "Asian" and “Western” dimensions to capture the diversity in dietary acculturation in AANPHI and other immigrant communities. Adapting dietary acculturation measures to the rapidly evolving, multicultural environment of the U.S. will be critical to designing effective food-related policies and interventions for immigrant communities.

ETHICS APPROVAL

The Institutional Review Board/Human Research Protection Program at the University of California San Francisco reviewed and approved this study (IRB study number 17-23454).

AUTHOR CONTRIBUTIONS

SLG and SSM designed the Study and directed the study’s implementation. AJC and AG designed the analytical strategy. All authors helped to interpret the findings. JYT and AEA conducted the literature review and prepared all sections of the text.

DATA AVAILABILITY

The data underlying this article will be shared on reasonable request to the corresponding author.

SUPPLEMENTARY DATA

Supplementary data are available at IJE online.

FUNDING

This work was supported by the California Breast Cancer Research Program [Grant number 17UB-8602].

ACKNOWLEDGEMENTS

We thank Pamela L. Horn-Ross, Trish Quema-La Chica, Roxanna Bautista, Winston Wang, Sandra Lee, Pancho Chang, and Thu Quach for their important contributions on earlier drafts.

CONFLICT OF INTEREST

None declared.

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Table I. Sample characteristics (N=440)

	N	%
Age (years)
<50	205	46.6%
50-59	131	29.8%
≥60	104	23.6%
Asian Ethnicity
Chinese	234	53.2%
Filipinx	83	18.9%
Other Asian American^a, Native Hawaiian or Pacific Islander	123	27.9%
Education‡
<High School	75	17.0%
Some college or Associates degree	91	20.7%
Bachelor’s degree	165	37.5%
Graduate degree	108	24.5%
Employment
Full-time	190	43.2%
Part-time	99	22.5%
Not working	95	21.6%
Retired	56	12.7%
Marital Status‡
Married or living with partner	291	66.1%
Formerly married	67	15.2%
Single	81	18.4%
Nativity
Foreign born	289	65.7%
US born	151	34.3%
Generational status‡
≥10 years old when immigrated to US	256	59.0%
<10 years old when immigrated to US	33	7.6%
First generation, US born	76	17.5%
Second generation, US born	29	6.7%
Third generation, US born	40	9.2%
English proficiency
Not good/Poor	70	15.9%
Okay/Well	119	27.0%
Very well	59	13.4%
Only speaks English	192	43.6%

Note: ^a Other Asian American groups included Japanese, Korean, South Asian, Southeast Asian, “Other Asian”, and Asian mixed ethnicity. ‡ Frequencies may not add up to 440 due to refusals

Table II. Loading weights from each food, beverage, or dietary practice item per extracted factor

	"Asian"	“Western”	"Multicultural"
Variable**	Factor 1	Factor 2	Factor 3	Uniqueness
American style supermarkets	-0.69			0.50
Non-Asian food in diet	-0.66			0.56
Packaged or prepared food such as frozen dinners or take out	-0.38	0.37		0.72
Alcohol consumption	-0.37			0.85
Non-fast-food restaurants that serve typically non-Asian food (e.g., American, Mexican, Italian restaurants)	-0.36	0.43		0.64
Think about the "hot" and "cold" characteristics of foods	0.36			0.78
Fish or fish stew	0.38			0.81
Asian-style bread, such as pan de sal and naan	0.42			0.82
Shop at [participant ethnicity] or other Asian food markets	0.62			0.56
[Participant ethnicity] or other Asian-style breakfast	0.63			0.59
Rice or rice dishes	0.68			0.52
Asian food in diet	0.69			0.49
[Participant ethnicity] or other Asian-style dinner	0.70			0.52
Butter or margarine		0.36		0.87
Cheese		0.40		0.78
Fruit Juices		0.42		0.81
Doughnuts		0.43		0.81
Pizza or American-style pasta, including spaghetti or lasagna		0.44	0.32	0.69
Chocolate or other candy		0.45		0.76
Coke or other soda		0.49		0.72
Cake, pie, or cookies		0.53		0.65
Ground beef or hamburgers		0.57		0.66
Fast-food restaurants that typically serve non-Asian food, such as McDonald's, Subway, or Domino's Pizza		0.58		0.61
Salty snacks		0.59		0.58
Fruit			0.59	0.64
Vegetables (not counting potatoes or light green lettuce)			0.54	0.67
Tofu			0.47	0.73
Dried apricots or dates			0.45	0.79
Homemade food or food that was prepared in your home			0.40	0.74
Edamame or soybeans			0.38	0.84
Cereal			0.38	0.79
Soy nuts			0.32	0.83
Western-style bread, rolls, or bagels				0.90
Protein or "power" bars made with soy				0.90
Black licorice				1.00
Soy milk				0.91
Whole milk, 2%, 1%, or skim milk				0.91
Tea				0.93
Foods that are fermented, pickled, or traditionally preserved				0.96

Factor loadings are only displayed for values ≥|0.3|

* Extraction method: principal components factor analysis using varimax rotation
** Dietary factors were assessed for the past 12 months from the time of the survey

Table III. Distribution of dietary acculturation factor by sociodemographic characteristics and acculturation among Asian American, Native Hawaiian, and Pacific Islander women (N=440)*

			Asian Diet												Western Diet											Multicultural Diet
			Low		Medium				High						Low		Medium				High					Low		Medium		High
Characteristic	n		Col %				Col %		Col %			p			Col %		Col %					Col %		p		Col %		Col %		Col %			p
Age years
<50	205		55.8		46.3				37.7					<0.01	36.7		45.6				57.5			<0.01		50.3		46.9		42.5			<0.01
50-59	131		26.5		34.0				28.8						34.7		26.5				28.1					33.3		32.0		24.0
60+	104		17.7		19.7				33.6						28.6		27.9				14.4					16.3		21.1		33.6
Ethnicity
Chinese	234		39.5		55.1				65.1					<0.001	65.3		57.8				36.3			<0.001		39.5		53.7		66.4			<0.001
Filipinx	83		12.2		21.1				23.3						15.0		16.3				25.3					28.6		15.7		12.3
NHPI	8		4.1		0.7				0.7						0.0		1.4				4.1					2.0		1.4		2.1
Other Asian American^a	115		48.2		23.1				11.0						19.7		24.5				34.3					29.9		29.3		19.2
Education
<High School	75		3.4		10.2				37.9					<0.001	28.6		13.7				8.9			<0.001		16.3		17.0		17.9			0.74
Some college or Associates	91		15.7		20.4				26.2						17.0		26.7				18.5					21.8		22.5		17.9
Bachelor’s degree	165		44.2		40.8				27.6						29.3		32.9				50.7					40.8		36.7		35.2
Graduate degree	108		36.7		28.6				8.3						25.2		26.7				21.9					21.1		23.8		29.0
Employment
Full-time	190		45.6		50.3				33.6					<0.01	42.2		40.8				46.6			0.31		45.6		44.2		39.7			0.45
Part-time	99		27.2		17.0				23.3						21.1		22.4				24.0					25.2		19.0		23.3
Not working	95		17.7		17.0				30.1						20.4		21.8				22.6					19.0		25.2		20.5
Retired	56		9.5		15.6				13.0						16.3		15.0				6.8					10.2		11.6		16.4
Per capita household income to poverty ratio^b
<1.00		131	17.7			21.1				50.7	<0.001				33.3			29.3	26.7						0.69	29.9	27.9				31.5		0.75
1.00-1.99		63	20.4			10.9				11.6					12.9			15.6	14.4							11.6	17.0				14.4
2.00+		176	50.3			52.4				17.1					38.8			37.4	43.8							41.5	38.8				39.7
Marital Status
Married/living w partner	291		61.9		64.6				72.4					<0.001	62.6		72.6				63.7			0.20		61.6		63.3		74.0			0.05
Formerly married	67		9.5		18.4				17.9						17.0		15.1				13.7					13.7		17.7		14.4
Single	81		28.6		17.0				9.7						20.4		12.3				22.6					24.7		19.1		11.6
Nativity
Foreign born	289		27.9		74.8				94.5					<0.001	76.9		68.7				51.4			<0.001		60.5		66.0		70.5			0.20
US born	151		72.1		25.2				5.5						23.1		31.3				48.6					39.5		34.0		29.5
Generational Status
Foreign born, age to US 10+	256		15.6		66.7				92.5					<0.001	72.1		60.5				41.8			<0.001		55.1		54.4		65.1			0.09
Foreign born, age to US <10	33		12.2		8.2				2.1						4.8		8.2				9.6					5.4		11.6		5.5
US born, 1st gen	76		35.4		14.3				2.1						10.2		16.3				25.3					20.4		17.0		14.4
US born, 2nd gen	29		15.0		3.4				1.4						4.8		5.4				9.6					6.1		9.5		4.1
US born, 3rd gen	40		18.4		6.8				2.1						6.8		8.2				12.3					10.9		6.1		10.3
English proficiency
Only speaks English	192		76.9	43.5				10.3					<0.001		36.1	39.5				55.5			<0.001			45.6	49.7		35.6			0.03
Not good/Poor	70		1.4		6.1				40.4						26.5		17.0				4.1					12.2		12.2		23.3
Okay/Well	119		4.1		34.7				42.5						27.9		31.3				21.9					26.5		28.6		26.0
Very well	59		17.7		15.6				6.8						9.5		12.2				18.5					15.6		9.5		15.1

*Tertiles of dietary acculturation factors were calculated using rotated estimates; percentages within each factor are by column, not row.

^a Other Asian American groups included Japanese, Korean, South Asian, Southeast Asian, “Other Asian”, and Asian mixed ethnicity

^b Income/poverty ratio <1.00 indicates that family income was less than the poverty threshold; Income/poverty ratio between 1.00-1.99 indicates that family income was equal to or higher than poverty threshold; Income/poverty ratio 2.00+ generally indicates that the family is relatively well off.

Table IV. Percentage distribution of each dietary acculturation factor by social characteristics, discrimination among Asian American, Native Hawaiian, and Pacific Islander women (n=440)*

		Asian Diet				Western Diet					Multicultural Diet
		Low	Medium	High		Low	Medium	High		Low		Medium			High
Characteristic	n	n=147	n=147	n=146	p	n=147	n=147	n=146	p	n=147		n=147			n=146		p
Home ownership
Yes	272	68.0	70.8	47.2	<0.001	60.3	61.0	65.1	0.66	59.2		63.7		63.5		0.67
No	166	32.0	29.3	52.8		39.7	39.0	34.9		40.8		36.3		36.6
Social network (# people)
0-2	109	14.3	26.5	33.6	<0.001	28.6	25.9	19.9	0.41	24.5		23.8		26.0		0.61
3-4	210	50.3	44.2	48.6		44.2	45.6	53.4		50.3		50.3		42.5
≥5	121	35.4	29.3	17.8		27.2	28.6	26.7		25.2		25.9		31.5
Neighbourhood collective efficacy
Lower	131	34.7	42.2	40.4	0.41	37.4	36.1	43.8	0.31	47.6		39.5		30.1		<0.001
Moderate	137	32.0	33.3	28.1		32.7	36.1	24.7		36.7		26.5		30.1
Higher	172	33.3	24.5	31.5		29.9	27.9	31.5		15.6		34.0		39.7
Experiences with discrimination¹
None	102	21.1	19.7	24.0	0.63	19.0	21.8	24.0	0.04	20.4		21.1		23.3		0.95
1-2	91	18.4	20.4	19.9		21.1	22.4	15.1		21.1		17.0		20.5
3-4	152	30.6	31.3	35.6		32.0	38.1	27.4		32.0		35.4		30.1
≥5	129	29.9	28.6	20.5		27.9	17.7	33.6		26.5		26.5		26.0
Perceived Stress Scale
Lower	127	41.5	24.5	19.9	<0.001	27.9	34.0	24.0	0.24	25.9		30.6		29.5		0.11
Moderate	143	26.5	36.7	34.2		32.0	27.2	38.4		36.1		24.5		37.0
Higher	175	32.0	38.8	45.9		40.1	38.8	37.7		38.1		44.9		33.6
Reason for immigration² (among foreign-born)**
Improve life	188	70.7	66.36	62.32	0.57	63.7	61.4	72.0	0.32	69.5		71.1	56.3			0.06
Better education	138	56.1	45.5	47.1	0.50	41.6	55.5	46.7	0.13	55.1		49.5	39.8			0.10
Join family	130	29.3	40.9	52.9	<0.05	40.7	46.5	49.3	0.47	44.9		43.3	46.6			0.90
Find a job	124	36.6	48.2	40.6	0.33	39.8	42.6	48.0	0.54	43.8		44.3	40.8			0.86
Immigration-related stress (among foreign-born)**
Lower	87	43.9	29.1	26.9	0.08	29.2	34.7	25.3	0.34	23.6		29.9	35.9			0.07
Moderate	84	34.2	30.0	26.8		24.8	28.7	26.0		23.6		32.0	31.1
Higher	118	22.0	40.9	46.4		46.0	36.6	38.7		52.8		38.1	33.0
Social standing regarding money (among foreign-born)**
Better before	85	37.5	32.5	45.8	0.29	37.1	42.9	42.2	0.78	46.0		35.3	40.0			0.47
Same	66	37.5	31.2	30.5		31.5	28.6	35.6		25.4		30.9	36.3
Better in the US	60	25.0	36.4	23.7		31.5	28.6	22.2		28.6		33.8	23.8
Social standing regarding education (among foreign-born)**
Better before	75	17.7	27.3	42.2	0.05	36.0	39.2	25.5	0.24	32.8		30.9	40.0			0.28
Same	81	41.2	37.7	37.2		37.1	30.4	51.1		46.3		35.3	32.5
Better in the US	59	41.2	35.1	20.7		27.0	30.4	23.4		20.9		33.8	27.5
Social standing regarding job/occupation (among foreign-born)**
Better before	91	50.0	42.9	52.5	0.26	55.8	43.1	44.4	0.15	44.6		50.9	50.8			0.95
Same	49	25.0	22.9	28.7		19.5	26.2	37.8		27.7		24.6	26.2
Better in the US	47	25.0	34.3	18.8		24.7	30.8	17.8		27.7		24.6	23.1

Note: Percentages represent column percentages. *Tertiles of dietary acculturation factors were calculated using rotated estimates. **Only for those who reported being foreign-born.

¹ Each of the 8 items in the Experiences with Discrimination scale was coded as "ever experienced" (i.e., 0=never experienced; 1=rarely, sometimes, or often experienced) and summed to reflect the total number of experiences in lifetime. Thus, summed scores ranged from 0 (none of the situations experienced) to 8 (experienced all the discriminatory situations). To facilitate interpretation, the score was categorized into 0, 1-2, 3-4, or 5 or more discriminatory situations experienced.

Appendix A. Factorability of the correlation matrix of the original nutrients: Bartlett's test of sphericity and Kaiser-Meyer-Olkin measure of sampling adequacy

Bartlett's test of sphericity: p-value <0.001

² Values not add up to 100% because respondents can select more than one reason for immigration.aiser-Meyer-Olkin statistic (overall measure of sampling adequacy*): 0.78
*Individual measures of sampling adequacy:**
<0.60	Soy milk; Black licorice; Western-style bread, rolls, or bagels; Foods that are fermented, pickled, or traditionally preserved in other ways (such as fish sauce, kimchi, Japanese pickled vegetables, salted or preserved eggs, preserved tofu, salted fish, or Chinese sausage (lap chong)); Tea; Whole milk, 2%, 1%, or skim milk; Soy nuts
0.60 to <0.80	Edamame or soybeans; Fruit; Tofu; Doughnuts; Vegetables (not counting potatoes or light green lettuce); Butter or margarine; Cereal; Dried apricots or dates; Ground beef or hamburgers; Fruit Juices; Cake, pie, or cookies; Protein or "power" bars; Asian style bread, such as pan de sal and naan; Homemade food or food that was prepared in your home; Fast-food restaurants that typically serve non-Asian food, such as McDonald's, Subway, or Domino's Pizza; Fish or fish stew; Chocolate or other candy; Cheese; Pizza or American-style pasta, including spaghetti or lasagna; Alcohol consumption
≥0.80	Rice or rice dishes; Salty snacks; Coke or other soda; Packaged or prepared food such as frozen dinners or take out; Non fast-food restaurants that serve typically non-Asian food (ex. American, Mexican, Italian restaurants) ; [Participant ethnicity] or other Asian style breakfast; [Participant ethnicity] or other Asian style dinner; Shop at [participant ethnicity] or other Asian food markets; American style supermarkets; Think about the "hot" and "cold" characteristics of foods; Asian food in diet

* Overall and individual measures of sampling adequacy range from between 0 and 1, with values >0.60 being satisfactory

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Acculturating to Multiculturalism: A New Dimension of Dietary Acculturation among Asian American, Native Hawaiian, and Pacific Islander Women in the San Francisco Bay Area, USA

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Abstract

Introduction

Methods

Materials and measures

Dietary acculturation

Social network and status

Overall stress and experiences of discrimination

Immigration experiences

Statistical analysis

Assessing factorability of the original matrix of dietary acculturation items

Results

Discussion

Limitations

Declarations

References

Tables

Appendix

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