Abstract
Objectives. We used population-based survey data to estimate the prevalence of self-reported racism across racial/ethnic groups and to evaluate the association between self-reported racism and cancer-related health behaviors.
Methods. We used cross-sectional data from the 2003 California Health Interview Survey. Questions measured self-reported racism in general and in health care. The cancer risk behaviors we assessed were smoking, binge drinking, not walking, being overweight or obese, and not being up to date with screenings for breast, cervical, colorectal, and prostate cancers. Analyses included descriptive analyses and logistic regression.
Results. Prevalences of self-reported racism varied between and within aggregate racial/ethnic groups. In adjusted analyses, general racism was associated with smoking, binge drinking, and being overweight or obese; health care racism was associated with not being up to date with screening for prostate cancer. Associations varied across racial/ethnic groups.
Conclusions. Associations between general racism and lifestyle behaviors suggest that racism is a potential stressor that may shape cancer-related health behaviors, and its impact may vary by race/ethnicity.
Racism is increasingly recognized as an important social determinant of health disparities.1–3 Researchers have examined exposure to racism in health care and other settings, across the life span, and at multiple levels.1–9 The health effects of emotional and behavioral responses to racism have also been studied.5,10 Although most of this research focuses on mental health effects, attention to physical health outcomes, including cardiovascular disease and low birth weight, is growing.1 Recent studies underscore the complexity of studying this phenomenon, both in measuring its multiple dimensions across diverse populations and in understanding specific ways in which it affects health.9,11,12
Health behaviors are a critical pathway through which racism can influence health.2,3,13,14 A better understanding of how racism affects health behaviors will elucidate how this social determinant gets under the skin and contributes to biological processes, including disease incidence and mortality.1,2,15,16 Exposure to racism may increase stress and maladaptive coping behaviors such as smoking, alcohol use, or poor diet.17–19 Racism may also limit socioeconomic resources, reducing health care access and potential uptake of health-promoting behaviors.3,18,20–23 Additionally, racism may deter utilization of health care because of patient mistrust and negative experiences patients had during previous encounters.24
Research on racism and health behaviors has focused on alcohol, tobacco, and substance use. Fewer studies have investigated links between racism and cancer screening, and none have studied links between racism and diet or physical activity.1–3 These behaviors are key modifiable factors for cancer control.25–28 The most robust finding in this area has been a positive association between racism and smoking, and studies on alcohol consumption have predominantly shown positive associations. Of 4 studies that investigated racism and cancer screening, only 1 found associations (with breast and colorectal cancer screening29).30–32
As increases in Asian American and Latino populations make US society more multicultural, there is a growing need to more fully assess racism across the spectrum of US racial/ethnic groups.9,27,33 We used population-based samples from California to examine the prevalence of self-reported racism across racial/ethnic groups, both in general and in health care, and we sought to investigate associations between prevalence of racism and cancer-related behaviors. We expected that general racism would be positively associated with unhealthful lifestyle behaviors (e.g., smoking, binge drinking, not walking, and being overweight or obese), and we expected health care racism to be positively associated with unhealthful cancer screening behaviors (i.e., not being current on 4 commonly offered screening tests). We also expected that African Americans would report the highest prevalence of racism, with non-Hispanic Whites reporting the lowest.
Our cross-sectional data come from the 2003 California Health Interview Survey (CHIS) adult component, a random-digit-dial telephone survey of 42 044 households conducted in English, Spanish, Mandarin, Cantonese, Vietnamese, and Korean.34,35 The 33.5% response rate is comparable to the California Behavioral Risk Factor Surveillance Survey and other telephone-administered population surveys. The survey sampling frame provides stable estimates for racial/ethnic groups statewide. Weights reflect 2000 US Census–based population demographics and are adjusted for nonresponse and households without telephones. More detailed information on CHIS is available at www.chis.ucla.edu.
The 2003 CHIS collected data on cancer risk behaviors and self-reported racism. There were relatively large unweighted samples across the major racial/ethnic groups: non-Hispanic Whites (n = 25 229), Latinos (n = 8770), Asian Americans and Pacific Islanders (APIs) (n = 3918), African Americans (n = 2550), and American Indians/Alaska Natives (AIANs) (n = 349).35 Analyses excluded proxy interviews (n = 171), prior cancer (n = 4727), pregnancy (n = 436), “other” or “multiracial” racial/ethnic groups (n = 1410), and missing responses to analysis variables (n = 448).
Outcomes included lifestyle and screening behaviors contributing to cancer risk.25,26 Lifestyle behaviors were grouped as “low risk” versus “risky”: never smoked or former smoker versus current smoking; 0–4 servings of alcohol per occasion versus 5 or more servings (binge drinking); walking for transportation or leisure versus no walking in the prior 7 days; and body mass index (BMI; weight in kilograms divided by height in meters squared) of normal (< 25 kg/m2) versus overweight or obese (≥ 25 kg/m2). Age- and gender-specific “low risk” versus “risky” screening behaviors were defined as up to date versus not up to date with screenings for the following cancers: breast (women aged 40 years or older, screened in the prior 2 years), cervical (women aged 18 years or older, screened in the prior 3 years), colorectal (men and women aged 50 years or older, screened in the prior 5 years), and prostate (men aged 50 years or older, screened in the prior 1 year).26,36
Self-reported racism was assessed by responses to 2 items.37 General racism was assessed with the question “Thinking about your race or ethnicity, how often have you felt treated badly or unfairly because of your race or ethnicity?” Health care racism was assessed with the question “Was there ever a time when you would have gotten better medical care if you had belonged to a different race or ethnic group?”
In descriptive analysis, response categories for general racism were dichotomized (never or rarely versus sometimes, often, or all the time). Regression analyses used 4 response categories for general racism (never, rarely, sometimes, often/all the time). Health care racism responses were “yes” or “no.” The validity of these racism measures was examined in association with physical and mental health outcomes known to be associated with other measures of racism. As anticipated, both racism measures were associated with multiple measures of poor physical and mental health (data not shown).
Covariates included self-reported race/ethnicity (Latino, non-Hispanic API, non-Hispanic AIAN, non-Hispanic African American, non-Hispanic White), age (18–22, 23–39, 40–49, 50–64, 65–102 years), gender, marital status (married, other, never), education (less than high school, high school/GED, some college, BA/BS, some graduate school), employment status (employed full time, employed part time or unknown, and not employed), household income as a percentage of the federal poverty level (0%–99%, 100%–199%, 200%–299%, 300%–399%, ≥ 400% of federal poverty level [as defined by the 1999 US Census]), health insurance coverage, immigration status (US-born, naturalized citizen, noncitizen), language spoken at home (English, English and other[s], other[s]), and years of US residency (≤ 1, 2–4, 5–9, 10–14, ≥ 15).
Analyses were conducted using SUDAAN,38 and we applied Jackknife replicate weights provided by CHIS to accommodate the survey design.39 We conducted descriptive analyses to estimate prevalence of racism by racial/ethnic group. Next, we estimated unadjusted odds ratios (ORs) of reporting racism by covariates. Standard critical P values of 0.05 were adjusted using the Bonferroni technique (divided by the total number of comparisons made).40 We also estimated ORs of risky behaviors for several key covariates stratifying on race/ethnicity. Finally, we estimated adjusted odd ratios (AORs) of risky behaviors for self-reported racism.
Total-sample models were adjusted for race/ethnicity, and all models were adjusted for age, gender, education, employment, martial status, and insurance coverage. Previous research shows these correlates to be associated with both the exposures and the outcomes of interest20,41,42; thus, the adjustment makes findings more comparable across studies. Previous literature has found widely varying associations between racism and these behaviors, depending on study population and adjustment for sociodemographic factors. We anticipated modest effects (ORs between 1.01 and 2.00), as racism is only one of many possible influences on these behaviors.
Column 1 of Table 1 presents weighted distributions of sociodemograhic characteristics for 35 203 respondents meeting inclusion criteria. Approximately half were non-Hispanic White, aged 40 years and older, male, and married; the majority had some college education, were employed full time, were insured, and resided in urban areas. Approximately one third were below 200% of the federal poverty level, Latino, foreign born, or bilingual.
TABLE 1 Characteristics of Total Sample and of Subgroups Reporting General Racism and Health Care Racism: California Health Interview Survey, 2003
| Total Study Sample | Reports General Racism At Least Sometimes | Reports Health Care Racism | ||||
| No. | Weighted % | Weighted % | OR (95% CI) | Weighted % | OR (95% CI) | |
| Sociodemographics | ||||||
| Race/ethnicitya | ||||||
| Non-Hispanic White (Ref) | 20 989 | 48 | 24 | 1.00 | 18 | 1.00 |
| Latino | 7 901 | 32 | 42 | 3.20* (2.94, 3.48) | 57 | 5.28* (4.55, 6.13) |
| Non-Hispanic API | 3 646 | 13 | 17 | 3.16* (2.81, 3.56) | 13 | 2.78* (2.27, 3.40) |
| Non-Hispanic AIAN | 306 | 1 | 1 | 3.89* (2.83, 5.37) | 1 | 3.76* (2.27, 6.25) |
| Non-Hispanic African American | 2 361 | 7 | 16 | 9.72* (8.57, 11.03) | 11 | 5.24* (4.26, 6.45) |
| Age, y | ||||||
| 18–22 | 2 204 | 10 | 10 | 0.86 (0.74, 0.99) | 10 | 0.91 (0.71, 1.17) |
| 23–39 | 10 293 | 36 | 40 | 0.99 (0.90, 1.08) | 46 | 1.20 (1.03, 1.40) |
| 40–49 (Ref) | 7 893 | 22 | 24 | 1.00 | 23 | 1.00 |
| 50–64 | 8 831 | 20 | 19 | 0.81* (0.73, 0.89) | 16 | 0.72* (0.60, 0.86) |
| 65–102 | 5 982 | 12 | 6 | 0.40* (0.35, 0.45) | 4 | 0.33* (0.26, 0.41) |
| Gender | ||||||
| Men (Ref) | 14 991 | 50 | 54 | 1.00 | 52 | 1.00 |
| Women | 20 212 | 50 | 46 | 0.83* (0.78, 0.89) | 48 | 0.94 (0.84, 1.04) |
| Marital status | ||||||
| Married (Ref) | 18 051 | 54 | 51 | 1.00 | 49 | 1.00 |
| Otherb | 10 305 | 22 | 24 | 1.18* (1.08, 1.29) | 26 | 1.28* (1.11, 1.49) |
| Never married | 6 847 | 23 | 26 | 1.26* (1.15, 1.37) | 25 | 1.20 (1.04, 1.39) |
| Education | ||||||
| Some graduate school (Ref) | 5 184 | 12 | 9 | 1.00 | 5 | 1.00 |
| BA/BS | 7 246 | 19 | 16 | 1.20 (1.06, 1.37) | 11 | 1.26 (0.95, 1.69) |
| Some college | 9 772 | 25 | 26 | 1.47* (1.31, 1.64) | 24 | 2.13* (1.67, 2.72) |
| High school diploma/GED | 8 436 | 24 | 24 | 1.41* (1.24, 1.61) | 21 | 2.02* (1.49, 2.74) |
| Less than high school | 4 565 | 21 | 25 | 1.82* (1.56, 2.12) | 39 | 4.58* (3.47, 6.05) |
| Employment status | ||||||
| Employed full time (Ref) | 19 407 | 58 | 60 | 1.00 | 56 | 1.00 |
| Employed < full time or unknown | 2 484 | 7 | 7 | 0.91 (0.79, 1.06) | 7 | 1.02 (0.80, 1.32) |
| Not employed | 13 312 | 35 | 33 | 0.88* (0.82, 0.95) | 37 | 1.07 (0.94, 1.21) |
| Poverty, % of federal poverty level | ||||||
| ≥ 400% (Ref) | 16 128 | 41 | 32 | 1.00 | 18 | 1.00 |
| 300%–399% | 3 873 | 10 | 10 | 1.29* (1.16, 1.44) | 8 | 1.81* (1.36, 2.39) |
| 200%–299% | 4 993 | 14 | 15 | 1.56* (1.39, 1.76) | 13 | 2.38* (1.92, 2.97) |
| 100%–199% | 6 057 | 19 | 24 | 1.89* (1.70, 2.10) | 30 | 4.11* (3.45, 4.88) |
| 0%–99% | 4 152 | 15 | 19 | 1.85* (1.67, 2.04) | 31 | 5.47* (4.62, 6.48) |
| Currently insured | ||||||
| Yes (Ref) | 30 619 | 82 | 77 | 1.00 | 66 | 1.00 |
| No | 4 584 | 18 | 23 | 1.57* (1.42, 1.74) | 34 | 2.63* (2.32, 2.99) |
| Rural/urban | ||||||
| Urban (Ref) | 29 595 | 91 | 92 | 1.00 | 91 | 1.00 |
| Rural | 5 608 | 9 | 8 | 0.82* (0.74, 0.92) | 9 | 1.01 (0.85, 1.21) |
| Immigration and acculturation | ||||||
| Citizenship/immigration status | ||||||
| US-born citizen (Ref) | 25 785 | 65 | 56 | 1.00 | 41 | 1.00 |
| Naturalized citizen | 4 788 | 16 | 19 | 1.60* (1.44, 1.77) | 17 | 1.84* (1.56, 2.18) |
| Noncitizen | 4 630 | 20 | 24 | 1.58* (1.44, 1.74) | 42 | 3.83* (3.35, 4.37) |
| Language spoken at home | ||||||
| English only (Ref) | 22 975 | 55 | 43 | 1.00 | 29 | 1.00 |
| English and other language(s) | 8 334 | 30 | 39 | 2.01* (1.87, 2.16) | 42 | 2.93* (2.60, 3.30) |
| Other language(s) only | 3 894 | 15 | 18 | 1.72* (1.54, 1.92) | 29 | 4.01* (3.42, 4.71) |
| US residency, y | ||||||
| ≥ 15 (Ref) | 31 443 | 84 | 81 | 1.00 | 68 | 1.00 |
| 10–14 | 1 600 | 7 | 9 | 1.67* (1.42, 1.96) | 14 | 2.77* (2.29, 3.34) |
| 5–9 | 1 223 | 5 | 6 | 1.20 (1.00, 1.44) | 11 | 2.94* (2.33, 3.71) |
| 2–4 | 746 | 3 | 4 | 1.27 (1.00, 1.60) | 7 | 2.85* (2.21, 3.67) |
| ≤ 1 | 191 | 1 | 1 | 0.82 (0.47, 1.41) | 1 | 0.69 (0.32, 1.50) |
| Health behaviors | ||||||
| Current smoker | ||||||
| No (Ref) | 29 602 | 83 | 80 | 1.00 | 76 | 1.00 |
| Yes | 5 601 | 17 | 20 | 1.38* (1.23, 1.54) | 24 | 1.63* (1.42, 1.87) |
| Binge drinking | ||||||
| No (Ref) | 30 429 | 84 | 83 | 1.00 | 81 | 1.00 |
| Yes | 4 774 | 16 | 17 | 1.14 (1.04, 1.26) | 19 | 1.23 (1.05, 1.44) |
| No walking | ||||||
| No (Ref) | 25 556 | 73 | 75 | 1.00 | 75 | 1.00 |
| Yes | 9 425 | 27 | 25 | 0.90 (0.82, 0.97) | 25 | 0.89 (0.77, 1.02) |
| Overweight/obese | ||||||
| No (Ref) | 15 917 | 44 | 39 | 1.00 | 39 | 1.00 |
| Yes | 19 286 | 56 | 61 | 1.29* (1.21, 1.38) | 61 | 1.27* (1.13, 1.44) |
| Recent Pap test | ||||||
| Yes (Ref) | 16 880 | 83 | 85 | 1.00 | 83 | 1.00 |
| No | 3 332 | 17 | 15 | 0.87 (0.76, 0.99) | 17 | 1.04 (0.87, 1.25) |
| Recent mammography | ||||||
| Yes (Ref) | 10 129 | 76 | 72 | 1.00 | 70 | 1.00 |
| No | 3 123 | 24 | 28 | 1.24 (1.08, 1.43) | 30 | 1.38 (1.09, 1.74) |
| Recent CRC screening | ||||||
| Yes (Ref) | 7 609 | 50 | 48 | 1.00 | 43 | 1.00 |
| No | 7 204 | 50 | 52 | 1.11 (0.98, 1.25) | 57 | 1.34 (1.08, 1.66) |
| Recent PSA test | ||||||
| Yes (Ref) | 2 337 | 37 | 34 | 1.00 | 18 | 1.00 |
| No | 3 623 | 63 | 66 | 1.19 (0.96, 1.47) | 82 | 2.75* (1.72, 4.40) |
Note. API = Asian American and Pacific Islander; AIAN: American Indian/Alaska Native; CI = confidence interval; CRC = colorectal cancer; GED = general equivalency diploma; OR = odds ratio; Pap = Papanicolau; PSA = prostate-specific antigen. Column percents may not add to 100% because of rounding.
aRaces/ethnicities were defined as self-reported, mutually exclusive single-race categories.
bDefined as divorced, separated, widowed, and living with partner.
*P = .002, with Bonferroni's adjustment.
Table 1 also presents weighted distributions of these sociodemographic characteristics for respondents who reported general racism sometimes, often, or all the time, as well as for those who reported health care racism. Compared with the total study sample, those who reported general racism at least sometimes were more likely to be non-White, younger, male, naturalized or not a citizen, uninsured, and unmarried; to have lower income and less than a high school education; and to speak a language other than English at home. Similar patterns were seen among those who reported health care racism.
For health behaviors, the distribution of study respondents varied by behavior, with no clear patterns. The majority of respondents reported walking and not smoking or binge drinking; however, more than half reported being overweight or obese. For cancer screenings, within age- and gender-appropriate subgroups, the majority of respondents were up to date with cervical and breast cancer screening. Half were up to date for colorectal cancer screening. The majority were not up to date with prostate cancer screening. Those reporting general racism at least sometimes were similar to the total sample for these behaviors, but those reporting health care racism were more likely to report smoking, binge drinking, and not being up to date with colorectal and prostate cancer screenings.
Figure 1 presents the prevalence of general and health care racism across racial/ethnic groups. The white bars represent the weighted percent of respondents who reported general racism sometimes, often, or all the time; the second series of black bars represents the weighted percent of respondents who reported health care racism. Twenty-nine percent of the total study population reported general racism sometimes, often, or all of the time. By racial/ethnic group the percents ranged from 12% among non-HispanicWhites to 56% among non-Hispanic African Americans. Overall, 7% reported health care racism, non-Hispanic Whites reported the lowest prevalence (3%), and Latinos (13%) and non-Hispanic African Americans (13%) reported the highest.
FIGURE 1 Weighted percent of self-reported general racism and health care racism, by racial/ethnic group: California Health Interview Survey, 2003.
Note. AIAN = American Indian/Alaska Native; API = Asian American and Pacific Islander. Sample sizes for each racial/ethnic group are given in parentheses.
Thirty percent of Latinos overall reported experiencing general racism at least sometimes, with subgroups ranging from 19% (Latino Europeans) to 37% (Guatemalans). The prevalence of reporting health care racism was 13% for Latinos; subgroups ranged from 8% (Latino Europeans and other Latinos) to 19% (Salvadorans). Twenty-nine percent of APIs experienced general racism at least sometimes, with subgroups ranging from 19% (Cambodians and other Asians) to 45% (Southeast Asians). Seven percent of APIs reported health care racism, ranging from 4% (Japanese and South Asians) to 20% (Cambodians and other Asians).
Table 1 also presents unadjusted odds of reporting general racism (at least sometimes versus never or rarely) and health care racism (yes versus no) by sociodemographic characteristics and health behaviors. The following groups had increased odds of reporting general and health care racism: Latinos, APIs, AIANs, and non-Hispanic African Americans; those never married or with other marital status; the uninsured; naturalized citizens or noncitizens; those who spoke English and another language or another language at home; those who had resided in the US for 10 to 14 years; current smokers; binge drinkers; those who were overweight or obese; and those not up to date with mammography. For general racism, respondents who were male, lived in urban areas, walked for transportation or leisure, or women who were up to date with the Papanicolau test had increased odds of reporting racism at least sometimes. For health care racism, respondents who were aged 23 to 49 years, had resided in the United States for 2 or more years, or were not up to date with screenings for colorectal and prostate cancers had increased odds of reporting health care racism.
Table 2 and Table 3 present unadjusted ORs for those who reported engaging in risky lifestyle and screening behaviors, respectively, by correlates. Age, race/ethnicity, marital status, and education were associated with all behaviors. Gender and employment status was associated with lifestyle behaviors. For 2 lifestyle behaviors (smoking and binge drinking) and all the screening behaviors, lack of health insurance coverage was associated with increased odds of these risky behaviors.
TABLE 2 Lifestyle Cancer-Risk Behaviors, by Sample Characteristics: California Health Interview Survey, 2003
| Current Smoker | Binge Drinking | No Walking | Overweight or Obese | |||||
| OR (95% CI) | P | OR (95% CI) | P | OR (95% CI) | P | OR (95% CI) | P | |
| Age, y | <.001 | <.001 | <.001 | <.001 | ||||
| 40–49 (Ref) | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| 18–22 | 0.93 (0.78, 1.10) | 1.82 (1.53, 2.17) | 0.75 (0.65, 0.87) | 0.40 (0.35, 0.45) | ||||
| 23–39 | 1.06 (0.95, 1.17) | 1.59 (1.41, 1.80) | 0.88 (0.81, 0.96) | 0.77 (0.72, 0.83) | ||||
| 50–64 | 0.82 (0.74, 0.91) | 0.70 (0.62, 0.79) | 1.05 (0.94, 1.16) | 1.22 (1.11, 1.33) | ||||
| 65–102 | 0.41 (0.36, 0.47) | 0.25 (0.20, 0.30) | 1.22 (1.10, 1.36) | 0.83 (0.77, 0.91) | ||||
| Gender | <.001 | <.001 | <.001 | <.001 | ||||
| Men (Ref) | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| Women | 0.56 (0.51, 0.61) | 0.24 (0.22, 0.26) | 0.88 (0.83, 0.94) | 0.48 (0.46, 0.51) | ||||
| Race/ethnicitya | <.001 | <.001 | <.001 | <.001 | ||||
| Non-Hispanic White (Ref) | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| Latino | 0.79 (0.71, 0.87) | 1.09 (1.00, 1.19) | 0.91 (0.83, 0.99) | 1.63 (1.50, 1.76) | ||||
| Non-Hispanic API | 0.74 (0.64, 0.85) | 0.61 (0.52, 0.72) | 1.01 (0.91, 1.12) | 0.44 (0.40, 0.49) | ||||
| Non-Hispanic AIAN | 1.87 (1.33, 2.62) | 1.03 (0.73, 1.46) | 1.23 (0.86, 1.75) | 1.71 (1.25, 2.35) | ||||
| Non-Hispanic African American | 1.15 (0.97, 1.35) | 0.54 (0.44, 0.66) | 1.28 (1.14, 1.45) | 1.68 (1.50, 1.88) | ||||
| Marital status | <.001 | <.001 | <.001 | <.001 | ||||
| Married (Ref) | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| Otherb | 2.01 (1.85, 2.18) | 1.19 (1.07, 1.32) | 1.07 (0.99, 1.16) | 0.93 (0.88, 0.99) | ||||
| Never married | 1.94 (1.73, 2.17) | 2.26 (2.04, 2.51) | 0.84 (0.77, 0.91) | 0.55 (0.51, 0.59) | ||||
| Education | <.001 | <.001 | <.001 | <.001 | ||||
| Some graduate school (Ref) | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| BA/BS | 1.45 (1.22, 1.71) | 1.47 (1.26, 1.70) | 1.10 (0.97, 1.25) | 1.06 (0.97, 1.15) | ||||
| Some college | 2.63 (2.29, 3.02) | 1.40 (1.21, 1.63) | 1.44 (1.27, 1.62) | 1.55 (1.43, 1.69) | ||||
| High school diploma/GED | 3.05 (2.65, 3.50) | 1.71 (1.48, 1.96) | 1.54 (1.36, 1.74) | 1.45 (1.32, 1.58) | ||||
| Less than high school | 2.78 (2.39, 3.23) | 1.50 (1.29, 1.74) | 1.47 (1.30, 1.67) | 2.23 (2.00, 2.48) | ||||
| Employment status | <.001 | <.001 | <.001 | <.001 | ||||
| Employed full time (Ref) | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| Employed < full time or unknown | 0.70 (0.58, 0.85) | 0.68 (0.57, 0.82) | 0.77 (0.66, 0.89) | 0.64 (0.57, 0.71) | ||||
| Not employed | 0.85 (0.78, 0.93) | 0.43 (0.39, 0.48) | 0.93 (0.86, 0.99) | 0.83 (0.78, 0.88) | ||||
| Currently insured | <.001 | <.001 | <.023 | .098 | ||||
| Yes (Ref) | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| No | 1.72 (1.54, 1.93) | 1.65 (1.48, 1.84) | 0.88 (0.78, 0.98) | 1.07 (0.99, 1.16) | ||||
Note. API = Asian American and Pacific Islander; AIAN: American Indian/Alaska Native; CI = confidence interval; GED = general equivalency diploma; OR = odds ratio. All ORs had P values that were less than the Bonferonni adjusted critical P value (P < .001), except no walking and employment status, no walking and currently insured, and overweight or obese and currently insured.
aRaces/ethnicities were defined as self-reported, mutually exclusive single-race categories.
bDefined as divorced, separated, widowed, and living with partner.
TABLE 3 Cancer Screening Behaviors, by Sample Characteristics: California Health Interview Survey, 2003
| Not Up to Date With Pap Test, Women ≥ 18 y (n = 20 212) | Not Up to Date With Mammography, Women ≥ 40 y (n = 13 252) | Not Up to Date With CRC Screening, Adults ≥ 50 y (n = 14 813) | Not Up to Date With PSA Test, Men ≥ 50 y (n = 5960) | |||||
| OR (95% CI) | P | OR (95% CI) | P | OR (95% CI) | P | OR (95% CI) | P | |
| Age, y | <.001 | <.001 | <.001 | <.001 | ||||
| 40–49a (Ref) | 1.00 | 1.00 | NA | NA | ||||
| 18–22 | 5.29 (4.33, 6.47) | NA | NA | NA | ||||
| 23–39 | 0.83 (0.68, 1.01) | NA | NA | NA | ||||
| 50–64 | 1.44 (1.23, 1.70) | 0.46 (0.40, 0.52) | 1.00 | 1.00 | ||||
| 65–102 | 4.12 (3.47, 4.89) | 0.56 (0.47, 0.66) | 0.56 (0.51, 0.62) | 0.58 (0.50, 0.68) | ||||
| Gender | .016 | |||||||
| Male (Ref) | NA | NA | 1.00 | NA | ||||
| Female | NA | NA | 1.12 (1.02,1.22) | NA | ||||
| Race/ethnicityb | <.001 | .002 | <.001 | <.001 | ||||
| Non-Hispanic White (Ref) | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| Latino | 0.95 (0.82, 1.10) | 1.36 (1.18, 1.57) | 1.59 (1.37, 1.84) | 2.00 (1.57, 2.56) | ||||
| Non-Hispanic API | 1.89 (1.63, 2.19) | 1.22 (1.01, 1.46) | 1.41 (1.19, 1.67) | 2.69 (1.99, 3.63) | ||||
| Non-Hispanic AIAN | 1.25 (0.71, 2.22) | 1.42 (0.76, 2.66) | 1.19 (0.71, 2.01) | 0.92 (0.42, 2.01) | ||||
| Non-Hispanic African American | 0.78 (0.64, 0.97) | 1.05 (0.84, 1.31) | 0.96 (0.79, 1.17) | 1.16 (0.85, 1.58) | ||||
| Marital status | <.001 | <.001 | <.001 | <.001 | ||||
| Married (Ref) | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| Otherc | 1.93 (1.71, 2.18) | 1.38 (1.24, 1.53) | 1.27 (1.15, 1.40) | 1.63 (1.39, 1.91) | ||||
| Never married | 3.29 (2.83, 3.81) | 1.74 (1.44, 2.11) | 1.50 (1.25, 1.80) | 1.87 (1.46, 2.40) | ||||
| Education | <.001 | <.001 | <.001 | <.001 | ||||
| Some graduate school (Ref) | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| BA/BS | 1.12 (0.92, 1.36) | 1.08 (0.89, 1.31) | 1.16 (0.99, 1.37) | 1.34 (1.08, 1.68) | ||||
| Some college | 1.74 (1.49, 2.04) | 1.12 (0.93, 1.34) | 1.23 (1.07, 1.41) | 1.60 (1.29, 2.00) | ||||
| High school diploma/GED | 2.34 (2.01, 2.73) | 1.20 (0.97, 1.48) | 1.27 (1.12, 1.45) | 1.77 (1.43, 2.21) | ||||
| Less than high school | 1.97 (1.66, 2.33) | 1.64 (1.34, 2.01) | 1.78 (1.49, 2.14) | 3.46 (2.65, 4.51) | ||||
| Employment status | <.001 | <.691 | <.001 | <.001 | ||||
| Employed full time (Ref) | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| Employed < full time or unknown | 1.64 (1.33, 2.02) | 1.05 (0.87, 1.27) | 0.82 (0.69, 0.99) | 0.75 (0.54, 1.04) | ||||
| Not employed | 1.80 (1.61, 2.02) | 0.97 (0.85, 1.10) | 0.70 (0.63, 0.77) | 0.72 (0.61, 0.85) | ||||
| Currently insured | <.001 | <.001 | <.001 | <.001 | ||||
| Yes (Ref) | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| No | 1.69 (1.47, 1.94) | 2.97 (2.41, 3.66) | 3.83 (3.11, 4.72) | 4.40 (3.10, 6.24) | ||||
Note. API = Asian American and Pacific Islander; AIAN: American Indian/Alaska Native; CI = confidence interval; CRC = colorectal cancer; GED = general equivalency diploma; OR = odds ratio; Pap = Papanicolau; PSA = prostate-specific antigen. All ORs had P values that were less than the Bonferonni adjusted critical P value (P < .001), except not up to date with mammography and race/ethnicity, not up to date with mammography and employment status, and not up to date with PSA test and employment status.
aFor CRC screening and PSA testing, the reference group is age 50 to 64 years.
bRaces/ethnicities were defined as self-reported, mutually exclusive single-race categories.
cDefined as divorced, separated, widowed, and living with partner.
Table 4 presents AORs for cancer-related health behaviors by self-reported racism. The first set of AORs shows associations between general racism (reference group is never) and lifestyle behaviors; the second set shows associations between health care racism (reference group is no) and screening behaviors. Although unadjusted ORs (Table 1) showed significant associations between general racism and smoking, binge drinking, no walking, and being overweight or obese, as well as with health care racism and not being current for screenings for breast cancer, colorectal cancer, and prostate cancer, overall these effects were attenuated, and some associations became statistically nonsignificant after adjusting for correlates (see Table 4).
TABLE 4 Adjusted Odds Ratios (AORs) of Self-Reported Racism and Cancer Risk Behaviors Among Total Study Sample and Racial/Ethnic Groups: California Health Interview Survey, 2003
| Total | African American | AIAN | API | Latino | White | |||||||
| No. | AOR (95% CI) | No. | AOR (95% CI) | No. | AOR (95% CI) | No. | AOR (95% CI) | No. | AOR (95% CI) | No. | AOR (95% CI) | |
| Smoking | ||||||||||||
| General racism | ||||||||||||
| Never (Ref) | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||||||
| Rarely | 1.10 (0.99, 1.22) | 0.76 (0.51, 1.13) | 1.35 (0.60, 3.03) | 1.09 (0.78, 1.53) | 0.98 (0.76, 1.25) | 1.12 (0.97, 1.28) | ||||||
| Sometimes | 1.24 (1.07, 1.43) | 0.71 (0.48, 1.05) | 1.32 (0.58, 2.98) | 1.14 (0.79, 1.64) | 1.21 (0.95, 1.54) | 1.37 (1.12, 1.67) | ||||||
| Often/all the time | 1.95 (1.60, 2.37) | 1.34 (0.85, 2.14) | 0.99 (0.31, 3.21) | 1.72 (0.87, 3.41) | 1.81 (1.27, 2.58) | 2.10 (1.56, 2.84) | ||||||
| Binge drinking | ||||||||||||
| General racism | ||||||||||||
| Never (Ref) | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||||||
| Rarely | 1.04 (0.92, 1.17) | 1.69 (0.68, 4.18) | 0.91 (0.33, 2.52) | 1.88 (1.15, 3.08) | 0.86 (0.67, 1.09) | 1.03 (0.90, 1.18) | ||||||
| Sometimes | 1.09 (0.95, 1.25) | 2.05 (0.81, 5.22) | 0.36 (0.08, 1.75) | 1.74 (1.10, 2.75) | 1.04 (0.84, 1.29) | 0.94 (0.76, 1.16) | ||||||
| Often/all the time | 1.31 (1.05, 1.63) | 1.54 (0.57, 4.11) | 1.44 (0.17, 2.14) | 2.14 (1.01, 4.54) | 1.23 (0.80, 1.89) | 1.37 (0.95, 1.97) | ||||||
| No walking | ||||||||||||
| General racism | ||||||||||||
| Never (Ref) | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | |||||||
| Rarely | 0.92 (0.84,1.00) | 1.15 (0.80, 1.67) | 0.96 (0.34, 2.74) | 0.99 (0.77, 1.27) | 1.01 (0.84, 1.20) | 0.83 (0.74, 0.92) | ||||||
| Sometimes | 0.82 (0.73, 0.91) | 0.94 (0.67, 1.33) | 0.31 (0.12, 0.80) | 0.85 (0.67, 1.08) | 0.89 (0.75, 1.06) | 0.72 (0.61, 0.84) | ||||||
| Often/all the time | 0.91 (0.76, 1.10) | 1.30 (0.78, 2.15) | 1.41 (0.53, 3.73) | 0.91 (0.52, 1.58) | 0.90 (0.62, 1.30) | 0.69 (0.50, 0.95) | ||||||
| Overweight or obese | ||||||||||||
| General racism | ||||||||||||
| Never (Ref) | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||||||
| Rarely | 1.07 (1.00, 1.14) | 1.12 (0.75, 1.69) | 0.62 (0.29, 1.33) | 1.03 (0.81, 1.31) | 1.01 (0.86, 1.20) | 1.08 (0.99, 1.19) | ||||||
| Sometimes | 1.18 (1.08, 1.29) | 0.90 (0.59, 1.37) | 0.49 (0.17, 1.42) | 1.13 (0.88, 1.45) | 1.14 (0.97, 1.35) | 1.32 (1.14, 1.52) | ||||||
| Often/all the time | 1.33 (1.12, 1.58) | 0.96 (0.59, 1.57) | 0.69 (0.21, 2.23) | 1.27 (0.75, 2.14) | 1.26 (0.90, 1.75) | 1.70 (1.29, 2.26) | ||||||
| Not up to date with Pap test | ||||||||||||
| Health care racism | ||||||||||||
| No (Ref) | 20 212 | 1.00 | 1466 | 1.00 | 160 | 1.00 | 2011 | 1.00 | 4429 | 1.00 | 12 137 | 1.00 |
| Yes | 1.16 (0.93, 1.45) | 1.71 (0.90, 3.24) | 0.94 (0.05, 19.29) | 1.36 (0.78, 2.39) | 0.74 (0.51, 1.07) | 2.18 (1.40, 3.41) | ||||||
| Not up to date with mammography | ||||||||||||
| Health care racism | ||||||||||||
| No (Ref) | 13 252 | 1.00 | 974 | 1.00 | 109 | 1.00 | 1208 | 1.00 | 2010 | 1.00 | 8 951 | 1.00 |
| Yes | 1.09 (0.86, 1.39) | 1.29 (0.75, 2.22) | 1.87 (0.25, 14.17) | 0.87 (0.38, 2.02) | 0.96 (0.64, 1.44) | 1.43 (0.97, 2.09) | ||||||
| Not up to date with CRC screening | ||||||||||||
| Health care racism | ||||||||||||
| No (Ref) | 14 813 | 1.00 | 997 | 1.00 | 123 | 1.00 | 1331 | 1.00 | 1850 | 1.00 | 10 502 | 1.00 |
| Yes | 0.97 (0.78, 1.22) | 0.74 (0.43, 1.27) | 1.26 (0.06, 24.42) | 1.07 (0.61, 1.89) | 1.11 (0.71, 1.73) | 1.02 (0.71, 1.48) | ||||||
| Not up to date with PSA test | ||||||||||||
| Health care racism | ||||||||||||
| No (Ref) | 5 960 | 1.00 | 360 | 1.00 | 50 | 1.00 | 557 | 1.00 | 790 | 1.00 | 4 197 | 1.00 |
| Yes | 1.79 (1.13, 2.85) | 1.79 (0.62, 5.15) | 2.94 (0.12, 72.17) | 2.96 (0.85, 10.34) | 1.42 (0.59, 3.47) | 1.87 (0.91, 3.86) | ||||||
Note. AIAN = American Indian/Alaska Native; API = Asian American and Pacific Islander; CI = confidence interval; CRC = colorectal cancer; Pap = Papanicolau; PSA = prostate-specific antigen. Races/ethnicities were defined as self-reported, mutually exclusive single-race categories. The model for the total study population was adjusted for race/ethnicity, age, gender, education, employment, marital status, and insurance coverage. The race/ethnicity-specific models were adjusted for all these variables except race/ethnicity. For models with screening outcomes, only CRC was adjusted for gender.
For the total study population, the odds of engaging in risky lifestyle behaviors increased for those who reported general racism compared with those who did not. For example, odds of being a current smoker increased by 24% for those who reported general racism sometimes; odds of being a current smoker increased by 95% for those who reported general racism often or all the time compared with those who did not (those who reported general racism rarely were not statistically significantly different from those who reported never). The odds of binge drinking increased by 31% for those reporting general racism often or all the time (those reporting general racism rarely or sometimes were not statistically significant from those who reported never). Similarly, the odds of being overweight or obese increased by 18% for those reporting general racism sometimes and by 33% for those reporting general racism often or all the time (those reporting general racism rarely were not statistically significantly different from those who reported never). The odds of not walking showed an inverse association with frequency of racism; that is, the odds of not walking decreased by 18% for those reporting general racism sometimes compared with those who reported never experiencing general racism (those who reported rarely or often/all the time were not statistically significantly different from those who reported never).
The only statistically significant association among cancer screenings and health care racism was for prostate-specific antigen (PSA) testing. Among men aged 50 years and older, the odds of not being up to date with PSA testing increased by 79% for those who reported health care racism compared with those who did not.
Associations observed between self-reported racism and cancer risk behaviors among the total study population did not hold after race/ethnicity stratification (see Table 4). In the AOR models, those who reported racism had increased risk of being current smokers among Latinos and Whites, increased risk of binge drinking among APIs, increased risk of being overweight or obese among Whites, and decreased risk of not walking among AIANs and Whites. For screening behaviors, adjusting for sociodemographic factors resulted in no statistically significant associations except for Whites, for whom there were increased odds of not being up to date with cervical cancer screening.
For all measures, non-White racial/ethnic groups reported higher prevalences of racism than Whites. African Americans reported the highest rates, and Latinos reported rates between those of African Americans and Whites. Previous studies support these differences, despite the wide range of rates reported.4,5,43–45 However, caution must be used when comparing racism prevalence across studies that use different measures and response metrics. For example, our prevalence rates for health care racism are similar to studies with comparable administration modes, questions, and samples.4,46 Studies drawing exclusively from patient pools reported much higher prevalence for health care racism than samples drawn from the general population.47,48
Our observed rates of racism are lower than those found in other studies, perhaps because of differences in population characteristics, survey modes, or measures. Self-administered surveys and multiple-item measures of racism tend to yield higher rates, perhaps because of minimized social desirability bias or increased reliability.4,5
It is also possible that California's relatively high racial/ethnic diversity may foster increased tolerance across groups; conversely, California's racial/ethnic diversity may increase the likelihood of ethnic enclaves, minimizing minority experiences of racism. Higher CHIS prevalence rates for Whites could also stem from changes in California's demographics, from White majority to a non-White “emerging majority” comprising minority racial/ethnic groups. Additionally, the “White” aggregate category includes groups such as Arab Americans, who report increased discrimination since the events of September 11, 2001.49
A unique feature of our study is inclusion of population-based estimates of self-reported racism for APIs and AIANs rather than combining these groups into an “other” category. Another distinguishing feature of our study is its estimates for Latinos and API subgroups. The subgroup analyses from our study demonstrated the anticipated heterogeneity within these aggregate racial/ethnic groups, with the broadest range among Asian Americans. APIs had a wide range of prevalence by ethnic group, with some ethnic groups reporting well above the aggregate group rate, which was unexpected. These subgroup analyses suggest that data can mask the experiences of specific ethnic groups or nationalities within the aggregate racial/ethnic groups. Disaggregating data into specific ethnic groups or nationalities is critical in identifying pockets of high-risk groups that might otherwise not be observed.
Ours is the only study we know of that uses a population-based data set to evaluate associations between racism and a broad range of cancer-related health behaviors, including both lifestyle and screening behaviors, across racial/ethnic groups. Although the tested associations did not achieve statistical significance in all cases, most point estimates of the effect of racism on both sets of behaviors were consistent across behaviors and racial/ethnic groups. Changes in some of the associations in the adjusted analyses, as compared with the unadjusted analyses, suggest that the relationship between self-reported racism and some lifestyle behaviors and screening outcomes, particularly binge drinking and screening for breast, cervical, and prostate cancers, may vary by race/ethnicity.
In the race/ethnicity-stratified analyses, patterns in the total population were not consistent across all groups. Associations between racism and risky behaviors remained most consistently among Whites, with other racial/ethnic groups showing few or no associations across these behaviors. Two studies have found associations between self-reported racism and coping mechanisms such as smoking, alcohol consumption, and substance use for White populations, suggesting that the underlying process of racism as a stressor can result in coping mechanisms of maladaptive health behaviors regardless of race/ethnicity.50,51 In addition, other studies have found null associations among minority racial/ethnic groups.31,32,51
This study may be underpowered to assess modest associations between self-reported racism and these behaviors with adjustment for a variety of sociodemographic factors among non-White racial/ethnic groups, and it may be overpowered to assess these associations for Whites, thus finding even the weak effects statistically significant. Because we studied exposures and outcomes that are relatively uncommon (e.g., binge drinking was reported by 16% of the study population and current smoking by 17%), and because we adjusted for several correlates, we are more likely to obtain results where the magnitude and direction are replicated in the stratified analyses with wider confidence limits because of the smaller sample sizes, such as for AIANs. In other instances, there may also be evidence of variation between subgroups for outcomes such as obesity, where social and cultural factors may be influencing pathways and contributing to the observed patterns. For example, Asian Americans, particularly first-generation immigrants, tend to have low rates of overweight or obesity, which may be attributed to cultural factors such as diet.
Consistent with other studies, we found that self-reported racism was associated with maladaptive coping behaviors, such as tobacco and alcohol consumption.1–3 This finding supports the concept of racism as a stressor that takes a psychological toll on those who experience it. Additional coping mechanisms may include physical inactivity and poor diet, both risks for overweight and obesity. Although the 2003 CHIS did not include questions on diet, we were able to evaluate the associations of 1 form of physical activity (walking) and BMI with racism.
Though we expected general racism to be associated with not walking, our results did not consistently suggest that walking for transportation or leisure may be a positive or health-promoting coping mechanism. This may be because of our measure, which combines walking for transportation with walking for leisure. In reality, these 2 types of walking may reflect different social circumstances: either a voluntarily healthful lifestyle or material disadvantage, including lack of car ownership or physically demanding work. The association between exercise as a coping mechanism and racism should be studied further with more comprehensive measures. Similarly, our findings that overweight and obesity are associated with racism suggest a need for future research to examine whether energy imbalance may stem in part from racism-related stress.
Similar to our study, previous studies have shown no association between exposure to racism and cancer screening.30–32 One explanation for this finding may be that insurance coverage is a key determinant of screening participation; more than 80% of our study sample reported having insurance coverage. Another explanation may be the comparatively low reports of health care racism, which may be the critical context for influencing screening participation. Alternatively, individuals who are willing to report racism may be more likely to have protective coping mechanisms that also enable them to engage in preventive health behaviors regardless of negative experiences with the health care system.3,32 The exception to this pattern was the PSA test for prostate cancer. Because the evidence supporting routine PSA screening is mixed, recommendations include informed decision-making discussions between patient and provider prior to testing. Our results suggest that this process may be especially vulnerable to perceived health care–related racism.
One study, using pooled data from 2003 and 2005 waves of CHIS, found some evidence suggesting an association between racism and cancer screenings, such that those in non-White racial/ethnic groups who reported health care racism were less likely to be up to date with cancer screening.29 That study differed from ours with regard to sample (men aged 50–75 years; women aged 40–75 years), focus on racism experienced in the prior 5 years and its association with colorectal and breast cancer screenings, and adjustment for health status factors beyond insurance coverage (e.g., self-reported health status, usual source of care, prior diagnosis of cancer). We could not examine these factors in our sample because of issues of multicollinearity; however, these factors should be considered where possible in studies of racism and health.
With these cross-sectional data, we cannot determine whether exposures to racism occurred prior to the behaviors of interest or vice versa. Therefore, these findings are most useful for generating hypotheses for further study of potential associative mechanisms operating between racism and cancer-related behaviors.
Some limitations in measurement of outcomes (e.g., exercise, diet) and exposure to racism created analytical challenges. Questions on general and health care–specific racism were neither nested nor phrased identically. A small proportion of respondents said they had experienced health care racism but had never experienced general racism. However, the individual items exhibit face validity by directly asking about experiences of racial/ethnic discrimination experiences, and they demonstrate concurrent validity through associations with poor physical and mental health.1–3
Cross-cultural validity of the 2003 CHIS racism measures has not been evaluated. Variation in reported prevalence across racial/ethnic groups may stem from differing exposures or differential interpretations of such experiences across groups. However, our observed patterns mirror those from other studies that use a range of measures, suggesting that our questions tap into similar experiences across these groups.
Future studies validating measures of racism across racial/ethnic groups should also examine survey mode effects. Although interview-administered surveys may use probes to reduce recall bias, they are vulnerable to social desirability bias, compared with self-administered surveys for racism or medical records for screening behaviors.4,5
CHIS respondents may differ from those not reached by the sampling strategies used. Furthermore, California is unique in both its current racial/ethnic composition and the sociopolitical histories of its racial/ethnic groups. However, California leads the United States in emerging diversity, so it may suggest where many states will be in 30 to 50 years. Moreover, although we focused on cancer risk, healthy lifestyles and use of preventive services such as screening are vital to controlling diseases such as diabetes and heart disease, which are growing threats to the health of these emerging groups.
Our findings add to the literature on racism and health by providing robust prevalence estimates for aggregate racial/ethnic groups and for Latino and Asian American subgroups. By evaluating the association between racism and a range of lifestyle and screening behaviors across racial/ethnic groups, this study underscores the challenge of studying this complex phenomenon, even with large population-based surveys. Our findings can be important in identifying potential mechanisms through which racism affects health and in efforts to mitigate the negative impact of racism on health behaviors.
Acknowledgments
Support for this study was provided by the National Cancer Institute's Cancer Epidemiology, Prevention and Control Training Grant (T32CA009314-25) at the Johns Hopkins Bloomberg School of Public Health. Additional support was provided by the Health, Behavior and Society Departmental Dissertation Award, the Carol Eliasberg Martin Scholarship in Cancer Prevention, and the John C. Hume Doctoral Award. We acknowledge the California Health Interview Survey respondents for completing the survey and California Health Interview Survey staff for their help in accessing the data. In addition, we thank Thomas A. Louis and Darrell J. Gaskin for their contribution to this study as dissertation committee members. We would also like to thank the anonymous reviewers at the American Journal of Public Health as well as Nancy Breen, Martin Brown, and Rachel Ballard-Barbash in the Applied Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, for their helpful comments on this article.
Human Participant Protection
This study was approved by the Johns Hopkins University Bloomberg School of Public Health committee on human research and the UCLA Center for Health Policy and Management Data Access Center.
