http://informahealthcare.com/ada ISSN: 0095-2990 (print), 1097-9891 (electronic) Am J Drug Alcohol Abuse, 2014; 40(2): 95–102 ! 2014 Informa Healthcare USA, Inc. DOI: 10.3109/00952990.2013.850503
ORIGINAL ARTICLE
Binge drinking by gender and race/ethnicity among California adults, 2007/2009 Jim E. Banta, PhD, MPH1, Pamela E. Mukaire, MEd, MPH, DrPH(c)2, and Mark G. Haviland, PhD3 1
Departments of Health Policy and Management, 2Health Promotion and Education, Loma Linda University School of Public Health, and Department of Psychiatry, Loma Linda University School of Medicine, Loma Linda, CA, USA
3
Abstract
Keywords
Background: This study provides binge drinking population estimates for California adults by gender and detailed race/ethnicity categories. This information may be helpful for planning targeted initiatives to decrease binge drinking. Method: Data were from the 2007 and 2009 California Health Interview Surveys. The 98 662 respondents represent an annual estimated population of 27.2 million adults. Survey adjusted binary logistic regression was used to calculate gender-specific binge drinking population rates and multinomial logit regression to estimate binge drinking frequency. Results: Adjusting for socio-demographics, any binge drinking during the past year was reported by 31.0% (95% Confidence Interval ¼ 30.5–31.4%) of men and 18.0% (17.7–18.3%) of women. Rates among White men and women were 30.5% and 19.6%, respectively. Binge drinking rates ranged from 11.9% among Chinese to 42.9% among Mexican men and from 4.8% among Vietnamese to 25.7% among ‘‘Other Latino’’ women. Five race/ethnicity categories of men and seven categories of women were significantly less likely to binge drink compared to Whites. Although Whites had the highest overall binge drinking rates, an estimated 12.5% of White men binge drank less than monthly, significantly exceeded by Mexican and Central American men, 19.9 and 19.6%, respectively. An estimated 9.6% of White women binge drank less than monthly, exceeded only by ‘‘Other Latino’’ women, 13.6%. Conclusion: These findings underscore the importance of detailed gender and race/ethnicity breakdowns when examining any binge drinking. Furthermore, there is variability across Asian and Latino subgroups in the frequency of binge drinking episodes, which is not evident in broad-group population studies.
Alcohol abuse, binge drinking, race/ethnicity, California Health Interview Survey (CHIS), population estimates
More than half of all alcohol consumed in the United States is in the form of binge drinking (1), commonly defined as five or more drinks at a time for men or four or more drinks at a time for women. Binge drinking is associated with several grim statistics: over 40 000 deaths and 1.5 million years of potential life were lost each year in the US between 2001 and 2005 (1). Moreover, binge drinking has long been linked to a host of negative outcomes across the lifespan: academic and personal problems in school, risk taking behaviors, physical injuries to self and others, driving while intoxicated, and failure to adhere to medications (2–8). Consequently, public health efforts, such as Healthy People 2020 (objective 14.3), call for a reduction in adult binge drinking rates from the 27.1% baseline rate (i.e. during a 30-day period) in 2008 to 24.4% in 2020 (9).
Address correspondence to: Jim E. Banta, PhD, MPH, 24951 North Circle Drive, Loma Linda, CA 92350, USA. Tel: +1 909 558 7753. Fax: +1 909 558 0469. E-mail: [email protected]
Received 13 February 2013 Revised 25 September 2013 Accepted 25 September 2013 Published online 12 February 2014
National reports document variation in binge drinking by gender, age group, and race/ethnicity (1,10,11). Based on estimates from the National Survey on Drug Use and Health (NSDUH), in 2011, binge drinking in the past 30 days was more common among men (32.5%) compared to women (21.4%), and it decreased as people aged: 36.6% among adults 18–44 years of age, 21.8% for those 45–64, and 9.8% for individuals 65 and older (9). According to age- and sexadjusted figures from the Behavioral Risk Factor Surveillance System (BRFSS) 2009 survey, binge drinking prevalence ranged from a low of 7.8% among Asian/Pacific Islanders to a high of 17.5% among Whites (12). According to BFRSS survey data from 2008–2010, of those who engaged in any binge drinking in the past month, the average was about four episodes during the month (13). Men who were binge drinking reported a higher frequency of binge drinking than did binge drinking women, with an average of 5.0 versus 3.2 episodes per month (13). Binge drinking frequency by racial/ethnic group also was similar to findings for any binge drinking; Whites had the highest frequency and Asian/Pacific Islanders the lowest. There were, however, differences between any binge drinking and
20 14
Background
History
96
J. E. Banta et al.
frequency among those engaging in binge drinking by age, education, and income. For those breakdowns, the groups with lowest overall rates of any binge drinking – that is persons 65 years of age and older, those not graduating from high school, and individuals with household income less than $25 000 – also were the groups who reported the highest frequency of binge drinking among those doing any binge drinking (13). Clearly, more research is needed to better understand alcohol consumption and consequences among racial/ethnic minorities (14); however, broad categorizations as noted above may mask within-group differences, which could prove useful to state planners and, particularly, at a time of limited public health resources. An interventional study of substance abusers in California, for example, revealed differences in alcohol use among Filipino, Vietnamese, and Chinese men (15). The need for detailed information regarding race/ethnicity is demonstrated further in that the 2000 Census revealed 92 different ancestries reported by 100 000 people or more, with 24 different ancestries being the largest in at least one county in the United States (16). Moreover, detailed race/ ethnicity categories will be captured in future Department of Health and Human Services surveys as a result of Section 4302 of the Affordable Care Act to help better characterize and compare the nature of health problems in underserved populations (17). This could lead to more effective, targeted health interventions for various racial/ethnic groups, perhaps, modeled after the cultural and linguistic services for Asian and Pacific Islanders within the educational system (18). Finally, knowing that Whites have higher rates of binge drinking, particularly compared to Asians (12), it may be necessary to use a more sensitive measure of binge drinking (such as 12 month versus 30 day recall) when evaluating data from states with many Asian residents. This would allow identifying a potentially harmful alcohol use pattern that may be rare in many race/ethnic subgroups and, thus, could provide useful information to those responsible for designing public health interventions. The purposes of the present study were for adult Californians, to (a) determine binge drinking population estimates by gender and detailed racial/ethnic categories, adjusting for relevant socio-demographic measures and (b) examine frequency of binge drinking by gender and detailed race/ethnicity, again, adjusting for socio-demographics. Combining survey data from the years 2007 and 2009 provides sufficient numbers to make reliable population estimates by gender and race/ethnicity, which are more detailed than the figures in national surveys.
Method Data source The California Health Interview Survey (CHIS, www.chis. ucla.edu) is the largest statewide survey conducted in the US. It is a bi-annual random-digit dial household telephone survey using multistage sampling, which is modeled after the National Health Interview Survey (NHIS). A primary objective of CHIS is to provide reliable estimates of health behaviors and status for all larger racial/ethnic groups as well
Am J Drug Alcohol Abuse, 2014; 40(2): 95–102
as for some smaller groups (19). The dataset includes extensive information about socio-demographics, health status, and health behaviors. CHIS primarily uses a landline and cell phone random digit dial method to identify subjects, although additional subjects from ethnic minorities are obtained by examining a surname phone list (19). There were 825 completed cell phone surveys in 2007, representing 1.6% of 51 048 total completed surveys (19) and 3047 in 2009, representing 6.4% of 47 614 completed surveys (20). Telephone surveys are conducted by Westat’s computer-assisted telephone interview system. The average adult survey in 2007 lasted 35 minutes (19), in 2009, about 40 minutes (20), with surveys in languages other than English generally requiring more time. Response rates are based on two factors – identifying/ screening respondents and completing the survey. Screening rates are higher for households who received an initial screening letter with $2. The 2007 household response rate for landlines was 35.5% screener response multiplied by 57.9% (0.579) survey completion, which equals 21.1% response rate (19), with response rates varying by sampling stratum. The 2009 household response rate for landlines was 36.1% screener response times 54.7% (0.547) survey completion equaling 19.7% household sample response rate (20). It is becoming more difficult to directly compare response rates to other surveys, such as the California BRFSS, which now uses a different method to determine response rates. The 2009 BRFSS shows that California had the highest refusal rate (32.2%) of any state, compared to a national median of 15.7% (20). To reduce potential bias based on response rates varying by demographic characteristics, survey weights were developed through an iterative raking process that accounts for non-response rates in relation to known population parameters obtained from the California Department of Finance. CHIS data also have been compared to results from other large population surveys, such as the NHIS, the Medical Expenditure Panel survey, and the California BRFSS (19). CHIS appears to do a reasonable job of measuring what it intends to measure; for example, the California BFRSS estimated that 14.0% of adults smoked in 2009, adjusting for Census 2000 population parameters (21), whereas CHIS estimates for current smoking, not adjusting for Census 2000 population, was 15.2% (22). Moreover, it has been shown that differences in questionnaire wording, mode of administration, sampling design, post-survey adjustments (e.g. weighting procedures), and use of incentives or proxy data are responsible for minor differences in population estimates between NHIS and BRFSS, although the generally small discrepancies in estimates have limited public health implications (23). An aspect of CHIS which distinguishes it from most other large surveys, including those that are national in scope, is the effort to gather information for those having little or no English-speaking skills. The CHIS designers culturally adapted and then translated the survey into Spanish, Chinese, Vietnamese, Korean, and Khmer, publishing in detail the process of survey adaption/translation (24). Moreover, some groups (e.g. Vietnamese and American Indian/Alaska Native) were oversampled. Eight and one half percent of all adult surveys in 2007 were conducted in a language other than
Binge drinking in California
DOI: 10.3109/00952990.2013.850503
English (19), as were 12% of the surveys in 2009 (20). Adult surveys for 2007 and 2009 were combined in the present study. Measures Binge drinking was defined by CHIS as 5þ drinks at one time for men and 4þ drinks at a time for women. The number of days in the past year with binge drinking was re-coded by CHIS staff into frequency categories: none, once a year, less than monthly, monthly, more than monthly, and daily or weekly. The primary independent variables were gender and race/ethnicity. The basic (aggregated) CHIS categories for race/ethnicity were: Latino, Pacific Islander, American Indian/Alaskan Native, Asian, African American, White, and ‘‘Other single/multiple race.’’ Detailed categories for Asian were Chinese, Filipino, South Asian, Japanese, Korean, Vietnamese, and Cambodian/other single/multi Asian type. Categories for Latinos were: Mexican, Salvadoran, Guatemalan, Central American, Puerto Rican, Latino European, South American, Other Latino, and ‘‘Two or more Latino types.’’ Due to small sample size for some of the detailed categories, particularly given the research design of stratifying by gender and examining a behavior occurring in a minority of respondents, some of the smaller sub-groups were combined. We grouped Cambodian, other single Asian, and multiple Asian types to form an ‘‘Other Asian’’ category. The existing Central American category for Latinos was expanded to also include those identified as Guatemalan or Salvadoran, and an ‘‘Other Latino’’ category was created by combining South American, Puerto Rican, Latino European, and ‘‘Two or more Latino types.’’ Finally, Pacific Islanders were included with ‘‘Other single/multiple race.’’ Similar to other studies in which binge drinking among specific racial/ethnic groups was examined (25,26), covariates included socio-economic status (education, household income, and employment) and family structure (marital status and presence of children in the home). Some research has shown that immigrant Latinos (27) and immigrant Asians (28) binge drink less than their US-born counterparts. Given that a majority of Asian and Latino adults in California are immigrants, we also included immigrant status (yes/no) and survey language (English vs. any other language) as shown in Table 2. Survey year was included to adjust for any temporal trends in binge drinking behavior. Statistical analyses Differences in socio-demographics and any binge drinking, stratified by gender, were examined by the Pearson test, using design-based F values to determine statistically significant differences among categorical variables. Multivariate binary logistic regression models, stratified by gender, were used to develop race-specific point estimates and 95% confidence intervals for any binge drinking. The frequency of binge drinking during the past year was examined further by multivariate multinomial logit models (29), run separately for men and women. A multinomial logit approach was used because ordered logistic regression models did not satisfy the proportional odds assumption (29).
97
Survey-specific procedures were used for all analyses, with survey weights based on the sampling design and response rates, and a jackknife approach was applied to properly compute 95% confidence intervals. Statistical analyses were conducted using Stata/IC 11.2 for Windows software (StataCorp LP, College Station, TX).
Results As shown in Table 1, for the 2007 and 2009 CHIS cycles, there were 98 662 completed surveys, representing an estimated annual population of 13.3 million adult men and 13.9 million adult women. There were several hundred to one thousand surveys by gender for American Indian/Alaska Native and most Asian and Latino sub-groups and thousands of responses for those identified as Mexican. Table 2 displays the characteristics of men and women and shows that for both genders, there were significant differences (at the p50.001 level) between binge drinkers and non-binge drinkers for nearly every socio-demographic breakdown. The only non-significant difference was survey year for men (no significant change over time) versus an increase in 2009 (approximately three percentage points) for women. Moreover, the four percentage point difference in binge drinking among men based on survey being conducted in English or another language was significant at the p ¼ 0.011 level. It is clear that binge drinking was more common among younger adults and those with higher incomes. On the other hand, binge drinking was less common among those 45 years of age and older, immigrants, those interviewed in a language other than English, and those having attended graduate school. Table 3 presents the estimated proportion of any binge drinking during the past year based on multivariate binary logistic regression. Estimated binge drinking among all men was 31.0% (95% Confidence Interval ¼ 30.5–31.4%) and among women, 18.0% (17.7–18.3%). Rates among White men and women were 30.5% and 19.6%, respectively. Among men, based on multivariate logistic regression, those significantly less likely to binge drink compared to White men were: Chinese (estimated proportion ¼ 11.9%, p50.001), Japanese Table 1. Sample and population sizes for California adults (CHIS survey years 2007 and 2009 combined). No. of men
No. of women a
Surveyed n Population N American Indian/ Alaska Native African American White Other/multiple race Chinese Filipino South Asian Japanese Korean Vietnamese Other Asian Mexican Central American Other Latino Total a
Surveyed n Population Na
464
125 624
678
124 199
1599 26 705 2446 1007 427 467 325 576 988 188 3964 414 365 39 935
720 769 6 500 912 1 188 466 479 570 395 421 251 336 96 794 122 305 210 583 92 773 2 588 841 348 498 223 953 13 345 841
2863 39 298 3679 1408 726 405 536 1016 945 241 5695 672 565 58 727
874 859 6 763 445 1 259 519 537 354 482 668 178 851 139 175 208 303 208 629 103 825 2 392 984 334 471 256 044 13 864 324
Estimated population for both years divided by two.
98
J. E. Banta et al.
Am J Drug Alcohol Abuse, 2014; 40(2): 95–102
Table 2. Socio-demographics for California adults (CHIS survey years 2007 and 2009 combined). Men
Women
Population
% binge drinking
Population
% binge drinking
18–24 years of age 25–34 35–44 45–54 55–65 65þ Education Less than 12 years High school graduate Some college College graduate Graduate school
14.6 18.1 20.8 19.5 14.0 13.1
51.8 53.9 44.0 35.1 25.5 12.4***
13.4 16.9 19.9 19.2 14.5 16.1
39.7 35.3 24.2 20.0 13.8 5.9***
15.6 28.9 22.2 19.2 14.1
35.7 42.2 41.9 37.9 29.1***
15.5 25.9 25.8 20.6 12.2
11.3 22.5 26.6 27.1 12.5***
Single, no children Single, children Married, children Married, no children
40.3 2.9 28.8 28.0
44.5 52.2 40.6 26.2***
36.6 8.4 27.8 27.2
27.3 26.9 21.2 20.8***
Household income $0–30 000 $30 001–65 000 $65 001–100 000 $100 001–150 000 $150 001þ Full-time employment Part-time employment Unemployed, looking for work Other Survey in English Survey in language other than English
28.7 26.6 19.7 13.8 11.3 65.6 7.0 6.8 20.6 83.4 16.6
34.8 37.0 40.0 40.8 45.6*** 43.3 38.3 40.9 22.3*** 39.1 35.1*
34.2 26.4 19.4 11.3 8.7 45.8 11.5 6.0 36.7 83.7 16.3
17.2 22.9 25.2 29.3 31.5*** 29.4 27.0 28.3 12.5*** 25.6 8.7***
Not foreign-born Foreign-born
67.1 32.9
40.8 33.6***
67.4 32.6
28.3 11.7***
Survey in 2007 Survey in 2009
49.4 50.6
38.2 38.6
49.4 50.6
21.4 24.3***
Binge drinking frequencies No binge drinking Once a year Less than monthly Monthly More than monthly Daily or weekly
61.6 4.6 16.3 4.8 7.3 5.4
77.1 4.3 11.0 2.8 2.9 1.9
*p50.05. ***p50.001.
(14.8%, p ¼ 0.001), South Asian (16.1%, p50.001), Vietnamese (16.8%, p50.001), and African American (24.6%, p50.001). Among women, again based on multivariate logistic regression, those less likely to binge drink compared to White women were: Chinese (24.6%, p50.001), South Asian (6.4%, p ¼ 0.047), Japanese (10.1%, p ¼ 0.008), Filipino (12.0%, p ¼ 0.014), African American (12.9%, p50.001), Central American (13.2%, p ¼ 0.028), and Mexican (17.5%, p ¼ 0.028). Table 4 (Panel a) presents the estimated proportions (and 95% confidence intervals) for binge drinking frequency by race/ethnicity based on the multinomial logit regression model for men. Significant p values also are presented. In most cases, White men had the highest proportion of binge drinking within each category, except for no binge drinking: binge drinking no times in the past year being 69.5% (68.9– 70.0%), only once in the past year 3.6% (3.4–3.8%), less than
monthly 12.5% (12.1–12.9%), monthly 4.2% (4.0–4.5%), more than monthly 5.1% (4.8–5.4%), and weekly or daily 5.1% (4.8–5.4%). The only instances of more frequent binge drinking compared to Whites, based on this regression, were Mexican and Central American men binge drinking in the category of ‘‘less than monthly’’ (p50.001, estimated proportion 19.9%, 18.7–21.1% and p50.001, 19.6%, 15.7– 23.4%, respectively. According to the multinomial logit regression model, African American and Chinese men were significantly less likely to binge drink compared to White men across all five categories of binge drinking (once per year through weekly or daily). Estimates for Vietnamese men were significantly lower in three categories (once per year through monthly), Japanese men lower in two categories (once per year and less than monthly), South Asians lower in two categories (less than monthly and weekly or daily), and Filipino men lower in one category (monthly). As previously noted, both Mexican and Central American men
Binge drinking in California
DOI: 10.3109/00952990.2013.850503
Table 3. Regression-adjusted percentages of adults reporting any binge drinking in the past year (CHIS survey years 2007 and 2009 combined).
Overall White American Indian/ Alaska Native African American Other/multiple race Chinese Filipino South Asian Japanese Korean Vietnamese Other Asian Mexican Central American Other Latino
Men % (95% CI)
Women % (95% CI)
31.0 (30.5–31.4) 30.5 (30.0–31.1) ref. 32.3 (28.1–36.6)
18.0 (17.7–18.3) 19.6 (19.2–20.0) ref. 19.0 (16.1–22.0)
24.6 37.5 11.9 27.6 16.1 14.8 28.1 16.8 29.8 42.9 38.6 37.5
12.9 18.7 5.5 12.0 6.4 10.1 13.6 4.8 11.2 17.5 13.2 25.7
(22.5–26.7)*** (35.6–39.4) (9.9–13.9)*** (23.4–31.9) (12.7–19.4)*** (10.9–18.6)** (24.4–31.8) (14.5–19.1)*** (23.2–36.3) (41.3–44.4) (34.0–43.3) (32.6–42.5)
99
were significantly more likely than Whites to binge drink less than monthly, although they were significantly less likely to binge drink weekly or daily. Table 4 (Panel b) presents the estimated proportions (and 95% confidence intervals) for binge drinking frequency by race/ethnicity based on the multinomial logit regression model for women. White women had the highest rate of binge drinking in all categories excluding ‘‘no binge drinking,’’ with the estimated percentage binge drinking no times during the past year being 80.4% (80.0–80.8%), only once in the past year at 3.2% (3.1–3.4%), less than monthly 9.6% (9.3–9.9%), monthly 2.6% (2.4–2.7%), more than monthly 2.5% (2.3–2.6%), and weekly or daily 1.7% (1.6–1.8%). The one exception was that ‘‘Other Latino’’ women were more likely to binge drink less than monthly 19.9% (18.7–21.1%). Similar to men, too, the multinomial logit regression revealed that only African American and Chinese women were significantly less likely than White women to binge drink across all five categories of binge drinking. South Asian women were less likely to drink in three categories (less than monthly, monthly, and weekly or daily). ‘‘Other Asian’’ women also were less likely to drink in three categories (once per year, less than monthly, and weekly or daily). American Indian/Alaska Native women were less likely to drink in two categories (more than monthly and weekly or daily, and monthly and weekly or daily, respectively). There were
(11.7–14.2)*** (17.4–20.0) (4.3–6.7)*** (9.6–14.3)* (4.0–8.8)* (7.5–12.6)** (11.5–15.7) (3.4–6.1) (7.2–15.2) (16.5–18.5)** (10.7–15.8)* (22.1–29.3)
*p50.05, **p50.01, ***p50.001. CI, Confidence Interval. ref. ¼ reference category for race in regression models. Binary logistic regression models used all independent variables shown in Table 2. Sample size for men was 39 935 and for women was 58 727.
Table 4. Regression-adjusted estimated binge drinking proportions for men and women.
Race/ethnicity
No binge
Once per year
Less than monthly
Monthly
Percent (CI)
Percent (CI)
Percent (CI)
Percent (CI)
Panel a: men White (ref.) 69.5 (68.9–70.0) 3.6 (3.4–3.8) American Indian/Alaska Native 67.7 (63.4–71.9) 3.2 (1.6–4.8) African American 75.4 (73.3–77.5) 4.1 (3.1–5.0)* Other/multiple race 62.5 (60.6–64.4) 5.8 (4.9–6.8) Chinese 88.1 (86.1–90.1) 2.4 (1.4–3.3)*** Filipino 72.4 (68.1–76.6) 4.9 (2.9–7.0) South Asian 83.9 (80.6–87.3) 1.3 (0.3–2.3) Japanese 85.2 (81.4–89.1) 2.8 (1.0–4.6)* Korean 71.9 (68.2–75.5) 2.8 (1.4–4.1) Vietnamese 83.2 (80.9–85.5) 2.6 (1.6–3.6)** Other Asian 70.2 (63.7–76.8) 4.3 (1.4–7.1) Mexican 57.1 (55.6–58.7) 5.8 (5.0–6.5) Central American 61.4 (56.7–66.0) 6.3 (3.9–8.6) Other Latino 62.5 (57.5–67.4) 5.5 (3.1–7.8)
12.5 12.5 11.5 15.8 5.8 13.3 8.6 6.2 10.2 8.4 10.6 19.9 19.6 15.9
Panel b: women White (ref.) 80.4 (80.0–80.8) 3.2 (3.1–3.4) 9.6 American Indian/Alaska Native 81.0 (78.0–83.9) 2.9 (1.7–4.2) 9.1 African American 87.1 (85.8–88.3) 3.0 (2.4–3.6)*** 6.5 Other/multiple race 81.3 (80.0–82.6) 4.7 (4.0–5.4) 9.4 Chinese 94.5 (93.3–95.7) 1.1 (0.6–1.7)*** 3.2 Filipino 88.0 (85.7–90.4) 4.4 (2.9–5.9) 5.8 South Asian 93.6 (91.2–96.0) 2.2 (0.8–3.7) 3.0 Japanese 89.9 (87.4–92.5) 2.1 (0.9–3.3) 4.5 Korean 86.4 (84.3–88.5) 2.6 (1.6–3.5)** 7.5 Vietnamese 95.2 (93.9–96.6) 1.6 (0.8–2.4) 2.1 Other Asian 88.8 (84.8–92.8) 1.7 (0.0–3.3)** 8.3 Mexican 82.5 (81.5–83.5) 4.7 (4.1–5.2) 8.8 Central American 86.8 (84.2–89.3) 4.3 (2.8–5.9) 6.0 Other Latino 74.3 (70.7–77.9) 4.8 (3.0–6.5) 13.6
More than monthly Weekly or daily Percent (CI)
Percent (CI)
(12.1–12.9) (9.5–15.5) (9.9–13.1)*** (14.3–17.2) (4.3–7.2)*** (10.1–16.6) (6.0–11.1)** (3.5–8.8)* (7.8–12.7) (6.7–10.1)** (6.2–15.1) (18.7–21.1)*** (15.7–23.4)*** (12.1–19.6)
4.2 4.3 2.4 4.3 1.8 2.8 2.6 2.2 4.3 2.1 3.7 5.0 5.1 6.8
(4.0–4.5) (2.5–6.2) (1.7–3.2)** (3.5–5.1) (1.0–2.6)** (1.2–4.4)* (1.1–4.0) (0.6–3.7) (2.7–6.0) (1.2–3.0)*** (1.0–6.4) (4.3–5.7) (3.0–7.2) (4.3–9.4)
5.1 5.6 3.6 7.2 1.8 5.2 2.6 1.8 4.9 2.1 6.4 7.2 4.8 5.8
(4.8–5.4) (3.5–7.7) (2.7–4.5)*** (6.1–8.2) (1.0–2.6)* (3.1–7.3) (1.1–4.0) (0.4–3.3) (3.1–6.6) (1.2–3.0) (2.9–9.9) (6.4–8.0) (2.8–6.9) (3.4–8.1)
5.1 6.7 3.0 4.4 0.2 1.4 1.1 1.8 5.9 1.5 4.8 5.0 2.9 3.6
(4.8–5.4) (4.4–9.0) (2.2–3.8)* (3.6–5.2) (0.1–0.5)*** (0.3–2.5) (0.1–2.0)*** (0.4–3.3) (4.0–7.8) (0.8–2.3) (1.7–7.8) (4.4–5.7)** (1.3–4.5)*** (1.7–5.5)
(9.3–9.9) (7.0–11.3) (5.6–7.4)*** (8.4–10.3) (2.3–4.1)*** (4.1–7.5) (1.3–4.6)*** (2.7–6.2) (5.9–9.1) (1.2–3.0) (4.8–11.8) (8.1–9.6) (4.2–7.7)* (10.8–16.5)*
2.6 2.4 1.2 1.6 0.4 1.0 0.5 1.1 1.6 0.3 0.8 1.5 1.2 3.7
(2.4–2.7) (1.2–3.5) (0.8–1.6)*** (1.2–2.0)* (0.1–0.8)*** (0.3–1.7)* (0.2–1.2)*** (0.2–2.0)* (0.8–2.3) (0.0–0.7) (0.3–2.0)* (1.2–1.8)* (0.4–2.0) (2.2–5.3)
2.5 2.5 1.3 2.0 0.2 0.7 0.7 1.5 1.4 0.5 0.4 1.8 1.3 2.3
(2.3–2.6) (1.3–3.7)* (0.9–1.7)*** (1.5–2.4) (0.0–0.5)*** (0.1–1.3)*** (0.0–1.6) (0.5–2.5) (0.7–2.1) (0.1–1.0) (0.4–1.2) (1.4–2.1) (0.5–2.2) (1.1–3.5)
1.7 2.1 0.9 1.1 0.5 0.1 0.0 0.9 0.6 0.2 0.0 0.7 0.4 1.2
(1.6–1.9) (1.0–2.9)* (0.6–1.3)*** (0.8–1.4)* (0.1–0.9)*** (0.1–0.4) (0.0–0.0)*** (0.1–1.7) (0.1–1.1) (0.1–0.5)** (0.0–0.0)*** (0.5–0.9)** (0.0–1.0)* (0.3–2.2)
CI, 95% Confidence Interval. p Values based on regression, *p50.05, **p50.01, ***p50.001. Multinomial logit model used all independent variables shown in Table 2. Sample size for men was 39 935 and for women was 58 727.
100
J. E. Banta et al.
three racial/ethnic groups that were significantly less likely than White women to binge drink in just one category: ‘‘Other/multiple race’’ (monthly), Filipino (more than monthly), and Japanese (monthly).
Discussion These results demonstrate sizable variation in binge drinking prevalence based on gender and race/ethnicity for adults in the State of California. Although the average estimated rate of any binge drinking among men during the past 12 months was 31.0%, it ranged from 11.9% among Chinese to 42.9% among Mexican respondents. Similarly, although the estimated average rate among women was 18.0%, it ranged from 4.8% among Vietnamese to 25.7% among ‘‘Other Latino’’ respondents. After adjusting for other sociodemographic measures, African American, Chinese, South Asian, Japanese, and Vietnamese men were significantly less likely to do any binge drinking compared to Whites. Likewise, African American, Chinese, Filipino, South Asian, Japanese, Mexican, and Central American women were significantly less likely to binge drink compared to Whites. In addition to having the highest rate of any binge drinking, White men and women also had the highest frequency of binge drinking weekly or daily, more than monthly, more monthly in the past year compared to other racial/ethnic groups. There were instances of racial/ethnic groups being more likely to engage in low-frequency binge drinking. As shown in Table 4, Mexican and Central American men and ‘‘Other Latino’’ women were significantly more likely than Whites to binge drink ‘‘more than once a year; but less than monthly’’. Standard reporting from the NSDUH presents binge drinking by race/ethnicity, with 2011 binge drinking rates among those 12 years of age and older being 24.3% for American Indians/Alaska Natives, 23.9% for Whites, 23.4% for Hispanics, 19.4% for Blacks, 18.6% for persons reporting two or more races, and 11.6% among Asians (30). Although helpful at a national level, such numbers may not be as useful in states, like California, with large numbers of Asian and Latino residents. With CHIS data, it is possible to provide a more detailed snapshot of binge drinking by gender and race/ethnicity, so that one can make sub-group comparisons. There are occasional national studies providing more details, however; Lee and others, for example, combined 2002–2008 NSDUH data for Asian Indian, Chinese, Filipino, Japanese, and Korean adults (26). They did not present binge drinking estimates by gender, but they did report that Koreans were significantly more likely to binge drink in the past 30 days compared to those in the other four Asian sub-groups. We believe that this detailed gender and race/ethnicity binge drinking rate information is useful for informing policy and public health efforts. These data provide a nonoverlapping complement to (a) population-level estimates using BFRSS and NSDUH data with limited race/ethnicity detail (12,30) and national studies examining in detail specific race/ethnicity groups, such as the NSDUH study of Asian adults (26), (b) regional studies of specific racial/ethnic groups (25,27,28), (c) state-level estimates based on national datasets (31), and (d) other CHIS-based research, such as
Am J Drug Alcohol Abuse, 2014; 40(2): 95–102
Bryant and Kim’s study of alcohol consumption among elderly Asians and Latino immigrants (28). Few other states are able to produce such detailed analyses, as most must rely on national data for their local binge-drinking estimates. Furthermore, the differences we reported by gender and race/ethnicity are not apparent in state or national reports where Asian and Latino data are aggregated.
Study strengths and limits Among the present study’s strengths are the size and scope of the survey, which enabled us to use more detailed race/ ethnicity categories. Moreover, CHIS over-sampled several racial/ethnic minorities and conducted the survey in five different languages. Among the limits are the binge drinking criterion (a single item for number of binge drinking episodes in the past year, which may have been affected by self-presentation and the possible disinclination to report a socially unacceptable activity). This is a state survey, and our findings may not generalize to other states or to the entire nation. There are regional differences in binge drinking patterns, with BFRSS age-adjusted overall prevalence of binge drinking in 2010 ranging from 10.9% in Utah to 25.6% in Wisconsin; with California and national prevalence rates in the middle of this range (16.5% and 17.1%, respectively) (13). State surveys regarding binge drinking, however, have been shown to have reasonable construct validity, by comparing results from surveys such as BRFSS and NSDUH with per capita alcohol sales and deaths due to high blood alcohol content (32), and, thus, clearly are valuable for exploring binge drinking within a state. There also is the issue of difference in binge drinking definition. CHIS evaluated binge drinking during the past year, whereas BFRSS evaluates binge drinking during the past 30 days as CHIS did in 2005 (33). The change by CHIS was in line with NIAAA’s Task Force on Recommended Alcohol Questions, which suggested that researchers who can ask only three to five questions about alcohol should ask about patterns over the past 12 months (34). Moreover, because between 50 to 75% of the binge drinkers identified in CHIS engaged in binge drinking on a less than monthly basis, with some only once during the year, many of these people might not be identified as binge drinkers in the national surveys. Although this may make our estimates higher compared to many other studies, an advantage of using the 12-month definition for binge drinking is that it identifies occasional binge drinkers. This information, particularly the figures for Latino subgroups, may be useful for program planning and outreach purposes. Another factor that may contribute to our findings differing from national survey binge drinking estimates is our estimates were made from regression models that accounted for several standard socio-demographic measures and were stratified by gender. Given the diversity in socio-demographics, such as age, education, income, and immigration, which varied both within and across racial/ethnic groups, we did not believe that unadjusted rates would be as useful for making comparisons in binge drinking rates across 14 different racial/ethnic groups in a large and diverse state.
DOI: 10.3109/00952990.2013.850503
Finally, a finer grained analysis of race/ethnicity itself can be challenging. The present results are based on self-report, with people being able to self-identify more than one category. There are differences if one categorizes race/ethnicity per the guidelines of the Census Bureau, federal Office of Management and Budget, or California Department of Finance. The detailed categories used by CHIS may not directly correspond to detailed categories used by other federal agencies. Moreover, we had to collapse some of the detailed race/ethnicity categories due to small sample size. Thus, our findings may not entirely match up with other surveys; however, California is a racially diverse state, and the categories chosen by CHIS are sensitive to the population, and the findings from our collapsed categories are statistically valid. Nevertheless, our results may be directly applicable to efforts to reduce the effects of excess drinking in states that are experiencing growth in the detailed race/ethnicity groups studied in CHIS. Mexicans are the predominant ancestry in many southwestern states, for example, whereas Japanese and Filipinos are predominant in Hawaii (16).
Conclusion Our data support the recommendation that binge drinking frequency should be monitored regularly by health agencies to both improve surveillance and better assess the effects of interventions designed to prevent and reduce binge drinking and its consequences (35,36). Findings from epidemiological surveys – ours and others – underscore the need to implement effective population-based prevention strategies and develop effective (evidence-based) interventions targeted at groups at higher risk (12,36). Our analyses point to several groups most in need of attention with respect to binge drinking. As shown in Table 3, among Asian men and women, Koreans, Filipinos, and ‘Other Asians’ have the highest rates of binge drinking. Moreover, as shown in Table 4 (Panels a and b), Mexican and Central American men and ‘Other Latino’ women were significantly more likely than Whites to binge drink occasionally, that is, less than monthly. Although not directly related to binge drinking, a ‘‘Keepin’it REAL’’ intervention to prevent substance abuse was translated and tested in Mexican public schools. Researchers found modest, but statistically significant, effects for females and a conclusion was that comprehensive cultural adaptation may be needed in addition to linguistic translation to achieve full effectiveness (37). Finally, it seems reasonable to recommend, too, that efforts to curtail binge drinking be coordinated with initiatives to reduce overall alcohol consumption; the majority of binge drinkers, in fact, may be considered moderate drinkers (e.g. not alcohol dependent) when looking at total alcohol consumption (38).
Declaration of interest The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.
References 1. Kanny D, Liu Y, Brewer RD. Binge drinking – United States, 2009. MMWR Surveill Summ 2011;60(Suppl):101–104.
Binge drinking in California
101
2. Perkins HW. Surveying the damage: a review of research on consequences of alcohol misuse in college populations. J Stud Alcohol Suppl 2002;14:91–100. 3. Wechsler H, Isaac N. ‘Binge’ drinkers at Massachusetts colleges. Prevalence, drinking style, time trends, and associated problems. JAMA 1992;267:2929–2931. 4. Wechsler H, Lee JE, Kuo M, Seibring M, Nelson TF, Lee H. Trends in college binge drinking during a period of increased prevention efforts. Findings from 4 Harvard School of Public Health College Alcohol Study surveys: 1993–2001. J Am Coll Health 2002;50: 203–217. 5. Hingson R, Heeren T, Winter M, Wechsler H. Magnitude of alcohol-related mortality and morbidity among U.S. college students ages 18–24: changes from 1998 to 2001. Annu Rev Public Health 2005;26:259–279. 6. Presley CA, Pimentel ER. The introduction of the heavy and frequent drinker: a proposed classification to increase accuracy of alcohol assessments in postsecondary educational settings. J Stud Alcohol 2006;67:324–331. 7. Haskard KB, Banta JE, Williams SL, Haviland MG, DiMatteo MR, Przekop P, Werner LS, Anderson DL. Binge drinking, poor mental health, and adherence to treatment among California adults with asthma. J Asthma 2008;45:369–376. 8. Read JP, Beattie M, Chamberlain R, Merrill JE. Beyond the ‘‘Binge’’ threshold: heavy drinking patterns and their association with alcohol involvement indices in college students. Addict Behav 2008;33:225–234. 9. U.S. Department of Health and Human Services. Disparities in Substance Abuse. Available at: http://www.healthypeople.gov/ 2020/lhi/substanceabuse.aspx?tab¼data [last accessed 5 September 2013]. 10. Harris KM, Gordon-Larsen P, Chantala K, Udry JR. Longitudinal trends in race/ethnic disparities in leading health indicators from adolescence to young adulthood. Arch Pediatr Adolesc Med 2006; 160:74–81. 11. Caetano R, Baruah J, Ramisetty-Mikler S, Ebama MS. Sociodemographic predictors of pattern and volume of alcohol consumption across Hispanics, Blacks, and Whites: 10-year trend (1992–2002). Alcohol Clin Exp Res 2010;34:1782–1792. 12. Centers for Disease Control and Prevention. Sociodemographic differences in binge drinking among adults – 14 States, 2004. MMWR Morb Mortal Wkly Rep 2009;58:301–304. 13. Kanny D, Liu Y, Brewer RD, Garvin WS, Balluz L. Vital signs: binge drinking prevalence, frequency, and intensity among adults – United States, 2010 (Reprinted from MMWR, vol. 61, pp. 14–19, 2012). JAMA 2012;307:908–910. 14. Chartier K, Caetano R. Ethnicity and health disparities in alcohol research. Alcohol Res Health 2010;33:152–160. 15. Toleran DE, Tran PD, Cabangun B, Lam J, Battle RS, Gardiner P. Substance use among Chinese, Filipino, and Vietnamese adult men living in San Jose, Daly City, and San Francisco, and its implications on ATOD prevention services. J Ethn Subst Abuse 2012;11:86–99. 16. Brittingham A, de le Cruz GP. Ancestry: 2000; Census 2000 Brief. U.S. Census Bureau. Available at: www.census.gov/prod/2004pubs/ c2bkr-35.pdf [last accessed 5 September 2013]. 17. U.S. Department of Health and Human Services Office of Minority Health. Reducing Health Disparities with Improved Data Collection: New Refined Data Standards for Race, Ethnicity, Sex, Primary Language, and Disability Status. Available at: http:// minorityhealth.hhs.gov/templates/content.aspx?ID¼9232&lvl¼2& lvlID¼208 [last accessed 5 September 2013]. 18. The White House. Initiative on Asian Americans and Pacific Islanders. Available at: http://www.whitehouse.gov/aapi [last accessed 5 September 2013]. 19. California Health Interview Survey. CHIS 2007 Methodology Report Series. Report 5: Weighting and Estimation of Variance in the CHIS Public Use Files. UCLA Center for Health Policy Research. Available at: http://healthpolicy.ucla.edu/chis/design/ Pages/methodology.aspx [last accessed 5 September 2013]. 20. California Health Interview Survey. CHIS 2009 Methodology Report Series. Report 4: Response Rates. UCLA Center for Health Policy Research. Available at: http://healthpolicy.ucla.edu/chis/ design/Pages/methodology.aspx [last accessed 5 September 2013.
102
J. E. Banta et al.
21. California Department of Public Health, California Tobacco Control Program. Smoking prevalence among California and U.S. minus California adults, 1984–2010. Available at: http:// www.cdph.ca.gov/programs/tobacco/Documents/Media/2010%20 adult%20and%20youth%20prevalence%20memo_figures%20(7)v2. pdf [last accessed 5 September 2013]. 22. Babey SH, Grant D, Brown ER. Adult smoking rate declines, while asthma, diabetes and obesity rates rise: UCLA Center for Health Policy Research, 2006. Available at: http://healthpolicy.ucla. edu/publications/Documents/PDF/CHIS2005_FS111306.pdf [last accessed 5 September 2013]. 23. Fahimi M, Link M, Mokdad A, Schwartz DA, Levy P. Tracking chronic disease and risk behavior prevalence as survey participation declines: statistics from the behavioral risk factor surveillance system and other national surveys. Prev Chronic Dis 2008;5:A80. 24. Ponce NA, Lavarreda SA, Yen W, Brown ER, DiSogra C, Satter DE. The California Health Interview Survey 2001: translation of a major survey for California’s multiethnic population. Public Health Rep 2004;119:388–395. 25. Caetano R, Mills B, Vaeth PA. Alcohol consumption and binge drinking among U.S.-Mexico border and nonborder Mexican Americans. Alcohol Clin Exp Res 2012;36: 677–685. 26. Lee HK, Han B, Gfroerer JC. Differences in the prevalence rates and correlates of alcohol use and binge alcohol use among five Asian American subpopulations. Addict Behav 2013;38: 1816–1823. 27. De La Rosa M, Dillon FR, Sastre F, Babino R. Alcohol use among recent Latino immigrants before and after immigration to the United States. Am J Addict 2013;22:162–168. 28. Bryant AN, Kim G. The relation between acculturation and alcohol consumption patterns among older Asian and Hispanic immigrants. Aging Ment Health 2013;17:147–156.
Am J Drug Alcohol Abuse, 2014; 40(2): 95–102
29. Long JS, Freese J. Regression models for categorical dependent variables using Stata, revised edition. College Station, Texas: Stata Press, 2003. 30. Substance Abuse and Mental Health Services Administration. Results from the 2011 National Survey on Drug Use and Health: Summary of National Findings, NSDUH Series H-44, HHS Publication No. (SMA) 12-4713. Rockville M.S.A.a.M.H.S.A., ed., 2012. 31. California Department of Alcohol and Drug Programs. 2012 California Needs Assessment Report. Available at: www.adp. ca.gov/library/pdf/2012_CA_Needs_Assessment_Report.pdf [last accessed 5 September 2013]. 32. Paschall MJ, Ringwalt CL, Gitelman AM. The validity of state survey estimates of binge drinking. Am J Prev Med 2010;39: 179–183. 33. Banta JE, Przekop P, Haviland MG, Pereau M. Binge drinking among California adults: results from the 2005 California Health Interview Survey. Am J Drug Alcohol Abuse 2008;34:801–809. 34. National Institute on Alcohol Abuse and Alcoholism. Available at: http://www.niaaa.nih.gov/research/guidelines-and-resources/ recommended-alcohol-questions [last accessed 5 September 2013]. 35. Naimi TS, Nelson DE, Brewer RD. The intensity of binge alcohol consumption among U.S. adults. Am J Prev Med 2010;38:201–207. 36. Centers for Disease Control and Prevention, Division of Population Health, National Center for Chronic Disease Prevention and Health Promotion. Fact sheets. Binge drinking. Available at: http:// www.cdc.gov/alcohol/fact-sheets/binge-drinking.htm [last accessed 5 September 2013]. 37. Marsiglia FF, Booth JM, Ayers SL, Nun˜o-Gutierrez BL, Kulis S, Hoffman S. Short-term effects on substance use of the Keepin’ It REAL Pilot Prevention Program: linguistically adapted for youth in Jalisco, Mexico. Prevention Sci 2013; Jul 23 [Epub ahead of print]. 38. Naimi TS, Brewer RD, Mokdad A, Denny C, Serdula MK, Marks JS. Binge drinking among US adults. JAMA 2003;289:70–75.
Copyright of American Journal of Drug & Alcohol Abuse is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
ORIGINAL ARTICLE
Binge drinking by gender and race/ethnicity among California adults, 2007/2009 Jim E. Banta, PhD, MPH1, Pamela E. Mukaire, MEd, MPH, DrPH(c)2, and Mark G. Haviland, PhD3 1
Departments of Health Policy and Management, 2Health Promotion and Education, Loma Linda University School of Public Health, and Department of Psychiatry, Loma Linda University School of Medicine, Loma Linda, CA, USA
3
Abstract
Keywords
Background: This study provides binge drinking population estimates for California adults by gender and detailed race/ethnicity categories. This information may be helpful for planning targeted initiatives to decrease binge drinking. Method: Data were from the 2007 and 2009 California Health Interview Surveys. The 98 662 respondents represent an annual estimated population of 27.2 million adults. Survey adjusted binary logistic regression was used to calculate gender-specific binge drinking population rates and multinomial logit regression to estimate binge drinking frequency. Results: Adjusting for socio-demographics, any binge drinking during the past year was reported by 31.0% (95% Confidence Interval ¼ 30.5–31.4%) of men and 18.0% (17.7–18.3%) of women. Rates among White men and women were 30.5% and 19.6%, respectively. Binge drinking rates ranged from 11.9% among Chinese to 42.9% among Mexican men and from 4.8% among Vietnamese to 25.7% among ‘‘Other Latino’’ women. Five race/ethnicity categories of men and seven categories of women were significantly less likely to binge drink compared to Whites. Although Whites had the highest overall binge drinking rates, an estimated 12.5% of White men binge drank less than monthly, significantly exceeded by Mexican and Central American men, 19.9 and 19.6%, respectively. An estimated 9.6% of White women binge drank less than monthly, exceeded only by ‘‘Other Latino’’ women, 13.6%. Conclusion: These findings underscore the importance of detailed gender and race/ethnicity breakdowns when examining any binge drinking. Furthermore, there is variability across Asian and Latino subgroups in the frequency of binge drinking episodes, which is not evident in broad-group population studies.
Alcohol abuse, binge drinking, race/ethnicity, California Health Interview Survey (CHIS), population estimates
More than half of all alcohol consumed in the United States is in the form of binge drinking (1), commonly defined as five or more drinks at a time for men or four or more drinks at a time for women. Binge drinking is associated with several grim statistics: over 40 000 deaths and 1.5 million years of potential life were lost each year in the US between 2001 and 2005 (1). Moreover, binge drinking has long been linked to a host of negative outcomes across the lifespan: academic and personal problems in school, risk taking behaviors, physical injuries to self and others, driving while intoxicated, and failure to adhere to medications (2–8). Consequently, public health efforts, such as Healthy People 2020 (objective 14.3), call for a reduction in adult binge drinking rates from the 27.1% baseline rate (i.e. during a 30-day period) in 2008 to 24.4% in 2020 (9).
Address correspondence to: Jim E. Banta, PhD, MPH, 24951 North Circle Drive, Loma Linda, CA 92350, USA. Tel: +1 909 558 7753. Fax: +1 909 558 0469. E-mail: [email protected]
Received 13 February 2013 Revised 25 September 2013 Accepted 25 September 2013 Published online 12 February 2014
National reports document variation in binge drinking by gender, age group, and race/ethnicity (1,10,11). Based on estimates from the National Survey on Drug Use and Health (NSDUH), in 2011, binge drinking in the past 30 days was more common among men (32.5%) compared to women (21.4%), and it decreased as people aged: 36.6% among adults 18–44 years of age, 21.8% for those 45–64, and 9.8% for individuals 65 and older (9). According to age- and sexadjusted figures from the Behavioral Risk Factor Surveillance System (BRFSS) 2009 survey, binge drinking prevalence ranged from a low of 7.8% among Asian/Pacific Islanders to a high of 17.5% among Whites (12). According to BFRSS survey data from 2008–2010, of those who engaged in any binge drinking in the past month, the average was about four episodes during the month (13). Men who were binge drinking reported a higher frequency of binge drinking than did binge drinking women, with an average of 5.0 versus 3.2 episodes per month (13). Binge drinking frequency by racial/ethnic group also was similar to findings for any binge drinking; Whites had the highest frequency and Asian/Pacific Islanders the lowest. There were, however, differences between any binge drinking and
20 14
Background
History
96
J. E. Banta et al.
frequency among those engaging in binge drinking by age, education, and income. For those breakdowns, the groups with lowest overall rates of any binge drinking – that is persons 65 years of age and older, those not graduating from high school, and individuals with household income less than $25 000 – also were the groups who reported the highest frequency of binge drinking among those doing any binge drinking (13). Clearly, more research is needed to better understand alcohol consumption and consequences among racial/ethnic minorities (14); however, broad categorizations as noted above may mask within-group differences, which could prove useful to state planners and, particularly, at a time of limited public health resources. An interventional study of substance abusers in California, for example, revealed differences in alcohol use among Filipino, Vietnamese, and Chinese men (15). The need for detailed information regarding race/ethnicity is demonstrated further in that the 2000 Census revealed 92 different ancestries reported by 100 000 people or more, with 24 different ancestries being the largest in at least one county in the United States (16). Moreover, detailed race/ ethnicity categories will be captured in future Department of Health and Human Services surveys as a result of Section 4302 of the Affordable Care Act to help better characterize and compare the nature of health problems in underserved populations (17). This could lead to more effective, targeted health interventions for various racial/ethnic groups, perhaps, modeled after the cultural and linguistic services for Asian and Pacific Islanders within the educational system (18). Finally, knowing that Whites have higher rates of binge drinking, particularly compared to Asians (12), it may be necessary to use a more sensitive measure of binge drinking (such as 12 month versus 30 day recall) when evaluating data from states with many Asian residents. This would allow identifying a potentially harmful alcohol use pattern that may be rare in many race/ethnic subgroups and, thus, could provide useful information to those responsible for designing public health interventions. The purposes of the present study were for adult Californians, to (a) determine binge drinking population estimates by gender and detailed racial/ethnic categories, adjusting for relevant socio-demographic measures and (b) examine frequency of binge drinking by gender and detailed race/ethnicity, again, adjusting for socio-demographics. Combining survey data from the years 2007 and 2009 provides sufficient numbers to make reliable population estimates by gender and race/ethnicity, which are more detailed than the figures in national surveys.
Method Data source The California Health Interview Survey (CHIS, www.chis. ucla.edu) is the largest statewide survey conducted in the US. It is a bi-annual random-digit dial household telephone survey using multistage sampling, which is modeled after the National Health Interview Survey (NHIS). A primary objective of CHIS is to provide reliable estimates of health behaviors and status for all larger racial/ethnic groups as well
Am J Drug Alcohol Abuse, 2014; 40(2): 95–102
as for some smaller groups (19). The dataset includes extensive information about socio-demographics, health status, and health behaviors. CHIS primarily uses a landline and cell phone random digit dial method to identify subjects, although additional subjects from ethnic minorities are obtained by examining a surname phone list (19). There were 825 completed cell phone surveys in 2007, representing 1.6% of 51 048 total completed surveys (19) and 3047 in 2009, representing 6.4% of 47 614 completed surveys (20). Telephone surveys are conducted by Westat’s computer-assisted telephone interview system. The average adult survey in 2007 lasted 35 minutes (19), in 2009, about 40 minutes (20), with surveys in languages other than English generally requiring more time. Response rates are based on two factors – identifying/ screening respondents and completing the survey. Screening rates are higher for households who received an initial screening letter with $2. The 2007 household response rate for landlines was 35.5% screener response multiplied by 57.9% (0.579) survey completion, which equals 21.1% response rate (19), with response rates varying by sampling stratum. The 2009 household response rate for landlines was 36.1% screener response times 54.7% (0.547) survey completion equaling 19.7% household sample response rate (20). It is becoming more difficult to directly compare response rates to other surveys, such as the California BRFSS, which now uses a different method to determine response rates. The 2009 BRFSS shows that California had the highest refusal rate (32.2%) of any state, compared to a national median of 15.7% (20). To reduce potential bias based on response rates varying by demographic characteristics, survey weights were developed through an iterative raking process that accounts for non-response rates in relation to known population parameters obtained from the California Department of Finance. CHIS data also have been compared to results from other large population surveys, such as the NHIS, the Medical Expenditure Panel survey, and the California BRFSS (19). CHIS appears to do a reasonable job of measuring what it intends to measure; for example, the California BFRSS estimated that 14.0% of adults smoked in 2009, adjusting for Census 2000 population parameters (21), whereas CHIS estimates for current smoking, not adjusting for Census 2000 population, was 15.2% (22). Moreover, it has been shown that differences in questionnaire wording, mode of administration, sampling design, post-survey adjustments (e.g. weighting procedures), and use of incentives or proxy data are responsible for minor differences in population estimates between NHIS and BRFSS, although the generally small discrepancies in estimates have limited public health implications (23). An aspect of CHIS which distinguishes it from most other large surveys, including those that are national in scope, is the effort to gather information for those having little or no English-speaking skills. The CHIS designers culturally adapted and then translated the survey into Spanish, Chinese, Vietnamese, Korean, and Khmer, publishing in detail the process of survey adaption/translation (24). Moreover, some groups (e.g. Vietnamese and American Indian/Alaska Native) were oversampled. Eight and one half percent of all adult surveys in 2007 were conducted in a language other than
Binge drinking in California
DOI: 10.3109/00952990.2013.850503
English (19), as were 12% of the surveys in 2009 (20). Adult surveys for 2007 and 2009 were combined in the present study. Measures Binge drinking was defined by CHIS as 5þ drinks at one time for men and 4þ drinks at a time for women. The number of days in the past year with binge drinking was re-coded by CHIS staff into frequency categories: none, once a year, less than monthly, monthly, more than monthly, and daily or weekly. The primary independent variables were gender and race/ethnicity. The basic (aggregated) CHIS categories for race/ethnicity were: Latino, Pacific Islander, American Indian/Alaskan Native, Asian, African American, White, and ‘‘Other single/multiple race.’’ Detailed categories for Asian were Chinese, Filipino, South Asian, Japanese, Korean, Vietnamese, and Cambodian/other single/multi Asian type. Categories for Latinos were: Mexican, Salvadoran, Guatemalan, Central American, Puerto Rican, Latino European, South American, Other Latino, and ‘‘Two or more Latino types.’’ Due to small sample size for some of the detailed categories, particularly given the research design of stratifying by gender and examining a behavior occurring in a minority of respondents, some of the smaller sub-groups were combined. We grouped Cambodian, other single Asian, and multiple Asian types to form an ‘‘Other Asian’’ category. The existing Central American category for Latinos was expanded to also include those identified as Guatemalan or Salvadoran, and an ‘‘Other Latino’’ category was created by combining South American, Puerto Rican, Latino European, and ‘‘Two or more Latino types.’’ Finally, Pacific Islanders were included with ‘‘Other single/multiple race.’’ Similar to other studies in which binge drinking among specific racial/ethnic groups was examined (25,26), covariates included socio-economic status (education, household income, and employment) and family structure (marital status and presence of children in the home). Some research has shown that immigrant Latinos (27) and immigrant Asians (28) binge drink less than their US-born counterparts. Given that a majority of Asian and Latino adults in California are immigrants, we also included immigrant status (yes/no) and survey language (English vs. any other language) as shown in Table 2. Survey year was included to adjust for any temporal trends in binge drinking behavior. Statistical analyses Differences in socio-demographics and any binge drinking, stratified by gender, were examined by the Pearson test, using design-based F values to determine statistically significant differences among categorical variables. Multivariate binary logistic regression models, stratified by gender, were used to develop race-specific point estimates and 95% confidence intervals for any binge drinking. The frequency of binge drinking during the past year was examined further by multivariate multinomial logit models (29), run separately for men and women. A multinomial logit approach was used because ordered logistic regression models did not satisfy the proportional odds assumption (29).
97
Survey-specific procedures were used for all analyses, with survey weights based on the sampling design and response rates, and a jackknife approach was applied to properly compute 95% confidence intervals. Statistical analyses were conducted using Stata/IC 11.2 for Windows software (StataCorp LP, College Station, TX).
Results As shown in Table 1, for the 2007 and 2009 CHIS cycles, there were 98 662 completed surveys, representing an estimated annual population of 13.3 million adult men and 13.9 million adult women. There were several hundred to one thousand surveys by gender for American Indian/Alaska Native and most Asian and Latino sub-groups and thousands of responses for those identified as Mexican. Table 2 displays the characteristics of men and women and shows that for both genders, there were significant differences (at the p50.001 level) between binge drinkers and non-binge drinkers for nearly every socio-demographic breakdown. The only non-significant difference was survey year for men (no significant change over time) versus an increase in 2009 (approximately three percentage points) for women. Moreover, the four percentage point difference in binge drinking among men based on survey being conducted in English or another language was significant at the p ¼ 0.011 level. It is clear that binge drinking was more common among younger adults and those with higher incomes. On the other hand, binge drinking was less common among those 45 years of age and older, immigrants, those interviewed in a language other than English, and those having attended graduate school. Table 3 presents the estimated proportion of any binge drinking during the past year based on multivariate binary logistic regression. Estimated binge drinking among all men was 31.0% (95% Confidence Interval ¼ 30.5–31.4%) and among women, 18.0% (17.7–18.3%). Rates among White men and women were 30.5% and 19.6%, respectively. Among men, based on multivariate logistic regression, those significantly less likely to binge drink compared to White men were: Chinese (estimated proportion ¼ 11.9%, p50.001), Japanese Table 1. Sample and population sizes for California adults (CHIS survey years 2007 and 2009 combined). No. of men
No. of women a
Surveyed n Population N American Indian/ Alaska Native African American White Other/multiple race Chinese Filipino South Asian Japanese Korean Vietnamese Other Asian Mexican Central American Other Latino Total a
Surveyed n Population Na
464
125 624
678
124 199
1599 26 705 2446 1007 427 467 325 576 988 188 3964 414 365 39 935
720 769 6 500 912 1 188 466 479 570 395 421 251 336 96 794 122 305 210 583 92 773 2 588 841 348 498 223 953 13 345 841
2863 39 298 3679 1408 726 405 536 1016 945 241 5695 672 565 58 727
874 859 6 763 445 1 259 519 537 354 482 668 178 851 139 175 208 303 208 629 103 825 2 392 984 334 471 256 044 13 864 324
Estimated population for both years divided by two.
98
J. E. Banta et al.
Am J Drug Alcohol Abuse, 2014; 40(2): 95–102
Table 2. Socio-demographics for California adults (CHIS survey years 2007 and 2009 combined). Men
Women
Population
% binge drinking
Population
% binge drinking
18–24 years of age 25–34 35–44 45–54 55–65 65þ Education Less than 12 years High school graduate Some college College graduate Graduate school
14.6 18.1 20.8 19.5 14.0 13.1
51.8 53.9 44.0 35.1 25.5 12.4***
13.4 16.9 19.9 19.2 14.5 16.1
39.7 35.3 24.2 20.0 13.8 5.9***
15.6 28.9 22.2 19.2 14.1
35.7 42.2 41.9 37.9 29.1***
15.5 25.9 25.8 20.6 12.2
11.3 22.5 26.6 27.1 12.5***
Single, no children Single, children Married, children Married, no children
40.3 2.9 28.8 28.0
44.5 52.2 40.6 26.2***
36.6 8.4 27.8 27.2
27.3 26.9 21.2 20.8***
Household income $0–30 000 $30 001–65 000 $65 001–100 000 $100 001–150 000 $150 001þ Full-time employment Part-time employment Unemployed, looking for work Other Survey in English Survey in language other than English
28.7 26.6 19.7 13.8 11.3 65.6 7.0 6.8 20.6 83.4 16.6
34.8 37.0 40.0 40.8 45.6*** 43.3 38.3 40.9 22.3*** 39.1 35.1*
34.2 26.4 19.4 11.3 8.7 45.8 11.5 6.0 36.7 83.7 16.3
17.2 22.9 25.2 29.3 31.5*** 29.4 27.0 28.3 12.5*** 25.6 8.7***
Not foreign-born Foreign-born
67.1 32.9
40.8 33.6***
67.4 32.6
28.3 11.7***
Survey in 2007 Survey in 2009
49.4 50.6
38.2 38.6
49.4 50.6
21.4 24.3***
Binge drinking frequencies No binge drinking Once a year Less than monthly Monthly More than monthly Daily or weekly
61.6 4.6 16.3 4.8 7.3 5.4
77.1 4.3 11.0 2.8 2.9 1.9
*p50.05. ***p50.001.
(14.8%, p ¼ 0.001), South Asian (16.1%, p50.001), Vietnamese (16.8%, p50.001), and African American (24.6%, p50.001). Among women, again based on multivariate logistic regression, those less likely to binge drink compared to White women were: Chinese (24.6%, p50.001), South Asian (6.4%, p ¼ 0.047), Japanese (10.1%, p ¼ 0.008), Filipino (12.0%, p ¼ 0.014), African American (12.9%, p50.001), Central American (13.2%, p ¼ 0.028), and Mexican (17.5%, p ¼ 0.028). Table 4 (Panel a) presents the estimated proportions (and 95% confidence intervals) for binge drinking frequency by race/ethnicity based on the multinomial logit regression model for men. Significant p values also are presented. In most cases, White men had the highest proportion of binge drinking within each category, except for no binge drinking: binge drinking no times in the past year being 69.5% (68.9– 70.0%), only once in the past year 3.6% (3.4–3.8%), less than
monthly 12.5% (12.1–12.9%), monthly 4.2% (4.0–4.5%), more than monthly 5.1% (4.8–5.4%), and weekly or daily 5.1% (4.8–5.4%). The only instances of more frequent binge drinking compared to Whites, based on this regression, were Mexican and Central American men binge drinking in the category of ‘‘less than monthly’’ (p50.001, estimated proportion 19.9%, 18.7–21.1% and p50.001, 19.6%, 15.7– 23.4%, respectively. According to the multinomial logit regression model, African American and Chinese men were significantly less likely to binge drink compared to White men across all five categories of binge drinking (once per year through weekly or daily). Estimates for Vietnamese men were significantly lower in three categories (once per year through monthly), Japanese men lower in two categories (once per year and less than monthly), South Asians lower in two categories (less than monthly and weekly or daily), and Filipino men lower in one category (monthly). As previously noted, both Mexican and Central American men
Binge drinking in California
DOI: 10.3109/00952990.2013.850503
Table 3. Regression-adjusted percentages of adults reporting any binge drinking in the past year (CHIS survey years 2007 and 2009 combined).
Overall White American Indian/ Alaska Native African American Other/multiple race Chinese Filipino South Asian Japanese Korean Vietnamese Other Asian Mexican Central American Other Latino
Men % (95% CI)
Women % (95% CI)
31.0 (30.5–31.4) 30.5 (30.0–31.1) ref. 32.3 (28.1–36.6)
18.0 (17.7–18.3) 19.6 (19.2–20.0) ref. 19.0 (16.1–22.0)
24.6 37.5 11.9 27.6 16.1 14.8 28.1 16.8 29.8 42.9 38.6 37.5
12.9 18.7 5.5 12.0 6.4 10.1 13.6 4.8 11.2 17.5 13.2 25.7
(22.5–26.7)*** (35.6–39.4) (9.9–13.9)*** (23.4–31.9) (12.7–19.4)*** (10.9–18.6)** (24.4–31.8) (14.5–19.1)*** (23.2–36.3) (41.3–44.4) (34.0–43.3) (32.6–42.5)
99
were significantly more likely than Whites to binge drink less than monthly, although they were significantly less likely to binge drink weekly or daily. Table 4 (Panel b) presents the estimated proportions (and 95% confidence intervals) for binge drinking frequency by race/ethnicity based on the multinomial logit regression model for women. White women had the highest rate of binge drinking in all categories excluding ‘‘no binge drinking,’’ with the estimated percentage binge drinking no times during the past year being 80.4% (80.0–80.8%), only once in the past year at 3.2% (3.1–3.4%), less than monthly 9.6% (9.3–9.9%), monthly 2.6% (2.4–2.7%), more than monthly 2.5% (2.3–2.6%), and weekly or daily 1.7% (1.6–1.8%). The one exception was that ‘‘Other Latino’’ women were more likely to binge drink less than monthly 19.9% (18.7–21.1%). Similar to men, too, the multinomial logit regression revealed that only African American and Chinese women were significantly less likely than White women to binge drink across all five categories of binge drinking. South Asian women were less likely to drink in three categories (less than monthly, monthly, and weekly or daily). ‘‘Other Asian’’ women also were less likely to drink in three categories (once per year, less than monthly, and weekly or daily). American Indian/Alaska Native women were less likely to drink in two categories (more than monthly and weekly or daily, and monthly and weekly or daily, respectively). There were
(11.7–14.2)*** (17.4–20.0) (4.3–6.7)*** (9.6–14.3)* (4.0–8.8)* (7.5–12.6)** (11.5–15.7) (3.4–6.1) (7.2–15.2) (16.5–18.5)** (10.7–15.8)* (22.1–29.3)
*p50.05, **p50.01, ***p50.001. CI, Confidence Interval. ref. ¼ reference category for race in regression models. Binary logistic regression models used all independent variables shown in Table 2. Sample size for men was 39 935 and for women was 58 727.
Table 4. Regression-adjusted estimated binge drinking proportions for men and women.
Race/ethnicity
No binge
Once per year
Less than monthly
Monthly
Percent (CI)
Percent (CI)
Percent (CI)
Percent (CI)
Panel a: men White (ref.) 69.5 (68.9–70.0) 3.6 (3.4–3.8) American Indian/Alaska Native 67.7 (63.4–71.9) 3.2 (1.6–4.8) African American 75.4 (73.3–77.5) 4.1 (3.1–5.0)* Other/multiple race 62.5 (60.6–64.4) 5.8 (4.9–6.8) Chinese 88.1 (86.1–90.1) 2.4 (1.4–3.3)*** Filipino 72.4 (68.1–76.6) 4.9 (2.9–7.0) South Asian 83.9 (80.6–87.3) 1.3 (0.3–2.3) Japanese 85.2 (81.4–89.1) 2.8 (1.0–4.6)* Korean 71.9 (68.2–75.5) 2.8 (1.4–4.1) Vietnamese 83.2 (80.9–85.5) 2.6 (1.6–3.6)** Other Asian 70.2 (63.7–76.8) 4.3 (1.4–7.1) Mexican 57.1 (55.6–58.7) 5.8 (5.0–6.5) Central American 61.4 (56.7–66.0) 6.3 (3.9–8.6) Other Latino 62.5 (57.5–67.4) 5.5 (3.1–7.8)
12.5 12.5 11.5 15.8 5.8 13.3 8.6 6.2 10.2 8.4 10.6 19.9 19.6 15.9
Panel b: women White (ref.) 80.4 (80.0–80.8) 3.2 (3.1–3.4) 9.6 American Indian/Alaska Native 81.0 (78.0–83.9) 2.9 (1.7–4.2) 9.1 African American 87.1 (85.8–88.3) 3.0 (2.4–3.6)*** 6.5 Other/multiple race 81.3 (80.0–82.6) 4.7 (4.0–5.4) 9.4 Chinese 94.5 (93.3–95.7) 1.1 (0.6–1.7)*** 3.2 Filipino 88.0 (85.7–90.4) 4.4 (2.9–5.9) 5.8 South Asian 93.6 (91.2–96.0) 2.2 (0.8–3.7) 3.0 Japanese 89.9 (87.4–92.5) 2.1 (0.9–3.3) 4.5 Korean 86.4 (84.3–88.5) 2.6 (1.6–3.5)** 7.5 Vietnamese 95.2 (93.9–96.6) 1.6 (0.8–2.4) 2.1 Other Asian 88.8 (84.8–92.8) 1.7 (0.0–3.3)** 8.3 Mexican 82.5 (81.5–83.5) 4.7 (4.1–5.2) 8.8 Central American 86.8 (84.2–89.3) 4.3 (2.8–5.9) 6.0 Other Latino 74.3 (70.7–77.9) 4.8 (3.0–6.5) 13.6
More than monthly Weekly or daily Percent (CI)
Percent (CI)
(12.1–12.9) (9.5–15.5) (9.9–13.1)*** (14.3–17.2) (4.3–7.2)*** (10.1–16.6) (6.0–11.1)** (3.5–8.8)* (7.8–12.7) (6.7–10.1)** (6.2–15.1) (18.7–21.1)*** (15.7–23.4)*** (12.1–19.6)
4.2 4.3 2.4 4.3 1.8 2.8 2.6 2.2 4.3 2.1 3.7 5.0 5.1 6.8
(4.0–4.5) (2.5–6.2) (1.7–3.2)** (3.5–5.1) (1.0–2.6)** (1.2–4.4)* (1.1–4.0) (0.6–3.7) (2.7–6.0) (1.2–3.0)*** (1.0–6.4) (4.3–5.7) (3.0–7.2) (4.3–9.4)
5.1 5.6 3.6 7.2 1.8 5.2 2.6 1.8 4.9 2.1 6.4 7.2 4.8 5.8
(4.8–5.4) (3.5–7.7) (2.7–4.5)*** (6.1–8.2) (1.0–2.6)* (3.1–7.3) (1.1–4.0) (0.4–3.3) (3.1–6.6) (1.2–3.0) (2.9–9.9) (6.4–8.0) (2.8–6.9) (3.4–8.1)
5.1 6.7 3.0 4.4 0.2 1.4 1.1 1.8 5.9 1.5 4.8 5.0 2.9 3.6
(4.8–5.4) (4.4–9.0) (2.2–3.8)* (3.6–5.2) (0.1–0.5)*** (0.3–2.5) (0.1–2.0)*** (0.4–3.3) (4.0–7.8) (0.8–2.3) (1.7–7.8) (4.4–5.7)** (1.3–4.5)*** (1.7–5.5)
(9.3–9.9) (7.0–11.3) (5.6–7.4)*** (8.4–10.3) (2.3–4.1)*** (4.1–7.5) (1.3–4.6)*** (2.7–6.2) (5.9–9.1) (1.2–3.0) (4.8–11.8) (8.1–9.6) (4.2–7.7)* (10.8–16.5)*
2.6 2.4 1.2 1.6 0.4 1.0 0.5 1.1 1.6 0.3 0.8 1.5 1.2 3.7
(2.4–2.7) (1.2–3.5) (0.8–1.6)*** (1.2–2.0)* (0.1–0.8)*** (0.3–1.7)* (0.2–1.2)*** (0.2–2.0)* (0.8–2.3) (0.0–0.7) (0.3–2.0)* (1.2–1.8)* (0.4–2.0) (2.2–5.3)
2.5 2.5 1.3 2.0 0.2 0.7 0.7 1.5 1.4 0.5 0.4 1.8 1.3 2.3
(2.3–2.6) (1.3–3.7)* (0.9–1.7)*** (1.5–2.4) (0.0–0.5)*** (0.1–1.3)*** (0.0–1.6) (0.5–2.5) (0.7–2.1) (0.1–1.0) (0.4–1.2) (1.4–2.1) (0.5–2.2) (1.1–3.5)
1.7 2.1 0.9 1.1 0.5 0.1 0.0 0.9 0.6 0.2 0.0 0.7 0.4 1.2
(1.6–1.9) (1.0–2.9)* (0.6–1.3)*** (0.8–1.4)* (0.1–0.9)*** (0.1–0.4) (0.0–0.0)*** (0.1–1.7) (0.1–1.1) (0.1–0.5)** (0.0–0.0)*** (0.5–0.9)** (0.0–1.0)* (0.3–2.2)
CI, 95% Confidence Interval. p Values based on regression, *p50.05, **p50.01, ***p50.001. Multinomial logit model used all independent variables shown in Table 2. Sample size for men was 39 935 and for women was 58 727.
100
J. E. Banta et al.
three racial/ethnic groups that were significantly less likely than White women to binge drink in just one category: ‘‘Other/multiple race’’ (monthly), Filipino (more than monthly), and Japanese (monthly).
Discussion These results demonstrate sizable variation in binge drinking prevalence based on gender and race/ethnicity for adults in the State of California. Although the average estimated rate of any binge drinking among men during the past 12 months was 31.0%, it ranged from 11.9% among Chinese to 42.9% among Mexican respondents. Similarly, although the estimated average rate among women was 18.0%, it ranged from 4.8% among Vietnamese to 25.7% among ‘‘Other Latino’’ respondents. After adjusting for other sociodemographic measures, African American, Chinese, South Asian, Japanese, and Vietnamese men were significantly less likely to do any binge drinking compared to Whites. Likewise, African American, Chinese, Filipino, South Asian, Japanese, Mexican, and Central American women were significantly less likely to binge drink compared to Whites. In addition to having the highest rate of any binge drinking, White men and women also had the highest frequency of binge drinking weekly or daily, more than monthly, more monthly in the past year compared to other racial/ethnic groups. There were instances of racial/ethnic groups being more likely to engage in low-frequency binge drinking. As shown in Table 4, Mexican and Central American men and ‘‘Other Latino’’ women were significantly more likely than Whites to binge drink ‘‘more than once a year; but less than monthly’’. Standard reporting from the NSDUH presents binge drinking by race/ethnicity, with 2011 binge drinking rates among those 12 years of age and older being 24.3% for American Indians/Alaska Natives, 23.9% for Whites, 23.4% for Hispanics, 19.4% for Blacks, 18.6% for persons reporting two or more races, and 11.6% among Asians (30). Although helpful at a national level, such numbers may not be as useful in states, like California, with large numbers of Asian and Latino residents. With CHIS data, it is possible to provide a more detailed snapshot of binge drinking by gender and race/ethnicity, so that one can make sub-group comparisons. There are occasional national studies providing more details, however; Lee and others, for example, combined 2002–2008 NSDUH data for Asian Indian, Chinese, Filipino, Japanese, and Korean adults (26). They did not present binge drinking estimates by gender, but they did report that Koreans were significantly more likely to binge drink in the past 30 days compared to those in the other four Asian sub-groups. We believe that this detailed gender and race/ethnicity binge drinking rate information is useful for informing policy and public health efforts. These data provide a nonoverlapping complement to (a) population-level estimates using BFRSS and NSDUH data with limited race/ethnicity detail (12,30) and national studies examining in detail specific race/ethnicity groups, such as the NSDUH study of Asian adults (26), (b) regional studies of specific racial/ethnic groups (25,27,28), (c) state-level estimates based on national datasets (31), and (d) other CHIS-based research, such as
Am J Drug Alcohol Abuse, 2014; 40(2): 95–102
Bryant and Kim’s study of alcohol consumption among elderly Asians and Latino immigrants (28). Few other states are able to produce such detailed analyses, as most must rely on national data for their local binge-drinking estimates. Furthermore, the differences we reported by gender and race/ethnicity are not apparent in state or national reports where Asian and Latino data are aggregated.
Study strengths and limits Among the present study’s strengths are the size and scope of the survey, which enabled us to use more detailed race/ ethnicity categories. Moreover, CHIS over-sampled several racial/ethnic minorities and conducted the survey in five different languages. Among the limits are the binge drinking criterion (a single item for number of binge drinking episodes in the past year, which may have been affected by self-presentation and the possible disinclination to report a socially unacceptable activity). This is a state survey, and our findings may not generalize to other states or to the entire nation. There are regional differences in binge drinking patterns, with BFRSS age-adjusted overall prevalence of binge drinking in 2010 ranging from 10.9% in Utah to 25.6% in Wisconsin; with California and national prevalence rates in the middle of this range (16.5% and 17.1%, respectively) (13). State surveys regarding binge drinking, however, have been shown to have reasonable construct validity, by comparing results from surveys such as BRFSS and NSDUH with per capita alcohol sales and deaths due to high blood alcohol content (32), and, thus, clearly are valuable for exploring binge drinking within a state. There also is the issue of difference in binge drinking definition. CHIS evaluated binge drinking during the past year, whereas BFRSS evaluates binge drinking during the past 30 days as CHIS did in 2005 (33). The change by CHIS was in line with NIAAA’s Task Force on Recommended Alcohol Questions, which suggested that researchers who can ask only three to five questions about alcohol should ask about patterns over the past 12 months (34). Moreover, because between 50 to 75% of the binge drinkers identified in CHIS engaged in binge drinking on a less than monthly basis, with some only once during the year, many of these people might not be identified as binge drinkers in the national surveys. Although this may make our estimates higher compared to many other studies, an advantage of using the 12-month definition for binge drinking is that it identifies occasional binge drinkers. This information, particularly the figures for Latino subgroups, may be useful for program planning and outreach purposes. Another factor that may contribute to our findings differing from national survey binge drinking estimates is our estimates were made from regression models that accounted for several standard socio-demographic measures and were stratified by gender. Given the diversity in socio-demographics, such as age, education, income, and immigration, which varied both within and across racial/ethnic groups, we did not believe that unadjusted rates would be as useful for making comparisons in binge drinking rates across 14 different racial/ethnic groups in a large and diverse state.
DOI: 10.3109/00952990.2013.850503
Finally, a finer grained analysis of race/ethnicity itself can be challenging. The present results are based on self-report, with people being able to self-identify more than one category. There are differences if one categorizes race/ethnicity per the guidelines of the Census Bureau, federal Office of Management and Budget, or California Department of Finance. The detailed categories used by CHIS may not directly correspond to detailed categories used by other federal agencies. Moreover, we had to collapse some of the detailed race/ethnicity categories due to small sample size. Thus, our findings may not entirely match up with other surveys; however, California is a racially diverse state, and the categories chosen by CHIS are sensitive to the population, and the findings from our collapsed categories are statistically valid. Nevertheless, our results may be directly applicable to efforts to reduce the effects of excess drinking in states that are experiencing growth in the detailed race/ethnicity groups studied in CHIS. Mexicans are the predominant ancestry in many southwestern states, for example, whereas Japanese and Filipinos are predominant in Hawaii (16).
Conclusion Our data support the recommendation that binge drinking frequency should be monitored regularly by health agencies to both improve surveillance and better assess the effects of interventions designed to prevent and reduce binge drinking and its consequences (35,36). Findings from epidemiological surveys – ours and others – underscore the need to implement effective population-based prevention strategies and develop effective (evidence-based) interventions targeted at groups at higher risk (12,36). Our analyses point to several groups most in need of attention with respect to binge drinking. As shown in Table 3, among Asian men and women, Koreans, Filipinos, and ‘Other Asians’ have the highest rates of binge drinking. Moreover, as shown in Table 4 (Panels a and b), Mexican and Central American men and ‘Other Latino’ women were significantly more likely than Whites to binge drink occasionally, that is, less than monthly. Although not directly related to binge drinking, a ‘‘Keepin’it REAL’’ intervention to prevent substance abuse was translated and tested in Mexican public schools. Researchers found modest, but statistically significant, effects for females and a conclusion was that comprehensive cultural adaptation may be needed in addition to linguistic translation to achieve full effectiveness (37). Finally, it seems reasonable to recommend, too, that efforts to curtail binge drinking be coordinated with initiatives to reduce overall alcohol consumption; the majority of binge drinkers, in fact, may be considered moderate drinkers (e.g. not alcohol dependent) when looking at total alcohol consumption (38).
Declaration of interest The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.
References 1. Kanny D, Liu Y, Brewer RD. Binge drinking – United States, 2009. MMWR Surveill Summ 2011;60(Suppl):101–104.
Binge drinking in California
101
2. Perkins HW. Surveying the damage: a review of research on consequences of alcohol misuse in college populations. J Stud Alcohol Suppl 2002;14:91–100. 3. Wechsler H, Isaac N. ‘Binge’ drinkers at Massachusetts colleges. Prevalence, drinking style, time trends, and associated problems. JAMA 1992;267:2929–2931. 4. Wechsler H, Lee JE, Kuo M, Seibring M, Nelson TF, Lee H. Trends in college binge drinking during a period of increased prevention efforts. Findings from 4 Harvard School of Public Health College Alcohol Study surveys: 1993–2001. J Am Coll Health 2002;50: 203–217. 5. Hingson R, Heeren T, Winter M, Wechsler H. Magnitude of alcohol-related mortality and morbidity among U.S. college students ages 18–24: changes from 1998 to 2001. Annu Rev Public Health 2005;26:259–279. 6. Presley CA, Pimentel ER. The introduction of the heavy and frequent drinker: a proposed classification to increase accuracy of alcohol assessments in postsecondary educational settings. J Stud Alcohol 2006;67:324–331. 7. Haskard KB, Banta JE, Williams SL, Haviland MG, DiMatteo MR, Przekop P, Werner LS, Anderson DL. Binge drinking, poor mental health, and adherence to treatment among California adults with asthma. J Asthma 2008;45:369–376. 8. Read JP, Beattie M, Chamberlain R, Merrill JE. Beyond the ‘‘Binge’’ threshold: heavy drinking patterns and their association with alcohol involvement indices in college students. Addict Behav 2008;33:225–234. 9. U.S. Department of Health and Human Services. Disparities in Substance Abuse. Available at: http://www.healthypeople.gov/ 2020/lhi/substanceabuse.aspx?tab¼data [last accessed 5 September 2013]. 10. Harris KM, Gordon-Larsen P, Chantala K, Udry JR. Longitudinal trends in race/ethnic disparities in leading health indicators from adolescence to young adulthood. Arch Pediatr Adolesc Med 2006; 160:74–81. 11. Caetano R, Baruah J, Ramisetty-Mikler S, Ebama MS. Sociodemographic predictors of pattern and volume of alcohol consumption across Hispanics, Blacks, and Whites: 10-year trend (1992–2002). Alcohol Clin Exp Res 2010;34:1782–1792. 12. Centers for Disease Control and Prevention. Sociodemographic differences in binge drinking among adults – 14 States, 2004. MMWR Morb Mortal Wkly Rep 2009;58:301–304. 13. Kanny D, Liu Y, Brewer RD, Garvin WS, Balluz L. Vital signs: binge drinking prevalence, frequency, and intensity among adults – United States, 2010 (Reprinted from MMWR, vol. 61, pp. 14–19, 2012). JAMA 2012;307:908–910. 14. Chartier K, Caetano R. Ethnicity and health disparities in alcohol research. Alcohol Res Health 2010;33:152–160. 15. Toleran DE, Tran PD, Cabangun B, Lam J, Battle RS, Gardiner P. Substance use among Chinese, Filipino, and Vietnamese adult men living in San Jose, Daly City, and San Francisco, and its implications on ATOD prevention services. J Ethn Subst Abuse 2012;11:86–99. 16. Brittingham A, de le Cruz GP. Ancestry: 2000; Census 2000 Brief. U.S. Census Bureau. Available at: www.census.gov/prod/2004pubs/ c2bkr-35.pdf [last accessed 5 September 2013]. 17. U.S. Department of Health and Human Services Office of Minority Health. Reducing Health Disparities with Improved Data Collection: New Refined Data Standards for Race, Ethnicity, Sex, Primary Language, and Disability Status. Available at: http:// minorityhealth.hhs.gov/templates/content.aspx?ID¼9232&lvl¼2& lvlID¼208 [last accessed 5 September 2013]. 18. The White House. Initiative on Asian Americans and Pacific Islanders. Available at: http://www.whitehouse.gov/aapi [last accessed 5 September 2013]. 19. California Health Interview Survey. CHIS 2007 Methodology Report Series. Report 5: Weighting and Estimation of Variance in the CHIS Public Use Files. UCLA Center for Health Policy Research. Available at: http://healthpolicy.ucla.edu/chis/design/ Pages/methodology.aspx [last accessed 5 September 2013]. 20. California Health Interview Survey. CHIS 2009 Methodology Report Series. Report 4: Response Rates. UCLA Center for Health Policy Research. Available at: http://healthpolicy.ucla.edu/chis/ design/Pages/methodology.aspx [last accessed 5 September 2013.
102
J. E. Banta et al.
21. California Department of Public Health, California Tobacco Control Program. Smoking prevalence among California and U.S. minus California adults, 1984–2010. Available at: http:// www.cdph.ca.gov/programs/tobacco/Documents/Media/2010%20 adult%20and%20youth%20prevalence%20memo_figures%20(7)v2. pdf [last accessed 5 September 2013]. 22. Babey SH, Grant D, Brown ER. Adult smoking rate declines, while asthma, diabetes and obesity rates rise: UCLA Center for Health Policy Research, 2006. Available at: http://healthpolicy.ucla. edu/publications/Documents/PDF/CHIS2005_FS111306.pdf [last accessed 5 September 2013]. 23. Fahimi M, Link M, Mokdad A, Schwartz DA, Levy P. Tracking chronic disease and risk behavior prevalence as survey participation declines: statistics from the behavioral risk factor surveillance system and other national surveys. Prev Chronic Dis 2008;5:A80. 24. Ponce NA, Lavarreda SA, Yen W, Brown ER, DiSogra C, Satter DE. The California Health Interview Survey 2001: translation of a major survey for California’s multiethnic population. Public Health Rep 2004;119:388–395. 25. Caetano R, Mills B, Vaeth PA. Alcohol consumption and binge drinking among U.S.-Mexico border and nonborder Mexican Americans. Alcohol Clin Exp Res 2012;36: 677–685. 26. Lee HK, Han B, Gfroerer JC. Differences in the prevalence rates and correlates of alcohol use and binge alcohol use among five Asian American subpopulations. Addict Behav 2013;38: 1816–1823. 27. De La Rosa M, Dillon FR, Sastre F, Babino R. Alcohol use among recent Latino immigrants before and after immigration to the United States. Am J Addict 2013;22:162–168. 28. Bryant AN, Kim G. The relation between acculturation and alcohol consumption patterns among older Asian and Hispanic immigrants. Aging Ment Health 2013;17:147–156.
Am J Drug Alcohol Abuse, 2014; 40(2): 95–102
29. Long JS, Freese J. Regression models for categorical dependent variables using Stata, revised edition. College Station, Texas: Stata Press, 2003. 30. Substance Abuse and Mental Health Services Administration. Results from the 2011 National Survey on Drug Use and Health: Summary of National Findings, NSDUH Series H-44, HHS Publication No. (SMA) 12-4713. Rockville M.S.A.a.M.H.S.A., ed., 2012. 31. California Department of Alcohol and Drug Programs. 2012 California Needs Assessment Report. Available at: www.adp. ca.gov/library/pdf/2012_CA_Needs_Assessment_Report.pdf [last accessed 5 September 2013]. 32. Paschall MJ, Ringwalt CL, Gitelman AM. The validity of state survey estimates of binge drinking. Am J Prev Med 2010;39: 179–183. 33. Banta JE, Przekop P, Haviland MG, Pereau M. Binge drinking among California adults: results from the 2005 California Health Interview Survey. Am J Drug Alcohol Abuse 2008;34:801–809. 34. National Institute on Alcohol Abuse and Alcoholism. Available at: http://www.niaaa.nih.gov/research/guidelines-and-resources/ recommended-alcohol-questions [last accessed 5 September 2013]. 35. Naimi TS, Nelson DE, Brewer RD. The intensity of binge alcohol consumption among U.S. adults. Am J Prev Med 2010;38:201–207. 36. Centers for Disease Control and Prevention, Division of Population Health, National Center for Chronic Disease Prevention and Health Promotion. Fact sheets. Binge drinking. Available at: http:// www.cdc.gov/alcohol/fact-sheets/binge-drinking.htm [last accessed 5 September 2013]. 37. Marsiglia FF, Booth JM, Ayers SL, Nun˜o-Gutierrez BL, Kulis S, Hoffman S. Short-term effects on substance use of the Keepin’ It REAL Pilot Prevention Program: linguistically adapted for youth in Jalisco, Mexico. Prevention Sci 2013; Jul 23 [Epub ahead of print]. 38. Naimi TS, Brewer RD, Mokdad A, Denny C, Serdula MK, Marks JS. Binge drinking among US adults. JAMA 2003;289:70–75.
Copyright of American Journal of Drug & Alcohol Abuse is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
