Alcohol Use Disorder Among Whites and Hispanics on and off the US/Mexico border in California

Abstract

This paper examines whether U.S./Mexico border residence in California is related to the prevalence of DSM-5 alcohol use disorder (AUD) among Whites and Hispanics. Household survey data were obtained from 1,209 adults (59.7% female) 18 to 39 years of age resident in four counties in California: Imperial on the U.S./Mexico border; and Kern, Tulare, and Madera in California’s Central Valley. Households were selected using a list assisted sample, with data collected on the phone or online. Results show that AUD rates were not different between border and non-border location and between Whites and Hispanics. AUD was negatively associated with higher income ($20,000 to $60,000: AOR=.38; 95%CI=.17-.86; p<.01 – more than $60,000: AOR=.27; 95%CI:.09-.81; p<.01) and poor risk perception (AOR=.86; 95%CI=.78-.94; p<.01). AUD was positively associated with continued volume of drinking (AOR=1.05; 95%CI=1.01-1.09; p<.01), drinking in Mexico (AOR=4.28; 95%CI=1.61-11.36; p<.01), marijuana use (AOR=4.11; 95%CI=1.73-9.77; p<.01), and impulsivity (AOR=1.55; 95%CI=1.23-1.94). Efforts to prevent AUD in the population in California, and especially among those who live close to the border with Mexico, should take into consideration factors such as impulsivity, marijuana use, border crossing to drink in Mexico, all of which increased risk of AUD.

Introduction

The U.S./Mexico border is approximately 2,000 miles long, extending from the Gulf of Mexico to the Pacific Ocean. In 2019, there were about 22 million pedestrian crossings in the 7 border crossing points in California alone (Bureau of Transportation Statistics, 2019). The U.S./Mexico border is also characterized by increased alcohol availability due to the younger minimum drinking age in Mexico (18 years of age) compared to the U.S., an increased number of bars, clubs, and restaurants, and lower alcohol costs. According to both the distribution of alcohol consumption model (Bruun et al., 1975; Skog, 1985) and alcohol availability theory (Babor et al., 2010; Gruenewald, 2011), the theoretical frameworks in which this paper is based, increased access to alcohol by the population will lead to higher levels of consumption, higher prevalence of heavier drinking, and a higher prevalence of AUD. Hispanics, especially Mexican Americans, are uniquely exposed to this environment by virtue of their location in large numbers in cities on the U.S./Mexico border. Young, 18 to 29 year old Mexican Americans, cross the border and drink in Mexico and those who do so tend to be heavier drinkers, reporting a higher average number of drinks per week, and more binge drinking (Caetano et al., 2013a).

There have been at least five previous papers on AUD and its correlates on and away from the border, none of which used data analyzed herein. All of them focus exclusively on Mexican Americans, without a comparison with other ethnic/racial groups such as Whites, a major ethnic/racial group in the U.S. population. The sample analyzed herein also covers a wider area, four counties in California, than previous samples, which were collected in selected cities. County-level sampling provides a wider geographical coverage and potentially increased respondent socioeconomic and cultural diversity and generalizability of results. Selected results from Caetano et al. (Caetano et al., 2008; Caetano et al., 2013b), Wallisch and Spence (2006), Cherpitel et al. (2015a), and Wallisch et al. (2017) show border rates of 12-month DSM-IV (abuse plus dependence) and DSM-5 AUD varying from 11.8% to 25.6%, which shows considerable variation in AUD prevalence within the border area. Three of these rates are higher than the rate among Whites in national data (Caetano et al., 2008; Cherpitel et al., 2015a; Wallisch et al., 2017).

The main objective of this paper is to examine 12-month rates of DSM-5 AUD by severity level (any, mild, moderate, severe) among Whites and Hispanics on and away from the U.S./Mexico border. Because of the focus on the potential effect of border location, the analyses examine frequency and volume of drinking, and whether drinking happened both in Mexico and the U.S. Previous analyses indicate that Hispanic drinkers who report drinking in Mexico and the U.S. are heavier drinkers than those who drink in the U.S. only (Caetano et al., 2013a; Caetano et al., 2022b). These previous analyses have also reported both null and positive effects of border location on AUD rates depending on the border and non-border sites compared (Caetano et al., 2013a; Cherpitel et al., 2015a; Wallisch et al., 2017). The focus on impulsivity is based on previous results showing a positive association between this construct and AUD (Rubio et al., 2008; Dick et al., 2010) and also with measures of hostility and drinking in bars (Treno et al., 2007; Gruenewald et al., 2018). The frequency of drinking in this latter context on the border has been positively associated with an increased number of alcohol related social problems, injuries, and fights (Caetano et al., 2022a).

Marijuana is a drug of interest because it is frequently used with alcohol (Midanik et al., 2007; Subbaraman and Kerr, 2015), because it is highly available in the border area, and because of the legalization of recreational marijuana use in California in 2016. Perceived risk associated with selected behaviors ( e.g., risky sex, drinking and driving) is also included in the analysis because of its importance for decision making and risk taking (e.g., heavier drinking) and potential variation across, gender, age, and ethnicity (Lowenstein et al., 2001; Pacek et al., 2015a).

Analyses with these foci have not been implemented before in California, where Whites and Hispanics constituted 76% of the state’s population (White, 37%; Hispanics, 39%) in 2018 (U.S. Census Bureau, 2019). The site on the border is Imperial County; the sites away from the border are three counties in California’s Central Valley: Kern, Tulare, and Madera. Two hypotheses will be tested in the analyses. First, rates of any AUD will be higher on the border than in the Central Valley and higher among Hispanics than Whites. This is based on expected effects of the increased availability of alcohol in the border area, including border crossing to drink in Mexico (Caetano et al., 2008; Caetano et al., 2013b). Second, controlling for drinking and sociodemographic characteristics there will be a positive association between AUD, marijuana use, drinking in Mexico, and impulsivity (Jackson et al., 2008; Rubio et al., 2008; Dick et al., 2010; Subbaraman and Kerr, 2015).

Methods

Participants and Data Collection

Data are from a general population household survey of 1,209 White or Hispanic adults, 18 to 39 years of age interviewed in four counties in California: Imperial, Kern, Tulare, and Madera. Imperial County is agricultural and one of two California counties which share a border with Mexico, the other is San Diego County. Kern, Tulare, and Madera, the comparison sites, are all located in California’s Central Valley with similar large agricultural rural areas punctuated by similarly small towns.

Households were selected from a list assisted address-based sample (ABS) purchased from a commercial vendor. Listed assisted sampling differs little from samples developed using random digit dialing (Brick et al., 1995; Tucker et al., 2002; Kempf and Remington, 2007). These samples are constituted by records derived from postal addresses, built on the USPS Computerized Delivery Sequence File (CDSF). They contain all available residential mailing addresses covering almost 100% of all households in the U.S. Sample records have the mailing address and postal service relevant geographic information. This sample of addresses was overlaid with Census data and then appended by other household data, including names, phones, and residents gender.

All address-based records purchased for the project received an invitation postcard with a unique ID code, which described the study and directed potential respondents to the study website to learn more about the study and to submit an eligibility screener. Households were eligible if at least one of the residents was 18-39 years of age. If respondents were eligible to participate in the study and entered their provided unique ID code in the online screening tool, they were rerouted to the web-based survey.

Surveys were conducted from June 2019 to May 2020 in both English and Spanish. Respondents who did not opt to respond to the questionnaire online were interviewed on the phone. These interviews were conducted by experienced interviewers working for the fieldwork agency contracted to conduct the survey. They were trained in the use of the questionnaire by the survey agency under supervision of the two co-directors of the project (Vaeth and Caetano). About 90% of the respondents opted to be interviewed in English or to respond to an online questionnaire in English, and 86% responded to the survey online. Respondents gave verbal consent to participate in the survey and received $20.00 remuneration.

Both the survey overall cooperation rate and the response rate, using version #4 of the American Association for Public Opinion Research (American Association for Public Opinion Research, 2016), were different for the Central Valley and the border samples. The response rate estimates the percentage of respondents who completed an interview over the total number of eligible respondents in the sample. The cooperation rate estimates the percentage of households with positive contacts with survey personnel over all households contacted. Cooperation and response rates for the Central Valley were 95% and 72.5%, while on the border they were 12% and 7%. These low rates may have resulted from time overlap between the survey and the COVID-19 pandemic, which disproportionately affected Imperial County, where 85% of the population is Hispanic. Responses to the survey invitation on the border may have been also affected by repressive immigration policies at the time by the U.S. federal administration.

Therefore, the sample should not be seen as representative of larger White and Hispanic groups in California. However, most important for the comparison between border location and the Central Valley is internal validity, which was secured by group matching respondents in both locales on gender, age, and ethnicity, maintaining uniformity of respondent selection and data collection, and conducting analyses with controls for potential confounders. Analyses were conducted on data weighted to adjust the sample to known population distributions on gender, age, and ethnic identification (Hispanic, White) on the U.S. Census Bureau 2018 American Community Survey (American Community Survey (ACS) (census.gov) accessed on July 7, 2020). This study was approved by the Pacific Institute for Research and Evaluation Institutional Review Board.

Measurements

Imperial County on the Border and the Central Valley away from the border

This variable identified sample respondents interviewed in the Central Valley of California (Kern, Madera, and Tulare counties), coded as “0”, and those who were interviewed in Imperial County, on the border, coded as “1”.

Alcohol use disorder (AUD)

Diagnosis of AUD was based on DSM-5 criteria (American Psychiatric Association, 2013) implemented with the World Health Organization’s Composite Diagnostic Interview (CIDI), Version 2.1. The CIDI has adequate concordance in clinical reappraisal studies with the Structured Clinical Interview for Axis I Disorders (SCID) (κ = .51; specificity = .82 for lifetime substance use disorders) (Alegria et al., 2009). According to DSM-5 criteria, respondents reporting the presence of two or more indicators in a 12-month period are positive for last 12-month AUD. Severity levels in DSM-5 are defined as follows: mild (2–3 indicators), moderate (4–5 indicators), severe (≥6 indicators) in a 12-month span.

Quantity and frequency of drinking

Respondents were asked the number of days on which they had at least one drink of beer, wine, or liquor in the past 4 weeks or, among those who had not used alcohol in the previous four weeks, the past year. Those who had used alcohol were then asked on how many days they drank more than one, three or more, six or more, and nine or more drinks. A “drink” was defined as a 12-ounce can of beer, a 5-ounce glass of wine, or a 1.5-ounce shot of liquor. These data were fit using a validated log-logistic model of these responses (Gruenewald et al., 2003a; Gruenewald et al., 2003b) providing measures of drinking frequency (F), average drinking quantity (Q), volume (F*Q), and continued drinking volumes, V - F, a dose-response measure of heavier drinking related to problems.(Gruenewald and Mair, 2015) Using these measures, F represents independent effects related to greater drinking exposures (drinking occasions) and V - F represents independent effects related to greater average drinks consumed per drinking occasion (two critical dimensions of dose-response) (Cunradi et al., in press). All drinking measures were scaled to a 365-day metric. Test retest reliabilities of these measures vary from r=0.65 for drinking quantities to r=0.85 for drinking frequencies (Gruenewald and Johnson, 2006).

Six drinks or more (heavy drinking)

Respondents were also asked on how many days they drank six or more drinks. Answers were recorded in integers representing the number of days reported by respondents. A similar question (six or more drinks on one occasion) had good sensitivity (men, .87; women, .60) and specificity ( men .84; women, .92) for detecting risky drinking (Bradley et al., 2003; Bradley et al., 2007).

Drinking in Mexico

Respondents were asked how many times they had visited Mexico in the past 12 months. Those that answered at least once were then asked on how many of those days they had had at least one drink of any alcoholic beverage. Answers were coded as “0” for no days and as “1” for one or more days.

Marijuana use

Respondents were asked on how many days in the past 12 months they had used marijuana. Answers were coded as “0” for none and “1” for 1 day or more.

Impulsivity

This was measured with three items assessing respondents’ agreement with the following statements: I often act on the spur-of-the-moment without stopping to think; You might say I act impulsively; Many of my actions seem to be hasty (Caetano et al., 2000; Cunradi et al., 2009). Four response categories ranged from “quite a lot” to “not at all” with scores ranging from 1 to 4 per item. Items were reverse coded, and scale values ranged from 0 to 9, with higher values indicating higher impulsivity. Cronbach’s alpha in the present data set is .86.

Perceived risk of selected drinking behaviors

This was assessed with five items asking respondents’ to evaluate the likelihood that “something bad would happen” if they drove over the speed limit, drove while drunk, drove without a seat belt, drank a lot, and had sex with someone they just met. Five response categories ranged from very likely to very unlikely. Scale values ranged from 0 to 20, with higher value indicating poorer risk perception. Cronbach’s alpha in the present data set is .88.

COVID shelter in place

The survey was conducted partially before and partially during the adoption of COVID related shelter-in-place orders. Thus, an indicator of the time when survey respondents were interviewed or completed the questionnaire online was included in multivariable analyses. This variable was coded as “0” for those interviewed before the stay-at-home order and as “1” for those interviewed during the order.

Sociodemographic variables

Gender. A dichotomous variable was coded as men and women. Age. This was coded as a categorical variable: 18-25, 26-29, and 30-39. Ethnicity. This was based on self-identification. This variable was coded as “0” if respondents were Hispanic and as “1” if they were White. Birthplace. Respondents were asked if they were born in the United States or elsewhere. Those born in the U.S. were coded as “0” and those born elsewhere were coded as “1”. Income. This variable had three categories: $20,000 or less, $20,001 to $60,000, and $60,001 or more. Employment. This was a dichotomy coded as: 0” for currently working and as “1” for not currently working. Education. Respondents were categorized into four education categories: less than high school, completed high school or had a general education diploma (GED), some college or technical or vocational school, and completed 4-year college or technical school or higher (reference group). Religion. This variable had four categories: Protestant, no religious preference, Catholic (reference), and other. Marital status. This is a 3-category variable: married, separated or divorced, and single.

Statistical Analyses

Analyses were conducted on weighted data using Stata’s 17.0 “svy” prefix (Stata, 2015). Statistical significance in Tables 1, 2 and 3 was assessed with Bonferroni corrected chi-square analysis. Multivariable analyses of AUD severity (Table 4) were conducted using Stata’s “oglm” command, an ordinal generalized linear analysis procedure that addresses sources of heteroskedasticity related to selected independent variables (Williams, 2009; 2010). All variables were entered in the model in one step. Variable selection was based on previous findings in the literature, and the need for control for potential confounding of the effect of border location and ethnicity on AUD (Caetano et al., 2013b; Cherpitel et al., 2015a; Caetano et al., 2018).

Table 1:

Sample demographic characteristics by location.

	Border	Central Valley	All
Sample Ns	(534)	(674)	(1208)
Gender				p= NS
Men (487)	53.7	52.2	52.9
Women 721)	46.3	47.8	47.1
Age				p= NS
18-25	39.5	35.0	36.9
26-29	16.4	15.9	16.1
30-39	44.1	49.0	46.9
Ethnicity				p<.001
White	7.3	28.9	19.4
Hispanic	92.7	71.1	80.6
Education				p=NS
Less than High School	4.5	5.0	4.8
High School	25.9	30.9	28.8
Some College or Technical	32.8	28.7	30.5
College or Technical Graduate	36.6	35.2	35.9
Marital Status				p=NS
Married or Living with Someone	41.7	50.0	46.3
Separated or Divorced	5.1	5.8	5.5
Single	53.2	44.1	48.2
Birthplace***				p<.001
United States	79.4	87.8	84.1
Employment ns				p=NS
Full or Part Time	62.7	61.0	61.8
Unemployed	36.3	38.4	37.5
Retired, Disabled Never Employed	.93	.53	.71
Annual Family Income				p=NS
$20,000 or less	33.7	31.6	32.5
$20,001 to $60,000	39.4	41.3	40.5
$60,001 or more	26.9	27.0	26.9
Religion				p<.001
Protestant	16.8	27.0	22.5
Catholic	50.8	32.1	40.4
No Religion	27.6	38.3	33.6
Other	4.7	2.6	3.5

Bonferroni corrected chi square: .05/9= 0.005; NS: Not significant.

Table 2:

Twelve Month Alcohol Use Disorder Rates by Selected Demographic Characteristics.

	Border % (Ns)	Central Valley % (Ns)
All Sample ns	11.3 (534)	13.5 (674)
Gender
Men ns	17.7 (233)	13.5 (254)
Women ns	8.8 (301)	8.7 (420)
Age
18-25 ns	12.8 (146)	10.0 (158)
26-29 ns	15.5 (87)	15.2 (106)
30-39 ns	13.3 (306)	10.9 (413)
Ethnicity
White ns	13.0 (83)	10.2 (264)
Hispanic ns	13.6 (455)	11.3 (413)
Birthplace
United States ns	14.3 (424)	11.9 (608)
Abroad ns	7.1 (114)	8.8 (69)

Bonferroni correct chi square .05/10=.005: ^ns not significant

Table 3:

Alcohol use disorder by gender, ethnicity and location among Whites and Hispanics 18 to 39 years of age, percent.

	Men					Women
	White		Hispanic		White		Hispanic
	Border	Central Valley	Border	Central Valley	Border	Central Valley	Border	Central Valley
Column Total N	(38)	(106)	(195)	(148)	(46)	(156)	(255)	(264)
Any AUD	16.8	13.9	13.3	19.2	2.9	11.9	9.1	7.5
Chi square	p=NS		p=NS		p=NS		p=NS
Mild	9.5	9.6	7.5	6.2	2.2	4.5	4.7	3.0
Moderate	6.6	3.7	3.5	6.9	0.0	2.6	2.4	2.7
Severe	0.0	7.0	2.3	6.1	2.2	3.8	1.9	1.7
Chi square	p=NS		p=NS		p=NS		p=NS

Bonferroni corrected chi square .05/8=.006. NS: Chi square not statistically significant for Any AUD border x Central Valley comparison within ethnic group and AUD severity border x Central Valley within ethnic group.

Table 4:

Multivariable heteroskedastic ordered logistic regression of AUD severity on sociodemographic, location, drinking, and psychological variables.

	AOR	95% CI
Independent Variables
Border (ref.: Central Valley) ns	.53	.21–1.37
Hispanic (ref.: White) ns	.78	.29–2.11
Income (Ref: less than $20,000)
$20,000 - $60,000**	.38	.17-.86
$60,001 and more**	.27	.09-.81
Drinking frequency ns	1.00	.87–1.1
Continued number of drinks**	1.05	1.01-1.09
Number of days had six drinks**	1.02	1.002–1.03
Drinking in Mexico (ref.: No)**	4.28	1.61-11.36
Marijuana use (ref.: No)**	4.11	1.73-9.77
Impulsivity scale***	1.55	1.23-1.94
Poor risk Perception**	.86	.78-.94
Heteroskedasticity	Coefficient	95%CI
Drinking frequency ns	−.076	.001-.150
Continued number of drinks ns	−.002	−.010-.006
Drinking frequency squared ns	−.001	−.003-.0007

Note: ^ns not significant

^*p<05;

^**p<.01;

^***p<.001.

Also controlling for gender, age, birthplace, education, employment, marital status, and religion, none of which had a statistically significant association with the outcome. Results in the heteroskedasticity part of the model are not transformed.

Results

Sample sociodemographic characteristics

The sample on the border had a significant higher percentage of Hispanics, of respondents born abroad, and Catholics (Table 1). There were no statistically significant differences in gender, age, education, marital status, employment, and annual family income.

AUD overall rates and by severity levels by location

There were no statistically significant differences between the rate of 12-month AUD on the border and in the Central Valley for the whole sample and for any of the sociodemographic categories compared in Table 2. There were no statistically significant differences in the distribution of AUD overall rates and by severity level between border and the Central Valley (Table 3).

Sociodemographic, location, substance use, and psychological correlates of AUD

The multivariable regression in Table 4 controlled for gender, age, birthplace, education, employment status, marital status, and religion, none of which had a statistically significant association with AUD. The variable indicating the time when respondents were interviewed relative to the COVID shelter-in-place order was not significantly associated with AUD (AOR=.74; 95%CI=.419-1.308; p=0.302). This variable was dropped from the model, which was then rerun with the results shown in Table 4 plus the sociodemographic variables identified above as controls.

Location and Hispanic ethnicity were not associated with AUD. Respondents who reported a higher annual family income were less likely to report AUD. Two of the three indicators of drinking were positively associated with AUD: Continued drinking beyond the first drink and the number of days in which six drinks were consumed. Those who went to Mexico and drank there were about four times more likely than those who did not drink in Mexico to report AUD. The same was true for those who had used marijuana in the past 12 months. Impulsivity was positively associated with AUD. However, those who had poorer perceived risk associated with various risky behaviors were more protected against AUD. None of the controls for heteroskedasticity were statistically significant.

Discussion

Two hypotheses were tested in this paper. The first, which proposed that rates of any AUD would be higher on the border than in the Central Valley and higher among Hispanics than Whites, was not confirmed. The second, which proposed that with appropriate controls for confounding, there would be a positive association between AUD, marijuana use, drinking in Mexico, and impulsivity was confirmed. First, the lack of difference between AUD rates on and off the border as well as by sociodemographic factors in Tables 2 and 3 is not entirely surprising. Border and off border comparisons of AUD rates in the U.S. have not always resulted in significantly different rates, as for example in findings reported by Caetano et al. (2013b) and Cherpitel et al. (2015a). Similarly, sociodemographic characteristics have not been consistently associated with AUD, especially when examined in a multivariable framework with control for potential confounding. For instance, there have been null findings for age, birthplace, religion (Caetano et al., 2008), income, employment, age, and education (Caetano et al., 2013b). The restricted age range of the sample may also be associated with the null results. While the selected age group has a higher prevalence of drinking and marijuana use and higher level of impulsivity than older adults, it may also have more uniformity in these behaviors.

However, the lack of difference in AUD rates between border and non-border locations should not automatically discount AUD risks associated with the border environment. For instance, crossing the border and drinking in Mexico, more than quadruples the likelihood of AUD. The magnitude of this effect is not surprising given previous research demonstrating that drinkers who cross the border to drink in Mexico are heavier drinkers, are more likely to binge, and have a higher rate of DSM-IV alcohol dependence (Caetano et al., 2013a; Cherpitel et al., 2015b). The cross-sectional nature of the data, however, makes it impossible to assess whether crossing the border leads to heavier drinking and AUD, or heavier drinkers with AUD cross the border more often seeking cheaper drinks in Mexico.

The lack of differences in AUD rates between Whites and Hispanics is also not entirely surprising. Recent national level data do not show differences in overall 12-month AUD rates and severity levels between Hispanics and Whites 18 years of age and older ( Whites: 14%; Hispanics: 13.6%) (Grant et al., 2015). Twelve month AUD rates from the 2018 National Survey on Drug Use and Health in the same age group are lower (White: 6%; Hispanic: 5.8%) but also show no difference between Whites and Hispanics (Center for Behavioral Health Statistics and Quality, 2019). No comparisons can be made with previous papers on the border because none of them compared Whites and Hispanics.

As expected, drinking more per any drinking occasion increases AUD risk by 5% with each additional drink. Consuming a larger number of drinks within certain timeframes, the best example of which is binge drinking, has been consistently associated with a higher risk of AUD (Caetano et al., 2008; Greenfield et al., 2017). Drinking in Mexico and use of marijuana in the past 12 months quadruple AUD risk. This latter result is important given the increased availability of this drug associated with the legalization of recreational use in various states in the U.S., including California. Simultaneous use of alcohol and marijuana has been related to increased social consequences, DUI, depression, harms to self, and alcohol dependence compared to alcohol-only users (Midanik et al., 2007; Subbaraman and Kerr, 2015). Two other important results are those related to impulsivity and perceived risk. Impulsivity had one of the strongest independent associations with AUD shown in Table 4 (Rubio et al., 2008; Dick et al., 2010; Coskunpinar and Cyders, 2012). Poor perceived risk is a predictor of alcohol-related risk taking (Coskunpinar and Cyders, 2012). Survey-based research examining perceived risk and substance use shows that lower perceived risk is usually associated with alcohol, marijuana, and other drug use (Pacek et al., 2015b; Votaw et al., 2017; Hanauer et al., 2019). , The results herein, are counterintuitive. Those with poor perceived risk are “protected” against AUD, perhaps because questions in this measure asked about specific behaviors (e.g., drove over the speed limit) and not about heavier drinking and AUD.

Finally, the study has many strengths. It analyzes a sample of younger adults in the population of a purposefully selected border county plus three other counties in the Central Valley of California, with matching for age and ethnicity to maximize internal validity. This was further enhanced by multivariable analyses controlling for sociodemographic and personality factors. Data collection covered drinking outcomes in detail and used state of the art interviewing techniques and questions both on the phone and online.

The study also has limitations. First, it is important to address the potential reasons for and consequences of the low survey response rate in Imperial County. If indeed repressive immigration policies, as mentioned above, influenced potential respondents behavior, it is likely that non-citizens and those born abroad would be less likely to participate in the study. For the same reason, non-participants should also be more likely to be men, younger and less educated, which would lead the border sample to have fewer respondents in these groups. But the data in Table 1 show that this is not the case. Results on the demographic composition of the sample in both locations (Table 1) are reassuring for they only show differences for ethnic composition, birthplace, and religion, which were expected given Census data (Ennis et al., 2011; Stepler and Brown, 2016) and previous border studies (Wallisch and Spence, 2006). Second, respondents in the sample may not represent larger populations on the border and in the Central Valley of California. Drinking amounts, AUD indicators, and other factors were self-reported, which may lead to under-reporting of alcohol consumption and other information. The study design was cross-sectional, which does not allow for assessments of temporal associations.

Conclusion

The higher availability of alcoholic beverages on the U.S./Mexico border area did not affect AUD rates. Overall AUD rates and AUD severity levels were similar for Whites and Hispanics in border and non-border areas. The same was true for rates by gender, age, and birthplace. It is possible that the restricted age range of sample respondents, 18 to 39 years old, contributed to this homogeneity in rates across population subgroups and locales.

Future research should focus on analyses of samples of adults with a wider age range to examine variations in drinking in older age groups by gender, locales, and ethnic groups. Given the cross-sectional nature of the findings herein, longitudinal studies are also needed to further understand the strong associations detected between drinking in Mexico and marijuana use with AUD. It is important to learn whether the association exists because those with AUD cross the border to drink in Mexico or whether border crossing leads to heavier drinking and AUD as a result. Until these and other longitudinal questions are answered, prevention policies should focus on minimizing border crossing and co-use of alcohol and marijuana both of which had the strongest associations with AUD in the analyses. The latter is particularly important now that recreational marijuana use is legal in California.