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. Author manuscript; available in PMC: 2020 Nov 1.
Published in final edited form as: Aging Ment Health. 2019 Jan 2;23(11):1503–1509. doi: 10.1080/13607863.2018.1506746

Regional and Racial/Ethnic Variations in Alcohol Consumption among Older Adults

Ami N Bryant 1,✉, Giyeon Kim 2
PMCID: PMC6606409  NIHMSID: NIHMS1512289  PMID: 30600687

Abstract

Objectives:

This paper sought to examine the role of region and race/ethnicity in alcohol consumption among older adults.

Methods:

Data were obtained from the 2010 Behavioral Risk Factor Surveillance System (BRFSS). Participants aged 60 and older were included (n=185, 190). Analyses of covariance (ANCOVAs) were conducted to examine the effects of region and race/ethnicity on alcohol consumption among older adults.

Results:

Results indicate that region and race/ethnicity are significantly related to the alcohol consumption of older adults. More specifically, results suggest that White older adults consume significantly more alcohol than other racial/ethnic groups among older adults. Regarding regional effects, results suggest that older adults in the West consume significantly more alcohol than older adults in the Midwest and South. Additionally, results suggest regional variation in alcohol consumption patterns by race/ethnicity.

Conclusion:

Results provide additional insight into how and where alcohol is being used among older adults in the United States. Further, results highlight the importance of taking race/ethnicity and geography into consideration when investigating the health behaviors of older adults.

Introduction

The National Institute on Alcoholism and Alcohol Abuse (NIAAA) recommends that adults aged 65 and older consume no more than one drink a day, a maximum of two drinks on any occasion, and that women should have even lower limits (NIAAA, 2005). Estimates suggest that at least one third of adults 65 and older consume alcohol on a regular basis (SAMHSA, 2002). It has been found that older adults have higher sensitivity to alcohol than their younger counterparts (Substance Abuse & Mental Health Services Administration [SAMHSA], 1998). This increased sensitivity is largely due to a change in body composition which alters the way that alcohol is distributed in the body when consumed (Blow & Barry, 2000; Smith, 1995).

A number of studies have found an inverse-U-shaped relation between alcohol consumption and risk for dementia, stroke, and cognitive decline (Anstey et al., 2005; Christensen et al., 2006; Espeland et al., 2005; Rodgers et al., 2005). While exactly what amount of alcohol is beneficial is not entirely clear, there is general consensus that excessive consumption is detrimental to many aspects of health. Among older adults, consumption over the NIAAA guidelines or problem drinking (terminology and functional definitions vary by study) is associated with various health problems as well as poor mental health and elevated levels of psychological distress (Bryant & Kim, 2013; Graham & Schmidt, 1999; Okoro, 2004). However, research also suggests that alcohol consumption within guidelines may have positive effects on both the physical and mental health of older adults (Graham & Schmidt, 1999; O’Connell, 2006; Tait & Hulse, 2006). For example, research indicates that moderate alcohol use is associated with a reduced risk for mental health related hospital admissions (O’Connell, 2006; Tait & Hulse, 2006). Thus, it appears that the alcohol consumption habits of older adults are highly significant as they may be related to improvements in or decreases in health outcomes depending on the nature of the consumption.

Whereas the number of older adults and alcohol consumption among this age group are predicted to increase, so is the racial/ethnic diversity of the United States (Cummings et al., 2011; IOM, 2012; US Census Bureau, 2012; Vincent and Velkoff, 2010; US Census Bureau, 2012). Research has shown variations in alcohol consumption patterns between racial/ethnic groups among both adults of all ages and older adults specifically (Bryant & Kim, 2012; Chartier & Caetano, 2010; Moore et al., 2006; Sacco, Bucholz & Spitznagel, 2009; SAMHSA, 2007). For instance, a recent study (Agic, Mann, & Kobus-Matthews, 2011) examining alcohol use in seven different ethnic communities in Canada found differences in the types and sizes of drinks consumed as well as differences in what amount of alcohol consumption was considered “normal” versus “excessive.” Such findings demonstrate that ethnic variations exist in alcohol consumption and that cultural factors may contribute to these variations. In addition to the reported racial/ethnic variations in alcohol consumption patterns, research has also found that the relation between alcohol use and mental health varies between racial/ethnic groups (Bryant & Kim, 2013; Smith et al., 2006).

Research has also demonstrated variability in alcohol consumption patterns by geographic location and geographic characteristics. In a study using the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), Borders and Booth (2007) examined rural/urban and regional differences in non-institutionalized Americans age 18 years and older. This study revealed that the relation between rural/urban residency and alcohol consumption is one that appears to vary by region (Borders & Booth, 2007). Regardless of rurality, it has consistently been found that the South has the lowest alcohol consumption rates while heavy drinking is most common in the Midwest (Borders & Booth, 2007; Dawson et al., 1995). Borders and Booth suggested (2007) that regional differences in alcohol consumption may be due to variations in cultural norms, variations in the health system, and the physical environment. Similarly, in a study of Filipino Americans living in Honolulu and San Francisco, Kim and colleagues (2010) found more evidence for an effect of geographic location on alcohol consumption: Filipino Americans living in San Francisco were more than twice as likely to have an alcohol use disorder than those living in Honolulu. This suggests that protective and risk factors for the development of alcohol use disorders varied by place of residence (Kim et al., 2010). These studies highlight the impact of geographic location and environment on alcohol consumption patterns.

The existing body of research indicates that geographic location as well as race/ethnicity appear to be contributors that may influence older adults’ alcohol consumption. Therefore, this study sought to examine the relation between geographic location and alcohol consumption among older adults while accounting for racial/ethnic variation.

Method

Dataset

Data from the 2010 Behavioral Risk Factor Surveillance System (BRFSS) were used. The BRFSS collects data on health risk behaviors, clinical preventive practices, and health care access and use, primarily related to chronic diseases and injury (CDC, 2010). BRFSS data are collected monthly by telephone through the use of random-digit dialing, by state health departments with technical and methodological assistance provided by the CDC. Data are collected in all 50 states as well as Puerto Rico, Guam, the US Virgin Islands, and the District of Colombia (CDC, 2010).

Sample

This study included adults age 60 and older from diverse racial/ethnic backgrounds. Adults who were not missing data on the study variables of interest (race/ethnicity, drinks consumed per month, and geographic location) were included for analyses. The self-reported racial/ethnic categories included White (n=161,711; 87.3%), Black (n=12,312; 6.6%), Asian (n=2,040; 1.1%), Pacific Islander (n=223; 0.1%), American Indian/Alaska Native (AIAN) (n=1,868; 1.0%), Hispanic (n=7,036; 3.8%). Due to their small sample sizes, the Asian and Pacific Islander racial/ethnic groups were merged into a larger single Asian/Pacific Islander group (API). Given the assumed exceptional level of heterogeneity of the Multiracial (n=2,929) and Other (n= 906) groups, they were excluded from analyses leaving a total sample size of n=185,190.

Measures

Outcome variable – monthly alcohol consumption

The main outcome variable, alcohol consumption, was assessed by examining number of alcoholic drinks consumed per month. In this dataset, drink is defined as follows: “One drink is equivalent to a 12-ounce beer, a 5-ounce glass of wine, or a drink with one shot of liquor” (CDC, 2009, p. 19). This is available as a calculated variable in the BRFSS data. This variable was calculated based on self-reported number of days alcohol was consumed in the past month and number of drinks consumed per drinking day in the past month. Research has shown that self-report methods are both valid and reliable for measuring alcohol consumption (Del Boca & Darkes, 2002). For the remainder of this document the term “drinks” refers to alcoholic drinks for the sake of brevity.

Independent variables

Demographic characteristics.

Age was measured continuously. Sex was measured dichotomously. Race/ethnicity was measured via self-report as previously described. Marital status was measured via self-report and was dichotomized into married and not married. Educational attainment was assessed utilizing the following 4 categories: did not graduate high school, graduated high school, attended college or technical school, and graduated from college or technical school. Employment status was dichotomized into unemployed and employed. Annual household income was assessed categorically with the following response options, less than $10,000; $10,000 to less than $15,000; $15,000 to less than $20,000; $20,000 to less than $25,000; $25,000 to less than $35,000; $35,000 to less than $50,000; $50,000 to less than $75,000; and $75,000 or more. However, to aid in interpretation annual household income was recoded into the following groups: less than $10,000, $10,000-$19,999, $20,000-$34,999, $35,000-$49,999, $50,000-$74,999, and 75,000 plus. Self-rated health was assessed based on the response to the following: Would you say that in general your health is excellent (1), very good (2), good (3), fair (4), or poor (5).

Geographic Region.

Geographic region was calculated by state, which is an available variable in the BRFSS. According to the US Census definition (US Census Bureau, 2012), categories included West, Midwest, Northeast, and South. State’s membership into specific regions was assigned based on the US Census defined regions. The US Census defined regions are as follows. The Northeast is comprised of Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont. The Midwest is comprised of Indiana, Illinois, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin. The South is comprised of Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and West Virginia. The West is comprised of Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, New Mexico, Oregon, Utah, Nevada, Washington, and Wyoming.

Data analysis

Analyses conducted on the publically available BRFSS data are preapproved by the University of Alabama’s institutional review board (IRB). To begin data analysis, descriptive statistics were conducted for demographic variables by geographic region. While this paper focuses on regional analyses, descriptive statistics regarding alcohol consumption were analyzed at the state level for the descriptive purpose. Past month alcohol consumption was computed by geographic region. In order to examine significant differences in these variables between geographic regions, one-way analyses of variance (ANOVAs) or chi-square tests were conducted as appropriate. To examine relations between independent variables, as well as to check for the presence of high multicollinearity, bivariate correlations were conducted using Pearson’s correlation coefficients.

In order to assess how geographic location (i.e. region) and race/ethnicity were associated with drinks consumed per month among older adults, an analysis of covariance (ANCOVA) was conducted. This allowed for an examination of the main effects of race/ethnicity and region as well as the conditional effects on alcohol consumption via the race/ethnicity by region interaction. Additionally, the use of ANCOVA allowed for in depth post hoc analyses including various pairwise comparisons without the limitation of a reference group. Simple effects tests relying on a Bonferroni adjustment were used to interpret interactions. Analyses were conducted in SPSS 17.

Results

Characteristics of the sample

As displayed in Table 1, there were significant regional differences in all demographic characteristics of the sample, as well as for self-rated health and drinks consumed per month. In regards to race/ethnicity, non-Hispanic Whites comprised the majority of the sample (87.3%), followed by Blacks (6.6%), Hispanics (3.8%), APIs (1.2%), and AIANs (1.0%). Non-Hispanic Whites comprised the highest percentage of the sample in the Northeast (91.4%) and the lowest percentage of the sample in the South (83.1%). In regards of racial/ethnic minority groups, the West had the highest percentage of AIANs (1.5%) while the Northeast had the lowest (0.4%). The West also had the highest percentage of APIs (3.7%) while the Midwest had the lowest (0.3%). The South had the highest percentage of Blacks (11.6%) while the West had the least (1.1%). Lastly, the West had the highest percentage of Hispanics (6.5%) while the Midwest had the lowest (1.4%). The mean age of the sample was 71.4 (SD=8.3). The sample was the oldest in the Midwest (72.0 years) and the youngest in the South and the West (71.2 years). The majority of the overall sample was female (64.6%). Females accounted for the largest percentage of the sample in the South (66%) and the smallest percentage of the sample in the West (62.4%). Approximately half of the sample was married (50.4%). Married individuals comprised the largest segment of the sample in the West (53.3%) and the smallest segment in the Northeast (47.0%). In regards to educational attainment, the majority of the sample had completed high school or a higher level of education (88.7%). Completion of high school or greater was most common in the West (92.3%) and least common in the South (85.0%). The most common annual income in the sample was $20,000-$34,999 (23.5%), while the least common annual income was less than $10,000 (4.3%). Annual incomes of less than $10,000 a year was most common in the South (6.5%). The mean self-rated health was 2.8 (SD=1.2) which is indicative of good to very good health. Self-rated health was the highest in the West (M=2.7, SD=1.2) and lowest in the South (M=2.9, SD=1.2). Average drinks consumed per month was 6.1 (SD=13.2) for the entire sample. The greatest mean drinks consumed per month was found in the Northeast (M=7.6, SD=14.2) and the least in the South (M=5.1, SD=12.5).

Table 1.

Characteristics of the Sample and Study Variables – Individual Level Variables (n=185,190)

Total Sample Northeast, n(%) Midwest, n(%) South, n(%) West, n(%) X2 or (F)
Total − 32,828 (17.7) 40,692 (22.0) 67,936 (36.7) 43,734 (23.6)
Race/Ethnicity 9,815.39**
 AIAN 1,868 (1.0) 128 (0.4) 448 (1.1) 641 (0.9) 651 (1.5)
 API 2,263 (1.2) 209 (0.6) 136 (0.3) 287 (0.4) 1,631 (3.7)
 Black 12,312 (6.6) 1,486 (4.5) 2,438 (6.0) 7,912 (11.6) 476 (1.1)
 Hispanic 7,036 (3.8) 997 (3.0) 560 (1.4) 2,637 (3.9) 2,842 (6.5)
 White 161,711 (87.3) 30,008 (91.4) 37,110 (91.2) 56,459 (83.1) 38,134 (87.2)
Age (Mean +/− SD) 71.4 (8.3) 71.5 ± 8.5 72.0 ± 8.6 71.2 ± 8.0 71.3 ± 8.3 (81.10)**
Female 119,678 (64.6) 21,213 (64.6) 26,357 (64.8) 44,830 (66.0) 27,278 (62.4) 152.70**
Married 93,425 (50.4) 15,434 (47.0) 20,559 (50.5) 34,134 (50.2) 23,298 (53.3) 295.83**
Educational Attainment 3,827.85**
 < High School 20,942 (11.3) 3,296 (10.0) 4,072 (10.0) 10,194 (15.0) 3,380 (7.7)
 Graduated High School 62,007 (33.5) 11,230 (34.2) 16,136 (39.7) 22,511 (33.1) 12,130 (27.7)
 Some College 46,816 (25.3) 7,190 (21.9) 10,292 (25.3) 16,566 (24.4) 12,768 (29.2)
 Graduated College 55,062 (29.8) 11,030 (33.6) 10,127 (24.9) 18,510 (27.2) 15,395 (36.2)
Employed 41,196 (22.3) 8,264 (25.2) 9,699 (23.8) 13,221 (19.5) 10,012 (22.9) 537.08**
Annual Income 1,489.24**
 Less than $10,000 8,003 (4.3) 1,244 (4.6) 1,421 (4.2) 3,554 (6.5) 1,784 (4.8)
 $10,000-$19,999 29,071 (15.7) 5,061 (18.7) 6,556 (19.5) 11,522 (21.0) 5,932 (15.8)
 $20,000-$34,999 43,449 (23.5) 7,411 (27.4) 10,631 (31.6) 15,342 (28.0) 10,065 (26.8)
 $35,000-$49,999 25,669 (13.9) 4,265 (15.7) 6,039 (17.9) 8,626 (15.7) 6,739 (18.0)
 $50,000-$74,999 20,748 (11.2) 3,699 (13.7) 4,353 (12.9) 6,847 (12.5) 5,849 (15.6)
 $75,000+ 26,107 (14.1) 5,407 (20.0) 4,657 (13.8) 8,891 (16.2) 7,152 (19.1)
Self-Rated Health (Mean +/− SD) 2.8 (1.2) 2.8 ± (1.2) 2.8 ± (1.1) 2.9 ± (1.2) 2.7 ± (1.2) (397.45)**
Drinks Per Month (Mean +/− SD) 6.1 (13.2) 7.6 (14.2) 5.2 (12.1) 5.1 (12.5) 7.4 (14.4) (444.92)**
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NOTES: AIAN=American Indian/Alaska Native; API=Asian/Pacific Islander

**

p<.001

Table 2 displays the average monthly drinks consumed by state. The lowest state mean for monthly drinks was found in Tennessee (1.98 drinks per month) while the highest state mean was found in New Hampshire (9.08 drinks per month). It should be noted that even higher average monthly alcohol consumption was found in Washington DC (11.12 drinks per month).

Table 2.

Mean Monthly Drinks by State (n=185,190)

State M
Alabama 3.00
Alaska 8.00
Arizona 7.57
Arkansas 3.91
California 8.78
Colorado 7.70
Connecticut 8.76
Delaware 7.13
District of Columbia1 11.12
Florida 7.28
Georgia 4.48
Hawaii 7.99
Idaho 5.54
Illinois 6.63
Indiana 4.11
Iowa 5.01
Kansas 4.59
Kentucky 2.69
Louisiana 4.26
Maine 8.62
Maryland 6.71
Massachusetts 7.88
Michigan 5.95
Minnesota 7.18
Mississippi 2.61
Missouri 4.30
Montana 7.30
Nebraska 4.87
Nevada 8.94
New Hampshire 9.08
New Jersey 6.80
New Mexico 5.72
New York 7.12
North Carolina 4.42
North Dakota 5.19
Ohio 4.79
Oklahoma 3.28
Oregon 8.63
Pennsylvania 5.14
Rhode Island 7.74
South Carolina 5.83
South Dakota 4.95
Tennessee 1.98
Texas 4.83
Utah 3.11
Vermont 8.85
Virginia 5.61
Washington 8.48
West Virginia 2.00
Wisconsin 7.08
Wyoming 6.15
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Correlations between study variables

Bivariate correlations between study variables are displayed in Table 3. Correlations between race/ethnicity and past month alcohol consumption for the overall sample as well as by region are displayed in Table 4. Race/ethnicity was dummy coded with the White sample as the reference group for Table 4. As compared to the reference group, every racial/ethnic group was significantly and negatively correlated with past month alcohol consumption.

Table 3.

Bivariate correlations between study variables (n=185,190

1 2 3 4 5 6 7
1. Drinks per month −
2. Female −.18** −
3. Age −.07** .06** −
4. Married .10** −.24** −.24** −
5. Educational attainment .10** −.11** −.15** .13** −
6. Employed .07** −.08** −.36** .10** .17** −
7. Annual income −.05** .10** .10** .02** −.04** .07** −
8. Self-rated health −.16** .00 .11** −.11** −.28** −.19** .00
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*

p<.01

**

p<.001

Table 4.

Bivariate correlations between race/ethnicity and drinks consumed per month by region (n=185,190)

Total Sample Northeast Midwest South West
AIAN1 −.02** −.01** −.02** −.02** −.03**
API1 −.02** −.03** −.01* −.01** −.05**
Black1 −.08** −.07** −.05** −.09** −.02**
Hispanic1 −.04** −.06** −.02** −.04** −.07**
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NOTES: AIAN=American Indian/Alaska Native, API=Asian/Pacific Islander; 1reference group=Whites;

**

p<.001

The effects of region and race/ethnicity

As displayed in Table 5, a two-way analysis of covariance (ANCOVA) was conducted to examine both the main effects and the interaction of region and race/ethnicity. A significant main effect of race/ethnicity was found [F(4,152491)=68.13, p<.001]. Means presented in the following section are estimated marginal means, or means adjusted for covariates. Post hoc testing relying on a Bonferroni adjustment revealed that Whites (M=6.94, 95%CI=6.87–7.00) had significantly higher average monthly alcohol consumption than other racial/ethnic groups: American Indian/Alaska Native (M=5.10, 95%CI=4.29–5.91), Asian/Pacific Islander (M=2.16, 95%CI=1.27–3.05), Black (M=4.68, 95%CI=4.28–5.07), and Hispanic (M=5.66, 95%CI=5.24–6.08). It was also found that the API sample had significantly lower alcohol consumption than all other racial/ethnic groups. Additionally, it was found that the Black sample had significantly higher alcohol consumption than the Hispanic sample.

Table 5.

ANCOVA of drinks consumed per month (n=185,190)

Variable Df SS F P-value
Main Effects
Race/Ethnicity 4 142,177.85 206.06 <.001**
Region 3 8,445.50 16.32 <.001**
Interaction Effect
Region × Race/Ethnicity 12 10,419.93 5.03 <.001**
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**

p<.001

A significant main effect was also found for region [F(3, 152491)=8.14, p<.001]. Post hoc testing relying on a Bonferroni adjustment revealed the following significant pairwise comparisons: average monthly alcohol consumption was significantly higher in the West (M=5.62, 95%CI=5.25–6.00) than the Midwest (M=4.22, 95%CI=3.62–4.83) and the South (M=4.46, 95%CI=4.04–4.88). Lastly, there was a significant race/ethnicity by region interaction [F(12, 152491)=2.71, p<.01], indicating that racial/ethnic alcohol consumption patterns varied by region of residence among the sample.

Table 6 displays the estimated marginal means for monthly alcohol consumption by region and race/ethnicity. A visual depiction of these results is illustrated in Figure 1. Simple effects testing relying on the Bonferroni adjustment revealed significant differences between regions within the White sample only [F (3,152491 = 171.52, p<.001)]. Among the White sample, those living in the Northeast (M=8.02, 95%CI=7.86–8.18) were predicted to have significantly higher alcohol consumption than those living in other regions: Midwest (M=5.99, 95%CI=5.85–6.14), South (M=6.23, 95%CI=6.11–6.35), West (M=7.51, 95%CI=7.36–7.65). Furthermore, among the White sample those living in the West (M=7.51, 95%CI=7.36–7.65) were predicted to have significantly higher alcohol consumption than those living in the Midwest (M=5.99, 95%CI=5.85–6.14) and then those living in the South (M=6.23, 95%CI=6.11–6.35).

Table 6.

Estimated Marginal Means of Drinks Consumed per Month (n=185,190)

AIAN API Black Hispanic White TOTAL
Northeast 4.33 2.95 3.04 2.73 7.99 4.21
Midwest 2.52 2.80 2.68 3.05 5.47 3.30
South 2.40 2.81 1.99 2.98 5.73 3.18
West 4.24 4.09 4.48 3.81 7.85 4.90
TOTAL 3.37 3.16 3.05 3.14 6.76
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NOTES: AIAN=American Indian/Alaska Native, API=Asian/Pacific Islander

Figure 1.

Figure 1.

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Estimated Marginal Means of Drinks Consumed per Month by Region and Race/Ethnicity

Simple effects testing was also conducted to examine racial/ethnic differences in monthly alcohol consumption within geographic regions. Results, which are displayed in Table 6 and Figure 1., indicated significant racial/ethnic differences within all four geographic regions: Northeast [F (4,152491=32.84, p<.001)], Midwest [F (4, 152491=10.77, p<.001)], South [F (4, 152491=40.90 , p<.001)], and West [F (4, 152491=38.63, p<.001)]. Within the Northeast, Whites (M=8.02, 95%CI=7.86–8.18) were predicted to have significantly higher monthly alcohol consumption than all other racial/ethnic groups except for AIANs (M=6.38, 95%CI=3.86–8.90): APIs (M=1.55, 95%CI=0–3.47), Blacks (M=4.81, 95%CI=4.09–5.53), Hispanics (M=2.73, 95%CI=1.92–3.55). Additionally, APIs (M=1.55, 95%CI=0–3.47) were predicted to have significantly lower monthly alcohol consumption than all other racial/ethnic groups: AIANs (M=6.38, 95%CI=3.86–8.90), Blacks (M=4.81, 95%CI=4.09–5.53), Hispanics (M=2.73, 95%CI=1.92–3.55), Whites (M=8.02, 95%CI=7.86–8.18). Within the Midwest, Whites (M=5.99, 95%CI=5.85–6.14) were predicted to have significantly higher alcohol consumption than APIs (M=1.30, 95%CI=0–3.68) and Blacks (M=4.65, 95%CI=4.09–5.21). Within the South, Whites (M=6.23, 95%CI=6.11–6.35) were predicted to have significantly higher monthly alcohol consumption than all other racial/ethnic groups with the exception of Hispanics (M=5.75, 95%CI=5.18–6.32): AIANs (M=4.06, 95%CI=2.95–6.32), APIs (M=2.02, 95%CI=.36–3.68), Blacks (M=4.24, 95%CI=3.92–4.56). Additionally, within the South, Blacks (M=4.24, 95%CI=3.92–4.56) and APIs (M=2.02, 95%CI=.36–3.68) were predicted to have significantly lower alcohol consumption than Hispanics (M=5.75, 95%CI=5.18–6.32). Within the West, Whites (M=7.51, 95%CI=7.36–7.65) were predicted to have significantly higher alcohol consumption than all other racial/ethnic groups: AIANs (M=5.63, 95%CI=4.55–6.72), APIs (M=3.77, 95%CI=3.11–4.43), Blacks (M=5.01, 95%CI=3.76–6.26), Hispanics (M=6.20, 95%CI=5.68–6.72). Additionally, APIs (M=3.77, 95%CI=3.11–4.43) were predicted to have significantly lower alcohol consumption than AIANs (M=5.63, 95%CI=4.55–6.72) and Hispanics (M=6.20, 95%CI=5.68–6.72).

Discussion

This study sought to examine the role of race/ethnicity and geographic location in the alcohol consumption of older adults in the United States. Data were analyzed on a nation-wide racially/ethnically diverse sample of adults 60 and older. Results suggest significant main effects for both region and race/ethnicity, as well as a significant region by race/ethnicity interaction on monthly alcohol consumption among older adults.

Regarding race/ethnicity, results suggest that White older adults are likely to consume significantly more alcohol on a monthly basis than members of other racial/ethnic groups. The strongest negative effect for race/ethnicity on monthly alcohol consumption was found for APIs. These findings are consistent with previous research indicating that among both adults in general and older adults, Whites are likely to consume the most alcohol of any racial/ethnic group while Asians are likely to consume the least (Blazer & Wu, 2009; Bryant & Kim, 2012; Chartier & Caetano, 2010; SAMHSA, 2007).

The observed regional pattern of alcohol consumption was not expected. Given the results of previous research examining regional patterns in alcohol consumption in a sample of adults 18 and older (Borders & Booth, 2007; Dawson et al., 1995), it was expected that the greatest alcohol consumption would be found in the Midwest while the least would be found in the South. Consistent with previous findings (Borders & Booth, 2007), the least alcohol consumption was found in the South, which may be related to cultural factors such as conservative religiosity. Unexpectedly, however, the greatest alcohol consumption was found in the West, closely followed by the Northeast, suggesting that the regional pattern of alcohol consumption may vary among older adults as compared to adults of a younger age. One possible explanation for this finding is that individuals who consumed alcohol in large quantities throughout adulthood may have increased mortality risk (IAS, 2014; Moore et al., 2006; Plunk et al., 2014; Waern, 2003). Specifically, research indicates that heavy alcohol use from early adulthood may shorten the lifespan by 10 to 15 years (IAS, 2013). This could lead to a reduction of heavy alcohol consumers and thus possibly alter the regional pattern of alcohol use between older adult hood and earlier adulthood.

The significant race/ethnicity by region interaction found was intriguing. For instance, it was found that among older AIANs and Whites the greatest alcohol consumption was found in the Northeast. Conversely, among older APIs, Blacks, and Hispanics, the greatest alcohol consumption was found in the West. This suggests that there may be individual race/ethnicity by regional characteristics interactions that account for these differences. Given previous research, it is likely that both protective and risk factors, such as social support and perceived discrimination, may vary by race/ethnicity and geographic location (Kim et al., 2010). For example, for certain minority groups, living in ethnic enclaves has been found to have protective effects on physical and mental health which may influence alcohol use, which outweighs risk factors associated with living in the low SES areas that these enclaves tend to be located (Eschbach et al., 2003; Ostir et al., 2003; Yen et al., 2009). It is reasonable to assume that the distribution of these ethnic enclaves varies by geographic region.

Identifying geographic and racial/ethnic variations in alcohol consumption among diverse older adults is significant for numerous reasons. First, these findings will help develop a better understanding of where alcohol is being used within this age group. Having a better understanding of where alcohol is used is an important first step for identifying potential alcohol related problems and benefits on both an individual and community wide level. Additionally, findings identify specific risk factors for increased alcohol use such as White race/ethnicity. Furthermore, findings add to the growing body of research, which highlights the role of geography in health and health behaviors. Better developing an understanding of the role of geography grants new insight and perspectives into physical and mental health and possible interventions. Lastly, findings from this study assist in laying the foundation for future research examining more specific elements of alcohol use in this age group.

This study is not without limitations. This study did not examine variables which may account for the regional difference in alcohol consumption. However, as suggested by previous research, it is likely that both psychosocial and physical characteristics of the environment including variations in cultural norms, variations in the health system, and the physical environment may be contributing factors to geographic variations in alcohol consumption (Borders & Booth, 2007; Kim et al., 2010). Future research may seek to find variables that account for geographic variance in alcohol consumption. This may be accomplished by taking a more narrow approach to closely examine additional topics that are be important factors in older adult alcohol consumption. Additionally, future research also may wish to focus on specific geographic areas or states with unique consumption patterns, such as very high or very low consumption, to identify characteristics associated with unique consumption patterns. For instance, given the results of this project, the states of Rhode Island, Tennessee, and the District of Columbia may provide interesting information on the role of geography for alcohol consumption among older adults if examined in depth. Additionally, it should be noted that racial/ethnic subgroup differences were not accounted for. Previous research has found that certain racial/ethnic groups, such as the Asian and Hispanic groups, have a large amount of within group heterogeneity (Borrell, 2005; Kim, Chiriboga, Jang et al., 2010). Therefore, it is quite possible that significant subgroup differences in alcohol consumption and the role of geography and geographic characteristics in this consumption may exist. Future research may also with to examine subgroup racial/ethnic differences. It is possible that an examination of within racial/ethnic group variation among older adults would provide novel and interesting findings. Additionally, the present study did not examine outcomes which may be related to alcohol consumption patterns such as mental or physical health variables. Future research may wish to directly examine relevant outcome variables to determine the influence of geographic consumption patterns such as average quality of life or average psychological distress.

The present study found that alcohol consumption among older adults varies by geographic location and race/ethnicity. Regional effects were found, suggesting that alcohol consumption among older adults varies by region of residence. Regarding race/ethnicity, findings indicate that non-Hispanic Whites consume the most alcohol while Asian/Pacific Islander older adults consume the least. A significant race/ethnicity by region interaction was found suggesting that regional patterns of alcohol consumption among older adults vary by individual race/ethnicity. Findings provide further insight into how and where alcohol is being used among older adults in the United States. Further, findings may lay the groundwork for future research examining the role of geography in alcohol use among this age group.

Contributor Information

Ami N Bryant, Department of Psychology, The University of Alabama, 253-345-1670, Ami.bryant@va.gov.

Giyeon Kim, Department of Psychology, Chung-Ang University.

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