BioMed Research International
Volume 2015 (2015), Article ID 596179, 10 pages
http://dx.doi.org/10.1155/2015/596179

Research Article

Examining the Relationship between Heavy Alcohol Use and Assaults: With Adjustment for the Effects of Unmeasured Confounders

Wenbin Liang and Tanya Chikritzhs

National Drug Research Institute, Curtin University, P.O. Box U1987, Perth, WA 6845, Australia

Received 20 November 2014; Revised 12 March 2015; Accepted 16 March 2015

Academic Editor: Nana Kwame Anokye

Copyright © 2015 Wenbin Liang and Tanya Chikritzhs. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Background. Experimental studies suggest that alcohol can lead to aggression in laboratory settings; however, it is impossible to test the causal relationship between alcohol use and real-life violence among humans in randomized clinical trials. Objectives. (i) To examine the relationship between heavy alcohol use and assaults in a population based study; (ii) to demonstrate the proxy outcome method, as a means of controlling the effects of unknown/unmeasured confounders in observational studies. Methods. This study used data collected from three waves of the National Survey on Drug Use and Health (NSDUH). The effects of heavy alcohol use on assault were measured using multivariable logistic regressions in conjunction with the proxy outcome method. Results. Application of the proxy outcome method indicated that effect sizes of heavy alcohol use on the risk of assault were overestimated in the standard models. After adjusting for the effects of unknown/unmeasured confounders, the risk of assault remained 43% and 63% higher among participants who consumed 5+ drinks/day for 5–8 days/month and 9–30 days/month, respectively. Conclusions. Even after adjustment for unknown/unmeasured confounders the association between heavy alcohol use and risk of violence remained significant. These findings support the hypothesis that heavy alcohol use can cause violence.

1. Introduction

Substantial evidence from experimental studies suggests that alcohol can lead to aggression in laboratory settings [1–5] and validated laboratory methods to measure physical aggression such as the Taylor aggression paradigm [6] and the hot sauce procedure [7] have been well-developed. Nevertheless, it is difficult to generalize laboratory results to real-life occurrences of alcohol-related violence. It is impossible to test the causal relationship between alcohol use and physical violence that occurs among people in real life (such as assault) in randomized clinical trials due to ethical concerns. Data from observational studies have shown a positive association between alcohol use and violence in general populations. However, due to the nature of observational studies, it is difficult to conclude whether the observed association between alcohol use and violence is due to alcohol use or whether it is due to common cause factors [8–12]. For example, the well-designed longitudinal study by Fergusson and Horwood showed that cohort participants who had been diagnosed with alcohol abuse were more likely to be involved in violence and property crime. However, use of the data from their study alone was unable to determine whether alcohol caused the law-breaking behaviors or whether violence, property crime, and alcohol abuse were caused by common factors, for example, changes in mental health which may increase the likelihood of high-risk behaviors [13–16]. Therefore, further studies are required to test the hypothesis that heavy alcohol use causes violence.

Recent published works have demonstrated that unmeasured/unknown confounding effects could be estimated by measuring the association between the exposure variable (heavy alcohol use in this case) and a proxy outcome, on which the exposure has no or very limited effect [17–20]. The proxy outcome method is a general approach which provides estimates and adjustments for effects of unmeasured confounders. Many types of physical and mental health outcomes are affected by clusters of socioeconomic determinants and genetic and behavioural psychological factors [21–31]. Field specific knowledge and experience can be applied to determine the best proxy outcome for estimating effects of such unmeasured confounders. The papers by Tchetgen Tchetgen and Liang et al. provided detailed discussion on the methodological framework of the proxy outcome method [17, 19]. A recent study by Liang and Chikritzhs applied the proxy outcome method to an investigation of the effect of alcohol use on health status. They used the health status of drinkers’ children as the proxy outcome to measure and control for unknown/unmeasured confounding effects that cluster within families such as socioeconomic determinants, environmental factors, lifestyle, and genetic susceptibility [18]. The findings from this study concurred with the results from a lately published Mendelian randomization analysis of pooled data from prospective studies that measured genetic variants [32]: both studies suggested no protective effect of moderate alcohol use on health.

In the current study, the proxy outcome method was adopted to control the effects of unknown confounders. The criteria for a proxy outcome are (i) the exposure of interest is not a cause for the proxy outcome and (ii) causes of the proxy outcome and the main outcome are subsets of a pool of correlated variables [17]. In order to avoid overadjustment, a positive outcome was used. The criteria for a positive outcome are (i) the exposure of interest is a cause for the positive outcome and (ii) causes of the positive outcome, the proxy outcome, and the main outcome are subsets of a pool of correlated variables. In relation to this study, driving without a seatbelt at all times met the criteria of a proxy outcome, and driving while under the influence of alcohol met the criteria of a positive outcome since (i) the three outcomes (violent behavior, driving without a seatbelt at all times, and driving while under the influence of alcohol) are all risk-taking illegal behaviors which share a set of similar social, environmental, and genetic risk factors [15, 33–35], as well as a similar direction and magnitude of response bias due to social desirability [36], and (ii) heavy alcohol use has minimum effect on driving without a seatbelt at all times while heavy alcohol use is an important cause of drink-driving (but not an essential cause, as some cases may be due to “moderate” level drinking) (also see Figure 1).

Figure 1: Illustration of an alternative approach to dealing with confounding effects.

2. Materials and Methods

This study used data collected from three waves (2009, 2010, and 2011) of the National Survey on Drug Use and Health (NSDUH). NSDUH surveys are conducted to measure the prevalence and correlates of drug use in the United States. Details of the survey method have been described previously [37–39]. Briefly, NSDUH surveys are multistage national surveys with representative samples of noninstitutionalized population of the United States aged 12 years or older. In-home, computer-assisted interviews were used to collect the data. Since 2002, each participant who completed the survey was provided a $30 cash payment to improve the response rate. The response rates were 75.7%, 74.7%, and 74.4% for the 2009, 2010, and 2011 survey, respectively [37–39]. In addition to the questions on demographics and use of tobacco products, alcohol, and illicit drugs, participants were also asked questions about risk behaviors in the past 12 months and questions that assess mental and physical health conditions. This study included the samples from the 2009, 2010, and 2011 NSDUH surveys who were 18 years or older at the time of the survey, had consumed alcohol, and drove a car in the past 12 months.

Responses to the following questions were used to define violence, driving without seatbelt, and driving under the influence of alcohol: (1) violent behavior: “During the past 12 months, how many times have you attacked someone with the intent to seriously hurt them?”; (2) driving without seatbelt: “How often do you wear a seatbelt when you drive a car?”; and (3) driving under the influence of alcohol: “During the past 12 months, have you driven a vehicle while you were under the influence of alcohol?”. Answers to these questions were converted to three corresponding binary variables (yes/no) to represent the presence/absence of violent behavior (i.e., a yes if ever tried to seriously hurt someone in the past 12 months), driving without seatbelt at all times (yes if never wore a seatbelt when driving in the past 12 months), and driving under the influence of alcohol (yes if ever drove under the influence of alcohol in the past 12 months), respectively.

NSDUH survey referred a “drink” as a “can or bottle of beer, or a wine cooler, a shot of liquor, or a mixed drink with liquor in it” [37–39]. Number of days in which five or more drinks were consumed on the same occasion (occasion: “at the same time or within a couple of hours”) in the past 30 days prior to the interview was used as the measurement of heavy alcohol use. Number of days when consuming 5+ drinks over the past 30 days was converted into a six-category variable: 0 days, 1 day, 2 days, 3-4 days, 5–8 days, and 9–30 days. These cutoff points were chosen to ensure that while having as many categories as possible, the smallest sample size of a category was at least 5% of the total sample.

Multivariable logistic regressions were used to examine the relationships between the outcome variables (assault, driving without wearing seatbelt all of the time, and driving under the influence of alcohol) and heavy alcohol use while controlling for demographics (age, gender, race, marital status, and type of country of living); socioeconomic status (income and highest academic achievement); health (general health status). Whether ever had a lifetime major depressive episode, and if yes then whether there was an episode in the past 12 months which was assessed based on criteria in the Diagnostic and Statistical Manual of Mental Disorders 4th Edition (DSM-IV); and use of tobacco or any illicit drug in the past 12 months. The association between driving without wearing seatbelt and heavy alcohol use was used as a proxy estimate of unknown confounding effects toward the relationship between violence and heavy alcohol use as well as the relationship between driving under the influence of alcohol and heavy alcohol use. A final regression model was then performed to obtain the new estimates for violence and alcohol use that controlled for the confounding effects estimated by the proxy models. STATA 12 developed by StataCorp was used to perform the analysis. For further illustration, additional logistic regression models were fitted with only alcohol use as a predictor variable—treating all potential confounding factors as unknown (i.e., factors shown in Table 1, such as age, gender, and race) and leaving all confounding effects for the proxy outcome to account for.

Table 1: Associations between heavy alcohol use and three outcome variables (violent behaviour, driving without wearing a seatbelt all of the time, and driving under the influence of alcohol). Estimates from standard logistic regression.

Violent behaviour
Driving without seatbelt
Driving while under the influence of alcohol

Odds ratio
95% CI
Odds ratio
95% CI∧
Odds ratio
95% CI∧

Days of having 5+ drinks last month

0
1.00

1.00

1.00

1
1.22
0.96
1.56
1.13
0.91
1.41
2.50*
2.27
2.76

2
1.43*
1.06
1.93
1.35*
1.02
1.79
3.30*
2.96
3.68

3 to 4
1.45*
1.16
1.82
1.53*
1.20
1.95
4.60*
4.13
5.12

5 to 8
1.98*
1.58
2.47
1.38*
1.08
1.77
5.77*
5.15
6.47

9 to 30
2.49*
2.01
3.10
1.53*
1.21
1.93
6.88*
6.09
7.77

Used tobacco last year

No
1.00

1.00

1.00

Yes
1.45*
1.19
1.76
1.50*
1.25
1.80
1.12*
1.04
1.20

Used illicit drug last year

No
1.00

1.00

1.00

Yes
2.09*
1.80
2.44
1.10
0.94
1.28
2.66*
2.47
2.86

Age

18
1.00

1.00

1.00

19
0.86
0.69
1.08
0.87
0.66
1.15
1.03
0.88
1.19

20
0.70*
0.55
0.90
0.97
0.73
1.29
1.11
0.95
1.30

21
0.65*
0.52
0.83
1.02
0.77
1.35
1.29*
1.11
1.49

22–23
0.56*
0.45
0.70
1.30*
1.01
1.68
1.43*
1.25
1.63

24–25
0.49*
0.39
0.62
1.18
0.90
1.55
1.43*
1.24
1.64

26–29
0.37*
0.28
0.50
1.11
0.81
1.50
1.43*
1.23
1.67

30–34
0.31*
0.22
0.43
0.95
0.70
1.31
1.38*
1.18
1.62

35–49
0.17*
0.12
0.24
0.68*
0.50
0.93
1.22*
1.05
1.42

50–64
0.10*
0.06
0.17
0.49*
0.33
0.72
1.10
0.93
1.30

65+
0.14*
0.05
0.38
0.48*
0.28
0.82
0.66*
0.52
0.84

Gender

Male
1.00

1.00

1.00

Female
0.68*
0.59
0.79
0.44*
0.37
0.52
0.69*
0.64
0.73

Race

White
1.00

1.00

1.00

Black
2.05*
1.69
2.47
0.60*
0.47
0.76
0.81*
0.73
0.91

Native American
1.91*
1.23
2.95
0.75
0.40
1.41
1.32
0.91
1.92

Native Pacific Islander
3.88
0.92
16.41
0.50
0.14
1.85
0.73
0.34
1.53

Asian
1.00
0.61
1.64
0.73
0.35
1.49
0.48*
0.40
0.58

Mixed race
1.88*
1.27
2.78
0.87
0.56
1.36
0.76*
0.62
0.93

Hispanic
1.29*
1.03
1.60
0.48*
0.38
0.61
0.78*
0.70
0.87

Education

Less than high school
1.00

1.00

1.00

High school graduate
0.83
0.68
1.02
1.02
0.83
1.25
1.32*
1.18
1.49

Some college
0.55*
0.44
0.69
0.59*
0.47
0.74
1.68*
1.49
1.90

College graduate
0.36*
0.26
0.50
0.28*
0.21
0.39
2.16*
1.90
2.45

Family income

<$20,000
1.00

1.00

1.00

$20,000–$49,999
1.07
0.91
1.26
0.93
0.76
1.14
1.28*
1.16
1.40

$50,000–$74,999
0.74*
0.60
0.92
0.82
0.64
1.04
1.58*
1.42
1.77

$75,000+
0.78*
0.63
0.97
0.77*
0.61
0.98
1.70*
1.53
1.88

Marital status

Married
1.00

1.00

1.00

Widowed
0.98
0.35
2.74
1.64
0.89
3.06
0.86
0.65
1.13

Divorced or separated
1.42*
1.05
1.94
1.50*
1.18
1.90
1.30*
1.17
1.45

Never been married
1.38*
1.07
1.77
1.26*
1.02
1.55
1.24*
1.14
1.36

Health status

Excellent
1.00

1.00

1.00

Very good
0.95
0.79
1.15
0.99
0.82
1.20
1.11
1.02
1.20

Good
1.16
0.94
1.42
1.29*
1.06
1.57
0.94
0.86
1.03

Fair
1.16
0.88
1.54
1.60*
1.21
2.12
0.78*
0.67
0.90

Poor
3.32*
1.53
7.19
3.48*
1.91
6.35
0.39*
0.29
0.55

Major depressive episode

Never
1.00

1.00

1.00

Ever, but not in the last 12 months
1.51*
1.13
2.01
1.11
0.78
1.58
1.58*
1.41
1.77

In the last 12 months
3.02*
2.52
3.62
1.28
0.98
1.68
1.89*
1.70
2.10

County metro status

Large metro
1.00

1.00

1.00

Small metro
1.05
0.89
1.23
0.95
0.80
1.13
1.12*
1.04
1.20

Nonmetro
1.22
0.99
1.50
1.32*
1.08
1.60
1.02
0.94
1.12

Year of survey

2009
1.00

1.00

1.00

2010
0.92
0.77
1.09
1.00
0.85
1.19
0.95
0.89
1.03

2011
0.84
0.70
1.00
1.05
0.88
1.26
0.99
0.91
1.07

95% confidence interval.

.

There were 82,790 participants that met the selection criteria. Less than 1% of these participants did not provide necessary information on the dependent variables or some of the independent variables and therefore were excluded from the analysis (,272 remained in the analysis). The sampling weight supplied with the dataset was used in all analyses [37].

3. Results

Multivariable analysis showed that all of the three outcomes were significantly associated with heavy alcohol use after controlling for a number of known potential confounders (Table 1). The descriptive statistics are showed in Tables 4, 5, and 6. The effect size of heavy alcohol use was largest on driving under the influence of alcohol, which is known to be at least partly caused by heavy use (i.e., moderate levels of alcohol use may also be a cause of positive responses for driving under the influence of alcohol). The effect size of heavy alcohol use on violence was the second largest. The effect of heavy alcohol use on driving without a seatbelt at all times was smallest but remained significant. This provided a useful indicator of the magnitude of unknown/unmeasured confounding effects on the association between alcohol use and violent behavior.

The natural logarithms of the adjusted odds ratio for driving without a seatbelt by number of days with heavy alcohol use in the last 30 days were 0 (reference level) for 0 days, 0.12 for 1 day, 0.30 for 2 days, 0.42 for 3-4 days, 0.32 for 5–8 days, and 0.42 for 9 days or more, respectively. After offsetting the residual confounding effects, the effect of heavy alcohol use on violence became nonsignificant for 4 days’ or less exposure in the last 30 days. Although reduced, the effects of 5–8 days’ and 9+ days’ heavy alcohol use on the risk of violence nevertheless remained significant. The estimates for the effects of heavy alcohol use on driving under the influence of alcohol were reduced but remained significant for all exposure categories (Table 2).

Table 2: Associations between heavy alcohol use and two outcome variables (assault and driving under the influence of alcohol). Estimates from standard logistic regression plus additional control for the effects of unmeasured confounders.

Violent behaviour
Driving while under the influence of alcohol

Odds ratio
95% CI∧
Odds ratio
95% CI∧

Days of having 5+ drinks last month

0
1.00

1.00

1
1.08
0.85
1.38
2.22*
2.01
2.44

2
1.06
0.78
1.43
2.44*
2.19
2.72

3 to 4
0.95
0.76
1.19
3.01*
2.70
3.35

5 to 8
1.43*
1.15
1.79
4.18*
3.73
4.69

9 to 30
1.63*
1.31
2.03
4.50*
3.99
5.08

Model controlled for the same potential confounding factors listed in Table 1 plus additional control for the effects of unmeasured confounders derived from proxy models.

95% confidence interval.

.

4. Discussion

This study examined the association between heavy alcohol use and risk of violence in uncontrolled settings (i.e., observational rather than experimental). The proxy outcome approach was employed to account for unknown/unmeasured confounding effects that may remain in estimates obtained using the standard approach. In both the standard and proxy outcome approaches it was observed that heavy alcohol use for 5 days or more in the past 30 days was significantly associated with increased risk of violence. This observation is consistent with findings from experimental studies which suggest that alcohol use may increase aggression in both humans and animals [1, 2, 40–42]. It has been hypothesized that physiological effects of alcohol on the central nervous system can impair cognitive functions that regulate emotion and behaviors [43, 44]. Individuals who have been suppressing angry feelings may express anger through acts of physical violence when self-control mechanisms are compromised due to alcohol use [11, 43–45].

The proxy outcome used in this study was driving without wearing a seatbelt at all times. Since not wearing a seatbelt at all times is not caused by alcohol, the apparent association is due to confounding. Similar confounding likely affected the apparent association between heavy alcohol use and violent behavior. Therefore, by controlling for confounding identified in the proxy outcome, we have more accurately described the magnitude of the true association between alcohol and violence. Thus, using a novel approach, this study has provided further evidence to support the notion that there is a causal relationship between alcohol use and violence.

We have described here an alternative approach to dealing with unknown/unmeasured confounding factors in observational studies that has application to the wider field of epidemiology. To further illustrate the application of this approach, we ran additional logistic regression models that were fitted with alcohol use as the only predictor (univariate model), thereby creating a hypothetical scenario where all potential confounding factors were unknown. We then applied the proxy outcome method as the only means of adjusting for potential confounding effects. As the estimates in Table 3 indicated, the confounding which dominated the crude results had been significantly removed using only the proxy outcome method and these estimates were, in fact, closely comparable to estimates derived using the standard approach that controlled for all known potential confounding factors (Table 1). Figure 1 was presented to further explain this approach. In order to measure “D” and “F” while adjusting for confounding, the effect size of “B” was used as approximation of the effect sizes of “C” and “E.” The certain causal effect of heavy alcohol use on driving under the influence of alcohol denoted as “F” was used as a positive control to detect whether any overadjustment had occurred.

Table 3: Comparison of estimates derived from standard univariate models and estimates derived from univariate models with proxy outcome method alone to account for all confounding effects.

Violent behaviour
Driving without seatbelt
Driving while under the influence of alcohol

Odds ratio
95% CI
Odds ratio
95% CI
Odds ratio
95% CI∧

Estimates from univariate models

Days of having 5+ drinks last month

0
1.00

1.00

1.00

1
1.99*
1.57
2.52
1.57*
1.25
1.96
2.94*
2.68
3.21

2
2.73*
2.09
3.56
2.14*
1.64
2.79
4.02*
3.64
4.44

3 to 4
3.17*
2.57
3.91
2.68*
2.14
3.35
6.01*
5.46
6.62

5 to 8
4.62*
3.75
5.70
2.64*
2.08
3.36
8.31*
7.51
9.19

9 to 30
6.53*
5.38
7.94
3.55*
2.87
4.39
10.20*
9.17
11.34

Estimates from univariate models with proxy outcome method

Days of having 5+ drinks last month

0
1.00

1.00

1
1.27
1.00
1.61

1.88*
1.71
2.05

2
1.27
0.98
1.66
Estimates not applicable for proxy outcome
1.88*
1.70
2.07

3 to 4
1.18
0.96
1.46
(will always be equal to 1)
2.25*
2.04
2.47

5 to 8
1.75*
1.42
2.15

3.14*
2.84
3.47

9 to 30
1.84*
1.52
2.24

2.87*
2.58
3.20

95% confidence interval.

.

Table 4: Descriptive frequency statistics for violent behaviours, alcohol use, and controlled potential confounders.

Violent behaviour

Yes
No
Total

Days of having 5+ drinks last month

0
45,069
842
45,911

1
10,356
293
10,649

2
7,102
306
7,408

3 to 4
6,800
334
7,134

5 to 8
5,672
341
6,013

9 to 30
4,693
464
5,157

Used tobacco last year

No
40,005
571
40,576

Yes
39,687
2,009
41,696

Used illicit drug last year

No
56,921
946
57,867

Yes
22,771
1,634
24,405

Age

18
4,144
395
4,539

19
4,374
322
4,696

20
4,689
268
4,957

21
5,419
311
5,730

22–23
10,500
460
10,960

24–25
10,367
354
10,721

26–29
6,046
152
6,198

30–34
6,601
127
6,728

35–49
17,165
155
17,320

50–64
7,234
27
7,261

65+
3,153
9
3,162

Gender

Male
38,700
1,608
40,308

Female
40,992
972
41,964

Race

White
54,968
1,392
56,360

Black
8,197
508
8,705

Native American
1,020
78
1,098

Native Pacific Islander
313
18
331

Asian
2,525
43
2,568

Mixed race
2,253
129
2,382

Hispanic
10,416
412
10,828

Education

Less than high school
9,125
663
9,788

High school graduate
24,301
1,096
25,397

Some college
25,729
659
26,388

College graduate
20,537
162
20,699

Family income

<$20,000
17,994
881
18,875

$20,000–$49,999
26,213
978
27,191

$50,000–$74,999
13,366
321
13,687

$75,000+
22,119
400
22,519

Marital status

Married
28,638
289
28,927

Widowed
1,266
11
1,277

Divorced or separated
7,376
187
7,563

Never been married
42,412
2,093
44,505

Health status

Excellent
21,488
557
22,045

Very good
32,969
980
33,949

Good
19,481
762
20,243

Fair
4,971
243
5,214

Poor
783
38
821

Major depressive episode

Never
67,989
1,908
69,897

Ever, but not in the last 12 months
5,450
176
5,626

In the last 12 months
6,253
496
6,749

County metro status

Large metro
34,746
1,052
35,798

Small metro
28,703
968
29,671

Nonmetro
16,243
560
16,803

Year of survey

2009
26,084
929
27,013

2010
27,080
872
27,952

2011
26,528
779
27,307

Table 5: Descriptive frequency statistics for driving without seatbelt, alcohol use and controlled potential confounders.

Driving without seatbelt

No
Yes
Total

Days had 5+ drinks last month

0
44,888
1,023
45,911

1
10,308
341
10,649

2
7,092
316
7,408

3 to 4
6,786
348
7,134

5 to 8
5,691
322
6,013

9 to 30
4,792
365
5,157

Used tobacco last year

No
39,911
665
40,576

Yes
39,646
2,050
41,696

Used illicit drug last year

No
56,381
1,486
57,867

Yes
23,176
1,229
24,405

Age

18
4,314
225
4,539

19
4,474
222
4,696

20
4,741
216
4,957

21
5,481
249
5,730

22–23
10,490
470
10,960

24–25
10,297
424
10,721

26–29
5,987
211
6,198

30–34
6,561
167
6,728

35–49
16,950
370
17,320

50–64
7,143
118
7,261

65+
3,119
43
3,162

Gender

Male
38,425
1,883
40,308

Female
41,132
832
41,964

Race

White
54,303
2,057
56,360

Black
8,474
231
8,705

Native American
1,056
42
1,098

Native Pacific Islander
325
6
331

Asian
2,539
29
2,568

Mixed race
2,294
88
2,382

Hispanic
10,566
262
10,828

Education

Less than high school
9,169
619
9,788

High school graduate
24,181
1,216
25,397

Some college
25,696
692
26,388

College graduate
20,511
188
20,699

Family income

<$20,000
18,159
716
18,875

$20,000–$49,999
26,110
1,081
27,191

$50,000–$74,999
13,286
401
13,687

$75,000+
22,002
517
22,519

Marital status

Married
28,363
564
28,927

Widowed
1,248
29
1,277

Divorced or Separated
7,273
290
7,563

Never been married
42,673
1,832
44,505

Health status

Excellent
21,479
566
22,045

Very good
33,005
944
33,949

Good
19,356
887
20,243

Fair
4,958
256
5,214

Poor
759
62
821

Major depressive episode

Never
67,589
2,308
69,897

Ever, but not in the last 12 months
5,483
143
5,626

In the last 12 months
6,485
264
6,749

County metro status

Large metro
34,873
925
35,798

Small metro
28,748
923
29,671

Nonmetro
15,936
867
16,803

Year of survey

2009
26,083
930
27,013

2010
27,049
903
27,952

2011
26,425
882
27,307

Table 6: Descriptive frequency statistics for driving while under the influence of alcohol, alcohol use and controlled potential confounders.

Driving while under the influence of alcohol

No
Yes
Total

Days had 5+ drinks last month

0
40,715
5,196
45,911

1
8,010
2,639
10,649

2
5,026
2,382
7,408

3 to 4
4,271
2,863
7,134

5 to 8
3,079
2,934
6,013

9 to 30
2,188
2,969
5,157

Used tobacco last year

No
34,317
6,259
40,576

Yes
28,972
12,724
41,696

Used illicit drug last year

No
48,963
8,904
57,867

Yes
14,326
10,079
24,405

Age

18
3,544
995
4,539

19
3,518
1,178
4,696

20
3,698
1,259
4,957

21
4,092
1,638
5,730

22–23
7,717
3,243
10,960

24–25
7,664
3,057
10,721

26–29
4,587
1,611
6,198

30–34
5,199
1,529
6,728

35–49
14,100
3,220
17,320

50–64
6,212
1,049
7,261

65+
2,958
204
3,162

Gender

Male
29,239
11,069
40,308

Female
34,050
7,914
41,964

Race

White
42,468
13,892
56,360

Black
7,149
1,556
8,705

Native American
814
284
1,098

Native Pacific Islander
255
76
331

Asian
2,131
437
2,568

Mixed race
1,811
571
2,382

Hispanic
8,661
2,167
10,828

Education

Less than high school
8,019
1,769
9,788

High school graduate
20,081
5,316
25,397

Some college
19,756
6,632
26,388

College graduate
15,433
5,266
20,699

Family income

<$20,000
14,557
4,318
18,875

$20,000–$49,999
21,107
6,084
27,191

$50,000–$74,999
10,530
3,157
13,687

$75,000+
17,095
5,424
22,519

Marital status

Married
24,193
4,734
28,927

Widowed
1,157
120
1,277

Divorced or separated
5,994
1,569
7,563

Never been married
31,945
12,560
44,505

Health status

Excellent
17,083
4,962
22,045

Very good
25,544
8,405
33,949

Good
15,670
4,573
20,243

Fair
4,277
937
5,214

Poor
715
106
821

Major depressive episode

Never
54,700
15,197
69,897

Ever, but not in the last 12 months
4,029
1,597
5,626

In the last 12 months
4,560
2,189
6,749

County metro status

Large metro
27,777
8,021
35,798

Small metro
22,535
7,136
29,671

Nonmetro
12,977
3,826
16,803

Year of survey

2009
20,449
6,564
27,013

2010
21,565
6,387
27,952

2011
21,275
6,032
27,307

The proxy outcome method is a general approach that enables analysts to control for the effects of unknown/unmeasured confounding factors. The advantage of this approach is that it will tend toward producing more conservative outcomes (i.e., effect sizes) than the standard approach which assumes that unknown/unmeasured confounding is minimal. However, under some circumstances, where the effect size of unmeasured confounding effects is severely overestimated, it may make a true causal effect (if it exists) undetectable. Therefore, a positive control outcome, such as the driving under the influence of alcohol variable used in this study, may be used to detect whether any overadjustment has occurred. In this case, after further adjustment, the effects of alcohol on the positive outcome remained significant at all levels of heavy alcohol use and therefore indicative that the true effect of alcohol exposure on violence is likely to remain detectable.

5. Conclusion

The association between heavy alcohol use and risk of violence remained significant after adjustment for the effects of known and unknown/unmeasured confounders. These findings support the hypothesis that heavy alcohol use is causal for violence. The novel proxy outcome method enabled the adjustment for the effects of unknown/unmeasured confounders and is a useful tool for improving the reliability of estimated effect sizes in observational research.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgment

This work was supported by the Australian Government Department of Health and Ageing under the National Drug Strategy’s funding of the National Drug Research Institute.

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