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Drug and Alcohol Dependence 132 (2013) 207– 215

Contents lists available at SciVerse ScienceDirect

Drug and Alcohol Dependence

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / d r u g a l c d e p

inge drinking and sleep problems among young adults�

oana Popovici a,1, Michael T. French b,∗

Nova Southeastern University, College of Pharmacy, Department of Sociobehavioral and Administrative Pharmacy, 3200 South University Drive, Fort Lauderdale, L 33328-2018, USA Health Economics Research Group, Department of Sociology, Department of Epidemiology and Public Health, and Department of Economics, 5202 University Drive, errick Building, Room 121F, P.O. Box 248162, Coral Gables, FL 33124-2030, USA

r t i c l e i n f o

rticle history: eceived 4 September 2012 eceived in revised form 31 January 2013 ccepted 2 February 2013 vailable online 5 March 2013

eywords: lcohol use inge drinking leep problems ational Longitudinal Study of Adolescent ealth (Add Health)

a b s t r a c t

Objective: As most of the literature exploring the relationships between alcohol use and sleep problems is descriptive and with small sample sizes, the present study seeks to provide new information on the topic by employing a large, nationally representative dataset with several waves of data and a broad set of measures for binge drinking and sleep problems. Methods: We use data from the National Longitudinal Study of Adolescent Health (Add Health), a nation- ally representative survey of adolescents and young adults. The analysis sample consists of all Wave 4 observations without missing values for the sleep problems variables (N = 14,089, 53% females). We esti- mate gender-specific multivariate probit models with a rich set of socioeconomic, demographic, physical, and mental health variables to control for confounding factors. Results: Our results confirm that alcohol use, and specifically binge drinking, is positively and significantly associated with various types of sleep problems. The detrimental effects on sleep increase in magnitude

with frequency of binge drinking, suggesting a dose–response relationship. Moreover, binge drinking is associated with sleep problems independent of psychiatric conditions. Conclusions: The statistically strong association between sleep problems and binge drinking found in this study is a first step in understanding these relationships. Future research is needed to determine the causal links between alcohol misuse and sleep problems to inform appropriate clinical and policy responses.

“I’ll sleep when I’m dead.” – Warren Zevon

. Introduction

Several studies have found significant associations between lcohol use and sleep disturbances (Roth, 2005; Vinson et al., 2010; itiello, 2006; Van Reen et al., 2011) as well as obstructive sleep pnea (OSA; Aldrich et al., 1993; Mamdani et al., 1989). Results of pidemiological and clinical studies show that alcohol use disorders re more prevalent in those with insomnia (Ford and Kamerow,

989) and that individuals with alcohol abuse/dependence are ore likely to suffer from sleep problems (Baekeland et al., 1974;

euerlein, 1974; Caetano et al., 1998; Foster et al., 1998; Brower

� Supplementary material can be found by accessing the online version of this aper. Please see Appendix A for more information. ∗ Corresponding author at: University of Miami, Department of Sociology, 5202 niversity Drive, Merrick Building, Room 121F, P.O. Box 248162, Coral Gables, FL 3124-2030, USA. Tel.: +1 305 284 6039; fax: +1 305 284 5310.

E-mail addresses: [email protected] (I. Popovici), [email protected] M.T. French).

1 Tel.: +1 954 262 1393; fax: +1 954 262 2278.

et al., 2001; Ehlers et al., 2010). These sleep disturbances can persist after months or even years of abstinence (Landolt and Gillin, 2001; Ford and Kamerow, 1989; Brower et al., 2001; Colrain et al., 2009; Landolt and Borbely, 2000; Williams and Rundell, 2008). Besides its disruptive effects on sleep, studies show that alcohol use, even in modest amounts, exacerbates snoring and sleep apnea in persons with OSA (Aldrich et al., 1993; Mamdani et al., 1989). Moreover, normal sleepers can develop snoring and OSA even after one drink (Dufour et al., 1992; Block et al., 1987).

Most studies examining the relationships between drinking and sleep problems are clinical and laboratory investigations. The anal- ysis samples are small, and considerable heterogeneity exists in study design, sleep disturbances, and alcohol use measures. Sur- prisingly, we found only two studies that examine this relationship in large samples (Johnson and Breslau, 2001; Bruck and Astbury, 2012). Johnson and Breslau (2001) use data from the U.S. National Household Survey on Drug Abuse and find significant associations between sleep problems and substance use among adolescents.

Adjusting for psychiatric problems reduces the magnitude of these associations, underscoring the importance of controlling for psy- chiatric problems. Bruck and Astbury (2012) use survey data to analyze potential predictors of ‘difficulty sleeping’ in a sample of

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08 I. Popovici, M.T. French / Drug and A

oung women. The authors find that psychiatric disorder symp- oms are the strongest predictors of sleep difficulty. A few other actors, including binge drinking, are also found to have a significant mpact on sleep difficulty.

Given the scarcity of studies employing large datasets, one of he most notable contributions to the current literature is our se of the National Longitudinal Study of Adolescent Health (Add ealth), a large, nationally representative data set of young adults,

o examine the relationship between alcohol consumption and leep problems. Our study makes several other contributions to he existing literature. First, we analyze a range of alcohol use pat- erns (any, occasional, approaching weekly, and weekly or more requent binge drinking) to shed light on a possible dose–response elationship between alcohol use and sleep disturbances, and to dentify particularly harmful patterns of use. Second, as men- ioned above, several studies find that part of the associations etween sleep disturbances and substance use can be attributed o psychiatric problems (Johnson and Breslau, 2001; Park et al., 010; Bruck and Astbury, 2012). Given the comorbidity between ubstance use and psychiatric disorders, we include in our regres- ions several indicators of psychiatric problems and disorders (i.e., ast year psychological or emotional counseling, depression, post- raumatic stress disorder, and panic disorder diagnoses) to further ur understanding of the relationships between drinking, sleep roblems, and mental health. Third, several personal character-

stics, physical health conditions, and other substance use are otentially correlated with both sleep patterns and alcohol use.

gnoring these variables would lead to biased estimates of the rela- ionships between drinking and sleep problems. The diversity of the dd Health data allows us to include a comprehensive set of con-

ounding factors, thus avoiding the potential bias that would result rom ignoring these variables or using a less comprehensive set of ata. Finally, we test the robustness of our findings with several ensitivity analyses.

.1. Conceptual background

The reported associations between alcohol use and sleep prob- ems can be explained, at least in part, by the pharmacologic ffects of alcohol. This effect seems to be dose-related (Stein and riedmann, 2005). At low to moderate doses, alcohol can have a timulating effect that might lead to problems falling asleep, usu- lly during the first hour after its use (Stein and Friedmann, 2005; an Reen et al., 2011). At high doses, alcohol has a sedating effect

Petrucelli et al., 1994; Roehrs et al., 1989; Zwyghuizen-Doorenbos t al., 1988; Maclean and Cairns, 1982; Roehrs and Roth, 2001). et, the sedative effect of alcohol wears off quickly and is followed y sleep disruptions, especially during the second half of the night Stein and Friedmann, 2005; Vitiello, 2006; Van Reen et al., 2011; oehrs and Roth, 2001; Landolt et al., 1996). During the first part f the sleep cycle, the body adjusts to the presence of alcohol in n effort to maintain a normal sleep pattern. Once alcohol has been liminated from the body, however, certain physiological variables, uch as REM-sleep patterns, change in the opposite direction of he body adjustments induced by alcohol. These changes result in leep disruptions (Roehrs and Roth, 2001). Studies find that, after bout a week of repeated nightly alcohol use, the sedative effect of lcohol diminishes, while its sleep disturbing effect remains (Stein nd Friedmann, 2005; Roehrs and Roth, 2001; Dufour et al., 1992; undell et al., 1972). Some studies report that tolerance to alco- ol’s sedative effect can develop after only three nights (Williams nd Salamy, 1972).

The association between alcohol use and sleep problems, how- ver, might be due to more than the pharmacologic effects of lcohol. Besides causing sleep problems, the use and misuse of lcohol could also be a reaction to sleep disturbances. Alcohol

l Dependence 132 (2013) 207– 215

is commonly perceived to aid sleep, and many individuals drink alcohol to self-treat insomnia (Vinson et al., 2010; Vitiello, 2006; Johnson et al., 1998; Kaneita et al., 2007b; Ancoli-Israel and Roth, 1999). One study found that 67% of the respondents who reported both insomnia and alcohol use as a sleep aid felt that it was effec- tive (Costa et al., 1996). Sleep problems might be early indicators of increased risk for substance use (Shibley et al., 2008). Moreover, sleep disturbances seem to be an important cause of relapse in alcohol dependent patients (Landolt and Gillin, 2001; Le Bon et al., 2003; Mahfoud et al., 2009; Ford and Kamerow, 1989).

Finally, it is possible that a common factor influences both alco- hol consumption and sleep disturbances with no direct causality between the two. Several studies show that sleep disturbances, alcohol use, and psychiatric disorders often co-exist (Johnson and Breslau, 2001; Bruck and Astbury, 2012; Merikangas et al., 1996; Roberts et al., 1999; Park et al., 2010). Psychiatric disorders could lead to increased alcohol consumption, as well as to a higher like- lihood of sleep problems. The comorbidity of substance use and psychiatric disorders is well known in the literature (Jane-Llopis and Matytsina, 2009). Some of the most common symptoms of anx- iety and depression are sleep problems, such as difficulty falling asleep and staying asleep (Benca et al., 1992).

The current study further investigates the associations between alcohol use and sleep problems by using a large, nationally rep- resentative dataset of young adults. Various analyses attempt to disentangle the effects of binge drinking on sleep problems while controlling for several other important variables.

2. Methods

2.1. Sample

We use data from Add Health, a nationally representative sur- vey of adolescents in grades 7–12 at Wave 1. Four waves of Add Health data are currently available. The first wave was initiated in 1994 and was, at that time, the largest, most comprehensive school- based survey of adolescents ever undertaken in the United States. The survey randomly selected 80 high schools stratified by region, school type (e.g., public, private), demographics, size, and level of urbanization from a sample frame of 26,666 schools throughout the country. Feeder schools that sent graduates to the 80 selected high schools were also identified. The final Wave 1 sample included a total of 134 middle and high schools.

Computer-assisted self-interviews were administered to 20,746 Wave 1 respondents in 1994. Wave 2 included only those Wave 1 respondents who were still attending school in 1995–1996 (71% of the Wave 1 sample). Wave 3 was collected in 2001 and 2002 and included 15,190 respondents (all original Wave 1 respondents who could be contacted and re-interviewed) between 18 and 26 years of age. A fourth in-home interview was conducted in 2007 and 2008 with 15,701 Wave 1 respondents. At the time of the interview, the Wave 4 participants were 24- to 32-years old.

The analysis sample consists only of Wave 4 observations. Of those, we kept the observations that contained complete infor- mation for the sleep problems variables (N = 14,089). We also considered incorporating data for sleep problems using informa- tion from the other three waves. Unfortunately, the sleep patterns questions in earlier waves were different and not as detailed as those from Wave 4.

2.2. Variables

2.2.1. Sleep problems. The Add Health survey asked how often respondents had trouble falling and staying asleep in the last four weeks. The respondents could choose from the following categories: never in the past four weeks, less than once a week, one

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r two times a week, three or four times a week, and five or more imes a week. We use this information to create two dichotomous ariables pertaining to problems falling asleep and problems taying asleep. We chose to collapse these response categories into ichotomous measures (i.e. presence or absence of the condition) ecause a large part of the sample (35% of males and 28% of females) eported no sleep problems during the past 4 weeks. Neverthe- ess, we also formed ordered categories from the sleep problem uestions and, using the same set of explanatory variables, esti- ated these relationships with the ordered probit technique (see

ection 3.3 that follows). In addition, the respondents were asked hether there were times when they snored or stopped breathing hile sleeping. We use this information to form a dichotomous

ariable indicating snoring/sleep apnea during the past four eeks.

.2.2. Binge drinking. We construct several dichotomous measures f drinking during the past 12 months. First, we create a binary mea- ure for any alcohol consumption during the past year to compare he sleep patterns of abstainers and drinkers. Second, to examine hether sleep problems are significantly associated with heavy

onsumption, we construct a dichotomous variable for any binge rinking during the past year. Binge drinking is defined as five or ore drinks in a row for men and four or more drink in a row for omen. The results for any binge drinking can be directly com- ared to the results for any drinking because both measures are inary. Finally, to explore whether more frequent binge drinking as a stronger association with sleep problems than less fre- uent binge drinking, we construct four binge-drinking categories nd re-estimate all models with these binge-drinking dummies. espondents could choose from seven binge-drinking categories: one, 1 or 2 days in the past 12 months, once a month or less, 2 r 3 days a month, 1 or 2 days a week, 3–5 days a week, and every ay or almost every day. To simplify the analysis and interpretation f results, we use these categories to construct four binge-drinking ummy variables: never binged, occasional binge drinker (1 or 2 ays in the past 12 month to once a month or less), approaching eekly binge drinker (2 or 3 days a month), and weekly or more

requent binge drinker (1 or 2 days a week to every day or almost very day) in the past year. Sensitivity analyses incorporate other lcohol use measures. Specifically, we construct four categories for lcohol use and four categories for being drunk or very high on lcohol in the past year in the same way as our four binge-drinking ummy variables were constructed.

.2.3. Control variables. To separate the effects of alcohol use on leep problems from the influence of other confounding factors, ll models include a rich set of control variables. Several studies ave found that age (Reyner et al., 1995; Ware et al., 2000; Ohayon t al., 2004), race/ethnicity (O’Connor et al., 2004; Mezick et al., 008; Hall et al., 2009), and socioeconomic status (Breslau et al., 997; Moore et al., 2002; Gellis et al., 2005; Mezick et al., 2008; all et al., 2009) are associated with individual sleep patterns and

leep problems. We therefore control for the following variables: ge, race/ethnicity (dichotomous indicators for Hispanic, African- merican, Asian, other race, with White as the reference group), ears of schooling, current employment and marital status, number f persons in the household, labor market income in the past year, nd being born outside the U.S.

Several studies also report that chronic medical conditions affect leep (Grunstein et al., 1993; Schwartz et al., 1995; Shahar et al., 001; Resnick et al., 2003; Pender and Pories, 2005; Vorona et al.,

005). Thus, we include a categorical measure for self-reported ealth status on a scale from 1 (excellent) to 5 (poor). Indicator ariables for underweight, overweight, and obese (with normal eight as the reference group) were constructed. These dummy

l Dependence 132 (2013) 207– 215 209

variables are based on height, weight, and waist circumference data collected by the Add Health interviewers from the study partici- pants. The interviewers measured these characteristics according to standardized protocols. Binary variables were added for heart problems, diabetes, and high blood pressure.

Numerous studies have found that sleep patterns are highly correlated with mental health status and mental health disorders (Johnson and Breslau, 2001; Bruck and Astbury, 2012; Ford and Kamerow, 1989; Thase, 1998; Kaneita et al., 2007a). Thus, all mod- els include dichotomous variables indicating lifetime diagnoses of depression, post-traumatic stress disorder, and panic disorder. Moreover, independent of these disorders, we include a dichoto- mous variable indicating whether the respondent received any psychological or emotional counseling in the past year for any type of mental health problem or disorder. Finally, because type of sub- stance use can affect sleep patterns in differential ways (Johnson et al., 1998; Johnson and Breslau, 2001; Bootzin and Stevens, 2005), we include indicators for any marijuana use in the past 30 days, any illicit drug use other than marijuana in the past 30 days, and cur- rent smoking. The control variables mentioned above are entered collectively in all models.

2.3. Empirical approach

The core specification is as follows:

S∗ = ˇAA + BC′ˇBC + X′ˇX + E′ˇE + ε (1) where S* is a latent variable indicating a particular sleep problem, A is a measure of alcohol use, BC′ is a vector of behavioral charac- teristics (e.g., smoking, illicit drug use) excluding current alcohol use, X′ is a vector of socioeconomic variables and personal charac- teristics, E′ is a vector of physical and mental health endowment, and ε is the error term. ˇA, ˇBC, ˇX, and ˇE are the coefficients to be estimated. Because S* is not observable, we define a dichotomous variable (S = 1 if S* > 0 and S = 0 otherwise) and estimate the above relationship using the probit technique.

Several studies have shown that gender differences exist in sleep patterns and sleep problems (Hume et al., 2002; Ware et al., 2000; Young et al., 1996; Reyner et al., 1995; Redline et al., 1994; Rediehs et al., 1990). Moreover, males and females have different alco- hol consumption patterns and experience different effects from drinking (Caetano, 1994; Hupkens et al., 1993; Robbins and Martin, 1993; Wilsnack et al., 2000). Thus, we follow convention in the sub- stance use literature and estimate separate regressions for males and females.

The analysis is conducted using the Stata 11 statistical software package (Stata, 2009). The results of heretoskedasticity tests reject the null of homoskedasticity in most of the models. Hence, we esti- mate all models with robust standard errors. To account for the complex sample design of the Add Health dataset, we execute the Stata survey command (svyset). As suggested by the Add Health administrators, we assume that the schools were selected with replacement in the survey design. After eliminating the observa- tions with missing sampling weights for Wave 4, these weights are employed to generate nationally representative estimates. We also use the post-stratification variable ‘Region’ and cluster at the school level. To conduct gender-specific analyses and obtain the correct standard errors, we employ the full dataset and use the ‘subpopulation’ option.

3. Results

3.1. Descriptive statistics

Table 1A (females) and Table 1B (males) present summary statis- tics for the variables used in the analysis. The means and standard

210 I. Popovici, M.T. French / Drug and Alcohol Dependence 132 (2013) 207– 215

Table 1A Mean values for all analysis variables: females.

Variables No binge drinkinga Occasional binge drinkingb

Approaching weekly binge drinkingc

Weekly binge drinkingd

(n = 4652) (n = 2032) (n = 529) (n = 509)

Sleep problems, past 4 weeks Trouble falling asleep (%)e , ** 52.35 61.93 63.38 64.51 Trouble staying asleep (%)f , ** 56.18 66.43 67.03 68.30 Snoring/sleep apnea (%)g , * 40.84 39.92 36.70 49.33

Explanatory variables Age (years) ** 29.02 (1.81) 28.76 (1.78) 28.48 (1.69) 28.58 (1.74) White (%) ** 59.81 77.62 78.55 78.54 Hispanic (%) * 13.75 9.88 8.61 9.82 African-American (%) ** 21.80 9.37 8.61 9.01 American Indian (%) ** 0.33 0.52 1.34 0.99 Asian (%) 3.41 2.32 2.63 1.56 Other race (%) 0.86 0.26 0.21 0.06

Years of schooling ** 14.01 (2.24) 14.59 (2.07) 14.53 (2.02) 14.34 (2.12) Currently employed (%) ** 55.51 66.44 69.99 62.43 Currently married (%) ** 45.45 37.58 30.62 22.29 Household size ** 2.59 (1.68) 2.08 (1.51) 1.87 (1.54) 1.72 (1.49) Labor market income, past year ** 23,665 (29,298) 31,672 (42,391) 31,018 (24,825) 28,728 (26.581) Born outside the U.S. (%) ** 5.15 3.17 1.67 1.70

Fair or poor health (%)h , ** 11.15 7.99 5.68 8.39 Good health (%)h 35.03 33.02 30.35 33.84 Very good or excellent health (%)h , ** 53.80 58.98 63.96 57.76

Normal Weight (%)i , ** 30.51 38.30 45.59 42.35 Underweight (%)i 2.33 1.91 1.18 1.77 Overweight (%)i 24.26 26.15 26.38 22.97 Obese (%)i , ** 42.88 33.62 26.84 32.89

Heart problem (%) 1.16 1.41 0.23 0.14 Diabetes (%) ** 3.26 2.71 1.17 1.08 High blood pressure (%) * 10.01 7.44 4.94 11.14

Any marijuana use, past 30 days (%) ** 7.20 15.96 24.80 33.22 Any other illicit drug use, past 30 days (%) ** 2.97 5.02 7.78 19.99 Current smoker (%) ** 35.72 50.69 59.07 70.67

Past year psychological or emotional counseling (%)j , ** 10.77 13.74 14.31 17.13 Depression (%)k , ** 20.74 23.95 23.22 31.60 Post-traumatic stress disorder (%)k 3.67 3.08 4.11 5.73 Panic disorder (%)k , ** 16.13 18.71 22.38 28.76

a A respondent who reported no episodes of binge drinking during the past 12 months. b An occasional binge drinker is an individual who reported binge drinking 1–12 days during the past 12 months. c An approaching weekly binge drinker is an individual who reported binge drinking 2 or 3 days a month during the past 12 months. d A weekly or more frequent binge drinker is an individual who reported binge drinking 1–7 days a week during the past 12 months. e Respondent had trouble falling asleep at least once during the past 4 weeks. f Respondent had trouble staying asleep at least once during the past 4 weeks. g Respondent reported snoring or sleep apnea during the past 4 weeks. h Self-reported health status measure. i Weight variables are based on data collected by the Add Health interviewers. j Past year psychological or emotional counseling for any type of mental health problem or disorder. k Lifetime diagnoses of depression, post-traumatic stress disorder, and panic disorder.

categ categ

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* Statistically significant difference in variable medians across the binge drinking ** Statistically significant difference in variable medians across the binge drinking

eviations are computed using the Add Health sampling weights o that the data are representative of the U.S. young adult pop- lation. As mentioned earlier, we conduct separate analyses by ender.

Table 1A reports weighted variable means for 7722 women, y binge drinking category. All control variables reveal highly ignificant differences in median values (Kruskal–Wallis (1952) ank-sum tests) across the binge drinking groups. Of particular nterest are the statistically significant differences in median alues for sleep problems measures across groups. The percentage f women who report having trouble falling asleep increases from bout 52% for those without any binge drinking episodes to about

5% of those reporting weekly binge drinking. The same linear elationship can be observed for the percentage of women repor- ing trouble staying asleep. The prevalence increases from 56% for he women without binge drinking episodes to 68% for weekly

ories, p < 0.05, Kruskal–Wallis equality of populations rank test. ories, p < 0.01, Kruskal–Wallis equality of populations rank test.

binge drinkers. Moreover, the prevalence of snoring and sleep apnea problems increases from 41% of the women without binge drinking episodes to 49% of those reporting weekly binge drinking.

Table 1B reports weighted variable means and standard devia- tions for 6781 men, by binge drinking category. Non-parametric Kruskal–Wallis (1952) rank-sum tests show statistically signifi- cant differences between binge drinking groups for most variables. While 45% of the men without any binge drinking episodes have trouble falling asleep, 55% of weekly binge drinkers report the same problem. The same linear relationship can be observed for the other two measures of sleep problems. The prevalence of men repor- ting trouble staying asleep increases from 48% for the men without

binge drinking episodes to 56% for weekly binge drinkers. More- over, while snoring and sleep apnea problems are reported by 51% of the men without binge drinking episodes, 61% of weekly binge drinkers report this problem.

I. Popovici, M.T. French / Drug and Alcohol Dependence 132 (2013) 207– 215 211

Table 1B Mean values for all analysis variables: males.

Variables No binge drinkinga Occasional binge drinkingb

Approaching weekly binge drinkingc

Weekly binge drinkingd

(n = 3071) (n = 1903) (n = 699) (n = 1108)

Sleep problems, past 4 weeks Trouble falling asleep (%)e , ** 45.08 50.54 51.57 54.62 Trouble staying asleep (%)f , ** 48.33 55.17 51.60 56.37 Snoring/sleep apnea (%)g , ** 50.96 62.53 59.59 61.23

Explanatory variables Age (years) ** 29.17 (1.86) 28.99 (1.80) 28.84 (1.82) 28.94 (1.8) White (%) ** 59.94 73.70 75.52 75.03 Hispanic (%) 12.46 11.65 11.27 11.05 African-American (%) ** 23.51 9.02 9.43 10.07 American Indian (%) 0.37 0.58 0.39 1.32 Asian (%) ** 3.02 3.91 2.40 1.79 Other race (%) 0.67 1.11 0.96 0.70

Years of schooling ** 13.49 (2.20) 14.18 (2.15) 14.01 (2.07) 13.45 (2.06) Currently employed (%) ** 64.61 71.63 72.04 72.82 Currently married (%) ** 36.55 38.35 29.12 21.85 Household size ** 2.20 (1.62) 1.95 (1.42) 1.95 (1.45) 1.81 (1.47) Labor market income, past year ** 33,018 (34,543) 46,336 (48,577) 42,482 (35,354) 37.303 (28.988) Born outside the U.S. ** 4.57 5.52 3.81 2.13

Fair or poor health (%)h , * 9.48 7.63 7.91 9.18 Good health (%)h , ** 34.81 29.18 32.23 35.36 Very good or excellent health (%)h , ** 55.69 63.18 59.85 55.44

Normal Weight (%)i , * 28.96 28.08 27.58 33.05 Underweight (%)i 0.80 0.87 0.26 1.05 Overweight (%)i , ** 31.28 36.33 40.54 35.51 Obese (%)i , ** 38.94 34.71 31.60 30.37

Heart problem (%) 0.84 0.50 0.52 0.81 Diabetes (%) ** 2.81 1.88 1.59 1.45 High blood pressure (%) 14.44 12.21 11.40 11.81

Any marijuana use, past 30 days (%) ** 13.38 20.09 29.47 39.58 Any other illicit drug use, past 30 days (%) ** 4.63 7.53 9.89 16.22 Current smoker (%) 42.00 50.39 57.38 68.81

Past year psychological or emotional counseling (%) j 8.67 8.32 6.65 7.02 Depression (%)k 10.89 9.33 9.57 10.96 Post-traumatic stress disorder (%)k 2.59 2.69 0.65 2.10 Panic disorder (%)k , * 7.82 7.51 8.07 9.96

categ categ

b n T d

p c a r

p b

* Statistically significant difference in variable medians across the binge drinking ** Statistically significant difference in variable medians across the binge drinking

Given the gender differences observed in the sleep problems and inge drinking variables, we also conduct rank-sum tests for sig- ificant differences in median values between males and females. he results of these tests reveal highly significant (p < .01) gender ifferences for all our sleep problems and binge drinking measures.

.2. Regression analysis

Although we find significant bivariate differences in sleeping roblems between the binge drinking categories, these differences ould be attenuated by confounding factors. Table 2A (women) nd Table 2B (men) present the results of the multivariate probit

egressions.

Overall, the estimates reveal that alcohol consumption is ositively associated with trouble falling and staying asleep for oth genders. Moreover, the results suggest a dose–response

ories, p < 0.05, Kruskal–Wallis equality of populations rank test. ories, p < 0.01, Kruskal–Wallis equality of populations rank test.

relationship as the estimated marginal effects increase with more frequent binge drinking. Most of the estimated marginal effects are statistically significant at the 1% or 5% levels.

The results for women indicate that drinking is associated with a 7.6–9.1 percentage point increase in the probability of having trouble falling asleep and an 8.4–9.1 percentage point increase in the likelihood of having trouble staying asleep. This is a substan- tial increase as, on average, about 56% (60%) of women reported problems falling (staying) asleep. The results for men tell a simi- lar story, albeit a slightly smaller effect size. Drinking is associated with a 4.8–7.1 percentage point increase in the probability of hav- ing trouble falling asleep and a 5.5–7.4 percentage point increase

in the likelihood of having trouble staying asleep for men.

Turning to our analysis of drinking patterns, snoring, and sleep apnea, the results reveal that drinking is positively associated with snoring and sleep apnea for men, but not for women. Specifically,

212 I. Popovici, M.T. French / Drug and Alcohol Dependence 132 (2013) 207– 215

Table 2A Selected regression results: females.

Alcohol use variables Trouble falling asleepa Trouble staying asleepa Snoring/sleep apneaa

N 7503 7507 7491 Baseline means 0.559 0.604 0.403 Current drinker 0.091** (0.019) 0.091** (0.017) 0.029 (0.019) Current binge drinker 0.076** (0.017) 0.084** (0.019) 0.008 (0.020) Occasional binge drinking 0.076** (0.019) 0.081** (0.023) 0.001 (0.022) Approaching weekly binge drinking 0.088** (0.033) 0.091** (0.035) −0.018 (0.032) Weekly binge drinking 0.066 (0.043) 0.084* (0.035) 0.070 (0.040)

Notes. Analyses used the Wave 4 sampling weights provided by Add Health and the strata variable Region. Robust standard errors clustered at school level are reported in parentheses. All specifications control for age, ethnicity, race, years of schooling, employment, marital status, household size, personal labor market income, born outside the U.S., health status indicators, BMI category, marijuana and other illicit drug use in the past 30 days, smoking status, ever diagnosed with a heart problem, diabetes, hypertension, depression, post-traumatic stress disorder, and panic disorder, and past year psychological or emotional counseling.

a Estimated with probit. Marginal effects are reported. * Statistically significant, p ≤ 0.05.

** Statistically significant, p ≤ 0.01.

Table 2B Selected Regression Results: Males.

Alcohol use variables Trouble falling asleepa Trouble staying asleepa Snoring/sleep apneaa

N 6584 6584 6584 Baseline means 0.487 0.517 0.579 Current drinker 0.071** (0.024) 0.055* (0.022) 0.111** (0.021) Current binge drinker 0.056** (0.019) 0.070** (0.020) 0.125** (0.020) Occasional binge drinking 0.048* (0.023) 0.073** (0.023) 0.130** (0.023) Approaching weekly binge drinking 0.060* (0.028) 0.045 (0.030) 0.104** (0.033) Weekly binge drinking 0.061* (0.026) 0.074** (0.027) 0.123** (0.029)

d i T l

e v r d a p o w s p c a c o s a o h h

b e t v

a Estimated with probit. Marginal effects are reported. * Statistically significant, p ≤ 0.05.

** Statistically significant, p ≤ 0.01.

rinking is associated with a 10.4–13.0 percentage point increase n the probability of reporting snoring/sleep apnea among men. he estimated marginal effects are statistically significant at the 1% evel.

Supplementary Tables A (women) and B (men)2 present the full stimation results. The estimated marginal effects for the control ariables follow our expectations in almost all cases. Overall, the esults for men are similar to those for women. Controlling for binge rinking, being African-American or born outside the U.S., having

larger number of persons in the household, and being in good hysical health are all associated with decreases in the probability f having trouble falling or staying asleep. In contrast, respondents ho are more educated, as well as those with a lifetime diagno-

is of depression or panic disorders, are more likely to report sleep roblems. A post-traumatic stress disorder diagnosis is not signifi- antly correlated with sleep problems in women, but it is positive nd statistically significant for men. Moreover, the results for psy- hiatric disorders suggest that depression is the strongest predictor f sleep disturbances for women, while post-traumatic stress is the trongest predictor among men. Snoring/sleep apnea is positively ssociated with age, being African-American, being overweight or bese, having diabetes, having high blood pressure, smoking, and aving a diagnosis of depression. Being in excellent physical health as a negative effect on snoring/sleep apnea among women.

As shown above, gender differences are present among all the inge-drinking estimates. To assess whether these gender differ-

nces are statistically significant, we re-ran all our models with he full sample of respondents and, besides all the previous control ariables, included a gender dummy and interaction terms between

2 Supplementary material can be found by accessing the online version of this aper. Please see Appendix A for more information.

gender and each of our binge drinking variables. The results are pre- sented in Supplementary Table C3. The estimated marginal effects for the gender dummy show that, while men are less likely to have difficulty falling and staying asleep, they are more likely than women to snore and have sleep apnea. These results are statisti- cally significant at the 1% level. Overall, the interaction terms are small in magnitude and lack statistical significance at conventional levels, probably due largely to multicollinearity among predictor variables.

3.3. Sensitivity analysis

We conduct several sensitivity analyses to check the robust- ness of our core findings. First, all specifications are re-estimated with linear probability models (i.e., OLS) instead of probit models to determine whether our findings are robust to functional form. The results are similar in sign and statistical significance relative to those produced by our core models.

Second, we re-estimate all models using two alternative sets of measures for alcohol use. Specifically, we construct four cate- gories for alcohol use and four categories for being drunk or very high on alcohol in the past year. The results are consistent in sign and statistical significance with our core models using the binge drinking categories. As expected, the estimated marginal effects are smaller in magnitude for the drinking categories and slightly larger in magnitude for the “drunk” categories.

Third, we are concerned about the potential endogeneity of our binge drinking variables in the sleep problems regression models. It is possible that an unobserved variable (e.g., a personality trait)

3 Supplementary material can be found by accessing the online version of this paper. Please see Appendix A for more information.

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I. Popovici, M.T. French / Drug and A

hat is omitted from the model influences both the decision to binge nd to engage in other risky behaviors that affect sleep patterns. oreover, numerous studies have shown that, besides alcohol

se causing sleep disturbances, some individuals with sleep prob- ems might consume alcohol to self-medicate to promote sleep. In hese instances, our analysis would be biased and causality can- ot be inferred. The optimal method to address this endogeneity roblem is instrumental variable (IV) estimation (Greene, 2008). nfortunately, the Add Health dataset does not contain state or

ocal identifiers, which results in our use of individual-level vari- bles as instruments. We chose religiosity and alcohol availability n the home during adolescence (i.e., Wave 1). In first-stage probit egressions, both instruments are strong predictors of the proba- ility of binge drinking (p < .01). Although the estimated marginal ffects from recursive bivariate probit models are not estimated ith precision (i.e., the estimates are not statistically significant

t conventional levels), they are consistent in sign and magnitude ith our core models. However, our individual-level instruments

ould be over identifying due to the fact that they are also poten- ially endogenous. Thus, we cautiously view the core results as ssociations rather than causal effects.

Fourth, to address potential reverse causality whereby sleep roblems directly affect alcohol use, we restrict the sample to indi- iduals who did not have a sleep problem in Wave 3. Unfortunately, he sleep-related questions asked in Wave 4 were not asked in

ave 3. Instead, we use the answers to the following question to roxy for a sleep problem in Wave 3: “In the past seven days, how ften did you fall asleep when you should have been awake (for xample, during class or at work)?” After dropping those individ- als who reported one or more cases in Wave 3, the estimates are imilar in sign, magnitude, and statistical significance to our core stimates.

Fifth, past binge drinking and a history of psychiatric problems re likely to affect current sleep problems as well as current binge rinking. Thus, we include two additional control variables in our odels: a dichotomous variable indicating whether the respon-

ent received any psychological or emotional counseling in the ast year at Wave 3 and a dichotomous variable indicating binge rinking in the past year at Wave 3. Selected estimation results rom these augmented models are presented in Supplementary ables D and E4. Overall, the key estimates change very little ompared to our core results.

Finally, we used alternative (ordinal) measures for having trou- le falling and staying asleep and estimate the relationships with rdered probit. Each variable includes integer values ranging from

to 5 for reporting sleep problems in the past four weeks: never, ess than once a week, one or two times a week, three or four imes a week, and five or more times a week. The results using hese ordinal measures again show positive and statistically sig- ificant associations between binge drinking and sleep problems. elected results of the ordered probit models are presented in upplementary Tables F and G5.

. Discussion

This study is, to the best of our knowledge, among the first o analyze the relationships between binge drinking and various

leep problems in a large and nationally representative sample of oung adults in the U.S. By employing nationally representative ata with corresponding sampling weights, the estimates are

4 Supplementary material can be found by accessing the online version of this aper. Please see Appendix A for more information. 5 Supplementary material can be found by accessing the online version of this

aper. Please see Appendix A for more information.

l Dependence 132 (2013) 207– 215 213

generalizable to young adults in the U.S. population. We find a strong and robust positive association between binge drinking and sleep problems even after controlling for numerous socio- demographic factors, psychiatric disorders, physical health, and other types of substance use. The estimated effects of binge drink- ing on sleep problems increase in magnitude with frequency of occurrence, which suggests a dose–response relationship. We also find statistically significant gender differences in the estimated relationship between drinking and sleep problems.

4.1. Limitations

While the previously mentioned findings are supported by our statistical analysis, there exist some limitations to our research. First, although our models include a rich set of control variables, it is still possible that other important variables that are associ- ated both with sleep problems and alcohol use are omitted from the models either because they are not observable (e.g., individual characteristic) or because they are not available in the data set.

Second, another limitation pertains to the statistical assump- tions underlying our empirical models. If binge drinking is strictly exogenous, our estimates represent unbiased causal effects of binge drinking on sleep problems. As mentioned above, however, it is pos- sible that unobserved explanatory variables in the sleep problems equations (e.g., personal characteristics and traits) are correlated with our binge drinking variables. Moreover, alcohol consumption could be directly influenced by sleep problems, as individuals are trying to promote sleep through drinking (i.e., reverse causality). In the absence of panel data with identical measures across waves (the sleep problems questions were added in Wave 4 of the Add Health survey), it is very difficult to effectively address this issue. We attempt to implement an IV approach (see Section 3.3 above), but the estimated marginal effects were not statistically significant at conventional levels. Hence, we cautiously present our results as associations between binge drinking and sleep problems, rather than as causal effects per se. Nevertheless, the results of the IV esti- mation were consistent in sign and magnitude with the core results and the dose–response relationships identified in our core regres- sions are suggestive of a causal relationship. Further research with better data and measures is necessary to more rigorously investi- gate causal effects.

Third, as mentioned earlier, we recognize the importance of controlling for mental health status/disorders and mental health treatment as important factors influencing both sleep patterns and alcohol use. While we control for several mental health disorders, our dichotomous “psychological and emotional counseling” mea- sure is very broad. It would have been preferable to separately include measures of mental health counseling for different psychi- atric disorders. However, these data are not available in Add Health. Future research should carefully examine how specific psychiatric disorders and their treatment affect the relationships between alco- hol misuse and sleep.

Finally, the manner in which the Add Health survey col- lected data poses as an additional limitation. Namely, Add Health respondents self-reported alcohol use. While the extent (if any) of misreporting in this area cannot be known with certainty, the literature on this topic suggests that self-reported substance use measures are generally reliable for use in statistical analyses (Darke, 1998; Del Boca and Darkes, 2003; Friesema et al., 2004; Lintonen et al., 2004).

4.2. Conclusion

In summary, we have established a significant, positive, and robust relationship between binge drinking and sleep problems. These findings could have important implications for policymakers

2 lcoho

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14 I. Popovici, M.T. French / Drug and A

nd health care providers. Sleep problems and the misuse of alcohol ave numerous negative consequences for society. Several clinical tudies find that alcohol consumption prior to sleep is associated ith severe daytime sleepiness and diminished reaction time and erformance (Roehrs et al., 1994; Walsh et al., 1991; Ohayon et al., 997). As a result, individuals who consume alcohol to promote leep are more prone to traffic accidents, as well as to accidents at ome and the workplace (Walsh et al., 1991). Besides contributing o accidents, sleep problems decrease quality of life, lead to work- lace conflicts, decrease labor market productivity, and increase ealth care costs (Roth, 2005).

The total economic costs associated with insomnia are esti- ated at $92.5–$107.5 billion annually (Stoller, 1994). In addition,

ndividuals with OSA who consume alcohol are at increased risk or heart attacks and strokes (Bassetti and Aldrich, 1996). Given he substantial societal costs associated with the negative conse- uences caused by sleep problems, appropriate clinical and policy esponses should be developed. This requires a clear understand- ng of the causal link between alcohol use and sleep disturbances. s alcohol use and sleep problems co-exist, it would be prudent to valuate patients with diagnosed sleep problems for co-occurring lcohol use disorders. If alcohol misuse leads to sleep problems, ffective alcohol abuse treatment programs may indirectly reduce he occurrence of sleep problems and thus have greater long-term conomic benefits than previously estimated. Nevertheless, sleep isturbances should be monitored closely during recovery from lcohol abuse/dependency as they have been linked to relapse. oreover, public policy tools such as alcohol taxation, purchasing

ge limits, and penalties for drunk driving that aim to reduce drink- ng may also reduce the negative consequences associated with he co-occurrence of sleep and drinking problems. If sleep distur- ances are followed by alcohol misuse, patients treated for sleep isturbances using behavioral and pharmacological interventions hould be monitored for problematic drinking. Finally, sleep prob- ems might be an early indicator of increased risk for substance se among adolescents and young adults (Johnson and Breslau, 001).

The strong association between binge drinking and sleep prob- ems found in this study should be seen as a first step in nderstanding these relationships and should encourage much eeded future research that explores the causal link between the wo. Future studies should employ rigorous statistical methods to ddress the issue of endogeneity of alcohol use (i.e., IV estima- ion and/or fixed-effects models with longitudinal data) in order to rovide a deeper understanding of the causal mechanisms between lcohol use and sleep patterns.

ole of funding source

This study was funded by the National Institute on Drug Abuse NIDA). NIDA had no further role in the collection, analysis and nterpretation of the data; in the writing of the report; or in the ecision to submit the paper for publication.

ontributors

All authors participated actively in the research design, methods evelopment, data analysis, results interpretation, and writing of he manuscript.

thical approval

All study procedures were approved by the Human Subjects esearch Office of the University of Miami.

l Dependence 132 (2013) 207– 215

Conflict of interest

No conflict declared.

Acknowledgments

Financial assistance for this study was provided by the National Institute on Drug Abuse (R01 DA018645). This research uses data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kath- leen Mullan Harris at the University of North Carolina at Chapel Hill and funded by grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Develop- ment, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due to Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Infor- mation on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth). We received no direct support from grant P01-HD31921 for this research. Thanks are due to Carmen Martinez and William Russell for editorial assistance, Audry Klossner for research assistance, and Rosalie Pacula for technical suggestions. The authors are entirely responsible for the research and results reported in this paper, and their position or opinions do not necessarily represent those of Nova Southeastern University, the University of Miami, the Carolina Population Center, or the National Institute on Drug Abuse.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.drugalcdep. 2013.02.001.

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Binge drinking and sleep problems among young adults

1 Introduction

1.1 Conceptual background

2 Methods

2.1 Sample
2.2 Variables

2.2.1 Sleep problems
2.2.2 Binge drinking
2.2.3 Control variables

2.3 Empirical approach

3 Results

3.1 Descriptive statistics
3.2 Regression analysis
3.3 Sensitivity analysis

4 Discussion

4.1 Limitations
4.2 Conclusion

Role of funding source
Contributors
Ethical approval
Conflict of interest
Acknowledgments
Appendix A Supplementary data
References