Alcohol drinking patterns and health-related quality of life reported in the Spanish adult population

Preventive Medicine 57 (2013) 703–707

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Alcohol drinking patterns and health-related quality of life reported in the Spanish adult population☆ José Lorenzo Valencia-Martín a,b,c,⁎, Iñaki Galán a,d, Pilar Guallar-Castillón a,b, Fernando Rodríguez-Artalejo a,b a

Department of Preventive Medicine and Public Health, School of Medicine, Universidad Autónoma de Madrid/IdiPAZ, Madrid, Spain CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain Department of Preventive Medicine, Móstoles University Hospital, Móstoles, Spain d National Centre for Epidemiology, Instituto de Salud Carlos III, Madrid, Spain b c

a r t i c l e

i n f o

Available online 17 September 2013 Keywords: Health-related quality of life SF-12 questionnaire Alcohol Binge drinking Drinking patterns

a b s t r a c t Objectives. To examine the association between alcohol drinking patterns and health-related quality of life (HRQL). Methods. Population-based cross-sectional study was conducted in 2008–2010 among 12,715 adult individuals in Spain. HRQL was assessed with the SF-12 questionnaire and alcohol intake with a diet history. The threshold between average moderate drinking and average heavy drinking was ≥40 g/day of alcohol in men and ≥24 g/day in women. Binge drinking was defined as the intake of ≥80 g in men and ≥60 g in women at any drinking session during the preceding 30 days. Analyses were performed with linear regression and adjusted for the main confounders. Results. Compared to non-drinkers, all types of average drinkers reported better scores on the SF-12 physical component: β = 1.42 (95% confidence interval 1.03 to 1.81) in moderate drinkers and β = 1.86 (1.07 to 2.64) in heavy drinkers. In contrast, average alcohol consumption was not associated with the mental component of the SF-12. The number of binge drinking episodes and most types of beverage preference showed no association with physical or mental HRQL. Conclusions. Alcohol drinkers, including those with heavy drinking, reported better physical HRQL than nondrinkers. © 2013 Elsevier Inc. All rights reserved.

Introduction Alcohol consumption is one of the main causes of disease burden (Lim et al., 2013; Rehm et al., 2010; World Health Organization, 2011). However, the association between alcohol drinking and other health indicators, including health-related quality of life (HRQL), is uncertain. HRQL can be assessed with the SF-12 and SF-36 questionnaires; these instruments include 8 health dimensions, which can be summarized by two global HRQL indicators: the physical component summary (PCS) and the mental component summary (MCS) (Schmidt et al., 2012; Vilagut et al., 2008; Ware and Sherbourne, 1992). Studies on the association between alcohol consumption and HRQL are scarce and have yielded apparently inconsistent results (Pisinger et al., 2009; Van Dijk et al., 2004; Volk et al., 1997). The heterogeneity of the results of

☆ Sources of funding: This work has been partly funded by grant no. 06/2010 from the Plan Nacional Sobre Drogas, Spanish Ministry of Health. ⁎ Corresponding author at: Departamento de Medicina Preventiva y Salud Pública, Facultad de Medicina, Universidad Autónoma de Madrid, C/Arzobispo Morcillo 4, 28029 Madrid, Spain. Fax: +34 914 975 353. E-mail address: [email protected] (J.L. Valencia-Martín). 0091-7435/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.ypmed.2013.09.007

studies on alcohol consumption and HRQL might partly be due to the effect of different drinking patterns; in fact, the available studies did not simultaneously account for some of the variables which best characterize a drinking pattern and its health impact: average alcohol intake and heavy episodic intake (“binge drinking”) (Rehm et al., 2010). Moreover, to our knowledge, no study has yet investigated the relationship between alcohol consumption and the PCS/MCS scores in a Southern European country, where the drinking patterns (e.g., regular consumption of wine, low frequency of binge drinking) differ from those in Anglo-Saxon countries. Accordingly, we have examined the association of average alcohol intake and binge drinking with reported HRQL in the adult population of Spain. Moreover, since drinking patterns are associated with beverage preference and drinking with meals, we also investigated the association between these latter variables and HRQL. Methods Study design and participants The study methods have been reported elsewhere (Rodríguez-Artalejo et al., 2011). In brief, we conducted a cross-sectional study from 2008 to 2010 on

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12,948 persons, who were representative of the non-institutionalized Spanish population aged ≥18 years. The ENRICA protocol was approved by the clinical research ethics committees of the University Hospital La Paz in Madrid and Hospital Clinic in Barcelona. Study variables Patterns of alcohol consumption The average intake of alcohol was estimated using a diet history, developed from that used in the EPIC-cohort study in Spain, which assesses the regular consumption of 34 alcoholic beverages (EPIC Group of Spain, 1997). A set of photographs was used for a more precise quantification of the total volume of the beverages. Average alcohol intake was obtained by asking individuals about their consumption in a typical week during the last twelve months; the daily intake was calculated by dividing the total weekly amount of pure alcohol by 7 days. Average heavy alcohol intake was defined as ≥ 40 g/day in men and ≥ 24 g/day in women (World Health Organization, 2000). Lower intakes were deemed to be average moderate intake. Individuals were classified into four categories: 1) non-drinkers; 2) ex-drinkers; 3) moderate drinkers; and 4) heavy drinkers. Non-drinkers included lifetime abstainers and sporadic drinkers. Binge drinking was defined as the intake of ≥80 g of alcohol in men and ≥60 g in women in women at any given drinking session (one evening or night) during the preceding 30 days (Strategy Unit Alcohol Harm Reduction Project, 2003). Study participants were classified according to the number of binge drinking episodes in the last month. Among drinkers, a preference for a specific type of alcoholic beverage (wine, beer or spirits) was deemed to exist when such drink accounted for over 80% of alcohol intake. Also, consumption of alcohol with meals (lunch or dinner) was used to classify drinkers into three groups (only with meals, only outside of meals, with or outside of meals). Information on beverage preference and drinking with meals also referred to the preceding 12 months. Health-related quality of life HRQL was assessed with the SF-12, version 2 questionnaire (Ware et al., 2002), previously validated in Spain (Schmidt et al., 2012). The PCS and MCS scores are standardized to a national norm with a mean of 50 and a standard deviation of 10; this allows for comparing the scores for each study participant against the mean score in the Spanish population. A higher score in PCS or MCS indicates a higher HRQL; a 2-point and an 8-point difference in the PCS or the MCS is deemed to be, respectively, a small and a moderate-to-large difference (Kazis et al., 1989; Vilagut et al., 2008). Despite being a short version of the SF-36 questionnaire, the SF-12 has excellent criterion validity because it explained over 90% of the variability in the PCS and MCS scores on the SF-36. The SF-12 has also shown good reliability for group comparisons (Vilagut et al., 2008). Other variables We collected information on sociodemographic variables, tobacco consumption, and physical activity using a validated instrument (Cust et al., 2008). We also asked subjects whether a physician had ever told them that they suffered from any of the following diseases: hypertension, hypercholesterolemia, myocardial infarction, stroke, heart failure, diabetes, cancer at any site, asthma or chronic bronchitis, sleep apnea, osteoarthritis or rheumatism, rheumatoid arthritis, gall bladder disease, duodenal ulcer, depression requiring treatment, and Parkinson disease. In our study sample, all of these diseases were associated (p b 0.05) with drinking patterns and with the PCS on the SF12; most of them were also associated with the MCS. Weight and height were measured with standardized procedures (GutiérrezFisac et al., 2012). Normal weight was defined as body mass index (BMI) 18.5– 24.9 kg/m2, overweight as BMI 25–29.9 kg/m2, and obesity as BMI ≥30 kg/m2. Statistical analysis Of the initial sample of 12,948 individuals, 11,782 reported alcohol drinking patterns and HRQL. Of these, 627 individuals lacked data on education or social class, 151 on BMI, and 293 on any of the chronic diseases studied. To reduce the number of missing values and optimize the study sample we followed a twostep procedure. First, we recoded variables with N1% missing values; specifically, we assigned a category named “no answer” to missing values in social class, BMI, hypertension and diabetes. Second, we imputed the missing values of the main independent variables (average alcohol consumption, episodes of binge drinking and drinking with meals) by using proper conditional methods such

as linear regression for a continuous variable, Poisson regression for a count variable, and multinomial logistic regression for a nominal variable, respectively; regression models included sociodemographic and lifestyle variables and health status. After this procedure, the number of subjects with missing values was reduced to 233 (1.8% of original sample); thus the analytical sample consisted of 12,715 individuals. The analyses on the association between beverage preference or drinking with meals and HRQL were performed only among the 7407 alcohol drinkers. The associations between alcohol drinking patterns and the PCS or the MCS of the SF-12 were summarized with β coefficients and their 95% confidence interval (CI) obtained from linear regression. Average alcohol consumption and binge drinking were modeled as separate variables, with mutual adjustment; specifically, average alcohol consumption was modeled as a categorical variable (non-drinkers, ex-drinkers, moderate drinkers, and heavy drinkers), while binge drinking was modeled as a continuous variable (number of binge drinking episodes). We built three types of models. The first one was a crude model; the second model adjusted for sociodemographic variables, lifestyle and BMI; and the third model additionally adjusted for diagnosed chronic diseases to assess their contribution to the study associations. The association between beverage preference or drinking with meals and HRQL among drinkers was further adjusted for average alcohol intake and binge drinking. All independent variables were modeled using dummy terms. We assessed whether the association of average alcohol consumption with HRQL vary with the number of episodes of binge drinking by constructing interaction terms defined as the product of the categories of alcohol consumption by the number of binging episodes. Then we used likelihood ratio tests to compare models with interaction terms and models without. No statistically significant interactions (p N 0.05) were found. We also examined the potential colinearity between alcohol drinking variables and chronic diseases by calculating Pearson correlations; since the Pearson coefficients were below ±0.3, colinearity was ruled-out. Analyses were performed using the survey procedures in Stata v.12 (StataCorp, 2012).

Results The PCS of the SF-12 was higher (better) in men than in women, in those with younger age, higher educational level and non-manual workers, and in individuals who had lower BMI and did more physical activity. Results were similar for the MCS, except that it tended to be better in older adults and showed no association with BMI (Table 1). Among study participants, 36.5% were non-drinkers, 5.3% ex-drinkers, 52,4% moderate drinkers, and 5.9% heavy drinkers. Moreover, 6.3% reported one or more episodes of binge drinking in the last month; among them, the average number of binging episodes was 2.2 (SE 0.1). Table 2 shows the association between alcohol drinking patterns (average alcohol intake and binge drinking episodes) and HRQL. Compared to the crude model, the magnitude of the association declined after adjusting for sociodemographic variables, lifestyle and BMI, and was further reduced after additional adjustment for chronic diseases. In the fully-adjusted analyses, no differences were observed between never drinkers and exdrinkers in their association with the PCS of the SF-12. However, compared to non-drinkers, all types of average drinkers reported a better score on the PCS: β = 1.42 (95% confidence interval [CI] 1.03 to 1.81) in moderate drinkers and β = 1.86 (1.07 to 2.64) in heavy drinkers. In contrast, average alcohol consumption was not associated with the MCS of the SF-12. Also, the number of binge drinking episodes showed no association with either the MCS or the MCS. Among the study subjects, 32.4% reported a beverage preference for wine, 18.5% for beer, and 6.2% for spirits. Also, 45.4% drank alcohol only with meals and 27.6% only outside meals. In fully-adjusted analyses, those who preferred spirits showed a worse score on the PCS than those with no beverage preference: β = −1.21 (−2.07 to −0.34), while those who preferred wine or spirits showed a better score on the MCS: β = 0.70 (0.07 to 1.33) and β = 1.25 (0.08 to 2.42), respectively (Table 3). Also, those drinking alcohol only outside of meals reported better MCS than those who drank only with meals: β = 1.11

J.L. Valencia-Martín et al. / Preventive Medicine 57 (2013) 703–707 Table 1 Physical and mental component summaries of the SF-12 by sociodemographic and lifestyle variables (Spain 2008–2010). Physical component summary Mean (SE)

Mental component summary Mean (SE)

12,715

50.1 (0.1)

50.0 (0.1)

6303 6412

51.5 (0.1) 48.7 (0.2)

51.8 (0.1) 48.2 (0.2)

2489 3972 3718 2536

54.2 (0.2) 52.5 (0.2) 49.2 (0.2) 43.5 (0.3)

49.2 (0.2) 49.4 (0.2) 50.2 (0.2) 51.5 (0.3)

3751 5341 3622

45.4 (0.3) 51.6 (0.2) 52.6 (0.2)

48.9 (0.2) 50.1 (0.2) 51.0 (0.2)

4212 7793

48.9 (0.2) 51.1 (0.1)

49.7 (0.2) 50.3 (0.1)

3542 3120 6054

51.2 (0.2) 49.9 (0.2) 49.5 (0.2)

48.8 (0.2) 51.2 (0.2) 50.1 (0.2)

3509 4266 2948 1992

46.4 (0.3) 50.2 (0.2) 52.0 (0.2) 53.5 (0.2)

49.1 (0.2) 49.8 (0.2) 50.6 (0.2) 51.1 (0.2)

4486 4659 2674

52.7 (0.2) 50.3 (0.2) 45.7 (0.3)

49.3 (0.2) 50.7 (0.2) 50.2 (0.3)

N

Total Sex Men Women Age 18–29 years 30–44 years 45–64 years ≥65 years Educational level Primary and lower Secondary University Social class Manual work Non-manual work Tobacco consumption Current smoker Ex-smoker Never smoker Physical activity Inactive Moderately inactive Moderately active Active Body mass index Normal weight Overweight Obesity SE: Standard error.

(0.48 to 1.73). No other associations were observed between beverage preference or drinking with meals and the PCS or MCS score (Table 3). Discussion In this study, alcohol drinkers, including moderate and heavy drinkers, reported better physical HRQL than non-drinkers. However,

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the associations were of small magnitude. Lastly, neither binge drinking episodes nor beverage preference and drinking alcohol with meals were consistently associated with HRQL. Average alcohol intake, binge drinking and HRQL Our results are in line with those of two studies in Holland and Denmark. In the first one, moderate and heavy drinkers reported better physical and mental HRQL than abstainers (Van Dijk et al., 2004). However, this study did not account for binge drinking and the analyses were not adjusted for lifestyles, other than tobacco smoking, or for chronic diseases. In the second study, the PCS on the SF-12 was 49.9 in non-drinkers, 52.2 in moderate drinkers and 51.6 in heavy drinkers. An association in the same direction was observed for the MSC (Pisinger et al., 2009). Of note was that the reference category in the analyses (non-drinkers) did not distinguish between never and former drinkers; moreover, binge drinkers were not considered, and the analyses did not adjust for chronic diseases. However, our results do not concur with those obtained in the US primary care patients where, in comparison with never drinkers, regular drinkers of only small amounts of alcohol reported better PCS and binge drinkers reported worse MCS (Volk et al., 1997). This article adjusted the analyses for chronic disease and tobacco consumption but not for other lifestyles. Some of the observed discrepancies could be due to culturally-based differences in drinking patterns. In Europe, alcohol is frequently consumed on a daily basis and with meals, with a progressive homogenization of drinking patterns across Mediterranean and Central European countries. In contrast, alcohol drinking in the US is more irregular and often concentrated in heavy drinking occasions (Gordon et al., 2012; Room and Makela, 2000), which could contribute to the worse HRQL observed in the US studies. In our study, the associations between drinking patterns and HRQL were of small magnitude (Kazis et al., 1989; Vilagut et al., 2008). To place our results into context, it should be noted that in the fully-adjusted analysis (model 3) the β coefficients for the PCS associated with heart failure, osteoarthritis, obesity and asthma were − 4.93, −5.38, − 2.45, and − 2.27, respectively. As regards the MCS, the β coefficient associated with depression requiring treatment was −12.29 and with sleep apnea was −3.25. In contrast, most previous studies have observed moderate associations (Pisinger et al.,

Table 2 Association between alcohol drinking patterns and the physical and mental component summaries of the SF-12 (Spain 2008–2010). N Average alcohol intake Physical component summary Non-drinker Ex-drinker Moderate drinker Heavy drinker Mental component summary Non-drinker Ex-drinker Moderate drinker Heavy drinker Binge drinking Physical component summary Number of binging episodes/month Mental component summary Number of binging episodes/month

Unadjusted Model β coefficient (95% CI) a

Adjusted Model I β coefficient (95% CI) a

Adjusted Model II β coefficient (95% CI) a

4638 670 6660 747

Ref. −2.33 (−3.56 to −1.10)⁎⁎ 3.02 (2.54 to 3.49)⁎⁎ 2.35 (1.50 to 3.20)⁎⁎

Ref. −0.97 (−2.04 to 0.09) 1.73 (1.33 to 2.13)⁎⁎ 2.28 (1.50 to 3.06)⁎⁎

Ref. −0.41 (−1.42 to 0.61) 1.42 (1.03 to 1.81)⁎⁎ 1.86 (1.07 to 2.64)⁎⁎

4638 670 6660 747

Ref. 0.46 (−0.62 to 1.53) 1.40 (0.94 to 1.86)⁎⁎ 2.40 (1.51 to 3.29)⁎⁎

Ref. −0.40 (−1.46 to 0.67) 0.34 (−0.12 to 0.80) 1.08 (0.23 to 1.93)⁎

Ref. 0.03 (−0.99 to 1.05) −0.03 (−0.46 to 0.41) 0.52 (−0.29 to 1.34)

12,715

0.45 (0.13 to 0.77)⁎

−0.03 (−0.22 to 0.17)

0.02 (−0.13 to 0.17)

12,715

−0.19 (−0.51 to 0.14)

−0.25 (−0.58 to 0.08)

−0.17 (−0.43 to 0.10)

SE: Standard error. CI: Confidence interval. a Linear regression models: Unadjusted Model; Adjusted Model I: adjusted for average alcohol intake, number of binge drinking episodes, age, sex, educational level, social class, tobacco consumption, physical activity, and body mass index; Adjusted Model II: adjusted for the variables in Adjusted Model I and for 15 chronic diseases: hypertension, hypercholesterolemia, myocardial infarction, stroke, heart failure, diabetes, cancer at any site, asthma or chronic bronchitis, sleep apnea, osteoarthritis or rheumatism, rheumatoid arthritis, gall bladder disease, duodenal ulcer, depression requiring treatment, and Parkinson disease. ⁎ p b 0.05. ⁎⁎ p b 0.001.

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Table 3 Association between beverage preference, drinking with meals and the physical and mental component summaries of the SF-12 among alcohol drinkers (Spain 2008–2010).

Beverage preference b Physical component summary No preference Wine preference Beer preference Spirits preference Mental component summary No preference Wine preference Beer preference Spirits preference Drinking with mealsc Physical component summary With meals only Outside of meals only With and outside of meals Mental component summary With meals only Outside of meals only With and outside of meals

N

Summary Mean (SE)

Unadjusted Model β coefficient (95% CI) a

Adjusted Model I β coefficient (95% CI) a

Adjusted Model II β coefficient (95% CI) a

3181 2398 1369 459

52.4 (0.2) 49.3 (0.2) 52.4 (0.3) 53.0 (0.4)

Ref. −3.17 (−3.75 to −2.60)⁎⁎ 0.02 (−0.57 to 0.62) 0.62 (−0.28 to 1.52)

Ref. −0.67 (−1.28 to −0.06)⁎ 0.00 (−0.62 to 0.62) −1.50 (−2.42 to −0.57)⁎

Ref. −0.53 (−1.11 to 0.06) −0.02 (−0.62 to 0.58) −1.21 (−2.07 to −0.34)⁎

3181 2398 1369 459

50.2 (0.2) 51.3 (0.2) 50.5 (0.3) 50.3 (0.5)

Ref. 1.13 (0.53 to 1.74)⁎⁎ 0.26 (−0.40 to 0.92) 0.13 (−0.98 to 1.25)

Ref. 0.68 (0.02 to 1.34)⁎ 0.30 (−0.38 to 0.98) 1.10 (−0.13 to 2.33)

Ref. 0.70 (0.07 to 1.33)⁎ 0.17 (−0.51 to 0.85) 1.25 (0.08 to 2.42)⁎

3365 2044 1998

50.3 (0.2) 52.8 (0.2) 51.9 (0.2)

Ref. 2.51 (1.97 to 3.04)⁎⁎ 1.54 (0.93 to 2.15)⁎⁎

Ref. 0.22 (−0.36 to 0.80) 0.21 (−0.40 to 0.82)

Ref. 0.37 (−0.18 to 0.92) 0.25 (−0.32 to 0.82)

3365 2044 1998

50.6 (0.2) 50.5 (0.2) 50.9 (0.3)

Ref. −0.07 (−0.68 to 0.53) 0.30 (−0.35 to 0.95)

Ref. 1.11 (0.46 to 1.76)⁎ 0.34 (−0.32 to 1.00)

Ref. 1.11 (0.48 to 1.73)⁎ 0.37 (−0.27 to 1.00)

SE: Standard error. CI: Confidence interval. a Linear regression models: Unadjusted Model; Adjusted Model I: adjusted for age, sex, educational level, social class, tobacco consumption, physical activity, body mass index, average alcohol intake, and number of binge drinking episodes; Adjusted Model II: adjusted for the variables in Adjusted Model I and for 15 chronic diseases: hypertension, hypercholesterolemia, myocardial infarction, stroke, heart failure, diabetes, cancer at any site, asthma or chronic bronchitis, sleep apnea, osteoarthritis or rheumatism, rheumatoid arthritis, gall bladder disease, duodenal ulcer, depression requiring treatment, and Parkinson disease. b Adjusted Models I and II are also adjusted for drinking with meals. c Adjusted Models I and II are also adjusted for beverage preference. ⁎ p b 0.05. ⁎⁎ p b 0.001.

2009; Van Dijk et al., 2004; Volk et al., 1997). Given that in our study the strength of the association decreased with progressive adjustment for lifestyle and chronic diseases, we speculate that the larger associations found in other studies are due not only to different population and cultural contexts but also to a lower degree of adjustment. After adjustment for a substantial number of confounders, we did not find substantial differences in HRQL between ex-drinkers and non-drinkers (most of them were never drinkers). One study observed worse PCS and MCS in ex-drinkers than in non-drinkers or current drinkers (Stranges et al., 2006); other investigation also found worse MCS in ex-drinkers with respect to non-drinkers (Volk et al., 1997), while in another ex-drinkers reported worse PCS but better MCS (Van Dijk et al., 2004). Beverage preference, drinking with meals and HRQL Beverage preference in our study was similar to that reported in previous research in Spain (Valencia-Martín et al., 2009; World Health Organization, 2010). The relationship between beverage preference and HRQL is inconsistent in the literature. A follow-up investigation found worse HRQL in drinkers who preferred spirits (Strandberg et al., 2007); however, this investigation did not assess binge drinking nor did it adjust for chronic disease. Other studies have linked beer preference to better MCS in women, and beer and spirits preference to worse PCS in men (Stranges et al., 2006). Given that most previous research did not fully account for drinking patterns, it is not possible to establish whether the observed association truly reflected the effect of the beverage or the effect of the associated drinking pattern (e.g. a preference for spirits is usually associated with binge drinking) (Barefoot et al., 2002; Valencia-Martín et al., 2009). In one study no association was observed between drinking with meals and HRQL (Stranges et al., 2006), while in other investigation drinking with meals was related to better self-rated health (ValenciaMartín et al., 2009). We found better mental HRQL among those who drank only outside of meals. Although this has been suggested to be a

risk behavior because of the potentially toxic effects of alcohol (Rehm et al., 2003), drinking outside of meals is usually observed as part of a socializing behavior which may also pose some health benefits. Limitations of the study The main limitation is the cross-sectional design of the study, which does not allow for ruling out reverse causation. Another limitation is that self-reported alcohol consumption may underestimate actual intake (Midanik, 1982; Perrine et al., 1995), particularly among heavy and binge drinkers, which tends to attenuate the effect estimates. However, it is also possible that heavy and binge drinkers overestimate their physical and mental capacities, particularly after alcohol intake. This may have led to overestimating the beneficial effects of alcohol on HRQL. The combined effect of both potential biases on the study results is difficult to predict. Also, we lacked information on formal diagnosis of alcoholism. It precluded an assessment of the impact of alcohol dependence on study results and of whether it mediates the study associations. Finally, the SF-12 does not include sleep quality or cognitive function. Thus, future research should examine the influence of alcohol on these important health dimensions. Conclusions Average alcohol consumption, including moderate and heavy drinking, was associated with better reported physical HRQL; the magnitude of the association was substantially reduced after adjustment for potential confounders, and was small. Finally, since heavy and binge drinking produces substantial health harms, our results should not be used to promote alcohol consumption. Conflict of interest The authors declare that there is no conflict of interest.

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