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Cross-cultural patterns of the association between varying levels of alcohol consumption and the common mental disorders of depression and anxiety: Secondary analysis of the WHO Collaborative Study on Psychological Problems in General Health Care

Drug and Alcohol Dependence 2013, Volume 133, pages 825–831

Editorial comment
This international study, involving a sample of 5348 primary care attenders who participated in the WHO Collaborative Study of Psychological Problems in General Health Care, aimed to investigate whether there are any differences in the association of the two most common mental disorders, depression and generalized anxiety disorder, with varying levels of alcohol consumption. In the study, light/moderate drinking was associated with a lower prevalence of depression, while excessive drinking was associated with an increased prevalence of depression. This was observed in all types of primary care centres. Only a marginally significant association was found between light/moderate alcohol consumption and a lower prevalence of generalized anxiety disorder, while in contrast to the finding for depression, heavy or excessive drinking was not associated with an increased prevalence of generalized anxiety disorder compared to abstinence with either measure of alcohol use. Due to its cross-sectional nature, the study cannot distinguish between issues of “causality” or reverse causality. Large longitudinal studies with more explicit recording of possible confounding or moderating variables are needed to explore whether light or moderate alcohol consumption may be beneficial for mental health.

Abstract

Background

Alcohol consumption is associated with several complications of both physical and mental health. Light or moderate alcohol consumption may have beneficial effects on physical or mental health but this effect is still controversial and research in the mental health field is relatively scarce. Our aim was to investigate the association between varying levels of alcohol consumption and the common mental disorders of depression and anxiety in a large international primary care sample.

Methods

The sample consisted of 5438 primary care attenders from 14 countries who participated in the WHO Collaborative Study of Psychological Problems in General Health Care. Alcohol use was assessed using Alcohol Use Disorders Identification Test (AUDIT) and the mental disorders were assessed with the Composite International Diagnostic Interview (CIDI).

Results

Light to moderate alcohol consumption was associated with a lower prevalence of depression and generalized anxiety disorder compared to abstinence while excessive alcohol consumption was associated with a higher prevalence of depression. This non-linear association was not substantially affected after adjustment for a range of possible confounding variables, including the presence of chronic disease and the current physical status of participants and was evident in different drinking cultures.

Conclusion

The study confirms that excessive drinking is associated with an increased prevalence of depression, but also raises the possibility that light/moderate drinking may be associated with a reduced prevalence of both depression and anxiety. Any causal interpretation of this association is difficult in the context of this cross-sectional study and further longitudinal studies are needed.

Keywords: Cross-cultural comparison, Alcohol drinking/*psychology, Alcohol drinking/*epidemiology, Anxiety disorders, Depressive disorders, Primary health care.

1. Introduction

Alcohol consumption is associated with several complications of both physical and mental health and contributes substantially to the global burden of disease (Room et al, 2005, Mathers and Loncar, 2006, and Murray et al, 2012). In contrast, the effect of light or moderate alcohol consumption on health is less clear.

Previous research has shown that light/moderate alcohol consumption may be associated with improved outcomes related to cardiovascular health (Corrao et al, 2000, Costanzo et al, 2010, and Ronksley et al, 2011), diabetes ( Howard et al., 2004 ), dementia ( Letenneur, 2004 ), and more generally has been linked with less disability, including reduced sickness absence and disability pension award (Upmark et al, 1999, Karlamangla et al, 2009, and Skogen et al, 2012), and less mortality (Di Castelnuovo et al, 2006 and Klatsky and Udaltsova, 2007). Although this has been suggested as a genuine preventive effect of moderate alcohol consumption ( Mukamal et al., 2010 ), others have interpreted this association as being due to systematic biases, confounding factors, or reverse causality (Jackson et al, 2005 and Fillmore et al, 2007).

The potentially positive effect of light/moderate alcohol consumption in physical health (as opposed to the harmful effect at higher doses) has not been adequately explored in relation to mental health. Most studies focus on the abuse/dependence extreme of the full range of alcohol consumption (Regier et al, 1990, Merikangas et al, 1998, Grant et al, 2004, and Haynes et al, 2005), ignoring the effects of light to moderate consumption when compared to abstinence from alcohol ( Rehm et al., 2003 ). One possible explanation for this omission is that average levels of alcohol consumption are typically grouped into too few categories that could hide differences between abstinence and light-moderate drinking or between heavy and excessive drinking (see Rehm et al., 2003 ). Studies that have explicitly looked at varying levels of average alcohol consumption have reported better mental health for light to moderate drinking compared to abstainers or excessive drinkers (Power et al, 1998, Peele and Brodsky, 2000, Rodgers et al, 2000b, Degenhardt et al, 2001, Caldwell et al, 2002, Alati et al, 2005, O’Donnell et al, 2006, and Graham et al, 2007,Smith and Shevlin, 2008, Skogen et al, 2009, and Mathiesen et al, 2012).

Other studies however failed to confirm such an association (Sareen et al, 2004, Paschall et al, 2005, Goldstein and Levitt, 2006, Graham et al, 2007, and Saarni et al, 2008). Part of this inconsistency could be attributed to the possible role of confounding factors such as the various socio-demographic variables (Rodgers et al, 2000a and Paschall et al, 2005) and the effect of chronic medical diseases that have not always been taken into account. In addition, most of these studies have drawn their samples from the community and only one from primary care ( Kirchner et al., 2007 ), where the average consumption and pattern of drinking may differ. Furthermore some of the above studies restricted their samples in younger (Caldwell et al, 2002, Alati et al, 2005, and O’Donnell et al, 2006) or older ( Kirchner et al., 2007 ) age groups only.

Furthermore, there are two particular aspects that have not been thoroughly examined so far: (a) previous studies have mainly concentrated on depression or general psychiatric morbidity (Paschall et al, 2005, Goldstein and Levitt, 2006, O’Donnell et al, 2006, and Graham et al, 2007). Regarding the two most common mental disorders, depression and generalized anxiety disorder (GAD), few studies have tried to investigate whether there are any differences in their association with varying levels of alcohol consumption (Kushner et al, 2000, Sareen et al, 2004, and Rodgers et al, 2007) even though there is evidence that this may differ ( Smith and Shevlin, 2008 ); (b) it is not known whether previous findings can be applied to cultures with different drinking patterns ( Room and Mäkelä, 2000 ). The above remarks are of particular interest for patients presenting in Primary Care where alcohol related problems are more likely to be comorbid with physical and/or mental disorders compared to the general population (Johnson et al, 1995 and Spitzer et al, 1999).

The aim of the present study was to investigate the association between varying levels of alcohol consumption and common mental disorders in primary care using data from the WHO study of Psychological Problems in General Health Care (PPGHC), an international study carried out between May 1991 and April 1992 in 15 primary care centres from 14 countries ( Üstün and Sartorius, 1995 ). To our knowledge, this is currently the only available data set from international primary care using the same methodology to assess the common mental disorders and the full range of alcohol consumption.

2. Materials and methods

2.1. General description of the data set

The WHO collaborative study of Psychological Problems in General Health Care (PPGHC) examined the prevalence, one-year outcome and public health implications of common mental disorders among patients in 15 primary care centres from 14 countries ( Üstün and Sartorius, 1995 ). Details on the methodology of the study are given elsewhere ( Von Korff and Üstün, 1995 ). Briefly, the study used a two-phase design in which 26969 primary care attenders aged between 18 and 64 years were approached in each participating centre and asked to complete the 12-item general health questionnaire (GHQ-12; Goldberg and Williams, 2000 ). 25,916 subjects agreed to participate (96% response rate, range across centres from 91% to 100%). Patients were selected for the second phase assessment using a stratified random sampling procedure according to site-specific GHQ-12 cut-points. On the basis of pilot test data and the recommendations for the more efficient use of the GHQ-12 as a screening instrument in two phase designs ( Dunn et al., 1999 ), the original investigators used two cut-points (the 60th and 80th percentile) to define three strata, the high GHQ scorers (those scoring above the 80th percentile), the medium GHQ scorers and the low GHQ scorers (those scoring below the 60th percentile). In order to increase the efficiency of prevalence estimation, a different sampling probability was used in each stratum, that is 100% of the high GHQ scorers, 35% of medium scorers and 10% of the low scorers ( Von Korff and Üstün, 1995 ). The second phase assessment included the Composite International Diagnostic Interview (CIDI; Wittchen et al., 1991 ) and involved 5438 out of 8698 eligible subjects (62% response rate, range from 43% to 99%). Data were collected between May 1991 and April 1992.

2.2. Measures

2.2.1. Measurement of common mental disorders

Common mental disorders were assessed with the Composite International Diagnostic Interview (CIDI) modified for use in primary care (Wittchen et al, 1991 and Von Korff and Üstün, 1995). The CIDI includes specific questions to rate each of the diagnostic criteria of the DSM-IV and ICD-10 classification systems. The validity and reliability of the CIDI have been tested in a study involving 20 countries ( Wittchen et al., 1991 ).

In the present study the following ICD-10 diagnostic categories derived from CIDI were used: (a) “Depression” (including both depressive episode [F32/33] and dysthymia [F34]), and (b) “Generalized Anxiety Disorder” (GAD; F41.1). All disorders refer to current (one-month) morbidity.

2.2.2. Measurement of alcohol use

Alcohol use was assessed with the Alcohol Use Disorders Identification Test (AUDIT; Saunders et al., 1993 ). AUDIT contains 10 questions related to alcohol use and its consequences in the previous 12 months. Each question is scored between 0 and 4 and the total score can have a range between 0 (total abstinence) and 40.

In the past AUDIT has been used in at least three ways in alcohol-related studies:(a) the first two questions in AUDIT ask about the average frequency of drinking in the specified time period and average amount of drinking in a typical drinking day in standard drinks (containing 10 g of alcohol each). These two questions can be combined to produce a “quantity-frequency index” ( Sobell and Sobell, 1995 ) that estimates the average pattern of alcohol consumption.(b) the third question is about occasional excessive drinking (6 or more drinks in one occasion) or binge drinking. As expected, there is a strong correlation between this question and the first two questions, especially the second question related to the amount of drinking ( Bergman and Källmén, 2002 ). For this reason, some researchers have also suggested the use of the AUDIT-C subscale, that is the score of the three first questions combined (Dawson et al, 2005b and Smith et al, 2010).(c) the total AUDIT score, which takes into account not only the pattern of average consumption and binge drinking (questions 1–3) but also symptoms of dependence (questions 4–6) and consequences from harmful use (questions 7–10).

Previous studies of the factorial structure of the AUDIT generally support the existence of two latent dimensions, namely alcohol consumption and alcohol-related problems ( Shevlin and Smith, 2007 ), although other studies have argued that the latter dimension should be further split into harmful alcohol use and dependence ( Rist et al., 2009 ). In addition, the items that constitute the consumption items (the first three AUDIT questions, i.e., the AUDIT-C subscale) have a complex relationship to each other and according to a large study in general practice the factorial structure is improved if the first two questions (quantity/frequency) are considered as indicators of another latent dimension, namely the consumption habits ( Rist et al., 2009 ). Taking this information into account, in the present study we chose the following two measures of alcohol consumption: (a) The quantity/frequency index, representing volume of alcohol consumed and (b) the total AUDIT score which combines both the concept of consumption and different patterns of use. In a sensitivity analysis we tested whether the use of AUDIT-C instead of the quantity/frequency index leads to different results.

A total AUDIT score of 8 for men or 6 for women is indicative of “hazardous” use ( Saunders et al., 1993 ). Similarly, one can use the recommended consumption in standard alcohol drinks to distinguish between safe and hazardous use. However, there is evidence from previous studies that the association between alcohol and mental health is not linear (Power et al, 1998, Rodgers et al, 2000b, Degenhardt et al, 2001, and Skogen et al, 2009). In order to highlight these non-linear associations we grouped the full range of alcohol consumption in 6 categories including that of abstinence or excessive drinking, as follows:(a) Regarding the total AUDIT scores we derived 6 groups of increasing severity defined using the 25th, 50th, 75th, 90th and 95th percentiles, this was done separately for men and women considering that safe consumption guidance and harmful use cut-offs are different for men and women ( Bergman and Källmén, 2002 ).(b) Regarding the quantity/frequency index (first two questions), we first calculated the average number of standard drinks consumed per week (see Table 1 ; Shakeshaft et al., 1999 ) and then we categorized participants into 6 groups of increasing consumption separately for males and females, in concordance with national and international guidelines ( National Health and Medical Research Council, 2009 ) and similarly to other studies ( Lucas et al., 2010 , Rodgers et al., 2007 ). The 6 groups included the following: (i) abstainers (ii) occasional drinkers (monthly or less) (iii) light drinkers (up to 14 standard drinks per week for men and 7 for women), (iv) moderate drinkers (up to 28 drinks per week for men and 14 for women), (v) heavy (up to 42 drinks per week for men and 28 for women) and (vi) excessive drinkers (>42 drinks for men or >28 for women).

Table 1 Calculation of average number of standard drinks consumed per week (according to Shakeshaft et al., 1999 ).

  Quantity (Number of drinks consumed on a typical drinking day)
Frequency (in the past 12 months) 1–2 3–4 5–6 7–9 10
Never 0 0 0 0 0
Monthly or less <2 <2 <2 <3 <3
2–4 times a month 2 3 4 6 8
2–3 times a week 4 9 14 21 25
4–7 times a week 9 20 31 45 55
2.2.3. Other variables

Data on possible confounders were systematically collected by the interviewers: (a) age was used as a continuous variable; (b) marital status was grouped into 5 categories: married, unmarried, widowed, divorced and separated); (c) years of education was used as a continuous variable; d) employment status was coded into 4 groups: employed, economically inactive (unemployed/student/retired/disabled), housekeeping and missing/other; (e) any self-reported chronic medical disease (yes/no) and (f) assessment of current physical health of participants as assessed by the primary care physician at the time of the visit (grouped into 3 categories: completely healthy/some non-specific symptoms; mildly ill; moderately/severely ill).

2.3. Classification of centres according to alcohol abstinence rates

To study potential cultural differences we classified centres into two rough categories using 50% of average abstinence rates as the cut-off ( Room and Mäkelä, 2000 ). Abstinence rates were calculated for all centres based on the proportion of the participants responded “never” on the first AUDIT question about frequency of drinking. Five participating centres had an average abstinence rate of ≥50% and were classified as “dry” centres while ten centres had an average abstinence rate of <50% and were classified as “wet” centres (Table S1 in Supplementary Material 1 shows details of this classification).

2.4. Statistical analysis

All analyses were performed with STATA/SE 10.0 (StataCorp, College Station, Texas). Weighted percentages were estimated using the “survey” commands in STATA. For the association between alcohol use and common mental disorders we used multilevel modelling. This is a method that can be used when data have a hierarchical structure and are organized at more than one level, i.e., they are nested ( Goldstein, 1995 ). This will create correlation of the data within clusters that needs to be taken into account. In our data, observations were organized into two levels (level 1: individuals, level 2: primary care centres). Since our outcome was dichotomous (presence or absence of depression or anxiety) we fitted a two-level logistic random-intercept model using the “gllamm” command in STATA ( Rabe-Hesketh and Skrondal, 2008 ). Further documentation for the gllamm command in STATA and the fitted model can be found elsewhere ( www.gllamm.org; Rabe-Hesketh and Skrondal, 2006 ). From these models we report odds ratios and 95% confidence intervals. The above models were adjusted for potential confounding variables (age, sex, marital status, years of schooling, working status, physician's rating of current physical health and self-reported chronic medical diseases). Our main analysis is with all centres combined. However, in the Supplementary Material 2 we also present the analyses separately for “wet” and “dry” centres (see previous section).

3. Results

3.1. Description of the sample

Sociodemographic and clinical characteristics of the sample are presented in Table 2 . It can be seen from the table that the sample was predominantly female (62%) and married (62%) with a mean age of 40.5 (range: 15–65). Prevalence of depression was 11.7% (95% CI: 10.8%–12.6%) while that of GAD was 7.9% (95% CI: 7.1%–8.8%).

Table 2 Sociodemographic characteristics of 5438 participants in psychological problems in General Health Care WHO Collaborative Study.

  Total Male n = 1919 (38.1%) Female n = 3519 (61.9%)
Mean age, (S.E.) 40.5 (0.4) 40.1 (0.6) 40.7 (0.3)
ICD-10 Diagnosis, N (%)
Depression 1287 (11.7%) 348 (8.2%) 939 (13.8%)
Generalized anxiety disorder 705 (7.9%) 207 (5.7%) 498 (9.3%)
Self reported presence of chronic disease, N (%)
No 2018 (39.3%) 704 (38.1%) 1314 (40.0%)
Yes 3420 (60.7%) 1215 (61.9%) 2205 (60.0%)
Physician rated health, N (%)
Completely healthy/some symptoms but not ill 2493 (48.6%) 826 (44.3%) 1667 (51.3%)
Mildly ill 1889 (35.9%) 696 (38.9%) 1193 (34.1%)
Moderately/severely ill 931 (15.5%) 363 (16.8%) 568 (14.6%)
Mean years of schooling, (S.E.) 9.4 (0.1) 10.4 (0.1) 8.9 (0.1)
Marital status, N (%)
Married 3331 (62.2%) 1161 (59.8%) 2170 (63.7%)
Widowed 243 (4.6%) 27 (2.2%) 216 (6.1%)
Divorced 347 (5.3%) 101 (4.5%) 246 (5.8%)
Separated 219 (4.0%) 51 (3.2%) 168 (4.5%)
Never married 1284 (23.9%) 576 (30.3%) 708 (19.9%)
Working status, N (%)
Unemployed/student/retired/disabled 1711 (32.2%) 735 (37.0%) 976 (29.3%)
Employed 2442 (44.2%) 1061 (56.5%) 1381 (36.7%)
Housekeeping 944 (16.7%) 4 (0.4%) 940 (26.7%)
Missing/other 341 (6.9%) 119 (6.1%) 222 (7.4%)

N = actual number of observations, percentages are weighted to take into account the two-phase design of the study

3.2. Alcohol use across centres

Table 3 presents the pattern of alcohol use in the sample. Overall, most participants abstained from alcohol (34.2%) or used alcohol monthly or less (30.7%). Approximately 12.9% of the participants (22.6% in male versus 7% in female) met the criteria for “hazardous” use taking into account the total AUDIT scores (≥8 for males and ≥6 for females). Regarding the differences between “wet” and “dry” centres, in the first abstinence rates for male and female primary care attenders were 7.4% and 17.9% respectively, while in the latter the abstinence rates were 53% and 83.4% respectively. Hazardous use, defined as previously, was reported by 18% in the “wet” centres versus 6% in “dry” centres with a significant gender effect in both.

Table 3 Alcohol consumption/use in 5438 primary care attenders in WHO Collaborative study.

Alcohol Measurement Male *

N (%) a
Female *

N (%) a
Total

N (%) a
A. Quantity/Frequency Index
Abstainers (not in the last year) 476 (22.7%) 1429 (41.2%) 1905 (34.2%)
Occasional drinkers (monthly or less) 498 (26.0%) 1201 (33.5%) 1699 (30.7%)
Light drinkers (up to 14 standard drinks per week for men and 7 for women) 586 (32.9%) 533 (15.5%) 1119 (22.1%)
Moderate drinkers (up to 28 standard drinks per week for men and 14 for women) 186 (10.9%) 242 (6.8%) 428 (8.4%)
Heavy drinkers (up to 42 standard drinks per week for men and 28 for women) 77 (3.5%) 73 (2.2%) 150 (2.7%)
Excessive drinkers (>42 standard drinks per week for men and 28 for women) 96 (4.0%) 41 (2.0%) 137 (2.0%)
 
B. Percentile range of total AUDIT-score b
≤25th 478 (22.7%) 1446 (41.9%) 1924 (34.6%)
26th–50th 493 (26.0%) 1109 (31.1%) 1602 (29.2%)
51st–75th 518 (28.7%) 161 (4.3%) 679 (13.6%)
76th–90th 245 (14.2%) 517 (15.7%) 762 (15.2%)
91st–95th 93 (4.8%) 131 (3.4%) 224 (3.9%)
≥96th 92 (3.7%) 155 (3.6%) 247 (3.6%)
 
C. Hazardous Alcohol use c 430 (22.6%) 286 (7.0%) 716 (12.9%)

* All comparisons between males and females are statistical significant (p < 0.01, designed-based F statistic).

a N=actual Number of observations/percentages are weighted to take into account the two-phase design of the study.

b Sex-specific percentiles.

c Based on total AUDIT- score: ≥8 for males and ≥6 for females.

3.3. Association between alcohol use and depression/GAD

Regarding depression, a nonlinear association, with a lower risk for light/moderate drinking and a higher risk for excessive drinking compared to abstinence, was observed ( Table 4 , left columns). This trend was evident with both measures of alcohol consumption, but the association between light/moderate drinking and reduced prevalence of depression was more evident with the quantity–frequency index; while that between excessive drinking and increased prevalence of depression was more evident with the percentile range of total AUDIT scores. Adjustment for potential confounders did not change substantially these results

Table 4 Association between alcohol use and common mental disorders in all 15 participating centres in the psychological problems in General Health Care WHO Collaborative Survey (N = 5438).

Alcohol Measurement Depression Generalized anxiety disorder
  N (%) a Crude OR b (95% CI) Adjusted OR b (95% CI) N (%) a Crude OR b (95% CI) Adjusted OR b (95% CI)
A. Quantity/Frequency Index c
Abstainers 473 (13.4%) 1.0 1.0 237 (7.8%) 1.0 1.0
Occasional drinkers 431 (12.6%) 0.79 * (0.66–0.95) 0.89 (0.68–1.17) 246 (9.2%) 0.67 * (0.51–0.88) 0.75 * (0.58–0.99)
Light drinkers 227 (8.7%) 0.49 * (0.37–0.63) 0.66 * (0.47–0.92) 122 (6.6%) 0.49 * (0.33–0.71) 0.71 * (0.51–0.99)
Moderate drinkers 82 (7.8%) 0.41 * (0.31–0.53) 0.53 * (0.38–0.75) 57 (6.9%) 0.51 * (0.37–0.70) 0.68 * (0.49–0.94)
Heavy drinkers 27 (8.6%) 0.54 * (0.31–0.93) 0.72 (0.42–1.23) 24 (7.7%) 0.74 (0.44–1.22) 1.06 (0.64–1.76)
Excessive drinkers 47 (23.4%) 1.54 * (1.02–2.35) 1.68 * (1.05–2.67) 19 (9.4%) 0.66 (0.32–1.37) 0.74 (0.39–1.40)
 
B. Percentile range of total Audit score d
≤25th 476 (13.3%) 1.0 1.0 238 (7.8%) 1.0 1.0
26th–50th 385 (11.9%) 0.79 * (0.64–0.98) 0.91 (0.67–1.24) 235 (9.5%) 0.71 * (0.57–0.88) 0.77 * (0.62–0.96)
51st–75th 104 (6.6%) 0.43 * (0.30–0.60) 0.66 (0.42–1.04) 58 (5.1%) 0.39 * (0.28–0.54) 0.59 * (0.40–0.88)
76th–90th 156 (8.6%) 0.53 * (0.29–0.97) 0.63 * (0.44–0.92) 88 (6.6%) 0.54 * (0.34–0.87) 0.66 * (0.44–0.99)
91st–95th 61 (12.2%) 0.64 (0.31–1.32) 0.77 (0.32–1.83) 38 (8.1%) 0.56 (0.29–1.07)

0
0.73 (0.38–1.44)
≥96th 105 (27.0%) 1.80 (0.75–4.30) 1.87 * (1.03–3.40) 48 (12.1%) 0.87 (0.49–1.55) 0.99 (0.63–1.57)

* p < 0.05.

a N = actual number of observations/percentages are weighted to take into account the two-phase design of the study.

b OR: odds ratios from two-level logistic regression adjusted for age, sex, marital status, working status, years of education, physician's rating of physical health, self-rated presence of chronic diseases.

c For definition see Table 3 and methods;

d Sex-specific percentiles.

Regarding GAD, in the crude analysis there was some evidence of a non-linear association (prevalence of GAD was lower for light/moderate drinkers compared to abstinent or heavy drinkers) especially with the quantity–frequency index; however after adjustment this was only marginally statistically significant. It is worth noting that, in contrast to what we observed in depression, heavy or excessive drinking was not associated with an increased prevalence of GAD compared to abstinence with either measure of alcohol use ( Table 4 , right columns).

We repeated the analyses separately for “wet” and “dry” centres but the results were very similar taking into account the resulting loss of power due to the smaller sample size, especially in “dry” centres (details of these analyses are given in the Supplementary Material Tables S2a and S2b 3 ). Furthermore, to perform sensitivity analysis we explored the association of AUDIT-C as a measure of alcohol consumption with Depression/Anxiety. The results were quite similar to those yielded with quantity/frequency index, but the latter was preferred as more easy to translate (Data available on request).

4. Discussion

4.1. Main findings

In this cross-national study from 14 countries, we found evidence for a non-linear association between alcohol consumption and depression/GAD. Light/moderate drinking was associated with a lower prevalence of depression, while excessive drinking was associated with an increased prevalence of depression. This was observed in all types of primary care centres, irrespective of the average abstinence rate. In contrast, heavy/excessive drinking was not associated with an increased prevalence of GAD, but there was some evidence that light/moderate drinking was associated with a lower prevalence. The observed associations were not substantially influenced by a range of potentially confounding variables including current or chronic medical diseases.

4.2. Limitations

These findings should be considered in the context of the following limitations: (a) as this is a cross-sectional study, the reported associations between alcohol use and anxiety or depression may not have any causal implications. Reverse causality, i.e., depressed patients drinking more heavily, may be equally true. Future longitudinal studies may clarify the direction of the observed associations; (b) this study was carried out in primary care and the results cannot be generalized to the general population. Factors related to health-seeking behaviour might be responsible for some of the associations found due to selection bias or bias of the Berkson's type; (c) The past alcohol history of the participants is unknown. It is possible that those who abstained from alcohol during the past year form a heterogeneous group, including never drinkers and subjects that stopped alcohol because they got sick of it in the past (“sick-quitters”) or because they were old ( Fillmore et al., 2007 ). These subgroups may have differences in personality and health related variables (Skogen et al, 2011 and Ng Fat and Shelton, 2012). In our adjusted analysis, we have taken into account some health related variables and we did not observe significant changes. This is in concordance with previous research not supporting (or partially supporting) the “quit-sickers effect” (Power et al, 1998, Caldwell et al, 2002, Alati et al, 2005, Graham et al, 2007, and Lucas et al, 2010); (d) although there was information on a number of potential confounders, some relevant variables were not measured: e.g., smoking (Caldwell et al, 2002 and Skogen et al, 2009), physical exercise ( Alati et al., 2007 ), social relationships (Alati et al, 2005, Lucas et al, 2010, and Skogen et al, 2009), personality characteristics like extroversion ( Caldwell et al., 2002 ), and stressful events or financial strain ( Lipton, 1994 ); (e) data were collected in the early 90s and the study was not specially designed to investigate the association between alcohol misuse and psychiatric disorders. Drinking patterns may have changed in many countries during the past 20 years and the association between alcohol consumption and common mental disorders could have been affected in specific countries included in the study. However, our main aim was to study the association between mental disorders and the full range of alcohol consumption and to investigate potential differences between two different alcohol-related cultures, namely “dry” and “wet” cultures, and not to report associations for individual countries. Therefore, our results can still be generalized into wet and dry cultures even if individual countries have moved from dry to wet (or vice versa) in more recent years.

4.3. Comparison with previous studies

As far as we know and based on a recent literature review, this is the first cross-cultural study conducted in primary care on the association between the full range of alcohol consumption and common mental disorders. Our results about the nonlinear association between the common mental disorders and the full range of alcohol use/consumption may be easier to generalize in health care settings compared to previous community studies supporting a J- or U- type association (Lipton, 1994, Power et al, 1998, Chick, 1999, Peele and Brodsky, 2000, Degenhardt et al, 2001, Alati et al, 2005, O’Donnell et al, 2006, El-Guebaly, 2007, and Skogen et al, 2009).

On the other hand, community studies that did not find an association between moderate alcohol use and lower prevalence of mental health problems (Sareen et al, 2004, Dawson et al, 2005a, Paschall et al, 2005, and Graham et al, 2007) have several methodological differences compared to the present study, e.g., they have restricted their samples in younger ages ( Paschall et al., 2005 ) and they have not assessed the full spectrum of alcohol use (Sareen et al, 2004 and Dawson et al, 2005a).

Regarding differences between depression and GAD, our study showed that excessive alcohol use was strongly associated with the first but not the latter. This is consistent with previous studies showing different patterns of association between alcohol use and these common mental disorders (Grant et al, 2004, Dawson et al, 2005a, and Falk et al, 2008). Furthermore, a higher prevalence of common mental disorders was not clearly associated with a high alcohol volume as measured by the quantity/frequency index (or AUDIT-C in our sensitivity analysis) but was clearly associated with a higher total AUDIT score in concordance with recent studies ( Smith et al., 2010 ).

4.4. Interpretation of main findings/implications

Our finding that light/moderate alcohol consumption may be associated with a lower prevalence of clinically significant common mental disorders in primary care, may be explained in at least four ways: low alcohol consumption could reduce the incidence or persistence of common mental disorders (causality hypothesis), the common mental disorders could lead to a decrease or abstinence from alcohol consumption (reverse causality hypothesis), several confounding variables not assessed in the current study could produce a plasmatic association (e.g., negative confounding) and, finally, the finding could be due to selection or information/measurement biases. Regarding confounding, it should be mentioned that we adjusted for both self-reported presence of chronic medical conditions and current medical morbidity assessed by local general practitioners (something that is rarely done in studies of this sample size). It is also worth noting that our adjusted analysis had a very small effect on the crude odds ratios. The primary care origin of our sample renders our results more clinically relevant, at least compared to community studies. The association between alcohol consumption and psychiatric disorders may have special importance in primary care where alcohol misuse and depression or anxiety disorders are very common ( Spitzer et al., 1999 ) and the target group for alcohol related intervention is still questionable (Volk et al, 1997 and Skog, 2006).

In the psychiatric literature, alcohol is traditionally considered as a harmful substance but it is not well appreciated that this refers mainly to heavy/excessive drinking. This has led to the assumption that the association between alcohol consumption and psychiatric disorders should be linear when in reality this may not be the case. Moreover, the use of binary cut-offs for AUDIT (e.g., ≥8 for hazardous drinking) may mask the non-linear association ( Haynes et al., 2005 ). On the other hand, in the public health literature there seems to be a consensus for the non-linear association between alcohol consumption and mortality in general or cardiovascular mortality in particular (Di Castelnuovo et al, 2006, Costanzo et al, 2010, Mukamal et al, 2010, and Ronksley et al, 2011). Given the large prevalence of common mental disorders (and an even larger proportion of subthreshold cases), it is important to distinguish between the harmful and potentially beneficial effects of alcohol in mental health. Whether a light or moderate consumption may be beneficial for mental health should be further explored in large longitudinal studies with more explicit recording of possible confounding or moderating variables. It is worth noting that we did not find important differences between “wet” and “dry” centres. However this does not necessarily imply that the same causal or intermediate mechanisms could explain this finding. Different typologies of drinking between “wet” and “dry” cultures ( Room and Mäkelä, 2000 ) combined with other culturally specific variables, related for example to stigma of mental disorders or social inequality issues, could lead to the same non-linear association ( Room, 2005 ).

Although the present study cannot distinguish between issues of “causality” or reverse causality due to its cross-sectional nature, our findings leave open the possibility that alcohol consumption may not always be bad for mental health. In several cultures around the world (for example in Southern European countries) alcohol is considered as an element of everyday nutrition rather that an intoxicant which is the prevalent view in Northern Europe or America ( Room and Mäkelä, 2000 ). These contrasting views influence public health policies on alcohol consumption and its regulation. Alcohol misuse or dependence, despite its detrimental health effects, is relatively rare compared to light/moderate drinking. Without underrating the impact of alcohol misuse or dependence on the global burden of disease, from a public health perspective it may be equally important to clarify whether light/moderate drinking has the potential to benefit mental health.

Role of funding

Nothing declared

Contributors

Stefanos Bellos was responsible for the conception of the study with PS, drafted the manuscript and contributed to the statistical analysis and in the critical interpretation of the results

Petros Skapinakis was responsible for the conception of the study with SB, helped in the writing of the manuscript and contributed to the statistical analysis and in the critical interpretation of the results.

Dheeraj Rai and Pedro Zitko, helped in the writing of the paper and made critical comments that helped in the interpretation of the results

Ricardo Araya, Glyn Lewis and Christos Lionis helped in the writing of the paper and made critical comments that helped in the interpretation of the results.

Venetsanos Mavreas contributed to data collection in the original study, provided the data, helped in the writing of the paper and made critical comments that helped in the interpretation of the results.

Conflict of interest

No conflict declared

Acknowledgements

The data reported in this article were collected as part of a World Health Organization's Psychological Problems in General Health Care project. Participating investigators include: O. Ozturk and M. Rezaki, Ankara, Turkey; C. Stefanis and V. Mavreas, Athens, Greece; S.M. Channabasavana and T.G. Sriram, Bangalore, India; H. Helmchen and M. Linden, Berlin, Germany; W. van der Brink and B. Tiemens, Groningen the Netherlands; M. Olatawura and O. Gureye, Ibadan, Nigeria; O. Benkert and W. Maier, Mainz, Germany; R. Gater and S. Kisely, Manchester, UK; Y. Nakane and S. Michitsuji, Nagasaki, Japan; Y. Lecrubier and P. Boyer, Paris, France; J. Costa e Silva and L. Villano, Rio de Janeiro, Brazil; R. Florenzano and J. Acuna, Santiago, Chile; G.E. Simon and M. von Korff, Seattle, Washington; Y. He-Quin and X. Shi Fu, Shanghai, China; and M. Tansella and C. Bellantuono, Verona, Italy. The study advisory group include J. Costa e Silva, D.P. Goldberg, Y. Lecrubier, M. von Korff and H-U Wittchen. Coordinating staff at World Health Organization headquarters include N. Sartorius and T.B. Ustun

Appendix A. Supplementary data

The following are Supplementary data to this article:

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Footnotes

a Department of Psychiatry, University of Ioannina School of Medicine, Ioannina 45110, Greece

b Centre for Mental Health, Addiction and Suicide Research, School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK

c Research Unit, Barros Luco General Hospital, Santiago, Chile

d Mental Health Sciences Unit, University College London, Charles Bell House, 67-73 Riding House St, London W1 W 7EJ, UK

e Clinic of Social and Family Medicine, School of Medicine, University of Crete, 71003, Greece

lowast Corresponding author. Tel.: +30 26510 07540; fax: +30 26510 07049.

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

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

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

Supplementary material can be found by accessing the online version of this paper. See Appendix A for more details.


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