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As well as having a negative impact on health , heavy alcohol consumption can also have a negative effect on an individual's welfare through its effects on work-place functioning and employment status [2-7]. Loss of employment itself can have many negative effects, including poverty, marginalization and adverse mental and physical health outcomes [8, 9].
Pathways from alcohol consumption to unemployment may be through chronic effects on health which make it difficult or impossible to remain in work, but also through acute dysfunctional consequences of alcohol such as hangover, which can directly affect an individual's ability to function in the work-place. Whether or not alcohol consumption results in acute dysfunctional behaviour could be particularly important for any effect of alcohol use on employment status. Acute dysfunction may be an important mediator between alcohol intake and loss of employment, because even when consuming the same volume of ethanol individuals can differ in their vulnerability to dysfunction and therefore the impact of their drinking on their work life.
The majority of longitudinal studies investigating alcohol consumption as a predictor of employment have investigated only the effects of volume of ethanol consumed [2, 3]. Only one study used a measure of acute dysfunction: Liira et al. found that self-reported drunkenness once a week or more predicted employment status in Scandinavian construction workers, but not forest workers . However, it is unlikely that drunkenness once a week or more captured men's experience of acute dysfunction adequately. In addition, this study did not measure the amount of ethanol consumed, so could not investigate how volume of ethanol, acute dysfunction and employment are related.
The aim of this study was to investigate the effects of alcohol intake and acute alcohol-related dysfunction on employment status using longitudinal data, in particular whether acute alcohol-related dysfunction was a mediator of the relationship between alcohol intake and employment. We used longitudinal data from the Izhevsk Family Studies carried out in Izhevsk, Russia, which collected detailed information on alcohol consumption, including several measures of acute dysfunctional behaviours.
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There were 1619 of 1941 men in regular paid employment at IFS-1, of whom 1502 had proxy-reported data available at IFS-1. Of these men, 1143 (76.1%) were re-interviewed at IFS-2. There was good evidence that men who were included were more likely to be married (82.4 versus 75.0%, P = 0.007) and have less frequent hangovers by proxy report (P = 0.05), but no evidence for a difference in any other socio-demographic, health or drinking variables compared to all men in employment at baseline. The baseline characteristics of these 1143 men by employment status at IFS-2 are shown in Table 1. At IFS-2, 115 of 1143 men (10.1%) were no longer in regular paid employment. The percentage of men no longer in regular employment at IFS-2 was higher for older men and lower for those who were married, had higher education and owned both a car and central heating at IFS-1.
Table 1. Baseline characteristics of men in regular paid employment at Izhevsk Family Study 1 (IFS-1) by employment status at IFS-2
|Characteristic at IFS-1||n (%)||Number no longer in regular paid employment at IFS-2 (row %)|
|Age (years)||25–29||77 (6.7)||10 (13.0)|
|30–34||101 (8.8)||5 (5.0)|
|35–39||104 (9.1)||8 (7.7)|
|40–44||192 (16.8)||9 (4.7)|
|45–50||272 (23.8)||24 (8.8)|
|50–54||390 (34.1)||57 (14.6)|
|55+||7 (0.6)||2 (28.6)|
|Marital status||Living with spouse in registered marriage||942 (82.4)||91 (9.7)|
|Living with spouse not in registered marriage||109 (9.5)||14 (12.8)|
|Divorced||36 (3.2)||4 (11.1)|
|Widower||6 (0.5)||2 (33.3)|
|Never married||50 (4.4)||4 (8.0)|
|Education||Incomplete secondary||56 (4.9)||9 (16.1)|
|Secondary||820 (71.7)||92 (11.2)|
|Higher||267 (23.4)||14 (5.2)|
|Amenity index||Neither car nor central heating||67 (5.9)||10 (14.9)|
|Car or central heating||584 (51.1)||67 (11.5)|
|Car and central heating||492 (43.0)||38 (7.7)|
|Smoking status (missing = 1)||Never smoked||227 (15.2)||22 (9.7)|
|Ex-smoker||174 (35.1)||12 (6.9)|
|Current smoker||741 (64.9)||81 (10.9)|
|Health problemsa (missing = 9)||No||646 (56.5)||57 (8.8)|
|Yes||488 (42.7)||58 (11.9)|
|Occupation type (missing = 6)||Manual||745 (65.2)||87 (11.7)|
|Non-manual||392 (34.3)||26 (6.6)|
|Total volume of ethanol from beverage alcohol in litres per year (missing = 14)||>0–2 litres||196 (17.4)||18 (9.2)|
|2–4 litres||243 (21.5)||19 (7.8)|
|5–9 litres||269 (23.8)||32 (11.9)|
|10–19 litres||171 (15.2)||17 (9.9)|
|20+ litres||104 (9.2)||12 (11.5)|
|Proxy report of drinking non-beverage alcohol (missing = 13)||Non-drinker||145 (12.7)||12 (8.3)|
|Drinks beverage alcohol only||922 (81.6)||86 (9.3)|
|Drinks non-beverage alcohol||63 (5.6)||15 (23.8)|
|Proxy report of zapoi in the past year||No (drinker)||924 (80.8)||83 (9.0)|
|Yes (drinker)||74 (6.5)||20 (27.0)|
|Proxy report of hangover (missing = 35)||Never||670 (60.5)||50 (7.5)|
|Less than once a month||218 (19.1)||18 (8.3)|
|Once a month||114 (10.0)||20 (17.5)|
|Several times a month||57 (5.0)||14 (24.6)|
|Once a week||27 (2.4)||4 (14.8)|
|Several times a week||16 (1.4)||0 (0.0)|
|Every day||6 (0.5)||3 (50.0)|
|Proxy report of excessive drunkenness (missing = 17)||Never||618 (54.1)||49 (7.9)|
|Less than once a month||255 (22.3)||19 (7.5)|
|Once a month||131 (11.5)||25 (19.1)|
|Several times a month||57 (5.0)||12 (21.1)|
|Once a week||36 (3.1)||3 (8.3)|
|Several times a week||20 (1.7)||3 (15.0)|
|Every day||9 (0.8)||3 (33.3)|
|Proxy report of sleeping in clothes at night because of drunkenness (missing = 6)||Never||934 (81.7)||75 (8.0)|
|Less than once a month||87 (7.6)||12 (13.8)|
|Once a month||58 (5.1)||14 (24.1)|
|Several times a month||23 (2.0)||5 (21.7)|
|Once a week||13 (1.1)||2 (15.4)|
|Several times a week||19 (1.7)||5 (26.3)|
|Every day||3 (0.3)||2 (66.7)|
|Proxy report of failing family or personal obligations because of drinking (missing = 19)||Never||901 (78.8)||81 (9.0)|
|Less than once a month||76 (6.6)||6 (7.9)|
|Once a month||65 (5.7)||10 (15.4)|
|Several times a month||39 (3.4)||7 (18.0)|
|Once a week||25 (2.2)||4 (16.0)|
|Several times a week||13 (1.1)||3 (23.1)|
|Every day||5 (0.4)||2 (40.0)|
|Proxy report of acute alcohol-related dysfunction (latent) (missing = 5)b||Drinker: no dysfunction||386 (33.9)||28 (7.3)|
|1st fifth of dysfunction||137 (12.0)||13 (9.5)|
|2nd fifth of dysfunction||125 (11.0)||6 (4.8)|
|3rd fifth of dysfunction||102 (9.0)||11 (10.8)|
|4th fifth of dysfunction||128 (11.3)||23 (18.0)|
|5th fifth of dysfunction||115 (10.1)||22 (19.1)|
|Total|| ||1143 (100)||115 (10.1)|
The measurement model used to define acute alcohol-related dysfunction is shown in Fig. 1 with standardized factor loadings and model fit indices. All four manifest variables were associated strongly with the underlying latent factor and the model had good fit.
The prevalence of sporadic (zapoi) and routine dysfunction by the two measures of alcohol intake are shown in Table 2. There was strong evidence that both measures of alcohol intake were associated with both types of dysfunction; however, compared to the highest category of beverage alcohol consumption (greater than 20 litres of ethanol per year), non-beverage alcohol users had a higher prevalence of both zapoi (47.6 versus 18.3%) and routine dysfunction (76.2% with scores in the top two-fifths of dysfunction versus 62.5%).
Table 2. Prevalence of sporadic (zapoi) and routine alcohol-related dysfunction by alcohol intake at Izhevsk Family Study 1 (IFS-1) among drinkers
|Alcohol intake variables at IFS-1||Prevalence of proxy-reported routine acute alcohol-related dysfunctiona||Prevalence of proxy-reported zapoi|
|n (%)||n (%)|
|Volume of ethanol from beverage alcohol (litres per year) missing = 14||>0–2 litres||17/194 (8.8)||6/196 (3.1)|
|2–4 litres||35/242 (14.5)||20/243 (8.2)|
|5–9 litres||69/268 (25.7)||22/269 (8.2)|
|10–19 litres||52/170 (30.6)||6/171 (3.5)|
|20+ litres||65/104 (62.5)||19/104 (18.3)|
|χ2 (df)||124.5 (4) P < 0.001||26.7 (4) P < 0.001|
|Test for linear trend||P < 0.001||P = 0.002|
|Non-beverage alcohol drinker missing = 13b||No||188/921 (20.4)||42/922 (4.6)|
|Yes||48/63 (76.2)||30/63 (47.6)|
|χ2 (df)||100.6 (1) P < 0.001||164.6 (1) P < 0.001|
|Totalc||242/993 (24.4)||74/998 (7.4)|
The relationship between alcohol intake and employment is shown in the top half of Table 3.There was only very weak evidence for a positive association between volume of ethanol and employment. This remained the case when men who drank non-beverage alcohol were excluded (data not shown). In contrast, there was good evidence that drinkers who drank non-beverage alcohols were more likely to have ceased regular paid employment at IFS-2 compared to beverage-only drinkers even after adjusting for socio-demographic factors and health problems. There was no evidence of an interaction between occupation type and either volume of ethanol (P = 0.35) or non-beverage alcohol use (P = 0.42).
Table 3. Association between alcohol variables at Izhevsk Family Study 1 (IFS-1) and not being in regular paid employment at IFS-2
|Alcohol use at IFS-1 (N = 1143)||Model 1 a,f||Model 2b,f||Model 3c,f|
|Odds ratio (95%CI)||P-value||Odds ratio (95%CI)||P-value||Odds ratio (95%CI)||P-value|
|Total volume of ethanol from beverage alcohol in litres per year (missing = 14)||Non-drinker||0.98 (0.46, 2.08)||Test for linear trend P = 0.19||0.94 (0.44, 2.02)||Test for linear trend P = 0.33||0.92 (0.43, 1.97)||Test for linear trend P = 0.36|
|>0–2 litres||1.00 (ref)||1.00 (ref)||1.00 (ref)|
|2–4 litres||0.86 (0.44, 1.69)||0.88 (0.45, 1.75)||0.86 (0.44, 1.71)|
|5–9 litres||1.40 (0.76, 2.59)||1.40 (0.75, 2.60)||1.37 (0.74, 2.55)|
|10–19 litres||1.18 (0.59, 2.39)||1.10 (0.54, 2.24)||1.08 (0.53, 2.19)|
|20+ litres||1.43 (0.65, 3.10)||1.22 (0.55, 2.70)||1.17 (0.52, 2.61)|
|Log total volume of ethanol (continuous)||1.07 (0.96, 1.20)||Test for linear trend P = 0.22||1.06 (0.95, 1.19)||Test for linear trend P = 0.30||1.06 (0.95, 1.19)||Test for linear trend P = 0.32|
|Proxy report of non-beverage alcohol use (missing = 13)||Non-drinker||0.85 (0.45, 1.60)||Test for heterogeneity P = 0.006||0.83 (0.43, 1.57)||Test for heterogeneity P = 0.03||0.82 (0.43, 1.57)||Test for heterogeneity P = 0.04|
|No||1.00 (ref)||1.00 (ref)||1.00 (ref)|
|Yes||2.88 (1.55, 5.38)||2.37 (1.24, 4.52)||2.30 (1.21, 4.40)|
|Proxy report of zapoi||Non-drinker||0.89 (0.47, 1.67)||Test for heterogeneity P < 0.001||0.86 (0.45, 1.65)||Test for heterogeneity P = 0.001||0.86 (0.45, 1.64)|| |
|No||1.00 (ref)||1.00 (ref)||1.00 (ref)|
|Yes||3.65 (2.08, 6.42)||3.10 (1.73, 5.53)||3.08 (1.71, 5.55)|
|Fifths of proxy report of acute alcohol-related dysfunction (latent) (missing = 7)||Non-drinkerd||1.15 (0.57, 2.33)||Test for linear trend P < 0.001||1.07 (0.52, 2.20)||Test for linear trend P < 0.001||1.07 (0.52, 2.19)||Test for linear trend P < 0.001|
|Drinker: no dysfunctiond||1.00 (ref)||1.00 (ref)||1.00 (ref)|
|First fifth of dysfunction||1.38 (0.69, 2.76)||1.28 (0.64, 2.58)||1.29 (0.64, 2.59)|
|Second fifth of dysfunction||0.70 (0.28, 1.72)||0.67 (0.27, 1.67)||0.66 (0.26, 1.65)|
|Third fifth of dysfunction||1.61 (0.77, 3.36)||1.50 (0.71, 3.17)||1.50 (0.71, 3.17)|
|Fourth fifth of dysfunction||2.89 (1.59, 5.25)||2.54 (1.38, 4.74)||2.57 (1.38, 4.78)|
|Fifth fifth of dysfunction||3.01 (1.65, 5.52)||2.77 (1.40, 4.95)||2.64 (1.40, 4.99)|
|Proxy report of acute alcohol-related dysfunction (latent)e||1.60 (1.29, 1.99)||Test for linear trend P < 0.001||1.51 (1.21, 1.89)||Test for linear trend P < 0.001||1.50 (1.20, 1.88)||Test for linear trend P < 0.001|
The relationship between alcohol-related dysfunction and employment is shown in the bottom half of Table 3.
After adjusting for socio-demographic confounders (model 2) there was strong evidence that men who had been on zapoi in the previous year at IFS-1 had more than three times higher odds of having ceased regular paid employment at IFS-2. It was not possible to assess evidence of interaction between zapoi and occupation type, because all the non-manual workers who had experienced zapoi (n = 12) remained in regular paid employment at IFS-2.
After adjusting for confounders (model 2), there was strong evidence that drinkers in the top two-fifths of latent routine dysfunction had more than twice the odds of being unemployed at IFS-2 than drinkers with no dysfunction. When the latent factor of routine acute alcohol-related dysfunction was used as a continuous variable, the odds of no longer being in regular paid employment increased by 51% [95% confidence interval (CI) = 20–89%] for every standard deviation unit increase in dysfunction score. Additional adjustment for health problems (model 3) had very little impact on the association between alcohol-related dysfunction and employment, suggesting that the association between alcohol and employment was not mediated importantly through any negative effect on chronic health problems. There remained strong evidence of an association between dysfunction and employment status when men whose proxies reported ‘serious work-related or employment problems’ at IFS-1 were excluded (data not shown). There was no evidence of interaction between fifth of dysfunction score and occupation type (P = 0.21).
The relationships between alcohol intake (volume of ethanol and non-beverage alcohol use) and acute alcohol-related dysfunction (latent factor of routine alcohol-related dysfunction and zapoi) with employment are shown in Fig. 2 and Table 4. All results are shown with adjustment for health problems. Direct and indirect effects of non-beverage alcohol use and volume of ethanol on employment status are shown in Table 4. Non-beverage alcohol use had strong indirect effects on employment via both zapoi and routine acute alcohol-related dysfunction, but there was no evidence that non-beverage alcohol use had a direct effect on employment status once zapoi and routine alcohol-related dysfunction were included in the model. Volume of ethanol had no indirect effect via zapoi, but there was strong statistical evidence of a small indirect effect via routine alcohol-related dysfunction. There was strong statistical evidence that both zapoi and the latent factor of routine alcohol-related dysfunction directly influenced employment status at IFS-2.
Table 4. Acute alcohol-related dysfunction (zapoi and latent factor of routine alcohol-related dysfunction) as mediators of the relationship between alcohol intake (volume of ethanol from beverage alcohol and non-beverage alcohol use) at Izhevsk Family Study 1 (IFS-1) and employment at IFS-.2
|Alcohol variable at IFS-1 n = 1107||Employment at IFS-2a|
|Direct||Indirect via acute alcohol-relate dysfunction||Indirect via zapoi|
|Probit coefficient (95% CI)||P-value||Probit coefficient (95% CI)||P-value||Probit coefficient (95% CI)||P-value|
|Self-reported log total volume of ethanol from beverage alcohol||−0.01 (−0.02, 0.002)||0.07||0.01 (0.002, 0.02)||0.002||0.001 (−0.001, 0.002)||0.54|
|Proxy-reported non-beverage alcohol use||−0.16 (−0.63, 0.32)||0.52||0.30 (0.11, 0.48)||0.002||0.25 (0.10, 0.39)||0.001|
|Proxy report of zapoi||0.58 (0.24, 0.91)||0.001||–|| ||–|| |
|Proxy-reported acute alcohol-related dysfunction (latent)||0.19 (0.08, 0.30)||0.001||–|| ||–|| |
|Model fit indices|| |
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To our knowledge, this is the first study to consider the effects of acute alcohol-related dysfunction on employment status. We were able to assess this longitudinally and found that high levels of both sporadic (zapoi) and routine alcohol-related dysfunction at baseline were associated with higher odds of no longer being in regular paid employment at follow-up. Acute alcohol-related dysfunction was also an important mediator of the effects of alcohol intake (volume of ethanol from beverage alcohol and non-beverage alcohol use) on employment. Once alcohol-related dysfunction was included in the model there was no evidence that either measure of alcohol intake directly increased the probability of no longer being in regular employment. No evidence was found of interaction by occupation type.
The findings for total volume of beverage alcohol are in contrast to findings from previous studies [2, 3], including analyses of the Russian Longitudinal Monitoring Survey (RLMS), which found that higher average daily consumption of alcohol increased the probability of job loss a year later . There are some differences between the two studies: the RLMS asked questions on the frequency of consumption of all alcohol and usual daily consumption of beer, wine, spirits and home-made liquor (which was not measured at IFS-1) in the past 30 days. The authors used a measure of daily alcohol intake calculated from these data but do not explain how this was calculated. In addition, the outcome of interest was specifically whether men were fired, and therefore at follow-up only men who were no longer employed but still participating in the work-force were of interest (i.e. men who were in irregular employment or unemployed but not seeking work were not included as ‘unemployed’) and the period of follow-up was shorter. It is unclear if these differences would be sufficient to explain the discrepancy in the results of the two studies. It is worth noting that in our study any effects of total volume of ethanol were very small in comparison to the effects of zapoi and routine alcohol-related dysfunction. The RLMS study had a larger sample size (n = 4173) than IFS-2, but the effect size found for average daily consumption of alcohol was also very small (probit regression coefficient 0.003 increase in probability of being fired per 10 g of alcohol per week) .
All data on alcohol use were obtained from self- or proxy report and therefore subject to measurement error. Using proxy reports of drinking behaviour may be more accurate than self-reported data, as proxies have less reason to under-report socially unacceptable behaviours. However, proxy report is not reliable for certain aspects of alcohol use such as volume of ethanol consumed per occasion, and therefore could not be used for measuring alcohol intake. Self-reported alcohol intake is very likely to be affected by measurement error, because when asked about usual frequency and volume of consumption participants often report their mode rather than mean consumption, ignoring less frequent heavy drinking episodes . Dysfunctional drinking behaviours such as hangover may be easier to report accurately than volume of alcohol consumed, especially for proxy respondents, and therefore results may partly reflect more accurate measurement of alcohol consumption. However, while ethanol must be consumed in order to experience acute alcohol-related dysfunction, experience of dysfunction is not entirely a function of the amount of ethanol consumed but represents interaction between a hazardous drinking pattern and individual-level susceptibility to the acute effects of alcohol. The strong association between alcohol-related dysfunction and employment, compared to the small effects of volume of ethanol which were mainly via dysfunction, seems to suggest that, when considering effects on employment, whether alcohol leads to dysfunctional behaviour is more important than the overall amount consumed. In this study the prevalence of both sporadic (zapoi) and routine dysfunction was higher in non-beverage alcohol drinkers compared to those in the highest category of beverage alcohol consumption (greater than 20 litres, of ethanol per year) which may explain why non-beverage alcohol consumption predicted employment status in the logistic regression model while volume of beverage alcohol did not. This is supported by the finding that the effects of non-beverage alcohol use were completely explained by alcohol-related dysfunction in the structural equation model.
In addition to the more general findings with respect to acute alcohol-related dysfunction, this is the first study to investigate the effects of two distinctive features of Russian drinking on employment: non-beverage alcohol consumption and zapoi. Both were associated strongly with transition out of regular paid employment. The findings of this study are particularly important given the high levels of hazardous drinking found in Russia [24, 25].
There are some limitations in terms of generalizability of these findings. First, the need for a proxy respondent at baseline meant that men who were living alone were excluded. These men are likely to have been different to those included and therefore results are not applicable to all men in Izhvesk. The age distribution of our study population was also skewed towards older men. Furthermore, men who were lost to follow-up were less likely to have been married at baseline; however, with the exception of frequency of hangover, drinking behaviour at baseline was not associated with whether or not they were followed-up, suggesting that these men do not represent a heavier drinking population. Despite the longitudinal study design there may have been some problems with reverse causality, as men may start to drink more hazardously in response to work-place problems even though they are still in employment at that time. Nevertheless, there remained strong evidence of an association between alcohol-related dysfunction and employment status even when men who were perceived as having ‘serious work or employment related problems’ at baseline were excluded. There was no evidence of interaction between occupation type and alcohol use on employment status at IFS-2, although the relatively small number of men who became unemployed between the studies meant that in order to increase power in detecting interaction we used a binary categorization of occupational type, which may not have been sensitive enough at identifying occupational groups at particular risk. Very few men with higher education became unemployed and therefore it was not possible to investigate interaction by education. There may also have been other effect modifiers related to employment, such as income, which were not measured in these studies. Therefore, these results should be interpreted with caution as they may not apply equally to men of all occupational types or educational level. Although we adjusted for chronic health problems, the measure used was relatively simple and so may not have accounted for all the effects of chronic ill health. However, adjusting for health problems made very little difference to the estimated effects of alcohol intake and dysfunction on employment, suggesting that this was not an important pathway. Finally, this study assessed quantitatively the relationship between alcohol use at baseline and employment status at follow-up (3–5 years later), but qualitative work is needed alongside this to understand fully the role of alcohol in employment transitions.
In conclusion, non-beverage alcohol use and both sporadic and routine alcohol-related dysfunction were related prospectively to remaining in employment. Acute alcohol-related dysfunction was an important mediator of the relationship between alcohol intake and employment and should be considered in addition to conventional measures of alcohol consumption when investigating the impact of alcohol consumption on work. If further studies support our findings, dysfunctional behaviour could be used for identifying those who would benefit from interventions to reduce alcohol consumption. Reducing dysfunctional behaviour should be considered an important treatment aim for hazardous drinkers alongside reducing overall consumption.