4.2
Literature Review
Kemm (2003) use data from the General Household Survey (GHS) from 1978 to 1998 to analyse the alcohol consumption over a twenty year period. They find that, after about age 20 years, both men and women become more likely to be non-drinkers or light drinkers with age. Moreover, men are also less likely to be heavy drinkers as they grow older, however for women these estimates are less precise than the estimates of men.
Kemm (2003) acknowledges that the questions regarding alcohol consumption were al- tered in 1986. Between 1978 to 1985 inclusive, the first two alcohol questions established if respondents drank at all. Those who had drunk even occasionally in the last year were then asked about their consumption of five types of alcohol of varying strengths: shandy, beer/cider, spirits, liqueur, and wine. First, respondents were asked how often
they consumed each beverage in the last 12 months, and then asked them how much they “usually had drunk on any one occasion during the last 12 months”. The product of fre- quency and quantity were used to derive quantity frequency (QF) ratings, that grouped drinkers into the five categories based on the frequency of drinking their main drink of choice and the quantity consumed. From 1986, drinkers were categorized on the basis of estimated total number of units of alcohol consumed per week into alcohol consumption (AC) ratings.
The main concern with this method of deriving quantity frequency is the possibility of measurement error. One can argue that measurement error might occur more often in infrequent drinkers, because their typical consumption is more difficult to define and capture in a snapshot questionnaire. In the GHS, prior to 1986, the optional responses for frequency for each respondent are: most days; 3-4 days per week; 1-2 per week; 1-2 per month; 1-2 per 6 months; 1-2 per year; or not at all. In 1990, the frequency categories were changed to almost every day, 5 or 6 days a week, 3 or 4 days a week, once or twice a week, once or twice a month, once every couple of months, once or twice a year and not at all in the last 12 months.
Kemm (2003) divides the sample into 5-year birth cohorts for males and females. Each cohort is then followed through a series up to 1998 or until they reach 83-87. Most 5-year age sex bands contained 500 or more individuals. The author splits their analysis into “non or very light drinkers”(less than one unit per week) and “heavy drinkers” (more than 21 units per week for men and 14 units per week for women). With respect to “non or very light drinkers”, they find that “non or very light drinking” increases as respondents get older, however there is very little difference across cohorts, especially amongst men although the oldest birth cohorts of men appear to have much higher rates of low drinking compared to their younger counterparts. Women appear to have much clearer differences between cohorts, furthermore there is a more apparent age effect with older respondents more likely to be abstainers or very light drinkers.
Kemm (2003) finds that the percentage of heavy drinkers fall with age in both men and women. At the age of 25 years, 35 percent of men and 15 percent of women were classified as heavy drinkers, but by age 80 years, only about 10 percent of men and 2 percent of women were heavy drinkers, which may be as to be expected.
Although Kemm (2003) was the first to apply birth cohort analysis to the UK, there is one key limitation with his analysis. They only analyse changes in the proportions of the pop- ulation who fall into each of the defined drinking groups i.e. the change in the number of respondents who are classified as heavy drinker by consuming over the recommended weekly amount. While this provides an interesting insight in the change in the preva- lence of heavy or light drinkers, it does not show how consumption has changed within the specified group.
Meng, Holmes, Hill-McManus, Brennan, and Meier (2014) also uses the GHS and its later waves from the General Lifestyle Survey (GLF) to model age-period-cohort effects. Throughout, survey respondents are assigned into 19 5-year cohorts for birth, observa- tion period (the year of the wave that the GHS/GLF took place), and age band. The study controls for household income, education, ethnicity, and country. The APC effects and the effects of other independent variables are estimated simultaneously and control for each other. All models are fitted separately for men and women to obtain gender-specific estimates.
The dependent variable in the consumption model is the drinker’s average weekly con- sumption in units, which was calculated using the same questions used by Kemm (2003) as well as the updated definition of beverage alcohol content used to convert quantity measures into alcohol units (1 unit = 10 ml ethanol) which was introduced in 2006. Meng, Holmes, et al. (2014) model the incident rate ratio (IRR) of drinkers’ average weekly al- cohol consumption using a negative binomial regression model to account for the count nature of the dependent variable. This is an advance from the previous literature where alcohol consumption, rather than the proportion of heavy drinkers, is used as the ex- plained variable, as this provides a more in depth picture of changes in the proportion
of heavy episodic drinkers and whether consumption within heavy drinkers has also increased.
They find that average weekly units consumed peaks at age 18–24 with men consuming 25.1 units, and women consuming 11.9 on average. By the age 25, alcohol consumption falls by approximately 5 units per week, and remains fairly consistent until age 50, with further declines in later life (age 65). Overall, the results are consistent with Kemm (2003) with respect to the abstention analysis. Meng, Holmes, et al. (2014) find that abstention rates are increasing over age and period for both men and women by roughly 5 percent.
One shortcoming, as with all APC models, is that identification remains a challenge as birth cohort is a linear function of age and time period, such that Cohort = Period− Age. As a result, APC models suffer from multicollinearity. In order to address for this, Meng, Holmes, et al. (2014) use overlapping groups to avoid the problem of a mechanical relationship between age, period, and birth year. Therefore, while a separate effect can be estimated, its true effect cannot be identified.
Pryce (2014) also uses the GHS and adapts the methods used by Kemm (2003), Meng, Holmes, et al. (2014) to examine changes across the drinking distribution. He further extends their analysis to explore whether price plays a role in determining alcohol con- sumption. He does this in two ways: looking at the long-run price elasticity across co- horts, and whether the price of alcohol when the cohort begins drinking (assumed to be when they are aged 18) affects the cohort’s alcohol consumption.
In line with the previous UK studies, Pryce (2014) finds that alcohol consumption has been increasing steadily across birth cohorts with younger birth cohorts tending to drink more than the preceding cohort at the same age. However, he finds that youngest cohorts (born 1986-1990) are beginning to drink less than their older peers with the youngest birth cohorts drinking on average 4 units per week compared to 7 for the older cohorts for men only.
Furthermore, Pryce (2014) estimates the quantile results from age-period-cohort mod- elling, finding age effects are not statistically significant for males, but are for females,
meaning, holding all else constant, older women drink more than younger women. In addition, he finds a downward trend in consumption for both genders and all quantiles across time period. This means thatceteris paribusevery type of drinker has decreased their consumption over time. Overall, Pryce (2014) finds that price does not have a sig- nificant effect on alcohol consumption in an APC setting.
In the United States (US), Kerr, Greenfield, Bond, Ye, and Rehm (2004) use data from the National Alcohol Survey, conducted for the Alcohol Research Group, between 1979 and 2000 inclusive. They estimate the influences of age, period and cohort on the consump- tion of beer, wine, and spirits separately.
The dependent variables used in this analysis are the monthly number of drinks of each beverage group; beer, wine, and spirits. The frequency of alcohol consumption is split into the number of drinks consumed in one occasion (where a drink is defined as 12g of ethanol) and how often this drinking pattern is repeated. This categorisation allows the calculation of both the volume of drinks per month consumed and the frequency of days in which five or more drinks were consumed for each of the beverages.
Instead of focusing on “heavy episodic drinking” or “binge drinking”, Kerr et al. (2004) analyse APC effects for beer, spirits, and wine separately. They find that changing cohort demographics have significant effects on beverage-specific consumption, namely strong cohort effects for spirits, significant negative effects of age are found for beer and spirits consumption for men and strong period effects for men’s beer and wine consumption and for women’s spirits consumption. Overall, the results are consistent with evidence from the UK (Meng, Holmes, et al., 2014) such that changing cohort demographics are found to have substantial effects on alcohol consumption.
Kerr, Greenfield, Bond, Ye, and Rehm (2009) extends this analysis by modelling alco- hol volume and heavy drinking days using data from six National Alcohol Surveys con- ducted over the 26-year period between 1979 and 2005, covering a 26 year period. Survey respondents were assigned either a 5 or 10-year age cohort and a 5-year birth cohort. The
study also controls for income, ethnicity, geographic region, marital status, and educa- tional attainment.
In order to conduct their analysis, the two main dependent variables they use are the monthly number of drinks consumed and the number of days in the past year on which five or more drinks were consumed (their definition of binge drinking). In the United States a standard drink is defined as having about 14 g of ethanol (in their earlier study a drink was defined as 12g of ethanol).
Kerr et al. (2009) find that while the mean values of drinking measures have continued to decline over time for those aged 26 and older, there has been a substantial increase in both alcohol volume and binge episodes among those aged 18–25 years. Specifically, they find that the more recent birth cohorts in the United States partake in more heavy drinking episodes of consuming 5 or more drinks in one occasion (and 8 or more drinks for men).
Similar to the earlier UK literature the effects are only shown at the mean for each age band, period, and birth cohort. Kerr et al. (2009) use 6 different surveys with alter the mode of interview, sampling procedures, and the general weighting of alcohol variables. As a result, comparisons across age, birth cohort and period may be biased through indi- viduals potentially effecting their attitudes in their responses.
Keyes and Miech (2013) uses age-period-cohort effects (APC) to analyse heavy episodic drinking from the National Household Survey on Drug Use and Health 1985-2009 (NHS- DUH). The authors find that heavy episodic drinking is decreasing in the US among adolescents and young adults. Moreover, they find that the younger cohorts born in the 1990s are at a lower risk of binge drinking than their older counterparts dating back to at least 1950–54.
Contrary to the results found in the UK, Keyes and Miech (2013) find similar trends for both males and females across the life course, with cohorts born in the 1960s, 1970s and 1980s at highest risk, and those born in the 1990s at lower risk of binge drinking. One difference that they found was that adolescent/young women have a higher probability
of heavy episodic drinking compared to the mean of all women, while there was no statistically significant difference for men. This means that men are more likely to drink heavily across the life course than women, which is consistent with the literature (Kemm, 2003; Meng, Holmes, et al.,2014)
Throughout Keyes and Miech (2013), heavy episodic drinking was defined as any in- stance of consuming five or more drinks in one occasion, in the month leading up to the interview. The authors note that while most studies now define a lower threshold of drinks for women, the answers from the respondents in the survey made it difficult to disentangle a more robust definition of heavy episodic drinking.
There are a few notable limitations to Keyes’ study. Firstly there are a few methodological changes that occurred throughout the course of the study. Prior to 1999 survey informa- tion was collected via chapter and pencil questionnaires, in 1999 the survey was admin- istered using computer assisted software. Evidence suggests that reporting of substance use and alcohol consumption increased when the responses were collected via computer- aided software (Barker, Gfroerer, Caspar, & J,1998). Secondly, in 2002 respondents were offered financial incentives to participate in the survey. Although this policy increased response rates, prevalence of substance use increased. These methodological changes may cause the estimates to be biased and limit inferential ability about the period effects that were estimated throughout this study.
Furthermore, there was no information in the study on the frequency of binge drinking within the population. One can expect problem drinkers to engage in weekly or daily heavy drinking while some individuals may have had a single episode of binge drinking. These two individuals are analysed with the same weight. Consequently, Keyes and Miech (2013) only considers the proportions of the population who fall into the defined drinking category, rather than the prevalence of binge episodes.
4.3
Data
Data from the Health Survey for England (HSE). The HSE is a repeated cross-sectional health interview survey of roughly 15,000 respondents conducted in England annually since 1991 by the National Centre for Social Research (NatCen). The HSE covers all of England and is a nationally representative sample of those residing at private residential addresses. The HSE collects detailed information on mental and physical health, health- related behaviour, and objective physical and biological measures in relation to demo- graphic and socio-economic characteristics of people aged 16 years and over (Mindell et al.,2012). The survey design is formed of two sections an interview with each eligible individual and a nurse visits. The interview includes a set of core questions on general health as well as social indicators; smoking, alcohol, demographic and socio-economic indicators.
While there are a range of alcohol consumption measures available in the HSE, our main focus throughout this study is alcohol abstention and heavy episodic drinking. Our ab- stention dependent variable is drawn from the questions: “Do you drink alcohol?” and “Whether you drink nowadays?”3. From these, we can determine the drinking status of the respondent, and how long they have held this status. Furthermore, our other variable of interest is heavy drinking, this is derived from “How many units of alcohol you con- sumed on your heaviest day of drinking in the last 7 days?”4 . We use this question as it provides a measure of heavy episodic drinking; the effect of which is of great significance due to the impact of heavy drinking on an individual’s health (Griswold et al.,2018).
One shortfall of the HSE is that the question “How many units of alcohol you consumed on your heaviest day of drinking in the last 7 days?” was first introduced in the 1998 wave of the HSE. However, this still leaves us with 18 years of repeated cross-sections for this section of our analysis (1998-2014). Prior to 1998, the HSE asked respondents to document
3These questions are available in all waves of the HSE for our period of analysis (1991-2014).
4Questions on heavy drinking days are available from 1998. Prior to 1998 respondents were asked about
their weekly alcohol consumption rather than their heaviest drinking day . Given this change in questioning, the years 1991-1997 are omitted from our heavy drinking analysis as the consistency of the data may introduce issues with consistency to our analysis. The change in the questions and the coding of such responses may pick up inaccurate period effects. Although this is not a perfect measure, as it may understate binge drinking insofar as heavy drinkers may be more likely to have numerous days of heavy drinking in the week, we test whether there is correlation between number of drinks consumed on their heaviest day and number of days alcohol is consumed and find little evidence of this. For our analysis on abstention we use the full sample 1991 to 2014. Finally, the HSE collects data on alcohol consumption from aged 16 years, however for the purpose of our study we keep only those aged between 16 and 90 years. Age is capped at 90 years as this is the capped age limit in the 2012, 2013, and 2014 waves of the HSE.
The HSE obtains information on alcohol consumption using two techniques. Individuals aged 16-24 years are asked to record their responses using either a computer-aided inter- view or a self completion questionnaire. Respondents aged over 25 years use only the computer-aided interview. The HSE asks questions regarding both frequency (whether an individual drank in past 12 months or the last week as well as the number of drinking days in the past week) and level (number of alcohol units drunk on the heaviest drinking day in the past week). Furthermore, the HSE depicts whether a respondent took part in binge drinking (whether men or women had drunk more than eight or six units, respec- tively, on their heaviest drinking day in the past 7 days). This definition of binge drinking is in line with UK guidelines.
As well as focusing on the rate of abstention and heavy episodic drinking across the life course, we apply our APC methodology to five health measures that can be found in the Health Survey for England. For the purpose of this study we focus on five outcomes that have been attributed to dangerous alcohol consumption (Burton & Sheron,2018; Gris- wold et al.,2018; Lieber, Seitz, Garro, & Worner,1979). We focus on high blood pressure
5Prior to 1998, respondents were asked to document their drinking from the question: “In the last week
(hypertension), cardiovascular disease or heart problems, respiratory problems, mental health issues such as depression or anxiety and finally self-assessed health. The health measures we use in the HSE relating to hypertension, cardiovascular disease, respiratory problems and mental health are all derived from binary questions “Have you ever suf- fered from or currently suffering fromhealth outcome” and “Do you take medication for health outcome”. We use these answers to calculate the proportion of respondents who suffer from these health issues as well as the odds ratio in the results section later in the chapter. Finally, we also use information relating to general health. Respondents were