a Centre for Health Services Studies, University of Kent at Canterbury, Kent, UK.
b Applied Statistics Research Unit (asru), St. Augustine's Business Park, Whitstable, Kent, UK.
c Department of Clinical Biochemistry, Guy's, King's and St Thomas' School of Medicine, London, UK.
Dr Colin Cryer, CHSS at Tunbridge Wells, Oak Lodge, David Salomons' Estate, Broomhill Road, Tunbridge Wells, Kent TN3 0TG, UK. E-mail: colin.cryer{at}hhc.umds.ac.uk
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Abstract |
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Methods The design was a health and lifestyle survey of 41 000 randomly-sampled adults in SE England. The response rate was 60%. Distinctive subgroups within the alcohol abstainer group were investigated using cluster analysis, based on socio-demographic and health status variables. Odds ratios for services use for the abstainer clusters, and three alcohol consumption groups were estimated from a logistic regression model which included age, social class, ethnic group, employment status, household composition, whether the respondent was a carer, smoking habit, use of private health insurance, and health status.
Results Two clusters were formed for both males and females. Cluster 1 comprised, on average, older, frailer, and more disabled people. Cluster 2 comprised younger, healthier people, a greater proportion of whom belonged to ethnic minority groups. Cluster 2 had similar rates of use of Accident & Emergency, GP, optician, and dental services compared with safe level drinkers. Cluster 1's rates differed from those of both Cluster 2 and safe level drinkers in almost all instances.
Conclusions The J- and inverted J-shaped relationships between alcohol consumption and service use are partly explained by a subgroup of abstainers who are older, of less good health, and who use hospital, clinic, and domiciliary healthcare services much more than safe level drinkers.
Keywords Alcohol drinking, temperance, health status, health surveys, health care surveys
Accepted 1 August 2000
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Introduction |
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J- or U-shaped relationships for the association between alcohol consumption and mortality have been reported in a number,28 but not all,9 studies. Such relationships have also been shown between alcohol consumption and coronary events or heart disease,4,915 self-rated health,7,1618 limiting long-term illness, and psychological distress.18 Reduced risk of diabetes mellitus19 and reported better health20 amongst the mild and moderate drinkers relative to those who never or rarely drink has also been found.
Relatively little work has been carried out to examine the characteristics of those who do not drink. It has been postulated that J-shaped curves may be the result of complex associations between demographic, physical, psychosocial, other confounding factors and health; the characteristics of the alcohol abstainers (other than their non-use of alcohol) may account for their poorer health outcomes.6,21,22
It has been suggested that the group of non-drinkers does not share the same characteristics as the alcohol consuming groups and is therefore not suitable as a reference group in analyses of relationships between alcohol and health outcomes.13,23 On this basis, it was also considered an unsuitable comparison group in our previous work.1
The characteristics of the alcohol abstainer group have not been fully investigated. Our previous observations led to the following proposed hypotheses:
The investigation of these hypotheses is the focus of this paper.
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Method |
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The survey took place in 1992 and produced an overall response rate of 60%. The response rate varied from 65% in a non-metropolitan area to 41% in inner London. An analysis was carried out to test the effect of non-response. Compared to the 1991 Census, the profile of responders matched reasonably well on a number of variables, but exhibited patterns often found in postal surveys: namely a better response from women than men, a poorer response rate from younger than older people, and a better response rate from married than from single people. The responder profile was similar to that in the census for housing tenure and for household composition.
Measurement
The variables used in this analysis include a range of socio-demographic variables, health status, alcohol and cigarette consumption, and service use measures. The socio-demographic variables were age, social class, ethnic group, employment status, whether the respondent lives with children or other adults, whether he/she is a carer, and use of private health insurance.
Health status was measured using SF-3625 and by limiting long-term illness, using a question similar to that used in the 1991 census. The SF-36 is a health measurement tool developed in the US, but which has been anglicized and validated for use in Britain. It comprises 36 questions, from which eight domain scores are derived, namely: physical functioning; role limitations due to physical problems; social functioning; role limitations due to emotional problems; bodily pain; mental health; vitality/ fatigue; and general health perceptions. Each domain score is scaled so that it takes values between 0 and 100, where 0 represents the worst and 100 the best possible state.
Alcohol consumption was measured in units consumed per week and banded to reflect drinking levels included in The Health of the Nation targets.26 It was calculated from questions which asked how often respondents drank and how much they drank on typical week and weekend days. Safe drinking was up to 21 units per week for men, and up to 14 units per week for women. Intermediate drinking levels were 2250 units per week for men, and 1535 units per week for women. Harmful drinking was defined as more than 50 units for men, and more than 35 units for women.
The use of services was recorded in differing ways for the differing services. This included how long since the respondent last consulted their GP, visited their dentist, had their eyes tested, had their blood pressure measured, had their blood cholesterol measured, had a cervical smear test, or had a mammogram. For use of Accident & Emergency (A&E) and outpatient visits, the number of attendances in the previous 3 months was recorded. For domiciliary visits (district nurse, health visitor, chiropodist, occupational therapist, physiotherapist, community psychiatric nurse, social worker, home help, meals on wheels, your GP, voluntary agency worker, midwife, environmental health officer), and hospital clinic attendances (dietician, chiropodist, OT, physiotherapist, psychotherapist), respondents recorded whether they had been seen in the last 3 months. Respondents were also asked whether, and how many times, they had been admitted to hospital as an inpatient or a day case in the last 12 months.
This survey did not ask any direct questions to determine the reason for not consuming alcohol. Consequently, the approach taken in this work was to identify distinctive subgroups within the alcohol abstainer group, using cluster analysis, based on socio-demographic and health status variables, and to consider both the characteristics of these subgroups and their relationship with rates of service use.
Statistical analysis
Cluster analysis was used, initially, to investigate the optimum number of the alcohol abstainer clusters separately for males and females. Cluster analysis is concerned with identification of groups of similar respondents. There are two main approaches to the production of clusters i.e. hierarchic techniques and partitioning techniques, where respondents are allowed to move in and out of groups at different stages of the analysis. Techniques of this latter type have been used in this study. Initially, some arbitrary cluster centres are chosen and individuals are allocated to the nearest one. New centres are then calculated for each of the clusters formed in this way. An individual is then moved to a new cluster if it is closer to that cluster's centre than it is to the centre of its present group. During each iteration, clusters close together are merged and dispersed clusters are split. The process continues iteratively until stability is achieved with a predetermined number of clusters. We considered a range of values for the final number of clusters.
Ward's Minimum Variance Cluster Analysis27 was used to estimate the optimum number of clusters. This resulted in the calculation of the Pseudo F statistic, which has been suggested by Calinski and Harabasz28 for estimating the optimum number of clusters. It is a measure of the separation between clusters, and is calculated by the formula:
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The SAS procedure FASTCLUS27 was then used to determine the clusters. This is more suitable when using very large data sets since the use of many other routines results in excessive computing times. The variables included in the cluster analysis were age, social class, ethnic origin, marital status, employment status, household composition, type of accommodation, use of private health insurance, smoking status, and health status as measured by the SF-36 domain scores. Dummy variables were used to enable categorical variables to be used in the cluster analysis (and also in the logistic regression analyses).
For the clusters that were formed in this way, the socio-demographic characteristics and health status were compared between clusters. For males and females separately, utilization rates were estimated for each of the two abstainer clusters and for the three alcohol consumption groups for the following service usage: A&E, GP, hospital inpatient and outpatient, domiciliary healthcare service, opticians, dental services, mammography (women only), and cervical cytology (women only) services. Linear modelling, both for ordered categorical outcomes and for dichotomized outcomes, was carried out using the SAS statistical procedure, LOGISTIC,27 to explore the relationship between the abstainer categories, alcohol use categories and service use, firstly unadjusted and subsequently adjusted for potential confounding by other variables. The potential confounding variables included in the logistic regression models were: age, social class, ethnic group, employment status, whether lives with children, whether lives with other adults, whether is a carer, limiting long-term illness, depression status, smoking, and use of private health insurance. For each service, the null hypothesis being tested was that there was no difference in the rate of service attendance between each abstainer cluster, and the safe, intermediate and harmful drinkers. This was tested against the alternative hypothesis that there were some real differences in the rates, wherever these may occur. The results are presented as odds ratios (OR) with 95% CI. We repeated this analysis but controlling only for socio-demographic variables; this additional analysis did not include the variables limiting long-term illness and depression status. This was to avoid adjusting for health status variables, which were hypothesized to be important in the definition of the abstainer clusters.
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Results |
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The results of the linear modelling of service use are shown in Table 6. These results have been adjusted for the potential confounding variables listed in Methods. The results of the logistic regressions using ordered categorical outcomes were similar to those after the outcomes had been dichotomized. Only the latter results are shown in Table 6
. Adjustment for confounding reduced the strength of many of the relationships shown in Tables 4 and 5
. Significant associations were sustained for use of GP (female only), dentist (male and female), optician (male only), mammography and cervical cytology services.
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Discussion |
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In the current study, our data were consistent with the existence of two clusters in the alcohol abstainer group for both males and females. The first abstainer cluster (Cluster 1) has characteristics similar to a group of sick quitters, while the second abstainer cluster (Cluster 2) have characteristics more similar to those of lifelong abstainers.
The pattern of hospital and clinic use, and the use of domiciliary services, found in our study, are consistent with abstainer Cluster 1 consisting of people with poorer health and/or older people. People belonging to Cluster 1 used the following services substantially more than either abstainer Cluster 2 or the alcohol use groups: district nurse, health visitor, chiropodist, and home help. The proportion of abstainer Cluster 1 using any domiciliary healthcare service was 34% for both males and females compared with 56% (males) and 613% (females) for the other groups.
It appears that these J- and inverted J-shaped curves, which were found in our previous paper,1 are due primarily to higher (or lower) rates for Cluster 1 relative to both Cluster 2 and the safe level drinkers. This is with the exception of mammography and cervical cytology for which Clusters 1 and 2 are more similar. This will be due in part to the fact that it was appropriate to analyse only females aged 5064 (mammography) and females 2064 (cervical cytology) for these services, since it was for these ages that these services were offered to women.
For these restricted age groups, Cluster 1 and Cluster 2 were more similar for some variables (age, social class, ethnicity, smoking status, and whether covered by private health insurance) but not for others (marital status, employment status, whether living alone, and health status). For example, the average age was 58.8 and 56.9 years in abstainer Clusters 1 and 2 for the mammography subset, and 52.4 and 41.3 years in Clusters 1 and 2 for the cervical cytology subset. In the total sample, the average ages of females in Clusters 1 and 2 were 68.7 and 43.2 years. On the other hand, for these restricted age groups those in Cluster 1, relative to Cluster 2, were much more likely to be single or divorced/separated, more likely to be unemployed due to disability or ill health, much more likely to be living alone, and to have poorer health status. Across all variables, however, abstainer Clusters 1 and 2 were much more similar for the age groups used in the analysis of utilization of mammography and cervical cytology services than in the female sample as a whole.
In conclusion, the J- and inverted J-shaped relationships in service use are partly explained by a subgroup of abstainers who are older or are retired people, of less good health, who use hospital, clinic, and domiciliary services much more than safe level drinkers. These findings support (but do not prove) the hypothesis that these may be a group of sick quitters, i.e. people who have given up drinking due to ill health or frailty. If so, it means that care must be taken in interpreting any J-shaped relationships between alcohol use and either service use or other health-related outcomes.
KEY MESSAGES
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Acknowledgments |
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