1. Social and Economic Correlates of Multiple Psychotropic Prescription Drug Use
William H. Laverty, PhD1; Ivan W. Kelly, PhD2; & Bonnie L. Janzen, PhD3
1Department of Mathematics and Statistics, 2Department of Educational Psychology & Special Education, 3Department of Community Health & Epidemiology
RESULTS
ABSTRACT
The use of prescribed psychotropic medication is patterned according to socio-demographic characteristics among elderly Canadians. However, little is known about prescribed multiple psychotropic drug use patterns among the working age population. This gap is important to address given that multiple drug use is related to more side effects. The purpose of this study was to:
determine the likelihood of an individual using two or more psychotropic prescription drugs, given usage of one type of psychotropic drug; and to
examine interactions between socio-demographic factors and the likelihood of being prescribed more than one psychotropic drug.
The data source was Statistics Canada’s Canadian Community Health Survey (Cycle 1.2); analyses were restricted to 20, year old Canadians. Self-reported use in the last year of antidepressants, anti-anxiety agents, sleeping pills, stimulants, or anti-psychotic medication was considered. Logit and log-linear models were applied. One-half of unemployed year olds with a less than high school education who used a single drug would go on to use 2 or more prescribed psychotropic drugs.
Further, almost 60% of year old divorced women using one psychotropic drug would go on to use 2 or more such drugs. The discussion highlights the elevated use of prescribed multiple psychotropic drugs among socially and economically vulnerable Canadians.
RESULTS
Table I indicates that almost 10% of year old Canadians used sleeping pills in the year previous to the survey, followed by anti-depressants (6.2%) and anti-anxiety medication (5.9%).
As shown in Table II, while most Canadians (83.4%) did not report any prescribed psychotropic drug use, 11.3% used one drug and over 5% used at least two drugs.
Table III shows which combinations of drugs were most common. Amongst those individuals who use two drugs, 36.2% used the combination of sleep and anxiety drugs and another 54.0% used antidepressants with either anxiety or sleep drugs.
Amongst drug users, the percentage who went on to use two or more drugs was 32.3 %. The logit model was fitted to see how that percentage was affected by the socio-demographic factors.
The tables that represent interactions (IV to VII) indicate cells where this percentage (32.3%) is higher. The final version of the fitted logit model for predicting multiple drug-use amongst all drug users from the socio-demographic variables had a generating class that contained one four-factor interaction (gender, age, employment status and marital status) and the additional three-factor interactions (employment status, age, education; employment status, education, gender; and employment status, marital status, education).
Table IV illustrates the statistically significant three way interaction of employment, education, and age on multiple drug use (two or more drugs). Among those who use at least one drug, 50.6% of the subgroup of Canadians aged with less than high school education go on to use two or more drugs.
Of further note is the sub-group between 20 and 39 years old, employed and with less than high school education: 45.9% of those who use at least one drug will use two or more prescription drugs.
Table V provides the percentages using multiple drugs based on the four-way interaction. Of particular note is the sub-group of female multiple drug users who are divorced, unemployed, and between 40 and 59 years of age; almost 60% of women in this sub-group use multiple drugs.
Table VI describes the three-way interaction between employment, educational level, and marital status on multi-drug use. It will be noted that overall, unemployment is associated with higher multiple drug usage, even among those individuals with post-secondary education.
It is also especially noteworthy that use among the sub-group consisting of those unemployed, single, with less than high school (58.4%) is especially high.
The results in Table VII clarify some of the above findings, especially indicating higher multiple-drug use with both males and females when unemployed (among those who use at least one drug).
CONCLUSION
Limitations: First, the survey used was cross-sectional. The direction of causation is not possible. Second, drug use was self-reported (and over a one year period). Third, even though the overall sample size was large (20,974), in a multi-way frequency table (or a log-linear analysis),the number of cells available must be limited. Fourth, we don’t have information on frequency of psychotropic drug use and MDE’s during the past year.
Conclusion: There is a relatively high proportion of Canadian adults that take prescription drugs. In this study, we analyzed the changes in drug use associated with different combinations of socio-demographic factors. We found, for example, that for those Canadians taking sleeping pills, the chance that they would go on to use anxiety drugs went from 3% to 27%.
Our analysis with socio-demographic factors and drug usage found higher multiple drug usage associated with factors and combinations of factors that included divorce, being single, unemployed, and having low educational attainment.
The findings are important since in the psychological and medical literature, the usage of multiple drugs is related to higher rates of side effects, beyond those found with separate individual prescription drug usage. Many of the very high and very low multiple drug use frequency cells uncovered  in our study would have been missed by looking simply at main effects. Groups that are particularly vulnerable to high multiple drug use would not have been identified. This illustrates the importance of considering interactions in the modeling of health data.
The sub-groups identified in this analysis could themselves be examined in future studies in more detail. We would also suggest that this situation be monitored in the future to determine changes in multiple drug utilization rates in Canada, and other countries.
Table III: Distribution over drug combinations when two drugs are taken
Table I: Percentage using one psychotropic drug
Drug
% Use
Sleeping pill
10.74
Anxiety medication
5.86
Mood stabilizers
1.13
Anti-depressants
6.15
Stimulants
0.24
Drug combination %
Sleep and anxiety
36.22
Antidep and anxiety
27.53
Antidep and sleep
26.52
Mood and antidep
5.06
Mood and sleep
2.49
Mood and anxiety
1.21
Sleep and stim
0.81
Antidep and stim
0.12
Anxiety and stim
0.03
Mood and stim
0.01
Table II: Percentage using psychotropic drugs by number of drugs
x (no. of drugs)
% taking x drugs
83.38 1
11.26 2
3.74 3
1.29 4
0.33 5
0.01
Table IV: Employment, education, and age interaction on multiple drug use
employed all year
not employed
20-39 years
40-59 years
Less than high school
45.9%
22.5%
39.5%
50.6%
High school graduate
37.8%
32.3%
37.2%
40.6%
Post secondary graduate
28.1%
28.5%
41.9%
41.6%
RESEARCH QUESTIONS
What percentage of Canadians use particular prescribed psychotropic drugs?
What percentage of Canadians use more than one prescribed psychotropic drug?
What are the most prevalent combinations of psychotropic drugs?
Which socio-demographic factors are associated with use of multiple psychotropic drugs?
Which socio-demographic factors are associated with the greatest prevalence of multiple psychotropic drug use?
Table V: Gender, employment, age, and marital status interaction on multiple drug use
MALE
FEMALE
employed all year
not employed
20-39 years
40-59 years
Partnered
22.7%
26%
34.9%
40.1%
30.6%
27.9%
33.8%
36.8%
Divorced
31.1%
35.0%
43.1%
44.7%
38.1%
33.1%
30.0%
58.4%
Single
40.8%
19.3%
50.0%
42.2%
33.5%
46.9%
52.4%
47.1%
REFERENCES
Cadieux, R.J (1989) Drug interactions in the elderly. How multiple drug use increases risk exponentially. Postgraduate Medicine,86,
Cone, E.J., Fant, R.V., Rohay, J.M., Caplan, Y.H., Ballina, M., Reder, R.F & Haddox, J.D (2004) Oxycodone involvement in drug abuse deaths. 11. Evidence for toxic multiple drug-drug interactions. Journal of Analytical Toxicology, 28,
Jorgensen, T., Johansson, S., Kennerfalk, A., Wallander, MA., & Svardsudd, K. (2001) The Annals of Pharmacotherapy, 35,
Kohler, G.I., Bode-Boger, S.M., Buss, R., Hoopman, M., Wette, T., & Boger, R.M (2000) Drug-interactions in medical patients: effects of in-hospital treatment and relation to multiple drug use. International Journal of Clinical Pharmacology and Therapeutics. 38,
Rao, J.N.K., & Thomas, D.R (1988) The analysis of cross-classified categorical data from complex survey samples. Sociological Methodology, 18,
METHODS
Data source: Statistics Canada’s 2002 Canadian Community Health Survey (Cycle 1.2, Mental Health and Well-being).
Participants: year old men and women (n= 20,974).
Dependent variables: Self-reported use in the previous year of prescribed antidepressants, anxiety pills, mood stabilizers, sleeping pills, and stimulants.
Independent variables: gender, age (20-39 years, years), employment (employed all year, not employed), income adequacy (low, medium or high), marital status (partnered, divorced, single), and education (less than high school, high school graduate, post secondary graduate).
Analyses: Our study was conducted to determine the frequency of psychotropic drug use, and the socio-demographic factors associated with use of multiple drugs to identify subgroups with elevated usage. We used sampling weights provided by Statistics Canada and bootstrap methods. The data were weighted using the techniques suggested by Rao and Taylor (1988). A backward elimination logit (logistic regression) model was conducted to determine statistically significant main effects and interactions. After the fitting of the log linear model, the statistically significant interactions were expressed in simplified multi-way tables that illustrate the important combined effects of the variables on multi-drug use.
Table VI: Employment, education, and marital status interaction on multiple drug use
employed all year
not employed
Partnered
Divorced
Single
Less than high school
34.2%
29.2%
17.2%
38.8%
48.1%
58.4%
High school graduate
36.9%
27.5%
33.6%
32.3%
52.7%
43.5%
Post secondary graduate
24.8%
38.7%
38.6%
38.2%
50.1%
48.3%
Table VII: Employment, education, and gender interaction on multiple drug use
employed all year
not employed
MALE
FEMALE
Less than high school
28.8%
28.9%
47.4%
47.1%
High school graduate
24.9%
41.2%
36.0%
Post secondary graduate
27.4%
29.2%
37.3%
43.6%