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IntroductionPharmacoepidemiologic data and methods

SummaryReferences

The use of prescription databases for thestudy of prescription drug abuse:

Prescriptions of Benzodiazepines in Denmark

Henrik Støvring

[email protected] Unit of General PracticeUniversity of Southern Denmark

Prescription Drug Abuse Conference, Oslo, November18-19, 2009

Henrik Støvring Prescriptions of Benzodiazepines in Denmark


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Conflicts of interest

None to declare



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Acknowledgment

Christiane GasseAssociate ProfessorNational Center for Register-based ResearchAarhus University



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Outline

Introduction

Pharmacoepidemiologic data and methods

Summary



SummaryReferences

RegulationsMonitoringRelevant epidemiologic measures

Danish Guidelines

◮ Specific guidance governs prescription of opioids andBenzodiazepines (BZD)

◮ Main instructions◮ Use should be limited to 28 days (at most!)◮ Prescriptions only by patients’ own GP (no doctor shopping)◮ Exception for elderly in long term treatment for sleeping

disorders

◮ Tightened repeatedly over the last two decades



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Monitoring by Danish Medicine Agency (DMA)

◮ Published report in 2009 on BZDprescriptions (Danish Medicines Agency 2009)

◮ Two main measures1. Number of users during calendar year← period prevalence2. Number of users redeeming more than 28 DDD per year←

period prevalence of heavy use

◮ Adequate for assessment of overall magnitude of problem(?)

◮ Inadequate for monitoring effect of interventions



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(Danish Medicines Agency 2009)



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Relevant epidemiologic measures

◮ Lorenz curves/Gini coefficients (Hallas and Stovring 2006)◮ Incidence rates (Støvring et al. 2003)◮ Cessation rates among incident users

(Støvring et al. 2007)◮ Types of BZD prescribed for incident users (future work)



SummaryReferences

Danish prescription dataScreening for potential abusePrevalence, incidence, and cessationSchematic representation of run-in period

Danish 25% sample

◮ Random 25% sample of all living in Denmark Jan 1, 2005(n = 1,414,089)

◮ Data used here◮ Date of birth◮ Gender◮ Dates of migration (in/out of DK), 2005–8◮ All BZD prescriptions, 2005–8 (cf. next slide)

◮ Future: Linkage with psychiatric diagnoses from Danishhospital discharge data



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Prescription data

◮ ATC codesAnxiolytics: N05BA, N03AE01Hypnotics and sedatives: N05CD, N05CF

◮ Date of redemption◮ Package size and number of tablets◮ Amount of DDDs



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Lorenz curve

◮ Developed to measure economic inequality (income,wealth, etc.)

◮ Definition of Lorenz curve◮ Rank subjects in order of income◮ Cumulate income◮ Compute percentiles of rank and cumulated income

◮ Yields statements of the type:“The top one percent earns 15% of the total income”



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Lorenz curve example

0.2

.4.6

.81

Pro

port

ion

of to

tal i

ncom

e

0 .2 .4 .6 .8 1Proportion of individuals



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Gini coefficient

◮ Measure of skewnessZero: All have equal incomeOne: One subject only has income

◮ Computed from areas of Lorenz curve



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Computation of Gini coefficient

A

B

Gini = A/(A+B)

0.2

.4.6

.81

Pro

port

ion

of to

tal i

ncom

e

0 .2 .4 .6 .8 1Proportion of individuals



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Lorenz and Gini for BZD

Gini−coefficient: 64.9%0

.2.4

.6.8

1P

erce

nt c

umul

ativ

e D

DD

−am

ount

0 .2 .4 .6 .8 1Percent of Benzodiazepine users

Year 2005



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Lorenz and Gini for BZD

Gini−coefficient: 68.0%0

.2.4

.6.8

1P

erce

nt c

umul

ativ

e D

DD

−am

ount

0 .2 .4 .6 .8 1Percent of Benzodiazepine users

Year 2008



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Percentiles for top users

Year 1% top users 10% top users 50% top users2005 13.6 47.2 93.82006 14.5 48.8 93.12007 14.5 49.7 93.32008 15.6 51.6 93.7



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Doctor shopping

◮ In 2005-8, the median number of annual prescriptions was3

◮ In 2005-8, 10% had 13 or more prescriptions◮ Let us consider subjects with > 12 prescriptions per year

(13,587 in 2008)◮ 59% had one GP Id only as prescriber◮ 39% had two to four◮ 2% (293) had five or more



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Calendar time

Subjects

Untreated

Treated

Redemptions



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Calendar time

Subjects

Observed



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t0 Calendar time

Subjects



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Definitions of treatment status

◮ Divide time into quarters of a year◮ Index date is first day of quarter◮ Treatment status on index date determined by

prescription(s) in previous date◮ Compute:

Prevalence: Proportion of treated on index dateIncidence: Proportion of untreated on index date

becoming treatedCessation: Proportion of incident in previous quarter who

are not treated in next



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BZD prevalence, quarterly, males

01

23

45

67

89

10P

reva

lenc

e pr

opor

tion

(/%

)

2005 2006 2007 2008 2009Year

Trend OR: 0.963 (0.960; 0.966)



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BZD prevalence, quarterly, females

01

23

Pre

vale

nce

prop

ortio

n (/

%)

2005 2006 2007 2008 2009Year

Trend OR: 0.954 (0.952; 0.956)



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BZD incidence, quarterly, males

01

23

Inci

denc

e pr

opor

tion

(/%

)

2005 2006 2007 2008 2009Year

Trend OR: 0.974 (0.969; 0.978)



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BZD incidence, quarterly, females

01

23

Inci

denc

e pr

opor

tion

(/%

)

2005 2006 2007 2008 2009Year

Trend OR: 0.963 (0.960; 0.966)



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BZD cessation among new users, quarterly, males

010

2030

4050

6070

80C

essa

tion

prop

ortio

n (/

%)

2005 2006 2007 2008 2009Year

Trend OR: 1.034 (1.022; 1.047)



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BZD cessation among new users, quarterly, females

010

2030

4050

6070

80C

essa

tion

prop

ortio

n (/

%)

2005 2006 2007 2008 2009Year

Trend OR: 1.038 (1.029; 1.047)



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Preliminary conclusions

◮ Clear evidence of potential drug abuse (Lorenz and Gini)◮ Use of BZD declines in Denmark in 2005–8◮ No clear effect of new regulations in mid-2008◮ Use should be more closely monitored by central health

authorities



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Future research

◮ Stratify on drug class◮ Account for age, diagnosis, etc.◮ Influence of social status◮ Apply more dedicated survival time models



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Thank you for your attention!

Slides prepared with LATEXand Beamer



SummaryReferences

Danish Medicines Agency (2009).Significant drop in the consumption of sleeping medicine and

anxiolytics in denmark - an analysis of consumption from2004 to early 2009.

Technical report, Danish Medicines Agency.

Hallas, J. and H. Stovring (2006, Mar).Templates for analysis of individual-level prescription data.Basic Clin Pharmacol Toxicol 98(3), 260–265.

Støvring, H., M. Andersen, H. Beck-Nielsen, A. Green, andW. Vach (2003, Aug).

Rising prevalence of diabetes: evidence from a Danishpharmaco-epidemiological database.

Lancet 362(9383), 537–8.



SummaryReferences

Støvring, H., M. Andersen, H. Beck-Nielsen, A. Green, andW. Vach (2007).

Counting drugs to understand the disease: The case ofmeasuring the diabetes epidemic.

Popul Health Metr 5(1), 2.


IntroductionRegulationsMonitoringRelevant epidemiologic measures

Pharmacoepidemiologic data and methodsDanish prescription dataScreening for potential abusePrevalence, incidence, and cessationSchematic representation of run-in period

SummaryReferences

the use of prescription databases for the study of ... › klinmed › forskning › sentre ›...

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