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Distribution of prescription drug exposures in the elderly: Description and implications
0895-4356/96/$15.00 PII SO895-4356(96)00055-S
] Clin Epidemiol Vol. 49, No. 8, pp. 929-935, 1996 Copyright 0 1996 Elsevier Science Inc. ELSEVIER
Distribution
of Prescription Drug Exposures in the Elderly: Description and Implications Geoffrey Arukrson’~* and Kerry Kerluke2
‘INSTITUTE
FOR CLINICAL SERVICES
EVALUATIVE AND
POLICY
SCIENCES, RESEARCH,
TORONTO, VANCOUVER,
ONTARIO, BRITISH
CANADA, COLUMBIA,
AND
2CENTRE
FOR HEALTH
CANADA
ABSTRACT. Using data from a comprehensive prescription drug benefit program in British Columbia, we studied the distribution of prescription drug expenditures and exposures in the community-dwelling elderly over a l-year period. Overall, 84% of the population was exposed to at least one prescription drug. The 11% of individuals with the highest level of use accounted for 50% of total drug expenditures. Individuals 65 to 74 years of age were exposed to a median of 2.2 different drugs during the year compared to a median of 3.8 for those 75 years of age and over. Twenty-four percent of the 65- to 74-year-old population were exposed to six or more different drugs during a l-year period compared to 37% of the 75 years and over population. Central nervous system and cardiovascular drugs were most commonly responsible for multiple drug exposures. Forty-eight percent of the individuals exposed to six or more different drugs received prescriptions from three or more different physicians. In British Columbia, 98% of the elderly receiving six or more different drugs received at least one prescription from a general practitioner or a family practitioner. J CLIN EPIDEMIOL
49;8:929-935, 1996. KEY WORDS. INTRODUCTION There is growing interest and concern over prescription drug use in the elderly [ 11. These concerns deal with both the costs of prescrip tion drugs [2] and the health impacts of their use [3]. Prescription drugs are an integral part of modem medicine and can be among the most cost-effective forms of care 141.However, there is evidence that prescription drug use in the elderly may not be optimal [5,6] and that in some cases the elderly may be put at risk for serious adverse effects [7,8]. The distribution of drug expenditures and exposures in the elderly has important cost and quality of care implications. In particular, the identification of individuals with multiple prescription drug exposures has become an area of interest [9]. It is important to note that exposure to multiple different drugs does not necessarily imply inappropriate prescribing. Although in some cases polypharmacy may involve the use of combinations of drugs that provide no benefit to the patient or that involve potentially dangerous drug-drug interactions [5], individuals with complex or multiple health care problems may best be treated with several different drugs. However, even if the multiple drug therapy is appropriate clinically, individuals exposed to multiple drugs are worthy of closer scrutiny both because of quality of care issues related to higher risk of adverse drug reactions [lo] and poor compliance (111 and because of cost control issues [12]. The more we understand about the distribution of drug exposures in the elderly, the better we will be able to identify effective cost control and quality of care policies. Understanding the distribution of prescription drug expenditures across the eligible population can ‘Address correspondence to: G. M. Anderson, Institute for Clinical tive Sciences,
G-106,
2075 Bayview
Avenue,
Toronto,
Canada. Accepted
for publication
on 2 January
1996.
Ontario,
EvaluaM4N 3M5
help target cost control policies and can provide information on the potential impact of different insurance coverage options. Understanding the characteristics of people who are exposed to multiple drugs, the type of drugs they receive, and the individuals who prescribed those drugs are key steps in designing strategies to monitor and improve the quality of care provided. Our study draws on claims data from Plan A of the British Columbia Pharmacare program. Plan A provides comprehensive drug coverage to the community-dwelling elderly in that province. Claims data can provide accurate information on drugs prescribed to elderly individuals [13], is not subject to problems with recall found with self-report data [14], and can provide a more representative picture of drug use than provider-based studies [15]. The purpose of this study was to use Pharmacare claims data to determine the overall distribution of exposures to different drugs in a defined population of elderly individuals living in the community, to study the relationship between drug exposures and drug expenditures, to examine exposures by age group and by type of drug, and to determine the number and characteristics of physicians writing prescriptions for individuals exposed to multiple drugs. METHODS
Data Source The Pharmacare program provided a machine-readable file of all prescriptions drug claims filed under Pharmacare Plan A in fiscal 1988/89. Plan A provided first-dollar coverage for drug ingredient costs to all residents of British Columbia who were 65 years of age and older and lived in the community. Individuals in extended or long-term care facilities received drug coverage under a separate benefit plan and individuals in hospitals received their drugs under the hospital budget. Each claim included a patient identifier, a prescriber identifier, a drug therapeutic code that can be used to identify drug types, the
930
G. Anderson
and K. Kerluke
TABLE 1. Claims and population data
Total eligible claims Claims linked to date of birth Claims linked to date of birth and physician identifier
Number in thousands
Percentage of total eligible
4009 3800
(100.0) (94.8)
3744
(93.4)
Age 65-74 Resident population in thousands Resident population not in extended or long-term care facilities (% of resident population)
Age 75+
Age 65+
230.3
147.4
377.7
227.0 (98.6)
128.6 (87.2)
355.6 (94.2)
drug quantity, and the drug ingredient costs. These data were cleaned to remove duplicate claims generated when pharmacists had to resubmit claims due to coding errors, claims with missing drug identification codes, and claims for nondrug items.
eight digits of the therapeutic code were used to identify individual drugs to which an individual could be exposed. The first two digits of the therapeutic code identify major drug types and were used to divide prescription drugs into major therapeutic categories.
Patient and Prescriber Linkages
Culculation
The British Columbia Ministry of Health maintains a registry file of all those eligible for Pharmacare coverage. This file contains a unique identifier for each beneficiary and information on the date of birth and sex of the individual. Each claim submitted under Plan A includes a patient identifier and the Ministry of Health linked the identifier on the claims with the data in the registery file. As shown in Table 1, it was possible to link 94.8% of the eligible claims to individuals in the registry. Failure to link 5.2% of the claims to the registry is the result of either miscoding of the identifier on the claim or missing data in the registry. Eligibility for Pharmacare is based on age and information on date of birth was available for all linked claims. However, at the time we did this study information on the sex of the claimant was missing in 20% of claims. Therefore, our analysis was limited to age and excluded sex. The current registry has been updated to include almost complete information on sex and future analyses will be able to provide information on use by sex. The claims file also contains an identifier for the provider who wrote the prescription. This identifier was linked to a data set containing information on the characteristics of the prescriber. of the claims already linked to the patient identifier, 98.5% were linked to an identifier for an individual physician. The remaining 1.5% of claims were linked to nonphysician prescribers (e.g., dentists, optometrists) or could not be linked to an individual provider.
The calculation of rates of exposure involves defining both a denominator (e.g., the number of individuals eligible for Plan A) and a numerator (e.g., the number of individuals exposed to prescription drugs). The claims and population data used to calculate these rates are summarized in Table 1. Under Plan A, all residents of British Columbia 65 years of age or older not residing in institutions (i.e., long-term care facilities, extended care hospitals, or acute care institutions) are eligible for coverage. Data from the Pharmacare registry may not be updated for deaths and individuals who migrate out of the province. We decided to use the census data to calculate denominators. However, census data from Statistics Canada covers all residents, including those in institutions. Because Plan A covers only those not in institutions, the census data had to be adjusted to provide an accurate estimate of the total eligible population. We considered adjusting the population for acute care use but decided against this option. Although when they are in acute care institutions the elderly receive drug coverage under the hospital budget, when they are discharged into the community their prescriptions are filled under Plan A. Average lengths of stay for the elderly in acute care institutions are about 14 days and virtually no elderly individual stays in an acute care institution for an entire year. Therefore, although many elderly are admitted to an acute care institution during the year, if they are not transferred to a long-term care institution, they are likely to be covered under Plan A for a substantial proportion of the year and should be included in the eligible population. The argument is quite different for extended or long-term care. In this case lengths of stay are very long and individuals in these institutions are not likely to be eligible for Plan A coverage at any time during the year. Because these individuals are unlikley to have the opportunity for a drug exposure under Plan A (i.e., are unlikely to show up in the numerator), they should be removed from the denominator when calculating rates. This was done using Ministry of Health data on the number of elderly who were residents of longterm care institutions or extended care hospitals in 1988/89. This number was then subtracted from the census data to yield the figures for the total eligible population presented in Table 1. The claims data were used to identify individuals exposed to pre-
Drug Expenditures Each claim indicates the amount paid by Pharmacare to the pharmacy for the drugs dispensed. Drug ingredient costs are the amounts paid in 1988/89 by Pharmacare for the drugs dispensed, measured in Canadian dollars. Drug ingredient costs do not include dispensing fees.
Drug Exposures Each claim in the file contains a nine-digit code that defines the therapeutic category of the prescribed drug. The first eight digits of the therapeutic code identify all drugs with the same ingredients independent of the specific dose or drug manufacturer. The first
of Rates
Prescription
931
Drug Exposure in the Elderly
scription drugs. All the analyses of overall drug exposure rates and the relationship between drug exposure and age were based on the 94.8% of eligible claims linked to the date of birth data in the population registry. These claims identified 299,346 individuals exposed to at least one prescription drug. All the analyses of the relationship of exposure to the number and speciality of the prescriber were based on the 93.4% of total eligible claims that could be linked to both the patient registry and unique physician identifiers. These claims identified 297,885 individuals exposed to at least one prescription drug. The number of individuals not exposed to any prescription drugs was calculated as the total number of eligible individuals minus the number of individuals exposed to at least one drug.
RFiSUL.TS The total value of claims used in the analysis was $85.5 million (Canadian dollars, CAD) with an average of $240 per eligible person and a median of $89 per eligible person. In the population 65 to 74 years of age the average total expenditures per person were $219 with a median of $65. In the population 75 years of age and older the average total expenditure was $279 per person with a median of $135. The difference between the means and medians indicates the extent to which the expenditure distribution is skewed to the right. Lorem curves provide a useful approach to presenting data on expenditure or income distributions [16]. These curves are constructed by ranking individuals from lowest to highest expenditures and then graphing the cumulative proportion of total expenditures against the cumulative proportion of the total population over that ranking. Figure 1 presents Lorenz curves for drug ingredient expenditures
in British Columbia in 1988/89. For the elderly population as a whole, 16% of the population was estimated not to have filled any prescription in 1988/89. At the other end of the Lorenz curves, it was estimated that the 11% of the overall elderly population with the highest drug expenditures accounted for about 50% of total expenditures. For the population 65 to 74 years of age, 21% of the population did not fill a prescription and compared to 7% of the population 75 years of age and over. Table 2 presents data on the number of individuals exposed to different numbers of drugs, the average expenditures for each level of drug exposure, and the average expenditure per drug exposure. Individuals exposed to a single drug had mean total expenditures of $43 and total expenditures increased steadily to $843 for those exposed to 11 or more different drugs. The average expenditure per drug exposure increases at low levels of exposure and then remain relatively stable over the higher range of exposures. Figure 2 presents data on the distribution of drug exposures by age group. In the population 65 to 74 years of age, the median number of different drug exposures was 2.2 and 24% of the population was exposed to six or more different drugs during the year. In the population 75 years of age and over, the median number of different drug exposures was 3.8 and 37% of the population was exposed to six or more different drugs during the year. Figure 3 provides data on drug exposures by drug category. The drug categories displayed in Fig. 3 (central nervous system [CNS] drugs including NSAIDs, antiinfective drugs, cardiovascular drugs, hormones, and gastrointestinal [GI] drugs) accounted for 82% of total drug expenditures in 1988/89 [2]. More than one-half of the elderly population was exposed to at least one drug in the CNS category and 13% of the population was exposed to three or more different drugs in this category. Almost
100 90 80 ---J.---.-i
.--I
.-j- .__. ;--.-i.
._-...i
1
P C
t
FIGURE 1. Percent drug exe penditure ve. percent population: total population (phs signs), population age 65-74 (open squares), and populae tion age 75+ (asfbivks).
D r ” Q E p” 8 : i ” r e
30
40
50
60
Pet Population
80
..i
932 TABLE
G. Anderson and K. Kerluke 2. Number
of exposures
Number of different drug exposures
to different
drugs and mean expenditures Percentage exposed population
Number of individuals
of
Mean
expenditures per person (8
Mean expenditures per exposure cs)
1 2
46,288 45,105
15.5 15.1
43.4 100.6
43.4 50.3
: 5 f
35,316 41,116 29,054 18,620 23,571
13.7 11.8 9.7 6.2 7.9
226.2 161.2 289.5 416.4 351.4
53.7 56.6 57.9 59.5 58.6
8 9 10 11+
14,735 11,271 8,707 25,563 299,346
4.9 3.8 2.9 8.5 100.0
483.7 529.9 607.6 842.5 285.7
60.5 58.9 60.8 60.9 57.9
25 1
FIGURE 2. Drug exposures by age group. The columns on the left represent ages 65-74 years; columns on the right represent ages 75+ years.
0
1
2
3
4
5
6
7
8
9
10 ll+
FIGURE 3. Exposure drug category.
rates by
Prescription
933
Drug Exposure in the Elderly
FIGURE 4. Distribution of exposed population by mum ber of drug exposures and number of prescribers.
Number of Prescribing Pbyslclam
1
k‘
40% of the elderly population was exposed to an antiinfective drug and 4.4% of the population was exposed to three or more different drugs in this category. Cardiovascular drugs account for almost 40% of total drug expenditures and 36% of the population was exposed to at least one drug in this category, with 7% of the population exposed to three or more different cardiovascular drugs. The other two drug categories, hormones and gastrointestinal drugs, had lower rates of any exposure than the other three categories, but the most striking difference between these two drug categories and the other three was the lower rates of multiple drug exposures. Only about 1% of the population was exposed to three or more drugs in either of these two categories. Figure 4 provides data on the relationship between the number of different drugs an individual was exposed to and the number of different physicians writing prescriptions for that individual. The total population exposed to at least one drug can be divided into three relatively equal-sized groups based on the number of drug exposures: (1) those exposed to one or two different drugs (30.8% of the exposed population), (2) those exposed to three to five different drugs (35.3% of the exposed population), and (3) those exposed to six or more different drugs (33.9% of the exposed population). Almost 80% of individuals exposed to one or two different drugs re-
TABLE
3. Distribution
of exposed
population
by number
of drug exposures
and prescriber
Drug specialty
No general practitioner and l-2 Specialists 23 Specialists l-2 general practitioners and 0 Specialists l-2 Specialists 23 Specialists 3 or more general practitioners 0 Specialists l-2 Specialists 23 Specialists Total:
n
15,049
254,552
and
28,284
297,885
l-2 96
5.1 4.8 0.2 85.5 57.6 26.1 1.8 9.5 4.9 4.0 0.5 100.0
Druglbrposures
ceived their prescriptions from only one physician, but only 21% of individuals receiving six or more different drugs received their prescriptions from only one physician. Almost 48% of the individuals exposed to six or more different drugs received their prescriptions from three or more different physicians. Table 3 examines the relationship between the number of different drugs to which an individual was exposed and the specialty of the physicians writing prescriptions for that individual. We have divided physicians into two categories. One is general practitioners, which includes all physicians without specialty certification and physicians with family practice certification. The other category is specialists, which includes all physicians with a certified specialty other than family practice. Overall, 95% of the elderly population who filled prescriptions in 1988/89 received at least one prescription from a general practitioner. The majority (58%) received prescriptions from only one or two general practitioners and no specialists. In the group exposed to one or two different drugs, only 19% received any prescriptions from a specialist and 90% received a prescription from at least one general practitioner. In the group exposed to six or more different drugs, 98% received at least one prescription from a general practitioner and 58% received at least one prescription from a specialst.
Overall Prescriber
t-2 rhgs
n
9,297
81,514
1,025
91,836
exposure
Drugs
specialty
group 3-5 Drugs
%
10.1 9.3 0.8 88.8 80.1 8.6 0.0 1.1 1.0 El 1OO:o
n
3,856 94,032
7,135
105,023
6+ Dmgs %
3.7 3.4 0.2 89.5 60.4 28.6 0.5 6.8 5.1 1.6 0.0 100.0
n
%
1,896
1.9 1.4
79,006
7::: 34.1 39.4 4.7 19.9 8.4 10.0 1.6 100.0
20,124
101,026
934 DISCUSSION Our analysis shows that prescription drug expenditures are not spread evenly through the elderly population. The expenditure distribution was skewed to the right, resulting in about 11% of the elderly population accounting for almost one-half of total expenditures. Any effective cost control effort will have to have an impact on the relatively small number of individuals with high expenditures. Analysis of the distribution of expenditures is essential to understanding the impacts of changes in drug benefit coverage, such as shifts in copayments or deductibles. An alternative to controlling or shifting costs through changes in coverage is the use of case managers who could target their efforts to the small number of high users. Our analysis also shows that high levels of drug expenditure are related to multiple drug exposures. The claims data used in our analysis do not have the clinical detail required to judge if the drug therapy received by individuals is clinically appropriate. The use of multiple different drugs may be the best treatment for patients with complicated conditions. Nonetheless, individuals who receive multiple drugs are at risk for adverse reactions [lo] and poor compliance [ 1 l] and they provide a reasonable target for focused quality assur, ante efforts. The identification of those individuals and the physi, cians who write the prescriptions for them is an important step in developing both cost containment and quality initiatives. We were able to show that it is the oldest old who are the most likely to have multiple drug exposures. This is consistent with studies in other jurisdictions that have shown that prescription drug use generally increases with age [ 12,171. We were also able to infer from the therapeutic drug categories that multiple drug exposure is often related to patients receiving long-term treatment for chronic diseases such as arthritis, hypertension, or ischemic heart disease. This suggests that many of the individuals with multiple drug exposures in 1 year are likely to continue to have multiple drug exposures in subsequent years. Furthermore, the concentration of multiple drug use in the treatment of a limited number of chronic conditions with drugs that can have important drug interactions [18] suggests initia, tives aimed at a few conditions could potentially have a large impact on the cost and quality of care. Our analysis was also able to examine some of the characteristics of those writing prescriptions for patients with multiple different drug exposures. As one would expect, our analysis showed that individuals receiving prescriptions for one or two different drugs generally received these drugs from a limited number of physicians. The relationship between high levels of drug exposure and the number of prescribers is less clear. Multiple drug exposures may be related to patient factors such as severity of illness, physician factors such as a high propensity to prescribe, or system factors including lack of a primary care provider who can coordinate care [19]. If multiple drugs are received from a single physician or a limited number of physicians, then interventions involving education or feedback [10,19] could be directed at these physicians. If multiple drugs are received from multiple physicians, the best intervention might be identifying a physician who could coordinate care for this patient. To help determine which type of initiative is likely to work, we divided the population receiving multiple prescriptions into those who received these prescriptions from one or two physicians and those who received these prescriptions from three or more physicians. In the population receiving prescriptions for six or more different drugs, there was an almost even split between those receiving scripts from one or two prescribers and those receiving scripts from
G. Anderson
and K. Kerluke
three or more different prescribers. This suggests that both identifying case managers and focusing on high-use physicians are important policy alternatives. Under the British Columbia medical care insurance program, a specialist can receive a consultant’s fee only if the patient is referred by another physician. This suggests that specialist care is often based on referrals from other physicians. Our analysis shows that 58% of individuals exposed to six or more different drugs received at least one prescription from a specialist compared to only 19% of individuals exposed to one or two drugs. The association between multiple drug exposure and the use of specialists is consistent with the notion that multiple drug users have complicated clinical conditions that require specialist care. Our analysis shows that about 98% of the elderly exposed to six or more different drugs received at least one prescription from a general practitioner and 39% received all of these prescriptions from a single general parctitioner. Because general practitioners provide at least some care for almost everyone who receives multple drugs and because in many cases they are the only physicians prescribing for these patients, they might be better targets than specialists for initiatives designed to improve practice and control costs.
CONCLUSIONS We used claims from a comprehensive drug benefit program to examine some important prescription drug cost and quality of care issues related to multiple drug exposures. Although our suggestion that general or family practitioners may be an appropriate target for initiatives aimed at controlling the cost and quality of prescription drug care for the elderly may be directly relevant only to the Canadian health care system, we hope that the general techniques for describing and examining drug exposures and expenditures we have outlined in this article will be of use to those studying prescription drug utilization in other jurisdictions. This study was funded under a grant from Health and Welfare Can& (NHRDP661 O-1 774-57). Dr. Anderson also received personal support from Health and Welfare Canada (NHRDP-6610-1737-48).
References 1. Avorn 1. The elderly and drug policy: Coming of age. Health Affairs 1990; 9: 6. GM, Kerluke K, P&ins I, et al. Trends and determinants 2. Anderson of prescription drug expenditures in the elderly: Data from the British Columbia Pharmacare program. Inquiry 1993; 30: 199. of acute 3. Brook RH. Kambere C. Maver Oaks A, et al. AoDrooriateness medical c&e for th; elherly: Health Pdlicy 199bi 14: 225-242. S, Goel V. Cost-effectiveness of intravenous 4. Naylor CD, Bronskill thrombolytic drugs for acute myocardial infarction. Can J Cardiol 1993; 6: 553-558. 5. Tamblyn RM, McLeod PJ, Abrahamowicz M, et al. Questionable prescribing for elderly patients in Quebec. Can Med Assoc J 1994; 150: 1801-1809. SM, Himmelstein DU, Woolhandler S. Inappropriate drug pre6. Willcox scribing for the community-dwelling elderly. J Am Med Assoc 1994; 272: 292-296. W, et al. Psychotropic drug use and 7. Ray WA, Griffin MR, Schaffner hip fracture. New Engl J Med 1987; 316: 363. 8. Griffin MR, Ray WA, Schaffner W. Nonsteroidal antiinflammatory drug use and death from peptic ulcer in elderly persons. Ann Intern Med 1988; 109: 359. 9. Isacson D, Smedby B. Defining heavy use of prescripton drugs, a methodological study. Med Care 1988; 26: 1103-l 110. in the elderly, a con10. Kroenke J, Pinholt EM. Reducing polypharmacy trolled trial of physician feedback. J Am Geriatr Sot 1990; 38: 31-36.
Prescription
Drug Exposure in the Elderly
11. Eraker SA, Kirscht JP, Becker MH. Understanding and improving patient compliance. Ann Intern Med 19&1; 100: 258-268. 12. Quinn K, Baker MJ, Evans B. A population-wide profile of prescription drug use in Saskatchewan, 1989. Can Med Assoc J 1992; 146: 21772186. 13. Tamblyn R, Lavoie G, Petrella L, Monette 1. The use of prescription claim databases in pharmacoepidemiological research: The accuracy and comprehensiveness of the prescription claims database in Quebec. J Clin Epidemiol 1995; 48: 999-1009. 14. Betk ML, Schur CL, Mohr P. Using survey data to estimate prescription drug costs. Health Affairs 1990; 9(3): 146-152.
935 15. Nolan L, O’Malley K. Prescribing for the elderly. 11. Prescribing patterns: Differences due to age. J Am Geriatr Sot 1988; 36: 245-251. 16. Lipsey RG, Sparks GR, Steiner PO. Economics, 2nd Ed. Harper and Row, New York, 1973. 17. Stuart B, Ahem F, Rabatin V, Johnson A. Patterns of outpatient prescription drug use among Pennsylvania elderly. Health Care Financial Rev 1991; 12: 61-75. 18. Lamy PP. The elderly and drug interactions. J Am Geriatr Sot 1986; 34: 586-591. 19. MeyerTJVanKootenD, MarshS,ProchazkaAV.Reductionofpolypharmacy by feedback to clinicians. J Gen Internal Med 1991; 6: 133-136.
] Clin Epidemiol Vol. 49, No. 8, pp. 929-935, 1996 Copyright 0 1996 Elsevier Science Inc. ELSEVIER
Distribution
of Prescription Drug Exposures in the Elderly: Description and Implications Geoffrey Arukrson’~* and Kerry Kerluke2
‘INSTITUTE
FOR CLINICAL SERVICES
EVALUATIVE AND
POLICY
SCIENCES, RESEARCH,
TORONTO, VANCOUVER,
ONTARIO, BRITISH
CANADA, COLUMBIA,
AND
2CENTRE
FOR HEALTH
CANADA
ABSTRACT. Using data from a comprehensive prescription drug benefit program in British Columbia, we studied the distribution of prescription drug expenditures and exposures in the community-dwelling elderly over a l-year period. Overall, 84% of the population was exposed to at least one prescription drug. The 11% of individuals with the highest level of use accounted for 50% of total drug expenditures. Individuals 65 to 74 years of age were exposed to a median of 2.2 different drugs during the year compared to a median of 3.8 for those 75 years of age and over. Twenty-four percent of the 65- to 74-year-old population were exposed to six or more different drugs during a l-year period compared to 37% of the 75 years and over population. Central nervous system and cardiovascular drugs were most commonly responsible for multiple drug exposures. Forty-eight percent of the individuals exposed to six or more different drugs received prescriptions from three or more different physicians. In British Columbia, 98% of the elderly receiving six or more different drugs received at least one prescription from a general practitioner or a family practitioner. J CLIN EPIDEMIOL
49;8:929-935, 1996. KEY WORDS. INTRODUCTION There is growing interest and concern over prescription drug use in the elderly [ 11. These concerns deal with both the costs of prescrip tion drugs [2] and the health impacts of their use [3]. Prescription drugs are an integral part of modem medicine and can be among the most cost-effective forms of care 141.However, there is evidence that prescription drug use in the elderly may not be optimal [5,6] and that in some cases the elderly may be put at risk for serious adverse effects [7,8]. The distribution of drug expenditures and exposures in the elderly has important cost and quality of care implications. In particular, the identification of individuals with multiple prescription drug exposures has become an area of interest [9]. It is important to note that exposure to multiple different drugs does not necessarily imply inappropriate prescribing. Although in some cases polypharmacy may involve the use of combinations of drugs that provide no benefit to the patient or that involve potentially dangerous drug-drug interactions [5], individuals with complex or multiple health care problems may best be treated with several different drugs. However, even if the multiple drug therapy is appropriate clinically, individuals exposed to multiple drugs are worthy of closer scrutiny both because of quality of care issues related to higher risk of adverse drug reactions [lo] and poor compliance (111 and because of cost control issues [12]. The more we understand about the distribution of drug exposures in the elderly, the better we will be able to identify effective cost control and quality of care policies. Understanding the distribution of prescription drug expenditures across the eligible population can ‘Address correspondence to: G. M. Anderson, Institute for Clinical tive Sciences,
G-106,
2075 Bayview
Avenue,
Toronto,
Canada. Accepted
for publication
on 2 January
1996.
Ontario,
EvaluaM4N 3M5
help target cost control policies and can provide information on the potential impact of different insurance coverage options. Understanding the characteristics of people who are exposed to multiple drugs, the type of drugs they receive, and the individuals who prescribed those drugs are key steps in designing strategies to monitor and improve the quality of care provided. Our study draws on claims data from Plan A of the British Columbia Pharmacare program. Plan A provides comprehensive drug coverage to the community-dwelling elderly in that province. Claims data can provide accurate information on drugs prescribed to elderly individuals [13], is not subject to problems with recall found with self-report data [14], and can provide a more representative picture of drug use than provider-based studies [15]. The purpose of this study was to use Pharmacare claims data to determine the overall distribution of exposures to different drugs in a defined population of elderly individuals living in the community, to study the relationship between drug exposures and drug expenditures, to examine exposures by age group and by type of drug, and to determine the number and characteristics of physicians writing prescriptions for individuals exposed to multiple drugs. METHODS
Data Source The Pharmacare program provided a machine-readable file of all prescriptions drug claims filed under Pharmacare Plan A in fiscal 1988/89. Plan A provided first-dollar coverage for drug ingredient costs to all residents of British Columbia who were 65 years of age and older and lived in the community. Individuals in extended or long-term care facilities received drug coverage under a separate benefit plan and individuals in hospitals received their drugs under the hospital budget. Each claim included a patient identifier, a prescriber identifier, a drug therapeutic code that can be used to identify drug types, the
930
G. Anderson
and K. Kerluke
TABLE 1. Claims and population data
Total eligible claims Claims linked to date of birth Claims linked to date of birth and physician identifier
Number in thousands
Percentage of total eligible
4009 3800
(100.0) (94.8)
3744
(93.4)
Age 65-74 Resident population in thousands Resident population not in extended or long-term care facilities (% of resident population)
Age 75+
Age 65+
230.3
147.4
377.7
227.0 (98.6)
128.6 (87.2)
355.6 (94.2)
drug quantity, and the drug ingredient costs. These data were cleaned to remove duplicate claims generated when pharmacists had to resubmit claims due to coding errors, claims with missing drug identification codes, and claims for nondrug items.
eight digits of the therapeutic code were used to identify individual drugs to which an individual could be exposed. The first two digits of the therapeutic code identify major drug types and were used to divide prescription drugs into major therapeutic categories.
Patient and Prescriber Linkages
Culculation
The British Columbia Ministry of Health maintains a registry file of all those eligible for Pharmacare coverage. This file contains a unique identifier for each beneficiary and information on the date of birth and sex of the individual. Each claim submitted under Plan A includes a patient identifier and the Ministry of Health linked the identifier on the claims with the data in the registery file. As shown in Table 1, it was possible to link 94.8% of the eligible claims to individuals in the registry. Failure to link 5.2% of the claims to the registry is the result of either miscoding of the identifier on the claim or missing data in the registry. Eligibility for Pharmacare is based on age and information on date of birth was available for all linked claims. However, at the time we did this study information on the sex of the claimant was missing in 20% of claims. Therefore, our analysis was limited to age and excluded sex. The current registry has been updated to include almost complete information on sex and future analyses will be able to provide information on use by sex. The claims file also contains an identifier for the provider who wrote the prescription. This identifier was linked to a data set containing information on the characteristics of the prescriber. of the claims already linked to the patient identifier, 98.5% were linked to an identifier for an individual physician. The remaining 1.5% of claims were linked to nonphysician prescribers (e.g., dentists, optometrists) or could not be linked to an individual provider.
The calculation of rates of exposure involves defining both a denominator (e.g., the number of individuals eligible for Plan A) and a numerator (e.g., the number of individuals exposed to prescription drugs). The claims and population data used to calculate these rates are summarized in Table 1. Under Plan A, all residents of British Columbia 65 years of age or older not residing in institutions (i.e., long-term care facilities, extended care hospitals, or acute care institutions) are eligible for coverage. Data from the Pharmacare registry may not be updated for deaths and individuals who migrate out of the province. We decided to use the census data to calculate denominators. However, census data from Statistics Canada covers all residents, including those in institutions. Because Plan A covers only those not in institutions, the census data had to be adjusted to provide an accurate estimate of the total eligible population. We considered adjusting the population for acute care use but decided against this option. Although when they are in acute care institutions the elderly receive drug coverage under the hospital budget, when they are discharged into the community their prescriptions are filled under Plan A. Average lengths of stay for the elderly in acute care institutions are about 14 days and virtually no elderly individual stays in an acute care institution for an entire year. Therefore, although many elderly are admitted to an acute care institution during the year, if they are not transferred to a long-term care institution, they are likely to be covered under Plan A for a substantial proportion of the year and should be included in the eligible population. The argument is quite different for extended or long-term care. In this case lengths of stay are very long and individuals in these institutions are not likely to be eligible for Plan A coverage at any time during the year. Because these individuals are unlikley to have the opportunity for a drug exposure under Plan A (i.e., are unlikely to show up in the numerator), they should be removed from the denominator when calculating rates. This was done using Ministry of Health data on the number of elderly who were residents of longterm care institutions or extended care hospitals in 1988/89. This number was then subtracted from the census data to yield the figures for the total eligible population presented in Table 1. The claims data were used to identify individuals exposed to pre-
Drug Expenditures Each claim indicates the amount paid by Pharmacare to the pharmacy for the drugs dispensed. Drug ingredient costs are the amounts paid in 1988/89 by Pharmacare for the drugs dispensed, measured in Canadian dollars. Drug ingredient costs do not include dispensing fees.
Drug Exposures Each claim in the file contains a nine-digit code that defines the therapeutic category of the prescribed drug. The first eight digits of the therapeutic code identify all drugs with the same ingredients independent of the specific dose or drug manufacturer. The first
of Rates
Prescription
931
Drug Exposure in the Elderly
scription drugs. All the analyses of overall drug exposure rates and the relationship between drug exposure and age were based on the 94.8% of eligible claims linked to the date of birth data in the population registry. These claims identified 299,346 individuals exposed to at least one prescription drug. All the analyses of the relationship of exposure to the number and speciality of the prescriber were based on the 93.4% of total eligible claims that could be linked to both the patient registry and unique physician identifiers. These claims identified 297,885 individuals exposed to at least one prescription drug. The number of individuals not exposed to any prescription drugs was calculated as the total number of eligible individuals minus the number of individuals exposed to at least one drug.
RFiSUL.TS The total value of claims used in the analysis was $85.5 million (Canadian dollars, CAD) with an average of $240 per eligible person and a median of $89 per eligible person. In the population 65 to 74 years of age the average total expenditures per person were $219 with a median of $65. In the population 75 years of age and older the average total expenditure was $279 per person with a median of $135. The difference between the means and medians indicates the extent to which the expenditure distribution is skewed to the right. Lorem curves provide a useful approach to presenting data on expenditure or income distributions [16]. These curves are constructed by ranking individuals from lowest to highest expenditures and then graphing the cumulative proportion of total expenditures against the cumulative proportion of the total population over that ranking. Figure 1 presents Lorenz curves for drug ingredient expenditures
in British Columbia in 1988/89. For the elderly population as a whole, 16% of the population was estimated not to have filled any prescription in 1988/89. At the other end of the Lorenz curves, it was estimated that the 11% of the overall elderly population with the highest drug expenditures accounted for about 50% of total expenditures. For the population 65 to 74 years of age, 21% of the population did not fill a prescription and compared to 7% of the population 75 years of age and over. Table 2 presents data on the number of individuals exposed to different numbers of drugs, the average expenditures for each level of drug exposure, and the average expenditure per drug exposure. Individuals exposed to a single drug had mean total expenditures of $43 and total expenditures increased steadily to $843 for those exposed to 11 or more different drugs. The average expenditure per drug exposure increases at low levels of exposure and then remain relatively stable over the higher range of exposures. Figure 2 presents data on the distribution of drug exposures by age group. In the population 65 to 74 years of age, the median number of different drug exposures was 2.2 and 24% of the population was exposed to six or more different drugs during the year. In the population 75 years of age and over, the median number of different drug exposures was 3.8 and 37% of the population was exposed to six or more different drugs during the year. Figure 3 provides data on drug exposures by drug category. The drug categories displayed in Fig. 3 (central nervous system [CNS] drugs including NSAIDs, antiinfective drugs, cardiovascular drugs, hormones, and gastrointestinal [GI] drugs) accounted for 82% of total drug expenditures in 1988/89 [2]. More than one-half of the elderly population was exposed to at least one drug in the CNS category and 13% of the population was exposed to three or more different drugs in this category. Almost
100 90 80 ---J.---.-i
.--I
.-j- .__. ;--.-i.
._-...i
1
P C
t
FIGURE 1. Percent drug exe penditure ve. percent population: total population (phs signs), population age 65-74 (open squares), and populae tion age 75+ (asfbivks).
D r ” Q E p” 8 : i ” r e
30
40
50
60
Pet Population
80
..i
932 TABLE
G. Anderson and K. Kerluke 2. Number
of exposures
Number of different drug exposures
to different
drugs and mean expenditures Percentage exposed population
Number of individuals
of
Mean
expenditures per person (8
Mean expenditures per exposure cs)
1 2
46,288 45,105
15.5 15.1
43.4 100.6
43.4 50.3
: 5 f
35,316 41,116 29,054 18,620 23,571
13.7 11.8 9.7 6.2 7.9
226.2 161.2 289.5 416.4 351.4
53.7 56.6 57.9 59.5 58.6
8 9 10 11+
14,735 11,271 8,707 25,563 299,346
4.9 3.8 2.9 8.5 100.0
483.7 529.9 607.6 842.5 285.7
60.5 58.9 60.8 60.9 57.9
25 1
FIGURE 2. Drug exposures by age group. The columns on the left represent ages 65-74 years; columns on the right represent ages 75+ years.
0
1
2
3
4
5
6
7
8
9
10 ll+
FIGURE 3. Exposure drug category.
rates by
Prescription
933
Drug Exposure in the Elderly
FIGURE 4. Distribution of exposed population by mum ber of drug exposures and number of prescribers.
Number of Prescribing Pbyslclam
1
k‘
40% of the elderly population was exposed to an antiinfective drug and 4.4% of the population was exposed to three or more different drugs in this category. Cardiovascular drugs account for almost 40% of total drug expenditures and 36% of the population was exposed to at least one drug in this category, with 7% of the population exposed to three or more different cardiovascular drugs. The other two drug categories, hormones and gastrointestinal drugs, had lower rates of any exposure than the other three categories, but the most striking difference between these two drug categories and the other three was the lower rates of multiple drug exposures. Only about 1% of the population was exposed to three or more drugs in either of these two categories. Figure 4 provides data on the relationship between the number of different drugs an individual was exposed to and the number of different physicians writing prescriptions for that individual. The total population exposed to at least one drug can be divided into three relatively equal-sized groups based on the number of drug exposures: (1) those exposed to one or two different drugs (30.8% of the exposed population), (2) those exposed to three to five different drugs (35.3% of the exposed population), and (3) those exposed to six or more different drugs (33.9% of the exposed population). Almost 80% of individuals exposed to one or two different drugs re-
TABLE
3. Distribution
of exposed
population
by number
of drug exposures
and prescriber
Drug specialty
No general practitioner and l-2 Specialists 23 Specialists l-2 general practitioners and 0 Specialists l-2 Specialists 23 Specialists 3 or more general practitioners 0 Specialists l-2 Specialists 23 Specialists Total:
n
15,049
254,552
and
28,284
297,885
l-2 96
5.1 4.8 0.2 85.5 57.6 26.1 1.8 9.5 4.9 4.0 0.5 100.0
Druglbrposures
ceived their prescriptions from only one physician, but only 21% of individuals receiving six or more different drugs received their prescriptions from only one physician. Almost 48% of the individuals exposed to six or more different drugs received their prescriptions from three or more different physicians. Table 3 examines the relationship between the number of different drugs to which an individual was exposed and the specialty of the physicians writing prescriptions for that individual. We have divided physicians into two categories. One is general practitioners, which includes all physicians without specialty certification and physicians with family practice certification. The other category is specialists, which includes all physicians with a certified specialty other than family practice. Overall, 95% of the elderly population who filled prescriptions in 1988/89 received at least one prescription from a general practitioner. The majority (58%) received prescriptions from only one or two general practitioners and no specialists. In the group exposed to one or two different drugs, only 19% received any prescriptions from a specialist and 90% received a prescription from at least one general practitioner. In the group exposed to six or more different drugs, 98% received at least one prescription from a general practitioner and 58% received at least one prescription from a specialst.
Overall Prescriber
t-2 rhgs
n
9,297
81,514
1,025
91,836
exposure
Drugs
specialty
group 3-5 Drugs
%
10.1 9.3 0.8 88.8 80.1 8.6 0.0 1.1 1.0 El 1OO:o
n
3,856 94,032
7,135
105,023
6+ Dmgs %
3.7 3.4 0.2 89.5 60.4 28.6 0.5 6.8 5.1 1.6 0.0 100.0
n
%
1,896
1.9 1.4
79,006
7::: 34.1 39.4 4.7 19.9 8.4 10.0 1.6 100.0
20,124
101,026
934 DISCUSSION Our analysis shows that prescription drug expenditures are not spread evenly through the elderly population. The expenditure distribution was skewed to the right, resulting in about 11% of the elderly population accounting for almost one-half of total expenditures. Any effective cost control effort will have to have an impact on the relatively small number of individuals with high expenditures. Analysis of the distribution of expenditures is essential to understanding the impacts of changes in drug benefit coverage, such as shifts in copayments or deductibles. An alternative to controlling or shifting costs through changes in coverage is the use of case managers who could target their efforts to the small number of high users. Our analysis also shows that high levels of drug expenditure are related to multiple drug exposures. The claims data used in our analysis do not have the clinical detail required to judge if the drug therapy received by individuals is clinically appropriate. The use of multiple different drugs may be the best treatment for patients with complicated conditions. Nonetheless, individuals who receive multiple drugs are at risk for adverse reactions [lo] and poor compliance [ 1 l] and they provide a reasonable target for focused quality assur, ante efforts. The identification of those individuals and the physi, cians who write the prescriptions for them is an important step in developing both cost containment and quality initiatives. We were able to show that it is the oldest old who are the most likely to have multiple drug exposures. This is consistent with studies in other jurisdictions that have shown that prescription drug use generally increases with age [ 12,171. We were also able to infer from the therapeutic drug categories that multiple drug exposure is often related to patients receiving long-term treatment for chronic diseases such as arthritis, hypertension, or ischemic heart disease. This suggests that many of the individuals with multiple drug exposures in 1 year are likely to continue to have multiple drug exposures in subsequent years. Furthermore, the concentration of multiple drug use in the treatment of a limited number of chronic conditions with drugs that can have important drug interactions [18] suggests initia, tives aimed at a few conditions could potentially have a large impact on the cost and quality of care. Our analysis was also able to examine some of the characteristics of those writing prescriptions for patients with multiple different drug exposures. As one would expect, our analysis showed that individuals receiving prescriptions for one or two different drugs generally received these drugs from a limited number of physicians. The relationship between high levels of drug exposure and the number of prescribers is less clear. Multiple drug exposures may be related to patient factors such as severity of illness, physician factors such as a high propensity to prescribe, or system factors including lack of a primary care provider who can coordinate care [19]. If multiple drugs are received from a single physician or a limited number of physicians, then interventions involving education or feedback [10,19] could be directed at these physicians. If multiple drugs are received from multiple physicians, the best intervention might be identifying a physician who could coordinate care for this patient. To help determine which type of initiative is likely to work, we divided the population receiving multiple prescriptions into those who received these prescriptions from one or two physicians and those who received these prescriptions from three or more physicians. In the population receiving prescriptions for six or more different drugs, there was an almost even split between those receiving scripts from one or two prescribers and those receiving scripts from
G. Anderson
and K. Kerluke
three or more different prescribers. This suggests that both identifying case managers and focusing on high-use physicians are important policy alternatives. Under the British Columbia medical care insurance program, a specialist can receive a consultant’s fee only if the patient is referred by another physician. This suggests that specialist care is often based on referrals from other physicians. Our analysis shows that 58% of individuals exposed to six or more different drugs received at least one prescription from a specialist compared to only 19% of individuals exposed to one or two drugs. The association between multiple drug exposure and the use of specialists is consistent with the notion that multiple drug users have complicated clinical conditions that require specialist care. Our analysis shows that about 98% of the elderly exposed to six or more different drugs received at least one prescription from a general practitioner and 39% received all of these prescriptions from a single general parctitioner. Because general practitioners provide at least some care for almost everyone who receives multple drugs and because in many cases they are the only physicians prescribing for these patients, they might be better targets than specialists for initiatives designed to improve practice and control costs.
CONCLUSIONS We used claims from a comprehensive drug benefit program to examine some important prescription drug cost and quality of care issues related to multiple drug exposures. Although our suggestion that general or family practitioners may be an appropriate target for initiatives aimed at controlling the cost and quality of prescription drug care for the elderly may be directly relevant only to the Canadian health care system, we hope that the general techniques for describing and examining drug exposures and expenditures we have outlined in this article will be of use to those studying prescription drug utilization in other jurisdictions. This study was funded under a grant from Health and Welfare Can& (NHRDP661 O-1 774-57). Dr. Anderson also received personal support from Health and Welfare Canada (NHRDP-6610-1737-48).
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935 15. Nolan L, O’Malley K. Prescribing for the elderly. 11. Prescribing patterns: Differences due to age. J Am Geriatr Sot 1988; 36: 245-251. 16. Lipsey RG, Sparks GR, Steiner PO. Economics, 2nd Ed. Harper and Row, New York, 1973. 17. Stuart B, Ahem F, Rabatin V, Johnson A. Patterns of outpatient prescription drug use among Pennsylvania elderly. Health Care Financial Rev 1991; 12: 61-75. 18. Lamy PP. The elderly and drug interactions. J Am Geriatr Sot 1986; 34: 586-591. 19. MeyerTJVanKootenD, MarshS,ProchazkaAV.Reductionofpolypharmacy by feedback to clinicians. J Gen Internal Med 1991; 6: 133-136.