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Original Contribution |

Adverse Events Associated With Prescription Drug Cost-Sharing Among Poor and Elderly Persons FREE

Robyn Tamblyn, PhD; Rejean Laprise, PhD; James A. Hanley, PhD; Michael Abrahamowicz, PhD; Susan Scott, MSc; Nancy Mayo, PhD; Jerry Hurley, PhD; Roland Grad, MD, MSc; Eric Latimer, PhD; Robert Perreault, MD; Peter McLeod, MD; Allen Huang, MD; Pierre Larochelle, MD; Louise Mallet, BPharm, PhD
[+] Author Affiliations

Author Affiliations: McGill University, Department of Medicine and Department of Epidemiology and Biostatistics, Montréal, Quebec.


JAMA. 2001;285(4):421-429. doi:10.1001/jama.285.4.421.
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Published online

Context Rising costs of medications and inequities in access have sparked calls for drug policy reform in the United States and Canada. Control of drug expenditures by prescription cost-sharing for elderly persons and poor persons is a contentious issue because little is known about the health impact in these subgroups.

Objectives To determine (1) the impact of introducing prescription drug cost-sharing on use of essential and less essential drugs among elderly persons and welfare recipients and (2) rates of emergency department (ED) visits and serious adverse events associated with reductions in drug use before and after policy implementation.

Design and Setting Interrupted time-series analysis of data from 32 months before and 17 months after introduction of a prescription coinsurance and deductible cost-sharing policy in Quebec in 1996. Separate 10-month prepolicy control and postpolicy cohort studies were conducted to estimate the impact of the drug reform on adverse events.

Participants A random sample of 93 950 elderly persons and 55 333 adult welfare medication recipients.

Main Outcome Measures Mean daily number of essential and less essential drugs used per month, ED visits, and serious adverse events (hospitalization, nursing home admission, and mortality) before and after policy introduction.

Results After cost-sharing was introduced, use of essential drugs decreased by 9.12% (95% confidence interval [CI], 8.7%-9.6%) in elderly persons and by 14.42% (95% CI, 13.3%-15.6%) in welfare recipients; use of less essential drugs decreased by 15.14% (95% CI, 14.4%-15.9%) and 22.39% (95% CI, 20.9%-23.9%), respectively. The rate (per 10 000 person-months) of serious adverse events associated with reductions in use of essential drugs increased from 5.8 in the prepolicy control cohort to 12.6 in the postpolicy cohort in elderly persons (a net increase of 6.8 [95% CI, 5.6-8.0]) and from 14.7 to 27.6 in welfare recipients (a net increase of 12.9 [95% CI, 10.2-15.5]). Emergency department visit rates related to reductions in the use of essential drugs also increased by 14.2 (95% CI, 8.5-19.9) per 10 000 person-months in elderly persons (prepolicy control cohort, 32.9; postpolicy cohort, 47.1) and by 54.2 (95% CI, 33.5-74.8) among welfare recipients (prepolicy control cohort, 69.6; postpolicy cohort, 123.8). These increases were primarily due to an increase in the proportion of recipients who reduced their use of essential drugs. Reductions in the use of less essential drugs were not associated with an increase in risk of adverse events or ED visits.

Conclusions In our study, increased cost-sharing for prescription drugs in elderly persons and welfare recipients was followed by reductions in use of essential drugs and a higher rate of serious adverse events and ED visits associated with these reductions.

Figures in this Article

Rising costs of medications and inequities in access to medication have sparked calls for drug policy reform in the United States and Canada.1,2 One of the most contentious issues is the introduction of cost-sharing to control drug expenditures. Cost-sharing is intended to deter the use of drug therapies that do little to improve health.36 But cost-effectiveness rests on the assumption that individuals will have the capacity to pay for essential drugs and that they will make rational choices about which drugs to use and abandon. Otherwise, the use of essential drugs will be curtailed to control drug expenditures and short-term savings in the drug budget may be offset by downstream costs in the treatment of potentially preventable illnesses.722

Cost-sharing policies that require the consumer to pay a percentage of the cost of the prescribed medication may be less likely to produce unintended health effects. But the impact of this type of cost-sharing on the use of health services has not been evaluated in poor persons, and only one inconclusive evaluation has been conducted for elderly persons.10

In 1996, the Canadian province of Quebec attempted to enhance equity of access to prescription drugs by legislating mandatory drug insurance for all residents.23 To help finance this coverage, a deductible and a 25% coinsurance charge was instituted for previously insured beneficiaries who received free medication: elderly persons and welfare recipients. The existence of comprehensive, linkable databases on drug and health care use in Quebec provided a unique opportunity to evaluate the impact of cost-sharing on the use of essential and less essential drugs and determine the rate of emergency department (ED) visits and serious adverse events (hospitalization, nursing home admission, and mortality) associated with reductions in drug use before and after the implementation of the cost-sharing policy.

Quebec Drug Policy Reform

Prior to the drug policy reform, welfare recipients and low-income elderly persons (5.7% of the total elderly population in Quebec) received free medication, and all other elderly persons paid $2 per prescription up to a maximum of $100 per year (all values are in Canadian dollars). On August 1, 1996, both groups became subject to a 25% coinsurance fee up to an annual maximum of $200 for welfare recipients and income-indexed ceilings of $200, $500, and $750 for elderly persons. On January 1, 1997, an annual deductible of $100 was added to the 25% coinsurance fee, with the annual deductible and ceiling prorated on a quarterly basis. On July 1, 1997, the annual ceiling and deductible were prorated on a monthly basis to reduce the amount a recipient would have to pay per month to a maximum of $16.67 ($200 plan) to $62.50 ($750 plan). Children and selected subgroups were exempted from cost-sharing and continued to receive free medication. A computerized, real-time link between the provincial health plan administration (Régie de l'assurance maladie du Québec [RAMQ]) and pharmacies was put in place to administer the policy.

Design and Study Populations

To study the intended impact of cost-sharing and to evaluate the health effects of reducing drug use, we identified a subset of essential and less essential drugs for evaluation using published classifications (Table 1).18,24,25Essential drugs were defined as medications that prevent deterioration in health or prolong life and would not likely be prescribed in the absence of a definitive diagnosis. Because there was no information on indication for treatment, the second criterion was added to limit the analysis to only those people who were using drugs for which reductions in use would be expected to have an impact on health status. The most commonly used drugs excluded using this criterion were antibiotics and gastro-protective medications, drugs targeted by many intervention programs to reduce overuse.26Less essential drugs were defined as medications that may provide relief of symptoms but will likely have no effect on the underlying disease process.25 Drugs included in the essential and less essential categories were independently identified and then rated by the 7 clinical coinvestigators. A medication was included if 6 of the 7 clinical coinvestigators agreed with the classification of the respective drug and it was insured through the RAMQ provincial health care plan. The subset of drugs included in the evaluation of essential and less essential drug use represented 21% of all insured drugs and 55% of all prescriptions dispensed.

Table Graphic Jump LocationTable 1. Drugs Included in the Essential and Less Essential Categories

An interrupted time-series design was used to estimate changes in prescription drug use after the introduction of the drug policy. Monthly measures of overall drug use as well as the use of essential and less essential drugs in the 3 years preceding the policy and in the first 17 postpolicy months were calculated for individual persons. A random sample of 120 000 welfare recipients and 120 000 elderly persons was selected. Persons were eligible for inclusion in the analysis if they were alive on the date of policy implementation, filled at least 1 prescription for a drug in the year before the policy, and were not exempted from the cost-sharing policy. The evaluation of essential and less essential drugs was limited to persons who had filled at least 1 prescription for drugs within these categories in the prepolicy year. Individuals contributed to the analysis only during the months in which they were enrolled.

Separate prepolicy control and postpolicy cohort studies were conducted to determine the impact of the policy on the rates of adverse events and ED visits associated with reductions in use of essential and less essential drugs. Each study produced an estimate of the risk associated with reduction in drug use, the proportion of the population making reductions, the population attributable fraction, and the rate of adverse events and ED visits associated with reductions in drug use. The 2 studies were conducted in comparable 10-month periods before (August 1995 to 1996) and after (August 1996-1997) policy implementation (Figure 1). The prepolicy control study provided an estimate of the expected rate of adverse events due to reductions in drug use prior to policy implementation. The estimation of an expected rate was important because even when drugs are free, individuals will experience adverse drug events due to injudicious reductions in needed therapy, because of forgetfulness, adverse effects, or misperceptions about the importance of drug treatment.27 The difference in the rate in the prepolicy control study vs the postpolicy study was used to estimate the impact of the drug reform on adverse events. This approach had several advantages. First, it voided biases related to ecological fallacy28 because changes in drug use were linked at the level of the individual with the occurrence of adverse events and ED visits. Second, it provided a means of isolating the effect of the drug policy from other health care policies that were implemented in the same 4-year period that may have reduced the rate of ED visits and hospitalizations, unrelated to prescription drug use (hospital closures and reallocation of service locations). Finally, the prepolicy and postpolicy cohort study approach verified the assumption that the primary impact of cost-sharing would be to increase the prevalence of reductions of drug use rather than changing the "biological risk" associated with rationing or stopping therapy. Thus, the studies were designed to estimate both the risk and the population attributable fraction or the share of adverse events and ED visits due to reductions in drug use, in the prepolicy and postpolicy periods.

Figure 1. Time Series and Prepolicy Control and Postpolicy Cohort Design
Graphic Jump Location
The first 3 months of the time series and of an individual's enrollment in the public plan were excluded because prescriptions filled prior to these dates were unknown and created artificially low values for monthly drug use. The month immediately prior to policy implementation also was excluded because of possible prescription stockpiling.

For this analysis, study populations were limited to regular recipients of essential or less essential drugs, defined as persons who had a supply of the respective medication in each of the 12 months prior to the follow-up period or new users with a minimum of 6 months of continuous use.

Data Sources

Four provincial health databases, validated in previous research,2931 were linked by unique encrypted health numbers. The beneficiary demographic database provided data on drug plan eligibility, death, and beneficiary characteristics. The prescription claims database, which includes the drug, quantity, date, and duration for each prescription dispensed from community-based pharmacies, was used to measure medication use. The physician claims database, which includes the date, type, and location of service delivery (eg, inpatient, emergency, clinic), was used to measure ED visits and hospitalization-institutionalization. The hospitalization database was used to validate claims-based measures of hospitalization-institutionalization.

Prescription Drug Use

The number of drugs available each day was calculated from prescription claims records using methods developed to convert the date, drug, and duration of prescriptions dispensed into a drug-by-day matrix.32 In each of the 53 months of the time series, a matrix of monthly mean daily drug use was then constructed for each beneficiary (for all drugs and separately for essential and less essential drugs). The first 3 months of the time series and of coverage for newly eligible recipients were excluded to avoid artificially lower values for drug use in the first few months of available prescription information. The month immediately prior to policy implementation also was excluded because of possible prescription stockpiling, leaving 49 months for analysis.

For the prepolicy and postpolicy cohort studies, reductions in drug use were measured first by estimating an expected daily drug use for each person. The resulting expected values were then compared with observed use in the 10-month follow-up period. The expected use value was estimated as the level predicted for the last baseline month by a linear trend fit to each person's mean monthly daily drug use in the baseline year. This method conservatively assumed that expected drug use would remain constant rather than increase during follow-up. In addition, it was assumed that the impact of reductions in drug use would cumulate over time. Therefore, time-dependent measures were used to summarize differences between expected and observed use during the follow-up period. Time-dependent measures of drug use also provided a means of adjusting for unusual drug consumption patterns triggered by the features of the drug policy. For instance, the deductible and maximum ceilings instituted a pattern whereby reductions in one month may be compensated for by increases in the next when drugs were free for those persons reaching the spending ceiling. Cumulative mean monthly increases (observed > expected) and reductions (observed < expected) in drug use were calculated as the sum (from the first follow-up month) of the monthly difference in observed and expected drug use divided by the number of follow-up months. For example, an individual who had an expected value of 5 drugs per month and who filled prescriptions for 3 drugs in the first 2 months of follow-up and 8 in the third month would have a mean cumulative reduction value in month 3 of 1.33 ([2 + 2 + 0]/3) and a mean cumulative increase of 1 ([0 + 0 + 3]/3). To respect the temporal sequence between reductions in drug use and adverse events, we linked the occurrence of adverse events in any 1 month to the patient's mean drug use pattern in the preceding months.

ED Visits and Adverse Events

For each person in each month, the number of distinct days with a claim for an ED visit was counted. Adverse events, defined as the first occurrence of acute care hospitalization, long-term care admission, or death, were established using the date and location of health service in the physician claims database (for hospitalizations and institutionalizations) and the beneficiary demographic database (for death). Follow-up was terminated after the occurrence of the first adverse event because prescription drugs dispensed during hospital stays are not recorded in claims files, and there are no available methods to establish long-term predictions of expected drug use at the level of the individual.

Covariates

The impact of the drug policy on monthly drug use was adjusted for linear trend across time, season (11 monthly indicator variables), and individual characteristics (age, sex, type of insurance plan, baseline medication use, education, and income). Education and income were approximated by the mean census value33 for residents within the same postal code area (mean, 366 households). Analysis of adverse events and ED visits was adjusted for age, sex, number of months hospitalized, Charlson comorbidity values34,35 in the baseline year, expected level of medication use, and mean cumulative increases in drug use. For ED visits, we also adjusted for the individual's frequency of ED visits in the baseline year.

Analysis

To estimate the impact of the policy on the level of drug use over time, random-effects, pooled-time series regression36 was used based on 49 prepolicy and postpolicy months. The individual was the unit of analysis, and an autoregressive first-order correlation structure (AR[1]) was assumed to represent the dependence among subsequent observations. Alternate correlation structures also were assessed (AR [2] and AR [12]), but as there was no material impact of the choice of correlation structure on the estimated regression coefficients or SEs, AR (1) results were presented. To assess postpolicy reductions in drug use, we first used the 32 prepolicy months to estimate the effect of time, seasonal variation, and beneficiary characteristics and then to predict, for each person, expected use for the 17 months postpolicy as well as the absolute and relative differences between observed and expected use.

The occurrence of adverse events associated with reduction in use of essential and less essential drugs was estimated by hazard ratios derived from a Cox model using time-dependent covariates, modeled as continuous variables, to represent monthly changes in drug use. Proportional hazards and linearity assumptions were verified. The frequency of ED visits was modeled by Poisson regression, assuming a first-order autoregressive correlation structure in a generalized estimating equation approach37,38 and empirical SEs to account for overdispersion. Outcomes in a particular month were examined in relation to cumulative mean monthly measures of drug use in the previous month. For the prepolicy control and postpolicy cohort studies, the overall fraction of adverse events and ED visits associated with reductions in drug use was estimated separately using the approach of Greenland and Drescher39 and multiplied by the overall adverse event and ED visit rates to estimate the monthly rate of adverse events and ED visits associated with reductions in drug use. As no estimate of the intercept is available in the Cox model, logistic regression was used to estimate the proportion of adverse events associated with reductions (population-attributable fraction) for adverse events. The regression coefficients produced by the Cox and logistic models were almost identical. Standard errors and confidence intervals (CIs) for the difference in these adverse event rates in the prepolicy and postpolicy studies were estimated by bootstrapping,40,41 using 200 bootstrap samples.

Among the 120 000 elderly persons sampled, 117 408 were eligible for cost-sharing. Of the 117 408, 93 950 (80.0%) used prescription medication in the prepolicy year, and 70 801 of the 93 950 (75.3%) used essential drugs (Table 2). Among the 120 000 welfare recipients sampled, 84 272 were adults of whom 55 333 (65.7%) used medication in the prepolicy year and 25 820 (46.7%) used medications classified as essential drugs. Elderly persons used more drugs per day and had higher costs for therapy than welfare recipients. However, the proportions hospitalized in the prepolicy year were similar. Elderly persons and welfare recipients used at least twice as many essential as less essential drugs. The most commonly used essential drugs in the elderly group were aspirin (13.2%), levothyroxine sodium (7.5%), and furosemide (6.0%). Among welfare recipients, the most commonly used essential drugs were salbutamol (5.9%) and levothyroxine sodium (5.0%). Benzodiazepines were the most commonly used less essential medication in the elderly and welfare recipient groups.

Table Graphic Jump LocationTable 2. Characteristics of the Recipients of Essential and Less Essential Drugs in the Prepolicy Year (August 1995-July 1996)

The introduction of the drug policy was followed by substantial and statistically significant reductions in the overall number of drugs used per day by both elderly persons (9.14%; 95% CI, 8.8%-9.5%) and adult welfare recipients (15.94%; 95% CI, 15.0%-16.9%). Similar reductions also were seen for the use of essential drugs as well as a slower rate of increase in drug use over time (policy/time interaction: elderly persons: = −0.02; P<.001; welfare recipients: = −0.009; P<.001) (Figure 2). In the postpolicy period, elderly persons showed a 9.12% (95% CI, 8.7%-9.6%) reduction in the number of essential drugs used per day (0.17 drugs; 95% CI, 0.16-0.18). Absolute and relative reductions were higher among welfare recipients (14.4%; 95% CI, 13.3%-15.6% and absolute reduction: 0.21; 95% CI, 0.19-0.23 essential drugs per day).

Figure 2. Observed and Predicted Use of Essential Medication in the Prepolicy and Postpolicy Periods
Graphic Jump Location

Relative reductions were greater in the use of less essential drugs by elderly persons and welfare recipients (15.14%; 95% CI, 14.4%-15.9% and 22.39%; 95% CI, 20.9%-23.9%, respectively) than for essential drugs (Figure 3). However, because fewer less essential drugs were used per day, the absolute size of the reduction was smaller for less essential drugs (elderly persons, 0.10 and welfare recipients, 0.15) than for essential drugs (elderly persons, 0.17 and welfare recipients, 0.21). Also, there was a significant decrease in the slope of less essential drug use over time in the postpolicy period (policy/time interaction) for the elderly persons ( = −0.009; P<.001) and for the welfare recipients ( = −0.008; P<.001).

Figure 3. Observed and Predicted Use of Less Essential Medication in the Prepolicy and Postpolicy Periods
Graphic Jump Location

As expected, in both the prepolicy and postpolicy studies, there was a significantly higher rate of adverse events and ED visits in those individuals who reduced their use of essential drugs vs those who did not (Table 3). Dose-response relationships were evident between the magnitude of the reduction and the rates of both outcomes. For example, in the prepolicy control study, the rates of adverse events in those with no reduction (≤0.1 drugs/d), minor reduction (>0.1 to 0.5 drugs/d), and major reductions (≥1 drugs/d) were 256, 272, and 385 per 10 000 person-months, respectively. Reduction of 1 medication would be equivalent to stopping 1 drug or rationing 2 drugs to half the expected use. Risks associated with reductions in drug use leveled off at higher reductions, reaching a plateau at approximately 1.5 medications per day. The estimated risk ratio for a 1-drug reduction, adjusted for cumulative increases, was similar for the prepolicy (1.57) and postpolicy (1.56) studies. Adjustment for beneficiary characteristics lowered the estimated risk ratios, primarily because individuals who had a greater number of comorbidities and used more medication were more likely to reduce their use of drugs and to experience adverse events and ED visits.

Table Graphic Jump LocationTable 3. Risk of Adverse Events With Reductions in Essential Drugs and Rates Associated With Reductions in the Prepolicy (n = 50 398) and Postpolicy (n = 56 548) Elderly Population*

A greater proportion of elderly persons (7.4% more) in the postpolicy study compared with the prepolicy control study reduced their use of essential drugs (Table 3). As a result, the estimated proportion of adverse events associated with drug reductions increased from 2.0% in the prepolicy to 3.9% in the postpolicy period. This change was accompanied by an estimated increase in the monthly rate of adverse events related to reductions in essential drug use from 5.8 to 12.6 per 10 000 in the prepolicy and postpolicy periods; a net increase of 6.8 adverse events per 10 000 per month. Reduction in the use of essential drugs also was associated with a significant increase in the rate of ED visits (risk ratio, 1.17-1.19). The higher frequency of reductions in essential drug use in the postpolicy period resulted in a net increased rate in monthly ED visits related to reductions in drug use of 14.2 per 10 000.

Welfare recipients, as compared with elderly persons, experienced similar risks of adverse events and ED visits in relationship to reductions in essential drug use (Table 4); however, the frequency of postpolicy reductions in essential drug use was considerably greater (13.6% and 7.4%, respectively). As a result, the drug policy was associated with a substantially greater net increase in the rates of adverse events and ED visits in relation to reductions in the use of essential drugs. In the postpolicy study, relative to the prepolicy study, the monthly rate of adverse events increased by 12.9 per 10 000 and ED visit rates by 54.2 per 10 000 person-months.

Table Graphic Jump LocationTable 4. Risk of Adverse Events With Reductions in Essential Drugs and Rates Associated With Reductions in the Prepolicy (n = 11 491) and Postpolicy (n = 13 311) Welfare Recipients*

Reductions in less essential drug use had no significant impact on adverse events or ED visits among elderly persons and welfare recipients in either the prepolicy control or postpolicy studies (Table 5).

Table Graphic Jump LocationTable 5. Prepolicy and Postpolicy Study Estimates of the Risk of Serious Adverse Events and Emergency Department Visits Associated With Reductions in Less Essential Drug Use in Elderly Persons and Welfare Recipients*

Increased cost-sharing for prescription drugs had the desired effect of reducing the use of less essential drugs but also the unintended effect of reducing the use of drugs that are essential for disease management and prevention. As a result, in the postpolicy period, there was an increase in the rate of adverse events and ED visits related to reductions in essential drug use. These effects are consistent with those reported by Soumerai et al18,20 in the evaluation of prescription reimbursement caps in US Medicaid programs.

This study had several limitations. In the absence of an out-of-province control group, we cannot confirm that reductions in drug use were solely related to cost-sharing policies. However, no other plausible explanation has emerged and the suddenness of reductions in drug use that occurred immediately after the introduction of the cost-sharing policy strengthens the conclusion that changes in drug use were likely to be related to the cost-sharing policy. Prescription claims files do not indicate what drugs were taken, only medication purchased. Although prescription refill rates provide a reasonably accurate measure of medication compliance,4244 reductions in drug use could have been overestimated if individuals received free samples or purchased equivalent over-the-counter preparations (eg, aspirin) after policy implementation. However, these individuals would be falsely classified as having reduced medication use, and as a result, the risk associated with reductions in drug use in the postpolicy studies would be underestimated.

Indications for therapy were unknown. Drugs classified as less essential may have been required therapy for some individuals (eg, benzodiazepines for panic disorder), whereas some essential therapeutic drugs may have been prescribed without adequate clinical indication (eg, diuretics for transitory elevation in blood pressure). This misclassification would likely lead to an underestimation of both the potential benefits of reducing the use of less essential drugs and the risks of reducing essential drug therapy.

Our study suggests that the primary mechanism by which cost-sharing affected the rate of adverse events was by increasing the proportion of people who made reductions in the use of essential drugs. We cannot confirm that reductions in essential drug use led to a deterioration in health status, but we believe that this is a plausible explanation for several reasons. First, there was a dose-response relationship between the magnitude of the reduction in the use of essential drugs and the risk of adverse events and ED visits. Second, reductions were associated with an increase in the risk of adverse events in the prepolicy and postpolicy period, a phenomenon that would be expected if reductions represented medication noncompliance. Finally, the risk associated with reduction was specific to essential drugs, for which there is clinical trial evidence of efficacy.

The challenge for insurers has been to craft health care policies that provide adequate access to drug therapy while simultaneously exercising fiscally responsible control over the drug budget. Consumer cost-sharing has been the principal method of fiscal control because it assumes that people will value what they pay for and as a result, they will reduce their use of unnecessary medication when they are required to contribute a portion of the payment.4,6 While this reasoning may apply to many consumer goods, cost-sharing has been shown to have unintended effects in health care, such as increasing hospital admissions.16,17,19,20,4548 Consumers may not have the information needed to make wise decisions about necessary treatment. We estimate that for elderly persons alone, the drug policy reform in Quebec may result in 7000 additional adverse events per million annually. In light of the substantial impact that drug policy can have on the population's health, there is a need to redress the relative scarcity of scientific data on the outcomes of policy interventions. Our results suggest that more stringent cost-sharing pharmaceutical cost containment policies in other parts of Canada49 and the United States1,4 may contribute to avoidable illnesses.

Soumerai SB, Ross-Degnan D. Inadequate prescription-drug coverage for medicare enrollees: a call to action.  N Engl J Med.1999;340:722-728.
Minister of Public Works and Government Services.  Canadian Health Action: Building on the Legacy: Synthesis Reports and Issues PapersOttawa, Ontario: National Forum on Health; 1997. Cat No. H21-126/5-2-1997E.
Soumerai SB, Ross-Degnan D, Fortess EE, Abelson J. A critical analysis of studies of state drug reimbursement policies: research in need of discipline.  Milbank Q.1993;71:217-252.
Levy R. Prescription cost sharing economic and health impacts, and implications for health policy.  Pharmacoeconomics.1992;2:219-237.
Huttin C. The use of prescription charges.  Health Policy.1994;27:53-73.
Reeder CE, Lingle EW, Schulz RM.  et al.  Economic impact of cost-containment strategies in third party programmes in the US (part 1).  Pharmacoeconomics.1993;4:92-103.
Hurley J, Arbuthnot Johnson N. The effects of co-payments within drug reimbursement programs.  Can Public Policy.1991;17:473-489.
Ryan M, Birch S. Charging for health care: evidence on the utilisation of NHS prescribed drugs.  Soc Sci Med.1991;33:681-687.
Martin BC, McMillan JA. The impact of implementing a more restrictive prescription limit on Medicaid recipients.  Med Care.1996;34:686-701.
Johnson RE, Goodman RM, Hornbrook MC, Eldredge MB. The effect of increased prescription drug cost-sharing on medical care utilization and expenses of elderly health maintenance organization members.  Med Care.1997;35:1119-1131.
Leibowitz A, Manning WG, Newhouse JP. The demand for prescription drugs as a function of cost-sharing.  Soc Sci Med.1985;21:1063-1069.
Harris BL, Stergachis A, Ried LD. The effect of drug co-payments on utilization and cost of pharmaceuticals in a health maintenance organization.  Med Care.1990;28:907-917.
O'Brien B. The effect of patient charges on the utilization of prescription medicines.  J Health Econ.1989;8:109-132.
Smith DG. The effects of copayments and generic substitution on the use and costs of prescription drugs.  Inquiry.1993;30:189-198.
Nelson AA, Reeder CE, Dickson M. The effect of a Medicaid drug copayment program on the utilization and cost of prescription services.  Med Care.1984;22:724-736.
Lohr KN, Brook RH, Kamberg C.  et al.  Use of medical care in the RAND health insurance experiment: diagnosis and service-specific analyses in a randomized controlled trial. Santa Monica, Calif: RAND; 1986.
Reeder CE, Nelson AA. The differential impact of copayment on drug use in a Medicaid population.  Inquiry.1985;22:396-403.
Soumerai SB, Avorn J, Ross-Degnan D, Gortmaker S. Payment restrictions for prescription drugs under Medicaid: effects on therapy, cost, and equity.  N Engl J Med.1987;317:550-556.
Soumerai SB, McLaughlin TJ, Ross-Degnan D, Casteris CS, Bollini P. Effects of limiting Medicaid drug-reimbursement benefits on the use of psychotropic agents and acute mental health services by patients with schizophrenia.  N Engl J Med.1994;331:650-655.
Soumerai SB, Ross-Degnan D, Avorn J, McLaughlin TJ, Choodnovskkiy I. Effects of Medicaid drug-payment limits on admission to hospitals and nursing homes.  N Engl J Med.1991;325:1072-1077.
Brook RH, Ware JE, Rogers WH.  et al.  Does free care improve adults' health? results from a randomized controlled trial.  N Engl J Med.1983;309:1426-1434.
Newhouse JP.The Insurance Experiment Group.  Free for All? Lessons From the RAND Health Insurance Experiment . London, England: Harvard University Press; 1993.
Martin M. Quebec considering universal drug insurance.  CMAJ.1996;154:1264.
World Health Organization.  Essential drugs.  WHO Drug Information.1999;13:249-262.
McGavock H, Wilson-Davis K, Niblock RWF. Unsuspected patterns of drug utilization revealed by interrogation of a regional general practitioner prescribing database.  Pharmacoepidemiol Drug Saf.1992;1:73-80.
Soumerai SB, McLaughlin TJ, Avorn J. Improving drug prescribing in primary care: a critical analysis of the experimental literature.  Milbank Q.1989;67:268-317.
Goetghebeur E, Molenberghs G, Katz J. Estimating the causal effect of compliance on binary outcomes in randomized controlled trials.  Stat Med.1998;17:341-355.
Morgenstern H. Ecological studies. In: Rothman K, Greenland S, eds. Modern Epidemiology. Philadelphia, Pa: Lippincott-Raven; 1998:459-480.
Tamblyn RM, Lavoie G, Petrella L, Monette J. The use of prescription claims databases in pharmacoepidemiological research: the accuracy and comprehensiveness of the prescription claims database in Quebec.  J Clin Epidemiol.1995;48:999-1009.
Tamblyn RM, Reid T, Mayo N, McLeod PJ, Churchill-Smith M. Using medical services claims to assess injuries in the elderly: the sensitivity of diagnostic and procedure codes for injury ascertainment.  J Clin Epidemiol.2000;53:183-194.
Demers M. Cout des services médicaux durant la dernière année de vie au Québec en 1991Québec, Direction générale de l'évaluation et du contrôle, Régie de l'assurance-maladie du Québec; 1994:ix-39.
Tamblyn R, Abrahamowicz M. Drug utilization patterns. In: Armitage P, Coulton T, eds. Encyclopedia of Biostatistics. West Sussex, England: John Wiley & Sons Ltd; 1998:1235-1247.
Wilkins K. Use of postal codes and addresses in the analysis of health data.  Health Rep.1993;5:157-177.
Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.  J Chronic Dis.1987;40:373-383.
Deyo RA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: a response.  J Clin Epidemiol.1993;46:1075-1079.
Ward MM, Leigh JP. Pooled time series regression analysis in longitudinal studies.  J Clin Epidemiol.1993;46:645-659.
Liang KY, Zeger SL. Longitudinal data analysis using generalized linear models.  Biometrics.1986;73:13-22.
Zeger SL, Liang KY. Longitudinal data analysis for discrete and continuous outcomes.  Biometrics.1986;42:121-130.
Greenland S, Drescher K. Maximum likelihood estimation of the attributable fraction from logistic models.  Biometrics.1993;49:865-872.
Rockhill B, Weinberg CR, Newman B. Population attributable fraction estimation for established breast cancer risk factors: considering the issues of high prevalence and unmodifiability.  Am J Epidemiol.1998;147:826-833.
Efron B, Tibshirani R. An Introduction to the BootstrapNew York, NY: Chapman & Hall; 1993.
Grymonpre RE, Didur CD, Montgomery PR, Sitar DS. Pill count, self-report, and pharmacy claims data to measure medication adherence in the elderly.  Ann Pharmacother.1998;32:749-754.
Inui TS, Carter WB, Pecoraro RE, Pearlman RA, Dohan JJ. Variations in patient compliance with common long-term drugs.  Med Care.1980;18:986-993.
Lau HS, de Boer A, Beuning KS, Porsius A. Validation of pharmacy records in drug exposure assessment.  J Clin Epidemiol.1997;50:619-625.
Lurie N, Ward NB, Shapiro MF, Gallego C, Vaghaiwalla R, Brook RH. Termination of medical benefits: a follow-up study one year later.  N Engl J Med.1986;314:1266-1268.
Simon GE, Grothaus L, Durham ML, VonKorff M, Pabiniak C. Inpact of visit copayments on outpatient mental health utilization by members of a health maintenance organization.  Am J Psychiatry.1996;153:331-338.
Bloom BS, Jacobs J. Cost effects of restricting cost-effective therapy.  Med Care.1985;23:872-879.
Simon GE, VonKorff M, Durham ML. Predictors of outpatient mental health utilization by primary care patients in a health maintenance organization.  Am J Psychiatry.1994;151:908-913.
Bacovsky RA. Federal, Provincial and Territorial Government-Sponsored Drug Plans and Drug Databases. Health Canada 1997; Background information Prepared for the Conference on National Approaches to Pharmacare on January 18-20, 1998 (Saskatoon, Saskatchewan).

Figures

Figure 1. Time Series and Prepolicy Control and Postpolicy Cohort Design
Graphic Jump Location
The first 3 months of the time series and of an individual's enrollment in the public plan were excluded because prescriptions filled prior to these dates were unknown and created artificially low values for monthly drug use. The month immediately prior to policy implementation also was excluded because of possible prescription stockpiling.
Figure 2. Observed and Predicted Use of Essential Medication in the Prepolicy and Postpolicy Periods
Graphic Jump Location
Figure 3. Observed and Predicted Use of Less Essential Medication in the Prepolicy and Postpolicy Periods
Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1. Drugs Included in the Essential and Less Essential Categories
Table Graphic Jump LocationTable 2. Characteristics of the Recipients of Essential and Less Essential Drugs in the Prepolicy Year (August 1995-July 1996)
Table Graphic Jump LocationTable 3. Risk of Adverse Events With Reductions in Essential Drugs and Rates Associated With Reductions in the Prepolicy (n = 50 398) and Postpolicy (n = 56 548) Elderly Population*
Table Graphic Jump LocationTable 4. Risk of Adverse Events With Reductions in Essential Drugs and Rates Associated With Reductions in the Prepolicy (n = 11 491) and Postpolicy (n = 13 311) Welfare Recipients*
Table Graphic Jump LocationTable 5. Prepolicy and Postpolicy Study Estimates of the Risk of Serious Adverse Events and Emergency Department Visits Associated With Reductions in Less Essential Drug Use in Elderly Persons and Welfare Recipients*

References

Soumerai SB, Ross-Degnan D. Inadequate prescription-drug coverage for medicare enrollees: a call to action.  N Engl J Med.1999;340:722-728.
Minister of Public Works and Government Services.  Canadian Health Action: Building on the Legacy: Synthesis Reports and Issues PapersOttawa, Ontario: National Forum on Health; 1997. Cat No. H21-126/5-2-1997E.
Soumerai SB, Ross-Degnan D, Fortess EE, Abelson J. A critical analysis of studies of state drug reimbursement policies: research in need of discipline.  Milbank Q.1993;71:217-252.
Levy R. Prescription cost sharing economic and health impacts, and implications for health policy.  Pharmacoeconomics.1992;2:219-237.
Huttin C. The use of prescription charges.  Health Policy.1994;27:53-73.
Reeder CE, Lingle EW, Schulz RM.  et al.  Economic impact of cost-containment strategies in third party programmes in the US (part 1).  Pharmacoeconomics.1993;4:92-103.
Hurley J, Arbuthnot Johnson N. The effects of co-payments within drug reimbursement programs.  Can Public Policy.1991;17:473-489.
Ryan M, Birch S. Charging for health care: evidence on the utilisation of NHS prescribed drugs.  Soc Sci Med.1991;33:681-687.
Martin BC, McMillan JA. The impact of implementing a more restrictive prescription limit on Medicaid recipients.  Med Care.1996;34:686-701.
Johnson RE, Goodman RM, Hornbrook MC, Eldredge MB. The effect of increased prescription drug cost-sharing on medical care utilization and expenses of elderly health maintenance organization members.  Med Care.1997;35:1119-1131.
Leibowitz A, Manning WG, Newhouse JP. The demand for prescription drugs as a function of cost-sharing.  Soc Sci Med.1985;21:1063-1069.
Harris BL, Stergachis A, Ried LD. The effect of drug co-payments on utilization and cost of pharmaceuticals in a health maintenance organization.  Med Care.1990;28:907-917.
O'Brien B. The effect of patient charges on the utilization of prescription medicines.  J Health Econ.1989;8:109-132.
Smith DG. The effects of copayments and generic substitution on the use and costs of prescription drugs.  Inquiry.1993;30:189-198.
Nelson AA, Reeder CE, Dickson M. The effect of a Medicaid drug copayment program on the utilization and cost of prescription services.  Med Care.1984;22:724-736.
Lohr KN, Brook RH, Kamberg C.  et al.  Use of medical care in the RAND health insurance experiment: diagnosis and service-specific analyses in a randomized controlled trial. Santa Monica, Calif: RAND; 1986.
Reeder CE, Nelson AA. The differential impact of copayment on drug use in a Medicaid population.  Inquiry.1985;22:396-403.
Soumerai SB, Avorn J, Ross-Degnan D, Gortmaker S. Payment restrictions for prescription drugs under Medicaid: effects on therapy, cost, and equity.  N Engl J Med.1987;317:550-556.
Soumerai SB, McLaughlin TJ, Ross-Degnan D, Casteris CS, Bollini P. Effects of limiting Medicaid drug-reimbursement benefits on the use of psychotropic agents and acute mental health services by patients with schizophrenia.  N Engl J Med.1994;331:650-655.
Soumerai SB, Ross-Degnan D, Avorn J, McLaughlin TJ, Choodnovskkiy I. Effects of Medicaid drug-payment limits on admission to hospitals and nursing homes.  N Engl J Med.1991;325:1072-1077.
Brook RH, Ware JE, Rogers WH.  et al.  Does free care improve adults' health? results from a randomized controlled trial.  N Engl J Med.1983;309:1426-1434.
Newhouse JP.The Insurance Experiment Group.  Free for All? Lessons From the RAND Health Insurance Experiment . London, England: Harvard University Press; 1993.
Martin M. Quebec considering universal drug insurance.  CMAJ.1996;154:1264.
World Health Organization.  Essential drugs.  WHO Drug Information.1999;13:249-262.
McGavock H, Wilson-Davis K, Niblock RWF. Unsuspected patterns of drug utilization revealed by interrogation of a regional general practitioner prescribing database.  Pharmacoepidemiol Drug Saf.1992;1:73-80.
Soumerai SB, McLaughlin TJ, Avorn J. Improving drug prescribing in primary care: a critical analysis of the experimental literature.  Milbank Q.1989;67:268-317.
Goetghebeur E, Molenberghs G, Katz J. Estimating the causal effect of compliance on binary outcomes in randomized controlled trials.  Stat Med.1998;17:341-355.
Morgenstern H. Ecological studies. In: Rothman K, Greenland S, eds. Modern Epidemiology. Philadelphia, Pa: Lippincott-Raven; 1998:459-480.
Tamblyn RM, Lavoie G, Petrella L, Monette J. The use of prescription claims databases in pharmacoepidemiological research: the accuracy and comprehensiveness of the prescription claims database in Quebec.  J Clin Epidemiol.1995;48:999-1009.
Tamblyn RM, Reid T, Mayo N, McLeod PJ, Churchill-Smith M. Using medical services claims to assess injuries in the elderly: the sensitivity of diagnostic and procedure codes for injury ascertainment.  J Clin Epidemiol.2000;53:183-194.
Demers M. Cout des services médicaux durant la dernière année de vie au Québec en 1991Québec, Direction générale de l'évaluation et du contrôle, Régie de l'assurance-maladie du Québec; 1994:ix-39.
Tamblyn R, Abrahamowicz M. Drug utilization patterns. In: Armitage P, Coulton T, eds. Encyclopedia of Biostatistics. West Sussex, England: John Wiley & Sons Ltd; 1998:1235-1247.
Wilkins K. Use of postal codes and addresses in the analysis of health data.  Health Rep.1993;5:157-177.
Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.  J Chronic Dis.1987;40:373-383.
Deyo RA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: a response.  J Clin Epidemiol.1993;46:1075-1079.
Ward MM, Leigh JP. Pooled time series regression analysis in longitudinal studies.  J Clin Epidemiol.1993;46:645-659.
Liang KY, Zeger SL. Longitudinal data analysis using generalized linear models.  Biometrics.1986;73:13-22.
Zeger SL, Liang KY. Longitudinal data analysis for discrete and continuous outcomes.  Biometrics.1986;42:121-130.
Greenland S, Drescher K. Maximum likelihood estimation of the attributable fraction from logistic models.  Biometrics.1993;49:865-872.
Rockhill B, Weinberg CR, Newman B. Population attributable fraction estimation for established breast cancer risk factors: considering the issues of high prevalence and unmodifiability.  Am J Epidemiol.1998;147:826-833.
Efron B, Tibshirani R. An Introduction to the BootstrapNew York, NY: Chapman & Hall; 1993.
Grymonpre RE, Didur CD, Montgomery PR, Sitar DS. Pill count, self-report, and pharmacy claims data to measure medication adherence in the elderly.  Ann Pharmacother.1998;32:749-754.
Inui TS, Carter WB, Pecoraro RE, Pearlman RA, Dohan JJ. Variations in patient compliance with common long-term drugs.  Med Care.1980;18:986-993.
Lau HS, de Boer A, Beuning KS, Porsius A. Validation of pharmacy records in drug exposure assessment.  J Clin Epidemiol.1997;50:619-625.
Lurie N, Ward NB, Shapiro MF, Gallego C, Vaghaiwalla R, Brook RH. Termination of medical benefits: a follow-up study one year later.  N Engl J Med.1986;314:1266-1268.
Simon GE, Grothaus L, Durham ML, VonKorff M, Pabiniak C. Inpact of visit copayments on outpatient mental health utilization by members of a health maintenance organization.  Am J Psychiatry.1996;153:331-338.
Bloom BS, Jacobs J. Cost effects of restricting cost-effective therapy.  Med Care.1985;23:872-879.
Simon GE, VonKorff M, Durham ML. Predictors of outpatient mental health utilization by primary care patients in a health maintenance organization.  Am J Psychiatry.1994;151:908-913.
Bacovsky RA. Federal, Provincial and Territorial Government-Sponsored Drug Plans and Drug Databases. Health Canada 1997; Background information Prepared for the Conference on National Approaches to Pharmacare on January 18-20, 1998 (Saskatoon, Saskatchewan).
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