Exposure misclassification as a result of free sample drug utilization in automated claims databases and its effect on a pharmacoepidemiology study of selective COX-2 inhibitors

Nondifferential Treatment Misclassification Biases Toward the Null? Not a Safe Bet for Active Comparator Studies

Active comparator studies are increasingly common, particularly in pharmacoepidemiology. In such studies, the parameter of interest is a contrast (difference or ratio) in the outcome risks between the treatment of interest and the selected active comparator. While it may appear treatment is dichotomous, treatment is actually polytomous as there are at least 3 levels: no treatment, the treatment of interest, and the active comparator. Because misclassification may occur between any of these groups, independent nondifferential treatment misclassification may not be toward the null (as expected with a dichotomous treatment). In this work, we describe bias from independent nondifferential treatment misclassification in active comparator studies with a focus on misclassification that occurs between each active treatment and no treatment. We derive equations for bias in the estimated outcome risks, risk difference, and risk ratio, and we provide bias correction equations that produce unbiased estimates, in expectation. Using data obtained from US insurance claims data, we present a hypothetical comparative safety study of antibiotic treatment to illustrate factors that influence bias and provide an example probabilistic bias analysis using our derived bias correction equations.

Use of prescription drug samples in the US and implications for pharmacoepidemiologic research: a systematic search of the literature

INTRODUCTION

Free drug samples are not captured in the pharmacy claims databases used in many pharmacoepidemiologic studies, which could lead to misclassification of drug exposure status and thus bias study results.

AREAS COVERED

We systematically searched the literature in PubMed/MEDLINE, Embase, and Scopus from database inception to August 2020 for studies assessing the magnitude of exposure misclassification in pharmacy claims data associated with uncaptured drug sample utilization. Our review identified five US-based studies with substantially different characteristics, contexts, methods, and results. Taken together, these studies suggest that the risk of sample-related bias may be higher for (1) studies of newly approved, patented brand-only drugs in specific classes and contexts; (2) studies of populations where sample use is common and the unexposed cohort is small; and (3) studies where the outcomes of interest are expected to be early-onset or acute, with non-constant hazards.

EXPERT OPINION

In light of declining overall trends in sample use, future research on sample-related exposure misclassification should focus on delineating bias across those modern contexts where sample use remains high and optimizing bias quantification methods to create a more standardized approach. Additionally, further assessment is warranted for other sources of misclassified exposure status in claims-based pharmacoepidemiology research.

Low-Cost Generic Program Use by Medicare Beneficiaries: Implications for Medication Exposure Misclassification in Administrative Claims Data

BACKGROUND

Administrative claims data are used for a wide variety of research and quality assurance purposes; however, they are prone to medication exposure misclassification if medications are purchased without using an insurance benefit. Low-cost generic drug programs (LCGPs) offered at major chain pharmacies are a relatively new and sparsely investigated source of exposure misclassification. LCGP medications are often purchased out of pocket; thus, a pharmacy claim may never be submitted, and the exposure may go unobserved in claims data. As heavy users of medications, Medicare beneficiaries have much to gain from the affordable medications offered through LCGPs. This use may put them at increased risk of exposure misclassification in claims data. Many high-risk medications (HRMs) and medications tracked for adherence and utilization quality metrics are available through LCGPs, and exposure misclassification of these medications may impact the quality assurance efforts reliant on administrative claims data. Presently, there is little information regarding the use of these programs among a geriatric population.

OBJECTIVES

To (a) quantify the prevalence of LCGP users in a nationally representative population of Medicare beneficiaries; (b) compare clinical and demographic characteristics of LCGP users and nonusers; (c) assess determinants of LCGP use and medications acquired through these programs; and (d) analyze patterns of LCGP use during the years 2007-2012.

METHODS

This study relied on data from the Medical Expenditure Panel Survey (MEPS) from 2007 to 2012. The first 3 objectives were completed with a cohort of individuals in the most recent MEPS panel, while the fourth objective was completed with a separate cohort composed of individuals who participated in MEPS from 2007 to 2012. Inclusion in either study cohort required that individuals were Medicare beneficiaries aged 65 years or greater, used at least 1 prescription drug during their 2-year panel period, and participated in all 5 rounds of data collection during their panel period. MEPS captures medication utilization by surveying individuals on current and previous medication use and verifies this information at the pharmacy level, so prescription fills can be observed irrespective of payment by an insurer or a filed claim. Pharmaceutical utilization was assessed at the individual level for each year of the study period, and LCGP use was recorded as a binary variable for each individual. An LCGP medication fill was identified if the total cost of the drug was paid out of pocket and matched the cost of medications listed on LCGP formularies available from major pharmacy retailers during these years. Cohort demographics and characteristics of interest included age, gender, race, employment status, marital status, family income level, education level, residence in a metropolitan statistical area, geographic region, prescription drug coverage, Medicare type, comorbidities, number of unique medications used, and number of medication fills. Comparisons were made between users and nonusers using chi-square and t-tests. Multivariable logistic regression was used to identify factors associated with LCGP use.

RESULTS

From the most recent MEPS panel, 1,861 individuals were included in the study cohort, of which 53.5% were observed to be LCGP users. The 995 LCGP users in this cohort represented over 20 million Medicare beneficiaries who used LCGPs from 2011 to 2012. Significant differences between LCGP users and nonusers existed in terms of race, educational attainment, comorbidity burden, type of Medicare insurance, number of unique medications used, and number of medication fills. Each additional unique medication filled increased the odds of LCGP use by 12% (95% CI = 1.09-1.14). Individuals with insurance in addition to Medicare (i.e., Tricare/Veteran's Affairs or Medicaid) had less than half the odds of using LCGPs compared with those with Medicare or Medicare managed care insurance coverage only. The proportion of LCGP users and the proportion of LCGP fills out of all medications available through LCGPs increased from 2007 to 2012.

CONCLUSIONS

There is a high rate of LCGP use among Medicare beneficiaries aged 65 years or greater. Claims-based research and quality assurance programs focusing on the benefits and harms of medications available through these programs are at risk of underestimating the true medication exposure in this population and should account for this possibility in sensitivity analyses. Managed care organizations should incentivize the reporting of LCGP medication use or make adjustments to generic medication benefit structures to more effectively capture true medication exposure.

DISCLOSURES

No direct sources of funding were used to conduct this study. Data acquisition was supported by the University of Kentucky Center for Clinical and Translational Science through funding from NIH NCATS grant #UL1TR000117. Brown is the Humana-Pfizer Research Fellow at the Institute for Pharmaceutical Outcomes & Policy at the University of Kentucky College of Pharmacy and is provided salary from these corporations. However, neither company provided any direct funding for the current study nor provided any input or guidance for the design, methods, or drafting of the manuscript. Pauly has no financial disclosures or conflicts of interest. Portions of these results were presented at the 20th International Society for Pharmacoeconomics and Outcomes Research International Meeting; May 16-20, 2015; Philadelphia, Pennsylvania. Study concept and design were primarily contributed by Brown, along with the other authors. Brown took the lead in data collection and interpretation, along with Pauly and Talbert. All authors participated in the writing and revision of the manuscript.

Use of Prescription Drug Samples in the USA: A Descriptive Study with Considerations for Pharmacoepidemiology

INTRODUCTION

Free prescription drug samples provided in physician offices can lead to exposure misclassification in pharmacoepidemiologic studies that rely on pharmacy claims data.

METHODS

We quantified drug-specific sample provision rates based on nationally projected data from a survey of over 3200 US office-based physicians for 1993-2013.

RESULTS

Between 2009 and 2013, a total of 44.7 % of newly initiated brand-only sitagliptin but only 3.6 % of generically available metformin therapy was provided as samples. We observed similar discrepancies between newly initiated rosuvastatin and simvastatin, dabigatran and warfarin, atomoxetine and methylphenidate, and between oral antibiotic drugs. During continued therapy, sample use was still present though to a lesser extent (sitagliptin 17.0 %, rosuvastatin 23.9 %), and remained high for some oral contraceptives (norethindrone 55.8 %). Oral contraceptives had the longest average days of sample supply (levonorgestrel, continued use 85.1 days). The average days of supply for all other chronically used study drugs ranged from 13.4 (dabigatran, new use) to 25.3 (exenatide, continued use) per sample provided. From 1993 to 2013, we found pronounced drops in sample provisions over time coinciding with more recent generic approval dates.

CONCLUSIONS

We observed markedly differential exposure to medication samples between branded and generic drugs. This can introduce bias in pharmacoepidemiologic studies, especially when adverse events that occur soon after drug initiation are of interest.

Prevalence of Low-Cost Generic Program Use in a Nationally Representative Cohort of Privately Insured Adults

BACKGROUND

Administrative claims data are used for a wide variety of research and quality assurance purposes. Despite their utility, they are prone to medication exposure misclassification if medications are purchased without utilizing an insurance benefit. Low-cost generic programs (LCGPs) offered at major chain pharmacies are a relatively new and sparsely investigated source of exposure misclassification. Since they were implemented in 2006, LCGPs are now available at 8 of the 10 largest pharmacy chains and include a wide variety of medication classes. LCGP medications are often purchased out of pocket; thus, a pharmacy claim may never be submitted and exposure may go unobserved in claims data. There are little data regarding the utilization of these programs, and estimates of their use can provide important insights into the potential impact LCGPs may have on exposure misclassification in claims data.

OBJECTIVES

To (a) quantify the prevalence of LCGP users in a privately insured adult population, (b) assess patterns of LCGP use, and (c) compare clinical and demographic characteristics associated with LCGP users and nonusers.

METHODS

The study cohort consisted of 19,037 privately insured adults aged 18-64 who participated in the Medical Expenditure Panel Survey (MEPS) from 2007-2011. MEPS captures medication utilization at the pharmacy level, so prescription fills can be observed irrespective of a claim being filed. Pharmaceutical utilization was assessed at the individual level for each year of the study period, and LCGP use was recorded as a binary variable for each individual. An LCGP medication fill was identified if the total cost of the drug was paid out of pocket and matched the cost of medications listed on LCGP formularies available from Target, Walmart, CVS, or other major pharmacy retailers during these years. Cohort demographics and characteristics of interest included age, gender, race, employment status, marital status, family income, education level, residence in a metropolitan statistical area (MSA), prescription drug coverage, geographic region, comorbidities, and number of unique medications and medication fills. Comparisons were made between users and nonusers using chi-square and t-tests. Multivariable logistic regression was used to identify factors associated with LCGP use.

RESULTS

Out of the entire study cohort (N = 19,037), 6,921 (36.4%) individuals were identified as LCGP users, representing 34 million LCGP users annually. Users tended to be older, had higher Charlson Comorbidity Index scores, filled more prescriptions per person, and used more unique medications. Proportions of LCGP users and uses nearly doubled from 2007-2011, while total prescription utilization per person remained relatively stable. Over 10% of all prescription fills were filled through LCGPs. Of all LCGP fills, approximately 42% were for cardiovascular medications, 12% for antidiabetics, and 14% for levothyroxine. Greater than 30% of fills for antigout, metronidazole, angiotensin-converting enzyme inhibitors, levothyroxine, metformin, and diuretics were obtained through LCGPs, as were 18.9% of all warfarin fills. Compared with the reference category aged 18-34, adults aged 35-54 had an adjusted odds ratio (AOR) of being an LCGP user of 1.39 (95% CI = 1.29-1.50) and adults aged 55-64 had an AOR of 1.86 (95% CI = 1.70-2.04). Additionally, those with prescription drug coverage were nearly twice as likely to be LCGP users (AOR = 1.96; 95% CI = 1.64-2.35) compared with those without. Gender, income, comorbidity burden, region, year of panel entry, and number of unique medications also significantly predicted LCGP use.

CONCLUSIONS

There is a high rate of LCGP use in the privately insured adult population. Users of LCGPs tend to be older, have more chronic comorbidities, and use more medications than nonusers. Claims-based research and quality assurance programs focusing on the benefits and harms of medications available through these programs are at risk of greatly underestimating the true medication exposure in this population and should account for this in sensitivity analyses. Managed care organizations should incentivize the reporting of LCGP medication use or make adjustments to generic medication benefit structures to more effectively capture true medication exposure.

The Prevalence and Predictors of Low-Cost Generic Program Use in the Pediatric Population

Background

Low-cost generic drug programs (LCGPs) increase the accessibility and affordability in the USA of prescription medication that can treat many common pediatric conditions. No studies have assessed the prevalence and predictors of LCGP use in the pediatric population, analyzed trends in LCGP use since their implementation, or analyzed which medications are most commonly purchased for children through LCGPs.

Objectives

Our objective was to determine the prevalence of LCGP use in the USA during the period 2007–2012 and to assess predictors of LCGP use in a nationally representative sample of children and adolescents.

Methods

We used cross-sectional data from the 2007–2012 Medical Expenditure Panel Survey (MEPS) and classified each prescription fill as an LCGP or non-LCGP fill. We assessed the proportions of LCGP fills and LCGP users each year from 2007 to 2012 and compared users and non-users during the latest available study cohort (2011–2012) using chi-squared and t-tests for users. We used multivariable logistic regression to identify factors associated with LCGP use in the most recent MEPS panel.

Results

Of 2754 children meeting all inclusion criteria, 23.7 % were classified as LCGP users, representing over 10 million adolescent LCGP users over the 2011–2012 period. LCGP users were significantly more likely to be female, privately insured, White, residing in urban areas, lacking prescription drug coverage, and in a higher income bracket than non-users. Significant predictors of LCGP use included age, prescription drug coverage, insurance type, race, region of residence, and number of unique medications used.

Conclusions

\While one in four children use LCGPs, certain subgroups that may benefit the most from the programs are using them at a lower rate, and use of these programs has important effects on medication utilization quality assurance and research.

Restrictive reimbursement policies: bias implications for claims-based drug safety studies

Restrictive reimbursement policies-including those based on non-formulary drug status and prior authorizations-can create situations in which patients' use of prescription medications is not fully captured in administrative claims data. This can create bias in drug safety studies that depend solely on these data. An analysis in two Canadian provinces found that primary administrative databases captured only 61 % of dispensations of drugs for which restrictive reimbursement policies were in place. A subsequent simulation study found that, in certain circumstances bias due to exposure misclassification resulting from restrictive reimbursement policies can be quite large in analyses comparing outcomes between drug exposure groups. Investigators need to be knowledgeable about the data they analyze and know whether restrictive reimbursement policies are in place that might affect the capture of drugs of interest. It is also critical to understand the mechanisms by which restrictive reimbursement might cause bias in claims-based drug safety studies, the direction and magnitude of the potential bias, and strategies that could be used to mitigate such bias.

Limited impact of drug exposure misclassification from non-benefit thiazolidinedione drug use on mortality and hospitalizations from Saskatchewan, Canada: a cohort study

PURPOSE

Our purpose was to measure the effect of non-benefit drug use on observed associations between exposure and outcome, thereby documenting an empirical example of the potential magnitude of biases introduced when exposure status is misclassified from a restrictive drug coverage policy.

METHODS

New users of antidiabetic agents were identified with a 1-year washout period between January 1, 1995, and December 31, 2005, in Saskatchewan, Canada, and were followed until December 31, 2008. Within this population-based cohort, persons were classified as users of benefit or non-benefit thiazolidinediones (TZDs) according to their first prescription record between January 1, 2006, and December 31, 2006 (non-benefit prescription records were not captured before 2006). An intention-to-treat approach was used to categorize TZD exposure over time. We evaluated the potential bias introduced by drug exposure misclassification by evaluating bootstrapped differences in hazard ratio (HR) estimates of all-cause hospitalization or death between users and nonusers of TZDs obtained from analyses that contained complete drug use (non-benefit and benefit drug use) versus benefit drug use only (non-benefit drug use was misclassified as unexposed). All analyses were replicated within the same cohort of new users of antidiabetic agents for clopidogrel and β-blocker (bisoprolol or carvedilol) users versus nonusers because these agents were also subject to exposure misclassification from non-benefit drug use during the period of the study.

FINDINGS

Among 27,333 new users of antidiabetic agents, we identified 5759 TZD users (28% non-benefit) and 21,574 nonusers of TZDs. The crude HR for hospitalization or death among TZD users versus nonusers was higher in a database that contained benefit-only prescriptions than in a database that contained all prescriptions (HR = 1.11 [95% CI, 1.05-1.18] vs HR = 0.99 [95% CI, 0.94-1.04]). However, the differences in HRs after adjustment for demographic characteristics, health care utilization, comorbidities, and medications suggested minimal bias was introduced when TZD exposure was misclassified in the benefit-only database (adjusted HR [aHR] = 1.04 [95% CI. 0.98-1.10] vs aHR = 0.99 [95% CI, 0.94-1.04]; bootstrapped aHR difference = +0.05 [95% CI, 0.02-0.08]). Minimal differences in aHRs were also observed within analyses of clopidogrel (1551 users [24% non-benefit]; bootstrapped aHR difference = +0.01 [95% CI, -0.04 to 0.06]) and β-blocker users (351 users [42% non-benefit]; bootstrapped aHR difference = +0.06 [95% CI, -0.09 to 0.20]) versus nonusers.

IMPLICATIONS

Although patient characteristics and outcomes differed between users of non-benefit and benefit drugs, misclassification of drug exposure did not meaningfully bias estimates of all-cause mortality and hospitalization after covariate adjustment in our study.

Evidence of sample use among new users of statins: implications for pharmacoepidemiology

BACKGROUND

Epidemiologic studies of prescription medications increasingly rely on large administrative health care databases. These data do not capture patients' use of medication samples. This could potentially bias studies of short-term effects where date of initiation may be inaccurate.

OBJECTIVES

To assess the extent of sample use among patients initiating statin therapy.

RESEARCH DESIGN

Retrospective cohort of patients who filled a first prescription for a statin after at least 6 months of statin-free period in 2007-2010. Low-density lipoprotein (LDL) values obtained within the 15 days preceding the first prescription were analyzed using a 2-component Gaussian mixture model to look for evidence of prior treatment.

SUBJECTS

A total of 26,033 statin initiators with at least 1 LDL laboratory result within the 15 days preceding the prescription fill.

MEASURES

Estimators for the proportion of patients filling a new prescription already on treatment.

RESULTS

Among 9256 patients filling a branded statin, LDL distribution was bimodal, consisting of 2 Gaussian distributions: one, which made up 13.4% of the total population, had much lower LDL values (mean=71.8 mg/dL) compared with the second (mean=148.0 mg/dL), suggesting drug use before first dispensed prescription. Among 16,777 patients filling a generic statin, LDL levels were substantially higher with no evidence of bimodality that would suggest prior sample use.

CONCLUSIONS

These results provide indirect evidence that the initial period of branded medication use may often be missed when using pharmacy claims data to define drug initiation. Further research is needed to examine approaches to better identify incident medication use when assessing short-term effects.

Evaluating the introduction of a computerized prior-authorization system on the completeness of drug exposure data

PURPOSE

Administrative databases that only capture records for benefit-approved prescriptions may underestimate exposure because they do not capture non-benefit prescriptions. Using a natural experiment, we illustrate the impact of automating a prior-authorization policy on the completeness of drug exposure.

METHODS

Using Saskatchewan (Canada) databases, weekly counts of benefit-approved and total prescription records in 2006 for new users of antidiabetic agents were examined across four categories: thiazolidinediones (TZDs), metformin, glyburide, and insulin. On July 1, 2006, Saskatchewan's public drug plan implemented an automated, online-adjudicated, prior-authorization process for TZDs; previously, prior approval was paper based. No such policy changes occurred for other drugs. We estimated the effect of this policy change on drug exposure using interrupted time-series analyses.

RESULTS

We examined 223 552 prescription records: 19% were for TZDs, 48% for metformin, 20% for glyburide, and 13% for insulin. Prior to automation, there were, on average, 571 benefit-approved TZD records per week; however, the number of benefit-approved TZD records increased immediately after the automated process was introduced by 240 prescriptions per week (95% CI 200-280, p < 0.001). The average proportion of TZD benefit-approved records was 73% before and increased to 93% immediately following policy change (20% absolute change, 95% CI 18.7-20.4%). No changes were observed for metformin, glyburide, or insulin (p > 0.1 for all).

CONCLUSIONS

Automating prior authorization for TZDs immediately increased the proportion of captured TZD records, suggesting in our study that one-fifth of TZD exposure was previously misclassified. If replicable, this indicates that even subtle changes in reimbursement policy may affect the validity of drug exposure data.

The incident user design in comparative effectiveness research

Comparative effectiveness research includes cohort studies and registries of interventions. When investigators design such studies, how important is it to follow patients from the day they initiated treatment with the study interventions? Our article considers this question and related issues to start a dialogue on the value of the incident user design in comparative effectiveness research. By incident user design, we mean a study that sets the cohort's inception date according to patients' new use of an intervention. In contrast, most epidemiologic studies enroll patients who were currently or recently using an intervention when follow-up began. We take the incident user design as a reasonable default strategy because it reduces biases that can impact non-randomized studies, especially when investigators use healthcare databases. We review case studies where investigators have explored the consequences of designing a cohort study by restricting to incident users, but most of the discussion has been informed by expert opinion, not by systematic evidence.

Restrictive drug coverage policies can induce substantial drug exposure misclassification in pharmacoepidemiologic studies

BACKGROUND

Drugs reimbursed through a single-party payer such as health maintenance organizations or provincial governments are generally captured in administrative data if they have full-benefit status on that payer's formulary. However, drugs subject to restrictive drug coverage policies are often not fully captured if patients receive these drugs through mechanisms other than the single-payer formulary.

OBJECTIVE

The goal of this study was to estimate the association between restrictive drug coverage and drug exposure misclassification across the Canadian provinces of Manitoba and Saskatchewan, which provide universal coverage for formulary-approved drugs to all citizens regardless of age or socioeconomic status.

METHODS

Monthly dispensations were compared for 75 drugs between 2005 and 2008 from Canada's National Prescription Drug Utilization System database, which captures provincial drug formulary claims only, versus the IMS Brogan CompuScript Database, which captures all drug dispensations irrespective of formulary status. The association between restrictive drug coverage and drug exposure misclassification was measured using generalized estimating equations and multivariable adjustment.

RESULTS

On average, 84% of monthly retail drug dispensations were captured by provincial claims data: 100% of monthly dispensations were captured for drugs with full-benefit status but only 61% of dispensations for drugs with restrictive drug coverage (adjusted risk ratio = 0.65 [95% confidence interval, 0.56-0.75]). The direction and magnitude of the potential misclassification bias between full-benefit and restricted policy drugs were consistent across all drug classes examined: acid-reducing drugs (97% vs 66%), analgesics (89% vs 64%), central nervous system drugs (103% vs 61%), cardiovascular drugs (100% vs 57%), diabetes drugs (98% vs 61%), osteoporosis drugs (96% vs 57%), and respiratory drugs (112% vs 60%).

CONCLUSIONS

Drugs subject to restrictive coverage policies are substantially under-captured in administrative databases, leading to potential drug exposure misclassification in pharmacoepidemiologic studies relying on administrative databases. Pharmacoepidemiologic studies should clearly describe whether evaluated drugs are available as full benefits or subject to restrictive coverage policies and the potential impact on their results.

Quantifying the impact of drug exposure misclassification due to restrictive drug coverage in administrative databases: a simulation cohort study

OBJECTIVE

Drug exposure misclassification may occur in administrative databases when individuals obtain nonreimbursed drugs by paying "out-of-pocket" or via alternative drug coverage plans. We examined the apparent association between oral antidiabetic therapy and mortality by simulating the effects of restrictive drug coverage policies.

METHODS

Population-based cohort study of 12,272 new patients using oral antidiabetic agents were identified using the administrative databases of Saskatchewan Health, 1991 to 1996. We randomly misclassified 0% [base case], 10%, 25%, and 50% of known patients taking metformin according to either overt drug exposure (e.g., metformin users switched to nonusers) or time of metformin initiation (e.g., delayed capture of exposure); thereby simulating the use of a "non-formulary" or "special authorization" policy, respectively. We also simulated an age-dependent coverage policy, mimicking a policy restricted to seniors.

RESULTS

Metformin use was associated with lower mortality compared with sulfonylurea use in the base case (adjusted hazard ratio [aHR] 0.88, 95% confidence interval [CI] 0.78-0.99) and the nonformulary simulations. The special authorization simulations demonstrated, however, an increasing relative mortality hazard of metformin versus sulfonylurea exposure: aHR 0.96, 95% CI 0.96-0.97 and aHR 1.34, 95% CI 1.31-1.37, for 10% and 50% delays in coverage capture respectively when 50% of metformin users were misclassified. Age-dependent drug coverage had a variable impact on mortality risk compared with the base-case cohort; however, a new-user simulation with a 1-year washout revealed consistent results to the base-case analysis.

CONCLUSION

Restrictive drug coverage policies may result in substantial drug exposure misclassification, potentially severely biasing the results of drug-outcome relationships using administrative databases.

A review of uses of health care utilization databases for epidemiologic research on therapeutics

OBJECTIVE

Large health care utilization databases are frequently used in variety of settings to study the use and outcomes of therapeutics. Their size allows the study of infrequent events, their representativeness of routine clinical care makes it possible to study real-world effectiveness and utilization patterns, and their availability at relatively low cost without long delays makes them accessible to many researchers. However, concerns about database studies include data validity, lack of detailed clinical information, and a limited ability to control confounding.

STUDY DESIGN AND SETTING

We consider the strengths, limitations, and appropriate applications of health care utilization databases in epidemiology and health services research, with particular reference to the study of medications.

CONCLUSION

Progress has been made on many methodologic issues related to the use of health care utilization databases in recent years, but important areas persist and merit scrutiny.