Drug Surveillance and Effectiveness Epidemiology Research - Questions to Ask your Epidemiologist

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1 Drug Surveillance and Effectiveness Epidemiology Research - Questions to Ask your Epidemiologist Roberta Glass, OptumInsight Life Sciences, Waltham, Massachusetts ABSTRACT As a SAS analyst starting work on a claims-based drug safety or effectiveness study, one of the most valuable skills you can have is the ability to ask the right questions. Many other types of research have clear start and end dates, and easily defined exposures and outcomes, the use of administrative claims data adds an extra layer of complexity. Drug surveillance analytics of administrative data requires an understanding of epidemiology concepts, the characteristics of the drugs under study, and the nature of the medical outcomes that are expected especially how these concepts can be operationalized using administrative data. INTRODUCTION This paper reviews important questions that the SAS analyst should clarify in the absence of sufficiently clear specifications. Questions about exposure identification, outcome definition, and person time calculation often need to be asked in order to understand the needs of the study and produce accurate results. The examples are based on a cohort study design, although many of the concepts apply to other designs. OptumInsight conducts post marketing safety and effectiveness studies using claims data from United Health Group, a private insurer. The questions posed in the paper also apply to administrative data from public health programs such as Medicare and Medicaid. EXPOSURE CLASSIFICATION How do I identify users of the drugs? Exposure definition will depend on how the drug is administered. National Drug Codes (NDC) or hierarchical classifications of NDC codes (e.g., hierarchical ingredient code list [HICL]) identify drugs dispensed at outpatient pharmacies. Drugs are administered by health care professionals in the form of an injection or infusion. These drugs will be identified by procedure codes. Often the first administration of a self-injectable drug will occur in the doctor s office and the claim will include a charge for patient education. For long-term studies, consider how often you should check to see if there are new codes. New NDC codes may be assigned over time as changes to dosage, combinations with other drugs, and modes of administration occur. Is dosage important? Some drugs are used in different dosages to treat different populations; for instance, methotrexate is used in higher doses to treat cancer and in lower doses to treat rheumatoid arthritis. Pharmacies charge insurance companies when they prepare a prescription for pick-up. If the patient never gets the prescription, the drug is returned to stock and the insurance claim is classified as no longer payable. Methods of identifying such claims will depend on how each insurance company handles this reversal of the claim. The claim may not be included in the data, or you may see a claim listing with a code explaining the non-payment, or two claims with one indicating the original charge and another reversing that charge. 1 of 5

2 If the study criteria require that subjects be first time users, determine how far back in the claims history you need to look to make sure the drug has not been previously used. Consider also whether the patient must be free of similar drugs (e.g., drugs in the same class). In many databases, there is turnover in insurance coverage which could reduce the size of your study cohort. For instance, if you require no prior use of the study drug in the past three years and there is 40% turnover in the covered population at two years, only the most stable insured population will qualify for the study, reducing sample size and perhaps generalizability. On the other hand, looking back too short a period will misclassify subjects who took the drug in the past as initiators. Women often stop taking drugs prior to getting pregnant, and then resume the drug after delivery of the baby. Looking back just 6 months would misclassify these women as initiating the drug at the end of the pregnancy. Also note the normal time between doses. Some biologic drugs are taken every 8 to 26 weeks the look back period should allow for dose spacing, and the possibility of a missed or late dose. OUTCOME CLASSIFICATION How do I identify outcomes? Claims data contain diagnosis codes that are attached to the claims for the purpose of justifying payment for a medical service. As a result, sometimes the presence of a particular diagnosis code means that the patient has the disease, and other times the codes is listed because the diagnosis was ruled-out i.e., the provider tested for the disease but found that it was not present. When given a list of diagnosis codes to identify an outcome, it is important to ask how many claims with the same or different codes should be present and how many days apart they can be to accurately differentiate an outcome from a rule=out. You should also ask whether the diagnosis needs to be, or cannot be, on a physician, ER, inpatient or lab claim, and whether the diagnosis must be associated with a particular procedure code. One claim may suffice if the presence of the code nearly always reflects the presence of disease or if a high false positive rate is tolerable. An example of the latter circumstance is if you plan to procure medical records to verify the outcome and do not wish to miss any outcomes. More complex algorithms may be required to lower the false positive rate if charts are not available. Are multiple outcomes of the same type allowed? If multiple outcomes of the same type are allowed, how much time must elapse between two claims with the diagnosis to qualify as 2 distinct episodes? If the diagnosis occurs during a hospital stay, do you count from the date the diagnosis occurred, or from the discharge date when calculating the gap? A common mistake is to find the first diagnosis claim, then search for the next claim that is the required number of days or more later. This method fails to consider that an illness can last for a period of time longer than the gap, and lead one to choose a follow-up visit as a new episode of the disease. A better method is to look for at least the required number of days free of any diagnosis code between episodes. To qualify as two distinct episodes of disease, consider a situation where there must be at least 7 days free of any claims with the diagnosis between two claims. In example 1 there is one episode of disease. Although there are 8 days between the diagnosis (dx) on 2/1 and the diagnosis on 2/9, there is an intervening diagnosis on 2/3. Example 1. 2/ / /9 dx dx dx If there must be 7 or more days free of claims for the diagnosis since the previous claim, in example 2 we have one episode of disease from 2/1 2/3, and a second episode starting 2/13. Example 2. 2/ / /13 2 of 5 dx dx dx

3 Example 3 shows an inpatient stay from 2/1 (IP Start) to 2/9 (IP End) with the diagnosis of interest on 2/3. Is this one episode of the disease (9 days free of disease between 2/3 and 2/13) or one episode ending with the hospital discharge on 2/9 and a follow-up visit 4 days later? The answer will depend on the condition under study. Example 3. IP Start IP End 2/ / / /13 dx dx CALCULATING PERSON TIME: When does follow-up person-time start? Calculating person-time seems like a simple task: take the end date of the person s time in the study and subtract the date that the drug was initiated. If you add one, then both the start date and the end date are included in the person-time. If you do not add one, then you are effectively excluding either the start or the end date. Cohort entry Outcome 2/1/ /15/2011 Person time = 15FEB2011 D 01FEB2011 D = 14 Person time = 15FEB2011 D 01FEB2011 D + 1 = 15 cohort entry date is excluded from follow-up follow-up starts on cohort entry date To make the decision about adding one, ask whether the date of drug initiation is to be included in baseline or follow-up. If immediate outcomes like allergic reactions are under examination, then the date of drug initiation must be included in follow-up. If you are testing the hypothesis that a drug used to treat cardiovascular events (CVE) decreases the incidence of future CVE, you would not include the initiation date in follow-up, because the drug may have been initiated as the result of a CVE, which you would not wish to count as an outcome. The claims for the date of initiation contain important information on the reason the doctor chose this particular drug, thus the epidemiologist may wish to include that day in the baseline period if the baseline characteristics are used to match comparator groups to the treated cohort. Thus you may need to search for one outcome on the date the drug was started, but search for other outcomes starting the next day, and produce two different measures of person time. When working with vaccines, an important question to ask is Does the vaccine take effect immediately? Vaccines may have immediate side effects, but immunity takes time to develop. So person-time for safety analysis would start on the date of the vaccine, while person time for an effectiveness study might start a week or two after the first dose. Vaccines are also expected to provide immunity for different periods of time. An influenza vaccine provides protection for one flu season while a full course of the polio vaccine is expected to provide many years of immunity. It is important to ask about the possibility of excluding event-depleted or event-rich person-time through your choice of exposure measure. A study that excludes or misclassify these types of person-time will misestimate the occurrence of adverse reactions. For example, a person who reacts poorly to the first dose of a vaccine is less likely to receive the subsequent doses, and a person who dies after the first dose is guaranteed not to be in the cohort that is defined according to vaccine course completion. If there is good reason to require a full course to qualify for cohort entry, be sure to ask whether person-time starts at the first or last dose. 3 of 5

4 When does person-time end? In a clinical trial, person-time usually ends at the earliest of the end of the study, occurrence of an outcome, withdrawal from the trial, or death. Claims-based studies are also censored by the loss of full insurance coverage. If your study requires both medical and prescription coverage, loss of either component of the coverage should censor a subject s person-time. If multiple outcomes of the same type are allowed, be sure to ask how person-time should be calculated for that outcome. If you have more than one type of outcome, you may need separate person time variables for each outcome, each censored by its own outcome date. Are there other censoring events? Studies involving children often limit follow-up by age. For instance, a study of infant vaccines might limit follow-up to the first year of life. It is always a good idea to ask whether the birthday is to be included in the follow-up period. Particularly with young children, a difference of one day can have a noticeable impact on the total amount of person-time. In some studies, the end of drug treatment will also censor person time. If a subject stops taking the drug, how long does it take for the drug to wash out of his body? The censor date for discontinued drugs may be defined as the date of last dispensing plus the days covered by the dispensing plus a wash-out period. For some drugs the wash-out period may be a few days, while for other drugs it could be several months. On the other hand, follow-up should continue if the outcome is a condition that takes many years to develop (e.g., cancer). Person-time can also be characterized into current use, recent use, and past use according to time since last dispensing or administration. For instance if the cohort entry date of January 1, 2010 is in the baseline period, the study ends on June 30, 2011, current use ended on March 3, 2010, and the Recent use ended on June 30, 2010, you would calculate person-time as follows (note that the current use start date is one day after cohort entry). Person Time Category Start Date End Date Person days Current 02JAN MAR2010 current_pt = 03MAR2010 D 02JAN2010 D + 1; 61 Recent 04MAR JUN2010 recent_pt = 04MAR2010 D 03JUN2010 D + 1; 92 Past 04JUN JUN2011 past_pt = 04MAR2010 D 03JUN2010 D + 1; 392 Total 01JAN JUN2011 total_pt = 30JUN2011 D 02JAN2010 D +1; 545 When creating such categories, test to see that the sum of person-time in the subgroups equals the total person time. It is easy to accidently exclude a day between sub categories, or double count the transition day between categories. Is the follow-up period a disease season? Effectiveness analyses may restrict follow-up to a seasonal period, for instance the influenza season. In this type of analysis it is important to ask how long before the start of the season the dose can occur, how long it takes for immunity to develop, and whether a person will be included in multiple seasons. For instance, a child might be immunized against rotavirus in March 2010 and followed to the end of the December 2009-March 2010 season, and also for the December 2010-March 2011 season. In contrast, the influenza vaccine s limited effect on future strains means that follow-up in a surveillance study of that product will generally be limited to one season. 4 of 5

5 CONCLUSION Claims-based epidemiology studies are complex and can drive an analyst to distraction trying to get all the details straight. Every study is different, combining different statistical methods, drugs with different modes of administration and different effects, and multiple medical outcomes that occur at different points in the study. Analysts must develop the habit of asking many questions, and the skill and experience to know which questions to ask. This paper presented important questions about cohort identification, outcome definition, and person-time calculation that commonly need to be answered in order to understand the needs of the study and produce accurate results. ACKNOWLEDGMENTS David Dore and Eva Ng of OptumInsight provided valuable feedback and edits. CONTACT INFORMATION Your comments and questions are valued and encouraged. Contact the author at: or by mail: Roberta Glass, OptumInsight, 950 Winter St., Waltham, MA SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. indicates USA registration. 5 of 5