Defined daily dose

The DDD is the assumed average daily maintenance dose for a drug for its main indication in adults.

Expressed as DDDs per 1000 inhabitants per day, for chronically used drugs

It can be interpreted as the proportion of the population that may receive treatment with a particular medicine on any given day.

For use in hospital settings, the unit is expressed as DDDs per 100 bed-days (adjusted for occupancy rate); it suggests the proportion of inpatients that may receive a DDD.

For medicines that are used for short-term periods, such as antimicrobials, the unit is expressed as DDDs per inhabitant per year; this provides an estimate of the number of days for which each person is treated with a particular medication in a year.

The defined daily dose (DDD) methodology was developed in response to the need to convert and standardize readily available volume data from sales statistics or pharmacy inventory data into medically meaningful units, to make crude estimates of the number of persons exposed to a particular medicine or class of medicines.

The DDD methodology is useful for working with readily available gross drug statistics and is relatively easy and inexpensive to use. However, the DDD methodology should

be used and interpreted with caution. The DDD is not a recommended or a prescribed dose, but a technical unit of comparison; it is usually the result of literature review and available information on use in various countries.

Prescribed daily dose

The prescribed daily dose (PDD) is another unit, developed as a means to validate the DDDs. The PDD is the average daily dose prescribed, as obtained from a representative sample of prescriptions.

Not useful to estimate incidence and prevalence of drug use or to quantify or identify patients who receive doses lower or higher than those considered effective and safe.

DRUG UTILIZATION REVIEW

Drug utilization review (DUR) programs have been defined as “structured, ongoing initiatives that interpret patterns of drug use in relation to predetermined criteria, and attempt to prevent or minimize inappropriate prescribing.”

DUR programs differ from drug utilization studies, which are time-limited investigations that measure drug use, but do not necessarily assess appropriateness or attempt to change practice.

Recently, the use of clinical decision support within computerized prescriber order entry (CPOE) programs has risen dramatically. The use of such programs to improve prescribing can be considered a form of prospective DUR in which prescribers are the targets of interventions

Generally, the DUR process involves comparing actual behavior to explicit, prospectively established standards, referred to as criteria. For example, a commonly used criterion is that patients should not receive more than one non steroidal anti-inflammatory agent at any one time. Criteria have been developed to identify the following types of problems: drug–drug interactions, drug–disease interactions, drug–age interactions, drug–allergy interactions, use of too high or too low a dose, duplication of therapeutic class, excessive duration of therapy, obtaining prescription refills sooner or later than should be needed, failure to prescribe a known effective agent in patients with certain conditions, abuse of psychoactive medications, and use of a more costly agent when a less costly agent is available. After developing criteria, the next step in the DUR process is to measure adherence to explicit criteria by examining individual-level data. Instances in which medication use does not agree with criteria are called exceptions.

Next, interventions are implemented where appropriate, often following an implicit review. Although the general model for DUR does not require that practitioners be made aware of individual exceptions occurring in their patients (that is, interventions can be made based on aggregate rather than individual findings), this step usually involves alerting the physician and/or pharmacy of record as to the occurrence of the specific exception.

There are different settings in which the DUR model is applied.

Outpatient retrospective DUR programs use

computerized administrative data (i.e., pharmacy and medical claims data maintained for billing and other administrative purposes) to identify exceptions that are then reviewed by a physician or pharmacist, or by a committee of health professionals, and result in an intervention (e.g., a mailed alert letter to the physician). The alert letter typically describes the DUR program and the criterion, and provides literature references supporting the criterion and a patient profile demonstrating that the criterion was violated.

Meta Ananlysis

Meta –analysis is a method which can be used to combine the results of two or more studies. The first Meta –analysis performed by karlpearson in 1904

Meta –analysis may conveniently be defined as a quantitative method of pooling information from independent studies concerning a single theme in order to draw conclusion.

Meta –analysis does not simply involve averaging the results of the individual studies, but requires a statistical method which combines the result whilst taking into account the size of the studies. Thus Meta –analysis is the statistical analysis of a large collection of analysis results for the purpose of integrating the findings.

Meta –analysis can provide researchers with single pooled results to answer whether treatment A is more beneficial than treatment B. This pooled result is usually more precise than the result from the individual studies. The precision with each of these studies calculates the treatment effect depends on many factors including the number of people in the study. Generally as the number of people increases in a study the precision of the treatment effect will increase.

Therefore by statistical combining the all the sample size together from the individual studies, the precision of our pooled result for the treatment effect can be improved.

Meta – analysis increases power. By combining the results from the smaller studies using Meta– analysis we can increase the overall power of the analysis. Therefore, the results from a Meta – analysis will usually have more power than the results from the individual studies.

STEPS TO PERFORM META – ANALYSIS

The theoretical  relationship of interest

Collect the population of studies that provide data on the relationship.

Code the studies and compute the effect sizes

Examine the distribution of effect sizes and analyze the impact of moderating variables

Interpret and report the results.

SUMMARY

 Meta – analysis leads to a shift of emphasis from single studies to multiple studies. It is performed with assistance of computer data bases (Microsoft access, paradox) and statistical software (DSTAT, SAS). The most common use of Meta – analysis has been in quantitative literature review. A valid meta – analysis however, require careful planning in the protocol stage as for any other research.

PHARMACOEPIDEMIOLOGIC STUDY DESIGNS

Pharmacoepidemiology

Ø Pharmacoepidemiology applies the methods of epidemiology to the content area of clinical pharmacology.

EPIDEMIOLOGIC STUDY DESIGNS:

CASE REPORTS

Case reports are simply reports of events observed in single patients. As used in Pharmacoepidemiology, a case report describes a single patient who was exposed to a drug and experiences a particular, usually adverse, outcome.

For example, one might see a published case report about a young woman who was taking oral contraceptives and who suffered a pulmonary embolism.

Case reports are useful for raising hypotheses about drug effects, to be tested with more rigorous study designs. However, in a case report one cannot know if the patient reported is either typical of those with the exposure or typical of those with the disease.

Certainly, one cannot usually determine whether the adverse outcome was due to the drug exposure or would have happened anyway. As such, it is very rare that a case report can be used to make a statement about causation.

One exception to this would be when the outcome is so rare and so characteristic of the exposure that one knows that it was likely to be due to the exposure, even if the history of exposure were unclear.

 An example of this is clear cell vaginal adenocarcinoma occurring in young women exposed inutero to diethylstilbestrol. Another exception would be when the disease course is very predictable and the treatment causes a clearly apparent change in this disease course.An example would be the ability of penicillin to cure streptococcal endocarditis, a disease that is nearly uniformly fatal in the absence of treatment. Case reports can be particularly useful to document causation when the treatment causes a change in disease course which is reversible, such that the patient returns to his or her untreated state when the exposure is withdrawn, can be treated again, and when the change returns upon repeat treatment. Consider a patient who is suffering from an overdose of methadone (a long-acting narcotic) and is comatose. If this patient is then treated with naloxone (a narcotic antagonist) and immediately awakens,this would be very suggestive that the drug indeed is efficacious as a narcotic antagonist. As the naloxone wears off the patient would become comatose again, and then if he or she were given another dose of naloxone the patient would awaken again. This, especially if repeated a few times,would represent strong evidence that the drug is indeed

effective as a narcotic antagonist. This type of challenge–re challenge situation is relatively uncommon, however, as physicians generally will avoid exposing a patient to a drug if the patient experienced an adverse reaction to it in the past.

CASE SERIES

Case series are collections of patients, all of whom have a single exposure, whose clinical outcomes are then evaluated and described.

Often they are from a single hospital or medical practice. Alternatively, case series can be collections of patients with a single outcome, looking at their antecedent exposures. For example, one might observe 100 consecutive women under the age of 50 who suffer from a pulmonary embolism, and note that 30 of them had been taking oral contraceptives.

After drug marketing, case series are most useful for two related purposes.

They can be useful for quantifying the incidence of an adverse reaction.

They can be useful for being certain that any particular adverse effect of concern does not occur when observed in a population which is larger than that studied prior to drug marketing.The so-called “Phase IV” post marketing surveillance study of prazosin was conducted for the former reason, to quantitate the incidence of first-dose syncope from prazosin. The“Phase IV” post marketing surveillance study of cimetidine was conducted for the latter reason.

Metiamide was an H-2 blocker, which was withdrawn after marketing outside the US because it caused agranulocytosis. Since cimetidineis chemically related to metiamide there was a concern that cimetidine might also cause agranulocytosis. In both examples, the manufacturer asked its sales representatives to recruit physicians to participate in the study. Each participating physician then enrolled the next series of patients for whom the drug was prescribed.

In this type of study, one can be more certain that the patients are probably typical of those with the exposure or with the disease, depending on the focus of the study.

However, in the absence of a control group, one cannot be certain which features in the description of the patients are unique to the exposure, or outcome. As an example,one might have a case series from a particular hospital of 100 individuals with a certain disease, and note that all were men over the age of 60. This might lead one to conclude that this disease seems to be associated with being a man over the age of 60.

 However, it would be clear that this would be an incorrect conclusion once one noted that the hospital this case series was drawn from was a Veterans Administration hospital, where most patients are men over the age of 60.

 In the previous example of pulmonary embolism and oral contraceptives, 30% of the women with pulmonary embolism had been using oral contraceptives.However, this information is not sufficient to determine whether this is higher, the same as, or even lower than would have been expected. For this reason, case series are also not very useful in determining causation, but provide clinical descriptions of a disease or of patients who receive an exposure.

CASE–CONTROL STUDIES

Case–control studies are studies that compare cases with a disease to controls  without the disease, looking for differences in antecedent exposures.

As an example, one could select cases of young women with venous thromboembolism and compare them to controls without venous thromboembolism, looking for differences in antecedent oral contraceptive use. Several such studies have been performed,generally demonstrating a strong association between the use of oral contraceptives and venous thromboembolism.

Case–control studies can be particularly useful when one wants to study multiple possible causes of a single disease,as one can use the same cases and controls to examine any number of exposures as potential risk factors.

This design is also particularly useful when one is studying a relatively rare disease, as it guarantees a sufficient number of cases with the disease.

 Using case–control studies, one can study rare diseases with markedly smaller sample sizes than those needed for cohort studies.

 For example, the classic study of diethylstilbestrol and clear cell vaginal adenocarcinoma required only 8 cases and 40 controls, rather than the many thousands of exposed subjects that would have been required for a cohort study of this question. Case–control studies generally obtain their information on exposures retrospectively, i.e., by recreating events that happened in the past. Information on past exposure to potential risk factors is generally obtained by abstracting medical records or by administering questionnaires or interviews.

As such, case–control studies are subject to limitations in the validity of retrospectively collected exposure information.In addition, the proper selection of controls can be a challenging task, and inappropriate control selection can lead to a selection bias, which may lead to incorrect conclusions.Nevertheless, when case–control studies are done well, subsequent well-done cohort studies or randomized clinical trials, if any, will generally confirm their results. As such, the case–control design is a very useful approach for pharmacoepidemiology studies.

Cross sectional studies

A cross sectional study measures the prevalence of health outcomes or determinants of health, or both, in a population at a point in time or over a short period.

They are usually done through surveys, chart reviews, or database analyses.

They provide a view of the state of affairs at that time, and provide an estimate of the prevalence of utilization and of outcomes.

Such information can be used to explore aetiology - for example, the relation between cataract and vitamin status has been examined in cross sectional surveys.

These types of studies have been used to compare drug use between countries or regions within a country. Very large differences suggest that reasons for those differences should be investigated and policies examined to determine whether outcomes and costs also differ and whether changes should be made.

However, associations must be interpreted with caution. Bias may arise because of selection into or out of the study population.

 A cross sectional survey of asthma in an occupational group of animal handlers would underestimate risk if the development of respiratory symptoms led people to seek alternative employment and therefore to be excluded from the study. A cross sectional design may also make it difficult to establish what is cause and what is effect.