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Comparison of adherence measures using claims data in the South African private health sector

Adherence to medication is the degree to which the drug-taking behaviours of patients agree with recommendations by the prescriber. [1] The measurement of adherence is becoming increasingly important, as it is critical to the success of pharmacotherapy.[2-5] There are several methods of computing adherence, and these include, inter alia, the use of biological assays and markers, directly observed therapy, self-reports, pill counts, use of surveys, questionnaires and electronic medication packaging.[2,4,6-11] These methods, although beneficial, may be limited by cost,[6,10,11] ethical concerns[11] and self-reported bias.[6,7,10] Administrative claims data offer an inexpensive, efficient and noninvasive means by which adherence can be measured.[2,4,9] In addition, administrative claims databases provide access to large populations for study under real clinical practice situations, and in a timely and effective manner.[12-15] Compared with other sources of data for health research, data obtained from such databases are less susceptible to recall and interviewer bias and can be linked to other databases,[14] such as medical records databases, to facilitate the determination of adherence. However, the use of administrative databases for measuring adherence has some inherent disadvantages. Administrative datasets estimate adherence based on medication possession and not consumption,[4] since they are unable to determine patients’ intake of prescribed and dispensed medications.[2] Grégoire and Moisan[1] add that dispensing data depend on conditions of reimbursement and will therefore not measure adherence to medications purchased over the counter and those not covered by a healthcare scheme. Adherence determined solely by claims data may mask periods of overand under-utilisation of medications.[11] Successful use of administrative claims data for estimating adherence requires that all relevant information is recorded accurately, and patients are eligible for the medication of interest during the period of study to allow for valid conclusions to be made.[9] Although several measures have been proposed to estimate adherence using medicines claims data and validated using other methods such as patient reports and pill counts, there are no specifications for their mathematical calculation.[16] With the wide range of adherence measures available, researchers are often faced with the decision of choosing which is appropriate.[16,17] Some adherence measures have been identified to be mathematically equivalent, yielding similar adherence values.[16] Hess et al.[4] suggest that it may not be necessary to have a variety of measures currently employed to assess adherence when administrative claims are used. An assessment of the various methods is therefore important for better understanding and to facilitate future adherence studies using administrative data. This open-access article is distributed under Creative Commons licence CC-BY-NC 4.0.


RESEARCH
Adherence to medication is the degree to which the drug-taking behaviours of patients agree with recommendations by the prescriber. [1] The measurement of adherence is becoming increasingly important, as it is critical to the success of pharmacotherapy. [2][3][4][5] There are several methods of computing adherence, and these include, inter alia, the use of biological assays and markers, directly observed therapy, self-reports, pill counts, use of surveys, questionnaires and electronic medication packaging. [2,4,[6][7][8][9][10][11] These methods, although beneficial, may be limited by cost, [6,10,11] ethical concerns [11] and self-reported bias. [6,7,10] Administrative claims data offer an inexpensive, efficient and noninvasive means by which adherence can be measured. [2,4,9] In addition, administrative claims databases provide access to large populations for study under real clinical practice situations, and in a timely and effective manner. [12][13][14][15] Compared with other sources of data for health research, data obtained from such databases are less susceptible to recall and interviewer bias and can be linked to other databases, [14] such as medical records databases, to facilitate the determination of adherence. However, the use of administrative databases for measuring adherence has some inherent disadvantages. Administrative datasets estimate adherence based on medication possession and not consumption, [4] since they are unable to determine patients' intake of prescribed and dispensed medications. [2] Grégoire and Moisan [1] add that dispensing data depend on conditions of reimbursement and will therefore not measure adherence to medications purchased over the counter and those not covered by a healthcare scheme. Adherence determined solely by claims data may mask periods of over-and under-utilisation of medications. [11] Successful use of administrative claims data for estimating adherence requires that all relevant information is recorded accurately, and patients are eligible for the medication of interest during the period of study to allow for valid conclusions to be made. [9] Although several measures have been proposed to estimate adherence using medicines claims data and validated using other methods such as patient reports and pill counts, there are no specifications for their mathematical calculation. [16] With the wide range of adherence measures available, researchers are often faced with the decision of choosing which is appropriate. [16,17] Some adherence measures have been identified to be mathematically equivalent, yielding similar adherence values. [16] Hess et al. [4] suggest that it may not be necessary to have a variety of measures currently employed to assess adherence when administrative claims are used. An assessment of the various methods is therefore important for better understanding and to facilitate future adherence studies using administrative data.

Objectives
To the best of our knowledge, no study has been published on the use of secondary data for the comparison of different adherence measures in South Africa (SA). This study aimed to use claims data from a medicines claims database in SA to illustrate the comparison of different adherence measures. Montelukast was employed for the purpose of illustration.

Study design
We performed a quantitative cross-sectional study analysing medicines claims data that are nationally representative for a 10-year period (1 January 2006 -31 December 2015).

Data source and study population
We employed nationally representative medicines claims data obtained from a privately owned SA pharmaceutical benefit management (PBM) company. This PBM is a large independent company that has been providing medicine claims processing services to about 1.6 million beneficiaries of about 42 medical schemes in South Africa for over 25 years. The data obtained represent about one-third of the SA patients registered with private medical aid schemes. [18] We employed information on prescribed medications dispensed to patients, including the active pharmaceutical ingredient, quantities dispensed, number of days' supply and the prescription fill date, as well as International Classification of Diseases, 10th revision (ICD-10) codes for diagnoses.
All patients who had a diagnosis code (ICD-10 code J45) for asthma in conjunction with at least two consecutive claims for montelukast based on the National Pharmaceutical Product Index (NAPPI) code 10.4.2, provided by the MIMS, [19] during the study period were included in the research. Patients had to be enrolled continuously with the PBM throughout the study period. A total of 9 141 claims for montelukast were analysed.

Measuring adherence
The medication possession ratio (MPR) is one of the most extensively used measures of adherence based on claims data. [2,17,20,21] Karve et al. [21] proposed that researchers consider the MPR first for the calculation of adherence, since several studies have discovered it to be valuable as an adherence measure. [2,17,22] Although the MPR is easy to compute and interpret, it may mask periods of oversupply. [2,17] There is currently no perfect adherence measure using claims data, [23] but the MPR serves as an acceptable standard against which other measures can be assessed. For this study, the MPR was used as a reference to which other measures of adherence were compared.
Six adherence measures, [4] namely the proportion of days covered (PDC) capped at 1, refill compliance rate (RCR), compliance ratio (CR), modified medication possession ratio (MPRm), continuous multiple-interval measurement of oversupply (CMOS) and continuous single-interval measure of medication acquisition (CSA) averaged over the period of observation were determined, three of which (capped PDC, MPRm and average CSA) were compared with the MPR using Bland-Altman plots. In interpreting the results, smaller bias levels together with narrow limits of agreements were considered representative of more equivalent measures than larger wide values with wider margins. The mathematical formulae for the determination of these adherence measures are described in Table 1.

Statistical analysis
SAS system version 9.4 (SAS Institute Inc., USA) was used to compute the means and standard deviations of the various measures. Bland-Altman plots, plotted using SPSS version 25 (IBM Corp.,

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USA), were used to compare selected adherence measures against the medication possession ratio as reference category.

Ethical considerations
The study was approved by the Health Research Ethics Committee of North-West University (ref. no. NWU-00179-14-A1-06) and the board of directors of the PBM.  Table 3. Varying degrees of equivalence were observed between MPR and each of the measures to which it was compared. Against PDC capped, there was a stronger agreement as evidenced by the small bias value (0.02) as well as narrow limits of agreement (-0.11 -0.15). However, this was not the case with the MPRm, for which there was a wide limit of agreement (-42.97 -2.06).

Discussion
This large cross-sectional study produced four key findings. Firstly, the study showed that two measures (CR and CMOS) produced equivalent mean adherence to the MPR. The observation confirms the results of the study by Hess et al. [4] in which CMOS and MPR produced the same mean values, but is contrary to the same study's finding of significant difference between the CR and MPR. This variation in findings can be attributed to the different ways in which the variables were defined for computing the measures. The formulae by which the MPR, CMOS and CR were calculated were almost the same, requiring the same variables and therefore resulting in adherence values that were equivalent. The CMOS, being a gap measure, produced a value of 0.14, which is equivalent to adherence levels of 0.86 in non-gap methods. For gap measures, adherence values closer to zero represent better adherence levels. [21] Secondly, it was also observed that besides the capped PDC, which produced adherence values lower than the MPR, all the other measures (Table 3) resulted in adherence values greater than the MPR (average CSA>>>RCR>>MPRm>MPR), a trend consistent with results of earlier studies. [4,20] The PDC capped resulted in adherence values lower than those from the MPR because this measure uses the total study period and does not consider excess medication on hand at the termination of the study. We observed a strong agreement between MPR and PDC capped for adherence values <1 (Fig. 1), as most of the data points that correspond with mean values <1 showed zero difference between these measures. This is also evidenced by a small bias value of 0.02 and the narrow range between the limits of agreement (-0.11 -0.15). Consequently, our results show that MPR and PDC capped are equivalent when adherence is <1, where there is no oversupply or excess medication on hand. In the event of adherence values >1 (oversupply) as shown in Fig. 2, however, there is an increasing difference with increasing mean. MPR therefore consistently produces values higher than PDC capped with increasing adherence. The presence of patients with some degree of medication oversupply in our investigation may therefore be the reason for the higher mean MPR value relative to the mean value of capped PDC.
The RCR evaluates the time period between dispensations instead of the entire study period, leading to a denominator which is smaller compared with that for measures that consider the entire period. This results in an overestimation of adherence reflected by the high mean RCR value. To overcome the drawback of RCR in overestimating adherence, the MPRm, which also assesses the period between fills, includes number of days equal to the number of days' supply of medication acquired at the last dispensation. This addition of days assumes that the patient is completely adherent during the period to be covered by the last dispensation, accounting for its relatively high

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value compared with the MPR. There is some degree of agreement between MPR and MPRm, as most data points fall within the limits of agreement (Fig. 2). A bias of -20.46 together with the wide range of limits of agreement between -42.97 and 2.06, however, suggests that the MPR and MPRm are not equivalent. The difference between the MPRm and MPR can be attributed to their difference in denominator and the consideration of the last dispensation in the computation of the MPRm.
In the calculation of the CSA, adherence for each dispensation period is calculated independently and averaged. The number of refills affects the weight of adherence in the cumulative analysis, so patients with single refills will not have the same weight as those with multiple refills. Patients' receipt of medications close to the end of the study period leads to bias, as this is reflected as an oversupply and accounts for the measure's high adherence values. This measure does not allow a carryover of excess medication from one refill interval to another, a very likely event in practical settings. [4,21] The MPR and average CSA also exhibit an agreement, as most data points fall within the limits of agreement (Fig. 3). With a bias of -0.59 and a range of limits of agreement between -3.06 and 1.88, the two measures can be said to be fairly equivalent. For mean adherence >2, it can be noticed that there is decreasing difference with increasing mean, the MPR consistently producing smaller values than the average CSA.
Thirdly, it was interesting to note that the definition of the denominator was critical to the results obtained from measure computations. Similar to what has been found by Hess et al. [4] and Karve et al., [21] adherence measures that presented the entire study period as the denominator (MPR, PDC capped and CMOS) in our study ensured uniformity and consistency, as opposed to measures that used the time between dispensations as the denominator. The measures that estimated the denominator as the difference between refills do not provide a uniform denominator. These measures may overestimate adherence since they may not be able to account for patients who discontinued medications early, resulting in overestimation of adherence for such patients. [21] Finally, it was observed that an appropriate choice of adherence measure depends largely on the data available to the researchers. The variables required as well as the complexity of calculations are features that also need to be considered. Determination of the variables for computation of adherence is critical to the accuracy of results in assessing adherence using these measures. [3,4,24] Days' supply must be cautiously estimated. Depending on the available data, this can be determined as the quotient of the prescribed dose and the number of units of the drug dispensed. [3,4] The number of evaluation days is also essential in the calculation of adherence measures. It is therefore important to determine a priori the time frame for the evaluation of adherence to obtain consistent values.

Study limitations
A notable limitation of this study was that the data used were intended for reimbursement purposes and not for research purposes, and may therefore lack certain data fields that would be relevant for the calculation of adherence methods, such as the exact number of days for which drugs were supplied. However, this limitation can be overcome by appropriate determination of variables for the computation of adherence and cautious estimation of supply and evaluation days based on the available data. Again, the use of administrative claims data limits the ability to draw definitive conclusions from such methods. Careful considerations must therefore be made in making inferences about results of studies that utilise administrative claims data, and these must be made in the context of the data sample used.

Conclusions
Data extracted from administrative databases ultimately determine the adherence measures that can be used. The most extensively used measure is the MPR. From this study, we found that adherence computed using CR, CMOS and PDC capped was equivalent to that obtained from MPR. Researchers can therefore consider the use of these measures when the MPR cannot be appropriately used.