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Title Medication Adherence: Commonly Used Definitions Author Xiangyang Ye, Pharmacotherapy Outcomes Research Center, Univertiy of Utah Maintainer Xiangyang Ye <xyexye08@gmail.com> Description Medication Adherence: Commonly Used Definitions adherence-package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
cmos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
medCMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
medCMG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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medDTMPR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
medMPR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . medMPRm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . medPDC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . postRxData . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . preRxData . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . rxGaps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Medication Adherence: Commonly Used Definitions Adherence is defined as "the extent to which a person’s behavior coincides with medical or healthadvice", which is very important, for both clinical researchers and physicians, to identify the treat-ment effect of a specific medication(s).
A variety of measures have been developed to calculate the medication adherence. Definitions andmethods to address adherence differ greatly in public literature. Choosing which definition shouldbe determined by overall study goals. This package provides the functions to calculate medicationadherence based on commonly used definitions.
Xiangyang Ye,Pharmacotherapy Outcomes Research Center,University of Utah< xyexye08@gmail.com > Haynes RB, Taylor DW, Sachett DL, eds. Compliance in health care. Baltimore: John HopkinsUniversity Press, 1979 Hess, LM, Raebel, MA, et al. Measurement of Adherence in Pharmacy Administrative Databases:A Proposal for Statndard Definitions and Preferred Measures The Annals of Pharmacotherapy2006;40:1280-1288 predt <- preRxData(df=cmos, id=ptid, rxDate=rxDay, daySupply=supplies) A sample dataset with 3 patients, 4 variables,14 records.
A data frame with 14 observations on the following 4 variables.
ptid a numeric vector
rxdate a character vector, prescription refill date
supplies a numeric vector
rxDay a numeric vector, prescription refill date
Continuous measure of Medication Availability medCMA function calculates the Continuous measure of Medication Acquisition.
Continuous measure of Medication Acquisition (CMA) was calculated by the days’ supplies ofmedication throughout the study period divided by the number of days from the first dispensationdate up to the patient’s participation completion (study end).
followUpDays days of follow up. 365 is the default, 12 month follow up Xiangyang Ye, Pharmacotherapy Outcomes Research Center, University of Utah Steiner, JG and Prochazka, AV. The Assessment of Refill Compliance Using Pharmacy Records:Methods, Validity, and Applications. Journal of Clinical Epidemiology 1997;50:105-116 Hess, LM, Raebel, MA, et al. Measurement of Adherence in Pharmacy Administrative Databases:A Proposal for Statndard Definitions and Preferred Measures The Annals of Pharmacotherapy2006;40:1280-1288 predt <- preRxData(df=cmos,id=ptid,rxDate=rxDay,daySupply=supplies) medCMG function calculates the Continuous measure of Medication Gaps. This is non-adherencerate. The higher CMG, the lower adherence rate.
Continuous measure of Medication Gaps (CMG) was estimated by total number of days in treat-ment gaps (days for which a drug was unavailable) divided by the duration of the time period ofinterest.
The formula is: (Total Days in Study - Total Days’ Supply)/(Total Days in Study) x 100. If numer-ator is negative, it will be set to ’0’.
followUpDays days of total study period. 365 is the default, 12 month follow up.
Xiangyang Ye, Pharmacotherapy Outcomes Research Center, University of Utah Steiner, JF and Prochazka, AV The Assessment of Refill Compliance Using Pharmacy Records:Methods, Validity, and Application Journal of Clinical Epidemiology 1997;50:105-116 Hess, LM, Raebel, MA, et al. Measurement of Adherence in Pharmacy Administrative Databases:A Proposal for Statndard Definitions and Preferred Measures The Annals of Pharmacotherapy2006;40:1280-1288 predt <- preRxData(df=cmos,id=ptid,rxDate=rxDay,daySupply=supplies) Continuous Multiple interval measure of Over-Supply medCMOS function calculates the Continuous Multiple interval measure of Over-Supply followUpDays days of follow up. 365 is the default, 12 month follow up Continuous Multiple interval measure of Over-Supply (CMOS) was calculated concurrently withCMG. The description of both calculations are as follows: From the first prescription refill to the next prescription refill, a patient can accumulate a surplus ora deficit by either coming to pick up their medicaiton too early (which would show up as a surplus)or too late (which is considered to be a deficit). Future deficits and surpluses are accumulated basedon existing deficits and surpluses.
If a person continuously has deficits or surpluses for each prescription refill period, the deficits orsurpluses are always accumulated into accumulated deficits or surplus categories, respectively. Anold surplus can cancel out a new deficit. If the accumulated surplus is more than the new deficit,the remaining surplus remains an accumulated surplus. If there is an accumulated surplus that pre-cedes a new deficit , but less than the new deficit, the remaining deficit goes to the accumulated gap At the end of the observation period, the accumulated gap is divided by the total days between thefirst and last prescription to get the CMG value for each patient. Similarly, the accumulated surplusis divided by the total days between the first and last presciption to get the measure of surplus foreach patient.
Xiangyang Ye, Pharmacotherapy Outcomes Research Center, University of Utah Morningstar, BA, Sketris IS, et al. Variation in Pharmacy Prescription Refill Adherence Measuresby Type of Oral Antihyperglycaemic Drug Therapy in Seniors in Nova Scotia, Canada Journal ofClinical Pharmacy and Therapeutics 2002;27:213-220 Hess, LM, Raebel, MA, et al. Measurement of Adherence in Pharmacy Administrative Databases:A Proposal for Statndard Definitions and Preferred Measures The Annals of Pharmacotherapy2006;40:1280-1288 predt <- preRxData(df=cmos,id=ptid,rxDate=rxDay,daySupply=supplies) medCR function calculates the compiance rate.
Compliance Rate (CR) were computed taking the ratio of the sum of days’ supplies (excluding thelast days’ supply) to the elapsed intervals between the last dispensation date and the first dispensa-tion date.
The formula is: (Total Days’ Supply - Last Days’ Supply)/(Last Dispensation Date - First Dispen-sation Date) x 100.
Xiangyang Ye, Pharmacotherapy Outcomes Research Center, University of Utah Ren XS, Kazis LE, et al. Identifying Patient and Physician Characteristics That Affect Compliancewith Antihypertensive Medications. Journal of Clinical Pharmacy and Therapeutics 2002;27:47-56 Hess, LM, Raebel, MA, et al. Measurement of Adherence in Pharmacy Administrative Databases:A Proposal for Statndard Definitions and Preferred Measures The Annals of Pharmacotherapy2006;40:1280-1288 predt <- preRxData(df=cmos,id=ptid,rxDate=rxDay,daySupply=supplies) Continuous Single-interval measure of medication Availability medCSA function calculates the Single-interval medication availability.
Continuous Single-interval measure of medication Availability (CSA) was calculated by the days’supply of a medication divided by the number of days in the interval from the dispensation date upto, but not include, the next dispensation date (or the study end).
Xiangyang Ye, Pharmacotherapy Outcomes Research Center, University of Utah Steiner, JG and Prochazka, AV. The Assessment of Refill Compliance Using Pharmacy Records:Methods, Validity, and Applications. Journal of Clinical Epidemiology 1997;50:105-116 Hess, LM, Raebel, MA, et al. Measurement of Adherence in Pharmacy Administrative Databases:A Proposal for Statndard Definitions and Preferred Measures The Annals of Pharmacotherapy2006;40:1280-1288 predt <- preRxData(df=cmos,id=ptid,rxDate=rxDay,daySupply=supplies) medDBR function calculates the Days Between Reills adherence rate.
Days Between Refills (DBR) adherence rate was estimated by comparing patients’ monthly phar-macy refill records to the prescribed regimen documented in their medical records. An assumptionwas made that any extra doses accumulated during the study period were used as needed by thepatients in order to adhere to the prescribed therapy if medication refills were not obtained on time.
The formula is: Adherence Rate = (1-(Days Between Refills - Total Days’ Supply)/(Days BetweenRefills)) x 100.
followUpDays days of follow up. If no follow up days provided, the elapsed interval from the first dispensation date to last dispensation date will be used.
Xiangyang Ye, Pharmacotherapy Outcomes Research Center, University of Utah Chisholm MA, Molly LL, et al. Comparing Renal Transplant Patients’ Adherence to Free Cy-closporine and Free Tacrolimus Immunosuppressant Therapy. Clinical Transplant 2005;19:77-82 Hess, LM, Raebel, MA, et al. Measurement of Adherence in Pharmacy Administrative Databases:A Proposal for Statndard Definitions and Preferred Measures The Annals of Pharmacotherapy2006;40:1280-1288 predt <- preRxData(df=cmos,id=ptid,rxDate=rxDay,daySupply=supplies) medDTMPR function calculates the Dual Therapy Medication Possession Ratio.
Dual Therapy Medication Possession Ratio (DTMPR) were computed taking the ratio of the sumof days’ supplies (devided by 2) to the intervals elapsed between date of first prescription refill andlast prescription refill plus the days’ supply of last refill.
Same as MPR, the ratio alone can’t be combined across patients due to different observation days(denomitor). DTMPR may exceed 100% due to early refills and/or polypharmacy. Very commonly,if DTMPR>100%, it will be truncated to 100%.
medDTMPR(df=data, followUpDays=NA,truncated="yes") limit MPR to 100% or not. "yes" is the default Xiangyang Ye, Pharmacotherapy Outcomes Research Center, University of Utah Vanderpoel, DR. , Hussein, MA, et al. Adherence to a Fixed-Dose Combination of RosiglitazoneMaleate/Metformin Hydrochloride in Subjects with Type 2 Diabetes Mellitus: A RetorspectiveDatabase Analysis Clinical Therapeutics 2004;26:2066-2075 predt <- preRxData(df=cmos,id=ptid,rxDate=rxDay,daySupply=supplies) medMPR function calculates the medication possession ratio.
Medication Possession Ratio (MPR) were computed taking the ratio of the sum of days’ supplies tonumber of days in study (important: the first day in study is not necessary a prescription refill date).
The ratio alone can’t be combined across patients due to different observation days (denomitor).
MPR may exceed 100% due to early refills and/or polypharmacy. Very commonly, if MPR>100%,it will be truncated to 100%.
medMPR(df=data, followUpDays=NA,truncated="yes") limit MPR to 100% or not. "yes" is the default Xiangyang Ye, Pharmacotherapy Outcomes Research Center, University of Utah Skaer TL, Sclar DA, et al. Effect of Pharmaceutical Formulation for Diltiazem on Health CareExpenditures fo Hypertension Clinical Therapeutics 1993;15:905-911 Hess, LM, Raebel, MA, et al. Measurement of Adherence in Pharmacy Administrative Databases:A Proposal for Statndard Definitions and Preferred Measures The Annals of Pharmacotherapy2006;40:1280-1288 predt <- preRxData(df=cmos,id=ptid,rxDate=rxDay,daySupply=supplies) medMPRm function calculates the medication possession ratio,modified.
Medication Possession Ratio, modified (MPRm) were computed taking the ratio of the sum ofdays’ supplies to the intervals elapsed between date of first prescription refill and last prescriptionrefill plus the days’ supply of last refill.
Same as MPR, the ratio alone can’t be combined across patients due to different observation days(denomitor). MPRm may exceed 100% due to early refills and/or polypharmacy. Very commonly,if MPRm>100%, it will be truncated to 100%.
medMPRm(df=data, followUpDays=NA,truncated="yes") limit MPR to 100% or not. "yes" is the default Xiangyang Ye, Pharmacotherapy Outcomes Research Center, University of Utah Vanderpoel, DR. , Hussein, MA, et al. Adherence to a Fixed-Dose Combination of RosiglitazoneMaleate/Metformin Hydrochloride in Subjects with Type 2 Diabetes Mellitus: A RetorspectiveDatabase Analysis Clinical Therapeutics 2004;26:2066-2075 Hess, LM, Raebel, MA, et al. Measurement of Adherence in Pharmacy Administrative Databases:A Proposal for Statndard Definitions and Preferred Measures The Annals of Pharmacotherapy2006;40:1280-1288 predt <- preRxData(df=cmos,id=ptid,rxDate=rxDay,daySupply=supplies) medPDC function calculates the proportion of days covered.
Proportion of Days Covered (PDC) was calculated by the nubmer of days with supply in study (nomatter how many medications were taken on the day) divided by total number of days in study. Themaximum of PDC is 100%.
followUpDays days of follow up. 365 is the default, 12 month follow up Xiangyang Ye, Pharmacotherapy Outcomes Research Center, University of Utah Benner JS, Glynn RJ, et al. Long-term Persisitence in Use of Statin Therapy in Elderly Patients theJournal of the American Medical Association 2002;288:455-461 Hess, LM, Raebel, MA, et al. Measurement of Adherence in Pharmacy Administrative Databases:A Proposal for Statndard Definitions and Preferred Measures The Annals of Pharmacotherapy2006;40:1280-1288 predt <- preRxData(df=cmos,id=ptid,rxDate=rxDay,daySupply=supplies) postRxData function further prepares the data for medical adherence calculation.
Xiangyang Ye, Pharmacotherapy Outcomes Research Center, University of Utah predt <- preRxData(df=cmos,id=ptid,rxDate=rxDay,daySupply=supplies) preRxData function prepares the data for medical adherence calculation.
preRxData(df=data,id=NULL,rxDate=NULL,daySupply=NULL,followUpDays=365) a dataframe. Patient id (id), prescription refill date (rxDate), days supply (Day-Supply) are required variables numeric variable name. Medication dispensation date numeric varable name. Prescription days’ supply followUpDays a scalar. 365 days is the default, 12 month follow up Xiangyang Ye, Pharmacotherapy Outcomes Research Center, University of Utah preRxData(df=cmos,id=ptid,rxDate=rxDay,daySupply=supplies) rxGaps function calculate the gaps between the date of dispensation plus days supplies and thenext dispensation date.
a dataframe. a object that rxGaps funtion created how to deal with negative gap. Negative gap is kept if any scalar is previded.
Otherwise, negative gap is considered as ’0’, which is the default.
Xiangyang Ye, Pharmacotherapy Outcomes Research Center, University of Utah predt <- preRxData(df=cmos,id=ptid,rxDate=rxDay,daySupply=supplies) medCMA, medCMG, medCMOS, medCR, medCSA, medDBR, medDTMPR, medMPR, medMPRm, medPDC,

Source: http://cran.uvigo.es/web/packages/medAdherence/medAdherence.pdf