title: Predictors of Nonadherence to Topical Intraocular Pressure Reduction Medications Among Medicare Members: A Claims-Based Retrospective Cohort Study.
abstract: BACKGROUND
  Reported adherence rates with ocular hypotensive medications typically range from 51% to 56% over the first year of therapy. As intraocular pressure (IOP) reduction slows the progression of vision loss from glaucoma, early identification of nonadherent members is crucial to effective disease management.
  
  
  OBJECTIVE
  To (a) identify member characteristics and other factors related to nonadherence with topical IOP-lowering medications available in administrative claims data and (b) create a predictive model incorporating these variables.
  
  
  METHODS
  This retrospective cohort study analyzed data from Humana's administrative claims database. The study cohort included members aged 65-89 years enrolled in a Medicare Advantage Prescription Drug plan (MAPD; medical and pharmacy benefits), with > 1 topical IOP-lowering medication claims between January 2011 and September 2012 and a minimum of 24 months of continuous enrollment-12 months before and 12 months after the initial (index) prescription claim for a topical IOP-lowering medication. Adherence was defined as the proportion of days covered (PDC) with drug supply (calculated from the number of drops per bottle and dose) over the first year after the index prescription. Members with PDC > 0.80 were considered adherent, while members with PDC < 0.80 were considered nonadherent. Multivariable stepwise logistic regression with backward elimination was used to construct a predictive model for the likelihood of nonadherence (PDC < 0.80). The model was developed using 28 input variables*#x2013;10 variables were retained in the final model.
  
  
  RESULTS
  73,256 MAPD members were included in this study; most (69%) of these members were continuing topical IOP-lowering medication users. The proportion of patients adherent (PDC > 0.80) to IOP-lowering medications was 51%. The study sample was split, using a 2:1 ratio, into a development sample (n = 48,840 members) and a validation sample (n=24,416 members). The model performed equally well in the development sample and the validation sample (area under the curve = 0.71 for development and validation sets), making it appear robust in this Medicare population. In the final predictive model, characteristics increasing the likelihood (P < 0.01) of nonadherence to IOP-lowering medication within the MAPD population included index IOP prescription filled through mail order, higher medical costs during the pre-index period, being a new IOP-lowering medication user, and being male. Characteristics that lowered the likelihood of nonadherence included advanced age, higher pharmacy costs during the pre-index period, receiving a low-income subsidy, residing in the South, and a previous diagnosis of open-angle glaucoma or history of glaucoma surgery.
  
  
  CONCLUSIONS
  Nonadherence to topical IOP-lowering medication can be predicted with 10 commonly available demographic, clinical, and treatment-related variables generally available in administrative claims data for an MAPD population. Given that this predictive model was constructed using these generally available data, it could potentially be replicated by other health plans for use in predicting nonadherence to topical IOP-lowering medications among MAPD plan members. This predictive model can be used to identify members that are likely to be nonadherent in order to target interventions intended to improve ocular hypotensive medication adherence.
  
  
  BACKGROUND
  Funding for this study was contributed by Allergan. Comprehensive Health Insights was contracted by Allergan to conduct this study. Sheer, Bunniran, and Uribe are employed by Comprehensive Health Insights/Humana and own stock in Humana. Fiscella, Chandwani, and Patel are employed by Allergan. Study concept and design were contributed by Sheer, Fiscella, and Patel, along with Bunniran and Uribe. Sheer and Bunniran took the lead in data collection, and data interpretation was performed by Bunniran and Uribe, along with the other authors. The manuscript was written and revised by Sheer, Bunniran, Chandwani, and Uribe, with assistance from Fiscella and Patel.
authors.affiliation: 1 Comprehensive Health Insights, Humana, Louisville, Kentucky.
authors.forename: Richard
authors.initials: R
authors.lastname: Sheer
authors.affiliation: 1 Comprehensive Health Insights, Humana, Louisville, Kentucky.
authors.forename: Suvapun
authors.initials: S
authors.lastname: Bunniran
authors.affiliation: 1 Comprehensive Health Insights, Humana, Louisville, Kentucky.
authors.forename: Claudia
authors.initials: C
authors.lastname: Uribe
authors.affiliation: 2 Health Economics and Outcomes Research Medical Science Liaison.
authors.forename: Richard G
authors.initials: RG
authors.lastname: Fiscella
authors.affiliation: 3 Global Health Economics and Outcomes Research.
authors.forename: Vaishali D
authors.initials: VD
authors.lastname: Patel
authors.affiliation: 4 Global Health Economics and Outcomes Research, Allergan, Irvine, California.
authors.forename: Hitesh S
authors.initials: HS
authors.lastname: Chandwani
mesh_terms: D000287:Administration, Topical; D000368:Aged; D000369:Aged, 80 and over; D000959:Antihypertensive Agents / Q000008:administration & dosage*; D015331:Cohort Studies; D005260:Female; D005544:Forecasting; D006801:Humans; D007345:Insurance Claim Review; D007429:Intraocular Pressure / Q000187:drug effects*; D008297:Male; D020398:Medicare Part C / Q000639:trends*; D055118:Medication Adherence; D054539:Medication Therapy Management / Q000639:trends*; D012189:Retrospective Studies; D014481:United States / Q000453:epidemiology
chemical_list: D000959:Antihypertensive Agents