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dc.contributor.authorZia, A.
dc.contributor.authorBrassart, A.
dc.contributor.authorThomas, S.
dc.contributor.authorYe, F.
dc.contributor.authorStephenson, J.J.
dc.contributor.authorMullins, C.D.en_US
dc.contributor.authorJones, C.A.en_US
dc.date.accessioned2021-02-08T20:13:40Z
dc.date.available2021-02-08T20:13:40Z
dc.date.issued2020-07-15
dc.identifier.urihttp://hdl.handle.net/10713/14615
dc.description.abstractBackground: Lack of adherence with prescribed medications among the asthma populations exacerbates health outcomes and increases social and economic costs. Objectives: The proposed study aims to model patient-centric structural determinants of adherence rates among asthma patients and explore the potential of mobile health apps such as the TRUSTR platform to improve adherence using its power of monetary and non-monetary chatbotting and non-monetary nudges. Following specific hypotheses are tested: (1) Patient attributes, such as their age and medical condition, have significant effect on their adherence with the prescribed treatment plans. (2) Behavioral nudging with rewards and engagement via mobile health apps will increase adherence rates. Methods: The patient population (N = 37 359) consists of commercially insured patients with asthma who have been identified from administrative claims in the HealthCore Integrated Research Database (HIRD) between April 1, 2018 and March 31, 2019. Two Structural Equation Models (SEMs) are estimated to quantify direct, indirect, and total effect sizes of age and medical condition on proportion of days covered (PDC) and medical possession ratio (MPR), mediated by patient medical and pharmacy visits. Fourteen additional SEMs were estimated to lateralize TRUSTR findings and conduct sensitivity analysis. Results: HIRD data reveal mean adherence rate of 59% (standard deviation (SD) 29%) for PDC and 58% for MPR (SD 36%). Key structural findings from SEMs derived from the HIRD dataset indicate that each additional year in the age of the patient has a positive total effect on the adherence rate. Patients with poor medical condition are likely to have lower adherence rate, but this direct effect is countered by mediating variables. Further, each additional reward and higher engagement with a mobile app is likely to have a positive total effect on increasing the adherence rate. Conclusions: HIRD data reveal mean adherence rate of 59% (SD 29%), providing the evidence for the opportunity to increase adherence rate by around 40%. Statistical modeling results reveal structural determinants, such as the opportunity to nudge, are higher among younger patients, as they have higher probability of being non-adherent. Methodologically, lateralization approach demonstrates the potential to capture real-world evidence beyond clinical data and merge it with clinical data. Copyright This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CCBY-4.0). View this license's legal deed at http://creativecommons.org/licenses/by/4.0 and legal code at http://creativecommons.org/licenses/by/4.0/legalcode for more information.en_US
dc.description.urihttps://doi.org/10.36469/JHEOR.2020.13607en_US
dc.language.isoen_USen_US
dc.publisherColumbia Data Analyticsen_US
dc.relation.ispartofJournal of Health Economics and Outcomes Research
dc.subjectAdherence ratesen_US
dc.subjectBehavioral nudgingen_US
dc.subjectMobile health appsen_US
dc.subjectRespiratory diseasesen_US
dc.subjectRewardsen_US
dc.subjectStructural modelsen_US
dc.titlePatient-centric structural determinants of adherence rates among asthma populations: Exploring the potential of patient activation and encouragement tool TRUSTR to improve adherenceen_US
dc.typeArticleen_US
dc.identifier.doi10.36469/JHEOR.2020.13607


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