Haya Ascher-Svanum
Research Subjects
Haya Ascher-Svanum has a total of 166 co-authors. Here are the top co-authors by number of shared publications:
- Douglas E. Faries
Eli Lilly and Company Indianapolis Indiana USA
16 shared publications - Virginia L. Stauffer
Lilly Research Laboratories, Lilly Corporate Center, Indianapolis, IN, USA.
7 shared publications - Bruce J. Kinon
Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, USA
6 shared publications Xiaomei Peng
Global Patient Outcomes and Real World Evidence
6 shared publications- Bruce J. Kinon
Lilly Research Laboratories, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, USA
5 shared publications - Allen W Nyhuis
Eli Lilly and Company, Lilly Corporate Center, DC 5024, Indianapolis, IN, 46285, USA
4 shared publications
Latest Publications
- Open Access
- 1 May 2016
- Published by Academy of Managed Care Pharmacy in Journal of Managed Care & Specialty Pharmacy
- Vol. 22 (5) , 467-482
Predictors of Change in Adherence Status from 1 Year to the Next Among Patients with Type 2 Diabetes Mellitus on Oral Antidiabetes Drugs
BACKGROUND: Diabetes is a leading cause of morbidity, mortality, and medical resource utilization in the United States and worldwide. Treatment is aimed at keeping blood glucose levels close to normal and preventing or delaying medical complications. It has been estimated that only 50% of patients with diabetes in the United States achieve glycosylated hemoglobin A1c level < 7%. Nonadherence to antidiabetic medications has been identified as a major factor related to poor glycemic control. OBJECTIVES: To (a) assess adult patients with type 2 diabetes mellitus (T2DM) whose adherence status to oral antidiabetic drugs (OADs) changed from 1 year to the next and (b) identify predictors of change in adherence status. METHODS: This retrospective study of the Humana Medicare Advantage Database included patients with T2DM and continuous enrollment between 2010 and 2012. Proportion of days covered (PDC) by OADs was calculated for each of the 3 study years (2010, 2011, 2012). Patients were classified as adherent (PDC ≥ 80%) or nonadherent (PDC < 80%) during each year. Patient characteristics from the baseline period (2010) were used as covariates, and adherence status changes from baseline to follow-up year (2011) were used as response variables. Data from the subsequent study periods (2011 as baseline, 2012 as follow-up) were used to validate the model (final model). RESULTS: A total of 238,402 patients met inclusion criteria. Among them, 144,216 (60.5%) were adherent, and 94,186 (39.5%) were nonadherent during the baseline period. Change in adherence status from baseline to follow-up year was observed in 31,320 (21.7%) patients that were adherent and 39,284 (41.7%) patients that were nonadherent during the baseline year. The final model for baseline adherent patients had a receiver-operating characteristic (ROC) index of 73% and a misclassification rate of 39%. The predictors of highest importance were identified as total number of prescriptions filled with 90-day supply, diabetes-related pill burden, longest gap in OADs, total number of antidiabetic classes filled, and copay for the last OAD filled. The final model had a sensitivity value of 76.4%. The final model for baseline nonadherent patients had a ROC index of 68%, a misclassification rate of 36.4%, and sensitivity value of 52.9%. The predictors of highest importance were diabetes-related pill burden, longest gap in OADs, month-wise patient oscillation from adherent to nonadherent during baseline year, total number of prescriptions filled with a 90-day supply, and total pill burden during the baseline year. CONCLUSIONS: One third of the T2DM patients changed adherence status from 1 year to the next, and factors associated with adherence status changes were identified. Predictive models such as those used in this study can serve as useful and cost-effective tools for payers, helping to identify members that should be targeted for adherence enhancement programs and, ultimately, to improve patients’ long-term outcomes. DISCLOSURES: Funding for this research was provided by Eli Lilly and Company. Comprehensive Health Insights, owned by Humana, completed this study. Peng, Fu, Ascher-Svanum, Ali, and Rodriguez are employees of Eli Lilly and Company. Saundankar and Louder are employed by Comprehensive Health Insights, and Slabaugh and Young are employed by Humana. Study concept and design were contributed by Peng, Ascher-Svanum, and Young. Saundankar and Louder took the lead in data collection, while Saundankar, Peng, Fu, and Louder interpreted the data. The manuscript was written by Saundankar, Peng, Fu, and Louder and revised by Saundankar, Rodriguez, Ali, and Louder.
- 1 May 2016
- Published by Academy of Managed Care Pharmacy in Journal of Managed Care & Specialty Pharmacy
- Vol. 22 (5) , 467-482
Open AccessPredictors of Change in Adherence Status from 1 Year to the Next Among Patients with Type 2 Diabetes Mellitus on Oral Antidiabetes Drugs
BACKGROUND: Diabetes is a leading cause of morbidity, mortality, and medical resource utilization in the United States and worldwide. Treatment is aimed at keeping blood glucose levels close to normal and preventing or delaying medical complications. It has been estimated that only 50% of patients with diabetes in the United States achieve glycosylated hemoglobin A1c level < 7%. Nonadherence to antidiabetic medications has been identified as a major factor related to poor glycemic control. OBJECTIVES: To (a) assess adult patients with type 2 diabetes mellitus (T2DM) whose adherence status to oral antidiabetic drugs (OADs) changed from 1 year to the next and (b) identify predictors of change in adherence status. METHODS: This retrospective study of the Humana Medicare Advantage Database included patients with T2DM and continuous enrollment between 2010 and 2012. Proportion of days covered (PDC) by OADs was calculated for each of the 3 study years (2010, 2011, 2012). Patients were classified as adherent (PDC ≥ 80%) or nonadherent (PDC < 80%) during each year. Patient characteristics from the baseline period (2010) were used as covariates, and adherence status changes from baseline to follow-up year (2011) were used as response variables. Data from the subsequent study periods (2011 as baseline, 2012 as follow-up) were used to validate the model (final model). RESULTS: A total of 238,402 patients met inclusion criteria. Among them, 144,216 (60.5%) were adherent, and 94,186 (39.5%) were nonadherent during the baseline period. Change in adherence status from baseline to follow-up year was observed in 31,320 (21.7%) patients that were adherent and 39,284 (41.7%) patients that were nonadherent during the baseline year. The final model for baseline adherent patients had a receiver-operating characteristic (ROC) index of 73% and a misclassification rate of 39%. The predictors of highest importance were identified as total number of prescriptions filled with 90-day supply, diabetes-related pill burden, longest gap in OADs, total number of antidiabetic classes filled, and copay for the last OAD filled. The final model had a sensitivity value of 76.4%. The final model for baseline nonadherent patients had a ROC index of 68%, a misclassification rate of 36.4%, and sensitivity value of 52.9%. The predictors of highest importance were diabetes-related pill burden, longest gap in OADs, month-wise patient oscillation from adherent to nonadherent during baseline year, total number of prescriptions filled with a 90-day supply, and total pill burden during the baseline year. CONCLUSIONS: One third of the T2DM patients changed adherence status from 1 year to the next, and factors associated with adherence status changes were identified. Predictive models such as those used in this study can serve as useful and cost-effective tools for payers, helping to identify members that should be targeted for adherence enhancement programs and, ultimately, to improve patients’ long-term outcomes. DISCLOSURES: Funding for this research was provided by Eli Lilly and Company. Comprehensive Health Insights, owned by Humana, completed this study. Peng, Fu, Ascher-Svanum, Ali, and Rodriguez are employees of Eli Lilly and Company. Saundankar and Louder are employed by Comprehensive Health Insights, and Slabaugh and Young are employed by Humana. Study concept and design were contributed by Peng, Ascher-Svanum, and Young. Saundankar and Louder took the lead in data collection, while Saundankar, Peng, Fu, and Louder interpreted the data. The manuscript was written by Saundankar, Peng, Fu, and Louder and revised by Saundankar, Rodriguez, Ali, and Louder.
- 25 February 2016
- Published by Academy of Managed Care Pharmacy in Journal of Managed Care & Specialty Pharmacy
- p. 1-16
Predictors of Change in Adherence Status from 1 Year to the Next Among Patients with Type 2 Diabetes Mellitus on Oral Antidiabetes Drugs
BACKGROUND: Diabetes is a leading cause of morbidity, mortality, and medical resource utilization in the United States and worldwide. Treatment is aimed at keeping blood glucose levels close to normal and preventing or delaying medical complications. It has been estimated that only 50% of patients with diabetes in the United States achieve glycosylated hemoglobin A1c level < 7%. Nonadherence to antidiabetic medications has been identified as a major factor related to poor glycemic control. OBJECTIVES: To (a) assess adult patients with type 2 diabetes mellitus (T2DM) whose adherence status to oral antidiabetic drugs (OADs) changed from 1 year to the next and (b) identify predictors of change in adherence status. METHODS: This retrospective study of the Humana Medicare Advantage Database included patients with T2DM and continuous enrollment between 2010 and 2012. Proportion of days covered (PDC) by OADs was calculated for each of the 3 study years (2010, 2011, 2012). Patients were classified as adherent (PDC ≥ 80%) or nonadherent (PDC < 80%) during each year. Patient characteristics from the baseline period (2010) were used as covariates, and adherence status changes from baseline to follow-up year (2011) were used as response variables. Data from the subsequent study periods (2011 as baseline, 2012 as follow-up) were used to validate the model (final model). RESULTS: A total of 238,402 patients met inclusion criteria. Among them, 144,216 (60.5%) were adherent, and 94,186 (39.5%) were nonadherent during the baseline period. Change in adherence status from baseline to follow-up year was observed in 31,320 (21.7%) patients that were adherent and 39,284 (41.7%) patients that were nonadherent during the baseline year. The final model for baseline adherent patients had a receiver-operating characteristic (ROC) index of 73% and a misclassification rate of 39%. The predictors of highest importance were identified as total number of prescriptions filled with 90-day supply, diabetes-related pill burden, longest gap in OADs, total number of antidiabetic classes filled, and copay for the last OAD filled. The final model had a sensitivity value of 76.4%. The final model for baseline nonadherent patients had a ROC index of 68%, a misclassification rate of 36.4%, and sensitivity value of 52.9%. The predictors of highest importance were diabetes-related pill burden, longest gap in OADs, month-wise patient oscillation from adherent to nonadherent during baseline year, total number of prescriptions filled with a 90-day supply, and total pill burden during the baseline year. CONCLUSIONS: One third of the T2DM patients changed adherence status from 1 year to the next, and factors associated with adherence status changes were identified. Predictive models such as those used in this study can serve as useful and cost-effective tools for payers, helping to identify members that should be targeted for adherence enhancement programs and, ultimately, to improve patients’ long-term outcomes. DISCLOSURES: Funding for this research was provided by Eli Lilly and Company. Comprehensive Health Insights, owned by Humana, completed this study. Peng, Fu, Ascher-Svanum, Ali, and Rodriguez are employees of Eli Lilly and Company. Saundankar and Louder are employed by Comprehensive Health Insights, and Slabaugh and Young are employed by Humana. Study concept and design were contributed by Peng, Ascher-Svanum, and Young. Saundankar and Louder took the lead in data collection, while Saundankar, Peng, Fu, and Louder interpreted the data. The manuscript was written by Saundankar, Peng, Fu, and Louder and revised by Saundankar, Rodriguez, Ali, and Louder.
- 25 February 2016
- Published by Academy of Managed Care Pharmacy in Journal of Managed Care & Specialty Pharmacy
- p. 1-16
Predictors of Change in Adherence Status from 1 Year to the Next Among Patients with Type 2 Diabetes Mellitus on Oral Antidiabetes Drugs
BACKGROUND: Diabetes is a leading cause of morbidity, mortality, and medical resource utilization in the United States and worldwide. Treatment is aimed at keeping blood glucose levels close to normal and preventing or delaying medical complications. It has been estimated that only 50% of patients with diabetes in the United States achieve glycosylated hemoglobin A1c level < 7%. Nonadherence to antidiabetic medications has been identified as a major factor related to poor glycemic control. OBJECTIVES: To (a) assess adult patients with type 2 diabetes mellitus (T2DM) whose adherence status to oral antidiabetic drugs (OADs) changed from 1 year to the next and (b) identify predictors of change in adherence status. METHODS: This retrospective study of the Humana Medicare Advantage Database included patients with T2DM and continuous enrollment between 2010 and 2012. Proportion of days covered (PDC) by OADs was calculated for each of the 3 study years (2010, 2011, 2012). Patients were classified as adherent (PDC ≥ 80%) or nonadherent (PDC < 80%) during each year. Patient characteristics from the baseline period (2010) were used as covariates, and adherence status changes from baseline to follow-up year (2011) were used as response variables. Data from the subsequent study periods (2011 as baseline, 2012 as follow-up) were used to validate the model (final model). RESULTS: A total of 238,402 patients met inclusion criteria. Among them, 144,216 (60.5%) were adherent, and 94,186 (39.5%) were nonadherent during the baseline period. Change in adherence status from baseline to follow-up year was observed in 31,320 (21.7%) patients that were adherent and 39,284 (41.7%) patients that were nonadherent during the baseline year. The final model for baseline adherent patients had a receiver-operating characteristic (ROC) index of 73% and a misclassification rate of 39%. The predictors of highest importance were identified as total number of prescriptions filled with 90-day supply, diabetes-related pill burden, longest gap in OADs, total number of antidiabetic classes filled, and copay for the last OAD filled. The final model had a sensitivity value of 76.4%. The final model for baseline nonadherent patients had a ROC index of 68%, a misclassification rate of 36.4%, and sensitivity value of 52.9%. The predictors of highest importance were diabetes-related pill burden, longest gap in OADs, month-wise patient oscillation from adherent to nonadherent during baseline year, total number of prescriptions filled with a 90-day supply, and total pill burden during the baseline year. CONCLUSIONS: One third of the T2DM patients changed adherence status from 1 year to the next, and factors associated with adherence status changes were identified. Predictive models such as those used in this study can serve as useful and cost-effective tools for payers, helping to identify members that should be targeted for adherence enhancement programs and, ultimately, to improve patients’ long-term outcomes. DISCLOSURES: Funding for this research was provided by Eli Lilly and Company. Comprehensive Health Insights, owned by Humana, completed this study. Peng, Fu, Ascher-Svanum, Ali, and Rodriguez are employees of Eli Lilly and Company. Saundankar and Louder are employed by Comprehensive Health Insights, and Slabaugh and Young are employed by Humana. Study concept and design were contributed by Peng, Ascher-Svanum, and Young. Saundankar and Louder took the lead in data collection, while Saundankar, Peng, Fu, and Louder interpreted the data. The manuscript was written by Saundankar, Peng, Fu, and Louder and revised by Saundankar, Rodriguez, Ali, and Louder.
- Open Access
- 30 July 2015
- Published by Springer Nature in Diabetes Therapy
- Vol. 6 (3) , 403
Erratum to: Associations Between Glycemic Control, Depressed Mood, Clinical Depression, and Diabetes Distress Before and After Insulin Initiation: An Exploratory, Post Hoc Analysis
- 30 July 2015
- Published by Springer Nature in Diabetes Therapy
- Vol. 6 (3) , 403
Open AccessErratum to: Associations Between Glycemic Control, Depressed Mood, Clinical Depression, and Diabetes Distress Before and After Insulin Initiation: An Exploratory, Post Hoc Analysis
- Open Access
- 10 July 2015
- Published by Springer Nature in Diabetes Therapy
- Vol. 6 (3) , 303-316
Associations Between Glycemic Control, Depressed Mood, Clinical Depression, and Diabetes Distress Before and After Insulin Initiation: An Exploratory, Post Hoc Analysis
Although depression is often associated with poor glycemic control in patients with type 2 diabetes mellitus (T2DM), this observation has been inconsistent. This exploratory, post hoc analysis investigated associations between depression parameters and glycemic control using data from a 24-month, prospective, observational, non-interventional study evaluating glycemic response following insulin initiation for T2DM.
- 10 July 2015
- Published by Springer Nature in Diabetes Therapy
- Vol. 6 (3) , 303-316
Open AccessAssociations Between Glycemic Control, Depressed Mood, Clinical Depression, and Diabetes Distress Before and After Insulin Initiation: An Exploratory, Post Hoc Analysis
Although depression is often associated with poor glycemic control in patients with type 2 diabetes mellitus (T2DM), this observation has been inconsistent. This exploratory, post hoc analysis investigated associations between depression parameters and glycemic control using data from a 24-month, prospective, observational, non-interventional study evaluating glycemic response following insulin initiation for T2DM.
- 1 July 2014
- Published by Longdom Group in Clinical Schizophrenia & Related Psychoses
- Vol. 8 (2) , 84-90A
Early Perception of Medication Benefit Predicts Subsequent Antipsychotic Response in Schizophrenia
- 1 July 2014
- Published by Longdom Group in Clinical Schizophrenia & Related Psychoses
- Vol. 8 (2) , 84-90A
Early Perception of Medication Benefit Predicts Subsequent Antipsychotic Response in Schizophrenia
- Open Access
- 5 December 2012
- Published by Springer Nature in BMC Psychiatry
- Vol. 12 (1) , 222
Long-term healthcare costs and functional outcomes associated with lack of remission in schizophrenia: a post-hoc analysis of a prospective observational study
Little is known about the long-term outcomes for patients with schizophrenia who fail to achieve symptomatic remission. This post-hoc analysis of a 3-year study compared the costs of mental health services and functional outcomes between individuals with schizophrenia who met or did not meet cross-sectional symptom remission at study enrollment. This post-hoc analysis used data from a large, 3-year prospective, non-interventional observational study of individuals treated for schizophrenia in the United States conducted between July 1997 and September 2003. At study enrollment, individuals were classified as non-remitted or remitted using the Schizophrenia Working Group Definition of symptom remission (8 core symptoms rated as mild or less). Mental health service use was measured using medical records. Costs were based on the sites’ medical information systems. Functional outcomes were measured with multiple patient-reported measures and the clinician-rated Quality of Life Scale (QLS). Symptoms were measured using the Positive and Negative Syndrome Scale (PANSS). Outcomes for non-remitted and remitted patients were compared over time using mixed effects models for repeated measures or generalized estimating equations after adjusting for multiple baseline characteristics. At enrollment, most of the 2,284 study participants (76.1%) did not meet remission criteria. Non-remitted patients had significantly higher PANSS total scores at baseline, a lower likelihood of being Caucasian, a higher likelihood of hospitalization in the previous year, and a greater likelihood of a substance use diagnosis (all p < 0.05). Total mental health costs were significantly higher for non-remitted patients over the 3-year study (p = 0.008). Non-remitted patients were significantly more likely to be victims of crime, exhibit violent behavior, require emergency services, and lack paid employment during the 3-year study (all p < 0.05). Non-remitted patients also had significantly lower scores on the QLS, SF-12 Mental Component Summary Score, and Global Assessment of Functioning during the 3-year study. In this post-hoc analysis of a 3-year prospective observational study, the failure to achieve symptomatic remission at enrollment was associated with higher subsequent healthcare costs and worse functional outcomes. Further examination of outcomes for schizophrenia patients who fail to achieve remission at initial assessment by their subsequent clinical status is warranted.
- 5 December 2012
- Published by Springer Nature in BMC Psychiatry
- Vol. 12 (1) , 222
Open AccessLong-term healthcare costs and functional outcomes associated with lack of remission in schizophrenia: a post-hoc analysis of a prospective observational study
Little is known about the long-term outcomes for patients with schizophrenia who fail to achieve symptomatic remission. This post-hoc analysis of a 3-year study compared the costs of mental health services and functional outcomes between individuals with schizophrenia who met or did not meet cross-sectional symptom remission at study enrollment. This post-hoc analysis used data from a large, 3-year prospective, non-interventional observational study of individuals treated for schizophrenia in the United States conducted between July 1997 and September 2003. At study enrollment, individuals were classified as non-remitted or remitted using the Schizophrenia Working Group Definition of symptom remission (8 core symptoms rated as mild or less). Mental health service use was measured using medical records. Costs were based on the sites’ medical information systems. Functional outcomes were measured with multiple patient-reported measures and the clinician-rated Quality of Life Scale (QLS). Symptoms were measured using the Positive and Negative Syndrome Scale (PANSS). Outcomes for non-remitted and remitted patients were compared over time using mixed effects models for repeated measures or generalized estimating equations after adjusting for multiple baseline characteristics. At enrollment, most of the 2,284 study participants (76.1%) did not meet remission criteria. Non-remitted patients had significantly higher PANSS total scores at baseline, a lower likelihood of being Caucasian, a higher likelihood of hospitalization in the previous year, and a greater likelihood of a substance use diagnosis (all p < 0.05). Total mental health costs were significantly higher for non-remitted patients over the 3-year study (p = 0.008). Non-remitted patients were significantly more likely to be victims of crime, exhibit violent behavior, require emergency services, and lack paid employment during the 3-year study (all p < 0.05). Non-remitted patients also had significantly lower scores on the QLS, SF-12 Mental Component Summary Score, and Global Assessment of Functioning during the 3-year study. In this post-hoc analysis of a 3-year prospective observational study, the failure to achieve symptomatic remission at enrollment was associated with higher subsequent healthcare costs and worse functional outcomes. Further examination of outcomes for schizophrenia patients who fail to achieve remission at initial assessment by their subsequent clinical status is warranted.
- 1 December 2012
- Published by Wolters Kluwer Health in Journal of Clinical Psychopharmacology
Comparing Symptom Response Among Antipsychotic Medications in CATIE
- 1 December 2012
- Published by Wolters Kluwer Health in Journal of Clinical Psychopharmacology
Comparing Symptom Response Among Antipsychotic Medications in CATIE
- Open Access
- 13 September 2012
- Published by Springer Nature in BMC Medical Research Methodology
- Vol. 12 (1) , 142
Construct validity of 2 measures to assess reasons for antipsychotic discontinuation and continuation from patients’ and clinicians’ perspectives in a clinical trial
Little is known about the specific reasons for antipsychotic discontinuation or continuation from patients’ or clinicians’ perspectives. This study aimed to assess the construct validity of 2 new measures of the Reasons for Antipsychotic Discontinuation/Continuation (RAD): RAD-I (a structured interview assessing the patient’s perspective) and RAD-Q (a questionnaire assessing the clinician’s perspective).
- 13 September 2012
- Published by Springer Nature in BMC Medical Research Methodology
- Vol. 12 (1) , 142
Open AccessConstruct validity of 2 measures to assess reasons for antipsychotic discontinuation and continuation from patients’ and clinicians’ perspectives in a clinical trial
Little is known about the specific reasons for antipsychotic discontinuation or continuation from patients’ or clinicians’ perspectives. This study aimed to assess the construct validity of 2 new measures of the Reasons for Antipsychotic Discontinuation/Continuation (RAD): RAD-I (a structured interview assessing the patient’s perspective) and RAD-Q (a questionnaire assessing the clinician’s perspective).
- Open Access
- 1 May 2012
- Published by Taylor & Francis in International Journal of General Medicine
- Vol. 5, 391-398
Treatment-completion rates with olanzapine long-acting injection versus risperidone long-acting injection in a 12-month, open-label treatment of schizophrenia: indirect, exploratory comparisons
Peer reviewed article authored by (Ascher-Svanum H, Montgomery WS, McDonnell DP, Coleman KA, Feldman PD). Read article or submit your manuscript for publishing.
- 1 May 2012
- Published by Taylor & Francis in International Journal of General Medicine
- Vol. 5, 391-398
Open AccessTreatment-completion rates with olanzapine long-acting injection versus risperidone long-acting injection in a 12-month, open-label treatment of schizophrenia: indirect, exploratory comparisons
Peer reviewed article authored by (Ascher-Svanum H, Montgomery WS, McDonnell DP, Coleman KA, Feldman PD). Read article or submit your manuscript for publishing.
- 1 April 2012
- Published by Elsevier in Schizophrenia Research
- Vol. 136, S253-S254
Poster #189 COMPARING SYMPTOM RESPONSE AMONG ANTIPSYCHOTIC MEDICATIONS IN CATIE
- 1 April 2012
- Published by Elsevier in Schizophrenia Research
- Vol. 136, S253-S254
Poster #189 COMPARING SYMPTOM RESPONSE AMONG ANTIPSYCHOTIC MEDICATIONS IN CATIE