Table 2. Distribution of Normalized Inverse Probability of Treatment Weights. Healthcare costs (US $2012) Notes:

Transcription

1 228 COMPARISON OF HEALTHCARE RESOURCE UTILIZATION AND MEDICAID SPENDING AMONG PATIENTS WITH SCHIZOPHRENIA TREATED WITH ONCE MONTHLY PALIPERIDONE PALMITATE OR ORAL ATYPICAL ANTIPSYCHOTICS USING THE INVERSE PROBABILITY OF TREATMENT WEIGHTING AROACH Erik Muser, 1 Marie-Hélène Lafeuille, 2 Yongling Xiao, 2 Patrick Lefebvre, 2 Jacqueline Pesa, 1 John Fastenau, 1 Mei Sheng Duh 3 1 Janssen Scientific Affairs, LLC, Titusville, NJ, USA; 2 Groupe d analyse, Ltée, Montréal, Québec, Canada; 3 Analysis Group, Inc., Boston, MA, USA BACKGROUND Schizophrenia ranks among the 20 leading causes of disability worldwide and one of the major contributors to the global burden of disease with a prevalence of about 4.6 per 1000 persons due to chronicity. 1, 2 The total (direct and indirect) annual cost of schizophrenia was estimated at US $62.7 billion in Nearly one-third of this cost was incurred as direct healthcare cost. 3 Lifelong antipsychotic (AP) use is required for disease management and relapse control. However, patients treated with oral antipsychotics often have high non-adherence rates. Non-adherence is a strong predictor of relapses and re-hospitalizations. 4 8 Atypical antipsychotic long-acting injectable therapy (LAT) has been shown to significantly improve adherence, and thus reduce symptoms, risk of relapse, and risk of re-hospitalization. 9,10 Observational studies have been commonly used by medical researchers to assess the effect of treatments on outcomes in the real-world setting. However, participants demographics and clinical characteristics in baseline may not be balanced between treatment groups. Without appropriately controlling for the baseline confounding and/or selection bias, the estimate of treatment effect will be biased. 11 OBJECTIVE To assess the impact of treatment initiation with paliperidone palmitate () versus oral atypical antipsychotics () on healthcare resource utilization and costs among Medicaid eligible patients with schizophrenia. METHODS Data Sources Health claims from Medicaid databases for New Jersey (1997Q1-2012Q1), Iowa (1998Q1-2012Q1), Missouri (1997Q1-2012Q1), and Kansas (2001Q1-2012Q1) were used. Medicaid databases contain medical claims (e.g., type of service, service unit, date, International Classification of Diseases, 9th revision [ICD-9] diagnoses, Current Procedural Terminology codes, physician specialty, and type of provider), prescription drug claims (e.g., supply days, units, date of service, and National Drug Codes), and eligibility information (e.g., age, gender, enrollment start and end dates, and date/year of death, if applicable). Study Design and Patient Selection A retrospective longitudinal cohort design was used (Figure 1). The study period began January 1st, 2010 (five months after the approval date) and ran through March 31st, 2012 (the last available date of the data). Index date was defined as the first of the two claims for or the same agent after January 1st, 2010 (five months after the approval date) within 90 days without evidence of prior use. The period of 6 months preceding the index date, was referred to as the baseline period. The end of the observation period for each patient was the earlier of the following dates: 1) disenrollment from Medicaid, 2) interruption of the treatment of interest (defined as no more claims for treatment of interest for more than 6 months continuously), 3) death, or 4) end of the follow-up data. The observation period started from 90 days after the index date. Figure 1. Study Design Diagram Baseline period 6 months before the index date 1 schizophrenia diagnosis Index Date: Date of the first claim for patients who had 2 claims for or 2 claims for within 90 days 90 days for assessment of treatment cohort Continuous Medicaid eligibility 2 schizophrenia diagnoses Observation period End of observation Inclusion Criteria Initiating or in 2010 or after was defined as having at least two pharmacy/medical claims for the same agent within 90 days and no claim of the same agent during the six months before the first claim (index date). Having at least two schizophrenia diagnoses, with at least one of the two diagnoses recorded during the 6 months prior to the index date. The ICD-9 codes for schizophrenia include (patients who had only ICD-9 code for schizoaffective disorder [295.7] were excluded). At least 18 years of age at the index date. Continuous Medicaid enrollment for 6 months prior to and at least 3 months after the index date. Treatment, Outcomes and Covariates The treatments of interest in this study include and the 9 FDA-approved s (including: aripiprazole, asenapine maleate, iloperidone, lurasidone, olanzapine, paliperidone, quetiapine fumarate, risperidone, and ziprasidone). Patients were classified into or cohorts based on the treatment received at index date. The outcomes of interest included all-cause, mental health-related, or schizophrenia-related healthcare resource utilization and direct costs, which were evaluated during the entire observation period. Baseline demographic and clinical characteristics were assessed during the 6 months prior to the index date and included: age, gender, race, state, region, number of unique mental health diagnosis, number of unique antipsychotics (APs), adherence status, use of concomitant medication, types of schizophrenia disorder, Charlson comorbidity index (CCI), individual comorbidities, and healthcare resource utilization and costs during baseline. Statistical Analysis Unadjusted analysis was conducted to compare patients baseline chracteristics between and cohorts. P-values were generated with chi-square tests for categorical variables and with Wilcoxon tests for continuous variables. To control for baseline confounding factors and selection bias, inverse probability of treatment weights (IPTW) were used. 12 First, the propensity score (PS), defined as the probability of initiating treatment was estimated using a multivariate logistic regression model conditional on the aforementioned baseline covariates. IPTWs were then calculated as the inverse of patients estimated probabilities of having their observed initiation treatments, that is, IPTWs were calculated as 1/PS for the group and 1/(1-PS) for the group. Finally, the normalized IPTWs were calculated by dividing each IPTW by the overall mean IPTW. Supported by Janssen Scientific Affairs, LLC Weighted regression models were used to estimate the association between the initial treatment and the healthcare resource utilization or cost. Specifically, Weighted linear regression models were used to estimate the impact of initial treatment ( versus ) on direct healthcare costs Weighted Poisson regression models were used to estimate the impact of initial treatment ( versus ) on healthcare resource utilization No adjustment was made for multiplicity RESULTS Demographic and Clinical Characteristics (Table 1) Table 1 presents the baseline demographic and clinical characteristics of the two cohorts (patients with initiation vs. patients with initiation) Among 13,126 patients, 952 and 12,174 initiated and at the index date, respectively. Patients who initiated with were older, more likely female, had more comorbidities, were more likely to have no antipsychotic use in the prior 6 months, and had lower prior antipsychotic adherence (among those that had antipsychotic use), compared with patients who initiated with. Table 1. Demographic and Clinical Characteristics Evaluated during the 6 Months Baseline Period (N=952) (N=12,174) P-value Age at index date (years), mean ± SD [median] 40.3 ± 12.7 [40.1] 45.3 ± 13.6 [46.6] <.0001 Age categories, n (%) < (12.8) 991 (8.1) < (26.3) 2,138 (17.6) < (21.4) 2,436 (20.0) (26.7) 3,759 (30.9) > (12.8) 2,850 (23.4) <.0001 Female, n (%) 351 (36.9) 5,163 (42.4) Race, n (%) White 445 (46.7) 7,098 (58.3) <.0001 Black 419 (44.0) 3,966 (32.6) <.0001 Hispanic 2 (0.2) 21 (0.2) Other 74 (7.8) 992 (8.1) Unknown 12 (1.3) 97 (0.8) State, n (%) Iowa 27 (2.8) 378 (3.1) Kansas 99 (10.4) 997 (8.2) New Jersey 285 (29.9) 4,662 (38.3) <.0001 Missouri 541 (56.8) 6,137 (50.4) Region characteristics, n (%) Urban 578 (60.7) 7,939 (65.2) Suburban 247 (25.9) 2,601 (21.4) Rural 127 (13.3) 1,631 (13.4) Time since availability (days) mean ± SD [median] ± [361.0] ± [341.0] <.0001 Number of unique mental health diagnoses, mean ± SD [median] 5.4 ± 5.3 [4.0] 6.2 ± 6.6 [4.0] Number of unique AP agents received, mean ± SD [median] 1.4 ± 1.0 [1.0] 0.7 ± 0.9 [0.0] <.0001 Proportion of days covered (PDC) by any AP agent, n (%) PDC < (45.5) 3,680 (30.2) <.0001 PDC (35.2) 2,287 (18.8) <.0001 AP use, n (%) Typical oral and short-term injectable antipsychotics 326 (34.2) 2,864 (23.5) <.0001 Atypical oral and short-term injectable antipsychotics 444 (46.6) 3,638 (29.9) <.0001 Typical LAT 140 (14.7) 625 (5.1) <.0001 Atypical LAT 286 (30.0) 712 (5.8) <.0001 Concomitant medication use, n (%) Antidepressant 383 (40.2) 4,733 (38.9) Anxiolytics 326 (34.2) 5,312 (43.6) <.0001 Mood stabilizer 301 (31.6) 3,383 (27.8) Quan-CCI, mean ± SD [median] 0.5 ± 1.1 [0.0] 0.9 ± 1.5 [0.0] <.0001 Type of schizophrenia disorder (ICD-9 code), 1 n (%) Simple type schizophrenia (295.0) 47 (4.9) 556 (4.6) Disorganized type schizophrenia (295.1) 52 (5.5) 396 (3.3) Catatonic type schizophrenia (295.2) 4 (0.4) 89 (0.7) Paranoid type schizophrenia (295.3) 573 (60.2) 5,263 (43.2) <.0001 Schizophreniform disorder (295.4) 25 (2.6) 289 (2.4) Latent schizophrenia (295.5) 3 (0.3) 45 (0.4) Schizophrenic disorder, residual type (295.6) 37 (3.9) 604 (5.0) Schizoaffective disorder (295.7) 339 (35.6) 3,987 (32.8) Other specified types of schizophrenia (295.8) 49 (5.1) 653 (5.4) Unspecified schizophrenia (295.9) 415 (43.6) 4,833 (39.7) Insurance Eligibility, n (%) Capitated or dual coverage 597 (62.7) 8,605 (70.7) <.0001 = Paliperidone palmitate; = Oral atypical antipsychotics, LAT = long-acting injectable therapy, PDC = proportion of days covered. 1. Types of schizophrenia disorder were identified based on the first 4 digits of the ICD-9-CM codes for schizophrenia diagnosis. Note that types of schizophrenia disorder are not mutually exclusive. Distribution of Normalized Inverse Probability of Treatment Weights (Table 2) Patients who actually initiated but had similar characteristics as those who had a higher probability for initiating were assigned higher weights. The same was true for patients who actually initiated but had similar characteristics as those who had a higher probability for initiating, such that selection bias and confounding were removed in the pseudopopulation (i.e., weighted-population). Median IPTW was 3.61 for patients and 0.53 for patients, with the interquartile-range of ( ) and ( ), respectively. Table 2. Distribution of Normalized Inverse Probability of Treatment Weights N (study population) ,174 N (weighted-population) 6, , IPTW Mean Minimum th percentile th percentile th percentile (median) th percentile th percentile Maximum Association between Monthly Healthcare Costs and Initial Treatment ( versus ) (Table 3) Patients initiating was associated with lower all-cause medical cost (with mean monthly cost difference [MMCD] = -$136 (p=0.0001)). Specifically, patients receiving had lower costs attributable to long-term care admissions, emergency room visits and home care services, compared to those who initiated. Patients initiating had higher all-cause pharmacy costs (with MMCD = $233 (p<0.0001)), compared to those who initiated. Resulting all-cause toal pharmacy and medical costs was found to be in higher among patients initiating, compared to those initiating (with MMCD = $97 (p=0.016)). Similar findings were obtained for the association between schizophrenia-related healthcare costs and initial treatment. Table 3. Association between Monthly Healthcare Costs and Initial Treatment ( versus ), Estimated Using Linear IPTW-Weighted Regression Models among Schizophrenia Patients (N = 13,126) Healthcare costs (US $2012) Mean monthly cost difference ( vs. ) All-cause pharmacy and medical costs (17.99, ) * All-cause pharmacy costs (207.74, ) <0.0001* All-cause medical costs ( , ) * Institute-related visits ( , -0.99) * Inpatient visits (-63.87, 25.81) Long-term care admissions (-69.12, ) * Mental institute admissions 2.78 (-27.16, 32.72) Emergency room visits (-7.27, -3.70) <0.0001* Outpatient visits (-12.42, 11.26) Home care ( , ) <0.0001* Other medical ancillary services (27.55, 60.69) <0.0001* Mental health-related pharmacy and medical costs (168.58, ) <0.0001* Mental health-related medical costs (-44.73, 76.49) Institute-related visits (-58.44, 45.74) Inpatient visits (-15.01, 64.75) Long-term care admissions (-47.94, ) <0.0001* Mental institute admissions 2.70 (-27.13, 32.52) Emergency room visits (-1.69, -0.97) <0.0001* Outpatient visits (3.19, 23.31) * Home care (-66.03, ) * Other medical ancillary services (34.78, 67.41) <0.0001* Schizophrenia-related pharmacy and medical costs (139.93, ) <0.0001* Schizophrenia-related medical costs (-72.34, -4.88) * Institute-related visits (-83.54, ) * Inpatient visits (-27.45, 19.42) Long-term care admissions (-38.22, ) <0.0001* Mental institute admissions (-37.09, -0.24) * Emergency room visits (-0.26, -0.07) * Outpatient visits (4.54, 18.96) * Home care (-15.96, -4.32) * Other medical ancillary services (7.14, 16.23) <0.0001* = Paliperidone palmitate; = Oral atypical antipsychotics; AP = Antipsychotics; CI = Confidence interval. Association between Healthcare Resource Utilization and Initial Treatment ( versus ) (Table 4) Compared to those receiving, patients initiating had more frequent outpatient visits (incidence rate ratio (IRR) = 1.15 (p<0.0001), but less frequent visits for most other healthcare resource utilization categories (including all-cause institute-related visits, long-term care, mental institute, emergency room visits and home care service). There is no difference between the rates of the all-cause inpatient visits for the two treatment groups, but patients initiating had less frequent schizophreniarelated inpatient visits (IRR =0.91 (p<0.0001)), compared to those initiating. Table 4. Association between Healthcare Resource Utilization and Initial Treatment ( versus ) Estimated Using Poisson IPTW-Weighted Regression Models among Schizophrenia Patients (N = 13,126) Healthcare Resource Utilization Incidence Rate Ratio ( vs. ) All medical services Institute-related visits 0.91 (0.90, 0.92) <0.0001* Inpatient visits 0.98 (0.95, 1.01) Long-term care admissions 0.51 (0.45, 0.57) <0.0001* Mental institute admissions 0.90 (0.89, 0.92) <0.0001* Emergency room visits 0.58 (0.57, 0.60) <0.0001* Outpatient visits 1.15 (1.15, 1.16) <0.0001* Home care 0.58 (0.57, 0.59) <0.0001* Other medical ancillary services 1.19 (1.16, 1.22) <0.0001* Mental health-related medical services 2 Institute-related visits 0.93 (0.91, 0.94) <0.0001* Inpatient visits 1.03 (1.00, 1.06) * Long-term care admissions 0.45 (0.39, 0.52) <0.0001* Mental institute admissions 0.91 (0.89, 0.92) <0.0001* Emergency room visits 0.68 (0.64, 0.71) <0.0001* Outpatient visits 1.31 (1.30, 1.32) <0.0001* Home care 0.72 (0.72, 0.73) <0.0001* Other medical ancillary services 1.97 (1.90, 2.04) <0.0001* Schizophrenia-related medical services 3 Institute-related visits 0.91 (0.89, 0.92) <0.0001* Inpatient visits 0.91 (0.87, 0.95) <0.0001* Long-term care admissions 0.38 (0.30, 0.47) <0.0001* Mental institute admissions 0.91 (0.90, 0.93) <0.0001* Emergency room visits 0.78 (0.71, 0.87) <0.0001* Outpatient visits 1.25 (1.23, 1.27) <0.0001* Home care 0.53 (0.52, 0.54) <0.0001* Other medical ancillary services 0.84 (0.78, 0.90) <0.0001* = Paliperidone palmitate; = Oral atypical antipsychotics; AP = Antipsychotics; CI = Confidence interval. LIMITATIONS The Medicaid data used in the study came from only four states and may not be representative of the nation or other states, or of non-medicaid patients. The data were subject to billing inaccuracies and missing data. As cohorts were determined at index date (intention-to-treat approach), interpretation of the estimates may become difficult if a large proportion of participants cross over between the treatment arms. As with all retrospective administrative claims data, the study results may be subject to residual confounding due to unmeasured confounders. Nevertheless, health insurance claims data remain a valuable source of information because they constitute a fairly valid large sample of patients characteristics and outcomes in a real-world setting. CONCLUSIONS Compared to, initiation with was associated with lower mean total medical cost and lower incidence rates in most healthcare resource utilization categories. REFERENCES 1. Saha S, Chant D, Welham J, McGrath J. A systematic review of the prevalence of schizophrenia. PLoS Med. 2005;2(5):e The World Health Organization (2008).The Global Burden of Disease: 2004 Update. Geneva, Switzerland, the World Health Organization. Accessed August 7, Wu EQ, Birnbaum HG, Shi L, Ball DE, Kessler RC, Moulis M, Aggarwal J. The Economics Burden of Schizophrenia in the United States in J Clin Psychiatry. 2005; 66(9): American Psychiatric Association. Practice guideline for the treatment of patients with schizophrenia. Am J Psychiatry. 1997;154: Kane JM. Pharmacologic treatment of schizophrenia. Biol Psychiatry. 1999;46: Lacro JP, Dunn LB, Dolder CR, Leckband SG, Jeste DV. Prevalence of and risk factors for medication nonadherence in patients with schizophrenia: a comprehensive review of recent literature. J Clin Psychiatry. 2002;63: Thieda P, Beard S, Richter A, Kane J. An economic review of compliance with medication therapy in the treatment of schizophrenia. Psychiatr Serv. 2003;54: Sun SX, Liu GG, Christensen DB, et al. Review and analysis of hospitalization costs associated with antipsychotic nonadherence in the treatment of schizophrenia in the United States. Curr Med Res Opin. 2007;23: Alphs L, Bossie CA, Sliwa JK, Ma Y-W, Turner N. Onset of efficacy with acute long acting injectable paliperidone palmitate treatment in markedly to severely ill patients with schizophrenia: post hoc analysis of a randomized, double-blind clinical trial. Ann Gen Psychiatry. 2011;10(1): Lambert T, Olivares JM, Peuskens J, et al. Effectiveness of injectable risperidone long-acting therapy for schizophrenia: data from the US, Spain, Australia, and Belgium. Ann Gen Psychiatry. 2011;10: Hernán, MA., Hernández-Díaz S, and Robins JM. A structural approach to selection bias. Epidemiology. 2004;15: Robins, JM, Hernan, MA, and Brumback, B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000;11: Presented at the 27th Annual U.S. Psychiatric and Mental Health Congress, September 20 23, 2014, Orlando, FL, USA

2 BACKGROUND Schizophrenia ranks among the 20 leading causes of disability worldwide and one of the major contributors to the global burden of disease with a prevalence of about 4.6 per 1000 persons due to chronicity. 1, 2 The total (direct and indirect) annual cost of schizophrenia was estimated at US $62.7 billion in Nearly one-third of this cost was incurred as direct healthcare cost. 3 Lifelong antipsychotic (AP) use is required for disease management and relapse control. However, patients treated with oral antipsychotics often have high non-adherence rates. Non-adherence is a strong predictor of relapses and re-hospitalizations. 4 8 Atypical antipsychotic long-acting injectable therapy (LAT) has been shown to significantly improve adherence, and thus reduce symptoms, risk of relapse, and risk of re-hospitalization. 9,10 Observational studies have been commonly used by medical researchers to assess the effect of treatments on outcomes in the real-world setting. However, participants demographics and clinical characteristics in baseline may not be balanced between treatment groups. Without appropriately controlling for the baseline confounding and/or selection bias, the estimate of treatment effect will be biased. 11 OBJECTIVE To assess the impact of treatment initiation with paliperidone palmitate () versus oral atypical antipsychotics () on healthcare resource utilization and costs among Medicaid eligible patients with schizophrenia. METHODS Data Sources Health claims from Medicaid databases for New Jersey (1997Q1-2012Q1), Iowa (1998Q1-2012Q1), Missouri (1997Q1-2012Q1), and Kansas (2001Q1-2012Q1) were used. Medicaid databases contain medical claims (e.g., type of service, service unit, date, International Classification of Diseases, 9th revision [ICD-9] diagnoses, Current Procedural Terminology codes, physician specialty, and type of provider), prescription drug claims (e.g., supply days, units, date of service, and National Drug Codes), and eligibility information (e.g., age, gender, enrollment start and end dates, and date/year of death, if applicable). Study Design and Patient Selection A retrospective longitudinal cohort design was used (Figure 1). The study period began January 1st, 2010 (five months after the approval date) and ran through March 31st, 2012 (the last available date of the data). Index date was defined as the first of the two claims for or the same agent after January 1st, 2010 (five months after the approval date) within 90 days without evidence of prior use. The period of 6 months preceding the index date, was referred to as the baseline period. The end of the observation period for each patient was the earlier of the following dates: 1) disenrollment from Medicaid, 2) interruption of the treatment of interest (defined as no more claims for treatment of interest for more than 6 months continuously), 3) death, or 4) end of the follow-up data. The observation period started from 90 days after the index date. Figure 1. Study Design Diagram Index Date: Date of the first claim for patients who had 2 claims for or 2 claims for within 90 days End of observation Baseline period 6 months before the index date 1 schizophrenia diagnosis 90 days for assessment of treatment cohort Continuous Medicaid eligibility 2 schizophrenia diagnoses Observation period Inclusion Criteria Initiating or in 2010 or after was defined as having at least two pharmacy/medical claims for the same agent within 90 days and no claim of the same agent during the six months before the first claim (index date). Having at least two schizophrenia diagnoses, with at least one of the two diagnoses recorded during the 6 months prior to the index date. The ICD-9 codes for schizophrenia include (patients who had only ICD-9 code for schizoaffective disorder [295.7] were excluded). At least 18 years of age at the index date. Continuous Medicaid enrollment for 6 months prior to and at least 3 months after the index date. Treatment, Outcomes and Covariates The treatments of interest in this study include and the 9 FDA-approved s (including: aripiprazole, asenapine maleate, iloperidone, lurasidone, olanzapine, paliperidone, quetiapine fumarate, risperidone, and ziprasidone). Patients were classified into or cohorts based on the treatment received at index date. The outcomes of interest included all-cause, mental health-related, or schizophrenia-related healthcare resource utilization and direct costs, which were evaluated during the entire observation period. Baseline demographic and clinical characteristics were assessed during the 6 months prior to the index date and included: age, gender, race, state, region, number of unique mental health diagnosis, number of unique antipsychotics (APs), adherence status, use of concomitant medication, types of schizophrenia disorder, Charlson comorbidity index (CCI), individual comorbidities, and healthcare resource utilization and costs during baseline. Statistical Analysis Unadjusted analysis was conducted to compare patients baseline chracteristics between and cohorts. P-values were generated with chi-square tests for categorical variables and with Wilcoxon tests for continuous variables. To control for baseline confounding factors and selection bias, inverse probability of treatment weights (IPTW) were used. 12 First, the propensity score (PS), defined as the probability of initiating treatment was estimated using a multivariate logistic regression model conditional on the aforementioned baseline covariates. IPTWs were then calculated as the inverse of patients estimated probabilities of having their observed initiation treatments, that is, IPTWs were calculated as 1/PS for the group and 1/(1-PS) for the group. Finally, the normalized IPTWs were calculated by dividing each IPTW by the overall mean IPTW. Supported by Janssen Scientific Affairs, LLC

3 Weighted regression models were used to estimate the association between the initial treatment and the healthcare resource utilization or cost. Specifically, Weighted linear regression models were used to estimate the impact of initial treatment ( versus ) on direct healthcare costs Weighted Poisson regression models were used to estimate the impact of initial treatment ( versus ) on healthcare resource utilization No adjustment was made for multiplicity RESULTS Demographic and Clinical Characteristics (Table 1) Table 1 presents the baseline demographic and clinical characteristics of the two cohorts (patients with initiation vs. patients with initiation) Among 13,126 patients, 952 and 12,174 initiated and at the index date, respectively. Patients who initiated with were older, more likely female, had more comorbidities, were more likely to have no antipsychotic use in the prior 6 months, and had lower prior antipsychotic adherence (among those that had antipsychotic use), compared with patients who initiated with. Table 1. Demographic and Clinical Characteristics Evaluated during the 6 Months Baseline Period (N=952) (N=12,174) P-value Age at index date (years), mean ± SD [median] 40.3 ± 12.7 [40.1] 45.3 ± 13.6 [46.6] <.0001 Age categories, n (%) < (12.8) 991 (8.1) < (26.3) 2,138 (17.6) < (21.4) 2,436 (20.0) (26.7) 3,759 (30.9) > (12.8) 2,850 (23.4) <.0001 Female, n (%) 351 (36.9) 5,163 (42.4) Race, n (%) White 445 (46.7) 7,098 (58.3) <.0001 Black 419 (44.0) 3,966 (32.6) <.0001 Hispanic 2 (0.2) 21 (0.2) Other 74 (7.8) 992 (8.1) Unknown 12 (1.3) 97 (0.8) State, n (%) Iowa 27 (2.8) 378 (3.1) Kansas 99 (10.4) 997 (8.2) New Jersey 285 (29.9) 4,662 (38.3) <.0001 Missouri 541 (56.8) 6,137 (50.4) Region characteristics, n (%) Urban 578 (60.7) 7,939 (65.2) Suburban 247 (25.9) 2,601 (21.4) Rural 127 (13.3) 1,631 (13.4) Time since availability (days) mean ± SD [median] ± [361.0] ± [341.0] <.0001 Number of unique mental health diagnoses, mean ± SD [median] 5.4 ± 5.3 [4.0] 6.2 ± 6.6 [4.0] Number of unique AP agents received, mean ± SD [median] 1.4 ± 1.0 [1.0] 0.7 ± 0.9 [0.0] <.0001 Proportion of days covered (PDC) by any AP agent, n (%) PDC < (45.5) 3,680 (30.2) <.0001 PDC (35.2) 2,287 (18.8) <.0001 AP use, n (%) Typical oral and short-term injectable antipsychotics 326 (34.2) 2,864 (23.5) <.0001 Atypical oral and short-term injectable antipsychotics 444 (46.6) 3,638 (29.9) <.0001 Typical LAT 140 (14.7) 625 (5.1) <.0001 Atypical LAT 286 (30.0) 712 (5.8) <.0001 Concomitant medication use, n (%) Antidepressant 383 (40.2) 4,733 (38.9) Anxiolytics 326 (34.2) 5,312 (43.6) <.0001 Mood stabilizer 301 (31.6) 3,383 (27.8) Quan-CCI, mean ± SD [median] 0.5 ± 1.1 [0.0] 0.9 ± 1.5 [0.0] <.0001 Type of schizophrenia disorder (ICD-9 code), 1 n (%) Simple type schizophrenia (295.0) 47 (4.9) 556 (4.6) Disorganized type schizophrenia (295.1) 52 (5.5) 396 (3.3) Catatonic type schizophrenia (295.2) 4 (0.4) 89 (0.7) Paranoid type schizophrenia (295.3) 573 (60.2) 5,263 (43.2) <.0001 Schizophreniform disorder (295.4) 25 (2.6) 289 (2.4) Latent schizophrenia (295.5) 3 (0.3) 45 (0.4) Schizophrenic disorder, residual type (295.6) 37 (3.9) 604 (5.0) Schizoaffective disorder (295.7) 339 (35.6) 3,987 (32.8) Other specified types of schizophrenia (295.8) 49 (5.1) 653 (5.4) Unspecified schizophrenia (295.9) 415 (43.6) 4,833 (39.7) Insurance Eligibility, n (%) Capitated or dual coverage 597 (62.7) 8,605 (70.7) <.0001 = Paliperidone palmitate; = Oral atypical antipsychotics, LAT = long-acting injectable therapy, PDC = proportion of days covered. 1. Types of schizophrenia disorder were identified based on the first 4 digits of the ICD-9-CM codes for schizophrenia diagnosis. Note that types of schizophrenia disorder are not mutually exclusive.

4 Distribution of Normalized Inverse Probability of Treatment Weights (Table 2) Patients who actually initiated but had similar characteristics as those who had a higher probability for initiating were assigned higher weights. The same was true for patients who actually initiated but had similar characteristics as those who had a higher probability for initiating, such that selection bias and confounding were removed in the pseudopopulation (i.e., weighted-population). Median IPTW was 3.61 for patients and 0.53 for patients, with the interquartile-range of ( ) and ( ), respectively. Table 2. Distribution of Normalized Inverse Probability of Treatment Weights N (study population) ,174 N (weighted-population) 6, , IPTW Mean Minimum th percentile th percentile th percentile (median) th percentile th percentile Maximum Association between Monthly Healthcare Costs and Initial Treatment ( versus ) (Table 3) Patients initiating was associated with lower all-cause medical cost (with mean monthly cost difference [MMCD] = -$136 (p=0.0001)). Specifically, patients receiving had lower costs attributable to long-term care admissions, emergency room visits and home care services, compared to those who initiated. Patients initiating had higher all-cause pharmacy costs (with MMCD = $233 (p<0.0001)), compared to those who initiated. Resulting all-cause toal pharmacy and medical costs was found to be in higher among patients initiating, compared to those initiating (with MMCD = $97 (p=0.016)). Similar findings were obtained for the association between schizophrenia-related healthcare costs and initial treatment. Table 3. Association between Monthly Healthcare Costs and Initial Treatment ( versus ), Estimated Using Linear IPTW-Weighted Regression Models among Schizophrenia Patients (N = 13,126) Healthcare costs (US $2012) Mean monthly cost difference ( vs. ) All-cause pharmacy and medical costs (17.99, ) * All-cause pharmacy costs (207.74, ) <0.0001* All-cause medical costs ( , ) * Institute-related visits ( , -0.99) * Inpatient visits (-63.87, 25.81) Long-term care admissions (-69.12, ) * Mental institute admissions 2.78 (-27.16, 32.72) Emergency room visits (-7.27, -3.70) <0.0001* Outpatient visits (-12.42, 11.26) Home care ( , ) <0.0001* Other medical ancillary services (27.55, 60.69) <0.0001* Mental health-related pharmacy and medical costs (168.58, ) <0.0001* Mental health-related medical costs (-44.73, 76.49) Institute-related visits (-58.44, 45.74) Inpatient visits (-15.01, 64.75) Long-term care admissions (-47.94, ) <0.0001* Mental institute admissions 2.70 (-27.13, 32.52) Emergency room visits (-1.69, -0.97) <0.0001* Outpatient visits (3.19, 23.31) * Home care (-66.03, ) * Other medical ancillary services (34.78, 67.41) <0.0001* Schizophrenia-related pharmacy and medical costs (139.93, ) <0.0001* Schizophrenia-related medical costs (-72.34, -4.88) * Institute-related visits (-83.54, ) * Inpatient visits (-27.45, 19.42) Long-term care admissions (-38.22, ) <0.0001* Mental institute admissions (-37.09, -0.24) * Emergency room visits (-0.26, -0.07) * Outpatient visits (4.54, 18.96) * Home care (-15.96, -4.32) * Other medical ancillary services (7.14, 16.23) <0.0001* = Paliperidone palmitate; = Oral atypical antipsychotics; AP = Antipsychotics; CI = Confidence interval.

5 Association between Healthcare Resource Utilization and Initial Treatment ( versus ) (Table 4) Compared to those receiving, patients initiating had more frequent outpatient visits (incidence rate ratio (IRR) = 1.15 (p<0.0001), but less frequent visits for most other healthcare resource utilization categories (including all-cause institute-related visits, long-term care, mental institute, emergency room visits and home care service). There is no difference between the rates of the all-cause inpatient visits for the two treatment groups, but patients initiating had less frequent schizophreniarelated inpatient visits (IRR =0.91 (p<0.0001)), compared to those initiating. Table 4. Association between Healthcare Resource Utilization and Initial Treatment ( versus ) Estimated Using Poisson IPTW-Weighted Regression Models among Schizophrenia Patients (N = 13,126) Healthcare Resource Utilization Incidence Rate Ratio ( vs. ) All medical services Institute-related visits 0.91 (0.90, 0.92) <0.0001* Inpatient visits 0.98 (0.95, 1.01) Long-term care admissions 0.51 (0.45, 0.57) <0.0001* Mental institute admissions 0.90 (0.89, 0.92) <0.0001* Emergency room visits 0.58 (0.57, 0.60) <0.0001* Outpatient visits 1.15 (1.15, 1.16) <0.0001* Home care 0.58 (0.57, 0.59) <0.0001* Other medical ancillary services 1.19 (1.16, 1.22) <0.0001* Mental health-related medical services 2 Institute-related visits 0.93 (0.91, 0.94) <0.0001* Inpatient visits 1.03 (1.00, 1.06) * Long-term care admissions 0.45 (0.39, 0.52) <0.0001* Mental institute admissions 0.91 (0.89, 0.92) <0.0001* Emergency room visits 0.68 (0.64, 0.71) <0.0001* Outpatient visits 1.31 (1.30, 1.32) <0.0001* Home care 0.72 (0.72, 0.73) <0.0001* Other medical ancillary services 1.97 (1.90, 2.04) <0.0001* Schizophrenia-related medical services 3 Institute-related visits 0.91 (0.89, 0.92) <0.0001* Inpatient visits 0.91 (0.87, 0.95) <0.0001* Long-term care admissions 0.38 (0.30, 0.47) <0.0001* Mental institute admissions 0.91 (0.90, 0.93) <0.0001* Emergency room visits 0.78 (0.71, 0.87) <0.0001* Outpatient visits 1.25 (1.23, 1.27) <0.0001* Home care 0.53 (0.52, 0.54) <0.0001* Other medical ancillary services 0.84 (0.78, 0.90) <0.0001* = Paliperidone palmitate; = Oral atypical antipsychotics; AP = Antipsychotics; CI = Confidence interval. LIMITATIONS The Medicaid data used in the study came from only four states and may not be representative of the nation or other states, or of non-medicaid patients. The data were subject to billing inaccuracies and missing data. As cohorts were determined at index date (intention-to-treat approach), interpretation of the estimates may become difficult if a large proportion of participants cross over between the treatment arms. As with all retrospective administrative claims data, the study results may be subject to residual confounding due to unmeasured confounders. Nevertheless, health insurance claims data remain a valuable source of information because they constitute a fairly valid large sample of patients characteristics and outcomes in a real-world setting. CONCLUSIONS Compared to, initiation with was associated with lower mean total medical cost and lower incidence rates in most healthcare resource utilization categories. REFERENCES 1. Saha S, Chant D, Welham J, McGrath J. A systematic review of the prevalence of schizophrenia. PLoS Med. 2005;2(5):e The World Health Organization (2008).The Global Burden of Disease: 2004 Update. Geneva, Switzerland, the World Health Organization. Accessed August 7, Wu EQ, Birnbaum HG, Shi L, Ball DE, Kessler RC, Moulis M, Aggarwal J. The Economics Burden of Schizophrenia in the United States in J Clin Psychiatry. 2005; 66(9): American Psychiatric Association. Practice guideline for the treatment of patients with schizophrenia. Am J Psychiatry. 1997;154: Kane JM. Pharmacologic treatment of schizophrenia. Biol Psychiatry. 1999;46: Lacro JP, Dunn LB, Dolder CR, Leckband SG, Jeste DV. Prevalence of and risk factors for medication nonadherence in patients with schizophrenia: a comprehensive review of recent literature. J Clin Psychiatry. 2002;63: Thieda P, Beard S, Richter A, Kane J. An economic review of compliance with medication therapy in the treatment of schizophrenia. Psychiatr Serv. 2003;54: Sun SX, Liu GG, Christensen DB, et al. Review and analysis of hospitalization costs associated with antipsychotic nonadherence in the treatment of schizophrenia in the United States. Curr Med Res Opin. 2007;23: Alphs L, Bossie CA, Sliwa JK, Ma Y-W, Turner N. Onset of efficacy with acute long acting injectable paliperidone palmitate treatment in markedly to severely ill patients with schizophrenia: post hoc analysis of a randomized, double-blind clinical trial. Ann Gen Psychiatry. 2011;10(1): Lambert T, Olivares JM, Peuskens J, et al. Effectiveness of injectable risperidone long-acting therapy for schizophrenia: data from the US, Spain, Australia, and Belgium. Ann Gen Psychiatry. 2011;10: Hernán, MA., Hernández-Díaz S, and Robins JM. A structural approach to selection bias. Epidemiology. 2004;15: Robins, JM, Hernan, MA, and Brumback, B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000;11: Presented at the 27th Annual U.S. Psychiatric and Mental Health Congress, September 20 23, 2014, Orlando, FL, USA