Introduction: Awareness and usage of Electronic cigarettes (e-cigs) among smokers have increased rapidly over the past few years, majorly in quitting smoking. The main objectives for this study were: 1) To estimate the prevalence and study sociodemographic predictors for e-cigs use among individuals with Chronic Obstructive Pulmonary Disease (COPD) 2) To examine the predictors and estimate the total healthcare costs among current smokers with COPD 3) To estimate the economic impact of adopting e-cigs as a smoking cessation tool among current smokers with COPD. Methods: The National Health Interview Survey data from the year 2014 was utilized to estimate the prevalence and identify sociodemographic predictors associated with e-cigs use among COPD adult population. Total healthcare costs and sociodemographic and clinical predictors among current smokers with COPD were estimated using the Medical Expenditure Panel Survey data from the year 2012-2013. Economic impact for adoption of e-cigs was obtained by developing an epidemiological cohort-Markov model from a societal perspective over the period of 5-year. The targeted population was current smokers with COPD and willing to quit smoking. Smoking abstinence for e-cigs was compared with Varenicline, Bupropion, and Nicotine Replacement therapy. Outcomes evaluated were the 1-year and accumulated 5-year total healthcare costs savings associated with e-cigs over other options. Results: Among individuals with COPD, 8.65% and 24.37% were current and ever e-cig users respectively. Current e-cigs use was found to be associated with individuals who have tried quitting smoking in the past (OR: 2.0; 95%CI: 1.05, 3.97). Adjusted total healthcare costs per patient per year among current smokers with COPD were found to be higher by $1,811 in comparison to non-smokers with COPD. The adoption of e-cigs among COPD current smokers can have a positive impact on the healthcare budget and can lead to healthcare cost savings of $37.71 million over the period of 5-year. Furthermore, a positive impact on budget were found among women and individuals with age 65 & above. Conclusion: E-cigs may be beneficial to the current US healthcare system if adopted as a smoking cessation tool among COPD individuals. However, uncertainty associated with product safety, efficacy and adherence for cessation warrants further studies and evaluation.

<p><strong>Background </strong></p> <p>The EQ-5D is one of the most widely used instruments to measure health status. It consists of a descriptive profile with a corresponding scoring algorithm. Multiple scoring algorithms have since been developed from EQ-5D preference elicitation studies.</p> <p><strong>Objectives </strong></p> <p>To identify key methodological issues in the construction of EQ-5D preference elicitation studies and to assess the validity of using a standard methodology in the construction of EQ-5D scoring algorithms.</p> <p><strong>Search methods </strong></p> <p>We searched the MEDLINE, EMBASE, Cochrane Library, NHS Economic Evaluation Database, and Health Economic Evaluation Database, (1990 to 2012). The EuroQol Group website was also searched.</p> <p><strong>Selection criteria </strong></p> <p>EQ-5D preference elicitation studies that reported the directly estimated health state scores and estimated scoring algorithm.</p> <p><strong>Data collection and analysis </strong></p> <p>Two reviewers independently assessed articles for inclusion. The observed and estimated EQ-5D preference scores were compared across studies. A standard scoring algorithm with fixed variables was estimated. The model performance of the standard algorithm and the study reported algorithm were assessed and compared.</p> <p><strong>Results </strong></p> <p>A total of 38 preference elicitation studies were included in this review. Key differences identified include: method of valuation, selection of health states, transformation of health state values, and method of estimation of the scoring algorithm. The observed health state values were found to be significantly different. The predicted health state values showed high levels of rank correlation. In general, a standard scoring algorithm was found to be no different in model performance than study specific scoring algorithms, with only three studies reporting a significant better model performance using the study specified scoring algorithm.</p> <p><strong>Conclusion</strong></p> <p>Methodological differences were identified across EQ-5D valuation studies. A standard scoring algorithm may yield similar model performance to study specific scoring algorithms, however further research is needed to identify when the use of a standard algorithm is appropriate.</p> / Master of Science (MSc)

Background: Pharmacy-related performance measures have gained significant attention in the transition to value-based healthcare. Pharmacy-level quality measures, including those developed by the Pharmacy Quality Alliance, are not yet publicly accessible. However, the publication of report cards for individual pharmacies has been discussed as a way to help direct patients towards high-quality pharmacies. This study aimed to measure the relative strength of patient preferences for community pharmacy attributes, including pharmacy quality. Additionally, this study aimed to identify and describe community pharmacy market segments based on patient preferences for pharmacy attributes. Methods: This study elicited patient preferences for community pharmacy attributes using a discrete choice experiment (DCE) among a sample of 773 adults aged 18 years and older. Six attributes were selected based on published literature, expert opinion, and pilot testing feedback. The attributes included hours of operation, staff friendliness/courtesy, pharmacist communication, pharmacist willingness to establish a personal relationship, overall quality, and a drug-drug interaction specific quality metric. Participants responded to a block of ten random choice tasks assigned by Sawtooth v9.2 and two fixed tasks, including a dominant and a hold-out scenario. The data were analyzed using conditional logit and latent class regression models, and Hierarchical Bayes estimates of individual-level utilities were used to compare preferences across demographic subgroups. Results: Among the 773 respondents who began the survey, 741 (95.9%) completed the DCE and demographic questionnaire. Overall, study participants expressed the strongest preferences for quality-related pharmacy attributes. The attribute importance values (AIVs) were highest for the specific, drug-drug interaction (DDI) quality measure, presented as, “The pharmacy ensured there were no patients who were dispensed two medications that can cause harm when taken together,” (40.3%) and the overall pharmacy quality measure (31.3%). The utility values for 5-star DDI and overall quality ratings were higher among women (83.0 and 103.8, respectively) than men (76.2 and 94.5, respectively), and patients with inadequate health literacy ascribed higher utility to pharmacist efforts to get to know their patients (26.0) than their higher literacy counterparts (16.3). The best model from the latent class analysis contained three classes, coined the Quality Class (67.6% of participants), the Relationship Class (28.3%), and the Convenience Class (4.2%). Conclusions: The participants in this discrete choice experiment exhibited strong preferences for pharmacies with higher quality ratings. This finding may reflect patient expectations of community pharmacists, namely that pharmacists ensure that patients are not harmed by the medications filled at their pharmacies. Latent class analysis revealed underlying heterogeneity in patient preferences for community pharmacy attributes.

Introduction The main objectives of this study were: 1) to examine the prevalence and characteristics of opioid exposures, 2) to estimate the economic costs associated with opioid poisonings, and 3) to examine the characteristics associated with opioid poisoning-related health care resource use (HCRU) and costs in children. Methods Data from the National Poison Data System from January 1, 2010 to December 31, 2014 were utilized to examine the prevalence and characteristics of opioid exposures and poisonings in children <18 years. Economic costs were estimated using the 2012 Nationwide Emergency Department Sample, Kids’ Inpatient Database, Multiple Cause-of-Death file and other published sources, applying a societal perspective. Direct costs included costs associated with ED visits, hospitalizations and ambulance transports. Indirect cost included productivity costs due to caregivers’ absenteeism and premature mortality among children. Results There were a total of 83,418 pediatric opioid exposures and nearly half of them resulted in poisoning. The epidemiology of opioid exposures differed considerably by age. Opioid exposures were more prevalent and mainly accidental in young children. Exposures in adolescents were more likely to be intentional and severe. The total economic costs of pediatric opioid poisonings in the United States were calculated at $230.8 million in 2012. Total direct costs were estimated to be over $21.1 million. Total productivity costs were calculated at $209.7 million, and 98.6% of these costs were attributed to opioid poisoning-related mortality. Conclusions Opioid exposures and poisonings in children continue to occur and impose an economic burden on the society.

Introduction: Atypical Antipsychotics (AAPs) are approved by the Food and Drug Administration (FDA) for the treatment of schizophrenia and bipolar disorder. AAPs are commonly used off-label to treat depression, post-traumatic stress disorder and neuropsychiatric symptoms in dementia due to lack of alternative treatment options and treatment resistance. Concerns for off-label use arise since AAPs increase the risk of cardiovascular events and death. The objectives were 1) describe patterns of RU and costs among off-label AAPs users in a nationally representative population 2) identify prevalence of off-label use in the Medicare population 3) compare RU and costs between off-label AAPs users and non-users with mental health conditions in Medicare. Methods: For the first objective, the Medical Expenditure Panel Survey (MEPS) datasets were used. AAPs users greater than 18 years were identified in this cross-sectional study. Generalized Linear Models (GLM) were used to estimate costs among users and non-users after controlling for age sex, gender, insurance type, marriage status, income and comorbidity index. For the second and third objective, Medicare datasets were used to identify prevalence, RU, and costs of off-label use in Medicare beneficiaries 18 years and older. RU and costs between propensity score matched AAPs user and non-user cohorts were compared in a retrospective cohort study. Results: The adjusted odds of having an office-based outpatient (OR=2.47, 95%CI: 1.55-3.92) or inpatient (OR=1.63, 95%CI: 1.26-2.10) visit were significantly higher among off-label AAPs users. Adjusted office-based visit ($1,943 vs. $1,346), prescription ($4,153 vs. $1,252) and total ($10,694 vs. $4,823) costs were significantly higher among users (p<0.0001). Among Medicare beneficiaries, approximately 37% of AAPs users had no FDA approved diagnosis. The typical off-label user was a white 70-year-old male. Common off-label uses were depression, anxiety and neurotic disorders and dementia. Off-label AAPs users had significantly higher mental health outpatient ($461 vs $297), prescription ($2,349 vs $282) and total ($3,665 vs $1,297) costs per beneficiary than non-users. About 30% of AAPs users had at least one mental health outpatient visit during the year versus 23% of non-users; no significant differences were found in inpatient visits. AAPs non-users had significantly higher all-cause inpatient costs ($6,945 vs. $4,841) per beneficiary (p Conclusion: In a nationally representative population comprising a younger age group AAPs users had higher all-cause RU and total costs than non-users. Off-label prescribing of AAPs continued to be a prevalent practice affecting 37% of Medicare AAPs users. Off-label AAPs users had higher mental health costs but no significant differences in all-cause total health care costs in a Medicare population. Off-label use of AAPs can be a cost-effective option if future research shows off-label use is associated with increased effectiveness, which offsets any additional costs.

No description available.

Introduction: Prescription drug monitoring programs (PDMPs) are one strategy established to curb the prescription opioid abuse epidemic. Prescriber use mandates has emerged as a promising practice to increase PDMPs impact on prescription opioid abuse; however, evidence of its effectiveness has not yet been established. Kentucky was the first state to implement comprehensive prescriber use mandates in July 2012. Objective: To assess the relationship between prescriber use mandates policy and emergency department (ED) visits related to prescription opioid poisoning among adults in Kentucky and North Carolina. Secondary aim: to evaluate the economic impact of prescriber use mandates in Kentucky. Methods: A controlled, pre-post study design. Data from the State Emergency Department Databases (SEDD) and the State Inpatient databases (SID) were used to identify prescription opioid poisoning ED visits among those ≥ 12 years old. Prevalence rate were estimated. Prescription opioid poisoning ED visits were characterized based on sociodemographic and clinical characteristics. Logistic regression was applied to compare occurrences of prescription opioid poisoning ED visits pre and post prescriber use mandates in Kentucky, and between Kentucky and North Carolina for the period 2011 to 2014. A cost of illness framework was applied to estimate direct medical costs associated with prescription opioid poisoning ED visits. The economic impact of prescriber use mandates was quantified based on logistic regression coefficient for the interaction term (state*time to implementation). Results: There were 7,419 and 12,598 prescription opioid poisoning -related ED visits in Kentucky and North Carolina, respectively. Young and Middle age, male gender, white, having one or more chronic conditions, and psychiatric conditions (such as depression and drug abuse) were significantly associated with prescription opioid poisoning ED visits (p-value<0.05). The odds of having a prescription opioid poisoning ED visit in Kentucky were significantly lower compared to North Carolina in 2012, 2013, and 2014 compared to 2011 (OR = 0.9, 0.7, and 0.7 respectively). The total estimated direct medical costs were $13.77 and $24.37 million in Kentucky and North Carolina, respectively. In Kentucky, the economic impact of prescriber use mandates was estimated at - $2.3 million. Conclusion: Prescriber use mandates is effective in reducing prescription opioid poisoning ED visits, and its economic impact is considerable.

Introduction: Warfarin is the most commonly prescribed drug for stroke prevention among Atrial Fibrillation (AF) patients, especially in older adult populations, but medication nonadherence reduces its effectiveness in clinical practice. Group Based Trajectory Models (GBTM) have been used to identify distinct patterns of adherence behavior related to various medications and understand the patient characteristics associated with each trajectory. The objectives of the study were: 1) Describe trajectories of warfarin adherence among Medicare AF patients, 2) Assess impact of adherence trajectories on AF-related hospitalization, 3) Estimate the AF-related direct costs for each adherence trajectory group. Methods: We identified elderly AF patients initiating warfarin treatment during 2008-2010 using data from a random sample of Medicare beneficiaries. The study’s first aim is to classify patients into different trajectory groups based on their monthly adherence patterns using a Group-Based Trajectory Model (GBTM). A multinomial regression model was used to assess associations between baseline characteristics and adherence trajectories. The second aim is to evaluate the association between adherence trajectories and time to first hospitalization related to stroke or bleeding event. Hospitalization events due to bleeding or stroke were identified using corresponding ICD-9 codes, and a Cox proportional hazard model was performed. The third aim of the study is to calculate AF-related direct medical costs associated with each trajectory group. SASv9.4 was used for analysis. Results: Among 3,246 beneficiaries who met inclusion criteria, six adherence trajectories were identified: 1) rapid-decline non-adherence group (11.5%), 2) moderate non-adherence group (24%), 3) rapid-decline then increasing adherence group (6.8%), 4) moderate-decline non-adherence group (8.2%), 5) slow-decline non-adherence group (24.3%), and 6) perfect adherence group (25.3%). Even though no statistical significances were found in the hazard of hospitalization among the adherence groups, there were higher odds of hospitalization among the lower adherence groups compared to perfect adherence group. Outpatient and monitoring costs were significantly higher in the lower adherence trajectories compared to perfect adherence group. Conclusion:The GBTM is considered an innovative methodological approach that can be applied to longitudinal medication adherence data and account for the dynamic nature of adherence behavior in a better way than traditional adherence measures.

This thesis aimed to elucidate the demographic characteristics associated with elevated or reduced rates of pneumococcal conjugate 13 (PCV13) vaccination. A retrospective cohort study was performed using the Truven Health MarketScan® Database. Three cohorts were created corresponding to populations for which the CDC recommends PCV13 vaccination. Cohort 1: children < 36 months of age. Cohort 2: adults 19-64 years of age with high infection risk. Cohort 3: adults > 65 years of age. Odds of having a PCV13 claim were calculated for each cohort. For Cohort 1, 78% out of a total of 353,214 subjects had a sufficient number of PCV13 doses to meet CDC recommendations. For Cohort 2, 3.7% out of a total of 673,157 subjects had a PCV13 claim. For Cohort 3, 18% of 1,262,531 subjects had a PCV13 claim. Odds of vaccination were generally lower in younger subjects, those with fewer outpatient claims, and those with residence in the Northeast and South regions. In Cohort 2, odds were reduced in subjects with generalized malignancy. Gender and urban residence were poor predictors of vaccination status. By understanding the demographic factors associated with lower rates of vaccination, clinicians may more effectively direct their efforts to increase pneumococcal vaccination coverage.

Objective. To examine the relationship between the NAPLEX and Pre-NAPLEX among pharmacy graduates, as well as determine effects of pre-pharmacy, pharmacy school, and demographic variables on NAPLEX performance. Methods. A retrospective review of pharmacy graduates' NAPLEX scores, Pre-NAPLEX scores, demographics, pre-pharmacy academic performance factors, and pharmacy school academic performance factors was performed. Bivariate (eg, ANOVA, independent samples t-test) and correlational analyses were conducted, as was stepwise linear regression to examine the significance of Pre-NAPLEX score and other factors as related to NAPLEX score. Results. One hundred fifty graduates were included, with the majority being female (60.7%) and white (72%). Mean NAPLEX score was 104.7. Mean Pre-NAPLEX score was 68.6. White students had significantly higher NAPLEX scores compared to Black/African American students. NAPLEX score was correlated to Pre-NAPLEX score, race/ethnicity, PCAT composite and section scores, undergraduate overall and science GPAs, pharmacy GPA, and on-time graduation. The regression model included pharmacy GPA and Pre-NAPLEX score. Conclusion. The findings provide evidence that, although pharmacy GPA is the most critical determinant, the Pre-NAPLEX score is also a significant predictor of NAPLEX score.