Economic impact of dry eye disease in Spain: A multicentre retrospective insurance claims database analysis

To analyse the occurrence and cost of dry eye disease in Spain in the recent years. A cross-sectional analysis based on anonymised data from an insurance claims database that includes data from 1997 to 2015 from public and private hospitals and healthcare centres; 36,081 patients were eligible for the study after duplicate elimination. Five ICD9 codes associated with dry eye were used for patient selection, including vitamin A deficiency with xerophthalmic scars of cornea, xerophthalmia due to vitamin A deficiency, keratoconjunctivitis sicca not specified as Sjögren's, dry eye syndrome and keratoconjunctivitis sicca Sjögren's disease

Patient clusters and cost trajectories in the Swiss Atrial Fibrillation cohort

Objective: Evidence on long-term costs of atrial fibrillation (AF) and associated factors is scarce. As part of the Swiss-AF prospective cohort study, we aimed to characterise AF costs and their development over time, and to assess specific patient clusters and their cost trajectories. Methods: Swiss-AF enrolled 2415 patients with variable duration of AF between 2014 and 2017. Patient clusters were identified using hierarchical cluster analysis of baseline characteristics. Ongoing yearly follow-ups include health insurance clinical and claims data. An algorithm was developed to adjudicate costs to AF and related complications. Results: A subpopulation of 1024 Swiss-AF patients with available claims data was followed up for a median (IQR) of 3.24 (1.09) years. Average yearly AF-adjudicated costs amounted to SFr5679 (€5163), remaining stable across the observation period. AF-adjudicated costs consisted mainly of inpatient and outpatient AF treatment costs (SFr4078; €3707), followed by costs of bleeding (SFr696; €633) and heart failure (SFr494; €449). Hierarchical analysis identified three patient clusters: cardiovascular (CV; N=253 with claims), isolated-symptomatic (IS; N=586) and severely morbid without cardiovascular disease (SM; N=185). The CV cluster and SM cluster depicted similarly high costs across all cost outcomes; IS patients accrued the lowest costs. Conclusion: Our results highlight three well-defined patient clusters with specific costs that could be used for stratification in both clinical and economic studies. Patient characteristics associated with adjudicated costs as well as cost trajectories may enable an early understanding of the magnitude of upcoming AF-related healthcare costs. Keywords: Atrial Fibrillation; Health Care Economics and Organization

External validation of decision-analytic models based on claims data of health insurance funds

Background: Decision-analytic models are used in the context of economic evaluation to bring together the best available evidence and to support the decision on the adoption of a health technology. A decision model’s credibility is, however, diminished by uncertainty which, to large part, stems from parameter uncertainty. Especially when novel technologies are evaluated, high quality evidence may not be available at the point of coverage decision making. A decision model incorporating uncertain parameter values eventually simulates uncertain effectiveness and cost outcomes. To enhance credibility of decision models, external validation of uncertain parameter values is vital. Data sources for external validation should be able to reflect the model’s study design and patient cohort, and estimate real-world effectiveness and costs. Objective: This study assesses whether claims data of health insurance funds are suitable to externally validate decision-analytic models. Methods: To answer the research question, a validation approach is developed which highlights critical steps in the validation of decision models based on claims data. The validation steps are: 1) selection of the validation level, 2) selection of the claims dataset, study design, and patient cohort, 3) selection of disease-relevant health technologies and costs, 4) statistical analysis of claims data, 5) changes to the decision model, 6) comparison between model and claims data, and 7) sensitivity analyses. The validation approach is exemplarily applied in the validation of a Markov model comparing treatment of localized prostate cancer (active surveillance and radical prostatectomy) in a German health care context, based on claims data of the German AOK statutory health insurance fund. An external validation of resource use, probability of utilization, and cost parameters is chosen, because these parameters are afflicted by a high degree of uncertainty in the decision model. Two different approaches to the analysis of relevant health technologies for prostate cancer treatment are presented in claims data analysis: an excess approach and a disease-related approach. Results: The decision model assumes that resource use and unit costs are identical in the two treatment groups; this is, however, not observed in claims data analysis. Excess cost analysis and disease-related cost analysis of AOK claims data as well as model analysis show that, overall, active surveillance is the less costly strategy compared to radical prostatectomy, with total incremental costs of €-6,611, €-6,260, and €-7,486 respectively. When testing differences between model and outcomes of claims data analysis, p-values of 0.61 (excess approach) and 0.18 (disease-related approach) indicate an agreement that is sufficient to assume that the decision model simulates real-world costs validly. Discussion: This study reveals general strengths and limitations of claims data based model validation. Claims data are able to provide evidence on real-world resource utilization and, with limitations regarding clinical information, effectiveness of a wide range of indications and treatments for a large patient cohort. Validation based on claims data is especially suitable when the decision maker, interested in the validity of the model in question, is the insurance fund providing access to the claims data. Suitability of claims data based validation is, however, limited concerning the replication of decision models’ structure and patient cohort. For one, the identification of distinct health states is limited, because clinical information is not included in sufficient detail. Secondly, due to non-randomization and a restricted number of variables available to adjust for confounding, comparability of treatment groups is limited in claims data analysis. Thirdly, distinct identification of health technology utilization and corresponding costs is not possible if the technology of interest is not specifically coded. Finally, claims data are, generally, collected for billing purposes; diagnoses and technology utilization are only coded if they are relevant for reimbursement by the insurance fund, which biases outcomes of model validation in cases where treatment is not covered by the insurance fund. Conclusion: The presented validation approach indicates critical aspects of the validation based on claims data, which may support researchers and decision makers in their decision on the suitability of claims data for model validation. The suitability of claims data for the external validation of a decision model ultimately depends on the ability of the claims data source to reflect the model’s patient cohort and outcome measures.Hintergrund: Entscheidungsanalytische Modelle kommen im Rahmen der gesundheitsökonomischen Evaluation von Gesundheitstechnologien zum Einsatz, um die beste verfügbare Evidenz zusammenzuführen und damit die Erstattungsentscheidung zu unterstützen. Bei der Evaluation von innovativen Technologien ist allerdings häufig zum Zeitpunkt der Erstattungsentscheidung keine hochwertige Evidenz, etwas aus klinischen Studien, verfügbar. Diese Parameterunsicherheit spiegelt sich letztlich in der im Entscheidungsmodell simulierten Kosteneffektivität der jeweiligen innovativen Technologien wieder. Für den Entscheidungsträger ist somit die Glaubwürdigkeit von Entscheidungsmodellen eingeschränkt. Um die Glaubwürdigkeit eines Entscheidungsmodells zu erhöhen, ist eine externe Validierung der unsicheren Parameterwerte von entscheidender Bedeutung. Datenquellen für eine externe Validierung sollten in der Lage sein, das Studiendesign und die Kohorte des Entscheidungsmodells zu reflektieren sowie reale Effekte und Kosten der evaluierten Technologie zu schätzen. Fragestellung: Im Rahmen dieser Studie wird untersucht, in wie weit sich Abrechnungsdaten von Krankenkassen für die externe Validierung von entscheidungsanalytischen Modellen eignen. Methoden: Um die Forschungsfrage zu beantworten, wurde ein Validierungsansatz entwickelt, welcher entscheidende Schritte bei der Validierung von Entscheidungsmodellen auf der Basis von Abrechnungsdaten beschreibt. Die einzelnen Validierungsschritte sind: 1) Auswahl der Validierungsebene, 2) Auswahl des externen Datensatzes, des Studiendesigns und der Patientenkohorte, 3) Definition von krankheitsrelevanten Gesundheitstechnologien und Kosten, 4) Auswahl der statistischen Methoden zur Analyse der Abrechnungsdaten, 5) Anpassung des Entscheidungsmodells, 6) Auswahl von Methoden zum Vergleich zwischen Modell und Abrechnungsdaten, und 7) Sensitivitätsanalysen. Der Validierungsansatz wird beispielhaft für die Validierung eines Markov-Modells angewendet, welches Behandlungsmethoden des lokalisierten Prostatakarzinoms (Active Surveillance und radikale Prostatektomie) in einem deutschen Versorgungskontext vergleicht. Zur Validierung werden Abrechnungsdaten einer deutschen gesetzlichen Krankenkasse, der AOK Baden-Württemberg, herangezogen. Es werden Parameterwerte des Entscheidungsmodells zum Ressourcenverbrauch, zur Inanspruchnahmewahrscheinlichkeit und zu Kosten validiert, da diese Parameter die größte Unsicherheit aufweisen. Dabei werden zwei verschiedene Vorgehensweisen zur Analyse der Abrechnungsdaten der AOK herangezogen: ein Excesskosten-Ansatz und ein Krankheitskosten-Ansatz. Ergebnisse: Im Entscheidungsmodell wird davon ausgegangen, dass Ressourcenverbrauch und Stückkosten in beiden Behandlungsgruppen identisch sind; in den Abrechnungsdaten der AOK ist diese Annahme allerdings nicht wiederzufinden. Sowohl die Excesskosten-Analyse und die krankheitskostenbezogene Analyse der AOK-Daten als auch die Modellanalyse zeigen, dass Active Surveillance insgesamt die kostengünstigere Strategie mit einer Ersparnis von jeweils 6.611€, 6.260€ und 7.486€ gegenüber der radikalen Prostatektomie ist. Der statistische Test der Kostendifferenz aus Modell und AOK-Daten ergibt p-Werte von 0,61 (Excesskosten-Ansatz) und 0,18 (Krankheitskosten-Ansatz), die auf eine signifikante Übereinstimmung der Schätzer aus Modell und AOK-Daten schließen lassen. Die Übereinstimmung der Schätzer lässt vermuten, dass das Entscheidungsmodell in der Lage ist, die Kosten der Behandlung des lokalisierten Prostatakarzinoms valide zu simulieren. Diskussion: Die beispielhafte Validierung des Markov-Modells anhand von Abrechnungsdaten der AOK Baden-Württemberg zeigt allgemeine Stärken und Schwächen der Kassendaten-basierten Modellvalidierung auf. Abrechnungsdaten sind in der Lage, Evidenz zur tatsächlichen Utilisierung von Gesundheitsleistungen und, mit Einschränkungen in Bezug auf klinische Informationen, Wirksamkeit einer Vielzahl von Behandlungsoptionen für eine große Patientenpopulation zu liefern. Die Validierung auf Basis von Abrechnungsdaten ist vor allem sinnvoll, wenn die Modellvalidierung aus der Perspektive einer Krankenkasse durchgeführt werden soll. Die Eignung von Abrechnungsdaten für die Modellvalidierung ist jedoch hinsichtlich der Nachbildung der Modellstruktur und der Patientenkohorte des Entscheidungsmodells limitiert. Erstens ist die Identifikation von Gesundheitszuständen in Kassendaten begrenzt, da klinische Informationen nicht ausreichend detailliert enthalten sind. Zweitens ist die Vergleichbarkeit der Behandlungsgruppen eingeschränkt, da eine Randomisierung nicht möglich ist und nur eine begrenzte Anzahl an Variablen zur Verfügung steht, um für Confounder zu adjustieren. Drittens ist eine eindeutige Identifizierung von Gesundheitsleistungen und deren Kosten schwierig, wenn die Leistung nicht explizit in den Abrechnungsdaten kodiert ist. Viertens werden Kassendaten zu Abrechnungszwecken gesammelt und deshalb werden auch nur solche Diagnosen und Gesundheitsleistungen kodiert, die für die Erstattung durch die Krankenkasse relevant sind. Für Gesundheitsleistungen, die nicht von der Krankenkasse vergütet werden, ist unter Umständen keine valide Schätzung zu Ressourcenverbrauch und Kosten möglich. Fazit: Der entwickelte Validierungsansatz zeigt kritische Aspekte der Modellvalidierung auf Basis von Abrechnungsdaten von Krankenkassen auf. Er soll Wissenschaftler und Entscheidungsträger bei der Entscheidung über die Eignung von Abrechnungsdaten für die externe Validierung eines Modells unterstützen. Die Eignung von Abrechnungsdaten für die externe Validierung eines Entscheidungsmodells hängt letztlich von der Fähigkeit ab, Modellstruktur, Kohorte und Zielparameter des Modells abzubilden

External validation of decision-analytic models based on claims data of health insurance funds

Background: Decision-analytic models are used in the context of economic evaluation to bring together the best available evidence and to support the decision on the adoption of a health technology. A decision model’s credibility is, however, diminished by uncertainty which, to large part, stems from parameter uncertainty. Especially when novel technologies are evaluated, high quality evidence may not be available at the point of coverage decision making. A decision model incorporating uncertain parameter values eventually simulates uncertain effectiveness and cost outcomes. To enhance credibility of decision models, external validation of uncertain parameter values is vital. Data sources for external validation should be able to reflect the model’s study design and patient cohort, and estimate real-world effectiveness and costs. Objective: This study assesses whether claims data of health insurance funds are suitable to externally validate decision-analytic models. Methods: To answer the research question, a validation approach is developed which highlights critical steps in the validation of decision models based on claims data. The validation steps are: 1) selection of the validation level, 2) selection of the claims dataset, study design, and patient cohort, 3) selection of disease-relevant health technologies and costs, 4) statistical analysis of claims data, 5) changes to the decision model, 6) comparison between model and claims data, and 7) sensitivity analyses. The validation approach is exemplarily applied in the validation of a Markov model comparing treatment of localized prostate cancer (active surveillance and radical prostatectomy) in a German health care context, based on claims data of the German AOK statutory health insurance fund. An external validation of resource use, probability of utilization, and cost parameters is chosen, because these parameters are afflicted by a high degree of uncertainty in the decision model. Two different approaches to the analysis of relevant health technologies for prostate cancer treatment are presented in claims data analysis: an excess approach and a disease-related approach. Results: The decision model assumes that resource use and unit costs are identical in the two treatment groups; this is, however, not observed in claims data analysis. Excess cost analysis and disease-related cost analysis of AOK claims data as well as model analysis show that, overall, active surveillance is the less costly strategy compared to radical prostatectomy, with total incremental costs of €-6,611, €-6,260, and €-7,486 respectively. When testing differences between model and outcomes of claims data analysis, p-values of 0.61 (excess approach) and 0.18 (disease-related approach) indicate an agreement that is sufficient to assume that the decision model simulates real-world costs validly. Discussion: This study reveals general strengths and limitations of claims data based model validation. Claims data are able to provide evidence on real-world resource utilization and, with limitations regarding clinical information, effectiveness of a wide range of indications and treatments for a large patient cohort. Validation based on claims data is especially suitable when the decision maker, interested in the validity of the model in question, is the insurance fund providing access to the claims data. Suitability of claims data based validation is, however, limited concerning the replication of decision models’ structure and patient cohort. For one, the identification of distinct health states is limited, because clinical information is not included in sufficient detail. Secondly, due to non-randomization and a restricted number of variables available to adjust for confounding, comparability of treatment groups is limited in claims data analysis. Thirdly, distinct identification of health technology utilization and corresponding costs is not possible if the technology of interest is not specifically coded. Finally, claims data are, generally, collected for billing purposes; diagnoses and technology utilization are only coded if they are relevant for reimbursement by the insurance fund, which biases outcomes of model validation in cases where treatment is not covered by the insurance fund. Conclusion: The presented validation approach indicates critical aspects of the validation based on claims data, which may support researchers and decision makers in their decision on the suitability of claims data for model validation. The suitability of claims data for the external validation of a decision model ultimately depends on the ability of the claims data source to reflect the model’s patient cohort and outcome measures.Hintergrund: Entscheidungsanalytische Modelle kommen im Rahmen der gesundheitsökonomischen Evaluation von Gesundheitstechnologien zum Einsatz, um die beste verfügbare Evidenz zusammenzuführen und damit die Erstattungsentscheidung zu unterstützen. Bei der Evaluation von innovativen Technologien ist allerdings häufig zum Zeitpunkt der Erstattungsentscheidung keine hochwertige Evidenz, etwas aus klinischen Studien, verfügbar. Diese Parameterunsicherheit spiegelt sich letztlich in der im Entscheidungsmodell simulierten Kosteneffektivität der jeweiligen innovativen Technologien wieder. Für den Entscheidungsträger ist somit die Glaubwürdigkeit von Entscheidungsmodellen eingeschränkt. Um die Glaubwürdigkeit eines Entscheidungsmodells zu erhöhen, ist eine externe Validierung der unsicheren Parameterwerte von entscheidender Bedeutung. Datenquellen für eine externe Validierung sollten in der Lage sein, das Studiendesign und die Kohorte des Entscheidungsmodells zu reflektieren sowie reale Effekte und Kosten der evaluierten Technologie zu schätzen. Fragestellung: Im Rahmen dieser Studie wird untersucht, in wie weit sich Abrechnungsdaten von Krankenkassen für die externe Validierung von entscheidungsanalytischen Modellen eignen. Methoden: Um die Forschungsfrage zu beantworten, wurde ein Validierungsansatz entwickelt, welcher entscheidende Schritte bei der Validierung von Entscheidungsmodellen auf der Basis von Abrechnungsdaten beschreibt. Die einzelnen Validierungsschritte sind: 1) Auswahl der Validierungsebene, 2) Auswahl des externen Datensatzes, des Studiendesigns und der Patientenkohorte, 3) Definition von krankheitsrelevanten Gesundheitstechnologien und Kosten, 4) Auswahl der statistischen Methoden zur Analyse der Abrechnungsdaten, 5) Anpassung des Entscheidungsmodells, 6) Auswahl von Methoden zum Vergleich zwischen Modell und Abrechnungsdaten, und 7) Sensitivitätsanalysen. Der Validierungsansatz wird beispielhaft für die Validierung eines Markov-Modells angewendet, welches Behandlungsmethoden des lokalisierten Prostatakarzinoms (Active Surveillance und radikale Prostatektomie) in einem deutschen Versorgungskontext vergleicht. Zur Validierung werden Abrechnungsdaten einer deutschen gesetzlichen Krankenkasse, der AOK Baden-Württemberg, herangezogen. Es werden Parameterwerte des Entscheidungsmodells zum Ressourcenverbrauch, zur Inanspruchnahmewahrscheinlichkeit und zu Kosten validiert, da diese Parameter die größte Unsicherheit aufweisen. Dabei werden zwei verschiedene Vorgehensweisen zur Analyse der Abrechnungsdaten der AOK herangezogen: ein Excesskosten-Ansatz und ein Krankheitskosten-Ansatz. Ergebnisse: Im Entscheidungsmodell wird davon ausgegangen, dass Ressourcenverbrauch und Stückkosten in beiden Behandlungsgruppen identisch sind; in den Abrechnungsdaten der AOK ist diese Annahme allerdings nicht wiederzufinden. Sowohl die Excesskosten-Analyse und die krankheitskostenbezogene Analyse der AOK-Daten als auch die Modellanalyse zeigen, dass Active Surveillance insgesamt die kostengünstigere Strategie mit einer Ersparnis von jeweils 6.611€, 6.260€ und 7.486€ gegenüber der radikalen Prostatektomie ist. Der statistische Test der Kostendifferenz aus Modell und AOK-Daten ergibt p-Werte von 0,61 (Excesskosten-Ansatz) und 0,18 (Krankheitskosten-Ansatz), die auf eine signifikante Übereinstimmung der Schätzer aus Modell und AOK-Daten schließen lassen. Die Übereinstimmung der Schätzer lässt vermuten, dass das Entscheidungsmodell in der Lage ist, die Kosten der Behandlung des lokalisierten Prostatakarzinoms valide zu simulieren. Diskussion: Die beispielhafte Validierung des Markov-Modells anhand von Abrechnungsdaten der AOK Baden-Württemberg zeigt allgemeine Stärken und Schwächen der Kassendaten-basierten Modellvalidierung auf. Abrechnungsdaten sind in der Lage, Evidenz zur tatsächlichen Utilisierung von Gesundheitsleistungen und, mit Einschränkungen in Bezug auf klinische Informationen, Wirksamkeit einer Vielzahl von Behandlungsoptionen für eine große Patientenpopulation zu liefern. Die Validierung auf Basis von Abrechnungsdaten ist vor allem sinnvoll, wenn die Modellvalidierung aus der Perspektive einer Krankenkasse durchgeführt werden soll. Die Eignung von Abrechnungsdaten für die Modellvalidierung ist jedoch hinsichtlich der Nachbildung der Modellstruktur und der Patientenkohorte des Entscheidungsmodells limitiert. Erstens ist die Identifikation von Gesundheitszuständen in Kassendaten begrenzt, da klinische Informationen nicht ausreichend detailliert enthalten sind. Zweitens ist die Vergleichbarkeit der Behandlungsgruppen eingeschränkt, da eine Randomisierung nicht möglich ist und nur eine begrenzte Anzahl an Variablen zur Verfügung steht, um für Confounder zu adjustieren. Drittens ist eine eindeutige Identifizierung von Gesundheitsleistungen und deren Kosten schwierig, wenn die Leistung nicht explizit in den Abrechnungsdaten kodiert ist. Viertens werden Kassendaten zu Abrechnungszwecken gesammelt und deshalb werden auch nur solche Diagnosen und Gesundheitsleistungen kodiert, die für die Erstattung durch die Krankenkasse relevant sind. Für Gesundheitsleistungen, die nicht von der Krankenkasse vergütet werden, ist unter Umständen keine valide Schätzung zu Ressourcenverbrauch und Kosten möglich. Fazit: Der entwickelte Validierungsansatz zeigt kritische Aspekte der Modellvalidierung auf Basis von Abrechnungsdaten von Krankenkassen auf. Er soll Wissenschaftler und Entscheidungsträger bei der Entscheidung über die Eignung von Abrechnungsdaten für die externe Validierung eines Modells unterstützen. Die Eignung von Abrechnungsdaten für die externe Validierung eines Entscheidungsmodells hängt letztlich von der Fähigkeit ab, Modellstruktur, Kohorte und Zielparameter des Modells abzubilden

Characteristics of Medicare Beneficiaries with Chronic Diseases Utilizing Telemedicine and the Impact on Overall Costs, Outpatient Costs, Inpatient Costs, and Number of Inpatient Admissions

In the healthcare industry in the United States, utilization of telemedicine to treat chronic and acute care conditions shows promise in increasing access, decreasing costs, and improving patient satisfaction. While telemedicine is not a new idea, only in recent years has there been the culmination of innovation, legislation, and advancement in practice to forge new virtual paths to high-quality treatment of patients through telemedicine utilization. The study design is a retrospective quasi-experimental cohort analysis of secondary patient claims data from 2012 to 2014. Using data from the Medicare Limited Data Set 5% Medicare sample, we compare healthcare costs for two groups of Medicare beneficiaries with chronic disease: those who utilized a telemedicine service and those who had a traditional face-to-face visit. Propensity score (PS) weighting was used to match the groups on age, race, sex, dual eligibility for Medicare and Medicaid. Analysis of the cost outcome utilized a gamma distributed models with log link functions controlling for age, Charlson Score, and Hypertension. When examining a six-month post visit period, results found a cost saving of $1,828 for the Telehealth group compared to the matched group of beneficiaries with an in-person visit. Telehealth is a promising approach to increase access to care and is associated with decreased costs for Medicare beneficiaries with chronic disease

Use of Western Medicine and Traditional Korean Medicine for Joint Disorders: A Retrospective Comparative Analysis Based on Korean Nationwide Insurance Data

This study aimed to compare the usage of Western medicine and traditional Korean medicine for treating joint disorders in Korea. Data of claims from all medical institutions with billing statements filed to HIRA from 2011 to 2014 for the four most frequent joint disorders were used for the analysis. Data from a total of 1,100,018 patients who received medical services from 2011 to 2014 were analyzed. Descriptive statistics are presented as type of care and hospital type. All statistical analyses were performed using IBM SPSS for Windows version 21. Of the 1,100,018 patients with joint disorders, 456,642 (41.5%) were males and 643,376 (58.5%) were females. Per diem costs of hospitalization in Western medicine clinics and traditional Korean medicine clinics were approximately 160,000 KRW and 50,000 KRW, respectively. Among costs associated with Western medicine, physiotherapy cost had the largest proportion (28.78%). Among costs associated with traditional Korean medicine, procedural costs and treatment accounted for more than 70%, followed by doctors’ fees (21.54%). There were distinct differences in patterns of medical care use and cost of joint disorders at the national level in Korea. This study is expected to contribute to management decisions for musculoskeletal disease involving joint disorders

Drug utilization and cost in a Medicaid population: A simulation study of community vs. mail order pharmacy

<p>Abstract</p> <p>Background</p> <p>Outpatient drugs are dispensed through both community and mail order pharmacies. There is no empirical evidence that substitution of community pharmacy with mail order reduces overall drug expenditures. The need for evaluating the potential effects on utilization and costs of the possible extension of mail order services in Medicaid provides the rationale for conducting this study. This study compares drug utilization and drug product cost in community vs. mail order pharmacy dispensing services in a Medicaid population.</p> <p>Methods</p> <p>This study is a retrospective cohort study comparing utilization and cost patterns in community vs. mail order pharmacy. A simulation model was employed to assess drug utilization and cost in mail order pharmacy using community pharmacy claim data. The model assumed that courses of drug therapy (CDT) in mail order pharmacy would have utilization patterns similar to those found in community pharmacy. A 95% confidence interval surrounding changes in average utilization and average cost were estimated using bootstrap analysis. A sensitivity analysis was performed by varying drug selection criteria and supply, fill point, and medication possession ratio (MPR). Sub-analyses were performed to address differences between mail order and community pharmacy related to therapeutic class and dual-eligible patients.</p> <p>Data for the study derived from pharmacy claims database of Ohio Medicaid State program for the period January 2000-September 2004. Drug claims were aggregated to obtain a set of CDTs representing unique patient IDs and unique drug products. Drug product cost estimates excluded dispensing fees and were used to estimate the cost reduction required in mail order to become cost neutral in comparison with community pharmacy.</p> <p>Results</p> <p>The baseline model revealed that the use of mail order vs. community pharmacy would result in a 5.5% increase in drug utilization and a 5.4% cost reduction required in mail order to become cost neutral. Results from Ohio Medicaid drugs for chronic use revealed a 5.1% increase in utilization and a 4.9% cost reduction required to become cost neutral in comparison with community pharmacy.</p> <p>Conclusion</p> <p>The results of the simulation model indicate that mail order pharmacy increases drug utilization and can also increase drug product cost if the cost per unit is not reduced accordingly. Prior consideration should be given to the patient population, day-supply, disease, therapy, and insurance characteristics to ensure the appropriate use of mail order pharmacy services.</p

Effectiveness of chronic obstructive pulmonary disease (COPD) management program at the University of Louisville.

Background: Chronic obstructive pulmonary disease (COPD) is a progressive disease of the respiratory system characterized by airflow limitation that is not completely reversible and is associated with systemic effects especially of the cardiovascular system. COPD is frequently complicated by acute exacerbations that contribute to physical impairment and increased health care use. As COPD is a chronic lung disease with significant systemic manifestations, it is important to have chronic disease management programs specifically targeting individuals with COPD designed to improve their overall quality of life, reduce the burden of disease and decrease the impact of COPD on daily life. Chronic disease management encompasses a multidisciplinary approach designed to enhance the quality and cost-effectiveness of health care for chronic conditions and has been defined as “an approach to patient care that emphasizes coordinated, comprehensive care along the continuum of disease and across health care delivery systems”. The purpose of this present study is to evaluate the effectiveness of a chronic obstructive pulmonary disease management program implemented at the University of Louisville in 2011. Methods: We conducted a retrospective observational cohort study of COPD subjects using clinical data from medical records and cost data from a claims dataset. Respiratory health was assessed by pulmonary function testing, St. George Respiratory questionnaire, COPD Assessment Test (CAT), 6 minute walk test (6MWT), Modified Medical Research Council (mMRC) dyspnea scale, and BODE index. General measures include Duke Profile for assessing overall health and Patient Health Questionnaire (PHQ-9) for assessing depression. At baseline, chi-square test for categorical variable and t-test for continuous variable was used to check for any difference between the two groups. To check for any longitudinal significant change in quality of life measures like SGRQ, CAT score, mMRC scale, BODE index, six minute walk distance and PFT measures from baseline paired t-test was performed. For each subject, the baseline probability of participation in the disease management program was calculated by the propensity score method using logistic regression analysis. Multiple linear regression analysis was performed to assess the rate of deterioration of various clinical parameters like FEV1 and FVC between two groups. Cost analysis was done by comparing the cost related to COPD among subjects in DMP group versus those under usual care. These costs includes total COPD cost, and also sub-categories of cost like office visit cost, in-patient hospitalization (IPH) cost, out-patient hospitalization (OPH) cost, pharmacy cost, cost related to home care and laboratory cost. Results: A total of 52 subjects were enrolled in the disease management program between February 1st 2011 and December 31st 2013: 37 in 2011, 11 in 2012 and 4 in 2013. The usual care group consists of 662 subjects diagnosed with COPD. There is a significant difference in average age of subjects between the two groups (54.2 in DMP versus 58.3 in usual care; P value 0.0094). Subjects who suffered from asthma, rhinitis and arthritis were significantly more likely to enroll in the disease management program. At baseline, the average PHQ9 was 6.3 which improved at the end of 12 months (mean = 4) and at 24 months (mean = 3.1). At baseline, the average duke score was 64.1 which were improved at the end of 12 months (mean = 71.6) and 24 months (mean = 68.3). At baseline average SGRQ score was 37.1 which were improved at the end of 12 months (mean = 28.4, P = 0.02) and 24 months from (mean = 30.2, P = 0.21). We found that not only did those subjects enrolled in the COPD program decrease their rate of loss of lung function, but remarkably showed a significant improvement in FEV1 from baseline to 12 months (mean difference: 140 ml, P value = 0.0046) and from baseline to 24 months (mean difference: 30 ml, P value 0.55). Average cost per person per year among subjects in DMP group in first year is $3693, which decreased to$3608.8 in second year and to $2934 in third year. Pharmacy cost contributes majority of total COPD cost followed by office cost and out-patient hospitalization. There is a significant decline in cost related to all major diseases like arthritis, hypertension, hyperlipidemia, diabetes, and osteoporosis after enrollment of COPD subjects into disease management program comparing to cost before enrollment into program. Average cost per person per year for in-patient hospitalization is significantly high for subjects in usual care ($5578.7) versus subjects in DMP group ($250.9). Conclusion: The University of Louisville COPD disease management program appears to be effective in improving lung health and reducing airflow limitations among COPD subjects as evidence by significant improvement in objective measures like FEV1. Program is also effective in reducing the impact of COPD on daily activities as evident by significant improvement in subjective measures for health related quality of life like St George Respiratory Questionnaire, COPD assessment test, PHQ9 and Duke Profile. Notwithstanding subjects in DMP had higher COPD related cost, they had significantly low in-patient hospitalization cost and also significant reduction in cost associated with major co-morbidities after enrollment in the disease management program

Direct and indirect costs of cluster headache : a prospective analysis in a tertiary level headache centre

Cluster headache (CH) is the most frequent trigemino-autonomic cephalgia. CH can manifest as episodic (ECH) or chronic cluster headache (CCH) causing significant burden of disease and requiring attack therapy and prophylactic treatment. The few data available on the economic burden of CH come from retrospective studies based on questionnaires, population surveys and medical insurance claims database. Although all these studies showed an important economic burden, they provided different estimates depending on variability of CH awareness and management, healthcare systems, available therapies and use of treatments according to different guidelines. This prospective study aimed to quantify the total direct and indirect cost of ECH and CCH over a cluster period, both for the patient and for the National Health System (NHS), using data from subjects who consecutively attended an Italian tertiary headache centre between January 1, 2018 and December 31, 2018. A total 108 patients (89 ECH, 19 CCH) were included. Mean attack frequency was 2.3 ± 1.4 per day. Mean total cost of a CH bout was €4398 per patient and total cost of CCH was 5.4 times higher than ECH (€13,350 vs. €2487, p <  0.001). Direct costs represented the 72.1% of total cost and were covered for the 94.8% by the NHS. The costs for any item of expense were higher for CCH than for ECH (p <  0.001). Mean indirect costs for a CH bout were €1226 per patient and were higher for CCH compared to ECH (€3.538 vs. €732), but the difference was not significant. Days with reduced productive capacity impacted for the 64.6% of the total indirect costs. The analysis of the impact CH on work showed that 27%% of patients felt that CH had limited their career, 40% had changed their work pattern, 20% had changed their place of employment and 10% had lost a job due to the disease. Our results provide a valuable estimate of the direct and indirect costs of ECH and CCH in the specific setting of a tertiary headache centre and confirm the high economic impact of CH on both the NHS and patients