J. Jaime Caro

A systematic review of low back pain cost of illness studies in the United States and internationally

BACKGROUND CONTEXT The economic burden of low back pain (LBP) is very large and appears to be growing. It is not possible to impact this burden without understanding the strengths and weaknesses of the research on which these costs are calculated. PURPOSE To conduct a systematic review of LBP cost of illness studies in the United States and internationally. STUDY DESIGN/SETTING Systematic review of the literature. METHODS Medline was searched to uncover studies about the direct or indirect costs of LBP published in English from 1997 to 2007. Data extracted for each eligible study included study design, population, definition of LBP, methods of estimating costs, year of data, and estimates of direct, indirect, or total costs. Results were synthesized descriptively. RESULTS The search yielded 147 studies, of which 21 were deemed relevant; 4 other studies and 2 additional abstracts were found by searching reference lists, bringing the total to 27 relevant studies. The studies reported on data from Australia, Belgium, Japan, Korea, the Netherlands, Sweden, the UK, and the United States. Nine studies estimated direct costs only, nine indirect costs only, and nine both direct and indirect costs, from a societal (n=18) or private insurer (n=9) perspective. Methodology used to derive both direct and indirect cost estimates differed markedly among the studies. Among studies providing a breakdown on direct costs, the largest proportion of direct medical costs for LBP was spent on physical therapy (17%) and inpatient services (17%), followed by pharmacy (13%) and primary care (13%). Among studies providing estimates of total costs, indirect costs resulting from lost work productivity represented a majority of overall costs associated with LBP. Three studies reported that estimates with the friction period approach were 56% lower than with the human capital approach. CONCLUSIONS Several studies have attempted to estimate the direct, indirect, or total costs associated with LBP in various countries using heterogeneous methodology. Estimates of the economic costs in different countries vary greatly depending on study methodology but by any standards must be considered a substantial burden on society. This review did not identify any studies estimating the total costs of LBP in the United States from a societal perspective. Such studies may be helpful in determining appropriate allocation of health-care resources devoted to this condition.

Anemia as an independent prognostic factor for survival in patients with cancer

Anemia is common in cancer patients, although the prevalence is influenced both by the type of malignancy and the choice of treatment. Individual studies have compared the survival of patients with and without anemia and have shown reduced survival times in patients with various malignancies, including carcinoma of the lung, cervix, head and neck, prostate, lymphoma, and multiple myeloma. The objective of this study was to systematically review, to summarize, and to obtain an overall estimate of the effect of anemia on survival in patients with malignant disease.

The Emerging Infectious Challenge of Clostridium difficile–Associated Disease in Massachusetts Hospitals: Clinical and Economic Consequences

OBJECTIVE To estimate the clinical and economic burden of Clostridium difficile-associated disease (CDAD) in Massachusetts over 2 years. DESIGN A retrospective analysis of Massachusetts hospital discharge data from 1999-2003 was conducted. Cases of CDAD in 2000 were identified using code 008.45 from the International Classification of Diseases, Ninth Revision, Clinical Modification; patients were excluded if they had a hospitalization in the prior year during which a diagnosis of CDAD was recorded. Hospitalizations for CDAD during 2001 and 2002 were examined. For primary case patients (ie, those for which CDAD was the principal diagnosis), all inpatient costs were deemed to be related, whereas for secondary case patients, all-patient refined diagnosis-related group assignment, case severity level, and length of stay (LOS) were used to calculate incremental costs attributable to CDAD. Costs were adjusted to the national level and reported in 2005 US dollars. RESULTS The CDAD cohort consisted of 3,692 patients; 59% were women, and the mean age was 70 years. This group represented 1% of all patients hospitalized in Massachusetts in 2000 (96% of hospitals treated at least 1 case; range, 1-257 cases). Of patients who received a first hospital diagnosis of CDAD in 2000, a total of 28% were primary case patients; their mean LOS was 6.4 days, and the mean cost per stay was

Model Transparency and Validation A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force–7

10,212. For secondary case patients, the mean CDAD-related incremental LOS was 2.95 days, and the mean incremental cost per stay was

Modeling good research practices--overview: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force--1.

13,675 per patient. Of patients with CDAD who survived their index stay in 2000, a total of 455 (14%) had at least 1 readmission for CDAD within the subsequent 2 years (mean number of readmissions, 1.4 per patient; range, 1-7 readmissions), with a mean time to first readmission of 3 months. Over 2 years, a total of 55,380 inpatient-days and

Budget impact analysis-principles of good practice: report of the ISPOR 2012 Budget Impact Analysis Good Practice II Task Force.

51.2 million were consumed by CDAD management. CONCLUSION CDAD is widespread in Massachusetts hospitals. Rehospitalization with CDAD, if it occurs, generally happens within a few months and happens multiple times for some patients. Based on this studys findings, a conservative estimate of the annual US cost for CDAD management is

The West of Scotland coronary prevention study: economic benefit analysis of primary prevention with pravastatin.

3.2 billion dollars.

Modeling Good Research Practices—Overview A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force–1

Trust and confidence are critical to the success of health care models. There are two main methods for achieving this: transparency (people can see how the model is built) and validation (how well it reproduces reality). This report describes recommendations for achieving transparency and validation, developed by a task force appointed by the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) and the Society for Medical Decision Making (SMDM). Recommendations were developed iteratively by the authors. A nontechnical description should be made available to anyone—including model type and intended applications; funding sources; structure; inputs, outputs, other components that determine function, and their relationships; data sources; validation methods and results; and limitations. Technical documentation, written in sufficient detail to enable a reader with necessary expertise to evaluate the model and potentially reproduce it, should be made available openly or under agreements that protect intellectual property, at the discretion of the modelers. Validation involves face validity (wherein experts evaluate model structure, data sources, assumptions, and results), verification or internal validity (check accuracy of coding), cross validity (comparison of results with other models analyzing same problem), external validity (comparing model results to real-world results), and predictive validity (comparing model results with prospectively observed events). The last two are the strongest form of validation. Each section of this paper contains a number of recommendations that were iterated among the authors, as well as the wider modeling task force jointly set up by the International Society for Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making.

Pharmacoeconomic analyses using discrete event simulation

Models--mathematical frameworks that facilitate estimation of the consequences of health care decisions--have become essential tools for health technology assessment. Evolution of the methods since the first ISPOR Modeling Task Force reported in 2003 has led to a new Task Force, jointly convened with the Society for Medical Decision Making, and this series of seven articles presents the updated recommendations for best practices in conceptualizing models; implementing state-transition approaches, discrete event simulations, or dynamic transmission models; and dealing with uncertainty and validating and reporting models transparently. This overview article introduces the work of the Task Force, provides all the recommendations, and discusses some quandaries that require further elucidation. The audience for these articles includes those who build models, stakeholders who utilize their results, and, indeed, anyone concerned with the use of models to support decision making.

Modeling Using Discrete Event Simulation A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force–4

BACKGROUND Budget impact analyses (BIAs) are an essential part of a comprehensive economic assessment of a health care intervention and are increasingly required by reimbursement authorities as part of a listing or reimbursement submission. OBJECTIVES The objective of this report was to present updated guidance on methods for those undertaking such analyses or for those reviewing the results of such analyses. This update was needed, in part, because of developments in BIA methods as well as a growing interest, particularly in emerging markets, in matters related to affordability and population health impacts of health care interventions. METHODS The Task Force was approved by the International Society for Pharmacoeconomics and Outcomes Research Health Sciences Policy Council and appointed by its Board of Directors. Members were experienced developers or users of BIAs; worked in academia and industry and as advisors to governments; and came from several countries in North America and South America, Oceania, Asia, and Europe. The Task Force solicited comments on the drafts from a core group of external reviewers and, more broadly, from the membership of the International Society for Pharmacoeconomics and Outcomes Research. RESULTS The Task Force recommends that the design of a BIA for a new health care intervention should take into account relevant features of the health care system, possible access restrictions, the anticipated uptake of the new intervention, and the use and effects of the current and new interventions. The key elements of a BIA include estimating the size of the eligible population, the current mix of treatments and the expected mix after the introduction of the new intervention, the cost of the treatment mixes, and any changes expected in condition-related costs. Where possible, the BIA calculations should be performed by using a simple cost calculator approach because of its ease of use for budget holders. In instances, however, in which the changes in eligible population size, disease severity mix, or treatment patterns cannot be credibly captured by using the cost calculator approach, a cohort or patient-level condition-specific model may be used to estimate the budget impact of the new intervention, accounting appropriately for those entering and leaving the eligible population over time. In either case, the BIA should use data that reflect values specific to a particular decision makers population. Sensitivity analysis should be of alternative scenarios chosen from the perspective of the decision maker. The validation of the model should include at least face validity with decision makers and verification of the calculations. Data sources for the BIA should include published clinical trial estimates and comparator studies for the efficacy and safety of the current and new interventions as well as the decision makers own population for the other parameter estimates, where possible. Other data sources include the use of published data, well-recognized local or national statistical information, and, in special circumstances, expert opinion. Reporting of the BIA should provide detailed information about the input parameter values and calculations at a level of detail that would allow another modeler to replicate the analysis. The outcomes of the BIA should be presented in the format of interest to health care decision makers. In a computer program, options should be provided for different categories of costs to be included or excluded from the analysis. CONCLUSIONS We recommend a framework for the BIA, provide guidance on the acquisition and use of data, and offer a common reporting format that will promote standardization and transparency. Adherence to these good research practice principles would not necessarily supersede jurisdiction-specific BIA guidelines but may support and enhance local recommendations or serve as a starting point for payers wishing to promulgate methodology guidelines.