R You Still Using Excel? The Advantages of Modern Software Tools for Health Technology Assessment

The Professional Society for Health Economics and Outcomes Research Economic models are used in health technology assessments (HTAs) to evaluate the cost-effectiveness of competing medical technologies and inform the efficient use of health care resources. Historically, these models have been developed with specialized commercial software (such as TreeAge) or more commonly with spreadsheet software (almost always Microsoft Excel). Although these tools may be sufficient for relatively simple analyses, they put unnecessary constraints on the analysis that may ultimately limit its credibility and relevance. In contrast, modern programming languages such as R, Python, Matlab, and Julia facilitate the development of models that are (i) clinically realistic, (ii) capable of quantifying decision uncertainty, (iii) transparent and reproducible, and (iv) reusable and adaptable. An HTA environment that encourages use of modern software can therefore help ensure that coverage and pricing decisions confer greatest possible benefit and capture all scientific uncertainty, thus enabling correct prioritization of future research

Essays in Health Economics and Political Economy

This dissertation examines issues in public policy---with an emphasis on health policy---from economic and political perspectives. It focuses on 1) how campaign donations influence party control in Congress, 2) racial/ethnic and educational inequalities in pharmaceutical spending, and 3) the effectiveness of cost containment policies in healthcare. The first chapter looks at whether Democratic and Republican parties optimally allocate resources in House elections. To do so, it estimates a probabilistic voting model using a Bayesian approach and compares actual spending patterns to the amount that should have been spent under the model. The correlation between actual spending and the amount that should have been spent is over 0.5 in each non-redistricting election from 2000 to 2010 and has generally increased over time. Surprisingly, these correlations are consistent across different types of campaign donors including political parties, political action committees, and individuals. Chapter two identifies differences in prescription drug utilization as a potential mechanism for the well known racial\ethnic and educational gradients in health. A two-part model predicts that, on average, blacks and Hispanics spend $350 and$560 less than whites respectively and that an additional 4 years of education increases prescription drug expenditures by $155. These documented disparities occur for two primary reasons: first, there are differences in the probability of being diagnosed with a disease; and second, there are gradients in expenditures conditional on diagnosis. The final chapter uses a dynamic Bayesian model to study two quantities of interest---out-of-pocket expenditure inequality and the uncertainty of longterm spending---that are essential for evaluating cost containment reforms. It shows the distribution of spending is less concentrated over longer periods than in a single period, but that there is still substantial inequality in long-term spending. Out-of-pocket expenditures are determined more by permanent and transitory shocks than observed data so individuals face considerable uncertainty in future health costs. Implications for policy are twofold. First, reforms that increase cost sharing must consider the risks of significant inequality and additional financial risk. Second, disease management programs have the potential to reduce costs significantly, but identifying high spenders will be difficult

The Lifetime Health Burden of Delayed Graft Function in Kidney Transplant Recipients in the United States

Background. Although delayed graft function (DGF) is associated with an increased risk of acute rejection and decreased graft survival, there are no estimates of the long-term or lifetime health burden of DGF. Objectives. To estimate the long-term and lifetime health burden of DGF, defined as the need for at least one dialysis session within the first week after transplantation, for a cohort representative of patients who had their first kidney transplant in 2014. Methods. Data from the United States Renal Data System (USRDS; 2001–2014) were used to estimate a semi-Markov parametric multi-state model with three disease states. Maximum length of follow-up was 13.7 years, and a microsimulation model was used to extrapolate results over a lifetime. The impact of DGF was assessed by simulating the model for each patient in the cohort with and without DGF. Results. At the end of 13.7 years of follow-up, DGF reduces the probability of having a functioning graft from 52% to 32%, increases the probability of being on dialysis from 10% to 19%, and increases the probability of death from 38% to 50% relative to transplant recipients who do not experience DGF. A typical transplant recipient with DGF (median age = 53) is observed to lose 0.87 quality-adjusted life-years (QALYs). Extrapolated over a lifetime, the same 53-year-old DGF patient is projected to lose 3.01 (95% confidence interval: 2.33, 3.70) QALYs relative to a transplant recipient with the same characteristics who does not experience DGF. Conclusions. The lifetime health burden of DGF is substantial. Understanding these consequences will help health care providers weigh kidney transplant decisions and inform policies for patients in the context of varying risks of DGF

Prognostic model to identify and quantify risk factors for mortality among hospitalised patients with COVID-19 in the USA

Objectives To develop a prognostic model to identify and quantify risk factors for mortality among patients admitted to the hospital with COVID-19.Design Retrospective cohort study. Patients were randomly assigned to either training (80%) or test (20%) sets. The training set was used to fit a multivariable logistic regression. Predictors were ranked using variable importance metrics. Models were assessed by C-indices, Brier scores and calibration plots in the test set.Setting Optum de-identified COVID-19 Electronic Health Record dataset including over 700 hospitals and 7000 clinics in the USA.Participants 17 086 patients hospitalised with COVID-19 between 20 February 2020 and 5 June 2020.Main outcome measure All-cause mortality while hospitalised.Results The full model that included information on demographics, comorbidities, laboratory results, and vital signs had good discrimination (C-index=0.87) and was well calibrated, with some overpredictions for the most at-risk patients. Results were similar on the training and test sets, suggesting that there was little overfitting. Age was the most important risk factor. The performance of models that included all demographics and comorbidities (C-index=0.79) was only slightly better than a model that only included age (C-index=0.76). Across the study period, predicted mortality was 1.3% for patients aged 18 years old, 8.9% for 55 years old and 28.7% for 85 years old. Predicted mortality across all ages declined over the study period from 22.4% by March to 14.0% by May.Conclusion Age was the most important predictor of all-cause mortality, although vital signs and laboratory results added considerable prognostic information, with oxygen saturation, temperature, respiratory rate, lactate dehydrogenase and white cell count being among the most important predictors. Demographic and comorbidity factors did not improve model performance appreciably. The full model had good discrimination and was reasonably well calibrated, suggesting that it may be useful for assessment of prognosis

An empirical tool for estimating the share of unmet need due to healthcare inefficiencies, suboptimal access, and lack of effective technologies

Background: Although there has been growing attention to the measurement of unmet need, which is the overall epidemiological burden of disease, current measures ignore the burden that could be eliminated from technological advances or more effective use of current technologies. Methods: We developed a conceptual framework and empirical tool that separates unmet need from met need and subcategorizes the causes of unmet need into suboptimal access to and ineffective use of current technologies and lack of current technologies. Statistical models were used to model the relationship between health-related quality of life (HR-QOL) and treatment utilization using data from the National Health and Wellness Survey (NHWS). Predicted HRQOL was combined with prevalence data from the Global Burden of Disease Study (GBD) to estimate met need and the causes of unmet need due to morbidity in the US and EU5 for five diseases: rheumatoid arthritis, breast cancer, Parkinson’s disease, hepatitis C, and chronic obstructive pulmonary disease (COPD). Results: HR-QOL was positively correlated with adherence to medication and patient-perceived quality and negatively correlated with financial barriers. Met need was substantial across all disease and regions, although significant unmet need remains. While the majority of unmet need was driven by lack of technologies rather than ineffective use of current technologies, there was considerable variation across diseases and regions. Overall unmet need was largest for COPD, which had the highest prevalence of all diseases in this study. Conclusion: We developed a methodology that can inform decisions about which diseases to invest in and whether those investments should focus on improving access to currently available technologies or inventing new technologie

DS_10.1177_2381468318781811 – Supplemental material for The Lifetime Health Burden of Delayed Graft Function in Kidney Transplant Recipients in the United States

<p>Supplemental material, DS_10.1177_2381468318781811 for The Lifetime Health Burden of Delayed Graft Function in Kidney Transplant Recipients in the United States by Devin Incerti, Nicholas Summers, Thanh G. N. Ton, Audra Boscoe, Anil Chandraker and Warren Stevens in MDM Policy & Practice</p

Cost-effectiveness of sequenced treatment of rheumatoid arthritis with targeted immune modulators

<p><b>Aims:</b> To determine the cost-effectiveness of treatment sequences of biologic disease-modifying anti-rheumatic drugs or Janus kinase/STAT pathway inhibitors (collectively referred to as bDMARDs) vs conventional DMARDs (cDMARDs) from the US societal perspective for treatment of patients with moderately to severely active rheumatoid arthritis (RA) with inadequate responses to cDMARDs.</p> <p><b>Materials and methods:</b> An individual patient simulation model was developed that assesses the impact of treatments on disease based on clinical trial data and real-world evidence. Treatment strategies included sequences starting with etanercept, adalimumab, certolizumab, or abatacept. Each of these treatment strategies was compared with cDMARDs. Incremental cost, incremental quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios (ICERs) were calculated for each treatment sequence relative to cDMARDs. The cost-effectiveness of each strategy was determined using a US willingness-to-pay (WTP) threshold of $150,000/QALY.</p> <p><b>Results:</b> For the base-case scenario, bDMARD treatment sequences were associated with greater treatment benefit (i.e. more QALYs), lower lost productivity costs, and greater treatment-related costs than cDMARDs. The expected ICERs for bDMARD sequences ranged from ∼$126,000 to $140,000 per QALY gained, which is below the US-specific WTP. Alternative scenarios examining the effects of homogeneous patients, dose increases, increased costs of hospitalization for severely physically impaired patients, and a lower baseline Health Assessment Questionnaire (HAQ) Disability Index score resulted in similar ICERs.</p> <p><b>Conclusions:</b> bDMARD treatment sequences are cost-effective from a US societal perspective.</p