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2. Meaningful and Measurable Health Domains in Huntington’s Disease: Large-Scale Validation of the Huntington’s Disease Health-Related Quality of Life Questionnaire Across Severity Stages

Author

Downie, Lorna, Jack, Roisin, Matheson, Kirsty, Miedzybrodzka, Zosia, Rae, Daniela, Simpson, Sheila A., Summers, Fiona, Ure, Alexandra, Vaughan, Vivien, Akhtar, Shahbana, Crooks, Jenny, Curtis, Adrienne, de Souza (Keylock), Jenny, Piedad, John, Rickards, Hugh, Wright, Jan, Coulthard, Elizabeth, Gethin, Louise, Hayward, Beverley, Sieradzan, Kasia, Wright, Abigail, Barker, Roger A., O’Keefe, Deidre, Gerrtiz (nee Di Pietro), Anna, Fisher, Kate, Goodman, Anna, Hill, Susan, Mason, Sarah, Swain, Rachel, Guzman, Natalie Valle, Busse, Monica, Butcher, Cynthia, Callaghan, Jenny, Dunnett, Stephen, Clenaghan, Catherine, Fullam, Ruth, Hunt, Sarah, Jones, Lesley, Jones, Una, Khalil, Hanan, Minster, Sara, Owen, Michael, Price, Kathleen, Townhill, Jenny, Rosser, Anne, Goudie, David, Buchanan, Lindsay, McFadyen, Paula, Tonner, Alison, Taylor, Anne-Marie, Edwards, Maureen, Ho, Carrie, McGill, Marie, Porteous, Mary, Pearson, Pauline, Harrower, Timothy, Irvine, Sarah, Brockie, Peter, Foster, Jillian, Johns, Nicola, McKenzie, Sue, Rothery, Jean, Thomas, Gareth, Yates, Shona, Deith, Catherine, Ireland, Jane, Ritchie, Stuart, Brown, Pauline, Burrows, Liz, Fletcher, Amy, Harding, Alison, Harrison, Kaye, Laver, Fiona, Silva, Mark, Thomson, Aileen, Chu, Carol, Evans, Carole, Gallentree, Deena, Hamer, Stephanie, Kraus, Alison, Markova, Ivana, Raman, Ashok, Rowett, Liz, Andrew, Alyson, Frost, Julie, Noad, Rupert, Cosgrove, Jeremy, Gallantree, Deena, Hobson, Emma, Jamieson, Stuart, Longthorpe, Mandy, Musgrave, Hannah, Peacy, Caroline, Toscano, Jean, Wild, Sue, Yardumian, Pam, Clayton, Carole, Dipple, Heather, Freire-Patino, Dawn, Hallam, Caroline, Middleton, Julia, Alusi, Sundus, Davies, Rhys, Foy, Kevin, Gerrans, Emily, Pate, Louise, Anjum, Uruj, Coebergh, Jan, Eddy, Charlotte, Lahiri, Nayana, McEntagart, Meriel, Patton, Michael, Peterson, Maria, Rose, Sarah, Andrews, Thomasin, Dougherty, Andrew, Golding, Charlotte, Kavalier, Fred, Laing, Hana, Lashwood, Alison, Robertson, Dene, Ruddy, Deborah, Santhouse, Alastair, Whaite, Anna, Gosling (nee Brown), Stefanie, Bruno, Stefania, Chu, Elvina, Doherty, Karen, Haider, Salman, Hensman, Davina, Lewis, Monica, Novak, Marianne, Patel, Aakta, Robertson, Nicola, Rosser, Elisabeth, Tabrizi, Sarah, Taylor, Rachel, Warner, Thomas, Wild, Edward, Arran, Natalie, Bek, Judith, Craufurd, David, Hare, Marianne, Howard, Liz, Huson, Susan, Johnson, Liz, Jones, Mary, Krishnamoorthy, Ashok, Murphy, Helen, Oughton, Emma, Partington-Jones, Lucy, Rogers, Dawn, Sollom, Andrea, Snowden, Julie, Stopford, Cheryl, Thompson, Jennifer, Trender-Gerhard, Iris, Verstraelen (formerly Ritchie), Nichola, Westmoreland, Leann, Cass, Ginette, Davidson, Lynn, Davison, Jill, Fullerton, Neil, Holmes, Katrina, Komati, Suresh, McDonnell, Sharon, Mohammed, Zeid, Morgan, Karen, Savage, Lois, Singh, Baldev, Wood, Josh, Nemeth, Andrea H., Siuda, Gill, Valentine, Ruth, Dixon, Kathryn, Armstrong, Richard, Burn, John, Weekes, Rebecca, Craven, Janet, Bailey, Wendy, Coleman, Caroline, Haig-Brown, Diane, Simpson, Steve, Majeed, Tahir, Verstraelen (Ritchie), Nicola, Barrett, Wendy, Ho, Aileen, Bandmann, Oliver, Bradbury, Alyson, Fairtlough, Helen, Fillingham, Kay, Foustanos, Isabella, Gill, Paul, Kazoka, Mbombe, O’Donovan, Kirsty, Nevitt, Louise, Peppa, Nadia, Quarrell, Oliver, Taylor, Cat, Tidswell, Katherine, Kipps, Christopher, MacKinnon, Lesley, Agarwal, Veena, Hayward, Elaine, Gunner, Kerry, Harris, Kayla, Anderson, Mary, Heywood, Melanie, Keys, Liane, Smalley, Sarah, El-Nimr, George, Duffell, Allison, Wood, Sue, Kennedy (nee Smith), Karen, Gowers, Lesley, Powell, Kingsley, Bethwaite, Pamela, Edwards, Rachel, Fuller, Kathleen, Phillips, Michelle, Bucher, Walter, de Schepper, Beatrice, Eden, John, Hendrikx, Victor, Hughes, Alis, King, Diana, Kleibrink, Ursula, Kuttruff-Wilschut, Rita, Lenon-Bird, Anne, Lohkamp, Christiane, Perrousseaux, Marie-Odile, Martinez, Asuncion, Rapaille, Lilliane, Santini, Helen, Sasinkova, Pavla, Soltysiak, Beverley, Smith, Steve, van der Leer, Hans, van der Meer, Lucienne, Wooldridge, Michael, Zinzi, Paola, Ho, Aileen K., Horton, Mike C., Landwehrmeyer, G. Bernhard, Burgunder, Jean-Marc, and Tennant, Alan

Published
2019
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4. Computer Modeling of Diabetes and Its Transparency: A Report on the Eighth Mount Hood Challenge

Author

Palmer, Andrew J., Si, Lei, Tew, Michelle, Hua, Xinyang, Willis, Michael S., Asseburg, Christian, McEwan, Phil, Leal, José, Gray, Alastair, Foos, Volker, Lamotte, Mark, Feenstra, Talitha, O’Connor, Patrick J., Brandle, Michael, Smolen, Harry J., Gahn, James C., Valentine, William J., Pollock, Richard F., Breeze, Penny, Brennan, Alan, Pollard, Daniel, Ye, Wen, Herman, William H., Isaman, Deanna J., Kuo, Shihchen, Laiteerapong, Neda, Tran-Duy, An, and Clarke, Philip M.

Published
2018
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19. Computer Modeling of Diabetes and Its Transparency

Author

Penny Breeze, Jose Leal, Andrew J. Palmer, Lei Si, Talitha L Feenstra, Alan Brennan, Alastair Gray, Michelle Tew, William J. Valentine, William H. Herman, Mark Lamotte, Volker Foos, James C. Gahn, Christian Asseburg, Harry J. Smolen, Xinyang Hua, Michael Brändle, Patrick J. O'Connor, Shihchen Kuo, Wen Ye, Philip Christopher McEwan, Daniel Pollard, An Tran-Duy, Michael Willis, Neda Laiteerapong, Deanna J. M. Isaman, Philip Clarke, Richard F Pollock, Real World Studies in PharmacoEpidemiology, -Genetics, -Economics and -Therapy (PEGET), and Value, Affordability and Sustainability (VALUE)

Subjects
Research design, Glycated Hemoglobin A, Computer science, 030209 endocrinology & metabolism, ECONOMIC-EVALUATION, GUIDELINES, Article, Mount Hood Challenge, Diabetes Complications, 03 medical and health sciences, 0302 clinical medicine, Documentation, Goodness of fit, Credibility, Statistics, Diabetes Mellitus, Humans, Computer Simulation, Data reporting, computer modeling, Glycated Hemoglobin, transparency, COMPLICATIONS, diabetes, 030503 health policy & services, Health Policy, Public Health, Environmental and Occupational Health, Linear model, Reproducibility of Results, Checklist, Economics, Medical, Treatment Outcome, Research Design, Transparency (graphic), Costs and Cost Analysis, Linear Models, Quality-Adjusted Life Years, 0305 other medical science, HEALTH TECHNOLOGY-ASSESSMENT
Abstract

Objectives\ud The Eighth Mount Hood Challenge (held in St. Gallen, Switzerland, in September 2016) evaluated the transparency of model input documentation from two published health economics studies and developed guidelines for improving transparency in the reporting of input data underlying model-based economic analyses in diabetes.\ud \ud Methods\ud Participating modeling groups were asked to reproduce the results of two published studies using the input data described in those articles. Gaps in input data were filled with assumptions reported by the modeling groups. Goodness of fit between the results reported in the target studies and the groups’ replicated outputs was evaluated using the slope of linear regression line and the coefficient of determination (R2). After a general discussion of the results, a diabetes-specific checklist for the transparency of model input was developed.\ud \ud Results\ud Seven groups participated in the transparency challenge. The reporting of key model input parameters in the two studies, including the baseline characteristics of simulated patients, treatment effect and treatment intensification threshold assumptions, treatment effect evolution, prediction of complications and costs data, was inadequately transparent (and often missing altogether). Not surprisingly, goodness of fit was better for the study that reported its input data with more transparency. To improve the transparency in diabetes modeling, the Diabetes Modeling Input Checklist listing the minimal input data required for reproducibility in most diabetes modeling applications was developed.\ud \ud Conclusions\ud Transparency of diabetes model inputs is important to the reproducibility and credibility of simulation results. In the Eighth Mount Hood Challenge, the Diabetes Modeling Input Checklist was developed with the goal of improving the transparency of input data reporting and reproducibility of diabetes simulation model results.

Published
2018

27. The Prime Diabetes Model: Novel Methods for Estimating Long-Term Clinical and Cost Outcomes in Type 1 Diabetes Mellitus

Author

Richard F Pollock, J.P. Bae, Rhodri Saunders, Kirsi Norrbacka, Kristina S. Boye, and William J. Valentine

Subjects
Male, medicine.medical_specialty, Time Factors, 030209 endocrinology & metabolism, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, Risk Factors, Diabetes mellitus, Outcome Assessment, Health Care, medicine, Humans, 030212 general & internal medicine, Risk factor, Intensive care medicine, Stroke, Type 1 diabetes, business.industry, Health Policy, Public Health, Environmental and Occupational Health, Reproducibility of Results, Models, Theoretical, medicine.disease, Surgery, Clinical trial, Diabetes Mellitus, Type 1, Models, Economic, Systematic review, chemistry, Cohort, Disease Progression, Female, Glycated hemoglobin, business
Abstract

Background Recent publications describing long-term follow-up from landmark trials and diabetes registries represent an opportunity to revisit modeling options in type 1 diabetes mellitus (T1DM). Objectives To develop a new product-independent model capable of predicting long-term clinical and cost outcomes. Methods After a systematic literature review to identify clinical trial and registry data, a model was developed (the PRIME Diabetes Model) to simulate T1DM progression and complication onset. The model runs as a patient-level simulation, making use of covariance matrices for cohort generation and risk factor progression, and simulating myocardial infarction, stroke, angina, heart failure, nephropathy, retinopathy, macular edema, neuropathy, amputation, hypoglycemia, ketoacidosis, mortality, and risk factor evolution. Several approaches novel to T1DM modeling were used, including patient characteristics and risk factor covariance, a glycated hemoglobin progression model derived from patient-level data, and model averaging approaches to evaluate complication risk. Results Validation analyses comparing modeled outcomes with published studies demonstrated that the PRIME Diabetes Model projects long-term patient outcomes consistent with those reported for a number of long-term studies. Macrovascular end points were reliably reproduced across five different populations and microvascular complication risk was accurately predicted on the basis of comparisons with landmark studies and published registry data. Conclusions The PRIME Diabetes Model is product-independent, available online, and has been developed in line with good practice guidelines. Validation has indicated that outcomes from long-term studies can be reliably reproduced. The model offers new approaches to long-standing challenges in diabetes modeling and may become a valuable tool for informing health care policy.

Published
2017
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30. PSY4 Evaluation of the Cost-Effectiveness of Iron Formulations for the Treatment of Iron Deficiency Anaemia in Patients with Inflammatory Bowel Disease in the UK

Author

Barnaby Hunt, William J. Valentine, A. Aksan, S. Gavata-Steiger, A. Ramirez de Arellano Serna, and G. Baxter

Subjects
medicine.medical_specialty, business.industry, Cost effectiveness, Health Policy, Internal medicine, Public Health, Environmental and Occupational Health, Medicine, In patient, Iron deficiency, business, medicine.disease, Inflammatory bowel disease, Gastroenterology
Published
2020
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31. PDB34 The PRIME Type 2 Diabetes MODEL: A New, Product-Independent MODEL to Evaluate LONG-TERM Clinical and Cost Outcomes in Type 2 Diabetes

Author

Richard F Pollock, Beatrice Osumili, William J. Valentine, KS Boye, and Kirsi Norrbacka

Subjects
Actuarial science, business.industry, Health Policy, New product development, Public Health, Environmental and Occupational Health, Medicine, Type 2 diabetes, business, medicine.disease, Prime (order theory), Term (time)
Published
2020
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32. PSY13 Iron Formulations for the Treatment of Iron Deficiency Anaemia in Patients with Inflammatory Bowel Disease: A Cost-Effectiveness Analysis in Switzerland

Author

S. Gavata-Steiger, A. Ramirez de Arellano Serna, Stephan R. Vavricka, A. Schöpfer, N. Morin, A. Aksan, M. Bettencourt, Barnaby Hunt, Pascal Juillerat, and William J. Valentine

Subjects
medicine.medical_specialty, business.industry, Health Policy, Internal medicine, Public Health, Environmental and Occupational Health, Medicine, In patient, Iron deficiency, Cost-effectiveness analysis, business, medicine.disease, Gastroenterology, Inflammatory bowel disease
Published
2020
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33. PDB54 EVALUATING THE IMPACT OF POOR GLYCEMIC CONTROL ASSOCIATED WITH THERAPEUTIC INERTIA ON LIFE EXPECTANCY IN PATIENTS WITH TYPE 2 DIABETES IN THE UK

Author

William J. Valentine, Stephen C. Bain, Barnaby Hunt, Solomon Nuhoho, L. Rizkallah, and Brian Bekker Hansen

Subjects
medicine.medical_specialty, business.industry, Health Policy, Poor glycemic control, Public Health, Environmental and Occupational Health, medicine, Life expectancy, In patient, Type 2 diabetes, medicine.disease, Intensive care medicine, business, Therapeutic inertia
Published
2020
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36. Meaningful and Measurable Health Domains in Huntington’s Disease: Large-Scale Validation of the Huntington’s Disease Health-Related Quality of Life Questionnaire Across Severity Stages

Author

Ho, Aileen K., primary, Horton, Mike C., additional, Landwehrmeyer, G. Bernhard, additional, Burgunder, Jean-Marc, additional, Tennant, Alan, additional, Downie, Lorna, additional, Jack, Roisin, additional, Matheson, Kirsty, additional, Miedzybrodzka, Zosia, additional, Rae, Daniela, additional, Simpson, Sheila A., additional, Summers, Fiona, additional, Ure, Alexandra, additional, Vaughan, Vivien, additional, Akhtar, Shahbana, additional, Crooks, Jenny, additional, Curtis, Adrienne, additional, de Souza (Keylock), Jenny, additional, Piedad, John, additional, Rickards, Hugh, additional, Wright, Jan, additional, Coulthard, Elizabeth, additional, Gethin, Louise, additional, Hayward, Beverley, additional, Sieradzan, Kasia, additional, Wright, Abigail, additional, Barker, Roger A., additional, O’Keefe, Deidre, additional, Gerrtiz (nee Di Pietro), Anna, additional, Fisher, Kate, additional, Goodman, Anna, additional, Hill, Susan, additional, Mason, Sarah, additional, Swain, Rachel, additional, Guzman, Natalie Valle, additional, Busse, Monica, additional, Butcher, Cynthia, additional, Callaghan, Jenny, additional, Dunnett, Stephen, additional, Clenaghan, Catherine, additional, Fullam, Ruth, additional, Hunt, Sarah, additional, Jones, Lesley, additional, Jones, Una, additional, Khalil, Hanan, additional, Minster, Sara, additional, Owen, Michael, additional, Price, Kathleen, additional, Townhill, Jenny, additional, Rosser, Anne, additional, Goudie, David, additional, Buchanan, Lindsay, additional, McFadyen, Paula, additional, Tonner, Alison, additional, Taylor, Anne-Marie, additional, Edwards, Maureen, additional, Ho, Carrie, additional, McGill, Marie, additional, Porteous, Mary, additional, Pearson, Pauline, additional, Harrower, Timothy, additional, Irvine, Sarah, additional, Brockie, Peter, additional, Foster, Jillian, additional, Johns, Nicola, additional, McKenzie, Sue, additional, Rothery, Jean, additional, Thomas, Gareth, additional, Yates, Shona, additional, Deith, Catherine, additional, Ireland, Jane, additional, Ritchie, Stuart, additional, Brown, Pauline, additional, Burrows, Liz, additional, Fletcher, Amy, additional, Harding, Alison, additional, Harrison, Kaye, additional, Laver, Fiona, additional, Silva, Mark, additional, Thomson, Aileen, additional, Chu, Carol, additional, Evans, Carole, additional, Gallentree, Deena, additional, Hamer, Stephanie, additional, Kraus, Alison, additional, Markova, Ivana, additional, Raman, Ashok, additional, Rowett, Liz, additional, Andrew, Alyson, additional, Frost, Julie, additional, Noad, Rupert, additional, Cosgrove, Jeremy, additional, Gallantree, Deena, additional, Hobson, Emma, additional, Jamieson, Stuart, additional, Longthorpe, Mandy, additional, Musgrave, Hannah, additional, Peacy, Caroline, additional, Toscano, Jean, additional, Wild, Sue, additional, Yardumian, Pam, additional, Clayton, Carole, additional, Dipple, Heather, additional, Freire-Patino, Dawn, additional, Hallam, Caroline, additional, Middleton, Julia, additional, Alusi, Sundus, additional, Davies, Rhys, additional, Foy, Kevin, additional, Gerrans, Emily, additional, Pate, Louise, additional, Anjum, Uruj, additional, Coebergh, Jan, additional, Eddy, Charlotte, additional, Lahiri, Nayana, additional, McEntagart, Meriel, additional, Patton, Michael, additional, Peterson, Maria, additional, Rose, Sarah, additional, Andrews, Thomasin, additional, Dougherty, Andrew, additional, Golding, Charlotte, additional, Kavalier, Fred, additional, Laing, Hana, additional, Lashwood, Alison, additional, Robertson, Dene, additional, Ruddy, Deborah, additional, Santhouse, Alastair, additional, Whaite, Anna, additional, Gosling (nee Brown), Stefanie, additional, Bruno, Stefania, additional, Chu, Elvina, additional, Doherty, Karen, additional, Haider, Salman, additional, Hensman, Davina, additional, Lewis, Monica, additional, Novak, Marianne, additional, Patel, Aakta, additional, Robertson, Nicola, additional, Rosser, Elisabeth, additional, Tabrizi, Sarah, additional, Taylor, Rachel, additional, Warner, Thomas, additional, Wild, Edward, additional, Arran, Natalie, additional, Bek, Judith, additional, Craufurd, David, additional, Hare, Marianne, additional, Howard, Liz, additional, Huson, Susan, additional, Johnson, Liz, additional, Jones, Mary, additional, Krishnamoorthy, Ashok, additional, Murphy, Helen, additional, Oughton, Emma, additional, Partington-Jones, Lucy, additional, Rogers, Dawn, additional, Sollom, Andrea, additional, Snowden, Julie, additional, Stopford, Cheryl, additional, Thompson, Jennifer, additional, Trender-Gerhard, Iris, additional, Verstraelen (formerly Ritchie), Nichola, additional, Westmoreland, Leann, additional, Cass, Ginette, additional, Davidson, Lynn, additional, Davison, Jill, additional, Fullerton, Neil, additional, Holmes, Katrina, additional, Komati, Suresh, additional, McDonnell, Sharon, additional, Mohammed, Zeid, additional, Morgan, Karen, additional, Savage, Lois, additional, Singh, Baldev, additional, Wood, Josh, additional, Nemeth, Andrea H., additional, Siuda, Gill, additional, Valentine, Ruth, additional, Dixon, Kathryn, additional, Armstrong, Richard, additional, Burn, John, additional, Weekes, Rebecca, additional, Craven, Janet, additional, Bailey, Wendy, additional, Coleman, Caroline, additional, Haig-Brown, Diane, additional, Simpson, Steve, additional, Majeed, Tahir, additional, Verstraelen (Ritchie), Nicola, additional, Barrett, Wendy, additional, Ho, Aileen, additional, Bandmann, Oliver, additional, Bradbury, Alyson, additional, Fairtlough, Helen, additional, Fillingham, Kay, additional, Foustanos, Isabella, additional, Gill, Paul, additional, Kazoka, Mbombe, additional, O’Donovan, Kirsty, additional, Nevitt, Louise, additional, Peppa, Nadia, additional, Quarrell, Oliver, additional, Taylor, Cat, additional, Tidswell, Katherine, additional, Kipps, Christopher, additional, MacKinnon, Lesley, additional, Agarwal, Veena, additional, Hayward, Elaine, additional, Gunner, Kerry, additional, Harris, Kayla, additional, Anderson, Mary, additional, Heywood, Melanie, additional, Keys, Liane, additional, Smalley, Sarah, additional, El-Nimr, George, additional, Duffell, Allison, additional, Wood, Sue, additional, Kennedy (nee Smith), Karen, additional, Gowers, Lesley, additional, Powell, Kingsley, additional, Bethwaite, Pamela, additional, Edwards, Rachel, additional, Fuller, Kathleen, additional, Phillips, Michelle, additional, Bucher, Walter, additional, de Schepper, Beatrice, additional, Eden, John, additional, Hendrikx, Victor, additional, Hughes, Alis, additional, King, Diana, additional, Kleibrink, Ursula, additional, Kuttruff-Wilschut, Rita, additional, Lenon-Bird, Anne, additional, Lohkamp, Christiane, additional, Perrousseaux, Marie-Odile, additional, Martinez, Asuncion, additional, Rapaille, Lilliane, additional, Santini, Helen, additional, Sasinkova, Pavla, additional, Soltysiak, Beverley, additional, Smith, Steve, additional, van der Leer, Hans, additional, van der Meer, Lucienne, additional, Wooldridge, Michael, additional, and Zinzi, Paola, additional

Published
2019
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38. PDB59 EVALUATING THE COST-UTILITY OF ADDING SODIUM-GLUCOSE COTRANSPORTER INHIBITORS TO A BASAL-BOLUS INSULIN REGIMEN IN PATIENTS WITH TYPE 1 DIABETES IN THE UK: AN ANALYSIS USING THE PRIME DIABETES MODEL

Author

R. Pollock and W.J. Valentine

Subjects
Type 1 diabetes, Basal bolus insulin, business.industry, Health Policy, Public Health, Environmental and Occupational Health, Sodium-Glucose Cotransporter Inhibitors, Pharmacology, medicine.disease, Prime (order theory), Regimen, Diabetes model, Cost utility, medicine, In patient, business
Published
2019
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41. Quantifying the Trade-off Between Improved Glycemic Control and Severe Hypoglycemia Risk in Patients with Type 1 Diabetes: A Health Economic Analysis in the Uk

Author

RF Pollock and WJ Valentine

Subjects
Type 1 diabetes, medicine.medical_specialty, business.industry, Health Policy, Public Health, Environmental and Occupational Health, medicine.disease, Severe hypoglycemia, medicine, Economic analysis, In patient, Medical emergency, Intensive care medicine, business, Glycemic
Published
2017
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42. PDB53 - EVALUATING THE RELATIONSHIP BETWEEN GLYCEMIC CONTROL, INCIDENCE OF SEVERE HYPOGLYCEMIA, AND LONG-TERM HEALTH ECONOMIC OUTCOMES IN PATIENTS WITH TYPE 1 DIABETES IN THE UNITED STATES

Author

R.F. Pollock and W.J. Valentine

Subjects
Type 1 diabetes, Pediatrics, medicine.medical_specialty, business.industry, Health Policy, Incidence (epidemiology), Public Health, Environmental and Occupational Health, medicine.disease, Severe hypoglycemia, Term (time), Medicine, In patient, business, Glycemic
Published
2018
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43. Nasal Glucagon Saves Professional Emergency Medical (EM) Costs Versus Intramuscular Glucagon —A Us Based Cost-Offset Model

Author

William J. Valentine, S Bajpai, Beth Mitchell, J Poehlmann, and Beatrice Osumili

Subjects
medicine.medical_specialty, business.industry, 030503 health policy & services, Health Policy, Cost offset, Public Health, Environmental and Occupational Health, Glucagon, 03 medical and health sciences, 0302 clinical medicine, Emergency medicine, Medicine, 030212 general & internal medicine, 0305 other medical science, business
Published
2018
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44. Cost-Effectiveness of Switching to Biphasic Insulin Aspart 30 from Human Insulin in Patients with Poorly Controlled Type 2 Diabetes in South Korea

Author

Jeremy White, Se Jin Seo, Kyoung Hee Lee, William J. Valentine, J.L. Palmer, and Jayne Smith-Palmer

Subjects
Adult, Male, Pediatrics, medicine.medical_specialty, Cost effectiveness, Cost-Benefit Analysis, Insulin, Isophane, Biphasic Insulins, Type 2 diabetes, Insulin aspart, Indirect costs, Life Expectancy, South Korea, Diabetes mellitus, Republic of Korea, medicine, Humans, Hypoglycemic Agents, Insulin, biphasic insulin aspart 30, cost-effectiveness, Insulin Aspart, Aged, business.industry, Health Policy, Public Health, Environmental and Occupational Health, Type 2 Diabetes Mellitus, modeling, Middle Aged, medicine.disease, Quality-adjusted life year, Surgery, Models, Economic, Diabetes Mellitus, Type 2, Life expectancy, Female, type 2 diabetes, Quality-Adjusted Life Years, business, medicine.drug
Abstract

ObjectivesTo estimate the cost-effectiveness of switching patients with poorly controlled type 2 diabetes mellitus from human insulin (HI) to biphasic insulin aspart 30 (BIAsp 30) in South Korea.MethodsA published and validated diabetes computer simulation model (the IMS CORE Diabetes Model) was used to evaluate the long-term clinical and economic outcomes associated with switching to BIAsp 30, using treatment effects from the South Korean subgroup of the Physician's Routine Evaluation of Safety and Efficacy of NovoMix® 30 Therapy study and cost data collected through primary research. Outcomes included life expectancy, quality-adjusted life expectancy, incidence of complications, direct medical costs, and cost-effectiveness. Analyses were performed from a third-party payer perspective over a 30-year time horizon. Future costs and clinical benefits were discounted at 5% per annum. Extensive sensitivity analyses were performed.ResultsSwitching patients uncontrolled on HI to BIAsp 30 was projected to increase discounted mean life expectancy by 0.15 ± 0.18 years per patient (8.62 ± 0.13 years vs. 8.47 ± 0.13 years) and improve discounted mean quality-adjusted life expectancy by 0.30 ± 0.12 quality-adjusted life-years (QALYs) per patient (5.68 ± 0.09 QALYs vs. 5.38 ± 0.09 QALYs). Conversion to BIAsp 30 was associated with a mean increase in direct costs of South Korean Won (KRW) 1,777,323 ± 359,209 over patient lifetimes. BIAsp 30 was associated with an incremental cost-effectiveness ratio of KRW5,916,758 per QALY gained versus HI.ConclusionSwitching patients uncontrolled on HI to BIAsp 30 was projected to improve life expectancy and quality-adjusted life expectancy. This analysis suggests that BIAsp 30 could be a cost-effective treatment option in type 2 diabetes patients poorly controlled on HI in South Korea.

Published
2009
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45. Cost-Effectiveness Analysis of Thiazolidinediones in Uncontrolled Type 2 Diabetic Patients Receiving Sulfonylureas and Metformin in Thailand

Author

Suphachai Chirakup, Petcharat Pongcharoensuk, Andrew J. Palmer, William J. Valentine, Stéphane Roze, Nathorn Chaiyakunapruk, Usa Chaikledkeaw, and Boonsong Ongphiphadhanakul

Subjects
Male, medicine.medical_specialty, Cost-Benefit Analysis, Diabetes Complications, Rosiglitazone, Internal medicine, Diabetes mellitus, Per capita, medicine, pioglitazone, Humans, Hypoglycemic Agents, Operations management, Economics, Pharmaceutical, Registries, Formulary, health care economics and organizations, business.industry, Incidence, Health Policy, Mortality rate, cost-effectiveness analysis, Public Health, Environmental and Occupational Health, Cost-effectiveness analysis, Middle Aged, Thailand, medicine.disease, Markov Chains, Metformin, Sulfonylurea Compounds, Treatment Outcome, Diabetes Mellitus, Type 2, Female, Thiazolidinediones, business, Pioglitazone, medicine.drug
Abstract

ObjectiveThe national essential drug committee in Thailand suggested that only one of thiazolidinediones be included in hospital formulary but little was know about their cost-effectiveness values. This study aims to determine an incremental cost-effectiveness ratio of pioglitazone 45 mg compared with rosiglitazone 8 mg in uncontrolled type 2 diabetic patients receiving sulfonylureas and metformin in Thailand.MethodsA Markov diabetes model (Center for Outcome Research model) was used in this study. Baseline characteristics of patients were based on Thai diabetes registry project. Costs of diabetes were calculated mainly from Buddhachinaraj hospital. Nonspecific mortality rate and transition probabilities of death from renal replacement therapy were obtained from Thai sources. Clinical effectiveness of thiazolidinediones was retrieved from a meta-analysis. All analyses were based on the government hospital policymaker perspective. Both cost and outcomes were discounted with the rate of 3%. Base-case analyses were analyzed as incremental cost per quality-adjusted life year (QALY) gained. A series of sensitive analyses were performed.ResultsIn base-case analysis, the pioglitazone group had a better clinical outcomes and higher lifetime costs. The incremental cost per QALY gained was 186,246 baht (US$ 5389). The acceptability curves showed that the probability of pioglitazone being cost-effective was 29% at the willingness to pay of one time of Thai gross domestic product per capita (GDP per capita). The effect of pioglitazone on %HbA1c decrease was the most sensitive to the final outcomes.ConclusionsOur findings showed that in type 2 diabetic patients who cannot control their blood glucose under the combination of sulfonylurea and metformin, the use of pioglitazone 45 mg fell in the cost-effective range recommended by World Health Organization (one to three times of GDP per capita) on average, compared to rosiglitazone 8 mg. Nevertheless, based on sensitivity analysis, its probability of being cost-effective was quite low. Hospital policymakers may consider our findings as part of information for the decision-making process.

Published
2008
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49. Cost-Effectiveness of Rapid-Acting Analog Insulin for Type 1 Diabetes In The Uk Setting

Author

K van Brunt, Rhodri Saunders, Richard F Pollock, William J. Valentine, and Kristina S. Boye

Subjects
medicine.medical_specialty, Type 1 diabetes, Text mining, business.industry, Cost effectiveness, Insulin, medicine.medical_treatment, Health Policy, Public Health, Environmental and Occupational Health, Medicine, business, Intensive care medicine, medicine.disease
Published
2015
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50. Verification and Validation of Health Economic Models for Diabetes

Author

ME Lage, Kristina S. Boye, Richard F Pollock, MJ Lage, Rhodri Saunders, and William J. Valentine

Subjects
Text mining, Information retrieval, business.industry, Health Policy, Public Health, Environmental and Occupational Health, Medicine, Economic model, business, Data science, Verification and validation
Published
2015
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