Publications by authors named "Katherine Payne"

133 Publications

Safety, Feasibility, and Outcomes of Frequent, Long-Duration Rehabilitation in an Inpatient Rehabilitation Facility after Prolonged Hospitalization for Severe COVID-19: An Observational Study.

Phys Ther 2021 Sep 6. Epub 2021 Sep 6.

University of Colorado School of Medicine, Department of Physical Medicine and Rehabilitation, University of Colorado Hospital. 12631 E 17th Ave, Mail Stop F493 Aurora, CO 80045.

Objectives: The objective of this study was to evaluate safety, feasibility, and outcomes of 30 patients within an inpatient rehabilitation facility following hospitalization for severe COVID-19 infection.

Methods: This was an Observational Study of 30 patients (ages 26-80) within a large, metropolitan academic hospital following hospitalization for complications from severe COVID-19. Ninety percent of the participants required critical care and 83% required mechanical ventilation during their hospitalization. Within an inpatient rehabilitation facility and model of care, frequent, long duration rehabilitation was provided by occupational therapists, physical therapists, and speech language pathologists.

Results: The average inpatient rehabilitation facility length of stay was 11 days (ranging from 4-22 days). Patients averaged 165 minutes per day (ranging from 140-205 minutes) total of physical therapy, occupational therapy, and speech therapy. Twenty eight of the 30 patients (93%) discharged to the community. One patient required readmission from the inpatient rehabilitation facility to the acute hospital. All 30 patients improved their functional status with inpatient rehabilitation.

Conclusion: In this cohort of 30 patients, inpatient rehabilitation after severe COVID-19 was safe and feasible. Patients were able to participate in frequent, long duration rehabilitation with nearly all patients discharging to the community. Clinically, inpatient rehabilitation should be considered for patients with functional limitations following severe COVID-19. Given 90% of our cohort required critical care, future studies should investigate the efficacy and effectiveness of inpatient rehabilitation following hospitalization for critical illness. Frequent, long duration rehabilitation shows promising potential to address functional impairments following hospitalization for severe COVID-19.

Impact Statement: Inpatient rehabilitation facilities should be considered as a discharge location for hospitalized survivors of COVID-19, especially severe COVID-19, with functional limitations precluding community discharge. Clinicians and administrators should consider inpatient rehabilitation and inpatient rehabilitation facilities to address the rehabilitation needs of COVID-19 and critical illness survivors.
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http://dx.doi.org/10.1093/ptj/pzab208DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8499953PMC
September 2021

Predicting presenteeism using measures of health status.

Qual Life Res 2021 Jul 27. Epub 2021 Jul 27.

Arthritis Research UK Centre for Epidemiology, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.

Objectives: To identify whether it is feasible to develop a mapping algorithm to predict presenteeism using multiattribute measures of health status.

Methods: Data were collected using a bespoke online survey in a purposive sample (n = 472) of working individuals with a self-reported diagnosis of Rheumatoid arthritis (RA). Survey respondents were recruited using an online panel company (ResearchNow). This study used data captured using two multiattribute measures of health status (EQ5D-5 level; SF6D) and a measure of presenteeism (WPAI, Work Productivity Activity Index). Statistical correlation between the WPAI and the two measures of health status (EQ5D-5 level; SF6D) was assessed using Spearman's rank correlation. Five regression models were estimated to quantify the relationship between WPAI and predict presenteeism using health status. The models were specified based in index and domain scores and included covariates (age; gender). Estimated and observed presenteeism were compared using tenfold cross-validation and evaluated using Root mean square error (RMSE).

Results: A strong and negative correlation was found between WPAI and: EQ5D-5 level and WPAI (r = - 0.64); SF6D (r =- 0.60). Two models, using ordinary least squares regression were identified as the best performing models specifying health status using: SF6D domains with age interacted with gender (RMSE = 1.7858); EQ5D-5 Level domains and age interacted with gender (RMSE = 1.7859).

Conclusions: This study provides indicative evidence that two existing measures of health status (SF6D and EQ5D-5L) have a quantifiable relationship with a measure of presenteeism (WPAI) for an exemplar application of working individuals with RA. A future study should assess the external validity of the proposed mapping algorithms.
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http://dx.doi.org/10.1007/s11136-021-02936-9DOI Listing
July 2021

Towards Personalising the Use of Biologics in Rheumatoid Arthritis: A Discrete Choice Experiment.

Patient 2021 Jun 18. Epub 2021 Jun 18.

Manchester Centre for Health Economics, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK.

Introduction: There have been promising developments in technologies and associated algorithm-based prescribing ('stratified approach') to target biologics to sub-groups of people with rheumatoid arthritis (RA). The acceptability of using an algorithm-guided approach in practice is likely to depend on various factors.

Objective: This study quantified preferences for an algorithm-guided approach to prescribing biologics (termed 'biologic calculator').

Methods: An online discrete choice experiment (DCE) was designed to elicit preferences from patients and the public for using a 'biologic calculator' compared with conventional prescribing. Treatment approaches were described by five attributes: delay to starting treatment; positive and negative predictive value (PPV/NPV); risk of infection; and cost saving to the UK national health service. Each survey contained six choice sets asking respondents to select their preferred option from two hypothetical biologic calculators or conventional prescribing. Background questions included sociodemographics, health status and healthcare experiences. DCE data were analysed using mixed logit models.

Results: Completed choice data were collected from 292 respondents (151 patients with RA and 142 members of the public). PPV, NPV and risk of infection were the most highly valued attributes to respondents deciding between prescribing strategies.

Conclusion: Respondents were generally receptive to personalised medicine in RA, but researchers developing personalised approaches should pay close attention to generating evidence on both the PPV and the NPV of their technologies.
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http://dx.doi.org/10.1007/s40271-021-00533-zDOI Listing
June 2021

Effect of competing mortality risks on predictive performance of the QRISK3 cardiovascular risk prediction tool in older people and those with comorbidity: external validation population cohort study.

Lancet Healthy Longev 2021 Jun;2(6):e352-e361

Usher Institute, University of Edinburgh, Edinburgh, UK.

Background: Primary prevention of cardiovascular disease (CVD) is guided by risk-prediction tools, but these rarely account for the risk of dying from other conditions (ie, competing mortality risk). In England and Wales, the recommended risk-prediction tool is QRISK2, and a new version (QRISK3) has been derived and internally validated. We aimed to externally validate QRISK3 and to assess the effects of competing mortality risk on its predictive performance.

Methods: For this retrospective population cohort study, we used data from the Clinical Practice Research Datalink. We included patients aged 25-84 years with no previous history of CVD or statin treatment who were permanently registered with a primary care practice, had up-to-standard data for at least 1 year, and had linkage to Hospital Episode Statistics discharge and Office of National Statistics mortality data. We compared the QRISK3-predicted 10-year CVD risk with the observed 10-year risk in the whole population and in important subgroups of age and multimorbidity. QRISK3 discrimination and calibration were examined with and without accounting for competing risks.

Findings: Our study population included 1 484 597 women with 42 451 incident CVD events (4·9 cases per 1000 person-years of follow-up, 95% CI 4·89-4·99), and 1 420 176 men with 53 066 incident CVD events (6·7 cases per 1000 person-years, 6·66-6·78), with median follow-up of 5·0 years (IQR 1·9-9·2). Non-CVD death rose markedly with age (0·4% of women and 0·5% of men aged 25-44 years had a non-CVD death 20·1% of women and 19·6% of men aged 75-84 years). QRISK3 discrimination in the whole population was excellent (Harrell's C-statistic 0·865 in women and 0·834 in men) but was poor in older age groups (<0·65 in all subgroups aged 65 years or older). Ignoring competing risks, QRISK3 calibration in the whole population and in younger people was excellent, but there was significant over-prediction in older people. Accounting for competing risks, QRISK3 systematically over-predicted CVD risk, particularly in older people and in those with high multimorbidity.

Interpretation: QRISK3 performed well at the whole population level when ignoring competing mortality risk. The tool performed considerably less well in important subgroups, including older people and people with multimorbidity, and less well again after accounting for competing mortality risk.

Funding: National Institute for Health Research.
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http://dx.doi.org/10.1016/S2666-7568(21)00088-XDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8175241PMC
June 2021

Sunbed Use among 11- to 17-Year-Olds and Estimated Number of Commercial Sunbeds in England with Implications for a 'Buy-Back' Scheme.

Children (Basel) 2021 May 14;8(5). Epub 2021 May 14.

Manchester Centre for Health Economics, University of Manchester, Manchester M13 9PL, UK.

Prior to 2011 legislation prohibiting children from using commercial sunbeds, the prevalence of sunbed use in 15- to 17-year-olds in some areas in England was as high as 50%. Despite significant decreases since 2011, children today still practice indoor tanning. We estimated current sunbed use in 11- to 17-year-olds in England, the number of available commercial sunbed units, and the associated cost of a 'buy-back' scheme to remove commercial sunbeds under a potential future policy to ban sunbeds. We undertook a calibration approach based on published prevalence rates in English adults and other sources. Internet searches were undertaken to estimate the number of sunbed providers in Greater Manchester, then we extrapolated this to England. Estimated mean prevalence of sunbed use was 0.6% for 11- to 14-year-olds and 2.5% for 15- to 17-year-olds, equating to 62,130 children using sunbeds in England. A predicted 2958 premises and 17,865 sunbeds exist nationally and a 'buy-back' scheme would cost approximately GBP 21.7 million. Public health concerns remain greatest for 11- to 17-year-olds who are particularly vulnerable to developing skin cancers after high ultraviolet exposure.
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http://dx.doi.org/10.3390/children8050393DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8156792PMC
May 2021

Higher donor body mass index is associated with increased hemolysis of red blood cells at 42-days of storage: A retrospective analysis of routine quality control data.

Transfusion 2021 02 24;61(2):449-463. Epub 2020 Nov 24.

Melbourne Dental School, The University of Melbourne, Melbourne, Victoria, Australia.

Background: For reasons unclear, some stored red blood cells (RBCs) have low hemolysis, while others have high hemolysis, which impacts quality consistency. To identify variables that influence hemolysis, routine quality control (QC) data for 42-days-stored RBCs with corresponding donor information were analyzed.

Study Design And Methods: RBC QC and donor data were obtained from a national blood supplier. Regression models and analyses were performed on total cohort stratified by donor sex and by high hemolysis (≥90th percentile) vs control (<90th percentile) samples, including matching.

Results: Data included 1734 leukoreduced RBCs (822 female, 912 male), processed by buffy coat-poor or whole blood filtration methods. Male RBCs had larger volume, hemoglobin content, and higher hemolysis than female RBCs (median hemolysis, 0.24% vs 0.21%; all P < .0001). Multivariable regression identified increased body mass index (BMI) and RBC variables were associated with higher hemolysis (P < .0001), along with older female age and buffy coat-poor processing method (P < .002). Logistic regression models comparing the high and control hemolysis subsets, matched for RBC component variables and processing method, identified overweight-obese BMI (>27 kg/m ) in males remained the single donor-related variable associated with higher hemolysis (P < .0001); odds ratio, 3 (95% confidence interval [CI], 1.3-6.7), increasing to 4 (95% CI, 1.8-8.6) for obese males (BMI > 30 kg/m ). Female donor obesity and older age trended toward higher hemolysis.

Conclusion: Donor BMI, sex, and female age influence the level of hemolysis of 42-days-stored RBCs. Other factors, not identified in this study, also influence the level of hemolysis.
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http://dx.doi.org/10.1111/trf.16203DOI Listing
February 2021

Implementing Interventions with Varying Marginal Cost-Effectiveness: An Application in Precision Medicine.

Med Decis Making 2020 10;40(7):924-938

Manchester Centre for Health Economics, Division of Population Health, Health Services Research & Primary Care, University of Manchester, Manchester, Greater Manchester, UK.

. A range of barriers may constrain the effective implementation of strategies to deliver precision medicine. If the marginal costs and consequences of precision medicine vary at different levels of implementation, then such variation will have an impact on relative cost-effectiveness. This study aimed to illustrate the importance and quantify the impact of varying marginal costs and benefits on the value of implementation for a case study in precision medicine. . An existing method to calculate the value of implementation was adapted to allow marginal costs and consequences of introducing precision medicine into practice to vary across differing levels of implementation. This illustrative analysis used a case study based on a published decision-analytic model-based cost-effectiveness analysis of a 70-gene recurrence score (MammaPrint) for breast cancer. The impact of allowing for varying costs and benefits for the value of the precision medicine and of implementation strategies was illustrated graphically and numerically in both static and dynamic forms. . The increasing returns to scale exhibited by introducing this specific example of precision medicine mean that a minimum level of implementation (51%) is required for using the 70-gene recurrence score to be cost-effective at a defined threshold of €20,000 per quality-adjusted life year. The observed variation in net monetary benefit implies that the value of implementation strategies was dependent on the initial and ending levels of implementation in addition to the magnitude of the increase in patients receiving the 70-gene recurrence score. In dynamic models, incremental losses caused by low implementation accrue over time unless implementation is improved. . Poor implementation of approaches to deliver precision medicine, identified to be cost-effective using decision-analytic model-based cost-effectiveness analysis, can have a significant economic impact on health systems. Developing and evaluating the economic impact of strategies to improve the implementation of precision medicine will potentially realize the more cost-effective use of health care budgets.
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http://dx.doi.org/10.1177/0272989X20954391DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7583450PMC
October 2020

Therapeutic monitoring of TNF inhibitors for rheumatoid arthritis: evidence required following NICE's recommendations.

Rheumatol Adv Pract 2020 22;4(2):rkaa023. Epub 2020 Jun 22.

NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre.

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http://dx.doi.org/10.1093/rap/rkaa023DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7474855PMC
June 2020

Generating evidence to inform health technology assessment of treatments for SLE: a systematic review of decision-analytic model-based economic evaluations.

Lupus Sci Med 2020 07;7(1)

Manchester Centre for Health Economics, Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.

This study aimed to understand and appraise the approaches taken to handle the complexities of a multisystem disease in published decision-analytic model-based economic evaluations of treatments for SLE. A systematic review was conducted to identify all published model-based economic evaluations of treatments for SLE. Treatments that were considered for inclusion comprised antimalarial agents, immunosuppressive therapies, and biologics including rituximab and belimumab. Medline and Embase were searched electronically from inception until September 2018. Titles and abstracts were screened against the inclusion criteria by two reviewers; agreement between reviewers was calculated according to Cohen's κ. Predefined data extraction tables were used to extract the key features, structural assumptions and data sources of input parameters from each economic evaluation. The completeness of reporting for the methods of each economic evaluation was appraised according to the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement. Six decision-analytic model-based economic evaluations were identified. The studies included azathioprine (n=4), mycophenolate mofetil (n=3), cyclophosphamide (n=2) and belimumab (n=1) as relevant comparator treatments; no economic evaluation estimated the relative cost-effectiveness of rituximab. Six items of the CHEERS statement were reported incompletely across the sample: target population, choice of comparators, measurement and valuation of preference-based outcomes, estimation of resource use and costs, choice of model, and the characterisation of heterogeneity. Complexity in the diagnosis, management and progression of disease can make decision-analytic model-based economic evaluations of treatments for SLE a challenge to undertake. The findings from this study can be used to improve the relevance of model-based economic evaluations in SLE and as an agenda for research to inform future health technology assessment and decision-making.
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http://dx.doi.org/10.1136/lupus-2019-000350DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7389518PMC
July 2020

Using qualitative methods for a conceptual analysis of measures of health status and presenteeism prior to a mapping study.

Qual Life Res 2020 Nov 22;29(11):3167-3177. Epub 2020 Jul 22.

Arthritis Research UK Centre for Epidemiology, Division of Musculoskeletal & Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.

Objectives: The inclusion of productivity in economic evaluations is a contentious issue. Methods are currently being developed to assess how it may feasibly be included for specific interventions, such as workplace interventions (WPIs), where productivity is a key outcome. Mapping (also called cross-walking or prediction modelling) may offer a solution. Prior to producing a mapping algorithm, it is recommended that the conceptual validity between 'source' and 'target' measures be understood first. This study aimed to understand the conceptual validity of two existing measures of health status (EQ-5D; SF-6D) and presenteeism to inform the potential for a subsequent mapping algorithm.

Methods: A purposive sample of individuals who were currently working and had either rheumatoid arthritis (RA), ankylosing spondylitis (AS) or psoriatic arthritis (PsA). Individuals were recruited through support groups. Semi-structured telephone interviews were conducted until data saturation (no new emerging themes) was reached. Deductive and inductive framework analysis methods were used to identify key aspects of the conditions (themes) that impact on presenteeism (working at reduced levels of health).

Results: Twenty-two (RA = 10; AS = 9; PsA = 3) employed individuals were interviewed. Deductive analysis identified evidence which confirmed the domains included in the EQ-5D and SF-6D capture those key aspects of RA, AS and PsA that increase presenteeism. Inductive analysis identified an additional theme; mental clarity, not captured by the EQ-5D or SF-6D, was also found to have a direct impact on presenteeism.

Conclusions: The results of the study indicate conceptual validity of both health status measures to predict presenteeism. The next step is to develop a mapping algorithm for presenteeism.
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http://dx.doi.org/10.1007/s11136-020-02570-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7591427PMC
November 2020

What are the benefits and harms of risk stratified screening as part of the NHS breast screening Programme? Study protocol for a multi-site non-randomised comparison of BC-predict versus usual screening (NCT04359420).

BMC Cancer 2020 Jun 18;20(1):570. Epub 2020 Jun 18.

NIHR Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Central Manchester University Hospitals NHS Foundation Trust, Manchester, England.

Background: In principle, risk-stratification as a routine part of the NHS Breast Screening Programme (NHSBSP) should produce a better balance of benefits and harms. The main benefit is the offer of NICE-approved more frequent screening and/ or chemoprevention for women who are at increased risk, but are unaware of this. We have developed BC-Predict, to be offered to women when invited to NHSBSP which collects information on risk factors (self-reported information on family history and hormone-related factors via questionnaire; mammographic density; and in a sub-sample, Single Nucleotide Polymorphisms). BC-Predict produces risk feedback letters, inviting women at high risk (≥8% 10-year) or moderate risk (≥5 to < 8% 10-year) to have discussion of prevention and early detection options at Family History, Risk and Prevention Clinics. Despite the promise of systems such as BC-Predict, there are still too many uncertainties for a fully-powered definitive trial to be appropriate or ethical. The present research aims to identify these key uncertainties regarding the feasibility of integrating BC-Predict into the NHSBSP. Key objectives of the present research are to quantify important potential benefits and harms, and identify key drivers of the relative cost-effectiveness of embedding BC-Predict into NHSBSP.

Methods: A non-randomised fully counterbalanced study design will be used, to include approximately equal numbers of women offered NHSBSP (n = 18,700) and BC-Predict (n = 18,700) from selected screening sites (n = 7). In the initial 8-month time period, women eligible for NHSBSP will be offered BC-Predict in four screening sites. Three screening sites will offer women usual NHSBSP. In the following 8-months the study sites offering usual NHSBSP switch to BC-Predict and vice versa. Key potential benefits including uptake of risk consultations, chemoprevention and additional screening will be obtained for both groups. Key potential harms such as increased anxiety will be obtained via self-report questionnaires, with embedded qualitative process analysis. A decision-analytic model-based cost-effectiveness analysis will identify the key uncertainties underpinning the relative cost-effectiveness of embedding BC-Predict into NHSBSP.

Discussion: We will assess the feasibility of integrating BC-Predict into the NHSBSP, and identify the main uncertainties for a definitive evaluation of the clinical and cost-effectiveness of BC-Predict.

Trial Registration: Retrospectively registered with clinicaltrials.gov (NCT04359420).
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http://dx.doi.org/10.1186/s12885-020-07054-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302349PMC
June 2020

Analysis of the Relaxometric Properties of Extremely Rapidly Exchanging Gd Chelates: Lessons from a Comparison of Four Isomeric Chelates.

Inorg Chem 2020 Jul 14;59(13):9037-9046. Epub 2020 Jun 14.

Department of Chemistry, Portland State University, 1719 SW 10th Avenue, Portland, Oregon 97201, United States.

Relaxometric analyses and in particular the use of fast-field cycling techniques have become routine in the study of paramagnetic metal complexes. The field dependence of the solvent proton relaxation properties (nuclear magnetic relaxation dispersion, NMRD) can provide unparalleled insights into the chemistry of these complexes. However, analyzing NMRD data is a multiparametric problem, and some sets of variables are mutually compensatory. Specifically, when fitting NMRD profiles, the metal-proton distance and the rotational correlation time constant have a push-pull relationship in which a change to one causes a predictable compensation in the other. A relaxometric analysis of four isomeric chelates highlights the pitfalls that await when fitting the NMRD profiles of chelates for which dissociative water exchange is extremely rapid. In the absence of independently verified values for one of these parameters, NMRD profiles can be fitted to multiple parameter sets. This means that NMRD fitting can inadvertently be used to buttress a preconceived notion of how the complex should behave when a different parameter set may more accurately describe the actual behavior. These findings explain why the effect of very rapid dissociative exchange on the hydration state of Gd has remained obscured until only recently.
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http://dx.doi.org/10.1021/acs.inorgchem.0c00905DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7423567PMC
July 2020

Being Precise About Precision Medicine: What Should Value Frameworks Incorporate to Address Precision Medicine? A Report of the Personalized Precision Medicine Special Interest Group.

Value Health 2020 05 1;23(5):529-539. Epub 2020 Apr 1.

The University of Manchester, Manchester, England, UK.

Precision medicine is a dynamic area embracing a diverse and increasing type of approaches that allow the targeting of new medicines, screening programs or preventive healthcare strategies, which include the use of biologic markers or complex tests driven by algorithms also potentially taking account of patient preferences. The International Society for Pharmacoeconomics and Outcome Research expanded its current work around precision medicine to (1) describe the evolving paradigm of precision medicine with examples of current and evolving applications, (2) describe key stakeholders perspectives on the value of precision medicine in their respective domains, and (3) define the core factors that should be considered in a value assessment framework for precision medicine. With the ultimate goal of improving health of well-defined patient groups, precision medicine will affect all stakeholders in the healthcare system at multiple levels spanning the individual perspective to the societal perspective. For an efficient, timely and practical precision medicine value assessment framework, it will be important to address these multiple perspectives through building consensus among the stakeholders for robust procedures and measures of value aspects, including performance of precision mechanism; aligned reimbursement processes of precision mechanism and subsequent treatment; transparent expectations for evidence requirements and study designs adequately matched to the intended use of the precision mechanism and to the smaller target patient populations; recognizing the potential range of value-generation such as ruling-in and ruling-out decisions.
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http://dx.doi.org/10.1016/j.jval.2019.11.010DOI Listing
May 2020

Factors that influence rheumatologists' anti-tumor necrosis factor alpha prescribing decisions: a qualitative study.

BMC Rheumatol 2019 19;3:47. Epub 2019 Dec 19.

2NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK.

Background: Treatment decisions for any disease are usually informed by reference to published clinical guidelines or recommendations. These recommendations can be developed to improve the relative cost-effectiveness of health care and to reduce regional variation in clinical practice. Anti-tumor necrosis factor alpha (anti-TNF) treatments are prescribed for people with rheumatoid arthritis according to specific recommendations by the National Institute for Health and Care Excellence in England. Evidence of regional variation in clinical practice for rheumatoid arthritis may indicate that different factors have an influence on routine prescribing decisions. The aim of this study was to understand the factors that influence rheumatologists' decisions when prescribing anti-TNF treatments for people with rheumatoid arthritis in England.

Methods: Semi-structured one-to-one telephone interviews were performed with senior rheumatologists in different regions across England. The interview schedule addressed recommendations by the National Institute for Health and Care Excellence, prescribing behavior, and perceptions of anti-TNF treatments. Interviews were recorded digitally, transcribed verbatim, and anonymized. Data were analyzed by thematic framework analysis that comprised six stages (familiarization; coding; developing the framework; applying the framework; generating the matrix; interpretation).

Results: Eleven rheumatologists (regional distribution - north 36%; midlands: 36%; south: 27%) participated (response rate: 24% of the sampling frame). The mean duration of the interviews was thirty minutes (range: 16 to 56 min). Thirteen factors that influenced anti-TNF prescribing decisions were categorized by three nested primary themes; specific influences were defined as subthemes: (i) External Environment Influences (National Institute for Health and Care Excellence Recommendations; Clinical Commissioning Groups; Cost Pressures; Published Clinical Evidence; Colleagues in Different Hospitals; Pharmaceutical Industry); (ii) Internal Hospital Influences (Systems to Promote Compliance with Clinical Recommendations; Internal Treatment Pathways; Hospital Culture); (iii) Individual-level Influences (Patient Influence; Clinical Autonomy; Consultant Experience; Perception of Disease Activity Score-28 (DAS28) Outcome).

Conclusions: Factors that influenced anti-TNF prescribing decisions were multifaceted, seemed to vary by region, and may facilitate divergence from published clinical recommendations. Strategic behavior appeared to illustrate a conflict between uniform treatment recommendations and clinical autonomy. These influences may contribute to understanding sources of regional variation in clinical practice for rheumatoid arthritis.
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http://dx.doi.org/10.1186/s41927-019-0097-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6921483PMC
December 2019

Effect of Fractionation and Chemical Characteristics on the Crystallization Behavior of Milk Fat.

J Food Sci 2019 Dec 24;84(12):3512-3521. Epub 2019 Nov 24.

College of Food Science and Nutritional Engineering, China Agricultural Univ., Beijing, 100083, China.

The experiments reported in this study provided a more comprehensive insight into the effect of chemical composition on the crystallization behavior of milk fat (MF). MF was fractionated between 20 and 40 °C into nine fractions with different melting points and was first subjected to the heating step (L20, L30, L40, and S40) followed by the cooling phase (SS40, SL40, SS30, SL30, and LL40). Furthermore, the species of fatty acids (FAs) and triglycerides (TAGs) of the MF fractions were identified. The thermodynamics, crystallization behavior, and polymorphs were determined using differential scanning calorimetry, pulsed nuclear magnetic resonance, and X-ray diffraction, respectively. The results indicated that L40 yielded the highest percentage (∼35% of the total MF) of all the fractions. Enthalpies of the melting and crystallization processes of solid fat content in this study were related to the different FA and TAG compositions of MF and its fractions. High melting fractions (HMFs) were enriched with long-chain saturated fatty acids and tri-saturated (S3) TAGs, and low melting fractions (LMFs) were enriched with short-chain unsaturated FAs and tri-unsaturated (U3) TAGs. Moreover, the various nucleation mechanisms of MF fractions were identified according to the Avrami equation. The polymorphic transformation from a β' form of double chain length structures to a β form of triple chain length occurred in the native MF and HMFs, whereas the LMFs displayed almost no crystals. PRACTICAL APPLICATION: This study represented the first time that nine fractions were obtained using MF fractionation via a heating step, followed by a cooling phase. Furthermore, the chemical composition of MF fractions was investigated. The results obtained from this study might be of specific value in understanding the functional properties of fat-based dairy food in both storage conditions and real-time applications.
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http://dx.doi.org/10.1111/1750-3841.14961DOI Listing
December 2019

A Picture is Worth a Thousand Words: The Role of Survey Training Materials in Stated-Preference Studies.

Patient 2020 04;13(2):163-173

Manchester Centre for Health Economics, The University of Manchester, Manchester, UK.

Background: Online survey-based methods are increasingly used to elicit preferences for healthcare. This digitization creates an opportunity for interactive survey elements, potentially improving respondents' understanding and/or engagement.

Objective: Our objective was to understand whether, and how, training materials in a survey influenced stated preferences.

Methods: An online discrete-choice experiment (DCE) was designed to elicit public preferences for a new targeted approach to prescribing biologics ("biologic calculator") for rheumatoid arthritis (RA) compared with conventional prescribing. The DCE presented three alternatives, two biologic calculators and a conventional approach (opt out), described by five attributes: delay to treatment, positive predictive value, negative predictive value, infection risk, and cost saving to the national health service. Respondents were randomized to receive training materials as plain text or an animated storyline. Training materials contained information about RA and approaches to treatment and described the biologic calculator. Background questions included sociodemographics and self-reported measures of task difficulty and attribute non-attendance. DCE data were analyzed using conditional and heteroskedastic conditional logit (HCL) models.

Results: In total, 300 respondents completed the DCE, receiving either plain text (n = 158) or the animated storyline (n = 142). The HCL showed the estimated coefficients for all attributes aligned with a priori expectations and were statistically significant. The scale term was statistically significant, indicating that respondents who received plain-text materials had more random choices. Further tests suggested preference homogeneity after accounting for differences in scale.

Conclusions: Using animated training materials did not change the preferences of respondents, but they appeared to improve choice consistency, potentially allowing researchers to include more complex designs with increased numbers of attributes, levels, alternatives or choice sets.
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http://dx.doi.org/10.1007/s40271-019-00391-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7075825PMC
April 2020

Opening the 'Black Box': An Overview of Methods to Investigate the Decision-Making Process in Choice-Based Surveys.

Patient 2020 02;13(1):31-41

Division of Population Health, Health Services Research and Primary Care, Manchester Centre for Health Economics, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK.

The desire to understand the preferences of patients, healthcare professionals and the public continues to grow. Health valuation studies, often in the form of discrete choice experiments, a choice based survey approach, proliferate as a result. A variety of methods of pre-choice process analysis have been developed to investigate how and why people make their decisions in such experiments and surveys. These techniques have been developed to investigate how people acquire and process information and make choices. These techniques offer the potential to test and improve theories of choice and/or associated empirical models. This paper provides an overview of such methods, with the focus on their use in stated choice-based healthcare studies. The methods reviewed are eye tracking, mouse tracing, brain imaging, deliberation time analysis and think aloud. For each method, we summarise the rationale, implementation, type of results generated and associated challenges, along with a discussion of possible future developments.
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http://dx.doi.org/10.1007/s40271-019-00385-8DOI Listing
February 2020

Modeling the Economic Impact of Interventions for Older Populations with Multimorbidity: A Method of Linking Multiple Single-Disease Models.

Med Decis Making 2019 10 20;39(7):842-856. Epub 2019 Aug 20.

Centre for Health Economics, the University of York, Heslington, York, UK.

Individuals from older populations tend to have more than 1 health condition (multimorbidity). Current approaches to produce economic evidence for clinical guidelines using decision-analytic models typically use a single-disease approach, which may not appropriately reflect the competing risks within a population with multimorbidity. This study aims to demonstrate a proof-of-concept method of modeling multiple conditions in a single decision-analytic model to estimate the impact of multimorbidity on the cost-effectiveness of interventions. Multiple conditions were modeled within a single decision-analytic model by linking multiple single-disease models. Individual discrete event simulation models were developed to evaluate the cost-effectiveness of preventative interventions for a case study assuming a UK National Health Service perspective. The case study used 3 diseases (heart disease, Alzheimer's disease, and osteoporosis) that were combined within a single linked model. The linked model, with and without correlations between diseases incorporated, simulated the general population aged 45 years and older to compare results in terms of lifetime costs and quality-adjusted life-years (QALYs). The estimated incremental costs and QALYs for health care interventions differed when 3 diseases were modeled simultaneously (£840; 0.234 QALYs) compared with aggregated results from 3 single-disease models (£408; 0.280QALYs). With correlations between diseases additionally incorporated, both absolute and incremental costs and QALY estimates changed in different directions, suggesting that the inclusion of correlations can alter model results. Linking multiple single-disease models provides a methodological option for decision analysts who undertake research on populations with multimorbidity. It also has potential for wider applications in informing decisions on commissioning of health care services and long-term priority setting across diseases and health care programs through providing potentially more accurate estimations of the relative cost-effectiveness of interventions.
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http://dx.doi.org/10.1177/0272989X19868987DOI Listing
October 2019

Crystal Structures of DOTMA Chelates from Ce to Yb : Evidence for a Continuum of Metal Ion Hydration States.

Chemistry 2019 Jul 3;25(42):9997-10005. Epub 2019 Jul 3.

Department of Chemistry, University of Minnesota, 207 Pleasant Street S.E., Minneapolis, MN, 55455, USA.

The crystal structures of chelates formed between each stable paramagnetic lanthanide ion and the octadentate polyamino carboxylate ligand DOTMA are described. A total of 23 individual chelates structures were obtained; in each chelate the coordination geometry around the metal ion is best described as a twisted square antiprism (torsion angle -25.0°--31.4°). Despite the uniformity of the general coordination geometry provided by the DOTMA ligand, there is a considerable variation in the hydration state of each chelate. The early Ln chelates are associated with a single inner sphere water molecule; the Ln-OH interaction is remarkable for being very long. After a clear break at gadolinium, the number of chelates in the unit cell that have a water molecule interacting with the Ln decreases linearly until at Tm no water is found to interact with the metal ion. The Ln-OH distance observed in the chelates of the later Ln ions are also extremely long and increase as the ions contract (2.550-2.732 Å). No clear break between hydrated and dehydrated chelates is observed; rather this series of chelates appear to represent a continuum of hydration states in which the ligand gradually closes around the metal ion as its ionic radius decreases (with decreased hydration) and the metal drops down into the coordination cage.
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http://dx.doi.org/10.1002/chem.201902068DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6700027PMC
July 2019

Assessing the Joint Value of Genomic-Based Diagnostic Tests and Gene Therapies.

J Pers Med 2019 May 21;9(2). Epub 2019 May 21.

Manchester Centre for Health Economics, Faculty of Biology, Medicine and Health, The University of Manchester, Oxford Road, Manchester M13 9PL, UK.

Gene therapy is an emerging type of treatment that may aim to provide a cure to individuals with a genetic mutation known to be causative of a specific disease. A diagnosis of the causative mutation must precede treatment with a in vivo gene therapy. Both achieving a genomic-based diagnosis and treatment with a gene therapy may result in substantial expenditures for health care systems. Uncertainties around the health care costs, risks, and benefits derived from diagnosis and treatment with a subsequent gene therapy suggests a need for developing an evidence base, underpinned by opportunity cost, to inform if, and how, these health technologies should be introduced into health care systems funded by finite budgets. This article discusses why current methods to evaluate health technologies (decision-analytic model-based cost-effectiveness analysis from the perspective of a health care system over a lifetime time horizon) are appropriate to quantify the costs and consequences of using genomic-based diagnostic tests and gene therapies in combination, rather than as separate interventions, within clinical practice. Evaluating the economic impact of test-and-treatment strategies will ensure that the opportunity cost of these health technologies is quantified fully for decision-makers who are responsible for allocating limited resources in health care systems.
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http://dx.doi.org/10.3390/jpm9020028DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6616850PMC
May 2019

Accounting for Capacity Constraints in Economic Evaluations of Precision Medicine: A Systematic Review.

Pharmacoeconomics 2019 08;37(8):1011-1027

Manchester Centre for Health Economics, Division of Population Health, Health Services Research and Primary Care, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK.

Background And Objective: Precision (stratified or personalised) medicine is underpinned by the premise that it is feasible to identify known heterogeneity using a specific test or algorithm in patient populations and to use this information to guide patient care to improve health and well-being. This study aimed to understand if, and how, previous economic evaluations of precision medicine had taken account of the impact of capacity constraints.

Methods: A meta-review was conducted of published systematic reviews of economic evaluations of precision medicine (test-treat interventions) and individual studies included in these reviews. Due to the volume of studies identified, a sample of papers published from 2007 to 2015 was collated. A narrative analysis identified whether potential capacity constraints were discussed qualitatively in the studies and, if relevant, which quantitative methods were used to account for capacity constraints.

Results: A total of 45 systematic reviews of economic evaluations of precision medicine were identified, from which 222 studies focusing on test-treat interventions, published between 2007 and 2015, were extracted. Of these studies, 33 (15%) qualitatively discussed the potential impact of capacity constraints, including budget constraints; quality of tests and the testing process; ease of use of tests in clinical practice; and decision uncertainty. Quantitative methods (nine studies) to account for capacity constraints included static methods such as capturing inefficiencies in trials or models and sensitivity analysis around model parameters; and dynamic methods, which allow the impact of capacity constraints on cost effectiveness to change over time.

Conclusions: Understanding the cost effectiveness of precision medicine is necessary, but not sufficient, evidence for its successful implementation. There are currently few examples of evaluations that have quantified the impact of capacity constraints, which suggests an area of focus for future research.
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http://dx.doi.org/10.1007/s40273-019-00801-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6597608PMC
August 2019

Differences in the Relaxometric Properties of Regioisomeric Benzyl-DOTA Bifunctional Chelators: Implications for Molecular Imaging.

Bioconjug Chem 2019 05 3;30(5):1530-1538. Epub 2019 May 3.

Department of Chemistry , Portland State University , 1719 SW 10th Avenue , Portland , Oregon 97201 , United States.

The bifunctional chelator S-2-(4-isothiocyanatobenzyl)-1,4,7,10-tetraazacyclododecane- N, N', N″, N‴-1,4,7,10-tetraacetate (IB-DOTA) is on paper the most attractive of the commercially available bifunctional chelators for magnetic resonance imaging (MRI) applications. The preserved DOTA scaffold is known to produce extremely kinetically and thermodynamically robust chelates with the Gd ion. Also, ligation through four acetate pendant arms should ensure that the rapid water exchange kinetics so, crucial to the function of an MRI contrast agent are retained. However, upon ligation of the Gd ion, IB-DOTA differentiates into two distinct isomers defined by the positions of the benzylic substituent (corner or side). A relaxometric analysis of these two isomers revealed marked differences in the property and behavior of the two chelates. Most notably the side isomer is found to be substantially more likely to aggregate in aqueous solution than its corner counterpart. This aggregation results in higher relaxivity for the side isomer versus the corner isomer, an observation that potentially obscures the impact of differences in water exchange kinetics between the two isomers. The side isomer is composed of a significant fraction of a twisted square antiprismatic coordination geometry that exchanges water more rapidly than optimal (τ = 7 ns) for maximizing relaxivity. The impact of this excessively fast exchange is not observed in the relaxivity of the side isomer only because in isolation this chelate tumbles much more slowly than the corner isomer. However, this situation is not expected to persist when the chelate is employed in a typical bioconjugate. These results imply that the corner isomer of IB-DOTA may represent a better choice of bifunctional chelator for bioconjugation applications in which a large macromolecule is to be tagged for MRI applications.
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http://dx.doi.org/10.1021/acs.bioconjchem.9b00223DOI Listing
May 2019

Preferences for aspects of antenatal and newborn screening: a systematic review.

BMC Pregnancy Childbirth 2019 Apr 16;19(1):131. Epub 2019 Apr 16.

Manchester Centre for Health Economics, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK.

Background: Many countries offer screening programmes to unborn and newborn babies (antenatal and newborn screening) to identify those at risk of certain conditions to aid earlier diagnosis and treatment. Technological advances have stimulated the development of screening programmes to include more conditions, subsequently changing the information required and potential benefit-risk trade-offs driving participation. Quantifying preferences for screening programmes can provide programme commissioners with data to understand potential demand, the drivers of this demand, information provision required to support the programmes and the extent to which preferences differ in a population. This study aimed to identify published studies eliciting preferences for antenatal and newborn screening programmes and provide an overview of key methods and findings.

Methods: A systematic search of electronic databases for key terms identified eligible studies (discrete choice experiments (DCEs) or best-worst scaling (BWS) studies related to antenatal/newborn testing/screening published between 1990 and October 2018). Data were systematically extracted, tabulated and summarised in a narrative review.

Results: A total of 19 studies using a DCE or BWS to elicit preferences for antenatal (n = 15; 79%) and newborn screening (n = 4; 21%) programmes were identified. Most of the studies were conducted in Europe (n = 12; 63%) but there were some examples from North America (n = 2; 11%) and Australia (n = 2; 11%). Attributes most commonly included were accuracy of screening (n = 15; 79%) and when screening occurred (n = 13; 68%). Other commonly occurring attributes included information content (n = 11; 58%) and risk of miscarriage (n = 10; 53%). Pregnant women (n = 11; 58%) and healthcare professionals (n = 11; 58%) were the most common study samples. Ten studies (53%) compared preferences across different respondents. Two studies (11%) made comparisons between countries. The most popular analytical model was a standard conditional logit model (n = 11; 58%) and one study investigated preference heterogeneity with latent class analysis.

Conclusion: There is an existing literature identifying stated preferences for antenatal and newborn screening but the incorporation of more sophisticated design and analytical methods to investigate preference heterogeneity could extend the relevance of the findings to inform commissioning of new screening programmes.
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http://dx.doi.org/10.1186/s12884-019-2278-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6469127PMC
April 2019

Estimating Joint Health Condition Utility Values.

Value Health 2019 Apr 22;22(4):482-490. Epub 2019 Feb 22.

Manchester Centre for Health Economics, Division of Population Health, Health Services Research & Primary Care, The University of Manchester, Manchester, UK.

Objectives: To predict health state utility values (HSUVs) for individuals with up to 4 conditions simultaneously.

Methods: Person-level data were taken from the General Practice Patient Survey, a national survey of adult patients registered with general practices in England. Individuals reported whether they had any 1 of 16 chronic conditions and completed the 3-level EuroQol 5-dimensional questionnaire. Four nonparametric methods (additive, multiplicative, minimum, and the adjusted decrement estimator) and 1 parametric estimator (the linear index) were used to predict HSUVs for individuals with a joint health condition (JHC). Predicted and actual utility scores were compared for precision using root mean square error and mean absolute error. Bias was assessed using mean error.

Results: The analysis included 929,565 individuals, of which 30.5% had at least 2 conditions. Of the nonparametric estimators, the multiplicative approach produced estimates with the lowest bias and most precision for 2 JHCs. For populations with a long-term mental health condition within the JHC, the multiplicative approach overestimated utility scores. All nonparametric methods produced biased results when estimating HSUVs for 3 or 4 JHCs. The linear index generally produced unbiased results with the highest precision.

Conclusions: The multiplicative approach was the best nonparametric estimator when estimating HSUVs for 2 JHCs. None of the nonparametric approaches for estimating HSUVs can be recommended with more than 2 JHCs. The linear index was found to have good predictive properties but needs external validation before being recommended for routine use.
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http://dx.doi.org/10.1016/j.jval.2018.09.2843DOI Listing
April 2019

A Systematic Review of Productivity in Economic Evaluations of Workplace Interventions: A Need for Reporting Criteria?

Appl Health Econ Health Policy 2019 10;17(5):591-613

Manchester Centre for Health Economics, The University of Manchester, 4.306, Jean McFarlane Building, Manchester, M13 9PL, UK.

Background: Rheumatic and musculoskeletal diseases (RMDs) are understood to reduce levels of paid productivity. Productivity, including absenteeism and presenteeism, is arguably an important factor for consideration in economic evaluations of workplace interventions for RMDs (WPI-RMDs). Existing methods available to quantify and value absenteeism and presenteeism are heterogeneous and produce estimates that vary substantially across studies. To date, there has been no systematic summary of the reporting quality of methods used to quantify paid productivity included in economic evaluations of WPI-RMDs.

Objective: The aim of this systematic review was twofold. First, the review was conducted to understand if, and how, the impact of WPI-RMDs on productivity was considered and incorporated in published economic evaluations. Second, we aimed to assess the reporting quality of productivity in published economic evaluations of WPI-RMDs and determine the need for a published set of reporting guidelines for productivity.

Methods: This systematic review was conducted in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A systematic review published in 2008 that focused on the cost effectiveness of WPIs, with limited information on productivity, was updated from 2007 to July 2018. A global search was conducted using three online databases: MEDLINE (1946 to August 2018, week 2), EMBASE (1974 to 10 July 2018); and EconLit (1886 to July 2018). The studies were double-screened by four independent reviewers. Data extraction was conducted using a bespoke data extraction table.

Results: Twenty-one economic evaluations of WPI-RMDs were identified. All studies evaluated absenteeism, but only five reported on levels of presenteeism. The methods used to identify and measure absenteeism were fairly consistent; however, methods used to identify and measure presenteeism, and value absenteeism and presenteeism, varied across studies. Two studies may have potentially double-counted productivity in their economic evaluations of WPI-RMDs. The results of this systematic review identified key elements potentially useful as a starting point to inform reporting quality guidelines for productivity.

Conclusions: Variation in the methods used to quantify productivity and how it is reported in economic evaluations suggests the need for specific published reporting guidelines for productivity. The development of standardised reporting guidelines for the identification, measurement, and valuation of absenteeism and presenteeism in economic evaluations may help reduce variation in the methods and promote transparency.
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http://dx.doi.org/10.1007/s40258-019-00473-8DOI Listing
October 2019

A Micro-Costing Study of Screening for Lynch Syndrome-Associated Pathogenic Variants in an Unselected Endometrial Cancer Population: Cheap as NGS Chips?

Front Oncol 2019 26;9:61. Epub 2019 Feb 26.

Gynaecological Oncology Research Group, Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.

Lynch syndrome is the most common inherited cause of endometrial cancer. Identifying individuals affected by Lynch syndrome enables risk-reducing interventions including colorectal surveillance, and cascade testing of relatives. We conducted a micro-costing study of screening all women with endometrial cancer for Lynch syndrome using one of four diagnostic strategies combining tumor microsatellite instability testing (MSI), immunohistochemistry (IHC), and/or methylation testing, and germline next generation sequencing (NGS). Resource use (consumables, capital equipment, and staff) was identified through direct observation and laboratory protocols. Published sources were used to identify unit costs to calculate a per-patient cost (£; 2017) of each testing strategy, assuming a National Health Service (NHS) perspective. Tumor triage with MSI and reflex methylation testing followed by germline NGS of women with likely Lynch syndrome was the cheapest strategy at £42.01 per case. Tumor triage with IHC and reflex methylation testing of MLH1 protein-deficient cancers followed by NGS of women with likely Lynch syndrome cost £45.68. Tumor triage with MSI followed by NGS of all women found to have tumor microsatellite instability cost £78.95. Immediate germline NGS of all women with endometrial cancer cost £176.24. The cost of NGS was affected by the skills and time needed to interpret results (£44.55/patient). This study identified the cost of reflex screening all women with endometrial cancer for Lynch syndrome, which can be used in a model-based cost-effectiveness analysis to understand the added value of introducing reflex screening into clinical practice.
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http://dx.doi.org/10.3389/fonc.2019.00061DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6399107PMC
February 2019

Measuring the economic value of genetic counselling.

Eur J Med Genet 2019 May 14;62(5):385-389. Epub 2018 Dec 14.

Manchester Centre for Health Economics, Division of Population Health, Health Services Research & Primary Care, The University of Manchester, Manchester, M13 9PL, UK.

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http://dx.doi.org/10.1016/j.ejmg.2018.12.007DOI Listing
May 2019

Developing a short-form of the Genetic Counselling Outcome Scale: The Genomics Outcome Scale.

Eur J Med Genet 2019 May 26;62(5):324-334. Epub 2018 Nov 26.

Centre for Medical Education, School of Medicine, Cardiff University, Cardiff, UK. Electronic address:

The Genetic Counselling Outcome Scale (GCOS-24) is a 24-item patient reported outcome measure for use in evaluations of genetic counselling and testing services. The aim of this study was to develop a short form of GCOS-24. The study comprised three phases. Phase I: Cognitive interviews were used to explore interpretability of GCOS-24 items and which GCOS-24 items were most valued by the target population. Phase II: The Graded Response Model was used to analyse an existing set of GCOS-24 responses (n = 395) to examine item discrimination. Phase III: Item Selection. Three principles guided the approach to item selection (i) Items with poor discriminative properties were not selected; (ii) To avoid redundancy, items capturing a similar outcome were not selected together; item information curves and cognitive interview findings were used to establish superior items. (iii) Rasch analysis was then used to determine the optimal scale. In Phase I, ten cognitive interviews were conducted with individuals affected by or at risk for a genetic condition, recruited from patient support groups. Analysis of interview transcripts identified twelve GCOS-24 items which were highly valued by participants. In Phase II, Graded Response Model item characteristic curves and item information curves were produced. In Phase III, findings from Phases I and II were used to select ten highly-valued items that perform well. Finally, items were iteratively removed and permutated to establish optimal fit statistics under the Rasch model. A six-item questionnaire with a five-point Likert Scale was produced (The Genomics Outcome Scale (GOS)). Correlation between GCOS-24 and GOS scores is high (r = 0.838 at 99% confidence), suggesting that GOS maintains the ability of GCOS-24 to capture empowerment, whilst providing a less burdensome scale for respondents. This study represents the first step in developing a preference-based measure which could be used in the evaluation of technologies and services used in genomic medicine.
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http://dx.doi.org/10.1016/j.ejmg.2018.11.015DOI Listing
May 2019

Understanding Midwives' Preferences for Providing Information About Newborn Bloodspot Screening.

MDM Policy Pract 2018 Jan-Jun;3(1):2381468317746170. Epub 2018 Jan 18.

Manchester Centre for Health Economics, Division of Population Health, Health Services Research & Primary Care (SJW, KP).

Understanding preferences for information provision in the context of health care service provision is challenging because of the number of potential attributes that may influence preferences. This study aimed to identify midwives' preferences for the process and outcomes of information provision in an expanded national newborn bloodspot screening program. A sample of practicing midwives completed a hybrid-stated preference survey including a conjoint analysis (CA) and discrete choice experiment to quantify preferences for the types of, and way in which, information should be provided in a newborn bloodspot screening program. Six conjoint analysis questions captured the impact of different types of information on parents' ability to make a decision, and 10 discrete choice experiment questions identified preferences for four process attributes (including parents' ability to make a decision). Midwives employed by the UK National Health Service (n = 134) completed the survey. All types of information content were perceived to improve parents' ability to make a decision except for the possibility of false-positive results. Late pregnancy was seen to be the best time to provide information, followed by day 3 postbirth. Information before 20 weeks of pregnancy was viewed as reducing parents' ability to make a decision. Midwives preferred information to be provided by an individual discussion and did not think parents should receive information on the Internet. A hybrid stated preference survey design identified that a wide variety of information should be provided to maximize parents' ability to make a decision ideally provided late in pregnancy or on day 3 postbirth.
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http://dx.doi.org/10.1177/2381468317746170DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6125045PMC
January 2018

Using "Big Data" in the Cost-Effectiveness Analysis of Next-Generation Sequencing Technologies: Challenges and Potential Solutions.

Value Health 2018 09 17;21(9):1048-1053. Epub 2018 Aug 17.

Canadian Centre for Applied Research in Cancer Control (ARCC), Cancer Control Research, BC Cancer, Vancouver, Canada; School of Population and Public Health, University of British Columbia, Vancouver, Canada.

Next-generation sequencing (NGS) is considered to be a prominent example of "big data" because of the quantity and complexity of data it produces and because it presents an opportunity to use powerful information sources that could reduce clinical and health economic uncertainty at a patient level. One obstacle to translating NGS into routine health care has been a lack of clinical trials evaluating NGS technologies, which could be used to populate cost-effectiveness analyses (CEAs). A key question is whether big data can be used to partially support CEAs of NGS. This question has been brought into sharp focus with the creation of large national sequencing initiatives. In this article we summarize the main methodological and practical challenges of using big data as an input into CEAs of NGS. Our focus is on the challenges of using large observational datasets and cohort studies and linking these data to the genomic information obtained from NGS, as is being pursued in the conduct of large genomic sequencing initiatives. We propose potential solutions to these key challenges. We conclude that the use of genomic big data to support and inform CEAs of NGS technologies holds great promise. Nevertheless, health economists face substantial challenges when using these data and must be cognizant of them before big data can be confidently used to produce evidence on the cost-effectiveness of NGS.
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http://dx.doi.org/10.1016/j.jval.2018.06.016DOI Listing
September 2018
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