Estimating the Treatment and Prophylactic Economic Value of New Antimicrobials in Managing Antibiotic Resistance and Serious Infections for Common Pathogens in the USA: A Population Modelling Study.

BACKGROUND: Antimicrobial resistance is a growing public health concern. There is a global need to estimate the population-level value of developing new antimicrobials and to ensure the effective use of existing antimicrobials as strategies to counteract antimicrobial resistance. To this aim, population-level value criteria need to be considered alongside conventional value measures. OBJECTIVE: The objective of this study was to develop a novel modelling approach to estimate the value of new antimicrobials, considering the transmission, diversity and enablement elements of STEDI value. METHODS: We developed a population-based mathematical model for the assessment of antimicrobial value considering both prophylactic use of antimicrobials and the treatment of selected serious hospital-acquired infections in hospitals in the USA at a population level. Large-scale clinical and population healthcare data were used to inform a modelling-based analysis assessing the impact of introducing a new antimicrobial compared with continuing with no new antimicrobial, accounting for the transmission, diversity and enablement value of antimicrobial agents. RESULTS: Over a 10-year period, the addition of a new antimicrobial as part of an antimicrobial stewardship strategy in the USA was estimated to result in a proportional reduction of 9.03% in projected antimicrobial resistance levels. This yielded an estimated reduction of $64.3 million in hospitalization costs and a gain of over 153,000 quality-adjusted life-years at an economic value of over $15.4 billion over 10 years. Considering input uncertainty, the estimate of monetary benefit ranged from $11.1 to $21.4 billion. CONCLUSIONS: The use of a new antimicrobial for treatment and prophylactic indications yields considerable clinical and economic benefits including transmission diversity and enablement value. These findings may provide decision makers with important evidence to support investment in new antimicrobials and antimicrobial stewardship policy that address the patient, population and system burden associated with antimicrobial resistance.

Capturing Value Attributes in the Economic Evaluation of Ceftazidime with Avibactam for Treating Severe Aerobic Gram-Negative Bacterial Infections in the United Kingdom.

INTRODUCTION: Antimicrobial resistance remains a serious and growing threat to public health, both globally and in the UK, leading to diminishing effectiveness of antimicrobials. Despite a clear need for new antimicrobials, the clinical pipeline is insufficient, driven by high research and development costs and limited expected returns on investment. To counteract this, National Institute for Health and Care Excellence (NICE) and National Health Service (NHS) England have launched a reimbursement mechanism, de-linked from volume of sales, that aims to reduce economic risk by recognising the broader population-level value of antimicrobials. The objective of this study was to quantify the value of ceftazidime-avibactam for treating gram-negative infections in the UK considering some of these broader value elements unique to antimicrobials. METHODS: A previously developed dynamic disease transmission and cost-effectiveness model was applied to assess the value of introducing ceftazidime-avibactam to UK treatment practice in the management of gram-negative hospital-acquired infections in line with the licenced indications for ceftazidime-avibactam. Model inputs were parameterised using sources aligned to the UK perspective. RESULTS: The introduction of ceftazidime-avibactam into a two-line treatment sequence saved over 2300 lives, leading to a gain of 27,600 life years and 22,000 quality-adjusted life years (QALY) at an additional cost of £17 million, over a ten-year transmission period. Ceftazidime-avibactam was associated with a net monetary benefit of £642 million at willingness to pay threshold of £30,000 per QALY; even at a lower threshold of £20,000 per QALY, the net monetary benefit is £422 million. DISCUSSION: Increasing the diversity of antimicrobial treatments through the introduction of an additional antimicrobial, in this instance ceftazidime-avibactam, was associated with substantial clinical and economic benefits, when considering broader population-level value. Despite revealing considerable benefits, the value of ceftazidime-avibactam is only partially reflected in this analysis. Further efforts are required to fully operationalise the spectrum, transmission, enablement, diversity and insurance (STEDI) value framework and accurately reflect the population-level value of antimicrobials.

Using Behavior Change Theory to Identify Drivers and Barriers for Antifungal Treatment Decisions: A Case Study in a Large Teaching Hospital in the East of England, UK.

INTRODUCTION: Antifungal stewardship (AFS) programs are recognized to contribute to optimizing antifungal prescribing for treatment and prophylaxis. However, only a small number of such programs are implemented. Consequently, evidence on behavioral drivers and barriers of such programs and learnings from existing successful AFS programs is limited. This study aimed to leverage a large AFS program in the UK and derive learnings from it. The objective was to (a) investigate the impact of the AFS program on prescribing habits, (a) use a Theoretical Domains Framework (TDF) based on the COM-B (Capability, Opportunity, and Motivation for Behavior) to qualitatively identify drivers and barriers for antifungal prescribing behaviors across multiple specialties, and (c) semiquantitatively investigate trends in antifungal prescribing habits over the last 5 years. METHODS: Qualitative interviews and a semiquantitative online survey were conducted across hematology, intensive care, respiratory, and solid organ transplant clinicians at Cambridge University Hospital. The discussion guide and survey used were developed to identify drivers of prescribing behavior, based on the TDF. RESULTS: Responses were received from 21/25 clinicians. Qualitative outcomes demonstrated that the AFS program was effective in supporting optimal antifungal prescribing practices. We found seven TDF domains influencing antifungal prescribing decisions-five drivers and two barriers. The key driver was collective decision-making among the multidisciplinary team (MDT) while key barriers were lack of access to certain therapies and fungal diagnostic capabilities. Furthermore, over the last 5 years and across specialties, we observed an increasing tendency for prescribing to focus on more targeted rather than broad-spectrum antifungals. CONCLUSIONS: Understanding the basis for linked clinicians' prescribing behaviors for identified drivers and barriers may inform interventions on AFS programs and contribute to consistently improving antifungal prescribing. Collective decision-making among the MDT may be leveraged to improve clinicians' antifungal prescribing. These findings may be generalized across specialty care settings.

A systematic literature review and network meta-analysis of effectiveness and safety outcomes in advanced melanoma.

BACKGROUND: Although a myriad of novel treatments entered the treatment paradigm for advanced melanoma, there is lack of head-to-head evidence. We conducted a network meta-analysis (NMA) to estimate each treatment's relative effectiveness and safety. METHODS: A systematic literature review (SLR) was conducted in Embase, MEDLINE and Cochrane to identify all phase III randomised controlled trials (RCTs) with a time frame from January 1, 2010 to March 11, 2019. We retrieved evidence on treatment-related grade III/IV adverse events, progression-free survival (PFS) and overall survival (OS). Evidence was synthesised using a Bayesian fixed-effect NMA. Reference treatment was dacarbazine. In accordance with RCTs, dacarbazine was pooled with temozolomide, paclitaxel and paclitaxel plus carboplatin. To increase homogeneity of the study populations, RCTs were only included if patients were not previously treated with novel treatments. RESULTS: The SLR identified 28 phase III RCTs involving 14,376 patients. Nineteen and seventeen treatments were included in the effectiveness and safety NMA, respectively. For PFS, dabrafenib plus trametinib (hazard ratio [HR] PFS: 0.21) and vemurafenib plus cobimetinib (HR PFS: 0.22) were identified as most favourable treatments. Both had, however, less favourable safety profiles. Five other treatments closely followed (dabrafenib [HR PFS: 0.30], nivolumab plus ipilimumab [HR PFS: 0.34], vemurafenib [HR PFS: 0.38], nivolumab [HR PFS: 0.42] and pembrolizumab [HR PFS: 0.46]). In contrast, for OS, nivolumab plus ipilimumab (HR OS: 0.39), nivolumab (HR OS: 0.46) and pembrolizumab (HR OS: 0.50) were more favourable than dabrafenib plus trametinib (HR OS: 0.55) and vemurafenib plus cobimetinib (HR OS: 0.57). CONCLUSIONS: Our NMA identified the most effective treatment options for advanced melanoma and provided valuable insights into each novel treatment's relative effectiveness and safety. This information may facilitate evidence-based decision-making and may support the optimisation of treatment and outcomes in everyday clinical practice.

Estimating Informal Caregiving Time from Patient EQ-5D Data: The Informal CARE Effect (iCARE) Tool.

BACKGROUND: Families and friends provide a considerable proportion of care for patients and elderly people. Caregiving can have substantial effects on caregivers' lives, health, and well-being. However, because clinical trials rarely assess these effects, no information on caregiver burden is available when evaluating the cost effectiveness of treatments. OBJECTIVE: This study develops an algorithm for estimating caregiver time using information that is typically available in clinical trials: the EQ-5D scores of patients and their gender. METHODS: Four datasets with a total of 8012 observations of dyads of caregivers and a gamma model with a log-link estimated with the Bayesian approach were used to estimate the statistical association between patient scores on the EQ-5D-3L dimensions and the numbers of hours of care provided by caregivers during the previous week. The model predicts hours of care as mean point estimates with 95% credible intervals or entire distributions. RESULTS: Model predictions of hours of care based on the five EQ-5D dimensions ranged from 13.06 (12.7-14.5) h/week for female patients reporting no health problems but receiving informal care to 52.82 (39.38-66.26) for male patients with the highest level of problems on all EQ-5D dimensions. CONCLUSIONS: The iCARE algorithm developed in this study allows researchers who only have patient-level EQ-5D data to estimate the mean hours of informal care received per week, including a 95% Bayesian credible interval. Caregiver time can be multiplied with a monetary value for caregiving, enabling the inclusion of informal care costs in economic evaluations. We recommend using the tool for samples that fall within the confidence intervals of the characteristics of our samples: men (age range 47.0-104.2 years), women (age range 55-103 years).

Health inequalities in the Netherlands: trends in quality-adjusted life expectancy (QALE) by educational level.

BACKGROUND: Quality-adjusted life expectancy (QALE) has been proposed as a summary measure of population health because it encompasses multiple health domains as well as length of life. However, trends in QALE by education or other socio-economic measure have not yet been reported. This study investigates changes in QALE stratified by educational level for the Dutch population in the period 2001-2011. METHODS: Using data from multiple sources, we estimated mortality rates and health-related quality of life (HRQoL) as functions of age, gender, calendar year and educational level. Subsequently, predictions from these regressions were combined for calculating QALE at ages 25 and 65. QALE changes were decomposed into effects of mortality and HRQoL. RESULTS: In 2001-2011, QALE increased for men and women at all educational levels, the largest increases being for highly educated resulting in a widening gap by education. In 2001, at age 25, the absolute QALE difference between the low and the highly educated was 7.4 healthy years (36.7 vs. 44.1) for men and 6.3 healthy years (39.5 vs. 45.8) for women. By 2011, the QALE difference increased to 8.1 healthy years (38.8 vs. 46.9) for men and to 7.1 healthy years (41.3 vs. 48.4) for women. Similar results were observed at age 65. Although the gap was largely attributable to widening inequalities in mortality, widening inequalities in HRQoL were also substantial. CONCLUSIONS: In the Netherlands, population health as measured by QALE has improved, but QALE inequalities have widened more than inequalities in life expectancy alone.

Trends in Dutch hospital spending by age and disease 1994-2010.

Understanding the age pattern of medical spending and changes therein - the purpose of this paper - is essential in an ageing society. We started by combining several data sources to create a comprehensive time-based data series of hospital spending by age group, gender and disease category for The Netherlands in the period 1994-2010. Subsequently, this time series enabled us to disentangle changes in the age pattern of hospital spending to various disease categories. Finally, we investigated to what extent trends in hospital spending by age and disease differed under the different hospital payment schemes - first global and fee-for-service budgeting and then patient-based budgeting - that were in place in The Netherlands between 1994 and 2010. Our results show that while hospital spending increased for all age groups, it grew most for the non-elderly aged. The greatest hospital spending growth for this age group related to the treatment of cardiovascular diseases and cancer. Furthermore, compared to global budget in 1990s, overall hospital spending grew considerably under fee-for-service and patient-based payment schemes, although this effect appears to be disease-specific.

Did the health of the Dutch population improve between 2001 and 2008? Investigating age- and gender-specific trends in quality of life.

Although many countries' populations have experienced increasing life expectancy in recent decades, quality of life (QoL) trends in the general population have yet to be investigated. This paper investigates whether QoL changed for the general Dutch population over the period 2001-2008. A beta regression model was employed to address specific features of the QoL distribution (i.e., boundedness, skewness, and heteroskedasticity), as well non-linear age and time trends. Quality-adjusted life expectancy (QALE) was calculated by combining model estimates of mean QoL with mortality rates provided by Statistics Netherlands. Changes in QALE were decomposed into those changes caused by QoL changes and those caused by mortality-rate changes. The results revealed a significant increase in QoL over 2001-2008 for both genders and most ages. For example, QALE for a man/woman aged 20 was found to have increased by 2.3/1.9 healthy years, of which 0.6/0.8 was due to QoL improvements.

Quality of life and time to death: have the health gains of preventive interventions been underestimated?

This article explores the implications of the relation between quality of life (QoL) and time to death (TTD) for economic evaluations of preventive interventions. By using health survey data on QoL for the general Dutch population linked to the mortality registry, we quantify the magnitude of this relationship. For addressing specific features of the nonstandard QoL distribution such as boundness, skewness, and heteroscedasticity, we modeled QoL using a generalized additive model for location, scale, and shape (GAMLSS) with a ß inflated outcome distribution. Our empirical results indicate that QoL decreases when approaching death, suggesting that there is a strong relationship between TTD and QoL. Predictions of different regression models revealed that ignoring this relationship results in an underestimation of the quality-adjusted life year (QALY) gains for preventive interventions. The underestimation ranged between 3% and 7% and depended on age, the number of years gained from the intervention, and the discount rate used.