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Aim: The global burden of colorectal cancer (CRC) is set to increase by 60% by 2030. An aging population and increasing treatment complexity add difficulties for patients and clinicians in CRC management. Patient preferences can be investigated using attribute‐based stated preference (AbSP) techniques to explore trade‐offs between different treatments. These techniques include discrete‐choice experiments (DCEs), conjoint analysis and time‐trade off (TTO) methods. This systematic review with a narrative synthesis aimed to determine the use and design of AbSP studies in CRC treatment and to identify patient choice themes. Methods: The searches were performed using MEDLINE, Embase, PsycInfo and Cochrane Library in March 2021. All manuscripts featuring the use of AbSP techniques in CRC treatment were included. Data synthesis was performed using a narrative approach. Results: The search strategy returned 271 articles. Eighteen AbSP studies were included featuring 1890 patients and 296 clinicians. AbSP techniques compromised DCE (38.9%, n = 7), TTO (38.9%, n = 7) and conjoint analysis (22.2%, n = 4). Eleven studies (61.1%) involved piloting of tasks and the average task completion rate was 75%. CRC treatments included chemotherapy (33%, n = 6), combined treatments (33%, n = 6), surgery (17%, n = 3), targeted therapy (11%, n = 2) and radiotherapy (6%, n = 1). The most examined domain was physical health, investigated with 49 (59.8%) attributes. Conclusions: Life expectancy was the main attribute in chemotherapy treatment. With surgery, patients were willing to trade life‐expectancy to avoid adverse outcomes or a permanent stoma. Communication skills, treatment cost, and clinicians' views were important attributes for patients in cancer services. Further research in the elderly population, and other quality of life domains, are needed to deliver patient‐centred CRC care. [ABSTRACT FROM AUTHOR]

OBJECTIVES: This overview aims to synthesise global evidence on factors affecting healthcare access, and variations across low‐ and middle‐income countries (LMICs) vs. high‐income countries (HICs); to develop understanding of where barriers to healthcare access lie, and in what context, to inform tailored policies aimed at improving access to healthcare for all who need it. METHODS: An overview of systematic reviews guided by a published protocol was conducted. Medline, Embase, Global Health and Cochrane Systematic Reviews databases were searched for published articles. Additional searches were conducted on the Gates Foundation, WHO and World Bank websites. Study characteristics and findings (barriers and facilitators to healthcare access) were documented and summarised. The methodological quality of included studies was assessed using an adapted version of the AMSTAR 2 tool. RESULTS: Fifty‐eight articles were included, 23 presenting findings from LMICs and 35 presenting findings from HICs. While many barriers to healthcare access occur in HICs as well as LMICs, the way they are experienced is quite different. In HICs, there is a much greater emphasis on patient experience; as compared to the physical absence of care in LMICs. CONCLUSIONS: As countries move towards universal healthcare access, evaluation methods that account for health system and wider cultural factors that impact capacity to provide care, healthcare finance systems and the socio‐cultural environment of the setting are required. Consequently, methods employed in HICs may not be appropriate in LMICs due to the stark differences in these areas. [ABSTRACT FROM AUTHOR]

General population health state values are used in healthcare resource allocation, including health technology assessment. We examine whether UK general population health valuations changed during the COVID‐19 pandemic. Ratings of EQ‐5D‐5L health states 11111 (no problems), 55555 (extreme problems), and dead were collected in a UK general population survey during the pandemic (April–May 2020) using the 0 = worst imaginable health, 100 = best imaginable health visual analog scale (EQ‐VAS). Ratings for 55555 were transformed to a full health = 1, dead = 0 scale. Responses were compared to similar data collected pre‐pandemic (2018). After propensity score matching to minimize sample differences, EQ‐VAS responses were analyzed using Tobit regressions. On the 0–100 scale, 11111 was rated on average 8.67 points lower, 55555 rated 9.56 points higher, and dead rated 7.45 points lower post‐pandemic onset compared to pre‐pandemic. On the full health = 1, dead = 0 scale, 55555 values were 0.09 higher post‐pandemic onset. There was evidence of differential impacts of COVID‐19 by gender, age, and ethnicity, although only age impacted values on the 1–0 scale. COVID‐19 may have affected how people value health. It is unknown whether the effect is large enough to have policy relevance, but caution should be taken in assuming pre‐COVID‐19 values are unchanged. [ABSTRACT FROM AUTHOR]

Background and Aims: To assess prevalence and predictors of e‐cigarettes/cigarettes patterns of use in adolescents in England. Design Prospective study with 24‐month follow‐up of e‐cigarette/cigarette ever/regular use with data from an intervention evaluation. Setting: Forty‐five schools in England (Staffordshire and Yorkshire). Participants: A total of 3210 adolescents who, at baseline, were aged 13–14 years and had never used e‐cigarettes/cigarettes. Measurements Based on e‐cigarette/cigarette ever use at follow‐up, six groups were created: (a) never user, (b) e‐cigarette only, (c) cigarette only, (d) dual use—order of use unclear, (e) dual use—e‐cigarettes used first and (f) dual use—cigarettes used first. Baseline measures were: gender, ethnicity, socio‐economic status, impulsivity, family plus friend smoking and smoking‐related beliefs (attitude and perceived behavioural control). Findings In groups (a) to (f), there were 71.5, 13.3, 3.3, 5.7, 2.9 and 3.4% adolescents, respectively. Among groups using cigarettes, regular smoking was more prevalent in group (f) (dual use—cigarettes used first) [17.6%, 95% confidence interval (CI) = 10.4, 24.8] than in groups (c), (d) and (e) combined (7.3%, 95% CI = 4.7, 9.9). Among groups using e‐cigarettes, regular use was less prevalent in group (b) (e‐cigarette only) (1.9%, 95% CI = 0.6, 3.2) than in groups (d), (e) and (f) combined (12.2%, 95% CI = 8.9, 15.5). Higher impulsivity plus friends and family smoking were predictive of being in groups (b) to (f) compared with group (a) (never users). Males were more likely to be in group (b) compared to group (a); females were more likely to be in groups (c) to (f) compared to group (a). Conclusions: Regular use of e‐cigarettes/cigarettes varies across groups defined by ever use of e‐cigarettes/cigarettes. Interventions targeted at tackling impulsivity or adolescents whose friends and family members smoke may represent fruitful avenues for future research. [ABSTRACT FROM AUTHOR]

It is well documented that the modelling of health‐related quality of life data is difficult as the distribution of such data is often strongly right/left skewed and it includes a significant percentage of observations at one. The objective of this study is to develop a series of two‐part models (TPMs) that deal with these issues. Data from the UK Medical Research Council Myeloma IX trial were used to examine the relationship between the European Organization for Research and Treatment of Cancer (EORTC) QLQ‐C30/QLQ‐MY20 scores and the European QoL‐5 Dimensions (EQ‐5D) utility score. Four different TPMs were developed. The models fitted included TPM with normal regression, TPM with normal regression with variance a function of participant characteristics, TPM with log‐transformed data, and TPM with gamma regression and a log link. The cohort of 1839 patients was divided into 75% derivation sample, to fit the different models, and 25% validation sample to assess the predictive ability of these models by comparing predicted and observed mean EQ‐5D scores in the validation set, unadjusted R2, and root mean square error. Predictive performance in the derivation dataset depended on the criterion used, with R2/adjusted‐R2 favouring the TPM with normal regression and mean predicted error favouring the TPM with gamma regression. The TPM with gamma regression performs best within the validation dataset under all criteria. TPM regression models provide flexible approaches to estimate mean EQ‐5D utility weights from the EORTC QLQ‐C30/QLQ‐MY20 for use in economic evaluation. [ABSTRACT FROM AUTHOR]

Objective: Treat-to-target approaches have proved to be effective in rheumatoid arthritis, but have not been studied in psoriatic arthritis (PsA). This study was undertaken to examine the cost-effectiveness of tight control (TC) of inflammation in early PsA compared to standard care.Methods: Cost-effectiveness analyses were undertaken alongside a UK-based, open-label, multicenter, randomized controlled trial. Taking the perspective of the health care sector, effectiveness was measured using the 3-level EuroQol 5-domain, which allows for the calculation of quality-adjusted life-years (QALYs). Incremental cost-effectiveness ratios (ICERs) are presented, which represent the additional cost per QALY gained over a 48-week time horizon. Sensitivity analyses are presented assessing the impact of variations in the analytical approach and assumptions on the cost-effectiveness estimates.Results: The mean cost and QALYs were higher in the TC group: £4,198 versus £2,000 and 0.602 versus 0.561. These values yielded an ICER of £53,948 per QALY. Bootstrapped uncertainty analysis suggests that the TC has a 0.07 probability of being cost-effective at a £20,000 threshold. Stratified analysis suggests that with certain costs being controlled, an ICER of £24,639 can be calculated for patients with a higher degree of disease severity.Conclusion: A tight control strategy to treat PsA is an effective intervention in the treatment pathway; however, this study does not find tight control to be cost-effective in most analyses. Lower drug prices, targeting polyarthritis patients, or reducing the frequency of rheumatology visits may improve value for money metrics in future studies. [ABSTRACT FROM AUTHOR]