Shan Jiang

This study explores public attitudes toward genetic sequencing (GS) services in China and identifies key factors influencing adoption. Although GS has the potential to strengthen precision public health, its uptake may be constrained by societal and attitudinal barriers. We used a sequential mixed-methods design, combining focus group discussions with a structured ranking questionnaire. A total of 28 participants (mean age 48.9 years) were included. The qualitative analysis identified five major themes reflecting participants’ understanding, concerns, and expectations regarding GS. Quantitatively, the most influential factors for adoption were the characteristics of genomic information and financial accessibility. Demographic differences were observed: men prioritized cost, whereas women emphasized clarity of information; older adults focused on affordability, whereas younger participants valued transparency and the utility of information. Psychological concerns also emerged as an important barrier, including fears of genetic discrimination and emotional distress arising from the implications of genetic information. In contrast to European studies that often highlight privacy and data protection, participants in this study placed greater emphasis on informational and financial considerations. Despite the modest sample, thematic saturation was reached and the mixed-methods approach provides complementary qualitative and quantitative evidence. Addressing public concerns through transparent communication, education, psychological support, and affordability-focused policies will be important to facilitate integration of GS into healthcare systems. These findings provide culturally grounded evidence to inform precision public health in China and similar contexts.

Health equity is a growing concern for policymakers across the globe. Conventional cost-effectiveness analysis (CEA), commonly used in evaluating health interventions, primarily focuses on the average and aggregate health outcomes in the targeted population, neglecting the distributional impacts on health equity. This gap calls for approaches that can quantify the impact of intervention of interest on health equity to support decision-making. Distributional Cost-Effectiveness Analysis (DCEA) offers a framework to assess the distributional impacts of health interventions. Based on DCEA, aggregate DCEA (A-DCEA) was proposed as a practical and simplified alternative to DCEA. Unlike full DCEA, which requires detailed subgroup data, A-DCEA utilizes aggregated data, making it accessible and feasible for broader use. In this commentary, we discuss the rationale for A-DCEA, outline the steps for its implementation, and highlight its applicability. The purpose of this article is to introduce A-DCEA as a pragmatic and accessible tool for evaluating the equity implications of healthcare interventions. A-DCEA can inform policymakers by incorporating equity considerations into healthcare decision-making, particularly when conducting a full DCEA is impractical due to data limitation. A-DCEA provides a valuable and accessible method for evaluating the distributional impact of interventions, promoting health equity in decision-making. Its adoption can lead to more informed health policy that considers health inequities as well as the efficient use of resources.

Productivity loss may contribute to a large proportion of costs of health conditions in an economic evaluation from a societal perspective, but there is currently a lack of methodological consensus on how productivity loss should be measured and valued. Despite the research progress surrounding this issue in other countries, it has been rarely discussed in China. We reviewed the official guidelines on economic evaluations in different countries and regions and screened the literature to summarise the extent to which productivity loss was incorporated in economic evaluations and the underlying methodological challenges. A total of 48 guidelines from 46 countries/regions were included. Although 32 (67%) guidelines recommend excluding productivity loss in the base case analysis, 23 (48%) guidelines recommend including productivity loss in the base case or additional analyses. Through a review of systematic reviews and the economic evaluation studies included in these reviews, we found that the average probability of incorporating productivity loss in an economic evaluation was 10.2%. Among the economic evaluations (n=478) that explicitly considered productivity loss, most (n=455) considered losses from paid work, while only a few studies (n=23) considered unpaid work losses. Recognising the existing methodological challenges and the specific context of China, we proposed a practical research agenda and a disease list for progress on this topic, including the development of the disease list comprehensively consisting of health conditions where the productivity loss should be incorporated into economic evaluations. An increasing number of guidelines recommend the inclusion of productivity loss in the base case or additional analyses of economic evaluation. We optimistically expect that more Chinese researchers notice the importance of incorporating productivity loss in economic evaluations and anticipate guidelines that may be suitable for Chinese practitioners and decision-makers that facilitate the advancement of research on productivity loss measurement and valuation.

The occurrence of future unrelated medical costs is a direct consequence of life-prolonging interventions, but most pharmacoeconomic guidelines recommend the exclusion of these costs. The Chinese guidelines were updated in 2020, taking an exclusion approach for the future unrelated medical cost. We notice the research surrounding this issue continues in other countries and leads to an inclusion recommendation in some guidelines. Meanwhile, this issue has not been discussed in China, reflecting an urgent need for extensive research on its impact. We reviewed the theoretical and practical studies surrounding the inclusion of future unrelated medical costs, summarised the landscape of guidelines in other jurisdictions. We found that the inclusion would increase the internal and external consistency of economic evaluation and the comparability of results between different jurisdictions. However, more research is needed surrounding this issue. We proposed a future research agenda to inform the update of Chinese guidelines. We recommend research on individual-level healthcare reimbursement data and end-of-life costs from hospital administrative data to generate the age-specific, sex-specific and condition-specific costs. We also recommend establishing a formal process to evaluate the ethical and economic impact of including future unrelated medical costs and adjust the threshold accordingly in the guidelines.