Strategies viewed as having the highest priority in Asia were monitoring at-risk populations, clinician education, national guidelines, multidisciplinary management, public awareness, and centers of excellence. Priorities were relatively concordant among the three groups of stakeholders (clinical, policy, and advocacy) and across the region. This said, three strategies, transplantation infrastructure, measuring social burden, and national guidelines received different priorities across the region. The fact that most priorities are shared across the four study sites provides justification for utilizing these strategies in the sites we studied, and suggests the findings could be generalized to other Asian countries with similar situations.
The strategy of monitoring at-risk populations is ranked as the top priority, consistent with some existing actions. Three of the four sites already have monitoring systems in place, suggesting either that the existing system is valued and should continue in a new comprehensive liver cancer control plan, or that the existing system could be improved. South Korea, Japan and Taiwan started national cancer screening programs in the 1990s with liver cancer screening as an important component [8, 10, 67]. Especially in Japan, where HCV infection is a major etiological cause of liver cancer, a HCV screening program has been implemented since the late 1980s . Japan’s National Project against Hepatitis and HCC specifically focuses on screening hepatitis carriers to prevent them from developing liver cancer and also to detect early-stage liver cancer patients . Screening is an effective method to control liver cancer and improve prognosis, and might be expected to be one of the most important strategies for liver cancer control given the large existing populations of HBV and HCV carriers in most Asian countries .
The finding of heterogeneity in transplantation infrastructure across sites is interesting. Respondents from China valued this strategy extremely low, compared to those from the other three sites. This phenomenon is consistent with evidence of barriers to liver transplantation in China. Although demand for liver transplantation has been growing rapidly in China, ethical criteria and governing legislation have not yet been fully established, which deters the utilization of liver transplantation in liver cancer management [32, 33]. Without legislation, the quality and safety of liver transplantation are not supervised and guaranteed for patients who undergo surgery, and the legal rights of healthcare providers are not protected [68, 69]. Faults in the regulatory system also leave space for illegal organ trades . In addition, liver cancer transplantation is an expensive surgical procedure that is not covered by health care insurance in China, so affordability is another barrier to full utilization . Thus, it is reasonable that stakeholders from China did not consider it a high priority despite the fact that it is considered an effective treatment for some liver cancer patients.
Our study demonstrated that conjoint analysis, a stated preference method, can be utilized to prioritize strategies for a comprehensive disease control plan. It combines qualitative research methods with quantitative methods, and provides a comprehensive way to explore stakeholders’ judgments in the policy decision-making process. Compared to CEA, the most prevalent prioritization tool in health economics, conjoint analysis has several advantages . First, it can take into consideration all possible outcomes (including risks and costs) of health policies rather than just using a single measurement to rank them . Conjoint analysis can integrate preferences from different stakeholders whose views are important when considering policy interventions, hence the results are a useful step towards developing consensus. Indeed, our study found good consensus among stakeholders from clinical, policy, and patient advocacy roles, who together should have a good understanding of decision making throughout their health systems. Second, the theoretical basis of conjoint analysis is less controversial than CEA, making it easier to justify the method to stakeholders such as physicians and policy makers .
This study demonstrated the usefulness of the conjoint-analysis method in studying stakeholders' priorities for CLCC strategies, although there are a number of limitations that need to be considered. First, given that the data come from subjective responses, there might be some variations in preference due to respondents’ specific positions, geography, or experience that do not truly reflect societal preferences and cannot be assessed with such a small sample. We tested concordance across the three selected stakeholder groups, and the results showed there was no statistically significant heterogeneity. However, to ensure representativeness and validity, additional important stakeholders should be included in future studies, such as patients and health policy researchers, and larger samples should be considered in countries such as China where there may be large differences in disease burden and resources within the country. In addition, if the sample size permitted, it would be useful to incorporate a latent class analysis in order to identify sources of heterogeneity that may not be captured by the characteristics that we chose to investigate.
A second limitation is that our study was conducted in countries where English is not the native language. Although the questionnaire was provided in both English and respondents’ first languages, there might be some misunderstandings due to translation.
The third limitation is that the generalizability of the study is limited by the study sites that were selected. Among our four sites, Japan, South Korea and Taiwan are high-income countries, while China is different in terms of health care resources, health financing and people’s socio-economic status. As most Asian countries are low or middle income countries, the results may not be applicable to all Asian countries. Further studies could be conducted in low or middle income parts of Asia to examine whether strategies are prioritized differently in such areas.
A fourth limitation of using conjoint analysis to assess priorities is that respondents may have simply preferred plans with more strategies than others. While all strategies were positively correlated with the number of strategies, this was uniform across the strategies, so bias in the prioritization would be difficult. This said, we did re-estimate the aggregate model by holding constant the number of attributes (which also required dropping the intercept to avoid the dummy variable trap). We did identify that there was a significant effect associated with the number of strategies presented in the model (p<0.001). In correcting for this bias, we found that only five strategies were significantly different to zero and positive (monitoring at-risk populations: 9.2, p<0.001; clinician education: 6.5, p<0.001; national guidelines: 4.4, p=0.003; multidisciplinary management: 3.8, p=0.008; and public awareness: 3.6, p=0.011) and one was statistically significant and negative (transplantation infrastructure: -4.1, p=0.011). In comparing these results to those reported above, the exact prioritization across the strategies was estimated. Future research is needed on separating the number of objects in a conjoint from the marginal effects estimated for each parameter.
Finally, the study sample size remains low and may lack generalizability to other Asian countries. The robustness of the results obtained in the aggregate model and the stratified models confirm the findings of the pilot study that small sample sizes can be used . This said, the lack of statistical difference between the groups can be attributed to the low sample size. While simulation techniques may provide some benefit in overcoming low sample sizes, such methods would also have to correct for the underlying differences in scale that may be present across the strata.