Within health economic studies, it is often necessary to adjust costs obtained from different time periods for inflation. Nevertheless, many studies do not report the methods used for this in sufficient detail. In this article, we outline the principal methods used to adjust for inflation, with a focus on studies relating to healthcare interventions in low- and middle-income countries. We also discuss issues relating to converting local currencies to international dollars and US$ and adjusting cost data collected from other countries or previous studies. We outlined the 3 main methods used to adjust for inflation for studies in these settings: exchanging the local currency to US$ or international dollars and then inflating using US inflation rates (method 1); inflating the local currency using local inflation rates and then exchanging to US$ or international dollars (method 2); splitting the costs into tradable and nontradable resources and using method 1 on the tradable resources and method 2 on the nontradable resources (method 3). In a hypothetical example of adjusting a cost of US$100 incurred in Vietnam from 2006 to 2016 prices, the adjusted cost from the 3 methods were US$116.84, US$172.09, and US$161.04, respectively. The different methods for adjusting for inflation can yield substantially different results. We make recommendations regarding the most appropriate method for various scenarios. Moving forward, it is vital that studies report the methodology they use to adjust for inflation more transparently.

Thailand faces a significant burden in terms of treating and managing degenerative and chronic diseases. Moreover, incidences of rare diseases are rising. Many of these—such as diabetes, cancer, and inherited inborn metabolic diseases—have no definite treatments or cure. Meanwhile, advanced health biotechnology has been found, in principle, to be an effective solution for these health problems.

Background: At present, health technology assessment (HTA) guidelines of many countries including Thailand have recommended EQ-5D as the preferred method for assessing utility. This study aims to generate an EQ-5D-5L value set based on societal preferences of Thai population. Methods: A 1,207 representative sample was recruited using a stratified multi-stage quota sampling technique. Face-to-face, computer-assisted interviews using the EuroQol Valuation Technology (EQ-VT) software were employed. To elicit preference score, each respondent was asked to value health states using composite time trade-off (cTTO), and discrete choice experiment (DCE). All data were integrated and analyzed using a hybrid regression model to estimate the value set. Results: Characteristics of 1,207 participants were generally similar to those of Thai general population. The coefficients generated from a hybrid model were logically consistent. The second best value is 0.9436 for health state 11121 and the worst state (55555) value is −0.4212. Mobility shows the greatest impact to utility decrement. Conclusions: Our study developed a Thai value set for EQ-5D using hybrid model. The findings from this study are of important to facilitate health technology assessment studies to inform policy decision-making as well as to promote the use of EQ-5D-5L in various health research in Thailand.