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Oral diseases are considered a significant public health problem due to their high prevalence and impact on people’s day-to-day activities and quality of life (QoL). Further, treatment of oral diseases imposes a substantial burden on health care systems, families and individuals. Cost-utility analysis (CUA) is a type of full economic evaluation preferred by most health technology assessment agencies to prioritise health care interventions in the context of limited resources. CUA compares the effects of interventions, in the form of a summary of health outcomes that incorporates both quantity and quality of life years. CUA is an important approach to evaluate oral heath interventions, as oral diseases have considerable impact on people’s QoL. Hence, the application of CUA in oral health research is worthy of exploration. Two systematic literature reviews were conducted to evaluate the application of CUA in oral health interventions and the paediatric QoL instruments used in oral health research. The first systematic review was performed to evaluate the usage of CUA in oral health interventions, the methods used and the reporting quality of CUA in publications on oral health interventions. This review identified an increasing trend of CUA use in oral health research over time, especially from 2011 to 2016. The majority of CUA publications were of good reporting quality and provided conclusions concerning the most cost-effective intervention among the different options compared; hence, these will assist in healthcare decision-making. However, among 23 CUAs published in dentistry, only four were conducted among paediatric populations and all four were related to dental caries interventions. As children are the main target group of the public health care system, the second systematic review was focused to identify the generic or disease-specific paediatric QoL instruments used in oral health research. This review provided an overview of 11 oral health-specific QoL instruments and five generic instruments used in oral health research among children and adolescents. Of these 11 oral health-specific QoL instruments, none were preference-based QoL measures (PBMs), whereas, two (CHU 9D and EQ-5D-Y) of the five generic instruments were PBMs. The background analysis identified only limited CUAs conducted among paediatric populations on oral health interventions and found no condition-specific PBMs in oral health to be used in CUA in this population. The limited number of CUAs identified in the first review was likely due to the fact that there is no oral health-specific paediatric PBM for use in economic evaluations. PBMs are important to assist estimation of more-accurate utility values for economic evaluations. Existing oral health-related QoL instruments are non-PBM and hence, cannot be used to generate utility values. The background analysis identified the necessity of paediatric utility measures to quantify outcomes in terms of the quality-adjusted life years (QALYs) and to promote the economic evaluation of oral health care interventions using CUA among children and adolescents. Therefore, the main aim of this study was to develop an oral health-specific preference-based QoL measure to facilitate the identification of high-value oral health interventions in adolescent populations. All four paediatric CUAs identified in the first systematic review were all related to interventions to prevent dental caries, the most prevalent childhood oral disease. This indicates that the economic evaluations of paediatric oral health studies were mainly concentrated on dental caries; thus, the availability of a paediatric CSPBM for dental caries would facilitate better evaluation of oral healthcare interventions among paediatric populations. Therefore, dental caries was the focus of the PBM. Preference-based measures consist of two components: a health state classification system and a set of utility weights that enable the generation of utility values for the health states defined by the classification system. The classification system for the PBM was developed as a de novo measure using literature reviews, qualitative interviews with adolescents and expert opinion using the Delphi technique. A systematic literature search of paediatric oral health-related QoL instruments was used to identify possible domains and items to be included in the classification system. Studies eliciting utility values for oral health outcomes and clinical dentistry references were also reviewed. Based on the findings, a draft classification system was developed and refined using semi-structured interviews with a convenience sample of 15 12–17-year-old adolescents who had active caries or previous experience with dental caries. The draft classification system was further refined and validated by a group of dental experts, using a modified Delphi technique. The classification system consists of five items (pain/discomfort, difficulty in eating food/drinking, worried, ability to participate in activities, and appearance) and each item has a four-level severity-based response scale. The resulting preference-based measure is named Dental Caries Utility Index (DCUI). The target group of this instrument was set as children above 12 years of age, considering that mixed dentition ends at 12 years and given the cognitive burden required for younger children to understand the concept and wordings of PBMs. Further, the Flesch Kincaid reading score of the finalised instrument was 64.6, indicating that adolescents aged 12–13 years can easily understand the classification system. The next stage was to generate a utility algorithm for the DCUI classification system. Discrete choice experiments (DCEs) were selected as the preference elicitation technique to value health states generated by the DCUI classification system. DCEs produce utility values on a latent scale; therefore, it is important to anchor them onto a full health-dead scale to calculate QALYs. As there were no previous studies to identify the most suitable method for dental caries health state valuation, two pilot studies were conducted prior to the main survey with two anchoring approaches: DCE with duration (DCETTO) and DCE with visual analogue scale (DCEVAS). The pilot surveys were conducted online among a sample from the Australian general population. Two separate DCE designs were created using Ngene software. The DCETTO design included duration as an additional attribute, whereas DCEVAS included DCE choice tasks with five attributes from the classification system and a separate visual analogue scale (VAS) task for the purpose of anchoring. Conditional logit was used to model the DCE data. Modelled DCETTO coefficients were anchored using the coefficient for the duration. Modelled DCEVAS data were anchored based on two methods: using worst heath state of VAS and mapping DCE onto VAS. A total of 200 participants completed the DCETTO survey and 191 participants completed the DCEVAS survey; there was no statistically significant difference between the two samples in relation to their sociodemographic characteristics. Further, there was no statistically significant difference between the participants’ self-reported difficulty in understanding and completing the valuation tasks between two approaches. The coefficient estimates from the unadjusted DCETTO model showed that the duration coefficient was in the expected direction and significant. Of the 15 coefficients estimated for each level in five dimensions, five were non-significant and one was not in the expected direction (level 2 of ‘difficulty in eating food/drinking’). The coefficients of all dimensions except ‘difficulty in eating food/drinking’ and ‘ability to participate activities’ were ordered as expected (i.e., higher utility decrements were associated with increasing severity levels). Therefore, an adjusted model was estimated by combining both levels 1 and 2 of ‘difficulty in eating food/drinking’ and levels 3 and 4 of ‘ability to participate activities’. All coefficients estimated from this adjusted model were in the expected direction and were logically consistent. Of the 14 coefficients estimated, 10 including the coefficient for duration were significant. Therefore, anchoring of the coefficients onto the full health-dead scale was performed based on the adjusted DCETTO model. In the DCEVAS approach, all coefficients estimated from the unadjusted model were in the expected direction, except that of level 2 of ‘appearance’, and the magnitude of the coefficients increased with the severity level of each dimension. Of the 15 coefficients estimated, four were non-significant. Therefore, an adjusted model was estimated by combining levels 1 and 2 of the dimension ‘appearance’. The anchoring of DCE data onto the full health-dead scale was completed based on adjusted DCEVAS, as all coefficients were in the expected direction, logically consistent and, of the 14 coefficients estimated, 11 were significant. The resultant utility values from these two valuation approaches were compared. Rescaled coefficients from the DCEVAS were in the expected order and significant compared to the rescaled coefficients from the DCETTO approach. Further, DCETTO produced more disperse utility decrements; as a result, the severe health states were valued as worse than death. This is unreliable for a condition like dental caries, in which participants may be reluctant to trade life years to avoid being in a severe dental caries health state. As the pilot data revealed that the DCEVAS model performed optimally and produced more-reliable utility values for dental caries health states compared to the DCETTO approach, the DCEVAS approach was utilised for the main survey. The main valuation survey was conducted as an online survey of an age and sex representative sample of the adult Australian general population. A total of 995 adults completed the survey. The survey included a set of DCE tasks and VAS tasks, basic social-demographic questions, the DCUI, a generic preference-based measure (EQ-5D-5L) and an oral health QoL instrument (OHIP-14). DCE data were modelled using conditional logit. All estimated coefficients from the DCE data were in the expected direction and order for the five dimensions. All coefficients were statistically significant except for the second levels of the ‘worried’ and ‘appearance’ domains. The estimated coefficients were rescaled onto full health-dead scale using two methods: VAS worst heath state and mapping DCE onto the VAS. Both of these methods produce largely similar utility values for the DCUI health states. The mean absolute error value for the DCE estimates based on the mapping approach was lower compared to anchoring based on VAS worst health state. Therefore, the final utility algorithm was generated based on the rescaled coefficients from mapping DCE onto VAS. The Australian-specific tariff of DCUI ranges from 0.1681 to 1.0000. The utility algorithm will enable the calculation of utility values from the participants’ responses for DCUI in economic evaluations. This study has certain limitations. Due to resource constraints and feasibility concerns, the DCUI was developed for dental caries economic evaluations among Australian adolescents. However, there is great potential for the DCUI to be used in adolescents in other countries, as an adult measure or to evaluate oral health conditions other than dental caries. Therefore, future research is recommended in these areas. Further, the DCUI should be validated rigorously with target group adolescents at dental clinic settings. Due to the methodological constraints, health state valuation was conducted using an adult sample, a common method to elicit preferences for paediatric PBMs. Therefore, future studies are recommended to assess whether there is any significant difference between the health state utility values for DCUI derived from an adult sample and the preferences of an adolescent sample. A new health state classification system and utility algorithm completed the new Preference-based QoL measure for dental caries: Dental Caries Utility Index (DCUI). The DCUI will facilitate the assessment of oral health interventions using a CUA framework and will aid resource allocation through economic evaluations for dental caries, the most prevalent childhood disease among Australian adolescents. |
