Fetal Down syndrome screening models for developing countries; Part II: Cost-benefit analysis.

BACKGROUND: To identify the most cost-beneficial model as a national policy of screening and diagnosis of fetal Down syndrome (DS) in developing countries. METHODS: Cost-benefit analysis (CBA) was performed based on the effectiveness and probabilities derived from a large prospective study on MSS (maternal serum screening) among Thai population. Various models including maternal age alone, STS (second trimester screen), I-S (independent screen: first or second trimester screen depending on the time of first visit), C-S (contingent serum screen) plus STS, maternal age with NIPS (non-invasive prenatal test), STS alone with NIPS, I-S with NIPS, C-S plus STS with NIPS, and Universal NIPS were compared. RESULTS: I-S with NIPS as a secondary screening was most cost-beneficial (Benefit/Cost ratio 4.28). Cost-benefit is directly related to the costs of NIPS. CONCLUSION: In addition to simplicity and feasibility, I-S with expensive NIPS as a secondary screening is the most cost-beneficial method for low resource settings and should be included in universal healthcare coverage as a national policy. This study could be a model for developing countries or a guideline for international health organizations to help low resource countries, probably leading to a paradigm shift in prenatal diagnosis of fetal DS in the developing world.

Handling time in economic evaluation studies.

The discount rates and time horizons used in a health technology assessment (HTA) can have a significant impact on the results, and thus the prioritization of technologies. Therefore, it is important that clear guidance be provided on the appropriate discount rates for cost and health effect and appropriate time horizons. In this paper we conduct a review of relevant case studies and guidelines and provide guidance for all researchers conducting economic evaluations of health technologies in the Thai context. A uniform discount rate of 3% is recommended for both costs and health effects in base case analyses. A sensitivity analysis should also be conducted, with a discount range of 0-6%. For technologies where the effects are likely to sustain for at least 30y ears, a rate of 4% for costs and 2% for health effects is recommended. The time horizon should be long enough to capture the full costs and effects of the programs.

Discounting money and health effects from communicable and noncommunicable diseases in Thailand.

The purpose of this study was to determine the discount rates for money and health outcomes in the Thai context, including the discount rates for communicable and non-communicable diseases. Moreover, this study aimed to explore the socio-demographic characteristics that influence discounting. The computer-based experimental design was used to obtain time preferences for money and health in a total of 1202 Chiang Mai province population, aged 25-50, individually interviewed by trained interviewers. Money-related questions were carried out in all subjects. For health-related questions, all subjects were randomly assigned in a 1:1 ratio for response to questions about Coronavirus Disease 2019 (COVID-19) (N = 602) and air pollution (N = 600). A choice-based elicitation procedure was performed in the experiment to obtain the indifference values from subjects' time preferences. The cumulative weighting functions were generated using the indifference values to indicate the degree of discounting. The discount factors were computed from the cumulative weighting functions. The discount rates were estimated using a continuous approximation based on the relationship between the discount factors and the parameters governing the discounting model. The Tobit model was applied to investigate the relationships between discounting and socio-demographic characteristics. Discounting for money was greater than discounting for health. Money and health had annual discount rates of 6.2% and 1.3%, respectively. Furthermore, in the COVID -19 situation, the annual discount rate for health was higher than that in the air pollution situation (2.4% vs. 0.7%). Generation X subjects (aged 42 years and above), children under the age of 15 in the household, and underlying diseases were positively related to discounting, while household income was negatively related to discounting. Health should be discounted at a lower rate than money. Moreover, different discount rates should be considered for different types of diseases.

Handling time in economic evaluation studies.

BACKGROUND: lime horizon and discounting are important issues in economic evaluation studies and have an impact on the priority of different programs. OBJECTIVE: Provides supporting rational and theories to propose the appropriate discount rate for both cost and effect and time horizon for conducting economic evaluation studies in Thailand. MATERIAL AND METHOD: Describe the theories that explain time horizon for both the cost of a study and its effect. CONCLUSION: The recommended uniform discount rate for both costs and effects at the base case is 3% with a variance in range from 0-6% by sensitivity analysis. The time horizon should be long enough to capture the full costs and effects of the programs.