BackgroundTo identify the most cost-beneficial model as a national policy of screening and diagnosis of fetal Down syndrome (DS) in developing countries.MethodsCost-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.ResultsI-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.ConclusionIn 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.

Minimum wage policies were designed to raise the wages of low-skilled workers. In this study, we use data from the Thai Labor Force Survey (2011-2020) to examine the impact of the minimum wage policy on the wage distribution using a quantile regression model corrected for sample selection with a copula. We find that the minimum wage has the strongest effect on the lowest quantile and the effect decreases toward the higher quantiles. This confirms the effectiveness of the minimum wage policy in raising the wages of low-income individuals. In addition, there is also a spill-over effect on individuals in higher wage quantiles. The effect of the minimum wage estimated by our model is smaller compared to the standard quantile regression. This suggests that without correcting for sampling bias, the estimated effect of the minimum wage leads to an upward bias.

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