How public health insurance expansion affects healthcare utilizations in middle and low-income households: an observational study from national cross-section surveys in Vietnam.

Public health insurance (PHI) has been implemented with different levels of participation in many countries, from voluntary to mandatory. In Vietnam, a law amendment made PHI compulsory nationwide in 2015 with a tolerance phase allowing people a flexible time to enroll. This study aims to examine mechanisms under which the amendment affected the enrollment, healthcare utilization, and out-of-pocket (OOP) expenditures by middle- and low-income households in this transitioning process.Using the biennial Vietnam Household Living Standard Surveys, the study applied the doubly robust difference-in-differences approach to compare outcomes in the post-amendment period from the 2016 survey with those in the pre-amendment period from the 2014 survey. The approach inheriting advantages from its predecessors, i.e., the difference-in-differences and the augmented inverse-probability weighting methods, can mitigate possible biases in policy evaluations due to the changes within the group and between groups over time in the cross-section observational study.The results showed health insurance expansion with extensive subsidies in premiums and medical coverage for persons other than the full-time employed, young children or elderly members in the family, significantly increased enrollments in the middle- and low-income groups by 9% and 8%, respectively. The number of visits for PHI-eligible services also increased, approximately 0.5 more visit per person in the middle-income and 1 more visit per person in the low-income. The amendment, however, so far did not show any significant effect on reducing OOP payments, neither for the low nor the middle-income groups. To further expand PHI coverage and financial protections, policymakers should focus on improving public health facilities, contracting PHI to more accredited private health providers, and motivating the high-income group's enrollments.

Automated Radiographic Evaluation of Adenoid Hypertrophy Based on VGG-Lite.

Adenoid hypertrophy is a pathological hyperplasia of the adenoids, which may cause snoring and apnea, as well as impede breathing during sleep. The lateral cephalogram is commonly used by dentists to screen for adenoid hypertrophy, but it is tedious and time-consuming to measure the ratio of adenoid width to nasopharyngeal width for adenoid assessment. The purpose of this study was to develop a screening tool to automatically evaluate adenoid hypertrophy from lateral cephalograms using deep learning. We proposed the deep learning model VGG-Lite, using the largest data set (1,023 X-ray images) yet described to support the automatic detection of adenoid hypertrophy. We demonstrated that our model was able to automatically evaluate adenoid hypertrophy with a sensitivity of 0.898, a specificity of 0.882, positive predictive value of 0.880, negative predictive value of 0.900, and F1 score of 0.889. The comparison of model-only and expert-only detection performance showed that the fully automatic method (0.07 min) was about 522 times faster than the human expert (36.6 min). Comparison of human experts with or without deep learning assistance showed that model-assisted human experts spent an average of 23.3 min to evaluate adenoid hypertrophy using 100 radiographs, compared to an average of 36.6 min using an entirely manual procedure. We therefore concluded that deep learning could improve the accuracy, speed, and efficiency of evaluating adenoid hypertrophy from lateral cephalograms.