Framing health taxes: learning from low- and middle-income countries.

Health taxes are effective policy instruments to save lives, raise government revenues and improve equity. Health taxes, however, directly conflict with commercial actors' interests. Both pro-tax health advocates and anti-tax industry representatives seek to frame health tax policy. Yet, little is known about which frames resonate in which settings and how framing can most effectively advance or limit policies. To fill this gap, we conducted qualitative research in 2022, including focus group discussions, in-depth interviews, document reviews and media analysis on the political economy of health taxes across eight low-income and middle-income countries. Studies captured multiple actors constructing context-specific frames, often tied to broader economic, health and administrative considerations. Findings suggest that no single frame dominates; in fact, a plurality of different frames exist and shape discourse and policymaking. There was no clear trade-off between health and economic framing of health tax policy proposals, nor a straightforward way to handle concerns around earmarking. Understanding how to best position health taxes can empower health policymakers with more persuasive framings for health taxes and can support them to develop broader coalitions to advance health taxes. These insights can improve efforts to advance health taxes by better appreciating political economy factors and constraining corporate power, ultimately leading to improved population-level health.

Evaluating the effectiveness and cost-effectiveness of a digital, app-based intervention for depression (VMood) in community-based settings in Vietnam: Protocol for a stepped-wedge randomized controlled trial.

The COVID-19 pandemic has amplified mental health problems and highlighted inequitable gaps in care worldwide. In response there has been an explosion of digital interventions such as smartphone applications ("apps") to extend care. The objective of this trial is to evaluate the effectiveness and cost-effectiveness of a digital depression intervention (VMood), delivered via a smartphone app. VMood is adapted from an in-person intervention that was delivered by non-specialist providers and shown to be effective in the Vietnamese context in our previous trial (2016-2019). A stepped-wedge, randomized controlled trial will be conducted across eight provinces in Vietnam. Adults aged 18 years and over will be recruited through community-based primary care centres and screened for depression using the embedded Patient Health Questionnaire-9 (primary outcome measure). Participants scoring 10-19, indicating depression caseness, will be randomly allocated to the intervention or control group until the target of 336 is reached. Secondary outcome measures will examine the effect of the intervention on commonly co-occuring anxiety, quality of life and work productivity, along with use of alcohol and tobacco products. Assessments will be administered through an online survey platform (REDCap) at baseline, and at every 3 months until 3 months post-intervention. Intervention-group participants will receive VMood for a 3-month period, with online support provided by social workers. Control-group participants will receive a limited version of the app until they cross into the intervention group. Generalized Linear Mixed-effect Models for clustered measures will be used for all outcomes data. We will conduct a cost-effectiveness analysis alongside the trial to capture VMood's costs and benefits. This trial will provide evidence on the effectiveness and cost-effectiveness of a digital mental health intervention adapted from an in-person intervention. This trial will also contribute important information to the growing and promising field of digital mental health. Trail regulation. Registered at ClinicalTrials.gov, identifier [NCT05783531].

Ecological and human health risk assessments of cyanotoxins and heavy metals in a drinking water supply reservoir.

Human beings are frequently exposed to a mixture of chemical pollutants through the ingestion of contaminated drinking water. The present study aimed to assess the ecological and human health risks associated with the contamination of cyanotoxins and heavy metals in a drinking water supply reservoir, the Tri An Reservoir (TAR), in Vietnam. Results demonstrated that the concentrations of individual heavy metals varied in the following order: iron (Fe) > lead (Pb) > arsenic (As) > zinc (Zn). Although the ecological potential risk of heavy metals was low during the study period, the concentration of Fe sometimes exceeded the Vietnamese standard for drinking water. Toxic cyanobacteria and microcystins (MCs) frequently occurred in the TAR with the highest density of 198.7 × 103 cells/mL and 7.8 µg/L, respectively, indicating a high risk of health impacts to humans. The results of the study indicate that exposure to heavy metals does not pose any non-carcinogenic health risks for both adults and children. However, the contamination of MCs in the surface water posed a serious disease enhancement to both adults and children through direct ingestion and dermal absorption.

Effects of cigarette price and packaging on hypothetical quit-smoking behaviour: a discrete choice experiment among Vietnamese adults who smoke.

INTRODUCTION: Raising cigarette prices, increasing graphic health warning label (HWL) coverage and requiring plain packaging could reduce cigarette smoking in Vietnam. This discrete choice experiment estimates the potential impact of these policies on smoking behaviour. METHODS: In February-May 2022, we conducted a phone-based, cross-sectional survey of 1494 Vietnamese adults who smoke. Participants were randomly assigned to view four individual cigarette pack images, varied on price (15 000 (reference group); 20 000; 30 000 or 40 000 Vietnamese dong (VND)) and packaging (branded pack with 50% graphic HWL (reference group); branded pack with 85% graphic HWL; plain pack with 50% graphic HWL or branded pack without HWL). Participants responded if they would quit or continue smoking if they could only purchase the pack shown. We used binomial logistic regressions to estimate the relative risk (RR) of price and packaging on hypothetical quitting. RESULTS: Participants were more likely to report they would quit when presented with 30 000 VND (RR 1.20, 95% CI 1.07 to 1.35) and 40 000 VND packs (RR 1.40, 95% CI 1.23 to 1.58) vs the 15 000 VND pack. Participants were also more likely to report they would quit when shown the branded pack with 85% HWL (RR 1.30, 95% CI 1.18 to 1.42) and plain pack with 50% HWL (RR 1.34, 95% CI 1.21 to 1.49) vs the branded pack with 50% HWL coverage. Participants had significantly lower quit likelihood (RR 0.41, 95% CI 0.35 to 0.48) when shown the branded pack without a HWL. CONCLUSIONS: Results suggest raising cigarette prices to at least 30 000 VND, implementing larger graphic HWLs or plain packaging could decrease smoking rates in Vietnam.

Estimating the health impacts of sugar-sweetened beverage tax for informing policy decisions about the obesity burden in Vietnam.

BACKGROUND: Taxation on sugar-sweetened beverages (SSBs) has been adopted in more than 40 countries but remained under discussion in Vietnam. This study aimed to estimate the health impacts of different SSBs tax plans currently under discussion to provide an evidence base to inform decision-making about a SSBs tax policy in Vietnam. METHOD & FINDINGS: Five tax scenarios were modelled, representing three levels of price increase: 5%, 11% and 19-20%. Scenarios of the highest price increase were assessed across three different tax designs: ad valorem, volume-based specific tax & sugar-based specific tax. We modelled SSBs consumption in each tax scenario; how this reduction in consumption translates to a reduction in total energy intake and how this relationship in turn translates to an average change in body weight and obesity status among adults by applying the calorie-to weight conversion factor. Changes in type 2 diabetes burden were then calculated based on the change in average BMI of the modelled cohort. A Monte Carlo simulation approach was applied on the conversion factor of weight change and diabetes risk reduction for the sensitivity analysis. We found that the taxation that involved a 5% price increase gave relatively small impacts while increasing SSBs' price up to 20% appeared to impact substantially on overweight and obesity rates (reduction of 12.7% and 12.4% respectively) saving 27 million USD for direct medical cost. The greatest reduction was observed for overweight and obesity class I. The decline in overweight and obesity rates was slightly higher for women than men. CONCLUSION: This study supports the SSB tax policy in pursuit of public health benefits, especially where the tax increase involves around a 20% price increase. The health benefit and revenue gains were evident across all three tax designs with the specific tax based on sugar density achieving greatest effects.

Application of Machine Learning in Epileptic Seizure Detection.

Epileptic seizure is a neurological condition caused by short and unexpectedly occurring electrical disruptions in the brain. It is estimated that roughly 60 million individuals worldwide have had an epileptic seizure. Experiencing an epileptic seizure can have serious consequences for the patient. Automatic seizure detection on electroencephalogram (EEG) recordings is essential due to the irregular and unpredictable nature of seizures. By thoroughly analyzing EEG records, neurophysiologists can discover important information and patterns, and proper and timely treatments can be provided for the patients. This research presents a novel machine learning-based approach for detecting epileptic seizures in EEG signals. A public EEG dataset from the University of Bonn was used to validate the approach. Meaningful statistical features were extracted from the original data using discrete wavelet transform analysis, then the relevant features were selected using feature selection based on the binary particle swarm optimizer. This facilitated the reduction of 75% data dimensionality and 47% computational time, which eventually sped up the classification process. After having been selected, relevant features were used to train different machine learning models, then hyperparameter optimization was utilized to further enhance the models' performance. The results achieved up to 98.4% accuracy and showed that the proposed method was very effective and practical in detecting seizure presence in EEG signals. In clinical applications, this method could help relieve the suffering of epilepsy patients and alleviate the workload of neurologists.

An Analysis of Vocal Features for Parkinson's Disease Classification Using Evolutionary Algorithms.

Parkinson's Disease (PD) is a brain disorder that causes uncontrollable movements. According to estimation, roughly ten million individuals worldwide have had or are developing PD. This disorder can have severe consequences that affect the patient's daily life. Therefore, several previous works have worked on PD detection. Automatic Parkinson's Disease detection in voice recordings can be an innovation compared to other costly methods of ruling out examinations since the nature of this disease is unpredictable and non-curable. Analyzing the collected vocal records will detect essential patterns, and timely recommendations on appropriate treatments will be extremely helpful. This research proposed a machine learning-based approach for classifying healthy people from people with the disease utilizing Grey Wolf Optimization (GWO) for feature selection, along with Light Gradient Boosted Machine (LGBM) to optimize the model performance. The proposed method shows highly competitive results and has the ability to be developed further and implemented in a real-world setting.

Magnetoimpedance Biosensors and Real-Time Healthcare Monitors: Progress, Opportunities, and Challenges.

A small DC magnetic field can induce an enormous response in the impedance of a soft magnetic conductor in various forms of wire, ribbon, and thin film. Also known as the giant magnetoimpedance (GMI) effect, this phenomenon forms the basis for the development of high-performance magnetic biosensors with magnetic field sensitivity down to the picoTesla regime at room temperature. Over the past decade, some state-of-the-art prototypes have become available for trial tests due to continuous efforts to improve the sensitivity of GMI biosensors for the ultrasensitive detection of biological entities and biomagnetic field detection of human activities through the use of magnetic nanoparticles as biomarkers. In this review, we highlight recent advances in the development of GMI biosensors and review medical devices for applications in biomedical diagnostics and healthcare monitoring, including real-time monitoring of respiratory motion in COVID-19 patients at various stages. We also discuss exciting research opportunities and existing challenges that will stimulate further study into ultrasensitive magnetic biosensors and healthcare monitors based on the GMI effect.

Cost-Effectiveness of Population-Based Tobacco Control Interventions on the Health Burden of Cardiovascular Diseases in Vietnam.

This study sought to assess the cost-effectiveness of population-based tobacco control interventions, which included health promotion and education, smoke-free models, cessation programs, warning on package, marketing bans, and raising tax. Standardized activity-based costing ingredient approach was applied with the provider perspective to calculate interventions cost from 2013 to 2017. The potential health impacts of the aforementioned interventions were calculated through a Microsoft Excel-based modeling adapted from Higashi et al and Ngalesoni et al. All six population-based tobacco control interventions were highly cost-effective with ranges from 1405 VND (Vietnamese Dong) to 135 560 VND per DALY (disability-adjusted life year) averted. It was identified that raising cigarette taxes and applying health warnings on tobacco packages are the most favorable, cost-effective interventions. The results from this study provide a robust message that calls for increased attention and efforts in developing an appropriate policy agenda, which jointly integrates both political and community-based interventions, to maximize intervention impact on tobacco use.

Real-Time Size and Mass Estimation of Slender Axi-Symmetric Fruit/Vegetable Using a Single Top View Image.

Among the physical attributes of agricultural materials, mass, volume, and sizes have always been important quality parameters. Previous research focused mostly on volume estimation using stereo-based approaches, which rely on manual intervention or require a multiple-cameras set up or multiple-frames captures from different viewing angles to reconstruct the three-dimensional point-cloud information. These approaches are tedious and not suitable for practical machine vision systems. In this work, we only use a single camera mounted on the ceiling of the imaging chamber, which is directly above the fruit/vegetable to capture its top-view, two-dimensional image. We developed a method to estimate the mass/volume of agricultural products with axi-symmetrical shapes such as a carrot or a cucumber. The mass/volume is estimated as the sum of smaller standard blocks, such as chopped pyramids, an elliptical cone, or a conical cone. The computed mass/volume showed good agreement with analytical and experimental results. The weight estimation error is 95% for the case of the carrot and 96.7% for the cucumber. The method proved to be sufficiently accurate, easy to use, and rotationally invariant.

Illicit Cigarette Consumption and Government Revenue Loss in Vietnam: Evidence from a Primary Data Approach.

This article provides the first comprehensive picture and independent estimates of both illicit cigarette consumption and the resulting government tax revenue loss in Vietnam using data from a representative survey of cigarette smokers in 12 Vietnamese provinces. The survey consisted of face-to-face interviews and on-site cigarette pack examinations. We find that more than 720 million illicit cigarette packs, or 20.7% of total cigarette consumption, circulated in Vietnam in 2012. Consequently, government tax revenue loss due to illicit trade ranged from US $223 to 295 million. Our estimates also indicate that 1) the most popular illicit brands were Jet and Hero, both were sold at higher prices than the average legal brand; 2) the average price of illicit cigarettes was 51% higher than the average price of legal cigarettes; and 3) majority of illicit cigarettes were sold at convenience stores, which were registered and licensed businesses. Our findings suggest that prices are not a driver of illicit cigarette consumption in Vietnam, and this illicit trade is at least partially a consequence of weak market control enforcement.