Making visible the cost of informal caregivers’ time in Latin America : a case study for major cardiovascular, cancer and respiratory diseases in eight countries

BACKGROUND: Informal care is a key element of health care and well-being for society, yet it is scarcely visible and rarely studied in health economic evaluations. This study aims to estimate the time use and cost associated with informal care for cardiovascular diseases, pneumonia and ten different cancers in eight Latin American countries (Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, Mexico and Peru). METHODS: We carried out an exhaustive literature review on informal caregivers' time use, focusing on the selected diseases. We developed a survey for professional caregivers and conducted expert interviews to validate this data in the local context. We used an indirect estimate through the interpolation of the available data, for those cases in which we do not found reliable information. We used the proxy good method to estimate the monetary value of the use of time of informal care. National household surveys databases were processed to obtain the average wage per hour of a proxy of informal caregiver. Estimates were expressed in 2020 US dollars. RESULTS: The study estimated approximately 1,900 million hours of informal care annually and $ 4,300 million per year in average informal care time cost for these fifteen diseases and eight countries analyzed. Cardiovascular diseases accounted for an informal care burden that ranged from 374 to 555 h per year, while cancers varied from 512 to 1,825 h per year. The informal care time cost share on GDP varied from 0.26% (Mexico) to 1.38% (Brazil), with an average of 0.82% in the studied American countries. Informal care time cost represents between 16 and 44% of the total economic cost (direct medical and informal care cost) associated with health conditions. CONCLUSIONS: The study shows that there is a significant informal care economic burden -frequently overlooked- in different chronic and acute diseases in Latin American countries; and highlights the relevance of including the economic value of informal care in economic evaluations of healthcare

The health, economic and social burden of smoking in Argentina, and the impact of increasing tobacco taxes in a context of illicit trade

Tobacco tax increases, the most cost-effective measure in reducing consumption, remain underutilized in low and middle-income countries. This study estimates the health and economic burden of smoking in Argentina and forecasts the benefits of tobacco tax hikes, accounting for the potential effects of illicit trade. Using a probabilistic Markov microsimulation model, this study quantifies smoking-related deaths, health events, and societal costs. The model also estimates the health and economic benefits of different increases in the price of cigarettes through taxes. Annually, smoking causes 45,000 deaths and 221,000 health events in Argentina, costing USD 2782 million in direct medical expenses, USD 1470 million in labor productivity loss costs, and USD 1069 million in informal care costs-totaling 1.2% of the national gross domestic product. Even in a scenario that considers illicit trade of tobacco products, a 50% cigarette price increase through taxes could yield USD 8292 million in total economic benefits accumulated over a decade. Consequently, raising tobacco taxes could significantly reduce the health and economic burdens of smoking in Argentina while increasing fiscal revenue

Genetic signature of differentiated thyroid carcinoma susceptibility: a machine learning approach

To identify a peculiar genetic combination predisposing to differentiated thyroid carcinoma (DTC), we selected a set of single-nucleotide polymorphisms (SNPs) associated with DTC risk, considering polygenic risk score (PRS), Bayesian statistics, and a machine learning (ML) classifier to describe cases and controls in 3 different datasets. Dataset 1 (649 DTC, 431 controls) has been previously genotyped in a genome-wide association study (GWAS) on Italian DTC. Dataset 2 (234 DTC, 101 controls) and dataset 3 (404 DTC, 392 controls) were genotyped. Associations of 171 SNPs reported to predispose to DTC in candidate studies were extracted from the GWAS of dataset 1, followed by replication of SNPs associated with DTC risk (P<0.05) in dataset 2. The reliability of the identified SNPs was confirmed by PRS and Bayesian statistics after merging the three datasets. SNPs were used to describe the case/control state of individuals by ML classifier. Starting from 171 SNPs associated with DTC, 15 were positive in both the datasets 1 and 2. Using these markers, PRS revealed that individuals in the fifth quintile had a 7-fold increased risk of DTC than those in the first. Bayesian inference confirmed that the selected 15 SNPs differentiate cases from controls. Results were corroborated by ML, finding a maximum AUC of about 0.7. A restricted selection of only 15 DTC-associated SNPs is able to describe the inner genetic structure of Italian individuals and ML allows a fair prediction of case or control status based solely on the individual genetic background

Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning

We investigate whether Deep Reinforcement Learning (Deep RL) is able to synthesize sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be composed into complex behavioral strategies in dynamic environments. We used Deep RL to train a humanoid robot with 20 actuated joints to play a simplified one-versus-one (1v1) soccer game. We first trained individual skills in isolation and then composed those skills end-to-end in a self-play setting. The resulting policy exhibits robust and dynamic movement skills such as rapid fall recovery, walking, turning, kicking and more; and transitions between them in a smooth, stable, and efficient manner - well beyond what is intuitively expected from the robot. The agents also developed a basic strategic understanding of the game, and learned, for instance, to anticipate ball movements and to block opponent shots. The full range of behaviors emerged from a small set of simple rewards. Our agents were trained in simulation and transferred to real robots zero-shot. We found that a combination of sufficiently high-frequency control, targeted dynamics randomization, and perturbations during training in simulation enabled good-quality transfer, despite significant unmodeled effects and variations across robot instances. Although the robots are inherently fragile, minor hardware modifications together with basic regularization of the behavior during training led the robots to learn safe and effective movements while still performing in a dynamic and agile way. Indeed, even though the agents were optimized for scoring, in experiments they walked 156% faster, took 63% less time to get up, and kicked 24% faster than a scripted baseline, while efficiently combining the skills to achieve the longer term objectives. Examples of the emergent behaviors and full 1v1 matches are available on the supplementary website.Comment: Project website: https://sites.google.com/view/op3-socce

Health, economic and social burden of tobacco in Latin America and the expected gains of fully implementing taxes, plain packaging, advertising bans and smoke-free environments control measures : a modelling study

OBJECTIVE: To investigate the tobacco-attributable burden on disease, medical costs, productivity losses and informal caregiving; and to estimate the health and economic gains that can be achieved if the main tobacco control measures (raising taxes on tobacco, plain packaging, advertising bans and smoke-free environments) are fully implemented in eight countries that encompass 80% of the Latin American population. DESIGN: Markov probabilistic microsimulation economic model of the natural history, costs and quality of life associated with the main tobacco-related diseases. Model inputs and data on labour productivity, informal caregivers' burden and interventions' effectiveness were obtained through literature review, surveys, civil registrations, vital statistics and hospital databases. Epidemiological and economic data from January to October 2020 were used to populate the model. FINDINGS: In these eight countries, smoking is responsible each year for 351 000 deaths, 2.25 million disease events, 12.2 million healthy years of life lost, US$22.8 billion in direct medical costs, US$16.2 billion in lost productivity and US$10.8 billion in caregiver costs. These economic losses represent 1.4$63.8, US$12.3, US$11.4 and US$5.7 billions in economic gains, respectively, on top of the benefits being achieved today by the current level of implementation of these measures. CONCLUSIONS: Smoking represents a substantial burden in Latin America. The full implementation of tobacco control measures could successfully avert deaths and disability, reduce healthcare spending and caregiver and productivity losses, likely resulting in large net economic benefits