RESUMO
This article aims to assess the association between household demographic and socioeconomic characteristics and catastrophic health expenditure (CHE) in Argentina during 2017-2018. CHE was estimated as the proportion of household consumption capacity (using both income and total consumption in separate estimations) allocated for Out-of-Pocket (OOP) health expenditure. For assessing the determinants, we estimated a generalized ordered logit model using different intensities of CHE (10%, 15%, 20% and 25%) as the ordinal dependent variable, and socioeconomic, demographic and geographical variables as explanatory factors. We found that having members older than 65 years and with long-term difficulties increased the likelihood of incurring CHE. Additionally, having an economically inactive household head was identified as a factor that increases this probability. However, the research did not yield consistent results regarding the relationship between public and private health insurance and consumption capacity. Our results, along with the robustness checks, suggest that the magnitude of the coefficients for the household head characteristics could be exaggerated in studies that overlook the attributes of other household members. In addition, these results emphasize the significance of accounting for long-term difficulties and indicate that omitting this factor could overestimate the impact of members aged over 65.
Assuntos
Características da Família , Gastos em Saúde , Fatores Socioeconômicos , Humanos , Argentina , Gastos em Saúde/estatística & dados numéricos , Idoso , Feminino , Masculino , Pessoa de Meia-Idade , Seguro Saúde/economia , Seguro Saúde/estatística & dados numéricos , Adulto , Financiamento Pessoal/estatística & dados numéricos , Renda/estatística & dados numéricos , Doença Catastrófica/economiaRESUMO
Results in contact sports like Rugby are mainly interpreted in terms of the ability and/or luck of teams. But this neglects the important role of the motivation of players, reflected in the effort exerted in the game. Here we present a Bayesian hierarchical model to infer the main features that explain score differences in rugby matches of the English Premiership Rugby 2020/2021 season. The main result is that, indeed, effort (seen as a ratio between the number of tries and the scoring kick attempts) is highly relevant to explain outcomes in those matches.
RESUMO
Causal graph extraction from news has the potential to aid in the understanding of complex scenarios. In particular, it can help explain and predict events, as well as conjecture about possible cause-effect connections. However, limited work has addressed the problem of large-scale extraction of causal graphs from news articles. This article presents a novel framework for extracting causal graphs from digital text media. The framework relies on topic-relevant variables representing terms and ongoing events that are selected from a domain under analysis by applying specially developed information retrieval and natural language processing methods. Events are represented as event-phrase embeddings, which make it possible to group similar events into semantically cohesive clusters. A time series of the selected variables is given as input to a causal structure learning techniques to learn a causal graph associated with the topic that is being examined. The complete framework is applied to the New York Times dataset, which covers news for a period of 246 months (roughly 20 years), and is illustrated through a case study. An initial evaluation based on synthetic data is carried out to gain insight into the most effective time-series causality learning techniques. This evaluation comprises a systematic analysis of nine state-of-the-art causal structure learning techniques and two novel ensemble methods derived from the most effective techniques. Subsequently, the complete framework based on the most promising causal structure learning technique is evaluated with domain experts in a real-world scenario through the use of the presented case study. The proposed analysis offers valuable insights into the problems of identifying topic-relevant variables from large volumes of news and learning causal graphs from time series.
RESUMO
We experimentally approach the discursive dilemma to gain insight into people's procedural appropriateness judgments. We relied on a vignette in which three people had formed opinions about two skills (premises) of a candidate to decide whether to hire her/him (conclusion). The dilemma arises when different outcomes (hire vs. not hire) are achieved depending on whether the majority opinion is independently considered for each premise or for the global conclusion of each judge. Participants were asked to choose the procedure they thought to be more appropriate to reach a decision. In Experiment 1, we found a leniency effect (a bias to prefer the aggregation procedure that led to hiring the candidate), which was reduced by introducing the participant as a juror with an exogenously provided negative opinion about the candidate's skills. In Experiment 2, we replicated the opinion effect, even when subjects did not participate as jury members. In Experiment 3, we found that the leniency bias was only reduced when participants' negative opinion was aligned with a majority of negative premises, but not with a majority of negative conclusions. We discuss present findings in terms of the identification of empirical regularities that may affect people's procedural legitimacy judgments.