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Objective: To determine which early‐stage variables best predicted the deterioration of coronavirus disease 2019 (COVID‐19) among community‐isolated people infected with severe acute respiratory syndrome coronavirus 2 and to test the performance of prediction using only inexpensive‐to‐measure variables. Methods: Medical records of 3145 people isolated in two Fangcang shelter hospitals (large‐scale community isolation centers) from February to March 2020 were accessed. Two complementary methods—machine learning algorithms and competing risk survival analyses—were used to test potential predictors, including age, gender, severity upon admission, symptoms (general symptoms, respiratory symptoms, and gastrointestinal symptoms), computed tomography (CT) signs, and comorbid chronic diseases. All variables were measured upon (or shortly after) admission. The outcome was deterioration versus recovery of COVID‐19. Results: More than a quarter of the 3145 people did not present any symptoms, while one‐third ended isolation due to deterioration. Machine learning models identified moderate severity upon admission, old age, and CT ground‐glass opacity as the most important predictors of deterioration. Removing CT signs did not degrade the performance of models. Competing risk models identified age ≥ 35 years, male gender, moderate severity upon admission, cough, expectoration, CT patchy opacity, CT consolidation, comorbid diabetes, and comorbid cardiovascular or cerebrovascular diseases as significant predictors of deterioration, while a stuffy or runny nose as a predictor of recovery. Conclusions: Early‐stage prediction of COVID‐19 deterioration can be made with inexpensive‐to‐measure variables, such as demographic characteristics, severity upon admission, observable symptoms, and self‐reported comorbid diseases, among asymptomatic people and mildly to moderately symptomatic patients. [ABSTRACT FROM AUTHOR]

Research question: We analyzed two questions. First, the effectiveness of public Assisted Reproductive Technologies (ART) in Spain compared with private ones, measured by the time since initiating ART treatment until achieving pregnancy, accounting for age and socioeconomic factors. Second, socioeconomic determinants of access to ART, referring primarily to financial means derived by employment, income, and wealth. Design: We applied statistical models on data extracted from the national Spanish Fertility Survey from 2018. The first topic was analyzed by competing risk survival analysis conducted on a sample of 667 women who initiate ART treatment since 2000. The second, by a Bivariate Probit model conducted on a sample of 672 women older than 41 years who required ART services. Results: The first analysis raised that throughout the treatment, patients treated exclusively in private clinics had on average a higher cumulative incidence of becoming pregnant compared with patients who approached public clinics. The second analysis raised that both higher household equivalent income and higher education increase the likelihood of accessing ART in a private clinic and decrease the tendency of accessing public clinics or failing to access any service. Moreover, being single decreases the likelihood of accessing public clinics or ART services in general. Conclusions: Long waiting periods could be the main reason for the lower incidence of getting pregnant in public healthcare, explaining why patients choose private over public care. We develop a broader discussion over the extent of Spanish public funding of ART, the unequal medical outcome, and potential options for optimization. [ABSTRACT FROM AUTHOR]

In this paper, we used the 10-wave Puget Sound Panel Dataset to investigate the response lag of a significant change in discretionary time use. In particular, we want to quantify the relative magnitude of the following factors: the built environment, family and social obligations, temporal constraints, or a psychological delay factor (people delay a behavioral change until the next life shock). To answer this question, we developed a survival model to treat (1) left-censoring, (2) partial observation, and (3) multi-type exits. The results suggest that family and social obligations, as well as temporal constraints, appear to play a more important role than the built environment. Support for the psychological delay factor is not evident. We also found that the probability of having a significant change in discretionary time use is negatively related to time progression, supporting the human adaptivity hypothesis. [ABSTRACT FROM AUTHOR]