To identify risk factors associated with transitions from cognitively normal to various forms of mild cognitive impairment (MCI) and then from MCI into early dementia with death as a competing state. Cognitive assessments from 554 subjects participating in a longitudinal study at the University of Kentucky AD Center were used to classify individuals into one of three transient states at any visit: cognitively normal, amnestic MCI, or mixed MCI. Between visits subjects could die or become demented. A series of polytomous logistic models were used to model transitions among these states over time and to determine how the log odds of these transitions vary with age, education, sex, family history of dementia, and APOE status. Age affects all transitions among transient states as well as those to dementia or death. Presence of at least one apolipoprotein 4 allele affects transitions from cognitively normal into amnestic MCI or into dementia. At most 12 years of education affects transitions into mixed MCI. Transitions do not vary with sex or family history. Aside from age, the usual risk factors associated with conversion from cognitively normal into dementia are likely risk factors for transitions into mild cognitive impairment.

The hedonic price regressions have mainly been used for inference. In contrast, machine learning employed on big data has a great potential for prediction. To contribute to the integration of these two strategies, this article proposes a machine learning approach to the regression analysis of big data, viz. real estate prices, for both inferential and predictive purposes. The methodology incorporates a new procedure of selecting variables, called ‘incremental sample with resampling’ (MINREM). The methodology is tested on two cases. The first is data from web advertisements selling used homes in Colombia (61,826 observations). The second considers the data (58,888 observations) from a sample of the Metropolitan American Housing Survey 2011 obtained and prepared by a reference study. The methodology consists of two stages. The first chooses the important variables under MINREM; the second focuses on the traditional training and validation procedure for machine learning, adding three activities. In both test cases, the methodology shows its value for obtaining highly parsimonious and stable models for different sample sizes, as well as taking advantage of the inferential and predictive use of the obtained regression functions. This paper contributes to an original methodology for big data regression analysis.

This work aims to propose and argue a new antecedent (critical thinking: CT) of the hard and soft dimensions of continuous improvement (CI) using a text mining perspective. The study employs a proposed methodological framework for scientific text mining (nine steps). The sample consists of 2,213 abstracts of articles that address the interest constructs in this study from the title. Three types of variables are used: investigation, reference, and control. The article presents theoretical and empirical evidence for an original model conformed by six propositions that relate CT, CI, dynamic capabilities that potentiate CI, and the context (academic, business), which place CT as a trigger for CI. The contrast of this model provides useful inputs for teachers, researchers, professionals, students, and leaders so that they can better motivate and argue the need to strengthen CT when CI is taught and applied (classically limited to the use of tools). This article provides two contributions. The first is related to the effect of CT on the development (process and result) of CI. The second consists of providing a framework to contrast structural hypotheses from a scientific text mining approach.

Bayesian multilevel models—also known as hierarchical or mixed models—are used in situations in which the aim is to model the random effect of groups or levels. In this paper, we conduct a simulation study to compare the predictive ability of 1-level Bayesian multilevel logistic regression models with that of 2-level Bayesian multilevel logistic regression models by using the prior Scaled Beta2 and inverse-gamma distributions to model the standard deviation in the 2-level. Then, these models are employed to estimate the correct answers in two questionnaires administered to university students throughout the first academic semester of 2018. The results show that 2-level models have a better predictive ability and provide more precise probability intervals than 1-level models, particularly when the prior Scaled Beta2 distribution is used to model the standard deviation in the second level. Moreover, the probability intervals of 1-level Bayesian multilevel logistic regression models proved to be more precise when Scaled Beta2 distributions, rather than an inverse-gamma distribution, are employed to model the standard deviation or when 1-level Bayesian multilevel logistic regression models, are used.

To assess the prevalence of and risk factors for cognitive decline and dementia in individuals greater than 65 years of age in Cumbayá, Quito, Ecuador. This is a cross-sectional observational study that was carried out in adults over age 65. The Mini Mental State Examination (MMSE), Ascertain Dementia Eight-Item Informant Questionnaire (AD8), and Mini Nutritional Assessment (MNA) were used to assess the cognitive status and nutritional habits of this population. A total of 144 patients (mean age 75.3 years, 77.1% female) participated in this study. Forty percent of patients had AD8 and MMSE scores consistent with cognitive impairment and possible dementia. Age (p < 0.01), lower educational level (p < 0.01), history of stroke (p < 0.01), history of intracerebral hemorrhage (p < 0.01), diabetes mellitus (p < 0.01), and malnutrition (p < 0.01) were statistically significant risk factors for cognitive impairment. Exercise was found to be protective against cognitive decline in our study group (p < 0.03). Gender, ethnicity, location, head trauma, Parkinson disease, hypercholesterolemia, myocardial infarction, thyroid disease, depression, anxiety, and family history of dementia were not found to be associated with cognitive decline in this population. The prevalence of cognitive impairment and possible dementia is 18–21% at age 65 and 54–60% at age 85 in Cumbayá, Quito, Ecuador. The major risk factors for cognitive impairment in this population are age, low educational level, malnutrition, prior stroke, prior intracerebral hemorrhage, and diabetes. Protective factors for cognitive decline include exercise and possibly modest consumption of alcohol.