Deviation in the Age Structure of Mortality as an Indicator of COVID-19 Pandemic Severity

Objectives. To test whether distortions in the age distribution of deaths can track pandemic activity. Methods. We compared weekly distributions of all-cause deaths by age during the COVID-19 pandemic in the United States from March to December 2020 with corresponding prepandemic weekly baseline distributions derived from data for 2015 to 2019. We measured distortions via Kolmogorov–Smirnov (K-S) and χ2 goodness-of-fit statistics as well as deaths among individuals aged 65 years or older as a percentage of total deaths (PERC65+). We computed bivariate correlations between these measures and the number of recorded COVID-19 deaths for the corresponding weeks. Results. Elevated COVID-19-associated fatalities were accompanied by greater distortions in the age structure of mortality. Distortions in the age distribution of weekly US COVID-19 deaths in 2020 relative to earlier years were highly correlated with COVID fatalities (K-S: r = 0.71, P < .001; χ2: r = 0.90, P < .001; PERC65+: r = 0.85, P < .001). Conclusions. A population-representative sample of age-at-death data can serve as a useful means of pandemic activity surveillance when precise cause-of-death data are incomplete, inaccurate, or unavailable, as is often the case in low-resource environments. (Am J Public Health. 2022;112(1):165–168. https://doi.org/10.2105/AJPH.2021.306567 )

A 3-Item Measure of Digital Healthcare Literacy: Development and Validation (Preprint)

BACKGROUND With increased reliance on digital healthcare, including telehealth, efficient and effective ways are needed to assess patients’ comfort and confidence with utilizing these services. OBJECTIVE The goal of this study was to develop and validate a brief scale that assesses digital healthcare literacy. METHODS We first developed an item pool using the existing literature and expert review. We then administered the items to participants as part of a larger study. Participants were caregivers of children receiving care at a pediatric clinic who completed a survey either online or over the phone. We randomized participants into a development and confirmatory sample stratifying by language so that exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) could be performed with a separate sample of participants. We assessed the scale’s validity by examining its associations with participants’ demographics, digital access, and prior digital healthcare use. RESULTS Participants (N=507) were, on average, aged 33.7 (SD 7.7) years and 89% female. Approximately half (55%) preferred English as their primary language, 31% preferred Spanish, and 14% Arabic. Around half (45%) had a high school degree or less and 45% had an annual household income less than US $35,000. Using the EFA, three items were retained in a reduced score with excellent reliability (Cronbach’s alpha = 0.90) and a high variance explained (78%). The reduced scale had excellent CFA fit with factor loadings between 0.82 and 0.94. All fit statistics exceeded the criteria for good fit between the proposed factor structure and the data. We refer to this scale as the Digital Healthcare Literacy Scale (DHLS). The scale was positively associated with education (ρ =0.139, p=.005) and income (ρ =0.379, p<.001). Arabic speakers had lower scores compared to English (p<.001) and Spanish speakers (p=.015), and Spanish speakers had lower scores relative to English speakers (p<.001). Participants who did not own a smartphone (p=0.13) or laptop (p<.001) had lower scores than those who did own these devices. Finally, participants who had not used digital tools, including health apps (p<.001) and video telehealth (p<.001), had lower scores than those who had. CONCLUSIONS Despite the potential for digital healthcare to improve quality of life and clinical outcomes, many individuals may not have the skills to engage with and benefit from it. Moreover, these individuals may be those who already experience worse outcomes. A screening tool like DHLS could be a useful resource to identify patients who require additional assistance to use digital health services and help ensure health equity. CLINICALTRIAL N/A

Validity and Reliability of Green Competencies Instrument for Automobile Technology Programme Using Rasch Model

This article aims to determine the validity of developed constructs and check the reliability of the newly developed instrument named as Questionnaire on Green Competencies for Automobile Engineering Technology (QGCAET) for the Automobile Technology Programme in Nigerian Universities. The instrument consists of 170 elements measuring four constructs namely Technical Green Competencies; Managerial Green Competencies; Personal Green Competencies and Social Green Competencies and was administered to 299 respondents made of Lecturers, Technologists and Final- Year Students of Automobile Engineering and Technology programme in Nigeria universities. The Rasch model was used to examine the validity and reliability of the items. From the analysis point of view, the polarity of the elements indicates that the correlation of the point measure (PTMEA CORR) of 170 elements of green competencies is between 0.00 and 0.55. The summary statistics show that the reliability of the items and the separation of the items of the green competencies instrument are 0.98 and 6.46, respectively. Similarly, the item reliability of each construct is between 0.96 and 0.99, and the reliability of the person is between 0.79 and 1.97, respectively. In terms of item fit statistics, a total of 157 items are found to be fit to achieve the objectives of the study. The result also indicates that the range of fit for the four (4) identified green competencies constructs is between 0.61 and 1.49 signifying that all the constructs are in harmony in measuring the items in the constructs, so suitable in achieving the objectives of the research.

Finite Mixture Modeling for Program Evaluation: Resampling and Pre-processing Approaches

Background Finite mixture models cluster individuals into latent subgroups based on observed traits. However, inaccurate enumeration of clusters can have lasting implications on policy decisions and allocations of resources. Applied and methodological researchers accept no obvious best model fit statistic, and different measures could suggest different numbers of latent clusters. Objectives The purpose of this article is to evaluate and compare different cluster enumeration techniques. Research Design Study I demonstrates how recently proposed resampling methods result in no precise number of clusters on which all fit statistics agree. We recommend the pre-processing method in Study II as an alternative. Both studies used nationally representative data on working memory, cognitive flexibility, and inhibitory control. Conclusions The data plus priors method shows promise to address inconsistencies among fit measures and help applied researchers using finite mixture models in the future.

The Origin of The Soft X-Ray Excess in the Seyfert 1.5 Galaxy ESO 362-G18

We review the Seyfert 1.5 Galaxy ESO 362-G18 for exploring the origin of the soft X-ray excess. The Warm Corona and Relativistic Reflection models are two main scenarios to interpret the soft X-ray excess in AGNs at present. We use the simultaneous X-ray observation data of XMM-Newton and NuSTAR on Sep. 24th, 2016 to perform spectral analysis in two steps. First, we analyze the time-average spectra by using Warm Corona and Relativistic Reflection models. Moreover, we also explore the Hybrid model, Double Reflection model and Double Warm Corona model. We find that both of Warm Corona and Relativistic Reflection models can interpret the time-average spectra well but cannot be distinguished easily based on the time-averaged spectra fit statistics. Second, we add the RMS and covariance spectra to perform the spectral analysis with time-average spectra. The result shows that the warm corona could reproduce all of these spectra well. The the hot, optical thin corona and neutral distant reflection will increase their contribution with the temporal frequency, meaning that the corona responsible for X-ray continuum comes from the inner compact X-ray region and the neutral distant reflection is made of some moderate scale neutral clumps.

Concurrent Validity of the Adult Eating Behavior Questionnaire in a Canadian Sample

The current study aimed to test the factor structure of the Adult Eating Behavior Questionnaire (AEBQ), its construct validity against the Three-Factor Eating Questionnaire (TFEQ-R18) and its associations with body mass index (BMI) in Canadian adults (n = 534, 76% female). Confirmatory factor analysis (CFA) revealed that a seven-factor AEBQ model, with the Hunger subscale removed, had better fit statistics than the original eight-factor structure. Cronbach’s alpha was used to assess the internal reliability of each subscale and resulted with α &gt; 0.70 for all subscales except for Hunger (α = 0.68). Pearson’s correlations were used to inform the convergent and discriminant validation of AEBQ against the TFEQ-R18 and to examine the relationship between AEBQ and BMI. All AEBQ Food Approach subscales positively correlated with that of the TFEQ-R18 Emotional Eating and Uncontrolled Eating subscales. Similarly, BMI correlated positively with Food Approach subscales (except Hunger) and negatively with Food Avoidance subscales (except Food Fussiness). These results support the use of a seven-factor AEBQ for adults self-reporting eating behaviors, construct validity of the AEBQ against TFEB-R18, and provide further evidence for the association of these traits with BMI.

Analysis of 29 Items of the Meaningful Activity Participation Assessment-Meaningful Scale Through Rasch Analysis

Individuals find meaning in their personal activities. Meaningful activities can improve an individual's emotional and physical health and quality of life. The Meaningful Activity Participation Assessment-Meaningful Scale (MAPA-M), which can measure these meaningful activities, is measured in 29 items. In this study, the psychometric properties of 29 items of MAPA-M were investigated through Rasch analysis. The data used in this study was the Well Elderly Study 2 data among public data provided by the Inter-university Consortium for Political and Social Research (ICPSR). We used 480 randomized samples from the Well Elderly Study 2 data. Before proceeding with the Rasch analysis, as a result of checking the unidimensionality assumption of 29 items, 19 items satisfied the unidimensionality assumption. As a result of Rasch analysis of 19 items, the Driving item was removed as misfit (infit mean-square = 2.04, infit z-standardized fit statistics = 9.90, outfit mean-square = 1.86, outfit z-standardized fit statistics = 8.99). The 18 items with the misfit items removed show a conceptual item-difficulty hierarchy, and there was no differential item functioning that worked for sex and age groups. The person strata value is 3.97, which corresponds to the confidence value of 0.88. These results indicate that the 18 items in MAPA-M show appropriate item-level psychometric properties. In other words, the modified MAPA-M 18 indicates that meaningful activities can be accurately and stably measured.

A Construct Revalidation of the Community of Inquiry Survey: Empirical Evidence for a General Factor Under a Bifactor Structure

The study revisited the community of inquiry (CoI) instrument for construct revalidation. To that end, the study used confirmatory factor analysis (CFA) to examine four competing models (unidimensional, correlated-factor, second-order factor, and bifactor models) on model fit statistics computed using parameter estimates from a statistical estimator for ordinal categorical data. The CFA identified as the optimal structure the bifactor model where all items loaded on their intended domains and the existence of the general factor was supported, essentially evidence of construct validity for the instrument. The study further examined the bifactor model using mostly model-based reliability measures. The findings confirmed the contributions of the general factor to the reliability of instrument scores. The study concluded with validity and reliability evidence for the bifactor model, supported the model as a valid and reliable representation of the CoI instrument and a fuller representation of the CoI theoretical framework, and recommended its use in CoI-related research and practice in online education.

Investigation of the Effects of Silage Type, Silage Consumption, Birth Type and Birth Weight on Live Weight in Kıvırcık Lambs With MARS and Bagging MARS Algorithms.

This study was carried out to determine the effect of silage type, silage consumption, birth type (single or twin) and birth weight on live weight at the end of fattening in Kıvırcık lambs. In the experiment, 40 male Kıvırcık lambs aged 2.5-3 months were used and the animals were fattened for 56 days. During the fattening period, the lambs fed with 5 different types of silage (100% sunflower silage, 75% sunflower + 25% corn silage, 50% sunflower + 50% corn silage, 25% sunflower + 75% corn silage, 100% corn silage) pure and mixed in different proportions and concentrate feed. Data on fattening results were analyzed with MARS and Bagging MARS algorithms. The main objective of this research is to predict live weight of lambs using Multivariate Adaptive Regression Splines (MARS) and Bagging MARS algorithms as a nonparametric regression technique. Live weight value was modeled based on factors such as birth type, birth weight, silage type and silage consumption. Correlation coefficient (r), determination coefficient (R2), Adjust R2, Root-mean-square error (RMSE), standard deviation ratio (SD ratio), mean absolute percentage error (MAPE), mean absolute deviation (MAD), and Akaike Information Criteria (AIC) values of MARS algorithm predicting live weight were as follows: 0.9986, 0.997, 0.977, 0.142, 0.052, 0.2389, 0.086 and -88 respectively. Like statistics for Bagging MARS algorithm were 0.754, 0.556, 0.453, 1.8, 0.666, 3.96, 1.47 and 115 respectively. It was observed that MARS and Bagging MARS algorithms have revealed correct results according to goodness of fit statistics. However, it has been revealed that MARS algorithm gives better results in live weight modeling.