ResearchPad - statistics https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[Forecasting the monthly incidence rate of brucellosis in west of Iran using time series and data mining from 2010 to 2019]]> https://www.researchpad.co/article/elastic_article_13811 The identification of statistical models for the accurate forecast and timely determination of the outbreak of infectious diseases is very important for the healthcare system. Thus, this study was conducted to assess and compare the performance of four machine-learning methods in modeling and forecasting brucellosis time series data based on climatic parameters.MethodsIn this cohort study, human brucellosis cases and climatic parameters were analyzed on a monthly basis for the Qazvin province–located in northwestern Iran- over a period of 9 years (2010–2018). The data were classified into two subsets of education (80%) and testing (20%). Artificial neural network methods (radial basis function and multilayer perceptron), support vector machine and random forest were fitted to each set. Performance analysis of the models were done using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Root Error (MARE), and R2 criteria.ResultsThe incidence rate of the brucellosis in Qazvin province was 27.43 per 100,000 during 2010–2019. Based on our results, the values of the RMSE (0.22), MAE (0.175), MARE (0.007) criteria were smaller for the multilayer perceptron neural network than their values in the other three models. Moreover, the R2 (0.99) value was bigger in this model. Therefore, the multilayer perceptron neural network exhibited better performance in forecasting the studied data. The average wind speed and mean temperature were the most effective climatic parameters in the incidence of this disease.ConclusionsThe multilayer perceptron neural network can be used as an effective method in detecting the behavioral trend of brucellosis over time. Nevertheless, further studies focusing on the application and comparison of these methods are needed to detect the most appropriate forecast method for this disease. ]]> <![CDATA[Operational method of reliability and content-validity analysis: Taking “trait-symptoms” screening of individuals at high-risk for OCD as an example]]> https://www.researchpad.co/article/elastic_article_13806 A well-designed self-reported scale is highly applicable to current clinical and research practices. However, the problems with the scale method, such as quantitative analysis of content validity and test-retest reliability analysis of state-like variables are yet to be resolved. The main purpose of this paper is to propose an operational method for solving these problems. Additionally, it aims to enhance understanding of the research paradigm for the scale method (excluding criterion-related validity). This paper used a study that involved screening of high-risk groups for OCD (Obsessive-Compulsive Disorder), conducted 5 rounds of tests, and developed scales, reliability, and validity analysis (using sample sizes of 496, 610, 600, 600 and 990). The operational method we propose is practical, feasible, and can be used to develop and validate a scale.

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<![CDATA[Using case-level context to classify cancer pathology reports]]> https://www.researchpad.co/article/elastic_article_7869 Individual electronic health records (EHRs) and clinical reports are often part of a larger sequence—for example, a single patient may generate multiple reports over the trajectory of a disease. In applications such as cancer pathology reports, it is necessary not only to extract information from individual reports, but also to capture aggregate information regarding the entire cancer case based off case-level context from all reports in the sequence. In this paper, we introduce a simple modular add-on for capturing case-level context that is designed to be compatible with most existing deep learning architectures for text classification on individual reports. We test our approach on a corpus of 431,433 cancer pathology reports, and we show that incorporating case-level context significantly boosts classification accuracy across six classification tasks—site, subsite, laterality, histology, behavior, and grade. We expect that with minimal modifications, our add-on can be applied towards a wide range of other clinical text-based tasks.

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<![CDATA[Pooling individual participant data from randomized controlled trials: Exploring potential loss of information]]> https://www.researchpad.co/article/elastic_article_7838 Pooling individual participant data to enable pooled analyses is often complicated by diversity in variables across available datasets. Therefore, recoding original variables is often necessary to build a pooled dataset. We aimed to quantify how much information is lost in this process and to what extent this jeopardizes validity of analyses results.MethodsData were derived from a platform that was developed to pool data from three randomized controlled trials on the effect of treatment of cardiovascular risk factors on cognitive decline or dementia. We quantified loss of information using the R-squared of linear regression models with pooled variables as a function of their original variable(s). In case the R-squared was below 0.8, we additionally explored the potential impact of loss of information for future analyses. We did this second step by comparing whether the Beta coefficient of the predictor differed more than 10% when adding original or recoded variables as a confounder in a linear regression model. In a simulation we randomly sampled numbers, recoded those < = 1000 to 0 and those >1000 to 1 and varied the range of the continuous variable, the ratio of recoded zeroes to recoded ones, or both, and again extracted the R-squared from linear models to quantify information loss.ResultsThe R-squared was below 0.8 for 8 out of 91 recoded variables. In 4 cases this had a substantial impact on the regression models, particularly when a continuous variable was recoded into a discrete variable. Our simulation showed that the least information is lost when the ratio of recoded zeroes to ones is 1:1.ConclusionsLarge, pooled datasets provide great opportunities, justifying the efforts for data harmonization. Still, caution is warranted when using recoded variables which variance is explained limitedly by their original variables as this may jeopardize the validity of study results. ]]> <![CDATA[A descriptive cross sectional study comparing barriers and determinants of physical activity of Sri Lankan middle aged and older adults]]> https://www.researchpad.co/article/elastic_article_7830 Benefits of physical activities are numerous. Barriers for physical exercise may differ among middle aged and older adults. Therefore, identifying and comparing the barriers for participating in regular physical exercises among middle aged and older adults will be useful in designing age specific physical exercise programmes.MethodsThis descriptive cross sectional study was carried out among 206 Sri Lankan adults in the age range of 40–84 years in the Colombo North region of Sri Lanka using culturally validated questionnaires to determine and compare the barriers and factors associated with regular physical activity participation. Majority were males (56%) and 54% were < 60 years. People in the age range of 40–59 years were considered as middle age and ≥ 60 years as older adults. Bivariate analysis and multivariate analysis was carried out to determine the significant factors that are associated with regular physical activity participation.ResultsLack of free time (52%), feeling too lazy (26%) and bad weather (29%) were the main barriers for the participants. In < 60 years, high level of income (p = 0.008) and in ≥ 60 years, being a male (p = 0.016), having a high level of education (P = 0.002) and a high BMI (p = 0.002) had a significant negative association with the level of physical activities.ConclusionsContrary to findings from surveys in several developed countries, this study showed that having a high level of education and being a male were strongly related with lack of physical activity participation. ]]> <![CDATA[The qualitative assessment of optical coherence tomography and the central retinal sensitivity in patients with retinitis pigmentosa]]> https://www.researchpad.co/article/elastic_article_7697 To analyze the relationships between qualitative and quantitative parameters of spectral-domain optical coherence tomography (SD-OCT) and the central retinal sensitivity in patients with retinitis pigmentosa (RP).Materials and methodsNinety-three eyes of 93 patients were finally enrolled, with a median age (quartile) of 58 (24.5) years. We assessed the patients using SD-OCT and the 10–2 program of a Humphry Field Analyzer (HFA). As a qualitative parameter, two graders independently classified the patients’ SD-OCT images into five severity grades (grades 1–5) based on the severity of damage to the photoreceptor inner and outer segments (IS/OS) layer. As quantitative parameters, we measured the IS-ellipsoid zone (IS-EZ) width, IS/OS thickness, outer nuclear layer (ONL) thickness, central macular thickness (CMT, 1 and 3 mm) and macular cube (6 × 6 mm) volume and thickness. The central retinal sensitivity was defined by the best-corrected visual acuity (BCVA; logMAR), average sensitivities of the central 4 (foveal sensitivity [FS]) and 12 (macular sensitivity [MS]) points of the HFA 10–2 program and the mean deviation (MD) of the 10–2 program. Spearman’s correlation was used to assess the association between both qualitative and quantitative parameters and variables of the central retinal sensitivity. In addition, we performed a multiple regression analysis using these parameters to identify the parameters most strongly influencing the central retinal sensitivity.ResultsThe IS/OS severity grade was significantly correlated with the BCVA (ρ = 0.741, P < 0.001), FS (ρ = −0.844, P < 0.001), MS (ρ = −0.820, P < 0.001) and MD (ρ = −0.681, P < 0.001) and showed stronger correlations to them than any other quantitative parameters including the IS-EZ width, IS/OS thickness, ONL thickness, CMTs and macular cube volume/thickness. Furthermore, a step-wise multiple regression analysis indicated that the IS/OS severity grade was more strongly associated with the BCVA (β = 0.659, P < 0.001), FS (β = −0.820, P < 0.001), MS (β = −0.820, P < 0.001) and MD (β = −0.674, P < 0.001) than any other quantitative parameters. The intraclass correlation coefficient between two graders indicated substantial correlation (κ = 0.70).DiscussionThe qualitative grading of OCT based on the severity of the IS/OS layer was simple and strongly correlated with the central retinal sensitivity in patients with RP. It may be useful to assess the central visual function in patients with RP, although there is some variation in severity within the same severity grade. ]]> <![CDATA[Prevalence, Severity and Mortality associated with COPD and Smoking in patients with COVID-19: A Rapid Systematic Review and Meta-Analysis]]> https://www.researchpad.co/article/elastic_article_7662 Coronavirus disease 2019 (COVID-19) is an evolving infectious disease that dramatically spread all over the world in the early part of 2020. No studies have yet summarized the potential severity and mortality risks caused by COVID-19 in patients with chronic obstructive pulmonary disease (COPD), and we update information in smokers.MethodsWe systematically searched electronic databases from inception to March 24, 2020. Data were extracted by two independent authors in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Study quality was assessed using a modified version of the Newcastle-Ottawa Scale. We synthesized a narrative from eligible studies and conducted a meta-analysis using a random-effects model to calculate pooled prevalence rates and 95% confidence intervals (95%CI).ResultsIn total, 123 abstracts were screened and 61 full-text manuscripts were reviewed. A total of 15 studies met the inclusion criteria, which included a total of 2473 confirmed COVID-19 patients. All studies were included in the meta-analysis. The crude case fatality rate of COVID-19 was 7.4%. The pooled prevalence rates of COPD patients and smokers in COVID-19 cases were 2% (95% CI, 1%–3%) and 9% (95% CI, 4%–14%) respectively. COPD patients were at a higher risk of more severe disease (risk of severity = 63%, (22/35) compared to patients without COPD 33.4% (409/1224) [calculated RR, 1.88 (95% CI, 1.4–2.4)]. This was associated with higher mortality (60%). Our results showed that 22% (31/139) of current smokers and 46% (13/28) of ex-smokers had severe complications. The calculated RR showed that current smokers were 1.45 times more likely [95% CI: 1.03–2.04] to have severe complications compared to former and never smokers. Current smokers also had a higher mortality rate of 38.5%.ConclusionAlthough COPD prevalence in COVID-19 cases was low in current reports, COVID-19 infection was associated with substantial severity and mortality rates in COPD. Compared to former and never smokers, current smokers were at greater risk of severe complications and higher mortality rate. Effective preventive measures are required to reduce COVID-19 risk in COPD patients and current smokers. ]]> <![CDATA[Life expectancy and survival analysis of patients with diabetes compared to the non diabetic population in Bulgaria]]> https://www.researchpad.co/article/elastic_article_7723 To evaluate the expected life expectancy in patients with diabetes in Bulgaria and to compare it to the expected life expectancy of the non-diabetic population in the country.MethodsIt is a retrospective observational population study on individuals diagnosed with diabetes, compared to the non-diabetic population in Bulgaria for the period 2012–2015. Data from the national diabetes register and national statistical institute were used to construct life-tables with probability of survival with t-test and Chi Square test. Confounder analysis was done by age, sex, and type of diabetes. All-cause mortality and deaths in diabetic patients were analyzed. Kaplan-Meier survival curves were constructed for each age group and a log-rank analysis was conducted.ResultsAverage life expectancy in the non-diabetic population, patients with Type 1 DM and with Type 2 DM is 74.8; 70.96 and 75.19 years, respectively. For 2012–2015 the mortality in the non-diabetic population remained constant and lower (average—1.48%) compared to type-1 DM (5.25%) and Type-2 (4.27%). Relative risk of death in diabetics was higher overall (12%), after the age of 70 before which the relative risk was higher for the non-diabetic population. This was observed as a trend in all analyzed years.ConclusionPatients with type 2 DM have a longer life-expectancy than patients with type-1 DM and overall Diabetics life expectancy equals that of the non-diabetic population, which could suggest improved disease control and its associated complications in Bulgaria. Male diabetics show slightly longer life expectancy than their counterparts in the non-diabetic population, by a marginal gain of 0.6 years for the entire observed period. Life expectancy in diabetic women increased by 1.3 years, which was not observed in the non-diabetic population. Prevalence of diabetes was higher for women. Improved diabetes control may explain this gain in life; however other studies are needed to confirm this. ]]> <![CDATA[Assessing the growth in clinical skills using a progress clinical skills examination]]> https://www.researchpad.co/article/N22edf03e-1df8-490d-b170-d8364e3b4da2 This study evaluates the generalizability of an eight-station progress clinical skills examination and assesses the growth in performance for six clinical skills domains among first- and second-year medical students over four time points during the academic year.MethodsWe conducted a generalizability study for longitudinal and cross-sectional comparisons and assessed growth in six clinical skill domains via repeated measures ANOVA over the first and second year of medical school.ResultsThe generalizability of the examination domain scores was low but consistent with previous studies of data gathering and communication skills. Variations in case difficulty across administrations of the examination made it difficult to assess longitudinal growth. It was possible to compare students at different training levels and the interaction of training level and growth. Second-year students outperformed first-year students, but first-year students’ clinical skills performance grew faster than second-year students narrowing the gap in clinical skills over the students’ first year of medical school.ConclusionsCase specificity limits the ability to assess longitudinal growth in clinical skills through progress testing. Providing students with early clinical skills training and authentic clinical experiences appears to result in the rapid growth of clinical skills during the first year of medical school. ]]> <![CDATA[Adherence to the Standards for Reporting of Diagnostic Accuracy (STARD) 2015 Guidelines in Acute Point-of-Care Ultrasound Research]]> https://www.researchpad.co/article/Nee6a5caa-fab9-467b-8d9f-86f377e063b5 Incomplete reporting of diagnostic accuracy research impairs assessment of risk of bias and limits generalizability. Point-of-care ultrasound has become an important diagnostic tool for acute care physicians, but studies assessing its use are of varying methodological quality.ObjectiveTo assess adherence to the Standards for Reporting of Diagnostic Accuracy (STARD) 2015 guidelines in the literature on acute care point-of-care ultrasound.Evidence ReviewMEDLINE was searched to identify diagnostic accuracy studies assessing point-of-care ultrasound published in critical care, emergency medicine, or anesthesia journals from 2016 to 2019. Studies were evaluated for adherence to the STARD 2015 guidelines, with the following variables analyzed: journal, country, STARD citation, STARD-adopting journal, impact factor, patient population, use of supplemental material, and body region. Data analysis was performed in November 2019.FindingsSeventy-four studies were included in this systematic review for assessment. Overall adherence to STARD was moderate, with 66% (mean [SD], 19.7 [2.9] of 30 items) of STARD items reported. Items pertaining to imaging specifications, patient population, and readers of the index test were frequently reported (>66% of studies). Items pertaining to blinding of readers to clinical data and to the index or reference standard, analysis of heterogeneity, indeterminate and missing data, and time intervals between index and reference test were either moderately (33%-66%) or infrequently (<33%) reported. Studies in STARD-adopting journals (mean [SD], 20.5 [2.9] items in adopting journals vs 18.6 [2.3] items in nonadopting journals; P = .002) and studies citing STARD (mean [SD], 21.3 [0.9] items in citing studies vs 19.5 [2.9] items in nonciting studies; P = .01) reported more items. Variation by country and journal of publication were identified. No differences in STARD adherence were identified by body region imaged (mean [SD], abdominal, 20.0 [2.5] items; head and neck, 17.8 [1.6] items; musculoskeletal, 19.2 [3.1] items; thoracic, 20.2 [2.8] items; and other or procedural, 19.8 [2.7] items; P = .29), study design (mean [SD], prospective, 19.7 [2.9] items; retrospective, 19.7 [1.8] items; P > .99), patient population (mean [SD], pediatric, 20.0 [3.1] items; adult, 20.2 [2.7] items; mixed, 17.9 [1.9] items; P = .09), use of supplementary materials (mean [SD], yes, 19.2 [3.0] items; no, 19.7 [2.8] items; P = .91), or journal impact factor (mean [SD], higher impact factor, 20.3 [3.1] items; lower impact factor, 19.1 [2.4] items; P = .08).Conclusions and RelevanceOverall, the literature on acute care point-of-care ultrasound showed moderate adherence to the STARD 2015 guidelines, with more complete reporting found in studies citing STARD and those published in STARD-adopting journals. This study has established a current baseline for reporting; however, future studies are required to understand barriers to complete reporting and to develop strategies to mitigate them. ]]> <![CDATA[Assessment of the genomic prediction accuracy for feed efficiency traits in meat-type chickens]]> https://www.researchpad.co/article/5989db51ab0ee8fa60bdc4ab

Feed represents the major cost of chicken production. Selection for improving feed utilization is a feasible way to reduce feed cost and greenhouse gas emissions. The objectives of this study were to investigate the efficiency of genomic prediction for feed conversion ratio (FCR), residual feed intake (RFI), average daily gain (ADG) and average daily feed intake (ADFI) and to assess the impact of selection for feed efficiency traits FCR and RFI on eviscerating percentage (EP), breast muscle percentage (BMP) and leg muscle percentage (LMP) in meat-type chickens. Genomic prediction was assessed using a 4-fold cross-validation for two validation scenarios. The first scenario was a random family sampling validation (CVF), and the second scenario was a random individual sampling validation (CVR). Variance components were estimated based on the genomic relationship built with single nucleotide polymorphism markers. Genomic estimated breeding values (GEBV) were predicted using a genomic best linear unbiased prediction model. The accuracies of GEBV were evaluated in two ways: the correlation between GEBV and corrected phenotypic value divided by the square root of heritability, i.e., the correlation-based accuracy, and model-based theoretical accuracy. Breeding values were also predicted using a conventional pedigree-based best linear unbiased prediction model in order to compare accuracies of genomic and conventional predictions. The heritability estimates of FCR and RFI were 0.29 and 0.50, respectively. The heritability estimates of ADG, ADFI, EP, BMP and LMP ranged from 0.34 to 0.53. In the CVF scenario, the correlation-based accuracy and the theoretical accuracy of genomic prediction for FCR were slightly higher than those for RFI. The correlation-based accuracies for FCR, RFI, ADG and ADFI were 0.360, 0.284, 0.574 and 0.520, respectively, and the model-based theoretical accuracies were 0.420, 0.414, 0.401 and 0.382, respectively. In the CVR scenario, the correlation-based accuracy and the theoretical accuracy of genomic prediction for FCR was lower than RFI, which was different from the CVF scenario. The correlation-based accuracies for FCR, RFI, ADG and ADFI were 0.449, 0.593, 0.581 and 0.627, respectively, and the model-based theoretical accuracies were 0.577, 0.629, 0.631 and 0.638, respectively. The accuracies of genomic predictions were 0.371 and 0.322 higher than the conventional pedigree-based predictions for the CVF and CVR scenarios, respectively. The genetic correlations of FCR with EP, BMP and LMP were -0.427, -0.156 and -0.338, respectively. The correlations between RFI and the three carcass traits were -0.320, -0.404 and -0.353, respectively. These results indicate that RFI and FCR have a moderate accuracy of genomic prediction. Improving RFI and FCR could be favourable for EP, BMP and LMP. Compared with FCR, which can be improved by selection for ADG in typical meat-type chicken breeding programs, selection for RFI could lead to extra improvement in feed efficiency.

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<![CDATA[Time-lapse imaging of HeLa spheroids in soft agar culture provides virtual inner proliferative activity]]> https://www.researchpad.co/article/Nceafa1bd-f75c-4e08-9c15-587118f668b1

Cancer is a complex disease caused by multiple types of interactions. To simplify and normalize the assessment of drug effects, spheroid microenvironments have been utilized. Research models that involve agent measurement with the examination of clonogenic survival by monitoring culture process with image analysis have been developed for spheroid-based screening. Meanwhile, computer simulations using various models have enabled better predictions for phenomena in cancer. However, user-based parameters that are specific to a researcher’s own experimental conditions must be inputted. In order to bridge the gap between experimental and simulated conditions, we have developed an in silico analysis method with virtual three-dimensional embodiment computed using the researcher’s own samples. The present work focused on HeLa spheroid growth in soft agar culture, with spheroids being modeled in silico based on time-lapse images capturing spheroid growth. The spheroids in silico were optimized by adjusting the growth curves to those obtained from time-lapse images of spheroids and were then assigned virtual inner proliferative activity by using generations assigned to each cellular particle. The ratio and distribution of the virtual inner proliferative activities were confirmed to be similar to the proliferation zone ratio and histochemical profiles of HeLa spheroids, which were also consistent with those identified in an earlier study. We validated that time-lapse images of HeLa spheroids provided virtual inner proliferative activity for spheroids in vitro. The present work has achieved the first step toward an in silico analysis method using computational simulation based on a researcher’s own samples, helping to bridge the gap between experiment and simulation.

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<![CDATA[Analysis and modeling of coolants and coolers for specimen transportation]]> https://www.researchpad.co/article/N4e3aeb5c-7b13-42da-a06e-637c738940f8

Maintaining cold chain while transporting medical supplies and samples is difficult in remote settings. Failure to maintain temperature requirements can lead to degraded sample quality and inaccuracies in sample analysis. We performed a systematic analysis on different types of transport coolers (polystyrene foam, injection-molded, and rotational molded) and transport coolants (ice, cold packs, frozen water bottles) frequently in use in many countries. Polystyrene foam coolers stayed below our temperature threshold (6°C) longer than almost all other types of coolers, but were not durable. Injection-molded coolers were durable, but warmed to 6°C the quickest. Rotational molded coolers were able to keep temperatures below our threshold for 24 hours longer than injection molded coolers and were highly durable. Coolant systems were evaluated in terms of cost and their ability to maintain cold temperatures. Long lasting commercial cold packs were found to be less cost effective and were below freezing for the majority of the testing period. Frozen plastic water bottles were found to be a reusable and economical choice for coolant and were only below freezing briefly. Finally, we modeled the coolers performance at maintaining internal temperatures below 6°C and built a highly accurate linear model to predict how long a cooler will remain below 6°C. We believe this data may be useful in the planning and design of specimen transportation systems in the field, particularly in remote or resource limited settings.

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<![CDATA[‘In search of lost time’: Identifying the causative role of cumulative competition load and competition time-loss in professional tennis using a structural nested mean model]]> https://www.researchpad.co/article/N4f3da08e-598e-44d5-a4f3-a2c64fcebd1f

Injury prevention is critical to the achievement of peak performance in elite sport. For professional tennis players, the topic of injury prevention has gained even greater importance in recent years as multiple of the best male players have been sidelined owing to injury. Identifying potential causative factors of injury is essential for the development of effective prevention strategies, yet such research is hampered by incomplete data, the complexity of injury etiology, and observational study biases. The present study attempts to address these challenges by focusing on competition load and time-loss to competition—a completely observable risk factor and outcome—and using a structural nested mean model (SNMM) to identify the potential causal role of cumulative competition load on the risk of time-loss. Using inverse probability of treatment weights to balance exposure histories with respect to player ability, past injury, and consecutive competition weeks at each time point; the SNMM analysis of 389 professional male players and 55,773 weeks of competition found that total load significantly increases the risk of time-loss (HR = 1.05 per 1,000 games of additional load 95% CI 1.01-1.10) and this effect becomes magnified with age. Standard regression showed a protective effect of load, highlighting the value of more robust causal methods in the study of dynamic exposures and injury in sport and the need for further applications of these methods for understanding how time-loss and injuries of elite athletes might be prevented in the future.

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<![CDATA[Effects of sea-level rise on physiological ecology of populations of a ground-dwelling ant]]> https://www.researchpad.co/article/N7f89605c-5421-4b76-a019-ba0e7ddd5b34

Introduction

Sea-level rise is a consequence of climate change that can impact the ecological and physiological changes of coastal, ground-dwelling species. Sea-level rise has a potential to inundate birds, rodents, spiders, and insects that live on the ground in coastal areas. Yet, there is still much to be learned concerning the specifics of these impacts. The red imported fire ant Solenopsis invicta (Buren) excavates soil for its home and is capable of surviving flooding. Because of their ground-dwelling life history and rapid reproduction, fire ants make an ideal model for discovery and prediction of changes that may be due to sea-level rise. There are up to 500,000 individuals in a colony, and these invasive ants naturally have a painful sting. However, observations suggest that colonies of fire ants that dwell in tidally-influenced areas are more aggressive with more frequent stings and more venom injected per sting (behavioral and physiological changes) than those located inland. This may be an adaption to sea-level rise. Therefore, the objective of this study is to elucidate differences in inland and coastal defensiveness via micro-dissection and comparison of head width, head length, stinger length, and venom sac volume. But first because fire ants’ ability to raft on brackish tidal water is unknown, it had to be determined if fire ants could indeed raft in brackish water and examine the behavior differences between those flooded with freshwater vs. saltwater.

Methods

To test the coastal-aggression hypothesis, inland colonies and coastal colonies, which experience relatively greater amounts of flooding, specifically regular tidal and windblown water and oscillations (i.e. El Nińo Southern Oscillation) from the Gulf of Mexico, were collected. To mimic sea-level rise, the colonies were flooded in salinities that correspond to both their collection site and conditions found in a variety of locales and situations (such as storm surge from a tropical storm). Individual ants were immediately taken from each colony for dissection before flooding, 1-hour into flooding, and 24-hours into flooding.

Results and discussion

Fire ants use their venom to defend themselves and to communicate alarm or aggression. Dissections and measurement of heads, venom sacs, and stingers revealed both coastal and inland colonies experience an increase in venom sac volume after 24 hours; in fact coastal colonies increased their venom volume by 75% after 24 h of flooding Whether this venom sac enlargement is due to diffusion of water or venom sac production is unknown. These ground-dwelling ants exhibit physiological and behavioral adaptations to ongoing sea-level rise possibly indicating that they are responding to increased flooding. Fire ants will raft on high-salinity water; and sea-level rise may cause stings by flooded ants to be more severe because of increased venom volume.

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<![CDATA[Class enumeration false positive in skew-t family of continuous growth mixture models]]> https://www.researchpad.co/article/Ne0623f60-4058-4fc0-9606-ac0f597752dc

Growth Mixture Modeling (GMM) has gained great popularity in the last decades as a methodology for longitudinal data analysis. The usual assumption of normally distributed repeated measures has been shown as problematic in real-life data applications. Namely, performing normal GMM on data that is even slightly skewed can lead to an over selection of the number of latent classes. In order to ameliorate this unwanted result, GMM based on the skew t family of continuous distributions has been proposed. This family of distributions includes the normal, skew normal, t, and skew t. This simulation study aims to determine the efficiency of selecting the “true” number of latent groups in GMM based on the skew t family of continuous distributions, using fit indices and likelihood ratio tests. Results show that the skew t GMM was the only model considered that showed fit indices and LRT false positive rates under the 0.05 cutoff value across sample sizes and for normal, and skewed and kurtic data. Simulation results are corroborated by a real educational data application example. These findings favor the development of practical guides of the benefits and risks of using the GMM based on this family of distributions.

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<![CDATA[Sample Size Calculation Guide - Part 7: How to Calculate the Sample Size Based on a Correlation]]> https://www.researchpad.co/article/N28aaf1bc-a194-4e98-b8b1-667fa93b6017 ]]> <![CDATA[Predicting 30-day hospital readmissions using artificial neural networks with medical code embedding]]> https://www.researchpad.co/article/N1f40719a-4631-45e6-bedb-5cf8a42ecf53

Reducing unplanned readmissions is a major focus of current hospital quality efforts. In order to avoid unfair penalization, administrators and policymakers use prediction models to adjust for the performance of hospitals from healthcare claims data. Regression-based models are a commonly utilized method for such risk-standardization across hospitals; however, these models often suffer in accuracy. In this study we, compare four prediction models for unplanned patient readmission for patients hospitalized with acute myocardial infarction (AMI), congestive health failure (HF), and pneumonia (PNA) within the Nationwide Readmissions Database in 2014. We evaluated hierarchical logistic regression and compared its performance with gradient boosting and two models that utilize artificial neural networks. We show that unsupervised Global Vector for Word Representations embedding representations of administrative claims data combined with artificial neural network classification models improves prediction of 30-day readmission. Our best models increased the AUC for prediction of 30-day readmissions from 0.68 to 0.72 for AMI, 0.60 to 0.64 for HF, and 0.63 to 0.68 for PNA compared to hierarchical logistic regression. Furthermore, risk-standardized hospital readmission rates calculated from our artificial neural network model that employed embeddings led to reclassification of approximately 10% of hospitals across categories of hospital performance. This finding suggests that prediction models that incorporate new methods classify hospitals differently than traditional regression-based approaches and that their role in assessing hospital performance warrants further investigation.

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<![CDATA[Psychometric characteristics and factorial structures of the Defensive Pessimism Questionnaire—Spanish Version (DPQ-SV)]]> https://www.researchpad.co/article/Nb6dcc03f-c5ae-4fce-8b03-b30a02ab227b

The aim of this study was to validate the Spanish version of the Defensive Pessimism Questionnaire. A sample of undergraduate students (N = 539) was measured on defensive pessimism using the Defensive Pessimism Questionnaire (DPQ), optimism and pessimism using the Life Orientation Test (LOT), positive and negative affect using the Positive and Negative Affect Schedule, and anxiety using the trait subscale of the State and Trait Anxiety Inventory. A Spanish version of the DPQ (DPQ-SV) is presented. Exploratory and Robust Confirmatory Factor Analysis had a bi-dimensional structure (Reflectivity and Negative Expectation). Omega coefficient showed a high internal consistency and the temporal stability was high in each dimension. Both DPQ-SV subscales (Negative Expectation and Reflectivity) showed adequate convergence with LOT-optimism and LOT-pessimism. Reflectivity showed adequate criterion validity with trait-anxiety and negative affect, but inadequate criterion validity with positive affect. Negative Expectation showed excellent criterion validity with trait-anxiety and negative affect and good criterion validity with positive affect. Finally, mediation analysis showed that Negative Expectation had a significant indirect mediating effect between trait-anxiety and negative affect. Reflectivity had a significant indirect mediating effect between trait-anxiety and negative and positive affect. Analysis of the psychometric properties of the DPQ-SV subscale scores showed that it is a two factor adequate measurement tool for its use in this type of samples.

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<![CDATA[Impact of law enforcement and increased traffic fines policy on road traffic fatality, injuries and offenses in Iran: Interrupted time series analysis]]> https://www.researchpad.co/article/N9254ca97-b759-40e9-8001-23227e05911a

Background

Road traffic law enforcement was implemented on 1st April 2011 (the first intervention) and traffic ticket fines have been increased on 1st March 2016 (the second intervention) in Iran. The aim of the current study was to evaluate the effects of the law enforcement on reduction in the incidence rate of road traffic fatality (IRRTF), the incidence rate of road traffic injuries (IRRTI) and the incidence rate of rural road traffic offenses (IRRRTO) in Iran.

Methods

Interrupted time series analysis was conducted to evaluate the impact of law enforcement and increased traffic tickets fines. Monthly data of fatality on urban, rural and local rural roads, injuries with respect to gender and traffic offenses namely speeding, illegal overtaking and tailgating were investigated separately for the period 2009–2016.

Results

Results showed a reduction in the incidence rate of total road traffic fatality (IRTRTF), the incidence rate of rural road traffic fatality (IRRRTF) and the incidence rate of urban road traffic fatality (IRURTF) by –21.44% (–39.3 to –3.59, 95% CI), –21.25% (–31.32 to –11.88, 95% CI) and –26.75% (–37.49 to –16, 95% CI) through the first intervention which resulted in 0.383, 0.255 and 0.222 decline in casualties per 100 000 population, respectively. Conversely, no reduction was found in the incidence rate of local rural road traffic fatality (IRLRRTF) and the IRRTI. Second intervention was found to only affect the IRURTF with –26.75% (–37.49 to –16, 95% CI) which led to 0.222 casualties per 100 000 population. In addition, a reduction effect was observed on the incidence rate of illegal overtaking (IRIO) and the incidence rate of speeding (IRS) with –42.8% (–57.39 to –28.22, 95% CI) and –10.54% (–21.05 to –0.03, 95% CI which implied a decrease of 415.85 and 1003.8 in monthly traffic offenses per 100 000 vehicles), respectively.

Conclusion

Time series analysis suggests a decline in IRTRTF, IRRRTF, and IRURTF caused by the first intervention. However, the second intervention found to be only effective in IRURTF, IRIO, and IRS with the implication that future initiatives should be focused on modifying the implementation of the traffic interventions.

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