Cross-classification Tables Research Articles

ANALISIS KETAHANAN PANGAN RUMAH TANGGA PETERNAK SAPI PERAH DI KECAMATAN GETASAN KABUPATEN SEMARANG

Food security is a problem that cannot be ignored, because food security is closely related to the problem of hunger and poverty, which is a problem for all countries in the world. One way to end hunger is to achieve food security from each individual. Food security status of a region depends on the food security of households in the region. This study was aim to analyze the status of food security of dairy farmers' households in Getasan District, Semarang Regency. The method used was a survey method with multiple stage sampling with cluster sampling as a determination of the location of research. The determination of respondents was carried out randomly at a predetermined location. The total respondents taken in this study were 102 respondents who were households of dairy farmers and wives who had cattle 2-6 heads. The data analysis method used was quantitative descriptive analysis, using calculation formulas and cross classification tables between two food security indicators, namely the share of food expenditure and the level of household energy consumption per capita per day. The study result showes that the average percentage of per capita energy consumption per day was 78.7791% and the average percentage share of food expenditure was 62.6404%. While the results shows that the level of food security was dominated by households with food vulnerable status. The advice that can be given to overcome this situation is to increase counseling, training and assistance to the housewives regarding nutritious food information and how to fulfill food independently, such as home gardening.

Health Satisfaction in Health Care between Patients of 3rd Tertiary Allergy Center in Kirkuk with Salam Primary Health Care Center Patients in Kirkuk

Background: Healthcare capacity in Iraq has deteriorated due to the effects of wars and political instability. The IraqiMinistry of Health expanded the provision of health services, by directing the public sector towards primary health care.This study aimed to assess patients satisfaction with the primary health care services provided in 3rd tertiary allergy centerand Salam primary health care center patients in Kirkuk, and to determine the relationship between patient satisfaction andtheir demographic data.Method: A descriptive study was carried out at the 3rd tertiary allergy center and Salam primary health care center in Kirkukcity, Iraq. Data were collected through the use of the constructed questionnaire from 100 patients who visit those centersfor routine care, through the period of the 1st December 2020 to 31st January 2021. Data were analyzed through (Measuresof central tendency and inferential statistics) using SPSS (V.22). Chi-square test for independence of two variables hasbeen utilized to test the hypothesis, through the use of cross-classification table to examine the nature of the relationshipbetween the variables. Ethical approval and permission to access the samples was obtained from Kirkuk Health Directoratein Iraq.Results: Overall, out of 100 clients responded to our questionnaire, age showed neutral score (Mean = 2.93, SD= 1.47).and the age group (21 -30 years) were the highest represented (n=27; 27%). Mean of score was used for reference aboutthe client satisfaction, if the mean of score up to more than 3 that means positive satisfaction with the available servicesotherwise it means dissatisfaction. Chi-square test for independence of two variables was applied, and the probailityof high level of satisfaction were reported as there were a significat relationship (P <0.005) in items related to (nursingcare services, availability of medical services, quality of prescribed medication, cooperation of pharmacist, availabilityof laboratory services and. laboratory staff cooperation about diagnosis than other items.Also, there were a statisticalsignificant difference (P <0.005) between respondents age more than other demographic characteristics.Conclusion: The study highlighted the sources of patient satisfaction related to primary health care services. The medicaland nursing staff have a crucial role in providing health services, so their job satisfaction can improve the quality of care forpatients, and reduce health care costs. Additionally, these results can provide priorities for developing healthcare services that can improve patient satisfaction. The utilization of telemedicine services, patient-centered medical visits and eHealth applications can be adopted to increase patient satisfaction with primary health care services. The use of remote patient monitoring programs are also effective ways to treat the signs and symptoms of COVID-19 at home and protect health workers.

Ei.Datasets: Real Data Sets for Assessing Ecological Inference Algorithms

Ecological inference models aim to infer individual-level relationships using aggregate data. They are routinely used to estimate voter transitions between elections, disclose split-ticket voting behaviors, or infer racial voting patterns in U.S. elections. A large number of procedures have been proposed in the literature to solve these problems; therefore, an assessment and comparison of them are overdue. The secret ballot however makes this a difficult endeavor since real individual data are usually not accessible. The most recent work on ecological inference has assessed methods using a very small number of data sets with ground truth, combined with artificial, simulated data. This article dramatically increases the number of real instances by presenting a unique database (available in the R package ei.Datasets) composed of data from more than 550 elections where the true inner-cell values of the global cross-classification tables are known. The article describes how the data sets are organized, details the data curation and data wrangling processes performed, and analyses the main features characterizing the different data sets.

Addressing Bias in Responder Analysis of Patient-Reported Outcomes

IntroductionQuantitative patient-reported outcome (PRO) measures ideally are analyzed on their original scales and responder analyses are used to aid the interpretation of those primary analyses. As stated in the FDA PRO Guidance for Medical Product Development (2009), one way to lend meaning and interpretation to such a PRO measure is to dichotomize between values where within-patient changes are considered clinically important and those that are not. But even a PRO scale with a cutoff score that discriminates well between responder and non-responders is fraught with some misclassification.MethodsUsing estimates of sensitivity and specificity on classification of responder status from a PRO instrument, formulas are provided to correct for such responder misclassification under the assumption of no treatment misclassification. Two case studies from sexual medicine illustrate the methodology.ResultsAdjustment formulas on cell counts for responder misclassification are a direct extension of correction formulas for misclassification on disease from a two-way cross-classification table of disease (yes, no) and exposure (yes, no). Unadjusted and adjusted estimates of treatment effect are compared in terms of odds ratio, response ratio, and response difference. In the two case studies, there was considerable underestimation of treatment effect.Discussion and conclusionsThe methodology can be applied to different therapeutic areas. Limitations of the methodology, such as when adjusted cell estimates become negative, are highlighted. The role of anchor-based methodology is discussed for obtaining estimates of sensitivity and specificity on responder classification. Correction for treatment effect bias from misclassification of responder status on PRO measures can lead to more trustworthy interpretation and effective decision-making.Clinicaltrials.gov: NCT00343200

A note on the three methods of item analysis

This paper contrasts three methods of item analysis for multiple-choice items based on classical test theory, generalized linear modeling, and item response theory. Illustrations of the methods are presented with contrived and real data. Specifically, the methods, respectively, use a cross-classification table under classical test theory, a new baseline-category logit model under generalized linear modeling, and a multiple-choice model under item response theory. Advantages and disadvantages of each method are discussed.

Reliability and validity of the four-level Chinese emergency triage scale in mainland China: A multicenter assessment

BackgroundAn effective emergency triage system is lacking in mainland China. The Chinese Emergency Triage Scale (CETS) was developed based on vital complaints and vital parameters, according to Chinese data. ObjectiveThe aim of this study was to assess the reliability and validity of the CETS by emergency department (ED) nurses in eight EDs in mainland China. DesignA cross-sectional multi-center study was conducted. SettingsEight EDs in the eastern, western, northern, and central areas of mainland China. ParticipantsA total of 51 ED nurses and 8000 active ED patients participated in the study between May and September 2018 in eight EDs. MethodsStandardized triage scenarios and active patients were assigned to nurses who used the CETS for evaluative purposes. Accuracy was defined by concordance with the key for the scenarios and was calculated as percentages. Inter-rater reliability was measured by weighted κ to compare the triage nurse's rating with the research nurse's acuity level for each patient. The triage time and clinical outcome of 1000 active patients per site also were collected. The criterion-related validity of the CETS was evaluated; criteria included ED mortality, number of patients discharged, and admission to ICU or general ward. The relationship between the CETS acuity levels triaged by the research nurse and four possible outcomes was assessed with a cross-classification table, using a chi-square test. The ability of the CETS to predict ED mortality was assessed via the receiver-operating characteristic (ROC) area under the curve (AUC). ResultsMean pooled accuracy from all sites was 89.4% (95% CI = 86.9%–91.8%), and the proportion of over-triage slightly exceeded under-triage (6.5% vs. 4.1%). There was no difference in accuracy between sites or according to nurse experience. Inter-rater reliability values for triage nurses and research nurses were 0.96 (95% CI = 0.95–0.97), based on the quadratic weight κ. Trauma cases were assigned to triage with greater accuracy than were nontrauma cases, both for the scenarios and for active patients. The average triage time of ED nurses in all sites was 151.5 ± 26.3 s, using the computer-based triage instrument. The chi-square test showed that there was a significant difference in triage time between the CETS levels (p < 0.001). The AUC was 0.968 (95% CI = 0.958–0.979). ConclusionsThe CETS is a reliable system for ED triage and can promote rapid and effective triage in mainland China.

Population Synthesis Based on Joint Distribution Inference Without Disaggregate Samples

Synthetic population is a fundamental input to dynamic micro-simulation in social applications. Based on the review of current major approaches, this paper presents a new sample-free synthesis method by inferring joint distribution of the total target population. Convergence of multivariate Iterative Proportional Fitting used in our method is also proved theoretically. The method, together with other major ones, is applied to generate a nationwide synthetic population database of China by using its overall cross-classification tables as well as a sample from census. Marginal and partial joint distribution consistencies of each database are compared and evaluated quantitatively. Final results manifest sample-based methods have better performances on marginal indicators while the sample-free ones match partial distributions more precisely. Among the five methods, our proposed method can significantly reduce the computational cost for generating synthetic population in large scale. An open source implementation of the population synthesizer based on C# used in this research is available at https://github.com/PeijunYe/PopulationSynthesis.git.

Individual trip rate transferability analysis based on a decision tree approach

ABSTRACTTransferring trip rates to areas without local survey data is a common practice which is typically performed in an ad hoc fashion using household-based cross-classification tables. This paper applies a rule-based decision tree method to develop individual-level trip generation models for eight different trip purposes as defined in the US National Household Travel Survey in addition to daily vehicle miles traveled. For each trip purpose, the models are obtained by finding the best fitted statistical distribution to each of the final decision tree clusters while considering the correlation between the trip rates for other trip purposes. The rule-based models are sensitive to changes in demographics. The performance of the models is then tested and validated in a transferability application to the Phoenix Metropolitan Region. These models can be employed in a disaggregate microsimulation framework to generate trips with different purposes at the individual or household level.

Misunderstood and Unattributed: Revisiting M. H. Doolittle's Measures of Association, With a Note on Bayes’ Theorem

In the 1880s, American scholars developed measures of association and chance for cross-classification tables that anticipated the more widely known work of Galton, Pearson, Yule, and Fisher. Three of the measures form the historical backdrop for the earliest known use of a joint probability measure that mirrored Bayes’ theorem long before the latter gained general interest among statisticians. The joint probability measure, which served as a foundational step in M. H. Doolittle's development of the first of the two “association ratios,” has not previously been reviewed in the statistical literature. It was reintroduced as if newly developed in a subfield of experimental psychology more than a century after Doolittle's work was published. It has flourished there, but it has not seen use in other academic venues. The article describes its properties and limitations and proposes that it be disseminated and debated beyond its current narrow application. The article notes that Doolittle's first association ratio can be expressed as another joint probability and that prior treatments in the literature are inconsistent with Doolittle's understanding of its purpose. The article also demonstrates that the equivalent of Cohen's kappa (κ) was developed by Doolittle in 1887, as his second association measure.[Received December 2014. Revised August 2015.]

Size invariant measures of association: Characterization and difficulties

A measure of association on cross-classification tables is row-size invariant if it is unaffected by the multiplication of all entries in a row by the same positive number. It is class-size invariant if it is unaffected by the multiplication of all entries in a class (i.e., a row or a column). We prove that every class-size invariant measure of association assigns to each cross-classification table a number which depends only on the cross-product ratios of its 2×2 subtables. We submit that the degree of association should increase when mass is shifted from cells containing a proportion of observations lower than what is expected under statistical independence to cells containing a proportion higher than expected–provided that total mass in each class remains unchanged. We prove that no continuous row-size invariant measure of association satisfies this monotonicity axiom if there are at least four rows.

An empirical comparison of univariate and multivariate meta‐analyses for categorical outcomes

Treatment effects for multiple outcomes can be meta-analyzed separately or jointly, but no systematic empirical comparison of the two approaches exists. From the Cochrane Library of Systematic Reviews, we identified 45 reviews, including 1473 trials and 258,675 patients, that contained two or three univariate meta-analyses of categorical outcomes for the same interventions that could also be analyzed jointly. Eligible were meta-analyses with at least seven trials reporting all outcomes for which the cross-classification tables were exactly recoverable (e.g., outcomes were mutually exclusive, or one was a subset of the other). This ensured known correlation structures. Outcomes in 40 reviews had an is-subset-of relationship, and those in 5 were mutually exclusive. We analyzed these data with univariate and multivariate models based on discrete and approximate likelihoods. Discrete models were fit in the Bayesian framework using slightly informative priors. The summary effects for each outcome were similar with univariate and multivariate meta-analyses (both using the approximate and discrete likelihoods); however, the multivariate model with the discrete likelihood gave smaller between-study variance estimates, and narrower predictive intervals for new studies. When differences in the summary treatment effects were examined, the multivariate models gave similar summary estimates but considerably longer (shorter) uncertainty intervals because of positive (negative) correlation between outcome treatment effects. It is unclear whether any of the examined reviews would change their overall conclusions based on multivariate versus univariate meta-analyses, because extra-analytical and context-specific considerations contribute to conclusions and, secondarily, because numerical differences were often modest.

Well-Being and Suicidal Ideation of Secondary School Students From Military Families

The mental health of children is a primary public health concern; adolescents of military personnel may be at increased risk of experiencing poorer well-being overall and depressive symptoms specifically. These adolescents experience individual and intrafamilial stressors of parental deployment and reintegration, which are directly and indirectly associated with internalizing behaviors. The present study sought to better understand the influence of parental military connectedness and parental deployment on adolescent mental health. Data from the 2011 California Healthy Kids Survey examined feeling sad or hopeless, suicidal ideation, well-being, and depressive symptoms by military connectedness in a subsample (n = 14,299) of seventh-, ninth-, and 11th-grade California adolescents. Cross-classification tables and multiple logistic regression analyses were used. More than 13% of the sample had a parent or sibling in the military. Those with military connections were more likely to report depressive symptoms and suicidal ideation. Controlling for grade, gender, and race/ethnicity, reporting any familial deployment compared with no deployments was associated with increasing odds of experiencing sadness or hopelessness, depressive symptoms, and suicidal ideation. Findings emphasize the increased risk of mental health issues among youth with parents (and siblings) in the military. Although deployment-related mental health stressors are less likely during peace, during times of war there is a need for increased screening in primary care and school settings. Systematic referral systems and collaboration with community-based mental health centers will bolster screening and services.

교통노면표시 이해도에 관한 연구

Traffic pavement markings are one of primary transportation facilities that provide drivers with various road information directly. Thus, a clear understanding of traffic pavement markings is utmost important to improve traffic safety as well as to establish a proper traffic culture. However, no past studies examined drivers` understanding of traffic pavement markings in Korea. Hence, this study investigated drivers` understanding of traffic pavement markings through an elaborated administrated survey, and analyzed the relationship between various drivers` characteristics and understanding regarding pavement marking via cross-classification table and logistic model. The analysis results show that drivers have limited understanding regarding the purpose of the markings. Specifically, the average understanding of pavement markings is 57.41%: the lowest understanding is 25.88% for yield pavement marking, and the highest understanding is 91.18% for advanced pedestrian crosswalk pavement marking. This study also revealed that the understanding of some pavement markings are somewhat influenced by user group such as drivers with suspended or revoked driver licenses, but the overall understanding of pavement markings are not significantly affected by drivers` characteristics such as gender and driving experiences at

The prevalence of asthma and declared asthma in Poland on the basis of ECAP survey using correspondence analysis.

Results of epidemiological and public health surveys are often presented in the form of cross-classification tables. It is sometimes difficult to analyze data described in this way and to understand relations between variables. Graphical methods such as correspondence analysis are more convenient and useful. Our paper describes an application of correspondence analysis to epidemiological research. We apply the basic concepts of correspondence analysis like profiles, chi-square distance to medical data concerning prevalence of asthma. We aim at describing the relationship between asthma, region, and age. The data presented in this paper come from Epidemiology of Allergy in Poland (ECAP) survey in years 2006–2008. Correspondence analysis shows that there is a fundamental difference in the structure of age groups for people with symptoms compared to those who have declared asthma (regardless of the level of symptoms of asthma and the level of declaration). The variable which best differentiates declared asthma in all regions is “wheezing and whistling.” Correspondence analysis also shows significant differences between locations. Our analyses are performed in the R package “ca”.

Abstract 3148A: MYOD1, hTERT, and RASSF1A gene promoter methylation is useful for prediction of outcome/ prognostication in patients with invasive cervical cancer

Abstract Background: Cervical cancer is the leading cause of cancer related mortality in women worldwide including India. DNA methylation, an epigenetic event regulates gene expression and chromatin structure. We hypothesized that the promoter methylation of genes involved in tumor suppression, DNA repair, cell cycle and apoptosis may affect response to radiation therapy and therefore the outcome of women with invasive cervical cancer. Objective: To perform DNA methylation profiling of BRCA1, RASSF1A, MLH1, ESR1, MYOD1 and hTERT genes in tissue samples of invasive cervical cancer and correlate the methylation pattern to radiation response and clinical outcome. Methodology: After obtaining informed consent, patients with invasive cervical cancer who underwent chemo-radiation as per protocol and with a minimum of 2 years follow-up after completion of treatment were included in the analysis [N=91; 2008-11]. The patients were followed up 3 monthly for evidence of local or systemic disease and their outcome documented. 60 patients were alive and well with No evidence of disease [NED] at the end of 24 months whereas 31 patients had evidence of disease [ED]. Out of these 31 patients, 3 died of disease, 9 had evidence of systemic disease [SED] and 19 patients had local disease recurrence [LED]. Genomic DNA was extracted from the snap frozen cervical biopsy tissues using TRIzol reagent, modified using the EZDNA methylation kit and promoter methylation of the individual genes determined by Real Time Polymerase Chain Reaction using Taqman probe chemistry using previously published primer and probe sets and appropriate controls. The probes were labelled with 5′-FAM and 3′-BHQ respectively. The housekeeping gene α-ACTIN was taken as the reference gene. Statistical analysis: Pearson's Chi-square test with Yates correction and Step-wise Logistic Regression was performed using the SPSS package version 18.0. A p-value &amp;lt;0.05 was considered as statistically significant. Results: In invasive cervical cancer, BRCA1, RASSF1A, MLH1, MYOD1, ESR1 and hTERT gene promoters were methylated in 51%, 47%, 40%, 51%, 38% and 60% cases respectively. Comparison of the NED vs LED groups revealed that methylated MYOD1 and hTERT genes in combination predicted for better outcome (Chi-Square test, p=0.03). Further logistic regression analysis revealed that methylation of RASSF1A, MYOD1 and hTERT genes predicted for better outcome following radiation therapy indicating that these genes play a role in determining radiation responsiveness of a tumor. The cross-classification table showed that 80% of the cases could be correctly classsified by the logistic model. Conclusion: A panel of 3 genes which includes MYOD1, hTERT and RASSF1A may be useful to predict the outcome of invasive cervical carcinoma patients treated with standard chemo-radiation therapy. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3148A. doi:1538-7445.AM2012-3148A

Modifiable Attribute Cell Problem and Solution Method for Population Synthesis in Land Use Microsimulation

Land use microsimulation requires the preparation of a set of microdata for the base year. Most existing procedures used for the synthesis of population data are based on the iterative proportional fitting method, in which the number of individuals in each cell of the cross-classification table is estimated. Such a procedure is referred to as the cell-based approach in this study. The approach is based on predefined categories of individuals. Originally, however, these individuals have continuous attributes. Therefore, a different type of categorization would yield a different classification table, which would change the end results of the analysis. In this paper, this phenomenon is referred to as the modifiable attribute cell problem (MACP). It is similar to the modifiable area unit problem that arises when spatial data are aggregated into zones. This paper addresses MACP and proposes a method to determine the best combination of the categories. The solution of MACP is considered to be the minimization of the number of cells in a table with respect to the key output variable that has been defined and used as an evaluation criterion. Because of the computational difficulty resulting from the combination explosion, symbiotic evolution, which is a kind of genetic algorithm, is used. Finally, a case study is presented for the Sapporo metropolitan area of Japan.

A weighted test of internal symmetry

This study examines extensions of McNemar's Test with multinomial responses, and proposes a linear weighting scheme, based on the distance of the response change, that is applied to one of these extensions (Bowker's test). This weighted version of Bowker's test is then appropriate for ordinal response variables. A Monte Carlo simulation was conducted to examine the Type I error rate of the weighted Bowker's test for a cross-classification table based on a five-category ordinal response scale. The weighted Bowker's test was also applied to a data set involving change in student attitudes towards mathematics. The results of the weighted Bowker's test were compared with the results of Bowker's test applied to the same set of data.

PoLCA: AnRPackage for Polytomous Variable Latent Class Analysis

poLCA is a software package for the estimation of latent class and latent class regression models for polytomous outcome variables, implemented in the R statistical computing environment. Both models can be called using a single simple command line. The basic latent class model is a finite mixture model in which the component distributions are assumed to be multi-way cross-classification tables with all variables mutually independent. The latent class regression model further enables the researcher to estimate the effects of covariates on predicting latent class membership. poLCA uses expectation-maximization and Newton-Raphson algorithms to find maximum likelihood estimates of the model parameters.

Validation of a quantitative FFQ for the Barbados National Cancer Study

To assess the validity of a 148-item quantitative FFQ (QFFQ) that was developed for the Barbados National Cancer Study (BNCS) to determine dietary intake over 12 months and examine the dietary risk factors. A cross-sectional validation study of the QFFQ against 4 d food diaries. Spearman's rank correlations (ρ), intra-class correlation coefficients (ICC) and weighted κ were computed as measures of concordance, adjusting for daily variations in the food diaries. Cross-classification tables and Bland-Altman plots were created for further assessment. BNCS is a case-control study of environmental risk factors for breast and prostate cancer in a predominantly African-origin population in Barbados. Fifty-four individuals (21 years and older) were recruited among controls in the BNCS who were frequency-matched on sex and age group to breast and prostate cancer cases. Similar mean daily energy intake was derived from the food diary (8201 kJ (1960 kcal)) and QFFQ (7774 kJ (1858 kcal)). Rho for energy and macronutrients ranged from 0·66 (energy) to 0·17 (dietary fibre). The percentage of energy from carbohydrates and protein showed the highest and lowest ICC among macronutrients (0·63 and 0·27, respectively). The highest weighted κ was observed for energy (0·45). When the nutrient intake was divided into quartiles, approximately 34 % of the observations were in the same quartile. This investigation supports the validity of the QFFQ as a method for assessing long-term dietary intake except for dietary fibre, folate, vitamins A, E and B12. The instrument will be a useful tool in the analysis of diet-cancer associations in the BNCS.

Freight Generation Models

This paper conducts a comparative analysis of two alternative approaches to freight generation modeling: ordinary least square (OLS) and cross classification. OLS models were estimated to identify a functional relationship between the number of freight deliveries per day and a set of company attributes used as independent variables. Cross-classification techniques aim at identifying a classification structure that provides a good representation of the freight generation process. To that effect, multiple classification analysis was used to identify groups of independent variables explaining freight generation, which provided the basis for constructing cross-classification tables. In both cases freight generation is explained as a function of company attributes. The model estimation process used data obtained from commercial establishments located in Manhattan and Brooklyn, New York. More than 190 different variables were tested as predictors for the number of deliveries received or carried per day. Six linear regression models found to be statistically significant and conceptually valid are discussed. Establishment attributes such as industry segment, commodity type, facility type, total sales, and number of employees were found to be statistically significant. The OLS models indicated that industry segment and commodity type are strong predictors of freight generation. It was also noticed that many of these variables played a significant role when interacting with economic variables such as total sales or employment. Twelve different groups of independent variables predicting freight generation were found to be significant as part of the cross-classification models. Results indicate that as in the case of regression models, commodity type, industry segment, and employment are strong predictors for freight generation.