A two-stage latent factor regression method to model the common and unique effects of multiple highly correlated exposure variables.

In many epidemiological and environmental health studies, developing an accurate exposure assessment of multiple exposures on a health outcome is often of interest. However, the problem is challenging in the presence of multicollinearity, which can lead to biased estimates of regression coefficients and inflated variance estimators. Selecting one exposure variable as a surrogate of multiple highly correlated exposure variables is often suggested in the literature as a solution to handle the multicollinearity problem. However, this may lead to loss of information, since the exposure variables that are highly correlated tend to have not only common but also additional effects on the outcome variable. In this study, a two-stage latent factor regression method is proposed. The key idea is to regress the dependent variable not only on the common latent factor(s) of the explanatory variables, but also on the residuals terms from the factor analysis as the explanatory variables. The proposed method is compared to the traditional latent factor regression and principal component regression for their performance of handling multicollinearity. Two case studies are presented. Simulation studies are performed to assess their performances in terms of the epidemiological interpretation and stability of parameter estimates.

Identifying the Influencing Factors for the BMI by Bayesian and Frequentist Multiple Linear Regression Models: A Comparative Study.

Background: In this article, we attempt to demonstrate the superiority of the Bayesian approach over the frequentist approaches of the multiple linear regression model in identifying the influencing factors for the response variable. Methods and Material: A survey was conducted among the 310 respondents from the Kathirkamam area in Puducherry. We have considered the response variable, body mass index (BMI), and the predictors such as age, weight, gender, nature of the job, and marital status of individuals were collected with the personal interview method. Jeffreys's Amazing Statistics Program (JASP) software was used to analyze the dataset. In the conventional multiple linear regression model, the single value of regression coefficients is determined, while in the Bayesian linear regression model, the regression coefficient of each predictor follows a specific posterior distribution. Furthermore, it would be most useful to identify the best models from the list of possible models with posterior probability values. An inclusion probability for all the predictors will give a superior idea of whether the predictors are included in the model with probability. Results and Conclusions: The Bayesian framework offers a wide range of results for the regression coefficients instead of the single value of regression coefficients in the frequentist test. Such advantages of the Bayesian approach will catapult the quality of investigation outputs by giving more reliability to solutions of scientific problems.

Domestic violence against women in Jordan: analysis of the demographic and health survey dataset 2017-2018.

This study analyzed the 2017-2018 Jordan Demographic and Health Survey (DHS) database to determine the prevalence of domestic violence (DV) against women in Jordan and its associated sociodemographic factors. The findings revealed that among Jordanian women, the lifetime prevalence of DV by husbands was 25.9%, with emotional (20.6%), physical (17.5%), and sexual (5.1%) violence being prominently reported. DV against women was significantly associated with the age, region, and educational status of women, as well as the wealth index, but not their husbands. While the results suggest a potential reduction in DV estimates compared to the last decade, DV still represents a public health issue in Jordan. The study highlights the direct association of DV with socio-demographic characteristics and provides a gateway to identifying high-risk women and implementing appropriate interventions to reduce DV.

Multilevel proportional odds modeling of anaemia prevalence among under five years old children in Ethiopia.

BACKGROUND: Despite anaemia is the leading cause of child morbidity and mortality in Africa including Ethiopia, there is inadequate evidence on modelling anaemia related factors among under five years old children in Ethiopia. Therefore, this study is aimed to assess factors that affect the anaemia status among under five years old children and estimate the proportion of overall child-level variation in anaemia status that is attributable to various factors in three regions of Ethiopia, namely Amhara, Oromiya and Southern Nation Nationalities People (SNNP). METHODS: This is a cross-sectional study, and the data was extracted from the 2011 Ethiopia National Malaria Indicator Survey which is a national representative survey in the country. A sample of 4,356 under five years old children were obtained from three regions. Based on child hemoglobin level, anaemia status was classified as non-anaemia (>11.0g/dL), mild anaemia (8.0-11.0g/dL), moderate anaemia (5.0-8.0g/dL) and severe anaemia (<5.0g/dL). Various multilevel proportional odds models with random Kebele effects were adopted taking into account the survey design weights. All the models were fitted with the PROC GLIMMIX in SAS. The Brant test for parallel lines assumption was done using the brant() function from brant package in R environment. RESULTS: The prevalence of anaemia status of under five years children varies among the three study regions, where the prevalence of severe child anaemia status was higher in Oromiya region as compared to Amhara and SNNP regions. The results of this study indicate that age (OR = 0.686; 95% CI: 0.632, 0.743), malaria RDT positive (OR = 4.578; 95% 2.804, 7.473), household had used mosquito nets while sleeping (OR = 0.793; 95%: 0.651, 0.967), household wealth status and median altitude (OR = 0.999; 95%: 0.9987, 0.9993), were significantly related to the prevalence of child anaemia infection. The percentage of Kebele-level variance explained by the region and median altitude, and child / household (Level 1) characteristics was 32.1 % . Hence, large part of the Kebele-level variance (67.9%) remain unexplained. CONCLUSIONS: The weighted multilevel proportional odds with random Kebele effects model used in this paper identified four child/household and one Kebele level risk factors of anaemia infection. Therefore, the public health policy makers should focus to those significant factors. The results also show regional variation in child anaemia prevalence, thus special attention should be given to those children living in regions with high anaemia prevalence.

A snapshot of climate drivers and temporal variation of Ixodes ovatus abundance from a giant panda living in the wild.

Ticks and tick-borne diseases have negative impacts on the health of wild animals including endangered and vulnerable species. The giant panda (Ailuropoda melanoleuca), a vulnerable and iconic flagship species, is threatened by tick infestation as well. Not only can ticks cause anemia and immunosuppression in the giant panda, but also bacterial and viral diseases. However, previous studies regarding tick infestation on giant pandas were limited in scope as case reports from sick or dead animals. In this study, an investigation focusing on the tick infestation of a reintroduced giant panda at the Daxiangling Reintroduction Base in Sichuan, China was conducted. Ticks were routinely collected and identified from the ears of the giant panda from March to September in 2021. A linear model was used to test the correlation between tick abundance and climate factors. All ticks were identified as Ixodes ovatus. Tick abundance was significantly different among months. Results from the linear model showed temperature positively correlated to tick abundance, while air pressure had a negative correlation with tick abundance. To the best of our knowledge, this study is the first reported investigation of tick species and abundance on a healthy giant panda living in the natural environment, and provides important information for the conservation of giant pandas and other species sharing the same habitat.

Bias Amplification and Variance Inflation in Distributed Lag Models Using Low-Spatial-Resolution Data.

Distributed lag models (DLMs) are often used to estimate lagged associations and identify critical exposure windows. In a simulation study of prenatal nitrogen dioxide (NO2) exposure and birth weight, we demonstrate that bias amplification and variance inflation can manifest under certain combinations of DLM estimation approaches and time-trend adjustment methods when using low-spatial-resolution exposures with extended lags. Our simulations showed that when using high-spatial-resolution exposure data, any time-trend adjustment method produced low bias and nominal coverage for the distributed lag estimator. When using either low- or no-spatial-resolution exposures, bias due to time trends was amplified for all adjustment methods. Variance inflation was higher in low- or no-spatial-resolution DLMs when using a long-term spline to adjust for seasonality and long-term trends due to concurvity between a distributed lag function and secular function of time. NO2-birth weight analyses in a Massachusetts-based cohort showed that associations were negative for exposures experienced in gestational weeks 15-30 when using high-spatial-resolution DLMs; however, associations were null and positive for DLMs with low- and no-spatial-resolution exposures, respectively, which is likely due to bias amplification. DLM analyses should jointly consider the spatial resolution of exposure data and the parameterizations of the time trend adjustment and lag constraints.

Lifestyle habits to prevent the development of benign prostatic hyperplasia: Analysis of Japanese nationwide datasets.

Objectives: Benign prostatic hyperplasia (BPH) refers to nonmalignant hyperplasia of prostate tissue, which causes lower urinary tract symptoms and has become a global public health concern in the aging population. The purpose of this study is to identify modifiable factors, which would prevent or delay BPH development. Methods: The association between BPH marker drugs and climate-, socioeconomic-, health condition-, and lifestyle habits-related variables was investigated by analyzing nationwide datasets which were collected in 2018, aggregated by prefecture (administrative unit), and published by Japanese ministries. Uroselective α1 receptor blockers and dutasteride were used as marker drugs referring to BPH prevalence. Correlation analysis, multiple linear regression analysis, and binomial logistic regression analysis were conducted with 47 Japanese prefectures as the unit. Results: The variables which showed |r| > 0.5 by correlation analysis were exercise habits (r = -0.5696), smoking habits (r = 0.6116), and daily drinking (r = 0.6001) for uroselective α1 receptor blockers, and antihypertensive medication (r = 0.5971), smoking habits (r = 0.6598), a small amount of drinking (r = -0.5292), and serum alanine aminotransferase (r = 0.6814) for dutasteride. Multiple linear regression equations were constructed by including these variables (R 2  = 0.5453 for uroselective α1 receptor blockers and R 2  = 0.5673 for dutasteride). Binomial logistic regression analysis found a significant association between climate in the resident area and BPH development. Conclusion: This ecological study, analyzing Japanese nationwide datasets, demonstrates that healthy lifestyle habits, especially avoidance of smoking, implementation of exercise in daily life, and a small amount of alcohol consumption, are important to prevent or delay BPH development. High blood pressure and high serum alanine aminotransferase are suggested as risk factors of BPH development.

Associations between paraspinal muscles fatty infiltration and lumbar vertebral bone mineral density - An investigation by fast kVp switching dual-energy CT and QCT.

Purpose: To investigate the relationship between paraspinal muscles fat content and lumbar bone mineral density (BMD). Methods: A total of 119 participants were enrolled in our study (60 males, age: 50.88 ± 17.79 years, BMI: 22.80 ± 3.80 kg·m-2; 59 females, age: 49.41 ± 17.69 years, BMI: 22.22 ± 3.12 kg·m-2). Fat content of paraspinal muscles (erector spinae (ES), multifidus (MS), and psoas (PS)) were measured at (ES L1/2-L4/5; MS L2/3-L5/S1; PS L2/3-L5/S1) levels using dual-energy computed tomography (DECT). Quantitative computed tomography (QCT) was used to assess BMD of L1 and L2. Linear regression analysis was used to assess the relationship between BMD of the lumbar spine and paraspinal muscles fat content with age, sex, and BMI. The variance inflation factor (VIF) was used to detect the degree of multicollinearity among the variables. P < .05 was considered to indicate a statistically significant difference. Results: The paraspinal muscles fat content had a fairly significant inverse association with lumbar BMD after controlling for age, sex, and BMI (adjusted R 2 = 0.584-0.630, all P < .05). Conclusion: Paraspinal muscles fat content was negatively associated with BMD.Paraspinal muscles fatty infiltration may be considered as a potential marker to identify BMD loss.

Trends and predictors of modern contraceptive use among married women: Analysis of 2000-2016 Ethiopian Demographic and Health Surveys.

Objectives: Accessing family planning is a key investment in reducing the broader costs of health care and can reduce a significant proportion of maternal, infant, and childhood deaths. In Ethiopia, use of modern contraceptive methods is still low but it is steadily increasing. Identifying the contributing factors to the changes in contraceptive use among women helps to improve women's contraceptive use and helps to plan strategies for family planning programs. Thus, the current study aimed to analyze the trends and predictors of changes in modern contraceptive use over time among married women in Ethiopia. Data source and study design: Secondary data analysis of the national representative data of 2000-2016 Ethiopian Demography and Health Survey was employed. Methods: This secondary data analysis was considered using 2000 through 2016 Ethiopian Demographic and Health Surveys. The study used data from the four DHSs conducted in Ethiopia (2000-2016). The data from all EDHS was collated so as to follow the trends throughout the period considered for the survey. Married women aged 15-49 years with sample sizes of 36,721 (9,203 in 2000, 8,438 in 2005, 9,478 in 2011, and 9,602 in 2016) were included. The analysis involved three levels, including trend analysis (to see changes from 2000 to 2005, 2005-2011, 2011-2016 and 2000-2016). Bivariate and multivariate analysis were also considered to identify predictors of modern contraceptive use. Data was extracted from the EDHS datasets for which authorization was obtained from the DHS Program/ICF International using a data extraction tool. SPSS 24 was employed for data management and analysis. Results: Among married women of reproductive age, modern contraceptive prevalence increased from 6.2% in 2000 to 35.2% in 2016. This 5-fold increment in modern contraceptive use was due to being in the age group of 25-29 years (AOR = 1.4; 95%CI (1.1, 1.7)), having two children (AOR = 1.3; 95%CI (1.1, 1.6)), the richest wealth category (AOR = 3.0; 95% CI (2.5, 3.5)), currently working (AOR = 1.3; 95%CI (1.2, 1.5)) and attending secondary and above education (AOR = 1.2; 95%CI (1.1, 1.6)) were found to be predictors. Conclusions: Over the past 15 years, an annual average of a 1.9% point increment has been observed in modern contraceptive use, but the country lags behind the SDGs's 2030 target of achieving zero unmet needs for contraception. Program interventions, and continued education of women, are mandatory, as education is one of the major factors contributing to increasing contraceptive use.

Fatigue and cognitive impairment after COVID-19: A prospective multicentre study.

Background: Reliable estimates of frequency, severity and associated factors of both fatigue and cognitive impairment after COVID-19 are needed. Also, it is not clear whether the two are distinct sequelae of COVID-19 or part of the same syndrome." Methods: In this prospective multicentre study, frequency of post-COVID fatigue and cognitive impairment were assessed in n = 969 patients (535 [55%] female) ≥6 months after SARS-CoV-2 infection with the FACIT-Fatigue scale (cut-off ≤30) and Montreal Cognitive Assessment (≤25 mild, ≤17 moderate impairment) between November 15, 2020 and September 29, 2021 at University Medical Center Schleswig-Holstein, Campus Kiel and University Hospital Würzburg in Germany. 969 matched non-COVID controls were drawn from a pre-pandemic, randomised, Germany-wide population survey which also included the FACIT-Fatigue scale. Associated sociodemographic, comorbid, clinical, psychosocial factors and laboratory markers were identified with univariate and multivariable linear regression models. Findings: On average 9 months after infection, 19% of patients had clinically relevant fatigue, compared to 8% of matched non-COVID controls (p < 0.001). Factors associated with fatigue were female gender, younger age, history of depression and the number of acute COVID symptoms. Among acute COVID symptoms, altered consciousness, dizziness and myalgia were most strongly associated with long-term fatigue. Moreover, 26% of patients had mild and 1% had moderate cognitive impairment. Factors associated with cognitive impairment were older age, male gender, shorter education and a history of neuropsychiatric disease. There was no significant correlation between fatigue and cognitive impairment and only 5% of patients suffered from both conditions. Interpretation: Fatigue and cognitive impairment are two common, but distinct sequelae of COVID-19 with potentially separate pathophysiological pathways. Funding: German Federal Ministry of Education and Research (BMBF).

Climate change diminishes the potential habitat of the bont tick (Amblyomma hebraeum): evidence from Mashonaland Central Province, Zimbabwe.

BACKGROUND: Understanding the response of vector habitats to climate change is essential for vector management. Increasingly, there is fear that climate change may cause vectors to be more important for animal husbandry in the future. Therefore, knowledge about the current and future spatial distribution of vectors, including ticks (Ixodida), is progressively becoming more critical to animal disease control. METHODS: Our study produced present (2018) and future (2050) bont tick (Amblyomma hebraeum) niche models for Mashonaland Central Province, Zimbabwe. Specifically, our approach used the Ensemble algorithm in Biomod2 package in R 3.4.4 with a suite of physical and anthropogenic covariates against the tick's presence-only location data obtained from cattle dipping facilities. RESULTS: Our models showed that currently (the year 2018) the bont tick potentially occurs in 17,008 km2, which is 60% of Mashonaland Central Province. However, the models showed that in the future (the year 2050), the bont tick will occur in 13,323 km2, which is 47% of Mashonaland Central Province. Thus, the models predicted an ~ 13% reduction in the potential habitat, about 3685 km2 of the study area. Temperature, elevation and rainfall were the most important variables explaining the present and future potential habitat of the bont tick. CONCLUSION: Results of our study are essential in informing programmes that seek to control the bont tick in Mashonaland Central Province, Zimbabwe and similar environments.

In-hospital mortality and severe outcomes after hospital discharge due to COVID-19: A prospective multicenter study from Brazil.

Background: We evaluated in-hospital mortality and outcomes incidence after hospital discharge due to COVID-19 in a Brazilian multicenter cohort. Methods: This prospective multicenter study (RECOVER-SUS, NCT04807699) included COVID-19 patients hospitalized in public tertiary hospitals in Brazil from June 2020 to March 2021. Clinical assessment and blood samples were performed at hospital admission, with post-hospital discharge remote visits. Hospitalized participants were followed-up until March 31, 2021. The outcomes were in-hospital mortality and incidence of rehospitalization or death after hospital discharge. Kaplan-Meier curves and Cox proportional-hazard models were performed. Findings: 1589 participants [54.5% male, age=62 (IQR 50-70) years; BMI=28.4 (IQR,24.9-32.9) Kg/m² and 51.9% with diabetes] were included. A total of 429 individuals [27.0% (95%CI,24.8-29.2)] died during hospitalization (median time 14 (IQR,9-24) days). Older age [vs<40 years; age=60-69 years-aHR=1.89 (95%CI,1.08-3.32); age=70-79 years-aHR=2.52 (95%CI,1.42-4.45); age≥80-aHR=2.90 (95%CI 1.54-5.47)]; noninvasive or mechanical ventilation at admission [vs facial-mask or none; aHR=1.69 (95%CI 1.30-2.19)]; SAPS-III score≥57 [vs<57; aHR=1.47 (95%CI 1.13-1.92)] and SOFA score≥10 [vs <10; aHR=1.51 (95%CI 1.08-2.10)] were independently associated with in-hospital mortality. A total of 65 individuals [6.7% (95%CI 5.3-8.4)] had a rehospitalization or death [rate=323 (95%CI 250-417) per 1000 person-years] in a median time of 52 (range 1-280) days post-hospital discharge. Age ≥ 60 years [vs <60, aHR=2.13 (95%CI 1.15-3.94)] and SAPS-III ≥57 at admission [vs <57, aHR=2.37 (95%CI 1.22-4.59)] were independently associated with rehospitalization or death after hospital discharge. Interpretation: High in-hospital mortality rates due to COVID-19 were observed and elderly people remained at high risk of rehospitalization and death after hospital discharge. Funding: Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Programa INOVA-FIOCRUZ.

Association of population migration with air quality: Role of city attributes in China during COVID-19 pandemic (2019-2021).

Atmospheric pollution studies have linked diminished human activity during the COVID-19 pandemic to improve air quality. This study was conducted during January to March (2019-2021) in 332 cities in China to examine the association between population migration and air quality, and examined the role of three city attributes (pollution level, city scale, and lockdown status) in this effect. This study assessed six air pollutants, namely CO, NO2, O3, PM10, PM2.5, and SO2, and measured meteorological data, with-in city migration (WCM) index, and inter-city migration (ICM) index. A linear mixed-effects model with an autoregressive distributed lag model was fitted to estimate the effect of the percent change in migration on air pollution, adjusting for potential confounding factors. In summary, lower migration was associated with decreased air pollution (other than O3). Pollution change in susceptibility is more likely to occur in NO2 decrease and O3 increase, but unsusceptibility is more likely to occur in CO and SO2, to city attributes from low migration. Cities that are less air polluted and population-dense may benefit more from decreasing PM10 and PM2.5. The associations between population migration and air pollution were stronger in cities with stringent traffic restrictions than in cities with no lockdowns. Based on city attributes, an insignificant difference was observed between the effects of ICM and WCM on air pollution. Findings from this study may gain knowledge about the potential interaction between migration and city attributes, which may help decision-makers adopt air-quality policies with city-specific targets and paths to pursue similar air quality improvements for public health but at a much lower economic cost than lockdowns.

D-optimal mixture design for optimization of topical dapsone niosomes: in vitro characterization and in vivo activity against Cutibacterium acnes.

This study aimed to illustrate the use of D-optimal mixture design (DOMD) for optimization of an enhancer containing Dapsone niosomal formula for acne topical treatment. Mixture components (MixCs) studied were: Span 20, Cholesterol, and Cremophor RH. Different responses were measured. Optimized formula (OF) was selected to minimize particle size and maximize absolute zeta potential and entrapment efficiency. Optimized formula gel (OF-gel) was prepared and characterized. OF-gel in vivo skin penetration using confocal laser scanning microscopy and activity against Cutibacterium acnes in acne mouse model were studied. Based on DOMD results analysis, adequate models were derived. Piepel and contour plots were plotted accordingly to explain how alteration in MixCs L-pseudo values affected studied responses and regions for different responses' values. The OF had suitable predicted responses which were in good correlation with the actually measured ones. The OF-gel showed suitable characterization and in vivo skin penetration up to the dermis layer. In vivo acne mouse-model showed that OF-gel-treated group (OF-gel-T-gp) had significantly better recovery (healing) criteria than untreated (UT-gp) and Aknemycin®-treated (A-T-gp) groups. This was evident in significantly higher reduction of inflammation percent observed in OF-gel-T-gp than both UT-gp and A-T-gp. Better healing in OF-gel-T-gp compared with other groups was also verified by histopathological examination. Moreover, OF-gel-T-gp and A-T-gp bacterial loads were non-significantly different from each other but significantly lower than UT-gp. Thus, DOMD was an adequate statistical tool for optimization of an appropriate enhancer containing Dapsone niosomal formula that proved to be promising for topical treatment of acne.

Statistical Methods for Item Reduction in a Representative Lifestyle Questionnaire: Pilot Questionnaire Study.

BACKGROUND: Reducing the number of items in a questionnaire while maintaining relevant information is important as it is associated with advantages such as higher respondent engagement and reduced response error. However, in health care, after the original design, an a posteriori check of the included items in a questionnaire is often overlooked or considered to be of minor importance. When conducted, this is often based on a single selected method. We argue that before finalizing any lifestyle questionnaire, a posteriori validation should always be conducted using multiple approaches to ensure the robustness of the results. OBJECTIVE: The objectives of this study are to compare the results of two statistical methods for item reduction (variance inflation factor [VIF] and factor analysis [FA]) in a lifestyle questionnaire constructed by combining items from different sources and analyze the different results obtained from the 2 methods and the conclusions that can be made about the original items. METHODS: Data were collected from 79 participants (heterogeneous in age and sex) with a high risk of metabolic syndrome working in a financial company based in Tokyo. The lifestyle questionnaire was constructed by combining items (asked with daily, weekly, and monthly frequency) from multiple validated questionnaires and other selected questions. Item reduction was conducted using VIF and exploratory FA. Adequacy tests were used to check the data distribution and sampling adequacy. RESULTS: Among the daily and weekly questions, both VIF and FA identified redundancies in sleep-related items. Among the monthly questions, both approaches identified redundancies in stress-related items. However, the number of items suggested for reduction often differed: VIF suggested larger reductions than FA for daily questions but fewer reductions for weekly questions. Adequacy tests always confirmed that the structural detection was adequate for the considered items. CONCLUSIONS: As expected, our analyses showed that VIF and FA produced both similar and different findings, suggesting that questionnaire designers should consider using multiple methods for item reduction. Our findings using both methods indicate that many questions, especially those related to sleep, are redundant, indicating that the considered lifestyle questionnaire can be shortened.

Associations of socioeconomic status indicators and migrant status with risk of a low vegetable and fruit consumption in children.

BACKGROUND: It is important to provide insight in potential target groups for interventions to reduce socioeconomic inequalities in children's vegetable/fruit consumption. In earlier studies often single indicators of socioeconomic status (SES) or migrant status have been used. However, SES is a multidimensional concept and different indicators may measure different SES dimensions. Our objective is to explore multiple associations of SES indicators and migrant status with risk of a low vegetable/fruit consumption in a large multi-ethnic and socioeconomically diverse sample of children. METHODS: We included 5,010 parents of 4- to 12-year-olds from a Dutch public health survey administered in 2018. Cross-sectional associations of parental education, material deprivation, perceived financial difficulties, neighbourhood socioeconomic status (NSES) and migrant status with low (≤4 days a week) vegetable and fruit consumption in children were assessed using multilevel multivariable logistic regression models. Results are displayed as odds ratios (OR) with 95% confidence intervals (CI). RESULTS: Of the 4- to 12-year-olds, 22.1% had a low vegetable consumption and 11.9% a low fruit consumption. Low (OR 2.51; 95%CI: 2.05, 3.07) and intermediate (OR 1.83; 95%CI: 1.54, 2.17) parental education, material deprivation (OR 1.45; 95%CI: 1.19, 1.76), low NSES (OR 1.28; 95%CI: 1.04, 1.58) and a non-Western migrant status (OR 1.94; 95%CI: 1.66, 2.26) were associated with a higher risk of a low vegetable consumption. Low (OR 1.68; 95%CI: 1.31, 2.17) and intermediate (OR 1.39; 95%CI: 1.12, 1.72) parental education and material deprivation (OR 1.63; 95%CI: 11.27, 2.08) were also associated with a higher risk of a low fruit consumption. CONCLUSION: Our findings indicate associations of multiple SES indicators and migrant status with a higher risk of a low vegetable/fruit consumption in children and thus help to identify potential target groups.

A variable selection method based on mutual information and variance inflation factor.

Feature selection plays a vital role in the quantitative analysis of high-dimensional data to reduce dimensionality. Recently, the variable selection method based on mutual information (MI) has attracted more and more attention in the field of feature selection, where the relevance between the candidate variable and the response is maximized and the redundancy of the selected variables is minimized. However, multicollinearity often is a serious problem in linear models. Collinearity can cause unstable parameter estimation, unreliable models, and weak predictive ability. In order to address this problem, the variance inflation factor (VIF) was introduced for feature selection. Therefore, a variable selection method based on MI combined with VIF was proposed in this paper, called Mutual Information-Variance Inflation Factor (MI-VIF). By calculating the MI between the independent variable and the response variable, the variable with greater MI was selected to maximize the correlation between the independent variable and the response variable. By calculating the VIF between the independent variables, the multicollinearity test was performed. The variables that cause the multicollinearity of the model were eliminated to minimize the collinearity between the independent variables. The proposed method was tested based on two high-dimensional spectral datasets. The regression models (PLSR, MLR) were established based on feature selection through MI-VIF and MI-based methods (MIFS, MMIFS) to compare the prediction accuracy of the models. The results showed that under two datasets, the MI-VIF showed a good prediction performance. Based on the tea dataset, the established MI-VIF-MLR model achieved accuracy with Rp2 of 0.8612 and RMSEP of 0.4096, the MI-VIF-PLSR model achieved accuracy with Rp2 of 0.8614 and RMSEP of 0.4092. Based on the diesel fuels dataset, the established MI-VIF-MLR model achieved accuracy with Rp2 of 0.9707 and RMSEP of 0.6568, the MI-VIF-PLSR model achieved accuracy with Rp2 of 0.9431 and RMSEP of 0.9675. In addition, the MI-VIF was compared with the Successive projections algorithm (SPA), which is a method to reduce the collinearity between variables in the wavelength selection of the near-infrared spectrum. It was found that MI-VIF also had a good predictive effect compared to SPA. It proves that the MI-VIF is an effective variable selection method.

Camel milk consumption is associated with less childhood stunting and underweight than bovine milk in rural pastoral districts of Somali, Ethiopia: a cross-sectional study.

Undernutrition is a major global health problem. Various types of animal milk are used for feeding children at early ages; however, associations of camel milk (CaM) and bovine milk (BM) with the nutritional status of children have not been explored. A comparative community-based cross-sectional study was conducted among pre-schoolers in rural pastoral districts of Somali, Ethiopia. Children were selected from households with lactating camels or cows. Anthropometric measurements followed standard procedures for height-for-age, weight-for-age and weight-for-height scores. Independent sample t-tests identified significant differences in anthropometric indices based on the type of milk consumed. Multivariable logistic regression was used to examine associations between milk consumption and other predictors of growth failures. The prevalence of stunting was 24⋅1 % [95 % confidence interval (CI) 20⋅5, 28⋅3] of pre-schoolers, 34⋅8 % (95 % CI 29⋅9, 39⋅6) were wasted and 34⋅7 % (95 % CI 30⋅1, 39⋅9) were underweight. Higher proportions of BM-fed children were severely stunted, wasted and underweight compared with CaM consumers. Using logistic regression models, children who consumed BM [adjusted odds ratio (AOR): 2⋅10; 95 % CI 1⋅22, 3⋅61] and who were anaemic (AOR: 4⋅22; 95 % CI 2⋅23, 7⋅98) were more likely to be stunted than their counterparts, while girls were less likely to be stunted than boys (AOR: 0⋅57; 95 % CI 0⋅34, 0⋅94). Similarly, children who consumed BM (AOR: 1⋅97; 95 % CI 1⋅20, 3⋅24), who were anaemic (AOR: 2⋅27; 95 % CI 1⋅38, 3⋅72) and who drank unsafe water (AOR: 1⋅91; 95 % CI 1⋅19, 3⋅07) were more likely to be underweight than their counterparts. In conclusion, CaM consumption was associated with lower prevalence of stunting and underweight than BM. Promoting CaM in pastoralist areas may help to curb the high level of undernutrition.

Understanding the impact of correlation within pair-bonds on Cormack-Jolly-Seber models.

The Cormack-Jolly-Seber (CJS) model and its extensions have been widely applied to the study of animal survival rates in open populations. The model assumes that individuals within the population of interest have independent fates. It is, however, highly unlikely that a pair of animals which have formed a long-term pairing have dissociated fates.We examine a model extension which allows animals who have formed a pair-bond to have correlated survival and recapture fates. Using the proposed extension to generate data, we conduct a simulation study exploring the impact that correlated fate data has on inference from the CJS model. We compute Monte Carlo estimates for the bias, range, and standard errors of the parameters of the CJS model for data with varying degrees of survival correlation between mates. Furthermore, we study the likelihood ratio test of sex effects within the CJS model by simulating densities of the deviance. Finally, we estimate the variance inflation factor c ^ for CJS models that incorporate sex-specific heterogeneity.Our study shows that correlated fates between mated animals may result in underestimated standard errors for parsimonious models, significantly deflated likelihood ratio test statistics, and underestimated values of c ^ for models taking sex-specific effects into account.Underestimated standard errors can result in lowered coverage of confidence intervals. Moreover, deflated test statistics will provide overly conservative test results. Finally, underestimated variance inflation factors can lead researchers to make incorrect conclusions about the level of extra-binomial variation present in their data.

A unified approach to sample size and power determination for testing parameters in generalized linear and time-to-event regression models.

To ensure that a study can properly address its research aims, the sample size and power must be determined appropriately. Covariate adjustment via regression modeling permits more precise estimation of the effect of a primary variable of interest at the expense of increased complexity in sample size/power calculation. The presence of correlation between the main variable and other covariates, commonly seen in observational studies and non-randomized clinical trials, further complicates this process. Though sample size and power specification methods have been obtained to accommodate specific covariate distributions and models, most existing approaches rely on either simple approximations lacking theoretical support or complex procedures that are difficult to apply at the design stage. The current literature lacks a general, coherent theory applicable to a broader class of regression models and covariate distributions. We introduce succinct formulas for sample size and power determination with the generalized linear, Cox, and Fine-Gray models that account for correlation between a main effect and other covariates. Extensive simulations demonstrate that this method produces studies that are appropriately sized to meet their type I error rate and power specifications, particularly offering accurate sample size/power estimation in the presence of correlated covariates.