95th Quantile Research Articles

The differential effect of maternal dietary patterns on quantiles of Birthweight

BackgroundMaternal dietary habits during pregnancy are considered essential for development and growth of the fetus as well as maternal health. It has an effect on the birthweight of infants. However, little is known about the effect of dietary patterns on birthweight in urban South Africa. This study aimed to investigate differential effect of dietary patterns of pregnant women on quantiles of birthweight.MethodsData for the study were obtained from a Mother and Child in the Environment birth cohort study in Durban South Africa. Quantile regression was used to investigate the effect of maternal dietary patterns on quantiles of birthweight. Data collection was conducted during the period of 2013 to 2017 in Durban South Africa. Using factor analysis, eight dietary groups were identified from 687 pregnant women in the cohort. Quantile regression analysis was employed to identify the differential effects of the seven dietary groups and demographic factors on the birthweight.ResultsThe quantile regression estimates at the 50th quantile and the ordinary regression estimates painted the same picture about the conditional mean effect of covariates on the birthweight. But unlike the quantile regression the ordinary regression fails to give insights about the covariates effect disparities at the low and/or upper birthweight quantiles. All the dietary groups show a significant differential effect at different birthweight quantiles. For instance, increased frequency of protein rich foods intake was associated with reduction in birthweight at lower and upper quantiles; increased frequency of junk foods intake has a slight increase in birthweight at the lower quantiles but significantly higher increase at the 95th quantile (p < 0.001); increase in consuming vegetable rich foods, reduced birthweight at 95th quantile (p < 0.001). The results further showed that employment (p = 0.006) and family size (p = 0.002) had differential effects across different birthweight quantiles.ConclusionsBoth maternal undernutrition and overnutrition of protein rich foods, junk foods, snack and energy foods and vegetable rich foods have shown a substantial varying effects on those infants with birthweights in the lower and upper birthweight quantiles.

Distributional Changes in U.S. Sugar-Sweetened Beverage Purchases, 2002–2014

U.S. policy actions focus on reducing sugar-sweetened beverage purchases. Yet, there are no studies on trends in overall purchase distribution and how it has changed by key subpopulations. This study examined changes in distributions of total sugar-sweetened beverage purchases and its major subtypes(regular carbonated soft drinks and fruit/sports/energy drinks)in 2002-2014 and distinguished among low, moderate, and high purchasers. Longitudinal data on sugar-sweetened beverage purchases of U.S. households from the 2002-2014 Nielsen Homescan Panel were used. Sugar-sweetened beverages were defined as all caloric non-alcoholic beverages containing added sugars. Longitudinal quantile regression model examined trends across distributions (from quantile 25 to 95) of purchases (measured in kcal/day/capita), while accounting for households' unobserved differences. All statistical analyses were conducted in 2019. All households across the total purchase distribution significantly reduced their purchases. High purchasers made less proportional reductions than low purchasers (e.g., 35% at 95th quantile vs 62% at 25th quantile). However, the smaller relative reductions among higher purchasers translated into larger absolute decreases (e.g., 134 kcal/day/capita at 95th quantile vs 23 kcal/day/capita at 25th quantile). Similar patterns in heterogeneity were observed across sugar-sweetened beverage subtype distributions and among racial/ethnic and income groups. In addition, there were significant racial/ethnic and income disparities in total sugar-sweetened beverage purchases in 2002-2003. Although racial/ethnic disparities among higher purchasers improved, income disparity patterns at all purchase levels persisted into 2013-2014. From 2002‒2003 to 2013-2014, U.S. households at all purchase levels made meaningful reductions in sugar-sweetened beverage purchases in both absolute and relative terms. Furthermore, racial/ethnic disparities in total sugar-sweetened beverage purchases narrowed, but income disparity patterns persisted.

Does an N-shaped association exist between pollution and ICT in Turkey? ARDL and quantile regression approaches.

It is a debated issue that the influence of information and communications technologies (ICTs) on the environment is positive or negative in the literature. For instance, usage and disposal of ICTs may lead to environmental degradation and it may also increase carbon emission arising from electricity generation. However, ICT applications may contribute to environmental quality creating great opportunities for smarter cities, transport systems, electrical grids, and industrial processes. These two effects reveal the possible relationship of an inverted U-shaped association also known as the environmental Kuznets curve (EKC) hypothesis relationship between ICT and CO2. The objective of this study is to determine the structure of the relationship between environmental degradation and ICTs in Turkey for 1980-2017. The findings of the autoregressive distributed lag (ARDL) analysis indicate the validity of inverted N-shaped EKC hypothesis. However, the results of the quantile regression test are inconsistent with those of ARDL. When the coefficients from 30th to 95th quantiles are examined, it is observed that there is an inverted N-shaped relationship between ICT and CO2, but these coefficients are not statistically significant. In addition, in the 95th quantile, coefficients of the square of ICT and ICT are statistically significant, contrary to the cube of ICT which is not. This reveals that the relationship between the variables is U-shaped.

Value at Risk Estimation Using the GARCH-EVT Approach with Optimal Tail Selection

A conditional Extreme Value Theory (GARCH-EVT) approach is a two-stage hybrid method that combines a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) filter with the Extreme Value Theory (EVT). The approach requires pre-specification of a threshold separating distribution tails from its middle part. The appropriate choice of a threshold level is a demanding task. In this paper we use four different optimal tail selection algorithms, i.e., the path stability method, the automated Eye-Ball method, the minimization of asymptotic mean squared error method and the distance metric method with a mean absolute penalty function, to estimate out-of-sample Value at Risk (VaR) forecasts and compare them to the fixed threshold approach. Unlike other studies, we update the optimal fraction of the tail for each rolling window of the returns. The research objective is to verify to what extent optimization procedures can improve VaR estimates compared to the fixed threshold approach. Results are presented for a long and a short position applying 10 world stock indices in the period from 2000 to June 2019. Although each approach generates different threshold levels, the GARCH-EVT model produces similar Value at Risk estimates. Therefore, no improvement of VaR accuracy may be observed relative to the conservative approach taking the 95th quantile of returns as a threshold.

Heterogeneous impacts of renewable energy and environmental patents on CO2 emission - Evidence from the BRIICS

The study explores the impacts of renewable energy, environmental patents, economic growth and other variables on the CO2 emission per capita from 2000 to 2013 for the BRIICS countries. Using both the panel OLS methods and panel quantile regression method, we find that the effects of the determinant variables are heterogeneous across quantiles. Specifically, renewable energy supply reduces CO2 emissions per capita, with the strongest effect at the 95th quantile. Development of environmental patents accelerates carbon emissions per capita, but only significantly affects the CO2 emissions per capita at the upper tail of the conditional distribution. GDP per capita enhances CO2 emissions per capita, with the most substantial effect in the 5th quantile. Exports increase carbon emissions per capita with an asymmetric inverted U-sharped impact. Foreign direct investment reduces carbon emissions per capita, but only significantly influences the carbon emissions per capita at the medium and upper of the conditional distribution. Domestic credit to private sectors raises carbon emissions per capita with gradually decreasing impacts along all quantiles. We propose several policy recommendations based on the results.

Reducing the Uncertainty of Lidar Measurements in Complex Terrain Using a Linear Model Approach

In complex terrain, ground-based lidar wind speed measurements sometimes show noticeable differences compared to measurements made with in-situ sensors mounted on meteorological masts. These differences are mostly caused by the inhomogeneities of the flow field and the applied reconstruction methods. This study investigates three different methods to optimize the reconstruction algorithm in order to improve the agreement between lidar measurements and data from sensors on meteorological masts. The methods include a typical velocity azimuth display (VAD) method, a leave-one-out cross-validation method, and a linear model which takes into account the gradients of the wind velocity components. In addition, further aspects such as the influence of the half opening angle of the scanning cone and the scan duration are considered. The measurements were carried out with two different lidar systems, that measured simultaneously. The reference was a 100 m high meteorological mast. The measurements took place in complex terrain characterized by a 150 m high escarpment. The results from the individual methods are quantitatively compared with the measurements of the cup anemometer mounted on the meteorological mast by means of the three parameters of a linear regression (slope, offset, R 2 ) and the width of the 5th–95th quantile. The results show that expanding the half angle of the scanning cone from 20 ∘ to 55 ∘ reduces the offset by a factor of 14.9, but reducing the scan duration does not have an observable benefit. The linear method has the lowest uncertainty and the best agreement with the reference data (i.e., lowest offset and scatter) of all of the methods that were investigated.

Grassland fragmentation and its influence on woody plant cover in the southern Great Plains, USA

Woodland and agricultural expansion are major causes of grassland fragmentation. Fire and rainfall play important roles in maintaining grasslands, however, fire activity has been reduced in fragmented landscapes. Quantify the degree to which basic landscape fragmentation metrics could be used as drivers of woody cover potential. Woody plant percent cover was calculated between 2004 and 2008 at > 2000 sites. At each site, we calculated these fragmentation metrics for grassland cover type (classified by the National Land Cover Database); # patches, landscape proportion, edge density, largest patch index, effective mesh size and patch cohesion index within 3 circular areas (10 km2, 360 km2 and 3600 km2) surrounding the sampling site. A quantile regression was performed to identify which metrics were useful at predicting the 25th, 50th, 75th or 95th quantile of woody cover distribution. Grassland proportion and edge density were significant predictors of the woody plant potential (75th and 95th quantile). Woody cover potential was positively associated with edge density suggesting that fragmented areas (i.e., areas with high number of edges) maintained higher woody cover, while grassland proportion was negatively associated with woody plant potential. We propose that in addition to a lack of fire, fragmented landscapes may facilitate further woodland expansion by reducing natural land and restricting grasslands to smaller, less connected patches, which can maintain higher woody cover. Given current trends in woodland expansion, special attention should be given to areas that are found within a fragmented landscape and climatically prone to woodland expansion.

Subway Opening, Traffic Accessibility, and Housing Prices: A Quantile Hedonic Analysis in Hangzhou, China

Most existing studies empirically investigated the impact of rail transit on housing prices using the traditional hedonic price model, which is based on ordinary least squares (OLS). This method can estimate only the average implicit prices of housing characteristics and may ignore possible heterogeneous impacts of rail transit at different housing price levels. As a useful supplement to OLS regression, quantile regression can measure how implicit prices of explanatory variables vary across different price levels, thereby providing a comprehensive picture of the relationship between housing characteristics and prices. By using Hangzhou, China as an example, this study adopts the traditional hedonic price model and quantile regression model to investigate the capitalization effect of a new subway line on housing prices. Empirical results suggest the significant impacts of accessibility to subway stations. The average housing price within 2 km of the station is 2.1% to 6.1% higher than those outside. We also find that the impacts of the subway differ significantly across the distribution of housing prices, wherein the absolute value of estimated coefficients increased from 0.023 for the 15th quantile to 0.086 for the 95th quantile. The subway opening strengthens the capitalization effect of traffic accessibility. The absolute value of price elasticity increases from 0.044 to 0.053, and the range of influence is expanded from 1500 m to 2000 m.

Radiocesium contamination in house dust within evacuation areas close to the Fukushima Daiichi nuclear power plant

Outdoor decontamination efforts have been ongoing since the Fukushima Daiichi nuclear power plant (FDNPP) accident; however, little is known about indoor contamination. Therefore, house dust was sampled based on particle size in 21 wooden buildings (19 residential houses and 2 community centers) within the evacuation area close to the FDNPP, Fukushima Prefecture, Japan. Activities of radiocesium (137Cs) per gram of house dust increased with decreasing particle size (mean: 6.1 × 103, 2.6 × 103, 1.6 × 103, 7.5 × 102, 5.0 × 102, and 4.6 × 102 Bq/g for <4–20, 20–63, 63–180, 180–500, 500–1000, and 1000–2000 μm, respectively). The 137Cs concentrations in house dust were inversely related to the square of distance from the FDNPP for <4–1000 μm particles. From the results of the multiple linear regression analysis, distance from the FDNPP and direction from the FDNPP (northwest) were significantly related to the radioactivity of house dust. It was found that 19%, 33%, and 48% of 137Cs in house dust were extracted in water, 1 M HCl, and not extracted, respectively. Considering the bioaccessibility and assuming a 20 mg/day daily intake of house dust, the daily doses would be 7.2 Bq/day (mean) and 18 Bq/day (95th percent quantile). These results provide valuable insight into indoor radioactive Cs contamination in the area around the FDNPP and possible oral exposure to indoor radioactive Cs after returning home.

Deriving Field-Based Ecological Risks for Bird Species.

Ecological risks (ERs) of pollutants are typically assessed using species sensitivity distributions (SSDs), based on effect concentrations obtained from bioassays with unknown representativeness for field conditions. Alternatively, monitoring data relating breeding success in bird populations to egg concentrations may be used. In this study, we developed a procedure to derive SSDs for birds based on field data of egg concentrations and reproductive success. As an example, we derived field-based SSDs for p,p′-DDE and polychlorinated biphenyls (PCBs) exposure to birds. These SSDs were used to calculate ERs for these two chemicals in the American Great Lakes and the Arctic. First, we obtained field data of p,p′-DDE and PCBs egg concentrations and reproductive success from the literature. Second, these field data were used to fit exposure-response curves along the upper boundary (right margin) of the response’s distribution (95th quantile), also called quantile regression analysis. The upper boundary is used to account for heterogeneity in reproductive success induced by other external factors. Third, the species-specific EC10/50s obtained from the field-based exposure-response curves were used to derive SSDs per chemical. Finally, the SSDs were combined with specific exposure data for both compounds in the two areas to calculate the ER. We found that the ERs of combined exposure to these two chemicals were a factor of 5–35 higher in the Great Lakes compared to Arctic regions. Uncertainty in the species-specific exposure-response curves and related SSDs was mainly caused by the limited number of field exposure-response data for bird species. With sufficient monitoring data, our method can be used to quantify field-based ecological risks for other chemicals, species groups, and regions of interest.

What We Should Know About House Reconstruction Costs?

This paper presents two important analyses, which have been derived from a rich dataset supplied by an important Property & Casualty Carrier in the Canadian Market (here we can introduce a footnote suggesting that due to the anonymity requirement we will abbreviate this important Property & Casualty Carrier in the Canadian Market as PCC). These analyses provide us with an improved understanding of house values, via a detailed analysis of their predicted reconstruction costs. In an initial step, we propose a new model that focuses on the modeling of house reconstruction cost (HRC) using 16 predictors, which include the materials and composition of the buildings. In a second step, we analyze the distribution of HRC using quantile regressions, in order to gain a better understanding of the influence of HRC skewness, which is driven by the most expensive houses. It is found that when a broad set of (16) predictors is used, the “Living Space” alone accounts for 54.87% of the cost variation, while the square of this variable accounts for another 9.4% of the variation in cost. Quantile analysis provides additional information, as the impact of certain coefficients on the cost of less expensive houses is different to that of expensive houses. In particular, the “Age of Construction” coefficient at the 25th quantile is 3 times higher (in absolute value) than at the 99th quantile, whereas the quantiles estimates for the nonlinear influence increase with quantile value, suggesting a move towards an exponential influence of age on the reconstruction cost of expensive houses. The “Living Space” predictor reveals that the living-space cost is approximately 4 times greater at the 25th than at the 95th quantile, whereas the nonlinear influence of living space varies from a negative effect (lower quantiles) to a positive effect (upper quantiles), suggesting that the cost curve changes from concave at lower quantiles to convex at higher quantiles.

Predictors of High Blood Pressure in South African Children: Quantile Regression Approach

Objective: To identify predictors of blood pressure (BP) in children and explore the predictors` effects on the conditional quantile functions of systolic blood pressure and diastolic blood pressure. Methods: A secondary data analysis was performed using data from the South African National Income Dynamics Study (2014-2015). From this particular secondary data, data for children aged between 10-17 years were extracted for analysis. The variables used in the study were systolic blood pressure (SBP), diastolic blood pressure (DBP), body mass index (BMI), age, smoking, alcohol consumption, exercises, gender and race. Two parameter estimation methods were used, ordinary least squares (OLS) and quantile regression (QR). Results: BMI had positive statistically significant estimated OLS and conditional quantile functions with both the BP measures except the 95th quantile for SBP. Age had also positive statistically significant estimated OLS and QR coefficients except for the 95th percentile, with both DBP and SBP respectively. Gender was found to be inversely related to both DBP and SBP except the 10th quantile for DBP. Race was partially significant to DBP. Smoking, alcohol consumption and exercises did not present any statistically significant relations with both DBP and SBP for all the estimated OLS and QR coefficients. Conclusion: BMI, age, gender and partially race were found to be predictors of BP in South African children using both OLS and QR techniques. Exercises, smoking and alcohol consumption did not present any statistically significant relations with both DBP and SBP probably because few participants exercise regularly, smoke and drink alcohol to bring out a significant change in both BP measurements.

Environmental and Anthropogenic Degradation of Vegetation in the Sahel from 1982 to 2006

There is a great deal of debate on the extent, causes, and even the reality of land degradation in the Sahel. Investigations carried out before approximately 2000 using remote sensing data suggest widespread reductions in biological productivity, while studies extending beyond 2000 consistently reveal a net increase in vegetation production, strongly related to the recovery of rainfall following the extreme droughts of the 1970s and 1980s, and thus challenging the notion of widespread, long-term, subcontinental-scale degradation. Yet, the spatial variations in the rates of vegetation recovery are not fully explained by rainfall trends. It is hypothesized that, in addition to rainfall, other meteorological variables and human land use have contributed to vegetation dynamics. Throughout most of the Sahel, the interannual variability in growing season ΣNDVIgs (measured from satellites, used as a proxy of vegetation productivity) was strongly related to rainfall, humidity, and temperature (mean r2 = 0.67), but with rainfall alone was weaker (mean r2 = 0.41). The mean and upper 95th quantile (UQ) rates of change in ΣNDVIgs in response to climate were used to predict potential ΣNDVIgs—that is, the ΣNDVIgs expected in response to climate variability alone, excluding any anthropogenic effects. The differences between predicted and observed ΣNDVIgs were regressed against time to detect any long-term (positive or negative) trends in vegetation productivity. Over most of the Sahel, the trends did not significantly depart from what is expected from the trends in meteorological variables. However, substantial and spatially contiguous areas (~8% of the total area of the Sahel) were characterized by negative, and, in some areas, positive trends. To explore whether the negative trends were human-induced, they were compared with the available data of population density, land use, and land biophysical properties that are known to affect the susceptibility of land to degradation. The spatial variations in the trends of the residuals were partly related to soils and tree cover, but also to several anthropogenic pressures.

Factors Associated with Adherence to Adjuvant Endocrine Therapy Among Privately Insured and Newly Diagnosed Breast Cancer Patients: A Quantile Regression Analysis.

Adherence to adjuvant endocrine therapy (AET) for estrogen receptor-positive breast cancer remains suboptimal, which suggests that women are not getting the full benefit of the treatment to reduce breast cancer recurrence and mortality. The majority of studies on adherence to AET focus on identifying factors among those women at the highest levels of adherence and provide little insight on factors that influence medication use across the distribution of adherence. To understand how factors influence adherence among women across low and high levels of adherence. A retrospective evaluation was conducted using the Truven Health MarketScan Commercial Claims and Encounters Database from 2007-2011. Privately insured women aged 18-64 years who were recently diagnosed and treated for breast cancer and who initiated AET within 12 months of primary treatment were assessed. Adherence was measured as the proportion of days covered (PDC) over a 12-month period. Simultaneous multivariable quantile regression was used to assess the association between treatment and demographic factors, use of mail order pharmacies, medication switching, and out-of-pocket costs and adherence. The effect of each variable was examined at the 40th, 60th, 80th, and 95th quantiles. Among the 6,863 women in the cohort, mail order pharmacies had the greatest influence on adherence at the 40th quantile, associated with a 29.6% (95% CI = 22.2-37.0) higher PDC compared with retail pharmacies. Out-of-pocket cost for a 30-day supply of AET greater than $20 was associated with an 8.6% (95% CI = 2.8-14.4) lower PDC versus $0-$9.99. The main factors that influenced adherence at the 95th quantile were mail order pharmacies, associated with a 4.4% higher PDC (95% CI = 3.8-5.0) versus retail pharmacies, and switching AET medication 2 or more times, associated with a 5.6% lower PDC versus not switching (95% CI = 2.3-9.0). Factors associated with adherence differed across quantiles. Addressing the use of mail order pharmacies and out-of-pocket costs for AET may have the greatest influence on improving adherence among those women with low adherence. This research was supported by a Ruth L. Kirschstein National Research Service Award for Individual Predoctoral Fellowship grant from the National Cancer Institute (grant number F31 CA174338), which was awarded to Farias. Additionally, Farias was funded by a Postdoctoral Fellowship at the University of Texas School of Public Health Cancer Education and Career Development Program through the National Cancer Institute (NIH Grant R25 CA57712). The other authors declare no conflicts of interest. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health. Farias was primarily responsible for the study concept and design, along with Hansen and Zeliadt and with assistance from the other authors. Farias, Hansen, and Zeliadt took the lead in data interpretation, assisted by the other authors. The manuscript was written by Farias, along with Thompson and assisted by the other authors, and was revised by Ornelas, Li, and Farias, with assistance from the other authors.

Factors associated with the level of CD4 cell counts at HIV diagnosis in a French cohort: a quantile regression approach.

The consensus definition of late presentation for human immunodeficiency virus patient based on a CD4 threshold of 350 cells/mm3 has limitations concerning risk factors identification since there is growing biomedical justification for earlier initiation of treatment. The objective was to overcome this problem by simultaneously determining factors associated with different levels of CD4 counts at the time of diagnosis. Between January 2000 and July 2014, 1179 patients with a first human immunodeficiency virus diagnosis and entering care in a French human immunodeficiency virus reference center were enrolled. Factors associated with each 5 percentile from 5th to 95th quantile of CD4 counts at diagnosis were simultaneously studied in a multivariable quantile regression model. At each of the quantiles, the factors identified as negatively associated with CD4 count at diagnosis were older age, male sex , foreign patients, hepatitis B virus or hepatitis C virus co-infection, employment status, non-MSM transmission, heterosexual transmission, suburban and rural's place of residence and earlier period of diagnosis. Association with CD4 count was not uniformly significant, most factors being significant for some quantiles. The only significant determinant for all quantiles was being born in a foreign country. These results are particularly helpful in the context of human immunodeficiency virus clinical care, management and prevention.

Gender difference in the association between food away-from-home consumption and body weight outcomes among Chinese adults.

The present study aimed to explore the associations between food away-from-home (FAFH) consumption and body weight outcomes among Chinese adults. FAFH was defined as food prepared at restaurants and the percentage of energy from FAFH was calculated. Measured BMI and waist circumference (WC) were used as body weight outcomes. Quantile regression models for BMI and WC were performed separately by gender. Information on demographic, socio-economic, diet and health parameters at individual, household and community levels was collected in twelve provinces of China. A cross-sectional sample of 7738 non-pregnant individuals aged 18-60 years from the China Health and Nutrition Survey 2011 was analysed. For males, quantile regression models showed that percentage of energy from FAFH was associated with an increase in BMI of 0·01, 0·01, 0·01, 0·02, 0·02 and 0·03 kg/m2 at the 5th, 25th, 50th, 75th, 90th and 95th quantile, and an increase in WC of 0·04, 0·06, 0·06, 0·04, 0·06, 0·05 and 0·07 cm at the 5th, 10th, 25th, 50th, 75th, 90th and 95th quantile. For females, percentage of energy from FAFH was associated with 0·01, 0·01, 0·01 and 0·02 kg/m2 increase in BMI at the 10th, 25th, 90th and 95th quantile, and with 0·05, 0·04, 0·03 and 0·03 cm increase in WC at the 5th, 10th, 25th and 75th quantile. Our findings suggest that FAFH consumption is relatively more important for BMI and WC among males rather than females in China. Public health initiatives are needed to encourage Chinese adults to make healthy food choices when eating out.

076 Using boar feeding patterns and classification trees to predict performance

Using feeding patterns to predict performance could allow producers to better sort animals in the finisher to optimize production efficiency. Selecting the correct animals for each market cut is important to maximize the profit from a finishing group. The objective of this study was to examine the predictive ability of a boar's feeding patterns on his growth and feed efficiency performance. Individual feed intake records from 2582 Yorkshire, 2275 Landrace, and 5267 Duroc boars were collected at a single finishing site using Osborne Feed Intake Recording Equipment (FIRETM). Pigs were tested from 11 wk of age to 23 wk of age. Feed intake was recorded every other week on each pig during the 12-wk testing period. The randomForest package in R was used to create 1000 classification trees using feeding patterns to sort boars by quartile for feed intake, feed efficiency (gain/feed), and growth rate in the finisher. The feeding patterns used in this analysis included average number of visits to the feeder per day, average time spent in the feeder per day, feed consumed per visit, the skewness, kurtosis, 95th quantile, and fifth quantile of the feed consumed per visit, and the skewness, kurtosis, 95th quantile, and fifth quantile of the time spent in the feeder per visit. The out-of-bag error rate for feed intake was 37%, 37%, and 36% for Yorkshire, Landrace, and Duroc boars, respectively. Similar out-of-bag error rates were found for feed efficiency and growth rate (62% and 53% for Yorkshire, 58% and 56% for Landrace, and 61% and 53% for Duroc). The feeding pattern variable with the largest mean decrease in accuracy for feed intake was average number of visits to feeder per day for Yorkshire and average feed consumed per visit for Landrace and Duroc. The variable with the largest mean decrease in accuracy for feed efficiency was average feed consumed per visit for Yorkshire and Landrace and average time spent in the feeder per day for Duroc. For growth rate, the variable with the largest mean decrease in accuracy was average feed consumed per visit for Yorkshire and Duroc, and 95th quantile for feed consumed per visit for Landrace. The feeding patterns examined in this study appear to provide little aid in predicting feed intake, feed efficiency, and growth rate in boars; more research should be done to determine if there are other feeding patterns that are more predictive.

Update Reference Charts: Fetal Biometry between the 15th and 20th Week of Gestation

Objective: The aim of this study was to derive reference charts between the 15th and 20th week of gestation for biparietal diameter (BPD), occipito-frontal diameter (OFD), head circumference (HC), abdominal circumference (AC) and femur length (FL) compared to commonly used curves and to evaluate the impact of maternal body mass index (BMI), ultrasound equipment, fetal gender and nicotine abuse. Methods: In a cross-sectional study of 4,265 low-risk pregnancies, biometric data were obtained between the 15th and 20th week of gestation. The statistical analysis included descriptive data, fitting regression curves for the 5th, 50th and 95th quantile as well as multivariate quantile regression analyses. Results: Reference curves for BPD, OFD, HC, AC and FL showed a linear increase between the 15th and 20th week of gestation. New equations and reference charts are presented in this study. In a comparison of influence factors sex, BMI and ultrasound systems have an impact. Conclusion: As a result of the study, we determined current growth curves for BPD, OFD, HC, AC and FL for both sexes as well as for males and females separately. The updated growth curves for all biometric parameters differ from the curves used commonly in the last two decades.

Understanding the Racial and Ethnic Differences in Cost and Mortality Among Advanced Stage Prostate Cancer Patients (STROBE).

The aims of the study were to understand the racial/ethnic differences in cost of care and mortality in Medicare elderly with advanced stage prostate cancer.This retrospective, observational study used SEER-Medicare data. Cohort consisted of 10,509 men aged 66 or older and diagnosed with advanced-stage prostate cancer between 2001and 2004. The cohort was followed retrospectively up to 2009. Racial/ethnic variation in cost was analyzed using 2 part-models and quantile regression. Step-wise GLM log-link and Cox regression was used to study the association between race/ethnicity and cost and mortality. Propensity score approach was used to minimize selection bias.Pattern of cost and mortality varies between racial/ethnic groups. Compared with other racial/ethnic groups, non-Hispanic white patients had higher unadjusted costs in treatment and follow-up phases. Quintile regression results indicated that in treatment phase, Hispanics had higher costs in the 95th quantile and non-Hispanic blacks had lower cost in the 95th quantile, compared with non-Hispanic white men. In terminal phase non-Hispanic blacks and Hispanics had higher cost. After controlling for treatment, all-cause and prostate cancer-specific mortality was not significant for non-Hispanic black men, compared with non-Hispanic white men. However, for Asians, mortality remained significantly lower compared with non-Hispanic white men.In conclusion, relationship between race/ethnicity, cost of care, and mortality is intricate. For non-Hispanic black men, disparity in mortality can be attributed to treatment differences. To reduce racial/ethnic disparities in prostate cancer care and outcomes, tailored policies to address underuse, overuse, and misuse of treatment and health services are necessary.

A mammoth mouthful? A test of the idea that larger animals ingest larger seeds

AbstractAimIt has been widely assumed that large seeds generally require large animals to ingest and disperse them. However, this relationship has only been quantified in single animal groups (e.g. birds) and in a few communities. Our goal was to provide the first broad‐scale study of the relationship between animal body mass and ingested seed size.LocationGlobal.MethodsWe compiled a dataset of 13,135 unique animal × seed interactions, animal body masses and seed sizes in these interactions, across all vertebrate groups (fish, amphibians, reptiles, birds and mammals).ResultsContrary to expectations, ingested seed size was negatively related to animal body mass. This negative relationship was largely driven by large ungulates ingesting small and dry seeds, and analyses excluding either ungulates or seeds with non‐fleshy fruit types showed a positive relationship between animal body mass and ingested seed size. Large animals ingested both seeds with a larger maximum size (the 95th quantile had a positive slope) and a smaller minimum size (the 5th quantile had a negative slope). Larger animals ingest larger seeds from fleshy fruits but smaller seeds from non‐fleshy fruits. A significant positive relationship was found between animal size and the number of seed species ingested.Main conclusionsOur data show that one of the assumptions that has underpinned the study of animal–seed interactions does not hold true across the full range of animal taxa and fruit types. These findings shed new light on theories about which types of plant species might be at risk if large animals go extinct, and cast doubt on the generality of a few theories (e.g. optimal diet theory, fruit‐size hypothesis) about the relationship between frugivores and seeds.