A new family of quantile regression models applied to nutritional data.

This paper introduces a new family of quantile regression models whose response variable follows a reparameterized Marshall-Olkin distribution indexed by quantile, scale, and asymmetry parameters. The family has arisen by applying the Marshall-Olkin approach to distributions belonging to the location-scale family. Models of higher flexibility and whose structure is similar to generalized linear models were generated by quantile reparameterization. The maximum likelihood (ML) method is presented for the estimation of the model parameters, and simulation studies evaluated the performance of the ML estimators. The advantages of the family are illustrated through an application to a set of nutritional data, whose results indicate it is a good alternative for modeling slightly asymmetric response variables with support on the real line.

A nationwide study on immunosenescence biomarkers profile in older adults: ELSI-Brazil.

Immunosenescence is a phenomenon caused by changes in the immune system, and part of these changes involves an increase in circulating immunological biomarkers, a process known as "Inflammaging." Inflammaging can be associated with many diseases related to older people. As the older population continues to grow, understanding changes in the immune system becomes essential. While prior studies assessing these alterations have been conducted in countries with Caucasian populations, this investigation marks a pioneering effort. The object of the study is to describe for the first time that the distribution of cytokines, chemokines, and growth factors serum levels, assessed by Luminex platform, has been examined in a Brazilian population-based study of older adult females and males by age. Blood samples from 2111 participants (≥50 years old) were analyzed at the baseline (2015/2016) of the ELSI-Brazil study. The exploratory variables considered in the study were age, sex, educational level, residence area, geographic region, alcohol and smoking consumption, physical activity, and self-reported medical diagnoses of hypertension, diabetes, asthma, arthritis, and cancer. The association between serum biomarker levels and age was assessed by a quantile regression model adjusted in the total population and stratified by sex. The significance level considered in the analysis was 0.05. The mean age of the participants was 62.9 years, with a slight majority of female (52.7 %). Differences were found between the sexes in the median circulating levels of the CCL11, CXCL10, and FGF biomarkers. Eight biomarkers showed significant associations with age, including the pro-inflammatory CXCL10, TNF-α, IL-6, IL-17, and IL-2; and type 2/regulatory CCL11 and IL-4, showing positive associations, and anti-inflammatory IL-1Ra showing a negative association. The results suggest similar associations between the sexes, revealing an inflammatory profile characterized by types 1 and 2. Remarkably, these findings reinforce the concept of the Inflammaging process in Brazilian population. These findings add novel insights to about the immunosenescence aspects in middle-income countries and help define biomarkers capable of monitoring inflammation in older adults.

Exploring factors influencing domestic violence: a comprehensive study on intrafamily dynamics.

Introduction: This econometric analysis investigates the nexus between household factors and domestic violence. By considering diverse variables encompassing mood, depression, health consciousness, social media engagement, household chores, density, and religious affiliation, the study aims to comprehend the underlying dynamics influencing domestic violence. Methods: Employing econometric techniques, this study examined a range of household-related variables for their potential associations with levels of violence within households. Data on mood, depression, health consciousness, social media usage, household chores, density, and religious affiliation were collected and subjected to rigorous statistical analysis. Results: The findings of this study unveil notable relationships between the aforementioned variables and levels of violence within households. Positive mood emerges as a mitigating factor, displaying a negative correlation with violence. Conversely, depression positively correlates with violence, indicating an elevated propensity for conflict. Increased health consciousness is linked with diminished violence, while engagement with social media demonstrates a moderating influence. Reduction in the time allocated to household chores corresponds with lower violence levels. Household density, however, exhibits a positive association with violence. The effects of religious affiliation on violence manifest diversely, contingent upon household position and gender. Discussion: The outcomes of this research offer critical insights for policymakers and practitioners working on formulating strategies for preventing and intervening in instances of domestic violence. The findings emphasize the importance of considering various household factors when designing effective interventions. Strategies to bolster positive mood, alleviate depression, encourage health consciousness, and regulate social media use could potentially contribute to reducing domestic violence. Additionally, the nuanced role of religious affiliation underscores the need for tailored approaches based on household dynamics, positioning, and gender.

Sectoral foreign direct investment and environmental degradation: new insights from diversification of energy mix containing fossil fuels and renewable energy.

The objective of the study is to extend the existing literature by investigating the effects of foreign direct investment, gross domestic products and per capita and energy diversification on the nitrogen oxide emissions in Brazil, Russia, India, China and South Africa (BRICS) by using annual data during the period 1992-2019. As per our knowledge, the present study is a first of its kind to examine the impact of a new energy diversification index, based on Herfindahl-Hirschman framework on pollution. This study has adopted a new quantile regression augmented method of moments, which is capable of producing the total impacts of the independent variables across the entire distribution of nitrogen oxides emissions. The findings suggest that an increase in foreign direct investment leads to a decrease in nitrogen oxides emissions at the aggregate level and in both manufacturing and service sectors. We observe that foreign direct investment leads to an increase in nitrogen oxides emissions in the agricultural sector in most of the quantiles. Diversification towards renewable energy causes a decrease in nitrogen oxides emissions in most quantiles at aggregate level, agricultural and manufacturing sectors, whilst diversification leads to an increase in nitrogen oxides emissions in the service sector. The findings also suggest that GDP per capita leads to an increase in NOx emissions in all the quantiles. The study suggests the policy to use and attract more clean energy through foreign direct investment for towards the achievement of sustainable development.

Modeling the evolution of deaths from infectious diseases with functional data models: The case of COVID-19 in Brazil.

In this paper, we apply statistical methods for functional data to explore the heterogeneity in the registered number of deaths of COVID-19, over time. The cumulative daily number of deaths in regions across Brazil is treated as continuous curves (functional data). The first stage of the analysis applies clustering methods for functional data to identify and describe potential heterogeneity in the curves and their functional derivatives. The estimated clusters are labeled with different "levels of alert" to identify cities in a possible critical situation. In the second stage of the analysis, we apply a functional quantile regression model for the death curves to explore the associations with functional rates of vaccination and stringency and also with several scalar geographical, socioeconomic and demographic covariates. The proposed model gave a better curve fit at different levels of the cumulative number of deaths when compared to a functional regression model based on ordinary least squares. Our results add to the understanding of the development of COVID-19 death counts.

Healthcare Costs of Major Morbidities Associated with Prematurity in US Children's Hospitals.

OBJECTIVE: To evaluate the healthcare costs attributed to major morbidities associated with prematurity, namely, bronchopulmonary dysplasia (BPD), intraventricular hemorrhage, necrotizing enterocolitis (NEC), retinopathy of prematurity (ROP), and nosocomial infections. STUDY DESIGN: This was a retrospective analysis of infants born at 24-30 weeks of gestation, admitted to children's hospitals in the Pediatric Health Information System between 2009 and 2018. Charges were adjusted by geographical price index, converted to costs using cost-to-charge ratios, inflated to 2018 US$, and total costs were accumulated for the initial hospitalization. Quantile regressions, which are less prone to bias from extreme outliers, were used to examine the incremental costs attributed to each morbidity across the entire cost distribution, including the median. RESULTS: There were 19 232 patients from 30 children's hospitals who were eligible. Higher costs were seen in lower gestational age, more severe morbidity, and those with higher number of comorbidities. Patients with surgical NEC, severe ROP, and severe BPD were the costliest with median total costs of $430 860, $413 825, and $399 495, respectively. Quantile regressions showed surgical NEC had the highest adjusted median incremental total cost ($48 621; 95% CI, $39 617-$57 626) followed by severe BPD ($35 773; 95% CI, $32 018-$39 528) and severe ROP ($22 561; 95% CI, $16 699-$28 423). Quantile regressions also revealed that surgical NEC, severe BPD, and severe ROP had increasing incremental costs at higher total cost percentiles, indicating these morbidities have a greater cost impact on the costliest patients. CONCLUSIONS: Severe BPD, surgical NEC, and severe ROP are the costliest morbidities and contribute the most incremental costs especially for the higher costs patients.

Empirical impact of financial service access on farmers income in Ghana / Impacto empírico do acesso a serviços financeiros na renda dos agricultores em Gana

The impact of access to financial services (AFS) and access to informal financial services (AIFS) on farmer income is examined in this study. After a multi-stage random sampling procedure, the study used a sample size of 478 people from two regions in Ghana. The endogenous treatment regression (ETR) model was used to account for selection bias while the unconditional quantile regression (UQR) model was used for a heterogenous analysis. The findings showed that education, financial literacy, IT access, farm size, and distance were all factors of access to financial services. Similarly, the findings revealed a positive and statistically significant link between household income and access to formal financial services. Similarly, there was a positive and significant association between access to informal financial services and household income. The findings showed that access to formal and informal financial services has different effects on household income. As a result, the effects of access to financial services on income varied by quantile. Based on the findings of the study, we developed policies to boost financial services accessibility as a means of increasing household income.

O impacto do acesso a serviços financeiros (AFS) e acesso a serviços financeiros informais (AIFS) na renda do agricultor é examinado neste estudo. Após um procedimento de amostragem aleatória em vários estágios, o estudo utilizou uma amostra de 478 pessoas de duas regiões de Gana. O modelo de regressão de tratamento endógeno (ETR) foi usado para explicar o viés de seleção, enquanto o modelo de regressão quantílica incondicional (UQR) foi usado para uma análise heterogênea. Os resultados mostram que educação, alfabetização financeira, acesso a TI, tamanho da fazenda e distância foram fatores de acesso a serviços financeiros. Da mesma forma, os resultados revelaram uma ligação positiva e estatisticamente significativa entre a renda familiar e o acesso a serviços financeiros formais. Da mesma forma, houve associação positiva e significativa entre acesso a serviços financeiros informais e renda familiar. Os resultados mostram que o acesso a serviços financeiros formais e informais tem efeitos diferentes na renda familiar. Como resultado, os efeitos do acesso a serviços financeiros sobre a renda variaram por quantil. Com base nos resultados do estudo, desenvolvemos políticas para aumentar a acessibilidade dos serviços financeiros como forma de aumentar a renda familiar.

On a log-symmetric quantile tobit model applied to female labor supply data.

The study of female labor supply has been a topic of relevance in the economic literature. Generally, the data are left-censored and the classic tobit model has been extensively used in the modeling strategy. This model, however, assumes normality for the error distribution and is not recommended for data with positive skewness, heavy-tails and heteroscedasticity, as is the case of female labor supply data. Moreover, it is well-known that the quantile regression approach accounts for the influences of different quantiles in the estimated coefficients. We take all these features into account and propose a parametric quantile tobit regression model based on quantile log-symmetric distributions. The proposed method allows one to model data with positive skewness (which is not suitable for the classic tobit model), to study the influence of the quantiles of interest, and to account for heteroscedasticity. The model parameters are estimated by maximum likelihood and a Monte Carlo experiment is performed to evaluate alternative estimators. The new method is applied to two distinct female labor supply data sets. The results indicate that the log-symmetric quantile tobit model fits better the data than the classic tobit model.

Association between leukocyte telomere length and sex by quantile regression analysis

ABSTRACT Introduction: Telomere length (TL) is a biomarker of cellular proliferative history. In healthy individuals, leukocyte TL shortens with age and associates with the lifespan of men and women. However, most of studies had used linear regression models to address the association of the TL attrition, aging and sex. Methods: We evaluated the association between the TL, aging and sex in a cohort of 180 healthy subjects by quantile regression. The TL of nucleated blood cells was measured by fluorescent in situ hypridization (flow-FISH) in a cohort of 89 men, 81 women, and 10 umbilical cord samples. The results were validated by quantitative polymerase chain reaction (qPCR) and compared to a linear regression analysis. Results: By quantile regression, telomere dynamics slightly differed between sexes with aging: women had longer telomeres at birth and slower attrition rate than men until the sixth decade of life; after that, TL eroded faster and became shorter than that in men. These differences were not observed by linear regression analysis, as the overall telomere attrition rates in women and men were similar (42 pb per year, p < 0.0001 vs. 45 pb kb per year, p < 0.0001). Also, qPCR did not recapitulate flow-FISH findings, as the telomere dynamics by qPCR followed a linear model. Conclusion: The quantile regression analysis accurately reproduced a third-orderpolynomial TL attrition rate in both women and men, but it depended on the technique applied to measure TL. The Flow-FISH reproduced the expected telomere dynamics through life and, differently from the qPCR, was able to detect the subtle TL variations associated with sex and aging.

Tourism adaptability amid the climate change and air pollution in BRICS: a method of moments quantile regression approach.

The main objective of this paper is to look at how environmental degradation in the form of climate change and air pollution affect international tourism for five countries such as Brazil, Russia, India, China, and South Africa (BRICS) over the years 1990-2019. Other independent variables include information and communication technology (ICT) and democracy. We also look at the role of environmental regulation to see the validity of porter hypothesis in the tourism sector. To achieve this objective, we apply a novel method of moments quantile regression approach as well as a robust causality technique. The result shows that at lower and medium quantile, CO2 emission has positive impact on tourism while at higher quantile, CO2 emission has negative but insignificant effect on tourism in BRICS countries. The result for PM2.5 is uniform across all the quantiles, showing the negative effect on tourism. ICT and human capital positively affect the tourism while democracy has negative impact on the tourism sector of the BRICS nations. The result also validated the Porter hypothesis for tourism sector. We conclude that tourism industry stakeholders and the environmental policymakers must work together to integrate tourism policies with BRICS countries' environmental conservation policies as part of the transition to sustainable tourism industry.

Association between leukocyte telomere length and sex by quantile regression analysis.

INTRODUCTION: Telomere length (TL) is a biomarker of cellular proliferative history. In healthy individuals, leukocyte TL shortens with age and associates with the lifespan of men and women. However, most of studies had used linear regression models to address the association of the TL attrition, aging and sex. METHODS: We evaluated the association between the TL, aging and sex in a cohort of 180 healthy subjects by quantile regression. The TL of nucleated blood cells was measured by fluorescent in situ hypridization (flow-FISH) in a cohort of 89 men, 81 women, and 10 umbilical cord samples. The results were validated by quantitative polymerase chain reaction (qPCR) and compared to a linear regression analysis. RESULTS: By quantile regression, telomere dynamics slightly differed between sexes with aging: women had longer telomeres at birth and slower attrition rate than men until the sixth decade of life; after that, TL eroded faster and became shorter than that in men. These differences were not observed by linear regression analysis, as the overall telomere attrition rates in women and men were similar (42 pb per year, p < 0.0001 vs. 45 pb kb per year, p < 0.0001). Also, qPCR did not recapitulate flow-FISH findings, as the telomere dynamics by qPCR followed a linear model. CONCLUSION: The quantile regression analysis accurately reproduced a third-order polynomial TL attrition rate in both women and men, but it depended on the technique applied to measure TL. The Flow-FISH reproduced the expected telomere dynamics through life and, differently from the qPCR, was able to detect the subtle TL variations associated with sex and aging.

Minding the curve: The influence of social origin on earnings inequality by education in Mexico.

Relying on data from the Mexican Mobility Survey for 2006, I evaluate by how much earnings inequality improves upon reducing the influence of social origin on educational attainment. A semiparametric estimation of a counterfactual distribution is used to simulate a distribution of earnings that is not overdetermined by the effect of social origin on education. The contrast between the simulated and the observed distribution reveals that social origin worsens inequality by inducing an earnings bonus. In particular, the earnings bonus associated with social origin, benefits most those with a high school degree or more. Those who have attained at most middle school obtain a small earnings bonus or none at all. Social origin's boosting of returns to education is most salient above the median of the earnings distribution and increases disproportionately above the 80th percentile. The results emphasize the importance of universal educational opportunities for reducing the effect of social origin on labor market outcomes. The absence of any indirect effect of social origin on the earnings of those with less than a high school education is not consistent with sociological perspectives that emphasize social reproduction mechanisms in explaining inter-generational persistence in the labor market.

A parametric quantile regression approach for modelling zero-or-one inflated double bounded data.

Over the last decades, the challenges in applied regression have been changing considerably, and full probabilistic modeling rather than predicting just means is crucial in many applications. Motivated by two applications where the response variable is observed on the unit-interval and inflated at zero or one, we propose a parametric quantile regression considering the unit-Weibull distribution. In particular, we are interested in quantifying the influence of covariates on the quantiles of the response variable. The maximum likelihood method is used for parameters estimation. Monte Carlo simulations reveal that the maximum likelihood estimators are nearly unbiased and consistent. Also, we define a residual analysis to assess the goodness of fit.

The direct and indirect effects of democracy on carbon dioxide emissions in BRICS countries: evidence from panel quantile regression.

This paper illustrates the direct and indirect effects of democracy on CO2 emissions in the BRICS countries (Brazil, Russia, India, China, and South Africa) from 1992 to 2018. In view of the distribution heterogeneity of CO2 emissions, the panel quantile regression model is especially used to explore the nexus among different variables. Furthermore, in order to predict the trends of CO2 emissions in different countries, we also estimate the kernel density function of CO2 emissions in the BRICS countries by the quantile-fitted values. The results indicate that the direct impact of democracy on carbon dioxide emissions is significantly negative and great at high-emission countries. Although the indirect effect of democracy is positive in China and negative in Brazil and South Africa, the total effect of democracy on CO2 emissions remains negative in all BRICS countries. The estimation of kernel density function shows that the distribution of CO2 emissions in each country is gradually concentrated. Moreover, there is an environmental Kuznets curve depicting the linkage of urbanization and carbon dioxide emissions in Brazil and South Africa. These findings further highlight that the impact of democracy on high-emission and low-emission countries should be taken into account in policymaking to achieve sustainable developments.

Nonlinear quantile regression to describe the dry matter accumulation of garlic plants / Regressão quantílica não linear para descrever o acúmulo de matéria seca de alho

The objective of this study was to adjust nonlinear quantile regression models for the study of dry matter accumulation in garlic plants over time, and to compare them to models fitted by the ordinary least squares method. The total dry matter of nine garlic accessions belonging to the Vegetable Germplasm Bank of Universidade Federal de Viçosa (BGH/UFV) was measured in four stages (60, 90, 120 and 150 days after planting), and those values were used for the nonlinear regression models fitting. For each accession, there was an adjustment of one model of quantile regression (τ=0.5) and one based on the least squares method. The nonlinear regression model fitted was the Logistic. The Akaike Information Criterion was used to evaluate the goodness of fit of the models. Accessions were grouped using the UPGMA algorithm, with the estimates of the parameters with biological interpretation as variables. The nonlinear quantile regression is efficient for the adjustment of models for dry matter accumulation in garlic plants over time. The estimated parameters are more uniform and robust in the presence of asymmetry in the distribution of the data, heterogeneous variances, and outliers.(AU)

Este trabalho teve como objetivo ajustar modelos de regressão quantílica não linear para o estudo do acúmulo de matéria seca total em plantas de alho ao longo do tempo, e compará-los com modelos ajustados pelo método dos mínimos quadrados. A matéria seca total de nove acessos de alho pertencentes ao Banco de Germoplasma de Hortaliças da Universidade Federal de Viçosa (BGH/UFV) foi avaliada em quatro períodos (60, 90, 120 e 150 dias após plantio), e estes valores foram utilizados para o ajuste de modelos de regressão - não linear - logística. Para cada acesso, foram ajustados um modelo de regressão quantílica (τ=0,5) e um modelo pela metodologia dos mínimos quadrados. Para avaliar a qualidade de ajuste dos modelos foi utilizado o Critério de Informação de Akaike. Os acessos foram agrupados pelo algoritmo UPGMA, utilizando as estimativas dos parâmetros com interpretação biológica como variáveis. A regressão quantílica não linear foi eficiente no ajuste de modelos para descrição do acúmulo de matéria seca ao longo do tempo. As estimativas de parâmetros foram mais uniformes e robustas na presença de dados assimétricos, variâncias heterogêneas e de valores discrepantes.(AU)

The unit-Weibull distribution as an alternative to the Kumaraswamy distribution for the modeling of quantiles conditional on covariates.

The Beta distribution is the standard model for quantifying the influence of covariates on the mean of a response variable on the unit interval. However, this well-known distribution is no longer useful when we are interested in quantifying the influence of such covariates on the quantiles of the response variable. Unlike Beta, the Kumaraswamy distribution has a closed-form expression for its quantile and can be useful for the modeling of quantiles in the absence/presence of covariates. As an alternative to the Kumaraswamy distribution for the modeling of quantiles, in this paper the unit-Weibull distribution was considered. This distribution was obtained by the transformation of a random variable with Weibull distribution. The same transformation applied to a random variable with Exponentiated Exponential distribution generates the Kumaraswamy distribution. The suitability of our proposal was demonstrated to model quantiles, conditional on covariates, with two simulated examples and three real applications with datasets from health, accounting and social science. For such data sets, the obtained fits of the proposed regression model were compared with those provided by the Beta and Kumaraswamy regression models.

Nonlinear quantile regression to describe the dry matter accumulation of garlic plants / Regressão quantílica não linear para descrever o acúmulo de matéria seca de alho

ABSTRACT: The objective of this study was to adjust nonlinear quantile regression models for the study of dry matter accumulation in garlic plants over time, and to compare them to models fitted by the ordinary least squares method. The total dry matter of nine garlic accessions belonging to the Vegetable Germplasm Bank of Universidade Federal de Viçosa (BGH/UFV) was measured in four stages (60, 90, 120 and 150 days after planting), and those values were used for the nonlinear regression models fitting. For each accession, there was an adjustment of one model of quantile regression (τ=0.5) and one based on the least squares method. The nonlinear regression model fitted was the Logistic. The Akaike Information Criterion was used to evaluate the goodness of fit of the models. Accessions were grouped using the UPGMA algorithm, with the estimates of the parameters with biological interpretation as variables. The nonlinear quantile regression is efficient for the adjustment of models for dry matter accumulation in garlic plants over time. The estimated parameters are more uniform and robust in the presence of asymmetry in the distribution of the data, heterogeneous variances, and outliers.

RESUMO: Este trabalho teve como objetivo ajustar modelos de regressão quantílica não linear para o estudo do acúmulo de matéria seca total em plantas de alho ao longo do tempo, e compará-los com modelos ajustados pelo método dos mínimos quadrados. A matéria seca total de nove acessos de alho pertencentes ao Banco de Germoplasma de Hortaliças da Universidade Federal de Viçosa (BGH/UFV) foi avaliada em quatro períodos (60, 90, 120 e 150 dias após plantio), e estes valores foram utilizados para o ajuste de modelos de regressão - não linear - logística. Para cada acesso, foram ajustados um modelo de regressão quantílica (τ=0,5) e um modelo pela metodologia dos mínimos quadrados. Para avaliar a qualidade de ajuste dos modelos foi utilizado o Critério de Informação de Akaike. Os acessos foram agrupados pelo algoritmo UPGMA, utilizando as estimativas dos parâmetros com interpretação biológica como variáveis. A regressão quantílica não linear foi eficiente no ajuste de modelos para descrição do acúmulo de matéria seca ao longo do tempo. As estimativas de parâmetros foram mais uniformes e robustas na presença de dados assimétricos, variâncias heterogêneas e de valores discrepantes.

A two decades study on ozone variability and trend over the main urban areas of the São Paulo state, Brazil.

In this paper, we analyze the variability of the ozone concentration over São Paulo Macrometropolis, as well the factors, which determined the tendency observed in the last two decades. Time series of hourly ozone concentrations measured at 16 automated stations from an air quality network from 1996 to 2017 were analyzed. The temporal variability of ozone concentrations exhibits well-defined daily and seasonal patterns. Ozone presents a significant positive correlation between the number of cases (thresholds of 100-160 µg m-3) and the fuel sales of gasohol and diesel. The ozone concentrations do not exhibit significant long-term trends, but some sites present positive trends that occurs in sites in the proximity of busy roads and negative trends that occurs in sites located in residential areas or next to trees. The effect of atmospheric process of transport and ozone formation was analyzed using a quantile regression model (QRM). This statistical model can deal with the nonlinearities that appear in the relationship of ozone and other variables and is applicable to time series with non-normal distribution. The resulting model explains 0.76% of the ozone concentration variability (with global coefficient of determination R1 = 0.76) providing a better representation than an ordinary least square regression model (with coefficient of determination R2 = 0.52); the effect of radiation and temperature are the most critical in determining the highest ozone quantiles.

Patterns and trends in the intake distribution of manufactured and homemade sugar-sweetened beverages in pre-tax Mexico, 1999-2012.

OBJECTIVE: To describe trends across the intake distribution of total, manufactured and homemade sugar-sweetened beverages (SSB) from 1999 to 2012, focusing on high SSB consumers and on changes by socio-economic status (SES) subgroup. DESIGN: We analysed data from one 24 h dietary recall from two nationally representative surveys. Quantile regression models at the 50th, 75th and 90th percentiles of energy intake distribution of SSB were used. SETTING: 1999 Mexican National Nutrition Survey and 2012 Mexican National Health and Nutrition Survey.ParticipantsSchool-aged children (5-11 years) and women (20-49 years) for trend analyses (n 7718). Population aged >1 year for 2012 (n 10 096). RESULTS: Over the 1999-2012 period, there were significant increases in the proportion of total and manufactured SSB consumers (5·7 and 10·7 percentage points), along with an increase in per-consumer SSB energy intake, resulting in significant increases in per-capita total SSB energy intake (142, 247 and 397 kJ/d (34, 59 and 95 kcal/d) in school-aged children and 155, 331 and 456 kJ/d (37, 79 and 109 kcal/d) in women at the 50th, 75th and 90th percentile, respectively). Total and manufactured SSB intakes increased sharply among low-SES children but remained similar among high-SES children during this time span. CONCLUSIONS: Large increases in SSB consumption were seen between 1999 and 2012 during this pre-tax SSB period, particularly for the highest consumers. Trends observed in school-aged children are a clear example of the nutrition transition experienced in Mexico. Policies to discourage high intake of manufactured SSB should continue, joined with strategies to encourage water and low-calorie beverage consumption.

The Unequal Impact of Food Insecurity on Cognitive and Behavioral Outcomes Among 5-Year-Old Urban Children.

OBJECTIVE: To examine the associations of food insecurity with children's cognitive and behavioral outcomes using quantile regression. DESIGN: Secondary analysis of the Fragile Families and Child Wellbeing Study dataset. PARTICIPANTS: A total of 2,046 children aged 5 years. MAIN OUTCOME MEASURES: Child behavioral outcomes were measured using externalizing (aggressive) and internalizing (emotional) behavior problems. Child cognitive outcomes were measured using the Peabody Vocabulary test and the Woodcock-Johnson letter-word identification test. Food insecurity was measured using the US Department of Agriculture's Food Security Module. ANALYSIS: Unconditional quantile regressions were employed. Statistical significance was set at P ≤ .05. RESULTS: Negative associations between food insecurity and child behavior problems (externalizing and internalizing) were largest for children with the most behavior problems. For Peabody Vocabulary scores, the negative association with food insecurity was statistically significant only for children in the top half of the distribution (≥50th percentile). The analysis found mixed evidence of an association between food insecurity and the Woodcock-Johnson letter-word identification test. These associations were similar for boys and girls. CONCLUSIONS AND IMPLICATIONS: Because children's cognitive skills and behavioral problems have long-lasting implications and effects later in life, reducing the risk of food insecurity might particularly benefit children with greater externalizing and internalizing behavior problems.