Association of citrulline concentration at birth with lower respiratory tract infection in infancy: Findings from a multi-site birth cohort study

Assessing the association of the newborn metabolic state with severity of subsequent respiratory tract infection may provide important insights on infection pathogenesis. In this multi-site birth cohort study, we identified newborn metabolites associated with lower respiratory tract infection (LRTI) in the first year of life in a discovery cohort and assessed for replication in two independent cohorts. Increased citrulline concentration was associated with decreased odds of LRTI (discovery cohort: aOR 0.83 [95% CI 0.70-0.99], p = 0.04; replication cohorts: aOR 0.58 [95% CI 0.28-1.22], p = 0.15). While our findings require further replication and investigation of mechanisms of action, they identify a novel target for LRTI prevention and treatment

Growth Dynamics of Dairy Processing Firms in the European Union

The structure of the dairy processing industry in the European Union has changed enormously in recent decades. In many countries the industry is characterized by a few large companies with a big market share accompanied by many small processors that often produce for niche markets. This paper investigates which factors relate to growth of dairy processing firms. Using a unique ten-year panel data set and recently developed dynamic panel data estimators, the growth process of dairy processors is investigated for six rather diverse European countries. The data structure and the estimation method allow for dealing with endogeneity issues in an appropriate way. Firm size growth measured in total assets is found to be affected by firm size, firm age and financial variables. Growth in number of employees is only affected by firm age and lagged labour productivity. Implications for these results are given in the final section of the paper

Spatial Distribution of Underweight, Overweight and Obesity among Women and Children: Results from the 2011 Uganda Demographic and Health Survey

While undernutrition and infectious diseases are still persistent in developing countries, overweight, obesity, and associated comorbidities have become more prevalent. Uganda, a developing sub-Saharan African country, is currently experiencing the public health paradox of undernutrition and overnutrition. We utilized the 2011 Uganda Demographic and Health Survey (DHS) to examine risk factors and hot spots for underweight, overweight, and obesity among adult females (N = 2,420) and their children (N = 1,099) using ordinary least squares and multinomial logit regression and the ArcGIS Getis-Ord Gi* statistic. Overweight and obese women were significantly more likely to have overweight children, and overweight was correlated with being in the highest wealth class (OR = 2.94, 95% CI = 1.99–4.35), and residing in an urban (OR = 1.76, 95% CI = 1.34–2.29) but not a conflict prone (OR = 0.48, 95% CI = 0.29–0.78) area. Underweight clustered significantly in the Northern and Northeastern regions, while overweight females and children clustered in the Southeast. We demonstrate that the DHS can be used to assess geographic clustering and burden of disease, thereby allowing for targeted programs and policies. Further, we pinpoint specific regions and population groups in Uganda for targeted preventive measures and treatment to reduce the burden of overweight and chronic diseases in Uganda

A new model of wheezing severity in young children using the validated ISAAC wheezing module: A latent variable approach with validation in independent cohorts

Background: The International Study of Asthma and Allergies in Children (ISAAC) Wheezing Module is commonly used to characterize pediatric asthma in epidemiological studies, including nearly all airway cohorts participating in the Environmental Influences on Child Health Outcomes (ECHO) consortium. However, there is no consensus model for operationalizing wheezing severity with this instrument in explanatory research studies. Severity is typically measured using coarsely-defined categorical variables, reducing power and potentially underestimating etiological associations. More precise measurement approaches could improve testing of etiological theories of wheezing illness. Methods: We evaluated a continuous latent variable model of pediatric wheezing severity based on four ISAAC Wheezing Module items. Analyses included subgroups of children from three independent cohorts whose parents reported past wheezing: infants ages 0–2 in the INSPIRE birth cohort study (Cohort 1; n = 657), 6-7-year-old North American children from Phase One of the ISAAC study (Cohort 2; n = 2,765), and 5-6-year-old children in the EHAAS birth cohort study (Cohort 3; n = 102). Models were estimated using structural equation modeling. Results: In all cohorts, covariance patterns implied by the latent variable model were consistent with the observed data, as indicated by non-significant χ2 goodness of fit tests (no evidence of model misspecification). Cohort 1 analyses showed that the latent factor structure was stable across time points and child sexes. In both cohorts 1 and 3, the latent wheezing severity variable was prospectively associated with wheeze-related clinical outcomes, including physician asthma diagnosis, acute corticosteroid use, and wheeze-related outpatient medical visits when adjusting for confounders Conclusion: We developed an easily applicable continuous latent variable model of pediatric wheezing severity based on items from the well-validated ISAAC Wheezing Module. This model prospectively associates with asthma morbidity, as demonstrated in two ECHO birth cohort studies, and provides a more statistically powerful method of testing etiologic hypotheses of childhood wheezing illness and asthma

Descriptive statistics for ISAAC-wheezing module severity items.

<p>Descriptive statistics for ISAAC-wheezing module severity items.</p

Participant characteristics for all cohorts.

<p>Participant characteristics for all cohorts.</p

Latent variable model of wheezing illness severity.

<p>Panel <i>a</i> shows the latent wheezing severity model used in cohorts 1 and 3. The severity of wheezing illness is estimated as a unidimensional latent variable (η₁) with four reflective ordinal indicators: wheezing episode frequency (y₁), frequency of wheeze-related sleep disturbance (y₂), wheeze-related speech disturbance (y₃), and exercise-induced wheeze (y₄). The ordinal indicators are presumed to be coarse measurements of underlying continuous variables (y₁*-y₄*). Panel <i>b</i> shows the multilevel wheezing severity model used in the Cohort 2 analyses. The within-schools level of the model is identical to panel <i>a</i>. The between-schools level of the model accounts for non-independence due to clustering within schools and study sites. Estimated parameters are depicted in red.</p

Cohort 1: Comparison of models testing stability in the latent wheezing severity factor across time and across child sex.

<p>Cohort 1: Comparison of models testing stability in the latent wheezing severity factor across time and across child sex.</p

Cohort 1: Concurrent associations between the latent wheezing severity factor and markers of wheezing illness in year-one.

<p>This figure shows estimated probabilities of having at least one respiratory hospitalization (panel <i>a;</i> model 1.3) and using asthma medication (panel <i>b</i>; model 1.5) against levels of the latent wheezing severity variable in the first year of life. As wheezing severity increases so does the estimated probability of respiratory hospitalization and medication use. These models held all covariates constant at their median values. Dotted lines represent 95% confidence intervals for estimated probability estimates.</p

Cohort 1: Year-3 estimated probability of acute corticosteroid treatment as a function of wheezing severity in year-2.

<p>These plots show the strength of associations between year-2 wheezing severity (x-axis) and year-3 acute corticosteroid treatment (y-axis), with all covariates held constant at their median values. Panels <i>a</i> shows estimated probabilities of corticosteroid treatment being present in the third year of life as a function of the discrete severity exposure variable; whereas panel <i>b</i> shows estimated probabilities vs. the latent continuous severity factor. In both models, as year-2 wheezing severity increases, so does the estimated probability of acute corticosteroid treatment, though the range of estimated probabilities is larger in the latent severity model. Values and 95% confidence intervals above the blue brackets show the increase in the estimated probabilities for a given increase in wheezing severity. Dotted lines represent 95% confidence intervals for estimated probability estimates.</p