Background: The univentricular heart with a predominant right ventricle morphology (uRV) has been associated with a higher rate of adverse cardiovascular events. It remains to be determined whether the specific type of uRV influences outcomes. Methods and Results: A North American multicenter retrospective cohort study was conducted by the Alliance for Adult Research in Congenital Cardiology on individuals with a uRV and total cavopulmonary connection Fontan. The incidence of a composite outcome consisting of all‐cause mortality, cardiac transplantation, atrial arrhythmias, or thromboembolic events was compared among patients with Fontan palliation who had hypoplastic left heart syndrome (HLHS) versus other forms of uRV (non‐HLHS). All components of the composite outcome were classified by a blinded adjudicating committee. Competing risks were taken into account in time‐to‐event analyses. A total of 171 patients with uRV of whom 76 (44.4%) had HLHS were followed for 10.2±5.7 years. The composite outcome occurred in 7.1 versus 2.1 cases per 100 person‐years in patients with HLHS versus non‐HLHS ( P <0.0001). In multivariable analyses, HLHS was associated with a significantly higher risk of the composite outcome (hazard ratio [HR], 6.13 [95% CI, 2.92–12.69], P <0.001). Moreover, HLHS was associated with significantly higher rates of all components of the primary outcome. Conclusions: Among patients with a uRV and Fontan palliation, HLHS is associated with a significantly higher rate of adverse cardiovascular events.

Most existing flexible count distributions allow only approximate inference when used in a regression context. This work proposes a new framework to provide an exact and flexible alternative for modeling and simulating count data with various types of dispersion (equi-, under-, and over-dispersion). The new method, referred to as “balanced discretization”, consists of discretizing continuous probability distributions while preserving expectations. It is easy to generate pseudo random variates from the resulting balanced discrete distribution since it has a simple stochastic representation (probabilistic rounding) in terms of the continuous distribution. For illustrative purposes, we develop the family of balanced discrete gamma distributions that can model equi-, under-, and over-dispersed count data. This family of count distributions is appropriate for building flexible count regression models because the expectation of the distribution has a simple expression in terms of the parameters of the distribution. Using the Jensen–Shannon divergence measure, we show that under the equidispersion restriction, the family of balanced discrete gamma distributions is similar to the Poisson distribution. Based on this, we conjecture that while covering all types of dispersions, a count regression model based on the balanced discrete gamma distribution will allow recovering a near Poisson distribution model fit when the data are Poisson distributed.

Agrarian systems dynamics of shea trees ( Vitellaria paradoxa Gaertn) parklands in Northern Benin

Most existing flexible count regression models allow only approximate inference. This work proposes a new framework to provide an exact and flexible alternative for modeling and simulating count data with various types of dispersion (equi-, under- and overdispersion). The new method, referred as “balanced discretization”, consists in discretizing continuous probability distributions while preserving expectations. It is easy to generate pseudo random variates from the resulting balanced discrete distribution since it has a simple stochastic representation in terms of the continuous distribution. For illustrative purposes, we have developed the family of balanced discrete gamma distributions which can model equi-, under- and overdispersed count data. This family of count distributions is appropriate for building flexible count regressionmodels because the expectation of the distribution has a simple expression in terms of the parameters of the distribution. Using the Jensen–Shannon divergence measure, we have shown that under equidispersion restriction, the family of balanced discrete gamma distributions is similar to the Poisson distribution. Based on this, we conjecture that while covering all types of dispersion, a count regression model based on the balanced discrete gamma distribution will allow recovering a near Poisson distribution model fit when the data is Poisson distributed.

BACKGROUND: Protein leverage (PL), the phenomenon of food consuming until absolute intake of protein meets a target value, regardless of shortfall or overconsuming for other nutrients in the diet and total energy intake (TEI). Evidence for PL was observed in humans, recently in a cohort of youth with obesity. This study aimed to test for PL and the protein leverage hypothesis (PLH) in a cohort of older adults. METHODS: We conducted a retrospective analysis of dietary intake in a cohort of 1699 community-dwelling older adults aged 67-84 years from the NuAge cohort. We computed TEI and the energy contribution (EC) from each macronutrient. The strength of leverage of macronutrients was assessed through power functions ( TEI = µ * EC ^L ). Body mass index (BMI) was calculated, and mixture models were fitted to predict TEI and BMI from macronutrient ECs. RESULTS: The mean TEI was 7,673 kJ and macronutrient ECs were 50.4 %, 33.2 % and 16.4 %, respectively for carbohydrates, fat, and protein. High carbohydrate intake was associated with low fat intake. There was a strong negative association ( L = -0.37; p < 0.001) between the protein EC and TEI. Each percent of energy intake from protein reduced TEI by 77 kJ on average, ceteris paribus . BMI was unassociated with TEI in this cohort, so the PLH could not be tested here. CONCLUSIONS: Findings indicate clear evidence for PL on TEI, but not on BMI, likely because TEI and BMI become increasingly uncoupled during aging.