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Over 70 million individuals are infected with hepatitis C virus (HCV) worldwide. Yet most prevalence data are in the adult population, with little focus on paediatrics, partially due to the scarcity of public data. The objective of this paper is to examine HCV prevalence in children by estimating prevalence rates among women, given the assumption that most cases are vertically transmitted. Between 2001 and 2017, maternal HCV infection affected ~ 0.24% of all births, with prevalence increasing by at least 261%. On average, approximately 0.01% of the total number of live births were infected with HCV, with a 245% increase in the number of children born with the infection. HCV epidemiology has evolved, with women of childbearing age representing a greater proportion of infected individuals in the United States, and infants born to infected mothers being at risk. We therefore recommend a greater public health focus of HCV on the paediatric population. [ABSTRACT FROM AUTHOR]

Classical threshold models assume that threshold values are constant and stable, which appears overly restrictive and unrealistic. In this article, we extend Hansen's (2000) constant threshold regression model by allowing for a time‐varying threshold which is approximated by a Fourier function. Least‐square estimation of regression slopes and the time‐varying threshold is proposed, and test statistics for the existence of threshold effect and threshold constancy are constructed. We also develop the asymptotic distribution theory for the time‐varying threshold estimator. Through Monte Carlo simulations, we show that the proposed estimation and testing procedures work reasonably well in finite samples, and there is little efficiency loss by the allowance for Fourier approximation in the estimation procedure even when there is no time‐varying feature in the threshold. On the contrary, the slope estimates are seriously biased when the threshold is time‐varying but being treated as a constant. The model is illustrated with an empirical application to a nonlinear Taylor rule for the United States. [ABSTRACT FROM AUTHOR]

Rates of change in final summer densities of two desert annuals, Eriogonum abertianum and Haplopappus gracilis , as constrained by their initial winter germination densities were estimated with regression quantiles and compared with mechanistic fits based on a self-thinning rule proposed by Guo et al. (1998); Oikos 83: 237–245). The allometric relation used was equivalent to S =N f (N i )-1=c f (N i )-1, where S is the ratio of final to initial densities (survivorship), c f is a constant that is a final density specific to the species and environment, N i is the initial plant density, and N f is final plant density. We used regression quantiles to estimate c f assuming the exponent of -1 was fixed (model 1, N f (N i )-1=c f (N i )-1) and also obtained estimates by treating the exponent as a parameter to estimate (model 2, N f (N i )-1=c f (N i )λ). Regression quantiles allow rates of change to be estimated through any part of a data distribution conditional on some linear function of covariates. We focused on estimates for upper (90–99th) quantiles near the boundary of the summer density distributions where we expected effects of self-thinning to operate as the primary constraint on plant performance. Allometric functions estimated with regression quantiles were similar to functions fit by Guo et al. (1998) when the exponent was constrained to -1. However, the data were more consistent with estimates for model (2), where exponents were closer to -0.4 than -1, although model fit was not as good at higher initial plant densities as when the exponent was fixed at -1. An exponential form (model 3, N f (N i )-1=c f (N i )λ eγNi) that is a generalization of the discrete logistic growth function, where estimates of λ were -0.23 to -0.28 and estimates of γ were -0.003 to -0.006, provided better fit from low to high initial germination densities. Model 3 predictions were consistent with an interpretation that final summer densities were constrained by initial germination densities when these were low (<40 per 0.25 m2 for Eriogonum and <100 per 0.25 m2 for Haplopappus ) and were constrained by the self-thinning process at higher germination densities. Our exponential model (3) estimated with regression quantiles had similar form to the mechanistic relation of Guo et al. (1998) when plotted as a survivorship function, but avoided the unrealistic assumption that all populations attained a similar final density, and was based on a statistical model that has formal rules for estimation and inference. [ABSTRACT FROM AUTHOR]