Global estimation of child mortality using a Bayesian B-spline Bias-reduction model

Estimates of the under-five mortality rate (U5MR) are used to track progress in reducing child mortality and to evaluate countries' performance related to Millennium Development Goal 4. However, for the great majority of developing countries without well-functioning vital registration systems, estimating the U5MR is challenging due to limited data availability and data quality issues. We describe a Bayesian penalized B-spline regression model for assessing levels and trends in the U5MR for all countries in the world, whereby biases in data series are estimated through the inclusion of a multilevel model to improve upon the limitations of current methods. B-spline smoothing parameters are also estimated through a multilevel model. Improved spline extrapolations are obtained through logarithmic pooling of the posterior predictive distribution of country-specific changes in spline coefficients with observed changes on the global level. The proposed model is able to flexibly capture changes in U5MR over time, gives point estimates and credible intervals reflecting potential biases in data series and performs reasonably well in out-of-sample validation exercises. It has been accepted by the United Nations Inter-agency Group for Child Mortality Estimation to generate estimates for all member countries.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS768 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Probabilistic projections of HIV prevalence using Bayesian melding

The Joint United Nations Programme on HIV/AIDS (UNAIDS) has developed the Estimation and Projection Package (EPP) for making national estimates and short-term projections of HIV prevalence based on observed prevalence trends at antenatal clinics. Assessing the uncertainty about its estimates and projections is important for informed policy decision making, and we propose the use of Bayesian melding for this purpose. Prevalence data and other information about the EPP model's input parameters are used to derive a probabilistic HIV prevalence projection, namely a probability distribution over a set of future prevalence trajectories. We relate antenatal clinic prevalence to population prevalence and account for variability between clinics using a random effects model. Predictive intervals for clinic prevalence are derived for checking the model. We discuss predictions given by the EPP model and the results of the Bayesian melding procedure for Uganda, where prevalence peaked at around 28% in 1990; the 95% prediction interval for 2010 ranges from 2% to 7%.Comment: Published at http://dx.doi.org/10.1214/07-AOAS111 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Probabilistic projections of HIV prevalence using Bayesian melding

The Joint United Nations Programme on HIV/AIDS (UNAIDS) has developed the Estimation and Projection Package (EPP) for making national estimates and short-term projections of HIV prevalence based on observed prevalence trends at antenatal clinics. Assessing the uncertainty about its estimates and projections is important for informed policy decision making, and we propose the use of Bayesian melding for this purpose. Prevalence data and other information about the EPP model's input parameters are used to derive a probabilistic HIV prevalence projection, namely a probability distribution over a set of future prevalence trajectories. We relate antenatal clinic prevalence to population prevalence and account for variability between clinics using a random effects model. Predictive intervals for clinic prevalence are derived for checking the model. We discuss predictions given by the EPP model and the results of the Bayesian melding procedure for Uganda, where prevalence peaked at around 28% in 1990; the 95% prediction interval for 2010 ranges from 2% to 7%.Comment: Published at http://dx.doi.org/10.1214/07-AOAS111 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Constitutional Law - Due Process - Scope of Inquiry in Habeas Corpus Petitions from Military Prisoner

Petitioners, military personnel, were convicted by courts martial of rape and murder. After exhausting military appellate remedies, they petitioned federal civil courts for writs of habeas corpus on the ground that they had been convicted in proceedings denying them basic constitutional rights. The petitions were denied. On appeal, held, affirmed, two justices dissenting. When the record shows that military courts have fairly considered all of the allegations of the petitioners and have found no denial of constitutional rights, civil courts in habeas corpus proceedings will not hear evidence on the merits of the allegations. Burns v. Wilson, 346 U.S. 137, 73 S.Ct. 1045 (1953)

REAL PROPERTY-RECORDING--LATENT DEFECT IN ACKNOWLEDGMENT OF DEED

In 1947 A executed a deed to B, which was recorded in July 1951. On May 7, 1951 A executed a deed of the same property to C, who did not record. C\u27s deed bore a certificate of acknowledgment, but in fact the acknowledgment was taken by telephone and not in person as required by law to make it eligible for record. On May 9, 1951 C conveyed to D, who recorded both conveyances in his chain of title on May 26, 1951. In an action by B to quiet title, held, for B. Since the deed from A to C was not properly acknowledged, the deed was not entitled to record, and its actual record was not notice to D of its execution. Therefore, D was not a purchaser in good faith and was not protected against the prior conveyance to B. Messersmith v. Smith, (N.D. 1953) 60 N.W. (2d) 276