Estimating percentile-specific treatment effects in counterfactual models: a case-study of micronutrient supplementation, birth weight and infant mortality

Summary.  Clinical trials of micronutrient supplementation are aimed at reducing the risk of infant mortality by increasing birth weight. Because infant mortality is greatest among the low birth weight (LBW) infants (2500 g or under), an effective intervention increases the birth weight among the smallest babies. The paper defines population and counterfactual parameters for estimating the treatment effects on birth weight and on survival as functions of the percentiles of the birth weight distribution. We use a Bayesian approach with data augmentation to approximate the posterior distributions of the parameters, taking into account uncertainty that is associated with the imputation of the counterfactuals. This approach is particularly suitable for exploring the sensitivity of the results to unverifiable modelling assumptions and other prior beliefs. We estimate that the average causal effect of the treatment on birth weight is 72 g (95% posterior regions 33–110 g) and that this causal effect is largest among the LBW infants. Posterior inferences about average causal effects of the treatment on birth weight are robust to modelling assumptions. However, inferences about causal effects for babies at the tails of the birth weight distribution can be highly sensitive to the unverifiable assumption about the correl-ation between the observed and the counterfactuals birth weights. Among the LBW infants who have a large causal effect of the treatment on birth weight, we estimate that a baby receiving the treatment has 5% less chance of death than if the same baby had received the control. Among the LBW infants, we found weak evidence supporting an additional beneficial effect of the treatment on mortality independent of birth weight.     

Formulating waste management strategies based on waste management practices of households in Santiago de Cuba, Cuba

Rapid urbanisation, population growth and changes in lifestyles in low- and middle-income countries contribute to increasing the per capita domestic waste generation. This trend leads to deplorable environmental and public health conditions, especially in rapidly expanding cities of low- and middle-income countries lacking appropriate waste management systems, Santiago de Cuba is no exception. To improve solid waste management in the city of Santiago de Cuba, the generation of household waste was studied and individual waste treatment approaches were assessed. The principle of a household level analysis was adopted to enable the development of strategies based on the specific conditions of households, their awareness and needs.In February 2004, a survey covering 1180 households was conducted on issues such as monthly consumed goods, waste generated and its treatment. The waste generated by the households was subsequently assessed by means of a measuring campaign. The measured volume not only comprised the collected waste fraction but also the waste materials treated on-site or recovered and recycled by all the different means available.The paper contains the results of the composition and distribution of the waste generated by the households as a function of socio-demographic data. Furthermore, the paper describes the various household treatment strategies dependent on specific waste material types. Several types of household waste can be differentiated by statistical cluster analysis. These household types are characterised not only by the amount of waste generated but also by socio-economic status and waste treatment practices. The results obtained are used to develop a range of waste management strategies, each relating to a household type and characterised by its generated amount, waste material type and spatial urban distribution. Since the strategies are adapted to the requirements and needs of the households, they are more readily accepted by the population concerned. Such socially anchored strategies will contribute to improving the waste situation in the city of Santiago de Cuba and allow an optimised allocation of local resources.     

A characterization of income distributions in terms of generalized Gini coefficients

Most commonly used parametric models for the size distribution of incomes possess only a few finite moments, and hence cannot be characterized by the sequence of their moments. However, all income distributions with a finite mean can be characterized by the sequence of first moments of the order statistics. This is an attractive feature since the generalized Gini coefficients of Kakwani (1980), Donaldson and Weymark (1980, 1983) and Yitzhaki (1983) are simple functions of expectations of sample minima. We present results which streamline these characterizations motivated by Aaberge (2000). Received: 8 March 2001/Accepted: 31 July 2001     

Survival analysis based on the proportional hazards model and survey data

The authors propose methods based on the stratified Cox proportional hazards model that account for the fact that the data have been collected according to a complex survey design. The methods they propose are based on the theory of estimating equations in conjunction with empirical process theory. The authors also discuss issues concerning ignorable sampling design, and the use of weighted and unweighted procedures. They illustrate their methodology by an analysis of jobless spells in Statistics Canada's Survey of Labour and Income Dynamics. They discuss briefly problems concerning weighting, model checking, and missing or mismeasured data. They also identify areas for further research.     

Influence on tests with focus on linear models

To assess the influence of single observations on the parameter estimates, case-deletion diagnostics are commonly used in linear regression models; one example is Cook's distance. For nested parametric models we consider a deletion diagnostic for evaluating the influence of a single observation on the likelihood ratio (LR) test. In order to have a common scale as reference, the asymptotic distribution of the diagnostic is derived and the values of the diagnostic are converted to percentiles. We focus on linear models and general linear models, and in these cases explicit results are derived. The performance of the diagnostic is explored in two small bench mark examples from linear regression and in a larger linear mixed model example.     

Régulation et compétition entre établissements scolaires dans six espaces locaux en Europe

According to this analysis of the dynamics of competition in six school zones in Europe (Charleroi, Budapest, Lisbon, London, Lille and the Paris area), secondary schools pursue “logics of action” that are shaped by an interdependence between local establishments. Parents’ strategies for choosing a school rank establishments and lead to a competitive interdependence with a pattern and intensity that vary depending on the place — the major issue being the capacity to attract and retain pupils. A typology of these “logics” is proposed: establishments pursue a logic of conquest, profit from the status quo, diversification or adaptation depending on their local position and the intensity of competition. Questions are raised about the potentials and limits of public interventions for regulating this competitive interdependence. Competition, along with its deviant effects (segregation and inequality), arises out of the shortcomings and limitations of public interventions and out of the fragmentation of local regulations. A lack of coordination between the authorities in charge, between districts and between the public and private supply of education affects the development of opportunistic tactics by certain establishments. The conclusion draws attention to the need for coordinating regulations.     

Estimations of the parameter of a Dirichlet distribution using residual allocation model representations and sampling properties

Estimating the parameter of a Dirichlet distribution is an interesting question since this distribution arises in many situations of applied probability. Classical procedures are based on sample of Dirichlet distribution. In this paper we exhibit five different estimators from only one observation. They are based either on residual allocation model decompositions or on sampling properties of Dirichlet distributions. Two ways are investigated: the first one uses fragments’ size and the second one uses size-biased permutations of a partition. Numerical computations based on simulations are supplied. The estimators are finally used to estimate birth probabilities per month.