Child Health and Developmental Problems and Child Maltreatment among AFDC Families

This paper explores the complex interrelationship among the physical health and developmental problems of a child, child abuse and neglect, and poverty. Gaps in agency attention to children\u27s medical needs are identified and recommendations made for reducing these gaps. The analysis is based on interview and agency data for 45 families randomly selected from a group of 365 AFDC recipient families under supervision for child abuse and neglect

Research Data as Aides in Formulating Agency Policy

Excerpt from the full-text article: Much is being written these days about the role of evaluation in the formulation of social policy. While few writers question the need for basing policy on systematic evaluation a good deal of the literature appears to focus on the obstacles in Larrying out as well as applying evaluative research. By contrast, the number of studies which in the eyes of critics measure up to minimum standards of scientific adequacy appears to be exceedingly small. Regardless of the problems inherent in the use of research data for policy formulation, the dearth of good studies constitutes the main reason why social policy is made, by and large, without reference to information secured with the aid of systematic research. The present paper endeavors to show how a set of empirical data, collected at four casework agencies, can serve as aids in choosing among policy alternatives. The size of the sample and problems in design make this study a demonstration in the use of policy-relevant research rather than a substantive contribution to knowledge in agency policy formulation. The data were produced as part of an effort to evaluate the outcome of services to clients. Whereas the agency executives, who encouraged and supported the study, were mainly concerned with the results of services, the researchers in this study were of the opinion that evaluation of outcome extends beyond a determination of whether treatment was or was not helpful to most clients. Questions that loomed large pertained to differences in criteria of outcome, effectiveness of techniques of service, effect of client characteristics on outcome, and others. Evaluation, in this study, was intended to encompass several areas of concern to agency decision-makers

SimEngine: A Modular Framework for Statistical Simulations in R

This article describes SimEngine, an open-source R package for structuring, maintaining, running, and debugging statistical simulations on both local and cluster-based computing environments. Several R packages exist for structuring simulations, but SimEngine is the only package specifically designed for running simulations in parallel via job schedulers on high-performance cluster computing systems. The package provides structure and functionality for common simulation tasks, such as setting simulation levels, managing seeds for random number generation, and calculating summary metrics (such as bias and confidence interval coverage). SimEngine also brings several unique features, such as automatic calculation of Monte Carlo error and information-sharing across simulation replicates. We provide an overview of the package and demonstrate some of its advanced functionality

Inferring HIV incidence trends and transmission dynamics with a spatio-temporal HIV epidemic model

Reliable estimation of spatio-temporal trends in population-level HIV incidence is becoming an increasingly critical component of HIV prevention policy-making. However, direct measurement is nearly impossible. Current, widely used models infer incidence from survey and surveillance seroprevalence data, but they require unrealistic assumptions about spatial independence across spatial units. In this study, we present an epidemic model of HIV that explicitly simulates the spatial dynamics of HIV over many small, interacting areal units. By integrating all available population-level data, we are able to infer not only spatio-temporally varying incidence, but also ART initiation rates and patient counts. Our study illustrates the feasibility of applying compartmental models to larger inferential problems than those to which they are typically applied, as well as the value of data fusion approaches to infectious disease modeling.Comment: 28 pages, 9 figures, submitted to Epidemics

Nonparametric variable importance for time-to-event outcomes with application to prediction of HIV infection

In survival analysis, complex machine learning algorithms have been increasingly used for predictive modeling. Given a collection of features available for inclusion in a predictive model, it may be of interest to quantify the relative importance of a subset of features for the prediction task at hand. In particular, in HIV vaccine trials, participant baseline characteristics are used to predict the probability of infection over the intended follow-up period, and investigators may wish to understand how much certain types of predictors, such as behavioral factors, contribute toward overall predictiveness. Time-to-event outcomes such as time to infection are often subject to right censoring, and existing methods for assessing variable importance are typically not intended to be used in this setting. We describe a broad class of algorithm-agnostic variable importance measures for prediction in the context of survival data. We propose a nonparametric efficient estimation procedure that incorporates flexible learning of nuisance parameters, yields asymptotically valid inference, and enjoys double-robustness. We assess the performance of our proposed procedure via numerical simulations and analyze data from the HVTN 702 study to inform enrollment strategies for future HIV vaccine trials.Comment: 91 total pages (31 main text, 60 supplementary); 14 total figures (4 main text, 10 supplementary

Evaluating distributional regression strategies for modelling self-reported sexual age-mixing

The age dynamics of sexual partnership formation determine patterns of sexually transmitted disease transmission and have long been a focus of researchers studying human immunodeficiency virus. Data on self-reported sexual partner age distributions are available from a variety of sources. We sought to explore statistical models that accurately predict the distribution of sexual partner ages over age and sex. We identified which probability distributions and outcome specifications best captured variation in partner age and quantified the benefits of modelling these data using distributional regression. We found that distributional regression with a sinh-arcsinh distribution replicated observed partner age distributions most accurately across three geographically diverse data sets. This framework can be extended with well-known hierarchical modelling tools and can help improve estimates of sexual age-mixing dynamics