Poverty, Educational Attainment and Health Among America’s Children: Current and Future Effects of Population Diversification and Associated Socioeconomic Change

A complex of interrelated factors including minority status, poverty, education, health status, and other factors determine the general welfare of children in America, particularly in heavily diverse states such as Texas. Although racial/ethnic status is clearly only a concomitant factor in that determination it is a factor for which future projections are available and for which the relationships with the other factors in the complex can be assessed. After examining the nature of the interrelationships between these factors we utilize direct standardization techniques to examine how the future diversification of the United States and Texas will affect the number of children in poverty, the educational status of the householders in households in which children in poverty live and the health status of children in 2040 assuming that the current relationships between minority status and these socioeconomic factors continue into the future. In the results of the analyses, data are compared with the total population of the United States and Texas in 2040 assumed in the first simulation scenario, to have the race/ethnicity characteristics of 2008 and in the second those projected for 2040 by the U.S. Census Bureau for the nation and by the Texas State Data Center for Texas in 2040. The results show that the diversification of the population could increase the number of children in poverty in the United States by nearly 1.8 million more than would occur with the lower levels of diversification evident in 2008. In addition, poverty would become increasingly concentrated among minority children with minority children accounting for 76.2 percent of all children in poverty by 2040 and with Hispanic children accounting for nearly half of the children in poverty by 2040. Results for educational attainment show an increasing concentration of minority children in households with householders with very low levels of education such that by 2040, 85.2 percent of the increase in the number of children in poverty would be in households with a householder with less than a high school level of education. Finally, the results related to several health status factors show that children in poverty will have a higher prevalence of nearly all health conditions. For example, the number of children with untreated dental conditions could increase to more than 4 million in the United States and to nearly 500,000 in Texas. The results clearly show that improving the welfare of children in America will require concerted efforts to change the poverty, educational, and health status characteristics associated with minority status and particularly Hispanic status. Failing to do so will lead to a future in which America’s children are increasingly impoverished, more poorly educated, and less healthy and which, as a result, is an America with a more tentative future.

Estimating the mean and standard deviation of concentration distributions using censored samples

Environmental data sets of pollutant concentrations in air, water, and soil frequently include unquantified sample values reported only as being below the analytical method detection limit. These values, referred to as censored values, should be considered in the estimation of distribution parameters as each represents some value of pollutant concentration between zero and the detection limit. Most of the currently accepted methods for estimating the population parameters of environmental data sets containing censored values rely upon the assumption of an underlying normal (or transformed normal) distribution. This assumption can result in unacceptable levels of error in parameter estimation due to the unbounded left tail of the normal distribution. With the beta distribution, which is bounded by the same range of a distribution of concentrations, $\rm\lbrack0\le x\le1\rbrack,$ parameter estimation errors resulting from improper distribution bounds are avoided. This work developed a method that uses the beta distribution to estimate population parameters from censored environmental data sets and evaluated its performance in comparison to currently accepted methods that rely upon an underlying normal (or transformed normal) distribution. Data sets were generated assuming typical values encountered in environmental pollutant evaluation for mean, standard deviation, and number of variates. For each set of model values, data sets were generated assuming that the data was distributed either normally, lognormally, or according to a beta distribution. For varying levels of censoring, two established methods of parameter estimation, regression on normal ordered statistics, and regression on lognormal ordered statistics, were used to estimate the known mean and standard deviation of each data set. The method developed for this study, employing a beta distribution assumption, was also used to estimate parameters and the relative accuracy of all three methods were compared. For data sets of all three distribution types, and for censoring levels up to 50%, the performance of the new method equaled, if not exceeded, the performance of the two established methods. Because of its robustness in parameter estimation regardless of distribution type or censoring level, the method employing the beta distribution should be considered for full development in estimating parameters for censored environmental data sets. ^

POPULATION ASSESSMENTS OF INFECTION WITH SCHISTOSOMA MANSONI: EFFECTS OF BIAS CAUSED BY THE RELATIVE SENSITIVITY OF DIAGNOSTIC TECHNIQUES (PUERTO RICO)

This study establishes the extent and relevance of bias of population estimates of prevalence, incidence, and intensity of infection with Schistosoma mansoni caused by the relative sensitivity of stool examination techniques. The population studied was Parcelas de Boqueron in Las Piedras, Puerto Rico, where the Centers for Disease Control, had undertaken a prospective community-based study of infection with S. mansoni in 1972. During each January of the succeeding years stool specimens from this population were processed according to the modified Ritchie concentration (MRC) technique. During January 1979 additional stool specimens were collected from 30 individuals selected on the basis of their mean S. mansoni egg output during previous years. Each specimen was divided into ten 1-gm aliquots and three 42-mg aliquots. The relationship of egg counts obtained with the Kato-Katz (KK) thick smear technique as a function of the mean of ten counts obtained with the MRC technique was established by means of regression analysis. Additionally, the effect of fecal sample size and egg excretion level on technique sensitivity was evaluated during a blind assessment of single stool specimen samples, using both examination methods, from 125 residents with documented S. mansoni infections. The regression equation was: Ln KK = 2.3324 + 0.6319 Ln MRC, and the coefficient of determination (r('2)) was 0.73. The regression equation was then utilized to correct the term "m" for sample size in the expression P ((GREATERTHEQ) 1 egg) = 1 - e('-ms), which estimates the probability P of finding at least one egg as a function of the mean S. mansoni egg output "m" of the population and the effective stool sample size "s" utilized by the coprological technique. This algorithm closely approximated the observed sensitivity of the KK and MRC tests when these were utilized to blindly screen a population of known parasitologic status for infection with S. mansoni. In addition, the algorithm was utilized to adjust the apparent prevalence of infection for the degree of functional sensitivity exhibited by the diagnostic test. This permitted the estimation of true prevalence of infection and, hence, a means for correcting estimates of incidence of infection. ^