1,721,089 research outputs found
Understanding socio-economic inequalities in childhood respiratory health
Get PDFAsthma is the most common chronic disease of childhood. Recent evidence has shown a socio-economic gradient in its distribution. This paper examines whether a number of factors argued to have led to a rise in the incidence of asthma might also explain the social gradient. Several of these have been the object of policy intervention, though not necessarily with the aim of lowering childhood respiratory conditions. Using a large cohort study (the Avon Longitudinal Study of Parents and Children) we find significant inequalities in three respiratory conditions in middle childhood. We investigate eight potential mediating factors: exposure to other children in infancy, child's diet, poor housing conditions, maternal smoking, parental history of asthma, poor child health at birth, maternal age at child's birth and local deprivation. We find that each of these alone typically explains a relatively modest part of each respiratory inequality, with child's diet, local deprivation and maternal smoking generally the most important. But taken together, the mediating factors account for a substantial part of the respiratory inequalities. So the socio-economic gradient appears to operate through a number of inter-correlated pathways, some of which may be amenable to policy intervention.Asthma, wheeze, socio-economic inequalities, mediating
Multiple-test procedures and smile plots
Get PDFmultproc carries out multiple-test procedures, taking as input a list of p-values and an uncorrected critical p-value, and calculating a corrected overall critical p-value for rejection of null hypotheses. These procedures define a confidence region for a set-valued parameter, namely the set of null hypotheses that are true. They aim to control either the family-wise error rate (FWER) or the false discovery rate (FDR) at a level no greater than the uncorrected critical p-value. smileplot calls multproc and then creates a smile plot, with data points corresponding to estimated parameters, the p-values (on a reverse log scale) on the y-axis, and the parameter estimates (or another variable) on the x-axis. There are y-axis reference lines at the uncorrected and corrected overall critical p-values. The reference line for the corrected overall critical p-value, known as the parapet line, is an informal “upper confidence limit” for the set of null hypotheses that are true and defines a boundary between data mining and data dredging. A smile plot summarizes a set of multiple analyses just as a Cochrane forest plot summarizes a meta-analysis
Multiple-test procedures and smile plots
No full textmultproc carries out multiple-test procedures, taking as input a list of p-values and an uncorrected critical p-value, and calculating a corrected overall critical pvalue for rejection of null hypotheses. These procedures define a conÞdence region for a set-valued parameter, namely the set of null hypotheses that are true. They aim to control either the family-wise error rate (FWER) or the false discovery rate (FDR) at a level no greater than the uncorrected critical p-value. smileplot calls multproc and then creates a smile plot, with data points corresponding to estimated parameters, the p-values (on a reverse log scale) on the y-axis, and the parameter estimates (or another variable) on the x-axis. There are y-axis reference lines at the uncorrected and corrected overall critical p-values. The reference line for the corrected overall critical p-value, known as the parapet line, is an informal Òupper confidence limitÓ for the set of null hypotheses that are true and defines a boundary between data mining and data dredging. A smile plot summarizes a set of multiple analyses just as a Cochrane forest plot summarizes a meta-analysis. Copyright 2003 by Stata Corporation.smile plot, multiple-test procedure, closed testing procedure, data mining, family-wise error rate, false discovery rate, Bonferroni, Sidak, Holm, Holland, Copenhaver, Hochberg, Rom, Simes, Benjamini, Yekutieli, Krieger, Liu
On the central role of Somers' D
Get PDFSomers' D and Kendall's tau-a are parameters behind rank or nonparametric statistics, interpreted as differences between proportions. Given two bivariate data pairs (X1, Y1) and (X2, Y2), Kendall’s tau-a parameter tau-XY is the difference between the probability that the two X–Y pairs are concordant and the probability that the two X–Y pairs are discordant, and Somers' D parameter DYX is the difference between the corresponding conditional probabilities, given that the X-values are ordered. The somersd package computes confidence intervals for both parameters. The Stata 9 version of somersd uses Mata to increase computing speed and greatly extends the definition of Somers' D, allowing the X and/or Y variables to be left- or right-censored and allowing multiple versions of Somers' D for multiple sampling schemes for the X–Y pairs. In particular, we may define stratified versions of Somers' D, in which we compare only X–Y pairs from the same stratum. The strata may be defined by grouping a Rubin–Rosenbaum propensity score, based on the values of multiple confounders for an association between exposure variable X and an outcome variable Y . Therefore, rank statistics can have not only confidence intervals but also confounder-adjusted confidence intervals. Usually, we either estimate DYX as a measure of the effect of X on Y , or we estimate DXY as a measure of the performance of X as a predictor of Y, compared with other predictors. Alternative rank-based measures of the effect of X on Y include the Hodges–Lehmann median difference and the Theil–Sen median slope, both of which are defined in terms of Somers' D.
Child abuse registration, fetal growth, and preterm birth: a population based study
Get PDFObjectives: To study the relation of intra-uterine growth and gestational age with child protection registration in a 20 year whole population birth cohort.
Setting: West Sussex area of England.
Study design: Retrospective whole population birth cohort.
Outcomes: Child protection registration; individual categories of registration—sexual abuse, physical abuse, emotional abuse, and neglect.
Population and participants: 119 771 infants born in West Sussex between January 1983 and December 2001 with complete data including birth weight, gestational age, maternal age, and postcode.
Results: In all categories of registration a linear trend was noted such that the lower the birth weight z score the higher the likelihood of child protection registration. Similar trends were noted for gestational age. All these trends were robust to adjustment for maternal age and socioeconomic status.
Conclusions: The results of this study suggest that lower levels of fetal growth and shorter gestational duration are associated with increased likelihood of child protection registration in all categories including sexual abuse independent of maternal age or socioeconomic status. This study does not permit comment on whether poor fetal growth or preterm birth predispose to child abuse and neglect or the association arises because they share a common pathway
Identification of the obese child: adequacy of the body mass index for clinical practice and epidemiology
No full textSwimming and birth weight
No full textBackground. Swimmers can be exposed to high levels of trihalomethanes, byproducts of chlorination disinfection. There are no published studies on the relation between swimming and birth weight. We explored this relation in a large birth cohort, the Avon (England) Longitudinal Study of Parents and Children (ALSPAC), in 1991-1992. Methods. Information on the amount of swimming per week during the first 18-20 weeks of pregnancy was available for 11,462 pregnant women. Fifty-nine percent never swam, 31% swam up to 1 hour per week, and 10% swam for longer. We used linear regression to explore the relation between birth weight and the amount of swimming, with adjustment for gestational age, maternal age, parity, maternal education level, ethnicity, housing tenure, drug use, smoking and alcohol consumption. Results. We found little effect of the amount of swimming on birth weight. More highly educated women were more likely to swim compared with less educated women, whereas smokers were less likely to swim compared with nonsmokers. Conclusions. There appears to be no relation between the duration of swimming and birth weight.Background. Swimmers can be exposed to high levels of trihalomethanes, byproducts of chlorination disinfection. There are no published studies on the relation between swimming and birth weight. We explored this relation in a large birth cohort, the Avon (England) Longitudinal Study of Parents and Children (ALSPAC), in 1991-1992. Methods. Information on the amount of swimming per week during the first 18-20 weeks of pregnancy was available for 11,462 pregnant women. Fifty-nine percent never swam, 31% swam up to 1 hour per week, and 10% swam for longer. We used linear regression to explore the relation between birth weight and the amount of swimming, with adjustment for gestational age, maternal age, parity, maternal education level, ethnicity, housing tenure, drug use, smoking and alcohol consumption. Results. We found little effect of the amount of swimming on birth weight. More highly educated women were more likely to swim compared with less educated women, whereas smokers were less likely to swim compared with nonsmokers. Conclusions. There appears to be no relation between the duration of swimming and birth weight
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