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      Childhood forecasting of a small segment of the population with large economic burden

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          Abstract

          <p class="first" id="P1">Policy-makers are interested in early-years interventions to ameliorate childhood risks. They hope for improved adult outcomes in the long run, bringing return on investment. How much return can be expected depends, partly, on how strongly childhood risks forecast adult outcomes. But there is disagreement about whether childhood determines adulthood. We integrated multiple nationwide administrative databases and electronic medical records with the four-decade Dunedin birth-cohort study to test child-to-adult prediction in a different way, by using a population-segmentation approach. A segment comprising one-fifth of the cohort accounted for 36% of the cohort’s injury insurance-claims; 40% of excess obese-kilograms; 54% of cigarettes smoked; 57% of hospital nights; 66% of welfare benefits; 77% of fatherless childrearing; 78% of prescription fills; and 81% of criminal convictions. Childhood risks, including poor age-three brain health, predicted this segment with large effect sizes. Early-years interventions effective with this population segment could yield very large returns on investment. </p>

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          Most cited references17

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          Cumulative risk and child development.

          Childhood multiple risk factor exposure exceeds the adverse developmental impacts of singular exposures. Multiple risk factor exposure may also explain why sociodemographic variables (e.g., poverty) can have adverse consequences. Most research on multiple risk factor exposure has relied upon cumulative risk (CR) as the measure of multiple risk. CR is constructed by dichotomizing each risk factor exposure (0 = no risk; 1 = risk) and then summing the dichotomous scores. Despite its widespread use in developmental psychology and elsewhere, CR has several shortcomings: Risk is designated arbitrarily; data on risk intensity are lost; and the index is additive, precluding the possibility of statistical interactions between risk factors. On the other hand, theoretically more compelling multiple risk metrics prove untenable because of low statistical power, extreme higher order interaction terms, low robustness, and collinearity among risk factors. CR multiple risk metrics are parsimonious, are statistically sensitive even with small samples, and make no assumptions about the relative strengths of multiple risk factors or their collinearity. CR also fits well with underlying theoretical models (e.g., Bronfenbrenner's, 1979, bioecological model; McEwen's, 1998, allostasis model of chronic stress; and Ellis, Figueredo, Brumbach, & Schlomer's, 2009, developmental evolutionary theory) concerning why multiple risk factor exposure is more harmful than singular risk exposure. We review the child CR literature, comparing CR to alternative multiple risk measurement models. We also discuss strengths and weaknesses of developmental CR research, offering analytic and theoretical suggestions to strengthen this growing area of scholarship. Finally, we highlight intervention and policy implications of CR and child development research and theory. © 2013 American Psychological Association
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            Comparing effect sizes in follow-up studies: ROC Area, Cohen's d, and r.

            In order to facilitate comparisons across follow-up studies that have used different measures of effect size, we provide a table of effect size equivalencies for the three most common measures: ROC area (AUC), Cohen's d, and r. We outline why AUC is the preferred measure of predictive or diagnostic accuracy in forensic psychology or psychiatry, and we urge researchers and practitioners to use numbers rather than verbal labels to characterize effect sizes.
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              Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker.

              M. S. Pepe (2004)
              A marker strongly associated with outcome (or disease) is often assumed to be effective for classifying persons according to their current or future outcome. However, for this assumption to be true, the associated odds ratio must be of a magnitude rarely seen in epidemiologic studies. In this paper, an illustration of the relation between odds ratios and receiver operating characteristic curves shows, for example, that a marker with an odds ratio of as high as 3 is in fact a very poor classification tool. If a marker identifies 10% of controls as positive (false positives) and has an odds ratio of 3, then it will correctly identify only 25% of cases as positive (true positives). The authors illustrate that a single measure of association such as an odds ratio does not meaningfully describe a marker's ability to classify subjects. Appropriate statistical methods for assessing and reporting the classification power of a marker are described. In addition, the serious pitfalls of using more traditional methods based on parameters in logistic regression models are illustrated.
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                Author and article information

                Journal
                Nature Human Behaviour
                Nat. hum. behav.
                Springer Nature
                2397-3374
                December 12 2016
                December 12 2016
                : 1
                : 1
                : 0005
                Article
                10.1038/s41562-016-0005
                5505663
                28706997
                a038721c-d05a-4c04-aab8-32686663299f
                © 2016
                History

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