Dataset Information

Hierarchical group testing for multiple infections.

ABSTRACT: Group testing, where individuals are tested initially in pools, is widely used to screen a large number of individuals for rare diseases. Triggered by the recent development of assays that detect multiple infections at once, screening programs now involve testing individuals in pools for multiple infections simultaneously. Tebbs, McMahan, and Bilder (2013, Biometrics) recently evaluated the performance of a two-stage hierarchical algorithm used to screen for chlamydia and gonorrhea as part of the Infertility Prevention Project in the United States. In this article, we generalize this work to accommodate a larger number of stages. To derive the operating characteristics of higher-stage hierarchical algorithms with more than one infection, we view the pool decoding process as a time-inhomogeneous, finite-state Markov chain. Taking this conceptualization enables us to derive closed-form expressions for the expected number of tests and classification accuracy rates in terms of transition probability matrices. When applied to chlamydia and gonorrhea testing data from four states (Region X of the United States Department of Health and Human Services), higher-stage hierarchical algorithms provide, on average, an estimated 11% reduction in the number of tests when compared to two-stage algorithms. For applications with rarer infections, we show theoretically that this percentage reduction can be much larger.

SUBMITTER: Hou P

PROVIDER: S-EPMC5362369 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Hierarchical group testing for multiple infections.

Hou Peijie P Tebbs Joshua M JM Bilder Christopher R CR McMahan Christopher S CS

Biometrics 20160922 2

Group testing, where individuals are tested initially in pools, is widely used to screen a large number of individuals for rare diseases. Triggered by the recent development of assays that detect multiple infections at once, screening programs now involve testing individuals in pools for multiple infections simultaneously. Tebbs, McMahan, and Bilder (2013, Biometrics) recently evaluated the performance of a two-stage hierarchical algorithm used to screen for chlamydia and gonorrhea as part of th ...[more]

PMID: 27657666

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Project description:BackgroundOppositional defiant disorder (ODD) is a major mental health concern and highly prevalent among children living in poverty-impacted communities. Despite that treatments for ODD are among the most effective, few children living in poverty receive these services due to substantial barriers to access, as well as difficulties in the uptake and sustained adoption of evidence-based practices (EBPs) in community settings. The purpose of this study is to examine implementation processes that impact uptake of an evidence-based practice for childhood ODD, and the impact of a Clinic Implementation Team (CIT)-driven structured adaptation to enhance its fit within the public mental health clinic setting.Methods/designThis study, a Hybrid Type II effectiveness-implementation research trial, blends clinical effectiveness and implementation research methods to examine the impact of the 4Rs and 2Ss Multiple Family Group (MFG) intervention, family level mediators of child outcomes, clinic/provider-level mediators of implementation, and the impact of CITs on uptake and long-term utilization of this model. All New York City public outpatient mental health clinics have been invited to participate. A sampling procedure that included randomization at the agency level and a sub-study to examine the impact of clinic choice upon outcomes yielded a distribution of clinics across three study conditions. Quantitative data measuring child outcomes, organizational factors and implementation fidelity will be collected from caregivers and providers at baseline, 8, and 16 weeks from baseline, and 6 months from treatment completion. The expected participation is 134 clinics, 268 providers, and 2688 caregiver/child dyads. We will use mediation analysis with a multi-level Structural Equation Modeling (SEM) (MSEM including family level variables, provider variables, and clinic variables), as well as mediation tests to examine study hypotheses.DiscussionThe aim of the study is to generate knowledge about effectiveness and mediating factors in the treatment of ODDs in children in the context of family functioning, and to propose an innovative approach to the adaptation and implementation of new treatment interventions within clinic settings. The proposed CIT adaptation and implementation model has the potential to enhance implementation and sustainability, and ultimately increase the extent to which effective interventions are available and can impact children and families in need of services for serious behavior problems.Trial registrationClinicalTrials.gov, ID: NCT02715414 . Registered on 3 March 2016.

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Hierarchical group testing for multiple infections.

Publications

Hierarchical group testing for multiple infections.

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