Family Maltreatment, Substance Problems, and Suicidality: Prevalence Surveillance and Ecological Risk/Protective Factors Models

Abstract

This study seeks to derive and validate an innovative public health surveillance system. Years of pilot work with the AF found that it is possible to derive accurate complex statistical estimation algorithms from data sets containing both nonsensitive information and assessments of secretive problems. These algorithms can then be applied to data sets that do not directly assess secretive problems to accurately estimate problem prevalences. In other words, a single survey administration and the algorithms can obviate the need for future secretive behavior surveys, making this a cost effective and sustainable planning tool. Further, the data set to be used for algorithm derivation will also be ideal to test a series of specific hypotheses about individual, family, workplace, and community risk and protective factors for each of the secretive problems.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2008
Accession Number
ADA494430

Entities

People

  • Amy M Smith
  • Richard E Heyman

Organizations

  • State University of New York

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Communities
  • Data Analysis
  • Data Science
  • Data Sets
  • Demography
  • Geographic Regions
  • Health
  • Hypotheses
  • Information Science
  • Public Health
  • Risk Factors
  • Statistical Algorithms
  • Statistical Analysis
  • Statistical Estimation
  • Surveillance
  • Surveys

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