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Estimating prevalences of sensitive problems from nonsensitive data.

Richard E Heyman1, Amy M Smith Slep

  • 1Family Translational Research Group, Department of Psychology, Stony Brook University, State University of New York, NY 11794-2500, USA. Richard.Heyman@Stonybrook.edu

Journal of Interpersonal Violence
|March 19, 2010
PubMed
Summary
This summary is machine-generated.

Accurate prevalence estimation for sensitive issues like child maltreatment is possible using non-sensitive data. This method aids public health policy and prevention planning when direct measurement is difficult.

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Area of Science:

  • Public Health
  • Epidemiology
  • Behavioral Science

Background:

  • Accurate prevalence data are crucial for public health policy and prevention strategies.
  • Sensitive behavioral and mental health issues, such as child maltreatment and intimate partner violence (IPV), are infrequently measured.
  • Existing data collection methods for sensitive issues are often costly or raise privacy concerns.

Purpose of the Study:

  • To evaluate the feasibility of estimating prevalences of sensitive behavioral problems using non-sensitive, dynamic variables.
  • To develop and validate a method for indirect prevalence estimation.

Main Methods:

  • Utilized archival datasets on partner and child maltreatment.
  • Randomly divided datasets into development and cross-validation subsets.
  • Employed sequential, backward, stepwise logistic regression to create estimation equations, subsequently validated.

Main Results:

  • Estimated prevalences closely matched measured prevalences.
  • Confidence intervals for estimated prevalences were comparable to those of measured prevalences (±1%-2%).
  • The developed method demonstrated accuracy in estimating sensitive outcomes.

Conclusions:

  • Regularly collected, non-sensitive data can accurately estimate the prevalence of unmeasured, sensitive outcomes.
  • This approach offers a viable solution for policy makers and prevention planners when direct assessment is impractical.
  • The findings support the use of proxy variables for monitoring public health issues.