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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Selective sampling, where data collection targets specific individuals, complicates accurate population inference.
  • Real-world scenarios like medical diagnoses and customs inspections often involve agents searching for high-value targets, leading to biased samples.

Purpose of the Study:

  • To develop and validate a novel statistical method for estimating population characteristics from selectively sampled data.
  • To apply this method to estimate the prevalence of multidrug-resistant tuberculosis (MDR-TB) and testing accuracy in South Africa.

Main Methods:

  • A model was developed to represent the agent's sampling decisions.
  • Generalized method of moments with instrumental variables and maximum likelihood were employed for estimation.
  • The method was applied to a national South African TB testing database (2004-2010).

Main Results:

  • The study estimated multidrug-resistant tuberculosis (MDR-TB) prevalence to be as high as 3.5%, significantly higher than the official estimate of 2.5%.
  • Approximately one-quarter of MDR-TB cases remained undiagnosed during the study period.
  • Estimated signal-to-noise ratios ranged from 0.5 to 1.

Conclusions:

  • The proposed method effectively corrects for biases introduced by selective sampling in prevalence estimation.
  • Routinely collected data and available instruments make this approach widely applicable for monitoring population health trends.
  • Accurate prevalence monitoring is crucial for evidence-based public health policy making, particularly for infectious diseases like TB.