RT Article
T1 Using Booking Data to Model Drug User Arrest Rates: A Preliminary to Estimating the Prevalence of Chronic Drug Use
JF Journal of quantitative criminology
VO 23
IS 1
SP 1
OP 22
A1 Rhodes, William
A2 Kling, Ryan
A2 Johnston, Patrick
LA English
YR 2007
UL https://krimdok.uni-tuebingen.de/Record/1764280199
AB Public policy is often concerned with the size and characteristics of special populations that are difficult to reach in household surveys. Chronic drug users, who often live outside conventional households, provide the illustration motivating this paper. An alternative to household surveys is to question chronic drug users where they congregate—jails, treatment programs, and shelters, for example. Using such opportunistic data for prevalence estimation raises difficult problems for statistical inference: Study subjects who arrive at the collection points cannot be deemed a random sample of the general population. However, if we could estimate the rates at which chronic drug users arrive at the collection points, then we could use those estimates to weight the sample to represent the population. This paper presents a modified Poisson mixture model used to estimate the stochastic process that accounts for how chronic drug users get arrested. It uses that model to estimate arrest rates for 38 counties using up to sixteen quarters of data from the Arrestee Drug Abuse Monitoring survey.
K1 Model-based estimation
K1 Poisson mixture models
K1 Endogenous stratification
K1 Arrest rates
K1 Hard-to-reach populations
K1 Chronic drug use
DO 10.1007/s10940-006-9016-9