  Violations of the positivity assumption (also called the common support
condition) challenge health policy research, and can result in significant
bias, large variance, and invalid inference. We define positivity in the
single- and multiple-timepoint (i.e., longitudinal) health policy evaluation
setting, and discuss real-world threats to positivity. We show empirical
evidence of the practical positivity violations that can result when attempting
to estimate effects of health policies (in this case, Naloxone Access Laws). In
such scenarios, an alternative is to estimate the effect of a shift in law
enactment (e.g., the effect if enactment had been delayed by some number of
years). Such an effect corresponds to what is called a modified treatment
policy, and dramatically weakens the required positivity assumption, thereby
offering a means to estimate policy effects even in scenarios with serious
positivity problems. We apply the approach to define and estimate longitudinal
effects of Naloxone Access Laws on opioid overdose rates.
