Opioid addition is a major health crisis in the United States. Deaths from overdose increased dramatically in the last 5 years. Opioid addiction sometimes starts when a person in pain is prescribed legal opioid drugs by a physician. Opioid prescriptions can also be sold illegally. For these reasons, opioid prescription rates are an indicator of opioid abuse in a particular region.
Some public health researchers have investigated whether legalizing marijuana can reduce rates of opioid use and abuse. Marijuana is an alternative for controlling chronic pain that, according to many experts, has a lower addiction risk. Recently, researchers published two studies, both with quasi-experimental designs, that tested whether legalized marijuana could lower the rates of opioid prescriptions. Like many quasi-experiments, the researchers took advantage of a real-world situation: Some U.S. states have legalized marijuana and other states have not.
ABC news covered the the research. There were two studies with similar designs, but we'll focus on the first one:
One looked at trends in opioid prescribing under Medicaid, which covers low-income adults, between 2011 and 2016. It compared the states where [medical] marijuana laws took effect versus states without such laws....
Results showed that laws that let people use marijuana to treat specific medical conditions were associated with about a 6 percent lower rate [over the years studied] of opioid prescribing for pain. That's about 39 fewer prescriptions per 1,000 people using Medicaid.
And when states with such a law went on to also allow recreational marijuana use by adults, there was an additional drop averaging about 6 percent.
a) What is the "independent" variable in this quasi-experiment? What is the dependent variable? Was the independent variable independent groups or within groups?
b) What makes this a quasi-independent variable?
c) Of the four quasi experimental designs, which seems to be the best fit: Non-equivalent control group posttest only? Non-equivalent control group pretest-posttest? Interrupted time series design? Non-equivalent control group posttest-only design?
d) How might you graph the results described above?
e) To what extent can these data support the causal claim that "legalizing marijuana, either for medical use or recreational use, can lower the rates of opioid prescriptions in the Medicaid system"?
a) The independent variable was whether a state had legalized marijuana or not. It was independent groups (states either had, or had not, legalized the drug). The dependent variable was the number of opioid prescription rates through Medicaid. Another variable, somewhat difficult to discern from the journalist's description, was year of study (from 2011 to 2016)
b) This IV was not manipulated/controlled by the experimenter. The researcher did not decide which states could legalize marijuana or not.
c) This is probably best characterized as a non-equivalent control group, pretest-posttest design. There were two types of states (legalized and not) and one main outcome variable: opioid prescriptions. The prescription rate was compared over time (from 2011 to 2016), making it pretest-posttest.
d) Your y-axis should have "opioid prescriptions" and the x-axis should include the years 2011 to 2016. You could then have "States with legalization" and "States without legalization" as two different colored lines.
e) The results of the study show covariance (States with legalized marijuana had lower opioid prescriptions). The fact that they compared opioid prescriptions over time (2011 to 2016) suggest that the design is able to establish temporal precedence. Presumably (although this is not clear from the articles), 2011 represents a year before many of the marijuana laws took effect and 2016 data occurred after the laws had been active.
As for internal validity, it's possible that states that legalize are different in systematic ways than states that do not. For example, states that legalize marijuana are more likely to be in the North and West, have lower poverty rates, and so on. However, the pretest-posttest design, in which they studied the "drop in opioid prescriptions over time" rather than "overall rate of opioid prescriptions" helps minimize some of these concerns. As with most quasi-experiments, causation is not a slam-dunk, because the experimenter does not have full control over the independent variable.