My fentanyl paper was mentioned in Congressional Testimony!

This is a new one for me! My research made it to Congress (in written testimony to a subcommittee). Bryce Pardo, a Rand Analyst, in Testimony presented before the House Committee on Homeland Security, Subcommittee on Intelligence and Counterterrorism and Subcommittee on Border Security, Facilitation, and Operations, on July 25, 2019 twice mentioned my paper about fentanyl and the changing geography of the overdose crisis in the US.

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(Tiny caveat; the dependent variable in my paper is all overdose deaths, not synthetic opioid overdose deaths, a deliberate choice because of heterogeneity in whether drugs are listed on death certificates).

(Here’s another link to the testimony via the Rand Institute website that might outlast the .gov link)

Norfolk 15th Primary Election (MA state House, 9/4/18)

In the weeks leading up to the primary election, I walked around this state house district and counted/geocoded all the lawn signs for the Norfolk 15th State House race in a smartphone application. (These are both Democrats; no Republican is running). Lawn signs are pretty consistent with vote shares and you can see “homefield advantage” type stuff in both (more signs & more votes in precincts where candidate lives).

norfolk15_lawnsigns.pngnorfolk15_precnorfolk15_prec_stone

MA-7th Democratic Primary (Pressley beats Capuano)

I was able to get a hold of and digitize the Boston precinct results for this race (maybe 189 of the 270 precincts in this race, though I only get 187 to merge with the Boston precinct shapefile [Edit: One of the oddities may be Long Island; the other is Ward 5, Precinct 2a in the unofficial election results]) without too much trouble. And since I have a Boston voterfile on my computer for research, I can link these precinct results to average precinct demographics among registered voters, like age (from birthdate) and predicted race.

The maps  of precinct results show mostly what you’d expect (white Neighborhoods like Charlestown and Brighton’s Oak Square were better for Capuano than the city as a whole; Black neighborhoods like Roxbury/Mattapan/Dorchester’s Fields Corner much better for Pressley). But Pressley did really well everywhere in Boston, and never gets less than a third of the vote (while Capuano gets <15% in some precincts). East Boston shows up as a relative bright spot for Capuano, which seems odd given the Hispanic predominance of that area (though Capuano also won Chelsea and non-Hispanic whites likely comprise a bigger proportion of the electorate than the population).  I can’t tell too much from the turnout map, though maybe it shows that college-heavy precincts (like around BU and Northeastern and maybe those Longwood-area college campuses) have less turnout (which is consistent with what I find with age and regression later).

Precinct-level regressions indicate that precincts with larger proportions of Black voters, Hispanic voters, and younger mean age gave a higher vote share to Pressley. Higher Hispanic percentage predicted lower turnout, precincts with an older average voter had higher turnout; association for Black percentage on turnout was null (no association with turnout conditional on Hispanic percent and mean age).

 

 

 

Precinct-Level Determinants (Boston only)
Dependent variable:
Pressley Vote Share Turnout
Black % 0.375*** -0.002
(0.02) (0.013)
Hispanic % 0.178*** -0.108***
(0.051) (0.034)
Mean Age -1.194*** 0.710***
(0.135) (0.089)
Observations 187 187
R2 0.676 0.289
Adjusted R2 0.671 0.277
F Statistic (df = 3; 183) 127.525*** 24.777***
Note: *p<0.1; **p<0.05; ***p<0.01

Fixing MA’s Good Samaritan Law: Calling 911 to report an overdose can still land you in legal trouble.

Before the legislative session concludes, Beacon Hill lawmakers will pass Governor Baker’s CARE Act, an important bill boosting services connecting overdose patients in the ER to recovery services. But the Care Act suffers from a critical omission: it leaves broken the Commonwealth’s limited Good Samaritan Law, which has failed to protect those who seek help during an overdose. Reforming the law is needed to ensure that patients call 911 in the first place.

The Good Samaritan Law is intended to protect people who call 911 from legal punishment, since studies show that fear of arrest stops bystanders from calling for help.  Politicians around the state have touted the law as doing just that. In May 2016,  Attorney General Healey and Governor Baker unveiled a $250,000 campaign encouraging bystanders who witnessed an overdose to “Make the Right Call.” The state distributed posters promising those who call 911 that “The Law Protects You.”

mtc_c

[Image: MA’s Department of Public Health  “Make the Right Call Poster”. Poster Text: “You might be high. You might be afraid. If you see an overdose call 911. The Law protects you.”]

Unfortunately, this is not true. The Good Samaritan Law that took effect on August 2nd, 2012 only narrowly exempts bystanders who call 911 and those who need medical attention from charge or prosecution for drug possession. What it does not do is protect bystanders or patients from being arrested, charged, or prosecuted for any offense other than drug possession. According to the Network for Public Health Laws, the Good Samaritan Law in Massachusetts—unlike progressive bastions like Nevada and Tennessee—does not preclude  arrest or prosecution for other crimes including, mindbogglingly, possessing drug paraphernalia. Nor does it confer protection from civil asset forfeiture, prosecution under any outstanding warrants, or violations of probation or parole.

Indeed, under the Massachusetts law, a person can still be arrested for simple drug possession, but not charged or prosecuted. The problem is, once a person is arrested for drug possession, that contact with the criminal justice system can lead to interviews, searches, and sanctions for unrelated crimes, including unpaid court fees or parole violations. What’s more, it is up to a prosecutor to determine whether you were a worthy Good Samaritan or not—something which can hinge on whether or not your drug possession was with intent to distribute. In our criminal justice system, such discretion usually leads to unequal outcomes across zipcodes, races, and economic strata.

Moreover, several cases illustrate how calling 911 for an overdose can lead to arrest, despite the Good Samaritan Law. In Attleboro, January 2013, a man who called 911 to report an overdose was arrested on drug possession charges. Though the drug possession charges were dropped under the Good Samaritan Law, he faced a three year sentence on an outstanding warrant in another state. In Swampscott, August 2014, police responding to a heroin overdose 911 call found that the residents were manufacturing cannabis oil (hash) in their house. Detectives shared pictures of the hash with the DEA and then arrested the residents on charges related to hash, heroin, and an unlicensed firearm. In Taunton, March 2016, a man called 911 to seek medical assistance for a woman who overdosed. When police arrived, the caller himself was protected by the Good Samaritan Law. But police arrested another person at the house—who hid under a blanket  in the bathroom—on an outstanding warrant. In Brockton, in April 2017, someone called 911 to report an apparent overdose of an unconscious man. When police arrived, he had woken up. Officers searched his truck and arrested him for drug possession and driving without a license.

Newspapers document these and many, many, other such cases where a witness’s call for help  is met with punishment. These cases of what can go wrong if you call for help fuel the apprehension about police that discourages calling 911 at all, a concern which is acute among Blacks, who may be especially wary of calling the police. (In Massachusetts, overdose mortality is rapidly increasing among Blacks). Undocumented residents and those close to them may also have reason to worry.

Expanding the protections of the Good Samaritan Law is essential to ensuring that medical emergencies are not treated like crime scenes. Though politicians around the Commonwealth promise that the law will protect you if you call 911, the experiences of some of our most vulnerable disagree.

Unions, Right-to-Work, and Occupational Deaths

Happily, my paper “Does ‘right to work’ imperil the right to health? The effect of labour unions on workplace fatalities” is attracting a lot of attention, which makes me feel good as an academic who cares at least a little about producing things that others find valuable (we will see how the sheriff stuff is received, once it’s finally done, though it seems like there’s something both quaint and gripping about them!). Although I wrote that short article in August 2017 — and had really no sense of the Janus case at that time — it’s really seemed to have caught the moment. It helps that there’s yet a non-trivial, vested constituency in organized labor that finds something useful, or reassuring, in these results. Anyway, in response to a journalist query about, essentially, what “14.1%” (the coefficient of the reduced form regression of the RTW variable) means exactly,  I wrote something like this:

predicted_annualfatalities_rtw

There were 138,736 total deaths on the job in the 50 states over 1992-2016 recorded in the Census of Fatal Occupational Injuries. Using a negative binomial regression (with the same predictors as the “reduced form” regression in the paper, but with the number of workers as an offset variable and the state/annual count of workplace deaths as the dependent variable) to model the incidence of occupational deaths,  the expected count of occupational deaths in a state during this time-period is about 118.7 in a right to work state and about 104.9 in an otherwise similar state without RTW (figure above; Stata margins command with coefplot for figure). Reassuringly, this 13.1% increase in RTW states in the negative binomial model is essentially the same result as in the “reduced form” model in the paper (about a 14% increase in RTW states relative to others). The Incident Rate Ratio on the right-to-work coefficient is also  1.1314 — which makes sense, as that is the ratio of the predicted values!

Over this same 1992-2016 period, 529 state-year dyads had right to work legislation (721 did not). Therefore, the model-implied count of occupational fatalities attributable to RTW  (assuming no confounders) is (118.7-104.9)*529 or about 7,300.That is, if no states had RTW over these years, the model implied counterfactual is that about 131,436 occupational deaths would have occurred instead of the observed 138,736 occupational deaths.

Semi-related: Using the original (non-logged) rate of fatalities, here’s a predicted effects plot of occupational fatality rate at different levels of unionization; I shared this on Twitter previously. This was used with an OLS model containing all the parameters of model 3 in the paper (or replication code), but with the non-logged dv (in the replication code labeled “robustness check using original unscaled variable”). This shows how the actual, observed decreases in unionization rates we’ve seen in states like Wisconsin in recent years may translate into meaningful increases in occupational mortality.

marginaleffects_origdv

White Supremacists are the biggest snowflakes in American History

[Cover Image from Jim Crow Songbook, 1847, admonishing Black people not to laugh at “them who happen to be white,” a rather strange play at victimhood in a year when almost all Blacks in America were enslaved.]

Of course, white supremacist prohibited Blacks from voting, holding office, serving on juries, having sex with or marrying whites, attending school with whites, eating at restaurants with whites, sharing street cars with whites (at issue in Plessy v. Ferguson), and so-on and so forth (Oregon just straight up banned Black people for many years). But I have only recently come to learn the depth, breadth, and sheer pettiness of Jim Crow and white supremacists, who must be among the most fragile people the world has ever seen. Deep down this brutal history really isn’t funny, but the fact that white racists felt it necessary to ban these things is at least a little bit funny–These people had really thin skin. Here are just some of the activities that so perturbed the White Supremacists that they sanctioned them with the law (and the lynch):

Like extreme conservatives in the US today, who have passed laws banning CDC research on guns, banning doctors from talking about guns with their patients, banning the promotion of non-binary pronouns, increasing penalties for protesting, and believe football players should face economic reprisals for protest, white supremacists during Jim Crow shared this belief in restricting the rights of people who disagree with them. Mississippi banned “printing, publishing or circulating printed, typewritten or written matter urging or presenting for public acceptance or general information, arguments or suggestions in favor of social equality or of intermarriage between whites and negroes.” Mississippi also had in its vagrancy law (a racist Jim Crow statute that allowed the police to arrest and “lease out” unemployed Blacks, often to plantation owners) a provision banning whites from “assembling themselves” with blacks “on terms of equality.”

 

Cannabis “strain” variation in THC:CBD ratio, by lab

Using the I-502 data from Washington State and the ggridges library in R can make some fun graphics. In my paper with Nick, we show the distribution of logged THC:CBD ratio, by strain, for 30 or so popular strains (according to Leafly review data) for two labs — Confidence and Peak.  Here are those plots for all six of the big labs.

Analytical 360, LLC._ridgeplotConfidence Analytics_ridgeplotGreen Grower Labs_ridgeplotIntegrity Labs_ridgeplotTesting Technologies_ridgeplotPeak Analytics Laboratory Testing Services_ridgeplot

Editors choose newspaper headlines, not authors.

Based on the comments on my recent Monkey Cage post (describing a paper Nick Jikomes and I wrote about inconsistent cannabanoid reporting, by lab in Washington’s i502 testing data and several other interesting things about legal cannabis revealed by the state’s testing data), I don’t think so many people realize that editors, not authors, choose headlines.

For the blog post, the Monkey Cage editors chose a not-so-great title: “How will you know if there’s E. coli in your marijuana? No one’s figured out how to test and regulate it yet.” I’ve been trying to pinpoint exactly what’s wrong with it. Several things come to mind.

  • The word “it” has an ambiguous antecedent (I think that’s the correct phrase). With lots of context and inside knowledge, I think the word “it” is referring to the previous sentence’s marijuana. But there’s no obvious syntactic reason why it wouldn’t refer to E. coli. In the case of the latter interpretation, the suggestion is that no body knows how to detect (regulate?) E. coli in marijuana. This is plainly not true. The point implied by the blog and article is much more subtle, that the kinds of regulatory regimes used by states is fluid and variable.
  • Almost none of the blog post, and absolutely none of the scholarly paper about which the post is written, is about E. coli. The paper is exclusively about THC and CBD, not contaminants. The word E. coli appears once in the blog post as an example of a contaminant.
  • While the title is click-baity by shock value, it is not succinct.

In general, the process of writing for Monkey Cage was exceptionally pleasant and smooth. I wish they chose a better title, but oh well. A silver-lining of all this is that maybe more folks will read our scholarly article, but it’s a shame I don’t feel more enthusiastic about promoting the piece.fig7.png

Luminous Nonlinearity

Jordan Ellenberg’s book How Not to Be Wrong: The Power of Mathematical Thinking was a pleasant part of my winter break. I don’t really care for the title, and it’s somewhat difficult for me to articulate what the book was about. Broadly, it intertwined anecdotes about mathematicians with mathematical concepts and illustrated these concepts in action with pithy vignettes about selection bias among surviving war planes, the expected value of a prior iteration of the Massachusetts lottery, and regression to the mean among the sons of tall fathers. Somehow, it was a page turner.

Non-linearity was a concept described brilliantly early in the book: “Nonlinear thinking means which way you should go depends on where you already are.” Ellenburg applies this concept to the Laffer Curve (and provides a humorous counterexample of a writer with a sharply discontinuous utility function with respect to wages who is incentivized to work more hours if tax rates go up) and a conservative polemic that America should not try to become more like Sweden (tax and redistribute more) if Sweden is trying to become more like America (tax and redistribute less) (Ellenburg implies in rebuttal that there may be an optimal amount of government intervention in the economy straddling these two points).

Shortly after finishing this book, I encountered a clear example of nonlinearity in my own life. I was buying light bulbs for my standing lamp, which is my primary light source for my apartment when the sun is down. I had the bright idea to purchase slightly less luminous bulbs than I was using previously — more environmentally efficient, I thought, and maybe better at night as I’m winding down. As it turns out, this was not correct. It’s now not quite bright enough for me to read, so I also turn on a small table lamp. The net effect: because I decreased the amount of light emitted by my standing lamp, I now use two lights; reducing the electricity consumed by one light increased my total electricity consumption. Brilliant and definitely non-linear.