Sandhya Kajeepeta, who, like myself, boasts of a “(grating) Michigan accent”, tweeted “We know the jail system harms the public rather than keeping us safe. In this new @AMJPublicHealth paper we quantify the population impact of jails in terms of the most basic public health indicator: mortality”.

Now I can’t speak for you, dear reader, but I support corporal and capital punishment as substitutes for jail in many cases for the sake of justice and community protection. I didn’t know that jails, inefficient as they might be, weren’t keeping us safe. An informal survey of the deplorables who associate with me were also ignorant of this apparently well known scientific fact. But then we drink beer and listen unironically to old country music.

Anyway, the peer-reviewed paper is County Jail Incarceration Rates and County Mortality Rates in the United States, 1987–2016 by Kajeepeta and others in the American Journal of Public Health.

Abstract:

Methods. We analyzed county jail incarceration rates from the Bureau of Justice Statistics from 1987 to 2016 for 1884 counties and mortality rates from the National Vital Statistics System. We fit 1-year-lagged quasi-Poisson 2-way fixed-effects models, controlling for unmeasured stable county characteristics, and measured time-varying confounders, including county poverty and crime rates.

If they were “unmeasured” how did they know what they were?

Results. A within-county increase in jail incarceration rates from the first to second quartile was associated with a 2.5% increase in mortality rates, adjusting for confounders (risk ratio [RR] = 1.03; 95% confidence interval [CI] = 1.02, 1.03). This association followed a dose-response relationship and was stronger for mortality among those aged 15 to 34 years (RR = 1.07; 95% CI = 1.06, 1.09). Conclusions. Within-county increases in jail incarceration rates are associated with increases in subsequent mortality rates after adjusting for important confounders. Public Health Implications. Our findings add to the growing body of empirical evidence of the harms of mass incarceration. The criminal justice reform and decarceration movements can use these findings as they develop strategies to end mass incarceration.

Decarceration sounds like something you’d go to the podiatrist to clean up.

So they made a regression-like model into which they tossed everything including the stainless steel jail sink. Here’s a small sample of measures:

“Because the fixed-effects regression models we employed control for all unobserved time-invariant confounders” Say what? All? “We adjusted for county median age using an 18-level variable with an indicator for the 5-year category containing the median age based on the Intercensal Estimates.” Only 18 levels?

“We obtained party control of state legislature data for all years from the National Conference of State Legislatures”…”To ensure the correct temporal order of the hypothesized association between jail incarceration rates and subsequent county mortality rates, we employed a time-lag in our analyses”…”using mortality rates for the following 5 age groups: younger than 15, 15 to 34, 35 to 54, 55 to 74, and 75 years and older”…etc. etc. etc.

It goes on like that for some length. Truth is, I lost count of all the stuff they crammed into their model. Actually three models, testing out various configurations, with all these “adjustments” or “controls.”

Stuffing an extra x into a regression is not “controlling” or “adjusting” for this measure. It is merely adding another thing, that may or may not, modify the probability math for the other measures or for the observable in some way that is repeatable. It is not control or adjustment in any real physical sense.

The results:

The results demonstrated that small increases in jail incarceration rate were associated with small increases in total mortality at the county level. In the fully adjusted model (model 3), a percentage point increase in jail incarceration rate was associated with a 0.4% increase in total mortality rate (risk ratio [RR]?=?1.0038; 95% confidence interval [CI]?=?1.0034, 1.0042).

This tiny boost in the rate with its confidence interval that does not “include 1” is equivalent to a wee p-value argument, with which we are now well familiar. Skip all that. The implicit argument is that small changes in the jail population caused more deaths. And not just deaths, deaths from anything.

It’s obvious that squeezing more (bad, violent, criminal, vicious) people into a jail will increase the mortality rate at the jail: we didn’t need a study for this. Our authors are implying more than this. We should fear people going to jail, because it’s going to cause the death of people outside the jail. Thus jails “harm the public.”

Naturally, jails aren’t usually put into the most desirable neighborhoods, and the bigger the jail gets the more undesirable the neighborhood becomes, all things considered. And bad neighborhoods have more people dying faster than in good neighborhoods. Again, no study is needed.

Our authors don’t appear to have considered this line. Instead they speak of things like “inequitable distribution of incarceration”. And they say things like this:

…in future research we aim to examine the role of institutional and structural racism, given that mass incarceration is a racialized social policy that disproportionately harms communities of color and given the existing research demonstrating heightened community health impacts of incarceration in Black communities.

Ah.

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