COVID Case Watch October 28th, 2020

This graph shows confirmed positive cases of COVID-19 per 1,000 individuals in the prison and general populations in each state as of October 28, 2020. The left side (orange) refers to the prison population and the right side (blue) refers to the general population. 

The rate of COVID in the general population is 26.61 per 1,000 individuals. In the prison population (in state prisons), the rate is 113.35 per 1,000 individuals. This means that, on average, the rate of COVID infections in prison populations is 4.26 times the rate of COVID infections in the general population. Arkansas, Kansas, and Vermont continue to have a prison infection rate that is over 10 times that of the general population. 

Despite a surge of COVID cases in the general population this month, in 45 of the 50 US states, the rate of COVID infections among those imprisoned exceeds the rate of COVID infections among the general population. As cases have risen in the Midwest, the general population of Montana, North Dakota, South Dakota, and Wisconsin have seen an increase in case rates of 4.9-7.9 per 1,000 in the past week. However, the case rates in Midwestern prisons have increased much more. Montana, North Dakota, South Dakota, and Wisconsin prisons have seen an increase in case rates in the past week of 54 per 1,000, 16 per 1,000, 302 per 1,000, and 38 per 1,000, respectively. 

Importantly, states have varying testing strategies within prisons and for their general population, indicating that these rates likely reflect a falsely low disease incidence with some states’ rates being more accurate than others.

Language Matters: Reporting COVID-19 in Prison Systems

CPP collects and analyzes data on five primary variables reported by 53 sources: each state prison system, ICE, the Federal Bureau of Prisons, and Puerto Rico. In an analysis of definitions available on each system’s website, discrepancies in language used to report COVID-related data were identified. Specifically, definitions of the number of people who are incarcerated who are tested for COVID-19 (“Inmates Tested”)  and positive cases in staff (“Staff Positive”) vary. These differences in terminology are important: reporting the number of tests given does not capture how many of them are re-tests of the same individual, due to either re-exposure to the virus or sentinel surveillance testing. 

Historically, CPP has defined “Inmates Tested” as the total number of incarcerated individuals in prisons who have received a COVID test. This was mostly the case early in the course of the pandemic wherein testing was slow to ramp up. However, more recently, as reductions in population have occurred and more robust testing efforts have been deployed, systems have begun defining their testing data disparately. Our team recently did a content analysis of reporting across all of the systems we are tracking. What we found is detailed here in Table 1. 

Definition of “Inmates Tested”Percentage  
Number of COVID tests given 23%
Number of people tested26%
Undefined 30%
Variable not reported by DOC 26%
Table 1. System Definitions of “Inmates Tested.” Note: Colorado, Vermont, and Washington report both the number of people tested and the number of tests given. 

Very few of the systems reporting data are providing information relevant to staff testing. Out of 53 systems, only 7 are reporting the number of staff that have been tested (defined by CPP as “Staff Tested”). Of these 7 systems, only 1 defines the variable as the number of DOC-administered tests to staff. For the remaining systems, 3 leave “staff tested” as undefined, and 3 specify that testing is self-reported by staff members. 

Systems should aim to be clear in how they define variables related to COVID-19 testing and cases, particularly when it comes to re-exposures and retesting incarcerated people and staff members. In light of these findings, CPP will begin to report two categories of data for relevant systems: both the number of people tested and the number of tests given. For more insight into how systems and CPP define these COVID-related variables, check out our “Data Dictionary” here. We continue to re-evaluate how these definitions differ between systems and what it means for the standardization of data on CPP’s platform.  

COVID-19 Tests per 1,000

In the last week, Wyoming has begun reporting testing information within prisons. Of the 41 states reporting testing information, 21, including Wyoming have administered more than 500 COVID tests per 1,000 inmates. Eight states have administered more than one test per inmate with Minnesota administering over three tests per inmate. Multiple prison systems are still testing fewer inmates per 1,000 than their state’s general population.  

COVID Prison Project Data Dictionary

Last updated: June 10, 2020

CPP collects daily data from 53 sources: each state prison system, ICE, the Federal Bureau of Prisons, and Puerto Rico. Over the past few months, we have carefully considered how to define our data. Each system reports their data differently, defines their data differently, and includes different data points. Some states are inclusive of community supervision, some include jail systems. And, many states’ data reporting systems change constantly. So, in the name of data integrity and transparency, we have created a data dictionary both for our team and for you who may be viewing and using the data. This document is living and breathing, changing constantly. You can find it here: https://docs.google.com/document/d/1XKLrAtT2UEsQ_fcicwWJRnHCh6zR5eZptG06bjtfEZg/edit?usp=sharing

For each state our data dictionary includes:

Where the data was sourced from, with a link to the system’s data page.

The system’s definition of its data and which variables they are reporting.

CPP’s definitions of the data. Sometimes systems remove positive cases when they are “recovered”. We add them back in to get a cumulative count. Sometimes systems don’t report cumulative testing numbers but instead report number of pending, positive, and negative numbers. We add those all up. In the data dictionary, we detail this unique process for each state.

If you have questions about the data dictionary or thoughts on how we can improve it please reach out to us!