To compare mortality among the prisoners in our cohort study to mortality in the general population, we also obtained the 5-year mortality rate of a randomly drawn sample from the full population of non-incarcerated individuals in Denmark with similar observation years, age, and sex distribution.

This cohort study included people that served a prison sentence in Denmark, for whatever reason and lasting at least 7 days, that both started and ended during 2006–11. This decision restricted our analyses to focus only on sentences that were shorter than 5 years; yet, because as little as 3% of sentences in Denmark were longer than 4 years during our data period, this decision was unlikely to be consequential. The advantage of this data restriction was that it allowed us to make all observations throughout the follow-up period. To avoid autocorrelation, we focused on each person's first prison sentence during the data window, and because we also observed mortality during new prison sentences, we did not adjust the follow-up period for re-incarceration. Some individuals died, emigrated, or otherwise went missing in the data (ie, we no longer observed them in the population register) before the 5-year follow-up period was completed. Informed consent to participate from the study participants was not needed for this register-based study, in accordance with the legislation that governs Statistics Denmark.

Procedures

25 Andersen LH Chapter 2—Danish register data: flexible administrative data and their relevance for studies of intergenerational transmission. To consider the association between being placed in solitary confinement during imprisonment and mortality after release, we relied on two sources of administrative data. The first source was Danish administrative registers (not publicly available). The registers were full population data (and thus had virtually no attrition) that consisted of administrative records from various collecting agencies, such as criminal justice agencies and other Danish agencies. Statistics Denmark collected these data and made them available for research, with the appropriate safeguards in place for ensuring confidentiality. An advantage of these data was that the administrative records were linkable at the individual level since all residents of Denmark had a unique identification number.We could therefore include an array of background information about the population (ie, sex, date of birth, ethnic background, education level). We also included information from the death register (dates and causes of death).

The second administrative dataset was made available to us by the Danish Prison and Probation Service and recorded which incarcerated individuals were subjected to disciplinary actions during confinement, including individual level information on whether someone was placed in solitary confinement while serving a prison sentence.

Our key predictor variable was whether a person was placed in solitary confinement during 2006–11. We assigned this variable a value of 1 if the individual had been placed in solitary confinement at any given time or 0 if they had not. This variable allowed us to observe whether mortality differed among formerly incarcerated individuals who spent time in solitary confinement and those who did not.

The main outcome of this study was mortality in the 5 years following release from a correctional facility. For each month after a person was released from prison, we constructed a dummy variable taking the value 1 if the person had died during that month and 0 otherwise. The outcome was right censored at 60 months after release (or when people had emigrated). We obtained the mortality information from the death register, which recorded all deaths in Denmark under the Danish Board of Health Data (Sundhedsdatastyrelsen). The death register was an appended mortality database, and the version we accessed included all deaths up to Dec 31, 2016. We censored everyone at 60 months from release, however, so the data window for mortality was Jan 12, 2006, to Dec 31, 2016.

The death register had information on date of death, which allowed us to determine the timing of death relative to prison release. We also merged our data with information on cause of death, which was recorded using the International Classification of Diseases tenth revision (ICD-10) in the DODSAASG register on causes of death that were also recorded by the Danish Board of Health Data. We used three broad categories: any cause of death (all ICD-10 codes, to capture total mortality), non-natural causes of death (ICD-10 codes V01-Y99 [accidents], X60-X84 [self-harm], and X85-Y09 [violence]), and natural causes of death (inverse of non-natural deaths).

Our models included many covariates potentially linked with both the risk of placement in solitary confinement (contingent upon being placed in a correctional facility) and mortality. The administrative dataset from the Danish Prison and Probation Service provided information on admission date, release date (we measured mortality from date of release but used release-year dummies as control variables to take general time trends into account); sentence length; the crime type for which the person served a prison sentence (violent crimes, property crimes, and the residual other crimes). From the Danish administrative population register we obtained date of birth (used to calculate the demeaned age of each person when they were admitted into prison), sex (this variable was assigned a value of 1 if female, 0 if male), and whether the person had ethnic minority background (this variable was assigned a value of 1 if yes, 0 if no). From the education register we assessed each person's highest education level at the time of admission into prison (we assigned this variable a value of 1 if it was basic schooling [ie, tenth grade or less], 0 if it was a higher level of education).

We also included information on other conditions of confinement (which were also available in the data from the Danish Prison and Probation Service), namely the security level of the facility in which the person was incarcerated (coded as a series of dummy variables: low-security prison; high-security facility; local jail), whether those who were not sent to solitary confinement were assigned other disciplinary actions (eg, fines, confiscation of contraband, etc; this parameter was assigned a value of 1 if yes, 0 if no), and, for individuals who were sent to solitary confinement, the total number of days spent in solitary (dummy coded 1–3 days, 4–7 days, or more than a week in descriptive statistics but entered continuously in the regression models). We collapsed multiple incidents of solitary confinement into a common measure of total days in solitary, which arguably introduced a risk of bias across repeat solitary confinement placement. Yet, 75% of incarcerated people who experienced solitary confinement only did so once, and 93% only did so once or twice; we therefore opted to use the simpler measure.Preliminary analyses that included extensive controls (eg, sibship size, more detailed offence categories, and information about each person's parents) did not relate substantially to solitary confinement, so we decided to use the above-described more parsimonious model.