The risk of non-communicable disease in people who have experienced imprisonment
Bibliographic record
Abstract
Abstract Introduction Imprisonment is associated with increased infectious disease and poor mental health, but risks of non-communicable diseases (NCDs) are unclear. We conducted a systematic review to describe the incidence of NCDs among people who have experienced imprisonment compared to the general population in high-income countries. Methods We searched four databases for comparative studies of NCD incidence. Reviewers conducted independent risk of bias (RoB) assessment using an adapted version of the Newcastle-Ottawa Scale, with data extraction checked independently. Data were synthesised using random-effects meta-analyses and vote counting based on effect direction. Heterogeneity was investigated using meta-regression and subgroup analysis. Findings Of 3,085 articles screened, 32 were included providing 341 datapoints. Most studies were at low or moderate RoB. Meta-analysis showed a higher risk of mortality from respiratory disease (RR 2·38, [1·18 - 4·80], I2=97%), cardiovascular disease (RR 1·80, [1·32- 2·46], I2=99%), liver disease and cirrhosis (2·50, [1·08-5·77], I2=99%), digestive disease (2·92, [1·09-7·78], I2=99%), neurological disease (1·94, [1·09-3·44], I2=92%), head and neck cancer (RR 21·31, [4·32-105·14], I2=97%), liver cancer (RR 4·07, [2·34-7·08], I2=94%), cervical cancer (3·95, [3·11-5·01], I2=0%), and lung cancer (RR 1·95, [1·34 - 2·85], I2=0%). Morbidity from liver cancer (5·58, [2·02-15·47], I2=78%), lung cancer (RR 1·99, [1·53-2·59], I2=83%), head and neck cancer (1·86, [1·53-2·27], I2=0%), and dementia (HR 2·30, [2·18-2·43], I2=0%) were increased. Patterns of results were similar across studies with differing RoB. Interpretation Incidence of NCDs is substantially higher in people who have experienced imprisonment compared to the general population in high-income countries. This has broad policy and practice implications across Europe, particularly for primary prevention and management of NCDs. Key messages • This review highlights the high burden of NCDs in people who have experienced imprisonment, adding to the evidence base which emphasises the burden of communicable diseases and mental illness. • Investment in policies and interventions to reduce and manage NCDs are required alongside continued management of substance use, mental illness, and communicable diseases in this population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".