An analysis of published study designs in PubMed prisoner health abstracts from 1963 to 2023: a text mining study

George Karystianis; Wilson Lukmanjaya; Iain Buchan; Paul Simpson; Natasha Ginnivan; Goran Nenadic; Tony Butler

doi:10.1186/s12874-024-02186-6

An analysis of published study designs in PubMed prisoner health abstracts from 1963 to 2023: a text mining study

BMC Med Res Methodol. 2024 Mar 17;24(1):68. doi: 10.1186/s12874-024-02186-6.

Authors

George Karystianis¹, Wilson Lukmanjaya², Iain Buchan³, Paul Simpson², Natasha Ginnivan², Goran Nenadic⁴, Tony Butler²

Affiliations

¹ School of Population Health, University of New South Wales, Sydney, Australia. g.karystianis@unsw.edu.au.
² School of Population Health, University of New South Wales, Sydney, Australia.
³ Institute of Population Health, University of Liverpool, Liverpool, UK.
⁴ School of Computer Science, University of Manchester, Manchester, UK.

Abstract

Background: The challenging nature of studies with incarcerated populations and other offender groups can impede the conduct of research, particularly that involving complex study designs such as randomised control trials and clinical interventions. Providing an overview of study designs employed in this area can offer insights into this issue and how research quality may impact on health and justice outcomes.

Methods: We used a rule-based approach to extract study designs from a sample of 34,481 PubMed abstracts related to epidemiological criminology published between 1963 and 2023. The results were compared against an accepted hierarchy of scientific evidence.

Results: We evaluated our method in a random sample of 100 PubMed abstracts. An F1-Score of 92.2% was returned. Of 34,481 study abstracts, almost 40.0% (13,671) had an extracted study design. The most common study design was observational (37.3%; 5101) while experimental research in the form of trials (randomised, non-randomised) was present in 16.9% (2319). Mapped against the current hierarchy of scientific evidence, 13.7% (1874) of extracted study designs could not be categorised. Among the remaining studies, most were observational (17.2%; 2343) followed by systematic reviews (10.5%; 1432) with randomised controlled trials accounting for 8.7% (1196) of studies and meta-analysis for 1.4% (190) of studies.

Conclusions: It is possible to extract epidemiological study designs from a large-scale PubMed sample computationally. However, the number of trials, systematic reviews, and meta-analysis is relatively small - just 1 in 5 articles. Despite an increase over time in the total number of articles, study design details in the abstracts were missing. Epidemiological criminology still lacks the experimental evidence needed to address the health needs of the marginalized and isolated population that is prisoners and offenders.

Keywords: Epidemiological criminology; Meta analysis; Observational research; Prisoner health research; Randomised controlled trials; Systematic reviews; Text mining.

Publication types

Review

MeSH terms

Criminals*
Data Mining
Humans
Prisoners*
Research Design