Evaluation of a text-mining application for the rapid analysis of free-text wildlife necropsy reports

by Stefan Saverimuttu, Kate McInnes, Kristin Warren, Lian Yeap, Stuart Hunter, Brett Gartrell, An Pas, James Chatterton, Bethany Jackson

The ability to efficiently derive insights from wildlife necropsy data is essential for advancing conservation and One Health objectives, yet close reading remains the mainstay of knowledge retrieval from ubiquitous free-text clinical data. This time-consuming process poses a barrier to the efficient utilisation of such valuable resources. This study evaluates part of a bespoke text-mining application, DEE (Describe, Explore, Examine), designed for extracting insights from free-text necropsy reports housed in Aotearoa New Zealand’s Wildbase Pathology Register. A pilot test involving nine veterinary professionals assessed DEE’s ability to quantify the occurrence of four clinicopathologic findings (external oiling, trauma, diphtheritic stomatitis, and starvation) across two species datasets by comparison to manual review. Performance metrics—recall, precision, and F1-score—were calculated and analysed alongside tester-driven misclassification patterns. Findings reveal that while DEE (and the principals underlying its function) offers time-efficient data retrieval, its performance is influenced by search term selection and the breadth of vocabulary which may describe a clinicopathologic finding. Those findings characterized by limited terminological variance, such as external oiling, yielded the highest performance scores and the most consistency across application testers. Mean F1-scores across all tested findings and application testers was 0.63–0.93. Results highlight the utility and limitations of term-based text-mining approaches and suggests that enhancements to automatically capture this terminological variance may be necessary for broader implementation. This pilot study highlights the potential of relatively simple, rule-based text-mining approaches to derive insights natural language wildlife data in the support of One Health goals.

Exploring factors contributing to patient decision-making in the care journey to elective hernia care in Kenya

by Helen W. Li, Jesse Kiprono Too, Sarah Nyanchama Nyariki, Charles Nathan Nessle, Sara Malone, Rachel Matsumoto, Teddy Ashibende Aurah, Jeffrey A. Blatnik, JoAnna Hunter-Squires, Ivan Seno Saruni

Background

Capacity for elective general surgical care is an important reflection of a health system’s ability to meet a population’s surgical needs and is currently known to be inadequate in many low- and middle-income countries. Patient agency is a key, understudied factor which shapes how and when patients ultimately decide to engage with formal care. Understanding factors which influence patient care seeking activity can have important implications for how current and future health systems may be utilized. This study aims to explore how patients approach the navigation and triage of their elective hernia condition within the Kenyan surgical care system.

Methods

We conducted a qualitative study of 38 convenience-sampled patients diagnosed with an elective hernia condition at a tertiary referral hospital in Kenya between November 2023 and March 2024. We utilized Braun and Clarke’s six-step model of thematic analysis to generate key themes across the phases of care seeking, reaching and receiving as modeled in the Three Delays Framework.

Results

We identified three main cross-cutting themes including (1) the flow of power from patients to providers, and vice versa, take the form of consent or knowledge, respectively; (2) trust is a limited currency required for patients to engage with formal care; and (3) internal and external contextual factors remain the foundation for patient-provider care activities. We incorporated these themes together in a framework which illustrates the cyclical nature by which each factor feeds back on the others, ultimately affecting patient care.

Conclusions

Fluctuating flows of patient power and trust interacts with existing infrastructural context to influence the ability of a health system to generate care. Recognizing the interaction of these key factors may have important bearing on the successful implementation of any larger systemic efforts or policies to improve access to elective surgical care.

Sociodemographic intersections and risk of multiple long-term conditions: A systematic review

by Mansuk Daniel Han, Thomas Yates, Kamlesh Khunti, Cameron Razieh, Francesco Zaccardi

Multimorbidity, or multiple long-term conditions (MLTC), is a growing public health concern with implications for quality of life, healthcare utilisation, and premature mortality. Classical explanations for MLTC often treat sociodemographic categories as independent predictors, overlooking the relational dynamics of health inequalities. This systematic review examines how MLTC outcomes vary at the intersections of sociodemographic factors within their relational context. We conducted a systematic search of PubMed, Medline, and Scopus to identify 792 studies. Four studies met inclusion criteria but none were longitudinal, which limits our ability to examine the role of intersectional effects on MLTC outcomes over the life course from this review. A narrative synthesis was conducted due to their wide heterogeneity among the MLTC outcomes of the studies included in this review. The limited evidence may potentially suggest that MLTC outcomes can vary considerably at the intersections of sociodemographic factors. All four studies in this review suggested that the association of income with MLTC outcomes can vary by what other sociodemographic factors it intersects with. The role of disability on MLTC outcomes varied when intersected with ethnicity, at least in the US racial context. A low level of education is a known MLTC risk factor, but when intersected with ethnicity for both men and women in the South African setting, definitive cumulative disadvantages were not found in the projected life expectancy. Future intersectionality-informed quantitative MLTC research should prioritise using longitudinal data and solution-linked variables to inform context-responsive interventions.