Introduction In sexual health services, availability of rapid and accurate point-of-care tests (POCTs) may enable major improvements in care pathway efficiencies and outcomes. Previous economic evaluations of nucleic acid amplification test (NAAT) POCTs for Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) indicate they may provide a cost-effective strategy for screening genitourinary medicine (GUM) attendees. We estimated costs, benefits and cost-effectiveness of three strategies using accurate, rapid NAAT POCTs that could detect different combinations of common multiple sexually transmitted infections (mSTIs) compared with standard care (SC; laboratory-based CT/NG NAAT).
Methods A decision tree was constructed to simulate a hypothetical cohort of 9 65 988 patients, representing annual numbers of GUM attendees in England, symptomatic for lower genitourinary tract infection. The model considered delivery costs (micro-costing) and reimbursement (tariff) to GUM services associated with diagnosing and managing STIs. POCT strategies compared to SC were: A) POCT for CT and NG; B) POCT for CT-NG and Mycoplasma genitalium (MG); C) POCT for CT-NG-MG and Trichomonas vaginalis. Data came from published literature and unpublished estimates.
Results SC was cheaper than all POCT strategies when micro-costing, but POCT C was the cheapest strategy for tariff costings. POCT C’s incremental cost-effectiveness ratio (ICER) was £36 585 per quality-adjusted life year (QALY) gained compared to SC when micro-costing; it was cost-saving (by £26,451,382) when tariff costing was applied. POCT C also generated most benefits, with 2 40 467 fewer clinic attendances, 808 fewer onward STI transmissions, and 2 35 135 averted inappropriate treatments compared to SC.
Conclusion POCTs that detect STI diagnoses may be cost-effective, cost-saving and improve patient management. However, there is variation by costing strategy, patient population, clinical setting and patient pathways. Further evidence is needed to populate model parameters to reduce uncertainty in economic analyses.
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