Cost-Effectiveness Analysis of Four Common Diagnostic Methods for Clostridioides difficile Infection

Treated, hospital-onset Clostridiodes difficile infection: An evaluation of predictors and feasibility of benchmarking comparing 2 risk-adjusted models among 265 hospitals

OBJECTIVES

To evaluate the incidence of a candidate definition of healthcare facility-onset, treated Clostridioides difficile (CD) infection (cHT-CDI) and to identify variables and best model fit of a risk-adjusted cHT-CDI metric using extractable electronic heath data.

METHODS

We analyzed 9,134,276 admissions from 265 hospitals during 2015-2020. The cHT-CDI events were defined based on the first positive laboratory final identification of CD after day 3 of hospitalization, accompanied by use of a CD drug. The generalized linear model method via negative binomial regression was used to identify predictors. Standardized infection ratios (SIRs) were calculated based on 2 risk-adjusted models: a simple model using descriptive variables and a complex model using descriptive variables and CD testing practices. The performance of each model was compared against cHT-CDI unadjusted rates.

RESULTS

The median rate of cHT-CDI events per 100 admissions was 0.134 (interquartile range, 0.023-0.243). Hospital variables associated with cHT-CDI included the following: higher community-onset CDI (CO-CDI) prevalence; highest-quartile length of stay; bed size; percentage of male patients; teaching hospitals; increased CD testing intensity; and CD testing prevalence. The complex model demonstrated better model performance and identified the most influential predictors: hospital-onset testing intensity and prevalence, CO-CDI rate, and community-onset testing intensity (negative correlation). Moreover, 78% of the hospitals ranked in the highest quartile based on raw rate shifted to lower percentiles when we applied the SIR from the complex model.

CONCLUSIONS

Hospital descriptors, aggregate patient characteristics, CO-CDI burden, and clinical testing practices significantly influence incidence of cHT-CDI. Benchmarking a cHT-CDI metric is feasible and should include facility and clinical variables.

Budget Impact Analysis of Adopting a One-Step Nucleic Acid Amplification Testing (NAAT) Alone Diagnostic Pathway for Clostridioides difficile in Japan Compared to a Two-Step Algorithm with Glutamate Dehydrogenase/Toxin Followed by NAAT

Clostridioides difficile infection (CDI) is a major healthcare-associated infection that leads to a significant health economic burden in Japan. Using a decision tree model, we evaluated the budget impact of adopting a one-step nucleic acid amplification test (NAAT) alone pathway compared to a two-step diagnostic algorithm with glutamate dehydrogenase (GDH) and toxin antigen, followed by NAAT. The analysis was conducted from the government payer's perspective for 100,000 symptomatic, hospitalized adults requiring a CDI diagnostic test. One-way sensitivity analysis was conducted for all data inputs. The NAAT alone strategy costed JPY 225,886,360 (USD 2,424,714) more, but was more effective, resulting in 1749 more patients accurately diagnosed and 91 fewer deaths compared to the two-step algorithm. Additionally, the NAAT alone pathway costed JPY 26,146 (USD 281) less per true positive CDI diagnosed. The total budget impact, and cost per CDI diagnosed was most sensitive to GDH sensitivity in one-way sensitivity analysis, where a lower GDH sensitivity resulted in greater cost savings with the NAAT alone pathway. Findings from this budget impact analysis can guide the adoption of a NAAT alone pathway for CDI diagnosis in Japan.

Defining the decision problem: A scoping review of economic evaluations for Clostridioides difficile interventions

BACKGROUND

Clostridioides difficile infection (CDI) is the leading cause of health care-associated infectious diarrhoea. Several preventative and treatment interventions exist, however, decisions for their use are typically made independent of other interventions along the care pathway.

AIM

To assess how the scope of the decision problem is defined in economic evaluations of C. difficile interventions.

METHODS

We conducted a scoping review following the Joanna Briggs Institute framework using a comprehensive literature search with C. difficile and economic evaluation as key search concepts. Study selection and extraction was performed independently by two reviewers. We conducted an in-depth analysis of all cost-utility and cost-effectiveness analyses. Care pathway domains (i.e., infection prevention and control, antimicrobial stewardship programs, prevention, diagnostics, treatment) were defined iteratively, and each study was classified according to the scope of the decision problem: a) one intervention, one domain; b) one intervention, multiple domains; c) multiple interventions, one domain; d) multiple interventions, multiple domains.

RESULTS

A total of 3,886 studies were identified, 116 included in our descriptive overview, and 46 included in our in-depth analysis. Most studies limited the scope of the decision problem to one intervention (43/46; 93%). Only three studies (3/46; 7%) assessed multiple interventions - either as bundled versus standalone interventions for prevention (i.e., a single domain), or as sequences of treatments for initial and recurrent infection (i.e., multiple domains). No study assessed multiple interventions across prevention and treatment domains.

CONCLUSIONS

Economic evaluations for CDI assess narrowly defined decision problems which may have implications for optimal healthcare resource allocation.

Efficiency of Diagnostic Testing for Helicobacter pylori Infections-A Systematic Review

Background: The most recommended treatment for a Helicobacter pylori infection is high doses of combined antibiotics. The objective of this article is to perform a systematic review of the economic evaluation studies applied to assess the efficiency of diagnostic testing for H. pylori infections, so that their main characteristics can be identified and to learn from the literature how the antimicrobial resistance (AMR) issue is incorporated into these economic evaluations. Methods: We conducted a systematic review to compare the costs and clinical effectiveness of diagnostic strategies for H. pylori infections. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and extracted the items from the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist. Results: We found thirteen articles that were of good quality according to CHEERS: six studies focused on diagnostics of Helicobacter pylori infections associated with dyspepsia and four on duodenal ulcers. Testing was found to be the most cost-effective strategy in eight articles. Four studies considered AMR. Conclusions: Testing was more cost-effective than empirical treatment, except in cases of high prevalence (as with developing countries) or when patients could be stratified according to their comorbidities. The introduction of AMR into the model may change the efficiency of the testing strategy.

A passive monitoring tool using hospital administrative data enables earlier specific detection of healthcare-acquired infections

BACKGROUND

Healthcare-associated infections impose a significant burden on the healthcare system. Current methods for detecting these infections are constrained by combinations of high cost, long processing times and imperfect accuracy, reducing their effectiveness.

METHODS

This study examined whether the amount of time a patient spends on a ward with other patients clinically suspected of infection, termed 'co-presence', can be used as a tool to predict subsequent healthcare-associated infection. Compared with contact tracing, this leverages passively collected electronic data rather than manually collected data, allowing for improved monitoring. All 133,304 inpatient records between 2011 and 2015 were abstracted from a healthcare system in the UK. The area under the receiver-operator curve (AUROC) for each of five pathogens was calculated based on co-presence time, sensitivity and specificity of the test, and how much earlier co-presence would have predicted infection for the true-positive cases.

FINDINGS

For the five pathogens, AUROC ranged from 0.92 to 0.99, and was 0.52 for the negative control. Optimal cut-points of co-presence ranged from 25 to 59 h, and would have led to detection of true-positive cases up to an average of 1 day earlier.

INTERPRETATION

These findings show that co-presence time would help to predict healthcare-acquired infection, and would do so earlier than the current standard of care. Using this measure prospectively in hospitals based on real-time data could limit the consequences of infection, both by being able to treat individual infected patients earlier, and by preventing potential secondary infections stemming from the original infected patient.