Determining the Attributable Costs of <i>Clostridium difficile</i> Infections When Exposure Time Is Lacking: Be Wary of “Conditioning on the Future”

Heister, Thomas; Wolkewitz, Martin; Kaier, Klaus

doi:10.1017/ice.2018.42

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…First, we presented a sensitivity analysis in which the time-dependency of exposure in HAP is ignored in order to quantify the potential overestimation of the true effect [33]. Next we conducted an analysis that was done when the exposure time was still unavailable [19, 34]. Specifically, we again ignored the time-dependency of exposure, and rather adjusted for total length of stay.…”

Section: Methodsmentioning

confidence: 99%

Measuring the in-hospital costs of Pseudomonas aeruginosa pneumonia: methodology and results from a German teaching hospital

et al. 2019

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BackgroundPseudomonas aeruginosa-related pneumonia is an ongoing healthcare challenge. Estimating its financial burden is complicated by the time-dependent nature of the disease.MethodsTwo hundred thirty-six cases of Pseudomonas aeruginosa-related pneumonia were recorded at a 2000 bed German teaching hospital between 2011 and 2014. Thirty-five cases (15%) were multidrug-resistant (MDR) Pseudomonas aeruginosa. Hospital- and community-acquired cases were distinguished by main diagnoses and exposure time. The impact of Pseudomonas aeruginosa-related pneumonia on the three endpoints cost, reimbursement, and length of stay was analyzed, taking into account (1) the time-dependent nature of exposure, (2) clustering of costs within diagnostic groups, and (3) additional confounders.ResultsPseudomonas aeruginosa pneumonia is associated with substantial additional costs that are not fully reimbursed. Costs are highest for hospital-acquired cases (€19,000 increase over uninfected controls). However, community-acquired cases are also associated with a substantial burden (€8400 when Pseudomonas aeruginosa pneumonia is the main reason for hospitalization, and €6700 when not). Sensitivity analyses for hospital-acquired cases showed that ignoring or incorrectly adjusting for time-dependency substantially biases results. Furthermore, multidrug-resistance was rare and only showed a measurable impact on the cost of community-acquired cases.ConclusionsPseudomonas aeruginosa pneumonia creates a substantial financial burden for hospitals. This is particularly the case for nosocomial infections. Infection control interventions could yield significant cost reductions. However, to evaluate the potential effectiveness of different interventions, the time-dependent aspects of incremental costs must be considered to avoid introduction of bias.

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Section: Methodsmentioning

confidence: 99%

Measuring the in-hospital costs of Pseudomonas aeruginosa pneumonia: methodology and results from a German teaching hospital

et al. 2019

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“…Conditioning on future events, for example, excluding patients who experience events that are not of primary interest, [22][23][24] violates one of the main principles in time-to-event analysis 25 by introducing selection bias. A recent COVID-19 example is the retrospective study by Mehra et al, 26 where patients for whom one of the treatments of interest was initiated more than 48 h after diagnosis or while they were on mechanical ventilation, as well as patients who received remdesivir, were excluded.…”

Section: Inappropriate Conditioning On Future Eventsmentioning

confidence: 99%

<p>Statistical Analysis of Clinical COVID-19 Data: A Concise Overview of Lessons Learned, Common Errors and How to Avoid Them</p>

Wolkewitz¹,

Lambert²,

Cube³

et al. 2020

CLEP

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By definition, in-hospital patient data are restricted to the time between hospital admission and discharge (alive or dead). For hospitalised cases of COVID-19, a number of events during hospitalization are of interest regarding the influence of risk factors on the likelihood of experiencing these events. The same is true for predicting times from hospital admission of COVID-19 patients to intensive care or from start of ventilation (invasive or non-invasive) to extubation. This logical restriction of the data to the period of hospitalisation is associated with a substantial risk that inappropriate methods are used for analysis. Here, we briefly discuss the most common types of bias which can occur when analysing inhospital COVID-19 data.

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Measuring the Financial Burden of Resistance: What Should Be Compared?

Kaier

Mutters

Wolkewitz

2019

Clinical Infectious Diseases

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Determining the Attributable Costs of Clostridium difficile Infections When Exposure Time Is Lacking: Be Wary of “Conditioning on the Future”

Cited by 3 publications

References 7 publications

Measuring the in-hospital costs of Pseudomonas aeruginosa pneumonia: methodology and results from a German teaching hospital

Measuring the in-hospital costs of Pseudomonas aeruginosa pneumonia: methodology and results from a German teaching hospital

<p>Statistical Analysis of Clinical COVID-19 Data: A Concise Overview of Lessons Learned, Common Errors and How to Avoid Them</p>

Measuring the Financial Burden of Resistance: What Should Be Compared?

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