A Step toward Tuberculosis Elimination in a Low-Incidence Country: Successful Diagnosis and Treatment of Latent Tuberculosis Infection in a Refugee Clinic

Objectives. Approximately 65 percent of tuberculosis (TB) cases in Canada each year occur from reactivation in foreign-born individuals. Refugees are at high risk after immigration. Routine screening of this population for latent TB infection (LTBI) is generally considered infeasible. We evaluated the outcome of LTBI screening and treatment amongst refugees. Methods. Government-sponsored refugees in Edmonton are seen at the New Canadians' Clinic and screened for TB and LTBI. We reviewed records of patients between 2009 and 2011. Completeness of initial assessment, diagnosis of latent infection, and completion of LTBI treatment were evaluated. Treatment for LTBI was offered when patients had a positive Tuberculin Skin Test (TST) and risk factors for progression to TB. An Interferon-Gamma Release Assay (IGRA) was performed on all other TST positives; treatment is only offered if it was positive. Results. 949 refugees were evaluated. 746 TSTs were read, with 265 positive individuals. IGRA testing was performed in 203 TST positive individuals without other TB risk factors; 110 were positive. LTBI treatment was offered to 147 of 151 eligible patients, 141 accepted, and 103 completed a treatment course. Conclusion. We observed high proportions of patient retention, completion of investigations, and treatment. This care model promises to be a component of effective TB prevention in this high-risk population

A Cost-Effectiveness Analysis of a Decolonization Protocol for Staphylococcus aureus Prior to Hip and Knee Arthroplasty in Alberta, Canada

There are over 100,000 knee/hip replacements yearly in Canada. While these procedures improve mobility and quality of life, approximately 1% develop complex surgical site infections (SSIs) after surgery. Detailed costing analysis of these infections, particularly in Canada, is lacking. We assessed incidence and cost of complex SSIs following primary hip/knee arthroplasty in patients across Alberta. We then evaluated the cost-effectiveness of an evidence-based decolonization protocol in patients prior to hip/knee arthroplasty in Alberta, compared with standard care (no decolonization) using decision analysis. Among 24,667 operations, 1.04% developed a complex SSI. The most common causative pathogen was Staphylococcus aureus (38%). Mean first-year costs for the infected and non-infected cohort were CAN $95,321 (IQR49,623 – 120,636) and$19,893 (IQR12,610 – 19,723), respectively. The decolonization protocol was associated with lower risk of complex SSI and cost savings of $153/person. A decolonization protocol should be considered for implementation in Alberta to reduce infections and save costs

Economic evaluations and their use in infection prevention and control: a narrative review

Abstract Background The objective of this review is to provide a comprehensive overview of the different types of economic evaluations that can be utilized by Infection Prevention and Control practitioners with a particular focus on the use of the quality adjusted life year, and its associated challenges. We also highlight existing economic evaluations published within Infection Prevention and Control, research gaps and future directions. Design Narrative Review. Conclusions To date the majority of economic evaluations within Infection Prevention and Control are considered partial economic evaluations. Acknowledging the challenges, which include variable utilities within infection prevention and control, a lack of randomized controlled trials, and difficulty in modelling infectious diseases in general, future economic evaluation studies should strive to be consistent with published guidelines for economic evaluations. This includes the use of quality adjusted life years. Further research is required to estimate utility scores of relevance within Infection Prevention and Control

A Step toward Tuberculosis Elimination in a Low-Incidence Country: Successful Diagnosis and Treatment of Latent Tuberculosis Infection in a Refugee Clinic

Objectives. Approximately 65 percent of tuberculosis (TB) cases in Canada each year occur from reactivation in foreign-born individuals. Refugees are at high risk after immigration. Routine screening of this population for latent TB infection (LTBI) is generally considered infeasible. We evaluated the outcome of LTBI screening and treatment amongst refugees. Methods. Government-sponsored refugees in Edmonton are seen at the New Canadians’ Clinic and screened for TB and LTBI. We reviewed records of patients between 2009 and 2011. Completeness of initial assessment, diagnosis of latent infection, and completion of LTBI treatment were evaluated. Treatment for LTBI was offered when patients had a positive Tuberculin Skin Test (TST) and risk factors for progression to TB. An Interferon-Gamma Release Assay (IGRA) was performed on all other TST positives; treatment is only offered if it was positive. Results. 949 refugees were evaluated. 746 TSTs were read, with 265 positive individuals. IGRA testing was performed in 203 TST positive individuals without other TB risk factors; 110 were positive. LTBI treatment was offered to 147 of 151 eligible patients, 141 accepted, and 103 completed a treatment course. Conclusion. We observed high proportions of patient retention, completion of investigations, and treatment. This care model promises to be a component of effective TB prevention in this high-risk population

Machine learning risk estimation and prediction of death in continuing care facilities using administrative data

Abstract In this study, we aimed to identify the factors that were associated with mortality among continuing care residents in Alberta, during the coronavirus disease 2019 (COVID-19) pandemic. We achieved this by leveraging and linking various administrative datasets together. Then, we examined pre-processing methods in terms of prediction performance. Finally, we developed several machine learning models and compared the results of these models in terms of performance. We conducted a retrospective cohort study of all continuing care residents in Alberta, Canada, from March 1, 2020, to March 31, 2021. We used a univariable and a multivariable logistic regression (LR) model to identify predictive factors of 60-day all-cause mortality by estimating odds ratios (ORs) with a 95% confidence interval. To determine the best sensitivity–specificity cut-off point, the Youden index was employed. We developed several machine learning models to determine the best model regarding performance. In this cohort study, increased age, male sex, symptoms, previous admissions, and some specific comorbidities were associated with increased mortality. Machine learning and pre-processing approaches offer a potentially valuable method for improving risk prediction for mortality, but more work is needed to show improvement beyond standard risk factors

A cost-effectiveness analysis of mupirocin and chlorhexidine gluconate for Staphylococcus aureus decolonization prior to hip and knee arthroplasty in Alberta, Canada compared to standard of care

Abstract Background While decolonization of Staphylococcus aureus reduces surgical site infection (SSI) rates following hip and knee arthroplasty, its cost-effectiveness is uncertain. We sought to examine the cost-effectiveness of a decolonization protocol for Staphylococcus aureus prior to hip and knee replacement in Alberta compared to standard care – no decolonization. Methods Decision analytic models and a probabilistic sensitivity analysis were used for a cost-effectiveness analysis, with the effectiveness of decolonization based on a large published pre- and post- intervention trial. The primary outcomes of the models were infections prevented and health care costs. We modelled the cost-effectiveness of decolonization in a hypothetical cohort of adult patients undergoing hip and knee replacement in Alberta, Canada. Information on the incidence of complex surgical site infections (SSIs), as well as the cost of care for patients with and without SSIs was taken from a provincial infection control database, and health administrative data. Results Use of the decolonization bundle was cost saving compared to usual care ($153/person), and resulted in 16 complex Staphylococcus aureus SSIs annually as opposed to 32 (with approximately 8000 hip or knee arthroplasties performed). The probabilistic sensitivity analysis demonstrated that the majority (84$161/person). Conclusions Decolonization for Staphylococcus aureus prior to hip and knee replacements resulted in cost savings and fewer SSIs, and should be considered prior to these procedures

Protocol for a parallel economic evaluation of a trial comparing two surgical strategies in severe complicated intra-abdominal sepsis: the COOL-cost study

Abstract Background The risk of death in severe complicated intra-abdominal sepsis (SCIAS) remains high despite decades of surgical and antimicrobial research. New management strategies are required to improve outcomes. The Closed Or Open after Laparotomy (COOL) trial investigates an open-abdomen (OA) approach with active negative pressure peritoneal therapy. This therapy is hypothesized to better manage peritoneal bacterial contamination, drain inflammatory ascites, and reduce the risk of intra-abdominal hypertension leading to improved survival and decreased complications. The total costs and cost-effectiveness of this therapy (as compared with standard fascial closure) are unknown. Methods We propose a parallel cost-utility analysis of this intervention to be conducted alongside the 1-year trial, extrapolating beyond that using decision analysis. Using resource use metrics (e.g., length of stay, re-admissions) from patients at all study sites and microcosting data from patients enrolled in Calgary, Alberta, the mean cost difference between treatment arms will be established from a publicly-funded health care payer perspective. Quality of life will be measured at 6 months and 1 year postoperatively with the Euroqol EQ-5D-5 L and SF-36 surveys. A within-trial analysis will establish cost and utility at 1 year, using a bootstrapping approach to provide confidence intervals around an estimated incremental cost-effectiveness ratio. If neither operative strategy is economically dominant, Markov modeling will be used to extrapolate the cost per quality-adjusted life years gained to 2-, 5-, 10-year, and lifetime horizons. Future costs and benefits will be discounted at 1.5% per annum. A cost-effectiveness acceptability curve will be generated using Monte Carlo simulation. If all trial outcomes are similar, the primary analysis will default to a cost-minimization approach. Subgroup analysis will be carried out for patients with and without septic shock at presentation, and for patients whose initial APACHE II scores are > 20 versus ≤ 20. Discussion In addition to an estimate of the clinical effectiveness of an OA approach for SCIAS, an understanding of its cost effectiveness will be required prior to its adoption in any resource-constrained environment. We will estimate this key parameter for use by clinicians and policymakers. Trial Registration ClinicalTrials.gov, NCT03163095, registered May 22, 2017

The impact of COVID-19 on hospital admissions and emergency department visits: A population-based study.

BackgroundAs a result of the novel coronavirus disease 2019 (COVID-19), there have been widespread changes in healthcare access. We conducted a retrospective population-based study in Alberta, Canada (population 4.4 million), where there have been approximately 1550 hospital admissions for COVID-19, to determine the impact of COVID-19 on hospital admissions and emergency department (ED visits), following initiation of a public health emergency act on March 15, 2020.MethodsWe used multivariable negative binomial regression models to compare daily numbers of medical/surgical hospital admissions via the ED between March 16-September 23, 2019 (pre COVID-19) and March 16-September 23, 2020 (post COVID-19 public health measures). We compared the most frequent diagnoses for hospital admissions pre/post COVID-19 public health measures. A similar analysis was completed for numbers of daily ED visits for any reason with a particular focus on ambulatory care sensitive conditions (ACSC).FindingsThere was a significant reduction in both daily medical (incident rate ratio (IRR) 0.86, pConclusionsDespite a relatively low volume of COVID-19 hospital admissions in Alberta, there was an extensive impact on our healthcare system with fewer admissions to hospital and ED visits. This work generates hypotheses around causes for reduced hospital admissions and ED visits which warrant further investigation. As most publicly funded health systems struggle with health-system capacity routinely, understanding how these reductions can be safely sustained will be critical

Healthcare utilization and adverse outcomes stratified by sex, age and long-term care residency using the Alberta COVID-19 Analytics and Research Database (ACARD): a population-based descriptive study

Abstract Background Understanding the epidemiology of Coronavirus Disease of 2019 (COVID-19) in a local context is valuable for both future pandemic preparedness and potential increases in COVID-19 case volume, particularly due to variant strains. Methods Our work allowed us to complete a population-based study on patients who tested positive for COVID-19 in Alberta from March 1, 2020 to December 15, 2021. We completed a multi-centre, retrospective population-based descriptive study using secondary data sources in Alberta, Canada. We identified all adult patients (≥ 18 years of age) tested and subsequently positive for COVID-19 (including only the first incident case of COVID-19) on a laboratory test. We determined positive COVID-19 tests, gender, age, comorbidities, residency in a long-term care (LTC) facility, time to hospitalization, length of stay (LOS) in hospital, and mortality. Patients were followed for 60 days from a COVID-19 positive test. Results Between March 1, 2020 and December 15, 2021, 255,037 adults were identified with COVID-19 in Alberta. Most confirmed cases occurred among those less than 60 years of age (84.3%); however, most deaths (89.3%) occurred among those older than 60 years. Overall hospitalization rate among those who tested positive was 5.9%. Being a resident of LTC was associated with substantial mortality of 24.6% within 60 days of a positive COVID-19 test. The most common comorbidity among those with COVID-19 was depression. Across all patients 17.3% of males and 18.6% of females had an unplanned ambulatory visit subsequent to their positive COVID-19 test. Conclusions COVID-19 is associated with extensive healthcare utilization. Residents of LTC were substantially impacted during the COVID-19 pandemic with high associated mortality. Further work should be done to better understand the economic burden associated with related healthcare utilization following a COVID-19 infection to inform healthcare system resource allocation, planning, and forecasting

Validating administrative data to identify complex surgical site infections following cardiac implantable electronic device implantation: a comparison of traditional methods and machine learning

Abstract Background Cardiac implantable electronic device (CIED) surgical site infections (SSIs) have been outpacing the increases in implantation of these devices. While traditional surveillance of these SSIs by infection prevention and control would likely be the most accurate, this is not practical in many centers where resources are constrained. Therefore, we explored the validity of administrative data at identifying these SSIs. Methods We used a cohort of all patients with CIED implantation in Calgary, Alberta where traditional surveillance was done for infections from Jan 1, 2013 to December 31, 2019. We used this infection subgroup as our “gold standard” and then utilized various combinations of administrative data to determine which best optimized the sensitivity and specificity at identifying infection. We evaluated six approaches to identifying CIED infection using administrative data, which included four algorithms using International Classification of Diseases codes and/or Canadian Classification of Health Intervention codes, and two machine learning models. A secondary objective of our study was to assess if machine learning techniques with training of logistic regression models would outperform our pre-selected codes. Results We determined that all of the pre-selected algorithms performed well at identifying CIED infections but the machine learning model was able to produce the optimal method of identification with an area under the receiver operating characteristic curve (AUC) of 96.8%. The best performing pre-selected algorithm yielded an AUC of 94.6%. Conclusions Our findings suggest that administrative data can be used to effectively identify CIED infections. While machine learning performed the most optimally, in centers with limited analytic capabilities a simpler algorithm of pre-selected codes also has excellent yield. This can be valuable for centers without traditional surveillance to follow trends in SSIs over time and identify when rates of infection are increasing. This can lead to enhanced interventions for prevention of SSIs