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Background The opioid epidemic in North America has been driven by an increase in the use and potency of prescription opioids, with ensuing excessive opioid-related deaths. Internationally, there are lower rates of opioid-related mortality, possibly because of differences in prescribing and health system policies. Our aim was to compare opioid prescribing rates in patients without cancer, across 5 centers in 4 countries. In addition, we evaluated differences in the type, strength, and starting dose of medication and whether these characteristics changed over time. Methods and findings We conducted a retrospective multicenter cohort study of adults who are new users of opioids without prior cancer. Electronic health records and administrative health records from Boston (United States), Quebec and Alberta (Canada), United Kingdom, and Taiwan were used to identify patients between 2006 and 2015. Standard dosages in morphine milligram equivalents (MMEs) were calculated according to The Centers for Disease Control and Prevention. Age- and sex-standardized opioid prescribing rates were calculated for each jurisdiction. Of the 2,542,890 patients included, 44,690 were from Boston (US), 1,420,136 Alberta, 26,871 Quebec (Canada), 1,012,939 UK, and 38,254 Taiwan. The highest standardized opioid prescribing rates in 2014 were observed in Alberta at 66/1,000 persons compared to 52, 51, and 18/1,000 in the UK, US, and Quebec, respectively. The median MME/day (IQR) at initiation was highest in Boston at 38 (20 to 45); followed by Quebec, 27 (18 to 43); Alberta, 23 (9 to 38); UK, 12 (7 to 20); and Taiwan, 8 (4 to 11). Oxycodone was the first prescribed opioid in 65% of patients in the US cohort compared to 14% in Quebec, 4% in Alberta, 0.1% in the UK, and none in Taiwan. One of the limitations was that data were not available from all centers for the entirety of the 10-year period. Conclusions In this study, we observed substantial differences in opioid prescribing practices for non-cancer pain between jurisdictions. The preference to start patients on higher MME/day and more potent opioids in North America may be a contributing cause to the opioid epidemic., Author(s): Meghna Jani 1,2, Nadyne Girard 3,4, David W. Bates 5,6,7, David L. Buckeridge 3,4, Therese Sheppard 1, Jack Li 8,9, Usman Iqbal 8,9, Shelly Vik 10,11, Colin Weaver 10,11, [...]

Objectives: Several automated algorithms for epidemiological surveillance in hospitals have been proposed. However, the usefulness of these methods to detect nosocomial outbreaks remains unclear. The goal of this review was to describe outbreak detection algorithms that have been tested within hospitals, consider how they were evaluated, and synthesize their results. Methods: We developed a search query using keywords associated with hospital outbreak detection and searched the MEDLINE database. To ensure the highest sensitivity, no limitations were initially imposed on publication languages and dates, although we subsequently excluded studies published before 2000. Every study that described a method to detect outbreaks within hospitals was included, without any exclusion based on study design. Additional studies were identified through citations in retrieved studies. Results: Twenty-nine studies were included. The detection algorithms were grouped into 5 categories: simple thresholds (n = 6), statistical process control (n = 12), scan statistics (n = 6), traditional statistical models (n = 6), and data mining methods (n = 4). The evaluation of the algorithms was often solely descriptive (n = 15), but more complex epidemiological criteria were also investigated (n = 10). The performance measures varied widely between studies: e.g., the sensitivity of an algorithm in a real world setting could vary between 17 and 100%. Conclusion: Even if outbreak detection algorithms are useful complementary tools for traditional surveillance, the heterogeneity in results among published studies does not support quantitative synthesis of their performance. A standardized framework should be followed when evaluating outbreak detection methods to allow comparison of algorithms across studies and synthesis of results. [ABSTRACT FROM AUTHOR]

Objective: The optimal way to measure antimicrobial use in hospital populations, as a complement to surveillance of resistance is still unclear. Using respiratory isolates and antimicrobial prescriptions of nine intensive care units (ICUs), this study aimed to identify the indicator of antimicrobial use that predicted prevalence and incidence rates of resistance with the best accuracy. Methods: Retrospective cohort study including all patients admitted to three neonatal (NICU), two pediatric (PICU) and four adult ICUs between April 2006 and March 2010. Ten different resistance / antimicrobial use combinations were studied. After adjustment for ICU type, indicators of antimicrobial use were successively tested in regression models, to predict resistance prevalence and incidence rates, per 4-week time period, per ICU. Binomial regression and Poisson regression were used to model prevalence and incidence rates, respectively. Multiplicative and additive models were tested, as well as no time lag and a one 4-week-period time lag. For each model, the mean absolute error (MAE) in prediction of resistance was computed. The most accurate indicator was compared to other indicators using t-tests. Results: Results for all indicators were equivalent, except for 1/20 scenarios studied. In this scenario, where prevalence of carbapenem-resistant Pseudomonas sp. was predicted with carbapenem use, recommended daily doses per 100 admissions were less accurate than courses per 100 patient-days (p = 0.0006). Conclusions: A single best indicator to predict antimicrobial resistance might not exist. Feasibility considerations such as ease of computation or potential external comparisons could be decisive in the choice of an indicator for surveillance of healthcare antimicrobial use. [ABSTRACT FROM AUTHOR]

Objective: Indicators of antimicrobial use have been described previously, but few studies have compared their accuracy in prediction of antimicrobial resistance in hospital settings. This study aimed to identify conditions under which significant differences would be observed in the predictive accuracy of indicators in the context of surveillance of intensive care units (ICUs). Methods: Ten resistance / antimicrobial use combinations were studied. We used simulation to determine if Québec’s network of 81 ICUs or the National Healthcare Safety Network (NHSN) of 2952 ICUs are large enough to allow the detection of predetermined differences between the most accurate and 1) the second most accurate indicator, and 2) the least accurate indicator, in more than 80% of simulations. For each indicator, we simulated absolute errors in prediction for each ICU and each 4-week period, for surveillance lasting up to 5 years. Absolute errors were generated following a binomial distribution, using mean absolute errors (MAEs) observed in 9 ICUs as the average proportion; simulated MAEs were compared using t-tests. This was repeated 1000 times per scenario. Results: When comparing the two most accurate indicators, 80% power was reached less often with the Québec network versus the NHSN (0/20 versus 2/20 scenarios, with 5 years of surveillance data), a finding reinforced when comparing the most and least accurate indicators (3/20 versus 20/20 scenarios). When simulating 1 year of data, scenarios reaching an 80% power dropped to 0/20, comparing the two most accurate indicators with the larger network, and to 1/20, comparing the most and least accurate indicators with the smaller network. Conclusion: Most of the time (72%), identifying an indicator of antimicrobial use predicting antimicrobial resistance with a better accuracy was not possible. The choice of an indicator for an eventual surveillance system should rely on criteria other that predictive accuracy. [ABSTRACT FROM AUTHOR]

Background: There is stigma attached to the identification of residents carrying antimicrobial resistant organisms (ARO) in long term care homes, yet there is a need to collect data about their prevalence for public health surveillance and intervention purposes. Objective: We conducted a point prevalence study to assess ARO rates in long term care homes in Ontario using a secure data collection system. Methods: All long term care homes in the province were asked to provide colonization or infection counts for methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant enterococci (VRE), and extended-spectrum beta-lactamase (ESBL) as recorded in their electronic medical records, and the number of current residents. Data was collected online during the October-November 2011 period using a Paillier cryptosystem that allows computation on encrypted data. Results: A provably secure data collection system was implemented. Overall, 82% of the homes in the province responded. MRSA was the most frequent ARO identified at 3 cases per 100 residents, followed by ESBL at 0.83 per 100 residents, and VRE at 0.56 per 100 residents. The microbiological findings and their distribution were consistent with available provincial laboratory data reporting test results for AROs in hospitals. Conclusions: We describe an ARO point prevalence study which demonstrated the feasibility of collecting data from long term care homes securely across the province and providing strong privacy and confidentiality assurances, while obtaining high response rates. [ABSTRACT FROM AUTHOR]

We evaluated a novel strategy to improve the performance of outbreak detection algorithms, namely setting the alerting threshold separately in each region according to the disease incidence in that region. By using data on hand, foot and mouth disease in Shandong province, China, we evaluated the impact of disease incidence on the performance of outbreak detection algorithms (EARS-C1, C2 and C3). Compared to applying the same algorithm and threshold to the whole region, setting the optimal threshold in each region according to the level of disease incidence (i.e., high, middle, and low) enhanced sensitivity (C1: from 94.4% to 99.1%, C2: from 93.5% to 95.4%, C3: from 91.7% to 95.4%) and reduced the number of alert signals (the percentage of reduction is C1∶4.3%, C2∶11.9%, C3∶10.3%). Our findings illustrate a general method for improving the accuracy of detection algorithms that is potentially applicable broadly to other diseases and regions. [ABSTRACT FROM AUTHOR]

Background: Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. Methods and Findings: We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998–1999). Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type). Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. Conclusions: Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers) with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve their predictive ability. [ABSTRACT FROM AUTHOR]

Introduction: Individual-level immunization data captured electronically can facilitate evidence-based decision-making and planning. Populating individual-level records through manual data entry is time-consuming. An alternative is to use scannable forms, completed at the point of vaccination and subsequently scanned and exported to a database or registry. To explore the suitability of this approach for collecting immunization data, we conducted a feasibility study in two settings in Ontario, Canada. Methods and Findings: Prior to the 2011-2012 influenza vaccination campaign, we developed a scannable form template and a corresponding database that captured required demographic and clinical data elements. We examined efficiency, data quality, and usability through time observations, record audits, staff interviews, and client surveys. The mean time required to scan and verify forms (62.3 s) was significantly shorter than manual data entry (69.5 s) in one organization, whereas there was no difference (36.6 s vs. 35.4 s) in a second organization. Record audits revealed no differences in data quality between records populated by scanning versus manual data entry. Data processing personnel and immunized clients found the processes involved to be straightforward, while nurses and managers had mixed perceptions regarding the ease and merit of using scannable forms. Printing quality and other factors rendered some forms unscannable, necessitating manual entry. Conclusions: Scannable forms can facilitate efficient data entry, but certain features of the forms, as well as the workflow and infrastructure into which they are incorporated, should be evaluated and adapted if scannable forms are to be a meaningful alternative to manual data entry. [ABSTRACT FROM AUTHOR]

Introduction: Several national and regional central line-associated bloodstream infections (CLABSI) surveillance programs do not require continuous hospital participation. We evaluated the effect of different hospital participation requirements on the validity of annual CLABSI incidence rate benchmarks for intensive care units (ICUs). Methods: We estimated the annual pooled CLABSI incidence rates for both a real regional (<100 ICUs) and a simulated national (600 ICUs) surveillance program, which were used as a reference for the simulations. We simulated scenarios where the annual surveillance participation was randomly or non-randomly reduced. Each scenario's annual pooled CLABSI incidence rate was estimated and compared to the reference rates in terms of validity, bias, and proportion of simulation iterations that presented valid estimates (ideal if≥90%). Results: All random scenarios generated valid CLABSI incidence rates estimates (bias -0.37 to 0.07 CLABSI/1000 CVC-days), while non-random scenarios presented a wide range of valid estimates (0 to 100%) and higher bias (-2.18 to 1.27 CLABSI/ 1000 CVC-days). In random scenarios, the higher the number of participating ICUs, the shorter the participation required to generate ≥90% valid replicates. While participation requirements in a countrywide program ranged from 3 to 13 surveillance blocks (1 block = 28 days), requirements for a regional program ranged from 9 to 13 blocks. Conclusions: Based on the results of our model of national CLABSI reporting, the shortening of participation requirements may be suitable for nationwide ICU CLABSI surveillance programs if participation months are randomly chosen. However, our regional models showed that regional programs should opt for continuous participation to avoid biased benchmarks. [ABSTRACT FROM AUTHOR]

Background: There is little empirical evidence in support of a relationship between rates of influenza infection and level of material deprivation (i.e., lack of access to goods and services) and social deprivation (i.e. lack of social cohesion and support). Method: Using validated population-level indices of material and social deprivation and medical billing claims for outpatient clinic and emergency department visits for influenza from 1996 to 2006, we assessed the relationship between neighbourhood rates of influenza and neighbourhood levels of deprivation using Bayesian ecological regression models. Then, by pooling data from neighbourhoods in the top decile (i.e., most deprived) and the bottom decile, we compared rates in the most deprived populations to the least deprived populations using age- and sex-standardized rate ratios. Results: Deprivation scores ranged from one to five with five representing the highest level of deprivation. We found a 21% reduction in rates for every 1 unit increase in social deprivation score (rate ratio [RR] 0.79, 95% Credible Interval [CrI] 0.66, 0.97). There was little evidence of a meaningful linear relationship with material deprivation (RR 1.06, 95% CrI 0.93, 1.24). However, relative to neighbourhoods with deprivation scores in the bottom decile, those in the top decile (i.e., most materially deprived) had substantially higher rates (RR 2.02, 95% Confidence Interval 1.99, 2.05). Conclusion: Though it is hypothesized that social and material deprivation increase risk of acute respiratory infection, we found decreasing healthcare utilization rates for influenza with increasing social deprivation. This finding may be explained by the fewer social contacts and, thus, fewer influenza exposure opportunities of the socially deprived. Though there was no evidence of a linear relationship with material deprivation, when comparing the least to the most materially deprived populations, we observed higher rates in the most materially deprived populations. [ABSTRACT FROM AUTHOR]

Background: Our understanding of the global scale of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection remains incomplete: Routine surveillance data underestimate infection and cannot infer on population immunity; there is a predominance of asymptomatic infections, and uneven access to diagnostics. We meta-analyzed SARS-CoV-2 seroprevalence studies, standardized to those described in the World Health Organization's Unity protocol (WHO Unity) for general population seroepidemiological studies, to estimate the extent of population infection and seropositivity to the virus 2 years into the pandemic., Methods and Findings: We conducted a systematic review and meta-analysis, searching MEDLINE, Embase, Web of Science, preprints, and grey literature for SARS-CoV-2 seroprevalence published between January 1, 2020 and May 20, 2022. The review protocol is registered with PROSPERO (CRD42020183634). We included general population cross-sectional and cohort studies meeting an assay quality threshold (90% sensitivity, 97% specificity; exceptions for humanitarian settings). We excluded studies with an unclear or closed population sample frame. Eligible studies-those aligned with the WHO Unity protocol-were extracted and critically appraised in duplicate, with risk of bias evaluated using a modified Joanna Briggs Institute checklist. We meta-analyzed seroprevalence by country and month, pooling to estimate regional and global seroprevalence over time; compared seroprevalence from infection to confirmed cases to estimate underascertainment; meta-analyzed differences in seroprevalence between demographic subgroups such as age and sex; and identified national factors associated with seroprevalence using meta-regression. We identified 513 full texts reporting 965 distinct seroprevalence studies (41% low- and middle-income countries [LMICs]) sampling 5,346,069 participants between January 2020 and April 2022, including 459 low/moderate risk of bias studies with national/subnational scope in further analysis. By September 2021, global SARS-CoV-2 seroprevalence from infection or vaccination was 59.2%, 95% CI [56.1% to 62.2%]. Overall seroprevalence rose steeply in 2021 due to infection in some regions (e.g., 26.6% [24.6 to 28.8] to 86.7% [84.6% to 88.5%] in Africa in December 2021) and vaccination and infection in others (e.g., 9.6% [8.3% to 11.0%] in June 2020 to 95.9% [92.6% to 97.8%] in December 2021, in European high-income countries [HICs]). After the emergence of Omicron in March 2022, infection-induced seroprevalence rose to 47.9% [41.0% to 54.9%] in Europe HIC and 33.7% [31.6% to 36.0%] in Americas HIC. In 2021 Quarter Three (July to September), median seroprevalence to cumulative incidence ratios ranged from around 2:1 in the Americas and Europe HICs to over 100:1 in Africa (LMICs). Children 0 to 9 years and adults 60+ were at lower risk of seropositivity than adults 20 to 29 (p < 0.001 and p = 0.005, respectively). In a multivariable model using prevaccination data, stringent public health and social measures were associated with lower seroprevalence (p = 0.02). The main limitations of our methodology include that some estimates were driven by certain countries or populations being overrepresented., Conclusions: In this study, we observed that global seroprevalence has risen considerably over time and with regional variation; however, over one-third of the global population are seronegative to the SARS-CoV-2 virus. Our estimates of infections based on seroprevalence far exceed reported Coronavirus Disease 2019 (COVID-19) cases. Quality and standardized seroprevalence studies are essential to inform COVID-19 response, particularly in resource-limited regions., Competing Interests: I have read the journal policy and the authors of this manuscript have the following competing interests: RKA, MW, HW, ZL, XM, CC, MYL, DB, JP, MPC, ML, MS, GRD, NI, CZ, SP, HPR, TY, KCN, DK, SAA, ND, CD, NAD, EL, RKI, ASB, ELB, AS, JC and NB report grants from Canada’s COVID-19 Immunity Task Force through the Public Health Agency of Canada, and the Canadian Medical Association Joule Innovation Fund. RKA, MW, HW, ZL, CC, MYL, NB also report grants from the World Health Organisation and the Robert Koch Institute. RKA reports personal fees from the Public Health Agency of Canada and the Bill and Melinda Gates Foundation Strategic Investment Fund, as well as equity in Alethea Medical (Outside the submitted work). MPC reports grants from McGill Interdisciplinary Initiative in Infection and Immunity and Canadian Institute of Health Research, and personal fees from GEn1E Lifesciences (Outside the submitted work), nplex biosciences (Outside the submitted work), Kanvas biosciences (Outside the submitted work). JP reports grants from MedImmune (Outside the submitted work) and Sanofi-Pasteur (Outside the submitted work), grants and personal fees from Merck (Outside the submitted work) and AbbVie (Outside the submitted work), and personal fees from AstraZeneca (Outside the submitted work). DB reports grants from the World Health Organization, Canadian Institutes of Health Research, Natural Sciences and Engineering Council of Canada (Outside the submitted work), Institute national d excellence en sante et service sociaux (Outside the submitted work), and personal fees from McGill University Health Centre (Outside the submitted work) and Public Health Agency of Canada (Outside the submitted work). CC reports funding from Sanofi Pasteur (Outside of the submitted work). TY reports working for Health Canada as a part-time Senior Policy Analyst with the COVID-19 Testing and Screening Expert Panel, from Nov 2020-Jun 2021 (Outside of the submitted work). TH reports funding recieved from the United States Centers for Disease Control and Prevention for Columbia University (Outside of the submitted work). Author HCL declares receiving funding as a WHO consultant from WHO Solidarity Response Fund and the German Federal Ministry of Health COVID-19 Research and Development., (Copyright: © 2022 Bergeri et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)