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Background: Understanding inequalities in SARS-CoV-2 transmission associated with the social determinants of health could help the development of effective mitigation strategies that are responsive to local transmission dynamics. This study aims to quantify social determinants of geographic concentration of SARS-CoV-2 cases across 16 census metropolitan areas (hereafter, cities) in 4 Canadian provinces, British Columbia, Manitoba, Ontario and Quebec. Methods: We used surveillance data on confirmed SARS-CoV-2 cases and census data for social determinants at the level of the dissemination area (DA). We calculated Gini coefficients to determine the overall geographic heterogeneity of confirmed cases of SARSCoV-2 in each city, and calculated Gini covariance coefficients to determine each city's heterogeneity by each social determinant (income, education, housing density and proportions of visible minorities, recent immigrants and essential workers). We visualized heterogeneity using Lorenz (concentration) curves. Results: We observed geographic concentration of SARS-CoV-2 cases in cities, as half of the cumulative cases were concentrated in DAs containing 21%-35% of their population, with the greatest geographic heterogeneity in Ontario cities (Gini coefficients 0.32-0.47), followed by British Columbia (0.23-0.36), Manitoba (0.32) and Quebec (0.28-0.37). Cases were disproportionately concentrated in areas with lower income and educational attainment, and in areas with a higher proportion of visible minorities, recent immigrants, high-density housing and essential workers. Although a consistent feature across cities was concentration by the proportion of visible minorities, the magnitude of concentration by social determinant varied across cities. Interpretation: Geographic concentration of SARS-CoV-2 cases was observed in all of the included cities, but the pattern by social determinants varied. Geographically prioritized allocation of resources and services should be tailored to the local drivers of inequalities in transmission in response to the resurgence of SARS-CoV-2. [ABSTRACT FROM AUTHOR]

To the Editor: School closure has been proposed as a strategy for slowing transmission of pandemic influenza (1). Studies of influenza A(H1N1)pdm 2009 (pH1N1) suggested that early and sustained school [...]

Abstract Background The quality of physician communication skills influences health-related decisions, including use of cancer screening tests. We assessed whether patient-physician communication examination scores in a national, standardized clinical skills examination predicted future use of screening mammography (SM). Methods Cohort study of 413 physicians taking the Medical Council of Canada clinical skills examination between 1993 and 1996, with follow up until 2006. Administrative claims for SM performed within 12 months of a comprehensive health maintenance visit for women 50–69 years old were reviewed. Multivariable regression was used to estimate the relationship between physician communication skills exam score and patients’ SM use while controlling for other factors. Results Overall, 33.8 % of 96,708 eligible women who visited study physicians between 1993 and 2006 had an SM in the 12 months following an index visit. Patient-related factors associated with increased SM use included higher income, non-urban residence, low Charlson co-morbidity index, prior benign breast biopsy and an interval >12 months since the previous mammogram. Physician-related factors associated with increased use of SM included female sex, surgical specialty, and higher communication skills score. After adjusting for physician and patient-related factors, the odds of SM increased by 24 % for 2SD increase in communication score (OR: 1.24, 95 % CI: 1.11 - 1.38). This impact was even greater in urban areas (OR 1.30, 95 % CI: 1.16, 1.46) and did not vary with practice experience (interaction p-value 0.74). Conclusion Physicians with better communication skills documented by a standardized licensing examination were more successful at obtaining SM for their patients.

Abstract Background Syndromic surveillance systems are plagued by high false-positive rates. In chronic disease monitoring, investigators have identified several factors that predict the accuracy of case definitions based on diagnoses in administrative data, and some have even incorporated these predictors into novel case detection methods, resulting in a significant improvement in case definition accuracy. Based on findings from these studies, we sought to identify physician, patient, encounter, and billing characteristics associated with the positive predictive value (PPV) of case definitions for 5 syndromes (fever, gastrointestinal, neurological, rash, and respiratory (including influenza-like illness)). Methods The study sample comprised 4,330 syndrome-positive visits from the claims of 1,098 randomly-selected physicians working in Quebec, Canada in 2005-2007. For each visit, physician-facilitated chart review was used to assess whether the same syndrome was present in the medical chart (gold standard). We used multivariate logistic regression analyses to estimate the association between claim-chart agreement about the presence of a syndrome and physician, patient, encounter, and billing characteristics. Results The likelihood of the medical chart agreeing with the physician claim about the presence of a syndrome was higher when the treating physician had billed many visits for the same syndrome recently (ORper 10 visit, 1.05; 95% CI, 1.01-1.08), had a lower workload (ORper 10 claims, 0.93; 95% CI, 0.90-0.97), and when the patient was younger (ORper 5 years of age, 0.96; 95% CI, 0.94-0.97), and less socially deprived (ORmost versus least deprived, 0.76; 95% CI, 0.60-0.95). Conclusions Many physician, patient, encounter, and billing characteristics associated with the PPV of surveillance case definition are accessible to public health, and could be used to reduce false-positive alerts by surveillance systems, either by focusing on the data most likely to be accurate, or by adjusting the observed data for known biases in diagnosis reporting and performing surveillance using the adjusted values.

Abstract Background Much remains unknown about the effect of timing and prioritization of vaccination against pandemic (pH1N1) 2009 virus on health outcomes. We adapted a city-level contact network model to study different campaigns on influenza morbidity and mortality. Methods We modeled different distribution strategies initiated between July and November 2009 using a compartmental epidemic model that includes age structure and transmission network dynamics. The model represents the Greater Vancouver Regional District, a major North American city and surrounding suburbs with a population of 2 million, and is parameterized using data from the British Columbia Ministry of Health, published studies, and expert opinion. Outcomes are expressed as the number of infections and deaths averted due to vaccination. Results The model output was consistent with provincial surveillance data. Assuming a basic reproduction number = 1.4, an 8-week vaccination campaign initiated 2 weeks before the epidemic onset reduced morbidity and mortality by 79-91% and 80-87%, respectively, compared to no vaccination. Prioritizing children and parents for vaccination may have reduced transmission compared to actual practice, but the mortality benefit of this strategy appears highly sensitive to campaign timing. Modeling the actual late October start date resulted in modest reductions in morbidity and mortality (13-25% and 16-20%, respectively) with little variation by prioritization scheme. Conclusion Delays in vaccine production due to technological or logistical barriers may reduce potential benefits of vaccination for pandemic influenza, and these temporal effects can outweigh any additional theoretical benefits from population targeting. Careful modeling may provide decision makers with estimates of these effects before the epidemic peak to guide production goals and inform policy. Integration of real-time surveillance data with mathematical models holds the promise of enabling public health planners to optimize the community benefits from proposed interventions before the pandemic peak.

Abstract Background Community clinics offer potential for timelier outbreak detection and monitoring than emergency departments. However, the accuracy of syndrome definitions used in surveillance has never been evaluated in community settings. This study's objective was to assess the accuracy of syndrome definitions based on diagnostic codes in physician claims for identifying 5 syndromes (fever, gastrointestinal, neurological, rash, and respiratory including influenza-like illness) in community clinics. Methods We selected a random sample of 3,600 community-based primary care physicians who practiced in the fee-for-service system in the province of Quebec, Canada in 2005-2007. We randomly selected 10 visits per physician from their claims, stratifying on syndrome type and presence, diagnosis, and month. Double-blinded chart reviews were conducted by telephone with consenting physicians to obtain information on patient diagnoses for each sampled visit. The sensitivity, specificity, and positive predictive value (PPV) of physician claims were estimated by comparison to chart review. Results 1,098 (30.5%) physicians completed the chart review. A chart entry on the date of the corresponding claim was found for 10,529 (95.9%) visits. The sensitivity of syndrome definitions based on diagnostic codes in physician claims was low, ranging from 0.11 (fever) to 0.44 (respiratory), the specificity was high, and the PPV was moderate to high, ranging from 0.59 (fever) to 0.85 (respiratory). We found that rarely used diagnostic codes had a higher probability of being false-positives, and that more commonly used diagnostic codes had a higher PPV. Conclusions Future research should identify physician, patient, and encounter characteristics associated with the accuracy of diagnostic codes in physician claims. This would enable public health to improve syndromic surveillance, either by focusing on physician claims whose diagnostic code is more likely to be accurate, or by using all physician claims and weighing each according to the likelihood that its diagnostic code is accurate.

Abstract Background Computer simulation models are used increasingly to support public health research and policy, but questions about their quality persist. The purpose of this article is to review the principles and methods for validation of population-based disease simulation models. Methods We developed a comprehensive framework for validating population-based chronic disease simulation models and used this framework in a review of published model validation guidelines. Based on the review, we formulated a set of recommendations for gathering evidence of model credibility. Results Evidence of model credibility derives from examining: 1) the process of model development, 2) the performance of a model, and 3) the quality of decisions based on the model. Many important issues in model validation are insufficiently addressed by current guidelines. These issues include a detailed evaluation of different data sources, graphical representation of models, computer programming, model calibration, between-model comparisons, sensitivity analysis, and predictive validity. The role of external data in model validation depends on the purpose of the model (e.g., decision analysis versus prediction). More research is needed on the methods of comparing the quality of decisions based on different models. Conclusion As the role of simulation modeling in population health is increasing and models are becoming more complex, there is a need for further improvements in model validation methodology and common standards for evaluating model credibility.

Abstract Background Immunization information systems (IISs) are electronic registries used to monitor individual vaccination status and assess vaccine coverage. IISs are currently not widely used across Canada, where health jurisdictions employ a range of approaches to capture influenza immunization information. Conducted in advance of the 2009 H1N1 vaccination campaign, the objectives of this study were to understand the perceived value of individual-level data and IISs for influenza control, identify ideal system functions, and explore barriers to implementation. Methods In July and August 2009, semi-structured interviews were conducted with key informants engaged in vaccine delivery and/or pandemic planning at regional, provincial/territorial and federal levels across Canada. Key informants were recruited using a combination of convenience and snowball sampling methodologies. Qualitative analysis was used to extract themes from interview content. Results Patient management, assessment of vaccine coverage, and evaluation of safety and effectiveness were identified as public health priorities that would be achieved in a more timely manner, and with greater accuracy, through the use of an IIS. Features described as ideal included system flexibility, rapid data entry, and universality. Financial and human resource constraints as well as coordination between immunization providers were expressed as barriers to implementation. Conclusions IISs were perceived as valuable by key informants for strengthening management capacity and improving evaluation of both seasonal and pandemic influenza vaccination campaigns. However, certain implementation restrictions may need to be overcome for these benefits to be achieved.

Abstract Background The collection of individual-level pandemic (H1N1) 2009 influenza immunization data was considered important to facilitate optimal vaccine delivery and accurate assessment of vaccine coverage. These data are also critical for research aimed at evaluating the new vaccine's safety and effectiveness. Systems used to collect immunization data include manual approaches in which data are collected and retained on paper, electronic systems in which data are captured on computer at the point of vaccination and hybrid systems which are comprised of both computerized and manual data collection components. This study's objective was to compare the efficiencies and perceptions of data collection methods employed during Canada's pandemic (H1N1) 2009 influenza vaccination campaign. Methods/Design A pan-Canadian observational study was conducted in a convenience sample of public health clinics and healthcare institutions during the H1N1 vaccination campaign in the fall of 2009. The study design consisted of three stages: Stage 1 involved passive observation of the site's layout, processes and client flow; Stage 2 entailed timing site staff on 20 clients through five core immunization tasks: i) client registration, ii) medical history collection, iii) medical history review, iv) vaccine administration record keeping and v) preparation of proof of vaccine administration for the client; in Stage 3, site staff completed a questionnaire regarding perceived usability of the site's data collection approach. Before the national study began, a pilot study was conducted in three seasonal influenza vaccination sites in Ontario, to both test that the proposed methodology was logistically feasible and to determine inter-rater reliability in the measurements of the research staff. Comparative analyses will be conducted across the range of data collection methods with respect to time required to collect immunization data, number and type of individual-level data elements collected, and clinic staff perceptions of the usability of the method employed at their site, using analysis of variance (ANOVA). Discussion Various data collection methods were employed at immunization sites across Canada during the pandemic (H1N1) 2009 influenza vaccination campaign. Our comparison of methods can facilitate planning an efficient, coordinated approach for collecting immunization data in future influenza seasons.

Large amounts of longitudinal health records are now available for dynamic monitoring of the underlying processes governing the observations. However, the health status progression across time is not typically observed directly: records are observed only when a subject interacts with the system, yielding irregular and often sparse observations. This suggests that the observed trajectories should be modeled via a latent continuous‐time process potentially as a function of time‐varying covariates. We develop a continuous‐time hidden Markov model to analyze longitudinal data accounting for irregular visits and different types of observations. By employing a specific missing data likelihood formulation, we can construct an efficient computational algorithm. We focus on Bayesian inference for the model: this is facilitated by an expectation‐maximization algorithm and Markov chain Monte Carlo methods. Simulation studies demonstrate that these approaches can be implemented efficiently for large data sets in a fully Bayesian setting. We apply this model to a real cohort where patients suffer from chronic obstructive pulmonary disease with the outcome being the number of drugs taken, using health care utilization indicators and patient characteristics as covariates. [ABSTRACT FROM AUTHOR]

Abstract Background There is strong support for educating physicians in medical informatics, and the benefits of such education have been clearly identified. Despite this, North American medical schools do not routinely provide education in medical informatics. Methods We conducted a qualitative study to identify issues facing the introduction of medical informatics into an undergraduate medical curriculum. Nine key informants at the University of Toronto medical school were interviewed, and their responses were transcribed and analyzed to identify consistent themes. Results The field of medical informatics was not clearly understood by participants. There was, however, strong support for medical informatics education, and the benefits of such education were consistently identified. In the curriculum we examined, medical informatics education was delivered informally and inconsistently through mainly optional activities. Issues facing the introduction of medical informatics education included: an unclear understanding of the discipline; faculty and administrative detractors and, the dense nature of the existing undergraduate medical curriculum. Conclusions The identified issues may present serious obstacles to the introduction of medical informatics education into an undergraduate medicine curriculum, and we present some possible strategies for addressing these issues.

The idea of “precision public health” (PPH) was proposed as an alternative to a one-size-fits-all approach to improving population health, which is not always effective. PPH aims to develop and apply interventions in a customized way, taking into account the detailed information about the target group. To enable the implementation of PPH in practice, we are developing an ontology-driven software platform that provides: a) access to detailed up-to-date information about population health, b) a structured machine-readable repository of evidence about public health interventions, and c) a set of intelligent tools to facilitate the assessment of evidence transferability, i.e. to determine how well certain interventions fit a given population. [ABSTRACT FROM AUTHOR]

Urinary tract infections caused by the bacterium Escherichia coli are among the most frequently encountered infections and are a common reason for antimicrobial prescriptions. Resistance to fluoroquinolone antimicrobial agents, particularly ciprofloxacin, has increased in recent decades. It is intuitive that variation in fluoroquinolone resistance is driven by changes in antimicrobial use, but careful study of this association requires the use of time-series methods. Between April 2010 and December 2014, we studied seasonal variation in resistance to ciprofloxacin, trimethoprim-sulfamethoxazole, and ampicillin in community-acquired urinary E. coli isolates in Montreal, Quebec, Canada. Using dynamic linear models, we investigated whether seasonal variation in resistance could be explained by seasonal variation in community antimicrobial use. We found a positive association between total fluoroquinolone use lagged by 1 and 2 months and the proportion of isolates resistant to ciprofloxacin. Our results suggest that resistance to ciprofloxacin is responsive to short-term variation in antimicrobial use. Thus, antimicrobial stewardship campaigns to reduce fluoroquinolone use, particularly in the winter when use is highest, are likely to be a valuable tool in the struggle against antimicrobial resistance. [ABSTRACT FROM AUTHOR]