David L. Buckeridge

Estimated epidemiologic parameters and morbidity associated with pandemic H1N1 influenza

Background: In the face of an influenza pandemic, accurate estimates of epidemiologic parameters are required to help guide decision-making. We sought to estimate epidemiologic parameters for pandemic H1N1 influenza using data from initial reports of laboratory-confirmed cases. Methods: We obtained data on laboratory-confirmed cases of pandemic H1N1 influenza reported in the province of Ontario, Canada, with dates of symptom onset between Apr. 13 and June 20, 2009. Incubation periods and duration of symptoms were estimated and fit to parametric distributions. We used competing-risk models to estimate risk of hospital admission and case-fatality rates. We used a Markov Chain Monte Carlo model to simulate disease transmission. Results: The median incubation period was 4 days and the duration of symptoms was 7 days. Recovery was faster among patients less than 18 years old than among older patients (hazard ratio 1.23, 95% confidence interval 1.06–1.44). The risk of hospital admission was 4.5% (95% CI 3.8%–5.2%) and the case-fatality rate was 0.3% (95% CI 0.1%–0.5%). The risk of hospital admission was highest among patients less than 1 year old and those 65 years or older. Adults more than 50 years old comprised 7% of cases but accounted for 7 of 10 initial deaths (odds ratio 28.6, 95% confidence interval 7.3–111.2). From the simulation models, we estimated the following values (and 95% credible intervals): a mean basic reproductive number (R0, the number of new cases created by a single primary case in a susceptible population) of 1.31 (1.25–1.38), a mean latent period of 2.62 (2.28–3.12) days and a mean duration of infectiousness of 3.38 (2.06–4.69) days. From these values we estimated a serial interval (the average time from onset of infectiousness in a case to the onset of infectiousness in a person infected by that case) of 4–5 days. Interpretation: The low estimates for R0 indicate that effective mitigation strategies may reduce the final epidemic impact of pandemic H1N1 influenza.

Medication-related falls in the elderly: causative factors and preventive strategies.

People are living to older age. Falls constitute a leading cause of injuries, hospitalization and deaths among the elderly. Older people fall more often for a variety of reasons: alterations in physiology and physical functioning, and the use (and misuse) of medications needed to manage their multiple conditions. Pharmacological factors that place the elderly at greater risk of drug-related side effects include changes in body composition, serum albumin, total body water, and hepatic and renal functioning. Drug use is one of the most modifiable risk factors for falls and falls-related injuries. Fall-risk increasing drugs (FRIDs) include drugs for cardiovascular diseases (such as digoxin, type 1a anti-arrhythmics and diuretics), benzodiazepines, antidepressants, antiepileptics, antipsychotics, antiparkinsonian drugs, opioids and urological spasmolytics. Psychotropic and benzodiazepine drug use is most consistently associated with falls. Despite the promise of a more favourable side-effect profile, evidence shows that atypical antipsychotic medications and selective serotonin reuptake inhibitor antidepressants do not reduce the risk of falls and hip fractures. Despite multiple efforts with regards to managing medication-associated falls, there is no clear evidence for an effective intervention. Stopping or lowering the dose of psychotropic drugs and benzodiazepines does work, but ensuring a patient remains off these drugs is a challenge. Computer-assisted alerts coupled with electronic prescribing tools are a promising approach to lowering the risk of falls as the use of information technologies expands within healthcare.

Methodological Review: Outbreak detection through automated surveillance: A review of the determinants of detection

Public health agencies and other groups have invested considerable resources in automated surveillance systems over the last decade. These systems generally follow syndromes in pre-diagnostic data drawn from sources such as emergency department visits. A main goal of syndromic surveillance systems is to detect outbreaks rapidly and the number of studies evaluating outbreak detection has increased recently. This paper reviews these studies with the goal of identifying the determinants of outbreak detection in automated syndromic surveillance systems. The review identified 35 studies with 22 studies (63%) relying on naturally occurring outbreaks and 13 studies (37%) relying on simulated outbreaks. In general, the results from these studies suggest that syndromic surveillance systems are capable of detecting some types of disease outbreaks rapidly with high sensitivity. The determinants of detection included characteristics of the system and of the outbreak. Influential system characteristics included representativeness, the outbreak detection algorithm, and the specificity of the algorithm. Important outbreak characteristics included the magnitude and shape of the signal and the timing of the outbreak. Future evaluations should aim to address inconsistencies in the evidence noted in this review and to identify the potential influence of other factors on outbreak detection.

The determinants of 2009 pandemic A/H1N1 influenza vaccination: A systematic review

BACKGROUND Pandemic A/H1N1 influenza vaccine coverage varied widely across countries. To understand the factors influencing pandemic influenza vaccination and to guide the development of successful vaccination programs for future influenza pandemics, we identified and summarized studies examining the determinants of vaccination during the 2009 influenza pandemic. METHODS We performed a systematic literature review using the PubMED electronic database from June 2009 to February 2011. We included studies examining an association between a possible predictive variable and actual receipt of the pandemic A/H1N1 influenza vaccine. We excluded studies examining intention or willingness to receive the vaccine. RESULTS Twenty-seven studies were identified from twelve countries. Pandemic influenza vaccine coverage varied from 4.8% to 92%. Coverage varied by population sub-group, country, and assessment method used. Most studies used questionnaires to estimate vaccine coverage, however seven (26%) used a vaccination registry. Factors that positively influenced pandemic influenza vaccination were: male sex, younger age, higher education, being a doctor, being in a priority group for which vaccination was recommended, receiving a prior seasonal influenza vaccination, believing the vaccine to be safe and/or effective, and obtaining information from official medical sources. CONCLUSIONS Vaccine coverage during the pandemic varied widely across countries and population sub-groups. We identified some consistent determinants of this variation that can be targeted to increase vaccination during future influenza pandemics.

A qualitative study of Canada’s experience with the implementation of electronic health information technology

Background In 2001, Canada Health Infoway unveiled a plan to implement a national system of interoperable electronic health records. This government-funded corporation introduced a novel model for interprovincial/territorial collaboration to establish core aspects of a national framework. Despite this

Information Technology and Global Surveillance of Cases of 2009 H1N1 Influenza

1.6 billion initiative, Canada continues to lag behind other Western countries in adopting electronic health records. We conducted a study to identify the success of different aspects of the Canadian plan and ways to improve the adoption of electronic health records. Methods We used a case study approach to assess the 10-year history of Canada’s e-health plan. National reports and documents were reviewed, and structured interviews were conducted with 29 key stakeholders representing national and provincial organizations responsible for establishing policy and strategic direction for health information technology. Using grounded theory, we analyzed transcripts of the interviews to identify themes and their relationships. Results Key stakeholders identified funding, national standards, patient registries and digital imaging as important achievements of the e-health plan. Lack of an e-health policy, inadequate involvement of clinicians, failure to establish a business case for using electronic health records, a focus on national rather than regional interoperability, and inflexibility in approach were seen as barriers to adoption of the plan. Interpretation To accelerate adoption of electronic health records and timely return on investment, an e-health policy needs to be tightly aligned with the major strategic directions of health care reform. Adoption needs to be actively fostered through a bottom-up, clinical-needs-first approach, a national policy for investment in electronic health records, and financial incentives based on patient outcomes that can be achieved with electronic health records.

Risk of Injury Associated with Opioid Use in Older Adults

Real-time forms of technology online are creating new ways to detect and track emerging disease threats, even weak signals from diverse areas.

Drug, Patient, and Physician Characteristics Associated With Off-label Prescribing in Primary Care

OBJECTIVES: To estimate the dose‐related risk of injuries in older adults associated with the use of low‐, medium‐, and high‐potency opioids.

Validation of population-based disease simulation models: a review of concepts and methods

BACKGROUND Off-label prescribing may lead to adverse drug events. Little is known about its prevalence and determinants resulting from challenges in documenting treatment indication. METHODS We used the Medical Office of the XXI Century electronic health record network in Quebec, Canada, where documentation of treatment indication is mandatory. One hundred thirteen primary care physicians wrote 253 347 electronic prescriptions for 50 823 patients from January 2005 through December 2009. Each drug indication was classified as on-label or off-label according to the Health Canada drug database. We identified off-label uses lacking strong scientific evidence. Alternating logistic regression was used to estimate the association between off-label use and drug, patient, and physician characteristics. RESULTS The prevalence of off-label use was 11.0%; of the off-label prescriptions, 79.0% lacked strong scientific evidence. Off-label use was highest for central nervous system drugs (26.3%), including anticonvulsants (66.6%), antipsychotics (43.8%), and antidepressants (33.4%). Drugs with 3 or 4 approved indications were associated with less off-label use compared with drugs with 1 or 2 approved indications (6.7% vs 15.7%; adjusted odds ratio [AOR], 0.44; 95% CI, 0.41-0.48). Drugs approved after 1995 were prescribed off-label less often than were drugs approved before 1981 (8.0% vs 17.0%; AOR, 0.46; 95% CI, 0.42-0.50). Patients with a Charlson Comorbidity Index of 1 or higher had lower off-label use than did patients with an index of 0 (9.6% vs 11.7%; AOR, 0.94; 95% CI, 0.91-0.97). Physicians with evidence-based orientation were less likely to prescribe off-label (AOR, 0.93; 95% CI, 0.88-0.99), a 7% reduction per 5 points in the evidence section of the Evidence-Practicality-Conformity Scale. CONCLUSIONS Off-label prescribing is common and varies by drug, patient, and physician characteristics. Electronic prescribing should document treatment indication to monitor off-label use.

Infection Acquisition Following Intensive Care Unit Room Privatization

BackgroundComputer 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.MethodsWe 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.ResultsEvidence 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.ConclusionAs 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.