Using Linked Data and Advanced Analytics to Prioritize Health Concerns within Regions

Introduction Linked population health data have the potential to inform evidence-based actions targeting serious public health concerns. However, large-scale data integration efforts can produce hundreds of population health indicators, which can overwhelm the ability of decision-makers to synthesize and interpret the information. Objectives and Approach Our research uses an existing semantic web application for population health surveillance, the Population Health Record (PopHR). PopHR automates a computational pipeline for linking data sources, building timely population health indicators, and uses artificial intelligence to organize indicators along a determinants of health framework. To assist users in interpreting the thousands of indicators, we developed computational algorithms combining values of multiple indicators across chronic diseases, to prioritize conditions within each region. This analytic approach can assist regional decision-makers in identifying their region’s priority conditions by facilitating the integration and analysis of multiple types of indicators (e.g. disease burden, temporal patterns). Results A pilot implementation of the regional prioritization algorithm focused on indicators defined in the Public Health Agency of Canada’s Chronic Disease Indicators Framework. Within this subset of diseases, we developed a computational algorithm to integrate into a priority index regional estimates of incidence, mortality, and prevalence taking into account the relative importance of each indicators’ outlier status and statistical significance of temporal trends. Our results allowed for the development of region-specific data visualizations dashboards, emphasizing the different factors driving the rankings of indicators within and across regions. For example, regions with higher socioeconomic status having generally lower disease burden are presented with visualizations emphasizing temporal trends and other statistically compelling patterns rather than simple indicators of magnitude. Conclusion/Implications This ranking approach represents initial stages ongoing research, expanding our methods to use machine learning strategies and additional expert knowledge. Current and future prioritization analyses within the PopHR platform offer the potential for public health to gain insights from an otherwise challenging complexity and richness of linked data

Monitoring non-pharmaceutical public health interventions during the COVID-19 pandemic.

Measuring and monitoring non-pharmaceutical interventions is important yet challenging due to the need to clearly define and encode non-pharmaceutical interventions, to collect geographically and socially representative data, and to accurately document the timing at which interventions are initiated and changed. These challenges highlight the importance of integrating and triangulating across multiple databases and the need to expand and fund the mandate for public health organizations to track interventions systematically

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

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

A Framework for Detecting and Classifying Outbreaks of Gastrointestinal Disease

OBJECTIVE: To develop a methodological framework for detecting and classifying outbreaks of gastrointestinal disease on the island of Montreal, with the goal of improving early outbreak detection using simulated surveillance data. INTRODUCTION: Outbreaks of waterborne gastrointestinal disease occur routinely in North America, resulting in considerable morbidity, mortality, and cost (Hrudey, Payment et al. 2003). Outbreak detection methods generally attempt to identify anomalies in time, but do not identify the type or source of an outbreak. We seek to develop a framework for both detection and classification of outbreaks using information in both space and time. Outbreak detection can be improved by using simulated outbreak data to build, validate, and evaluate models that aim to improve accuracy and timeliness of outbreak detection. METHODS: To generate outbreak data, we used a previously validated microsimulation model depicting waterborne outbreaks of gastrointestinal disease (Okhmatovskaia et al. 2010). The model is parameterized based on outbreak characteristics such as concentration and duration of contamination, and calibrated to produce realistic outbreak data (e.g., emergency department visits from GI-illness, laboratory reporting to public health) in space and time. We are interested in identifying unique space-time signatures in the data that would allow not only detection, but also classification based on outbreak type. For example, to be able to detect and classify an outbreak as due to a water plant failure versus an food-borne illness based on unique space-time patterns, even though symptoms and temporal outbreak patterns may be similar. For the detection step, we use a hidden Markov model (HMM) that accounts for spatial information through a spatially correlated random effect with an exponential decay. HMMs have been used previously in disease mapping (Green 2002) but not widely in space-time disease outbreak detection. For the classification step, we use a supervised clustering algorithm to classify the outbreak by source (e.g., water plant location) and type (e.g., disease). RESULTS: Preliminary results for the detection step show that the HMM can distinguish accurately between regions in an outbreak state versus those in a normal state at each time period. Ongoing work for the detection step includes further evaluation of the HMM accuracy as a function of outbreak characteristics. For the classification step, we are evaluating the suitability of different supervised clustering algorithms for identifying the type of outbreak from the HMM results. CONCLUSIONS: If outbreaks are detected rapidly, interventions, such as boil-water advisories, are available to quickly and effectively limit the human and economic impacts. Traditional public health surveillance systems, however, frequently fail to detect waterborne disease outbreaks. Every disease outbreak has unique characteristics; simulation is the best method to estimate the capacity of syndromic surveillance to more efficiently detect different types of enteric disease outbreaks based on a variety of parameters. Outbreak detection can be improved with advances in data availability, such as syndromic surveillance data that will increase timeliness of detection, and space-time information to allow for simultaneous detection and classification of outbreaks by important characteristics (type of outbreak, source of outbreak)

A Framework for Detecting and Classifying Outbreaks of Gastrointestinal Disease

Outbreaks of gastrointestinal disease occur with some frequency in North America, resulting in considerably morbidity, mortality, and cost. Outbreak detection can be improved by using simulated outbreak data to build, validate, and evaluate models that aim to improve accuracy and timeliness of outbreak detection. We have constructed a microsimulation model that depicts reasonable outbreak scenarios in space and time, and explore the use of a hidden Markov model along with supervised learning algorithms to find unique space-time outbreak signatures useful for outbreak classification

Reducing the burden of low back pain: results from a new microsimulation model

Background Low back pain (LBP) causes the highest morbidity burden globally. The purpose of the present study was to project and compare the impact of three strategies for reducing the population health burden of LBP: weight loss, ergonomic interventions, and an exercise program. Methods We have developed a microsimulation model of LBP in Canada using a new modeling platform called SimYouLate. The initial population was derived from Cycle 1 (2001) of the Canadian Community Health Survey (CCHS). We modeled an open population 20 years of age and older. Key variables included age, sex, education, body mass index (BMI), type of work, having back problems, pain level in persons with back problems, and exercise participation. The effects of interventions on the risk of LBP were obtained from the CCHS for the effect of BMI, the Global Burden of Disease Study for occupational risks, and a published meta-analysis for the effect of exercise. All interventions lasted from 2021 to 2040. The population health impact of the interventions was calculated as a difference in years lived with disability (YLDs) between the base-case scenario and each intervention scenario, and expressed as YLDs averted per intervention unit or a proportion (%) of total LBP-related YLDs. Results In the base-case scenario, LBP in 2020 was responsible for 424,900 YLDs in Canada and the amount increased to 460,312 YLDs in 2040. The effects of the interventions were as follows: 27,993 (95% CI 23,373, 32,614) YLDs averted over 20 years per 0.1 unit change in log-transformed BMI (9.5% change in BMI) among individuals who were overweight and those with obesity, 19,416 (16,275, 22,557) YLDs per 1% reduction in the proportion of workers exposed to occupational risks, and 26,058 (22,455, 29,661) YLDs averted per 1% increase in the proportion of eligible patients with back problems participating in an exercise program. Conclusions The study provides new data on the relationship between three types of interventions and the resultant reductions in LBP burden in Canada. According to our model, each of the interventions studied could potentially result in a substantial reduction in LBP-related disability.Medicine, Faculty ofNon UBCMedicine, Department ofPhysical Therapy, Department ofPopulation and Public Health (SPPH), School ofReviewedFacult

Integrated Disease Surveillance to Reduce Data Fragmentation – An Application to Malaria Control

The lack of access to timely health indicators can preclude the design and the effective implementation of infectious diseases control interventions. Our project aims to foster the integration of existing surveillance data to support evidence-based decision-making in malaria. The cornerstone of our approach is the use of a common knowledge platform to scale-up and extend structural and semantic mapping across existing data sources to other geographical regions and global health priority diseases. Upon completion of our project, we will have designed an open-access prototype system capable of sharing comparable surveillance data within and across countrie