Objectives To validate case ascertainment algorithms for identifying individuals experiencing homelessness in health administrative databases between 2007 and 2014; and to estimate homelessness prevalence trends in Ontario, Canada, between 2007 and 2016. Design A population-based retrospective validation study. Setting Ontario, Canada, from 2007 to 2014 (validation) and 2007 to 2016 (estimation). Participants Our reference standard was the known housing status of a longitudinal cohort of housed (n=137 200) and homeless or vulnerably housed (n=686) individuals. Two reference standard definitions of homelessness were adopted: the housing episode and the annual housing experience (any homelessness within a calendar year). Main outcome measures Sensitivity, specificity, positive and negative predictive values and positive likelihood ratios of 30 case ascertainment algorithms for detecting homelessness using up to eight health service databases. Results Sensitivity estimates ranged from 10.8% to 28.9% (housing episode definition) and 18.5% to 35.6% (annual housing experience definition). Specificities exceeded 99% and positive likelihood ratios were high using both definitions. The most optimal algorithm estimates that 59 974 (95% CI 55 231 to 65 208) Ontarians (0.53% of the adult population) experienced homelessness in 2016, a 67.3% increase from 2007. Conclusions In Ontario, case ascertainment algorithms for identifying homelessness had low sensitivity but very high specificity and positive likelihood ratio. The use of health administrative databases may offer opportunities to track individuals experiencing homelessness over time and inform efforts to improve housing and health status in this vulnerable population.

https://bmjopen.bmj.com/content/bmjopen/9/10/e030221.full.pdf

Validation study of health administrative data algorithms to identify individuals experiencing homelessness and estimate population prevalence of homelessness in Ontario, Canada.

INTRODUCTION The Public Health Agency of Canada's Canadian Chronic Disease Surveillance System (CCDSS) produces population-based estimates of chronic disease prevalence and incidence using administrative health data. Our aim was to assess trends in incidence rates over time, trends are essential to understand changes in population risk and to inform policy development. METHODS Incident cases of diagnosed asthma, chronic obstructive pulmonary disease (COPD), diabetes, hypertension, ischemic heart disease (IHD), and stroke were obtained from the CCDSS online infobase for 1999 to 2012. Trends in national and regional incidence estimates were tested using a negative binomial regression model with year as a linear predictor. Subsequently, models with year as a restricted cubic spline were used to test for departures from linearity using the likelihood ratio test. Age and sex were covariates in all models. RESULTS Based on the models with year as a linear predictor, national incidence rates were estimated to have decreased over time for all diseases, except diabetes; regional incidence rates for most diseases and regions were also estimated to have decreased. However, likelihood ratio tests revealed statistically significant departures from a linear year effect for many diseases and regions, particularly for hypertension. CONCLUSION Chronic disease incidence estimates based on CCDSS data are decreasing over time, but not at a constant rate. Further investigations are needed to assess if this decrease is associated with changes in health status, data quality, or physician practices. As well, population characteristics that may influence changing incidence trends also require exploration.

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Trends in chronic disease incidence rates from the Canadian Chronic Disease Surveillance System.

Background Familial hypercholesterolemia (FH) is an inherited condition of elevated serum low-density lipoprotein (LDL) cholesterol leading to premature coronary heart disease. We evaluated whether FH mutations are independently associated with the development of myocardial infarction (MI), after adjusting for LDL cholesterol level and clinical risk factors. Methods In 182 unrelated patients from different families referred with clinically suspected FH, targeted next-generation DNA sequencing was performed on 73 lipid-related genes and 178 single nucleotide polymorphisms, at 300-times mean read depth, to identify monogenic mutations and high-risk single nucleotide polymorphisms. Results Pathogenic FH mutations were identified in 27% of patients. Patients with mutations, compared with those without, were 12 years younger when referred to the lipid clinic (P Conclusion FH is a genetically diverse condition. FH mutations are independently associated with higher risk of premature MI in patients referred for hypercholesterolemia. Therefore, genotyping could guide cardiovascular risk stratification in the personalized treatment of FH.

Genetic Determinants of Myocardial Infarction Risk in Familial Hypercholesterolemia.

The aim of this study was to explore the experiences of occupational disruption during a pandemic among individuals living with chronic disease, with particular attention to experiences related to ...

https://www.tandfonline.com/doi/pdf/10.1080/14427591.2020.1871401

Occupational disruption during a pandemic: Exploring the experiences of individuals living with chronic disease

Non-communicable diseases (NCDs) remain the largest global public health threat. The emerging field of precision public health (PPH) offers a transformative opportunity to capitalize on digital health data to create an agile, responsive and data-driven public health system to actively prevent NCDs. Using learnings from digital health, our aim is to propose a vision toward PPH for NCDs across three horizons of digital health transformation: Horizon 1—digital public health workflows; Horizon 2—population health data and analytics; Horizon 3—precision public health. This perspective provides a high-level strategic roadmap for public health practitioners and policymakers, health system stakeholders and researchers to achieving PPH for NCDs. Two multinational use cases are presented to contextualize our roadmap in pragmatic action: ESP and RiskScape (USA), a mature PPH platform for multiple NCDs, and PopHQ (Australia), a proof-of-concept population health informatics tool to monitor and prevent obesity. Our intent is to provide a strategic foundation to guide new health policy, investment and research in the rapidly emerging but nascent area of PPH to reduce the public health burden of NCDs.

/pdf/precision-public-health-for-non-communicable-diseases-an-296rbi7h.pdf

Precision Public Health for Non-communicable Diseases: An Emerging Strategic Roadmap and Multinational Use Cases

Chronic diseases have a major impact on populations and healthcare systems worldwide. Administrative health data are an ideal resource for chronic disease surveillance because they are population-based and routinely collected. For multi-jurisdictional surveillance, a distributed model is advantageous because it does not require individual-level data to be shared across jurisdictional boundaries. Our objective is to describe the process, structure, benefits, and challenges of a distributed model for chronic disease surveillance across all Canadian provinces and territories (P/Ts) using linked administrative data. The Public Health Agency of Canada (PHAC) established the Canadian Chronic Disease Surveillance System (CCDSS) in 2009 to facilitate standardized, national estimates of chronic disease prevalence, incidence, and outcomes. The CCDSS primarily relies on linked health insurance registration files, physician billing claims, and hospital discharge abstracts. Standardized case definitions and common analytic protocols are applied to the data for each P/T; aggregate data are shared with PHAC and summarized for reports and open access data initiatives. Advantages of this distributed model include: it uses the rich data resources available in all P/Ts; it supports chronic disease surveillance capacity building in all P/Ts; and changes in surveillance methodology can be easily developed by PHAC and implemented by the P/Ts. However, there are challenges: heterogeneity in administrative databases across jurisdictions and changes in data quality over time threaten the production of standardized disease estimates; a limited set of databases are common to all P/Ts, which hinders potential CCDSS expansion; and there is a need to balance comprehensive reporting with P/T disclosure requirements to protect privacy. The CCDSS distributed model for chronic disease surveillance has been successfully implemented and sustained by PHAC and its P/T partners. Many lessons have been learned about national surveillance involving jurisdictions that are heterogeneous with respect to healthcare databases, expertise and analytical capacity, population characteristics, and priorities.

The Canadian Chronic Disease Surveillance System: A model for collaborative surveillance

Background In 2012 the Northwest Territories (NWTs) Public Health Act mandated that all health care providers report immunizations administered in the territory to the Chief Public Health Officer. Shortly after, the Department of Health and Social Services’(DHSS) Disease Registry unit begun working toward the implementation of a new information system to support their immunization registry. Today an amalgamation of excel spreadsheets submitted from the community health centres form the NWT immunization registry.

/pdf/case-5-vaccination-under-the-midnight-sun-validation-of-an-2v9xjvriwh.pdf

Shannon LeBlanc

Papers

The Canadian Chronic Disease Surveillance System: A model for collaborative surveillance

Case 5 : Vaccination Under the Midnight Sun: Validation of an Immunization Registry in the Northwest Territories

Demographic patterns of exposure and transmission for a rural Canadian community outbreak of COVID-19, 2020