Cases and Traces, Platforms and Publics: Big Data and Health Surveillance

2022 ◽  
pp. 271-289
Author(s):  
Sandra Robinson
Keyword(s):  
Big Data ◽  
Health Surveillance

scholarly journals Linking big data for cardiovascular health surveillance – opportunities and challenges using the CANHEART cohort

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Anna Chu ◽  
Deirdre Hennessy ◽  
Sharon Johnston ◽  
Jacob Udell ◽  
Dennis Ko ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Big Data ◽  
Risk Factor ◽  
Cardiovascular Risk ◽  
Population Health ◽  
Cardiovascular Risk Factor ◽  
Cardiovascular Health ◽  
Population Based ◽  
Health Surveillance ◽  
Risk Factor Prevalence

IntroductionOur increasing ability to link large population-based health administrative datasets to create ‘big data’ cohorts offers unique opportunities to conduct health and health services surveillance at lower costs than traditional methods using surveys or primary data collection. However, comparability of findings from big data with traditional methods is unknown. Objectives and ApproachIn the CArdiovascular HEalth in Ambulatory Care Research Team (CANHEART) ‘big data’ initiative, we linked 19 population-based health databases to obtain baseline and 5-year follow-up health information on a cohort of 9.8 million adult residents of Ontario, Canada as of January 2008. We compared cardiovascular risk factor prevalence with results from 3500 participants in the 2007-09 Canadian Health Measures Survey (CHMS), a traditional population health surveillance survey. Additionally, we determined cardiovascular preventative care use and clinical event rates by sex and age. Planned linkages to new data sources will enable continued cohort surveillance of population health-related and care indicators. ResultsCholesterol and glucose levels determined from the CANHEART cohort were comparable to the CHMS, whereas blood pressure values and obesity rates were substantially higher. Overall, receipt of cardiovascular preventive care in the CANHEART cohort was high, with 85.7% of males and 91.8% of females having blood pressure assessments, and 67.8% of males and 79.4% of females having weight assessments. Cholesterol and diabetes screening rates among those recommended for screening were over 75%. Incidence of myocardial infarction, stroke or cardiovascular death was 51% higher among males than females (3.8 and 2.5 events per 1000 person-years, respectively). Challenges encountered in analyzing data included treatment of repeated and time-varying measures, selection of valid diagnostic and physician billing codes, changing coding practices and handling of missing and outlying data. Conclusion/ImplicationsComparability of cardiovascular risk factor prevalence using linked administrative data with survey methods varies by indicator. Selection biases amongst survey participants and different measurement methods could explain discrepancies. The added ability to examine health care indicators longitudinally and by subgroup supports use of linked population-based data to enhance health surveillance.

scholarly journals Augmenting Public Health Surveillance with Big Data: Measurement Framework and Selected Applications

2021 ◽  
Author(s):  
Dominic Vincent Ligot
Keyword(s):  
Public Health ◽  
Big Data ◽  
Public Health Surveillance ◽  
Health Surveillance ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Time Varying ◽  
Data Measurement ◽  
Measurement Framework ◽  
Data Surveillance

This paper examines opportunities for big data in augmenting public health surveillance. We describe the use of social listening, remote sensing and mobility indices in supplementing surveillance data. We discuss the time varying reproductive number as an alternative to the basic reproductive number for monitoring an ongoing pandemic, present the INFORM risk management model for integrating insights, and discuss modalities for decision support systems. Selected examples of big data surveillance applications are presented along with considerations for further work.

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