Comparing Twitter data to routine data sources in public health surveillance for the 2015 Pan/Parapan American Games: an ecological study

Abstract Background The availability of data generated from different sources is increasing with the possibility to link these data sources together. However, linked administrative data can be complex to use and may require advanced expertise and skills in statistical analysis. The main objectives of this study were to describe the current use of data linkage at the individual level and the artificial intelligence (AI) in routine public health activities, and to identify the related health outcome and intervention indicators and determinants of health for non-communicable diseases. Method We performed a survey across European countries to explore the current practices applied by national institutes of public health and health information and statistics for innovative use of data sources (i.e., the use of data linkage and/or the AI). Results The use of data linkage and the AI at national institutes of public health and health information and statistics in Europe varies. The majority of European countries use data linkage in routine by applying a deterministic method or a combination of two types of linkages (i.e., deterministic & probabilistic) for public health surveillance and research purposes. The use of AI to estimate health indicators is not frequent at national institutes of public health and health information and statistics. Using linked data, 46 health outcome indicators related to seven health conditions, 34 indicators related to determinants and 23 to health interventions were estimated in routine. Complex data regulation laws, lack of human resources, skills and problems with data governance, were reported by European countries as obstacles to link different data sources in routine for public health surveillance and research. Conclusions Our results highlight that the majority of European countries have integrated data linkage in routine public health activities but a few use the AI. A sustainable national health information system and a robust data governance framework allowing to link different data sources are essential to support evidence-informed health policy development process. Building analytical capacity and awareness of the added value of data linkage in national institutes is necessary for improving the utilization of linked data in order to improve the monitoring of public health activities.

Download Full-text

Using Twitter for Public Health Surveillance from Monitoring and Prediction to Public Response

Data ◽

10.3390/data4010006 ◽

2018 ◽

Vol 4 (1) ◽

pp. 6 ◽

Cited By ~ 16

Author(s):

Sophie Jordan ◽

Sierra Hovet ◽

Isaac Fung ◽

Hai Liang ◽

King-Wa Fu ◽

...

Keyword(s):

Public Health ◽

Public Health Surveillance ◽

Public Health Research ◽

Global Scale ◽

Health Surveillance ◽

Short Text ◽

Related Information ◽

Twitter Data ◽

Public Health Information ◽

Health Related

Twitter is a social media platform where over 500 million people worldwide publish their ideas and discuss diverse topics, including their health conditions and public health events. Twitter has proved to be an important source of health-related information on the Internet, given the amount of information that is shared by both citizens and official sources. Twitter provides researchers with a real-time source of public health information on a global scale, and can be very important in public health research. Classifying Twitter data into topics or categories is helpful to better understand how users react and communicate. A literature review is presented on the use of mining Twitter data or similar short-text datasets for public health applications. Each method is analyzed for ways to use Twitter data in public health surveillance. Papers in which Twitter content was classified according to users or tweets for better surveillance of public health were selected for review. Only papers published between 2010–2017 were considered. The reviewed publications are distinguished by the methods that were used to categorize the Twitter content in different ways. While comparing studies is difficult due to the number of different methods that have been used for applying Twitter and interpreting data, this state-of-the-art review demonstrates the vast potential of utilizing Twitter for public health surveillance purposes.

Download Full-text

Using Dynamic Bayesian Networks for Incorporating Nontraditional Data Sources in Public Health Surveillance

Lecture Notes in Social Networks - Public Health Intelligence and the Internet ◽

10.1007/978-3-319-68604-2_3 ◽

2017 ◽

pp. 45-58

Author(s):

Masoumeh Izadi ◽

Katia Charland ◽

David L. Buckeridge

Keyword(s):

Public Health ◽

Bayesian Networks ◽

Public Health Surveillance ◽

Dynamic Bayesian Networks ◽

Health Surveillance ◽

Data Sources

Download Full-text

Comparing Syndromic Data to Discharge Data to Measure Opioid Overdose Emergency Department Visits

Online Journal of Public Health Informatics ◽

10.5210/ojphi.v11i1.9742 ◽

2019 ◽

Vol 11 (1) ◽

Author(s):

Emilia S. Pasalic ◽

Alana Marie Vivolo-Kantor ◽

Pedro Martinez

Keyword(s):

Public Health ◽

Emergency Department ◽

Public Health Surveillance ◽

Health Surveillance ◽

Data Sources ◽

Opioid Overdose ◽

Public Health Response ◽

Discharge Data ◽

Emergency Department Data ◽

Health Response

ObjectiveEpidemiologists will understand the differences between syndromic and discharge emergency department data sources, the strengths and limitations of each data source, and how each of these different emergency department data sources can be best applied to inform a public health response to the opioid overdose epidemic.IntroductionTimely and accurate measurement of overdose morbidity using emergency department (ED) data is necessary to inform an effective public health response given the dynamic nature of opioid overdose epidemic in the United States. However, from jurisdiction to jurisdiction, differing sources and types of ED data vary in their quality and comprehensiveness. Many jurisdictions collect timely emergency department data through syndromic surveillance (SyS) systems, while others may have access to more complete, but slower emergency department discharge datasets. State and local epidemiologists must make decisions regarding which datasets to use and how to best operationalize, interpret, and present overdose morbidity using ED data. These choices may affect the number, timeliness, and accuracy of the cases identified.MethodsCDC partnered with 45 states and the District of Columbia to combat the worsening opioid overdose epidemic through three cooperative agreements: Prevention for States (PFS), Data Driven Prevention Initiative (DDPI), and Enhanced State Opioid Overdose Surveillance (ESOOS). To support funded jurisdictions in monitoring non-fatal opioid overdoses, CDC developed two different sets of indicator guidance for measuring non-fatal opioid overdoses using ED data, with each focusing on different ED data sources (SyS and discharge). We report on the following attributes for each type of ED data source1,2: 1) timeliness; 2) data quality (e.g., percent completeness by field); 3) validity; and 4) representativeness (e.g., percent of facilities included).ResultsWhen comparing timeliness across data sources, SyS data has clear advantages, with many jurisdictions receiving data within 24 hours of an event. For discharge data, timeliness is more variable with some jurisdictions receiving data within weeks while others wait over 1.5 years before receiving a complete discharge dataset. Data quality and completeness tends to be stronger in discharge datasets as facilities are required to submit complete discharge records with valid ICD-10-CM codes in order to be reimbursed by payers. By contrast, for SyS data systems, participating facilities may not consistently submit data for all possible fields, including diagnosis. Validity is dependent on the data source as well as the case definition or syndrome definition used; with this in mind, SyS data overdose indicators are designed to have high sensitivity, with less attention to specificity. Discharge data overdose indicators are designed to have a high positive predictive value, while sensitivity and specificity are both important considerations. Discharge datasets often include records for 100% of ED visits from all nonfederal, acute care-affiliated facilities in a state included. By contrast, representativeness of facilities in SyS data systems varies widely across states with some states having less than 50% of facilities reporting.ConclusionsCDC funded partners share overdose morbidity data with CDC using either ED SyS data, ED discharge data, or both. CDC indicator guidance for ED discharge data is designed for states to track changes in health outcomes over time for descriptive, performance monitoring, and evaluation purposes and to create rates that are more comparable across injury category, time, and place. Considering these objectives, CDC placed a higher priority on data quality, validity (i.e., positive predictive value), and representativeness, all of which are stronger attributes of discharge data. CDC’s indicator guidance for ED SyS data is designed for states to rapidly identify changes in nonfatal overdoses and to identify areas within a particular state that are experiencing rapid change in the frequency or types of overdose events. When considering these needs, CDC prioritized timeliness and validity in terms of sensitivity, both of which are stronger attributes of SyS data. SyS and discharge ED data each lend themselves to different informational applications and interpretations based on the strengths and limitations of each dataset. An effective, informed public health response to the opioid overdose epidemic requires continued investment in public health surveillance infrastructure, careful consideration of the needs of the data user, and transparency regarding the unique strengths and limitations of each dataset.References1. Pencheon, D. (2006). Oxford handbook of public health practice. 2nd ed. Oxford: Oxford University Press.2. Centers for Disease Control and Prevention (CDC) Evaluation Working Group on Public Health Surveillance Systems for Early Detection of Outbreaks. (May 7, 2004). Framework for Evaluating Public Health Surveillance Systems for Early Detection of Outbreaks. MMWR. Morbidity and Mortality Weekly Reports. Retrieved from: https://www.cdc.gov/mmwr/preview/mmwrhtml/rr5305a1.htm

Download Full-text

Using Poison Center Data for Postdisaster Surveillance

Prehospital and Disaster Medicine ◽

10.1017/s1049023x14000983 ◽

2014 ◽

Vol 29 (5) ◽

pp. 521-524 ◽

Cited By ~ 4

Author(s):

Amy Wolkin ◽

Amy H. Schnall ◽

Royal Law ◽

Joshua Schier

Keyword(s):

Public Health ◽

Health Care Professionals ◽

Disaster Response ◽

Public Health Surveillance ◽

Health Surveillance ◽

Data Sources ◽

Morbidity And Mortality ◽

Accurate Information ◽

Poison Center ◽

The Impact

AbstractThe role of public health surveillance in disaster response continues to expand as timely, accurate information is needed to mitigate the impact of disasters. Health surveillance after a disaster involves the rapid assessment of the distribution and determinants of disaster-related deaths, illnesses, and injuries in the affected population. Public health disaster surveillance is one mechanism that can provide information to identify health problems faced by the affected population, establish priorities for decision makers, and target interventions to meet specific needs. Public health surveillance traditionally relies on a wide variety of data sources and methods. Poison center (PC) data can serve as data sources of chemical exposures and poisonings during a disaster. In the US, a system of 57 regional PCs serves the entire population. Poison centers respond to poison-related questions from the public, health care professionals, and public health agencies. The Centers for Disease Control and Prevention (CDC) uses PC data during disasters for surveillance of disaster-related toxic exposures and associated illnesses to enhance situational awareness during disaster response and recovery. Poison center data can also be leveraged during a disaster by local and state public health to supplement existing surveillance systems. Augmenting traditional surveillance data (ie, emergency room visits and death records) with other data sources, such as PCs, allows for better characterization of disaster-related morbidity and mortality. Poison center data can be used during a disaster to detect outbreaks, monitor trends, track particular exposures, and characterize the epidemiology of the event. This timely and accurate information can be used to inform public health decision making during a disaster and mitigate future disaster-related morbidity and mortality.WolkinA, SchnallAH, LawR, SchierJ. Using poison center data for postdisaster surveillance. Prehosp Disaster Med. 2014;29(5):1-4.

Download Full-text

NextGen Public Health Surveillance and the Internet of Things (IoT)

Frontiers in Public Health ◽

10.3389/fpubh.2021.756675 ◽

2021 ◽

Vol 9 ◽

Author(s):

Kirti Sundar Sahu ◽

Shannon E. Majowicz ◽

Joel A. Dubin ◽

Plinio Pelegrini Morita

Keyword(s):

Public Health ◽

Internet Of Things ◽

Large Scale ◽

Public Health Surveillance ◽

Health Surveillance ◽

Current Data ◽

Data Sources ◽

Surveillance Systems ◽

Data Generation ◽

Data Source

Recent advances in technology have led to the rise of new-age data sources (e.g., Internet of Things (IoT), wearables, social media, and mobile health). IoT is becoming ubiquitous, and data generation is accelerating globally. Other health research domains have used IoT as a data source, but its potential has not been thoroughly explored and utilized systematically in public health surveillance. This article summarizes the existing literature on the use of IoT as a data source for surveillance. It presents the shortcomings of current data sources and how NextGen data sources, including the large-scale applications of IoT, can meet the needs of surveillance. The opportunities and challenges of using these modern data sources in public health surveillance are also explored. These IoT data ecosystems are being generated with minimal effort by the device users and benefit from high granularity, objectivity, and validity. Advances in computing are now bringing IoT-based surveillance into the realm of possibility. The potential advantages of IoT data include high-frequency, high volume, zero effort data collection methods, with a potential to have syndromic surveillance. In contrast, the critical challenges to mainstream this data source within surveillance systems are the huge volume and variety of data, fusing data from multiple devices to produce a unified result, and the lack of multidisciplinary professionals to understand the domain and analyze the domain data accordingly.

Download Full-text

Innovative use of data sources: A Cross-sectional study of Data Linkage and Artificial Intelligence Practices across European Countries

10.21203/rs.2.22164/v2 ◽

2020 ◽

Author(s):

Romana Haneef ◽

Marie Delnord ◽

Michel Vernay ◽

Emmanuelle Bauchet ◽

Rita Gaidelyte ◽

...

Keyword(s):

Public Health ◽

Health Information ◽

Public Health Surveillance ◽

Data Linkage ◽

Health Surveillance ◽

European Countries ◽

Data Sources ◽

Health Determinants ◽

Data Governance ◽

Use Of Data

Abstract Background: The availability of data generated from different sources is increasing with the possibility to link these data sources with each other. However, linked administrative data can be complex to use and may require advanced expertise and skills in statistical analysis. The main objectives of this study were to describe the current use of data linkage at the individual level and artificial intelligence (AI) in routine public health activities, to identify the related estimated health indicators (i.e., outcome and intervention indicators) and health determinants of non-communicable diseases and the obstacles to linking different data sources. Method: We performed a survey across European countries to explore the current practices applied by national institutes of public health, health information and statistics for innovative use of data sources (i.e., the use of data linkage and/or AI). Results: The use of data linkage and AI at national institutes of public health, health information and statistics in Europe varies. The majority of European countries use data linkage in routine by applying a deterministic method or a combination of two types of linkages (i.e., deterministic & probabilistic) for public health surveillance and research purposes. The use of AI to estimate health indicators is not frequent at national institutes of public health, health information and statistics. Using linked data, 46 health outcome indicators, 34 health determinants and 23 health intervention indicators were estimated in routine. The complex data regulation laws, lack of human resources, skills and problems with data governance, were reported by European countries as obstacles to routine data linkage for public health surveillance and research. Conclusions: Our results highlight that the majority of European countries have integrated data linkage in their routine public health activities but only a few use AI. A sustainable national health information system and a robust data governance framework allowing to link different data sources are essential to support evidence-informed health policy development. Building analytical capacity and raising awareness of the added value of data linkage in national institutes is necessary for improving the use of linked data in order to improve the quality of public health surveillance and monitoring activities.

Download Full-text