Surveillance

2013 ◽  
pp. 140-147 ◽  
Author(s):  
Daniel M. Sosin ◽  
Richard S. Hopkins
Keyword(s):  
Public Health ◽  
Public Health Surveillance ◽  
Public Health Practice ◽  
Health Surveillance ◽  
Surveillance Systems ◽  
Public Health Programme ◽  
Public Health Action ◽  
Effective Implementation ◽  
Health Action ◽  
Effective Use

Effective use of health data is a foundation of public health practice. Surveillance produces an ongoing stream of data that, when appropriately analysed, supports and directs public health action. This chapter provides an introduction to the purposes, design, methods, and uses of public health surveillance systems. Public health programme managers and staff, decision makers, epidemiologists, and students of public health can use this information to assure effective implementation of public health surveillance systems.

Monitoring disease and risk factors: surveillance

2020 ◽  
pp. 154-163
Author(s):  
Richard Hopkins ◽  
Aaron Kite-Powell
Keyword(s):  
Public Health ◽  
Surveillance System ◽  
Public Health Surveillance ◽  
Health Surveillance ◽  
Surveillance Systems ◽  
Public Health Informatics ◽  
Public Health Action ◽  
Health Related ◽  
Health Action ◽  
Public Health Surveillance System

Public health surveillance is ‘the ongoing, systematic collection, analysis, interpretation, and dissemination of data about a health-related event for use in public health action to reduce morbidity and mortality and to improve health. Data disseminated by a public health surveillance system can be used for immediate public health action, program planning and evaluation, and formulating research hypotheses. This chapter discusses purposes for surveillance, surveillance opportunities, surveillance system design, public health informatics, evaluating a surveillance system, and general principles for effective surveillance systems.

scholarly journals Epidemic intelligence in the European Union: strengthening the ties

2008 ◽  
Vol 13 (6) ◽  
pp. 1-2
Author(s):  
D Coulombier
Keyword(s):  
Public Health ◽  
European Union ◽  
Public Health Surveillance ◽  
Union Member ◽  
Health Surveillance ◽  
The European Union ◽  
Public Health Action ◽  
Epidemic Intelligence ◽  
Health Action ◽  
European Union Member States

Public health surveillance remains the cornerstone of the detection of health threats requiring public health action. Two articles in this issue of Eurosurveillance refer to the challenges of epidemic intelligence activities in European Union Member States.

Symposium on Public Health Law Surveillance: The Nexus of Information Technology and Public Health Law

2003 ◽  
Vol 31 (S4) ◽  
pp. 41-42 ◽  
Author(s):  
Angela McGowan ◽  
Michael Schooley ◽  
Helen Narvasa ◽  
Jocelyn Rankin ◽  
Daniel M. Sosin
Keyword(s):  
Public Health ◽  
Surveillance System ◽  
Public Health Surveillance ◽  
Program Planning ◽  
Health Surveillance ◽  
Health Law ◽  
Public Health Law ◽  
Public Health Action ◽  
Health Action ◽  
Health Laws

The Centers for Disease Control and Prevention’s (CDC) goal is to develop a surveillance system of public health laws that would both support research and analysis among policymakers and legislators, and support the scientific basis for public health law. This session was convened, in part, to discuss the value of creating an electronic system to track public health legal information. Public health surveillance is the “ongoing, systematic collection, analysis, interpretation, and dissemination of data regarding a health-related event for use in public health action to reduce morbidity and mortality and to improve health. Data disseminated by a public health surveillance system can be used for immediate public health action, program planning and evaluation, and formulating research hypotheses.” There is currently no system available that meets the goals of this definition of “surveillance” for public health laws.

scholarly journals Evaluation of Measles Surveillance Systems in Ginnir District, Bale Zone, Southeast Ethiopia: A Concurrent Embedded Mixed Quantitative/Qualitative Study 

2020 ◽  
Author(s):  
Falaho Sani ◽  
Mohammed Hasen ◽  
Mohammed Seid ◽  
Nuriya Umer
Keyword(s):  
Public Health ◽  
Qualitative Study ◽  
Surveillance System ◽  
Public Health Surveillance ◽  
Health Surveillance ◽  
Health Facilities ◽  
Surveillance Data ◽  
Surveillance Systems ◽  
Southeast Ethiopia ◽  
Bale Zone

Abstract Background: Public health surveillance systems should be evaluated periodically to ensure that the problems of public health importance are being monitored efficiently and effectively. Despite the widespread measles outbreak in Ginnir district of Bale zone in 2019, evaluation of measles surveillance system has not been conducted. Therefore, we evaluated the performance of measles surveillance system and its key attributes in Ginnir district, Southeast Ethiopia.Methods: We conducted a concurrent embedded mixed quantitative/qualitative study in August 2019 among 15 health facilities/study units in Ginnir district. Health facilities are selected using lottery method. The qualitative study involved purposively selected 15 key informants. Data were collected using semi-structured questionnaire adapted from Centers for Disease Control and Prevention guidelines for evaluating public health surveillance systems through face-to-face interview and record review. The quantitative findings were analyzed using Microsoft Excel 2016 and summarized by frequency and proportion. The qualitative findings were narrated and summarized based on thematic areas to supplement the quantitative findings.Results: The structure of surveillance data flow was from the community to the respective upper level. Emergency preparedness and response plan was available only at the district level. Completeness of weekly report was 95%, while timeliness was 87%. No regular analysis and interpretations of surveillance data, and the supportive supervision and feedback system was weak. The participation and willingness of surveillance stakeholders in implementation of the system was good. The surveillance system was found to be useful, easy to implement, representative and can accommodate and adapt to changing conditions. Report documentation and quality of data was poor at lower level health facilities. Stability of the system has been challenged by shortage of budget and logistics, staff turnover and lack of update trainings.Conclusions: The surveillance system was acceptable, useful, simple, flexible and representative. Data quality, timeliness and stability of the system were attributes that require improvement. The overall performance of measles surveillance system in the district was poor. Hence, regular analysis of data, preparation and dissemination of epidemiological bulletin, capacity building and regular supervision and feedback are recommended to enhance performance of the system.

Structural Transphobia, Homophobia, and Biphobia in Public Health Practice: The Example of COVID-19 Surveillance

2021 ◽  
pp. e1-e7
Author(s):  
Randall L. Sell ◽  
Elise I. Krims
Keyword(s):  
Public Health ◽  
United States ◽  
Race And Ethnicity ◽  
Public Health Surveillance ◽  
Public Health Practice ◽  
The United States ◽  
Health Surveillance ◽  
Essential Information ◽  
And Gender ◽  
Public Health Surveillance System

Public health surveillance can have profound impacts on the health of populations, with COVID-19 surveillance offering an illuminating example. Surveillance surrounding COVID-19 testing, confirmed cases, and deaths has provided essential information to public health professionals about how to minimize morbidity and mortality. In the United States, surveillance has also pointed out how populations, on the basis of geography, age, and race and ethnicity, are being impacted disproportionately, allowing targeted intervention and evaluation. However, COVID-19 surveillance has also highlighted how the public health surveillance system fails some communities, including sexual and gender minorities. This failure has come about because of the haphazard and disorganized way disease reporting data are collected, analyzed, and reported in the United States, and the structural homophobia, transphobia, and biphobia acting within these systems. We provide recommendations for addressing these concerns after examining experiences collecting race data in COVID-19 surveillance and attempts in Pennsylvania and California to incorporate sexual orientation and gender identity variables into their pandemic surveillance efforts. (Am J Public Health. Published online ahead of print June 10, 2021: e1–e7. https://doi.org/10.2105/AJPH.2021.3062727 )

FluHMM: A simple and flexible Bayesian algorithm for sentinel influenza surveillance and outbreak detection

2018 ◽  
Vol 28 (6) ◽  
pp. 1826-1840 ◽  
Author(s):  
Theodore Lytras ◽  
Kassiani Gkolfinopoulou ◽  
Stefanos Bonovas ◽  
Baltazar Nunes
Keyword(s):  
Public Health ◽  
Public Health Practice ◽  
R Package ◽  
Sentinel Surveillance ◽  
Surveillance Data ◽  
Outbreak Detection ◽  
Influenza Surveillance ◽  
Bayesian Algorithm ◽  
Public Health Action ◽  
Health Action

Timely detection of the seasonal influenza epidemic is important for public health action. We introduce FluHMM, a simple but flexible Bayesian algorithm to detect and monitor the seasonal epidemic on sentinel surveillance data. No comparable historical data are required for its use. FluHMM segments a typical influenza surveillance season into five distinct phases with clear interpretation (pre-epidemic, epidemic growth, epidemic plateau, epidemic decline and post-epidemic) and provides the posterior probability of being at each phase for every week in the period under surveillance, given the available data. An alert can be raised when the probability that the epidemic has started exceeds a given threshold. An accompanying R package facilitates the application of this method in public health practice. We apply FluHMM on 12 seasons of sentinel surveillance data from Greece, and show that it achieves very good sensitivity, timeliness and perfect specificity, thereby demonstrating its usefulness. We further discuss advantages and limitations of the method, providing suggestions on how to apply it and highlighting potential future extensions such as with integrating multiple surveillance data streams.

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