scholarly journals Validation of Four-dimensional Components for Measuring Quality of the Public Health Data Collection Process: Expert Elicitation (Preprint)

10.2196/17240 ◽  
2019 ◽  
Author(s):  
Hong Chen ◽  
Ping Yu ◽  
David Hailey ◽  
Tingru Cui
Keyword(s):  
Public Health ◽  
Data Collection ◽  
Health Data ◽  
Expert Elicitation ◽  
The Public ◽  
Public Health Data ◽  
Collection Process ◽  
Data Collection Process

scholarly journals Validation of Four-dimensional Components for Measuring Quality of the Public Health Data Collection Process: Expert Elicitation (Preprint)

2019 ◽  
Author(s):  
Hong Chen ◽  
Ping Yu ◽  
David Hailey ◽  
Tingru Cui
Keyword(s):  
Public Health ◽  
Data Collection ◽  
Expert Elicitation ◽  
The Public ◽  
Component Framework ◽  
Collection Process ◽  
Data Collection Process ◽  
Public Health Information ◽  
Aids Information

BACKGROUND Identification of the essential components of quality of data collection is the starting point for the design of effective data quality management strategies in public health information systems. An inductive analysis of global public health informatics literature on the data collection process derived a four-dimensional (4D) component framework that focuses on four dimensions of the process: management, personnel, data collection system, and environment. It is necessary to empirically validate the framework for further research and practice. OBJECTIVE This study aimed to obtain empirical evidence to confirm the components of the 4D framework, and if needed, to further develop this preliminary framework. METHODS Expert elicitation was used to evaluate the preliminary framework in the context of Chinese national AIDS information management system. The research processes included the development of an interview guide and data collection form, data collection, and data analysis. Twenty-eight experts, including three public health administrators, fifteen public health work-ers, and ten healthcare practitioners participated in the elicitation session. A framework quali-tative data analysis approach was followed to elicit themes from interview transcripts and to compare with the elements of the 4D framework. RESULTS A total of 302 codes were extracted from the interview transcripts, which verified 116 (78%) original indicators and generated 47 new indicators. The final 4D component framework consists of 116 indicators including 82 facilitators and 34 barriers. The first component, data collection management, includes data collection protocol and quality assurance, which is measured by 41 (35% of the 116) indicators. It was followed by data collection environment measured by 37 (32%) indicators, which comprises leadership, training, and funding, as well as three newly added subcomponents, i.e., organisational policy, high-level management support, collaboration among parallel organisations. The third component, data collection personnel, is described by a perception of data collection, skill/competence, communication, and staffing pattern, which is measured by 22 (19%) indicators. The fourth, data collection system, contain-ing functions, integration of different data collection systems, technical support, and device for data collection, is measured by 16 (14%) indicators. CONCLUSIONS This expert elicitation study situated in national AIDS information management systems validated and made improvements to the 4D component framework measuring the quality of the data collection process for public health information systems. The validated 4D component framework can be applied by researchers and practitioners in designing and managing the public health data collection process.

Public Health Data Collection and Implementation of the Revised Common Rule

2019 ◽  
Vol 47 (2) ◽  
pp. 232-237 ◽  
Author(s):  
Lisa M. Lee
Keyword(s):  
Public Health ◽  
Data Collection ◽  
Historical Development ◽  
Public Health Surveillance ◽  
Health Data ◽  
Health Surveillance ◽  
Common Rule ◽  
The Public ◽  
Public Health Data ◽  
First Time

For the first time, the revised Common Rule specifies that public health surveillance activities are not research. This article reviews the historical development of the public health surveillance exclusion and implications for other foundational public health practices.

scholarly journals Opportunities and Challenges in Public Health Data Collection in Southern Asia: Examples from Western India and Kathmandu Valley, Nepal

2017 ◽  
Vol 9 (7) ◽  
pp. 1106 ◽  
Author(s):  
Amruta Nori-Sarma ◽  
Anobha Gurung ◽  
Gulrez Azhar ◽  
Ajit Rajiva ◽  
Dileep Mavalankar ◽  
...  
Keyword(s):  
Public Health ◽  
Data Collection ◽  
Health Data ◽  
Kathmandu Valley ◽  
Western India ◽  
Southern Asia ◽  
Public Health Data

scholarly journals Public Health Infographic Design for the public – the evidence base

2021 ◽  
Vol 31 (Supplement_3) ◽  
Author(s):  
C Stones
Keyword(s):  
Public Health ◽  
Behavioral Change ◽  
Evidence Base ◽  
Health Data ◽  
Information Design ◽  
The Public ◽  
Graph Comprehension ◽  
Florence Nightingale ◽  
Public Health Data ◽  
John Snow

Abstract In order to make effective infographics, one needs to understand the science behind public health infographic design. This presentation introduces guidelines for public health infographic design based on gathered academic evidence of effectiveness as well as information design principles. We tackle the topic from a variety of angles exploring issues of attention, comprehension, recall and behavioral change and focuses on infographics designed for a lay audience. Despite the exhaustive research conducted on say, graph comprehension, there remains a gap in how we account for the effectiveness of public health infographic design more broadly. The presentation also covers a brief examination of ‘hidden' historical precedents for the design of engaging health infographics, beyond the oft-cited visual work of John Snow or Florence Nightingale. We argue that notions of data spectacle and the need to grab attention remain vital today. The presentation concludes by reflecting on the future of infographics for displaying public health data, particularly with reference to the use of COVID-19 graphics in 2020/21.

Facing the Nonresponse Challenge

2012 ◽  
Vol 645 (1) ◽  
pp. 23-35 ◽  
Author(s):  
Frauke Kreuter
Keyword(s):  
Data Collection ◽  
Survey Research ◽  
Survey Data ◽  
Quality Measures ◽  
Data Sources ◽  
Collection Process ◽  
Data Collection Process ◽  
Survey Estimates ◽  
Survey Designs

This article provides a brief overview of key trends in the survey research to address the nonresponse challenge. Noteworthy are efforts to develop new quality measures and to combine several data sources to enhance either the data collection process or the quality of resulting survey estimates. Mixtures of survey data collection modes and less burdensome survey designs are additional steps taken by survey researchers to address nonresponse.

scholarly journals A Survey to Assess the Quality of the Data Obtained by Radio-Frequency Technologies and Microelectromechanical Systems to Measure External Workload and Collective Behavior Variables in Team Sports

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2271 ◽  
Author(s):  
Markel Rico-González ◽  
Asier Los Arcos ◽  
Daniel Rojas-Valverde ◽  
Filipe M. Clemente ◽  
José Pino-Ortega
Keyword(s):  
Data Collection ◽  
Radio Frequency ◽  
Collective Behavior ◽  
Microelectromechanical Systems ◽  
Team Sports ◽  
Positioning Systems ◽  
Collection Process ◽  
Data Collection Process ◽  
Global Positioning

Electronic performance and tracking systems (EPTS) and microelectromechanical systems (MEMS) allow the measurement of training load (TL) and collective behavior in team sports so that match performance can be optimized. Despite the frequent use of radio-frequency (RF) technology (i.e., global positioning navigation systems (GNSS/global positioning systems (GPS)) and, local position systems (LPS)) and MEMS in sports research, there is no protocol that must be followed, nor are there any set guidelines for evaluating the quality of the data collection process in studies. Thus, this study aims to suggest a survey based on previously used protocols to evaluate the quality of data recorded by RF technology and MEMS in team sports. A quality check sheet was proposed considering 13 general criteria items. Four additional items for GNSS/GPS, eight additional items for LPS, and five items for MEMS were suggested. This information for evaluating the quality of the data collection process should be reported in the methods sections of future studies.

scholarly journals Collection of Data on Sex, Sexual Orientation, and Gender Identity by U.S. Public Health Data and Monitoring Systems, 2015–2018

2021 ◽  
Vol 18 (22) ◽  
pp. 12189
Author(s):  
Alissa C. Kress ◽  
Asia Asberry ◽  
Julio Dicent Taillepierre ◽  
Michelle M. Johns ◽  
Pattie Tucker ◽  
...  
Keyword(s):  
Public Health ◽  
Sexual Orientation ◽  
Data Collection ◽  
Gender Identity ◽  
Health Data ◽  
Monitoring Systems ◽  
Data Systems ◽  
Public Health Data ◽  
Race Ethnicity ◽  
And Gender

We aimed to assess Centers for Disease Control and Prevention (CDC) data systems on the extent of data collection on sex, sexual orientation, and gender identity as well as on age and race/ethnicity. Between March and September 2019, we searched 11 federal websites to identify CDC-supported or -led U.S. data systems active between 2015 and 2018. We searched the systems’ website, documentation, and publications for evidence of data collection on sex, sexual orientation, gender identity, age, and race/ethnicity. We categorized each system by type (disease notification, periodic prevalence survey, registry/vital record, or multiple sources). We provide descriptive statistics of characteristics of the identified systems. Most (94.1%) systems we assessed collected data on sex. All systems collected data on age, and approximately 80% collected data on race/ethnicity. Only 17.7% collected data on sexual orientation and 5.9% on gender identity. Periodic prevalence surveys were the most common system type for collecting all the variables we assessed. While most U.S. public health data and monitoring systems collect data disaggregated by sex, age, and race/ethnicity, far fewer do so for sexual orientation or gender identity. Standards and examples exist to aid efforts to collect and report these vitally important data. Additionally important is increasing accessibility and appropriately tailored dissemination of reports of these data to public health professionals and other collaborators.

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