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

2014 ◽  
Vol 2014 (1) ◽  
pp. 2008
Author(s):  
Amruta Nori-Sarma* ◽  
Anobha Gurung ◽  
Michelle L. Bell
2017 ◽  
Vol 9 (7) ◽  
pp. 1106 ◽  
Author(s):  
Amruta Nori-Sarma ◽  
Anobha Gurung ◽  
Gulrez Azhar ◽  
Ajit Rajiva ◽  
Dileep Mavalankar ◽  
...  

2019 ◽  
Vol 47 (2) ◽  
pp. 232-237 ◽  
Author(s):  
Lisa M. Lee

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.


Author(s):  
Alissa C. Kress ◽  
Asia Asberry ◽  
Julio Dicent Taillepierre ◽  
Michelle M. Johns ◽  
Pattie Tucker ◽  
...  

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.


Author(s):  
Holly A. Taylor

The systematic collection and analysis of data is central to public health. Some public health activities are easily classified as either research or nonresearch, while the distinction is more nuanced for other activities. How an activity gets classified has ethical implications—additional oversight, requirements for consent of participants, and potentially whether the activity can be undertaken at all. Scholarly analysis of this issue suggests that an important aspect distinguishing research from other public health data collection activities is to consider the intent of the activity and whether experimentation is involved. The three ethical principles of respect for persons, beneficence, and (distributive) justice guide researchers in their relationships with individual participants. Because public health research can be directed at an entire community, this chapter posits that these three principles must be extended to appropriately include and consider the community as a stakeholder.


Author(s):  
Holly A. Taylor

Collection of data is essential to the practice of public health. This chapter provides a brief introduction to ethics and public health data collection, as well as an overview of chapters in the related section of The Oxford Handbook on Public Health Ethics. A key ethics challenge has been, and will remain, how best to balance the health of the community with the respect owed to individual citizens. The four chapters in this section examine various aspects of those ethics challenges, including those related to the scope of public health surveillance activities, the distinction between public health practice and public health research, community-based participatory research (CBPR), and the use of big data to answer public health research questions.


2005 ◽  
Vol 29 (4) ◽  
pp. 303-316 ◽  
Author(s):  
Min Wu ◽  
Tian Zhao ◽  
Changshan Wu

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