eMagzine Spring 2016 (Trusha Hake)

Sep 26, 20160 likes44 views

This document summarizes the author's experience with data quality assurance procedures during their practicum working on an epidemiological study on autism in Jamaica. Their responsibilities included double data entry in REDCap, data cleaning using SAS to identify discrepancies and missing data, and communicating issues to the Jamaican research team. The final cleaned dataset was uploaded to NDAR following data sharing rules. They gained experience using REDCap and SAS software and learning research processes from basic levels.

Data management in Public Health Research
DOI: 10:1371journal.pmed.0020267.g001
A data cleaning framework
Public Health Significance
Now a days and in future health
information from hospitals and
epidemiological surveys will increasingly
be stored as electronic information
than paper documents. Therefore, it is
of utmost importance to understand
how to make information as accurate
as possible.
Reliable and accurate public health
and research information is essential for
monitoring health status & disease
conditions and for evaluating &
improving the delivery of health care
services.
Data management involves the aspect
of data quality assurance that includes
data cleaning and data quality
control. This aspect is important to
improve the validity of the data that
produces the study result.
Finally, BERD collaborates with several
projects providing data management
support and support with statistical
analysis in studies.
I worked at Biostatistics/ Epidemiology/
Research Design (BERD) core in Center
of Clinical and Translational Sciences
(CCTS) at University of Texas Health
Science Center under Dr. Mohammad
H. Rahbar.
My major responsibilities in data
management included double data
entry, data cleaning, data quality
assurance and descriptive analysis of
the data of an NIH funded autism
research case control study in Jamaica.
I did data entry in REDCap (Research
Electronic Data Capture), a secure web
An Experience of data quality assurance procedures in
Epidemiological Research on Autism in Jamaica-Phase II (ERAJ-II).
By: Trusha Hake.
application for building and managing
online surveys and databases. Used SAS
codes for comparing double data entry
and data cleaning rules were followed to
look for discrepancies, error and missing
data. All issues were reported and
communicated to the Jamaican team at
University of West Indies, Mona campus
and then resolved by them.
The final product included a cleaned
dataset that was uploaded to NDAR
(National Database for Autism Research)
by the data manager based on data
sharing rules of NIH.
Special duties
during your
practicum
 Use of REDCap, a secure
web application.
• Use of SAS statistical
software.
 Learning research from the
most basic level
Advice for future
students
• Start your search early to
find a suitable practicum
of your interest.
 Learn new things and
make the most of the
opportunity.
Spring 2016 ● Trusha Hake ● UT Health CCTS BERD ● Data management in Public Health Research

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eMagzine Spring 2016 (Trusha Hake)

1. Data management in Public Health Research DOI: 10:1371journal.pmed.0020267.g001 A data cleaning framework Public Health Significance Now a days and in future health information from hospitals and epidemiological surveys will increasingly be stored as electronic information than paper documents. Therefore, it is of utmost importance to understand how to make information as accurate as possible. Reliable and accurate public health and research information is essential for monitoring health status & disease conditions and for evaluating & improving the delivery of health care services. Data management involves the aspect of data quality assurance that includes data cleaning and data quality control. This aspect is important to improve the validity of the data that produces the study result. Finally, BERD collaborates with several projects providing data management support and support with statistical analysis in studies. I worked at Biostatistics/ Epidemiology/ Research Design (BERD) core in Center of Clinical and Translational Sciences (CCTS) at University of Texas Health Science Center under Dr. Mohammad H. Rahbar. My major responsibilities in data management included double data entry, data cleaning, data quality assurance and descriptive analysis of the data of an NIH funded autism research case control study in Jamaica. I did data entry in REDCap (Research Electronic Data Capture), a secure web An Experience of data quality assurance procedures in Epidemiological Research on Autism in Jamaica-Phase II (ERAJ-II). By: Trusha Hake. application for building and managing online surveys and databases. Used SAS codes for comparing double data entry and data cleaning rules were followed to look for discrepancies, error and missing data. All issues were reported and communicated to the Jamaican team at University of West Indies, Mona campus and then resolved by them. The final product included a cleaned dataset that was uploaded to NDAR (National Database for Autism Research) by the data manager based on data sharing rules of NIH. Special duties during your practicum  Use of REDCap, a secure web application. • Use of SAS statistical software.  Learning research from the most basic level Advice for future students • Start your search early to find a suitable practicum of your interest.  Learn new things and make the most of the opportunity. Spring 2016 ● Trusha Hake ● UT Health CCTS BERD ● Data management in Public Health Research

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eMagzine Spring 2016 (Trusha Hake)

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