Centralized Data Management in a Multicountry, Multisite Population-based Study
A centralized data management system was developed for data collection and processing for the Aetiology of Neonatal Infection in South Asia (ANISA) study. ANISA is a longitudinal cohort study involving neonatal infection surveillance and etiology detection in multiple sites in South Asia. The primar...
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| Veröffentlicht in: | The Pediatric infectious disease journal Jg. 35; H. 5 Suppl 1; S. S23 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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United States
01.05.2016
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| ISSN: | 1532-0987, 1532-0987 |
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| Abstract | A centralized data management system was developed for data collection and processing for the Aetiology of Neonatal Infection in South Asia (ANISA) study. ANISA is a longitudinal cohort study involving neonatal infection surveillance and etiology detection in multiple sites in South Asia. The primary goal of designing such a system was to collect and store data from different sites in a standardized way to pool the data for analysis.
We designed the data management system centrally and implemented it to enable data entry at individual sites. This system uses validation rules and audit that reduce errors. The study sites employ a dual data entry method to minimize keystroke errors. They upload collected data weekly to a central server via internet to create a pooled central database. Any inconsistent data identified in the central database are flagged and corrected after discussion with the relevant site. The ANISA Data Coordination Centre in Dhaka provides technical support for operations, maintenance and updating the data management system centrally. Password-protected login identifications and audit trails are maintained for the management system to ensure the integrity and safety of stored data.
Centralized management of the ANISA database helps to use common data capture forms (DCFs), adapted to site-specific contextual requirements. DCFs and data entry interfaces allow on-site data entry. This reduces the workload as DCFs do not need to be shipped to a single location for entry. It also improves data quality as all collected data from ANISA goes through the same quality check and cleaning process. |
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| AbstractList | A centralized data management system was developed for data collection and processing for the Aetiology of Neonatal Infection in South Asia (ANISA) study. ANISA is a longitudinal cohort study involving neonatal infection surveillance and etiology detection in multiple sites in South Asia. The primary goal of designing such a system was to collect and store data from different sites in a standardized way to pool the data for analysis.
We designed the data management system centrally and implemented it to enable data entry at individual sites. This system uses validation rules and audit that reduce errors. The study sites employ a dual data entry method to minimize keystroke errors. They upload collected data weekly to a central server via internet to create a pooled central database. Any inconsistent data identified in the central database are flagged and corrected after discussion with the relevant site. The ANISA Data Coordination Centre in Dhaka provides technical support for operations, maintenance and updating the data management system centrally. Password-protected login identifications and audit trails are maintained for the management system to ensure the integrity and safety of stored data.
Centralized management of the ANISA database helps to use common data capture forms (DCFs), adapted to site-specific contextual requirements. DCFs and data entry interfaces allow on-site data entry. This reduces the workload as DCFs do not need to be shipped to a single location for entry. It also improves data quality as all collected data from ANISA goes through the same quality check and cleaning process. A centralized data management system was developed for data collection and processing for the Aetiology of Neonatal Infection in South Asia (ANISA) study. ANISA is a longitudinal cohort study involving neonatal infection surveillance and etiology detection in multiple sites in South Asia. The primary goal of designing such a system was to collect and store data from different sites in a standardized way to pool the data for analysis.BACKGROUNDA centralized data management system was developed for data collection and processing for the Aetiology of Neonatal Infection in South Asia (ANISA) study. ANISA is a longitudinal cohort study involving neonatal infection surveillance and etiology detection in multiple sites in South Asia. The primary goal of designing such a system was to collect and store data from different sites in a standardized way to pool the data for analysis.We designed the data management system centrally and implemented it to enable data entry at individual sites. This system uses validation rules and audit that reduce errors. The study sites employ a dual data entry method to minimize keystroke errors. They upload collected data weekly to a central server via internet to create a pooled central database. Any inconsistent data identified in the central database are flagged and corrected after discussion with the relevant site. The ANISA Data Coordination Centre in Dhaka provides technical support for operations, maintenance and updating the data management system centrally. Password-protected login identifications and audit trails are maintained for the management system to ensure the integrity and safety of stored data.METHODSWe designed the data management system centrally and implemented it to enable data entry at individual sites. This system uses validation rules and audit that reduce errors. The study sites employ a dual data entry method to minimize keystroke errors. They upload collected data weekly to a central server via internet to create a pooled central database. Any inconsistent data identified in the central database are flagged and corrected after discussion with the relevant site. The ANISA Data Coordination Centre in Dhaka provides technical support for operations, maintenance and updating the data management system centrally. Password-protected login identifications and audit trails are maintained for the management system to ensure the integrity and safety of stored data.Centralized management of the ANISA database helps to use common data capture forms (DCFs), adapted to site-specific contextual requirements. DCFs and data entry interfaces allow on-site data entry. This reduces the workload as DCFs do not need to be shipped to a single location for entry. It also improves data quality as all collected data from ANISA goes through the same quality check and cleaning process.CONCLUSIONCentralized management of the ANISA database helps to use common data capture forms (DCFs), adapted to site-specific contextual requirements. DCFs and data entry interfaces allow on-site data entry. This reduces the workload as DCFs do not need to be shipped to a single location for entry. It also improves data quality as all collected data from ANISA goes through the same quality check and cleaning process. |
| Author | Rahman, Qazi Sadeq-ur Ahmed, A S M Nawshad Uddin Ahmed, Imran Hossain, Tanvir Ali, Murtaza Connor, Nicholas E Moin, Syed Mamun Ibne Saha, Samir K Mullany, Luke Rahman, Md Mahmudur El Arifeen, Shams Islam, Mohammad Shahidul Jaman, Md Jahiduj Hossain, Belal |
| Author_xml | – sequence: 1 givenname: Qazi Sadeq-ur surname: Rahman fullname: Rahman, Qazi Sadeq-ur organization: From the Centre for Child and Adolescent Health, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh; †Child Health Research Foundation, Dhaka, Bangladesh; ‡International Center for Maternal and Newborn Health, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland; §Aga Khan University, Karachi, Pakistan; and ¶Centers for Disease Control and Prevention, Atlanta, Georgia – sequence: 2 givenname: Mohammad Shahidul surname: Islam fullname: Islam, Mohammad Shahidul – sequence: 3 givenname: Belal surname: Hossain fullname: Hossain, Belal – sequence: 4 givenname: Tanvir surname: Hossain fullname: Hossain, Tanvir – sequence: 5 givenname: Nicholas E surname: Connor fullname: Connor, Nicholas E – sequence: 6 givenname: Md Jahiduj surname: Jaman fullname: Jaman, Md Jahiduj – sequence: 7 givenname: Md Mahmudur surname: Rahman fullname: Rahman, Md Mahmudur – sequence: 8 givenname: A S M Nawshad Uddin surname: Ahmed fullname: Ahmed, A S M Nawshad Uddin – sequence: 9 givenname: Imran surname: Ahmed fullname: Ahmed, Imran – sequence: 10 givenname: Murtaza surname: Ali fullname: Ali, Murtaza – sequence: 11 givenname: Syed Mamun Ibne surname: Moin fullname: Moin, Syed Mamun Ibne – sequence: 12 givenname: Luke surname: Mullany fullname: Mullany, Luke – sequence: 13 givenname: Samir K surname: Saha fullname: Saha, Samir K – sequence: 14 givenname: Shams surname: El Arifeen fullname: El Arifeen, Shams |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/27070059$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Asia, Western - epidemiology Communicable Diseases - epidemiology Communicable Diseases - etiology Community-Acquired Infections - epidemiology Community-Acquired Infections - etiology Data Collection - methods Data Collection - standards Epidemiological Monitoring Female Humans Infant Infant, Newborn Infant, Newborn, Diseases - epidemiology Infant, Newborn, Diseases - etiology Male |
| Title | Centralized Data Management in a Multicountry, Multisite Population-based Study |
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