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Centralized Data Management in a Multicountry, Multisite Population-based Study.

Qazi Sadeq-ur Rahman1, Mohammad Shahidul Islam, Belal Hossain

  • 1From 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.

The Pediatric Infectious Disease Journal
|April 13, 2016
PubMed
Summary
This summary is machine-generated.

A centralized data management system was developed for the Aetiology of Neonatal Infection in South Asia (ANISA) study. This system standardizes data collection and improves data quality for neonatal infection research.

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Area of Science:

  • Clinical Research
  • Data Management
  • Epidemiology

Background:

  • The Aetiology of Neonatal Infection in South Asia (ANISA) study is a longitudinal cohort study focused on neonatal infection surveillance and etiology detection across multiple South Asian sites.
  • A centralized data management system was crucial for standardizing data collection and processing from diverse study locations.
  • The primary objective was to create a unified dataset for comprehensive analysis.

Purpose of the Study:

  • To establish a robust, centralized data management system for the ANISA study.
  • To ensure standardized data collection and processing across multiple international research sites.
  • To facilitate pooled data analysis for neonatal infection research.

Main Methods:

  • A centralized data management system was designed and implemented with site-specific data entry interfaces.
  • Dual data entry and validation rules were employed at study sites to minimize errors.
  • Weekly data uploads to a central server created a pooled database, with a dedicated coordination center providing technical support.

Main Results:

  • The system enabled the use of common data capture forms (DCFs) adapted to local needs, allowing for on-site data entry.
  • Centralized data management streamlined the process, eliminating the need to ship DCFs, thereby reducing workload.
  • Implementation of standardized quality checks and data cleaning processes enhanced overall data quality.

Conclusions:

  • Centralized data management significantly improved the efficiency and quality of data collection for the ANISA study.
  • The system facilitated standardized data handling across multiple sites, crucial for a multi-center longitudinal study.
  • This approach enhanced the integrity and usability of the pooled dataset for neonatal infection research.