Importance of capacity assessment for an early staged-research network designed to eliminate health disparity: lessons from RTRN

Ethn Dis. 2010 Winter;20(1 Suppl 1):S1-150-4.

Abstract

Background: RCMI Translational Research Network (RTRN) is the first academic-based national network to address the problem of health disparities by integrating minority medical schools in a collaborative effort. While there was a great interest in forming the research network, limited systematic effort has been made in understanding members' existing capacity and future demand.

Objective: The aim of this study was to report the results of the RTRN Statistical Capacity Assessment and discuss the importance of an initial capacity assessment in building the biostatistical capacity of a research network in its early stage.

Methods: The assessment was based on survey responses submitted by program directors/managers from 12 of the 18 RTRN institutions. In this assessment the capacity is defined as the statistical tools and human resources which are required for effective and efficient performance.

Results: A total of 52 biostatisticians (mean of 4.5 per site) were working for 12 RTRN institutions; 84% were fulltime employees, and 53% held a doctoral degree. On average, they had about 13 years of job experience. SAS, SPSS and STATA were the most frequently used and were selected as their major statistical software. A wide inter-institutional variability was found in number of biostatisticians (ranged from 1 to 8), mean years of experience in their position (4.5-20 years) and in major software (5-20), and the number of statistical software in use (1-11).

Conclusion: The initial capacity assessment provided valuable information on members' background and the network's research capacity which will be used as the basic data in developing programs to build research capacity. Therefore, it is important to include the initial capacity survey and on-going evaluation of network activities when making business plans of research networks intended to reduce health disparities.

MeSH terms

  • Health Status Disparities*
  • Humans
  • Needs Assessment*
  • Software
  • Translational Research, Biomedical / organization & administration*
  • United States