Engmann C, Jehan I, Ditekemena J, Garces A, Phiri M, Mazariegos M, Chomba E, Pasha O, Tshefu A, Hemed Y, McClure EM, Thorsten V, Bann C, Goldenberg RL, Bose C, Setel P, Carlo WA, Wright LL
Division of Neonatal-Perinatal Medicine, Department of Pediatrics, University of North Carolina

Trop Med Int Health. 2009 Dec;14(12):1496-504

OBJECTIVES: To develop a standardized verbal autopsy (VA) training program and evaluate whether its implementation resulted in comparable knowledge required to classify perinatal cause of death (COD) by physicians and non-physicians.

METHODS: Training materials, case studies, and written and mock scenarios for this VA program were developed using conventional VA and ICD-10 guidelines. This program was used to instruct physicians and non-physicians in VA methodology using a train-the-trainer model. Written tests of cognitive and applied knowledge required to classify perinatal COD were administered before and after training to evaluate the effect of the VA training program.

RESULTS: Fifty-three physicians and non-physicians (nurse-midwives/nurses and Community Health Workers [CHW]) from Pakistan, Zambia, the Democratic Republic of Congo, and Guatemala were trained. Cognitive and applied knowledge mean scores among all trainees improved significantly (12.8 and 28.8% respectively, P < 0.001). Cognitive and applied knowledge post-training test scores of nurse-midwives/nurses were comparable to those of physicians. CHW (high-school graduates with 15 months or less formal health/nursing training) had the largest improvements in post-training applied knowledge with scores comparable to those of physicians and nurse-midwives/nurses. However, CHW cognitive knowledge post-training scores were significantly lower than those of physicians and nurses.

CONCLUSIONS: With appropriate training in VA, cognitive and applied knowledge required to determine perinatal COD is similar for physicians and nurses-midwives/nurses. This suggests that midwives and nurses may play a useful role in determining COD at the community level, which may be a practical way to improve the accuracy of COD data in rural, remote, geographic areas.

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