Effect of an integrated intervention on the availability and completeness of Robson ten group classification system-related data in district hospitals in Bangladesh

综合干预对孟加拉国地区医院Robson十组分类系统相关数据的可用性和完整性的影响

阅读:3

Abstract

BACKGROUND: The Government of Bangladesh's routine data capturing system of health care facilities doesn't adequately capture Robson ten group classification system (TGCS)-related variables required to monitor caesarean section rates. To address this gap, we developed an integrated intervention and assessed its impact on the Robson TGCS-related variables availability and completeness. METHODS: We conducted implementation research in eight district hospitals in Bangladesh from January 2021 to June 2023, where the primary population was mothers who gave birth within the implementing health care facilities. Our integrated intervention included the introduction of Robson TGCS; development and implementation of 'Robson TGCS Report Form'; capacity development training; a multi-level monitoring and supportive supervision system; and collaborative stakeholders' advisory workshops and review meetings. The primary outcome was the percentage of availability and completeness of TGCS-related data across study phases. The study was divided into five phases. Phase one focused on developing the data-capturing system, while data collection began in Phase two. RESULTS: The average availability of six variables improved significantly (P < 0.001) from 76% (95% confidence interval (CI) = 75.7-76.8%) in phase two to 99% (95% CI = 98.8-99.1%) in Phase five. Similarly, data completeness for these variables increased significantly (P < 0.001) from 53% (95% CI = 52.2-54.2%) to 97% (95% CI = 96.2-97%). Consequently, the percentage of classified groups improved significantly (P < 0.001) from 55% (95% CI = 54.1-56.1%) to 97% (95% CI = 96.8-97.5%). CONCLUSIONS: The integrated intervention significantly improved data availability and completeness. Active engagement of stakeholders, including governmental bodies and technical experts from local and central levels, is crucial to ensure data quality for identifying, planning and implementing targeted interventions.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。