建立常规免疫接种和疫苗可预防疾病监测计划的数据三角化能力,以确定免疫接种覆盖率的不平等
Audrey Rachlin1, Oluwasegun Joel Adegoke1, Rajendra Bohara2
1Global Immunization Division, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA.
Vaccines
|June 27, 2024
概括
整合多个数据源,称为数据三角化,有助于识别错过疫苗接种的儿童. 这种方法改善了免疫计划的性能,并减少了疫苗可以预防的疾病爆发.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 医疗信息学 医疗信息学
背景情况:
- 免疫扩展计划 (EPI) 和疫苗可预防疾病 (VPD) 监测 (VPDS) 计划产生了多样化但孤立的数据.
- 数据可访问性和及时分析方面的挑战阻碍了有效的程序决策.
- 孤岛数据有助于错过接种疫苗,免疫缺口和VPD爆发,特别是在服务不足的人群中.
研究的目的:
- 描述数据三角化过程,以提高免疫程序的性能.
- 确定评估免疫覆盖率和识别差距的关键指标.
- 分享在孟加拉国,尼日利亚和卢旺达实施数据三角化的经验教训.
主要方法:
- 数据三角化涉及日常行政数据,VPD案例数据和覆盖调查的整合.
- 优先考虑关键指标,以确定免疫覆盖率差距,免疫不足 (UI) 和零剂量 (ZD) 的儿童.
- 能力建设工作和具体分析,以产生有意义的数据,用于编程决策.
主要成果:
- 数据三角化增强了关键指标的可用性,用于识别免疫覆盖率不平等.
- 该过程有效地识别了免疫不足 (UI) 和零剂量 (ZD) 的儿童,这对于有针对性的干预至关重要.
- 通过更好地识别医疗保健系统各个层面的绩效差距,观察到计划性改进.
结论:
- 数据三角化是克服免疫计划中的数据孤岛的有价值策略.
- 利用三角测量过程和经验教训可以带来改进方案,改善疫苗接种服务的获取和利用.
- 这种方法支持识别UI和ZD儿童,最终加强VPD预防工作.
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