Health Economics, Policy and Management, Karolinska Institutet, Research and Advocacy Intern, Shifo Foundation, Stockholm, Sweden.
Partnerships and Advocacy, Shifo Foundation, Sweden.
Vaccine. 2020 Jun 19;38(30):4652-4663. doi: 10.1016/j.vaccine.2020.02.091. Epub 2020 May 20.
Few public health interventions can match the immense achievements of immunization in terms of mortality and morbidity reduction. However, progress in reaching global coverage goals and achieving universal immunization coverage have stalled; with key stakeholders concerned about the accuracy of reported coverage figures. Incomplete and incorrect data has made it challenging to obtain an accurate overview of immunization coverage, particularly in low- and middle-income countries (LMIC). To date, only one literature review concerning immunization data quality exists. However, it only included articles from Gavi-eligible countries, did not go deep into the characteristics of the data quality problems, and used a narrow 'data quality' definition. This scoping review builds upon that work; exploring the "state of data quality" in LMIC, factors affecting data quality in these settings and potential means to improve it. Only a small volume of literature addressing immunization data quality in LMIC was found and definitions of 'data quality' varied widely. Data quality was, on the whole, considered poor in the articles included. Coverage numerators were seen to be inflated for official reports and denominators were inaccurate and infrequently adjusted. Numerous factors related to these deficiencies were reported, including health information system fragmentation, overreliance on targets and poor data management processes. Factors associated with health workers were noted most frequently. Authors suggested that data quality could be improved by ensuring proper data collection tools, increasing workers' capacities and motivation through training and supervision, whilst also ensuring adequate and timely feedback on the data collected. The findings of this scoping review can serve as the basis to identify and address barriers to good quality immunization data in LMICs. Overcoming said barriers is essential if immunization's historic successes are to continue.
在降低死亡率和发病率方面,很少有公共卫生干预措施能与免疫接种取得的巨大成就相媲美。然而,在实现全球覆盖目标和实现全民免疫覆盖方面的进展已经停滞不前;主要利益攸关方对报告的覆盖数据的准确性表示担忧。不完整和不正确的数据使得难以准确了解免疫覆盖情况,特别是在中低收入国家(LMIC)。迄今为止,仅有一篇关于免疫数据质量的文献综述。然而,它只包括了符合 Gavi 资格的国家的文章,没有深入探讨数据质量问题的特征,也使用了狭隘的数据质量定义。这项范围界定审查建立在这项工作的基础上;探讨了 LMIC 中“数据质量状况”、影响这些环境中数据质量的因素以及提高数据质量的潜在方法。只发现了少量针对中低收入国家免疫数据质量的文献,并且“数据质量”的定义差异很大。总体而言,被纳入的文章认为数据质量较差。官方报告中的覆盖分子被认为被夸大了,而分母不准确且很少调整。报告中还报道了许多与这些缺陷相关的因素,包括卫生信息系统碎片化、过度依赖目标和数据管理流程不佳。与卫生工作者相关的因素被认为是最常见的。作者建议通过确保适当的数据收集工具、通过培训和监督提高工作人员的能力和积极性,同时确保对收集的数据进行及时和充分的反馈,可以提高数据质量。本范围界定审查的结果可以作为确定和解决中低收入国家良好免疫数据质量障碍的基础。如果要继续保持免疫接种的历史成功,克服这些障碍至关重要。