背景:麻疹血清阳性率数据有可能成为了解传播动态和加强免疫规划的决策努力的有用工具。在这项研究中,我们对1962-2021年发表的低收入和中等收入国家麻疹血清流行率的所有主要数据(由世界银行2021年收入分类定义)进行了系统回顾和偏倚评估.
方法:2022年3月9日,我们搜索了PubMed的所有可用数据。我们纳入了包含麻疹血清阳性率主要数据的研究,如果是临床试验或简短报告,则排除研究。只有医护人员,疑似麻疹病例,或只接种疫苗的人。我们提取了所有可用的麻疹血清阳性率信息,研究设计,和血清测定方案。我们基于多个类别进行了偏见评估,并将每项研究分类为低,中度,严重,或临界偏见。这篇评论在PROSPERO(CRD420223226075)注册。
结果:我们在世界卫生组织所有地区确定了221项相关研究,几十年,和独特的年龄范围。所有研究的总体粗平均血清阳性率为78.0%(SD:19.3%),血清阳性率中位数为84.0%(IQR:72.8-91.7%)。我们将80项(36.2%)研究归类为严重或严重的总体偏差。麻疹疫苗覆盖率较低或麻疹发病率较高的国家年的研究总体偏倚较高。
结论:虽然许多研究有实质性的潜在偏差,许多研究仍然提供了一些见解或数据,可用于为建模工作提供信息,以检查麻疹动态和减少麻疹易感性的方案决策.
BACKGROUND: Measles
seroprevalence data have potential to be a useful tool for understanding transmission dynamics and for decision making efforts to strengthen immunization programs. In this study, we conducted a systematized review and bias assessment of all primary data on measles
seroprevalence in low- and middle-income countries (as defined by World Bank 2021 income classifications) published from 1962 to 2021.
METHODS: On 9 March 2022, we searched PubMed for all available data. We included studies containing primary data on measles
seroprevalence and excluded studies if they were clinical trials or brief reports, from only health-care workers, suspected measles cases, or only vaccinated persons. We extracted all available information on measles
seroprevalence, study design, and seroassay protocol. We conducted a bias assessment based on multiple categories and classified each study as having low, moderate, severe, or critical bias. This review was registered with PROSPERO (CRD42022326075).
RESULTS: We identified 221 relevant studies across all World Health Organization regions, decades, and unique age ranges. The overall crude mean
seroprevalence across all studies was 78.0% (SD: 19.3%), and the median
seroprevalence was 84.0% (IQR: 72.8-91.7%). We classified 80 (36.2%) studies as having severe or critical overall bias. Studies from country-years with lower measles vaccine coverage or higher measles incidence had higher overall bias.
CONCLUSIONS: While many studies have substantial underlying bias, many studies still provide some insights or data that could be used to inform modelling efforts to examine measles dynamics and programmatic decisions to reduce measles susceptibility.