肯尼亚西部麻疹第二剂疫苗常规数据和调查数据覆盖率估计的一致性。

Frontiers in epidemiology Pub Date : 2025-09-11 eCollection Date: 2025-01-01 DOI:10.3389/fepid.2025.1663372
Angela K Moturi, Moses M Musau, Samuel K Muchiri, Peter M Macharia, Robert W Snow, Emelda A Okiro
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引用次数: 0

摘要

背景:错过关键疫苗接种机会继续加剧疾病暴发。准确监测免疫覆盖率对于确定疫苗提供方面的差距和及时通报行动至关重要。本研究评估了肯尼亚西部常规和基于调查的第二剂麻疹疫苗(MCV2)覆盖率估计之间的一致性。方法:本研究利用基于模型的地质统计学估计2022年肯尼亚人口与健康调查(DHS)的MCV2覆盖率,来自常规卫生信息系统(2019-2022年)的月度免疫接种数据,以及来自世界人口普查的2019年肯尼亚西部62个次国家地区(次县)的人口数据。常规MCV2接种覆盖率是使用MCV2剂量作为分子和两个单独的分母来计算的:(i)五价1剂量,用于计算已经在卫生设施接种过疫苗的儿童(以服务为基础的覆盖率);(ii)存活婴儿,用于计算所有符合条件的儿童(以人口为基础的覆盖率)。使用调查模型估计的95%置信区间(ci)、类内相关系数(ICC)和Bland-Altman (BA)图评估一致性。结果:在55个(89%)和39个(63%)副县中,调查模型估计与基于人口和基于服务的覆盖率估计相比存在很大差异。不同的方法在基于调查模型的覆盖率估计与基于人口的覆盖率估计(ICC: 0.10, p = 0.229)和基于调查模型的覆盖率估计与基于服务的覆盖率估计(ICC: 0.42, p = p =结论:基于调查模型的、常规人口的和基于服务的覆盖率估计之间的巨大差异暴露了每种方法结果的重要差异。虽然所有方法都提供不同的见解,但改进调查模型、常规数据质量和改进人口集水区估计对于可靠的小规模疫苗交付监测至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Concordance of coverage estimates from routine and survey data of measles second dose vaccine in Western Kenya.

Background: Missed opportunities for key vaccinations continue to exacerbate disease outbreaks. Accurately monitoring immunisation coverage is fundamental to identifying gaps in vaccine delivery and informing timely action. This study assesses the agreement between routine and survey-based coverage estimates for the second dose of the measles vaccine (MCV2) in Western Kenya.

Methods: This study utilised model-based geostatistics estimates MCV2 coverage from the 2022 Kenya Demographic and Health Survey (DHS), monthly immunisation data from routine health information systems (2019-2022) imputed for missingness and population data from WorldPop for 2019 across 62 Western Kenyan subnational areas (sub-counties). Routine MCV2 coverage was computed using MCV2 doses as a numerator and two separate denominators: (i) Pentavalent 1 doses to account for children already receiving prior vaccines at health facilities (service-based coverage) and (ii) surviving infants to account for all eligible children (population-based coverage). Concordance was assessed using the 95% confidence intervals (CIs) of survey-modelled estimates, intra-class correlation coefficient (ICC), and Bland-Altman (BA) plots.

Results: Survey-modelled estimates differed substantially in 55 (89%) and 39 (63%) sub-counties compared to population and service-based coverage estimates respectively. The different approaches showed poor congruence in survey-modelled vs. population-based coverage estimates (ICC: 0.10, p = 0.229) and survey-modelled vs. service-based coverage estimates (ICC: 0.42, p = <0.001); there was moderate congruence of population vs. service-based coverage estimates (ICC: 0.65, p = <0.001). Survey-modelled vs. population-based coverage estimates showed the highest bias in BA plots of 18.80 percent points (p.p) compared to 11.02 p.p. and 7.79 p.p. between survey-modelled vs. service-based coverage and population vs. service-based coverage estimates, respectively.

Conclusions: Substantial discrepancies among survey-modelled, routine population, and service-based coverage estimates expose important variations in each approaches' results. While all approaches offer distinct insights, improving survey models, routine data quality and refining estimates of population catchment is imperative for reliable fine-scale vaccine delivery monitoring.

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