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Modeled trends of the probability of data quality flags for immunization coverage reports for DTP1, DTP3, MCV1, and BCG, by different country classifications, 194 WHO Member States, 2000-2019. Notes: Shading represents 95% confidence intervals (CI). Country data as reported by 15 July 2020. BCG = Bacillus Calmette-Guérin vaccine birth dose. DTP1 = first dose of diphtheria-tetanus-pertussiscontaining vaccine. DTP3 = third dose of diphtheria-tetanus-pertussis-containing vaccine. FCS = fragile and conflict-affected situations. MCV1 = first dose of measles-containing vaccine. Countries were grouped separately for each year by World Bank income groups, population size, and fragile and conflict-affected situations (FCS) classification. All other groupings were done for all years together. FCS status was available for 2004-2019 only. Immunization coverage level was based on average DTP1 and DTP3 coverage estimated by WHO and UNICEF for 2017-2019, as of July 2020. Birth registration levels refer to children under age five who have been registered based on the latest available
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Analyzing immunization coverage data is crucial to guide decision-making in national immunization programs and monitor global initiatives such as the Immunization Agenda 2030. We aimed to assess the quality of reported child immunization coverage data for 194 countries over 20 years. We analyzed child immunization coverage as reported to the World...
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Citations
... 18 It has been reported that the Americas and Africa are more likely to have potential data quality issues. 32 Although the quality of data has improved over the past two decades, 32 data obtained during the COVID-19 pandemic might suffer from further issues. Finally, the study only examined cross-country disparities in routine childhood vaccine coverage, as is typical of prior studies. ...
Background Global routine childhood vaccine coverage has plateaued in recent years, and the COVID-19 pandemic further disrupted immunisation services. We estimated global and regional inequality of routine childhood vaccine coverage from 2019 to 2021, particularly assessing the impacts of the COVID-19 pandemic.
... 52 Despite improved data quality over the last 2 decades, gains were not universal, with resource-constrained countries and those with lower immunization performance continuing to have limited to poorer quality data. 53 Although data on numerous indicators are often collected and reported by countries, we identified few indicators in the resources included in our review that measured data-driven decisionmaking and program planning at the national level. Availability of data does not necessarily translate into action; mechanisms and accountability frameworks to incorporate data into decision-making are needed. ...
... Secondly, the different input data sets have their inherent biases (e.g., admin estimates being greater than 100) which are likely the result of inaccurate denominator and/or numerator estimates, large differences between consecutive coverage estimates (in time) and recall bias associated with survey data for multi-dose vaccines (Cutts et al., 2016). A complete overview and analysis of data quality issues associated with these data sources are provided in Rau et al. (2022); Stashko et al. (2019). Although we implemented some ad hoc measures to correct these biases wherever possible, e.g., recall-bias adjustment for survey estimates of DTP3 and PCV3 and rounding down of administrative estimates greater than 100 whilst persevering the differential between multi-dose vaccines, they are better addressed at the point of data collection and summarization. ...
Estimates of national immunization coverage are crucial for guiding policy and decision-making in national immunization programs and setting the global immunization agenda. WHO and UNICEF estimates of national immunization coverage (WUENIC) are produced annually for various vaccine-dose combinations and all WHO Member States using information from multiple data sources and a deterministic computational logic approach. This approach, however, is incapable of characterizing the uncertainties inherent in coverage measurement and estimation. It also provides no statistically principled way of exploiting and accounting for the interdependence in immunization coverage data collected for multiple vaccines, countries and time points. Here, we develop Bayesian hierarchical modeling approaches for producing accurate estimates of national immunization coverage and their associated uncertainties. We propose and explore two candidate models: a balanced data single likelihood (BDSL) model and an irregular data multiple likelihood (IDML) model, both of which differ in their handling of missing data and characterization of the uncertainties associated with the multiple input data sources. We provide a simulation study that demonstrates a high degree of accuracy of the estimates produced by the proposed models, and which also shows that the IDML model is the better model. We apply the methodology to produce coverage estimates for select vaccine-dose combinations for the period 2000-2019. A contributed R package {\tt imcover} implementing the No-U-Turn Sampler (NUTS) in the Stan programming language enhances the utility and reproducibility of the methodology.
Background
Several studies have indicated that universal health coverage (UHC) improves health service utilization and outcomes in countries. These studies, however, have primarily assessed UHC's peacetime impact, limiting our understanding of UHC's potential protective effects during public health crises such as the Coronavirus Disease 2019 (COVID-19) pandemic. We empirically explored whether countries' progress toward UHC is associated with differential COVID-19 impacts on childhood immunization coverage.Methods and findingsUsing a quasi-experimental difference-in-difference (DiD) methodology, we quantified the relationship between UHC and childhood immunization coverage before and during the COVID-19 pandemic. The analysis considered 195 World Health Organization (WHO) member states and their ability to provision 12 out of 14 childhood vaccines between 2010 and 2020 as an outcome. We used the 2019 UHC Service Coverage Index (UHC SCI) to divide countries into a "high UHC index" group (UHC SCI ≥80) and the rest. All analyses included potential confounders including the calendar year, countries' income group per the World Bank classification, countries' geographical region as defined by WHO, and countries' preparedness for an epidemic/pandemic as represented by the Global Health Security Index 2019. For robustness, we replicated the analysis using a lower cutoff value of 50 for the UHC index. A total of 20,230 country-year observations were included in the study. The DiD estimators indicated that countries with a high UHC index (UHC SCI ≥80, n = 35) had a 2.70% smaller reduction in childhood immunization coverage during the pandemic year of 2020 as compared to the countries with UHC index less than 80 (DiD coefficient 2.70; 95% CI: 0.75, 4.65; p-value = 0.007). This relationship, however, became statistically nonsignificant at the lower cutoff value of UHC SCI
