Lessons from Recent Measles Post-Campaign Coverage Surveys Worldwide
by
, , , and
M. Carolina Danovaro-Holliday
1,*
,
Mitsuki Koh
1,
Claudia Steulet
1,
Dale A. Rhoda
2 and
Mary Kay Trimner
2 1
World Health Organization, 1211 Geneva, Switzerland
2
Biostat Global Consulting, Worthington, OH 43085, USA
*
Author to whom correspondence should be addressed.
Vaccines 2024, 12(11), 1257; https://doi.org/10.3390/vaccines12111257
Submission received: 27 August 2024
/
Revised: 16 October 2024
/
Accepted: 20 October 2024
/
Published: 6 November 2024
(This article belongs to the Special Issue A World without Measles and Rubella: Meeting the Regional Elimination Targets on the Path to Global Eradication)
Abstract
Background: Measles elimination strategies include supplementary immunization activities (SIAs) to rapidly fill immunity gaps. Post-campaign coverage surveys (PCCSs) are recommended to assess SIA coverage. We characterized selected PCCSs performed following recent SIAs, highlighting specific challenges and strengths, and provide recommendations for improvement. Methods: We extracted national SIA data from the global measles/MR SIA database for the period of 2020–2023 and reviewed PCCS reports available at the World Health Organization headquarters. We extracted selected information on PCCS implementation, including information about the implementer, sampling, and main results. Results: Only 15 of 66 countries (23%) with a national-level SIA performed since 2020 had a PCCS report available. We reviewed those reports, plus six more, following three 2019 SIAs with a delayed PCCS and two PCCSs following large subnational SIAs (Kenya 2021 and Yemen 2023). All 24 PCCS reports available were from Gavi-eligible countries, with 15 from South Saharan Africa (Cameroon, the Democratic Republic of the Congo, and Ethiopia had two PCCSs). Eleven (45.8%) PCCSs were conducted within three months of the end of the SIA. All included sampling information and most had percentage of participation. Description of the interviewers’ profiles varied but was limited. PCCS coverage was lower than administrative data in all but two instances. All PCCSs collected data on previous measles vaccination status that would allow exploring indicators on the SIA reaching previously measles zero-dose children. Of the 12 PCCSs reporting coverage among previously measles zero-dose children, nine reported coverage among this group of more than 50% (range: 12% and 91.6%). Conclusion: Even though a PCCS following an SIA is recommended and a requirement in Gavi-supported countries, most SIAs are not followed by a PCCS and, when performed, the timeliness of survey implementation needs improvement. Recent PCCSs were independently conducted and reports included basic survey information, but analysis and presentation of survey results vary particularly for measles zero-dose-related indicators. More guidance and technical support on how to implement PCCSs, including standardization of reports and more in-depth PCCS analyses, may help improve reporting and use of available PCCS data.
1. Background
Since the launch of the Expanded Program on Immunization (EPI) 50 years ago, over 154 million vaccine-preventable deaths have been averted, with measles vaccination contributing in a great extent to this achievement [1]. The World Health Organization (WHO) recommends two doses of a measles-containing vaccine (MCV) to be given in routine immunization (RI) [2]. In addition, supplementary immunization activities (SIAs) are a common strategy used to fill immunity gaps resulting from incomplete vaccination through RI services [3,4]. SIAs have been part of measles elimination strategies since their development in the 1990s [4,5]. During an SIA, age- and place-eligible persons, most often children, are vaccinated regardless of their previous vaccination status. In most low- and middle-income countries, SIAs occur every 2–5 years, as the number of measles-susceptible persons accumulate from incomplete vaccination coverage with one or two doses [3,6,7,8]. SIAs have been found to be more equitable in reaching children than routine immunization in several countries [9], and their timeliness and performance have been under much scrutiny recently [10,11]. Most countries have now also introduced rubella vaccination and use measles-rubella-containing (MR) vaccines, with SIAs effectively contributing to rubella control and elimination [12,13]. During the COVID-19 pandemic, not only did MCV1 and MCV2 coverage decline globally [14,15,16], but also many SIAs were cancelled or postponed [17]. MCV coverage is yet to reach pre-pandemic levels globally. As a result, several outbreaks are now occurring across the globe [18,19,20].
To be effective, SIAs need to reach high and homogenous coverage; reaching communities and children previously missed by RI is particularly important. To evaluate the coverage reached by an SIA, the Measles and Rubella Partnership (former Measles Rubella Initiative) recommends implementing an independent post-campaign coverage survey (PCCS) within three months of SIA implementation. For measles and MR SIAs implemented with support from Gavi, the Vaccine Alliance, a PCCS following WHO guidance is required [21,22,23]. A PCCS does not replace intra-campaign monitoring using real-time monitoring of the process, administrative data, and rapid convenience monitoring [3,24,25,26]. However, a PCCS is considered an important tool to estimate the coverage reached by the SIA, to evaluate coverage among children who were previously zero-dose measles vs. those who had been previously vaccinated, and to better understand enablers and barriers to vaccination [22,27].
Concerns have been raised about the quality of vaccination coverage surveys in general and for those following measles campaigns in particular [25,28]. In 2015, the WHO released new survey guidance, first as a working draft and in 2018 as a final product, and accompanying analytical software to overcome some of the challenges [22,28]. Countries have since moved toward conducting independent, probability sampling, vaccination coverage surveys that use probability sampling. This manuscript explores the implementation of PCCSs following measles and MR SIAs since 2020, in addition to selected PCCSs not included in a related publication manuscript published in 2021 [21]. It also characterizes the PCCSs conducted, highlighting specific challenges and strengths, and provides recommendations for improvement.
2. Methods
We extracted SIA data from the measles/MR SIA database and reviewed the PCCS reports available at the World Health Organization headquarters as of 11 July 2024. We restricted the analysis to PCCSs conducted since 2020, as a summary of PCCSs conducted between 2017 and 2019 was published by Cutts et al. [21]. However, PCCS reports for 2019 SIAs that were not included in that publication were also included here.
We extracted data on country, vaccine type, SIA date, target age group, estimated target population, administrative coverage, and conduct of the PCCS. For the available PCCS reports, we extracted information on whether it was a national-level survey, dates of field work, sample size, SIA cards or finger marking seen, profile of the implementing partner, sampling approach, length of training, the composition of teams and the supervision provided, and whether the PCCS included questions related to barriers and enablers to vaccination or on reasons for no vaccination. Regarding the results, we extracted information on whether a description of the sample, including the exclusion of certain areas and clusters, was presented. We also extracted MCV RI coverage, SIA overall coverage, and information related to measles zero-dose children, including the proportion receiving their first MCV dose in the SIA, SIA coverage among previously measles zero-dose children, and proportion who remained measles zero-dose after the SIA. Two of the authors (MCD-H and MK) extracted the data in an Excel file, and when one was unsure, both authors reviewed at the report together.
3. Results
Review of the WHO SIA database identified 66 countries that had conducted non-outbreak response, national-level, or rolling SIAs between 2020 and 21 June 2024 (Supplemental Table S1). Of them, 15 (23%) had a PCCS with a report available; Ethiopia had two. We also included six additional PCCS reports following 2019 SIAs that were not included by Cutts et al. [21], as well as one PCCS report from Kenya, for a subnational SIA in 2021 and one from Yemen, for a subnational SIA in 2023, but both covered a large proportion of the country. Cameroon and DRC had PCCSs that evaluated 2019 SIAs. In total, our analysis included 24 PCCS reports (Table 1 and Table 2). All PCCSs were implemented at the national level, except for Kenya, which included only the 22 counties where the SIA took place, and Yemen, where the SIA and PCCS were conducted only in the southern governorates. The Somalia PCCS excluded one large northern region, although the SIA was also conducted there. For the Central African Republic, only 57% of enumeration areas available in the list of enumeration areas were deemed accessible and kept in the sampling frame. Eleven PCCSs (45.8%) were implemented within three months of the SIA; four PCCSs (16.7%) were implemented at about one year or more after the SIA. In terms of implementers, four were conducted by the National Bureau of Statistics (NBS) and all but two at least mentioned collaborating or using a sampling frame from the NBS. Twenty-three PCCSs reported having performed a weighted analysis, although in one it was unclear if weighting was performed according to the sampling strategy or only to aggregate the country-level estimates. For Syria, weighting was not performed as per NBS guidance due to the current situation of moving populations and unavailability of accurate population data at any administrative level. Ten (43%) were led by independent consultants or organizations and four (20.8%) were led by an academic institution or a research organization.
Twenty-one (87.5%) reports indicated having referred to the 2018 WHO Vaccination Coverage Cluster Survey Reference Manual, with one referring to an outdated version (2005), and most described the key sampling elements. All but two reports described exclusions or furnished the response rate. Sample sizes varied, but most PCCSs were large, with all having >1000 children. Regarding field implementation, dates were explicitly stated in all but one report. The description of the composition of the teams varied broadly, with the profile and gender of interviewers rarely described (data not displayed in the table, as the reports varied widely). Seventeen PCCS reports mentioned ethical clearance being obtained and two (Chad and Kenya) indicated that the survey was considered a programmatic evaluation and thus ethical clearance not required. The other five reports had no mention of ethical review, although explicit information on informed consent was available for three (the two PCCSs from Cameron and the one in Nigeria) of these five. Copies of the questionnaires were included in 19 reports (79.2%). Of relevance, all but one of the PCCSs were performed using computer-assisted personal interviewing (CAPI) and at least seventeen (70.8%) collected GPS coordinates. We did not explicitly extract information on whether geospatial analyses were conducted, but from reading the reports, we noted that maps were not frequently included. Finally, all PCCSs included a section on reasons for no vaccination, with two mentioning the Behavioral and Social Drivers of Vaccination (BeSD) framework.
The main PCCS results and comparisons to administrative data and routine coverage with the first dose of a measles-containing vaccine (MCV1) are included in Table 2. PCCS SIA coverage was lower than the administrative coverage included in the SIA database, apart from DPR Korea and the accessible areas of Syria. Regarding ascertainment of SIA vaccination, 19 had vaccination cards or finger marking as proof of vaccination in the SIA (range: 6.9–81.1%). One third of the PCCSs reported SIA coverage that was below the WHO/UNICEF Estimate of National Immunization Coverage (WUENIC) for one dose of a measles-containing vaccine. Twenty PCCS reports (83.3%) included at least one of the indicators related to zero-dose measles. Of the 12 PCCSs reporting coverage among previously measles zero-dose children, nine had coverage of more than 50% (range: 12% and 91.6%). DPR Korea and Nepal explored this indicator but unvaccinated children in the sample were extremely few. Only the Zambia PCCS report did not allow ascertaining whether the collected data would allow calculating at least one of the three measles zero-dose SIA coverage indicators included in our table.
Only one anonymized database was available to WHO-HQ. This PCCS had been re-analyzed, in 2023, using the tool Vaccination Coverage Quality Indicators (VCQI) [29] upon request from stakeholders. In this PCCS, which excluded one large northern region and security-compromised areas, SIA coverage among children previously vaccinated with either one or 2+ measles doses was strikingly higher than among those who had no previous MCV dose. Table 3 illustrates the VCQI output for a given age-group.
Secondary analyses were conducted to better understand SIA coverage heterogeneity. Table 4 describes measles SIA coverage, the number of children in the sample, and the number of clusters, by state. It then indicates the number of clusters where all surveyed children were found vaccinated, those where the proportion was >50% but <100%, and then the clusters where the proportion of vaccinated children was ≤50%. The design effect (DEFF) and intra-cluster correlation coefficient (ICC) are also included for each stratum. Both parameters were high, with the DEFF ranging from 10.8 to 28.5, which is largely above the parameters used in sample size calculations. The large value observed here is due to the high ICC and an unusually high number of respondents per cluster.
An organ pipe plot [21,30], as recommended by the WHO Vaccination Coverage Survey Manual, was produced for all 6 states included in the PCCS (Figure 1—illustrative). Cluster-level SIA proportion vaccinated (indicated by the individual columns) varied considerably across State X.
Figure footnote: Clusters are sorted left-to-right in descending order of coverage. In this representation, clusters with 100% sample coverage are light blue, those with 50.01–99.99% are dark blue, and those with 0–50% are shown in orange. The thin gray dashed line that varies in height from cluster to cluster indicates the number of respondents per cluster and its values are read using the right vertical axis.
Only two other PCCS reports reviewed for this work included calculation of the intra-cluster correlation coefficient (ICC) or design effect (DEFF), parameters that are useful to inform sample size calculations in future PCCS.
4. Discussion
Though measles SIAs are performed in many parts of the world, few countries use PCCSs to evaluate their performance. All PCCSs for which the WHO had access to reports were conducted in Gavi-eligible countries, and most in Sub Saharan Africa. If we compare our findings with previous reviews [21,25], the use of probability sampling and weighted analysis is encouraging; most PCCSs are explicit in indicating that current WHO guidance is being considered. The independence of implementers and quality of reports has also improved compared to previous studies.
Many challenges remain, however. Not all countries can implement a PCCS, and this is evident from the fact that only Gavi eligible countries had PCCS reports available. Also, the timing of PCCS implementation is of particular concern. Only about half of the PCCSs explored here were implemented within three months of the SIA, though our findings are confounded by the limitations to field activities imposed by the COVID-19 pandemic.
Administrative coverage levels tend to overestimate campaign coverage compared to coverage obtained from PCCS. This finding is not surprising. Administrative coverage can be affected by inaccurate denominators and errors in tallying and aggregating doses. For SIAs, vaccination of children outside the target age while tallying them as within the age range is not uncommon.
Only five PCCS coverage levels were above 90%, and only one >95%. We also observed that measles SIA coverage was lower than routine MCV1 coverage for a third of the PCCSs included. This was unexpected, as SIAs, by their nature, conduct intensive outreach and social mobilization. This warrants more exploration, as it may also be that as documentation of vaccine doses received improves, children who are already vaccinated may be less likely to go for an additional dose.
The next questions after seeing coverage that is insufficient to stop measles transmission are: where coverage was not reached and why. While PCCSs can provide clues, these answers would be better addressed by better intra-campaign monitoring and rapid convenience monitoring before stopping vaccination. We did not explore these aspects of SIA implementation but would argue that they need strengthening judging by the SIA coverage reached in most recent campaigns and their heterogeneity in reaching previously unvaccinated children [4,21,27,31]. Furthermore, to our knowledge, only campaign coverage is broadly accepted and recommended as an indicator of measles SIA quality and impact. The establishment of more criteria to assess SIAs was recommended by the Measles & Rubella Strategic Framework 2021–2030 published at the beginning of the decade [32].
Conducting a PCCS is not always easy. The PCCSs included here tend to be large, with over 1000 children. The more clusters in a survey, the more resources are needed to reach the different areas. In addition to the challenges linked to the normal work of reaching selected places, interviewing families takes time and effort. Four reports explicitly mentioned the violence that survey teams faced, with one survey team even being kidnapped in a country in central Africa. To this end, one should consider under what situations a PCCS may not be the best use of resources or may not warrant the risk that field work may entail. Also, complementary monitoring tools may need to be used in insecure areas and others that are not included in normal sampling frames. Decision support tools may be worth considering. Time elapsed between the SIA and the PCCS may be a consideration. Senegal implemented a selective SIA in 2021. It was an SIA of particular interest because it involved screening and listing children aged 9 to 59 months (performed in Oct 2021) and then vaccination of these children at fixed and outreach sites (in November 2021); children not on the lists but who came to a site without documentation of having previously received two doses of MR vaccine were also to be vaccinated. Unfortunately, a PCCS was not conducted until 2023, and at the time of this writing, in July 2024, the report is not available. Another example where a PCCS may not be the best option is when an SIA is known to not have reached high coverage, using administrative data and a narrative of the issues, complemented or not with rapid convenience monitoring, may be a better option. This was the case in Indonesia outside of Java in 2017–2018 and the Gambia in 2022. Another example might be that of South Sudan, where after performing an SIA in 2023 decided to prioritize campaign evaluation and rapid field assessments, and their PCCS was paired with a planned RI vaccination coverage survey that was scheduled for late 2023, after the rainy season. This survey is yet to be implemented. Recent outbreaks affecting mainly children aged 1–4 years suggest that coverage was likely insufficient [33]. More needs to be done to triangulate data from intra-campaign monitoring and administrative coverage with PCCS findings to better inform the proposed decision support algorithms [3,31].
When PCCSs are implemented, exploiting the findings and the available data should be of utmost priority. The WHO and partners make available analytical software and a detailed list of indicators [29]. However, most of the surveys described in the reports accessible to us did not use them. This may reflect limited awareness or other barriers that we need to further explore. In addition to coverage and the availability of sociodemographic variables that would allow exploring factors associated with no vaccination, most PCCSs now collect GPS coordinates. They also have information about reasons for no vaccination, and more recent PCCSs are starting to use the Behavioral and Social Drivers of Immunization (BeSD) [34] framework to explore barriers and enablers to vaccination.
Some of the countries included here used survey data to conduct exploratory analysis on factors linked to not being vaccinated. Nigeria explored geographic differences in measles vaccination in RI vs. SIAs [27]. The analysis showed that the SIA reached more homogenous coverage than the RI, but that there are areas where many children are missed by both approaches. Furthermore, geolocated campaign and RI coverage data can be extremely useful to modelers when modeling disease transmission and outbreak risk, which can help to inform disease control targeting [10,11,31,35,36,37,38,39]. However, unlike well-established household survey programs, there is no PCCS database repository. The example presented here allowed exploring heterogeneity in coverage. The results, which represent areas that were accessible to surveyors, warrant better exploring why coverage among measles zero-dose children was so much lower than that for those who had been previously vaccinated. Countries should be encouraged to make anonymized databases available to the WHO, the IA2030 Measles and Rubella Partnership, and other stakeholders, as is done by most household surveys. Furthermore, we support the initiative by the IA2030 Partnership to establish data analysis cooperation in countries conducting coverage surveys with additional analyses performed by local analysts, close in time to the PCCS, and engaging with EPI programs to promote data use.
Our analysis has several limitations. First, we included only national SIAs that were not for outbreak response. We may have accidentally left out SIAs that should have been included. Second, we may not have the reports of all PCCSs conducted. This is less likely for Gavi-eligible countries, as the authors reached out to Gavi and the US Centers for Disease Control and Prevention (CDC) to seek reports. However, non-Gavi eligible countries may not share PCCS reports with the WHO. Third, we selected only a few characteristics of the PCCS and the reports from a long list of recommended items in the survey checklist recommended by Cutts et al. [21] and adopted by Gavi. Also, we did not compare the reports to the elements recommended in the recently introduced “Preferred Reporting Items for Complex Sample Survey Analysis” (PRICSSA) checklist [40]. Fourth, two authors extracted data from the PCCS reports, and as they were not structured according to our list of items, we may have misrepresented some data; we did not attempt to reach PCCS teams for clarifications. Lastly, having an item in a report does not guarantee good survey implementation. There are many challenges to conducting field enumeration and sampling, identifying pre-selected clusters boundaries and pre-selected households, and to interviewing people, particularly to ascertain vaccination status when a card or other documentation is not available. Caregivers’ ability to accurately recall vaccination experience for each of their children can be challenging, and more so the longer the time lag between the SIA and the survey. Analysis of data collected using complex sampling, particularly the appropriate weight calculations, and use of sample design parameters in statistical packages is challenging. We did not have access to any analytical codes, except for the VCQI code used for one PCCS performed by two co-authors (DR and MKT). Nevertheless, we believe that our main findings remain solid: not all SIAs are followed by a PCCS and, when performed, timeliness remains a concern; more PCCSs appear to be following current recommendations; survey coverage tends to be lower than administrative SIA results; and more analysis could be performed with the data that are collected by PCCS analysts and, if made available, by measles modelers and other stakeholders.
Gavi contracted out two PCCSs in 2024, one for Burkina Faso and one for Laos, in an attempt to ensure PCCS timeliness and quality survey implementation. Neither report is available for review yet. This work will present an opportunity to explore the pros and cons of a centralized contracting approach, as well as to explicitly triangulate PCCS results with other data to better inform decision making.
The WHO is coordinating a thorough update to the 2018 Vaccination Coverage Cluster Survey Reference Manual [22] and accompanying software tools [29] in 2024–2026. This presents an opportunity to further engage with countries and the IA2030 Measles and Rubella Partnership to ensure that the revised guidance includes options for areas where regular sampling is problematic, clearer standardization for reports, more guidance on analyses beyond descriptive statistics, and more hands-on capacity-building activities if a stakeholder analysis identifies this as useful.
Measles is so exceedingly contagious that very high vaccination coverage is required to prevent outbreaks from spreading through a population. High-quality, independent, probability-based assessments of campaign coverage will be useful both for refining SIA planning and execution to consistently achieve high coverage rates, especially among measles zero-dose children.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/vaccines12111257/s1. Table S1: List of measles campaigns, global measles supplemental immunization activity (SIA) database.
Author Contributions
Conceptualization, M.C.D.-H.; Data compilation: C.S.; Data extraction M.C.D.-H. and M.K.; Survey data analysis, D.A.R. and M.K.T. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
This study did not require ethical approval as it is a secondary data analysis.
Informed Consent Statement
Informed consent was obtained from all subjects participating in the surveys described in this study.
Data Availability Statement
Data is contained within the article.
Conflicts of Interest
M.C.D.-H., C.S., and M.K. for the World Health Organization (WHO). The authors alone are responsible for the views expressed in this publication and they do not necessarily represent the decisions, policy, or views of the WHO. D.A.R. and M.K.T. declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Authors Dale A. Rhoda and Mary Kay Trimner are employed by the company Biostat Global Consulting. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
References
- Shattock, A.J.; Johnson, H.C.; Sim, S.Y.; Carter, A.; Lambach, P.; Hutubessy, R.C.W.; Thompson, K.M.; Badizadegan, K.; Lambert, B.; Ferrari, M.J.; et al. Contribution of vaccination to improved survival and health: Modelling 50 years of the Expanded Programme on Immunization. Lancet 2024, 403, 2307–2316. [Google Scholar] [CrossRef] [PubMed]
- World Health Organization. Measles vaccines: WHO position paper. Wkly. Epidemiol. Rec. 2017, 92, 205–227. [Google Scholar]
- World Health Organization. Planning and Implementing High-Quality Supplementary Immunization Activities for Injectable Vaccines: Using an Example of Measles-Rubella Vaccines—Field Guide; WHO: Geneva, Switzerland, 2016; Available online: https://www.who.int/publications/i/item/9789241511254 (accessed on 15 October 2024).
- Strebel, P.; Grabowsky, M.; Hoekstra, E.; Gay, A.; Cochi, S. Evolution and Contribution of a Global Partnership against Measles and Rubella, 2001–2023. Vaccines 2024, 12, 693. [Google Scholar] [CrossRef] [PubMed]
- De Quadros, C.A.; Hersh, B.S.; Nogueira, A.C.; A Carrasco, P.; Da Silveira, C.M. Measles eradication: Experience in the Americas. Bull. World Health Organ. 1998, 76 (Suppl. 2), 47–52. [Google Scholar] [PubMed]
- Verguet, S.; Johri, M.; Morris, S.K.; Gauvreau, C.L.; Jha, P.; Jit, M. Controlling measles using supplemental immunization activities: A mathematical model to inform optimal policy. Vaccine 2015, 33, 1291–1296. [Google Scholar] [CrossRef]
- Prada, J.; Metcalf, C.; Takahashi, S.; Lessler, J.; Tatem, A.; Ferrari, M. Demographics, epidemiology and the impact of vaccination campaigns in a measles-free world—Can elimination be maintained? Vaccine 2017, 35, 1488–1493. [Google Scholar] [CrossRef]
- Auzenbergs, M.; Fu, H.; Abbas, K.; Procter, S.R.; Cutts, F.T.; Jit, M. Health effects of routine measles vaccination and supplementary immunisation activities in 14 high-burden countries: A Dynamic Measles Immunization Calculation Engine (DynaMICE) modelling study. Lancet Glob. Health 2023, 11, e1194–e1204. [Google Scholar] [CrossRef]
- Portnoy, A.; Jit, M.; Helleringer, S.; Verguet, S. Comparative Distributional Impact of Routine Immunization and Supplementary Immunization Activities in Delivery of Measles Vaccine in Low- and Middle-Income Countries. Value Health 2020, 23, 891–897. [Google Scholar] [CrossRef] [PubMed]
- Portnoy, A.; Jit, M.; Helleringer, S.; Verguet, S. Impact of measles supplementary immunization activities on reaching children missed by routine programs. Vaccine 2018, 36, 170–178. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
- Rosenfeld, K.A.; Frey, K.; McCarthy, K.A. Optimal Timing Regularly Outperforms Higher Coverage in Preventative Measles Supplementary Immunization Campaigns. Vaccines 2024, 12, 820. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
- Yan, L.; Jing, M.; Dapeng, Y. Rubella vaccines: WHO position paper. Wkly. Epidemiol. Rec. 2020, 95, 27. [Google Scholar]
- Ou, A.C.; Zimmerman, L.A.; Alexander, J.P.; Crowcroft, N.S.; O’connor, P.M.; Knapp, J.K. Progress Toward Rubella and Congenital Rubella Syndrome Elimination—Worldwide, 2012–2022. Mmwr-Morb. Mortal. Wkly. Rep. 2024, 73, 162–167. [Google Scholar] [CrossRef] [PubMed]
- Minta, A.A.; Ferrari, M.; Antoni, S.; Portnoy, A.; Sbarra, A.; Lambert, B.; Hatcher, C.; Hsu, C.H.; Ho, L.L.; Steulet, C.; et al. Progress Toward Measles Elimination—Worldwide, 2000–2022. Mmwr-Morbidity Mortal. Wkly. Rep. 2023, 72, 1262–1268. [Google Scholar] [CrossRef] [PubMed]
- Rachlin, A.; Danovaro-Holliday, M.C.; Murphy, P.; Sodha, S.V.; Wallace, A.S. Routine Vaccination Coverage—Worldwide, 2021. Mmwr-Morbidity Mortal. Wkly. Rep. 2022, 71, 1396–1400. [Google Scholar] [CrossRef]
- Muhoza, P.; Danovaro-Holliday, M.C.; Diallo, M.S.; Murphy, P.; Sodha, S.V.; Requejo, J.H.; Wallace, A.S. Routine Vaccination Coverage—Worldwide, 2020. Mmwr-Morbidity Mortal. Wkly. Rep. 2021, 70, 1495–1500. [Google Scholar] [CrossRef]
- Ho, L.L.; Gurung, S.; Mirza, I.; Nicolas, H.D.; Steulet, C.; Burman, A.L.; Danovaro-Holliday, M.C.; Sodha, S.V.; Kretsinger, K. Impact of the SARS-CoV-2 pandemic on vaccine-preventable disease campaigns. Int. J. Infect. Dis. 2022, 119, 201–209. [Google Scholar] [CrossRef]
- CDC. Global Measles Outbreaks. Global Measles Vaccination. Published 13 May 2024. Available online: https://www.cdc.gov/global-measles-vaccination/data-research/global-measles-outbreaks/index.html (accessed on 15 October 2024).
- e Clinical Medicine Concerning global rise in measles cases. eClinicalMedicine 2024, 68, 102502. [CrossRef]
- Parums, D.V. A Review of the Resurgence of Measles, a Vaccine-Preventable Disease, as Current Concerns Contrast with Past Hopes for Measles Elimination. Med. Sci. Monit. 2024, 30, e944436-1–e944436-10. [Google Scholar] [CrossRef]
- Cutts, F.T.; Danovaro-Holliday, M.C.; Rhoda, D.A. Challenges in measuring supplemental immunization activity coverage among measles zero-dose children. Vaccine 2021, 39, 1359–1363. [Google Scholar] [CrossRef]
- World Health Organization. Vaccination Coverage Cluster Surveys: Reference Manual; WHO: Geneva, Switzerland, 2018. [Google Scholar]
- Measles and Measles-Rubella Vaccine Support. Available online: https://www.gavi.org/types-support/vaccine-support/measles-and-measles-rubella (accessed on 15 October 2024).
- Luman, E.T.; Cairns, K.L.; Perry, R.; Dietz, V.; Gittelman, D. Use and abuse of rapid mnitoring to assess coverage during mass vaccination campaigns. Bull. World Health Organ. 2007, 85, 651. [Google Scholar] [CrossRef]
- Kaiser, R.; E Shibeshi, M.; Chakauya, J.M.; Dzeka, E.; Masresha, B.G.; Daniel, F.; Shivute, N. Surveys of measles vaccination coverage in eastern and southern Africa: A review of quality and methods used. Bull. World Health Organ. 2015, 93, 314–319. [Google Scholar] [CrossRef] [PubMed]
- Cutts, F.T.; Izurieta, H.S.; Rhoda, D.A. Measuring Coverage in MNCH: Design, Implementation, and Interpretation Challenges Associated with Tracking Vaccination Coverage Using Household Surveys. PLoS Med. 2013, 10, e1001404. [Google Scholar] [CrossRef] [PubMed]
- Utazi, C.E.; Wagai, J.; Pannell, O.; Cutts, F.T.; Rhoda, D.A.; Ferrari, M.J.; Dieng, B.; Oteri, J.; Danovaro-Holliday, M.C.; Adeniran, A.; et al. Geospatial variation in measles vaccine coverage through routine and campaign strategies in Nigeria: Analysis of recent household surveys. Vaccine 2020, 38, 3062–3071. [Google Scholar] [CrossRef] [PubMed]
- Danovaro-Holliday, M.C.; Dansereau, E.; Rhoda, D.A.; Brown, D.W.; Cutts, F.T.; Gacic-Dobo, M. Collecting and using reliable vaccination coverage survey estimates: Summary and recommendations from the “Meeting to share lessons learnt from the roll-out of the updated WHO Vaccination Coverage Cluster Survey Reference Manual and to set an operational research agenda around vaccination coverage surveys”, Geneva, 18–21 April 2017. Vaccine 2018, 36, 5150–5159. [Google Scholar] [CrossRef] [PubMed]
- Biostat Global Consulting. Vaccination Coverage Quality Indicators (VCQI)—Resources [Internet]. 2024. Available online: http://www.biostatglobal.com/VCQI_RESOURCES.HTML (accessed on 23 August 2024).
- Prier, M.L.; Rhoda, D.A. Organ Pipe Plots for Clustered Datasets—Visualize Disparities in Cluster-Level Coverage [Internet]. Stata Conference 2018; Columbus, Ohio. Available online: https://github.com/BiostatGlobalConsulting/organ-pipe-plots/blob/master/opplot_presentation.pptx (accessed on 23 August 2024).
- Cutts, F.T.; Ferrari, M.J.; Krause, L.K.; Tatem, A.J.; Mosser, J.F. Vaccination strategies for measles control and elimination: Time to strengthen local initiatives. BMC Med. 2021, 19, 2. [Google Scholar] [CrossRef]
- World Health Organization. Measles and Rubella Strategic Framework 2021–2030; World Health Organization: Geneva, Switzerland, 2020. [Google Scholar]
- Measles Cases in the AFRO Region. South Sudan. Available online: https://www.afro.who.int/sites/default/files/2024-06/Measles%20Outbreak%20and%20Response%20Weekly%20Situation%20Update_Week%2019%2C%202024.pdf (accessed on 23 August 2024).
- Behavioral and Social Drivers of Immunization. Available online: https://www.who.int/teams/immunization-vaccines-and-biologicals/essential-programme-on-immunization/demand (accessed on 15 October 2024).
- Akpan, G.U.; Mohammed, H.F.; Touray, K.; Kipterer, J.; Bello, I.M.; Ngofa, R.; Stein, A.; Seaman, V.; Mkanda, P.; Cabore, J. Conclusions of the African Regional GIS Summit (2019): Using geographic information systems for public health decision-making. BMC Proc. 2022, 16 (Suppl. 1), 3. [Google Scholar] [CrossRef]
- Utazi, C.E.; Aheto, J.M.; Wigley, A.; Tejedor-Garavito, N.; Bonnie, A.; Nnanatu, C.C.; Wagai, J.; Williams, C.; Setayesh, H.; Tatem, A.J.; et al. Mapping the distribution of zero-dose children to assess the performance of vaccine delivery strategies and their relationships with measles incidence in Nigeria. Vaccine 2022, 41, 170–181. [Google Scholar] [CrossRef]
- Arambepola, R.; Yang, Y.; Hutchinson, K.; Mwansa, F.D.; Doherty, J.A.; Bwalya, F.; Ndubani, P.; Musukwa, G.; Moss, W.J.; Wesolowski, A.; et al. Using geospatial models to map zero-dose children: Factors associated with zero-dose vaccination status before and after a mass measles and rubella vaccination campaign in Southern province, Zambia. BMJ Glob. Health 2021, 6, e007479. [Google Scholar] [CrossRef] [PubMed]
- Gebreslasie, M.T. A review of spatial technologies with applications for malaria transmission modelling and control in Africa. Geospat. Health 2015, 10, 239–247. [Google Scholar] [CrossRef] [PubMed]
- Fatima, M.; O’keefe, K.J.; Wei, W.; Arshad, S.; Gruebner, O. Geospatial Analysis of COVID-19: A Scoping Review. Int. J. Environ. Res. Public Health 2021, 18, 2336. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
- Seidenberg, A.B.; Moser, R.P.; West, B.T. Preferred Reporting Items for Complex Sample Survey Analysis (PRICSSA). J. Surv. Stat. Methodol. 2023, 11, 743–757. [Google Scholar] [CrossRef]
Figure 1.
Organ pipe plot of proportion of children vaccinated per cluster in State X. Somalia (minus one large northern region one large northern region) PCCS 2023.
Figure 1.
Organ pipe plot of proportion of children vaccinated per cluster in State X. Somalia (minus one large northern region one large northern region) PCCS 2023.
Table 1.
Summary of basic characteristics of post-campaign coverage surveys (PCCSs) following measles supplementary immunization activities.
Table 1.
Summary of basic characteristics of post-campaign coverage surveys (PCCSs) following measles supplementary immunization activities.
| Measles Supplementary Immunization Activities (SIAs) | Post-Campaign Coverage Surveys (PCCSs) | ||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Basics | Basics | Sampling | |||||||||||||||||||||
| # | Year of PCCS | Country | WHO Region | Target Age Group in Months | Estimated Target Population | Admin Coverage % | SIA Month/Year | Month/Year of Field Work | Delay Between SIA & PCCS (in Months) | Lead Implementer | Ethical Clearance Mentioned | Questionnaire/Data Collection Form Included in Report | # of Clusters Selected/Included | # of Households Interviewed | # of Children Included | Description of Response Rates of Clusters | Description of Response Rates of Households | Primary Sampling Units | Household Enumeration Done | Involvement of NBS/CSO | Data Collection Tools | GPS Coordinates Collected | Software Used for Analysis |
| 1 | 2019 | Democratic People’s Republic of Korea | SEAR | 9 months-15 years old | 5,873,914 | 99.7 | Oct-19 | Winter | NA | Country-led | Yes | Yes | 41 | 1312 (31 HH in each cluster) | 1180 | Yes | Yes | Census enumeration blocks | Yes | Yes | CSPro | No | SPSS |
| 2 | 2019 | Zimbabwe | AFR | 9–59 months | 1,776,546 | 91 | Sep-19 | Nov-19 | 2 months | Not specified | Yes | Yes | 470 | 8110 planned *actual number included missing | 3796 planned *actual number included missing | No | No | Enumeration areas | Yes | Yes | ODK | NR | Stata |
| 3 | 2020 | Burkina Faso | AFR | 9–59 months | 3,078,334 | 106 | Nov-19 | Aug–Sept 2020 | 9–10 months | Independent consultant | Yes | No | 548 | 17,650 | 8457 | Yes | Yes | Enumeration areas | Not mentioned | Sampling frame from NBS | ODK | NA | SPSS and Stata |
| 4 | 2020 | Cameroon | AFR | 9–59 months | 3,339,090 | 92 | Dec-19 | June–July 2020 | 6–7 months | Research Organization | Not mentioned. Informed consent was obtained. | Yes | 396 | 4866 | 4671 | Yes | Yes | Enumeration areas | Yes | Sampling frame from NBS | ODK | Yes | SPSS |
| 5 | 2020 | Ethiopia | AFR | 9–59 months | 14,181,143 | 102.8 | June–July 2020 | Oct 2020–Jan 2021 | 4–6 months | Research Organization | Yes | Yes | 1016 Tigray was excluded | 10,143 | 12,867 | Yes | Yes | Enumeration areas | Yes | Sampling frame from NBS | CSPro | Yes | Stata, VCQI |
| 6 | 2020 | Uganda | AFR | 9 months to less than 15 years old | 18,200,970 | 107 | Oct-19 | Dec 2019–Jan 2020 | 2–3 months | Academic Institution | Yes | No | 363 | 5174 | 12,025 | Yes (written as coverage rate) | Yes | Enumeration areas | Yes | Sampling frame from NBS | ODK | NA | Stata |
| 7 | 2021 | Central African Republic † | AFR | 6–59 months 5–10 years | 1,209,256 | 102 for 6–59 m 85.5 for 5–10 yrs | Phase 1: Mar-2020 Phase 2: Aug-2020 | Dec 2020 Bangui, March–April 2021 rest | 8–11 months | Independent consultant | Yes | Yes | 240 | 3172 | 5674 | Yes | Yes | Enumeration areas | Yes | Sampling frame from NBS | ODK | Yes (difficulties noted) | SPSS |
| 8 | 2021 | Chad | AFR | 9–59 months | 1,792,830 (Phase1) 1,623,518 (Phase2) | 108.8 (Phase1) 107.5 (Phase2) | Jan- 2021 (Phase1) Mar-2021 (Phase2) | Mar–April 2022 | ~1 year | Independent consultant | The survey technical committe considered that the survey was not reasearch and thus it was not needed to present the protocol to an ethical comittee. Informed consent was obtained. | Yes | 695 | 15,793 | 11,601 | Yes | Yes | Enumeration areas, with segmentation | Yes | Yes | ODK | Yes, at least for clusters | SPSS and Stata |
| 9 | 2021 | Democratic Republic of the Congo | AFR | 6–59 months | 18,167,926 | 101.6 | Oct-Dec 2019 | Nov–Dec 2021 | ~2 years | Independent consultant | Yes | Yes | 728 | 81,740 | 21,575 | Yes | Yes | Enumeration areas | Yes | Yes | CSPro | Yes | SPSS, EpIInfo |
| 10 | 2021 | Kenya ‡ | AFR | 9–59 months | 3,374,464 | 111 | June–July 2021 | Jul-21 | <3 months | NBS | Considered programmatic evaluation. No ethical clearance was sought. Informed consent was obtained. | No | 477 | 4404 | 5409 | Yes, but by Region | Yes | Enumeration areas | Not for the PCCS. Sample was from available up-to-date sampling frame maintained by NBS. | Yes | Survey Solution application with digitization of the questionnaire and integration of maps. | Yes | Not mentioned |
| 11 | 2021 | Nepal | SEAR | 9–59 months | 2,548,336 | 101 | Feb–Mar 2020 (Phase 1) Mar–July 2020 (Phase 2) | Sept–Nov 2021 | >1 year | Family Welfare Division Department of Health Services MOH | Yes | Yes | 342 | 7180 | 3715 | Yes | Yes | List of wards (some were merged or segmented) | Yes | Yes | CSPro | Yes | SPSS |
| 12 | 2021 | Pakistan | EMR | 9 months-15 years old | 90,262,980 | 105 | Nov-21 | Nov 2021–Jan 2022 | <3 months | Contech International Health Consultants | Yes | Yes | 1584 | 15,840 | 31,871 | Yes | Yes | Enumeration areas | Yes | Yes. Technical and sampling frame | Proprietary android-based CAPI application from implementing partner | No | Stata, VCQI |
| 13 | 2021 | Zambia | AFR | 9–59 months | 3,398,230 | 91.3 | Nov-20 | Oct-21 | >1 year | Not specified | No | No | 272 | 5155 | 4590 | No | Yes | Enumeration areas | Yes | Yes | The questionnaire was programmed into a CAPI application using survey solutions, a World Bank software application | Not mentioned? | |
| 14 | 2022 | Burundi | AFR | 9–59 months | 1,683,300 | 93 | Jan-22 | Oct–Nov 2022 | 10–11 months | NBS | Not mentioned | Yes | 540 | 32,177 | 20,618 | Yes | Yes | Ennumeration areas | Yes | Yes | KoboCollect | Yes | SPSS and Stata |
| 15 | 2022 | Madagascar | AFR | 6–59 months | 4,355,433 | 95.11 | May–June 2022 | Aug–Sept 2022 | 3 months | International consultant with Directorate of Demography and Social Statistics (DDSS) of the NBS | Yes | Yes | 123 | 3055 | 1421 | Yes | Yes | Enumeration areas | Yes | Yes | CSPro | Yes | SPSS and Stata |
| 16 | 2022 | Somalia | EMR | children of 0–59 months for tOPV, 06–59 months for MCV and Vitamin A and 12–59 months for deworming | 2,566,955 | 90 | Nov-22 | Feb-23 | 3 months | Independent consultant/cabinet | Yes | Yes | 450 | 17,539 | 21,740 | No | No | Lists of accessible areas (not mentioned in detail) | Not mentioned | Not mentioned | Survey123 | Yes | SPSS, Stata, ArcGIS |
| 17 | 2022 or 2023 | Syrian Arab Republic | EMR | 6 months-5 years | 2,494,498 | 75.62 | Oct–Nov 2022 | Dates not provided. 2023? | NA | Independent consultant | Yes | No | 99 | 5982 | 3581 | Yes | Yes | Sub-districts | Yes | Yes, technical advise | Paper-based | No | EpiInfo |
| 18 | 2023 | Cameroon | AFR | 9–59 months | 5,564,940 | 94.38 | Jul-23 | Sept–Oct 2023 | <3 months | NBS | No. Informed consent was obtained. | Yes | 395 | 5711 | 3546 | Yes | Yes | Enumeration areas | Yes | Yes | CSPro | Yes | SPSS, Stata, VCQI |
| 19 | 2023 | Democratic Republic of the Congo | AFR | 6–59 months | 6,454,490 (May) 7,359,339 (August) 5,273,383 (September) | 103.6 (May) 85.7 (August) 101.1 (September) | Three phases: April, June and Aug 2023. | October–December 2023 in Block 1, Phase 2 in Block 2 and part of Block 3 from January to March 2024, then Phase 3 in the rest of the provinces in March–April 2024. | 6 to 10 months | Independent consultant | Yes | Yes | 728 | 10,920 | 9627 | Yes | Yes | Enumeration areas | Yes | Yes | CSPro | Yes | SPSS, EpIInfo |
| 20 | 2023 | Malawi | AFR | 9–59 months | 3,169,522 | 82.4 | May-23 | Jun-23 | <3 months | International consultant and NBS | Yes | Yes | 205 | 4715 | 8485 | Yes | Yes | Enumeration areas | Yes | Yes | CSPro | Yes | Stata, VCQI |
| 21 | 2023 | Niger | AFR | 6–59 m or 9–59 m depending on district | 5,098,682 | 105 | Dec 2022–Jan 2023 | Mar–April 2023 | 3 months | Independent consultant | Yes | Yes | 270 | 5334 | 4655 | Yes | Yes | Ennumeration areas | Yes | Yes | ODK | Yes | SPSS |
| 22 | 2023 | Nigeria | AFR | 9–59 months | 4,298,149 (October) 1,090,330 (November) 5,021,611 (December) | 96.67 (October) 103.95 (November) 4,823,266 (December) | Oct 2023–Jan 2024 | Dec 2023–Jan 2024 | <3 months | NBS | No. Informed consent was obtained. | Yes | 740 (560 in 14 states plus 180 in 6 local area governments in Borno) | 7399 | 6987 | Yes | Yes | Enumeration areas | Yes | Yes | CSPro | Yes | Stata, VCQI |
| 23 | 2023 | Yemen ‡ | EMR | 6–59 months | 1,267,083 | 91 | September–October 2023 | Oct-23 | <3 months | Research Organization | Yes | Yes | 1547 | 18,564 | 29,549 | Yes | Yes | Harahs | No | No | ODK | No | Excel |
| 24 | 2024 | Ethiopia | AFR | 98.7 | Dec 2022–April 2023 | Dec 2023–Jan 2024 | >1 year | Research Organization | Yes | Yes | 1272 | 12,702 | 15,763 | Yes | Yes | Enumeration areas | Yes | Sampling frame from NBS | Kobo Toolbox | Yes | Stata, VCQI | ||
AFR = World Health Organization African Region; EMR = World Health Organization Middle Eastern Region; SEAR = World Health Organization South-East Asian Region NBS = National Bureau of Statistics; NA = not available; NR = not reported, but potentially available; VCQI = Vaccination Coverage Quality Indicators tool; † Areas considered secure; ‡ Subnational.
Table 2.
Summary of main results of post-campaign coverage surveys (PCCSs) following measles supplementary immunization activities (SIAs) and related coverage levels.
Table 2.
Summary of main results of post-campaign coverage surveys (PCCSs) following measles supplementary immunization activities (SIAs) and related coverage levels.
| Results | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| # | Year of PCCS | Country | WHO Region | MCV1 Coverage in Year of Start of SIA | Admin Coverage % | SIA Cards or Finger Marking Seen | SIA Coverage (95% CI) | % Routine Immunization (RI) Cards Seen (Age Group If Not All) | RI MCV1 Coverage % (95% CI) (Age Group If Not All) | SIA Dose Was the First MCV Dose Received by Child % (95%CI) | SIA Coverage Among Zero-Dose % (95%CI) | Child Remained Measles Zero-Dose After SIA % (95%CI) |
| 1 | 2019 | Democratic People’s Republic of Korea | SEAR | 98 | 99.7 | Almost all were verified by record in Primary Health Facility. Only recall were 7 children who had records in a different health facility | 99.9% (99.09–99.87) | NA | 81% yes, 18.6% unsure, 0.4% no | NR | NA | NA |
| 2 | 2019 | Zimbabwe | AFR | 85 | 91 | NR | 78.7% (77.35– 79.98) | NA | NA | NR | NR | NR |
| 3 | 2020 | Burkina Faso | AFR | 88 | 106 | 38.7% (87.8% received one) | 84.4% (83.6–85.2%) | 93% 12–23 months 81% 24–35 months | 87,8% 12–23 m 84.8% 24–35 m | NR | NR | 4.80% |
| 4 | 2020 | Cameroon | AFR | 61 | 92 | 8.30% | 69.7% (68.34–71.01) | NA | NR | 35.70% | NR | NR |
| 5 | 2020 | Ethiopia | AFR | 59 | 102.8 | 81.10% | 81.5% (80.0–83.0%) | NA | 80.30% | NR | 56.70% | 8.10% |
| 6 | 2020 | Uganda | AFR | 87 | 107 | 48.5 (78.4% received one) | 94.4% (93.0–95.5) | 25.30% | 85.40% | 12.00% | NR | 2.60% |
| 7 | 2021 | Central African Republic † | AFR | 41 | 102 for 6–59 m 85.5 for 5–10 yrs | 25% (83% received one) | 94.6% (92.9–96.0%) | NA | 25.2% 12–23 m 15.3% 24–35 m | NR | NR | 3.40% |
| 8 | 2021 | Chad | AFR | 53 | 108.8 (Phase1) 107.5 (Phase2) | 11% (19% reported not having received a card) | 77.4% (74.8–79.8%) | NA | Available as an analysis added later, not in main report. 41.1% 12–23 m 28.2% 24–35 m | NR | 73.5% (IC95%:70.2–76.5%) | 20% (IC95%: 17.6–22.5%) |
| 9 | 2021 | Democratic Republic of the Congo | AFR | 65 | 101.6 | 6.89%; 4.93% (correctly filled in) | 87.52% (87.50–87.53%) | NR | 80.70% | NR | 61.06%. | 7.61% |
| 10 | 2021 | Kenya ‡ | AFR | 90 | 111 | NR | 84.20% | NA | NA | NR | 80.10% | NR |
| 11 | 2021 | Nepal | SEAR | 87 | 101 | 43% | 84% (82–87) Phase 1: 87% (85–89) phase 2: 81% (77–85) | 51.40% | 95.50% | Small sample size of previously zero-dose | Small sample size of previously zero-dose | NR |
| 12 | 2021 | Pakistan | EMR | 81 | 105 | 80% | 93.6% (92.7–94.4) | 47% | 71.5% (and 10% unkown status) 79% for children under 2 years | NR | NR | NR |
| 13 | 2021 | Zambia | AFR | 96 | 91.3 | NA | 68.90% | 63% had documented evidence | 88.5 | NA | NA | NA |
| 14 | 2022 | Burundi | AFR | 89 | 93 | 15% | 88.60% | 91.8% 12–23 m 86.1% 24–35 m | 85.90% | NR | NR | NR |
| 15 | 2022 | Madagascar | AFR | 44 | 95.11 | 56.30% | 65.3% (56.8–72.9) | NA | 69.50% | NR | NR | 19.20% |
| 16 | 2022 | Somalia | EMR | 46 | 90 | NA | 86.0 (83.7–88.0) | 25% 12–23 | 65% | NR | 33.1% 14.1% 12–23 m 37.2% 24–35 m | NR |
| 17 | 2022 or 2023 | Syrian Arab Republic | EMR | 52 | 75.62 | 54.40% | 80.7% (79.35%–81.94%) | NA | 82.8 12–23 m 93.6 24–59 m | NR | 54.30% | NR |
| 18 | 2023 | Cameroon | AFR | 71 | 94.38 | 31.8% (84.9% received a card) | 69.5 (66.4–72.5) | NA | NR | 22.10% | 62.30% | 13.3% |
| 19 | 2023 | Democratic Republic of the Congo | AFR | 52 | 103.6 (May) 85.7 (August) 101.1 (September) | 12.88% | 94.60% (94.60%–94.61%) | ~3.5% (3.47% vaccinated by card seen) | 78.30% | NR | 75.01% | NA |
| 20 | 2023 | Malawi | AFR | 87 | 82.4 | 38.8% | 75.7% (72.3–79.0) | NA | NR | NR | 27.6% for 12–35 m | NR |
| 21 | 2023 | Niger | AFR | 65 | 105 | 71.8% (86.2% received one) | 92.7% (90.8–94.1) | NA | NA | NR | 91.60% | 4.60% |
| 22 | 2023 | Nigeria | AFR | 60 | 96.67 (October) 103.95 (November) 4,823,266 (December) | 47% card and 9.9% finger marking | 87% (84–90) | NR | NR | 12% | NR | NR |
| 23 | 2023 | Yemen ‡ | EMR | 45 | 91 | NA | 84.2% (95% CI: 83.8–84.6) | NA | NR | NR | 12% | NR |
| 24 | 2024 | Ethiopia | AFR | 55 | 98.7 | 16% | 87.10% | 3940 (~25%) | 73.80% | 14.00% | 72% | NR |
AFR = World Health Organization African Region; EMR = World Health Organization Middle Eastern Region; SEAR = World Health Organization South-East Asian Region; NBS = National Bureau of Statistics; NA = not available; NR = not reported, but potentially available; † Areas considered secure; ‡ Subnational.
Table 3.
Measles SIA vaccination coverage by previous vaccination status, children aged 24–35 months, Somalia (minus one large northern region) PCCS 2023.
Table 3.
Measles SIA vaccination coverage by previous vaccination status, children aged 24–35 months, Somalia (minus one large northern region) PCCS 2023.
| Number of Measles Vaccine Doses Prior to SIA | Vaccinated During SIA (%) | 95% CI (%) | Vaccinated During SIA (Weighted N) | Weighted N |
|---|---|---|---|---|
| Zero | 37.2 | (31.8, 42.8) | 81 | 217 |
| 1 Dose | 92.8 | (89.5, 95.1) | 540 | 582 |
| 2+ Doses | 90.9 | (86.6, 94.0) | 812 | 894 |
| Somalia 2023 (Total) | 84.7 | (81.6, 87.3) | 1433 | 1692 |
Table 4.
Measles SIA coverage, the number of children in the sample, and the number of clusters, by state. Somalia (minus one large northern region) PCCS 2023.
Table 4.
Measles SIA coverage, the number of children in the sample, and the number of clusters, by state. Somalia (minus one large northern region) PCCS 2023.
| State | % Vac’d During SIA | Total # Children | Total # of Clusters | # Clusters with 100% Children vac’d’ | # Clusters with >50–99.9% Children vac’d | # Clusters with ≤50% Children vac’d | DEFF | ICC |
|---|---|---|---|---|---|---|---|---|
| Banadir | 89.8 | 3604 | 74 | 13 | 57 | 4 | 11.7 | 0.2216 |
| Galmudug | 86.1 | 3338 | 76 | 10 | 61 | 5 | 10.8 | 0.1634 |
| Hirshabelle | 82.6 | 4372 | 76 | 17 | 47 | 12 | 28.5 | 0.4284 |
| Jubbaland | 83.8 | 2781 | 76 | 29 | 39 | 8 | 26.7 | 0.4465 |
| Puntland | 88.6 | 3279 | 73 | 18 | 52 | 3 | 14.0 | 0.2361 |
| Southwest | 87 | 3542 | 75 | 20 | 48 | 7 | 17.7 | 0.2791 |
| Total | 87 | 20,916 | 450 | 107 | 304 | 39 | 21.3 | 0.3116 |
DEFF: design effect; ICC: intra-cluster correlation coefficient. For PCCS, ICC values of 1/6 = 0.167 are considered conservative [21].
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Danovaro-Holliday, M.C.; Koh, M.; Steulet, C.; Rhoda, D.A.; Trimner, M.K. Lessons from Recent Measles Post-Campaign Coverage Surveys Worldwide. Vaccines 2024, 12, 1257. https://doi.org/10.3390/vaccines12111257
AMA Style
Danovaro-Holliday MC, Koh M, Steulet C, Rhoda DA, Trimner MK. Lessons from Recent Measles Post-Campaign Coverage Surveys Worldwide. Vaccines. 2024; 12(11):1257. https://doi.org/10.3390/vaccines12111257
Chicago/Turabian StyleDanovaro-Holliday, M. Carolina, Mitsuki Koh, Claudia Steulet, Dale A. Rhoda, and Mary Kay Trimner. 2024. "Lessons from Recent Measles Post-Campaign Coverage Surveys Worldwide" Vaccines 12, no. 11: 1257. https://doi.org/10.3390/vaccines12111257
APA StyleDanovaro-Holliday, M. C., Koh, M., Steulet, C., Rhoda, D. A., & Trimner, M. K. (2024). Lessons from Recent Measles Post-Campaign Coverage Surveys Worldwide. Vaccines, 12(11), 1257. https://doi.org/10.3390/vaccines12111257
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