Clustered lot quality assurance sampling: a pragmatic tool for timely assessment of vaccination coverage

Greenland, K.; Rondy, Marc; Chévez, Ana Elena; Sadozai, N.A.; Gasasira, Alex; Abanida, Emmanuel; Pate, Muhammad Ali; Ronveaux, Olivier; Okayasu, Hiromasa; Pedalino, Biagio; Pezzoli, Lorenzo

doi:10.1111/j.1365-3156.2011.02770.x

Cited by 23 publications

(44 citation statements)

References 5 publications

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“…Random selection of sites from a list of HTR sites in preference to proportional population size sampling (PPS) or Lot Quality Assurance Survey (LQAS) was implemented to ensure vulnerable populations were specifically sampled [24,25]. In rapidly urbanizing settings the pre-MDA census may be inaccurate.…”

Section: Discussionmentioning

confidence: 99%

Maintaining effective mass drug administration for lymphatic filariasis through in-process monitoring in Sierra Leone

et al. 2012

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BackgroundSince 2007 Sierra Leone has conducted mass drug administration (MDA) for the elimination of lymphatic filariasis (LF) implemented by unpaid community health volunteers (CHVs). Other health campaigns such as Mother and Child Health Weeks (MCHW) pay for services to be implemented at community level and these persons are then known as community health workers (CHWs). In 2010, the LF MDA in the 12 districts of the Southern, Northern and Eastern Provinces un-expectantly coincided with universal distribution of Long Lasting Insecticide Treated Nets (LLITNs) during the MCHW. In-process monitoring of LF MDA was performed to ensure effective coverage was attained in hard to reach sites (HTR) in both urban and rural locations where vulnerable populations reside.MethodsIndependent monitors interviewed individuals eligible for LF MDA and tallied those who recalled having taken ivermectin and albendazole, calculated program coverage and reported results daily by phone. Monitoring of coverage in HTR sites in the 4 most rapidly urbanizing towns was performed after 4 weeks of LF MDA and again after 8 weeks throughout all 12 districts. End process monitoring was performed in randomly selected HTR sites not previously sampled throughout all 12 districts and compared to coverage calculated from the pre-MDA census and reported treatments.ResultsOnly one town had reached effective program coverage (≥80%) after 4 weeks following which CHWs were recruited for LF MDA in all district headquarter towns. After 8 weeks only 4 of 12 districts had reached effective coverage so LF MDA was extended for a further month in all districts. By 12 weeks effective program coverage had been reached in all districts except Port Loko and there was no significant difference between those interviewed in communities versus households or by sex. Effective epidemiological coverage (≥65%) was reported in all districts and overall was significantly higher in males versus females.ConclusionsThe challenges to LF MDA included the late delivery in country of ivermectin, the availability and motivation of unpaid CHVs, concurrent LLITN distribution and the MCHW, remuneration for CHWs, rapid urbanization and employment seeking population migrations. 'In process' monitoring ensured modifications of LF MDA were made in a timely manner to ensure effective coverage was finally attained even in HTR locations.

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Section: Discussionmentioning

confidence: 99%

Maintaining effective mass drug administration for lymphatic filariasis through in-process monitoring in Sierra Leone

et al. 2012

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“…Alternative methods based on geographic sampling could be used to allow that sampling locations are more evenly spread across the lots [27-29]. Third, as previously seen with the C-LQAS approach, the high inter-cluster variability (SE > 0.1) seen in some lots, reaching a maximum level of SE = 0.21, may indicate that the plans may have had errors (alpha and beta) above the levels defined in some lots [9,17], although this finding should be interpreted with caution, since it is not possible to support it statistically given the small sample sizes per lot used to calculate the SE. One way to reduce the inter-cluster variability and consequently increase the precision would be to increase the number of clusters sampled to more than five [22].…”

Section: Discussionmentioning

confidence: 99%

Intervene before leaving: clustered lot quality assurance sampling to monitor vaccination coverage at health district level before the end of a yellow fever and measles vaccination campaign in Sierra Leone in 2009

Pezzoli

Conteh²,

Kamara³

et al. 2012

BMC Public Health

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BackgroundIn November 2009, Sierra Leone conducted a preventive yellow fever (YF) vaccination campaign targeting individuals aged nine months and older in six health districts. The campaign was integrated with a measles follow-up campaign throughout the country targeting children aged 9–59 months. For both campaigns, the operational objective was to reach 95% of the target population. During the campaign, we used clustered lot quality assurance sampling (C-LQAS) to identify areas of low coverage to recommend timely mop-up actions.MethodsWe divided the country in 20 non-overlapping lots. Twelve lots were targeted by both vaccinations, while eight only by measles. In each lot, five clusters of ten eligible individuals were selected for each vaccine. The upper threshold (UT) was set at 90% and the lower threshold (LT) at 75%. A lot was rejected for low vaccination coverage if more than 7 unvaccinated individuals (not presenting vaccination card) were found. After the campaign, we plotted the C-LQAS results against the post-campaign coverage estimations to assess if early interventions were successful enough to increase coverage in the lots that were at the level of rejection before the end of the campaign.ResultsDuring the last two days of campaign, based on card-confirmed vaccination status, five lots out of 20 (25.0%) failed for having low measles vaccination coverage and three lots out of 12 (25.0%) for low YF coverage. In one district, estimated post-campaign vaccination coverage for both vaccines was still not significantly above the minimum acceptable level (LT = 75%) even after vaccination mop-up activities.ConclusionC-LQAS during the vaccination campaign was informative to identify areas requiring mop-up activities to reach the coverage target prior to leaving the region. The only district where mop-up activities seemed to be unsuccessful might have had logistical difficulties that should be further investigated and resolved.

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“…To overcome this problem, Pezzoli et al and Greenland et al [8-10] have recently put forward a more field-friendly “clustered LQAS” (CLQAS) approach, whereby the lot sample is divided into clusters, as in any multi-stage cluster sample. The critical assumption behind this approach is that, within any given lot (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…The CLQAS approach has been used in different settings including Nigeria and Cameroon [8,9]; however, the accuracy of classifications generated by this design and implications of this accuracy for operational decisions have not been sufficiently documented [11]. Using data from a vaccination coverage survey carried out in Mali in January 2011, we aimed to evaluate the performance of CLQAS in a typical field setting.…”

Section: Introductionmentioning

confidence: 99%

Performance of small cluster surveys and the clustered LQAS design to estimate local-level vaccination coverage in Mali

Minetti¹,

Riera-Montes²,

Nackers³

et al. 2012

Emerg Themes Epidemiol

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BackgroundEstimation of vaccination coverage at the local level is essential to identify communities that may require additional support. Cluster surveys can be used in resource-poor settings, when population figures are inaccurate. To be feasible, cluster samples need to be small, without losing robustness of results. The clustered LQAS (CLQAS) approach has been proposed as an alternative, as smaller sample sizes are required.MethodsWe explored (i) the efficiency of cluster surveys of decreasing sample size through bootstrapping analysis and (ii) the performance of CLQAS under three alternative sampling plans to classify local VC, using data from a survey carried out in Mali after mass vaccination against meningococcal meningitis group A.ResultsVC estimates provided by a 10 × 15 cluster survey design were reasonably robust. We used them to classify health areas in three categories and guide mop-up activities: i) health areas not requiring supplemental activities; ii) health areas requiring additional vaccination; iii) health areas requiring further evaluation. As sample size decreased (from 10 × 15 to 10 × 3), standard error of VC and ICC estimates were increasingly unstable. Results of CLQAS simulations were not accurate for most health areas, with an overall risk of misclassification greater than 0.25 in one health area out of three. It was greater than 0.50 in one health area out of two under two of the three sampling plans.ConclusionsSmall sample cluster surveys (10 × 15) are acceptably robust for classification of VC at local level. We do not recommend the CLQAS method as currently formulated for evaluating vaccination programmes.

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Clustered lot quality assurance sampling: a pragmatic tool for timely assessment of vaccination coverage

Cited by 23 publications

References 5 publications

Maintaining effective mass drug administration for lymphatic filariasis through in-process monitoring in Sierra Leone

Maintaining effective mass drug administration for lymphatic filariasis through in-process monitoring in Sierra Leone

Intervene before leaving: clustered lot quality assurance sampling to monitor vaccination coverage at health district level before the end of a yellow fever and measles vaccination campaign in Sierra Leone in 2009

Performance of small cluster surveys and the clustered LQAS design to estimate local-level vaccination coverage in Mali

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