Clustered Lot Quality Assurance Sampling Research Articles

National Stop Transmission of Polio Program support for polio supplemental immunization activities in Nigeria 2012-2016: deployment of management support team.

Introductionto support polio eradication activities in Nigeria, in 2012 the National Polio Emergency Operation Center (NEOC) created the Management Support Teams (MST) to address gaps in the quality of supervision of polio vaccination teams. The National Stop Transmission of Polio (NSTOP) Program supported the polio eradication activities by deploying trained supervisors as part of the MST for polio and non-polio immunization campaigns.Methodstrained MST members were deployed approximately 4 days before the start of the campaign to participate in pre-implementation activities and supervise vaccination teams during campaigns. Terms of reference (TOR) developed by NEOC was provided to MST members to guide their activities. Qualified MSTs that met pre-determined criteria were selected and deployed to the field to support pre, intra and post campaigns activities.Resultsa pool of over 400 MST personnel have been identified, trained, and repeatedly deployed from 2012 till 2016. The number of deployed MST personnel rose from 40 per campaign in October 2012 to 342 in May 2016. Of these, 270 (79%) MST personnel were deployed to 11 polio high-risk states of northern Nigeria, where campaigns are conducted between eight and ten times yearly as planned by NEOC. For measles campaigns, about 300 (75%) MST personnel were deployed for the one-off northern and southern campaigns in 2016. The results of clustered Lot Quality Assurance Sampling (LQAS) post-campaign vaccination coverage surveys, a measure of campaign quality, of which introduction into the polio program coincided with deployment of MSTs, showed improvement over time, from 10% (very poor quality) in February 2012 to about 90% (good quality) in December 2016.Conclusionthe deployment of MST personnel increased the number of trained supervisors in the field, frequency of supervisory visits and had a positive impact on the quality of polio campaigns.

Data for Program Management: An Accuracy Assessment of Data Collected in Household Registers by Community Health Workers in Southern Kayonza, Rwanda.

Community health workers (CHWs) collect data for routine services, surveys and research in their communities. However, quality of these data is largely unknown. Utilizing poor quality data can result in inefficient resource use, misinformation about system gaps, and poor program management and effectiveness. This study aims to measure CHW data accuracy, defined as agreement between household registers compared to household member interview and client records in one district in Eastern province, Rwanda. We used cluster-lot quality assurance sampling to randomly sample six CHWs per cell and six households per CHW. We classified cells as having 'poor' or 'good' accuracy for household registers for five indicators, calculating point estimates of percent of households with accurate data by health center. We evaluated 204 CHW registers and 1,224 households for accuracy across 34 cells in southern Kayonza. Point estimates across health centers ranged from 79 to 100% for individual indicators and 61 to 72% for the composite indicator. Recording error appeared random for all but the widely under-reported number of women on modern family planning method. Overall, accuracy was largely 'good' across cells, with varying results by indicator. Program managers should identify optimum thresholds for 'good' data quality and interventions to reach them according to data use. Decreasing variability and improving quality will facilitate potential of these routinely-collected data to be more meaningful for community health program management. We encourage further studies assessing CHW data quality and the impact training, supervision and other strategies have on improving it.

Cluster lot quality assurance sampling: effect of increasing the number of clusters on classification precision and operational feasibility.

To assess the quality of supplementary immunization activities (SIAs), the Global Polio Eradication Initiative (GPEI) has used cluster lot quality assurance sampling (C-LQAS) methods since 2009. However, since the inception of C-LQAS, questions have been raised about the optimal balance between operational feasibility and precision of classification of lots to identify areas with low SIA quality that require corrective programmatic action. To determine if an increased precision in classification would result in differential programmatic decision making, we conducted a pilot evaluation in 4 local government areas (LGAs) in Nigeria with an expanded LQAS sample size of 16 clusters (instead of the standard 6 clusters) of 10 subjects each. The results showed greater heterogeneity between clusters than the assumed standard deviation of 10%, ranging from 12% to 23%. Comparing the distribution of 4-outcome classifications obtained from all possible combinations of 6-cluster subsamples to the observed classification of the 16-cluster sample, we obtained an exact match in classification in 56% to 85% of instances. We concluded that the 6-cluster C-LQAS provides acceptable classification precision for programmatic action. Considering the greater resources required to implement an expanded C-LQAS, the improvement in precision was deemed insufficient to warrant the effort.

Extending cluster lot quality assurance sampling designs for surveillance programs

Lot quality assurance sampling (LQAS) has a long history of applications in industrial quality control. LQAS is frequently used for rapid surveillance in global health settings, with areas classified as poor or acceptable performance on the basis of the binary classification of an indicator. Historically, LQAS surveys have relied on simple random samples from the population; however, implementing two-stage cluster designs for surveillance sampling is often more cost-effective than simple random sampling. By applying survey sampling results to the binary classification procedure, we develop a simple and flexible nonparametric procedure to incorporate clustering effects into the LQAS sample design to appropriately inflate the sample size, accommodating finite numbers of clusters in the population when relevant. We use this framework to then discuss principled selection of survey design parameters in longitudinal surveillance programs. We apply this framework to design surveys to detect rises in malnutrition prevalence in nutrition surveillance programs in Kenya and South Sudan, accounting for clustering within villages. By combining historical information with data from previous surveys, we design surveys to detect spikes in the childhood malnutrition rate.

From Agadez to Zinder: estimating coverage of the MenAfriVac™ conjugate vaccine against meningococcal serogroup A in Niger, September 2010 – January 2012

MenAfriVac™ is a conjugate vaccine against meningitis A specifically designed for Africa. In Niger, the MenAfriVac™ vaccination campaign was conducted in people aged 1–29 years in three phases. The third phase was conducted in November/December 2011 targeting more than 7 million people. We estimated vaccination coverage for the third phase; classified the 31 target districts according to vaccination coverage levels; analysed the factors associated with being vaccinated; described the reasons for non-vaccination; and estimated coverage of the MenAfriVac™ introduction in Niger by aggregating data from all three phases.We classified the districts by clustered lot quality assurance sampling according to a 75% lower threshold and a 90% upper threshold. We estimated coverage using a minimum cluster-sample of 30×10 in each region. Two criteria were used to document vaccination status: presentation of vaccination card only or by card and/or verbal history of vaccination (card+history).We surveyed 2390 persons. After the third phase, estimated coverage was 68.8% (95% CI 64.9–72.8) by card only and 90.9% (95% CI 88.6–93.2) by card+history. Five districts were accepted for coverage above 75% based on card only, whereas 25 were accepted based on card+history. Factors positively associated with being vaccinated were younger age (<15 years), female sex, residing in the same household for more than three months, and being informed about the vaccination campaign. The main reason for non-vaccination was not being at home during the campaign. Overall coverage for MenAfriVac™ introduction via 3 phases was 76.1% (95% CI: 72.5–79.6) by card only and 91.9% (95%CI: 89.7–94.1) by card+history.Although estimated coverage was high, pockets of non-vaccination probably still exist in the country; thus, the implementation of mop-up campaigns should be considered. Priorities for the future should include incorporating meningitis A vaccination into the existing immunization schedule and assessing its impact at a population level.

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

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.

Whom and where are we not vaccinating? Coverage after the introduction of a new conjugate vaccine against group A meningococcus in Niger in 2010.

MenAfriVac is a new conjugate vaccine against Neisseria meningitidis serogroup A developed for the African “meningitis belt”. In Niger, the first two phases of the MenAfriVac introduction campaign were conducted targeting 3,135,942 individuals aged 1 to 29 years in the regions of Tillabéri, Niamey, and Dosso, in September and December 2010. We evaluated the campaign and determined which sub-populations or areas had low levels of vaccination coverage in the regions of Tillabéri and Niamey. After Phase I, conducted in the Filingué district, we estimated coverage using a 30×15 cluster-sampling survey and nested lot quality assurance (LQA) analysis in the clustered samples to identify which subpopulations (defined by age 1–14/15–29 and sex) had unacceptable vaccination coverage (<70%). After Phase II, we used Clustered Lot Quality Assurance Sampling (CLQAS) to assess if any of eight districts in Niamey and Tillabéri had unacceptable vaccination coverage (<75%) and estimated overall coverage. Estimated vaccination coverage was 77.4% (95%CI: 84.6–70.2) as documented by vaccination cards and 85.5% (95% CI: 79.7–91.2) considering verbal history of vaccination for Phase I; 81.5% (95%CI: 86.1–77.0) by card and 93.4% (95% CI: 91.0–95.9) by verbal history for Phase II. Based on vaccination cards, in Filingué, we identified both the male and female adult (age 15–29) subpopulations as not reaching 70% coverage; and we identified three (one in Tillabéri and two in Niamey) out of eight districts as not reaching 75% coverage confirmed by card. Combined use of LQA and cluster sampling was useful to estimate vaccination coverage and to identify pockets with unacceptable levels of coverage (adult population and three districts). Although overall vaccination coverage was satisfactory, we recommend continuing vaccination in the areas or sub-populations with low coverage and reinforcing the social mobilization of the adult population.

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

To evaluate oral poliovirus vaccine (OPV) coverage of the November 2009 round in five Northern Nigeria states with ongoing wild poliovirus transmission using clustered lot quality assurance sampling (CLQAS). We selected four local government areas in each pre-selected state and sampled six clusters of 10 children in each Local Government Area, defined as the lot area. We used three decision thresholds to classify OPV coverage: 75-90%, 55-70% and 35-50%. A full lot was completed, but we also assessed in retrospect the potential time-saving benefits of stopping sampling when a lot had been classified. We accepted two local government areas (LGAs) with vaccination coverage above 75%. Of the remaining 18 rejected LGAs, 11 also failed to reach 70% coverage, of which four also failed to reach 50%. The average time taken to complete a lot was 10 h. By stopping sampling when a decision was reached, we could have classified lots in 5.3, 7.7 and 7.3 h on average at the 90%, 70% and 50% coverage targets, respectively. Clustered lot quality assurance sampling was feasible and useful to estimate OPV coverage in Northern Nigeria. The multi-threshold approach provided useful information on the variation of IPD vaccination coverage. CLQAS is a very timely tool, allowing corrective actions to be directly taken in insufficiently covered areas.

Clustered lot quality assurance sampling: a tool to monitor immunization coverage rapidly during a national yellow fever and polio vaccination campaign in Cameroon, May 2009

We used the clustered lot quality assurance sampling (clustered-LQAS) technique to identify districts with low immunization coverage and guide mop-up actions during the last 4 days of a combined oral polio vaccine (OPV) and yellow fever (YF) vaccination campaign conducted in Cameroon in May 2009. We monitored 17 pre-selected districts at risk for low coverage. We designed LQAS plans to reject districts with YF vaccination coverage <90% and with OPV coverage <95%. In each lot the sample size was 50 (five clusters of 10) with decision values of 3 for assessing OPV and 7 for YF coverage. We 'rejected' 10 districts for low YF coverage and 14 for low OPV coverage. Hence we recommended a 2-day extension of the campaign. Clustered-LQAS proved to be useful in guiding the campaign vaccination strategy before the completion of the operations.

Clustered lot quality assurance sampling to assess immunisation coverage: increasing rapidity and maintaining precision

Vaccination programmes targeting disease elimination aim to achieve very high coverage levels (e.g. 95%). We calculated the precision of different clustered lot quality assurance sampling (LQAS) designs in computer-simulated surveys to provide local health officers in the field with preset LQAS plans to simply and rapidly assess programmes with high coverage targets. We calculated sample size (N), decision value (d) and misclassification errors (alpha and beta) of several LQAS plans by running 10 000 simulations. We kept the upper coverage threshold (UT) at 90% or 95% and decreased the lower threshold (LT) progressively by 5%. We measured the proportion of simulations with < or =d individuals unvaccinated or lower if the coverage was set at the UT (pUT) to calculate beta (1-pUT) and the proportion of simulations with >d unvaccinated individuals if the coverage was LT% (pLT) to calculate alpha (1-pLT). We divided N in clusters (between 5 and 10) and recalculated the errors hypothesising that the coverage would vary in the clusters according to a binomial distribution with preset standard deviations of 0.05 and 0.1 from the mean lot coverage. We selected the plans fulfilling these criteria: alpha < or = 5% beta < or = 20% in the unclustered design; alpha < or = 10% beta < or = 25% when the lots were divided in five clusters. When the interval between UT and LT was larger than 10% (e.g. 15%), we were able to select precise LQAS plans dividing the lot in five clusters with N = 50 (5 x 10) and d = 4 to evaluate programmes with 95% coverage target and d = 7 to evaluate programmes with 90% target. These plans will considerably increase the feasibility and the rapidity of conducting the LQAS in the field.