Choosing a Cluster Sampling Design for Lot Quality Assurance Sampling Surveys
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paper_title: Clustered lot quality assurance sampling to assess immunisation coverage: increasing rapidity and maintaining precision.

paper_content:

OBJECTIVE ::: 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. ::: ::: ::: METHODS ::: 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. ::: ::: ::: RESULT ::: 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. ::: ::: ::: CONCLUSION ::: These plans will considerably increase the feasibility and the rapidity of conducting the LQAS in the field.
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paper_title: Cluster-sample surveys and lot quality assurance sampling to evaluate yellow fever immunisation coverage following a national campaign, Bolivia, 2007.

paper_content:

OBJECTIVE ::: To estimate the yellow fever (YF) vaccine coverage for the endemic and non-endemic areas of Bolivia and to determine whether selected districts had acceptable levels of coverage (>70%). ::: ::: ::: METHODS ::: We conducted two surveys of 600 individuals (25 x 12 clusters) to estimate coverage in the endemic and non-endemic areas. We assessed 11 districts using lot quality assurance sampling (LQAS). The lot (district) sample was 35 individuals with six as decision value (alpha error 6% if true coverage 70%; beta error 6% if true coverage 90%). To increase feasibility, we divided the lots into five clusters of seven individuals; to investigate the effect of clustering, we calculated alpha and beta by conducting simulations where each cluster's true coverage was sampled from a normal distribution with a mean of 70% or 90% and standard deviations of 5% or 10%. ::: ::: ::: RESULTS ::: Estimated coverage was 84.3% (95% CI: 78.9-89.7) in endemic areas, 86.8% (82.5-91.0) in non-endemic and 86.0% (82.8-89.1) nationally. LQAS showed that four lots had unacceptable coverage levels. In six lots, results were inconsistent with the estimated administrative coverage. The simulations suggested that the effect of clustering the lots is unlikely to have significantly increased the risk of making incorrect accept/reject decisions. ::: ::: ::: CONCLUSIONS ::: Estimated YF coverage was high. Discrepancies between administrative coverage and LQAS results may be due to incorrect population data. Even allowing for clustering in LQAS, the statistical errors would remain low. Catch-up campaigns are recommended in districts with unacceptable coverage.
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paper_title: Choosing a design to fit the situation: how to improve specificity and positive predictive values using Bayesian lot quality assurance sampling.

paper_content:

BACKGROUND ::: Lot Quality Assurance Sampling (LQAS) is a provably useful tool for monitoring health programmes. Although LQAS ensures acceptable Producer and Consumer risks, the literature alleges that the method suffers from poor specificity and positive predictive values (PPVs). We suggest that poor LQAS performance is due, in part, to variation in the true underlying distribution. However, until now the role of the underlying distribution in expected performance has not been adequately examined. ::: ::: ::: METHODS ::: We present Bayesian-LQAS (B-LQAS), an approach to incorporating prior information into the choice of the LQAS sample size and decision rule, and explore its properties through a numerical study. Additionally, we analyse vaccination coverage data from UNICEF's State of the World's Children in 1968-1989 and 2008 to exemplify the performance of LQAS and B-LQAS. ::: ::: ::: RESULTS ::: Results of our numerical study show that the choice of LQAS sample size and decision rule is sensitive to the distribution of prior information, as well as to individual beliefs about the importance of correct classification. Application of the B-LQAS approach to the UNICEF data improves specificity and PPV in both time periods (1968-1989 and 2008) with minimal reductions in sensitivity and negative predictive value. ::: ::: ::: CONCLUSIONS ::: LQAS is shown to be a robust tool that is not necessarily prone to poor specificity and PPV as previously alleged. In situations where prior or historical data are available, B-LQAS can lead to improvements in expected performance.
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paper_title: Extending cluster lot quality assurance sampling designs for surveillance programs

paper_content:

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.
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paper_title: The effect of clustering on lot quality assurance sampling: a probabilistic model to calculate sample sizes for quality assessments

paper_content:

Traditional Lot Quality Assurance Sampling (LQAS) designs assume observations are collected using simple random sampling. Alternatively, randomly sampling clusters of observations and then individuals within clusters reduces costs but decreases the precision of the classifications. In this paper, we develop a general framework for designing the cluster(C)-LQAS system and illustrate the method with the design of data quality assessments for the community health worker program in Rwanda. To determine sample size and decision rules for C-LQAS, we use the beta-binomial distribution to account for inflated risk of errors introduced by sampling clusters at the first stage. We present general theory and code for sample size calculations. The C-LQAS sample sizes provided in this paper constrain misclassification risks below user-specified limits. Multiple C-LQAS systems meet the specified risk requirements, but numerous considerations, including per-cluster versus per-individual sampling costs, help identify optimal systems for distinct applications. We show the utility of C-LQAS for data quality assessments, but the method generalizes to numerous applications. This paper provides the necessary technical detail and supplemental code to support the design of C-LQAS for specific programs.
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paper_title: 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

paper_content:

In 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. We 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. During 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. C-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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paper_title: Clustered lot quality assurance sampling: a pragmatic tool for timely assessment of vaccination coverage

paper_content:

OBJECTIVES ::: 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). ::: ::: ::: METHODS ::: 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. ::: ::: ::: RESULTS ::: 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. ::: ::: ::: CONCLUSIONS ::: 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.
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paper_title: A simplified general method for cluster-sample surveys of health in developing countries.

paper_content:

Emphasizing methods for practitioners with little expertise and no background in sampling this paper presents a set of guidelines to follow in planning cluster-sample surveys of appropriate size in developing countries without undue bias. A self-weighting design based upon the World Health Organizations Expanded Program on Immunization is employed. The paper covers the topics of sample design methods of random selection of areas and households sample-size calculation and estimating proportions ratios and means with standard errors appropriate to the survey design. Extensions including stratification and multiple stages of selection are also discussed. Giving guidance on possible values the authors pay close attention to allow for survey structure in estimating sample size using the design effect and the rate of homogeneity. A spreadsheet is finally included to aid in calculating standard errors.
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paper_title: Who should be undertaking population-based surveys in humanitarian emergencies?

paper_content:

BackgroundTimely and accurate data are necessary to prioritise and effectively respond to humanitarian emergencies. 30-by-30 cluster surveys are commonly used in humanitarian emergencies because of their purported simplicity and reasonable validity and precision. Agencies have increasingly used 30-by-30 cluster surveys to undertake measurements beyond immunisation coverage and nutritional status. Methodological errors in cluster surveys have likely occurred for decades in humanitarian emergencies, often with unknown or unevaluated consequences.DiscussionMost surveys in humanitarian emergencies are done by non-governmental organisations (NGOs). Some undertake good quality surveys while others have an already overburdened staff with limited epidemiological skills. Manuals explaining cluster survey methodology are available and in use. However, it is debatable as to whether using standardised, 'cookbook' survey methodologies are appropriate. Coordination of surveys is often lacking. If a coordinating body is established, as recommended, it is questionable whether it should have sole authority to release surveys due to insufficient independence. Donors should provide sufficient funding for personnel, training, and survey implementation, and not solely for direct programme implementation.SummaryA dedicated corps of trained epidemiologists needs to be identified and made available to undertake surveys in humanitarian emergencies. NGOs in the field may need to form an alliance with certain specialised agencies or pool technically capable personnel. If NGOs continue to do surveys by themselves, a simple training manual with sample survey questionnaires, methodology, standardised files for data entry and analysis, and manual for interpretation should be developed and modified locally for each situation. At the beginning of an emergency, a central coordinating body should be established that has sufficient authority to set survey standards, coordinate when and where surveys should be undertaken and act as a survey repository. Technical expertise is expensive and donors must pay for it. As donors increasingly demand evidence-based programming, they have an obligation to ensure that sufficient funds are provided so organisations have adequate technical staff.
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paper_title: Rapid nutrition surveys : how many clusters are enough ?

paper_content:

On the basis of theoretical considerations, population-based nutrition surveys of 30 clusters of 30 children should provide reasonably valid estimates of the prevalence of malnutrition with at least 95 per cent confidence that the estimated prevalence differs from the true value by no more than 5 per cent. In areas of famine in Africa, where an urgent need often exists for rapid nutritional assessment to determine the extent and severity of the problem, visiting 30 sites is often logistically difficult. To determine the effects of using fewer than 30 clusters on the validity and precision of the estimated level of undernutrition, we used data from the 1983 Swaziland National Nutrition Survey and from rapid nutrition surveys performed in 1984 and 1985 in Burkina Faso, Guinea, and Niger. Fewer than 30 clusters may result in point prevalence estimates that differ dramatically from the true prevalence and, in most instances, are less precise. In contrast, little is gained by collecting more than 30 clusters. In summary, around 30 clusters provides relatively valid and precise estimates of the prevalence of undernutrition, and every effort should be made to obtain the logistic support required to study this number of clusters.
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paper_title: The Intra‐Cluster Correlation Coefficient in Cluster Randomized Trials: A Review of Definitions

paper_content:

The intra-cluster correlation coefficient (ICC) of the primary outcome plays a key role in the design and analysis of cluster randomized trials (CRTs), but the precise definition of this parameter is somewhat elusive, especially in the context of non-normally distributed outcomes. In this paper, we provide a unified treatment of ICC as used in CRTs. We present a general definition of the ICC that may be expressed in different ways depending on the modelling approach used to describe the data, illustrating how this general definition is applied to continuous and dichotomous outcomes. Greater complexity arises for dichotomous outcomes; in particular, the usual definition of the ICC cannot be related directly to the parameters of the logistic-normal model that is commonly used for dichotomous outcomes. We show how the definition of the ICC is different when covariates are introduced. Finally, we use our framework and definition of the ICC to draw out implications for those interpreting and choosing values of the ICC when planning CRTs. Copyright (c) 2009 The Authors. Journal compilation (c) 2009 International Statistical Institute.
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paper_title: Clustered lot quality assurance sampling to assess immunisation coverage: increasing rapidity and maintaining precision.

paper_content:

OBJECTIVE ::: 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. ::: ::: ::: METHODS ::: 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. ::: ::: ::: RESULT ::: 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. ::: ::: ::: CONCLUSION ::: These plans will considerably increase the feasibility and the rapidity of conducting the LQAS in the field.
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paper_title: Cluster-sample surveys and lot quality assurance sampling to evaluate yellow fever immunisation coverage following a national campaign, Bolivia, 2007.

paper_content:

OBJECTIVE ::: To estimate the yellow fever (YF) vaccine coverage for the endemic and non-endemic areas of Bolivia and to determine whether selected districts had acceptable levels of coverage (>70%). ::: ::: ::: METHODS ::: We conducted two surveys of 600 individuals (25 x 12 clusters) to estimate coverage in the endemic and non-endemic areas. We assessed 11 districts using lot quality assurance sampling (LQAS). The lot (district) sample was 35 individuals with six as decision value (alpha error 6% if true coverage 70%; beta error 6% if true coverage 90%). To increase feasibility, we divided the lots into five clusters of seven individuals; to investigate the effect of clustering, we calculated alpha and beta by conducting simulations where each cluster's true coverage was sampled from a normal distribution with a mean of 70% or 90% and standard deviations of 5% or 10%. ::: ::: ::: RESULTS ::: Estimated coverage was 84.3% (95% CI: 78.9-89.7) in endemic areas, 86.8% (82.5-91.0) in non-endemic and 86.0% (82.8-89.1) nationally. LQAS showed that four lots had unacceptable coverage levels. In six lots, results were inconsistent with the estimated administrative coverage. The simulations suggested that the effect of clustering the lots is unlikely to have significantly increased the risk of making incorrect accept/reject decisions. ::: ::: ::: CONCLUSIONS ::: Estimated YF coverage was high. Discrepancies between administrative coverage and LQAS results may be due to incorrect population data. Even allowing for clustering in LQAS, the statistical errors would remain low. Catch-up campaigns are recommended in districts with unacceptable coverage.
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paper_title: A field test of three LQAS designs to assess the prevalence of acute malnutrition

paper_content:

Results Hypothesis tests showed GAM as <10% for the 33 � 6 design and GAM as 510% for the 67 � 3 and Sequential designs. Point estimates for the 33 � 6 and 67 � 3 designs were similar to those of the 30 � 30 cluster-survey for GAM (6.7%, CI ¼ 3.2–10.2%; 8.2%, CI ¼ 4.3–12.1%, 7.4%, CI ¼ 4.8–9.9%) and all other indicators. The CIs for the LQAS designs were only slightly wider than the CIs for the 30 � 30 cluster-survey; yet the LQAS designs required substantially less time to administer. Conclusions The LQAS designs provide statistically appropriate alternatives to the more timeconsuming 30 � 30 cluster-survey. However, additional field-testing is needed using independent samples rather than a nested study design.
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paper_title: Sampling: Design and Analysis

paper_content:

For a current, practical introduction to the field of sampling that you'll want to keep close at hand, Sharon L. Lohr's SAMPLING: DESIGN AND ANALYSIS, 2ND EDITION, answers the call. Practical and authoritative, the book is listed as a standard reference for training on real-world survey problems by a number of prominent surveying organizations. Lohr concentrates on the statistical aspects of taking and analyzing a sample, incorporating a multitude of applications from a variety of disciplines. The text gives guidance on how to tell when a sample is valid or not, and how to design and analyze many different forms of sample surveys. Recent research on theoretical and applied aspects of sampling is included, as well as technology instructions for using statistical software with survey data.
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paper_title: Extending cluster lot quality assurance sampling designs for surveillance programs

paper_content:

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.
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paper_title: A Novel Approach to Evaluating the Iron and Folate Status of Women of Reproductive Age in Uzbekistan after 3 Years of Flour Fortification with Micronutrients

paper_content:

BACKGROUND ::: The Uzbekistan 1996 Demographic Health Survey reported 60.4% of women of reproductive age (WRA) had low hemoglobin concentrations (<120 g/L), and anemia was an important public health problem. Fortification of wheat flour was identified as an appropriate intervention to address anemia due to the ubiquitous consumption of wheat flour. A National Flour Fortification Program (NFFP) was implemented in 2005. ::: ::: ::: METHODOLOGY/PRINCIPAL FINDINGS ::: After 3-years of the NFFP, a national survey using large country-lot quality assurance sampling was carried out to assess iron, folate, hemoglobin and inflammation status of WRA; the coverage and knowledge of the fortified first grade UzDonMakhsulot (UDM) flour/grey loaf program; and consumption habits of women to investigate the dietary factors associated with anemia. Estimated anemia prevalence was 34.4% (95% CI: 32.0, 36.7), iron depletion 47.5% (95% CI: 45.1, 49.9) and folate deficiency 28.8% (95% CI: 26.8, 30.8); the effect of inflammation was minimal (4% with CRP >5 mg/L). Severe anemia was more prevalent among folate deficient than iron depleted WRA. Presence of UDM first grade flour or the grey loaf was reported in 71.3% of households. Among WRA, 32.1% were aware of UDM fortification; only 3.7% mentioned the benefits of fortification and 12.5% understood causes of anemia. Consumption of heme iron-containing food (91%) and iron absorption enhancers (97%) was high, as was the consumption of iron absorption inhibitors (95%). ::: ::: ::: CONCLUSIONS/SIGNIFICANCE ::: The NFFP coincided with a substantial decline in the prevalence of anemia. Folate deficiency was a stronger predictor of severe anemia than iron depletion. However, the prevalence of iron depletion was high, suggesting that women are not eating enough iron or iron absorption is inhibited. Fortified products were prevalent throughout Uzbekistan, though UDM flour must be adequately fortified and monitored in the future. Knowledge of fortification and anemia was low, suggesting consumer education should be prioritized.
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paper_title: The effect of clustering on lot quality assurance sampling: a probabilistic model to calculate sample sizes for quality assessments

paper_content:

Traditional Lot Quality Assurance Sampling (LQAS) designs assume observations are collected using simple random sampling. Alternatively, randomly sampling clusters of observations and then individuals within clusters reduces costs but decreases the precision of the classifications. In this paper, we develop a general framework for designing the cluster(C)-LQAS system and illustrate the method with the design of data quality assessments for the community health worker program in Rwanda. To determine sample size and decision rules for C-LQAS, we use the beta-binomial distribution to account for inflated risk of errors introduced by sampling clusters at the first stage. We present general theory and code for sample size calculations. The C-LQAS sample sizes provided in this paper constrain misclassification risks below user-specified limits. Multiple C-LQAS systems meet the specified risk requirements, but numerous considerations, including per-cluster versus per-individual sampling costs, help identify optimal systems for distinct applications. We show the utility of C-LQAS for data quality assessments, but the method generalizes to numerous applications. This paper provides the necessary technical detail and supplemental code to support the design of C-LQAS for specific programs.
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paper_title: Cluster designs to assess the prevalence of acute malnutrition by lot quality assurance sampling: a validation study by computer simulation

paper_content:

Traditional lot quality assurance sampling (LQAS) methods require simple random sampling to guarantee valid results. However, cluster sampling has been proposed to reduce the number of random starting points. This study uses simulations to examine the classification error of two such designs, a 67×3 (67 clusters of three observations) and a 33×6 (33 clusters of six observations) sampling scheme to assess the prevalence of global acute malnutrition (GAM). Further, we explore the use of a 67×3 sequential sampling scheme for LQAS classification of GAM prevalence. Results indicate that, for independent clusters with moderate intracluster correlation for the GAM outcome, the three sampling designs maintain approximate validity for LQAS analysis. Sequential sampling can substantially reduce the average sample size that is required for data collection. The presence of intercluster correlation can impact dramatically the classification error that is associated with LQAS analysis. Copyright (c) 2009 Royal Statistical Society.
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paper_title: 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

paper_content:

In 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. We 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. During 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. C-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.
---
paper_title: Clustered lot quality assurance sampling: a pragmatic tool for timely assessment of vaccination coverage

paper_content:

OBJECTIVES ::: 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). ::: ::: ::: METHODS ::: 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. ::: ::: ::: RESULTS ::: 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. ::: ::: ::: CONCLUSIONS ::: 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.
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paper_title: Precision, time, and cost: a comparison of three sampling designs in an emergency setting

paper_content:

The conventional method to collect data on the health, nutrition, and food security status of a population affected by an emergency is a 30 × 30 cluster survey. This sampling method can be time and resource intensive and, accordingly, may not be the most appropriate one when data are needed rapidly for decision making. In this study, we compare the precision, time and cost of the 30 × 30 cluster survey with two alternative sampling designs: a 33 × 6 cluster design (33 clusters, 6 observations per cluster) and a 67 × 3 cluster design (67 clusters, 3 observations per cluster). Data for each sampling design were collected concurrently in West Darfur, Sudan in September-October 2005 in an emergency setting. Results of the study show the 30 × 30 design to provide more precise results (i.e. narrower 95% confidence intervals) than the 33 × 6 and 67 × 3 design for most child-level indicators. Exceptions are indicators of immunization and vitamin A capsule supplementation coverage which show a high intra-cluster correlation. Although the 33 × 6 and 67 × 3 designs provide wider confidence intervals than the 30 × 30 design for child anthropometric indicators, the 33 × 6 and 67 × 3 designs provide the opportunity to conduct a LQAS hypothesis test to detect whether or not a critical threshold of global acute malnutrition prevalence has been exceeded, whereas the 30 × 30 design does not. For the household-level indicators tested in this study, the 67 × 3 design provides the most precise results. However, our results show that neither the 33 × 6 nor the 67 × 3 design are appropriate for assessing indicators of mortality. In this field application, data collection for the 33 × 6 and 67 × 3 designs required substantially less time and cost than that required for the 30 × 30 design. The findings of this study suggest the 33 × 6 and 67 × 3 designs can provide useful time- and resource-saving alternatives to the 30 × 30 method of data collection in emergency settings.
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paper_title: The Intra‐Cluster Correlation Coefficient in Cluster Randomized Trials: A Review of Definitions

paper_content:

The intra-cluster correlation coefficient (ICC) of the primary outcome plays a key role in the design and analysis of cluster randomized trials (CRTs), but the precise definition of this parameter is somewhat elusive, especially in the context of non-normally distributed outcomes. In this paper, we provide a unified treatment of ICC as used in CRTs. We present a general definition of the ICC that may be expressed in different ways depending on the modelling approach used to describe the data, illustrating how this general definition is applied to continuous and dichotomous outcomes. Greater complexity arises for dichotomous outcomes; in particular, the usual definition of the ICC cannot be related directly to the parameters of the logistic-normal model that is commonly used for dichotomous outcomes. We show how the definition of the ICC is different when covariates are introduced. Finally, we use our framework and definition of the ICC to draw out implications for those interpreting and choosing values of the ICC when planning CRTs. Copyright (c) 2009 The Authors. Journal compilation (c) 2009 International Statistical Institute.
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paper_title: A new dependence parameter approach to improve the design of cluster randomized trials with binary outcomes

paper_content:

BACKGROUND AND PURPOSE ::: Power and sample size calculations for cluster randomized trials require prediction of the degree of correlation that will be realized among outcomes of participants in the same cluster. This correlation is typically quantified as the intraclass correlation coefficient (ICC), defined as the Pearson correlation between two members of the same cluster or proportion of the total variance attributable to variance between clusters. It is widely known but perhaps not fully appreciated that for binary outcomes, the ICC is a function of outcome prevalence. Hence, the ICC and the outcome prevalence are intrinsically related, making the ICC poorly generalizable across study conditions and between studies with different outcome prevalences. ::: ::: ::: METHODS ::: We use a simple parametrization of the ICC that aims to isolate that part of the ICC that measures dependence among responses within a cluster from the outcome prevalence. We incorporate this parametrization into sample size calculations for cluster randomized trials and compare our method to the traditional approach using the ICC. ::: ::: ::: RESULTS ::: Our dependence parameter, R, may be less influenced by outcome prevalence and has an intuitive meaning that facilitates interpretation. Estimates of R from previous studies can be obtained using simple statistics. Comparison of methods showed that the traditional ICC approach to sample size determination tends to overpower studies under many scenarios, calling for more clusters than truly required. ::: ::: ::: LIMITATIONS ::: The methods are developed for equal-sized clusters, whereas cluster size may vary in practice. ::: ::: ::: CONCLUSIONS ::: The dependence parameter R is an alternative measure of dependence among binary outcomes in cluster randomized trials that has a number of advantages over the ICC.
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paper_title: Extending cluster lot quality assurance sampling designs for surveillance programs

paper_content:

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.
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paper_title: Intraclass correlation coefficient and outcome prevalence are associated in clustered binary data.

paper_content:

BACKGROUND AND OBJECTIVE ::: To describe the association between values for a proportion and the intraclass correlation coefficient (ICC). ::: ::: ::: METHODS ::: Analysis of data obtained from the General Practice Research Database (GPRD) for variation between United Kingdom general practices and results from a Health Technology Assessment (HTA) review for a range of outcomes in community and health services settings. ::: ::: ::: RESULTS ::: There were 188 ICCs from the GPRD, the median prevalence was 13.1% (interquartile range IQR 3.5 to 28.4%) and median ICC 0.051 (IQR 0.011 to 0.094). There were 136 ICCs from the HTA review, with median prevalence 6.5% (IQR 0.4 to 20.7%) and median ICC 0.006 (IQR 0.0003 to 0.036). There was a linear association of log ICC with log prevalence in both datasets (GPRD, regression coefficient 0.61, 95% confidence interval 0.53 to 0.69, P < 0.001; HTA, 0.91, 0.81 to 1.01, P < 0.001). When the prevalence was 1% the predicted ICC was 0.008 from the GPRD or 0.002 from the HTA, but when the prevalence was 40% the predicted ICC was 0.075 (GPRD) or 0.046 (HTA). ::: ::: ::: CONCLUSION ::: The prevalence of an outcome may be used to make an informed assumption about the magnitude of the intraclass correlation coefficient.
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paper_title: Clustered lot quality assurance sampling: a pragmatic tool for timely assessment of vaccination coverage

paper_content:

OBJECTIVES ::: 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). ::: ::: ::: METHODS ::: 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. ::: ::: ::: RESULTS ::: 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. ::: ::: ::: CONCLUSIONS ::: 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.
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paper_title: Performance of small cluster surveys and the clustered LQAS design to estimate local-level vaccination coverage in Mali

paper_content:

Estimation 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. We 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. VC 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. Small 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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paper_title: A new dependence parameter approach to improve the design of cluster randomized trials with binary outcomes

paper_content:

BACKGROUND AND PURPOSE ::: Power and sample size calculations for cluster randomized trials require prediction of the degree of correlation that will be realized among outcomes of participants in the same cluster. This correlation is typically quantified as the intraclass correlation coefficient (ICC), defined as the Pearson correlation between two members of the same cluster or proportion of the total variance attributable to variance between clusters. It is widely known but perhaps not fully appreciated that for binary outcomes, the ICC is a function of outcome prevalence. Hence, the ICC and the outcome prevalence are intrinsically related, making the ICC poorly generalizable across study conditions and between studies with different outcome prevalences. ::: ::: ::: METHODS ::: We use a simple parametrization of the ICC that aims to isolate that part of the ICC that measures dependence among responses within a cluster from the outcome prevalence. We incorporate this parametrization into sample size calculations for cluster randomized trials and compare our method to the traditional approach using the ICC. ::: ::: ::: RESULTS ::: Our dependence parameter, R, may be less influenced by outcome prevalence and has an intuitive meaning that facilitates interpretation. Estimates of R from previous studies can be obtained using simple statistics. Comparison of methods showed that the traditional ICC approach to sample size determination tends to overpower studies under many scenarios, calling for more clusters than truly required. ::: ::: ::: LIMITATIONS ::: The methods are developed for equal-sized clusters, whereas cluster size may vary in practice. ::: ::: ::: CONCLUSIONS ::: The dependence parameter R is an alternative measure of dependence among binary outcomes in cluster randomized trials that has a number of advantages over the ICC.
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paper_title: Intraclass correlation coefficient and outcome prevalence are associated in clustered binary data.

paper_content:

BACKGROUND AND OBJECTIVE ::: To describe the association between values for a proportion and the intraclass correlation coefficient (ICC). ::: ::: ::: METHODS ::: Analysis of data obtained from the General Practice Research Database (GPRD) for variation between United Kingdom general practices and results from a Health Technology Assessment (HTA) review for a range of outcomes in community and health services settings. ::: ::: ::: RESULTS ::: There were 188 ICCs from the GPRD, the median prevalence was 13.1% (interquartile range IQR 3.5 to 28.4%) and median ICC 0.051 (IQR 0.011 to 0.094). There were 136 ICCs from the HTA review, with median prevalence 6.5% (IQR 0.4 to 20.7%) and median ICC 0.006 (IQR 0.0003 to 0.036). There was a linear association of log ICC with log prevalence in both datasets (GPRD, regression coefficient 0.61, 95% confidence interval 0.53 to 0.69, P < 0.001; HTA, 0.91, 0.81 to 1.01, P < 0.001). When the prevalence was 1% the predicted ICC was 0.008 from the GPRD or 0.002 from the HTA, but when the prevalence was 40% the predicted ICC was 0.075 (GPRD) or 0.046 (HTA). ::: ::: ::: CONCLUSION ::: The prevalence of an outcome may be used to make an informed assumption about the magnitude of the intraclass correlation coefficient.
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paper_title: Clustered lot quality assurance sampling: a pragmatic tool for timely assessment of vaccination coverage

paper_content:

OBJECTIVES ::: 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). ::: ::: ::: METHODS ::: 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. ::: ::: ::: RESULTS ::: 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. ::: ::: ::: CONCLUSIONS ::: 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.
---
paper_title: Performance of small cluster surveys and the clustered LQAS design to estimate local-level vaccination coverage in Mali

paper_content:

Estimation 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. We 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. VC 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. Small 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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paper_title: A novel sequential sampling technique for the surveillance of transmitted HIV drug resistance by cross-sectional survey for use in low resource settings.

paper_content:

This article describes the development of a novel sequential sampling method for the surveillance of transmitted HIV drug resistance by cross-sectional survey. Two commonly used sequential sampling methods are described and their applicability to the problem of classifying the prevalence of transmitted HIV drug resistance investigated. Both methods are rejected due to insufficient savings in sample size and operational complexity. A novel method is proposed and this is tested using computer-based simulation. This method provides useful sample size savings and operational simplicity and could provide the basis for a rapid and reliable survey method for classifying the prevalence of transmitted HIV drug resistance in circumstances where monitoring HIV drug resistance is an important issue, but resources do not allow fullscale surveillance to be established. The method is currently being used in several such settings.
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paper_title: The Intra‐Cluster Correlation Coefficient in Cluster Randomized Trials: A Review of Definitions

paper_content:

The intra-cluster correlation coefficient (ICC) of the primary outcome plays a key role in the design and analysis of cluster randomized trials (CRTs), but the precise definition of this parameter is somewhat elusive, especially in the context of non-normally distributed outcomes. In this paper, we provide a unified treatment of ICC as used in CRTs. We present a general definition of the ICC that may be expressed in different ways depending on the modelling approach used to describe the data, illustrating how this general definition is applied to continuous and dichotomous outcomes. Greater complexity arises for dichotomous outcomes; in particular, the usual definition of the ICC cannot be related directly to the parameters of the logistic-normal model that is commonly used for dichotomous outcomes. We show how the definition of the ICC is different when covariates are introduced. Finally, we use our framework and definition of the ICC to draw out implications for those interpreting and choosing values of the ICC when planning CRTs. Copyright (c) 2009 The Authors. Journal compilation (c) 2009 International Statistical Institute.
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paper_title: A new dependence parameter approach to improve the design of cluster randomized trials with binary outcomes

paper_content:

BACKGROUND AND PURPOSE ::: Power and sample size calculations for cluster randomized trials require prediction of the degree of correlation that will be realized among outcomes of participants in the same cluster. This correlation is typically quantified as the intraclass correlation coefficient (ICC), defined as the Pearson correlation between two members of the same cluster or proportion of the total variance attributable to variance between clusters. It is widely known but perhaps not fully appreciated that for binary outcomes, the ICC is a function of outcome prevalence. Hence, the ICC and the outcome prevalence are intrinsically related, making the ICC poorly generalizable across study conditions and between studies with different outcome prevalences. ::: ::: ::: METHODS ::: We use a simple parametrization of the ICC that aims to isolate that part of the ICC that measures dependence among responses within a cluster from the outcome prevalence. We incorporate this parametrization into sample size calculations for cluster randomized trials and compare our method to the traditional approach using the ICC. ::: ::: ::: RESULTS ::: Our dependence parameter, R, may be less influenced by outcome prevalence and has an intuitive meaning that facilitates interpretation. Estimates of R from previous studies can be obtained using simple statistics. Comparison of methods showed that the traditional ICC approach to sample size determination tends to overpower studies under many scenarios, calling for more clusters than truly required. ::: ::: ::: LIMITATIONS ::: The methods are developed for equal-sized clusters, whereas cluster size may vary in practice. ::: ::: ::: CONCLUSIONS ::: The dependence parameter R is an alternative measure of dependence among binary outcomes in cluster randomized trials that has a number of advantages over the ICC.
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paper_title: An assessment of Lot Quality Assurance Sampling to evaluate malaria outcome indicators: extending malaria indicator surveys.

paper_content:

BACKGROUND ::: Large investments and increased global prioritization of malaria prevention and treatment have resulted in greater emphasis on programme monitoring and evaluation (M&E) in many countries. Many countries currently use large multistage cluster sample surveys to monitor malaria outcome indicators on a regional and national level. However, these surveys often mask local-level variability important to programme management. Lot Quality Assurance Sampling (LQAS) has played a valuable role for local-level programme M&E. If incorporated into these larger surveys, it would provide a comprehensive M&E plan at little, if any, extra cost. ::: ::: ::: METHODS ::: The Mozambique Ministry of Health conducted a Malaria Indicator Survey (MIS) in June and July 2007. We applied LQAS classification rules to the 345 sampled enumeration areas to demonstrate identifying high- and low-performing areas with respect to two malaria program indicators-'household possession of any bednet' and 'household possession of any insecticide-treated bednet (ITN)'. ::: ::: ::: RESULTS ::: As shown by the MIS, no province in Mozambique achieved the 70% coverage target for household possession of bednets or ITNs. By applying LQAS classification rules to the data, we identify 266 of the 345 enumeration areas as having bednet coverage severely below the 70% target. An additional 73 were identified with low ITN coverage. ::: ::: ::: CONCLUSIONS ::: This article demonstrates the feasibility of integrating LQAS into multistage cluster sampling surveys and using these results to support a comprehensive national, regional and local programme M&E system. Furthermore, in the recommendations we outlined how to integrate the Large Country-LQAS design into macro-surveys while still obtaining results available through current sampling practices.
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paper_title: A field test of three LQAS designs to assess the prevalence of acute malnutrition

paper_content:

Results Hypothesis tests showed GAM as <10% for the 33 � 6 design and GAM as 510% for the 67 � 3 and Sequential designs. Point estimates for the 33 � 6 and 67 � 3 designs were similar to those of the 30 � 30 cluster-survey for GAM (6.7%, CI ¼ 3.2–10.2%; 8.2%, CI ¼ 4.3–12.1%, 7.4%, CI ¼ 4.8–9.9%) and all other indicators. The CIs for the LQAS designs were only slightly wider than the CIs for the 30 � 30 cluster-survey; yet the LQAS designs required substantially less time to administer. Conclusions The LQAS designs provide statistically appropriate alternatives to the more timeconsuming 30 � 30 cluster-survey. However, additional field-testing is needed using independent samples rather than a nested study design.
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paper_title: A Novel Approach to Evaluating the Iron and Folate Status of Women of Reproductive Age in Uzbekistan after 3 Years of Flour Fortification with Micronutrients

paper_content:

BACKGROUND ::: The Uzbekistan 1996 Demographic Health Survey reported 60.4% of women of reproductive age (WRA) had low hemoglobin concentrations (<120 g/L), and anemia was an important public health problem. Fortification of wheat flour was identified as an appropriate intervention to address anemia due to the ubiquitous consumption of wheat flour. A National Flour Fortification Program (NFFP) was implemented in 2005. ::: ::: ::: METHODOLOGY/PRINCIPAL FINDINGS ::: After 3-years of the NFFP, a national survey using large country-lot quality assurance sampling was carried out to assess iron, folate, hemoglobin and inflammation status of WRA; the coverage and knowledge of the fortified first grade UzDonMakhsulot (UDM) flour/grey loaf program; and consumption habits of women to investigate the dietary factors associated with anemia. Estimated anemia prevalence was 34.4% (95% CI: 32.0, 36.7), iron depletion 47.5% (95% CI: 45.1, 49.9) and folate deficiency 28.8% (95% CI: 26.8, 30.8); the effect of inflammation was minimal (4% with CRP >5 mg/L). Severe anemia was more prevalent among folate deficient than iron depleted WRA. Presence of UDM first grade flour or the grey loaf was reported in 71.3% of households. Among WRA, 32.1% were aware of UDM fortification; only 3.7% mentioned the benefits of fortification and 12.5% understood causes of anemia. Consumption of heme iron-containing food (91%) and iron absorption enhancers (97%) was high, as was the consumption of iron absorption inhibitors (95%). ::: ::: ::: CONCLUSIONS/SIGNIFICANCE ::: The NFFP coincided with a substantial decline in the prevalence of anemia. Folate deficiency was a stronger predictor of severe anemia than iron depletion. However, the prevalence of iron depletion was high, suggesting that women are not eating enough iron or iron absorption is inhibited. Fortified products were prevalent throughout Uzbekistan, though UDM flour must be adequately fortified and monitored in the future. Knowledge of fortification and anemia was low, suggesting consumer education should be prioritized.
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paper_title: Large country-lot quality assurance sampling : a new method for rapid monitoring and evaluation of health, nutrition and population programs at sub-national levels

paper_content:

Sampling theory facilitates development of economical, effective and rapid measurement of a population. While national policy maker value survey results measuring indicators representative of a large area (a country, state or province), measurement in smaller areas produces information useful for managers at the local level. It is often not possible to disaggregate a national survey to obtain local information if that was not the intent of the original survey design. Cluster sampling is typically used for national or large area surveys because sampling in clusters lowers the cost of a survey. Lot Quality Assurance Sampling (LQAS) is used to measure results at a local level, since it requires small random samples and produces results useful to local managers. However, current LQAS methodology requires all local areas (strata) be included in the survey in order to be aggregated to produce point estimates for the nation or state. In large countries it is not feasible to sample all strata for logistical and financial reasons. This paper resolves this problem by presenting Large Country (LC)-LQAS, a method with two concurrent objectives: 1) provide local managers with accurate local information to enable data driven decisions, and 2) provide central policy makers with the aggregate information they require. These are achieved by integrating cluster sampling with LQAS methodologies. Two examples of the implementation of LC-LQAS are provided, in an HIV/AIDS program in Kenya and a Malaria Booster Project in Nigeria. Classifications of local health units into performance categories and aggregate estimates of coverage, with associated confidence intervals, are provided for select indicators in order to demonstrate its use, analysis, and costs. This paper is written as a manual to support the use of LC-LQAS by others.
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paper_title: Cluster designs to assess the prevalence of acute malnutrition by lot quality assurance sampling: a validation study by computer simulation

paper_content:

Traditional lot quality assurance sampling (LQAS) methods require simple random sampling to guarantee valid results. However, cluster sampling has been proposed to reduce the number of random starting points. This study uses simulations to examine the classification error of two such designs, a 67×3 (67 clusters of three observations) and a 33×6 (33 clusters of six observations) sampling scheme to assess the prevalence of global acute malnutrition (GAM). Further, we explore the use of a 67×3 sequential sampling scheme for LQAS classification of GAM prevalence. Results indicate that, for independent clusters with moderate intracluster correlation for the GAM outcome, the three sampling designs maintain approximate validity for LQAS analysis. Sequential sampling can substantially reduce the average sample size that is required for data collection. The presence of intercluster correlation can impact dramatically the classification error that is associated with LQAS analysis. Copyright (c) 2009 Royal Statistical Society.
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paper_title: Intraclass correlation coefficient and outcome prevalence are associated in clustered binary data.

paper_content:

BACKGROUND AND OBJECTIVE ::: To describe the association between values for a proportion and the intraclass correlation coefficient (ICC). ::: ::: ::: METHODS ::: Analysis of data obtained from the General Practice Research Database (GPRD) for variation between United Kingdom general practices and results from a Health Technology Assessment (HTA) review for a range of outcomes in community and health services settings. ::: ::: ::: RESULTS ::: There were 188 ICCs from the GPRD, the median prevalence was 13.1% (interquartile range IQR 3.5 to 28.4%) and median ICC 0.051 (IQR 0.011 to 0.094). There were 136 ICCs from the HTA review, with median prevalence 6.5% (IQR 0.4 to 20.7%) and median ICC 0.006 (IQR 0.0003 to 0.036). There was a linear association of log ICC with log prevalence in both datasets (GPRD, regression coefficient 0.61, 95% confidence interval 0.53 to 0.69, P < 0.001; HTA, 0.91, 0.81 to 1.01, P < 0.001). When the prevalence was 1% the predicted ICC was 0.008 from the GPRD or 0.002 from the HTA, but when the prevalence was 40% the predicted ICC was 0.075 (GPRD) or 0.046 (HTA). ::: ::: ::: CONCLUSION ::: The prevalence of an outcome may be used to make an informed assumption about the magnitude of the intraclass correlation coefficient.
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paper_title: Precision, time, and cost: a comparison of three sampling designs in an emergency setting

paper_content:

The conventional method to collect data on the health, nutrition, and food security status of a population affected by an emergency is a 30 × 30 cluster survey. This sampling method can be time and resource intensive and, accordingly, may not be the most appropriate one when data are needed rapidly for decision making. In this study, we compare the precision, time and cost of the 30 × 30 cluster survey with two alternative sampling designs: a 33 × 6 cluster design (33 clusters, 6 observations per cluster) and a 67 × 3 cluster design (67 clusters, 3 observations per cluster). Data for each sampling design were collected concurrently in West Darfur, Sudan in September-October 2005 in an emergency setting. Results of the study show the 30 × 30 design to provide more precise results (i.e. narrower 95% confidence intervals) than the 33 × 6 and 67 × 3 design for most child-level indicators. Exceptions are indicators of immunization and vitamin A capsule supplementation coverage which show a high intra-cluster correlation. Although the 33 × 6 and 67 × 3 designs provide wider confidence intervals than the 30 × 30 design for child anthropometric indicators, the 33 × 6 and 67 × 3 designs provide the opportunity to conduct a LQAS hypothesis test to detect whether or not a critical threshold of global acute malnutrition prevalence has been exceeded, whereas the 30 × 30 design does not. For the household-level indicators tested in this study, the 67 × 3 design provides the most precise results. However, our results show that neither the 33 × 6 nor the 67 × 3 design are appropriate for assessing indicators of mortality. In this field application, data collection for the 33 × 6 and 67 × 3 designs required substantially less time and cost than that required for the 30 × 30 design. The findings of this study suggest the 33 × 6 and 67 × 3 designs can provide useful time- and resource-saving alternatives to the 30 × 30 method of data collection in emergency settings.
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Title: Choosing a Cluster Sampling Design for Lot Quality Assurance Sampling Surveys

Section 1: Introduction
Description 1: Introduce the primary goal of global health monitoring and evaluation (M&E) surveys and the relevance of Lot Quality Assurance Sampling (LQAS) surveys.

Section 2: Cluster surveys
Description 2: Describe the implementation of two-stage cluster sampling in global health M&E surveys, including the use of self-weighted designs and the impact of clustering.

Section 3: Cluster sampling in LQAS
Description 3: Discuss the initial approaches to cluster sampling in LQAS, the challenges posed by clustering, and the development of three methods (Pezzoli, Hedt, and Hund) to accommodate cluster sampling in the design phase.

Section 4: Evaluating the designs
Description 4: Compare the Pezzoli, Hedt, and Hund methods by evaluating their distributional assumptions and clustering parameterizations, using example designs from vaccination and nutrition surveys.

Section 5: Distributional assumptions
Description 5: Present simulation studies to evaluate the impact of different distributional models (binomial-scaled, beta, and quasi-binomial) on the classification precision of cluster LQAS designs.

Section 6: Clustering parameterization
Description 6: Analyze the differences between fixing σ or ρ as the clustering parameter and evaluate these differences using data examples, including the impact on the final sample size and α and β errors.

Section 7: Discussion
Description 7: Summarize the findings, providing recommendations for choosing a cluster LQAS design based on the evaluation results, and discuss the implications for future research and practice in global health M&E surveys.