Improving the collection of knowledge, attitude and practice data with community surveys: a comparison of two second-stage sampling methods

Davis, Rosemary; Valadez, Joseph J.

doi:10.1093/heapol/czt088

Cited by 10 publications

(6 citation statements)

References 13 publications

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“…The first stage sample uses probability proportional to size to randomly sample villages [typically, n = 19 (14)]. In each random location a second stage sample identifies an index household using segmentation sampling (15, 16) from locally constructed hand-drawn maps. A random number table is used to select an index house in the segment.…”

Section: Methodsmentioning

confidence: 99%

Hybrid prevalence estimation: Method to improve intervention coverage estimations

Jeffery

Pagano

Hemingway

et al. 2018

Proc. Natl. Acad. Sci. U.S.A.

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SignificanceOver the last 2 decades, low- and middle-income countries have moved from fragmented paper-based systems to electronic health information systems (HIS). Although these are a major advance, they are not well suited to drive operational policy decisions and detect gaps in service coverage. To avoid overreliance on administrative estimates from HIS, probability surveys of population coverage are increasingly used operationally. By combining the probability survey and HIS data, a hybrid prevalence estimate can be generated that can be used to validate and/or moderate HIS administrative estimates of coverage.

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Section: Methodsmentioning

confidence: 99%

Hybrid prevalence estimation: Method to improve intervention coverage estimations

Jeffery

Pagano

Hemingway

et al. 2018

Proc. Natl. Acad. Sci. U.S.A.

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“…A number of alternative approaches have been proposed for second‐stage sampling, including the random walk, quartering and segmentation sampling (Henderson & Sundaresan ; Turner et al . ; Davis & Valadez ). Despite the wide application of the random walk, the methodological rigour of this technique has been questioned, as the household selection may be open to bias (Milligan et al .…”

Section: Discussionmentioning

confidence: 99%

“…In practice, it may be impractical to implement a purely random sample, however. A number of alternative approaches have been proposed for second-stage sampling, including the random walk, quartering and segmentation sampling (Henderson & Sundaresan 1982;Turner et al 1996;Davis & Valadez 2013). Despite the wide application of the random walk, the methodological rigour of this technique has been questioned, as the household selection may be open to bias (Milligan et al 2004) with households at the centre of villages or clusters being favoured for selection (Kok 1986).…”

Section: Discussionmentioning

confidence: 99%

Estimation after classification using lot quality assurance sampling: corrections for curtailed sampling with application to evaluating polio vaccination campaigns

Olives

Valadez

Pagano

2014

Tropical Med Int Health

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Abstractobjectives To assess the bias incurred when curtailment of Lot Quality Assurance Sampling (LQAS) is ignored, to present unbiased estimators, to consider the impact of cluster sampling by simulation and to apply our method to published polio immunization data from Nigeria.methods We present estimators of coverage when using two kinds of curtailed LQAS strategies: semicurtailed and curtailed. We study the proposed estimators with independent and clustered data using three field-tested LQAS designs for assessing polio vaccination coverage, with samples of size 60 and decision rules of 9, 21 and 33, and compare them to biased maximum likelihood estimators.Lastly, we present estimates of polio vaccination coverage from previously published data in 20 local government authorities (LGAs) from five Nigerian states.results Simulations illustrate substantial bias if one ignores the curtailed sampling design. Proposed estimators show no bias. Clustering does not affect the bias of these estimators. Across simulations, standard errors show signs of inflation as clustering increases. Neither sampling strategy nor LQAS design influences estimates of polio vaccination coverage in 20 NigerianLGAs. When coverage is low, semicurtailed LQAS strategies considerably reduces the sample size required to make a decision. Curtailed LQAS designs further reduce the sample size when coverage is high.conclusions Results presented dispel the misconception that curtailed LQAS data are unsuitable for estimation. These findings augment the utility of LQAS as a tool for monitoring vaccination efforts by demonstrating that unbiased estimation using curtailed designs is not only possible but these designs also reduce the sample size.

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“…Within each block, the primary sampling unit is the Anganwadi Centre (Community Health Subcentre) Catchment Area (ACCA); 19 ACCAs are selected from each block with probability proportional to size. From each ACCA, one respondent is randomly selected from each age group under study using segmentation sampling 22 23. The sample of 19 mothers in each block is chosen to maximise the probability of correctly classifying a block with reference to performance targets on health-related indicators (95% reliability) while balancing the probability (10% margin of error) of incorrectly classifying a block and thereby failing to recognise either the accomplishments of local healthcare delivery systems or the local population's healthcare needs 22.…”

Section: Methodsmentioning

confidence: 99%

How well do mothers recall their own and their infants’ perinatal events? A two-district study using cross-sectional stratified random sampling in Bihar, India

et al. 2019

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ObjectiveGlobal monitoring of maternal, newborn and child health (MNCH) programmes use self-reported data subject to recall error which may lead to incorrect decisions for improving health services and wasted resources. To minimise this risk, samples of mothers of infants aged 0–2 and 3–5 months are sometimes used. We test whether a single sample of mothers of infants aged 0–5 months provides the same information.DesignAn annual MNCH household survey in two districts of Bihar, India (n=6 million).ParticipantsIndependent samples (n=475 each) of mothers of infants aged 0–5, 0–2 and 3–5 months.Outcome measuresMain analyses compare responses from the samples of infants aged 0–5 and 0–2 months with Mantel-Haenszel-Cochran statistics using 51 indicators in two districts.ResultsNo measurable differences are detected in 79.4% (81/102) comparisons; 20.6% (21/102) display differences for the main comparison. Subanalyses produce similar results. A difference detected for exclusive breast feeding is due to premature complementary feeding by older infants. Measurable differences are detected in 33% (8/24) of the indicators on Front Line Worker (FLW) support, 26.9% (7/26) of indicators of birth preparedness and place of birth and attendant, and 9.5% (4/42) of the indicators on neonatal and antenatal care.ConclusionsDifferences in FLW visits and compliance with their advice may be due to seasonal effects: mothers of older infants aged 3–5 months were pregnant during the dry season; mothers of infants aged 0–2 months were pregnant during the monsoons, making transportation difficult. Useful coverage estimates can be obtained by sampling mothers with infants aged 0–5 months as with two samples suggesting that mothers of young infants recall their own perinatal events and those of their children. For some indicators (eg, exclusive breast feeding), it may be necessary to adjust targets. Excessive stratification wastes resources, does not improve the quality of information and increases the burden placed on data collectors and communities which can increase non-sampling error.

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Improving the collection of knowledge, attitude and practice data with community surveys: a comparison of two second-stage sampling methods

Cited by 10 publications

References 13 publications

Hybrid prevalence estimation: Method to improve intervention coverage estimations

Hybrid prevalence estimation: Method to improve intervention coverage estimations

Estimation after classification using lot quality assurance sampling: corrections for curtailed sampling with application to evaluating polio vaccination campaigns

How well do mothers recall their own and their infants’ perinatal events? A two-district study using cross-sectional stratified random sampling in Bihar, India

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