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

Emerging Themes in Epidemiology

Marcello Pagano¹  Casey Olives²  Lauren Hund³  Tisha Mitsunaga⁴  Bethany L Hedt-Gauthier⁵ 

[1] Department of Biostatistics, Harvard School of Public Health, Boston, USA;Department of Biostatistics, University of Washington, Seattle, USA;Department of Family and Community Medicine, University of New Mexico, Albuquerque, New Mexico, USA;Inshuti Mu Buzima (IMB)/Partners In Health (PIH), Rwinkwavu, Rwanda;Department of Global Health and Social Medicine, Harvard Medical School, Boston, USA

关键词: Community health workers;    Survey;    Program evaluation;    Lot quality assurance sampling;    Cluster-LQAS;   

Others  :  803615
DOI  :  10.1186/1742-7622-10-11

 received in 2012-12-30, accepted in 2013-09-17,  发布年份 2013

【 摘 要 】

Background

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.

Results

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.

Conclusions

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.

【 授权许可】

   
2013 Hedt-Gauthier et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

【 参考文献 】

• [1]Dodge H, Romig H: A method of sampling inspection. The Bell System Technical Journal 1929, 8:613-631.
• [2]Lanata CF, Stroh G, Black RE: Lot quality assurance sampling in health monitoring. Lancet 1988, 1:122-3.
• [3]Valadez JJ: Assessing child survival programs in developing countries: testing lot quality assurance sampling. Cambridge, MA: Harvard University Press; 1991.
• [4]Lwanga S, Lemeshow S: Sample size determination in health studies: a practical manual. Geneva: World Health Organization; 1991.
• [5]Lemeshow S, Taber S: Lot quality assurance sampling: single- and double-sampling plans. Bull World Health Organ 1991, 44:115-32.
• [6]Robertson SE, Valadez JJ: Global review of health care surveys using lot quality assurance sampling (LQAS), 1984–2004. Soc Sci Med 2006, 63:1648-60.
• [7]Olives C, Pagano M, Deitchler M, et al.: Cluster designs to assess the prevalence of acute malnutrition by lot quality assurance sampling: a validation study by computer simulation. J R Stat Soc A Stat Soc 2009, 172:495-510.
• [8]Pezzoli L, Tchio R, Dzossa AD, et al.: 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. Epidemiol Infect 2012, 140:14-26.
• [9]Minetti A, Riera-Montes M, Nackers F, et al.: Performance of small cluster surveys and the clustered LQAS design to estimate local-level vaccination coverage in Mali. Emerg Themes Epidemiol 2012, 9:6. BioMed Central Full Text
• [10]Government of Rwanda: Economic development & poverty reduction strategy 2008–2012. Kigali: Government of Rwanda; 2007.
• [11]Pezzoli L, Pineda S, Halkyer P, et al.: Cluster-sample surveys and lot quality assurance sampling to evaluate yellow fever immunisation coverage following a national campaign, Bolivia, 2007. Trop Med Int Health 2009, 14:355-61.
• [12]Pezzoli L, Andrews N, Ronveaux O: Clustered lot quality assurance sampling to assess immunisation coverage: increasing rapidity and maintaining precision. Trop Med Int Health 2010, 15:540-6.
• [13]Yee TW, Wild CJ: Vector generalized additive models. J R Stat Soc Ser B Methodol 1996, 58:481-493.
• [14]Ridout MS, Demétrio CG, Firth D: Estimating intraclass correlation for binary data. Biometrics 1999, 55:137-48.
• [15]Fenn B, Morris SS, Frost C: Do childhood growth indicators in developing countries cluster? implications for intervention strategies. Public Health Nutr 2004, 7:829-34.
• [16]Campbell MK, Elbourne DR, Altman DG: CONSORT statement: extension to cluster randomized trials. BMJ 2004, 328:702-8.
• [17]Pagel C, Prost A, Lewycka S, et al.: Intracluster correlation coefficients and coefficients of variation for perinatal outcomes from five cluster-randomized trials in low and middle-income countries: results and methodological implications. BMC Trials 2011, 12:151. 10.1186/1745-6215-12-151 BioMed Central Full Text
• [18]Gilroy K, Winch PJ, Diawara A, et al.: Impact of IMCI training and language used by provider on quality of counseling provided to parents of sick children in Bougouni District, Mali. Patient Educ Couns 2004, 54:35-44.
• [19]Rowe AK, Lama M, Onikpo F, et al.: Design effects and intraclass correlation coefficients from a health facility cluster survey in Benin. Int J Qual Health Care 2002, 14:521-523.
• [20]Sandiford P: Lot quality assurance sampling for monitoring immunization programmes: cost-efficient or quick and dirty? Health Policy Plan 1993, 8(3):217-223.
• [21]Bilukha OO, Blanton C: Interpreting results of cluster surveys in emergency settings: is the LQAS test the best option? Emerg Themes Epidemiol 2008, 5:25. BioMed Central Full Text
• [22]Olives C, Pagano M: Bayes-LQAS: classifying the prevalence of global acute malnutrition. Emerg Themes Epidemiol 2010, 7:3. BioMed Central Full Text
• [23]Olives C, Pagano M: Choosing a design to fit the situation: improving specificity and positive predictive values using Bayesian-LQAS. Int J Epidemiol 2013, 42(1):346-355.
• [24]Cotter B, Bremer V, Stroh G, et al.: Assessment of neonatal tetanus elimination in an African setting by lot quality assurance cluster sampling (LQA-CS). Epidemiol Infect 2003, 130:221-226.
• [25]Deitchler M, Deconinck H, Bergeron G: Precision, time, and cost: a comparison of three sampling designs in an emergency setting. Emerging Themes in Epidemiology. 2008, 5:6. BioMed Central Full Text
• [26]Deitchler M, Valadez JJ, Egge K, et al.: A field test of three LQAS designs to assess the prevalence of acute malnutrition. Int J Epidemiol 2007, 36:858-64.
• [27]Greenland K, Rondy M, Chevez A, et al.: Clustered lot quality assurance sampling: a pragmatic tool for timely assessment of vaccination coverage. Trop Med Int Health 2011, 16:863-8.
• [28]Kim SH, Pezzoli L, Yacouba H, et al.: Whom and where are we not vaccinating? coverage after the introduction of a New conjugate vaccine against group a meningococcus in Niger in 2010. PLoS ONE 2012, 7:e29116.