Optimum strata boundaries and sample sizes in health surveys using auxiliary variables

Using convenient stratification criteria such as geographical regions or other natural conditions like age, gender, etc., is not beneficial in order to maximize the precision of the estimates of variables of interest. Thus, one has to look for an efficient stratification design to divide the whole p...

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Bibliographic Details
Published in:PloS one 2018-04, Vol.13 (4), p.e0194787-e0194787
Main Authors: Reddy, Karuna Garan, Khan, Mohammad G M, Khan, Sabiha
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Anemia
Boundaries
Boundary layers
Computer and Information Sciences
Computer simulation
Diseases
Dynamic programming
Feasibility studies
Frequency distribution
Gene expression
Health
Health surveys
Hemoglobin
Iron
Iron deficiency
Mathematical programming
Mathematics
Medicine and Health Sciences
Methods
Nutrient deficiency
Optimization
Parameter estimation
Physical Sciences
Polls & surveys
Population
Population statistics
Prevalence studies (Epidemiology)
Research and Analysis Methods
Simulation
Strata
Stratigraphy
Variables
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Summary:Using convenient stratification criteria such as geographical regions or other natural conditions like age, gender, etc., is not beneficial in order to maximize the precision of the estimates of variables of interest. Thus, one has to look for an efficient stratification design to divide the whole population into homogeneous strata that achieves higher precision in the estimation. In this paper, a procedure for determining Optimum Stratum Boundaries (OSB) and Optimum Sample Sizes (OSS) for each stratum of a variable of interest in health surveys is developed. The determination of OSB and OSS based on the study variable is not feasible in practice since the study variable is not available prior to the survey. Since many variables in health surveys are generally skewed, the proposed technique considers the readily-available auxiliary variables to determine the OSB and OSS. This stratification problem is formulated into a Mathematical Programming Problem (MPP) that seeks minimization of the variance of the estimated population parameter under Neyman allocation. It is then solved for the OSB by using a dynamic programming (DP) technique. A numerical example with a real data set of a population, aiming to estimate the Haemoglobin content in women in a national Iron Deficiency Anaemia survey, is presented to illustrate the procedure developed in this paper. Upon comparisons with other methods available in literature, results reveal that the proposed approach yields a substantial gain in efficiency over the other methods. A simulation study also reveals similar results.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0194787