Introducing an efficient sampling method for national surveys with limited sample sizes: application to a national study to determine quality and cost of healthcare

BMC Public Health

Mojdeh Saadati¹  Ali Sheidaei²  Mahboubeh Parsaeian²  Shahab Khatibzadeh³  Saeid Shahraz⁴  Mahdi Mahdavi⁵  Parinaz Mehdipour⁶  Farshad Farzadfar⁷ 

[1] Department of Computer Science, Iowa State University, 50010, Ames, IA, USA;Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran;Heller School for Social Policy and Management, Brandeis University, Waltham, MA, USA;Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA;National Institute of Health Research (NIHR), Tehran University of Medical Sciences, Tehran, Iran;Harvard T.H. Chan School of Public Health, 677 Huntington Ave, 02115, Boston, MA, USA;Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran;Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran;Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran;

关键词: Survey sampling method;    Small sample size;    Model-based clustering;    Validity;    Sampling efficiency;    Iran quality of Care in Medicine Program (IQCAMP);   

DOI  :  10.1186/s12889-021-11441-0

来源: Springer

【 摘 要 】

BackgroundSampling a small number of participants from an entire country is not straightforward. In this case, researchers reluctantly sample from a single setting or few settings, which limits the generalizability of findings. Therefore, there is a need to design efficient sampling method for small sample size surveys that can produce generalizable results at the country level.MethodsData comprised of twenty proxy variables to measure health services demands, structures, and outcomes of 413 districts of Iran. We used two data mining methods (hierarchical clustering method (HCM) and model-based clustering method (MCM)) to create homogenous groups of districts, i.e., strata based on these variables. We compared the internal and stability validity of the methods by statistical indices. An expert group checked the face validity of the methods, particularly regarding the total number of strata and the combination of districts in each stratum. The efficiency of selected method, which is measured by the inverse of variance, was compared with a simple random sampling (SRS) through simulation. The sampling design was tested in a national study in Iran, which aimed to evaluate the quality and costs of medical care for eight selected diseases by only recruiting 300 participants per disease at the country level.ResultsMCM and HCM divided the districts into eight and two clusters, respectively. The measures of internal and stability validity showed that clusters created by MCM were more separated, compact, and stable, thus forming our optimum strata. The probability of death from stroke, chronic obstructive pulmonary disease, and in-hospital mortality rate were the most important indicators that distinguished the eight strata. Based on the simulation results, MCM increased the efficiency of the sampling design up to 1.7 times compared to SRS.ConclusionsThe use of data mining improved the efficiency of sampling up to 1.7 times greater than SRS and markedly reduced the number of strata to eight in the entire country. The proposed sampling design also identified key variables that could be used to classify districts in Iran for sampling from these target populations in the future studies.

【 授权许可】

CC BY