Abstract
Background:This study was carried out to use multiple imputation (MI) in order to correct for the potential nonresponse bias in measurements related to variable fasting blood glucose (FBS) in non-communicable disease risk factors survey conducted in Iran in 2007.Methods:Five multiple imputation methods as bootstrap expectation maximization, multivariate normal regression, univariate linear regression, MI by chained equation, and predictive mean matching were applied to impute variable fasting blood sugar. To make FBS consistent with normality assumption natural logarithm (Ln) and Box-Cox (BC) transformations were used prior to imputation. Measurements from which we intended to remove nonresponse bias included mean of FBS and percentage of those with high FBS.Results:For mean of FBS results didn’t considerably change after applying MI methods. Regarding the prevalence of high blood sugar all methods on original scale tended to increase the estimates except for predictive mean matching that along with all methods on BC or Ln transformed data didn’t change the results.Conclusions:FBS-related measurements didn’t change after applying different MI methods. It seems that nonresponse bias was not an important challenge regarding these measurements. However use of MI methods resulted in more efficient estimations. Further studies are encouraged on accuracy of MI methods in these settings.
Highlights
fasting blood glucose (FBS)-related measurements didn’t change after applying different multiple imputation (MI) methods. It seems that nonresponse bias was not an important challenge regarding these measurements
Use of MI methods resulted in more efficient estimations
Further studies are encouraged on accuracy of MI methods in these settings
Summary
According to the fact sheets of World Health Organization, updated in January 2015, diabetes account for 1.5 million deaths annually while more than 80% of these deaths happen in low- and middle-income countries (World Health Organization [WHO], 2014).Provision of accurate information on prevalence of diabetes and its major risk factors will enable us to understand the importance of burden which it imposes on societies and can be regarded as a necessary part to devise and monitor preventive and controlling programs.One of the best tools to reach this purpose is STEPS study, devised by WHO, which is a nationwide population based cross-sectional survey to gather information on non-communicable disease risk factors as well as diabetes.One important issue about STEPS studies is that usually a fraction of sample don’t take part in the whole study or some part of it known as nonresponse that can threaten the results of the study by reducing the sample size www.ccsenet.org/gjhsGlobal Journal of Health ScienceVol 8, No 1; 2016 and in some cases biasing descriptive and analytic statistics.For example there have been about 17% nonresponses in Fasting Blood Glucose measurement in almost all rounds of these surveys in Iran while no advanced strategies has been applied to treat possible nonresponse bias (Asgari, Mirzazadeh, & Heidarian, 2009; Esteghamati et al, 2008).There are a wide range of methods to deal with nonresponses (Little & Rubin, 1989). Provision of accurate information on prevalence of diabetes and its major risk factors will enable us to understand the importance of burden which it imposes on societies and can be regarded as a necessary part to devise and monitor preventive and controlling programs. One of the best tools to reach this purpose is STEPS study, devised by WHO, which is a nationwide population based cross-sectional survey to gather information on non-communicable disease risk factors as well as diabetes. This study was carried out to use multiple imputation (MI) in order to correct for the potential nonresponse bias in measurements related to variable fasting blood glucose (FBS) in non-communicable disease risk factors survey conducted in Iran in 2007
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