Iranian Journal of

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Background and Objectives: Missing data is a big challenge in the research. According to the type of the study and of the variables, different ways have been proposed to work with these data. This study compared five popular imputation approaches in addressing missing data in the questionnaires.

Methods: In this study, 500 questionnaires were used for self-medication in diabetic patients. Missing in the observations was artificially generated by random selection of questions and then deleting them. Five imputation ways included: 1) the mean of the questions, 2) the mean of the person, 3) the mode of the person, 4) linear regression, and 5) EM algorithm. For each method, the mean and standard deviation were compared with imputation. The Spearman correlation coefficient, the percentage of incorrectly classified and kappa statistic were also calculated.

Results: A kappa higher than 0.81 represented almost perfect agreement at 10% missingness. The EM algorithm showed the highest level of agreement with the results of actual data with a Kappa of 0.886. With increasing missingness to 30%, the EM algorithm and the mean of  the person showed a rather similar agreement with a Kappa of 0.697 and 0.687, respectively.

Conclusion: In this study, the EM algorithm was the most accurate method for handling missing data in all models. The mean of the person method is easy for handling missing data, especially for most non statisticians.

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Background and Objectives: Fair financial protection against health expenditures is one of the most critical goals of health systems. This study was conducted to investigate the most appropriate threshold for measuring the exposure to catastrophic health expenditure (CHE) in Iran.
 
Methods: The present study was conducted using 2018 national household income and expenditure data of the Iran statistics center. The occurrence and intensity of the households' exposure to CHE at different thresholds and for economic quintiles was measured using two World Health Organization (WHO) and World Bank (WB) methodologies. The most appropriate threshold for each methodology was also determined using the Kappa statistics adaptation estimation.
 
Results: The occurrence of households’ exposure to CHE was 3.02% and 8.51% in urban households and 4.66% and 9.22% in rural households, respectively. According to asset quintiles, the highest occurrence was in the first quintile and the lowest in the fifth quintile. In addition, the results of adaptation estimation based on Kappa statistics showed that a threshold of 40% in the WHO methodology and 25% in the WB methodology were the most appropriate thresholds.
 
Conclusion: The results showed a relatively high rate of exposure to CHE. CHE occurrence was also more in poorer households, and common thresholds were the most appropriate thresholds for estimating CHE.