Background: Incomplete data are of particular important influence in mental measurement questionnaires. Most experts, however, mostly focus on clinical trials and cohort studies and generally pay less attention to this deficiency. We aim is to compare the accuracy of four common methods for handling items missing from different psychology questionnaires according to the items non-response rates. Method: All data were drawn from the previous studies including the self-acceptance scale (SAQ), the activities of daily living scale (ADL) and self-esteem scale (RSES). SAQ and ADL dataset, simulation group, were used to compare and assess the ability of four imputation methods which are direct deletion, mode imputation, Hot-deck (HD) imputation and multiple imputation (MI) by absolute deviation, the root mean square error and average relative error in missing proportions of 5, 10, 15 and 20%. RSES dataset, validation group, was used to test the application of imputation methods. All analyses were finished by SAS 9.4. Results: The biases obtained by MI are the smallest under various missing proportions. HD imputation approach performed the lowest absolute deviation of standard deviation values. But they got the similar results and the performances of them are obviously better than direct deletion and mode imputation. In a real world situation, the respondents' average score in complete data set was 28.22 ± 4.63, which are not much different from imputed datasets. The direction of the influence of the five factors on self-esteem was consistent, although there were some differences in the size and range of OR values in logistic regression model. Conclusion: MI shows the best performance while it demands slightly more data analytic capacity and skills of programming. And HD could be considered to impute missing values in psychological investigation when MI cannot be performed due to limited circumstances.

BackgroundReducing premature deaths is an important step towards achieving the World Health Organization’s sustainable development goal. Redeployed miners are more prone to disease or premature death due to the special occupational characteristics. Our aims were to describe the deaths of redeployed miners, assess the losses due to premature death and identify their main health problems. All the records of individuals were obtained from Fuxin Mining Area Social Security Administration Center. Year of life lost (YLL) and average year of life lost were used to assess the loss due to premature death. YLL rates per 1000 individuals were considered to compare deaths from different populations.ResultsCirculatory system diseases contributed the most years of life lost in the causes of death, followed by neoplasms. But average year of life lost in neoplasms was 6.85, higher than circulatory system diseases, 5.63. Cerebrovascular disease and ischemic heart disease were the main causes of death in circulatory system diseases. And average years of life lost in cerebrovascular disease and ischemic heart disease were 5.85 and 5.62, higher than those in other circulatory system diseases. Lung cancer was the principal cause of death in neoplasms. Average year of life lost in liver cancer was 7.92, the highest in neoplasms.ConclusionsFor redeployed miners, YLL rates per 1000 individuals in cerebrovascular disease, ischemic heart disease and lung cancer were higher than those in other populations, especially in men. It is important to attach importance to the health of redeployed miners, take appropriate measures to reduce premature death and achieve the sustainable development goal. Our findings also contribute to a certain theoretical reference for other countries that face or will face the same problem.Electronic supplementary materialThe online version of this article (10.1186/s12992-019-0450-5) contains supplementary material, which is available to authorized users.

Background Incomplete data are of particular important influence in mental measurement questionnaires. Most experts, however, mostly focus on clinical trials and cohort studies and generally pay less attention to this deficiency. We aim is to compare the accuracy of four common methods for handling items missing from different psychology questionnaires according to the items non-response rates. Method All data were drawn from the previous studies including the self-acceptance scale (SAQ), the activities of daily living scale (ADL) and self-esteem scale (RSES). SAQ and ADL dataset, simulation group, were used to compare and assess the ability of four imputation methods which are direct deletion, mode imputation, Hot-deck (HD) imputation and multiple imputation (MI) by absolute deviation, the root mean square error and average relative error in missing proportions of 5%, 10%, 15% and 20%. RSES dataset, validation group, was used to test the application of imputation methods. All analyses were finished by SAS 9.4. Results The biases obtained by MI are the smallest under various missing proportions. HD imputation approach performed the lowest absolute deviation of standard deviation values. But they got the similar results and the performances of them are obviously better than direct deletion and mode imputation. In a real world situation, the respondents' average score in complete data set was 28.22 ± 4.63, which are not much different from imputed datasets. The direction of the influence of the five factors on self-esteem was consistent, although there were some differences in the size and range of OR values in logistic regression model. Conclusion MI shows the best performance while it demands slightly more data analytic capacity and skills of programming. And HD could be considered to impute missing values in psychological investigation when MI cannot be performed due to limited circumstances.

Background Incomplete data are of particular important influence in mental measurement questionnaires. Most experts, however, mostly focus on clinical trials and cohort studies and generally pay less attention to this deficiency. We aim to compare the performance of in terms of accuracy and precision of four common methods for handling items missing from different psychology questionnaires according to the items non-response rates. Method All data were drawn from the previous studies including the self-acceptance scale (SAQ), the activities of daily living scale (ADL) and self-esteem scale (RSES). SAQ and ADL dataset, simulation group, were used to compare and assess the ability of four imputation methods which are direct deletion, mode imputation, Hot-deck imputation and multiple imputation by absolute deviation, the root mean square error and average relative error in missing proportions of 5%, 10%, 15% and 20%. RSES dataset, validation group, was used to test the application of imputation methods. All analyses were finished by SAS 9.4. Results The biases obtained by MI are the smallest under various missing proportions. HD imputation approach performed the lowest absolute deviation of standard deviation values. But they got the semblable results and the performances of them are obviously better than direct deletion and mode imputation. In a real world situation, the respondents' average score in complete data set was 28.22 ± 4.63, which are not much different from imputed datasets. The direction of the influence of the five factors on self-esteem was consistent, although there were some differences in the size and range of OR values in logistic regression model. Conclusion MI shows the best performance while it demands slightly more data analytic capacity and skills of programming. And HD could be considered to impute missing values in psychological investigation when MI cannot be performed due to limited circumstances.