Objective: Similarity of drug names is one factor of dispensing incidents.  The aim of this study was to survey the relation between sensual similarities of drug names and the occurrence of taking errors for pharmacists who actually prepare medicine.
Methods: A pair of drugs (15 incident pairs and 104 control pairs) was displayed on a computer screen at random.  The subject’s task was to determine the sensual similarity of them.  Thirteen pharmacists who prepared these pairs and caused their incidents participated in the experiment.
Results: The result showed that the sensual similarity of drug names of incident pairs was found to be highly significant in comparison to one of the control pairs [p=0.026].  However, the similarity in incident pairs is not necessarily high.  It was suggested that the similarity of drug name was not the only factor of taking error.  Multiple linear regression analyses of the sensual similarity in control pairs were performed, in which 10 variables were reported as quantitative indicators of similarity of drug name and were able to be measured on the internet.  The correlation was good [R²=0.828].  However, this regression model was not useful when adjusting to incident pairs.  In incident pairs, the similarity value calculated by the regression model was lower than the measured sensual similarity.
Conclusion: The result suggested that measured sensual similarity includes other risk factors of taking error, such as appearance similarity and/or efficacy similarity and/or short distance arrangement.  It seemed that the pharmacist’s ability complicated the factor of taking error.

Objective: Taking the wrong medicine or medication error is a serious concern to patient safety.  The aim of this study was to statistically survey the relation between the placement of drugs on medicine shelf and the occurrence of error in taking a medicine.
Methods: The study comprised 2 groups.  The incident group contained 43 cases that were erroneously taken in the Obihiro Kosei General Hospital.  The control group contained 43 drug pairs matched by the similarity index of the drug names from among the drugs used in the hospital at random.  The similarity index of drug names was based on 10 quantitative indicators.  The distance of medicine shelf arrangement was represented by three variables: the horizontal distance, the vertical distance and the distance of shelf block.  Conditional logistic regression analyses of the occurrence of medication errors were performed by evaluating the three variables of the distance factor and their interaction for error in taking a similar-sounding named drugs.
Results: Conditional logistic regression analysis revealed that the vertical distance (OR: 0.64, 95%CI: 0.42-0.99) and the distance of the shelf block (OR: 0.74, 95%CI: 0.57-0.97) were significant risk-reduction factors of medication errors.  Four variables were extracted as the most suitable logistic regression model in terms of the interaction between them.  As the interaction between 3 variables (the horizontal distance, the vertical distance and the distance of shelf block) was significant (OR: 0.93, 95%CI: 0.86-0.99), they may be considered as synergistic risk-reduction factors.  Moreover, the horizontal distance was found to be a risk-enhancement factor (OR: 1.52, 95%CI: 0.93-2.48).
Discussion: In order to reduce the risk of medication errors due to similar-sounding drug names, placement of drug on the medicine shelf should take into consideration the three coordinates of the distance factor.

Objective: Similarity in drug appearance is one of the major environmental factors influencing dispensing errors, such as picking the wrong medication (picking error).  The purpose of this study is to verify if the index values of appearance similarity calculated objectively for multiple-specification drugs are the factors of picking error.
Methods: Four variables (number of total prescription, deviation of prescriptions between the specifications, sheet size, and color similarity of the sheet surface) were calculated.  The number of total prescription and deviation of prescriptions were extracted from the dispensing system.  Sheet size and color similarity were calculated, respectively, from the area ratio and by the Histogram Intersection method using the press through package (PTP) sheet image.  To evaluate the relationship between the picking error rate and these four variables, univariate and multivariate analyses were performed.
Results: The number of total prescription and the deviation of prescriptions were not significant factors.  In contrast, sheet size and color similarity significantly influenced the picking error rates.
Conclusion: Similarity in appearance between multiple-specification drugs is a risk factor of picking error.  When the multiple-specification pair has the same sheet size or high color similarity, one needs to be caution of picking error.  Further, in the pharmaceutical industry, to reduce the risk of dispensing errors, it is desirable to carry out the devise to enhance the identity of each specification.

Background: The use of transport other than cars is a modifiable factor in the association between driving cessation and social frailty. Clarifying this relationship may serve as a new preventive measure against social frailty among current non-drivers. This study examined the potential association of driving status and transport use with social frailty, as well as between the frequency of transport use and social frailty, among current non-drivers. Methods: This study included 977 middle-aged and older adults (average age, 65.3±4.8 years). The participants were classified as transport users (more than a few times a week) and transport non-users (less than a few times a month). Based on driving status and transport use, the groups were further classified into current driver, current non-driver/transport user, and current non-driver/transport non-user groups. The relationships between driving status, transport use, and social frailty were examined using multiple logistic regression. Results: The current non-driver group and the transport non-user group were significant association with a higher social frailty. The current non-driver/transport user group showed no association with social frailty compared with the current driver group. The odds ratio for the social frailty rate for The current non-driver/transport non-user group was 2.14 (95% confidence interval, 1.25–3.73). Conclusions Participants who neither drive nor take transport showed significant associations with increased social frailty. Compared with current driver/transport use, current non-driver/transport non-use was associated with social frailty.

Background: The use of transport other than cars is a modifiable factor in the association between driving cessation and social frailty. Clarifying this relationship may serve as a new preventive measure against social frailty among current non-drivers. This study examined the potential association of driving status and transport use with social frailty, as well as between the frequency of transport use and social frailty, among current non-drivers. Methods: This study included 977 middle-aged and older adults (average age, 65.3±4.8 years). The participants were classified as transport users (more than a few times a week) and transport non-users (less than a few times a month). Based on driving status and transport use, the groups were further classified into current driver, current non-driver/transport user, and current non-driver/transport non-user groups. The relationships between driving status, transport use, and social frailty were examined using multiple logistic regression. Results: The current non-driver group and the transport non-user group were significant association with a higher social frailty. The current non-driver/transport user group showed no association with social frailty compared with the current driver group. The odds ratio for the social frailty rate for The current non-driver/transport non-user group was 2.14 (95% confidence interval, 1.25–3.73). Conclusions Participants who neither drive nor take transport showed significant associations with increased social frailty. Compared with current driver/transport use, current non-driver/transport non-use was associated with social frailty.

Background: The use of transport other than cars is a modifiable factor in the association between driving cessation and social frailty. Clarifying this relationship may serve as a new preventive measure against social frailty among current non-drivers. This study examined the potential association of driving status and transport use with social frailty, as well as between the frequency of transport use and social frailty, among current non-drivers. Methods: This study included 977 middle-aged and older adults (average age, 65.3±4.8 years). The participants were classified as transport users (more than a few times a week) and transport non-users (less than a few times a month). Based on driving status and transport use, the groups were further classified into current driver, current non-driver/transport user, and current non-driver/transport non-user groups. The relationships between driving status, transport use, and social frailty were examined using multiple logistic regression. Results: The current non-driver group and the transport non-user group were significant association with a higher social frailty. The current non-driver/transport user group showed no association with social frailty compared with the current driver group. The odds ratio for the social frailty rate for The current non-driver/transport non-user group was 2.14 (95% confidence interval, 1.25–3.73). Conclusions Participants who neither drive nor take transport showed significant associations with increased social frailty. Compared with current driver/transport use, current non-driver/transport non-use was associated with social frailty.

Background: This study aimed to investigate the potential association between the combination of low physical activity and low dietary diversity with mild cognitive impairment (MCI) in older Japanese adults. Methods: Data from 600 older adults (mean age, 74.1±6.4 years; women, 62.0%) were analyzed. We evaluated dietary variety based on the Food Frequency Score (FFS; maximum 30 points) by assessing the 1-week consumption frequencies of ten foods. An FFS of ≤16 indicated low dietary diversity. We assessed MCI using the National Center for Geriatrics and Gerontology Functional Assessment Tool. Physical activity levels was determined based on participant responses to two questions: “Do you engage in moderate levels of physical exercise or sports aimed at health?” and “Do you engage in low levels of physical exercise aimed at health?” Participants who responded “No” to both questions were classified as having low physical activity levels. We classified the participants into robust, low-dietary diversity, low-physical activity, and coexistence groups. Results: The overall prevalence of MCI was 20.7%, with rates in the robust, low dietary diversity, low physical activity, and coexistence groups of 17.7%, 24.7%, 25.0%, and 41.9%, respectively. Multiple logistic regression analysis revealed that low dietary diversity and physical activity were associated with MCI in older adults (odds ratio=2.80, 95% confidence interval 1.22–6.28). Conclusion The results of the present study demonstrated the association of the co-occurrence of low dietary diversity and low physical activity with MCI. Older adults with both risk factors may require early detection, as well as physical activity and dietary interventions.

Background: This study aimed to investigate the potential association between the combination of low physical activity and low dietary diversity with mild cognitive impairment (MCI) in older Japanese adults. Methods: Data from 600 older adults (mean age, 74.1±6.4 years; women, 62.0%) were analyzed. We evaluated dietary variety based on the Food Frequency Score (FFS; maximum 30 points) by assessing the 1-week consumption frequencies of ten foods. An FFS of ≤16 indicated low dietary diversity. We assessed MCI using the National Center for Geriatrics and Gerontology Functional Assessment Tool. Physical activity levels was determined based on participant responses to two questions: “Do you engage in moderate levels of physical exercise or sports aimed at health?” and “Do you engage in low levels of physical exercise aimed at health?” Participants who responded “No” to both questions were classified as having low physical activity levels. We classified the participants into robust, low-dietary diversity, low-physical activity, and coexistence groups. Results: The overall prevalence of MCI was 20.7%, with rates in the robust, low dietary diversity, low physical activity, and coexistence groups of 17.7%, 24.7%, 25.0%, and 41.9%, respectively. Multiple logistic regression analysis revealed that low dietary diversity and physical activity were associated with MCI in older adults (odds ratio=2.80, 95% confidence interval 1.22–6.28). Conclusion The results of the present study demonstrated the association of the co-occurrence of low dietary diversity and low physical activity with MCI. Older adults with both risk factors may require early detection, as well as physical activity and dietary interventions.

Background: Dementia is a critical late-life health issue that occurs among members of aging societies. This study examined the relationships between eating out, dietary diversity, and mild cognitive impairment (MCI) among community-dwelling older adults. Methods: We analyzed data from 597 older adults (median age 73.0 years, interquartile range 69.0–78.0 years; 62.6% females). We applied the food frequency score to evaluate diet variety and the weekly consumption frequencies of ten food items were determined. The National Center for Geriatrics and Gerontology Functional Assessment Tool (NCGG-FAT) was used to evaluate MCI. Finally, we asked the participants how often they ate out each month; those who replied "none" were categorized into the "non-eating out" group. Results: The overall prevalence of MCI was 122 (20.4%), with a higher prevalence in the low dietary diversity group than in the high dietary diversity group (28.6% vs. 18.6%). After adjusting for covariates, the participants who self-described as not eating out were independently associated with low dietary diversity (odds ratio [OR]=1.97, 95% confidence interval [CI] 1.20–3.20), while low dietary diversity was associated with MCI (OR=1.72; 95% CI 1.02–2.87). Structural equation models revealed that not eating out had no direct effect on MCI but was associated with MCI via low dietary diversity (root mean square error of approximation=0.030, goodness-of-fit index=0.999, and adjusted goodness-of-fit index=0.984). Conclusions Although non-eating out may not have a direct effect on MCI, an indirect relationship may exist between eating-out habits and MCI via dietary diversity status.

Background: This study aimed to investigate the potential association between the combination of low physical activity and low dietary diversity with mild cognitive impairment (MCI) in older Japanese adults. Methods: Data from 600 older adults (mean age, 74.1±6.4 years; women, 62.0%) were analyzed. We evaluated dietary variety based on the Food Frequency Score (FFS; maximum 30 points) by assessing the 1-week consumption frequencies of ten foods. An FFS of ≤16 indicated low dietary diversity. We assessed MCI using the National Center for Geriatrics and Gerontology Functional Assessment Tool. Physical activity levels was determined based on participant responses to two questions: “Do you engage in moderate levels of physical exercise or sports aimed at health?” and “Do you engage in low levels of physical exercise aimed at health?” Participants who responded “No” to both questions were classified as having low physical activity levels. We classified the participants into robust, low-dietary diversity, low-physical activity, and coexistence groups. Results: The overall prevalence of MCI was 20.7%, with rates in the robust, low dietary diversity, low physical activity, and coexistence groups of 17.7%, 24.7%, 25.0%, and 41.9%, respectively. Multiple logistic regression analysis revealed that low dietary diversity and physical activity were associated with MCI in older adults (odds ratio=2.80, 95% confidence interval 1.22–6.28). Conclusion The results of the present study demonstrated the association of the co-occurrence of low dietary diversity and low physical activity with MCI. Older adults with both risk factors may require early detection, as well as physical activity and dietary interventions.