Association between Kihon check list score and geriatric depression among older adults from ORANGE registry

Objective Older adults in Japan are tackling health-related challenges brought by comprehensive geriatric symptoms, such as physical and cognitive problems and social-psychological issues. In this nationwide study, we mainly focused on the Kihon checklist (KCL) as certificated necessity of long-term care for Japanese older adults and investigated whether the KCL score was associated with geriatric depression. In addition, we aimed to identify critical factors that influence the relationship between the KCL score and geriatric depression. Methods This survey was a cross-sectional observational study design, performed from 2013 to 2019. A total of 8,760 participants aged 65 years and over were recruited from five cohorts in Japan, consisting of 6,755 persons in Chubu, 1,328 in Kanto, 481 in Kyushu, 49 in Shikoku and 147 in Tohoku. After obtaining informed consent from each participant, assessments were conducted, and outcomes were evaluated according to the ORANGE protocol. We collected data on demographics, KCL, physical, cognitive and mental evaluations. To clarify the relationship between the KCL and geriatric depression or critical factors, a random intercept model of multi-level models was estimated using individual and provincial variables depending on five cohorts. Results The KCL score was correlated with depression status. Moreover, the results of a random intercept model showed that the KCL score and geriatric depression were associated, and its association was affected by provincial factors of slow walking speed, polypharmacy and sex difference. Conclusions These results suggest that provincial factors of low walking performance, polypharmacy and sex difference (female) might be clinically targeted to improve the KCL score in older adults.

Identifying and quantifying variation between healthcare organisations and geographical regions: using mixed-effects models

When the degree of variation between healthcare organisations or geographical regions is quantified, there is often a failure to account for the role of chance, which can lead to an overestimation of the true variation. Mixed-effects models account for the role of chance and estimate the true/underlying variation between organisations or regions. In this paper, we explore how a random intercept model can be applied to rate or proportion indicators and how to interpret the estimated variance parameter.

Multilevel Analysis of Determinants of Anemia Prevalence among Children Aged 6–59 Months in Ethiopia: Classical and Bayesian Approaches

Background. Anemia is a widely spread public health problem and affects individuals at all levels. However, there is a considerable regional variation in its distribution. Objective. Thus, this study aimed to assess and model the determinants of prevalence of anemia among children aged 6–59 months in Ethiopia. Data. Cross-sectional data from Ethiopian Demographic and Health Survey was used for the analysis. It was implemented by the Central Statistical Agency from 27 December 2010 through June 2011 and the sampling technique employed was multistage. Method. The statistical models that suit the hierarchical data such as variance components model, random intercept model, and random coefficients model were used to analyze the data. Likelihood and Bayesian approaches were used to estimate both fixed effects and random effects in multilevel analysis. Result. This study revealed that the prevalence of anemia among children aged between 6 and 59 months in the country was around 42.8%. The multilevel binary logistic regression analysis was performed to investigate the variation of predictor variables of the prevalence of anemia among children aged between 6 and 59 months. Accordingly, it has been identified that the number of children under five in the household, wealth index, age of children, mothers’ current working status, education level, given iron pills, size of child at birth, and source of drinking water have a significant effect on prevalence of anemia. It is found that variances related to the random term were statistically significant implying that there is variation in prevalence of anemia across regions. From the methodological aspect, it was found that random intercept model is better compared to the other two models in fitting the data well. Bayesian analysis gave consistent estimates with the respective multilevel models and additional solutions as posterior distribution of the parameters. Conclusion. The current study confirmed that prevalence of anemia among children aged 6–59 months in Ethiopia was severe public health problem, where 42.8% of them are anemic. Thus, stakeholders should pay attention to all significant factors mentioned in the analysis of this study but wealth index/improving household income and availability of pure drinking water are the most influential factors that should be improved anyway.