Diabetes mellitus is a metabolic disorder where by glucose cannot effectively get transported out of the blood. It is a chronic disease with a high prevalence and growing concern in world wide. There are two Types of diabetes, which are Type I and Type II. A longitudinal data analysis retrospective based study was conducted between 1 st September, 2012 to 30 th August 2015 in Debre Berhan referral hospital. The main objective of the study was Gaussian longitudinal analysis of progression of Diabetes mellitus patients using fasting blood sugar level count following insulin, metformin and to identify factors predicting the progression of diabetic infection. A total of 248 Diabetes mellitus patients were included in the study whom 111 (44.8%) were females and the rest 137 (55.8%) were males. The generalized linear mixed model would be used to model the progression of diabetic infection. The appropriate variance covariance structure was Compound symmetry selected for this study. This study showed that age, sex, time, illiterate with time, primary with time, address with time, age with time and time with time were statistically significant factors for the progression of fasting blood sugar level at a logarithmic fasting sugar level over time in generalized linear mixed model. The mean fasting blood sugar level showed an increasing progress over time after patients were initiated on insulin and metformin. The statistical modelling approaches linear mixed model and generalized linear mixed model have been compared for the analysis of fasting data and we obtained generalized linear mixed model exhibited the best fit for this data with smaller disturbance than linear mixed model for their estimated standard error.
Background The World Health Organization (WHO) defined: - low birth weight as a weight at birth less than 2500g. Adverse birth outcomes, low birth weight, and preterm birth, constitute an important danger to public health since they raise the likelihood of future diseases and developmental problems for children as well as fetal health status at birth. The study aimed to investigate propensity score methods for causal inference by removing selection bias from observational studies for adverse birth outcomes. Methods We analyzed quasi-experimental studies for the maternal and neonatal health outcome datasets, including adverse birth outcomes for allocated groups of mothers within the period of time from August 2019 to September 2020. We applied different propensity score algorithms, matching, inverse probability weighting, stratification and overlap weighting for covariate balance between midwives-led continuity care and shared model care for adverse birth outcomes. Results The result of the current investigation indicates that mothers who were provided midwife-led continuity (OR=0.48, 95% CI∶( 0.35, 0.894)) with inverse probability treatment weighting (OR=0.36, 95% CI: (0.19, 0.69)) had significant contribution for the improvement of advance birth outcomes. Conclusion Midwife-led continuity care of mothers had a significant enrollment for improving adverse birth outcomes of newborn babies and the propensity score has only controls for measured covariates, propensity score methods are the most recommended approach to adjust confounding and recover treatment effects.
Objective: Diabetes mellitus is a metabolic disorder that develops over time and affects the cardiovascular system, eyes, kidneys, nerves, and blood sugar levels. The aim of this investigation was to determine the prevalence of diabetic mellitus patients, identify the associating risk factors using a multilevel longitudinal model, and understand the multilevel model changes for the level-1 and level-2 models. Material and Methods: We examined such types of scenarios using multilevel longitudinal models such as the simple random intercept multilevel model, the random coefficient model, and the null model. Results: There were 248 individuals with diabetes mellitus enrolled in the study for follow-up measurements over 4 time points, among these 248 individuals, 211 had complete data for all four time points. Based on the intraclass correlation coefficient, much of the variability (88.35%) in diabetes mellitus patients was accounted for by the follow-up time in this study, whereas 11.65% of the variability could not be accounted for by the follow-up time. Moreover, the data analysis suggested that sex had a significant effect on diabetes mellitus patients with the progression of time. Conclusion: Based on the results of our study, sex, baseline fasting and educational status had a significant effect on diabetes mellitus patients over time. The educational status of diabetes mellitus patients was found to have a significant effect throughout the follow-up time; this shows that when treating diabetes mellitus patients, the physician should beware of the nature of the disease and how to management diabetes requires a high level of awareness and motivation on part of the patients regarding self-care.
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