Volunteer Surgical Camp at Gombe Hospital in Uganda

Background: The Islamic University Habib Medical School in Uganda (IUIU), in collaboration with Doctors Worldwide (DWW) from Turkey, organized a surgical camp in April 2014. In this camp, different types of hernia repair, among other general surgical procedures were conducted. The target population was the population within the Gombe hospital serving districts.Methods: The defined area for the surgical camp was Butambala and neighboring districts including Mpigi; Gomba, Mityana, and parts of Wakiso district. The IUIU team and Gombe hospital team were respectful to the sensitivities of the community, district and government officials. The surgical team composed of 4 surgeons (three from DWW-Turkey and one from Uganda), 3 Anesthesiologists, (two from DWW-Turkey and one from Uganda), 2 nurses and 2 intern doctor, (one from DWW-Turkey and one from Uganda).Results: The total number of patients operated was 115; however the total number of operations performed was 130. One hundred and fourteen operations were different types of hernia repair. The ages of hernia patients ranged between 1 and 80 years (mean±SD is 27.46±24.55). Hemoglobin values ranged between 9.2 and 17 (mean±SD is 12.5±1.48). Only two (1.8%) of 114 hernia patients had positive results on HIV serology. Sixteen patients underwent circumcision. Of those, only two (12.5%) patients had positive results on HIV serology.Conclusion: Hernia is a common surgical problem in all age groups. It is more common in men. In addition to the operations conducted, the need for surgery for 187 patients was detected. This condition shows that the hernia operation is commonly accepted as a negligible condition.Keywords: Global surgery; Provincial; Hernia; World Wide Doctors; Ugand

Adjusting the Effect of Baseline Differences Between Groups in Trials with Which Have Two or More Groups

WOS: 000264851900013Objective: In many clinical and experimental trials, researchers assess the effect of treatment by measuring the value of a continuous variable before and after the treatment. If there is an imbalance in baseline values between groups, some statistical comparisons may result with mistakes in estimation of the treatment effect. The aim of this study was to explain which statistical methods were more suitable in the estimation of the treatment effect when there was an imbalance for the baseline values between groups. Material and Methods: Different statistical methods, which are used in estimation of treatment effects, were briefly explained and were applied to a hypothetical data set, which had significant differences between groups according to baseline values of the related variable. In addition, a limited simulation study for several conditions was carried out to determine suitable statistical methods. Results: Baseline values were different between two groups and correlation was low between baseline and follow up values of related variable in each group for hypothetical data set. In this condition, comparison of simple differences between baseline and follow up values was the best method for the estimation of treatment effect. In the simulation study, the power of the test for simple differences was higher (85%) than the value in the analysis of covariance (40%) when correlations were low and sample sizes were small in each group. Moreover, the powers of these two tests were high and similar to each other, when sample sizes were moderate. When the correlation was high, the powers of both tests were high in both small and moderate sample sizes. Conclusion: The presence of a significant difference should be sought between groups according to baseline values of the related variable even though groups are randomly assigned. In addition, the degree of the correlation between baseline and follow up values should be taken into consideration. When significant differences exist between baseline values and the correlation is low, we suggest that the classical methods should be used to determine the significance of the effect; however, when the correlation is high, covariance analysis is a suitable method

Which Measure Should be Used for Testing in a Paired Design: Simple Difference, Percent Change, or Symmetrized Percent Change?

WOS: 000261655200013We aimed to determine the most proper change measure among simple difference, percent, or symmetrized percent changes in simple paired designs. For this purpose, we devised a computer simulation program. Since distributions of percent and symmetrized percent change values are skewed and bimodal, paired t-test did not give good results according to Type I error and the test power. To be to able use percent change or symmetrized percent change as change measure, either the distribution of test statistics should be transformed to a known theoretical distribution by transformation methods or a new test statistic for these values should be developed

Statistical Properties of Sampling Distributions of Different Test Statistics for Different Measures of Change and a New Test: Simulation Study

The aim of this study is to analyse the distribution characteristics of four different test statistics, namely the Mean/Standard Error of Mean (Mean/SEmean), Median/Interquartile Range (Median/IQR), Trimmed Mean/Standard Error of Mean (TrMean/SEmean), and Trimmed Mean/Interquartile Range (TrMean/IQR), which can be used to test two measures of change, namely percent change (PC) and modified symmetrised percent change (MSPC). To ensure the selection of suitable test statistics using the two measures of change, the observed type-I errors and powers of the test statistics have been computed. Results demonstrate that the sampling distributions of the four different test statistics by using PC values exhibit skewness. The Mean/SEmean statistic for the MSPC measure exhibits a two-peak value and platykurtic distribution, while the TrMean/SEmean statistic shows a leptokurtic distribution. The Median/IQR test yields robust and powerful results, especially for large sample sizes. This new statistical measure is referred to as the HS test.WOS:00059204480000

Assessment of the dissimilarities of totally 186 countries and regions according to COVID-19 indicators at the end of March 2020

BackgroundThis study is aimed at evaluating the relationship between the number of days elapsed since a country’s first case(s) of coronavirus disease 2019 (COVID-19), the total number of tests conducted, and outbreak indicators such as the total numbers of cases, deaths, and patients who recovered. The study compares COVID-19 indicators among countries and clusters them according to similarities in the indicators. MethodsDescriptive statistics of the indicators were computed and the results were presented in figures and tables. A fuzzy c-means clustering algorithm was used to cluster/group the countries according to the similarities in the total numbers of patients who recovered, deaths, and active cases. ResultsThe highest numbers of COVID-19 cases were found in Gibraltar, Spain, Switzerland, Liechtenstein and Italy were also of that order with about 1500 cases per million population. Spain and Italy had the highest total number of deaths, which were about 140 and 165 per million population, respectively. In Japan, where exposure to the causative virus was longer than in most other countries, the total number of deaths per million population was less than 0.5. According to cluster analysis, the total numbers of deaths, patients who recovered, and active cases were higher in Western countries, especially in central and southern European countries, which had the highest numbers when compared with other countries.ConclusionThere may be various reasons for the differences between the clusters obtained by fuzzy c-means clustering. These include quarantine measures, climatic conditions, economic levels, health policies, and the duration of the fight against the outbrea