Vol 6 No 3 (2020)
Risk Factors Associated with Lost to Follow up Among Multidrug Resistance Tuberculosis Patients Registered for Treatment in Programmatic Management of Drug Resistant Tuberculosis Sites of Punjab, Pakistan
Introduction: Pakistan, a country with a 27 high burden countries of multidrug resistance tuberculosis. To predict the associated risk factors and proportion of loss to follow up among MDR-TB patients treated at PMDT sites of Punjab from 2017 to 2019.
Methodology: This case control study based on the standardized reporting and recording case record forms called as Electronic Nominal Review System (ENRS) of National TB Control Program, Pakistan. A logistic regression model was used to assess risk factors of lost to follow up MDR-TB patients.
Results: A total of 539 patients with MDR-TB were included in the final analysis. Among them, 207 patients (7.5%) had lost to follow up outcome at the end of the study. MDR-TB lost to follow up patients were more likely to report older age (AOR: 1.40, CI: 1.14-1.71, p=0.000), history of lost to follow up from first line drugs treatment (AOR: 0.39, CI: 0.28-0.56, p=0.000), co-morbid (AOR:1.54, CI: 1.24-1.91, p=0.000), adverse reaction of second line drugs (AOR: 0.45, CI: 0.37-0.56, p=0.000), long distance between patient’s home and PMDT site (AOR: 0.68, CI: 0.52-0.89, p=0.001).
Conclusion: The history of lost to follow up from first line drugs treatment, co-morbid, older age and long distance were independent risk factors of MDR-TB. Proper training for PMDT sites staff, friendly follow up services and psychological counseling may help to reduce lost to follow up.
Using Multinomial Logistic Regression for Modeling Obesity and Overweight Among People in Urban Area of Ardabil City, Ardabil, Iran
Introduction: Overweight and obesity are defined as abnormal or excessive fat accumulation that may impair health and increase the risk of more diseases in future. Body mass index (BMI) is a good method for measure the overweight and obesity and waist to hip ration is a good index for measure the abdominal obesity.
Methods: This cross-sectional study was done on 1316 people who selected randomly from Ardabil city. Demographic data and anthropometric parameters such as age, sex, height, weight, waist circumference and hip circumference were measured by interviewers. Data were analyzed by statistical methods such as t-test, chi-square test, Pearson correlation and multinomial logistic regression model in SPSS version 21.
Results: The mean age of the people was 28.5±7.4 years of them, 63.1% were in age group 20-30 years. The mean height of the samples was 162.7±8.6 cm in range 110-194 and the mean weight of them was 68.9±11.7 kg in range 43-111. The mean BMI of patients was 25.7. According to BMI, 35.6% of all samples had overweight and 18.6% had obesity. According to the WHR, 28.1% of male and 22.1% of female had high WHR (abdominal obesity). The prevalence of abdominal obesity based WHR was 25.2%.
Conclusions: By using Multinomial Logistic Regression we showed that the relation between BMI and Age was positive and significant and by increasing one year at age of people, the rate of overweight increased 13% and the rate of obesity increased 17% in compare with normal patients.
Introduction: Coronavirus disease 2019 (COVID-19), a respiratory disease caused by the coronavirus SARSCoV-2, has had an immense impact on a variety of sectors both worldwide and nationwide. Vast differences are observed among states within the United States of America in terms of COVID-19 cases and deaths.
Objective: The objective of this paper is to present a means through which we can compare deaths between multiple states, using the index date approach applied by Middelburg and Rosendaal.
Materials and Methods: Using the CDC COVID-19 tracker, we created two sets of ten states focusing on states with (1) the highest number of deaths and (2) the highest number of deaths per 100,000. We applied features of the authors’ technique in order to compare deaths between certain states through visualizations. We referred to the cumulative number of deaths on each day from January 21st, 2020 to September 30th, 2020, as a percentage of the cumulative deaths 40 days after the first death.
Results and Discussion: Comparability was established by synchronizing each state to a baseline date, which allows us to adjust for issues that arise from the scales used within a standard cumulative deaths graph, such as a tendency to be driven by the states with the highest cumulative number of deaths. This technique also normalized each state to a standard start date.
Conclusion: This paper shows the application of a technique that allows for clearer comparisons of COVID19 related deaths between states, as opposed to the use of a standard cumulative deaths graphs.
Corona Virus Outbreak in Iran: A Comparison with China, Italy and South Korea in One Month after Infection
Introduction: The coronavirus outbreak has become a serious issue of the entire world. In some ways, the ability to provide outbreak rate prediction is helpful. Therefore the main purpose of this study was to investigate the incidence pattern of Confirmed COVID-19 Cases in Iran, and comparison between countries with high infected person such as China, Italy and South Korea.
Method: In this cross sectional study 126789 infected cases with COVID 19 related countries with highest infection, China, Italy, Iran and South Korea in 30 day timespan was extracted from the cumulative frequency chart at https://www.worldmeters. Info/coronavirus/. The incidence rate pattern was presented.
Results: The findings show the frequency pattern of the infected person’s frequency within 30 days since the first case has observed in Iran are similar. Although incidence rate coronavirus is similar to other countries in during 14 days, but after the 14th day, there is a noticeable difference between the obtained pattern of confirmed cases in Iran and other countries. There is a spectacular difference in the number of patients in Iran and South Korea after the seventh day and between Iran and Italy after the fourteenth day.
Conclusion: The Covid-19 quickly spread across the world and caused varying challenges. Thus the prevention strategies aimed at reducing transmission in the community are a necessity.
Epidemiological Study of Mortality Caused by Road Accidents in the Intensive Care Unit of Besat Educational-Medical Hospital in Hamadan
Introduction: Road accidents are one of the causes of death and it's important to investigate the epidemiological indicators in this context. In this regard, this study aimed to determine the epidemiological causes of road accident mortality in the intensive care unit of Besat Educational-medical Hospital in Hamadan during Nowruz Holidays 2018.
Material and Methods: In this descriptive cross-sectional study, data of the patients died for 13 days in Nowruz Holidays in the intensive care unit of Besat Hospital in Hamadan, by census method, was extracted from the medical records of patients by using a checklist from a researcher-made questionnaire. Data were analyzed by Fisher's exact test and SPSS 21 software.
Results: Of the 28 Patient admitted to the intensive care unit,16 patients died. Most deaths were in the third decade of life, among single men, with a self-employment job, with diploma education level or cycle education level, with more than 10 days of hospitalization and in the night shift. Based on the results, most of the dead persons were rider (81.25%), suffered head trauma (50%), with head surgery (56.25%), without any underlying illness (93.75%) and without successful experience of cardiopulmonary resuscitation (87.5%). There were no statistically significant differences between the variables and mortality rate (P value > 0/05).
Conclusion: Young single men with low level of education are a wide range of road accidents victims. Therefore, it seems it's necessary to reduce mortality from these accidents, health planning such as traffic culture training and driving rules, by using social media, should focus on learning and improving the level of community awareness, especially in this group of people
Validity and Reliability of American College of Healthcare Executive Competencies Assessment Tool for the Managers of Healthcare Networks in Zanjan Province
Background: The importance of using the competent managers in the healthcare system, shows the need to recognize their competencies and having standards to measure competencies. In this regard, the purpose of this article is to determine the validity and reliability of the competency assessment tool of the American College of Health Care Executives for the managers of healthcare networks in Zanjan province.
Methods: This study is a descriptive-analytical study in which data collection was performed using the American College of Health Care Executives Competencies Assessment tool. This tool was provided to 30 healthcare management professors and experts, in Zanjan province. To investigate the validity, internal consistency and repeatability Content Validation methods, Cronbach's Alpha coefficient and Retesting were used respectively. Data were analyzed using Excel 2010 and SPSS 18 software.
Results: The results showed that 235 out of 302 questions related to the American College of Health Care Executives Competencies Assessment tool had low content validity and should be rejected. The content validity index of the final questionnaire was calculated to be 0.84, which is acceptable. The results also showed that the final questionnaire was reliable with α=0.98 and repeatable.
Conclusion: Utilizing a framework to assess the competencies of healthcare network managers can be of benefit in choosing qualified managers. According to the results of this study, the provided tool shows a desirable reliability and a fairly convenient validity to be used in healthcare networks of Zanjan province.
Introduction: Previous studies on the quality of life of strabismus patients have not examined the existence of censoring to express the relation between the response variable and its predictors.
Methods & Materials: The information used in this study is a conducted cross-sectional study in 2012. The sample size is 90 children in the age range (4-18) years and with congenital strabismus. We used the RAND Health Insurance Study questionnaire with ten subscales to evaluate the quality of life, which was increased to 11 dimensions by adding some items related to eye alignment concerns introduced by Archer et al. The demographic profile is also recorded by 13 other questions. We have expressed the relationship between the independent and response variables in each of the 11 dimensions of the questionnaire and the overall quality of life score by fitting the multiple linear regression model. Then we fitted the two models of classic Tobit and CLAD, which are for censoring, to all dimensions of the questionnaire.
Results: We showed that in fitting the models to the overall quality of life scale variable, the best model is the multiple linear regression. Because the response variable was normal, and there was no censoring (ceiling and floor effect). However, in the depression subscale, due to the high censoring (28.89% of the ceiling effect) and the almost normal distribution of the response variable (p-value of skewness< 0.05), the appropriate model according to the criteria is the classic Tobit (AIC = 546.33). That is, the classic Tobit model is the best alternative to the multiple linear regression model in the presence of censoring. But these conditions did not exist in all variables. In the subscale, there was a severe censoring performance constraint (67.78% of the ceiling effect). When censoring is high, the distribution of the response variable becomes very skewed, and the distribution of response variables deviates drastically from normal. The distribution of the performance constraint variable was very skewed (p-value <0.001). Here the RMSE standard scale for the classic Tobit model was 28.74, which is much higher than the standard scale for the multiple linear regression model (14.23). The best model for the high censoring was CLAD.
Conclusion: To use the appropriate statistical method in the analysis, one must look at how the response variable is distributed. The multiple linear regression model is very widely used, but in the presence of censoring, the use of this model gives skewed results. In this case, the classic Tobit model and its derived model, CLAD, are replaced. The nonparametric CLAD model calculates accurate estimates with minimum defaults and censoring.
Introduction: Iran is one of the seven countries in the world with natural disaster distress. Moreover, due to its geopolitical situation and strategic importance, it has always been invaded by neighbors or the developed countries. Medical healthcare of the injured people and distributing medicine in the beginning hours of the disasters and crises have great roles in decreasing pain, fetal injuries and increase in the healing and the survival of the injured. In the present study, to evaluate the defects in drug distribution and necessary health care applications during disasters, the corresponding research in Iran and selected countries have been presented to discuss damages resulted by mismanagements during crises.
Methods: The PubMed, Google Scholar and Iranian databases were searched about crisis management with “management”, “medicine”, ‘drug”, “crisis” and “disaster” as keywords individually and in combination.
Results: The efforts during disasters, the shortcomings and defects were evaluated. The related data in some countries were also reviewed and the viewpoints for drug management in disasters and crises in Iran were presented.
Conclusion: Regarding the damages caused by drug deficiency and the importance of well-organized drug management, it will be possible to save peoples life by efficient drug management and well-planned distribution of medications during natural and man-made disasters.