scholarly journals Prevalence and Predictors of Willingness and Attitudes towards Organ Donation among Medical and Non-Medical University Students in Karachi

2021 ◽  
Keyword(s):  
Social Media ◽  
Logistic Regression ◽  
Organ Donation ◽  
The Body ◽  
Categorical Variables ◽  
Common Source ◽  
Organ Transplants ◽  
Multiple Logistic Regression ◽  
Significant Information ◽  
Willingness To Donate

Background: Even though organ transplants save thousands of lives globally, there exists a wide mismatch between an increasing demand for organ transplants and limited number of organ donors. The aim of the study was to assess the reasons and inspect differences between donors and non-donors in both students of medical and non-medical universities. Methods: A cross-sectional study was conducted over a period of 6 months (May 2018-March 2021). Sample of 400 participants enrolled from three medical colleges, two business/social sciences institutions and one engineering university of Karachi. Categorical variables were compared using the Chi-squared test. Continuous variables were compared using a student t-test. Variables with a univariate p-value ≤ 0.10 were included in multiple logistic regression models. Adjusted odds ratio (OR) with 95% confidence interval were reported. Results: With a very high response rate 383(96%) for knowledge about organ donation, a subdued response 201(52.3%) for willingness to donate. Most common source of knowledge regarding organ donation was social media 249(65%), followed by university 207(54%), family/friends 184(48%) and television 172(45%). Multiple logistic regression model identified being comfortable discussing organ donation as a positive predictor of willingness to donate (OR: 2.71, p=0.005), and the belief that organ donation disfigures the body as a negative predictor (OR: 0.40, p<0.001). Conclusion: Attitude towards organ donation was not up to the mark among the participants (201/383=52.3%). Better knowledge may ultimately yield into the act of donation. Valuable measures should be taken to teach young people with significant information with the contribution of social media, institutions and families. Keywords: Organ Donation; Donor Card; Organ Transplants; Ethics.

Abstract 395: State-Level CPR Education Legislation Associated With Higher Survival After Out-Of-Hospital Cardiac Arrest in Older Adults

Circulation ◽  
2019 ◽  
Vol 140 (Suppl_2) ◽  
Author(s):  
David G Buckler ◽  
Megan Barnes ◽  
Tyler D Alexander ◽  
Marissa Lang ◽  
Alexis M Zebrowski ◽  
...  
Keyword(s):  
Older Adults ◽  
Logistic Regression ◽  
Cardiac Arrest ◽  
State Level ◽  
Categorical Variables ◽  
Multiple Logistic Regression ◽  
Education Legislation ◽  
Chi Squared ◽  
Patient State ◽  
Hospital Cardiac Arrest

Introduction: State-level legislation requiring CPR education prior to high school graduation (CPR Legislation) is associated with an increased likelihood of community-level CPR training. CPR Legislation has also been shown to be associated with increased bystander CPR. We hypothesized that states with recent CPR Legislation would be associated with higher survival in older adults following out-of-hospital cardiac arrest (OHCA). Methods: Utilizing 2014 Medicare Claims data for emergency department (ED) visits and inpatient stays, we identified OHCA via ICD-9-CM code. CPR Legislation data was collected through online statute review. Exposure to CPR Legislation was assessed using the patient state of residence reported on the first claim. Patient dispositions were coded as home, SNF, death/hospice, rehab or other. All categories were considered survival to discharge except for death/hospice. Associations between categorical variables were assessed by chi-squared test. Multiple logistic regression was used to calculate the odds ratio associated with OHCA survival and CPR Legislation, controlling for patient age and sex. Results: In 2014, 256,277 OHCAs were identified. Mean age was 79 ±8 y, 48% were female, 23% were non-white, and survival to discharge was 22%. Prior to 2013, 4 states had passed CPR Legislation and 6 others passed legislation in 2013. These states account for 12% of OCHA for the study year. States that passed CPR Legislation in 2013 had the highest survival compared to states with earlier passage or no CPR Legislation (22.2% vs 20.6% vs. 21.8%, respectively, p < 0.001). Among those who survived to discharge, more patients were discharged home from states with 2013 CPR Legislation, than earlier or no legislation (50.8% vs. 41.3% vs. 42.8%, p <0.001). Results of the multiple logistic regression showed CPR Legislation passed in 2013 was associated with a 12% increase in the odds of survival to discharge compared to states with CPR Legislation prior to 2013 (OR: 1.12, p <0.001). Conclusion: States with CPR Legislation passed in 2013 were associated with higher survival to discharge and discharge to home, compared to earlier adopters and states with no legislation. Further work is needed to assess the mechanisms underlying this relationship.

scholarly journals Interaction Effects of Religiosity Level on the Relationship between Religion and Willingness to Donate Organs

Religions ◽  
2018 ◽  
Vol 10 (1) ◽  
pp. 8
Author(s):  
Sung Yoon
Keyword(s):  
Organ Donation ◽  
Interaction Effect ◽  
Interaction Effects ◽  
The Body ◽  
Post Mortem ◽  
Willingness To Donate ◽  
High Level ◽  
The Relationship

This study aims to investigate the interaction effect of religiosity level on the relationship between religion and willingness to donate organs. Prior studies have suggested that a high level of religiosity indicates a high level of willingness to donate organs. However, these previous works ignore the interaction effect of the level of religiosity and the doctrinal characteristics of each religion regarding one’s own body preservation. Organ donation is an act of transplanting part of one’s own body after death to another person and is influenced by the viewpoint of the post-mortem world and the attitude toward the preservation of the body. Therefore, this study analyzes the effects of religious characteristics and belief levels on the relationship between religion and organ donation. Results show that Christianity, such as Catholicism and Protestantism, positively affects the willingness to donate organs as compared with Buddhism. Religiosity level also exerts an interaction effect that strengthens the relationship between Christianity and willingness to donate organs.

scholarly journals Analysis of Environmental Factors with Chronic Diarrhea in Toddlers in Jambi City in 2019

2020 ◽  
Vol 2 (4) ◽  
pp. 300-310
Author(s):  
Eko Misriyanto ◽  
Rico J. Sitorus ◽  
Misnaniarti
Keyword(s):  
Logistic Regression ◽  
Diarrheal Disease ◽  
Chronic Diarrhea ◽  
Growth Failure ◽  
The Body ◽  
P Value ◽  
Multiple Logistic Regression ◽  
Chi Square ◽  
Confidence Degree ◽  
The Impact

Chronic diarrhea is defecation with a frequency of 3 or more times in infants and children lasting for 14 days. The impact of diarrheal disease in general causes loss of fluid in the body (dehydration) and chronic diarrhea can cause a child to experience poor nutritional status and experience growth failure. This study uses a case-control design using a retrospective approach. The number of samples in this study was 135 respondents. Instruments for collecting data in the form of questionnaires and observations. Data were analyzed by univariate, bivariate using the Chi-Square test, and multivariate analysis with multiple logistic regression. The statistical test results obtained p-value on the variables of clean water supply (0.007), latrine ownership (0.001), sewerage system (0.04), confidence degree 95% Confidence Interval (95% CI) and p-value ˂ 0, 05, it can be concluded that there is a significant relationship with chronic diarrheal disease in infants. The results of multiple logistic regression tests, on the variable wastewater discharge obtained OR = 3.801, meaning that sewerage is closely related to causing chronic diarrheal disease in infants.

scholarly journals Knowledge, attitudes and willingness to organ donation among the general public: A cross-sectional survey in China

2020 ◽  
Author(s):  
Xiaojing Fan ◽  
Meng Li ◽  
Heike Rolker ◽  
Jiaoyang Du ◽  
Duolao Wang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Organ Donation ◽  
General Public ◽  
Logistic Regression Model ◽  
Central China ◽  
Western China ◽  
Knowledge And Attitudes ◽  
Willingness To Donate ◽  
The Mean

Abstract Background The purpose of this study is to assess the level of knowledge, attitudes, and willingness to organ donation among the general public in China. Methods The study population consisted of 4274 participants from Eastern, Central and Western China. The participants’ knowledge, attitudes and willingness to organ donation were collected by a self-designed questionnaire consisting of 30 items. Knowledge is measured by 10 items and presented as a 10 point score, attitudes is measured by 20 items using a 5-step Likert scale and total score ranged between 0 and 80; while the willingness to donate is assessed as binary variable (0 = No; 1 = Yes). A logistic regression model was used to assess the association of knowledge and attitudes with willingness to organ donation, controlling for demographic and socioeconomic confounders. Results The questionnaire response rate was 94.98%. The mean score (± SD) of the general public’s knowledge to organ donation was 6.84 ± 1.76, and the mean score (± SD) of attitudes to organ donation was 47.01 ± 9.07. The general public’s knowledge and attitudes were the highest in Eastern China, followed by West and Central China. The logistic regression model indicated a positive association between knowledge and the willingness to organ donation (OR = 1.12, 95%CI: 1.08, 1.17; P < 0.001); attitudes were also positively potential determinant of more willingness to organ donation (OR = 1.08, 95%CI: 1.07, 1.09; P < 0.001). Conclusions Knowledge and attitudes were found to be positively associated with the Chinese general public’s willingness to organ donation. Knowledge about the concept of brain death and the transplant procedure may help raise the rate of willingness to organ donation.

scholarly journals Understanding public opinion regarding organ donation in China: A social media content analysis

2021 ◽  
Vol 104 (2) ◽  
pp. 003685042110096
Author(s):  
Xiling Xiong ◽  
Kaisheng Lai ◽  
Wenshi Jiang ◽  
Xuyong Sun ◽  
Jianhui Dong ◽  
...  
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Organ Donation ◽  
Public Information ◽  
Negative Attitude ◽  
Medical Decision ◽  
Media Content Analysis ◽  
Life Saving ◽  
Willingness To Donate ◽  
Media Platform

Organ donation provides a life-saving opportunity for patients with organ failure. China, like most countries, is faced with organ shortages. Understanding public opinion regarding organ donation in China is critical to ensure an increased donation rate. Our study explored public concerns and attitudes toward organ donation, factors involved, and how the public pays attention to organ donation. Sixteen million users’ public information (i.e. gender, age, and geographic information) and posts from January 2017 to December 2017 were collected from Weibo, a social media platform. Of these, 1755 posts related to organ donation were included in the analysis. We categorized the posts and coded the users’ attitudes toward organ donation and the associations between the demographics. The most popular posts mentioning organ donation were “publicly expressing the willingness to donate organs.” Furthermore, 87.62% of posts exhibited a positive attitude toward organ donation, whereas only 7.44% exhibited a negative attitude. Most positive posts were “saluting the organ donors,” and most negative posts involved “fear of the family’s passive medical decision.” There was no significant gender difference in the users’ attitudes, but older people generally had a more negative attitude. Users with negative attitudes mainly distrust the medical system and are worried that the donated organs may be used in improper trading. Social media may be an important channel for promoting organ donation activities, and it is important to popularize scientific knowledge related to organ donation in order to eliminate the public’s misunderstanding of organ donation and transplantation.

scholarly journals Level and Determinants of Willingness to Donate Organs Among the General Public: A Cross-Sectional Survey in China

2020 ◽  
Author(s):  
Xiaojing Fan ◽  
Sirui Zheng ◽  
Meng Li ◽  
Duolao Wang ◽  
Enchang Li ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Organ Donation ◽  
Eastern China ◽  
Organ Transplant ◽  
Central China ◽  
Cross Sectional ◽  
Regression Modelling ◽  
Transplant Patients ◽  
Willingness To Donate ◽  
Logistic Regression Modelling

Abstract Background: This study aims to assess the level and determinants of the general public’s willingness to organ donation. Methods: We conducted a population-based cross-sectional study of 4261 participants in China. The primary outcome was the willingness to donate organs. Logistic regression modelling was used to determine the factors that affect willingness to donate organs. Results: Overall, the proportion of participants who showed a willingness to donate organs was 47.45% (95%CI: 0.46, 0.49) in this study. Logistic regression modelling showed participants from Western (OR=1.35, 95%CI=1.13-1.62) and Eastern China (OR=1.54, 95%CI=1.20-1.66) were more willing to donate organs compared with those from Central China. The odds of being willing to donate organs was higher in females than males (OR=1.34, 95%CI=1.17-1.55); and was higher in those participants with experience of organ donation (OR=1.57, 95%CI=1.12-2.20), experience of caring for organ transplant patients (OR=1.47, 95%CI=1.03-2.10), and those undertaking related voluntary activities (OR=1.68, 95%CI=1.45-1.94), than those without. Conclusion: The general public’s level of willingness to organ donation was not high in China. Geographical region, gender, experience of organ donation related activities, taking care of organ transplant patients and volunteering in related activities were independently associated with participants’ willingness to donate organs.

scholarly journals Level and determinants of willingness to donate organs among the general public: A cross-sectional survey in China

2020 ◽  
Author(s):  
Xiaojing Fan ◽  
Sirui Zheng ◽  
Meng Li ◽  
Duolao Wang ◽  
Enchang Li ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Organ Donation ◽  
Eastern China ◽  
Organ Transplant ◽  
Central China ◽  
Cross Sectional ◽  
Regression Modelling ◽  
Transplant Patients ◽  
Willingness To Donate ◽  
Logistic Regression Modelling

Abstract Background: This study aims to assess the level and determinants of the general public’s willingness to organ donation. Methods: We conducted a population-based cross-sectional study of 4261 participants in China. The primary outcome was the willingness to donate organs. Logistic regression modelling was used to determine the factors that affect willingness to donate organs. Results: Overall, the proportion of participants who showed a willingness to donate organs was 47.45% (95%CI: 0.46, 0.49) in this study. Logistic regression modelling showed participants from Western (OR=1.33, 95%CI=1.11-1.59) and Eastern China (OR=1.40, 95%CI=1.19-1.65) were more willing to donate organs compared with those from Central China. The odds of being willing to donate organs was higher in females than males (OR=1.35, 95%CI=1.17-1.55); and was higher in those participants with experience of organ donation (OR=1.58, 95%CI=1.13-2.21), experience of caring for organ transplant patients (OR=1.45, 95%CI=1.01-2.07), and those undertaking related voluntary activities (OR=1.67, 95%CI=1.45-1.94), than those without. Conclusion: The general public’s level of willingness to organ donation was not high in China. Geographical region, gender, experience of organ donation related activities, taking care of organ transplant patients and volunteering in related activities were independently associated with participants’ willingness to donate organs.

Determining the Most Valuable Clinical Variables : a Stepwise Multiple Logistic Regression Program

1980 ◽  
Vol 19 (01) ◽  
pp. 42-49 ◽  
Author(s):  
B. W. Brown ◽  
C. Engelhard ◽  
J. Haipern ◽  
J. F. Fries ◽  
L. S. Coles
Keyword(s):  
Logistic Regression ◽  
Predictor Variable ◽  
Clinical Problem ◽  
Relevant Information ◽  
Stepwise Logistic Regression ◽  
Multiple Logistic Regression ◽  
Binary Outcome ◽  
Medical Problems ◽  
Valuable Predictor ◽  
Data Banks

In solving a clinical problem of diagnosis, prognosis, or treatment choice, a physician must select from among a large group of possible tests. In general, an ordering exists specifying which tests are most valuable in providing relevant information concerning the problem on hand. The computer program package to be described (MW) extracts appropriate data from the ARAMIS data banks and then analyzes the data by stepwise logistic regression. A binary outcome (diagnosis, prognostic event, or treatment response) is sequentially associated with possible tests, and the most powerful combination of tests is identified. For example, the most valuable predictor variable of early mortality in SLE is proteinuria, followed sequentially by anemia and absence of arthritis. Experience with these techniques suggests : 1. optimal certainty is usually reached after only three or four tests; 2. several different test sequences may lead to the same level of certainty; 3. diagnosis may usually be ascertained with greater certainty than prognosis; 4. many medical problems contain considerable non-reducible uncertainty; 5. a relatively small group of tests are typically found among the most powerful; 6. results are consistent across several patient populations; 7. results are largely independent of the particular statistic employed. These observations suggest strategies for maximizing information while minimizing risk and expense.

Utilizing Twitter Data Analysis and Deep Learning to Identify Drug Use (Preprint)

2019 ◽  
Author(s):  
Joseph Tassone ◽  
Peizhi Yan ◽  
Mackenzie Simpson ◽  
Chetan Mendhe ◽  
Vijay Mago ◽  
...  
Keyword(s):  
Social Media ◽  
Logistic Regression ◽  
Deep Learning ◽  
Decision Tree ◽  
Semantic Meaning ◽  
Predictive Capability ◽  
Logistic Regression Models ◽  
Twitter Data ◽  
Data Points ◽  
Positive Classification

BACKGROUND The collection and examination of social media has become a useful mechanism for studying the mental activity and behavior tendencies of users. OBJECTIVE Through the analysis of a collected set of Twitter data, a model will be developed for predicting positively referenced, drug-related tweets. From this, trends and correlations can be determined. METHODS Twitter social media tweets and attribute data were collected and processed using topic pertaining keywords, such as drug slang and use-conditions (methods of drug consumption). Potential candidates were preprocessed resulting in a dataset 3,696,150 rows. The predictive classification power of multiple methods was compared including regression, decision trees, and CNN-based classifiers. For the latter, a deep learning approach was implemented to screen and analyze the semantic meaning of the tweets. RESULTS The logistic regression and decision tree models utilized 12,142 data points for training and 1041 data points for testing. The results calculated from the logistic regression models respectively displayed an accuracy of 54.56% and 57.44%, and an AUC of 0.58. While an improvement, the decision tree concluded with an accuracy of 63.40% and an AUC of 0.68. All these values implied a low predictive capability with little to no discrimination. Conversely, the CNN-based classifiers presented a heavy improvement, between the two models tested. The first was trained with 2,661 manually labeled samples, while the other included synthetically generated tweets culminating in 12,142 samples. The accuracy scores were 76.35% and 82.31%, with an AUC of 0.90 and 0.91. Using association rule mining in conjunction with the CNN-based classifier showed a high likelihood for keywords such as “smoke”, “cocaine”, and “marijuana” triggering a drug-positive classification. CONCLUSIONS Predictive analysis without a CNN is limited and possibly fruitless. Attribute-based models presented little predictive capability and were not suitable for analyzing this type of data. The semantic meaning of the tweets needed to be utilized, giving the CNN-based classifier an advantage over other solutions. Additionally, commonly mentioned drugs had a level of correspondence with frequently used illicit substances, proving the practical usefulness of this system. Lastly, the synthetically generated set provided increased scores, improving the predictive capability. CLINICALTRIAL None

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