Search results for: Statistical%20package%20IBM%20SPSS%2020

Authors: Antonio Marco Miotti, Antonella Ruffatto, Giampaola Basso, Antonio Madia, Giulia Zavatta, Emanuela Salvatico, Emanuela Zilli

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A National Action Plan to fight antimicrobial resistance was launched in Italy in 2017. In order to reduce inappropriate exposure to antibiotics and infections from multi-drug resistant bacteria, it is essential to set up a structured system of surveillance and monitoring of the implementation of National Action Plan standards, including antimicrobial consumption, with a special focus on quinolones, third generation cephalosporins and carbapenems. A quantitative estimate of antibiotic consumption (defined daily dose - DDD - consumption per 100 days of hospitalization) has been provided by the Pharmaceutical Service to the Hospital of Cittadella, ULSS 6 Euganea – Health Trust (District of Padua) for the years 2019 (before the pandemic), 2020 and 2021 for all classes of antibiotics. Multidisciplinary meetings have been organized monthly by the local Antimicrobial Stewardship Group. Between 2019 and 2021, an increase in the consumption of carbapenems in the Intensive Care Unit (from 12.2 to 18.2 DDD, + 49.2%) and a decrease in Medical wards (from 5.3 to 2.6 DDD, - 50.9%) was reported; a decrease in the consumption of quinolones in Intensive Care Unit (from 17.2 to 10.8 DDD, - 37.2%), Medical wards (from 10.5 to 6.6 DDD, - 37.1%) and Surgical wards (from 10.2 to 9.3 DDD, - 8.8%) was highlighted; an increase in the consumption of third generation cephalosporins in Medical wards (from 18.1 to 22.6 DDD, + 24,1%) was reported. Finally, after an increase in the consumption of macrolides between 2020 and 2019, in 2021, a decrease was reported in the Intensive Care Unit (DDD: 8.0 in 2019, 18.0 in 2020, 6.4 in 2021) and Medical wards (DDD: 9.0 in 2019, 13.7 in 2020, 10.9 in 2021). Constant monitoring of antimicrobial consumption and timely identifying of warning situations that may need a specific intervention are the cornerstone of Antimicrobial Stewardship programs, together with analysing data on bacterial resistance rates and infections from multi-drug resistant bacteria.

Keywords: carbapenems, quinolones, antimicrobial, stewardship

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Authors: Shishen Xie, Yingda L. Xie

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Tuberculosis (TB) is a potentially serious infectious disease that remains a health concern. The Interferon Gamma Release Assay (IGRA) is a blood test to find out if an individual is tuberculous positive or negative. This study applies statistical analysis to the clinical data of interferon-gamma levels of seventy-three subjects who diagnosed pulmonary TB in an anti-tuberculous treatment. Data analysis is performed to determine if there is a significant decline in interferon-gamma levels for the subjects during a period of six months, and to infer if the anti-tuberculous treatment is effective.

Keywords: data analysis, interferon gamma release assay, statistical methods, tuberculosis infection

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Authors: Mohammed E. Ibrahim, Baraa A. Taha, Kamil M. A. Shabban

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Background: Epilepsy is a common but widely misunderstood illness. Consequently, patients with epilepsy suffer from considerable stigmatization in society. This social stigma and discrimination often cause more suffering for the patients than the disease itself. Since very few studies have explored the misperceptions about epilepsy among university students in Sudan, it is not possible to provide focused intervention aimed at eliminating this discrimination. Methods: A cross-sectional study was applied among the first-year medical students at the University of Khartoum between December (2020) and February (2021). A 29-item standardized questionnaire was self-administered by 198 students (out of 320) who agreed to participate in this study. Google form was the tool used to collect the data. The data were analyzed using the Statistical Package for Social Science software version 26. Result: Overall, the results indicate a negative trend in knowledge and attitude toward epilepsy. The vast majority of the respondents (84.8%) have read or heard about epilepsy, while 43.9% had seen someone with epilepsy. Only 7.5% of the participants reported that epilepsy is contagious, whereas 43.4% of them think that epilepsy is a psychological disorder. About 62.2% of students think head/birth trauma is a cause of epilepsy. On the other side, about 15.7% and 5.1% believed that evil spirits and punishment from god can also be a possible cause of epilepsy; we found these false beliefs are more common in participants from rural areas (p-value < 0.05). In regard to attitude, 19.7% of students thought that it is inappropriate for a patient with epilepsy to have a child. This attitude correlates with the mother’s education as the percentage is higher for those who have lower mother’s education (through secondary school education and below) (p < 0.05). The majority of Our participant knew that some people with epilepsy need life-long drug treatment; this belief was found to be more common in females than their counterparts(p < 0.05). . Finally, most of the respondents (93.9%) thought that a child with epilepsy Can be successful in a normal class. This belief is four-time as common in participants whose mothers have higher education (through university education and above) compared with corresponding respondents (p < 0.05). Conclusion: This study concludes that students' knowledge about epilepsy is limited and requires immediate intervention through educational campaigns to develop a well-informed and tolerant community.

Keywords: epilepsy, awareness, attitude, university students, Sudan

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Authors: Kumaressan Ragunathan, Arisudhan Anantharachagan

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Gestational diabetes mellitus (GDM) is the subtype of diabetes that has been rapidly increasing in numbers in Australia. The annual percentage of GDM has increased more than 50 percent in the last decade. According to Diabetes Australia, more than five hundred thousand women in Australia will be diagnosed with GDM. Globally, the prevalence of GDM ranges from single-digit to more than 45%. The prevalence of GDM has increased significantly last five years after the introduction of new diagnostic criteria. Hence, we have decided to investigate the trend in GDM prevalence in a tertiary maternity unit at Western Australia and compare it to national prevalence. Data is derived from STORK Perinatal Database which has been used by Maternity services in Western Australia to populate information on pregnancy and labour. We have selected data from 2015 until 2020, which includes 17508 women. Among 17508 women, 3850 women were diagnosed with GDM. In 2015, we had a total of 2213 deliveries with 345 of them were complicated by GDM. GDM prevalence was 15.6% compared to the Australian national prevalence of 12%. In 2016, total deliveries increased to 2759 with 590 of were with GDM. GDM prevalence was 21.4% compared to the Australian national prevalence of 12%. In 2017, total deliveries further increased to 3049 with 675 with GDM. GDM prevalence was 22.1%, with an Australian national prevalence of 13%. In 2018, total deliveries continued to increase, with numbers reaching 3231 with 749 with GDM. GDM prevalence was 23.2%, with an Australian National prevalence of 14%. In 2019, total deliveries were 3110, with 712 complicated by GDM. GDM prevalence was 22.9%, with Australian national prevalence 14%. In 2020, total deliveries 3146 with 819 complicated by GDM. GDM prevalence increased to 26% and we were unable to compare this to national standard as national prevalence has not been released. Among 3890 women with GDM, 2482 (64%) of them required insulin. Apart from that, a total 1642(42%) from the GDM group were delivered via the Caesarean section. 2121 (55%) women with GDM required induction of labour. Overall, we demonstrated an increase in the prevalence of GDM in our unit from 2015 until 2020. Our prevalence is also higher compared to national prevalence. This could be contributed by the increasing number of obesity and in addition, our unit accepts referrals of women with a body mass index (BMI) of more than 40. Hence, further studies are required to look at other risk factors like ethnicity, socio-economic status, health literacy and age, which could contribute to this high prevalence.

Keywords: gestational diabetes mellitus, prevalence, Western Australia, Australia

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Authors: Jirawan Jitthavech, Vichit Lorchirachoonkul

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A statistical procedure using multiple comparisons test for proportions is proposed for variable selection in a data envelopment analysis (DEA) model. The test statistic in the multiple comparisons is the proportion of efficient decision making units (DMUs) in a DEA model. Three methods of multiple comparisons test for proportions: multiple Z tests with Bonferroni correction, multiple tests in 2Xc crosstabulation and the Marascuilo procedure, are used in the proposed statistical procedure of iteratively eliminating the variables in a backward manner. Two simulation populations of moderately and lowly correlated variables are used to compare the results of the statistical procedure using three methods of multiple comparisons test for proportions with the hypothesis testing of the efficiency contribution measure. From the simulation results, it can be concluded that the proposed statistical procedure using multiple Z tests for proportions with Bonferroni correction clearly outperforms the proposed statistical procedure using the remaining two methods of multiple comparisons and the hypothesis testing of the efficiency contribution measure.

Keywords: Bonferroni correction, efficient DMUs, Marascuilo procedure, Pastor et al. method, 2xc crosstabulation

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Authors: Gülcan Taşkıran, Ayla Bayık Temel

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Background: Florence Nightingale, the modern founder of the nursing, is most famous for her role as a nurse. But not so much known about her contributions as a mathematician and statistician. Aim: In this conceptual article it is aimed to examine Florence Nightingale's statistics education, how she used her passion for statistics and applied statistical data in nursing care and her scientific contributions to statistical science. Design: Literature review method was used in the study. The databases of Istanbul University Library Search Engine, Turkish Medical Directory, Thesis Scanning Center of Higher Education Council, PubMed, Google Scholar, EBSCO Host, Web of Science were scanned to reach the studies. The keywords 'statistics' and 'Florence Nightingale' have been used in Turkish and English while being screened. As a result of the screening, totally 41 studies were examined from the national and international literature. Results: Florence Nightingale has interested in mathematics and statistics at her early ages and has received various training in these subjects. Lessons learned by Nightingale in a cultured family environment, her talent in mathematics and numbers, and her religious beliefs played a crucial role in the direction of the statistics. She was influenced by Quetelet's ideas in the formation of the statistical philosophy and received support from William Farr in her statistical studies. During the Crimean War, she applied statistical knowledge to nursing care, developed many statistical methods and graphics, so that she made revolutionary reforms in the health field. Conclusions: Nightingale's interest in statistics, her broad vision, the statistical ideas fused with religious beliefs, the innovative graphics she has developed and the extraordinary statistical projects that she carried out has been influential on the basis of her professional achievements. Florence Nightingale has also become a model for women in statistics. Today, using and teaching of statistics and research in nursing care practices and education programs continues with the light she gave.

Keywords: Crimean war, Florence Nightingale, nursing, statistics

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Authors: S. Bangphan, P. Bangphan, T.Boonkang

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Quality control helps industries in improvements of its product quality and productivity. Statistical Process Control (SPC) is one of the tools to control the quality of products that turning practice in bringing a department of industrial engineering process under control. In this research, the process control of a turning manufactured at workshops machines. The varying measurements have been recorded for a number of samples of a rice polished cylinder obtained from a number of trials with the turning practice. SPC technique has been adopted by the process is finally brought under control and process capability is improved.

Keywords: rice polished cylinder, statistical process control, control charts, process capability

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Authors: Ramin Rostamkhani, Thurasamy Ramayah

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One of the most important topics of today’s industrial organizations is the challenging issue of supply chain management. In this field, scientists and researchers have published numerous practical articles and models, especially in the last decade. In this research, to our best knowledge, the discussion of data modeling of supply chain management components using well-known statistical distribution functions has been considered. The world of science owns mathematics, and showing the behavior of supply chain data based on the characteristics of statistical distribution functions is innovative research that has not been published anywhere until the moment of doing this research. In an analytical process, describing different aspects of functions including probability density, cumulative distribution, reliability, and failure function can reach the suitable statistical distribution function for each of the components of the supply chain management. It can be applied to predict the behavior data of the relevant component in the future. Providing a model to adapt the best statistical distribution function in the supply chain management components will be a big revolution in the field of the behavior of the supply chain management elements in today's industrial organizations. Demonstrating the final results of the proposed model by introducing the process capability indices before and after implementing it alongside verifying the approach through the relevant assessment as an acceptable verification is a final step. The introduced approach can save the required time and cost to achieve the organizational goals. Moreover, it can increase added value in the organization.

Keywords: analyzing, process capability indices, statistical distribution functions, supply chain management components

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Authors: Innocent C. Abazie, Odukoya A. Olatunde, Ochuko Orakpoghenor, Bamidele J. Kolawole, Hannah L. Kolawole

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This study was carried out to obtain five-year retrospective data on dog bite cases and antirabies vaccination of dogs in relation to rabies control in Aba, Abia state, Nigeria. Data for retrospective studies (2016 - 2020) was collected from both government and private veterinary clinics in Aba. A total of 66 dog bite cases were reported between January 2016 and December 2020 with a majority of victims being adults (51.5%) within the ages of 18 – 50 years. On the prevalence of dog bite cases according to the gender of victims, males (75.8%) were mostly involved compared to female victims (24.2%). Anatomically, the majority of bite wounds (65.2%) were on the legs. The monthly distribution of bites indicates a higher frequency in the months of January and March (dry season). Local 28 (43.8%) and exotic breeds 30(46.8%) of dogs were mostly involved in the dog bite cases. The majority of the dogs (92.2%) involved in the bite cases were known dogs whose owners could be contacted, while a very few proportion (7.8%) were stray dogs. Results of this study indicated that only 16 (25%) of the offending dogs had a current antirabies vaccination record, 8 (12.5%) had an unknown vaccination record, while the largest proportion, 40 (62.5%) of the offending dogs had no current vaccination record. Public awareness of the danger of dog bites in relation to rabies and increased antirabies vaccination of dogs as a means of controlling the disease is recommended.

Keywords: Aba, dog bite, rabies, vaccination profile

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Authors: Amanda Julia Binay, Patricia Elise Suarez

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Female representation in media is an important subject in the discussion of gender equality, especially in impactful and influential media like film. As the Filipino film industry continues to grow and evolve, the need for analysis on Filipino female representation on screen is imperative. Additionally, there has been limited research made on female representation in the Philippine film scene. Thus, the paper aims to analyze the presence and evolution of female representation in Philippine cinema and compare the findings with that of American films to see how Filipino filmmakers hold their own against the standards of international movements that call for more and better female representation, especially in Hollywood. The participants selected were Filipino and American films released within the years 1975 to 2020 in five (5) year intervals. Twenty (20) critically acclaimed and highest-grossing Filipino films and twenty (20) critically acclaimed and highest-grossing Hollywood films were then subject to the Bechdel and Peirce tests to obtain statistical measures of their female representation. The findings of the study reveal that the presence of female representation in Philippine film history has been consistent and has continued to grow and evolve throughout the years, with strong female leads with vibrant characteristics and diverse stories. However, analysis of female representation regarding American films has shown an extreme lack thereof with more misogynistic, sexist, and limiting ideals. Thus, the study concludes that the state of female representation in Philippine cinema and film industry holds its own when compared to American cinema and film industry and even outperforms it in many aspects of female representation, such as consistent inclusion and depiction of multi-dimensional female leads and female relationships. Hence, the study implies that women’s consistent presence in Philippine cinema mirrors Filipino women’s prominent role in Philippine society and that American cinema must continue to make efforts to change their portrayals of female characters, leads, and relationships to make them more grounded in reality.

Keywords: female representation, gender studies, feminism, philippine cinema, American cinema, bechdel test, peirce test, comparative analysis

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Authors: Acumsiri Tantikarnpanit

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This paper aims to examine the relationship between urbanization and income inequality in Thailand during the period 2002–2020. Using a panel of data for 76 provinces collected from Thailand’s National Statistical Office (Labor Force Survey: LFS), as well as geospatial data from the U.S. Air Force Defense Meteorological Satellite Program (DMSP) and the Visible Infrared Imaging Radiometer Suite Day/Night band (VIIRS-DNB) satellite for nineteen selected years. This paper employs two different definitions to identify urban areas: 1) Urban areas defined by Thailand's National Statistical Office (Labor Force Survey: LFS), and 2) Urban areas estimated using nighttime light data from the DMSP and VIIRS-DNB satellite. The second method includes two sub-categories: 2.1) Determining urban areas by calculating nighttime light density with a population density of 300 people per square kilometer, and 2.2) Calculating urban areas based on nighttime light density corresponding to a population density of 1,500 people per square kilometer. The empirical analysis based on Ordinary Least Squares (OLS), fixed effects, and random effects models reveals a consistent U-shaped relationship between income inequality and urbanization. The findings from the econometric analysis demonstrate that urbanization or population density has a significant and negative impact on income inequality. Moreover, the square of urbanization shows a statistically significant positive impact on income inequality. Additionally, there is a negative association between logarithmically transformed income and income inequality. This paper also proposes the inclusion of satellite imagery, geospatial data, and spatial econometric techniques in future studies to conduct quantitative analysis of spatial relationships.

Keywords: income inequality, nighttime light, population density, Thailand, urbanization

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Authors: Nadine Ayasha

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Hail happened in Sukabumi (August 23, 2020), Sekadau (August 22, 2020), and Bogor (September 23, 2020), where this extreme weather phenomenon occurred in the dry season. This study uses the ERA5 reanalysis model data, it aims to examine the vertical velocity impact on the hail occurrence in the dry season, as well as its relation to other weather parameters such as relative humidity, streamline, and wind velocity. Moreover, HCAI product satellite data is used as supporting data for the convective cloud development analysis. Based on the results of graphs, contours, and Hovmoller vertical cut from ERA5 modeling, the vertical velocity values in the 925 Mb-300 Mb layer in Sukabumi, Sekadau, and Bogor before the hail event ranged between -1.2-(-0.2), -1.5-(-0.2), -1-0 Pa/s. A negative value indicates that there is an upward motion from the air mass that trigger the convective cloud growth, which produces hail. It is evidenced by the presence of Cumulonimbus cloud on HCAI product when the hail falls. Therefore, the vertical velocity has significant effect on the hail event. In addition, the relative humidity in the 850-700 Mb layer is quite wet, which ranges from 80-90%. Meanwhile, the streamline and wind velocity in the three regions show the convergence with slowing wind velocity ranging from 2-4 knots. These results show that the upward motion of the vertical velocity is enough to form the wet atmospheric humidity and form a convergence for the growth of the convective cloud, which produce hail in the dry season.

Keywords: hail, extreme weather, vertical velocity, relative humidity, streamline

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Authors: Dipak Subedi, Anil Giri, Christine Whitt, Tia McDonald

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This study aims to understand the impact of the pandemic on the overall economic well-being of the agricultural sector of the United States. The two key metrics used to examine the economic well-being are the bankruptcy rate of the U.S. farm operations and the operating profit margin. One of the primary reasons for farm operations (in the U.S.) to file for bankruptcy is continuous negative profit or a significant decrease in profit. The pandemic caused significant supply and demand shocks in the domestic market. Furthermore, the ongoing trade disruptions, especially with China, also impacted the prices of agricultural commodities. The significantly reduced demand for ethanol and closure of meat processing plants affected both livestock and crop producers. This study uses data from courts to examine the bankruptcy rate over time of U.S. farm operations. Preliminary results suggest there wasn’t an increase in farm operations filing for bankruptcy in 2020. This was most likely because of record high Government payments to producers in 2020. The Federal Government made direct payments of more than $45 billion in 2020. One commonly used economic metric to measure farm profitability is the operating profit margin (OPM). Operating profit margin measures profitability as a share of the total value of production and government payments. The Economic Research Service of the United States Department of Agriculture defines a farm operation to be in a) a high-risk zone if the OPM is less than 10 percent and b) a low-risk zone if the OPM is higher than 25 percent. For this study, OPM was calculated for small, medium, and large-scale farm operations using the data from the Agriculture Resource Management Survey (OPM). Results show that except for small family farms, the share of farms in high-risk zone decreased in 2020 compared to the most recent non-pandemic year, 2019. This was most likely due to higher commodity prices at the end of 2020 and record-high government payments. Further investigation suggests a lower share of smaller farm operations receiving lower average government payments resulting in a large share (over 70 percent) being in the critical zone. This study should be of interest to multiple stakeholders, including policymakers across the globe, as it shows the resilience of the U.S. agricultural system as well as (some) impact of government payments.

Keywords: U.S. farm sector, COVID-19, operating profit margin, farm bankruptcy, ag finance, government payments to the farm sector

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Authors: Sheikh Omar Sillah

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Introduction: Monitoring is an important means of ensuring the smooth implementation and quality of clinical trials. For many years, traditional site monitoring approaches have been critical in detecting data errors but not optimal in identifying fabricated and implanted data as well as non-random data distributions that may significantly invalidate study results. The objective of this paper was to provide recommendations based on best statistical monitoring practices for detecting data-integrity issues suggestive of fabrication and implantation early in the study conduct to allow implementation of meaningful corrective and preventive actions. Methodology: Electronic bibliographic databases (Medline, Embase, PubMed, Scopus, and Web of Science) were used for the literature search, and both qualitative and quantitative studies were sought. Search results were uploaded into Eppi-Reviewer Software, and only publications written in the English language from 2012 were included in the review. Gray literature not considered to present reproducible methods was excluded. Results: A total of 18 peer-reviewed publications were included in the review. The publications demonstrated that traditional site monitoring techniques are not efficient in detecting data anomalies. By specifying project-specific parameters such as laboratory reference range values, visit schedules, etc., with appropriate interactive data monitoring, statistical monitoring can offer early signals of data anomalies to study teams. The review further revealed that statistical monitoring is useful to identify unusual data patterns that might be revealing issues that could impact data integrity or may potentially impact study participants' safety. However, subjective measures may not be good candidates for statistical monitoring. Conclusion: The statistical monitoring approach requires a combination of education, training, and experience sufficient to implement its principles in detecting data anomalies for the statistical aspects of a clinical trial.

Keywords: statistical monitoring, data anomalies, clinical trials, traditional monitoring

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Authors: Johnson C. Y. Pang, Amy S. N. Fu, Ryan K. L. Lee, Allan C. L. Fu

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Dry needling (DN) is one of the puncturing methods that involves the insertion of needles into the tender spots of the human body without the injection of any substance. DN has long been used to treat the patient with knee pain caused by knee osteoarthritis (KOA) and patellofemoral pain syndrome (PFPS), but the effectiveness is still inconsistent. This study aimed to conduct a systematic review and meta-analysis to assess the intervention methods and effects of DN with and without ultrasound guidance for treating pain and dysfunctions in people with KOA and PFPS. Design: This systematic review adhered to the PRISMA reporting guidelines. The registration number of the study protocol published in the PROSPERO database was CRD42021221419. Six electronic databases were searched manually through CINAHL Complete (1976-2020), Cochrane Library (1996-2020), EMBASE (1947-2020), Medline (1946-2020), PubMed (1966-2020), and Psychinfo (1806-2020) in November 2020. Randomized controlled trials (RCTs) and controlled clinical trials were included to examine the effects of DN on knee pain, including KOA and PFPS. The key concepts included were: DN, acupuncture, ultrasound guidance, KOA, and PFPS. Risk of bias assessment and qualitative analysis were conducted by two independent reviewers using the PEDro score. Results: Fourteen articles met the inclusion criteria, and eight of them were high-quality papers in accordance with the PEDro score. There were variations in the techniques of DN. These included the direction, depth of insertion, number of needles, duration of stay, needle manipulation, and the number of treatment sessions. Meta-analysis was conducted on eight articles. DN group showed positive short-term effects (from immediate after DN to less than 3 months) on pain reduction for both KOA and PFPS with the overall standardized mean difference (SMD) of -1.549 (95% CI=-0.588 to -2.511); with great heterogeneity (P=0.002, I²=96.3%). In subgroup analysis, DN demonstrated significant effects in pain reduction on PFPS (p < 0.001) that could not be found in subjects with KOA (P=0.302). At 3-month post-intervention, DN also induced significant pain reduction in both subjects with KOA and PFPS with an overall SMD of -0.916 (95% CI=-0.133 to -1.699, and great heterogeneity (P=0.022, I²=95.63%). Besides, DN induced significant short-term improvement in function with the overall SMD=6.069; 95% CI=8.595 to 3.544; with great heterogeneity (P<0.001, I²=98.56%) when analyzed was conducted on both KOA and PFPS groups. In subgroup analysis, only PFPS showed a positive result with SMD=6.089, P<0.001; while KOA showed statistically insignificant with P=0.198 in short-term effect. Similarly, at 3-month post-intervention, significant improvement in function after DN was found when the analysis was conducted in both groups with the overall SMD=5.840; 95% CI=9.252 to 2.428; with great heterogeneity (P<0.001, I²=99.1%), but only PFPS showed significant improvement in sub-group analysis (P=0.002, I²=99.1%). Conclusions: The application of DN in KOA and PFPS patients varies among practitioners. DN is effective in reducing pain and dysfunction at short-term and 3-month post-intervention in individuals with PFPS. To our best knowledge, no study has reported the effects of DN with ultrasound guidance on KOA and PFPS. The longer-term effects of DN on KOA and PFPS are waiting for further study.

Keywords: dry needling, knee osteoarthritis, patellofemoral pain syndrome, ultrasound guidance

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Authors: Zamri Abu Bakar Zamri, Normaly Kamal Ismail Normaly, Mohd Izani Mohamed Rawi Izani

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Malay noun compound are phrases that consist of two or more nouns. The key characteristic behind noun compounds lies on its frequent occurrences within the text. Therefore, extracting these noun compounds is essential for several domains of research such as Information Retrieval, Sentiment Analysis and Question Answering. Many research efforts have been proposed in terms of extracting Malay noun compounds using linguistic and statistical approaches. Most of the existing methods have concentrated on the extraction of bi-gram noun+noun compound. However, extracting noun+verb, noun+adjective and noun+prepositional is challenging due to the difficulty of selecting an appropriate method with effective results. Thus, there is still room for improvement in terms of enhancing the effectiveness of compound word extraction. Therefore, this study proposed a combination of linguistic approach and statistical measures in order to enhance the extraction of compound words. Several preprocessing steps are involved including normalization, tokenization, and stemming. The linguistic approach that has been used in this study is Part-of-Speech (POS) tagging. In addition, a new linguistic pattern for named entities has been utilized using a list of Malays named entities in order to enhance the linguistic approach in terms of noun compound recognition. The proposed statistical measures consists of NC-value, NTC-value and NLC value.

Keywords: Compound Word, Noun Compound, Linguistic Approach, Statistical Approach

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Authors: Melissa Vang, Raheyma Khan, Haihua Chen

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The coronavirus disease 2019 (COVID‐19) pandemic has changed people's lives from all over the world. More people have turned to Twitter to engage online and discuss the COVID-19 vaccine. This study aims to present a text mining approach to identify people's attitudes towards the COVID-19 vaccine on Twitter. To achieve this purpose, we collected 54,268 COVID-19 vaccine tweets from September 01, 2020, to November 01, 2020, then the BERT model is used for the sentiment and topic analysis. The results show that people had more negative than positive attitudes about the vaccine, and countries with an increasing number of confirmed cases had a higher percentage of negative attitudes. Additionally, the topics discussed in positive and negative tweets are different. The tweet datasets can be helpful to information professionals to inform the public about vaccine-related informational resources. Our findings may have implications for understanding people's cognitions and feelings about the vaccine.

Keywords: BERT, COVID-19 vaccine, sentiment analysis, topic modeling

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Authors: Dmitriy Syssoyev, Aslan Seitkamzin, Natalya Lim, Kamilla Mussina, Abduzhappar Gaipov, Dimitri Poddighe, Dinara Galiyeva

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Background: Data on the epidemiology of congenital heart defects (CHD) in Kazakhstan is scarce. Therefore, the aim of this study was to describe the incidence, prevalence and all-cause mortality of patients with CHD in Kazakhstan, using national large-scale registry data from the Unified National Electronic Healthcare System (UNEHS) for the period of 2014-2020. Methods: In this retrospective cohort study, the included data pertained to all patients diagnosed with CHD in Kazakhstan and registered in UNEHS between January 2014 and December 2020. CHD was defined based on International Classification of Diseases 10th Revision (ICD-10) codes Q20-Q26. Incidence, prevalence, and all-cause mortality rates were calculated per 100,000 population. Survival analysis was performed using Cox proportional hazards regression modeling and the Kaplan-Meier method. Results: In total, 66,512 patients were identified. Among them, 59,534 (89.5%) were diagnosed with a single CHD, while 6,978 (10.5%) had more than two CHDs. The median age at diagnosis was 0.08 years (interquartile range (IQR) 0.01 – 0.66) for people with multiple CHD types and 0.39 years (IQR 0.04 – 8.38) for those with a single CHD type. The most common CHD types were atrial septal defect (ASD) and ventricular septal defect (VSD), accounting for 25.8% and 21.2% of single CHD cases, respectively. The most common multiple types of CHD were ASD with VSD (23.4%), ASD with patent ductus arteriosus (PDA) (19.5%), and VSD with PDA (17.7%). The incidence rate of CHD decreased from 64.6 to 47.1 cases per 100,000 population among men and from 68.7 to 42.4 among women. The prevalence rose from 66.1 to 334.1 cases per 100,000 population among men and from 70.8 to 328.7 among women. Mortality rates showed a slight increase from 3.5 to 4.7 deaths per 100,000 in men and from 2.9 to 3.7 in women. Median follow-up was 5.21 years (IQR 2.47 – 11.69). Male sex (HR 1.60, 95% CI 1.45 - 1.77), having multiple CHDs (HR 2.45, 95% CI 2.01 - 2.97), and living in a rural area (HR 1.32, 95% CI 1.19 - 1.47) were associated with a higher risk of all-cause mortality. Conclusion: The incidence of CHD in Kazakhstan has shown a moderate decrease between 2014 and 2020, while prevalence and mortality have increased. Male sex, multiple CHD types, and rural residence were significantly associated with a higher risk of all-cause mortality.

Keywords: congenital heart defects (CHD), epidemiology, incidence, Kazakhstan, mortality, prevalence

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Authors: Gleda Kutrolli, Maksi Kutrolli, Etjon Meco

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SARS-CoV-2 virus is currently one of the most infectious pathogens for humans. It started in China at the end of 2019 and now it is spread in all over the world. The origin and diffusion of the SARS-CoV-2 epidemic, is analysed based on the discussion of viral phylogeny theory. With the aim of understanding the spread of infection in the affected countries, it is crucial to modelize the spread of the virus and simulate its activity. In this paper, the prediction of coronavirus outbreak is done by using SIR model without vital dynamics, applying different numerical technique solving ordinary differential equations (ODEs). We find out that ABM and MRT methods perform better than other techniques and that the activity of the virus will decrease in April but it never cease (for some time the activity will remain low) and the next cycle will start in the middle July 2020 for Norway and Denmark, and October 2020 for Sweden, and September for Finland.

Keywords: forecasting, ordinary differential equations, SARS-COV-2 epidemic, SIR model

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Authors: Miguel Cañon, Darwin Mena, Ivan Cabeza

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In this work was compared and evaluated the applicability of statistical methods for the estimation of missing precipitations data in the basin of the river Lenguazaque located in the departments of Cundinamarca and Boyacá, Colombia. The methods used were the method of simple linear regression, distance rate, local averages, mean rates, correlation with nearly stations and multiple regression method. The analysis used to determine the effectiveness of the methods is performed by using three statistical tools, the correlation coefficient (r2), standard error of estimation and the test of agreement of Bland and Altmant. The analysis was performed using real rainfall values removed randomly in each of the seasons and then estimated using the methodologies mentioned to complete the missing data values. So it was determined that the methods with the highest performance and accuracy in the estimation of data according to conditions that were counted are the method of multiple regressions with three nearby stations and a random application scheme supported in the precipitation behavior of related data sets.

Keywords: statistical comparison, precipitation data, river subbasin, Bland and Altmant

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Authors: Ashwani Kumar

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GPS plays an important role in everyday life for safe and convenient transportation. While pedestrians use hand held devices to know their position in a city, vehicles in intelligent transport systems use relatively sophisticated GPS receivers for estimating their current position. However, in urban areas where the GPS satellites are occluded by tall buildings, trees and reflections of GPS signals from nearby vehicles, GPS position estimation becomes poor. In this work, an exhaustive GPS data is collected at a single point in urban area under different times of day and under dynamic environmental conditions. The data is analyzed and statistical refinement methods are used to obtain optimal position estimate among all the measured positions. The results obtained are compared with publically available datasets and obtained position estimation refinement results are promising.

Keywords: global positioning system, statistical approach, intelligent transport systems, least squares estimation

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Authors: Messis A., Adjebli A., Ayeche R., Talbi M., Tighilet K., Louardiane M.

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Cancers are the second cause of death worldwide. Prevalence and incidence of cancers is getting increased by aging and population growth. This study aims to predict and modeling the evolution of breast, Colorectal, Lung, Bladder and Prostate cancers over the period of 2014-2019. In this study, data were analyzed using time series analysis with double exponential smoothing method to forecast the future pattern. To describe and fit the appropriate models, Minitab statistical software version 17 was used. Between 2014 and 2019, the overall trend in the raw number of new cancer cases registered has been increasing over time; the change in observations over time has been increasing. Our forecast model is validated since we have good prediction for the period 2020 and data not available for 2021 and 2022. Time series analysis showed that the double exponential smoothing is an efficient tool to model the future data on the raw number of new cancer cases.

Keywords: cancer, time series, prediction, double exponential smoothing

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Authors: Michele Romanello

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COVID-19 has spread rapidly in Brazil since March 2020, making the country one of the most affected in the world by the pandemic. From an economic point of view, Brazil came from a pre-pandemic period characterized by low or negative growth, with a resulting increase in the number of unemployed and informal workers. This paper considers lockdown implementation in the situation of the large presence of informality in the economy. The objective of the paper is to analyze how the country has tried to help workers affected by economic crisis after the implementation of measures against COVID-19 and whether the emergency assistance from the government has been adequate to contain the increase of informal workers and unemployed. The methodology used in this paper is survival analysis. Through this methodology, the formality – informality, and informality – unemployment transitions are analyzed. This analysis draws data from the Continuous National Household Sample Survey (Continuous PNAD) and from the National Household Sample Survey COVID-19 (PNAD COVID-19) covering the period of January 2020 – July 2020. The results indicate that emergency aid has been not sufficient to reduce the transitions of workers from formal to informal jobs and from informal jobs to unemployment. Emergency aid has been not sufficient considering the previous situation of the country, with levels of poverty and inequality very high. In the next months, another fundamental determinant of the income trajectory in the context of the COVID-19 crisis will be the continuity of the emergency aid, especially considering the fiscal adjustment policy pursued by the government. Therefore, the current negative portrait may be even worse in the coming months.

Keywords: Brazil, COVID-19, informality, survival analysis

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Authors: Xiaoqin Wang, Li Yin

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Background and purpose: In many practices, one estimates causal effects arising from a complex stochastic process, where a sequence of treatments are assigned to influence a certain outcome of interest, and there exist time-dependent covariates between treatments. When covariates are plentiful and/or continuous, statistical modeling is needed to reduce the huge dimensionality of the problem and allow for the estimation of causal effects. Recently, Wang and Yin (Annals of statistics, 2020) derived a new general formula, which expresses these causal effects in terms of the point effects of treatments in single-point causal inference. As a result, it is possible to conduct the modeling via point effects. The purpose of the work is to study the modeling of these causal effects via point effects. Challenges and solutions: The time-dependent covariates often have influences from earlier treatments as well as on subsequent treatments. Consequently, the standard parameters – i.e., the mean of the outcome given all treatments and covariates-- are essentially all different (null paradox). Furthermore, the dimension of the parameters is huge (curse of dimensionality). Therefore, it can be difficult to conduct the modeling in terms of standard parameters. Instead of standard parameters, we have use point effects of treatments to develop likelihood-based parametric approach to the modeling of these causal effects and are able to model the causal effects of a sequence of treatments by modeling a small number of point effects of individual treatment Achievements: We are able to conduct the modeling of the causal effects from a sequence of treatments in the familiar framework of single-point causal inference. The simulation shows that our method achieves not only an unbiased estimate for the causal effect but also the nominal level of type I error and a low level of type II error for the hypothesis testing. We have applied this method to a longitudinal study of COVID-19 mortality among Scandinavian countries and found that the Swedish approach performed far worse than the other countries' approach for COVID-19 mortality and the poor performance was largely due to its early measure during the initial period of the pandemic.

Keywords: causal effect, point effect, statistical modelling, sequential causal inference

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Authors: Shahrzad Maghsoodi, Hamid Reza Ranazi

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This paper is devoted to exploration of Chichak (hematite) deposit, using electrical resistivity, chargeability and statistical methods. Chichak hematite deposit is located in Chichak area west Azarbaijan, northwest of Iran. There are some outcrops of hematite bodies in the area. The goal of this study was to identify the depth, thickness and shape of these bodies and to explore other probabile hematite bodies. Therefore nine profiles were considered to be surveyed by RS and IP method by utilizing an innovative electrode array so called CRSP (Combined Resistivity Sounding and Profiling). IP and RS sections were completed along each profile. In addition, the RS and IP data were analyzed and relation between these two variables was determined by statistical tools. Finally, hematite bodies were identified in each of the sections. The results showed that hematite bodies have a resistivity lower than 125 Ωm and very low chargeability, lower than 8 mV⁄V. After geophysical study some points were proposed for drilling, results obtained from drilling confirm the geophysical results.

Keywords: Hematite deposit, Iron exploration, Electrical resistivity, Chargeability, Iran, Chichak, Statistical, CRSP electrodes array

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Authors: Ubaidillah Zuhdi, Shunsuke Mori, Kazuhisa Kamegai

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The purpose of this study is to forecast the influences of Information and Communication Technology (ICT) on the structural changes of Japanese economies based on Leontief Input-Output (IO) coefficients. This study establishes a statistical analysis to predict the future interrelationships among industries. We employ the Constrained Multivariate Regression (CMR) model to analyze the historical changes of input-output coefficients. Statistical significance of the model is then tested by Likelihood Ratio Test (LRT). In our model, ICT is represented by two explanatory variables, i.e. computers (including main parts and accessories) and telecommunications equipment. A previous study, which analyzed the influences of these variables on the structural changes of Japanese industrial sectors from 1985-2005, concluded that these variables had significant influences on the changes in the business circumstances of Japanese commerce, business services and office supplies, and personal services sectors. The projected future Japanese economic structure based on the above forecast generates the differentiated direct and indirect outcomes of ICT penetration.

Keywords: forecast, ICT, industrial structural changes, statistical analysis

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Authors: Christopher Godwin Udomboso, Angela Unna Chukwu, Isaac Kwame Dontwi

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In selecting a Statistical Neural Network model, the Network Information Criterion (NIC) has been observed to be sample biased, because it does not account for sample sizes. The selection of a model from a set of fitted candidate models requires objective data-driven criteria. In this paper, we derived and investigated the Adjusted Network Information Criterion (ANIC), based on Kullback’s symmetric divergence, which has been designed to be an asymptotically unbiased estimator of the expected Kullback-Leibler information of a fitted model. The analyses show that on a general note, the ANIC improves model selection in more sample sizes than does the NIC.

Keywords: statistical neural network, network information criterion, adjusted network, information criterion, transfer function

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Authors: Haoxue Cui, Sirui Zhang, Shanshan Gao, Weiyi Zhang, Lantian Wang, Xuanwen Zheng

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The 2020 "multi-school zoning" policy makes students ineligible for direct attendance in their district. To study whether the housing price trend of the school district is affected by the policy, This paper studies housing prices based on the school district division in Xicheng District, Beijing. In this paper, we collected housing prices and the basic situation of communities from "Anjuke", which were divided into two periods of 15 months before and after the 731 policy in the Xicheng District, Beijing. Then we used DID model and time fixed effect to investigate the DIFFERENTIAL statistics, that is, the overall net impact of the policy. The results show that the coefficient is negative at a certain statistical level. It indicates that the housing prices of school districts in the Xicheng district decreased after the "multi-school zoning" policy, which shows that the policy has effectively reduced the housing price of school districts in the Xicheng District and laid a foundation for the "double reduction" policy in 2022.

Keywords: “multi-school zoning”policy, DID, time fixed effect, housing prices

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Authors: Mimoza Canga, Irene Malagnino, Giulia Malagnino, Alketa Qafmolla, Ruzhdie Qafmolla, Vito Antonio Malagnino

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The goal of the present study was to analyze the risk factors regarding dental fear. This observational study was conducted during the period of February 2020 - April 2022 in Albania. The sample was composed of 200 participants, of which 40% were males and 60% were females. The participants' age range varied from 35 to 75 years old. We divided them into four age groups: 35-45, 46-55, 56-65, and 66-75 years old. Statistical analysis was performed using IBM SPSS Statistics 23.0. Data were scrutinized by the Post Hoc LSD test in analysis of variance (ANOVA). The P ≤ 0.05 values were considered significant. Data analysis included Confidence Interval (95% CI). The prevailing age range in the sample was mostly from 55 to 65 years old, 35.6% of the patients. In all, 50% of the patients had extreme fear about the fact that the dentist may be infected with Covid-19, 12.2% of them had low dental fear, and 37.8% had extreme dental fear. However, data collected from the current study indicated that a large proportion of patients 49.5% of them had high dental fear regarding the dentist not respecting the quarantine due to COVID-19, in comparison with 37.2% of them who had low dental fear and 13.3% who had extreme dental fear. The present study confirmed that 22.2% of the participants had an extreme fear of poor hygiene practices of the dentist that have been associated with the transmission of COVID-19 infection, 57.8% had high dental fear, and 20% of them had low dental fear. The present study showed that 50% of the patients stated that another factor that causes extreme fear was that the patients feel pain after interventions in the oral cavity. Strong associations were observed between dental fear and pain 95% CI; 0.24-0.52, P-value ˂ .0001. The results of the present study confirmed strong associations between dental fear and the fact that the dentist may be infected with Covid-19 (95% CI; 0.46-0.70, P-value ˂ .0001). Similarly, the analysis of the present study demonstrated that there was a statistically significant correlation between dental fear and poor hygiene practices of the dentist with 95% CI; 0.82-1.02, P-value ˂ .0001. On the basis of our statistical data analysis, the dentist did not respect the quarantine due to COVID-19 having a significant impact on dental fear with a P-value of ˂ .0001. This study shows important risk factors that significantly increase dental fear.

Keywords: Covid-19, dental fear, pain, past dreadful experiences

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Authors: Hamda Ajmal, Karen Young, Ruth Melia, John Bogue, Mary O'Sullivan, Jim Duggan, Hannah Wood

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The Covid-19 pandemic led to a range of public health mitigation strategies in order to suppress the SARS-CoV-2 virus. The drastic changes in everyday life due to lockdowns had the potential for a significant negative impact on public mental health, and a key public health goal is to now assess the evidence from available Irish datasets to provide useful insights on this issue. Text-50808 is an online text-based mental health support service, established in Ireland in 2020, and can provide a measure of revealed distress and mental health concerns across the population. The aim of this study is to explore statistical associations between public mental health in Ireland and the Covid-19 pandemic. Uniquely, this study combines two measures of emotional wellbeing in Ireland: (1) weekly text volume at Text-50808, and (2) emotional wellbeing indicators reported by respondents of the Amárach public opinion survey, carried out on behalf of the Department of Health, Ireland. For this analysis, a multivariate graphical exploratory data analysis (EDA) was performed on the Text-50808 dataset dated from 15th June 2020 to 30th June 2021. This was followed by time-series analysis of key mental health indicators including: (1) the percentage of daily/weekly texts at Text-50808 that mention Covid-19 related issues; (2) the weekly percentage of people experiencing anxiety, boredom, enjoyment, happiness, worry, fear and stress in Amárach survey; and Covid-19 related factors: (3) daily new Covid-19 case numbers; (4) daily stringency index capturing the effect of government non-pharmaceutical interventions (NPIs) in Ireland. The cross-correlation function was applied to measure the relationship between the different time series. EDA of the Text-50808 dataset reveals significant peaks in the volume of texts on days prior to level 3 lockdown and level 5 lockdown in October 2020, and full level 5 lockdown in December 2020. A significantly high positive correlation was observed between the percentage of texts at Text-50808 that reported Covid-19 related issues and the percentage of respondents experiencing anxiety, worry and boredom (at a lag of 1 week) in Amárach survey data. There is a significant negative correlation between percentage of texts with Covid-19 related issues and percentage of respondents experiencing happiness in Amárach survey. Daily percentage of texts at Text-50808 that reported Covid-19 related issues to have a weak positive correlation with daily new Covid-19 cases in Ireland at a lag of 10 days and with daily stringency index of NPIs in Ireland at a lag of 2 days. The sudden peaks in text volume at Text-50808 immediately prior to new restrictions in Ireland indicate an association between a rise in mental health concerns following the announcement of new restrictions. There is also a high correlation between emotional wellbeing variables in the Amárach dataset and the number of weekly texts at Text-50808, and this confirms that Text-50808 reflects overall public sentiment. This analysis confirms the benefits of the texting service as a community surveillance tool for mental health in the population. This initial EDA will be extended to use multivariate modeling to predict the effect of additional Covid-19 related factors on public mental health in Ireland.

Keywords: COVID-19 pandemic, data analysis, digital health, mental health, public health, digital health

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