Risk and Survival Analysis from COVID Outbreak Data
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This document summarizes the results of a study analyzing COVID-19 outbreak data from India to derive lessons. It used clustering analysis to group states based on active cases, finding Maharashtra as the worst affected and others like Kerala, Delhi, Uttar Pradesh, and West Bengal among the second worst. Survival analysis on patient data from Karnataka found the median recovery time was 8 days, with older patients (>60) having only 4 days and adults (18-60) having 33 days. It also found males had a median survival time 7 days less than females, and a Cox proportional hazards model showed males had a 1.26 times higher risk of decreased survival.
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Running head PHASE 1 SCENARIO NCLEX MEMOORIAL HOSPITAL1PHASE .docx
Running head: PHASE 1 SCENARIO NCLEX MEMOORIAL HOSPITAL 1
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Institution: Rasmussen College
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Date: 02/17/17
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The scenario I will be working with is that I am working at NCLEX Memorial Hospital in the infectious disease unit. As a healthcare professional, I need to work to improve the health of individuals, families and communities in various settings. The current situation that has posed as a problem at the hospital and raised eyebrows is that in the past few days, there has been an increase in patients admitted with a particular infectious disease. The basic statistical analysis shows that the disease does not affect minors hence the ages of the infected patients does play a critical role in the method that shall be required to treat the patients in order to impact positively on the health and well-being of the clients being served whether infected with the disease or associated with those infected. After speaking to the manager, we decided that we shall work together in utilising the available statistical analysis to look closer into the ages of the infected patients. To do that, I had to put together a spreadsheet with the data containing the information we shall need to carry out the analysis.
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From the data collected and input on an Excel sheet, there are sixty patients with the infectious disease. Of the patient’s whose data has already been collected an input on the excel sheet, the ages range from thirty-five years of age to seventy-six. There is only one patient in their thirties with the age of thirty-five. There are five patients in their forties, One forty-five, one forty-six, two at forty-eight and two at forty-nine. There are fifteen patients in their fifties, two at fifty, one fifty-two, one fifty-three, one fifty-four, four at fifty-five, one fifty-six, one at fifty-eight and four at fifty-nine. There are twenty-three patients in their sixties, five at sixty, one at sixty-two, one at sixty-three, two at sixty-four, one at sixty-five, three at sixty-eight and seven at sixty-nine. Finally, we have fifteen infected patients in their seventies, six at seventy, three at seventy-one, three at seventy-two, one at seventy-three, one at seventy-four and one at seventy-six. From the graph in Figure 1 below, the horizontal axis depicts the age group of patients infected with the disease and the vertical axis depicts the number of patients in the age group infected with the disease.
Figure 1
Data Classification
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Data Classification
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This scientific paper presents research on the prevalence of COVID-19 appropriate behaviors among the general public in selected areas of Hyderabad, Telangana, India. The study found that 40% of respondents tested positive for COVID-19, while 60% tested negative. An attitude scale also found that 75% of respondents agreed with practicing COVID-19 appropriate behaviors, while 16.6% disagreed and 8.4% remained undecided. The paper concludes that assessing COVID-19 prevalence and behaviors in the community can help promote preventive measures and inform nursing practice, education, administration, and further research.
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Introduction Balance exercise is an important element that prevent fall in older age people observed throughout the world. balance training programme for old age people, including multi task exercises, was designed and developed based on well established principles of exercise and on the knowledge that balance control relies on the interaction of several physiological systems, as well as interaction with environmental factors and the performed task. Objective This study aims to assess the effectiveness of balance exercises on fall prevention among older people. Methods The research design selected for this study was experimental research design. The investigator used Randomized control trial to select a sample of 88 exprimental 44 and control 44 of old age people for this study. In this study socio demographic data tool, POMA Tinetti performance oriented mobility assessment were used to measure and observe the study accurately. Fall rate was measured with the POMA tool. Data were analyzed through descriptive and inferencial statistic. Results In the group 1 i.e. experimental group, 8 18.2 cases were high fall risk, 13 29.5 medium fall risk and 23 52.3 low fall risk. In the control group group 2 , 29 65.9 were high fall risk, 10 22.7 medium fall risk and 5 11.4 low fall risk. In the experimental group the high fall risk is very low and low fall risk is very high in comparison to control group. The difference was highly significant p=0.000 . This implied the balance exercise on fall prevention is highly effective. Conclusion This study concluded that significant increasing the strength, prevent fall and maintaining the balance among older people in a 4 week balance exercise program. Debojita Chatterjee "A Study to Assess the Effectiveness of Balance Exercise on Risk of Fall Prevention among Older People in Selected Old Age Homes at Purba Medinipur, West Bengal - Pilot Study" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38386.pdf Paper Url: https://www.ijtsrd.com/medicine/nursing/38386/a-study-to-assess-the-effectiveness-of-balance-exercise-on-risk-of-fall-prevention-among-older-people-in-selected-old-age-homes-at-purba-medinipur-west-bengal--pilot-study/debojita-chatterjee
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Eama presentation nicolas martinez velilla avila 2015 final
This document discusses challenges in applying clinical trial results for chronic kidney disease (CKD) to elderly patients. It notes that CKD is defined based on kidney structure/function abnormalities for over 3 months, assessed via estimated glomerular filtration rate and albuminuria levels. Frailty is common in CKD patients over 60 and associated with higher mortality and dialysis need. CKD is also linked to impaired cognition and physical function in older adults. However, clinical trials often exclude elderly patients, so guidance is largely based on younger populations. The document calls for more research accounting for frailty, function, and including more representative elderly patients.
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The imbalance of sodium and chloride ions occurs frequently in patients with lung cancer. However, the correlation between ion concentration change and patients prognosis have not been studied thoroughly. Our research will fill the gap, especially for high ion concentration.
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The imbalance of sodium and chloride ions occurs frequently in patients with lung cancer. However, the correlation between ion concentration change and patients prognosis have not been studied thoroughly. Our research will fill the gap, especially for high ion concentration.
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Eama presentation nicolas martinez velilla avila 2015 final
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Abnormal Sodium and Chlorine Level Is Associated With Prognosis of Lung Cance...
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Risk and Survival Analysis from COVID Outbreak Data
- 1. RISK AND SURVIVAL ANALYSIS FROM COVID OUTBREAK DATA: LESSONS FROM INDIA Prasad Bankar | Subhasis Panda | Vaibhav Anand | Vineet Kumar ISI Kolkata | IIT Kharagpur | IIM Calcutta AAAI SYMPOSIUM 2021 SURVIVAL PREDICTION ALGORITHMS, CHALLENGES AND APPLICATIONS
- 2. OUTLINE INTRODUCTION & MOTIVATION DATASET METHODOLOGY 1 2 3 4 RESULTS AND DISCUSSIONS 2
- 3. INTRODUCTION & MOTIVATION ▪ Spread of Coronavirus – Possibility of a 2nd wave, country attempting to fully unlock ▪ Forecasting COVID-19 is difficult – given the unreasonable modelling assumption and lack of good quality data ▪ Developed a real time monitoring dashboard with risk metrics to provide actionable insights ▪ Difference in trends for the risk metrics across states 3
- 4. A GLIMPSE OF THE DASHBOARD Source: https://covid-isical.tech/ 4
- 5. SOME OF THE RISK ASSESSMENT METRICS 5
- 6. DATASET Source: https://api.covid19india.org/ ▪ COVID-19 India Org Data Operations Group ▪ Opensource Platform ▪ Uses state bulletins and official handles to update numbers and publish it to APIs 6
- 7. METHODOLOGY (1/2) ▪ Some states/ regions were more affected compared to others ▪ Motivation to identify underlying structures within COVID-19 cases ▪ Performed Euclidean measure based vanilla k-means clustering on the top 20 most affected states ▪ Value of k was chosen iteratively experimenting and checking the standard silhouette score and elbow metric ▪ Performed the hierarchical clustering on the cases using the Ward distance 1 Cluster Analysis 7
- 8. METHODOLOGY (2/2) ▪ Used the data of testing date and the date of discharge from hospitals for individual patients in Karnataka to calculate the length of stay in hospitals ▪ Calculated the days progress using Kaplan-Meier estimator of survival function ▪ Carried out cohort selection with Age and Gender as the variates ▪ Calculated the Gender and Age stratified Kaplan-Meier estimate ▪ Performed the standard non-parametric Log Rank Test to statistically compare the difference between the survival probability of various stratum 2 Survival Analysis 8
- 9. RESULTS AND DISCUSSIONS 1 Cluster Analysis ▪ MH is the worst affected state while KL,DL,UP,WB are among the second worst-hit states. ▪ Such clustering analysis, performed across the last 7-8 months, gives a better view of the shift of the states in the cluster Cluster Analysis based on Active Cases 9
- 10. 10 RESULTS AND DISCUSSIONS 2 Survival Analysis ▪ In our cohort, the mean age is 45 years and the mean/ median time to recovery is 8 days. Ages & Stay distribution of patients
- 11. RESULTS AND DISCUSSIONS 2 Survival Analysis ▪ Vanilla KM estimate on the entire cohort shows median survival time as 16 days. Survival Analysis on the entire cohort 11
- 12. RESULTS AND DISCUSSIONS 2 Survival Analysis ▪ We stratified the cohort based on gender and found an interesting difference on median survival time across male and female was varying by 7 days Survival Analysis on the Gender stratified cohort n events median 0.95LCL 0.95UCL Sex=F 9290 3531 21 19 23 Sex=M 17451 7979 14 14 15 12
- 13. RESULTS AND DISCUSSIONS 2 Survival Analysis ▪ We stratified the cohort based on age and found that older people have lesser median survival time of 4 days and adults have significantly high median survival time of 33 days. Survival Analysis on the Age stratified cohort n events median 0.95LCL 0.95UCL <18 yr 2295 37 NA NA NA 18-60 yr 16380 4571 33 31 37 >60 yr 8066 6902 4 4 4 13
- 14. RESULTS AND DISCUSSIONS 2 Survival Analysis Gender and Age stratified KM estimate 14
- 15. RESULTS AND DISCUSSIONS 2 Survival Analysis Cohort Characteristics of Gender and Age stratified KM estimate ▪ For older people (>60 years), there is not much difference in the median survival time across gender but for adult cohort (18-60 years) median survival time is varying by 4 days across gender stratum. 15
- 16. RESULTS AND DISCUSSIONS 2 Survival Analysis Cox Proportional Hazard ▪ Fitted a semiparametric cox proportional hazard model to quantify the effect of age on mortality ▪ Cox-ph model gives Hazard Ratio, which is interpretated as ratio of hazards between two groups (Male & Female) at any particular point of time. ▪ The estimated Hazard ratio came out to be 1.26 with 95% confidence interval which implies Male gender is associated with 1.26 times increased risk or decreased survival. ▪ The male population in our cohort is more vulnerable and the reason could be traced to higher comorbidity and exposure to virus due to relatively higher contact with riskier environment. 16
- 17. THANK YOU