Top^Clinic ^%[+27785538335__Safe*Abortion Pills For Sale In Soweto
Are there differences in the number of days from symptom onset to admission
1. 1
Are There Differences In The Number Of Days From Symptom Onset To Admission?
Name:
Institution Affiliation:
Date:
2. 2
Table of Contents
Study design.................................................................................................................................... 3
Variables ......................................................................................................................................... 3
Hypotheses...................................................................................................................................... 3
Univariate Analysis......................................................................................................................... 4
Final Diagnosis............................................................................................................................ 4
Time_symptoms_admitted.......................................................................................................... 5
Bivariate Analysis........................................................................................................................... 7
Statistical test and assumptions..................................................................................................... 10
Assumptions.............................................................................................................................. 10
Test of Statistics ........................................................................................................................ 11
Summary....................................................................................................................................... 13
3. 3
Study design
The patients’ data were collected through two online questionnaires sent to randomize and
a sample size of 5000. One of the questionnaires had questions on admission, and the second
contained data on final diagnosis. The data collected were reported for all Covid-19 patients. At
the time of this study, Brazil already had over 13.5 million people infected and over 354,000
deaths. The kind of data design used to collect patients' data here is practical design because this
study involves analysis of statistical data collected from an online interview.
Variables
The dependent variable in this analysis is the Time_sympton_admitted (Continuous), while
the independent variable is the final diagnosis (categorical dichotomous)
There is no much change that will be done on the variable, and only the final diagnosis data
shall be transformed into a group of less than 30 to enable practical application of the Central Limit
Theorem in the analysis.
Hypotheses
Null hypothesis:
There are differences in the number of days from symptom onset to admission (Time -
symptom_admitted) between final diagnoses (Diagnosis_final.
𝑯𝟎: 𝝁𝟏 = 𝝁𝟐 = 𝝁𝟑 = 𝝁𝟒 = 𝝁𝟓
Alternate hypothesis
H1: At least one of the means is different from other population means.
4. 4
Univariate Analysis
Frequencies of Diagnosis_final
Levels Counts % of
Total
Cumulative
%
Influenza 17 0.3 % 0.3 %
Other
respiratory
viruses
16 0.3 % 0.7 %
Other
aetiological
agents
24 0.5 % 1.1 %
Unspecified 2046 40.9 % 42.1 %
Covid-19 2897 57.9 % 100.0 %
Table 1: Frequencies of Final Diagnosis
Final Diagnosis
About 27/5000 (0.3%) of the patients surveyed had influenza, while 16/5000 (0.3%) had
other respiratory viruses. We can lasso see that 24/5000 (0.5%) of the respondents had other
aetiological agents while 2046/50000 (40.9%) had unspecified conditions. Finally, 2897/5000
(57.9%) of the Brazilian patients surveyed had contracted covid-19.
5. 5
Figure 1: Bar blot for the final diagnosis
The chart shows that a higher percentage (about 2897) of the patients surveyed had
contracted covid-19. Unfortunately, another 2046 patients had unknown diseases.
Time_symptoms_admitted
Data collected for the number of days of symptom admission
(Time_symptoms_admitted) indicates that different conditions have a different number of days
from symptom onset to admission. The average number of days from symptoms to admission is
5.52 (SD= 4.69). The median number of days is 5. Because this analysis will use Central Limit
Theorem, more than 30 days were filtered out to make this possible.
Time_symptoms_admitted
N 4923
Missing 0
Mean 5.52
Median 5
6. 6
Standard deviation 4.69
Variance 22
Range 29
Minimum 0
Maximum 29
Skewness 1.25
Std. error skewness 0.0349
Table 2: Descriptive Statistics for Time_symptoms_admitted
The data for days from symptom notification to admission is not normally distributed. This is
indicated by a positive skewness (1.25) and standard skewness error of 0.0349.
Figure 2: Histogram for Time_symptoms_admission
The distribution is skewed to the right, indicated by a long tail towards the right.
7. 7
Figure 3: Box plot for the Time symptoms admission
The interquartile range is (IQR=6.0) and mean=5.52. There are 12 outliers from the data.
Bivariate Analysis
Data related to the time of admission and the final results were analyzed according to the
disease type. Of 5000 patients who participated in the survey, 0.3% were found to have influenza
and other respiratory viruses, 0.5% had other aetiological agents, while 40.9% had the unspecified
disease, and finally, about 57.9% had covid-19. Data for both final diagnosis and
Time_symptoms_admitted were not normally distributed. The median days for influenza one day
(IQR= 2), the median days for other respiratory virus is 2.5 days (IQR=2.25), the median for other
aetiological agent is 2.5 days (IQR=2.25), the median for Unspecified diseases is 3 days (IQR=5),
and finally, the median for covid-19 is 6 days (IQR=6). Generally, covid-19 appeared to have a
longer time from symptom notification to admission.
Diagnosis_final Time_symptoms_admitted
N Influenza 17
8. 8
Other respiratory viruses 16
Other aetiological agents 24
Unspecified 2011
Covid-19 2855
Mean Influenza 1.53
Other respiratory viruses 2.75
Other aetiological agents 3.88
Unspecified 4.27
Covid-19 6.45
Median Influenza 1
Other respiratory viruses 2.5
Other aetiological agents 2.5
Unspecified 3
Covid-19 6
Standard
deviation
Influenza 2.03
Other respiratory viruses 2.02
Other aetiological agents 4.24
Unspecified 4.42
Covid-19 4.69
Variance Influenza 4.14
Other respiratory viruses 4.07
Other aetiological agents 17.9
Unspecified 19.5
Covid-19 21.9
IQR Influenza 2
Other respiratory viruses 2.25
Other aetiological agents 2.25
Unspecified 5
Covid-19 6
Maximum Influenza 7
Other respiratory viruses 8
9. 9
Other aetiological agents 14
Unspecified 29
Covid-19 29
Skewness Influenza 1.54
Other respiratory viruses 1
Other aetiological agents 1.83
Unspecified 1.86
Covid-19 0.989
Std. error
skewness
Influenza 0.55
Other respiratory viruses 0.564
Other aetiological agents 0.472
Unspecified 0.0546
Covid-19 0.0458
Kurtosis Influenza 2.16
Other respiratory viruses 1.88
Other aetiological agents 2.29
Unspecified 4.91
Covid-19 1.85
Std. error
kurtosis
Influenza 1.06
Other respiratory viruses 1.09
Other aetiological agents 0.918
Unspecified 0.109
Covid-19 0.0916
25th percentile Influenza 0
Other respiratory viruses 1.75
Other aetiological agents 1.75
Unspecified 1
Covid-19 3
50th percentile Influenza 1
Other respiratory viruses 2.5
10. 10
Other aetiological agents 2.5
Unspecified 3
Covid-19 6
75th percentile Influenza 2
Other respiratory viruses 4
Other aetiological agents 4
Unspecified 6
Covid-19 9
Table 3: Descriptive Statistics for final diagnosis and Time_symptoms_admitted
Figure 4: Box plots of the groups
From figure 4 above, we can see that the variability in Time_symptoms_admitted vary among the
groups and the data within each condition is not normally distributed.
Statistical test and assumptions
Assumptions
Since the grouping variable “Diagnosis_final” has more than 2 groups, we cannot make an
independent t-test for analysis, but we will use the ANOVA one way to test our hypothesis. It is
11. 11
assumed that all the samples used were taken from normally distributed data. Besides, each sample
is independent of one another. The dependent variable is Time_sympotoms_admitted, which is
measured on a continuous scale, makes it more certain to use one-way ANOVA.
Test of Statistics
Homogeneity of Variances Test (Levene's)
F df1 df2 p
Time_symptoms_admitted 8.75 4 4918 < .001
Table 4: Test of Equality of Variances (Levene's)
The Levene's test has p=0.001 which is lower than α=0.05; we reject the levene’s null
hypothesis and concludes that the variances are not equal. Now that we have known that the
variances are not equal, we can comfortably use a t-test for unequal variances. However, the
challenge comes in this case because our grouping variable has more than two levels. Hence, a t-
test for independent samples is not suitable for our variables. We, therefore, use ANOVA tests to
accommodate our many group levels.
One-Way ANOVA (Welch's)
F df1 df2 p
Time_symptoms_admitted 88.1 4 47.6 < .001
Table 5: jamovi ANOVA output
The p-value (p=0.001) is less than α=0.05. We reject the null hypothesis and concludes that the
data provides enough evidence to suggest that at least one means for diagnosis final is different.
Influenza Other
respiratory
viruses
Other
aetiological
agents
Unspecified Covid-
19
Influenza Mean
difference
— -1.22 -2.35 -2.74 -4.92
p-value — 0.94 0.483 0.099 < .001
12. 12
Other
respiratory
viruses
Mean
difference
— -1.13 -1.519 -3.7
p-value — 0.941 0.675 0.011
Other
aetiological
agents
Mean
difference
— -0.394 -2.58
p-value — 0.993 0.047
Unspecified Mean
difference
— -2.18
p-value — < .001
Covid-19 Mean
difference
—
p-value —
Table 6: Tukey Post-Hoc Test – Time_symptoms_admitted
The turkey comparison also revealed that all means are different.
95%
Confidence
Interval
statistic df p
Mean
difference
SE
difference
Lower Upper
Time_symptoms_admitted Diagnosis_final
Student's
t
14.6 4922< .0010.959 0.0655 0.831 1.09
Table 7: Paired Samples T-Test
The test statistics are 14.6, with the degrees of freedom (df=4922) and a p-value (p-
value=0.001). The mean difference calculated is 0.959 with a 95% confidence interval of 0.831
and 1.09. We, therefore, conclude that the mean difference for time_symptom_admitted is not the
same for all the diseases(𝑡4922 = 14,𝑝 = 0.001).
13. 13
Table 8: T Confidence interval chart
There different means between the groups.
Summary
From the patient’s data collected in Brazil from a sample size of 5000 over the second wave
of the Coronavirus pandemic, it has been found that 57.9% of the patients admitted in various
hospitals had covid-19, while 40.9% had unspecified diseases. Influenza and other respiratory
viruses both had 0.3%, while other etiological agents had 0.5%. It hypothesized that these diseases
had equal means; however, both the and t-paired tests done reveals that the p-value =0.01 less than
the 5% significant level. It is concluded that at least one of the diseases has a different
Time_symptom_admitted from others. Ideally, different conditions have different incubation
periods, and this may explain why the disease under study had different days to admission.
