A Review Of Published Studies Looking At Statistical Models And
Methods And Their Application To Problems Of Infectious Diseases Such
As COVID-19 In BMJ
Dr. Nancy Agnes, Head,
Technical Operations, Tutorsindia
info@ tutorsindia.com
or age category. The correlation technique
Keywords:
statistical
regression
meta-analysis
analysis,
factor
service,
analysis,
Confirmatory Factor Analysis, clinical
trial analysis, data mining services,
biostatistics services, Time series analysis
will be useful to identify the relationship
between these two variables. And suppose
the researcher wants to predict the
effectiveness of future outcomes. In that
case, the regression analysis will be useful
as
using R.
it
identifies
the
average
linear
relationship between the dependent and
independent variables. Apart from the
I. INTRODUCTION TO HEALTH
usual correlation and regression analysis,
SCIENCE:
many researchers adopt dimensionality
reduction
Health science research is the most
techniques
such
as
factor
analysis.
interesting research area as we identify the
III. FACTOR ANALYSIS
pattern of Genomic diseases and various
other kinds of diseases.
Factor analysis reduces the dimensions and
II. STATISTICAL MODELS IN HUMAN
creates the latent variables. Each latent
HEALTH SCIENCE:
variable acts as another variable in the
study. With those latent variables, one can
The most common statistical approach for
any human health studies is correlation and
regression analysis. Suppose, consider a
vaccine effectiveness
researcher
wants
study,
to
and the
identify
the
construct linear regression analysis and
predict future outcomes or simply identify
the
variables'
linear
relationship.For
example, Goni et al. (2020) considered a
Confirmatory factor analysis to study
effectiveness of vaccine among the gender
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1
respiratory tract infections in Hajj and
Umrah. They collected the data in the form
survey involving 72 variables. In practice,
analysing the entire 72 variables will yield
poor results. Thus, the dimensionality
reduction
technique
is
adopted
and
measured by the confirmatory factor
analysis,
which
uses
the
chi-square
statistic. Also, Saefi et al. (2020) studied
the undergraduate student's knowledge
about COVID19, measures taken by them
to prevent the disease, and maintaining the
health style during COVID19. They
IV. BAYESIAN META-ANALYSIS
With this information, they conducted a
Bayesian meta-analysis and performed
10000 Markov Chain iterations using fixed
effects and random effects separately and
found no statistical incoherence in the
analysis. Furthermore, Xu et al. (2020)
studied the characteristics of patients
affected by COVID19 outside Wuhan in
China. The study revealed that people
affected with COVID outside Wuhan city
are very mild than the people affected in
Wuhan.
conducted a survey and investigated the
properties of the KAP questionnaire by
Apart from the viral infectious disease,
adopting Confirmatory Factor Analysis
numerous diseases are of interest to the
(CFA) and RASCH model and the results
researchers
of these analyses revealed that each of the
remedies, risk factors, etc. One such
items in the questionnaire possesses
increasing research area is cancer studies.
unique qualities and this questionnaire is
Calster et al. (2020) considered a cohort
adequate enough to measure the student's
study on ovarian cancer and identified the
knowledge, attitude and practice during
best model to detect cancer and properly
COVID19. Further, Siemieniuk et al.
distinguish cancer types. The dataset has
(2020) compared the effects of COVID19
been collected from IOTA and selected a
treatments from literature using Meta-
proper sample for the analysis. Five
analysis. Data for this study has been
different models have been conducted, and
collected daily from different sources such
the results revealed that SRRisk and
as the WHO website, Centre for Disease
ADNEX
Control and Prevention in the U.S.,
classifying the type of cancer. Healthcare
PubMed, etc. The data includes detailed
research is to diagnose the disease or find
information of the patient affected with
the risk factor associated with the disease.
COVID19, like the length of stay in ICU,
Statistical techniques can be used to
duration of ventilation, etc.
analyse the causes of the diseases. In that
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in
models
finding
the
performed
causes,
well
in
2
sense, Tian et al. (2019) estimated the risk
80%.The fifth model uses a mobile
factors of hospital admission related to
application to collect data and to risk-
cardiovascular disease. A total of 184
stratify patients. It uses demographics,
cities in China are included in the study,
symptoms, and contact history of users.It
and the information related to pollution
further expanded into two more models:
and hospital admissions are collected.
blood values and blood values plus
They adopted Time series analysis to
computed tomography (C.T.) images.
investigate
the
association
between
pollution and disease. The results showed
Table: Overview of prediction models for
that short-term exposure to pollution leads
diagnosis and prognosis of covid-1911
to
increased
cardiovascular
hospital
admissions
disease.
for
Statswork
provides high quality biostatistics services
which helps precise estimation of the
effect
size
and
increases
the
generalizability of the results of individual
studies.
V. MODELS TO FORECAST THE RISK
OF COVID-19 IN THE GENERAL
POPULATION
VI. DIAGNOSTIC MODELS TO
DISCOVER COVID-19 IN PATIENTS
WITH SUSPECTED INFECTION
They acknowledged seven models that
It is a type of method or test used to
help in predicting the risk of covid-19 in
help diagnose a disease or condition. It
the general population. Three models from
includes imaging tests and tests to measure
one study used hospital admission based
blood pressure, pulse, and temperature are
on non-tuberculosis pneumonia, influenza,
examples
acute bronchitis, or upper respiratory tract
Diagnosis has
infections as substitution outcomes in a
for patient care, research, and policy.
of diagnostic
techniques.
significant
implications
dataset without any patients with covid191. The fourth model uses a deep learning
technique detecting thermal video from the
VII .PREDICTIVE MODELS TO
DIAGNOSE COVID-19
faces of people wearing facemasks to
A predictive
detecting abnormal breathing (not covid
combining at least two prognostic factors,
related) with a reported sensitivity of
based
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on
model was
multivariable
defined
analysis,
as
as
3
REFERENCES
estimating the individual risk of a specific
outcome, presented as regression formula,
nomogram, or in a simplified form, such as
risk score.
A predictive model is a formal grouping of
multiple predictors from which a particular
endpoint's risks can be calculated for
individual patients. Other names for
a predictive
model include prognostic (or
prediction) index or rule, risk (or clinical)
prediction model, and predictive model.
VIII. CONCLUSION:
Further, statistical techniques have been
widely used in epidemiological research.
Moustgaard et al. (2020) studied the
impact of treatment and therapeutically
effects in clinical trials using metaanalysis. The results showed no difference
in the effects of treatments of patients from
the healthcare providers with and without
blinding. Furthermore, Fabbri et al. (2020)
presented a review on the health care
providers and South African patients'
funding
using
recommended
companies
meta-analysis.
that
provide
the
They
corporate
transparency
in
providing funds to patients, and this type
of funding can be seen in high-income
countries. If you are struggling with metaanalysis you can reach our statistical metaanalysis service.
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1.
Dauda Goni M, Hasan H, Naing N.N., et al.
Assessment of Knowledge, Attitude and
Practice towards Prevention of Respiratory
Tract Infections among Hajj and Umrah
Pilgrims from Malaysia in 2018. International
Journal of Environmental Research and Public
Health. 2019 Nov;16(22).
2. Saefi, M., Fauzi, A., Kristiana, E., Adi, W. C.,
Muchson, M., Setiawan, M. E., Ramadhani, M.
(2020). Validating of Knowledge, Attitudes,
and Practices Questionnaire for Prevention of
COVID-19 infections among Undergraduate
Students: A RASCH and Factor Analysis.
Eurasia Journal of Mathematics, Science and
Technology Education, 16(12), em1926.
3. Siemieniuk R A, Bartoszko J J, Ge L,
Zeraatkar D, Izcovich A, Kum E et al. Drug
treatments for covid-19: living systematic
review and network meta-analysis BMJ 2020;
370 :m2980
4. Van Calster B, Valentin L, Froyman W,
Landolfo C, Ceusters J, Testa A C et al.
Validation of models to diagnose ovarian
cancer in patients managed surgically or
conservatively: multicentre cohort study BMJ
2020; 370 :m2614
5. Whaley C M, Arnold D R, Gross N, Jena A B.
Practice composition and sex differences in
physician income: observational study BMJ
2020; 370 :m2588
6. Tian Y, Liu H, Wu Y, Si Y, Song J, Cao Y et
al. Association between ambient fine
particulate pollution and hospital admissions
for cause specific cardiovascular disease: time
series study in 184 major Chinese cities BMJ
2019; 367 :l6572
7. Forbes H, Douglas I, Finn A, Breuer J,
Bhaskaran K, Smeeth L et al. Risk of herpes
zoster after exposure to varicella to explore the
exogenous boosting hypothesis: self controlled
case series study using U.K. electronic
healthcare data BMJ 2020; 368 :l6987
8. Moustgaard H, Clayton G L, Jones H E,
Boutron I, Jørgensen L, Laursen D R T et al.
Impact of blinding on estimated treatment
effects in randomised clinical trials: metaepidemiological study BMJ 2020; 368 :l6802
9. Fabbri A, Parker L, Colombo C, Mosconi P,
Barbara G, Frattaruolo M P et al. Industry
funding of patient and health consumer
organisations: systematic review with metaanalysis BMJ 2020; 368 :l6925
10. Xu X, Wu X, Jiang X, Xu K, Ying L, Ma C et
al. Clinical findings in a group of patients
infected with the 2019 novel corona virus
(SARS-Cov-2) outside of Wuhan, China:
retrospective case series BMJ 2020; 368 :m606
11. Wynants, L., Van Calster, B., Collins, G. S.,
Riley, R. D., Heinze, G., Schuit, E., ... & van
4
Smeden, M. (2020). Prediction models for
diagnosis and prognosis of covid-19:
systematic
review
and
critical
appraisal. bmj, 369.
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