Atul Bhargava1* and Pankaj Nagar2
1Ph.D Scholar, School of Business and Management, Jaipur National University, Jaipur, India
2Associate Professor, Department of Statistics, Rajasthan University, Jaipur, India
Citation: Atul Bhargava and Pankaj Nagar. “Impact of Family Dependents on Asthma”. EC Pulmonology and Respiratory Medicine 9.5
(2020): 78-81.
*Corresponding Author: Atul Bhargava, Ph.D Scholar, School of Business and Management, Jaipur National University, Jaipur, India.
Received: February 29, 2020; Published: April 29, 2020

Abstract
Asthma and Chronic Obstructive Pulmonary Disease (COPD) are increasing at a rapid pace in the last decade [1]. Research is going
on, but the fact remains that prevalence is on the increasing trend, confirmed by CDC reports. A study conducted on Asthma patients
related to socio- economic factor like family size or number of dependents in the family and found out that among asthmatic patient
there are few factors which significantly affect asthmatic patients [2].
Keywords: Asthma; COPD; ANOVA; Linear Regression Model

Introduction
Health is a state of complete physical, mental and social well-being and not merely absence of disease or infirmity as defined by WHO
[3]. Health is a basic fundamental right of all citizens and health-promoting forms an intrinsic part of healthcare

Definition of asthma
“Asthma is a chronic lung disorder which is caused due to inflammation of the airways. The airways are filled with mucus, become narrower
and blocking the airflow which then results in recurring periods of wheezing, short gasping breaths, chest tightness and coughing”.
In recent decade there has been an increase in the burden of the disease among both children and adults. This is mainly attributed to
increasing atmospheric pollution, the changing lifestyle, increasing industrialization and urbanization [4].
Status of asthma in India
Around 15 million people in India are suffering from asthma but the management of the disease continues to remain unsatisfactory,
according to a study Genetic factors interact with environmental factors such as pollution to trigger asthma. Some alarming statistics are
one among every ten persons affected by asthma globally is an Indian, with the country having 20 million such patients and the number
is on the rise. So, there is greater sense of awareness, preventive measures and proper detection and treatment is necessary. The statistics
on asthma in India have been worrying for a while now [5-9].
Materials and Methods
After an extensive review of literature the research problem/objective was identified as “impact of Family size/dependents on Asthma
in Rajasthan. As the study is of descriptive in nature, detailed questionnaire was designed using as primary data. The limitation of the
Citation: Atul Bhargava and Pankaj Nagar. “Impact of Family Dependents on Asthma”. EC Pulmonology and Respiratory Medicine 9.5
(2020): 78-81.
Impact of Family Dependents on Asthma
79
study is the respondent should be an asthma patient diagnosed by chest physician/physician. To keep the homogeneity of data respondent
age group was taken 18 – 60 years. The universe was taken as Rajasthan. Total number of patient completed study were 500. Statistical
analysis done by using SPSS Version 22.0 with various Statistical Tools to examine the significance of various characteristics of study:
• Dependent variables- Age, Gender, Profession, income dependents and education.
• Independent variables- Related to prevalence, management, trigger and Asthma related factors.
Hypothesis of the study
• H0: Socio-demographic factor like dependents in the family do not have significant effect on attacks of Asthma.
• H1: Socio-demographic factors like dependents in the family have significant effect on attacks of Asthma.
Analytical and statistical tools
Descriptive statistics: Frequency, mean, mean percentage and Standard deviation are used to describe demographic variables. Inferential
Statistics: ANOVA, Multiple linear regression to study the effect of Selected Socio Demographic factors and the awareness and prevention
on Asthma.
No. of Patients
0 106
1 41
10 1
12 1
2 103
3 106
4 61
5 42
6 20
7 14
8 5
Grand Total 500
Table 1 and 2: Showing number of dependents, patients are having in the family.
Testing the equality of dependent-wise mean-score in 4 major subjects
• H01: Dependent—Wise Mean Scores are equal in Asthma factor of Subjects.
• H02: Dependent—Wise Mean Scores are equal in Trigger factor of Subjects.
• H03: Dependent–Wise Mean Scores are equal in Prevalence factor of Subjects.
• H04: Dependent—Wise Mean Scores are equal in Life Management of Subjects.
Citation: Atul Bhargava and Pankaj Nagar. “Impact of Family Dependents on Asthma”. EC Pulmonology and Respiratory Medicine 9.5
(2020): 78-81.
Impact of Family Dependents on Asthma
80
The p-value of F Test, in case of Asthma factor of Subjects, is 0.001(< 0.05). Therefore, the null hypothesis (H01) is to be rejected. Hence it can be concluded that there is impact of Dependent Factor on Asthma factor of subjects. The p-value of F Test, in case of Trigger factor of Subjects, is 0.008(<0.05). Therefore, the null hypothesis (Ho1) is to be rejected. Hence it can be concluded that there is an impact of Dependents Factor on Trigger Factor of the disease on the subjects. The p-value of F Test, in case of Prevalence of Subjects, is 0.001(<0.05). Therefore, the null hypothesis (Ho1) is to be rejected. Hence it can be concluded that there is an impact of dependents Factor on Prevalence of the disease on the subjects. The p-value of F Test, in case of Life management of Subjects, is 0.965(>0.05). Therefore, the null hypothesis (Ho1) is to be rejected.
Hence it can be concluded that there is an impact of dependents factor on Life management of subjects.
Result and Conclusion
In descriptive table 1 and 2 showing how many family members are there in each asthma patients family and there percentage distribution
among 500 asthma patients.
Table 3 is representing the significance of testing the equality of mean values of different sub-grouping of all the attributes on the basis
of dependent present in the family of asthma patient. From table 3 it can be observed dependent mean values of three attributes (Prevalence
of Asthma, Asthma Factor, Triggering Factor) has significant difference as p < 05. Whereas in case of Management factor as p > .05
so it has no significance. For such testing, t-test has been used through Analysis of Variance (ANOVA) for all attributes.
Sum of Squares df Mean Square F Sig.
AF_Sum Between Groups 443.165 4 110.791 4.608 .001
Within Groups 11902.083 495 24.045
Total 12345.248 499
TF_Sum Between Groups 54.750 4 13.687 3.510 .008
Within Groups 1930.408 495 3.900
Total 1985.158 499
Prvlce_Sum Between Groups 296.151 4 74.038 4.993 .001
Within Groups 7340.201 495 14.829
Total 7636.352 499
MGT_Sum Between Groups 2.619 4 .655 .146 .965
Within Groups 2213.931 495 4.473
Total 2216.550 499
Table 3: ANOVA (Dependents).
Bibliography

1. Cavkaytar O and Sekerel BE. “Baseline management of asthma control”. Allergologia et Immunopathologia 42.2 (2014): 162-168.
2. Aggarwal AN., et al. “Prevalence and Risk Factors for Bronchial Asthma in Indian Adults: A Multicentre”. Indian Journal of Chest Diseases
   and Allied Sciences 48 (2006): 13-22.
3. WHO definition of health (2019).
   Citation: Atul Bhargava and Pankaj Nagar. “Impact of Family Dependents on Asthma”. EC Pulmonology and Respiratory Medicine 9.5
   (2020): 78-81.
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   81
4. Global strategy for asthma management and prevention (2019).
5. Hussein S and Partridge M. “Perceptions of asthma in South Asians and their views on educational materials and self-management
   plans: a qualitative study”. Patient Education and Counseling 48 (2002): 189-194.
6. Christensen Ronald. “Plane Answers to Complex Questions: The Theory of Linear Models (Third edition)”. New York: Springer (2002).
7. Rawlings John O., et al. “Applied Regression Analysis”. Springer Texts in Statistics (1998).
8. Rutherford. “Introducing Anova and Ancova: A GLM Approach”. SAGE (2001).
9. Stroup W. “Generalized Linear Mixed Models: Modern Concepts, Methods and Applications”. CRC Press (2016).
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