BMI and Asthma: A New York Case Study on Elementary School Kids

This case study investigates the relationship between Body Mass Index (BMI) and asthma in children and adults in New York, utilizing a mixed research design with both qualitative and quantitative methods. The study aims to examine the correlation between BMI and asthma prevalence in elementary school children and adults from selected medical clinics, considering demographic and ecological factors. Statistical measures, including mean, standard deviation, and multiple logistic regression analysis, are employed to analyze the data collected through questionnaires, focusing on demographics, environmental exposures, and asthma treatment history. Potential measurement issues, such as researcher subjectivity and data collection challenges, are addressed to ensure the validity and reliability of the findings. The research seeks to contribute to the understanding of the association between BMI and asthma, which has seen increasing prevalence in recent years.

Running head: Epidemiology 1
Relationship between Body Mass Index and Asthma in both Children and Adults: Case study
of New York Elementary School and Medical Clinics
by
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Study Design
The study will use a mixed research design (qualitative and quantitative methods). A mixed
research design is chosen because it will enable a blending of different techniques which will
make the study all-inclusive due to the statistical analyses in addition to observation,
interpretation, and the use of questionnaires (Creswell and Creswell, 2017). The qualitative
research approach will be used to provide a comprehensive insight into the study topic
through a critical review of extant literature. Quantitative method will enable descriptive and
inferential analyses (Creswell and Creswell, 2017). Descriptive analysis will provide the
demographics of the study variables whereas inferential analysis (multiple logistic regression
analysis) will be used to explore the relationship between the study variables.
The prevalence of asthma and obesity in both children and adults has been on the rise for the
past years in most nations across the globe (Baïz & Annesi-Maesano, 2012; Sin &
Sutherland, 2008). Incidences of Asthma in children of Taiwan has been on the rise from
1.3% to 19.0% within a span of thirty years (Ho et al., 2007; Hwang et al., 2010). Similarly, it
seems that obesity incidences are following the same trend at a doubling rate (Pan et al.,
2008). In the Republic of Korea, obesity in children has increased by 5.9% within a span of
three years from 1998, and asthma have also increased by 2.6% within a span of five years
from 1995 (Suh et al., 2011). Several studies on epidemiology have linked asthma with
obesity in both children and adults (Hjellvik, Tverdal, & Furu, 2010; Scholtens et al., 2009).
However, the cause of this alarming association is still unknown. The purpose of this study
will be to examine the relationship between Body Mass Index (BMI) and Asthma in both
Children and Adults using USA children and adults.
Statistical Measures

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The mean and standard deviation (SD) will be used to compare the specific features of the
selected population for continuous variables and percentage for categorical variables in all the
BMI clusters. All missing answers to vital issues such as informed consent or questions on
age, sex, height and weight will not be included in the analysis. BMI will be ascertained
using the collected measurements of weight and height and then grouped into four clusters
namely (≤ 25 percentile, 26-50 percentile, 51-75 percentile, and > 75 percentile). The
research will also examine the relationship between demographic or ecological features and
asthma using a ranked model (two-stage) due to the likelihood of a circumstantial impact of
the area of residence on the risk of asthma. Under stage one, multiple logistic regression
analysis will be carried out to ascertain the existence of any independent relationship between
possible confounders and asthma in each area of residence. Under stage two, the normal two-
tier random-intercept model will be used. This model is to enable the combination of the
specific estimates of residential area and multilevel logistic regression analysis using SPSS
Version 20. Level one units will include the factors linked to demographic or the residential
environment, whereas, level two units will include the areas of residence. Gender will be the
key separator in all the analyses.
Subject Selection
The target population will consist of 500 elementary school children in New York, USA,
randomly selected on the basis of their areas of residence (city, provincial, rural, industrial
areas) by the use of cluster sampling approach. Additionally, the target population will also
comprise of 500 adults in selected medical clinics in New York, USA.
The prevalence of asthma will be measured using designed questionnaires which will
comprise of sections on demographics, electricity expenses per month, exposure to tobacco
smoking, and presence of allergens. The research will also ask questions regarding the

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ecological settings of the areas of residence and movement history from one house to the
other. The BMI will be calculated using the actual measured height and weight and classified
into quartiles of BMI age percentile.
Invitation letters, requests for informed consent and questionnaires will be sent via mail to the
chosen elementary schools. The questionnaires will be completed by the parents or care
providers. Data from the adults will as well be collected using structured questionnaires
which will comprise of questions on socio-demographic and anthropomorphic features such
as height and weight, history of asthma treatment. The collected data will then be cross-
examined for inclusivity, coded and then analysed and the findings presented in the form of
graphs and tables.
Measurement Issues
There are possible measurement issues in the study. The first one is that there is a possibility
of the outcomes being influenced by the subjectivity of the researcher. The researcher is to
handle this matter by comparing his own opinions with the scholarly literature in order to
minimize subjectivity. Secondly, some of the care givers or parents might withhold or refuse
to provide significant information to the researcher, thus compelling the researcher to re-
assure the respondents of the confidentiality of the data to be collected so that they can be
free to provide the required information for this study. Thirdly, the use of questionnaires in
gathering data; despite of its advantages, might consume a lot time seeing that the target
population is varied. The researcher to address this issue by setting timelines for each activity
in order to save on time and minimize costs.

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References
Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and
mixed methods approaches. Sage publications.
Baïz, N., & Annesi-Maesano, I. (2012). Is the asthma epidemic still ascending?. Clinics in
chest medicine, 33(3), 419-429.
Hjellvik, V., Tverdal, A., & Furu, K. (2010). Body mass index as predictor for asthma: a
cohort study of 118 723 males and females. European Respiratory Journal.
Ho, W. C., Hartley, W. R., Myers, L., Lin, M. H., Lin, Y. S., Lien, C. H., & Lin, R. S. (2007).
Air pollution, weather, and associated risk factors related to asthma prevalence and
attack rate. Environmental research, 104(3), 402-409.
Hwang, C. Y., Chen, Y. J., Lin, M. W., Chen, T. J., Chu, S. Y., Chen, C. C., ... & Liu, H. N.
(2010). Prevalence of atopic dermatitis, allergic rhinitis and asthma in Taiwan: a
national study 2000 to 2007. Acta dermato-venereologica, 90(6), 589-594.
Pan, W. H., Lee, M. S., Chuang, S. Y., Lin, Y. C., & Fu, M. L. (2008). Obesity pandemic,
correlated factors and guidelines to define, screen and manage obesity in
Taiwan. Obesity Reviews, 9, 22-31.
Scholtens, S., Wijga, A. H., Seidell, J. C., Brunekreef, B., de Jongste, J. C., Gehring, U., ... &
Smit, H. A. (2009). Overweight and changes in weight status during childhood in
relation to asthma symptoms at 8 years of age. Journal of Allergy and Clinical
Immunology, 123(6), 1312-1318.

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Sin, D. D., & Sutherland, E. R. (2008). Obesity and the lung: 4· Obesity and
asthma. Thorax, 63(11), 1018-1023.
Suh, M., Kim, H. H., Sohn, M. H., Kim, K. E., Kim, C., & Shin, D. C. (2011). Prevalence of
allergic diseases among Korean school-age children: a nationwide cross-sectional
questionnaire study. Journal of Korean medical science, 26(3), 332-338.