Asthma Presence

Author

Crows: Artemas Souder, Danielle Clarke, Kensley House

Introduction

Knowing the factors that influence asthma can be integrally in determining what factors might influence asthma severity. This data set provides plenty of general information in the healthcare domain, to explore this topic.

Research Questions

What factors are associated with the presence of Asthma?
How does BMI interact with Asthma Risk and Asthma Severity?

Statement of Purpose

The purpose of this research is to gain a better understanding of what influences Asthma Risk and Severity. Our aim is provide adequate analysis, and to understand how certain personal factors might prove important for people with Asthma.

Data

The following data set was collected from Kaggle: Asthma Risk & Severity Dataset

Variable Names in Asthma Risk Dataset
Variable_Names
Patient_ID
Age
Gender
BMI
Smoking_Status
Family_History
Allergies
Air_Pollution_Level
Physical_Activity_Level
Occupation_Type
Comorbidities
Medication_Adherence
Number_of_ER_Visits
Peak_Expiratory_Flow
FeNO_Level
Has_Asthma
Asthma_Control_Level

Of the seventeen variables present in the data set, nine will be kept for further analysis. BMI and Age will be turned into categorical groups for general comparison purposes based on broad categories affecting Asthma Levels.

asthma <- asthma|>
  mutate( Age_groups = case_when(
          Age < 26 ~ " 25 & Under",
          Age >= 26 & Age < 51 ~ "26 - 50",
          Age >= 51 & Age < 76 ~ "51 - 75",
          Age >= 76 ~ "76+"), 
          BMI_groups = case_when(
            BMI < 18.5 ~ "Underweight",
            BMI <= 24.9 ~ "Normal",
            BMI <= 29.9 ~ "Overweight",
            BMI >29.9 ~ "Obese"),
          asthma_y_n = case_when(
              Has_Asthma == 0 ~ "No Asthma",
              Has_Asthma == 1 ~ "Has Asthma")
          )|>select(Age, BMI, Gender, Allergies, Physical_Activity_Level, Occupation_Type, Comorbidities, Has_Asthma, Age_groups, BMI_groups, Medication_Adherence)

asthma_clean <- asthma

The table below is interactive allowing you to explore the data set through the use of the search box.

Analysis

# A tibble: 2 × 5
  Has_Asthma     n mean_BMI mean_age mean_adherence
       <int> <int>    <dbl>    <dbl>          <dbl>
1          0  7567     24.8     45.0          0.498
2          1  2433     25.9     44.7          0.499

Within the sample, 7,567 participants do not have asthma while 2,433 participants do. The Mean BMI for individuals with without asthma is 24.8 while those with asthma have a mean BMI of 25.9. For those with asthma the mean age of participants is 44.7 while it is 45.0 for their counterparts without asthma.

# A tibble: 4 × 5
  Comorbidities     n mean_BMI mean_age mean_adherence
  <chr>         <int>    <dbl>    <dbl>          <dbl>
1 Both            986     25.0     46.3          0.494
2 Diabetes       2029     25.1     44.5          0.494
3 Hypertension   2018     25.1     44.3          0.499
4 None           4967     25.0     45.1          0.500

Within the sample, most participants (4,967) do not have a co-morbidity. Nearly the same amount of participants presented with one co-morbidity (either diabetes or hypertension) while less than 1,000 participants have both diabetes and hypertension.

We began the analysis by looking at table below which highlights the differences for BMI groups when also looking at Age groups (figure 1). Next we faceted bar charts of the variables to visualize the numbers as a percentage of their group (figure 2).

Figure 1a:Table of Comorbidities by Age Group
	Both	Diabetes	Hypertension	None
25 & Under	265	583	612	1387
26 - 50	261	575	551	1380
51 - 75	281	547	552	1438
76+	179	324	303	762

Figure 1b:Table of BMI and Age as categorical variables
	Normal	Obese	Overweight	Underweight
25 & Under	1133	467	990	257
26 - 50	1112	441	969	245
51 - 75	1158	443	945	272
76+	632	241	546	149

For males without asthma, hypertension was reported more than diabetes while it was reported less than diabetes in males with asthma.

From Figure 2, we see individuals with asthma in the 26–50 age group seem to have a higher proportion of obesity when compared to individuals without asthma in the same age range while individuals with wihtout asthma in the 25 & under age range seem to have a higher proportion of obesity when compared to individuals with asthma in the same age range.

Figure 3a & 3b show the distribution of age. Age has a pretty normal distribution with a slightspike of individuals in their 30’s and least ammount of members in the group 76 +.

Figure 4 shows the count of individuals with and without asthma by Gender. Both groups there are an approximately equal numbers of males and females at 48%

In the following histogram (figure 5) we compared BMI categories of individuals with and without asthma based on whether they worked indoors or outdoors. BMI does not show any correlation with asthma presence in this visualization.

ggplot(asthma_clean,aes(x = , y = ))

Interactive Elements

In figure 6 we used a density graph to analyse the distribution of BMI by occupation (indoor or outdoor) and asthma status.

In figure 7a we used stacked histograms to show the distribution of medication adherence for individuals with asthma, faceted by physical activity level (Active, Moderate, Sedentary) and colored by gender. Underneath in figure 7b, we used a density plot to represent the medication adherence for individuals with asthma, faceted by gender. Based on figures 7a & 7b we concluded that medication adherence had no significant correlation with gender or physical activity level.

Results

Medication adherence had no significant correlation with gender or physical activity level
BMI does not show any correlation with asthma presence

Conclusion

This project analyzied factors associated with asthma prevalence. Of the eleven variables analysed, no strong correlations with asthma presence were present for most demographic and lifestyle factors, including gender, occupation, and physical activity level.

While BMI appears to have a weak relationship with asthma suggesting a need for further analysis with different variables. The density plot used for figure six showed a slight shift toward higher BMI values for individuals with asthma when observing by occupation type (indoor or outdoor). However, the overlap between groups indicates that BMI is not a strong predictor of asthma presence in this dataset. Likewise, medication adherence did not show a significant variation across gender or physical activity levels to suggest correlation between the variables.

In conclusion, the absence of evidence supporting strong correlations between the observed variables does not imply that asthma is random but rather it highlights the need for additional variables like individual triggers/allergies and family history in future analysis.

Contact Information

xsouder@students.kennesaw.edu

dclar175@students.kennesaw.edu

khouse15@students.kennesaw.edu