Asthma among American Adults
The progress made in science, particularly health care, depends majorly on the findings from systematic research carried out. By researching the health care sector, investigators can answer various questions and understand different challenges by understanding the relationship between varying events and characteristics. The results of the research are the cornerstone of new decisions made to improve the healthcare sector. Therefore, this paper describes a data analysis plan to answer the research questions on American adults’ asthma occurrence.
Asthma is among the leading high-cost chronic conditions in the United States of America. According to studies, the condition is likely to affect the United States exceeding 43 million during their life, subsequently leading to the use of an annual figure of approximately $30 billion in costs. Further, government reports suggest that some 3500 persons succumb to asthma and other related complications every year. Therefore, minimizing the significant burden and succeeding in asthma management needs a long-term approach that takes in place various factors that include medical intervention and patient education. To achieve these, it is essential to use data-driven decisions towards managing the chronic condition.
Research Questions
According to Ratan et al. (2019), research questions are vital for scientific investigations since they are based on a study’s hypothesis. Further, research questions are essential in displaying the study’s direction and aim and provide the context for developing logical arguments of the research. The research questions for the present study are as follows.
- What is the most prevalent age in developing an asthmatic condition?
- Is there a relationship between smoking and asthma?
- Does asthma-related medication help to improve the condition?
- What is the relationship between the time since diagnosis of asthma condition and the recovery?
Population and Data
The current study dataset consists of data found in the United States’ Center for Disease Control and Prevention public repository and collected by the Asthma Call-back Survey in 2016. The survey is conducted after the collection of data on the behavioral Risk Factor Surveillance System. According to Banerjee & Chaudhury (2010), a target population is an entire group under which information is extracted. This study’s population involves 18 years or older persons who have had a history of asthma from the United States of America. The population used for the present study is suitable because it gives the general outlook of the prevalence of asthma in the country, hence answering the research questions.
Variables
Independent Variables
According to several studies, independent variables are used in research to predict the outcome variable. This study’s explanatory variables include:
- Age at diagnosis
The above variable is continuous, and it shows the age at which the respondent was first diagnosed with the asthmatic condition. The variable is essential in answering the first question of the current study.
- Smoking Status
The variable is categorically coded from with numbers 1 to 4 representing “Current smoker – now smokes every day,” “Current smoker – now smokes some days,” “Former Smoker,” and “Never smoked.” The variable answers the second research question.
- Last took asthma medication.
The variable indicates the time since a respondent living with the condition took the medication. It categorical and coded from 1 to 7. The variable is vital in answering the third research question.
- Time since diagnosis
The variable represents the time since the respondent was diagnosed with asthma. Does it answer whether there is a relationship between the time since diagnosis of asthma condition and the recovery? The variable is categorically coded from 1 to 7.
Dependent Variable
- Asthma during the last 12 months
The variable is the outcome factor in the present study, and it indicates if the respondent has encountered an asthmatic attack in the previous 1 month. The variable is categorical coded 1 and 2 representing “Yes” and “No.”
Data Analysis
Data analysis is essential in research as it is the foundation under which conclusions concerning the data are made. This study carries out both descriptive and inferential statistics for the data set. Descriptive statistics is an essential tool in statistical analysis as it forms the basis of understanding data by simplifying it (Banerjee & Chaudhury, 2010). Data is described using summary statistics such as the mean and standard deviation on variables containing continuous data, for example, the age at diagnosis. On the other hand, frequencies and percentages are calculated for categorical variables. Further, bar charts and histograms are employed to display the data visually.
The current study uses correlation and regression analysis statistical approach to answer the research questions. Correlations are useful in showing the association between varying factors in a study. The statistical technique will provide both the strength and the direction of the relationship between the predictor and the outcome variable, thus providing the answers to the study’s research questions. According to Faguet and Davis (1984), regression analysis is important in medical research for patient management. Regression analysis is also vital to show the relationship between two or more explanatory variables in predicting the dependent variable. This study employs regression to show the association between the characteristics in predicting that a patient will experience an asthmatic attack. The statistical technique is, therefore, essential in determining the conclusions of the study.
References
Banerjee, A., & Chaudhury, S. (2010). Statistics without tears: Populations and samples. Industrial Psychiatry Journal, 19(1), 60. https://doi.org/10.4103/0972-6748.77642.
2016 Behavioral Risk Factor Surveillance System Asthma Call-back Survey History and Analysis Guidance National Asthma Control Program. (2016). https://www.cdc.gov/brfss/acbs/2016/pdf/acbs_history 2016-final-version-2-508 .pdf
Faguet, G., & Davis, H. (1984, July 22). (PDF) Regression Analysis in Medical Research. ResearchGate. Retrieved from https://www.researchgate.net/publication/16468514_Regression_Analysis_in_Medical_Research
Ratan, S., Anand, T., & Ratan, J. (2019). Formulation of research question – Stepwise approach. Journal of Indian Association of Pediatric Surgeons, 24(1), 15. https://doi.org/10.4103/jiaps.jiaps_76_18.