Prevalence of Asthma in the United States in 2009

Summary of the study

In the U.S., the prevalence of Asthma escalated significantly in the period between 2001 and 2009. This medical report indicates that about 25.5 million people had the illness in 2009. According to the report, asthma prevalence was high in particular demographic groups. For instance, the prevalence of the disease among females, children under the age of 17, the blacks, multiple raced individuals, Puerto Ricans and the poor varied significantly.

The intended purpose of this report was to inspect different levels of asthma outcomes: health care experiences and mortality for asthmatic people only in the U.S. population. The level of asthmatic people in the population considers variation in the prevalence of asthma among varied demographic categories.

According to this document, the percentage of asthmatic patients who visited the ‘emergency department’ and were hospitalized remained the same in the period between 2001 and 2009. The rate of asthmatic patients who visited the primary care unit reduced significantly by 2009 (U.S. National Centre for Health Statistics, 2009).

In 2009, the percentage of Black patients who reported asthmatic symptoms was equivalent to that of the White patients. The level of asthmatic reports was high among children under the age of 17 compared to adults. The rate was also high in people who reported poor backgrounds compared to those who were economically privileged.

How to use sampling and confidence interval knowledge to explain statistical findings

Both meta-examinations and individual studies are only effectively reported if a point of approximation is used together with a related confidence interval. Using either sampling method or confidence intervals, one can accurately interpret the findings of a particular study. The study above was an example of ‘individual studies’ and evaluated the prevalence of asthma in the United States in 2009.

If I was to explain the findings of this study to a person with minimal understanding of statistics, confidence intervals would work. It would be possible in the sense that confidence intervals provide possible scale of possible outcomes for the general population. However, this is only possible if the findings of the study hold true (Harold & Christopher, 1989).

Confidence intervals are used to statistically relate different risk factors for the development of asthmatic condition. Confidence Intervals can be used to explain the findings by helping the person understand the relationship between different variables qualitatively. For instance, one may relate the high prevalence of asthma in a population with reported physical and sexual maltreatment or neighbourhood conditions (Harold & Christopher, 1989).

Sampling methods can be used to describe the findings by qualitatively examining the assumptions of the study. This approach does not require statistical knowledge for one to interpret and understand the findings of the study. The hypotheses of the study can be qualitatively evaluated to find out whether the outcomes of the study correlate (Lisa, 2011). For instance, it one of the hypotheses assumes that environmental factors contribute to increased asthma prevalence in a population, it is possible to check the correspondence with the actual findings.

Importance of Confidence interval in Public Health

In many situations practitioners in the public health sector depend on statistical data to understand and interpret relevant events. At times, they may be interested in finding the actual number of particular events related to health issues.

Confidence interval becomes significant when these practitioners are analysing the true fundamental risks of a health discrepancy in a community. Health statistics that are observed does not always show a perfect reflection of basic perils in the population (Fay & Feuer, 1997).

Speaking in technical terms, confidence intervals can help researchers know an approximation of the possible gap between the true population variables and observed percentages. Therefore, to understand the possible size of that gap can give public health officers vast information about ways of interpreting the statistical data observed.


Fay, M., P. & Feuer, E., F. (1997). Confidence Intervals for Directly Standardized Rates: A Method based on the Gamma Distribution. Statistics in medicine, 16(1), 798-801.

Harold, A., K. & Christopher, T., S. (1989). Statistical Methods in Epidemiology. New York: Oxford University Press.

Lisa, M., S. (2011). Essentials of Biostatistics in Public Health: Confidence Interval Estimates. Boston: Boston University Press.

U.S. National Centre for Health Statistics. (2009). National Centres for Health Statistics: Prevalence of asthma in the United States in 2009. National Health Interview Survey, 233(111), 123-339.