Statistics is a very important tool in decision-making. Information from statistical methods provides important facts that are used as the basis for making informed decisions. Statistics is applied in virtually all businesses and it is an essential tool in organizations seeking to improve operations, improve conditions of a certain phenomenon, or even to evaluate performance (Biggeri, 2004, p. 7). This paper explores the use of statistics in a hospital researching people with HIV.
The hospital has a policy of using every requisition for a test involving blood to test if the patient has HIV. The information is then recorded along with personal details of the patient like gender, age, etc. More data is obtained from the Voluntary Counselling and Testing center in the hospital. Here more data is collected. Among the data collected includes the age of the client, gender, number of sexual encounters in the last one year, whether the client uses condoms or not, whether the client has HIV or not, the frequency of hospitalization of the client which is recorded as weekly, monthly, yearly or after more than one year, etc. The two sets of data are then merged and analyzed together to give information related to the distribution and prevalence of HIV.
In the analysis of the aforementioned data, the hospital uses a variety of descriptive statistics to get a good interpretation of the data. An example of descriptive statistics used in the analysis of this data is the mean of the clients with HIV. This helps to approximate the number of people in the area with HIV and can be used in conjunction with similar statistical information from other areas to help in the allocation of Antiretroviral (ARV) drugs (Kirk, 2006, p. 1). The information can also be used to help in the distribution of medication for common opportunistic infections. This includes infections like tuberculosis, candidiasis, etc.
The data analysts in the hospital also use inferential statistics in analyzing the HIV data. An example of inferential statistics used in the analysis of this data is a test to check if HIV is significantly prevalent in women. Such analysis is very important since it gives information that can be used to plan for public education regarding HIV (Kirk, 2006, p. 1). The information is also used to determine the prevalence of HIV infection through breastfeeding or during birth.
The HIV data from the hospital uses virtually all the levels of measurement. For instance, the gender of the client/patient is under the nominal level of measurement. The age of the client/patient is under the interval level of measurement, the number of sexual encounters in the last one year is in the ratio level of measurement and lastly, the frequency of hospitalization of the client/patient lies under the ordinal level of measurement (Kirk, 2006, p. 1). Below is an explanation of each level of measurement to justify the assignment of the variables to the levels of measurement.
Nominal variables are normally derived from names or categories of responses. Examples of nominal variables include religion, color, gender, etc. Ordinal variables are used in cases where comparisons of the degree of possession of the dependent variable are desirable. Thus the values of an ordinal variable can be ordered. An example is a variable “size” taking the values small, medium, and big. The interval level of measurement is used for variables that can easily be categorized into intervals. For instance, “age” can be categorized into young, medium-aged, and old (Kirk, 2006, p. 1). The ratio level of measurement is used on variables on which all computations can be performed e.g. the mean and standard deviation of the number of sexual encounters in the last one year can be easily computed.
As mentioned earlier, statistical analysis of data is very useful in decision-making. The information from this analysis is, especially, useful if it is accurate. The discussed hospital makes sensitive decisions informatively and thus they can be able to give substantial reasons for their decisions. For instance, the hospital can request more ARVs if the mean number of people infected with HIV is higher than that of another hospital. The results from the statistical analysis of data may also help the hospital to make informed decisions about the protection of staff and patients from infections (Biggeri, 2004, p. 11). For instance, policies can be put in place to limit the spread of tuberculosis within the hospital. As stated earlier, the information from the analyzed data can be used in educating the public about HIV.
Reference List
Biggeri, L. (2004). Measuring for decision making. Web.
Kirk, M. (2006). Levels of measurement. Web.