Measures of Summarizing Data in Epidemiology

Measures of Summarizing Data in Epidemiology

Abstract

Epidemiology is a scientific inquiry of data. Epidemiology  is often referred as the basic science of public health, This paper will explore 4 different measures of data used by epidemiologists in a systemic and unbiased approach, this paper will describe and analyze how frequency, morbidity, natality and measure of association could be apply in different health scenarios in specific populations.

Keywords:  Frequency Measure, Morbidity Frequency, Natality measures and Measure of Association

 Frequency Measure is a measure of a central location which provides a value that summarizes an entire distribution of data, whereas frequency measure characterizes only part of the distribution. Frequency measures compare one part of the distribution to another part of the distribution, or the entire distribution. The most common frequently measures are ratios, proportions, and rates (Cdc.gov, 2019).

            In my example of frequency measure, I will use the data from Neoplasms- associate death in HIV infected and non-infected patients in Bahia, Brazil, between December 2000, to December 2010; the opening line by Cancer Epidemiology was that:

HIV-infected patients are at a higher risk to develop malignancies than the general population. AIDS-related malignancies are a common factor feature of late-stagedisease; patients under successful antiretroviral therapy also have an increased risk of development of non-AIDS malignancies (Marques et al., 2018).

 The cause of death registry of Bahia from January 1^st. 2000 to December 31^st

2010 shows a total of 7333,546 deaths. At least one diagnosed of malignant neoplasm was reported I 77,174 (11.4%) of cases. HIV/AIDS was reported in 5156 (0.8%) death certificates, 307 (6%) of them in association with a malignant neoplasm. Among the death certificates reporting neoplasms related deaths, in 76,867 (98%) only one malignant neoplasms was reported. In 1589 (1.97%) cases two neoplasms were reported, and 3 different neoplasms were found on 24 (%0.03) death certificates. In the group with HIV infection 307 (98%) had only one associated neoplasm (Cancer Epidemiology, 2018).

  According to the study findings there is a higher probability that patients with HIV are more at risk of developing malignant neoplasms more than HIV-negative patients.

Morbidity Frequency Measure- Morbidity has been defined as any departure subjective or objective, from a state of physiological or psychological well-being.

Morbidity encompasses disease, injury and disability. In addition it also includes the number of peoples who are ill, it can also describe periods of illness that these people experienced. Morbidity Measure is usually characterized by the people in a population who become ill (incident) or are ill at a given time (prevalence)(Cdc.gov, 2019).

The morbidity case example I found is from BMC Medicine Journal (2016). The association of cancer related to smoking, which stated that:

 Smoking is the most individual risk factor for many cancers sites but its association with breast and prostate cancer is not entirely clear. Rate advancement periods may enhance communication of smoking related risk to general population; as a result the rate advancement period is an estimation of smoking exposure cancer incident as in (BMC Med, 2016).

 The study of 897,021 smoking adults results, stated that 140,205 people had the first cancer health issue, and 53,164 people ended up dying from cancer in a period of a 12 year follow-up.

Natality Measures is the birthrate measure in a country or in a population. The example I found for Natality Measures is the number of births for the United States in 2015. The number of birth was 3,978,497. Birth rate was 12.3 per 1,000. In comparison with live birth recorded in England and Wales for the year 2015, there were 697,852 live births, in total of 2 children per woman. (Ons.gov.uk, 2019)

Measure of Association. Measure of Association refers to a comparison among an incident rate in a population. Measure of Association quantifies the relationship between exposure and disease among two groups. (CDC, 2018)

The example for measure of association I found is the Shingles virus, which is Herpes zosster. The (CDC, 2019) reoported, that Shingles is caused by the reaction of the varicella-zoster virus, the same virus that caused varicella (chickenpox), and also why some people seem to get it while others don’t.  According to The Centers for Disase Control and Prevantion, also known as the (CDC) which stated that:

Only people who had had natural infection with wild-type (VZV) or had varicella vaccination can develop herpes zoster. Children who get the varicella vaccine apperar to have a lower risk of herpes zoster compared with people infected with wild-type VZV. Many people do not remember having varicella as children; however aproximately 99.5% of people born in the United States who are 40 and older have been infected with wild-type (CDC, 2019).

  The Centers for Disease Control and Prevention expressed that “as a result, almost all adults in the United States are at risk of herpes zoster. 1 out of 3 persons will develop herpes zoster during their lifetime.” (Cdc.gov, 2019)

References

• Births in the United States, 2015. Retrieved from: https://eds- a-ebscohost-com.libauth.purdueglobal.edu/eds/detail/detail?vid=14&sid=aa6d58cd-bbce-453e-b757-ec6746ee2d6f%40sessionmgr4009&bdata=JnNpdGU9ZWRzLWxpdmU%3d#AN=30156535&db=mdc
• Cdc.gov. (2019). Shingles / Clinical Overview – Varicella Vaccine / Herpes Zoster / CDC. Retrieved from: https://www.cdc.gov/shingles/hcp/clinical-overview.html
• Cdc.gov. (2019). Principles of Epidemiology | Lesson 3 – Section 2. [online] Available at: https://www.cdc.gov/ophss/csels/dsepd/ss1978/lesson3/section2.html [Accessed 7 Feb. 2019]
• Neoplasm-associated deaths in HIV- infected and non-infected patients in Bahia, Brazil. (2018). Retrieve from: : https://search-proquest-com.libauth.purdueglobal.edu/central/docview/2042689228/fulltext/9F31C9A993E84E02PQ/1?accountid
• Ons.gov.uk. (2019). Births in England and Wales – Office for National Statistics. Retrieved from: https://www.ons.gov.uk/peoplepopulationandcommunity
• Quantification of the smoking-associated cancer risk. (2013). Retrieved from: : https://eds-a-ebscohost-com.libauth.purdueglobal.edu/eds/detail/detail?vid=5&sid=aa6d58cd-bbce-453e-b757ec6746ee2d6f%40sessionmgr4009&bdata=JnNpdGU9ZWRzLWxpdmU%3d#db=mdc&AN=27044418