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Introduction
A satisfactory conclusion about the level of morbidity can only be obtained using a set of evaluation criteria when conducting a retrospective analysis. Indicators can be calculated concerning the number of persons (such as those who first applied to a medical institution, injured, or deceased) and the number of cases taken into account. The incidence of new cases per 1000 person-years that had a culture-positive chance during the first survey can be calculated based on the following formula: Total no. of new cases of a disease divided into Total population at risk multiplied by Population size (Davidsm et al., 2019). Therefore, taking into account the basic formula, the 1000 person-years case, the number of culture-positive cases of 500, and culture-negative of 10000, the incidence rate will be 20 new cases.
Discussion
The incidence of new cases per 1000 person-years that did not have a culture-positive case during the first survey can be calculated based on the formula mentioned in the first question. In general, the rate of cases without culture-positive is 1 new case per 1,000 populations. The total number of new cases of the disease was ten, and the total population at risk was ten thousand.
Relative risk is a statistical term that refers to the risk of a particular event occurring in one group relative to another. According to the researchers, the most frequent formula for addressing the question of relative risk is RR = [a / (a + b)] / [c / (c + d)] (Jantschi, 2021). The interpretation of namings is as follows:
- a = Number of people exposed to the factor and falling ill
- b = Number of people exposed to the factor but not sick
- c = Number of people who were not exposed to the factor but became ill
- d = Number of people who were not exposed to the factor and did not get sick (Jantschi, 2021).
The results of the calculations are limited due to the lack of information gained. As far as the collected data provides only the information regarding the cases with and without culture-positive results, the outcome of the calculation is not accurate enough. According to the defined issue, the a, c, and b, d inquiries were identified as the same numbers 500 ad 10000, respectively. Such a decision is justified by the results, which correspond with the general risk of relevant risk calculations (Jantschi, 2021). Based on the calculations, the risk is 1, meaning there is no difference in risk between the two groups. Considering these results, we can conclude that the studied factor does not affect the probability of the outcome (lack of relationship between the factor and the outcome).
Epidemic of Rubella
In the investigation, the five age categories were included. The formula which can be used for calculating the incidence of illness in percentage is IR = NC / AP * 100, where NC is the number of ill people in the particular chosen age group, and AP is the average population during the chosen period. The first is 0-9 years with a total population of 20400, the number of new cases at 11000, and the percentage of incidence of illness will be 53.9. The second age group is from 10 to 19 years with a total population of 12900 and several ill of 7000. The incidence of illness in percentage will be 54.2%. The incidence rate of the third age group (from 30 to 39 with a total number of 16100 and ill of 8800) will be 54.6%. The fourth group includes a population of 7800 with 4200 cases of illness from 40 to 59, counting the 53.8% incidence rate. The fifth group includes people over sixty years old. The total population is 61400, while the number of ill people was only 200, making the incidence of illness rate 4.7%.
To find the percentage of asymptomatic patients, the number of infected and the total number of age groups in the city were used. To find how many percent one number is of another, it is necessary to divide the part that is being asked about by the total number and multiply by 100% (Jantschi, 2021). Therefore, the following results for different age groups can be highlighted: 0-9 – 9.8%; 10-19 – 10%; 20-39 – 9.9%; 40-59 – 10.2%; 60+ – 90%.
An increase in the susceptible layer due to the birth rate determines the formation of a pathogen with higher epidemic potential and an increase in the incidence. Breast cancer occurs throughout the world in women of any age after puberty, but the incidence increases with older age. The activation of the epidemic process, in turn, is accompanied by an increase in the immune layer, which reduces the epidemic potential of the pathogen and determines the decline in the incidence even before the susceptible layer is exhausted. Based on the information provided about the disease concerning age groups, it can be noted that the most vulnerable group is newborns and children under nine years of age. The replenishment of the population with non-immune carriers due to the birth rate creates the prerequisites for increasing the virulence of circulating pathogens (due to selection in the host population), which leads to another increase in the incidence (Kamiya et al., 2022). Thus, the previous increase in the birth rate can be estimated as a span of about ten years. In other words, the previous outbreak roughly occurred ten years ago, corresponding to the increase in the birth rate. The results of the epidemiologists’ investigations regarding the epidemic process of rubella development show that outbreaks usually occur over a 6-9 years period. Therefore, the suggestions regarding the period when rules previously occurred in the city correspond with the theory.
Garden Pesticide and Breast Cancer
In the US, breast cancer is the most common form of cancer in women. Men can also develop this pathology, but it is less common, accounting for less than 1% of all cases of breast cancer. There is a growing perception that expensive organic foods are safer than conventional fruits and vegetables grown with pesticides. Consumer fears are fueled by media reports that pesticides are a direct or indirect cause of cancer. Pesticides and industrial pollutants do not increase the risk of breast cancer.
Researchers from the Yale Center for Cancer Research refuted on Friday the data obtained earlier by Danish scientists (Cockburn et al., 2019). The study followed one thousand women living in the state of Connecticut, known for its environmental troubles (Cockburn et al., 2019). Scientists have not found any connection between the level of pesticides and industrial pollutants in the blood of women and the occurrence of breast cancer. Over the past few years, evidence has emerged in the scientific literature about the potential for pesticides to cause breast cancer (Gammon et al., 2019). According to the hypotheses of Danish scientists, pesticides, acting like female sex hormones – estrogens, stimulate the growth of cancer cells (Cockburn et al., 2019). However, various studies, such as the one conducted by Gammon et al. (2019), proved the suggestion of the researcher from Yale. Therefore, based on the analyzed literature, there is no direct correlation between the use of lawn and garden pesticides and breast cancer occurrence.
Conclusion
In conclusion, the number of women who had breast cancer and used pesticides and women who did not use pesticides and had breast cancer is almost equivalent. Therefore, the association between the two mentioned factors cannot be drawn. The relation between the two events is fifty percent which is relatively low in stating the fundamental connection between the two events. Considering the theoretical data discussed above, it is rational to conclude that there is no association between lawn and garden pesticides and breast cancer. Additional research focused on vaster or worldwide investigations is required to make accurate conclusions. The resources of such an organization as the World Health Association should be allocated to provide precise research results. Existing research is too local, showing no correlation between the discussed illness and pesticides.
References
Cockburn, M., Langholz, B., Mills, P., Shahabi, K., Tayour, C.,Ritz, B., & Wu, P. (2019). A case–control study of breast cancer risk and ambient exposure to pesticides.Environmental Epidemiology, 3(5), 1-7. Web.
Davidsm, R., Hilderink, H., Nielen, M., Korevaar, J.C., Poos, R., Schellevis, F., Spronk, I., & Verheij, R. (2019) Calculating incidence rates and prevalence proportions: Not as simple as it seems. BMC Public Health 19(512).
Gammon, M., Neugut, A., Niehoff, N., Parada, H., Stellman, S., & Teitelbau, S. (2019). Self-reported residential pesticide uses and survival after breast cancer. International Journal of Hygiene and Environmental Health, 222(8), 1077-1083.
Jantschi, L. (2021). Formulas, algorithms and examples for binomial distributed data confidence interval calculation: Excess risk, relative risk and odds ratio.Mathematics, 9(19). Web.
Kamiya, H., Kanai, M., Kitajima, H., Matsui, T., Mori, Y., Oishi, K., Okuno, H., Sungawa, T., & Tanaka-Taya, K. (2022). Epidemiology of congenital rubella syndrome related to the 2012–2013 rubella epidemic in Japan. Journal of the Pediatric Infectious Diseases Society, 43.
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