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Posted: July 14th, 2023

# The incidence rate per 100,000 for type 2 diabetes in 2018

Initial discussion post will all discussion posts must be minimum 250 words, references must be cited in APA format 7th Edition, and must include minimum of 2 scholarly resources published within the past
5-7 years (not part of the classroom coursework).

3.1 In 2018, there were 105 new cases of type 2 diabetes reported in Smithville, a city of 500,000. This brought the total number of active cases of type 2 diabetes in Smithville to 3,075. During this time, there were 105 deaths attributable to the disease.
1. What was the incidence rate per 100, 000 for type 2 diabetes in 2018?
2. What was the prevalence rate of type 2 diabetes per 100,000 in 2018?
3. What was the cause-specific death rate of type 2 diabetes in 2018?

3.2 A city contains 100,000 people (45,000 male and 55,000 females), and 1,000 people die per year (600 males and 400 females). There were 50 cases (40 males and 10 females) of lung cancer per year of whom 45 died (36 males and 9 females).
Using this information compute:
1. The crude mortality rate per 1,000
2. The sex-specific mortality rate per 1,000
3. The cause-specific mortality rate per 1,000 for lung cancer
4. The case fatality rate for lung cancer
5. The proportionate mortality ratio for lung cancer
3.3 A new rapid blood test was created to test for human papillomavirus (HPV) in a rural clinic. The following is a 2 × 2 chart that describes the results of the test. Answer questions 1–7 using the 2 × 2 chart.
HPV No HPV Totals
Positive Test 95 37 132
Negative Test 39 278 317
Totals 134 315 449

1.What is the sensitivity of this test?
2. What is the specificity of this test?
3. What is the positive predictive value?
4. What is the negative predictive value?
5. Describe in words the sensitivity of this test.
6. Describe in words the negative predictive value.
7. What is the disease prevalence in this population?

3.4 An epidemiological study is conducted to learn about the relationship between celiac disease and colon cancer. Suppose there are 77 cases of colon cancer in 68,000 person-years in persons with celiac disease and 54 cases of colon cancer in 215,000 person-years in those without celiac disease. (The overall rate in both groups combined = 131 cases in 283,000 person-years overall.) Use this information to answer questions 1–3.
1. Calculate the rate of colon cancer in the celiac group (R1), in the no celiac group (R0), and overall (R). Express all rates in “per 100,000 person-years.”
2. Calculate and interpret the relative risk of colon cancer associated with celiac disease.
3. Calculate and interpret the attributable risk of colon cancer associated with celiac disease.
3.5 This week you have been learning about from your readings about the critical components of data analysis, including bias, causality, confounding, and interaction. It also covers more in-depth discussion of study designs, as well as a comprehensive review of ways to report on randomized and nonrandomized studies. So what do these mean to you as an advanced practice nurse? How are you able to see the connection between the numbers on the page and ways you might apply this in a practice situation? How is this data important to your role as an advanced practice nurse. Why is it important to know how to calculate and interpret prevalence and incidence rates of diseases? Why are mortality and morbidity rates important? Why is the sensitivity of a specific test important? What about relative risks.
Most nurses don’t like what we call numbers or statistics it is as if our brains just want to shut off but understanding how to interpret these results and numbers is important to population health interventions in what way?

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3.1

The incidence rate per 100,000 for type 2 diabetes in 2018 can be calculated by dividing the number of new cases by the population at risk and then multiplying by 100,000. In this case, the number of new cases is 105 and the population at risk is 500,000. Therefore, the incidence rate per 100,000 is (105/500,000) * 100,000 = 21.

The prevalence rate of type 2 diabetes per 100,000 in 2018 can be calculated by dividing the total number of active cases by the population and then multiplying by 100,000. In this case, the total number of active cases is 3,075 and the population is 500,000. Therefore, the prevalence rate per 100,000 is (3,075/500,000) * 100,000 = 615.

The cause-specific death rate of type 2 diabetes in 2018 can be calculated by dividing the number of deaths attributable to the disease by the population and then multiplying by 100,000. In this case, the number of deaths attributable to type 2 diabetes is 105 and the population is 500,000. Therefore, the cause-specific death rate per 100,000 is (105/500,000) * 100,000 = 21.

3.2

The crude mortality rate per 1,000 can be calculated by dividing the total number of deaths by the total population and then multiplying by 1,000. In this case, the total number of deaths is 1,000 and the total population is 100,000. Therefore, the crude mortality rate per 1,000 is (1,000/100,000) * 1,000 = 10.

The sex-specific mortality rate per 1,000 can be calculated by dividing the number of deaths in each sex by the corresponding population and then multiplying by 1,000. For males, the number of deaths is 600 and the population is 45,000. Therefore, the male-specific mortality rate per 1,000 is (600/45,000) * 1,000 = 13.33. For females, the number of deaths is 400 and the population is 55,000. Therefore, the female-specific mortality rate per 1,000 is (400/55,000) * 1,000 = 7.27.

The cause-specific mortality rate per 1,000 for lung cancer can be calculated by dividing the number of deaths due to lung cancer by the total population and then multiplying by 1,000. In this case, the number of deaths due to lung cancer is 45 and the total population is 100,000. Therefore, the cause-specific mortality rate per 1,000 for lung cancer is (45/100,000) * 1,000 = 0.45.

The case fatality rate for lung cancer can be calculated by dividing the number of deaths due to lung cancer by the number of cases of lung cancer and then multiplying by 100. In this case, the number of deaths due to lung cancer is 45 and the number of cases of lung cancer is 50. Therefore, the case fatality rate for lung cancer is (45/50) * 100 = 90%.

The proportionate mortality ratio for lung cancer can be calculated by dividing the number of deaths due to lung cancer by the total number of deaths and then multiplying by 100. In this case, the number of deaths due to lung cancer is 45 and the total number of deaths is 1,000. Therefore, the proportionate mortality ratio for lung cancer is (45/1,000) * 100 = 4.5%.

3.3

The sensitivity of this test can be calculated by dividing the true positive results (95) by the sum of true positive results and false negative results (95 + 39) and then multiplying by 100. Therefore, the sensitivity of this test is (95/(95 + 39)) * 100 = 70.76%.

The specificity of this test can be calculated by dividing the true negative results (278) by the sum of true negative results and false positive results (278 + 37) and then multiplying by 100. Therefore, the specificity of this test is (278/(278 + 37)) * 100 = 88.27%.

The positive predictive value can be calculated by dividing the true positive results (95) by the sum of true positive results and false positive results (95 + 37) and then multiplying by 100. Therefore, the positive predictive value is (95/(95 + 37)) * 100 = 71.88%.

The negative predictive value can be calculated by dividing the true negative results (278) by the sum of true negative results and false negative results (278 + 39) and then multiplying by 100. Therefore, the negative predictive value is (278/(278 + 39)) * 100 = 87.66%.

The sensitivity of this test indicates the proportion of individuals with the disease who test positive. In other words, it measures the ability of the test to correctly identify those who have the disease.

The negative predictive value indicates the proportion of individuals without the disease who test negative. It measures the ability of the test to correctly identify those who do not have the disease.

The disease prevalence in this population can be calculated by dividing the number of positive test results (132) by the total population (449) and then multiplying by 100. Therefore, the disease prevalence in this population is (132/449) * 100 = 29.4%.

3.4

The rate of colon cancer in the celiac group (R1) can be calculated by dividing the number of cases of colon cancer in the celiac group (77) by the person-years in the celiac group (68,000) and then multiplying by 100,000. Therefore, the rate of colon cancer in the celiac group is (77/68,000) * 100,000 = 113.24 per 100,000 person-years.
The rate of colon cancer in the no celiac group (R0) can be calculated by dividing the number of cases of colon cancer in the no celiac group (54) by the person-years in the no celiac group (215,000) and then multiplying by 100,000. Therefore, the rate of colon cancer in the no celiac group is (54/215,000) * 100,000 = 25.12 per 100,000 person-years.

The overall rate (R) can be calculated by dividing the total number of cases of colon cancer (131) by the total person-years (283,000) and then multiplying by 100,000. Therefore, the overall rate is (131/283,000) * 100,000 = 46.25 per 100,000 person-years.

The relative risk of colon cancer associated with celiac disease can be calculated by dividing the rate of colon cancer in the celiac group (R1) by the rate of colon cancer in the no celiac group (R0). Therefore, the relative risk is 113.24/25.12 = 4.51. This means that individuals with celiac disease have a 4.51 times higher risk of developing colon cancer compared to those without celiac disease.

The attributable risk of colon cancer associated with celiac disease can be calculated by subtracting the rate of colon cancer in the no celiac group (R0) from the rate of colon cancer in the celiac group (R1). Therefore, the attributable risk is 113.24 – 25.12 = 88.12 per 100,000 person-years. This means that 88.12 cases of colon cancer per 100,000 person-years can be attributed to celiac disease.

3.5

As an advanced practice nurse, understanding the critical components of data analysis is crucial for effective population health interventions. Bias, causality, confounding, and interaction are important concepts to consider when interpreting research findings and applying them in practice. These concepts help in identifying potential sources of error, establishing causal relationships, addressing confounding factors, and understanding how variables interact with each other.

The ability to calculate and interpret prevalence and incidence rates of diseases is essential for assessing the burden of diseases in a population. Prevalence rates provide information about the total number of cases of a disease in a population at a specific point in time, while incidence rates indicate the rate at which new cases of a disease occur in a population over a given period. These rates help in identifying trends, planning interventions, and evaluating the impact of public health programs.

Mortality and morbidity rates are important measures of disease burden and health outcomes. Mortality rates provide information about the number of deaths due to specific causes in a population, while morbidity rates indicate the occurrence of diseases or health conditions. These rates help in understanding the impact of diseases on population health, identifying high-risk groups, and targeting interventions.

The sensitivity of a specific test is important as it measures the test’s ability to correctly identify individuals who have the disease. A high sensitivity indicates that the test can accurately detect true positive cases, reducing the chances of false negatives.

Relative risks are important in epidemiology as they measure the strength of association between a risk factor and a disease outcome. They provide information about the likelihood of developing a disease in individuals exposed to a specific risk factor compared to those unexposed. Understanding relative risks helps in identifying risk factors, designing preventive strategies, and evaluating the effectiveness of interventions.

Being able to interpret and analyze data is crucial for advanced practice nurses in their role as advocates for population health. It helps in making informed decisions, developing evidence-based interventions, identifying health disparities, and evaluating the outcomes of healthcare programs. By understanding the numbers and statistics, advanced practice nurses can contribute to improving the health and well-being of individuals and communities.

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