# PUB 540 Measuring Morbidity: Prevalence and Incidence

## PUB 540 Measuring Morbidity: Prevalence and Incidence

PUB 540 Measuring Morbidity: Prevalence and Incidence

**Part 1**

How many people had HIV in January 2012? Present or describe the formula you used to arrive at your answer.

Total population of the community = 20, 0000

Number of people with the diseases (HIV) = 2.2/100 (20,000) = 440

**Calculate the Incidence Rate**

Incidence rate = (Shivashankar et al., 2017)

Number of new HIV cases = 50

Denominator = 20,000 (population remained constant for a period of one year)

= 1000

Incidence Rate = 50/20,000 (1000)

= 2.5 new cases for every 1000 people

Prevalence refers to the number of people in the population who have the disease (Auld et al., 2017). In the above case, there was the determination of the HIV prevalence among the population of 20,000. Given that 2.2% of the population had the disease, this was a representation of 440 people. In other words, the prevalence was 440. The prevalence included all the cases, both new and the preexisting cases (Cai et al., 2017). The HIV prevalence above was measured at a particular point in time. Prevalence of the diseases may also be attributed to the proportion of the population or the persons suffering from the diseases on a specified date or point in time. Incidence on the other hand, is the rate or proportion of the population who develop a condition in a given period. In the above case, 5 more people became HIV positive in a period of 12 months (Cho et al., 2017). The prevalence rate was therefore determined to be 2.5 for every 1000 people. Both the prevalence rates and the incidence rates can be applied in the determination of the rate of infections. In other words, they can be used to determine the risk of the diseases for a given population (Meymandi et al., 2017).

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Given the increase in the incidence rate from 0.5 per every 1,000 persons to 2.5 for every 1,000 persons, the epidemiologist should be more concerned with the disease (HIV). In other words, going by the calculation, there is the significant increase in the rate of infections in a period of 12 months.

**Part 2**

**Solution**

Total Population = 20,000

Disease prevalence = 2.2%

= 440 people

Number of people who are diseases negative = (20,000 – 440)

= 19, 560 people

Sensitivity = TP/(FN+TP) = 99.1% (Nazir, 2017)

TP/440 = 0.991

TP = 0.991 x 440 = 436

FN = 440- 436 = 4

Specificity = TN/(TN+FP) = 90%

TN/19560 = 0.90

TN = 0.90 x 19560 = 17,604

FP = 19560- 17,604 = 1956

Positive predictive value (PPV) = TP/(TP+FP) (Siegel et al., 2017)

436/(436+1956) = 18.2%

Negative predictive value (NPV) = TN/(TN+FN)

17604/(17604+4) = 99.97%

A rapid test used for diagnosing HIV has a sensitivity of 99.1% and a specificity of 90%.

The rapid testing applied in the diagnosis of HIV has a sensitivity of 99.1% and the specificity of 90% (Fan et al., 2016). The above test increase the accuracy in the determination of the disease prevalence and the cases that exist among the population. Under normal testing processes, the level of significance if often taken at 95%. However, in this case, the accuracy level has been increased to 99.1%. On the other hand, the specificity is used at 90%. Specificity and sensitivity are statistical measures that are applied in the determination of the performance of a binary classification tests that are commonly used in medicine (Bujang & Adnan, 2016). In the above case, specificity was applied to measure the proportion of true negatives that were correctly identified i.e. the proportion of the population of those who truly did not have the condition, the unaffected individuals. The sensitivity, on the other hand, was applied to measure the proportion of the true positives that were correctly identified (Zalesky et al., 2016). The formula applied to arrive at the answer was accurately used to determine the accuracy at each stage. In other words, the formula takes into consideration of different factors that can be used to determine the accuracy in the outcomes.

**References**

Cai, Y., Kang, Y., & Wang, W. (2017). A stochastic SIRS epidemic model with nonlinear incidence rate. *Applied Mathematics and Computation*, *305*, 221-240. Retrieved from: https://doi.org/10.1016/j.amc.2017.02.003

Siegel, R. L., Miller, K. D., Fedewa, S. A., Ahnen, D. J., Meester, R. G., Barzi, A., & Jemal, A. (2017). Colorectal cancer statistics, 2017. *CA: a cancer journal for clinicians*, *67*(3), 177-193. Retrieved from: **https://doi.org/10.3322/caac.21395**

Cho, J. H., Oh, D. S., Hong, S. H., Ko, H., Lee, N. H., Park, S. E., … & Choi, C. W. (2017). A nationwide study of the incidence rate of herb-induced liver injury in Korea. *Archives of toxicology*, *91*(12), 4009-4015. Retrieved from: https://link.springer.com/article/10.1007/s00204-017-2007-9

Nazir, M. A. (2017). Prevalence of periodontal disease, its association with systemic diseases and prevention. *International journal of health sciences*, *11*(2), 72. Retrieved from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5426403/

Shivashankar, R., Tremaine, W. J., Harmsen, W. S., & Loftus Jr, E. V. (2017). Incidence and prevalence of Crohn’s disease and ulcerative colitis in Olmsted County, Minnesota from 1970 through 2010. *Clinical Gastroenterology and Hepatology*, *15*(6), 857-863. Retrieved from: https://doi.org/10.1016/j.cgh.2016.10.039

Meymandi, S. K., Forsyth, C. J., Soverow, J., Hernandez, S., Sanchez, D., Montgomery, S. P., & Traina, M. (2017). Prevalence of Chagas disease in the Latin American–born population of Los Angeles. *Clinical Infectious Diseases*, *64*(9), 1182-1188. Retrieved from: https://doi.org/10.1093/cid/cix064

Auld, A. F., Shiraishi, R. W., Oboho, I., Ross, C., Bateganya, M., Pelletier, V., … & Delcher, C. (2017). Trends in prevalence of advanced HIV disease at antiretroviral therapy enrollment—10 countries, 2004–2015. *MMWR. Morbidity and mortality weekly report*, *66*(21), 558. Retrieved from: 10.15585/mmwr.mm6621a3

Bujang, M. A., & Adnan, T. H. (2016). Requirements for minimum sample size for sensitivity and specificity analysis. *Journal of clinical and diagnostic research: JCDR*, *10*(10), YE01. Retrieved from: 10.7860/JCDR/2016/18129.8744

Zalesky, A., Fornito, A., Cocchi, L., Gollo, L. L., van den Heuvel, M. P., & Breakspear, M. (2016). Connectome sensitivity or specificity: which is more important?. *Neuroimage*, *142*, 407-420. Retrieved from: https://doi.org/10.1016/j.neuroimage.2016.06.035

Fan, Y., Xi, L., Hughes, D. S., Zhang, J., Zhang, J., Futreal, P. A., … & Wang, W. (2016). MuSE: accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling from sequencing data. *Genome biology*, *17*(1), 1-11. Retrieved from: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1029-6