PATCH 1:

You have just been  appointed to cover maternity leave in a research team. The team is about to report results of a study that followed 810 pregnant professional working women. The study aimed to determine if there is an association between working remotely or the office during pregnancy and pre-term births.

Sixty per cent (60%) of the total participants worked from home. Out of this, 30 had preterm births while 12 out of those that worked from the office had preterm births.

a)    Represent the information provided in a 2×2 contingency table.
Ans:
 

b)    What type of study design is most likely and calculate the corresponding measure of  association? Provide an appropriate interpretation of this result.
Ans:

This study design fits the criteria for an observational cohort study, in which working women are studied to determine their working style and the results are documented over a period of time. With its emphasis on tracking and comparing groups of working women across time according to their working style, the scenario fits the criteria of an observational cohort research design.

Women who work from home and those who do their jobs in an office are the two groups that make up the participants in this study. A longitudinal design, data collected over a predetermined time frame, and the lack of intervention or random assignment are the defining characteristics of an observational cohort research. Hence, the study design is most likely to be ‘Observational Cohort Study’.

Interpretation: 

From the above table, it can be stated that out of 486 working women who completed their jobs remotely, 30 gave delivery prematurely, whereas 456 did not. On the other hand, out of 324 office workers, 12 had unfavorable results. Hence, there is a clear difference in the rates of preterm birth outcomes among working women depending on how they do their jobs.

From the above data, it can be seen that 6.17 % women had preterm birth who had worked form home whereas 3.7% women working form off office had preterm birth. Therefore, preterm birth outcomes may be associated with the manner of professional job based on the above mentioned disparity.

However, it is prudent to proceed with caution because conclusive results require more research that accounts for any confounding circumstances and this study's findings lay the groundwork for future research into maternal health and wellness in the workplace, as well as possible interventions to improve this area.

c)    Calculate the prevalence of pre-term birth

Ans:
Prevalence of preterm birth = number of cases/total sample size
                          = 42/810 = 0.0518 or 5.2 percent


d)    Calculate the attributable risk. What does this mean?

Ans:
Attributable Risk (AR) = (Risk in exposed – Risk in unexposed) / Risk in exposed
                = ((30/486) – (12/324))/ (30/486)
                = (0.062 – 0.037)/0.062
                = 0.40 
This above result means that 40% of the preterm birth is potentially attributable to the factor ‘Worked from Home’. 

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PATCH 2:

If a screening test for women with ovarian cancer among 300 women reported a sensitivity of           90% and specificity of 80% as shown in the table below. Using the information provided answer the follow questions.

Complete the table provided.

 A). complete a table with the result.

b) Calculate the positive predictive value and negative predictive value. Comment on your results.  

Positive Predictive value =    a / (a +b) = 27/81 = 0.333  
Therefore, NPV = 33.3%

This indicates that 33.3% of the individuals in this group who get a positive test result are affected by ovarian cancer. The percentage of people who have a positive test result and truly have the disease is what is meant by the term "positive predictive value." In this particular instance, it is anticipated that around one-third of the individuals of the group that were tested and had a positive result will be diagnosed with ovarian cancer.  

Negative Predictive Value = d / (c +d) = 216/219 = 0.986 
Therefore, NPV = 98.6 % 

It can be concluded that 98.6% of the individuals in this category who get a negative test result do not have ovarian cancer. According to the negative predictive value, the percentage of people who have a negative test result but do not have the ailment is the proportion of their population. In this particular scenario, it is anticipated that about 99 percent of persons who obtain a negative test do not have ovarian cancer. As a result, the test results are quite reliable in determining whether or not this particular population is affected by ovarian cancer.

c) Calculate the accuracy:

Accuracy = (a + d) / (a + b +c + d) = (27+216)/300 = 0.81 
Hence, accuracy of the test is 81%

d) Calculate the prevalence

Prevalence = number of cases/total sample size
        = 30/300 = 0.1 or 10%

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