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Use the Shapiro Library and identify a primary source, peer-reviewed public health or medical journal article that used any of the statistical methodologies covered in this class to analyze their data, and use it to respond to the following prompts:
State one of the research questions that the authors researched data to answer.
What are the variables that had to be measured on each subject to answer this research question? Were they categorical or continuous?
What statistical methodology did the authors use to find the answer to this research question? Does it seem appropriate to you? Explain.
What were the results? Provide the actual statistical results and interpret them using your own words.
Provide any graph, figure, or table that displays these results and interpret it.
Do you feel that this article fully answered the research question? Explain why or why not. Include a reference citation for your chosen article.
In response to your peers, comment on their answers and conclusions drawn. Is there anything you can add? What questions do you have or need for further clarification? Are there any similarities to the article you chose?
Student 1
Article: Negin, J., Abimbola, S., & Marais, B. J. (2015). Tuberculosis among older adults–time to take notice. International Journal of Infectious Diseases, 32, 135-137.
1. Research Question
One of the research questions addressed in this article is: “What is the burden of tuberculosis among older adults in different regions of the world?”
2. Variables Measured
The primary variables measured to answer this research question include:
Age of tuberculosis patients (continuous variable)
Incidence rates of tuberculosis in different age groups (continuous variable)
Disability-Adjusted Life Years (DALYs) lost due to tuberculosis (continuous variable)
HIV infection status among tuberculosis patients (categorical variable)
3. Statistical Methodology
The authors used descriptive statistics and comparative analysis to evaluate the burden of tuberculosis among older adults. They provided prevalence rates, incidence rates, and DALYs lost due to tuberculosis in various age groups across different regions. This methodology is appropriate as it allows for a clear comparison of tuberculosis impacts among different age groups and regions, highlighting the significant burden among older adults.
4. Results
The results indicate a high burden of tuberculosis among older adults. For instance, the 2010 Global Burden of Disease estimates showed that 57% of all tuberculosis deaths globally occurred among people older than 50, with more than half of these deaths in those aged 65 and above. Additionally, older adults contributed significantly to DALYs attributable to tuberculosis, with 34% of tuberculosis DALYs occurring in patients aged 50 years and older globally.
5. Graph, Figure, or Table Interpretation
Table 1 from the article illustrates the DALYs lost due to tuberculosis among older adults in various regions. For example, in East and Central Asia, 44.9% of tuberculosis DALYs occurred in patients aged 50 years and older. This table highlights the disproportionate impact of tuberculosis on older adults, particularly in regions like East Asia and South Asia.
6. Article Evaluation
Yes, the article effectively answers the research question by providing comprehensive data on the burden of tuberculosis among older adults across different regions. The analysis underscores the need for targeted interventions to address the high incidence and mortality rates of tuberculosis in this age group, especially in low- and middle-income countries.
Reference
Negin, J., Abimbola, S., & Marais, B. J. (2015). Tuberculosis among older adults–time to take notice. International Journal of Infectious Diseases, 32, 135-137. https://www.sciencedirect.com/science/article/pii/S120197121401707X
student 2
Internet use has become an integral part of daily life for many individuals, providing access to information, communication, and entertainment. However, excessive or problematic internet use can negatively affect both physical and mental health. One area of concern is the impact of internet use on depression. The national sample (young adults) was used in the authors’ attempt to find a significant association between the use of social media and depression. This research question permits us to investigate if social media use has an impact on our feelings and has the potential to cause depression. These days, in particular, social media is the primary addiction for those young people. It occurs when people look to the quantity of likes they receive for affirmation.
Age (categorical), educational level (categorical), household income (continuous), living situation (categorical), race/ethnicity (categorical), relationship status (categorical), and sex/gender (categorical) are the variables and categorical measurements for this study. The total number of variables in this study was seven.
The study employed the Bivariates and Covariates analysis statistical approach. Regression analysis was used to see whether there was a general linear relationship between each ordered categorical independent and dependent variable, and these two methods seemed adequate for the data. All primary analyses were conducted using survey weights, considering sex/gender, age, race/ethnicity, education, household income, census region, metropolitan area, and Internet access. This allowed for the advantageous and beneficial use of the results’ nationally representative nature. The results were extensive and significant for each of the variables.
Everything on social media was associated with being younger, having sex with women, having less education, and spending more time online each day. The only significant covariate that has been shown to be connected with weekly social media visits is age (P<01.001), with younger age groups showing the greatest weekly site visitation rates. Age, living circumstances, and household income all had a significant correlation with the global frequency score. Media-site visits per week (AOR = 2,74, 95% CI = 1,86-4,04) and global frequency score (AOR = 3,05, 95% CI = 2,03–4,59) have been linked to increased depression. All connections between independent and depressive factors have excellent linear dosage resolutions, according to sensitivity analysis.
The correlations revealed modest connections among all Internet usage characteristics (mean r =.50). Furthermore, older respondents were less likely to use the Internet than younger ones (mean correlation between multiple Internet use metrics and age = -.26). The outcome measures were pretty steady. The indices of general health and depression were moderately negatively linked (mean r across a single time period = -.35).
Using a nationally representative sample of young adults, the study correctly analyzed depression and the usage of social media across a variety of social media platforms. The apparent link between social media use and depression has important implications for future research and intervention, especially in light of the rising use of social media and the high rates of illness and mortality linked to depression globally. Therefore, those who are more engaged could feel more invested in social media interactions and own greater social capital. Active individuals may, however, also be more susceptible to negative exposures that affect their ability to recognize themselves. Determining the active vs passive character of social media interactions and their effect on mood may therefore prove helpful in the future.
References
Bessière, K., Pressman, S., Kiesler, S., & Kraut, R. (2010). Effects of internet use on health anddepression: A longitudinal study. Journal of Medical Internet Research, 12(1), 78–91. https://doi-org. /10.2196/jmir.1149
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