Casting your vote on risk

Policy making is dependent on information. Quantitative data can serve as the evidence used to support or oppose a given initiative. However, because data is so open to analysis and varied interpretations, each side of a position can shape and present the data to support their distinct positions. Consider this:
“Coverage of mammograms for breast cancer screening is mandated by the Affordable Care Act, which provides that these be given without a co-pay or deductible beginning with plan years starting after August 1, 2012. This doesn’t apply to health plans that were in place before it was passed (called grandfathered plans). Those plans are covered by state laws, which vary, and other federal laws” (American Cancer Society, 2013).
Imagine that you are a member of Congress voting on a bill to amend the Affordable Care Act to mandate “grandfathered” and “individual” (self –paid) insurance plans to provide full coverage for bi-annual mammography screening for all females of 35 years and older and all females of 20 years and older who have documented breast cancer in an immediate family member. The legislation would nullify the individual state laws that have varied mandates and coverage, and grant all women access to free screening. Insurance companies have lobbied you to vote against the amendment, and cancer awareness organizations (i.e. ACS, Komen) have lobbied you to vote for it. The insurance companies highlight the costs of screening. While the advocates for the amendment use data to support the cost to benefit analysis. As a member of Congress, you decide to carry out your own research on the benefits of screening (read the module texts) and using varied sources of data to inform your decision. After extensive research, you are now ready to cast your vote and prepare a statement for your constituency.

Preparation:

STEP 1
Review the course texts and navigate the various data/statistics available through the NCI, Komen Foundation, and other valid sources. Select two (2) data visualizations that would serve to support your chosen position
and one (1)misleading data visualization, which you will critique.
STEP 2
In addition to the texts supplied, you will need to research financial costs of breast cancer screening (focusing on individuals, insurance plans, the
government). You should locate at least one (1) valid –preferably government or academic journal – source of data for your cost analysis.
Cancer can be a very emotional subject, but the statements that you make and data that you report about the importance, harms, benefits or effects of mammography must be substantiated by a cited source.
It is also important that additional sources you find are valid and not driven by bias.
STEP 3
Analysis Guidelines:
Structure your paper as a letter to your constituents, explaining why you support or oppose such a policy.
This statement would be posted to your congressional website.
Introduce the proposed bill and state your position.
Provide a context for your position, explaining the sources of
information you have studied to come to this conclusion.
Argue the rationality of routine screening or not screening based on the scientific research and social critique you have read.
*Make sure to ‘Talk back to the opposing argument/position with data.*
Identify specific data (rates, probability, mean, etc) to support your argument.
Include at least 5 varied data points to integrate into your analysis
Explain your interpretation of the two data visualizations you selected
to support your argument. Critique a misleading data visualization.

Discuss the implications of supporting or not supporting the bill mandating
full coverage for routine screening.
What health implications does it make?
What are the financial costs?
– include the data you researched
What might the cultural effects be?
Who would benefit most from such a provision?
What harmor backlash could you foresee from passing such a law?.
Paper requirements:
Statement of position
Cited statistics supporting this position
Appropriately cited use of three (3)published data visualizations
Interpretation of data visualizations and critique of a misleading one
Critical assessment of the collective data and information sourced
Paper Formatting (standard):
3 pages of text (approximately 1650 words, excluding reference list
and charts/data visualization)
1” Margins Standard font (i.e. 12pt Times New Roman, 11pt Calibri)
Introduction –Body –Conclusion
3 Data visualizations (cited)
Reference List
APA formatting

Supporting reading materials:
https://bbhosted.cuny.edu/bbcswebdav/pid-28523133-dt-content-rid-126142784_1/courses/SPS01_QUAN_201_01_1172_1/SPS01_QUAN_201_01_1172_1_ImportedContent_20170126050851/SPS01_QUAN_201_01_1169_1_ImportedContent_20160729085427/OL_v9_QUAN_201_Pathways_DEV_ImportedContent_20131219013425/OL_QUAN_201_01_201309_ImportedContent_20130825110247/Effect%20of%20Three%20Decades%20of%20Screening%20Mammography%20on%20Breast-Cancer%20Incidence.pdf

https://bbhosted.cuny.edu/bbcswebdav/pid-28523138-dt-content-rid-126142787_1/courses/SPS01_QUAN_201_01_1172_1/SPS01_QUAN_201_01_1172_1_ImportedContent_20170126050851/SPS01_QUAN_201_01_1169_1_ImportedContent_20160729085427/OL_v9_QUAN_201_Pathways_DEV_ImportedContent_20131219013425/OL_QUAN_201_01_201309_ImportedContent_20130825110247/The_Problem_With_Pink_NYT.PDF

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