Using as references:
Haws, K. L., & Winterich, K. (2013). When value trumps health in a supersized world.Journal of Marketing,77(3), 48–64.
Santry, H. P., Collins, C. E., Wiseman, J. T., Psoinos, C. M., Flahive, J. M., & Kiefe, C. I. (2014). Rates of insurance for injured patients before and after health care reform in Massachusetts: A possible case of double jeopardy.American Journal Of Public Health,104(6), 1066–1072. doi:10.2105/AJPH.2013.301711
Cegielski, J., Griffith, D. E., McGaha, P. K., Wolfgang, M., Robinson, C. B., Clark, P. A., & … Wallace, C. (2014). Eliminating tuberculosis one neighborhood at a time.American Journal of Public Health,104(S2), S225–S233. doi:10.2105/AJPH.2012.300781
Wyshak, G. (2014). Height, socioeconomic and subjective well-being factors among U.S. women, ages 49–79.Plos ONE,9(6), 1–17. doi:10.1371/journal.pone.0096061
Answer the following questions:
In two diferent paragraph give your personal opinion to Dianna Adair and Diamond Jones
This week, I picked the study that discusses insurance rates for injured patients before and after healthcare reform in Massachusetts.
The quantitative problem statement is that uncompensated care causes undue burden on both the healthcare system and the individual that is uninsured in the event of an unexpected serious illness or accident.
The quantitative purpose statement is to determine if healthcare reform positively impacted insurance status among patients at a level I trauma center.
The quantitative research questions I discovered were as follows:What quantifiable effects are measurable in regards to healthcare reform on the burden of uncompensated trauma care in Massachusetts?How does an individual mandate affect the insurance status of injured residents?
The primary hypothesis was that fewer uninsured patients would be treated after the Massachusetts health care reform went into effect.
The data used in the study was collected by retrospective query of trauma registry for adults aged 18-64 years old (not eligible for Medicare) treated before healthcare reform during 2004-2005 and after the reform during 2009-2010. The study excluded 2006-2008 to allow for implementation and compliance variance. All out of state patients were also excluded. Payer source was considered unless it was unavailable, upon which the fact sheet of that patient was reviewed instead. Other relevant data was collected to determine if the patient had insurance coverage at the time the patient presented to the hospital, regardless of if it was charged (such as when no-fault auto insurance from another party paid or the patient was given retroactive insurance coverage).
Rates and insurance types were compared across both years used in the study. Univariate tests of association were used to compare mean, median, and patient proportions by group. Additionally, a multiple logistic regression model estimated insurance status. Analyses were performed using the program SAS.
Univariate tests of association are more simple than multivariate techniques, potentially leaving less room for error. The advantages of logistic regression are simplicity, transparency, and giving probabilistic output, to start (Phung, 2019). Using SAS to perform analyses is no better or worse than SPSS. It is all up to personal/business choice. Granted, I do not have any personal experience with SAS and I have only used SPSS for one semester during my Bachelor’s degree, so I am definitely not an expert on the matter!
Insurance coverage rates in trauma patients increased from 76.7% to 84.3% post reform. Patient sex, race/ethnicity, and physiological measures of trauma severity were similar in both years. A key difference is that injuries were more likely to be a result of blunt trauma with higher severity post-reform as compared to before. Length of stay and percentage of patients discharged with access to rehab or skilled nursing improved post-reform. Essentially, the hypothesis was proven correct with an overall increase of 7.6% in insurance coverage among injured Massachusetts residents.
Regards,DiannaReferences:Phung, T. M. (2019). Logistic regression: Advantages and disadvantages. Retrieved from: https://tungmphung.com/logistic-regression-advantages-and-disadvantages/Santry, H. P., Collins, C. E., Wiseman, J. T., Psoinos, C. M., Flahive, J. M., & Kiefe, C. I. (2014). Rates of insurance for injured patients before and after health care reform in Massachusetts: A possible case of double jeopardy. American Journal Of Public Health, 104(6), 1066–1072. doi:10.2105/AJPH.2013.301711
Testing and collecting data on Tuberculosis (TB) is difficult to do in large populations. Researchers wanted to find a strategy to test those most at risk and most effective to combat it.
Researchers sought to analyze and compare proportions and cumulative incidence of TB cases and compare continuous variables from those cases.
When individuals test positive for TB, local health departments keep track of where the case is. The number of cases and locations can be tracked manually or through a computerized geographic information system (GIS). Researchers hypothesized that with this information, they could identify communities most at risk of TB. With that information, they would be able to concentrate on screening and prevention in those areas.
Using GIS, researchers were able to locate two communities with TB clusters. They went to these neighborhoods and sought voluntary engagement to collect data. Researchers spread the word of their work through pamphlets, media, and community leaders. After spreading their message, researchers went door-to-door for five months to offer free TB skin testing (TST). When agreed upon, they performed on-the-spot TST and collected data on demographics such as age, race, and gender. Anyone with a skin reaction of 10-mm on the TST was sent to a mobilized clinic, where they received chest radiography, clinical evaluation, and treatment. Researchers repeated this again after 10 years
Analysis of the data focused on the participation of community members, isoniazid treatment, and TB incidence. A x^2 test was conducted to evaluate and compare proportions and cumulative incidence from the cases. A t-test was conducted to compare continuous variables from the cases. Also, a multivariable logistic regression model was used to identify the independent effects of each variable.
Using these tests, researchers were able to identify variables and factors that are associated with the probability of participation in TB studies and TST reactivity.
Researchers concluded that by being able to identify the factors associated with TB screening in communities, they can better combat TB and aid in its elimination.
Cegielski, J. P., Griffith, D. E., Mcgaha, P. K., Wolfgang, M., Robinson, C. B., Clark, P. A., . . . Wallace, C. (2014). Eliminating Tuberculosis One Neighborhood at a Time. American Journal of Public Health, 104(S2). doi:10.2105/ajph.2012.300781r
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