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Health Disparities and Improvement After Policies Essay


Background

Health data is used in many countries to allocate scarce resources, distribute funds to health care organizations, regulate patient flow, as well as inform and enhance decision making and patient care outcomes (Lucyk, Lu, Sajobi, & Quan, 2015). As such, it is important to collect high-quality data using valid and reliable techniques to ensure that the decisions made reflect the true picture on the ground (Deering, 2013; Schoenman, Sulton, Kintala, Love, & Maw, 2005). This paper uses two real life events to not only discuss how health data is collected and submitted to relevant authorities, but also to make several recommendations on how the status quo could be improved to achieve better outcomes.

Description of the Events

The first event relates to data that was collected to show disparities in health insurance after Obamacare was signed into law. The news article demonstrates that most of uninsured Americans reside in the South and the Southwest (Republican-leaning states), and are generally poor as demonstrated by the socioeconomic indicators used. The news article also shows that obtaining health insurance is still seen as a major hurdle for many Americans due to low incomes and decisions made by local politicians to abstain from Medicaid expansion (Bui & Sanger-Katz, 2015).

In the second event, an independent global health research organization known as the Institute for Health Metrics and Evaluation (IHME) used data from two studies to report how child and maternal deaths have declined in most counties after the implementation of United Nation’s Millennium Development Goals (Murray, Wang, & Kassebaum, 2016).

How Information was Gathered and Submitted

In the first event, the newspaper enrolled the services of two organizations to collect self-report data from participants through the use of questionnaires administered in the form of a survey. Information in the second event was gathered through synthesizing the findings of two studies that were published in the Lancet scholarly journal. It not clear how the information from the two events was submitted to relevant authorities, though it is apparent that data from the two events could be used by federal health agencies to inform policies on health insurance and child and maternal health. However, due to their presence as news items that can be accessed through online protocols, it is argued that the information was reported through electronic means.

Proposals for Improving the Status Quo

Although available literature shows that self-report is one of the mostly used techniques of collecting health data (Pyone et al., 2015), it is often faced with problems of validation and variation in the timeliness of data. In the first event, for example, it is evident that one organization used census data that was not current to project uninsured patterns. This problem can be solved by using up-to-date measures and tools in collecting health information from the field. It is however difficult to use contemporary measures as most organizations prefer to use census reports due to their high validity (Lucyk et al., 2015).

Another proposal relating to the second event is to ensure that primary health data is corrected from the field so that the findings of the two studies used can be compared against objective data. However, it is often difficult for organizations to collect new health data from the field due to financial constraints, time considerations, and lack of capacity (Lucyk et al., 2015). The last proposal relates to ensuring that proper reporting mechanisms are implemented to ensure that such information and data become useful to relevant agencies and stakeholders. However, it is clear that most organizations do not invest in proper reporting mechanisms for health data due to lack of awareness on the importance of such data.

References

Bui, Q., & Sanger-Katz, M. (2015, October 30). We mapped the uninsured. You’ll notice a pattern. New York Times. Web.

Deering, M.J. (2013). . Web.

Lucyk, K., Lu, M., Sajobi, T., & Quan, H. (2015). Administrative health data in Canada: Lessons from History. BMC Medical Informatics & Decision Making, 15(1), 1-6.

Murray, C.J.L., Wang, H., & Kassebaum, N. (2016). . Web.

Pyone, T., Dickinson, F., Kerr, R., Baschi-Pinto, C., Mathai, M., & van den Broek, N. (2015). Data collection tools for maternal and child health in humanitarian emergencies: A systematic review. Bulletin of the World Health Organization, 93(9), 648-658.

Schoenman, J.A., Sulton, J.P., Kintala, S., Love, D., & Maw, R. (2005). . Web.

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IvyPanda. (2020, September 11). Health Disparities and Improvement After Policies. Retrieved from https://ivypanda.com/essays/health-disparities-and-improvement-after-policies/

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"Health Disparities and Improvement After Policies." IvyPanda, 11 Sept. 2020, ivypanda.com/essays/health-disparities-and-improvement-after-policies/.

1. IvyPanda. "Health Disparities and Improvement After Policies." September 11, 2020. https://ivypanda.com/essays/health-disparities-and-improvement-after-policies/.


Bibliography


IvyPanda. "Health Disparities and Improvement After Policies." September 11, 2020. https://ivypanda.com/essays/health-disparities-and-improvement-after-policies/.

References

IvyPanda. 2020. "Health Disparities and Improvement After Policies." September 11, 2020. https://ivypanda.com/essays/health-disparities-and-improvement-after-policies/.

References

IvyPanda. (2020) 'Health Disparities and Improvement After Policies'. 11 September.

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