Analysis

Health economics can be defined with the effective implementation of the economic tools to control and examine the of the problems faced by the healthcare sector to promote the public health program. The application of health economics is the most effective step for the healthcare providers of both the public and private sector organizations to develop the decision-making process. This study will unfold the effectiveness of the effective implementation of health economics to formulate a proper treatment plan for the patients suffering from lymphoma. Lymphoma is a cancer of lymphatic system that adversely affects the whole blood cells and damages the immune system completely. The Nano-surgery technology is considered to be the most prominent treatment for lymphoma. This is a laser treatment with ultra-short pulses along with gold. It helps to change the DNA of the cells that are cancerous.  The cost of the treatment is considered to be $2.5million along with the staffing cost of $1.5 million. Thus, the study will state the effectiveness of the economic tool to mitigate the and control the treatment cost concerning in the healthcare betterment of the patients. On the contrary, phage therapy can also be identified as an effective treatment for the treatment of leukemia. The required cost for the treatment is $2 million as the initial cost and $1.3 million is the ongoing  cost The study will also focus the benefits of the treatment process, costs, operations along with several cases based on their age wise morbidity and mortality rate, compare and justify the two treatment proposals with the help of the economic tolls cost-benefit analysis and input-output. The conclusion will summarise the whole concept and will outline the decision-making process in the healthcare facilities to choose the most cost-effective treatment.

Determinants of health economic

The determinants of health economics are the cost of the treatment, the income of the consumers and the demand for the health service(Kuhar et al., 2013).

Cost factor

The cost of the treatment can be identified as a great concern for lymphoma treatment(Gallamini et al., 2014). It is notable that the cost of the training and staffing of nano-surgery is around $2.5 million per year along with the recurrent cost of $ 1.5million. However, the cots of the leukemia treatment for the children is the most expensive cancer treatment in Australia that takes $51,000 for each patient. Thus, the lymphoma treatment is more cost friendly (Paul et al., 2013).

Determinants of Health Economic

Income of the patient factor

The income of the consumers is one of the most integral parts of the health economics where the where a low-cost treatment should be beneficial for the target consumers. The consumers prefer to select the treatment process that is most cost-effective with maximum healthcare benefit (Fan, 2014). The conventional cost of the chemotherapy for lymphoma is $50,000 while in it is $30,000 in the nano-therapy. Thus, the acceptance of the nano-therapy is more applicable than the conventional chemotherapy (Weber et al., 2015). The survival chances for lymphoma after nano-therapy is 5.4 deaths per 100,000 patients which is comparatively lower than the leukemia treatment which is 6.4 deaths per 100,000. Hence, the nano-therapy treatment for lymphoma is more cost effective with health benefit than that of phage therapy for leukemia.

The demand for health service factor

The demand for the health service is also an essential criterion for health economics. The target consumers prefer to avail the best healthcare service at a cost-effective price (Neuman, Cohen & Weinstein, 2014). The demand for the healthcare signifies a speedy healthcare recovery with least side effects. The treatment for lymphoma is nano-surgery technology that is conducted by the nano-particles to demolish the cancer cells from DNA (Oxnard et al., 2014). The nano-particles kills the germs of lymphoma from one location and soon transfers itself from another location to destroy the cancerous germs. It takes less than a year to provide 90% fruitful result within $ 4million (staffing and recurrent cost). On the contrary, the recovery procedure of phage therapy for leukemia is a lengthy process (Chan, Abedon& Loc-Carrillo, 2014). The phage therapy generally targets the leukemic cells and turn it into phage cells and then restarts the process. There is no secured long-term available for the treatment process. However, the cost for this therapeutic treatment is $3.3 million (including initial cost and ongoing cost). Thus, the nano-surgery for lymphoma should be most cost-effective to be implemented in a healthcare facility (Dudecket al., 2013).

Economic evaluation

The economic evaluation generally signifies the effectiveness of the existing costs for the treatment procedure for the holistic development of the cost-benefit for a healthcare facility (Svensson, 2016). The economic evaluation of both the treatment processes is essential to choose a cost-effective program for a healthcare organization (Fan, 2014). Thee economic evolution always suggests the cost-effective treatment process in the healthcare facility (Fan, 2014). Thus, lymphoma treatment takes $30,000 for the surgery which is cost effective for the patients and helpful to expand the target consumer for the organization

Cost Factor

Economic tool:

Cost-benefit analysis

The cost-benefit analysis uses to analyze the estimates the monetary value of the costs and benefits of the project in order to locate its effectiveness. As per the given context, the benefits should be chosen from the effective outcome of the applied cost (Svensson, 2016). The effective utilization of the cost-benefit analysis of a new threats surgery relates the costs and outcomes of the associated treatment. The nano-surgery technology for lymphoma takes around $4 million for the betterment process. Moreover, It takes only 8 months for 90% recovery. The diagnosed case for lymphoma treatment from 1982 to 2013 is 53,516 where the mortality rate is only 6.55% with the overall cost of $4million (Neumann et al., 2014). On the contrary, the reported cases for leukemia from 1982 to 2013 is 27,049 where the rate of mortality is 6.7% and it is considered as the costliest treatment process (Svensson, 2016). The patients generally chose a cost-friendly treatment where the nano-surgery for lymphoma treatment can be most suited.

Leontief’s input-output model is productive in terms of selecting the positive and negative impact of the cost investment (Svensson, 2016). In the case of lymphoma treatment, the investment along with staffing, training and the recurrent cost is $4 million (including staffing training and recurrent) and the operational expense is $50,000 for general therapy and $30,000 in the nano-therapy.However, the overall cost for leukemia treatment process is $3.3 million and the operational expense is $51,000 for patients. The patients prefer small input with maximum healthcare output (Svensson, 2016). Thus, the lymphoma treatment should be most applicable for the patients and organization can expect maximum return with this investment (Svensson, 2016).

The analysis has justified choosing the lymphoma treatment in the healthcare facility (Kuhar et al., 2013). The decision has been taken based on the sustainability between cost-benefit and quality treatment.  The treatment cost for lymphoma is $30000 with 90% assurance to be a cure within 8 months. However, the cost of leukemia treatment is $51,000 and it takes longer to be cured. Thus, the lymphoma treatment can attract a maximum client that can increase the target market of the healthcare facility organization (Kuhar et al., 2013). The mortality rate is higher in the leukemia case where the phage therapy is not effective as the mortality rate for he age group 40-80 is 6.2 per 100,000 as compared to the nano-surgery treatment for lymphoma which is 4.8 per 100,000. Thus, the evaluation suggests that the nano-surgery technology for lymphoma treatment is most applicable in a healthcare facility.

Income of the Patient Factor

Conclusion

The study has analyzed the effectiveness and cost of the nano-surgery for lymphoma treatment and phage therapy for leukemia. It is seen that the effectiveness of the lymphoma treatment is quick and it is cost effective for patients with minimum mortality rate. However, the leukemia treatment process is not as cost friendly for the target audience as lymphoma and the curing process is lengthy that can be unsuited for the healthcare facility. Thus, the nano-surgery for lymphoma should be the best choice for the healthcare facility.

Reference List

Chan, B. K., Abedon, S. T., & Loc-Carrillo, C. (2013). Phage cocktails and the future of phage therapy. Future microbiology, 8(6), 769-783.

Dudeck, M. A., Weiner, L. M., Malpiedi, P. J., Edwards, J. R., Peterson, K. D., & Sievert, D. M. (2013). Risk adjustment for healthcare facility-onset C. difficile and MRSA bacteremia laboratory-identified event reporting in NHSN. Published March, 12.

Fan, A. Y. (2014). “Obamacare” covers fifty-four million Americans for acupuncture as Essential Healthcare Benefit. Journal of integrative medicine, 12(4), 390-393.

Gallamini, A., Barrington, S. F., Biggi, A., Chauvie, S., Kostakoglu, L., Gregianin, M., … & Seymour, J. F. (2014). The predictive role of interim Positron Emission Tomography on Hodgkin lymphoma treatment outcome is confirmed using the 5-point scale interpretation criteria. Haematologica, haematol-2013.

Kuhar, D. T., Henderson, D. K., Struble, K. A., Heneine, W., Thomas, V., Cheever, L. W., … & US Public Health Service Working Group. (2013). Updated US Public Health Service guidelines for the management of occupational exposures to human immunodeficiency virus and recommendations for postexposure prophylaxis. Infection Control & Hospital Epidemiology, 34(9), 875-892.

Neumann, P. J., Cohen, J. T., & Weinstein, M. C. (2014). Updating cost-effectiveness—the curious resilience of the $50,000-per-QALY threshold. New England Journal of Medicine, 371(9), 796-797.

Oxnard, G. R., Paweletz, C. P., Kuang, Y., Mach, S. L., O’Connell, A., Messineo, M. M., … & Jänne, P. A. (2014). Noninvasive detection of response and resistance in EGFR-mutant lung cancer using quantitative next-generation genotyping of cell-free plasma DNA. Clinical cancer research.

Paul, M., Ford, T., Kramer, T., Islam, Z., Harley, K., & Singh, S. P. (2013). Transfers and transitions between child and adult mental health services. The British Journal of Psychiatry, 202(s54), s36-s40.

Svensson, L. E. (2016). Cost-Benefit Analysis of Leaning Against the Wind (No. w21902). National Bureau of Economic Research.

Weber, J. S., D’Angelo, S. P., Minor, D., Hodi, F. S., Gutzmer, R., Neyns, B., … & Linette, G. P. (2015). Nivolumab versus chemotherapy in patients with advanced melanoma who progressed after anti-CTLA-4 treatment (CheckMate 037): a randomised, controlled, open-label, phase 3 trial. The lancet oncology, 16(4), 375-384.