The Burden of Covid-19 Cases on the Payer Institution: The Case of Aydın Provincial Ambulance Service

Year 2023, Volume: 11 Issue: 3, 645 - 658, 30.12.2023

Hüseyin Demir , Merve Tekinarslan , Selma Kaplan

https://doi.org/10.18506/anemon.1229517

Abstract

The study aims to assess the economic burden of Covid-19 cases on the payer institution in the context of ambulance services and to investigate the factors affecting costs. Data were obtained from Aydın Provincial Ambulance Service. Cumulative frequency, cumulative percentage and mean (standard deviation) statistics were used for descriptive findings. Logistic regression model was used to examine the factors affecting costs. Covid-19 cases posed a significant economic burden. The cost per case was approximately 466.40 TL and this amount was higher with additional diagnoses. The pandemics created a higher economic burden in 2020. Accordingly, Covid-19 cases accounted for 14.92% of the number of cases and 14.75% of the costs, with an average cost per case of 447.12 TL. In the second year (2021), the economic burden of the pandemic decreased significantly. Accordingly, the share of Covid-19 cases was 7.69%, while the cost share was 7.12%. The average cost in that year was 503.6 TL. Age, triage, reason for call, urban & rural and region variables had a significant effect on costs. There was a statistically significant relationship between advanced age and high costs per case. Covid-19 cases caused a significant economic burden on the payer. It is considered that the implementation of principles and practices related to risk and crisis management for provincial ambulance services will contribute to reducing costs. It is thought that the findings will be useful for other decision makers and policy makers, especially the payer institution.

Keywords

Ambulance Service, Payer Institution, Economic Burden

The Burden of Covid-19 Cases on the Payer Institution: The Case of Aydın Provincial Ambulance Service

Abstract

Çalışmanın amacı Covid-19 vakalarının ödeyici kurum üzerindeki ekonomik yükünü ambulans servisi hizmetleri bağlamında değerlendirmek, maliyetleri etkileyen faktörleri araştırmaktır. Veri Aydın İl Ambulans Servisi’nden temin edilmiştir. Tanımlayıcı bulgular için kümülatif sıklık, kümülatif yüzde ve ortalama (standart sapma) istatistikleri kullanılmıştır. Maliyetleri etkileyen faktörlerin incelenmesi amacıyla lojistik regresyon modeli kullanılmıştır. Covid-19 vakaları, önemli ölçüde ekonomik yük oluşturmuştur. Vaka başına maliyet yaklaşık 466,40 TL olup ek tanılar ile birlikte bu tutar daha yüksek olmuştur. Pandemi 2020 yılında daha fazla ekonomik yük oluşturmuştur. Buna göre, Covid-19 vakaları, vaka sayısının %14,92'sini, maliyetlerin ise %14,75'ini oluşturmuş, vaka başına ortalama maliyet 447,12 TL olmuştur. İkinci yılda (2021) pandeminin ekonomik yükü önemli ölçüde azalmıştır. Buna göre, Covid-19 vakalarının payı %7,69 iken maliyet payı ise %7,12 olmuştur. Söz konusu yılda ortalama maliyet 503,6 TL olmuştur. Yaş, triaj, çağrı nedeni, kent & kır ve bölge değişkenleri maliyetler üzerinde anlamlı etkiye sahiptir. İleri yaşta bulunma ile vaka başına yüksek maliyetler arasında istatistiksel olarak anlamlı ilişki bulunmaktadır. Covid-19 vakaları ödeyici kurum üzerinde önemli ölçüde ekonomik yük oluşturmuştur. İl ambulans servisleri için risk ve kriz yönetimi ile ilgili ilke ve uygulamaların hayata geçirilmesinin maliyetlerin düşürülmesine katkı sağlayacağı değerlendirilmektedir. Çalışma bulgularının başta ödeyici kurum olmak üzere diğer karar verici ve politikacılar için yararlı olacağı düşünülmektedir.

Keywords

Covid-19, Ambulans Servisi, Ödeyici Kurum, Ekonomik Yük

References

• Abiad, A., Arao, R. M., & Dagli, S. (2020). The economic impact of the COVID-19 outbreak on developing Asia. (Erişim: 10.04.2022), www.adb.org/publications/economic-impact-covid19-developing-asia
• Ajmal, M. M., Khan, M., & Shad, M. K. (2021). The global economic cost of coronavirus pandemic: Current and future implications. Public Administration and Policy, 24(3), 290-305.
• Broughel, J., & Kotrous, M. (2021). The benefits of coronavirus suppression: A cost-benefit analysis of the response to the first wave of covid-19 in the United States. PLoS ONE, 16(6), 1-20.
• Chen, N., Zhou, M., Dong, X., Qu, J., Gong, F., Han, Y., ... & Zhang, L. (2020). Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study. Lancet, 395(10223), 507-13.
• Cunningham, A. C., Goh, H.P., & Koh D. (2020). Treatment of COVID-19: old tricks for new challenges. Crit Care, 24(1), 91.
• Drummond, M. F., Sculpher, M. J., Claxton, K., Stoddart, G. L., & Torrance, G. W. (2015). Methods for the economic evaluation of health care programmes. United Kingdom: Oxford University Press.
• Fox, J., & Weisberg S. (2022). Car: Companion to Applied Regression. [R package]. Retrieved from https://cran.r-project.org/package=car.
• Göçen, A., & Ateş, M. (2013). Acil sağlık hizmetleri. (A. Göçen (Ed.), Sağlık hizmetleri yönetimi içinde (ss. 39-103). İstanbul: Beta Yayınları.
• Gray, A. M., Clarke, P.M., Wolstenholme, J.L., & Wordsworth, S. (2011). Applied methods of cost-effectiveness analysis in health care. India: Oxford University Press.
• Guan, W. J., Ni, Z. Y., Hu, Y., Liang, W. H., Ou, C. Q., He, J. X., ... & Zhong, N. S. (2020). Clinical characteristics of coronavirus disease 2019 in China. New England journal of medicine, 382(18), 1708-1720.
• Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., ... & Cao, B. (2020). Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The lancet, 395(10223), 497-506.
• Jordan, R. E., Peymane, A., & Cheng, K. K. (2020). Covid-19: risk factors for severe disease and death. The BMJ, 368, 1-2.
• Kraus, S., Thomas, C., Matthias, B., Johanna, G., Alessandro, Z., & Victor, T. (2020). The economics of Covid-19: Initial empirical evidence on how family firms in five european countries cope with the corona crisis. International Journal of Entrepreneurial Behaviour and Research, 26(5), 1067-92.
• Kuhn, M., & Johnson, K. (2013). Applied Predictive Modeling. USA: Springer.
• Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., ... & Feng, Z. (2020a). Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia. New England journal of medicine, 382(13), 1199-207.
• Li, X. Z., Jin, F., Zhang, J. G., Deng, Y. F., Shu, W., Qin, J. M., ... & Pang, Y. (2020b). Treatment of coronavirus disease 2019 in Shandong, China: a cost and affordability analysis. Infectious Diseases of Poverty, 9(3), 31-38.
• Liew, M. F., Siow, W.T., MacLaren, G., See, K. C. (2020). Preparing for Covid-19: early experience from an intensive care unit in Singapore. Crit Care, 24(1), 83.
• R Core Team. (2020). R: A Language and environment for statistical computing. (Version 4.0) [Computer software]. Retrieved from https://cran.r-project.org. (R packages retrieved from MRAN snapshot 2020-08-24).
• Sing, T., Sander, O., Beerenwinkel, N., & Lengauer, T. (2022). ROCR: Visualizing the Performance of Scoring Classifiers. [R package]. Retrieved from https://cran.r-project.org/package=ROCR.
• The Jamovi Project. (2022). Jamovi. (Version 1.6) [Computer Software]. Retrieved from https://www.jamovi.org.
• Tian, S., Hu, N., Lou, J., Chen, K., Kang, X., Xiang, Z., ... & Zhang, J. (2020). Characteristics of COVID-19 infection in Beijing. Journal of infection, 80(4), 401-406.
• WHO. (2022). Novel coronavirus (Covid-19) situation report - 101. (Erişim: 30.04.2022), https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200430-sitrep-101-covid-19.pdf?sfvrsn=2ba4e093_2.
• Wu, C., Chen, X., Cai, Y., Zhou, X., Xu, S., Huang, H., ... & Song, Y. (2020). Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China. JAMA Internal Medicine, 180(7), 934-943.
• Xie J, Tong Z, Guan X, Du B, Qiu H, Slutsky AS. (2020). Critical care crisis and some recommendations during the COVID-19 epidemic in China. Intensive Care Med, 46(5), 837-40.
• Yang, X., Yu, Y., Xu, J., Shu, H., Liu, H., Wu, Y., ... & Shang, Y. (2020). Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. The Lancet Respiratory Medicine, 8(5), 475-481.