Türkiye genelinde renal replasman tedavisine ihtiyaç duyacak olan hasta sayısının GM (1,1) ve OGM (1,1) ile tahmin edilmesi

Amaç: 2018-2023 yılları arasında RRT tedavisi görmesi gerekecek hasta sayısını tahmin etmektir. Yöntem: Tahmin etme sürecinde genel olarak zaman serilerinin tahmin edilmesinde kullanılan bir yöntem olan gri tahmin etme yöntemleri kullanılmıştır. Gri sistemlerde tahmin edebilmek için çeşitli modeller geliştirilmiş olmakla birlikte bu çalışmada GM (1,1) ve OGM (1,1) modelleri kullanılmıştır. Verilerin analizinde Microsoft Excel 2016 tabanlı Genel İndirgenmiş Gradyan metodundan yararlanılmıştır. Araştırma verileri, 2006-2017 yılları arasında Türkiye’de RRT gören hasta sayılarından oluşmaktadır. Modellerin tahmin performansı ortalama mutlak yüzde hata (MAPE) ve kök ortalama kare hata (RMSE) ile ölçülmüştür. Bulgular: Karşılaştırmalar sonucunda OGM (1,1)’in (MAPE: %2.0 RMSE: 1484) GM (1,1) modeline (MAPE: %2.1 RMSE: 1740) göre daha iyi performans gösterdiği tespit edilmiştir. 2006-2017 verilerine dayanarak tahmin edilen ve gerçekleşen veriler bazında yakınsama oranları karşılaştırıldığında da OGM (1,1) modelinin daha başarılı olduğu belirlenmiştir. 2018-2023 yılları arasında RRT görecek hasta sayısındaki ortalama yıllık büyüme oranı, GM (1,1) modeline göre %4.12; OGM (1,1) modeline göre ise %4.64’tür. Bu modellere göre, hasta sayısı her yıl bir önceki yıla göre artış göstereceği tahmin edilmektedir. 2017’de 77311 olan hasta sayısı 2023 yılında OGM (1,1) modeline göre 104105’e ulaşacağı öngörülmektedir. Sonuç: Bu yükseliş nedeniyle insidansı gittikçe artma eğilimi gösteren kronik böbrek hastalığının önlenmesi ve topluma ve devlete sosyo-ekonomik yükünün azaltılması için etkili önlemler (renal transplantasyon, organ bağışının özendirilmesi vs.) alınması gerekliliği gün yüzüne çıkmaktadır

Anahtar Kelimeler:

Hasta tahmini, Gri tahminleme yöntemi, Renal replasman tedavisi, Zaman serileri analizi

Prediction of number of patients will need to renal replacement therapy in Turkey with GM (1,1) and OGM (1,1)

Aim: The main purpose of the study is to estimate the number of patients requiring RRT treatment between 2018-2023. Method: In the estimation process, grey estimation methods, which are generally used for estimating time series, are used. Although various models have been developed to predict grey systems, GM (1,1) and OGM (1,1) models are used in this study. General Reduced Gradient method based on Microsoft Excel 2016 was used to analyze the data. Research data consists of the number of patients receiving RRT between the years 2006-2017 in Turkey. Estimated performance of the models was measured by mean absolute percent error (MAPE) and root mean square error (RMSE). Results: As a result of the comparisons, it was found that OGM (1,1) (MAPE: 2% RMSE: 1484) performed better than GM (1,1) (MAPE: 2.1% RMSE: 1740). When the convergence rates are compared on the basis of estimated and actual data based on 2006-2017, it is found that OGM (1,1) is more successful. The average annual growth rate of the number of patients who will see RRT between 2018-2023 is 4.12% according to GM (1,1) and 4.64% according to OGM (1,1). According to these data, the number of patients will increase each year compared to the previous year. The number of patients who were 77311 in 2017 will reach 104105 in 2023 according to OGM (1,1). Conclusion: Due to this increase, effective preventions (renal transplantation, promotion of organ donation, etc.) should be taken to prevent chronic kidney disease whose incidence tends to increase gradually and to reduce the socio-economic burden on society and the state.

Keywords:

Patient forecasting, Grey prediction model, Renal replacement therapy, time series analysis,

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