Medical waste management in a mid-populated Turkish city and development of medical waste prediction model

Çetinkaya A. Y., KUZU S. L., DEMİR A.

ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY, cilt.22, sa.7, ss.6233-6244, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 22 Sayı: 7
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1007/s10668-019-00474-6
  • Dergi Adı: ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, International Bibliography of Social Sciences, PASCAL, ABI/INFORM, Agricultural & Environmental Science Database, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, Business Source Elite, Business Source Premier, CAB Abstracts, Geobase, Greenfile, Index Islamicus, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.6233-6244
  • Anahtar Kelimeler: Medical waste, Aksaray, Waste estimation, Multiple linear regression, HOSPITAL SOLID-WASTE, GENERATION, IMPLEMENTATION, REGRESSION, DISPOSAL, TEHRAN
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

Waste remains a very complex problematic issue on human society due to its health and environmental effects, economic aspects and social impacts. Medical waste is in a special classification and is supposed to be more hazardous compared to municipal wastes. Therefore, collection, storage, transfer and disposal of medical waste need more delicate measures. These four cycles of waste handling comprise the waste management system. The amount of future medical waste is important in studies on waste management. In order to form an accurate strategy, the amount of waste that is generated must be known with high precision. Accurate estimation can help both planning and designing medical waste management systems. In the present work, a regression model was performed in order to estimate the amount of waste generated by the hospitals in Aksaray city. The inputs of the model were the patient number in three different age classes (0-15; 15-65; 65 <) and gross domestic product per capita. The model had very high determination coefficient of 0.979. This model provides important inputs to ready-to-use solutions to decision makers in medical waste management, which has dynamic conditions due to activities in health services.