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Avrasya Ekonometri �statistik ve Ampirik Ekonomi DergisiYl:2021 Say: 20 Alan: alma ktisad ve ktisadi Demografi

Murat FT
KORONA PANDEMSNE KARI BLGELERARASI SALIK PLANLAMASINDA YEN BR YNTEM NERS: YALILARDA BLGESEL YIILMALARIN LOKASYON KATSAYISI LE TESPT
 
Zamanla deise de korona virsn yallar daha iddetli etkiledii bilinmektedir. Yallarn blgesel dalmlar ise dengeli deildir. Yallarn dengesiz blgesel dalmlaryla uyumlu salk hizmetlerinin sunulmas, korona pandemisiyle mcadelede baary arttrabilir. Ancak bunun iin nce blgesel ylmalarn tespiti gerekir. Gini, Atkinson, Theil, CV gibi eitsizlik endeksleri, sadece dengesiz dalmn genel iddetini gsterir. htiya duyulansa dengesiz dalmn iddeti deil, hangi blgesel birimlerde ne iddette ylmann olduudur. Belki asal bileenler analizi kullanlarak blgesel birimlerdeki ylmalarn sralamas yaplabilir. Ancak bu defa da iddet tespiti yaplamamaktadr. Lokasyon katsaylar her blgesel birim iin hesaplanmas durumunda, hem ylma iddeti tespit edilir hem de ylma iddetine dayal gruplamaya gidilebilir. nk lokasyon katsays teorisinde ylma deerlerine gre genel kabul grm eikler vardr. Bu almada da 65 ya ve stndeki yal nfusun, ile Dzey 4 dzeyindeki ylma iddetleri hesaplanmtr. Ardndan iddet eiklerine gre toplam yal saylar tespit edilmitir. Analizde kullanlan veriler TUKten derlenen 2019 yl ile nfus istatistiklerinden alnmtr. Elde edilen bulgular, 11,1 milyon 65 ya ve stndeki yalnn %13nn tam ylma olan ilelerde, %51inin gl ylmann olduu ilelerde, %28inin yksek blgesel ylmann, %7sinin ortalama ylmann, %1ininse zayf ylmann olduu ilelerde yaadklar tespit edilmitir. Buradan anlalan, yallar belli blgelerde ylmaktadr ve gl-yksek ylma yaanan ilelerde salk hizmetlerinin ok daha youn sunulmas gerekir. Bu almayla hem yntemsel hem de mevcut durumun analizini birlikte gerekletirme imkn olumutur.

Anahtar Kelimeler: Salk ynetimi, salk planlamas, salk planlama yntemleri, lokasyon katsays, korona pandemisi


PROPOSAL OF A NEW METHOD IN INTER-REGIONAL HEALTH PLANNING AGAINST THE COVID-19 PANDEMIC: DETERMINATION OF REGIONAL AGGLOMERATION IN THE ELDERLY BY LOCATION QUOTIENT
 
Although it changes over time, it is known that the coronavirus affects the elderly more severely. The regional dispersion of the elderly is not balanced. Providing health services compatible with the uneven regional dispersion of the elderly may increase success in the fight against the coronavirus pandemic. However, this requires the determination of regional agglomerations firstly. Inequality indices such as Gini, Atkinson, Theil, CV only show the overall intensity of the uneven dispersion. What is needed is not to find the intensity of the balanced dispersion, but to find in which regional units the intensity of the agglomeration is. Perhaps a ranking of the agglomerations in regional units can be made using the principal components analysis. However, this time, intensity cannot be determined. If the location quotients are calculated for each regional unit, both the agglomeration intensity can be determined, and grouping can be made based on the agglomeration intensity. Because in LQ theory, there are generally accepted thresholds according to agglomeration values. In this study, the agglomeration intensities of the elderly population aged 65 and over at the district "NUTS 4" level are calculated. Then, the total number of elderly people was determined according to the intensity thresholds. The data used in the analysis is taken from the district population statistics of 2019 which is compiled from TSI. The findings show that 13% of the 11.1 million elderly people aged 65 and over are in districts with full agglomeration, 51% is in districts with strong agglomeration, 28% is in districts with high regional agglomeration, 7% is in districts with average agglomeration, and 1% is in districts with high agglomeration. It can be understood from this that, the elderly people are agglomerated in certain regions and health services should be provided much more intensively in districts with strong-high concentration. With this study, it was possible to carry out both methodological analysis and current situation analysis together.

Keywords: Health management, health planning, the methods of health planning, location quotient, Corona pandemic


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