Turkiye Klinikleri Journal of Health Sciences

.: ORIGINAL RESEARCH
Türkiye'de COVID-19 Pandemisinin Yönetimi ve Joinpoint Regresyon Yöntemiyle Analizi
Management of Pandemic COVID-19 in Turkey and Analysis with Joinpoint Regression Method
Erman GEDİKLİa, Yeter DEMİR USLUa, Pakize YİĞİTb, Emre YILMAZa
aİstanbul Medipol Üniversitesi Sağlık Bilimleri Fakültesi, Sağlık Yönetimi Bölümü, İstanbul, TÜRKİYE
bİstanbul Medipol Üniversitesi Tıp Fakültesi, Biyoistatistik ve Tıp Bilişimi ABD, İstanbul, TÜRKİYE
Turkiye Klinikleri J Health Sci. 2021;6(4):911-20
doi: 10.5336/healthsci.2020-79033
Article Language: TR
Full Text
ÖZET
 Amaç: Araştırmada, salgın yönetimi sürecinde karar vericiler tarafından uygulamaya konulan kararların koronavirüs hastalığı-2019 [coronavirus disease-2019 (COVID-19)] verilerinde meydana getirdiği anlamlı değişiklikleri ve kırılma noktalarını Joinpoint Regresyon Analizi (JRA) yöntemi kullanarak açıklamak amaçlanmıştır. Gereç ve Yöntemler: Çalışmada COVID-19 verileri olarak; yeni vaka sayısı, yeni vakaların yapılan testlere oranı, hastalığa bağlı ölüm sayısı, aktif vaka sayısı ve aktif vaka büyüme hızı kullanılmıştır. Yeni vaka sayısında 10 Mart 2020 ve 12 Mayıs 2020, diğer değişkenlerde ise 28 Mart 2020 ve 12 Mayıs 2020 tarihleri arası araştırmaya dâhil edilmiştir. Verilerin analizinde Joinpoint Regression Programı (JRP) 4.8.0.1 (Nisan 2020), SPSS Statistics 20 ve Microsoft Excel programları kullanılmıştır. Bulgular: Uzun dönemli eğilimde, günlük ortalama değişime göre yeni vakalarda %9, hastalığa bağlı ölümlerde %2,6, aktif vaka sayısında %3,9 anlamlı artışların olduğu, yeni vakaların yapılan testlere oranında ise %3,3 anlamlı düşüşün olduğu; aktif vakaların büyüme hızında 24 Nisan 2020 tarihine kadar pozitif, bu tarihten sonra ise negatif bölgede yer aldığı tespit edilmiştir. Kısa dönemli eğilimlerde, yeni vakalarda anlamlı 2 kırılma, 3 trend; hastalığa bağlı ölümlerde anlamlı 4 kırılma ve 4 trend; aktif vaka sayısında ise anlamlı 5 kırılma, 6 trendin olduğu, yeni vakaların yapılan testlere anlamlı bir Joinpoint olmadığı ve tek bir anlamlı trendin olduğu tespit edilmiştir. Sonuç: Dünya'da görülen ilk COVID-19 vakasından sonra alınan ulusal önlemler öncelikle virüsün Türkiye'ye geç gelmesini sağlamış, sonrasında ise alınan hızlı tedbirler ise salgının kontrol edilmesini de önemli derecede kolaylaştırmıştır. İlk dönemlerde alınan sınırlayıcı önlemlerin yeni ve aktif vakaların artışında yavaşlatıcı bir etkisinin olduğu, sokağa çıkma kısıtlamalarıyla birlikte ise verilerdeki negatif yönlü değişime önemli derecede katkı yaptığı söylenebilir. Özellikle sokağa çıkma kısıtlamasını takiben 8 gün sonra yeni vakalarda, 10 gün sonra da aktif vakalarda etkisini gösterdiğini ifade etmek mümkündür.

Anahtar Kelimeler: COVID-19; regresyon çözümlemesi; sağlık planlaması; sağlık politikası
ABSTRACT
 Objective: In the study, it is aimed to explain the significant changes and joinpoints in the coronavirus disease-2019 (COVID19) data by using the Joinpoint Regression Analysis (JRA) method that caused by the decisions made by decision makers in the process of epidemic management. Material and Methods: In the study, as COVID19 data; new cases, the ratio of new cases to the number of tests performed, the daily deaths due to the disease, the number of active cases per day and the active case growth rate were used. When the data are analysed variables Turkey March 10-12 May 2020 for new cases and for other variables between the date's March 28-12 May 2020 were included in the study. Joinpoint Regression Program (JRP) 4.8.0.1 (April 2020), SPSS Statistics 20 and Microsoft Excel programs were used to analyze the data. Results: According to the long-term trend, there was a significant increase in 9% in new cases, 2.6% in disease-related deaths, and 3.9% in the number of active cases; 3.3% significant decrease in the ratio of new cases to the tests performed; It was determined that active cases were in the positive zone until April 24, 2020, and in the negative zone after this date. It was determined that there were two significant joinpoints, three trends in new cases; four significant joinpoints and four trends in disease-related deaths, five significant joinpoints, six trends in the number of active cases, new cases had no significant point in the tests and there was only one significant trend, in short-term trends. Conclusion: National precautions are taken after the first cases in the world COVID-19, the virus is primarily led to the late arrival of Turkey. Afterwards, the quick measures made significantly easier to control the outbreak. In the early periods, it can be said that limiting measures have a slowing effect on the increase of new cases and active cases, and the lackdowns have an important contribution to negative changes. Especially after eight days following the lackdown in new cases, ten days after the active case stated that it is possible to show the effect on.

Keywords: COVID-19; regression analysis; health planning; health policy
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