Estimating hazard function and survival analysis of tuberculosis patients in Erbil city

Abstract EN

The study aimed to estimate the effects of prognostic factors on tuberculosis (TB) survival.

Two models have been studied (Logistic model and Cox regression models) in survival analysis.

Kaplan Meier has been applied to estimate the hazard function.

The Kaplan Meier curve has been used to show the risks of dyeing of the factors in this study of tuberculosis data set.

The data was obtained from Kurdistan Regional Government, Iraq /Ministry of health/General Directorate of Health, Hawler/Chest and Respiratory disease Center, in period 11th January 2015 through 23th November 2019 of all tuberculosis patients followed up by the hospital until 14th April 2020.

Kaplan Meier estimator results indicates that in the factor X-ray result TB has the highest value of estimated mean time until death, the Kaplan Meier curves are clearly indicated that the risk of dying increased with the time especially after 15 months.

The logistic regression model identifies that (Gender, Chest symptoms, Type of patient, Site of TB, Transpupillary thermotherapy (TTT-outcome)) are the prognostic factors that influence in tuberculosis survival.

Moreover, the Cox regression model identifies that (Age group, Gender, Site of TB, TTT-outcome) are the most common factors that have an impact on tuberculosis.

Logistic regression model was selected to be the best model for our study data of tuberculosis by using the criterions; Akaike Information Criterion (AIC) and Bayesian information criterion (BIC) to comparing two models.

It's worth mentioning; the results obtained by utilizing the statistical packages in Mat-lab and SPSS V.25, which was used to analyze our study data.