Background:Predicting the incidence of tuberculosis (TB) plays an important role in planning health control strategies for the future, developing intervention programs and allocating resources.Objectives:The present longitudinal study estimated the incidence of tuberculosis in 2014 using Box-Jenkins methods.Materials and Methods:Monthly data of tuberculosis cases recorded in the surveillance system of Iran tuberculosis control program from 2005 till 2011 was used. Data was reviewed regarding normality, variance equality and stationary conditions. The parameters p, d and q and P, D and Q were determined, and different models were examined. Based on the lowest levels of AIC and BIC, the most suitable model was selected among the models whose overall adequacy was confirmed.Results:During 84 months, 63568 TB patients were recorded. The average was 756.8 (SD = 11.9) TB cases a month. SARIMA (0,1,1)(0,1,1)12 with the lowest level of AIC (12.78) was selected as the most adequate model for prediction. It was predicted that the total nationwide TB cases for 2014 will be about 16.75 per 100,000 people.Conclusions:Regarding the cyclic pattern of TB recorded cases, Box-Jenkins and SARIMA models are suitable for predicting its prevalence in future. Moreover, prediction results show an increasing trend of TB cases in Iran.
OBJECTIVESThe risk of transmission of Mycobacterium tuberculosis from patients to health care workers (HCWs) is a neglected problem in many countries, including Iran. The aim of this study was to estimate the prevalence of latent tuberculosis (TB) infection (LTBI) among TB laboratory staff in Iran, and to elucidate the risk factors associated with LTBI.METHODSAll TB laboratory staff (689 individuals) employed in the TB laboratories of 50 Iranian universities of medical sciences and a random sample consisting of 317 low-risk HCWs were included in this cross-sectional study. Participants with tuberculin skin test indurations of 10 mm or more were considered to have an LTBI.RESULTSThe prevalence of LTBI among TB laboratory staff and low-risk HCWs was 24.83% (95% confidence interval [CI], 21.31 to 27.74%) and 14.82% (95% CI, 11.31 to 19.20%), respectively. No active TB cases were found in either group. After adjusting for potential confounders, TB laboratory staff were more likely to have an LTBI than low-risk HCWs (prevalence odds ratio, 2.06; 95% CI, 1.35 to 3.17).CONCLUSIONSThis study showed that LTBI are an occupational health problem among TB laboratory staff in Iran. This study reinforces the need to design and implement simple, effective, and affordable TB infection control programs in TB laboratories in Iran.
OBJECTIVES:The tuberculin skin test (TST) and the QuantiFERON-TB Gold test (QFT) are used to identify latent tuberculosis infections (LTBIs). The aim of this study was to determine the agreement between these two tests among health care workers in Iran.METHODS:This cross-sectional study included 177 tuberculosis (TB) laboratory staff and 67 non-TB staff. TST indurations of 10 mm or more were considered positive. The Student’s t-test and the chi-square test were used to compare the mean score and proportion of variables between the TB laboratory staff and the non-TB laboratory staff. Kappa statistics were used to evaluate the agreement between these tests, and logistic regression was used to assess the risk factors associated with positive results for each test.RESULTS:The prevalence of LTBIs according to both the QFT and the TST was 17% (95% confidence interval [CI], 12% to 21%) and 16% (95% CI, 11% to 21%), respectively. The agreement between the QFT and the TST was 77.46%, with a kappa of 0.19 (95% CI, 0.04 to 0.34).CONCLUSIONS:Although the prevalence of LTBI based on the QFT and the TST was not significantly different, the kappa statistic was low between these two tests for the detection of LTBIs.
The study aimed to evaluate the accuracy of empirical equations (Hargreaves-Samani; HS, Irmak; IR and Dalton; DT) and multivariate linear regression models (MLR1–6) for estimating reference evapotranspiration (ETRef) in different climates of Iran based on the Köppen method including arid desert (Bw), semiarid (Bs), humid with mild winters (C), and humid with severe winters (D). For this purpose, climatic data of 33 meteorological stations during 30 statistical years 1990–2019 were used with a monthly time step. Based on various meteorological data (minimum and maximum temperature, relative humidity, wind speed, solar radiation, extraterrestrial radiation, and vapor pressure deficit), in addition to 6 multivariate linear regression models and three empirical equations were used as MLR1, MLR2, and HS (temperature-based), MLR3 and IR (radiation-based), MLR4, MLR5 and DT (mass transfer-based), and MLR6 (combination-based) were also used to estimate the reference evapotranspiration. The results of these models were compared using the root mean square error (RMSE), mean absolute error (MAE), scatter index (SI), determination coefficient (R2), and Nash-Sutcliffe efficiency (NSE) statistical criteria with the evapotranspiration results of the FAO56 Penman-Monteith reference as target data. All MLR models gave better results than empirical equations. The results showed that the simplest regression model (MLR1) based on the minimum and maximum temperature data was more accurate than the empirical equations. The lowest and highest accuracy related to the MLR6 model and HS empirical equation with RMSE was 10.8–15.1 mm month−1 and 22–28.3 mm month−1, respectively. Also, among all the evaluated equations, radiation-based models such as IR in Bw and Bs climates with MAE = 8.01–11.2 mm month−1 had higher accuracy than C and D climates with MAE = 13.44–14.48 mm month−1. In general, the results showed that the ability of regression models was excellent in all climates from Bw to D based on SI < 0.2.
The accurate estimation of reference evapotranspiration (ETref) is a crucial component for modeling hydrological and ecological cycles. The goal of this study was the calibration of 32 empirical equations used to determine ETref in the three classes of temperature-based, solar radiation-based and mass transfer-based evapotranspiration. The calibration was based on measurements taken between the years 1990 and 2019 at 41 synoptic stations located in very dry, dry, semidry and humid climates of Iran. The performance of the original and calibrated empirical equations compared to the PM-FAO56 equation was evaluated based on model evaluation techniques including: the coefficient of determination (R 2 ), the root mean square error (RMSE), the average percentage error (APE), the mean bias error (MBE), the index of agreement (D) and the scatter index (SI). The results show that the calibrated Baier and Robertson equation for temperature-based models, the Makkink equation for solar radiation-based models and the Penman equation for mass transfer-based models performed better than the original empirical equations. The calibrated
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