Showing 6 results for Measles
K Holakouie Naeini, A Moradi, F Pourmalek, S.r Majdzadeh,
Volume 1, Issue 1 (12-2005)
Abstract
Background and Objectives: The campaign to eliminate measles and rubella (MR) is one of the most important current health projects in Iran. One of the basic requirements of this program is the creation of an efficient system for collecting data on MR morbidity and mortality, people's knowledge, attitude and practice (KAP) regarding MR prevention, and people's participation in the eradication drive. The present study was done to assess people's knowledge, attitude and practice in relation to the current anti-MR campaign.
Materials and Methods: This cross- sectional study was carried out fur months after the mass (anti-MR campaign (performed in May 2004). The target population included at the people aged 20-25y who lived in areas covered by Tehran University of Medical Sciences and Health Services. The calculated sample size was 384, using a 2-stage sampling procedure. We used X2 tests, odds ratios and confidence intervals to detect relationships between various categorical variables. We also performed Cronbach's alpha test to assess questionnaire reliability, and principal component analysis to ensure construct validity.
Results: Data were collected on 390 individuals. The percentages of people with an acceptable level of knowledge, attitude and practice were 63.3%, 53.6% and 93.1% respectively. After controlling for confounders in a logistic regression model, it became apparent that knowledge concerning the mass immunization campaign was related to the individual's own education and that of his/her mother. Attitude was found to be affected by factors such as education, marital status and the family's main income level. The practice component, on the other hand, was not significantly related to any of the variables included in this study.
Conclusion: The positive achievements of this program should be used in planning any future immunization campaigns. Particular attention should be paid to factors that affect overall coverage. These include human resources, equipment, vaccines and other materials, service uality, the cold chain, information provided to the public, and people's as well as providers' knowledge of the immunization program, the target diseases (s) and the vaccines.
M Karami, H Soori, Y Mehrabi, Aa Haghdoost, Mm Gouya,
Volume 8, Issue 3 (12-2012)
Abstract
Background & Objectives: Knowledge of the presence of seasonal trends and other explainable patterns in the prediagnostic data sources and removing such patterns before applying outbreak detection methods seem very important. This study aimed to detect and remove the explainable patterns such as seasonality, day-of-week (DOW) and holiday effects of the daily counts of suspected cases of measles in Iran.Methods: Data on daily counts of suspected cases of measles as a pre-diagnostic data source were obtained from Iranian national surveillance system between 21 March 2008 and 20 March 2011. We used lines plot, moving average chart, autocorrelation and partial autocorrelation functions for detecting explainable patterns. Moving average (MA) and Holt- Winters (HW) exponential smoothing method are used for removing explainable patterns.
Results: Our findings indicate the presence of seasonality, DOW effect, holidays and weekend effects in the daily counts of suspected cases of measles. The good performance of HW exponential smoothing technique in removing seasonal patterns is evident. MA technique showed better performance regarding assumption violation on outbreak detection methods.
Conclusion: Because of the presence of explainable patterns in the daily counts of suspected cases of measles, considering such patterns before applying outbreak detection algorithms is very important. Implementing both MA (7 days) techniques for its simplicity as a pre- processing method and HW method for its efficacy in removing seasonal patterns is recommended.
M Karami, H Soori, Y Mehrabi, Aa Haghdoost, Mm Gouya,
Volume 8, Issue 3 (12-2012)
Abstract
Background & Objectives: Evaluating the performance of outbreak detection methods using real data testing provide the highest degree of validity. The aim of this study was to determine the performance of the Exponentially Weighted Moving Average (EWMA) in real time detection of two local outbreaks in Iran.
Methods: The EWMA algorithm (both ƛ= 0.3 and 0.6) applied on daily counts of suspected cases of measles to detect local outbreaks which had been occurred in Mashhad and Bandar Abbas cities during 2010. The performance of The EWMA algorithms were evaluated using real data testing approach and reported by correlation analysis.
Results: Mashhad outbreak was detected with a delay of about 2 to 7 days using EWMA algorithms as outbreak detection method while the utility of EWMA algorithms in real time detection of Bandar Abbas’ outbreak were on time good optimal. Maximum correlation value for EWMA 2 in relation to Mashhad outbreak was 0.60 at lag 2.
Conclusion: Applying the EWMA algorithm as an outbreak detection method at local levels is not suggested. However the characteristics of data are determinant of the performance of such detection methods.
M Karami, H Soori, Y Mehrabi, Aa Haghdoost, Mm Gouya, N Esmailnasab,
Volume 9, Issue 2 (10-2013)
Abstract
Background & Objectives: Timely response to emerging diseases and outbreaks are a major public health and health systems priority. There are few published studies that evaluate the performance of cumulative sum (CUSUM) on identical data using semi- synthetic simulation approach. This study was undertaken to determine the performance of the CUSUM in timely detection of 831 days of simulated outbreaks.
Methods: We evaluated the performances of the CUSUM as an outbreak detection method on simulated outbreaks injected to daily counts of suspected cases of measles as baseline data in Iran between 21 March 2008 till 20 March 2011. Data obtained from the Iranian national surveillance system. The performance of algorithms was evaluated using sensitivity, false alarm rate, likelihood ratios and Area under the Receiver Operating Characteristic (ROC) curve.
Results: Generally the sensitivity of the CUSUM algorithm in detecting simulated outbreaks was 50%
(95% CI: 47- 54). The corresponding values are disaggregated according to outbreak size, shape and duration. The CUSUM algorithm detected the half of outbreaks after 13.84 days on average.
Conclusion: We concluded that CUSUM algorithm performed good in detection of large outbreaks with short periods and poorly in detecting long period outbreaks, particularly those simulated outbreaks that did not begin with a surge of cases.
M Karami, Kh Rahmani, Gh Moradi, Mm Gouya, A Sabouri , K Entezar Mahdi , Gh Kamali, Sm Zahraei,
Volume 16, Issue 1 (6-2020)
Abstract
Background and Objectives: Elimination and eradication of measles requires designing and implementing an enhanced surveillance system. The purpose of this study was to review the measles surveillance system in Iran.
Methods: The data of this study were obtained from the surveillance system of the Center for Communicable Disease Control; a review of the records, documents, books, and published articles; and interviews with process owners and experts of measles surveillance in 2017-2019.
Results: Iran has a surveillance plan to eliminate the measles. The current design for suspected cases of measles in Iran is a case-based surveillance system, in which for each identified case with fever and maculopapular rashes, some activities such reporting, laboratory confirmation, clinical and epidemiological investigation and case registration in individual forms are done.
Conclusion: Complete surveillance of cases suspicious of measles and high coverage of vaccination in children less than 2 years in a cohesive surveillance system and rapid response to the outbreak have led to lack of occurrence of indigenous measles in Iran. Although the current status of the measles surveillance system in Iran seems to be favorable, since Iran is at the stage of measles elimination, it is essential to increase the sensitivity of the reporting system for suspected cases of measles and to maintain the status of vaccine coverage to save elimination status.
P Maroofi, , Z Cheraghi, L Tapak,
Volume 17, Issue 4 (3-2022)
Abstract
Introduction: Identifying the epidemiological features of reported measles outbreaks including the size, period, and generation of the outbreaks plays a significant role in preventing new outbreaks and estimating effective reproduction number (R) as an indication of measles elimination. This study was conducted to describe the reported measles outbreaks in the world in 2018.
Method: The PubMed, Scopus, and Web of Sciences databases were searched using related keywords to retrieve articles that reported 2018 measles outbreaks. From the full-texts of the articles that met the inclusion criteria, the data including gender, season, age group, country, genotype, and vaccination status as well as shape, size, period of outbreaks and number of generations of each outbreak were extracted and reported using the relevant epidemiological curves.
Results: The search results led to the retrieval of 2806 articles. After screening, 16 studies were used for final analysis. Most outbreaks were reported in the winter (56.25%) with genotypes B3 and D8. The sex female (38.64%, 308 cases) was mostly in Asia and Europe. On average, the minimum and maximum number of outbreaks size was 1 and 23, which spread to 3-4 generations. In terms of death, only one case of death was reported in Ethiopia.
Conclusion: The results of this study are useful for identifying measles outbreaks in other countries according to the at-risk groups. However, publication bias and non-reporting of all outbreaks should be considered as limitations in the generalization of the results.
