Showing 25 results for Gis
Z Asadollahi, P Jafari, M Rezaeian,
Volume 10, Issue 1 (6-2014)
Abstract
Background & Objectives: Due to the increasing tendency to measure the quality of life in recent years and the extensive quality of life questionnaires, it is important to determine the appropriate method of analyzing data derived from these studies. The aim of the present study was to introduce ordinal logistic regression models as an appropriate method for analyzing the data of quality of life.
Methods: The data was derived from a cross-sectional study on quality of life survey of 938 students. For data analysis, two binary logistic regression models and ordinal logistic regression models were used and the results of these models were compared.
Results: The results of goodness of fit showed that all three models were fitted well. Based on the ordinal logistic regression models, the three variables out of the explanatory variables were statistically associated with the response while based on the binary logistic regression model, after combining two categories of response variable, only two variables were significant. Therefore, combining the categories of the response variable should be avoided as much as possible because it may lead to data loss due to ignoring some of the response categories.
Conclusion: It is concluded that to analyze quality of life data, due to the nature of the response variable, ordinal logistic regression models are recommended considering the fewer parameter estimates and easier interpretation of the results
A Asadabadi , A Bahrampour, Aa Haghdoost,
Volume 10, Issue 3 (12-2014)
Abstract
Background and Objectives : recent years, considerable attention has been paid to statistical models for classification of medical data according to various diseases and their outcomes. Artificial neural networks have been successfully used for pattern recognition and prediction since they are not based on prior assumptions in clinical studies. This study compared two statistical models, artificial neural network and logistic regression, to predict the survival of patients with breast cancer.
Methods: Two models were applied on cancer registry data, Kerman, southeast of Iran, to predict survival. The data of 712 breast cancer patients in the age group 15 to 85 years was used in this study. The logistic regression and three-layer perceptron neural network models were compared in terms of predicting the survival. Sensitivity, specificity, prediction accuracy, and the area under ROC curve were used for comparing the two models.
Results : In this study, the sensitivity and specificity of logistic regression and artificial neural network models were (0.594, 0.70) and (0.621, 0.723), respectively. Prediction accuracy and the area under ROC curve for two models were (0.688, 0.725) and (0.70, 0.725), respectively.
Conclusion: Although there were insignificant differences in the performance of the two models for predicting the survival of the patients with breast cancer, the corresponding results of artificial neural network were more appropriate for predicting survival in such data.
H Akbarein, Ar Bahonar, S Bokaie, N Mosavar, A Rahimi- Foroushani , H Sharifi, As Makenali, Nd Rokni, B Marhamati- Khameneh , S Broumanfar,
Volume 10, Issue 3 (12-2014)
Abstract
Background & Objectives: Bovine Tuberculosis (BTB) is one of the most important zoonoses. Mycobacterium bovis is the responsible agent of BTB in the cattle. The current study was conducted to investigate the determination factors of BTB in dairy farms covered by the tuberculin screening test.
Methods: A herd level case- control study was carried out in 124 (62 cases & 62 controls) dairy farms in the provinces of Tehran, Alborz, Hamedan, Isfahan, Qazvin, Qom, Mazandaran and Semnan. The control farms were individually matched with case farms by farm capacity and distance. Statistical analyses were done by Stata 11.2 using conditional logistic regression.
Results: Proper management of manure (OR=0.12 95% CI: 0.03-0.49), regular flaming of stalls (OR= 0.21 95% CI: 0.04-0.92) and complete fencing around the farm (OR= 0.17 95% CI: 0.03-0.81) decreased while the presence of rodents (rat) (OR= 4.90 95% CI: 1.04-23.01) increased the risk of infection. The interaction among these variables was not statistically significant
Conclusion: According to the results, there is an essential need to pay more attention to rodent control in farms.
M Khodadost, P Yavari, Ss Hashemi Nazari , M Babaei, A Abadi, F Sarvi,
Volume 10, Issue 4 (3-2015)
Abstract
Background and Objectives : Awareness of the cancer incidence is essential for cancer prevention and control programs. Capture-recapture methods have been recommended for reducing bias and increasing the accuracy of cancer incidence estimation. This study aimed to estimate the incidence of gastric cancer by the capture-recapture method based on Ardabil population-based cancer registry data.
Methods: All new cases of gastric cancer reported by three sources, i.e. pathology reports, death certificates, and medical records, reported to Ardabil population-based cancer registry between 2006 and 2008 were enrolled in the study. The duplicate cases based on the similarity of the first name, surname, and father's name were identified among sources. The estimated incidence was calculated by the log-linear method using the Stata 12 software.
Results : A total 857 new cases of gastric cancer were reported from three sources. After removing duplicates, the reported incidence rate was 35.3 and 32.5 per 100,000 population for the years 2006 and 2008, respectively. The estimated incidence rate calculated by the log-linear method for these years was 96.2 and 90.4 per 100,000 population, respectively.
Conclusion: The results showed that none of the sources of pathology reports, death certificates, and medical records, individually or collectively, fully covered the incidence of gastric cancer. We can obtain more accurate estimates of the incidence rate using the capture-recapture method.
M Khodadost, P Yavari, M Babaei, F Sarvi, Ss Hashemi Nazari ,
Volume 11, Issue 3 (11-2015)
Abstract
Background and Objectives: completeness of registration is used as one of the measures of the quality of a cancer registry, which is the degree to which reportable incident cases of cancer in the population of interest is actually recorded in the registry.
Methods: After removing the duplicates, a total of 471 new cases of esophagus cancer reported by three sources of pathology reports, medical records, and death certificates to Ardabil Province Cancer Registry Center in 2006 and 2008 were enrolled in the study. The incidence rate was estimated based on the capture-recapture method and the use of the log-linear models. BIC, G2 and Akaike statistics were used to select the best-fit model.
Results: In this study, a model with linkage between pathology reports and medical records and a model with death certificates alone, independent of the previous two sources, was the best fitted model. The estimated total completeness of esophagus cancer in 2006 and 2008 was 36% .The source that had the most completeness for esophagus cancers was pathology reports with 21.17%. The estimated incidence rate calculated by the log-linear method for the years 2006 and 2008 was 49.71 and 53.87 per 100,000 population, respectively.
Conclusion: Based on the obtained results, it can be concluded that the low degree of completeness in Ardabil Province requires some changes in data abstracting and case finding such as the use of personal national code and electronic health records to create a more accurate cancer registry.
M Aram Ahmadi , A Bahrampour,
Volume 11, Issue 3 (11-2015)
Abstract
Background and Objectives: Diabetes is a chronic and common metabolic disease which has no curative treatment. Logistic regression (LR) is a statistical model for the analysis and prediction in multivariate statistical techniques. Discriminant analysis is a method for separating observations in terms of dependent variable levels which can allocate any new observation after making discriminating functions. The aim of this study was to compare and determine the effective variables in type 2 diabetes.
Methods: The data included 5357 persons obtained through a cohort study in Kerman, southeastern Iran, in 2009-11. Diabetes was considered the response variable. The independent variables after deleting colinearity and correlated variables included height, waist circumference, age, gender, occupation, education, drugs, systolic blood pressure, HDL, LDL, drug abuse, activities, and triglyceride. Sensitivity, specificity, accuracy, and ROC curve were applied for determining and comparing the prediction power of the models.
Results: The results in the reduced model with extracted significant variables from the full model, the sensitivity of the LR model and DA was 74% and 22.4%, the specificity of the LR model and DA was 71.1 % and 95.4 %, the prediction accuracy of the LR model and DA was 71.5% and 85.3%, and the ROC curve of the LR model and DA was 80.3% and 80.1%, respectively.Simulation showed the sensitivity, specificity, accuracy, and ROC curve was 99.18%, 98.49%, 98.59%, and 99.9% for the LR model and 92.62%, 99.19%, 98.26%, and 99.56% for DA, respectively.
Conclusion: The results showed that the risk factors of diabetes in the logistic regression reduced model were waist circumference, age, gender, LDL level, systolic pressure, and drugs. Also, the sensitivity of the LR model was more than DA while DA had a higher specificity and prediction accuracy. Comparison of the ROC curve showed that the prediction estimated values were rather similar in both models, but the two models were the same asymptotically.
F Zayeri, Sh Seyedagha, H Aghamolaie, F Boroumand, P Yavari,
Volume 12, Issue 2 (8-2016)
Abstract
Background and Objectives: Breast cancer is one of the most common malignancies in women which accounts for the highest number of deaths after lung cancer. The aim of the current study was to compare the logistic regression and classification tree models in determining the risk factors and prediction of breast cancer.
Methods: We used from the data of a case-control study conducted on 303 patients with breast cancer and 303 controls. In the first step, we included 16 potential risk factors of breast cancer in both the logistic regression and classification tree models. Then, the area under the ROC curve (AUC), sensitivity, and specificity indexes were used for comparing these models.
Results: From 16 variables included in the models, 5 variables were statistically significant in both models. Sensitivity, specificity, and AUC was 71%, 69%, and 74.7% for the logistic regression and 63.3%, 68.8%, and 71.1% for the classification tree, respectively.
Conclusion: The obtained results suggest that the classification tree has more power for separating patients from healthy people. Menopausal status, number of breast cancer cases in the family, and maternal age at the first live birth were significant indicators in both models.
S Aghamohammadi , E Kazemi, A Khosravi, H Kazemeini ,
Volume 12, Issue 4 (2-2017)
Abstract
Background and Objectives: By identifying the causes of death, interventions can be designed and implemented to reduce the risk factors of different diseases. The aim of this study is to determine the trend of ten leading causes of death in the Islamic Republic of Iran in 2011.
Methods: The study population comprised all deaths recorded in the death registration system of the Ministry of Health and Medical Education (MOHME) from 2006 to 2011. The data related to causes of death reviewed and modified in terms of quality, underreporting of deaths, and garbage codes using the Global Burden of Disease study methods. Finally, the data were analyzed by sex and age groups.
Results: The leading causes of death were cardiovascular diseases (46.12%), cancers and tumors (13.63%) and unintentional injuries (11.55%) in 2011. The 10 leading causes of death in the general population were myocardial infarction, stroke, transportation-related accidents; blood pressure induced heart disease, other cardiovascular diseases, diabetes, chronic pulmonary and bronchial diseases, gastric cancer, other heart diseases and renal failure.
Conclusion: Deaths from non-communicable diseases still account for a large proportion of total deaths. According to the Heath System Reform Plan in Iran and the need for new interventions, it is very important to register the exact causes of death to design service packages and also evaluate the success rate of ongoing interventions.
F Zayeri, M Amini, H Hasanzadeh,
Volume 13, Issue 4 (3-2018)
Abstract
Background and Objectives: Shift work as a pervasive phenomenon in various industrial sectors is one of the most stressful factors in the workplace. Considering the contradictory reports on the relationship of shift work and hypertension, the main objective of the present study was to investigate the relationship between these two variables among petrochemical industry staff of Mahshahr, Iran.
Methods: In this longitudinal study, 3254 petrochemical staff were investigated during 2008-2011. According to work schedule, shift workers were divided into two groups of shift work and day work (1872 day workers and 1382 shift workers). The aim of this research was to assess the effect of shift work on hypertension by adjusting confounding variables such as gender, age, body mass index, and smoking. The data were analyzed using a random-effects logistic regression model.
Results: Of 3254 (3142 males and 112 females) subjects, 37.85% (860 subject) were hypertensive. The random effects model, with controlling covariates, showed no significant relationship between shift work and hypertension (OR=1.04, 95% CI= (0.98, 1.10). Moreover, the variance of the random effects was significant.
Conclusion: Generally, according to the results of this study, shift work is not a significant risk factor for hypertension.
A Alipour, Sa Ghadiri, L Khazaei,
Volume 14, Issue 2 (9-2018)
Abstract
Background and Objectives: The cause of death in children under one year can be an important tool for designing prevention strategies and reducing the mortality rate. The aim of this study was to estimate the number of deaths in children under one year using the Mr. Murray’s estimation index in Mazandaran Province, and to compare this estimation with reported cases of civil registration organization.
Methods: All deaths of children under one year between 2011 to 2014 registered in hospitals across Mazandaran Province were included in this study. The cause of death as coded in the International Classification of Diseases (ICD-10) was converted to Murray classification. The coefficients in each of the Murray levels were used to estimate actual death cases. We compared this estimation with the number of deaths that is reported annually by civil registration organization.
Results: Seven hundred and sixty four deaths occurred in this period. The leading causes of death in children under one year were conditions of the perinatal period, congenital anomalies and chromosomal disorders, respiratory diseases, and diseases of the cardiovascular system. The Murray method estimated 1711 deaths for the entire Province.
Conclusion: the Murray method predicted that from 2011 to 2014, 390-445 children under one year died in Mazandaran Province annually. There is a controversy between the estimates obtained in this study and the number of deaths reported by the civil registration organization, which may indicate a defect in a complete registration of deaths by this organization.
F Feizmanesh, Aa Safaei,
Volume 14, Issue 3 (12-2018)
Abstract
Background and Objectives: Pulmonary embolism is a potentially fatal and prevalent event that has led to a gradual increase in the number of hospitalizations in recent years. For this reason, it is one of the most challenging diseases for physicians. The main purpose of this paper was to report a research project to compare different data mining algorithms to select the most accurate model for predicting pulmonary embolism in hospitalized patients. This model would provide the knowledge needed by the medical staff fir better decision making.
Methods: In this research, we designed a prediction model using different methods of machine learning that would best predict the probability of pulmonary embolism in patients at risk. Among data mining algorithms, Bayesian network, decisions tree (J48), logistic regression (LR), and sequential minimal optimization (SMO) were used. The data used in the study included risk factors and past history of patients admitted to the Lung Department of Shariati Hospital, Tehran, Iran.
Results: The results showed that the accuracy and specificity of all prediction models were satisfactory. The Bayesian model had the highest sensitivity in predicting pulmonary embolism.
Conclusion: Although the results showed a little difference in the performance of prediction models, the Bayesian model is a more appropriate tool to predict the occurrence of pulmonary embolism in hospitalized patients in this type of data. It can be considered a supportive approach along medical decisions to improve disease prediction.
S Dehghani, A Abadi, M Namdari, Z Ghorbani,
Volume 14, Issue 4 (3-2019)
Abstract
Background and Objectives: Periodontal disease is one of the most common oral health problems. Clinical attachment loss occurs in sever periodontal cases (CAL>3). In this study, we applied a classic regression model and the models that consider the hierarchical structure of the data to estimate and compare the effect of different factors on CAL.
Methods: This cross-sectional study was performed in 375 pregnant women and 192 mothers of three-year-old children. The data were gathered from 16 health networks of Shahid Beheshti University of Medical Sciences, Tehran, Iran. CAL was determined for 6 teeth per person by a dentist according to WHO standard oral health examination form. Three-level and ordinary logistic regression analyses were applied for data analysis using the STATA software 14.
Results: Of 3,402 examined teeth, 6.3% had CAL> 3mm. Based on the obtained results, the odds of CAL>3mm were 2.4 in the third semester compared to non-pregnant women. The odds of CAL>3mm were 2.86 in women without daily floss use compared to women with routine daily floss use. Posterior teeth were more likely to have CAL>3m than anterior teeth (OR = 1.65) (P-value < 0.05).
Conclusion: According to the AIC index, multi-level logistic regression model has a better fit than ordinary logistic regression model and can estimate the coefficients of factors related to CAL>3mm more precisely. The use of the ordinary logistic regression model in hierarchical data can result in underestimated standard errors of the estimated parameters.
Am Keshtvarz Hesam Abadi , E Hajizadeh, Ma Pourhoseingholi, E Nazemalhossein Mojarad ,
Volume 14, Issue 4 (3-2019)
Abstract
Background and Objectives: The purpose of this study was to predict the mortality rate of colorectal cancer in Iranian patients and determine the effective factors on the mortality of patients with colorectal cancer using random forest and logistic regression methods.
Methods: Data from 304 patients with colorectal cancer registry from the Gastroenterology and Liver Research Center of Shahid Beheshti University of Medical Sciences during the years 2009 to 2014 were used as a retrospective study. Data analysis was performed using random forest and logistic regression methods. To analyze the data, R software version 3.4.3 was considered.
Results: Ten important variables related to colorectal cancer deaths were selected by random forest method. Several criteria such as the area under the characteristic curve (AUC) were used to compare the random forest method with logistic regression. According to both criteria, five important variables ranked by random forest were Cancer stage, age of diagnosis, patient's age, HLA, and degree of differentiation (tumor differentiation). In terms of different criteria, the random forest method had better performance than logistic regression (Area under the ROC curve for random forest and logistic regression methods was: 98%; 80% respectively).
Conclusion: Variables such as Cancer stage, age of diagnosis, patient's age, HLA, and degree of differentiation are considered as the most important factors affecting mortality in colorectal cancer, that the patients' longevity can be increased with the early diagnosis of cancer and screening programs.
M Chehrazi, R Omani Samani , E Tehraninejad, H Chehrazi, A Arabipoor,
Volume 14, Issue 4 (3-2019)
Abstract
Background and Objectives: Analysis of ordinal data outcomes could lead to bias estimates and large variance in sparse one. The objective of this study is to compare parameter estimates of an ordinal regression model under maximum likelihood and Bayesian framework with generalized Gibbs sampling. The models were used to analyze ovarian hyperstimulation syndrome data.
Methods: This study used the data from 138 patients of a clinical trial phase III to compare the efficacy of intravenous Albumin and Cabergoline in prevention of ovarian hyperstimulation syndrome. The original study was done between 2010 to 2011 in Royan institute. We compared maximum likelihood and Bayesian estimation with generalized Gibbs sampling for an ordinal regression model based on confidence intervals and standard errors. The model were fit through R 3.3.2 software version.
Results: Markov Chain Monte Carlo results reduced the standard errors for estimates and consequently, narrower confidence intervals. Autocorrelations for generalized Gibbs sampler reached to zero in compare to standard Gibbs sampler for shorter time.
Conclusion: It seems that confidence intervals of an ordinal regression model are shorter for generalized Gibbs sampler in compare to standard Gibbs and maximum likelihood. It suggests doing more studies to warrant the results.
Hr Bahrami Taghanaki , E Mosa Farkhani , R Eftekhari Gol , P Bahrami Taghanaki , S Bokaei, A Taghipour, B Beygi,
Volume 16, Issue 3 (11-2020)
Abstract
Background and Objectives: Diabetes is considered as one of the most common endocrine disorders worldwide. The aim of this study was to investigate the factors associated with diabetic complications.
Methods: A case-control study was performed on the data of 70089 diabetic patients (4622 cases and 53613 controls) extracted from the SINA Electronic Health Record (SinaEHR®) in a population covered by Mashhad University of Medical Sciences in 2018. The effect of independent variables on the likelihood of diabetic complications was investigated using single-variable and multivariate logistic regression models with the control of the potential confounding effects.
Results: Using the multivariate logistic regression, the odds of developing diabetic complications were 0.35 (0.31-0.38) for living in the city, 0.73(0.67-0.79) for living in the suburbs and 0.31(0.28-0.33) for living in rural areas relative to the metropolises, 0.84 (0.78-0.91) for illiterate subjects, 0.70 (0.66-0.75) for physical activity, 1.51(1.34-1.71) for stage 1 hypertension and 1.87 (1.43-2.44) for stage 2 hypertension relative to normal blood pressure, 0.79(0.74-0.85) for uncontrolled low density lipoprotein and 1.42(1.33-1.51) for uncontrolled hemoglobin A1C.
Conclusion: Various risk factors were identified to increase the odds ratio of diabetic complications. The most important risk factors were uncontrolled glycosylated hemoglobin and stage 1 and 2 hypertension. Control of these factors can reduce the chance of diabetic complications in diabetic patients.
M Gholamhoseinzadeh, L Ghadirian Marnani, E Ehsani-Chimeh, F Rajabi,
Volume 18, Issue 1 (5-2022)
Abstract
Background and Objectives: The distribution of causes of death indicates the distribution of risk factors for death, and is a basis of planning and intervention to reduce risk factors. The quality of the registered information has problems due to the weakness of the processes of completing and issuing the death certificate or the coding method. The purpose of this study was to explain the challenges of death registration and to provide a solution in this regard.
Methods: This qualitative study was conducted in the second half of 2019 in Guilan University of Medical Sciences. The target population was the directors and experts of the death registration program. Sampling was done purposefully by counting. Data was collected through in-depth interviews using a questionnaire and simultaneous contractual content analysis to identify key themes. To ensure the validity and acceptability of the data, the participants and two research colleagues reviewed the data frequently.
Results: According to the content analysis of 24 interviews, the main challenges of death registration included manpower, organizing the death registration system in the country, and death registration software system and its implementation. These themes were abstracted from 45 subcategories and 13 main categories.
Conclusion: Considering the challenges described by death registration managers and experts, the main proposed interventions to improve the death registration system include recruiting appropriate staff, empowering and motivating various human resources departments, developing internal and external cooperation, increasing public participation, monitoring and continuous assessment to identify the strengths and weaknesses of the death registration system and adressing them, attention to the development of death registration software and its required infrastructure such as Internet access and equipment, attention to the multiplicity of systems, and efforts to integrate them.
Kiumarss Nasseri,
Volume 18, Issue 2 (9-2022)
Abstract
Epidemiology is generally defined as the basic science and art of disease prevention and health promotion. Historically, it began with the accounting of death in major epidemics in the Middle Ages. Over the years, it has evolved into the basic science and art of dealing with mass phenomena of disease occurrence and public health. It is now gaining eminence in dealing with all kinds of mass phenomena beyond disease and public health.
Prior to the 1970s when teaching of epidemiology became a distinct training in academia, most epidemiologists were highly experienced practitioners of infectious and parasitic diseases and drew from their vast experiences in suggesting interventions for infectious disease control. With the prominence of non-infectious and chronic diseases, the need for special training with particular emphasis on biostatistics became apparent and has extensively developed to the present state. In Iran, epidemiological practice and training began with the national efforts in combating the main scourges of Malaria, Trachoma, Schistosoma infestation, cholera, and other diseases that impacted the country with high endemicity and regular epidemic outbreaks. This brief paper describes the development of epidemiology training in Iran in more detail.
Nasrin Talkhi, Nooshin Akbari Sharak, Zahra Rajabzadeh, Maryam Salari, Seyed Masoud Sadati, Mohammad Taghi Shakeri,
Volume 18, Issue 3 (12-2022)
Abstract
Background and Objectives: Due to the high prevalence of COVID-19 disease and its high mortality rate, it is necessary to identify the symptoms, demographic information and underlying diseases that effectively predict COVID-19 death. Therefore, in this study, we aimed to predict the mortality behavior due to COVID-19 in Khorasan Razavi province.
Methods: This study collected data from 51, 460 patients admitted to the hospitals of Khorasan Razavi province from 25 March 2017 to 12 September 2014. Logistic regression and Neural network methods, including machine learning methods, were used to identify survivors and non-survivors caused by COVID-19.
Results: Decreased consciousness, cough, PO2 level less than 93%, age, cancer, chronic kidney diseases, fever, headache, smoking status, and chronic blood diseases are the most important predictors of death. The accuracy of the artificial neural network model was 89.90% in the test phase. Also, the sensitivity, specificity and area under the rock curve in this model are equal to 76.14%, 91.99% and 77.65%, respectively.
Conclusion: Our findings highlight the importance of some demographic information, underlying diseases, and clinical signs in predicting survivors and non-survivors of COVID-19. Also, the neural network model provided high accuracy in prediction. However, medical research in this field will lead to complementary results by using other methods of machine learning and their high power.
Shoboo Rahmati, Reza Goujani, Zahra Abdolahinia, Naser Nasiri, Sakineh Narouee, Amir Hossein Nekouei, Hamid Sharifi, Ali Akbar Haghdoost,
Volume 19, Issue 3 (12-2023)
Abstract
Background and Objectives: The influential role of epidemiologists in improving health outcomes and conducting pertinent research becomes apparent when they are strategically positioned and available in sufficient numbers within a nation. This study aims to identify potential job positions in epidemiology within both governmental and non-governmental sectors while estimating the necessary workforce of epidemiologists in the country until 2027.
Materials and Methods: The present study was conducted as a combination in two quantitative and qualitative parts. In the qualitative part, interviews were conducted with experts, policy makers, graduates and students of this field in the field of job opportunities. In the quantitative part, the number of epidemiologists needed was estimated using modeling and parameters obtained from the review of the literature and the opinions of experts in this field. In this study, the current and near future needs up to 1406 have been considered.
Results: Based on the interviewes, job opportunities for epidemiologists in the country encompass diverse domains, including problem management and analysis, conducting applied research, data analysis, dashboard development, teaching, training, and future-oriented work (forecasting). Acounting for lost job opportunities, the estimated number of epidemiologists required in the country until 2027 is 1122 individuals, that most of them contribute to the country's health system if job opportunities are created. The highest demand for epidemiologists was identidied in units of the Ministry of Health, medical universities, research centers, and hospitals.
Conclusion: Estimating the number of epidemiologists needed using modeling in the country and paying attention to the current number of graduates, reveals that the growth of this field and the increase in graduates can only occur if job opportunities are clearly defined, created, and implemented across proposed job levels.
Shoboo Rahmati, Zahra Abdolahinia, Sakineh Narouee, Naser Nasiri, Reza Goujani, Ali Akbar Haghdoost,
Volume 19, Issue 4 (3-2024)
Abstract
Background and Objectives: Given the significant migration of educated individuals, particularly epidemiologists, throughout the country, it is crucial to investigate the underlying causes. This article presents the findings of an extensive study that sought to explore this issue by gathering insights from experts in the field.
Methods: The study was conducted using qualitative methods, employing content analysis. Data were collected between summer and autumn 2023 through semi-structured interviews with 32 epidemiologists in the country. Analysis followed the steps outlined by Lundman and Graneheim, with coding facilitated by MAXQDA software.
Results: The main challenges of epidemiology in Iran were divided into six main categories. In the field of graduate students, problems in recruitment and employment due to the lack of suitable careers, infrastructure and structural problems of the system, research problems and insufficient knowledge about the performance of this field are the most important challenges, and in the field of students, education problems, structural and infrastructure problems were reported. Also, the main reasons for the migration of epidemiologists were the lack of meritocracy and specialization, inappropriate privileges (including the application of unconventional recruitment quotas), lack of a job market and opportunities for graduates, and inadequate compensation and benefits.
Conclusion: Based on the results of this study, it seems that the migration of epidemiologists as a valuable human resource abroad is a serious concern and problem. The lack of a suitable job position and ambiguity in the path to career advancement due to non-adherence to the meritocracy and skill-building system have increased the motivation to migrate in this group.
